Time-sensitive networking (TSN) is an emerging technology widely used in real-time systems for its high bandwidth and deterministic timing properties. To ensure the deterministic transmission of Time-triggered (TT) messages, a guard band mechanism is employed to prevent interference from other messages, such as Audio-Video Bridging (AVB) and Best-effort (BE) messages, before transmitting the TT messages in TSN. However, this mechanism introduces transmission delays for non-TT messages and bandwidth wastes for the physical links. Another challenge arises from the default First-in-first-out (FIFO) order of incoming messages, resulting in a relatively low acceptance rate for non-TT messages. To address these issues, a hybrid scheduling algorithm based on the min-heap structure (HSMH) is proposed. For AVB messages, HSMH sorts them in ascending style on the basis of deadlines, guaranteeing the earliest deadline message to be sent first. For BE messages, a threshold is designed to diverge them into two queues: a FIFO queue and a STF (shortest-time-first) queue. The former outputs the messages in a FIFO style, while the latter outputs messages in a STF style. All the output order of AVB messages and STF-queue messages are arranged in a min-heap structure. The algorithm can efficiently improve the transmission rate of AVB messages, the sending rate of BE messages, and the overall link utilization. Experimental results demonstrate that the proposed algorithm outperforms existing approaches in all these three aspects.

Extensive investigations are being conducted on gold nanoparticles focusing on their applications in biosensors, laser phototherapy, targeted drug delivery and bioimaging utilizing advanced detection techniques. In this work, an electrochemical sensor was developed based on graphite carbon nitride supported gold nanoparticles. Carbon nitride supported gold nanoparticles (Au-CN) was synthesized by applying a deposition-precipitation route followed by a chemical reduction technique. The composite system was characterized by X-ray diffraction and Xray photo electron spectroscopy methods. Electron microscopy analysis confirmed the formation of gold nanoparticles within the size range of 5-15 nm on the carbon nitride support. Carbon nitride supported gold based sensor was employed for the electrochemical detection of iodide ion and L-cysteine. The limit of detection and sensitivity of the sensor was attained 8.9 mu M and 0.96 mu A mu M(-1)cm( 2), respectively, for iodide ion, while 0.48 mu M and 5.8 mu A mu M(-1)cm( 2), respectively, was achieved for the recognition of cysteine. Furthermore, a paper-based electrochemical device was developed using the Au-CN hybrid system that exhibited promising results in detecting iodide ions, highlighting its potential for economic and portable device applications.

Environmental pollution is of global concern. In Southeast Sweden, historical glass production has contaminated communities with toxic metals. Long-term residency in these communities and high consumption of local foods may constitute a risk for cardiovascular disease (CVD) or stroke. The current study investigates if residency in these contaminated sites and long-term consumption of local foods is associated with self-reported CVD and stroke. In addition, the body burden of the toxic metals arsenic, cadmium, and lead, as well as inflammatory protein markers, were studied for association with CVD and stroke. From an existing questionnaire cohort and biomonitoring sub cohort (n = 2290/882) of people living in the contaminated areas, self-reported CVD cases (n = 366/166) and stroke cases (n = 78/25) were identified. Individuals were grouped based on their residency within a 2 km radius of glassworks with historical high, moderate, or low air-borne lead emissions. Body burden of arsenic, cadmium, and lead was analyzed using ICPMS. Inflammatory markers were investigated using electrochemiluminescence. Long-term residency near glassworks with historically high levels of lead emissions, and high consumption of local foods, were associated with CVD. The risk was increasing for each year of residency in high emission areas. Increased body burden of arsenic in blood, and lead in urine, were associated with stroke. Five and two inflammatory markers, respectively, were elevated in CVD and stroke cases after adjusting for confounders. An increased risk for CVD was found in areas with historically high emissions of lead but possibly also other toxic metals. Interestingly, there was an indication of a dose-response relationship with increasing risk for CVD per year of residency time. Inhalation and consumption of local food may constitute major pathways for this association. The study shows that long-term exposure to toxic metals in these contaminated areas is associated with CVD and that there is a need to limit exposure in the general population.

Five-axis machining, especially flank milling, is popular in machining thin-walled freeform surface parts with high energy consumption. Reducing the machining energy consumption is paramount for advancing green manufacturing. Therefore, this paper proposes an energy-efficient integration optimisation of cutting parameters and tool path with hierarchical reinforcement learning (HRL). Firstly, a novel multi-pass machining energy consumption model is developed with cutting and path parameters, based on which the integrated optimisation problem is modelled considering a dynamic workpiece deformation constraint. Secondly, HRL with a Soft Actor Critic agent (HSAC) decouples the model into two Markov Decision Processes at different timescales. The higherlayer plans cutting parameters for each pass on a macro timescale, while the micro-timescale lower-layer performs multiple tool path expansions with the planned cutting parameters, and provides feedback to the higher layer. By hierarchical optimisation and non-hierarchical interaction, the model is efficiently solved. Moreover, curriculum transfer learning is applied to expedite task completion of the lower layer, enhancing interaction efficiency between the two layers. Experiments show that, compared with two benchmarks, the proposed method improves machining energy consumption by 35.02 % and 30.92 %, and reduces machining time by 38.57 % and 27.17 %, providing a promising paradigm of green practices for thin-walled freeform parts and the broader manufacturing industry.

We consider the Helmholtz equation with a variable coefficient in a semi-infinite strip. Homogeneous Neumann conditions are prescribed on a part of the boundary of the strip. Our aim is to find the unknown function in the Dirichlet boundary condition on the remaining part of the boundary from measurements taken on a segment inside the semi-infinite strip. We assume that the radiation condition at infinity is unknown and must be found also. The main difficulty here is the variable coefficient in the Helmholtz equation which does not allow to apply the method of separation of variable as was done in [2]. Such problems appear in acoustics to determine acoustical sources and surface vibrations from acoustic field measurements in non-uniform mediums. We split the problem in two parts. One consists of finding the radiation condition and this problem is well-posed. We derive the equation for finding the parameter of the radiation condition, that holds at infinity. The second one is to find the Dirichlet data and this problem is ill-posed. We present an operator equation with compact operator for finding the Dirichlet data. (c) 2024 The Author(s). Published by Elsevier Inc. This is an open access article under the CC BY license (http://creativecommons .org /licenses /by /4 .0/).

Objective The objective was to investigate 10-year mortality, causes of death and cardiovascular comorbidity in idiopathic normal pressure hydrocephalus (iNPH) and to evaluate their mutual associations.Methods This prospective cohort study included 176 CSF-shunted iNPH patients, and 368 age- and sex-matched controls. At inclusion, participants were medically examined, had blood analyzed and answered a questionnaire. The vascular comorbidities investigated were smoking, diabetes, body mass index, blood pressure (BP), hyperlipidemia, kidney function, atrial fibrillation and, cerebro- and cardiovascular disease.Results Survival was observed for a mean period of 10.3 +/- 0.84 years. Shunted iNPH patients had an increased risk of death compared to controls (hazard ratio (HR) = 2.5, 95% CI 1.86-3.36; p < 0.001). After 10 years, 50% (n = 88) of iNPH patients and 24% (n = 88) of the controls were dead (p < 0.001). The risk of dying from cardiovascular disease, falls and neurological diseases were higher in iNPH (p < 0.05). The most common cause of death in iNPH was cardiovascular diseases (14% vs 7% for controls). Seven out of nine iNPH dying from falls had subdural hematomas. Systolic BP (HR = 0.985 95% CI 0.972-0.997, p = 0.018), atrial fibrillation (HR = 2.652, 95% CI 1.506-4.872, p < 0.001) and creatinine (HR = 1.018, 95% CI 1.010-1.027, p < 0.001) were independently associated with mortality for iNPH.Discussion This long-term and population-matched cohort study indicates that in spite of CSF-shunt treatment, iNPH has shorter life expectancy. It may be important to treat iNPH in supplementary ways to reduce mortality. Both cardiovascular comorbidities and lethal falls are contributing to the excess mortality in iNPH and reducing these preventable risks should be an established part of the treatment plan.

Relational information between different types of entities is often modelled by a multilayer network (MLN) - a network with subnetworks represented by layers. The layers of an MLN can be arranged in different ways in a visual representation, however, the impact of the arrangement on the readability of the network is an open question. Therefore, we studied this impact for several commonly occurring tasks related to MLN analysis. Additionally, layer arrangements with a dimensionality beyond 2D, which are common in this scenario, motivate the use of stereoscopic displays. We ran a human subject study utilising a Virtual Reality headset to evaluate 2D, 2.5D, and 3D layer arrangements. The study employs six analysis tasks that cover the spectrum of an MLN task taxonomy, from path finding and pattern identification to comparisons between and across layers. We found no clear overall winner. However, we explore the task-to-arrangement space and derive empirical-based recommendations on the effective use of 2D, 2.5D, and 3D layer arrangements for MLNs.

PurposeTo investigate the performance of dynamic 3D diuretic renal scintigraphy using a hybrid whole body CZT SPECT/CT for the evaluation of acute ureteric obstruction in patients with urinary stone disease.Methods20 patients who presented to the Emergency Department with acute renal colic due to urinary stone disease confirmed by means of CT were prospectively included. Three observers evaluated and graded hydronephrosis, hydroureter, perirenal stranding, and thickening of the renal fascia from the CT as well as the renal scintigraphy curves from the dynamic SPECT study. The normalized residual activity from dynamic SPECT was analysed at 16 min in all patients and at 20 min in suspected obstruction.ResultsRenal scintigraphy curves showed a sensitivity of 100%, specificity of 93%, PPV 83% and a NPV 100% for obstruction, while normalized residual activity showed a sensitivity of 100%, specificity of 73%, PPV 56% and a NPV 100%. All patients presented at least 2 secondary signs of obstruction on the CT, showing a PPV of only 25% for obstruction.ConclusionDynamic 3D diuretic renal scintigraphy CZT SPECT/CT provides valuable functional and anatomical information from one single examination. The combination of pathological renogram curves and high normalized residual activity values provide the most valuable imaging information to determine the presence of acute ureteric obstruction. The secondary signs of obstruction observed on CT are not specific and should not be used to confirm or discard obstruction in patients with urinary stone disease.Trial registration: ISRCTN15338358. Registration date 03/01/2024. Retrospectively registered. https://www.isrctn.com/ISRCTN15338358?q=miguel%20ochoa%20figueroa&filters=&sort=&offset=1&totalResults=2&page=1&pageSize=10ConclusionDynamic 3D diuretic renal scintigraphy CZT SPECT/CT provides valuable functional and anatomical information from one single examination. The combination of pathological renogram curves and high normalized residual activity values provide the most valuable imaging information to determine the presence of acute ureteric obstruction. The secondary signs of obstruction observed on CT are not specific and should not be used to confirm or discard obstruction in patients with urinary stone disease.Trial registration: ISRCTN15338358. Registration date 03/01/2024. Retrospectively registered. https://www.isrctn.com/ISRCTN15338358?q=miguel%20ochoa%20figueroa&filters=&sort=&offset=1&totalResults=2&page=1&pageSize=10

Background Most women experience vasomotor symptoms (VMS) during the menopausal transition. A 15-week resistance training intervention (RTI) significantly reduced moderate-to-severe VMS (MS-VMS) and improved health-related quality of life (HRQoL) and cardiovascular risk markers in postmenopausal women. Whether a short RTI could have long-term effects is unknown. We aimed to investigate whether there were intervention-dependent effects two years after a 15-week RTI on MS-VMS frequency, HRQoL, and cardiovascular risk markers in postmenopausal women. Methods This observational prospective cohort study is a follow-up to a randomized controlled trial (RCT) on a 15-week RTI in postmenopausal women (n = 57). The control group had unchanged low physical activity during these first 15 weeks. At the follow-up contact two years post-intervention, 35 women agreed to participate in an additional physical visit at the clinic with clinical testing, blood sampling, and magnetic resonance imaging, identical to the protocol at the baseline visit at the start of the RCT. Results Although all women showed reduced MS-VMS and increased moderate-to-vigorous physical activity (MVPA) over the 2-year follow-up compared to baseline, the groups from the original RCT (intervention group; IG, control group; CG) changed differently over time (p < 0.001 and p = 0.006, respectively) regarding MS-VMS. The IG maintained a significantly lower MS-VMS frequency than the CG at the 6-month follow-up. At the 2-year follow-up, there was no significant difference between the original RCT groups. No significant changes over time or differences between groups were found in HRQoL or cardiovascular risk markers. However, significant interactions between original RCT groups and time were found for visceral adipose tissue (p = 0.041), ferritin (p = 0.045), and testosterone (p = 0.010). Conclusions A 15-week resistance training intervention reduced MS-VMS frequency up to six months post-intervention compared to a CG, but the effect was not maintained after two years. The RTI did neither contribute to preserved improvements of cardiovascular risk markers nor improved HRQoL after two years compared to a CG. Trial registration Clinical trials.gov registered ID: NCT01987778, trial registration date 2013-11-19.

Industrial symbiosis can be seen as a collaborative process where actors jointly identify, develop, and maintain resource management innovations, primarily related to secondary material and energy flows, to reduce waste generation and increase resource efficiency. Although industrial symbiosis is commonly recognized and promoted as a process creating business values for involved businesses, knowledge of what kind of values are created and how remains unstructured-and possibly incomplete. This is problematic because, without wider considerations as to what business values can be created through industrial symbiosis, the concept may not garner sufficient support from businesses and policy makers. Therefore, this paper aims to develop and present a framework that captures the wide range of business value propositions of industrial symbiosis, including benefits and sacrifices, along with their enabling mechanisms. The framework was developed based on a synthesis of the literature and interviews with actors involved with existing industrial symbiosis networks and is divided into four domains: costs, revenues, risks, and soft values. Beyond providing an overview of industrial symbiosis business values, the framework has additional uses, such as assisting in negotiations of relationship governance terms, including pricing and compensation mechanisms.

The Paris Agreement ushered in an era of climate governance underpinned by a polycentric theory of change, emphasizing experimentation, collaboration, and innovation while downplaying political contestation, power asymmetries, and the need for regulatory action by the state. This article explores the roles the state plays in polycentric climate governance, focusing on the tension between the regulatory state, where authorities set, monitor, and enforce rules, and the orchestrating state, which facilitates collaboration with nonstate actors to induce behavioral change. Using decarbonization in Sweden as an illustrative case study, the article synthesizes the results of two research projects evaluating the promises and limits of polycentric climate governance. The results problematize the view that Sweden is a forerunner in climate governance, suggesting that while the Swedish government has mobilized support from important industries and cities in favor of decarbonization, that support may be insufficient to achieve necessary societal transformation for deep decarbonization. Finally, the study reflects on the conditions necessary for polycentric governance to effectively decarbonize society, highlighting the pivotal role of the regulatory state.

The aggregation of alpha-Synuclein (alpha Syn) is strongly linked to neuronal death in Parkinson's disease and other synucleinopathies. The spreading of aggregated alpha Syn between neurons is at least partly dependent on electrostatic interactions between positively charged stretches on alpha Syn fibrils and the negatively charged heparan sulphate proteoglycans on the cell surface. To date there is still no therapeutic option available that could halt the progression of Parkinson's disease and one of the major limitations is likely the relatively low proportion of alpha Syn aggregates accessible to drugs in the extracellular space. Here, we investigated whether a negatively charged peptide tail fused to the alpha Syn aggregate-specific antibodies SynO2 and 9E4 could enhance the antibodies' avidity to alpha Syn aggregates in order to improve their potential therapeutic effect through inhibiting cell-to-cell spreading and enhancing the clearance of extracellular aggregates. We performed ELISAs to test the avidity to alpha Syn aggregates of both monovalent and bivalent antibody formats with and without the peptide tail. Our results show that the addition of the negatively charged peptide tail decreased the binding strength of both antibodies to alpha Syn aggregates at physiological salt conditions, which can likely be explained by intermolecular repulsions between the tail and the negatively charged C-terminus of alpha Syn. Additionally, the tail might interact with the paratopes of the SynO2 antibody abolishing its binding to alpha Syn aggregates. Conclusively, our peptide tail did not fulfil the required characteristics to improve the antibodies' binding to alpha Syn aggregates. Fine-tuning the design of the peptide tail to avoid its interaction with the antibodies' CDR and to better mimic relevant characteristics of heparan sulphates for alpha Syn aggregate binding may help overcome the limitations observed in this study.

Plant image analysis is a significant tool for plant phenotyping. Image analysis has been used to assess plant trails, forecast plant growth, and offer geographical information about images. The area segmentation and counting of the leaf is a major component of plant phenotyping, which can be used to measure the growth of the plant. Therefore, this paper developed a convolutional neural network-based leaf counting model called LC-Net. The original plant image and segmented leaf parts are fed as input because the segmented leaf part provides additional information to the proposed LC-Net. The well-known SegNet model has been utilised to obtain segmented leaf parts because it outperforms four other popular Convolutional Neural Network (CNN) models, namely DeepLab V3+, Fast FCN with Pyramid Scene Parsing (PSP), U-Net, and Refine Net. The proposed LC-Net is compared to the other recent CNN-based leaf counting models over the combined Computer Vision Problems in Plant Phenotyping (CVPPP) and KOMATSUNA datasets. The subjective and numerical evaluations of the experimental results demonstrate the superiority of the LC-Net to other tested models.

Purpose: Scaphoid fractures in patients and assessment of healing using PCD-CT have, as far as we know, not yet been studied. Therefore, the aim was to compare photon counting detector CT (PCD-CT) with energy integrating detector CT (EID-CT) in terms of fracture visibility and evaluation of fracture healing. Method: Eight patients with scaphoid fracture were examined with EID-CT and PCD-CT within the first week posttrauma, and with additional scans at 4, 6 and 8 weeks. Our clinical protocol for wrist examination with EID-CT was used (CTDIvol 3.1 +/- 0.1 mGy, UHR kernel Ur77). For PCD-CT matched radiation dose, reconstruction kernel Br89. Quantitative analyses of noise, CNR, trabecular and cortical sharpness, and bone volume fraction were conducted. Five radiologists evaluated the images for fracture visibility, fracture gap consolidation and image quality, and rated their confidence in the diagnosis. Results: The trabecular and cortical sharpness were superior in images obtained with PCD-CT compared with EIDCT. A successive reduction in trabecular bone volume fraction during the immobilized periods was found with both systems. Despite higher noise and lower CNR with PCD-CT, radiologists rated the image quality of PCD-CT as superior. The visibility of the fracture line within 1 -week post -trauma was rated higher with PCD-CT as was diagnostic confidence, but the subsequent assessments of fracture gap consolidation during healing process and the confidence in diagnosis were found equivalent between both systems. Conclusion: PCD-CT offers superior visibility of bone microstructure compared with EID-CT. The evaluation of fracture healing and confidence in diagnosis were rated equally with both systems, but the radiologists found primary fracture visibility and overall image quality superior with PCD-CT.

Exploiting low-cost, highly active, and robust oxygen evolution reaction (OER) electrocatalysts based on earth-abundant elements by a simple synthesis approach holds paramount importance for green hydrogen production through water electrolysis. In this work, the NiO, Co3O4 and NiCo2O4 nanoparticle layers with identical surface morphologies are prepared under same deposition conditions by a simple spray pyrolysis method and their OER activities are comparatively investigated. Among all these three electrocatalysts, NiCo2O4 shows the lowest overpotential of 420 mV to drive benchmark current density of 10 mA cm(-2) and the smallest Tafel slope (84.1 mV dec(-1)), which are comparable to the OER performance of the benchmark commercial RuO2 electrocatalyst. The high OER activity of NiCo2O4 is attributed to the synergy effect and the modulation of electronic properties between Co and Ni atoms, which drastically reduces the overpotential required to drive OER activities. Therefore, it is believed that the NiCo2O4 synthesized by this simple method would be a competitive candidate as an industrial electrocatalyst with high-efficiency and low cost for large-scale green hydrogen production via water electrolysis.

Eye tracking is a common tool to assess drivers' attentional state, either in real-time with the goal to prevent incidents, or offline, to understand underlying processes. While seemingly objective, eye tracking data can be coded and interpreted in different ways, which can have substantial effects on the results. The objective of this paper is to highlight and discuss the possibilities and limitations of three different approaches to code glance data: the direction-based encoding, the target-based approach, and the purpose-based approach. The direction-based coding scheme de-scribes glances relative to the direction of travel. The target-based approach classifies the glance targets into different categories. The purpose-based approach needs additional layers of information to deduce the reason for the glance. This information encompasses road layout, traffic rules, and the presence and relevance of other traffic. Data from a field study with 23 participants driving an instrumented vehicle on an urban route was used to illustrate differences between the three methods. The results showed that the coding approach clearly affected the interpretation of the measured glance data. A purely target-based approach is limited by its inability to account for spare visual capacity and that also the absence of a target constitutes valuable information, and a purely direction-based approach does not account for the need to scan areas located off forward. The purpose-based approach requires contextual information that can be cumbersome to integrate. Regardless of the approach used, additional layers of contextual information increase understanding and interpretability, potentially at the cost of increased complexity. The three approaches are suitable for different contexts and their feasibility also depends on the availability of additional data. A key message is that context awareness improves the accuracy of driver attention monitoring and inattention identification.

Introduction: Domestication is the process of modifying animals for human benefit through selective breeding in captivity. One of the traits that often diverges is the size of the brain and its constituent regions; almost all domesticated species have relatively smaller brains and brain regions than their wild ancestors. Although the effects of domestication on the brain have been investigated across a range of both mammal and bird species, almost nothing is known about the neuroanatomical effects of domestication on the world's most common bird: the chicken (Gallus gallus). Methods: We compared the quantitative neuroanatomy of the telencephalon of white leghorn chickens with red junglefowl, their wild counterpart, and several wild galliform species. We focused specifically on the telencephalon because telencephalic regions typically exhibit the biggest differences in size in domesticate-wild comparisons. Results: Relative telencephalon size was larger in chickens than in junglefowl and ruffed grouse (Bonasa umbellus). The relative size of telencephalic regions did not differ between chickens and junglefowl, but did differ in comparison with ruffed grouse. Ruffed grouse had larger hyperpallia and smaller entopallial, nidopallial, and striatal volumes than chickens and junglefowl. Multivariate analyses that included an additional three wild grouse species corroborated these findings: chicken and junglefowl have relatively larger nidopallial and striatal volumes than grouse. Conversely, the mesopallial and hyperpallial volumes tended to be relatively smaller in chickens and junglefowl. Conclusion: From this suite of comparisons, we conclude that chickens do not follow a pattern of widespread decreases in telencephalic region sizes that is often viewed as typical of domestication. Instead, chickens have undergone a mosaic of changes with some regions increasing and others decreasing in size, and there are few differences between chickens and junglefowl.

Diffuse correlation spectroscopy (DCS) is a powerful tool for assessing microvascular hemodynamic in deep tissues. Recent advances in sensors, lasers, and deep learning have further boosted the development of new DCS methods. However, newcomers might feel overwhelmed, not only by the already-complex DCS theoretical framework but also by the broad range of component options and system architectures. To facilitate new entry to this exciting field, we present a comprehensive review of DCS hardware architectures (continuous-wave, frequency-domain, and time-domain) and summarize corresponding theoretical models. Further, we discuss new applications of highly integrated silicon single-photon avalanche diode (SPAD) sensors in DCS, compare SPADs with existing sensors, and review other components (lasers, sensors, and correlators), as well as data analysis tools, including deep learning. Potential applications in medical diagnosis are discussed and an outlook for the future directions is to offer effective to embark on DCS research.

Interaction is critical for data analysis and sensemaking. However, designing interactive physicalizations is challenging as it requires cross-disciplinary knowledge in visualization, fabrication, and electronics. Interactive physicalizations are typically produced in an unstructured manner, resulting in unique solutions for a specific dataset, problem, or interaction that cannot be easily extended or adapted to new scenarios or future physicalizations. To mitigate these challenges, we introduce a computational design pipeline to 3D print network physicalizations with integrated sensing capabilities. Networks are ubiquitous, yet their complex geometry also requires significant engineering considerations to provide intuitive, effective interactions for exploration. Using our pipeline, designers can readily produce network physicalizations supporting selection-the most critical atomic operation for interaction-by touch through capacitive sensing and computational inference. Our computational design pipeline introduces a new design paradigm by concurrently considering the form and interactivity of a physicalization into one cohesive fabrication workflow. We evaluate our approach using (i) computational evaluations, (ii) three usage scenarios focusing on general visualization tasks, and (iii) expert interviews. The design paradigm introduced by our pipeline can lower barriers to physicalization research, creation, and adoption.

Effective individual wound management, particularly in cases of prolonged healing and increased infection vulnerability, has prompted the development of wound theranostics, combining real-time diagnostic assessment and on-demand treatment. Here, we present a multifunctional conducting polymer-based smart theranostic bandage that integrates pH sensing, pH-compensated uric acid (UA) biosensing, and on-demand antibiotic release using different conducting polymers, each leveraging their advantageous intrinsic properties. Specifically, the polyaniline-based pH sensor operates reversibly across a pH range of 4-10, while the functionalized poly(3,4-ethylenedioxythiophene)-based UA biosensor exhibits a linear response up to 0.9 mM UA. Simultaneous detection of pH and UA allows accurate UA determination via pH compensation. Upon detecting abnormal pH/UA levels, the polypyrrole-based drug carrier releases ciprofloxacin via 0.6 V electrical stimulation, successfully inhibiting bacterial growth in vitro. The array is assembled as a 3D patch, connected to a flexible printed circuit board, and embedded in a wound bandage, offering potential for remote wound monitoring, targeted treatment, and wireless wound management. Smart devices for wound management combine biosensing with drug release. Here, a smart theranostic bandage is reported that can detect pH and uric acid levels and release antibiotics as necessary, all of which can be done remotely.

Background and Objective

The accurate evaluation of bone mechanical properties is essential for predicting fracture risk based on clinical computed tomography (CT) images. However, blurring and noise in clinical CT images can compromise the accuracy of these predictions, leading to incorrect diagnoses. Although previous studies have explored enhancing trabecular bone CT images to super-resolution (SR), none of these studies have examined the possibility of using clinical CT images from different instruments, typically of lower resolution, as a basis for analysis. Additionally, previous studies rely on 2D SR images, which may not be sufficient for accurate mechanical property evaluation, due to the complex nature of the 3D trabecular bone structures. The objective of this study was to address these limitations.

Methods

A workflow was developed that utilizes convolutional neural networks to generate super-resolution 3D models across different clinical CT instruments. The morphological and finite-element-derived mechanical properties of these super-resolution models were compared with ground truth models obtained from micro-CT scans.

Results

A significant improvement in analysis accuracy was demonstrated, where the new SR models increased the accuracy by up to 700% compared with the low-resolution data, i.e. clinical CT images. Additionally, we found that the mixture of different CT image datasets may improve the super-resolution model performance.

Conclusions

Super-resolution images, generated by convolutional neural networks, outperformed clinical CT images in the determination of morphological and mechanical properties. The developed workflow could be implemented for fracture risk prediction, potentially leading to improved diagnoses and subsequent clinical decision making.

The chapter discusses shifting trends in global migration and the precarization oflabour on the background of processes of commodification and recommodification, against a theory of a neoliberal ‘regulatory state’. It explores aspects of the other side of thi sproblem in terms of perspectives for, to paraphrase Polanyi (2001 [1944]), a countermovementof, for, or with the migrant precariat. Reviewing processes leading up to the confirmation of the Global Compact for Safe, Orderly and Regular Migration (GCM 2018 ) and the subsequent  UN International Migration Review Forum, the authors ask what space there is for migrant rights movements in the global governance of migration and discuss the handling of the discursive emblemof ‘human rights’ in the context.

Industrial device scanners allow anyone to scan devices on private networks and the Internet. They were intended as network security tools, but they are commonly exploited as attack tools, as scanning can reveal vulnerable devices. However, from a defensive perspective, this vulnerability disclosure could be used to secure devices if characteristics such as type, model, manufacturer, and firmware could be identified. Automated scanning reports can help to apply security measures before an attacker finds a vulnerability. A complete device recognition procedure can then be seen as the basis for auditing networks and identifying vulnerabilities to mitigate cyber-attacks, especially among Industrial Internet of Things (IIoT) devices that are part of critical systems. In this survey, considering SCADA (Supervisory Control and Data Acquisition) systems as monitoring and control components of essential infrastructure, we focus on analyzing the architectures, specifications, and constraints of several industrial device scanners. In addition, we examine the information revealed by the scanners to identify the threats posed by them on industrial systems and networks. We analyze monthly and yearly statistics of cyber-attack incidents to investigate the role of these scanners in accelerating attacks. By presenting the findings of an experimentation, we highlight how easily anyone could identify hundreds of Internet-connected industrial devices in Sweden, which could lead to a major service interruption in industrial environments designed for minimal human involvement. We also discuss several methods to avoid scanners or reduce their identifying capabilities to conceal industrial devices from unauthorized access.

The purpose of this contribution is to explore how a dialectical approach may further Integrational Linguistics into transcending structuralism. The strategy employed is to articulate the biomechanical and macrosocial factors in the communicational infrastructure as neurobiological attractors and social anchors respectively. Hence, the inquiry focuses on the dialectical relation between an individual’s neurobiological structure and her ability to confer signhood onto activity-relevant phenomena in the social environment. This makes it possible to conceive new directions for Integrational Linguistics by rethinking central aspects such as contextuality, indeterminacy, cotemporality, integration, and coordination. Conjectural as these directions may be, I put forward that the dialectical approach provides a promising launchpad for further inquiries into transcending structuralism and beyond.

The present study focuses on challenges to the work of teachers as a result of a Swedish educational reform that mandates schools guarantee pupils receive early learning support concerning reading, writing, and number sense. The outcome of reforms is connected to how teachers achieve agency in their work , and we use the ecolo- gical model of teacher agency as an overarching theoretical frame- work and analytical dimension. The study involved interviews with 14 teachers, and the focus was on a) how teachers identify pupils in need of support and how this support is carried out, (b) how teachers would like this identification to be carried out, and how teachers would like to support pupils with weak number sense. Teachers described a ‘dilemmatic space’ (cf. Fransson and Grannäs 2013), which affects how they achieved agency in developing pupils’ num- ber sense. The dilemmatic space described by teachers in this study is framed by necessary priorities pertaining to the cultural, structural, and material conditions of the current situation. According to the study’s results, whether teachers should perform the assessment merits discussion. The study concludes there is a risk that conducting assessments and offering support risks cancelling each other out.

Background Tuberculosis (TB) is among the largest infectious causes of death worldwide, and there is a need for a time- and resource-effective diagnostic methods. In this novel and exploratory study, we show the potential of using buccal swabs to collect human DNA and investigate the DNA methylation (DNAm) signatures as a diagnostic tool for TB.Methods Buccal swabs were collected from patients with pulmonary TB (n = 7), TB-exposed persons (n = 7), and controls (n = 9) in Sweden. Using Illumina MethylationEPIC array, the DNAm status was determined.Results We identified 5644 significant differentially methylated CpG sites between the patients and controls. Performing the analysis on a validation cohort of samples collected in Kenya and Peru (patients, n = 26; exposed, n = 9; control, n = 10) confirmed the DNAm signature. We identified a TB consensus disease module, significantly enriched in TB-associated genes. Last, we used machine learning to identify a panel of 7 CpG sites discriminative for TB and developed a TB classifier. In the validation cohort, the classifier performed with an area under the curve of 0.94, sensitivity of 0.92, and specificity of 1.Conclusions In summary, the result from this study shows clinical implications of using DNAm signatures from buccal swabs to explore new diagnostic strategies for TB. In this work we show that individuals with tuberculosis display distinct DNA methylation patterns in the buccal mucosa compared to healthy controls and tuberculosis-exposed individuals, showing the potential of using this DNA methylation signature as a diagnostic tool. Graphical Abstract

This paper presents the first study to investigate ethnic differential treatment in public housing through a correspondence test field experiment. The experiment involved sending inquiries from fictitious couples with Swedish or Arabic names to all public housing companies in Sweden. Four outcomes were examined: whether the public housing companies responded to the inquiries, whether they initiated their response with a greeting, whether they had a priority system in place, and whether they provided information about problematic neighborhoods. The findings revealed disparities in the treatment of the couples. The Swedish couple received greetings and information about problematic neighborhoods at a greater rate than the Arab couple. This study contributes to existing literature on ethnic differences in the housing market by providing evidence of differential treatment within the public housing sector. Additionally, it explores the content and quality of public housing companies' responses, offering valuable insights for policymakers and housing professionals in designing interventions to promote equality and counteract differential treatment.

Ammonia (NH3) of high concentration will pose a threat to ecological environment or human health, and exhaled NH3 is significant in disease monitoring and diagnosis. Thus, developing a highly sensitive gas sensor is significant to monitor NH3 concentration in complex environments. However, traditional NH3 sensors either need high working temperature, or face the challenge of poor conductivity/ sensitivity. In this work, NH3 sensors based on self-assembled MXene membrane have been fabricated. As-prepared sensors show a high sensitivity of 2.10 ppm-1 towards extremely low concentrations of NH3 (ppb level) at room temperature, attributed to large surface area and high conductivity. In addition, the sensors also display low detection limit (50 ppb), fast response time (41 s), good recoverability, long-term stability (15 days) and excellent flexibility (1000 bending cycles) towards NH3. The results provide insights into the development of highly sensitive NH3 sensors for industrial or biomedical applications. image

Multiple Sclerosis (MS) is a heterogeneous inflammatory and neurodegenerative disease with an unpredictable course towards progressive disability. Treating progressive MS is challenging due to limited insights into the underlying mechanisms. We examined the molecular changes associated with primary progressive MS (PPMS) using a cross-tissue (blood and post-mortem brain) and multilayered data (genetic, epigenetic, transcriptomic) from independent cohorts. In PPMS, we found hypermethylation of the 1q21.1 locus, controlled by PPMS-specific genetic variations and influencing the expression of proximal genes (CHD1L, PRKAB2) in the brain. Evidence from reporter assay and CRISPR/dCas9 experiments supports a causal link between methylation and expression and correlation network analysis further implicates these genes in PPMS brain processes. Knock-down of CHD1L in human iPSC-derived neurons and knock-out of chd1l in zebrafish led to developmental and functional deficits of neurons. Thus, several lines of evidence suggest a distinct genetic-epigenetic-transcriptional interplay in the 1q21.1 locus potentially contributing to PPMS pathogenesis.

Integrating electrochemically actuated soft robotics with ultra-flexible microelectrodes enables reversible and gentle wrapping around nerves for high-quality recordings.

The Danish Reproducibility Network (DKRN) is a grassroots initiative for establishing a peer-supportive reproducibility-focused academic network in Denmark. We modelled our approach on already existing national Reproducibility Networks. We consulted with researchers and research support professionals to identify the needs of the research community. Three themes emerged around policy implementation, training and the appropriate application of reproducible practices. The network aims to address these three themes in a strategic plan, which harnesses the benefits of grassroots initiatives. The mission of the DKRN is therefore to facilitate communication, peer-support, and the exchange of ideas through a network of topic and geographical nodes. The network is open to researchers and research support professionals from all career stages and disciplines. It aligns with broader international initiatives, and national institutions, positioning itself as a contributor to the Danish research ecosystem.

Poly(3,4-ethylenedioxythiophene):poly(styrenesulfonate) (PEDOT:PSS) is a benchmark hole-transporting (p-type) polymer that finds applications in diverse electronic devices. Most of its success is due to its facile synthesis in water, exceptional processability from aqueous solutions, and outstanding electrical performance in ambient. Applications in fields like (opto-)electronics, bioelectronics, and energy harvesting/storage devices often necessitate the complementary use of both p-type and n-type (electron-transporting) materials. However, the availability of n-type materials amenable to water-based polymerization and processing remains limited. Herein, we present a novel synthesis method enabling direct polymerization in water, yielding a highly conductive, water-processable n-type conjugated polymer, namely, poly[(2,2 '-(2,5-dihydroxy-1,4-phenylene)diacetic acid)-stat-3,7-dihydrobenzo[1,2-b:4,5-b ']difuran-2,6-dione] (PDADF), with remarkable electrical conductivity as high as 66 S cm(-1), ranking among the highest for n-type polymers processed using green solvents. The new n-type polymer PDADF also exhibits outstanding stability, maintaining 90% of its initial conductivity after 146 days of storage in air. Our synthetic approach, along with the novel polymer it yields, promises significant advancements for the sustainable development of organic electronic materials and devices.

In this paper, the Hierarchical Multi Switch Multi Echelon Vehicle Routing Problem with Service Times (HMSME-VRP-ST) is presented. This novel problem considers distribution applications in which goods are delivered either directly from a central depot or through intermediate facilities called switch points. Commodities are loaded into interchangeable containers called swap bodies at the central depot, and can be transferred from one vehicle to another at the switch points. The goal is to minimize fixed and variable costs of the vehicle fleet and swap -bodies while serving a predetermined set of customers. The routes are constrained by time and swap bodies by loading capacity. A mathematical model of the HMSME-VRP-ST is presented, and a Hybrid Clustered Ant Colony Optimization (HCACO) algorithm is proposed to address the complexity of the problem. The approach utilizes the ant -based clustering algorithm, combining the density and connectivity properties of clustering for creating promising neighborhoods with the solution construction methodology and pheromonebased memory of the Ant Colony Optimization framework. Additionally, two local search schemes based on Variable Neighborhood Descent are incorporated to further improve the generated solutions. The behavior of each HCACO variant is analyzed, and their results are compared to a Greedy Randomized Adaptive Search Procedure metaheuristic in 36 newly generated benchmarks comprising of clustered, uniformly random, and mixed clustered -random instances.

The inverse problem of supervised reconstruction of depth-variable (time-dependent) parameters in ordinary differential equations is considered, with the typical application of finding weights of a neural ordinary differential equation (NODE) for a residual network with time continuous layers. The differential equation is treated as an abstract and isolated entity, termed a standalone NODE (sNODE), to facilitate for a wide range of applications. The proposed parameter reconstruction is performed by minimizing a cost functional covering a variety of loss functions and penalty terms. Regularization via penalty terms is incorporated to enhance ethical and trustworthy AI formulations. A nonlinear conjugate gradient mini-batch optimization scheme (NCG) is derived for the training having the benefit of including a sensitivity problem. The model (differential equation)-based approach is thus combined with a data-driven learning procedure. Mathematical properties are stated for the differential equation and the cost functional. The adjoint problem needed is derived together with the sensitivity problem. The sensitivity problem itself can estimate changes in the output under perturbation of the trained parameters. To preserve smoothness during the iterations, the Sobolev gradient is calculated and incorporated. Numerical results are included to validate the procedure for a NODE and synthetic datasets and compared with standard gradient approaches. For stability, using the sensitivity problem, a strategy for adversarial attacks is constructed, and it is shown that the given method with Sobolev gradients is more robust than standard approaches for parameter identification.

Developmental psychology and cultural evolution are concerned with the same research questions but rarely interact. Collaboration between these fields could lead to substantial progress. Developmental psychology and related fields such as educational science and linguistics explore how behavior and cognition develop through combinations of social and individual experiences and efforts. Human developmental processes display remarkable plasticity, allowing children to master complex tasks, many which are of recent origin and not part of our biological history, such as mental arithmetic or pottery. It is this potency of human developmental mechanisms that allow humans to have culture on a grand scale. Biological evolution would only establish such plasticity if the combinatorial problems associated with flexibility could be solved, biological goals be reasonably safeguarded, and cultural transmission faithful. We suggest that cultural information can guide development in similar way as genes, provided that cultural evolution can establish productive transmission/teaching trajectories that allow for incremental acquisition of complex tasks. We construct a principle model of development that fulfills the needs of both subjects that we refer to as Incremental Functional Development. This process is driven by an error-correcting mechanism that attempts to fulfill combinations of cultural and inborn goals, using cultural information about structure. It supports the acquisition of complex skills. Over generations, it maintains function rather than structure, and this may solve outstanding issues about cultural transmission. The presence of cultural goals gives the mechanisms an open architecture that become an engine for cultural evolution.

Lagrangian relaxation is a common and often successful way to approach computationally challenging single-objective discrete optimization problems with complicating side constraints. Its aim is often twofold; first, it provides bounds for the optimal value, and, second, it can be used to heuristically find near-optimal feasible solutions, the quality of which can be assessed by the bounds. We consider bi-objective discrete optimization problems with complicating side constraints and extend this Lagrangian bounding and heuristic principle to such problems. The Lagrangian heuristic here produces non-dominated candidates for points on the Pareto frontier, while the bounding forms a polyhedral outer approximation of the Pareto frontier, which can be used to assess the quality of the candidate points. As an illustration example we consider a facility location problem in which both CO2 emission and cost should be minimized. The computational results are very encouraging, both with respect to bounding and the heuristically found non-dominated solutions. In particular, the Lagrangian bounding is much stronger than the outer approximation given by the Pareto frontier of the problem's linear programming relaxation.

KOL är en kronisk, irreversibel obstruktiv lungsjukdom. I Sverige är 400000-700000 personer drabbade. KOL är den tredje vanligaste diagnosen. Cirka tre procent har KOL i stadie III och IV. Personer drabbade av KOL har symtom i form av andningsbesvär, hosta och ökad slemproduktion. Personer drabbade av KOL och deras närstående har ett förändrat liv. De drabbade personerna har ett behov av stöd från närstående och det omgivande samhället.

Den här avhandlingens syfte är att identifiera, beskriva och fördjupa kunskapen om symtombördan hos personer drabbade av KOL och vårdgivarbördan hos närstående samt deras behov av stöd.

Avhandlingen innehåller kvalitativa intervjuer med 25 personer drabbade av KOL i GOLD stadie III och IV och 22 närstående till de drabbade personerna. Intervjuerna är analyserade med tematisk analys utifrån Braun och Clarke. Data från frågeformulär innehåller bakgrundsinformation, skattning av symtombörda med RMSAS, vårdgivarbörda med CRA, känsla av sammanhang med SOC13 och stöd är skattat med SSQ6. Kvantitativ data via dessa frågeformulär är insamlad från 112 personer drabbade av KOL och 71 närstående. Denna datan är analyserad med frekvens, medelvärden och t-test. Den totala datan, både kvalitativ och kvantitativ, är sedan jämförd och kontrasterad utifrån likheter och skillnader utifrån mixad metod enligt Teddlie och Tashakkori. Slutligen är en integrativ litteraturstudie genomförd med 68 studier som identifierar och syntetiserar vilket stöd som ges till personer drabbade av KOL och deras närstående. Även effekten av det givna stödet är identifierat och syntetiserat.

Resultatet visar att personer drabbade av KOL har en symtombörda i form av ett förändrat liv. De närstående upplever en vårdgivarbörda i form av förändrade roller, att sätta livet på väntan och att stå vid sidan om. All data tillsammans, kvalitativ och kvantitativ, visar att personer drabbade av KOL och deras närstående skattar sin börda på ett sätt via olika frågeformulär och uttrycker det på ett annat sätt med egna ord. Både de drabbade personerna och deras närstående behöver stöd för att hantera sin livssituation. Det vanligaste stödet som ges i dag är i forma av information, fysisk aktivitet och medicinsk behandling och har inte alltid den önskade effekten.

Slutsatsen är att personer drabbade av KOL och deras närstående önskar ett personcentrerat stöd via ett fysiskt möte eller i hemmet för att minska bördan.

The aim of this paper is to contribute with an understanding of the governance capacity of local public actors to be place leaders for local collaborative initiatives aimed at addressing climate mitigation, and how this agency is embedded within place-specific institutional conditions. The research design is a single case study of a climate mitigation initiative in the city of Uppsala in Sweden. The discussion based on the empirical results highlights three findings. Firstly, that the political and administrative arms of local government interact in their capacity to be place leaders in local interventions. Secondly, that the capacity of local government to provide place leadership needs to be conceived in relation to the place and the institutional structure of the local environment. Thirdly, that the role of place leader for governance is imbued with governance dilemmas deriving from how the steering role is negotiated. The concluding discussion argues for the need of a detailed understanding of place-leading agency to achieve a deeper understanding of governance capacity. This paper illustrates that place leadership is multifaceted and means different things across the cycles of collaborative governance. The concluding discussion also stresses the importance of understanding governance dilemmas in making actors prepared to strategically take position for their involvement and engagement in collaborations. This means that maintaining collaborative governance is an important strategy for meeting social challenges, but that this needs to be developed based on thorough knowledge about the intrinsic conditions for such arrangements and processes.

AimThis study addresses the scarcity of longitudinal research on the influence of screen media on children. It aims to explore the longitudinal relationship between children's vocabulary development and their exposure to screen media.MethodsThe study, initiated in 2017, included 72 children (37 boys) in ostergotland, Sweden, at three key developmental stages: preverbal (9.7 months), early verbal (25.5 months) and preliterate (5.4 years). Parents completed online surveys at each time point, reporting their child's screen time. At 10 months and 2 years, age-appropriate vocabulary assessments were conducted online. At age 5, children's vocabulary was laboratory assessed.ResultsCorrelational analysis revealed a negative relationship between language scores and screen media use across all time points. Furthermore, a cross-lagged panel model demonstrated that screen media use showed significant continuity over time, with screen use at age 2 predicting language development at ages 2 and 5.ConclusionThis longitudinal study, spanned from 9 months to 5 years of age, established a predictive negative association between children's exposure to screen media and their vocabulary development. These findings underscore the need to consider the impact of screen media on early childhood development and may inform guidelines for screen media use in young children.

Background

Licorice, through the effects of glycyrrhizic acid (GA), raises blood pressure (BP). The World Health Organization has suggested that 100 mg GA/d would be unlikely to cause adverse effects, but of 13 previously published studies none have been randomized and controlled and independently quantified the GA content.

Objective

Our aim was to analyze the effects on home BP of a daily licorice intake containing 100 mg GA.

Methods

Healthy volunteers were randomly assigned to start with either licorice or a control product in a nonblinded, 2 × 2 crossover study. Home BP was measured daily, and blood samples were collected at the end of each 2-wk period.

Results

There were 28 participants and no dropouts. The median age was 24.0 y (interquartile range 22.8–27.0 y). During the licorice compared with control intake period, the systolic home BP increased [mean difference: 3.1 mm Hg (95% confidence interval [CI]: 0.8, 5.4 mm Hg) compared with −0.3 mm Hg (95% CI: −1.8, 1.3 mm Hg); P = 0.018] and renin and aldosterone were suppressed [mean change: −30.0% (95% CI: −56.7%, −3.3%) compared with 15.8% (95% CI: −12.8%, 44.4%); P = 0.003; and −45.1% (95% CI: −61.5%, −28.7%) compared with 8.2% (95% CI: −14.7%, 31.1%); P <0.001, respectively]. In the quartile of participants with the most pronounced suppression of renin and aldosterone, N-terminal prohormone of brain natriuretic peptide concentration increased during the licorice compared with control period [mean change: 204.1% (95% CI: −11.6%, 419.7%) compared with 72.4% (95% CI: −52.2%, 197.1%); P = 0.016].

Conclusions

We found licorice to be more potent than previously known, with significant increases in BP, after a daily intake of only 100 mg GA. Thus, the safe limit of intake of this substance might need to be reconsidered.

This paper proposes a six-step maturity ladder for inter-organizational collaborative capability in crisis management and response operations. The paper argues that collaborative crisis management and response can be seen as a case of complex adaptive systems, where awareness of the organizational context of crisis response, technical interoperability, and individual relations are drivers that enable rapid and successful mobilization of crisis response. The maturity ladder provides a link between the capabilities necessary for collaboration possessed by individual crisis response entities and the overall system maturity in terms of collaborative crisis management capability. It can inter alia be used as a basis for a discussion when inter-organizational exercises are appropriate and when focus must remain on the development of the capabilities of individual crisis response entities.

Wearable electronics are some of the most promising technologies with the potential to transform many aspects of human life such as smart healthcare and intelligent communication. The design of self-powered fabrics with the ability to efficiently harvest energy from the ambient environment would not only be beneficial for their integration with textiles, but would also reduce the environmental impact of wearable technologies by eliminating their need for disposable batteries. Herein, inspired by classical Archimedean spirals, we report a metastructured fiber fabricated by scrolling followed by cold drawing of a bilayer thin film of an MXene and a solid polymer electrolyte. The obtained composite fibers with a typical spiral metastructure (SMFs) exhibit high efficiency for dispersing external stress, resulting in simultaneously high specific mechanical strength and toughness. Furthermore, the alternating layers of the MXene and polymer electrolyte form a unique, tandem ionic-electronic coupling device, enabling SMFs to generate electricity from diverse environmental parameters, such as mechanical vibrations, moisture gradients, and temperature differences. This work presents a design rule for assembling planar architectures into robust fibrous metastructures, and introduces the concept of ionic-electronic coupling fibers for efficient multimodal energy harvesting, which have great potential in the field of self-powered wearable electronics. In this work, a concept of ionic-electronic coupling fibers by integrating a 2D MXene and a polymer electrolyte to fabricate spiral metastructures is proposed to realize multimodal power generation from various sources simultaneously.

Open innovation can support firms looking to deploy strategic agility through product innovations during periods of market and technological change. However, existing research lacks a comprehensive understanding of the microfoundations that underlie strategic agility in the context of open innovation. We address this gap using an in-depth analysis of a firm's open innovation activities in support of new product development (NPD). Our analysis reveals that open innovation can help leverage NPD processes to drive technological innovations in response to changing market conditions. Under such circumstances, open innovation enables firms to deploy strategic agility by continually developing the product portfolio. Our study reveals six mechanisms that enable three mutually complementary practices of agility: knowledge-based agility at the firm-environment interface, behavioural agility in the firm's decision-making process, and organisational agility in the internal NPD process. We theorise the interplay between the mechanisms that constitute each practice and, in doing so, shed light on how they contribute to firm-level strategic agility.

Optimal power flow (OPF) problem solution is a crucial task for the operators and decision makers to assign the best setting of the system components to obtain the most economic, environmental, and technical suitable state. Artificial Hummingbird Algorithm is a recent optimization algorithm that has been applied to solving several optimization problems. In this paper, a Modified Artificial Hummingbird Algorithm (MAHA) is proposed for improving the performance of the orignal Artificial Hummingbird Algorithm as well as effectivelly solve the OPF problem. The proposed MAHA is based on improving the searching capability by boosting the exploitation using the bandwidth motion around the best solution, while the exploration process is improved using the Levy flight distribution motion and the fitness-distance balance selection. This modified version helps overcome issues such as stagnation, premature convergence, and a propensity for local optima when tackling complex, nonlinear, and non-convex optimization problems like OPF. In order to confirm the effectiveness of the proposed algorithm, a series of tests are conducted on 23 standard benchmark functions, including CEC2020. The resulting outcomes are then compared to those obtained using other algorithms such as fitness-distance balance selection-based stochastic fractal search (FDBSFS), antlion optimizer (ALO), whale optimization algorithm (WOA), sine-cosine algorithm (SCA), fitness-distance balance and learning based artificial bee colony (FDB-TLABC), and traditional artificial hummingbird algorithm (AHA).The proposed algorithm is evaluated by solving the OPF problem with multiple objective functions on the IEEE 30-bus system. These objectives include fuel cost, fuel cost with valve loading effects, power losses, emissions, and voltage profile. Additionally, the algorithm's effectiveness is further assessed by testing it on single objective functions using medium and large-scale IEEE 57 and 118-bus networks.The results obtained by the proposed MAHA demonstrate its power and superiority for solving the OPF problem as well as the standard benchmark functions , surpassing the performance of other reported techniques.

Current display screens are typically only used for information display, but can have a range of different sensors integrated into them for functions such as touch control, ambient light sensing and fingerprint sensing. Photo-responsive light-emitting diodes (LEDs), which can display information and respond to light excitation, could be used to develop future ultra-thin and large screen-to-body ratio screens. However, photo-response is difficult to achieve with conventional display technologies. Here, we report a multifunctional display that uses photo-responsive metal halide perovskite LEDs as pixels. The perovskite LED display can be simultaneously used as a touch screen, ambient light sensor and image sensor (including for fingerprint drawing) without integrating any additional sensors. The light-to-electricity conversion efficiency of the pixels also allow the display to act as a photovoltaic device that can charge the equipment. Photo-responsive metal halide perovskite light-emitting diodes can be used to create a multifunctional display that can function as a touch screen, ambient light sensor and image sensor.

With the growing demand for sustainable and optimal packaging solutions, this study proposes a novel two-stage algorithm for the multi-container three-dimensional bin packing problem. The research addresses this problem within the context of a real-world industrial scenario and implements several practical constraints including: full shipment, customer positioning requirements, and product geometric interlocking, for increased stability and with the purpose of minimizing the use of plastic wrapping and/or additional supporting surfaces. The main optimization target is to minimize the total number of containers used in the palletization process of custom orders with varying degrees of complexity. The proposed algorithm includes two stages/phases of processing. In the first phase, the algorithm uses constructive heuristics to generate homogeneous product layers. The layers are then stacked to produce blocks, which are then placed on individual containers or pallets. The second phase packs the leftover items using a genetic algorithm. The performance of the proposed solution is benchmarked using real-world industrial data, as well as a more classic academic benchmark. It is demonstrated, across a very large set of orders, that the algorithm always achieves solutions for full palletization of the orders. The analysis shows that the approach is generic and the quality of the solutions generated is relatively even for both small and large, homogeneous and heterogeneous problem instances.

Wind power production is expanding globally and with this comes social resistance. In this article, we study controversies concerning the expansion of wind power in Sweden between 2009 and 2022. To this end, we study national press coverage and local press coverage from Sweden's 290 municipalities. Despite previous studies showing that Swedish people are generally in favour of expanding wind power, our results show that this policy is controversial and often is met with significant local resistance. We identify a clash between two environmental concerns: developing national policies to mitigate the effects of climate change and local opinions calling for the preservation of natural landscapes, which are often coupled with concerns about whether expansion of wind power leads to positive climate effects. We analyse national and local discourses through a multi-level governance perspective, a view that argues Sweden's market-oriented and decentralized wind power planning regime feeds the controversy. In conclusion, the dispersal of power throughout the planning system facilitates scaling up of local resistance to an energy policy issue of national concern, creating significant uncertainty about the expansion of wind power in Sweden.