Abstract: Objectives: Local positioning systems (LPS) used in indoor team sports generate a large number of external load variables, often exceeding practical monitoring capacity. The redundancy and overlap among these variables make it difficult to identify the most informative metrics for performance analysis and load management. This study aimed to reduce the dimensionality of external load variables derived from LPS data and to identify data-driven external-load observation profiles using principal component analysis and clustering techniques. Methods: A total of 188 observations from indoor team sports (basketball, handball, and futsal) were analyzed. Continuous external load variables were standardized and subjected to principal component analysis (PCA), with component retention based on a ≥90% cumulative explained variance threshold. K-means clustering was applied in both the full standardized feature space and the PCA-reduced space. The optimal number of clusters was determined using silhouette analysis and the elbow method. Agreement between clustering solutions was assessed using Adjusted Rand Index (ARI) and Normalized Mutual Information (NMI). Cluster characteristics were further examined using descriptive statistics and variable separation analysis. Results: The first two principal components explained 53.7% of the total variance, representing high-intensity external load and neuromuscular load dimensions, while 12 components were required to exceed 90% cumulative explained variance. Clustering analysis consistently identified three moderately separated clusters in both the full and PCA-reduced spaces. The PCA-based solution demonstrated improved separation (silhouette = 0.362) compared to the full-space solution (silhouette = 0.319). Agreement between clustering approaches was high (ARI = 0.981; NMI = 0.971), indicating that dimensionality reduction largely preserved the main clustering structure within the analyzed dataset. The most discriminative variables included jump load, acceleration load, metabolic power, and anaerobic activity distance. Conclusions: A large set of external load variables can be reduced into interpretable latent dimensions that supported exploratory external-load profile identification. The combination of PCA and clustering provides an exploratory, structure-preserving, and interpretable framework for simplifying load monitoring in indoor team sports, supporting the selection of key performance indicators and more efficient training load management.

Abstract: This paper presents new families of triangular norms and conorms specifically constructed for type-2 fuzzy sets. Our approach considers several partially ordered sets of membership functions defined on the unit interval, each characterized by different structural properties and suited to representing particular kinds of uncertainty. For these settings, we define operators that consistently represent fuzzy intersection and union, filling gaps where no such type-2 operators were previously available. It is the first time that triangular norms and conorms are obtained with respect to both usual partial orders in the set of normal membership functions. Building on this framework, we integrate the proposed operators into a type-2 fuzzy time series. A large-scale evaluation on multiple benchmark time series shows that the new operators consistently achieve the best predictive accuracy, in terms of MAPE, in comparison with classical type-2 and type-1 fuzzy time series baselines. These results demonstrate that the algebraic design of type-2 operators has a direct impact on forecasting performance and can substantially improve the modeling of uncertainty in time-dependent data.

Abstract: The analysis of basketball performance has increasingly incorporated advanced analytics and machine learning methods to understand better the factors that influence offensive efficiency and game dynamics. The present study aimed to identify performance profiles in basketball using unsupervised machine learning techniques and to examine the physical load and performance indicators that differentiate these profiles. Quarters from Greek U16 basketball matches were analyzed in two contexts: quarters from games with large score differences and quarters from games with small score differences. K-means clustering was applied separately to each dataset to identify latent performance patterns. The optimal number of clusters was determined using the Elbow method and silhouette analysis, which indicated a two-cluster solution for both datasets. Cluster visualization using t-distributed stochastic neighbor embedding (t-SNE) confirmed the presence of distinct performance profiles. Independent samples t-tests revealed significant differences between clusters across several physical load indicators, including jump load, total distance covered, accumulated acceleration load, and distance covered across different speed zones (p < .001). Clusters characterized by higher movement intensity also exhibited higher basketball performance efficiency indicators. Although higher-performance clusters showed numerically higher winning proportions in both contexts (large score differences: 66.7% vs. 42.9%; small score differences: 59.4% vs. 36.7%), chi-square analyses indicated that cluster membership was not significantly associated with game outcomes. Overall, the findings suggest that performance profiles in basketball are primarily differentiated by movement intensity and physical load characteristics, highlighting the importance of integrating both physical and technical performance indicators in basketball performance analysis.

Abstract: Obsessive–compulsive disorder (OCD) is a chronic and disabling psychiatric condition whose neurobiological underpinnings remain incompletely characterized. A growing body of evidence suggests that vitamin D, through its modulatory actions on neuroinflammation, serotonin synthesis and cortico-striato-thalamo-cortical circuitry, may be implicated in its clinical expression. The present cross-sectional study examined the association between serum 25-hydroxyvitamin D levels and OCD severity in 306 adult outpatients with a diagnosis of OCD, of whom 173 had vitamin D measurements available. Symptom severity was assessed through the Yale–Brown Obsessive–Compulsive Scale (Y-BOCS), and associations were examined using non-parametric tests, partial correlations and multivariable linear regression adjusted for age, gender, age at onset and bipolar comorbidity. Mean vitamin D was 20.0 ± 13.1 ng/mL, with 60.1% of patients meeting criteria for deficiency. Lower vitamin D levels correlated inversely with Y-BOCS total score (ρ = −0.26, p = 0.001) and with both subscales, and deficient patients showed a mean Y-BOCS total approximately 5.5 points higher than non-deficient ones. In multivariable models, lower vitamin D (β = −0.253, p = 0.001) and earlier age at onset (β = −0.278, p = 0.001) independently predicted greater severity (R² = 0.133), while a history of suicide attempts neither predicted severity nor moderated the vitamin D association. These findings support vitamin D status as a biological correlate of OCD severity and warrant longitudinal and interventional investigation.

Abstract: Adoptive cellular therapy has emerged as one of the most pivotal modalities in cancer immunotherapy. Although chimeric antigen receptor (CAR)-T cell therapy has achieved remarkable clinical success in hematological malignancies, its broad application is hampered by severe immune-related toxicities, autologous manufacturing limitations, and suboptimal efficacy against most solid tumors, highlighting an urgent unmet clinical need for safer and more versatile cellular therapeutics. As a promising alternative, CAR-NK cell therapy exhibits inherent advantages in safety profile, off-the-shelf accessibility, and multimodal antitumor mechanisms. NKG2D, a pivotal activating receptor broadly expressed on NK cells, specifically recognizes stress-inducible ligands such as MICA/B and ULBP family molecules. These ligands are abundantly overexpressed in various hematological and solid malignancies yet rarely detected in normal tissues, thereby rendering NKG2D an ideal candidate for universal targeted immunotherapy. In this review, we systematically examine the structural design and iterative optimization of NKG2D CAR constructs and elucidate the inherent advantages of CAR-NK over conventional CAR-T cells. We also comprehensively review recent research advances in NKG2D CAR-NK across multiple malignancies, and critically analyze three pivotal translational bottlenecks, including tumor antigen escape, the immunosuppressive tumor microenvironment, and inadequate in vivo persistence. Moreover, we highlight current genetic engineering and combinatorial strategies to overcome these limitations, and outline future research directions focusing on the development of universal off-the-shelf products, multifunctional cell engineering, and rational combination regimens. By integrating mechanistic advances, preclinical findings and early clinical evidence, this review provides a systematic theoretical basis and translational guidance for the structural optimization, clinical translation, and widespread clinical adoption of NKG2D CAR-NK therapy, laying a solid foundation for its future development as a standardized universal anticancer cellular therapeutic.

Abstract: This paper develops a new generalized model that describes the complex dynamical behavior of a financial system through three state variables, namely the interest rate, investment demand and the price index. The developed model extends and improves numerous financial models available in the literature by incorporating two time delays. The first delay accounts for the time lag in price adjustment, whereas the second captures the delayed feedback effect on investment demand. For the first time in the context of financial systems, a novel threshold parameter is introduced to characterize the existence of equilibria. The dynamical properties of the proposed model, including the stability and the occurrence of Hopf bifurcation, are rigorously analyzed. Furthermore, sensitivity analysis and numerical simulations are conducted to investigate the influence of model parameters on the dynamics of the financial system and to illustrate the analytical results.

Abstract: Brain-specific angiogenesis inhibitor 1-associated protein 2 (BAIAP2), which encodes Insulin Receptor Substrate Protein 53 (IRSp53), has emerged as a brain-disease gene. BAIAP2 involvement in pathogenic CNS disorders is due to its roles in molecular architecture, synaptic signaling, circuit computation, and human neurodevelopmental pathology can be examined within a single mechanistic framework. Unlike many synaptic risk genes that are assigned broad scaffolding functions, IRSp53 is distinguished by the convergence of membrane-shaping capacity, actin control, and postsynaptic condensate organization, through which N-methyl-D-aspartate receptor (NMDAR) -dependent signaling is tuned rather than merely supported. Across mouse models, disrupted IRSp53 has been linked to altered postsynaptic density assembly, abnormal glutamatergic physiology, reduced prefrontal population activity, and impaired social behavior. These phenotypes have not only been observed across scales but, in part, have been reversed in adulthood, indicating that pathogenic effects are not fixed once development has ended. In parallel, emerging human studies have extended BAIAP2 beyond idiopathic neuropsychiatric association toward defined neurodevelopmental disorders, including cortical migration defects and developmental epileptic encephalopathy. A multi-scale view is that the BAIAP2 is not considered as a synaptic organizer, but a causal bridge between nanoscale postsynaptic structure, systems-level dysfunction, and disease expression.

Abstract: Huperzine A (HupA) is a natural Lycopodium alkaloid known for its potent neuro-protective properties through the inhibition of acetylcholinesterase. Nevertheless, the limited understanding of its biosynthesis restricts its broader application. This study integrates full-length and second-generation transcriptomes with the quantification of HupA and its precursor, huperzine B, across various tissues of Huperzia serrata, the primary source plant. By employing phylogenetic clustering, expression profiling, and correlation analysis between gene expression and metabolite abundance, we identified 71 candidate genes from seven enzyme families potentially involved in the synthesis of the HupA backbone, including lysine/ornithine decarboxylases, copper amine oxidases (CAOs), chalcone synthases, and cytochrome P450 monooxygenases. Additionally, 28 genes from two families were identified for modification reactions, specifically 2-oxoglutarate/Fe(II)-dependent dioxygenases and caffeoyl shikimate esterases. Comparative analysis between young and mature leaves revealed 3,801 up-regulated genes in young leaves, with 84 showing a high correlation with HupA content across seven families. Protein–protein interaction network analysis indicated interactions with transcription factors from the MYB, NF-YC, GRAS, ERF, BHLH, and SAP families. Functional validation of two candidate CAOs in planta confirmed their catalytic roles in amine/alkaloid metabolism. This study provides a theoretical foundation and a set of candidate genes for elucidating the biosynthetic pathway of HupA and related alkaloids in H. serrata.

Abstract: Phosphate is an essential element for energy metabolism, bone mineralization and chondrocyte function, particularly during growth. Its homeostasis involves complex interactions between dietary intake, renal excretion and hormonal regulation, notably through fibroblast growth factor 23 (FGF23) and growth hormone (GH). The intricate balance of phosphate and its interplay with FGF23 and GH are examined in the context of normal development and disorders such as X-linked hypophosphatemia (XLH) and chronic kidney disease (CKD), which disrupt this balance through mechanisms of hypophosphatemia or hyperphosphatemia. FGF23 emerges as a key regulator, mediating phosphate excretion and vitamin D metabolism, while GH exerts a counter-regulatory effect by promoting phosphate reabsorption and chondrocyte proliferation. This hormonal interaction maintains skeletal integrity but becomes dysregulated in pathological conditions, leading to impaired growth and mineralization. Recent insights into the molecular mechanisms of phosphate transporters and the role of the PHEX gene in FGF23 regulation provide promising avenues for therapeutic interventions. Emerging treatments, including phosphate binders, calcitriol supplementation and anti-FGF23 antibodies such as burosumab, offer potential to address the complications of phosphate dysregulation. However, challenges remain in optimizing therapies for pediatric populations and mitigating adverse effects like vascular calcification. Future research into phosphate homeostasis, transporter dynamics and gene-based approaches holds the promise of advancing clinical outcomes for phosphate-related disorders. This review explores the dual role of phosphate in skeletal growth, highlighting its critical contributions to chondrocyte maturation, apoptosis and hydroxyapatite formation during endochondral ossification.

Abstract: IoT devices operate as integrated systems spanning hardware, firmware/software layers, and communication layers. In operational settings, many faults and performance degradations are emergent: they arise from cross-layer interactions, workload changes, and telemetry artifacts rather than a single physics-of-failure mechanism. These realities make traditional supervised fault classification difficult because labeled fault data are rarely available during deployment, and the fault surface is unknown a priori. This paper presents a practitioner-oriented, label-free fault detection and diagnosis (FDD) pattern based on Dynamic Time Warping (DTW) for rapid implementation in production IoT telemetry. The method represents a device as a sequence of overlapping episodes and organizes telemetry into interpretable layers (hardware sensors, communication health proxies, and software/firmware-derived KPIs). A reference library of regular episodes is built from an assumed-healthy training window; new episodes are scored using constrained DTW distances against this library, while retaining per-layer and per-channel contributions for attribution. We show that production performance depends strongly on operational parameterization, including episode length, DTW constraints, robust threshold learning, and temporal validation. Within a verified-healthy evaluation window, the tuned configuration achieves an AUROC of 0.97 for the temporally-structured faults DTW is suited to (bias, drift, and interaction faults, with spikes detected at AUROC 0.93), detecting 100% of injected faults at a mean delay under 25 minutes. We further show that constant-value (stuck-at) and missing-data (dropout) faults fall outside DTW's shape-matching scope (AUROC about 0.66) and are better served by complementary variance- and missingness-based detectors, a consequence of DTW's shape-matching scope rather than a parameter choice. This work contributes a system-level methodological framework for deploying DTW as an IoT fault-detection-and-diagnosis capability: an episode-and-layer architecture aligned with hardware, communication, and software/firmware ownership; a label-free reference library requiring only assumed-healthy data; per-layer and per-channel attribution for cross-domain triage; and a reproducible operational tuning procedure. Together these deliver a fast-to-deploy, scalable, and accurate first-line detector for label-scarce IoT systems.

Abstract: Large Language Models (LLMs) are increasingly deployed for editorial and text-processing tasks, yet their application to high-stakes domain editing exposes a class of failures distinct from hallucination. Where hallucination involves a model generating false information, constraint-adherence failure involves a model ignoring or overriding explicit user constraints in favour of its parametrically weighted training data — producing output that is fluent, stylistically polished, and wrong in precisely the ways that matter most. This paper documents the development, iterative testing, and deployment of a dual-pass HITL (Human-in-the-Loop) architecture designed to perform source-critical developmental editing on an 8,000-word academic manuscript analyzing the genealogy of nineteenth-century homeopathic materia medica. Through systematic prompt engineering conducted under a HITL framework, we identified and categorized ten distinct constraint-adherence failure modes, including Parametric Memory Override, Constraint Poisoning, Invented Math Anchors, and Source-Category Collapse. We demonstrate that effective mitigation of these failures requires not elaborated natural-language requests — the conventional understanding of prompt engineering — but the application of rigid, software-like constraints that directly counteract specific neural network behaviors at the token-generation level. Our findings reframe the role of LLMs in scholarly editorial work: not as autonomous “Auto-Editors” capable of independent philological judgment, but as “AI Surgical Assistants” operating under the continuous direction of a human Lead Surgeon whose domain expertise cannot be encoded in any prompt. The Subject Matter Expert Override Protocol documented here provides a replicable model for high-fidelity LLM-assisted editing in any domain where semantic precision is non-negotiable.

Abstract: Electric micromobility may reduce transport emissions, but its system-level benefits depend on which journeys and vehicles it replaces. This study assesses e-bike, e-scooter and e-cargo-bike pathways in an electrifying UK transport system using an expanded Transport Energy Air pollution Model (TEAM-UK). Socio-technical scenarios are modelled annually to 2050, estimating travel activity, energy demand, direct CO₂ and NO_X emissions, life-cycle greenhouse gas emissions and local air pollutants, including non-exhaust sources. Fleet electrification dominates long-run reductions in tailpipe CO₂ and NO_X. However, e-bike-led pathways deliver the largest additional reductions in car-kilometres, energy demand, cumulative life-cycle emissions and non-exhaust PM2.5. E-cargo bike pathways provide more targeted benefits, while e-scooter pathways produce modest system-level gains. Electric micromobility is most effective when embedded in policies that substitute car and van travel with lighter, low-energy modes.

Abstract: Let R be a commutative ring with nonzero identity, and δ an expansion function of its ideals. In this paper, we introduce and study the concept of cube-difference factor absorbing δ(0)-ideals. A proper ideal I of R is called a cube-difference factor absorbing δ(0)-ideal (cdf-absorbing δ(0)-ideal, for short) if, for all a, b ∈ R, the condition a³ − b³ ∈ I implies that a − b ∈ I or a² + ab + b² ∈ δ(0). We establish various properties and characterizations of such ideals. We also examine the transfer of this property across several ring constructions, including polynomial rings, localizations, trivial ring extensions, and amalgamated algebras along an ideal. In particular, we characterize cdf-absorbing δ₀(0)-ideals of Z: among prime ideals via the third cyclotomic polynomial Φ3(x) = x² + x + 1 and its connection to Eisenstein primes, and among all ideals via a squarefreeness condition. We further extend the prime ideal characterization to arbitrary principal ideal domains.

Abstract: The increasing contamination of aquatic environments by heavy metals, together with the growing ecological impact of invasive aquatic species, highlights the need for sustainable remediation strategies. In this study, shells of the invasive crayfish Faxonius limosus were investigated as a low-cost biosorbent for the removal of Zn(II) and Cd(II) ions from aqueous solutions. The material was characterized using FTIR, SEM, and BET/BJH analyses, which confirmed the presence of amino, hydroxyl, amide, and carbonate functional groups and a predominantly mesoporous structure favorable for adsorption. Batch adsorption experiments showed rapid adsorption kinetics, with equilibrium reached after approximately 60 min. The pseudo-second-order kinetic model provided the best fit for both metal ions, indicating chemisorption as the dominant mechanism. Adsorption isotherms were best described by the Langmuir model, suggesting monolayer adsorption on homogeneous active sites. The maximum adsorption capacities predicted by the Langmuir model were 70.69 mg/g for Zn(II) and 302.12 mg/g for Cd(II). The study also demonstrated the potential reuse of metal-loaded shells as biofillers in natural rubber composites, contributing to sustainable waste valorization and circular economy principles.

Abstract: Brushless DC (BLDC) motors are now the dominant propulsion choice for electric vehicles (EVs) because of their high torque density, efficiency and reliability, but their nonlinear dynamics, electronic commutation and wide load and speed range make fixed-gain control difficult. A single set of proportional–integral–derivative (PID) gains tuned at one operating point degrades when inertia, back-EMF or load torque change. This paper presents a hybrid adaptive PID speed controller for a BLDC EV drive that couples an online PID auto-tuner, which re-estimates the gains from a frequency-response estimate of the plant, with a fast fixed-structure PID that supplies the rapid corrective action the auto-tuner cannot provide during its estimation interval. The novelty of the work is this explicit two-element decomposition operating on a cascaded speed/voltage loop driven by Hall-sensor feedback, which removes the need for an exact analytical feedback model while retaining the transparency of classical PID. A full analytical model of the BLDC machine and the closed-loop transfer functions is derived and implemented in MATLAB/Simulink. Across step references of 1000–1800 rpm and load steps to 10 N·m, and against a conventional fixed-gain PID and a Flower Pollination Algorithm (FPA) tuned PID, the proposed controller holds overshoot below 1% at low-to-mid speed and consistently lower torque ripple, while a 12.4% transient undershoot at 1800 rpm under sudden load identifies the present operating limit and a direction for future work.

Abstract: Urban public parks are vital for recreation, public health, environmental quality, and sustainable urban growth. Nonetheless, access to these parks remains uneven across many cities, disproportionately impacting low-income, marginalised, and spatially segregated communities. This study systematically examines socio-spatial disparities in park access and their effects on spatial justice. Using the PRISMA 2020 framework, 108 peer-reviewed articles published from 2000 to 2025 were analysed through thematic narrative synthesis. The results identify key factors influencing accessibility disparities, including socio-economic status, residential location, race and ethnicity, transport infrastructure, urban form, governance, and demographic vulnerability. Socio-economic status (24%), residential location and spatial distribution (20%), race and ethnicity (18%), and urban form and transport systems (17%) were the most common determinants. Limited park access exacerbates socio-economic inequality, worsens environmental injustice, contributes to health gaps, reinforces spatial segregation, hampers urban sustainability, and marginalises vulnerable populations. The review integrates Spatial Justice Theory, Environmental Justice Theory, and Urban Political Ecology into a comprehensive analytical framework and introduces a conceptual model linking accessibility factors to spatial justice outcomes. These findings underscore the importance of equitable green infrastructure planning, inclusive governance, and better access in underserved urban areas.

Abstract: Background: The way individuals perceive and interact with the food environment may influence their dietary practices and health outcomes. This study analyzed associations between social and housing conditions, food environment perception, and food security, and their effects on the nutritional status of children and mothers. Methods: A cross-sectional study conducted between 2022 and 2023 with 314 participants in Vitória de Santo Antão, Pernambuco, Brazil. Socioeconomic variables, housing conditions, perceptions of domestic and community food environments, food security, and anthropometric indicators were assessed using generalized structural equation modeling (GSEM, p < 0.05). Results: Better social and housing conditions were associated with more favorable domestic food environment perceptions and lower food insecurity. No significant associations were observed between the domestic food environment and food security, nor between the community food environment and other model variables. No significant indirect effects were identified, indicating absence of mediation through food environment perceptions. Associations were found between food security and maternal BMI, and between maternal and child BMI. Conclusions: Social and housing conditions play a central role in shaping food security and nutritional outcomes, while food environment perception showed limited relevance. These findings reinforce the need for intersectoral public policies addressing social inequalities and promoting healthy eating.

Abstract: Background: Tuberculosis (TB) remains the leading cause of death among persons with advanced HIV disease (AHD) in high HIV-burden settings. Digital chest X-ray (dCXR) with computer-aided detection (CAD) is a promising tool to overcome human resource constraints and improve TB case detection. This study evaluates the real-world im-plementation and performance of dCXR/CAD for TB screening within a specialized AHD clinic in Maputo, Mozambique. Methods: We conducted a retrospective cohort analysis of 487 new AHD patients at Centro de Referência do Alto Maé (CRAM) from October 2023 to September 2024. Of these, 238 underwent dCXR with CAD interpreta-tion. All patients underwent systematic TB screening according to Ministry of Health (MoH) guidelines. Using the recorded diagnosis of TB (bacteriologically confirmed or clinically diagnosed) as the reference standard, we calculated the sensitivity, specifici-ty, positive predictive value (PPV), and negative predictive value (NPV) of the nation-ally adopted CAD threshold (≥0.5). Results: Among 238 AHD patients screened with CAD, 116 (49%) were diagnosed with TB. At the ≥0.5 threshold, sensitivity was 50% (58/116; 95% CI: 41–59), specificity 92% (112/122; 95% CI: 85–96), PPV 85% (58/68; 95% CI: 75–92), and NPV 65.9% (112/170; 95% CI: 58–73). TB diagnosis rates increased sharply with CAD score: 30% (43/143) in normal, 52% (15/27) in abnormal non-suggestive, and 85% (58/68) in suggestive cases. Bacteriological confirmation was low across all groups (19–26%), reflecting reliance on clinical diagnosis. Conclu-sion: Integrating dCXR/CAD into AHD care is feasible and identifies a high TB burden. However, at the adopted threshold of ≥0.5, CAD demonstrated high specificity but low sensitivity (50%) in this population, missing half of all TB cases. These findings suggest that CAD functions better as a confirmatory decision-support tool than a standalone screening test in AHD. Threshold optimization for this specific population warrants prospective evaluation. Implementation challenges — including fragmented systems and lack of dedicated human resources — must be addressed to realize CAD's full po-tential in TB programs.

Abstract: This paper examines how the travel intention of students is correlated with their mental health, in terms of stress reduction and psychological wellbeing. The study employs a combined model that integrates the Theory of Planned Behavior and Stress Recovery Theory to determine the role of travel intention, which is based on the travel self-efficacy, the pressure of social influence, the value of the travel experience, the perception of affordability, and the attitude towards tourism in affecting the mental health outcome of students. The paper examines the mediating capacities of travel involvement and social connectedness, and the moderating capacities of nature connectedness and travel frequency. Semi-structured interviews with 15 university students and survey among 604 students in various universities in Bangladesh were used to collect data. Hypotheses were tested using PLS-SEM. Findings indicate that travel intention enhance psychological wellbeing and reduce stress, and travel involvement and social connectedness are important mediators. These relations are mediated by nature connectedness and frequency of travel, which increases the restorative effect of travel. The results indicate that tourism could be a viable approach to student mental health promotion, which has both practical and theoretical consequences on university policy and future tourism psychological studies.

Abstract: Irregular migration across the Mediterranean Sea causes thousands of deaths annually, mostly due to shipwrecks involving structurally inadequate vessels navigating under severe meteo-marine conditions. The forensic investigation of human remains recovered in such contexts is particularly challenging due to advanced decomposition and the absence of documentary evidence linking victims to a specific departure event. In the present study, a methodology is developed and validated for reconstructing the most probable departure location of human remains recovered at sea, through the integration of backward Lagrangian drift simulations with large-scale oceanographic and atmospheric datasets provided by the Copernicus Marine Service (CMEMS). The methodology was applied to five bodies recovered in the Aeolian Islands area (Sicily, Italy) between March and June 2024. Simulations were performed using the OpenDrift Leeway model, with an ensemble of several drifters released across five temporal offsets per recovery site. Results were synthesised through a drift probability metric Pd and a newly proposed Hydrodynamic Connectivity Index (HCI), cross-referenced with documented shipwreck incidents and complemented by a wave climate analysis. The methodology successfully identified the port of Bizerte (Tunisia) and the shipwreck event of 5-6 February 2024 as the most probable origin, in full agreement with independent forensic findings, demonstrating the reliability of the proposed approach for forensic reconstruction of shipwreck events in the central Mediterranean and the possibility of being used as aid in recovering further remains.