Background Clinical guidelines recommend a biopsychosocial approach to low back pain (LBP) management, with physiotherapists playing a key role in occupational health primary care (OHPC). However, little is known about how their clinical behaviours at the first visit align with guideline-oriented biopsychosocial principles. Therefore, we evaluated LBP management quality in OHPC by applying predefined criteria to physiotherapists' documentation. Methods Based on a cluster-randomised implementation study data (ISRCTN11875357) we analysed 98 electronic patient records (EPRs) documented by 28 physiotherapists across diverse OHPC units. The intervention arm had received 3-7 days of biopsychosocial training. A stratified random sample of EPRs from individuals with LBP was reviewed using a structured researcher's evaluation tool. Each item was scored dichotomously (yes/no) and evaluated against predefined quality criteria with stepwise thresholds for different work disability risk groups. Results Step I, multidimensional biopsychosocial assessment of LBP, was documented in fewer than half of the records (36.5% in the intervention vs 16.7% in the control arm, p=0.081). The biological dimension was well documented in both arms (100% vs 95.8%, p=0.245), while psychological (58.1% vs 25%, p=0.009) and social (54.1% vs 29.2%, p=0.038) dimensions were more frequently documented in the intervention arm. Step II quality criteria (low-risk patients) were met in 58.1% of intervention versus 4.2% of control records (p<0.001), and step III (medium-risk) in 55.4% versus 4.2% (p<0.001). No EPRs met step IV (high-risk) quality criteria. The intervention arm more often documented psychosocial assessments, risk stratification, behavioural strategies and advice to stay active. Person-centredness (ie, goals, values, resources, expectations) was rarely documented (36.5% vs 0%, p<0.001).Results Step I, multidimensional biopsychosocial assessment of LBP, was documented in fewer than half of the records (36.5% in the intervention vs 16.7% in the control arm, p=0.081). The biological dimension was well documented in both arms (100% vs 95.8%, p=0.245), while psychological (58.1% vs 25%, p=0.009) and social (54.1% vs 29.2%, p=0.038) dimensions were more frequently documented in the intervention arm. Step II quality criteria (low-risk patients) were met in 58.1% of intervention versus 4.2% of control records (p<0.001), and step III (medium-risk) in 55.4% versus 4.2% (p<0.001). No EPRs met step IV (high-risk) quality criteria. The intervention arm more often documented psychosocial assessments, risk stratification, behavioural strategies and advice to stay active. Person-centredness (ie, goals, values, resources, expectations) was rarely documented (36.5% vs 0%, p<0.001).Results Step I, multidimensional biopsychosocial assessment of LBP, was documented in fewer than half of the records (36.5% in the intervention vs 16.7% in the control arm, p=0.081). The biological dimension was well documented in both arms (100% vs 95.8%, p=0.245), while psychological (58.1% vs 25%, p=0.009) and social (54.1% vs 29.2%, p=0.038) dimensions were more frequently documented in the intervention arm. Step II quality criteria (low-risk patients) were met in 58.1% of intervention versus 4.2% of control records (p<0.001), and step III (medium-risk) in 55.4% versus 4.2% (p<0.001). No EPRs met step IV (high-risk) quality criteria. The intervention arm more often documented psychosocial assessments, risk stratification, behavioural strategies and advice to stay active. Person-centredness (ie, goals, values, resources, expectations) was rarely documented (36.5% vs 0%, p<0.001). Conclusion Training in guideline-oriented biopsychosocial approach was associated with more frequent documentation of behaviours aligned with high-quality LBP management. However, overall quality varied, and person-centred aspects remained underreported. Complementary implementation strategies are required to ensure consistent delivery and documentation of biopsychosocial clinical practice in OHPC.

Objectives This study aimed to investigate the effect of a brief training intervention for occupational health services (OHS) professionals on multiprofessional resource utilization and the costs of biopsychosocial management of patients with low-back pain (LBP) compared to usual care among all participants and those in work disability-based risk groups. Methods OHS utilization and back-related sick leave data were collected from electronic patient records over one-year follow-up comparing 232 patients in the intervention arm and 80 control-arm patients, stratified for risk of work disability based on the Orebro Musculoskeletal Pain Screening Questionnaire. We estimated costs using linear mixed models by multiplying unit costs (in euros) by each type of OHS resource use (visits to physicians, physiotherapists, nurses, use of imaging) and the number of sick leaves. Estimated mean cost differences with confidence intervals (CI) were reported using bootstrapping to deal with skewed cost data. Results The median number of visits to physicians and physiotherapists in the intervention versus control arms was 1 [interquartile range (IQR) 0-3] and 2 (IQR 1-4) versus 2 (IQR 1-3) and 1 (IQR 0-2), respectively. The intervention arm accrued lower physician costs (<euro>-43, 95% CI <euro>-82--3, P=0.034) and higher physiotherapist costs (<euro>55, 95% CI <euro>26-84, P<0.001) compared to the control arm. There was no statistically significant difference in average total costs between the arms (<euro>-1908, 95% CI <euro>-6734-2919). In the low- and medium-risk groups of work disability, physiotherapist costs were higher in the intervention than control arm, but no statistically significant differences were observed between the arms in the total resource utilization or sickness absence costs. Conclusions Brief biopsychosocial training may support shifting OHS resources towards multiprofessional physiotherapist-driven care, instead of solely physician-driven care, for management of patients with LBP in differing risk groups of work disability with no substantial differences in total costs.

BackgroundWe explored Finnish occupational healthcare professionals' (HCP) perceptions of biopsychosocial (BPS) low back pain (LBP) management after an educational intervention.MethodsWe conducted twelve group interviews of 51 physicians, physiotherapists and nurses from intervention units in a cluster randomized controlled trial (ISRCTN11875357). We used deductive and inductive content analysis to examine the data, and the Capability-Opportunity-Motivation-Behaviour (COM-B) model to identify the facilitators of and barriers to changes in three target behaviours: (A) forming a common BPS-based understanding with patients, (B) systematically using risk stratification tools, and (C) multidisciplinary collaboration in individualized care planning.ResultsFacilitators and barriers were categorized into the following COM-B domains. Most of the facilitators were in the Capability and Motivation domains: increased confidence regarding managing treatment decisions, improved therapeutic alliance and renewed professional identity. Significant system-level barriers were mostly in the Opportunity domain: time constraints, limited resources and unclear treatment pathways. The HCPs reported improved individual skills and awareness after the training, but varying organizational policies and lacking incentives hindered the adoption of BPS methods in multidisciplinary teams. Initial resistance to change decreased as positive patient outcomes emerged. The perceived benefits were increased multidisciplinary collaboration and a shift toward holistic pain management. Those who embraced BPS management reported greater professional satisfaction and confidence when handling LBP patients.ConclusionsTo effectively implement BPS management in occupational health services, organizational and system-level barriers must be addressed and HCPs' skills and motivation enhanced. For sustained support through policy initiatives and reinforced multidisciplinary collaboration, future strategies should integrate BPS practices into routine workflows.Trial registrationThe trial was retrospectively registered on 13.05.2019 ISRCTN11875357.

BACKGROUND: The Determinants of Implementation Behavior Questionnaire (DIBQ) measures facilitators or barriers of healthcare professionals implementation behaviors based on the current implementation research on practice and policy. The DIBQ covers 18 domains of the Theoretical Domains Framework and consists of 93 items. A previously tailored version (DIBQ-t) covering 10 domains and 28 items focuses on implementing best-practice low back pain care. AIM: To tailor a shortened version of DIBQ to multiprofessional rehabilitation context with cross-cultural adaptation to Finnish language. DESIGN: A two-round Delphi study. SETTING: National-level online survey. POPULATION: Purposively recruited experts in multiprofessional rehabilitation (N.=25). METHODS: Cross-cultural translation of DIBQ to Finnish was followed by a two-round Delphi survey involving diverse experts in rehabilitation (physicians, physiotherapists, occupational therapists, psychologists, nursing scientists, social scientists). In total, 25 experts in Round 1, and 21 in Round 2 evaluated the importance of DIBQ items in changing professionals implementation behavior by rating on a 5-point Likert Scale (1 = Strongly Disagree, 5 = Strongly Agree) of including each item in the final scale. Consensus to include an item was defined as a mean score of &gt;= 4 by &gt;= 75% of Delphi participants. Open comments were analyzed using inductive content analysis. Items with agreement of &lt;= 74% were either directly excluded or reconsidered and modified depending on qualitative judgements, amended with experts suggestions. After completing an analogous second-round, a comparison with DIBQ-t was performed. Lastly, the relevance of each item was indexed using content validity index on item-level (I-CVI) and scale-level (S-CVI/Ave). RESULTS: After Round 1, 17 items were included and 48 excluded by consensus whereas 28 items were reconsidered, and 20 items added for Round 2. The open comments were categorized as: 1) "modifying"; 2) "supportive"; and 3) "critical". After Round 2, consensus was reached regarding all items, to include 21 items. After comparison with DIBQ-t, the final multiprofessional DIBQ (DIBQ-mp) covers 11 TDF domains and 21 items with I-CVIs of &gt;= 0.78 and S-CVI/Ave of 0.93. CONCLUSIONS: A Delphi study condensed a DIBQ-mp with excellent content validity for multiprofessional rehabilitation context. CLINICAL REHABILITATION IMPACT: A potential tool for evaluating determinants in implementing evidence-based multiprofessional rehabilitation interventions.

Controller-Pilot Data Link Communications (CPDLC) are rapidly replacing voice-based Air Traffic Control (ATC) communications worldwide. Being digital, CPDLC is highly resilient and bandwidth efficient, which makes it the best choice for traffic-congested airports. Although CPDLC initially seems to be a perfect solution for modern-day ATC operations, it suffers from serious security issues. For instance, eavesdropping, spoofing, man-in-the-middle, message replay, impersonation attacks, etc. Cyber attacks on the aviation communication network could be hazardous, leading to fatal aircraft incidents and causing damage to individuals, service providers, and the aviation industry. Therefore, we propose a new security model called AKAASH, enabling several paramount security services, such as efficient and robust mutual authentication, key establishment, and a secure handover approach for the CPDLC-enabled aviation communication network. We implement the approach on hardware to examine the practicality of the proposed approach and verify its computational and communication efficiency and efficacy. We investigate the robustness of AKAASH through formal (proverif) and informal security analysis. The analysis reveals that the AKAASH adheres to the CPDLC standards and can easily integrate into the CPDLC framework.

Controller Pilot Data Link Communications (CPDLC) enhances air traffic communication by replacing traditional voice transmissions with digital messages over Very High Frequency (VHF) radio systems. This transition improves communication resilience by providing clear, text-based instructions that reduce misunderstandings and increase bandwidth efficiency by enabling more data to be transmitted simultaneously. It benefits congested airspace by reducing radio frequency congestion and minimizing communication errors. However, due to the plain-text nature of its messages, CPDLC faces significant security challenges, making it vulnerable to cyber-attacks such as eavesdropping, modification, injection, and man-in-the-middle (MITM) attacks. This vulnerability allows motivated attackers to intercept CPDLC messages using inexpensive devices like Software-Defined Radio (SDR), HACKRF-one, and an antenna. Such breaches can lead to fatal safety incidents, severely impacting passengers and the aviation industry. To address this, we proposed a robust security framework for securing CPDLC communication by implementing critical measures, including mutual authentication, secure key establishment, and handover. The proposed framework has been tested on hardware to verify its effectiveness in practical scenarios, ensuring it aligns with existing CPDLC standards and integrates seamlessly into current systems without impacting operational efficiency. Our findings indicate that the proposed security framework enhances CPDLC's defenses against potential cyber threats while maintaining system performance, making it feasible to protect global air traffic communications.

The aviation industry faces significant challenges due to rising global air travel demand. Frequency saturation in Air Traffic Management (ATM) leads to communication problems, necessitating the enhancement of traditional systems. The Single European Sky ATM Research (SESAR) initiative, backed by the European Commission, aims to digitize ATM, with the L-band Digital Aeronautical Communications System (LDACS) as a key component. LDACS aims to improve communication, enhance surveillance, and optimize airspace usage for safer, more efficient ATM. Although LDACS is protected against most cyberattacks, a critical security objective, anonymity, is currently overlooked. To strengthen LDACS's security, robust authentication mechanisms, Post-Quantum security, and measures to ensure aircraft anonymity are crucial. Therefore, we propose a comprehensive security framework to enhance LDACS's cybersecurity, focusing on mutual authentication and key agreement. The protocol uses Physical Unclonable Function (PUF) for robust mutual authentication and Bit-flipping Key Encapsulation (BIKE) for secure session key establishment utilizing Post-Quantum Cryptography (PQC). This framework ensures anonymity and secure communication between aircraft and ground stations while minimizing message exchange, latency, and data overhead. An informal security analysis confirms our proposed framework's potential to augment the efficiency and security of ATM operations.

The L-band Digital Aeronautical Communications System (LDACS) is a key advancement for next-generation aviation networks, enhancing Communication, Navigation, and Surveillance (CNS) capabilities. It operates with VHF Datalink mode 2 (VDLm2) and features a seamless handover mechanism to maintain uninterrupted communication between aircraft and ground stations (GSs), improving safety and efficiency in air traffic management (ATM). However, LDACS’ handover process encounters significant security risks due to inadequate authentication and key agreement between aircraft and ground station controllers (GSCs) during handovers. This vulnerability threatens communications’ confidentiality, integrity, and authenticity, posing risks to flight safety and sensitive data. Therefore, developing and implementing a robust security framework to protect aviation communications is essential. In response, we have proposed a security solution specifically designed to protect LDACS handovers. Our solution uses a mutual authentication and key agreement mechanism tailored for LDACS handovers, ensuring robust security for all types of handovers, including Intra GSC - Intra Aeronautical Telecommunication Network (ATN), Inter GSC - Intra ATN, and Inter GSC - Inter ATN. Our approach utilizes post-quantum cryptography to protect aviation communication systems against potential post-quantum threats, such as unauthorized access to flight data, interception of communication, and spoofing of aircraft identity. Furthermore, our proposed solution has undergone a thorough informal security analysis to ensure its effectiveness in addressing handover challenges and offering robust protection against various threats. It seamlessly integrates with the LDACS framework, delivering low Bit Error Rate (BER) and latency levels, making it a highly reliable approach in practice.

Ports are striving for innovative technological solutions to cope with the ever-increasing growth of transport, while at the same time improving their environmental footprint. An emerging technology that has the potential to substantially increase the efficiency of the multifaceted and interconnected port processes is the digital twin. Although digital twins have been successfully integrated in many industries, there is still a lack of cross-domain understanding of what constitutes a digital twin. Furthermore, the implementation of the digital twin in complex systems such as the port is still in its infancy. This paper attempts to fill this research gap by conducting an extensive cross-domain literature review of what constitutes a digital twin, keeping in mind the extent to which the respective findings can be applied to the port. It turns out that the digital twin of the port is most comparable to complex systems such as smart cities and supply chains, both in terms of its functional relevance as well as in terms of its requirements and characteristics. The conducted literature review, considering the different port processes and port characteristics, results in the identification of three core requirements of a digital port twin, which are described in detail. These include situational awareness, comprehensive data analytics capabilities for intelligent decision making, and the provision of an interface to promote multi-stakeholder governance and collaboration. Finally, specific operational scenarios are proposed on how the ports digital twin can contribute to energy savings by improving the use of port resources, facilities and operations.

Digital twins have gained tremendous momentum since their conceptualization over 20 years ago, as more and more domains discover their value in driving efficiencies and reducing costs, while enabling technologies continue to advance. Originally aimed at product optimization and intelligent manufacturing, the range of applications for digital twins now spans entire complex, often highly interconnected systems such as ports, cities, and supply chains. Despite the increasing demand for sophisticated digital twinning solutions across all domains and scopes, their development is often still constrained by differing definitions, different understandings of their functional scope and design, and a lack of concrete methodology toward implementing a comprehensive digital twinning solution. Although there are already papers that evaluate the capabilities of existing digital twinning solutions on the basis of maturity levels, these usually consider the object to be twinned in isolation and are often domain-specific. With this article we address exactly this gap discussing how interoperability of digital twins can break physical boundaries of an isolated system, enabling system of systems joint optimization. We therefore consider interoperable digital twins to be the most mature twinning platforms, thus, we discuss in detail six digital twin maturity levels, departing from the interrelated contexts of ports, cities, and supply chains. Examples drawn from these domains demonstrate the need for interoperability toward optimizing processes and systems in realistic contexts, rather than in assumed isolation.

Ports are striving to improve operational efficiency in the context of constantly growing volumes of trade. In this context, port terminal storage yard operation is key, since complexity and poor coordination lead to containers stacked without consideration of retrieval schedules, resulting in time- and energy-consuming reshuffling operations. This problem, known as the block relocation (and retrieval) problem (BRP), has recently gained considerable attention. Indeed, there are promising solutions to the BRP. However, the literature views the problem in isolation, optimizing one operational parameter for one of the many port stakeholders. This often leads to efficiency losses since port processes involve different stakeholders and port parts. In this work, we explicitly focus on scheduling trucks for pick-up for hinterland distribution. Appointments are often postponed in order to minimize reshuffling operations, leading to losses for the transport forwarders and decreasing the competitiveness of the port. We discuss the trade-off between minimizing container reshuffling operations while maintaining scheduled time windows for container retrieval. We describe the multi-objective optimization problem as a weighted sum of the two objectives. Given the complexity of the problem, we also present a greedy heuristic. Our results indicate that the number of schedule deviations can be reduced without significantly affecting the number of relocations compared to solutions that consider only the latter. Ideally, a weighting of 0.4 and 0.6 should be applied, reflecting schedule deviations and relocations, respectively, to achieve the highest joint optimization potential. This demonstrates that in complex environments, such as ports, with multiple interacting stakeholders and processes, coordination of solutions yields significant benefits.

Quay cranes (QCs) play a vital role in ship-to-shore operations, enabling the seamless transfer of cargo between sea and land. However, increasing trade volumes require faster and more costeffective container handling, exerting significant pressure on QCs and leading to greater wear on critical components such as wires, hoists, and rope clamps. While operations research has explored maintenance scheduling to improve terminal performance, comparatively little work has examined how machine learning can exploit the growing volume of QC monitoring and operational data to predict breakdowns before they occur. This study contributes to this area by integrating terminal operations data, QC monitoring logs, and meteorological observations into a unified analytical framework. We employ explainable artificial intelligence (XAI), using both global and local SHapley Additive exPlanations (SHAP) to identify the operational and environmental factors most strongly associated with QC failures and to illustrate concrete, instance-level examples of how specific conditions contribute towards breakdowns. In parallel, we develop a robust machine learning pipeline built around nested cross-validation to assess the predictive capability of multiple classifiers for forecasting QC breakdowns. Our XAI analysis reveals that breakdown risk is closely linked to QC working time, the distribution of moves across simultaneously operating QCs, hoist overload and trolley alignment warnings, and adverse weather conditions. Among the evaluated models, LightGBM achieved the highest predictive accuracy, reaching up to 83% in identifying breakdown-prone scenarios. These findings demonstrate the feasibility and value of data-driven predictive maintenance for QCs, providing insights that support safer, more reliable, and more efficient terminal operations.

Efficient port operations depend on the disruption free operation of quay cranes (QCs), which transfer containers between vessels and internal trucks. As global container through put rises, QCs face increased pressure, resulting in accelerated wear and tear. This can lead to QC downtime, which could interrupt the entire chain of port operations. Therefore, timely identification and prediction of critical errors is essential to enable timely maintenance to lower the risk of downtime. This study utilizes two years of QC monitoring data, enriched with weather conditions and terminal operational context, alongside twenty critical error events identified by the terminal operator. The goal is to predict the occurrence and timing of these critical errors through a three-stage machine learning model. The first stage predicts the type of the next critical event based on historical error patterns, warnings, and contextual data. The second stage estimates a time window in which the event will occur. The third stage refines timing predictions when more than one hour remains. The first two stages are formulated as multiclass classification problems, and the third as a regression task. All stages utilize eXtreme Gradient Boosting (XGBoost). SHapley Additive exPlanations (SHAP) are used to identify influential features. Results show that the model predicts the next critical error type with 83% accuracy and its immediacy with 71% accuracy. However, approximating the timing of events anticipated to occur beyond one hour remains challenging. These findings support proactive maintenance planning and operational adjustments, helping port operators mitigate disruptions and enhance QC reliability.

Enhancing fuel efficiency in ferry operations is essential for reducing emissions and advancing maritime sustainability. This study presents a data-driven framework that uses second-level GPS data enriched with operational and environmental variables to identify and explain fuel consumption patterns. Vessel movements are segmented into trip legs and journeys, and operational metrics such as speed, wind exposure, and fuel use are computed. A hybrid machine learning approach combines unsupervised clustering to detect recurring operational patterns with gradient boosting models and explainable methods to quantify feature impacts. The framework achieves strong performance, with a cluster classification accuracy of 94 percent and a coefficient of determination of 0.97 for fuel prediction. Results indicate that operational speed is the dominant driver of fuel consumption, while analysis of captain assignments reveals the influence of human factors. The proposed framework provides actionable insights for speed management and operational optimization, enabling cost-effective emission reductions in ferry services.