Search Results (735)

Smart safety helmets equipped with vision systems are emerging as powerful tools for industrial infrastructure inspection. This paper presents a comprehensive state-of-the-art review of such VSLAM-enabled (Visual Simultaneous Localization and Mapping) helmets. We surveyed the evolution from basic helmet cameras to intelligent, sensor-fused inspection platforms, highlighting how modern helmets leverage real-time visual SLAM algorithms to map environments and assist inspectors. A systematic literature search was conducted targeting high-impact journals, patents, and industry reports. We classify helmet-integrated camera systems into monocular, stereo, and omnidirectional types and compare their capabilities for infrastructure inspection. We examine core VSLAM algorithms (feature-based, direct, hybrid, and deep-learning-enhanced) and discuss their adaptation to wearable platforms. Multi-sensor fusion approaches integrating inertial, LiDAR, and GNSS data are reviewed, along with edge/cloud processing architectures enabling real-time performance. This paper compiles numerous industrial use cases, from bridges and tunnels to plants and power facilities, demonstrating significant improvements in inspection efficiency, data quality, and worker safety. Key challenges are analyzed, including technical hurdles (battery life, processing limits, and harsh environments), human factors (ergonomics, training, and cognitive load), and regulatory issues (safety certification and data privacy). We also identify emerging trends, such as semantic SLAM, AI-driven defect recognition, hardware miniaturization, and collaborative multi-helmet systems. This review finds that VSLAM-equipped smart helmets offer a transformative approach to infrastructure inspection, enabling real-time mapping, augmented awareness, and safer workflows. We conclude by highlighting current research gaps, notably in standardizing systems and integrating with asset management, and provide recommendations for industry adoption and future research directions. Full article

Background: While several test-automation maturity models (e.g., CMMI, TMMi, TAIM) exist, none explicitly integrate ISO 9001-based quality management systems (QMS), leaving a gap for organizations that must align E2E test automation with formal quality assurance. Objective: This study proposes a test-automation maturity model (TAMM) that bridges E2E automation capability with ISO 9001/ISO 9004 self-assessment principles, and evaluates its reliability and practical impact in industry. Methods: TAMM comprises eight maturity dimensions, 39 requirements, and 429 checklist items. Three independent assessors applied the checklist to three software teams; inter-rater reliability was ensured via consensus review (Cohen’s κ = 0.75). Short-term remediation actions based on the checklist were implemented over six months and re-assessed. Synergy with the organization’s ISO 9001 QMS was analyzed using ISO 9004 self-check scores. Results: Within 6 months of remediation, mean TAMM score rose from 2.75 → 2.85. Inter-rater reliability is filled with Cohen’s κ = 0.75. Conclusions: The proposed TAMM delivers measurable, short-term maturity gains and complements ISO 9001-based QMS without introducing conflicting processes. Practitioners can use the checklist to identify actionable gaps, prioritize remediation, and quantify progress, while researchers may extend TAMM to other domains or automate scoring via repository mining. Full article

Cardiovascular disease (CVD) remains Europe’s leading cause of mortality, responsible for >45% of deaths. Beyond established risk factors (hypertension, diabetes, dyslipidaemia, smoking, obesity), psychosocial elements—depression, anxiety, financial stress, personality traits, and trauma—significantly influence CVD development and progression. Behavioural Cardiology addresses this connection by systematically incorporating psychosocial factors into prevention and rehabilitation protocols. This review examines the HEARTBEAT model, developed by Greece’s first Behavioural Cardiology Unit, which aligns with current European guidelines. The model serves dual purposes: primary prevention (targeting at-risk individuals) and secondary prevention (treating established CVD patients). It is a personalised medicine approach that integrates psychosocial profiling with traditional risk assessment, utilising tailored evaluation tools, caregiver input, and multidisciplinary collaboration to address personality traits, emotional states, socioeconomic circumstances, and cultural contexts. The model emphasises three critical implementation aspects: (1) digital health integration, (2) cost-effectiveness analysis, and (3) healthcare system adaptability. Compared to international approaches, it highlights research gaps in psychosocial interventions and advocates for culturally sensitive adaptations, particularly in resource-limited settings. Special consideration is given to older populations requiring tailored care strategies. Ultimately, Behavioural Cardiology represents a transformative systems-based approach bridging psychology, lifestyle medicine, and cardiovascular treatment. This integration may prove pivotal for optimising chronic disease management through personalised interventions that address both biological and psychosocial determinants of cardiovascular health. Full article

This follow-up scientific case study builds on prior research to explore the computational challenges of applying quantum algorithms to financial asset management, focusing specifically on solving the graph-cut problem for investment recommendation. Unlike our prior study, which focused on idealized QAOA performance, this work introduces a graph compression pipeline that enables QAOA deployment under real quantum hardware constraints. This study investigates quantum-accelerated spectral graph compression for financial asset recommendations, addressing scalability and regulatory constraints in portfolio management. We propose a hybrid framework combining the Quantum Approximate Optimization Algorithm (QAOA) with spectral graph theory to solve the Max-Cut problem for investor clustering. Our methodology leverages quantum simulators (cuQuantum and Cirq-GPU) to evaluate performance against classical brute-force enumeration, with graph compression techniques enabling deployment on resource-constrained quantum hardware. The results underscore that efficient graph compression is crucial for successful implementation. The framework bridges theoretical quantum advantage with practical financial use cases, though hardware limitations (qubit counts, coherence times) necessitate hybrid quantum-classical implementations. These findings advance the deployment of quantum algorithms in mission-critical financial systems, particularly for high-dimensional investor profiling under regulatory constraints. Full article

The escalating climate crisis and global disruptions have prompted a critical re-evaluation of operations management within manufacturing and supply systems. This conceptual article addresses the theoretical and strategic gap in aligning resilience and sustainability by proposing an Integrated Sustainable Operational Strategy (ISOS) framework. Drawing on systems theory, circular economy principles, and sustainability science, the framework synthesizes multiple operational domains—circularity, localization, digital adaptation, and workforce flexibility—across macro (policy), meso (organizational), and micro (process) levels. This study constructs a conceptual model that explains the interdependencies and trade-offs among strategic operational responses in the Anthropocene era. Supported by multi-level logic and a synthesis of domain constructs, the model provides a foundation for empirical investigation and strategic planning. Key propositions for future research are developed, focusing on causal relationships and boundary conditions. The novelty of ISOS lies in its simultaneous integration of three strategic pillars—circularity, localization, and digital resilience—within a unified, multi-scalar architecture that bridges fragmented operational theories. The article advances theory by redefining operational excellence through regenerative logic and adaptive capacity, responding directly to SDG 9 (industry innovation), SDG 12 (responsible consumption and production), and SDG 13 (climate action). This integrative framework offers both theoretical insight and practical guidance for transforming operations into catalysts of sustainable transition. Full article

Obesity is a growing global health concern with widespread impacts on physical, psychological, and social well-being. Clinically, it is a major driver of type 2 diabetes (T2D), cardiovascular disease (CVD), non-alcoholic fatty liver disease (NAFLD), and cancer, reducing life expectancy by 5–20 years and imposing a staggering economic burden of USD 2 trillion annually (2.8% of global GDP). Despite its significant health and socioeconomic impact, earlier obesity medications, such as fenfluramine, sibutramine, and orlistat, fell short of expectations due to limited effectiveness, serious side effects including valvular heart disease and gastrointestinal issues, and high rates of treatment discontinuation. The advent of glucagon-like peptide-1 (GLP-1) receptor agonists (e.g., semaglutide, tirzepatide) has revolutionized obesity management. These agents demonstrate unprecedented efficacy, achieving 15–25% mean weight loss in clinical trials, alongside reducing major adverse cardiovascular events by 20% and T2D incidence by 72%. Emerging therapies, including oral GLP-1 agonists and triple-receptor agonists (e.g., retatrutide), promise enhanced tolerability and muscle preservation, potentially bridging the efficacy gap with bariatric surgery. However, challenges persist. High costs, supply shortages, and unequal access pose significant barriers to the widespread implementation of obesity treatment, particularly in low-resource settings. Gastrointestinal side effects and long-term safety concerns require close monitoring, while weight regain after medication discontinuation emphasizes the need for ongoing adherence and lifestyle support. This review highlights the transformative potential of incretin-based therapies while advocating for policy reforms to address cost barriers, equitable access, and preventive strategies. Future research must prioritize long-term cardiovascular outcome trials and mitigate emerging risks, such as sarcopenia and joint degeneration. A multidisciplinary approach combining pharmacotherapy, behavioral interventions, and systemic policy changes is critical to curbing the obesity epidemic and its downstream consequences. Full article

The maritime sector faces escalating demands to minimize emissions and optimize operational efficiency under tightening environmental regulations. Although technologies such as the Internet of Things (IoT), Artificial Intelligence (AI), and Digital Twins (DT) offer substantial potential, their deployment in real-time ship performance analytics is at an emerging state. This paper proposes a machine learning-driven framework for real-time ship performance management. The framework starts with data collected from onboard sensors and culminates in a decision support system that is easily interpretable, even by non-experts. It also provides a method to forecast vessel performance by extrapolating Key Performance Indicator (KPI) values. Furthermore, it offers a flexible methodology for defining KPIs for every crucial component or aspect of vessel performance, illustrated through a use case focusing on fuel oil consumption. Leveraging Artificial Neural Networks (ANNs), hybrid multivariate data fusion, and high-frequency sensor streams, the system facilitates continuous diagnostics, early fault detection, and data-driven decision-making. Unlike conventional static performance models, the framework employs dynamic KPIs that evolve with the vessel’s operational state, enabling advanced trend analysis, predictive maintenance scheduling, and compliance assurance. Experimental comparison against classical KPI models highlights superior predictive fidelity, robustness, and temporal consistency. Furthermore, the paper delineates AI and ML applications across core maritime operations and introduces a scalable, modular system architecture applicable to both commercial and naval platforms. This approach bridges advanced simulation ecosystems with in situ operational data, laying a robust foundation for digital transformation and sustainability in maritime domains. Full article

Collaborative business ecosystems (CBEs) are increasingly exposed to disruptive events (e.g., pandemics, supply chain breakdowns, cyberattacks) that challenge organizational adaptability and value creation. Traditional approaches to resilience and robustness often fail to capture the full range of systemic responses. This study introduces a unified mathematical framework to evaluate four crisis response modes—plasticity, resilience, transformative resilience, and antifragility—within complex adaptive networks. Grounded in complex systems and collaborative network theory, our model formalizes both internal organizational capabilities (e.g., adaptability, learning, innovation, structural flexibility) and strategic interventions (e.g., optionality, buffering, information sharing, fault-injection protocols), linking them to pre- and post-crisis performance via dynamic adjustment functions. A composite performance score is defined across four dimensions (Innovation, Contribution, Prestige, and Responsiveness to Business Opportunities), using capability–strategy interaction matrices, weighted performance change functions, and structural transformation modifiers. The sensitivity analysis and scenario simulations enable a comparative evaluation of organizational configurations, strategy impacts, and phase-transition thresholds under crisis. This indicator-based formulation provides a quantitative bridge between resilience theory and practice, facilitating evidence-based crisis management in networked business environments. Full article

Heating, ventilation, and air-conditioning (HVAC) systems account for the largest share of energy consumption in European Union (EU) buildings, representing approximately 40% of the final energy use and contributing significantly to carbon emissions. Latent thermal energy storage (LTES) using phase change materials (PCMs) has emerged as a promising strategy to enhance HVAC efficiency. This review systematically examines the role of latent thermal energy storage using phase change materials (PCMs) in optimizing HVAC performance to align with EU climate targets, including the Energy Performance of Buildings Directive (EPBD) and the Energy Efficiency Directive (EED). By analyzing advancements in PCM-enhanced HVAC systems across residential and commercial sectors, this study identifies critical pathways for reducing energy demand, enhancing grid flexibility, and accelerating the transition to nearly zero-energy buildings (NZEBs). The review categorizes PCM technologies into organic, inorganic, and eutectic systems, evaluating their integration into thermal storage tanks, airside free cooling units, heat pumps, and building envelopes. Empirical data from case studies demonstrate consistent energy savings of 10–30% and peak load reductions of 20–50%, with Mediterranean climates achieving superior cooling load management through paraffin-based PCMs (melting range: 18–28 °C) compared to continental regions. Policy-driven initiatives, such as Germany’s renewable integration mandates for public buildings, are shown to amplify PCM adoption rates by 40% compared to regions lacking regulatory incentives. Despite these benefits, barriers persist, including fragmented EU standards, life cycle cost uncertainties, and insufficient training. This work bridges critical gaps between PCM research and EU policy implementation, offering a roadmap for scalable deployment. By contextualizing technical improvement within regulatory and economic landscapes, the review provides strategic recommendations to achieve the EU’s 2030 emissions reduction targets and 2050 climate neutrality goals. Full article

The transition to electric mobility has significantly increased the demand for lithium-ion batteries, raising concerns about their end-of-life management. Therefore, this study presents the design, development and first implementation steps of a stationary energy storage system utilizing second-life electric vehicle (EV) batteries. These batteries, no longer suitable for traction applications due to a reduced state of health (SoH) below 80%, retain sufficient capacity for less demanding stationary applications. The proposed system is designed to be flexible and scalable, serving both research and commercial purposes. Key challenges include heterogeneous battery characteristics, safety considerations due to increased internal resistance and battery aging, and the need for flexible power electronics. An optimized dual active bridge (DAB) converter topology is introduced to connect several batteries in parallel and to ensure efficient bidirectional power flow over a wide voltage range. A first prototype, rated at 50 $\mathrm{k}$$\mathrm{W}$, has been built and tested in the laboratory. This study contributes to sustainable energy storage solutions by extending battery life cycles, reducing waste, and promoting economic viability for industrial partners. Full article

Recent advances in microbiome studies have deepened our understanding of endosymbionts and gut-associated microbiota in host biology. Of those, lepidopteran systems in particular harbor a complex and diverse microbiome with various microbial taxa that are stable and transmitted between larval and adult stages, and others that are transient and context-dependent. We highlight key microorganisms—including Bacillus, Lactobacillus, Escherichia coli, Pseudomonas, Rhizobium, Fusarium, Aspergillus, Saccharomyces, Bifidobacterium, and Wolbachia—that play critical roles in microbial ecology, biotechnology, and microbiome studies. The fitness implications of these microbial communities can be variable; some microbes improve host performance, while others neither positively nor negatively impact host fitness, or their impact is undetectable. This review examines the central position played by the gut microbiota in interactions of insects with plants, highlighting the functions of the microbiota in the manipulation of the behavior of herbivorous pests, modulating plant physiology, and regulating higher trophic levels in natural food webs. It also bridges microbiome ecology and applied pest management, emphasizing S. frugiperda as a model for symbiont-based intervention. As gut microbiota are central to the life history of herbivorous pests, we consider how these interactions can be exploited to drive the development of new, environmentally sound biocontrol strategies. Novel biotechnological strategies, including symbiont-based RNA interference (RNAi) and paratransgenesis, represent promising but still immature technologies with major obstacles to overcome in their practical application. However, microbiota-mediated pest control is an attractive strategy to move towards sustainable agriculture. Significantly, the gut microbiota of S. frugiperda is essential for S. frugiperda to adapt to a wide spectrum of host plants and different ecological niches. Studies have revealed that the microbiome of S. frugiperda has a close positive relationship with the fitness and susceptibility to entomopathogenic fungi; therefore, targeting the S. frugiperda microbiome may have good potential for innovative biocontrol strategies in the future. Full article

Aiming to enable intelligent vehicles to achieve autonomous charging under low-battery conditions, this paper presents a navigation system for autonomous charging that integrates an improved bidirectional A* algorithm for path planning and an optimized YOLOv11n model for visual recognition. The system utilizes the improved bidirectional A* algorithm to generate collision-free paths from the starting point to the charging area, dynamically adjusting the heuristic function by combining node–target distance and search iterations to optimize bidirectional search weights, pruning expanded nodes via a greedy strategy and smoothing paths into cubic Bézier curves for practical vehicle motion. For precise localization of charging areas and piles, the YOLOv11n model is enhanced with a CAFMFusion mechanism to bridge semantic gaps between shallow and deep features, enabling effective local–global feature fusion and improving detection accuracy. Experimental evaluations in long corridors and complex indoor environments showed that the improved bidirectional A* algorithm outperforms the traditional improved A* algorithm in all metrics, particularly in that it reduces computation time significantly while maintaining robustness in symmetric/non-symmetric and dynamic/non-dynamic scenarios. The optimized YOLOv11n model achieves state-of-the-art precision (P) and mAP@0.5 compared to YOLOv5, YOLOv8n, and the baseline model, with a minor 0.9% recall (R) deficit compared to YOLOv5 but more balanced overall performance and superior capability for small-object detection. By fusing the two improved modules, the proposed system successfully realizes autonomous charging navigation, providing an efficient solution for energy management in intelligent vehicles in real-world environments. Full article

Nature-based solutions (NbS) are increasingly recognized as strategic alternatives and complements to grey infrastructure for addressing water-related challenges in the context of climate change, urbanization, and biodiversity decline. This article presents a critical, theory-informed review of the state of NbS implementation in European water management, drawing on a structured synthesis of empirical evidence from regional case studies and policy frameworks. The analysis found that while NbS are effective in reducing surface runoff, mitigating floods, and improving water quality under low- to moderate-intensity events, their performance remains uncertain under extreme climate scenarios. Key gaps identified include the lack of long-term monitoring data, limited assessment of NbS under future climate conditions, and weak integration into mainstream planning and financing systems. Existing evaluation frameworks are critiqued for treating NbS as static interventions, overlooking their ecological dynamics and temporal variability. In response, a dynamic, climate-resilient assessment model is proposed—grounded in systems thinking, backcasting, and participatory scenario planning—to evaluate NbS adaptively. Emerging innovations, such as hybrid green–grey infrastructure, adaptive governance models, and novel financing mechanisms, are highlighted as key enablers for scaling NbS. The article contributes to the scientific literature by bridging theoretical and empirical insights, offering region-specific findings and recommendations based on a comparative analysis across diverse European contexts. These findings provide conceptual and methodological tools to better design, evaluate, and scale NbS for transformative, equitable, and climate-resilient water governance. Full article

Aquaculture is a crucial food-producing sector that can supply more essential nutrients to nourish the growing human population. However, it faces challenges, including limited water quality and space competition. These constraints have led to the intensification of culture systems for more efficient resource use while maintaining or increasing production levels. However, intensification introduces stress risks to cultured organisms by, for instance, overcrowding, waste accumulation, and water quality deterioration, which can negatively affect the growth, health, and immunity of animals and cause diseases. Additionally, environmental changes due to climate and anthropogenic activities further intensify the environmental stress for aquaculture organisms, including crustaceans. Shrimp are one of the most widely cultured and consumed farmed crustacea. Relative to aquatic vertebrates such as fish, the physiology of crustaceans has simpler physiological structures, as they lack a spinal cord. Consequently, their stress response mechanisms follow a single pathway, resulting in less complex responses to stress exposure compared to those of fish. While stress is considered a primary factor influencing the growth, health, and immunity of shrimp, comprehensive research on crustacean stress responses remains limited. Understanding the stress response at the organismal and cellular levels is essential to identify sensitive and effective stress biomarkers which can inform the development of targeted intervention strategies to mitigate stress. This review provides a comprehensive overview of the physiological changes that occur in crustaceans under stress, including hormonal, metabolic, hematological, hydromineral, and phenotypic alterations. By synthesizing current knowledge, this article aims to bridge existing gaps and provide insights into the stress response mechanisms, paving the way for advancements in crustacean health management. Full article

In this study, we introduce a novel interdisciplinary framework that applies concepts from statistical physics, specifically lattice-gas models, to the classical order lot-sizing problem in supply chain management. Traditional methods often rely on heuristic or deterministic approaches, which may fail to capture the inherently probabilistic and dynamic nature of decision-making across multiple periods. Drawing on structural parallels between inventory decisions and adsorption phenomena in physical systems, we constructed a mapping that represented order placements as particles on a lattice, governed by an energy function analogous to thermodynamic potentials. This formulation allowed us to employ analytical tools from statistical mechanics to identify optimal ordering strategies via the minimization of a free energy functional. Our approach not only sheds new light on the structural characteristics of optimal planning but also introduces the concept of configurational entropy as a measure of decision variability and robustness. Numerical simulations and analytical approximations demonstrate the efficacy of the lattice gas model in capturing key features of the problem and suggest promising avenues for extending the framework to more complex settings, including multi-item systems and time-varying demand. This work represents a significant step toward bridging physical sciences with supply chain optimization, offering a robust theoretical foundation for both future research and practical applications. Full article