Abstract: Intimate partner violence-related brain injury is the most recent condition in a 150-year arc in which biological brain injury has been misattributed to psychological or moral causes before formal clinical recognition emerged. Earlier conditions in this pattern were each marked by decades of recognition lag before formal diagnostic frameworks emerged. In each prior case, that lag was driven by limits in available diagnostic technology. Intimate partner violence-related brain injury is the first condition in which diagnostic technology, including computed tomography, magnetic resonance imaging, diffusion tensor imaging, and neurocognitive assessment, has been continuously available throughout the recognition gap. The review identifies three structural barriers that sustain this recognition gap: a diagnostic barrier that leaves the injury without formal criteria, an administrative coding barrier that leaves it absent from ICD architecture, and a population surveillance barrier that leaves it indistinguishable from broader assault categories. Each barrier reinforces the others, limiting visibility, resource allocation, and access to care. Across these conditions, recognition lag has reflected an institutional imperative that has shaped which injured populations became clinically legible. Recent neuroimaging and cognitive studies make the biological imperative explicit. A cognitive entrapment framework reframes the reduced capacity to engage the cognitive and material resources leaving requires as injury-driven rather than as ambivalence or motivational deficit. The framework explains mechanistically why brain injury disrupts the multistep planning that leaving demands. Intimate partner violence-related brain injury is not only underdiagnosed but structurally underserved; correcting the mechanisms of recognition failure is necessary for access to treatment and rehabilitation.

Abstract: This case study examines an encryption failure incident involving the exposure of sensitive personal data within a governmental information system environment. The analysis is based on the well-documented data breach that occurred within the U.S. Department of Veterans Affairs, in which a government employee stored a large dataset containing veterans’ personal information on a portable laptop device that lacked adequate encryption protection. Following the theft of the device from the employee’s residence, the personal records of approximately 26.5 million individuals were placed at risk of unauthorized exposure. Rather than interpreting the incident as an isolated technical failure, this study analyzes it through the Swiss cheese model, proposed by James Reason, showing that the breach resulted from the alignment of weaknesses across multiple layers of defence. These weaknesses included the absence of full-disk encryption, insufficient enforcement of data handling policies, weak access control procedures, inadequate oversight of sensitive data transfers outside controlled environments, and excessive reliance on individual user compliance. Based on this analysis, the study proposes corrective and preventive measures, including mandatory strong encryption for portable devices, formal cryptographic key management procedures, strengthened data access and handling controls, and enhanced monitoring and auditing mechanisms. These measures are intended to reinforce multiple defensive layers, improve the protection of sensitive information, and reduce the likelihood of similar incidents in operational environments handling confidential data.

Abstract: A recently proposed CMB temperature relation, obtained from applying the Stefan Boltzmann law to the Hubble sphere and from related Hawking–Planck–Hubble scale arguments, may be written in the compact form T_CMB(t) = T_P/(8π √(N_P(t))). Here N_P is the effective Poisson-shot count. In an R_H = ct cosmology, the normalization consistent with the Stefan–Boltzmann radiation density is N_P(t) = (R_H(t))/2l_P = t/2t_P = (M_c(t))/m_P, where M_c(t) = c²R_H(t)/(2G) is the critical Hubble mass. If instead one defines the doubled Hubble-sphere mass M_u(t) = c²R_H(t)/G, then M_u/m_P = 2N_P. The formula has the mathematical structure of a Poisson relative-fluctuation law, since σ_N/N = 1/√N for a Poisson count, and may equivalently be written T_CMB(t) = T_P/8π σ_NP/N_P.We call this the Poisson-shot CMB formula. Substitution back into the Stefan–Boltzmann law gives u_γ ∝ T_CMB⁴ ∝ R_H^-2, matching the critical-density scaling in R_H = ct and yielding a constant photon radiation density parameter. This provides additional blackbody support for the formula and connects it to the observed near-perfect blackbody spectrum of the CMB. By contrast, in the standard ΛCDM framework the present CMB temperature is normally an observational input: the model predicts the redshift scaling T(z) = T₀(1 + z) once T₀ is supplied, but it does not derive the absolute present value T₀ from the Planck scale and the Hubble scale.

Abstract: An approach for the control of linear control systems with a single time-delay is proposed. The main contribution is the inclusion of a symmetric-injection virtual reference trajectory into the controller to render stability robustness of single-delay linear control systems. The dynamics of the virtual trajectory is included into the closed-loop dynamics allowing theoretical computation of the critical time-delay before losing stability. Moreover, an energy-based symmetry interpretation of the proposed approach is drawn. Numerical simulations considering stable and unstable linear systems are shown, and experiments to control a DC-motor with time-delay measurements validate our proposal.

Abstract: Plant factories have evolved from automated cultivation facilities into data-driven crop production systems. Over the last decade, artificial intelligence has been applied to non-destructive crop monitoring, sensor correction, nutrient-solution diagnosis, growth prediction, environmental control, digital twins, and product-level inspection. This review summarizes AI technologies for plant factories, focusing on machine vision, deep learning, nutrient-solution intelligence, reinforcement learning, and digital-twin interfaces. The main argument is that plant-factory AI should not be understood only as image-based phenotyping; practical systems require an integrated intelligence stack connecting visual perception, sensor calibration, nutrient modeling, control, remote operation, and industrial inspection. Remaining challenges include dataset scarcity, model generalization, sensor drift, explainability, energy-aware control, and closed-loop decision-making.

Abstract: Floods are among the most damaging natural hazards, threatening human safety and causing substantial economic losses. Their risk results from the interaction between hazard, exposure, and vulnerability, and has been increasing due to the rising frequency and intensity of extreme hydrometeorological events. This issue is particularly relevant in Mediterranean regions, where floods often affect small, densely populated, and highly urbanised basins.This study applies a comprehensive climate risk assessment to the Foglia River basin (Marche, Italy) using the framework and tools developed within the Horizon Europe CLIMAAX project. Locally developed flood hazard maps were integrated with exposure and vulnerability data, focusing on the city of Pesaro at the river mouth. Risk was quantified in terms of building damage and population exposure for different return periods.To further investigate changes in flood hazard, projected river discharge under climate scenarios was analysed. The results indicate a relative increase in flood recurrence exceeding 20% for the 5-, 10-, and 50-year return periods, suggesting a significant intensification of flood risk. The study provides spatially explicit estimates of potential economic losses and supports the refinement of regional climate adaptation strategies, offering valuable insights for the integration of climate risk considerations into urban and territorial planning.

Abstract: The most widely used method for trend estimation in economics is the Ho-drick-Prescott (HP) filter. The HP filter has various disadvantages as the arbitrary and frequency-dependent choice of the smoothing parameter λ, boundary problems and difficult interpretation when linking to economic theory. We suggest an alternative method by improving some of these disadvantages using a purely data-driven, endog-enous nonparametric trend estimation. A simulation study and different applications demonstrate the advantages of the nonparametric trend compared to the HP filter. We identify optimal time windows supporting the momentary growth trend. Within this window economic fundamentals smoothly change and drive the trend.

Abstract: Objectives: This study investigated the interplay between pre- and post-match physiological responses and subsequent emotional changes in elite male water polo players, focusing on differences between official championship (competitive) and non-competitive (training) settings. Methods: Sixteen male Italian Serie C water polo players participated. Salivary biomarkers (cortisol, immunoglobulin A (IgA), and uric acid) were measured, alongside psychological assessments of cognitive anxiety, somatic anxiety, and self-confidence. Measurements were taken before and after both training and competition matches. Results: A significant anticipatory rise in salivary cortisol was observed before competition matches compared to training, highlighting the psychological stress associated with competitive events. Post-match, cortisol levels remained elevated to a greater extent after competition. Salivary IgA levels decreased significantly following both training and competition, with a more pronounced reduction after official matches, and exhibited a negative correlation with cortisol. Salivary uric acid, a marker of oxidative stress, increased post-exercise and was significantly higher after competition. Players reported higher somatic and cognitive anxiety and lower self-confidence before competition compared to training, and pre-competition cortisol levels were positively correlated with both anxiety measures and negatively correlated with self-confidence. Conclusions: These findings highlight the distinct physiological and psychological responses elicited by competitive versus non-competitive settings in water polo, emphasizing the importance of considering the emotional context when monitoring athletes' stress and recovery. The social meaning of competitive contexts may be embodied, impacting stress and immune responses.

Abstract: Sub-Andean forests of the Colombian Central Cordillera are among the most biodiverse and threatened Neotropical ecosystems, yet their floristic, structural, and carbon dynamics remain poorly documented. We characterized the floristic diversity, vegetation structure, and aboveground carbon storage of the El Mangón sub-Andean forest remnant (24 ha; 1,600–1,700 m a.s.l., Cauca, Colombia) using free collections across the total area and a structural inventory in five 50 × 4 m transects (498 individuals, 35 species). A total of 281 species, 209 genera, and 100 families were recorded; epiphytes represented 44.13% of species, exceeding typical values (25–35%) for this forest type. Diversity indices were intermediate (H′ = 2.55; DMg = 5.47; 1−D = 0.89). Palicourea crocea dominated structurally (IVI = 45.12) and concentrated 31.2% of total carbon storage (894.41 kg·ha⁻¹; total = 2,866.23 kg·ha⁻¹). Three novel carbon indices (CVI, CCEI, CSI) integrate storage magnitude with ecological efficiency and spatial stability. The CSI was highest in low-aggregation species (1,618.23), with no significant differences among spatial groups (Kruskal–Wallis, p = 0.088). El Mangón ranks among the most diverse sub-Andean remnants of southwestern Colombia, underscoring its conservation priority in an increasingly fragmented landscape.

Abstract: Since the launch of the Advanced Land Observing Satellite (ALOS) in 2006, the Japan Aerospace Exploration Agency (JAXA) has been leading both disaster response within Japan and Sentinel Asia, which targets disasters in the Asia-Pacific region, as well as participating in the International Charter: Space and Major Disasters, which is international framework. The author was involved in these activities from 2006 to 2014. Based on this experience, this article proposes an approach to “how to utilize satellite remote sensing in the activities of disaster management users.” The methodology involves treating each activity as a case study for “how to utilize satellite remote sensing in the activities of disaster management users.” and examining them from a holistic perspective.

Abstract: Video world models have emerged as a critical framework, offering a powerful approach to modeling dynamic environments through lens of video data, and serving as a key tool for understanding and predicting complex systems. While prior papers have focused on specific domains such as 3D modeling, autonomous driving, and robotics, they have largely overlooked the growing importance of video modality in the development of future world models. These papers often concentrate on particular data representations, failing to account for how video-based representations can bridge the gap between perception, prediction, and decision-making in intelligent systems. This paper aims to fill this gap by providing a standardized and systematic classification of video world models. We introduce a comprehensive taxonomy that distinguishes between implicit state deduction which, focuses on learning compact latent representations and explicit visual modeling, which emphasizes frame level video processing. Additionally, we analyze indepth review of experimental setups, specific applications, and open problems. By focusing on video world models, this paper offers a unified reference that highlights their critical role in the future of world modeling research.

Abstract: Background: Respiratory syncytial virus (RSV) is a leading cause of hospitalization in children, but predictors of critical illness remain poorly defined. This study aimed to identify risk factors for critical RSV pneumonia and to develop a predictive model. Methods: A retrospective analysis of 12,035 hospitalized children with RSV infection (2019-2025) yielded 304 eligible patients after exclusions. Based on critical illness criteria, 30 critical cases and 90 randomly selected non critical controls from the remaining patients were enrolled. Clinical characteristics, laboratory parameters, and co-infection patterns were compared. Univariate, Lasso, and multivariable logistic regression analyses were performed to identify independent predictors, which were then incorporated into a nomogram. Model performance was assessed using ROC curve, calibration plot, and decision curve analysis. Results: Among the 304 eligible children, 30 (9.9%) had critical illness. Co infection with three or more pathogens was most common in the critical group (43.3%), whereas single RSV infection predominated in the non critical group (38.9%). Multivariable logistic regression identified four independent predictors of critical illness: interleukin 6 (IL 6), creatine kinase MB (CK MB), serum bilirubin excretion (SBE), and neutrophil percentage. The nomogram combining these factors exhibited excellent discriminative ability (AUC = 0.921, 95% CI: 0.868-0.974). The calibration curve agreed well with the 45° reference line (Hosmer-Lemeshow χ² = 3.233, p = 0.919), and decision curve analysis demonstrated clinical benefit across threshold probabilities ranging from 0.01 to 0.99. Conclusions: Elevated IL-6, CK-MB, neutrophil percentage, and SBE are independent predictors of critical RSV infection in children. The nomogram based on these readily available biomarkers provides a robust tool for early risk stratification and clinical decision-making.

Abstract: Emerging materials often face challenges in market adoption due to limited comparability and reliability of measurement-based material information, despite their potential to drive technological innovation. While standardization is widely recognized as an important mechanism for market diffusion, existing approaches provide limited insight into how material specifications facilitate the comparative evaluation of material characteristics and their use in market decision-making. This study proposes a complementary perspective that interprets standardization as an infrastructure for organizing the generation, sharing, and evaluation of measurement-based material information across industry, standard development organizations (SDOs), and markets. Within this framework, the study distinguishes between two complementary types of standards for material specifications. Type A standards enable the structured disclosure of measured characteristic values and associated measurement uncertainties, allowing application-specific evaluation without predefined acceptance criteria. In contrast, Type B standards define predefined characteristic values and compliance criteria, providing a basis for conformity assessment, certification, and quality assurance. These two types may be understood as complementary mechanisms that fulfill different functions of comparability and compliance under varying technological and market conditions in emerging material systems. Consequently, they contribute to both innovation-oriented market evaluation and quality-assured market acceptance.

Abstract: Timelike boundaries provide a natural setting for organizing geometric, quasilocal, and coarse-grained information in general relativity. This work develops a cut-level reference framework for finite-radius timelike interfaces in Lorentzian spacetime. Starting from a timelike boundary, a tangent observer field, and observer-adapted spatial cuts, the construction assigns selected boundary quantities, coarse-grained reference structures, channel-specific comparison values, resolved deviations, local event closure, and cut-level response terms to the same geometric surface. The framework is local in its physical reading. The coarse-grained reference structure is not treated as a single resolved boundary record, but as the macroscopic comparison structure relative to which local deviations are defined. A local boundary event is represented by a boundary-relative deviation that becomes resolvable at the candidate event. The causal condition fixes the Lorentzian admissibility domain; it does not by itself define a resolved trajectory or microscopic propagation history between spacetime points. In the classical realization developed here, the selected variables are supplied by the Brown--York cut-level dictionary. Observer-adapted projections of the boundary stress tensor define surface energy density, momentum density, spatial cut stress, and isotropic pressure. A coarse-grained boundary reference package specifies which variables are resolved, on which cut they are evaluated, and which reference structure serves as their comparison level. The corresponding deviation map and channel-dependent resolution norms identify the locally resolved boundary content. The same cut-level variables also enter a classical balance structure in which cut-energy variation separates into normal exchange and tangential mechanical response. In isotropic spherical symmetry, this response reduces to the pressure--area form, linking cut-level stress to the area-response channel of a timelike shell. Timelike thin-shell dynamics and macroscopic shell-balance laws then appear as concrete realizations of the general reference-cut structure. The resulting formulation provides a classical boundary-reference language for finite-radius timelike systems, relating local Lorentzian geometry, quasilocal stress, coarse-grained reference structure, resolved deviations, causal admissibility, and area response within one common cut-level framework.

Abstract: Rigid 2D-3D image registration plays a critical role in modern image-guided interventions by enabling the alignment of intraoperative X-ray images with preoperative volumetric data such as computed tomography (CT). Accurate 2D-3D registration allows clinicians to localize anatomical structures in three-dimensional space while relying on fast and low-dose intraoperative imaging, which is essential for applications including orthopedic surgery, spine navigation, radiation therapy, and interventional radiology. Despite significant progress over the past two decades, achieving robust and accurate registration remains challenging due to factors such as limited imaging viewpoints, occlusions, imaging noise, and the large search space of rigid transformations. This paper provides a comprehensive survey of rigid 2D-3D registration methods with a particular focus on X-ray-to-CT alignment. We first introduce the mathematical formulation of the registration problem and present a taxonomy of existing approaches. We then review the key technical components underlying modern registration systems. In addition, we summarize commonly used datasets and evaluation protocols, discussing widely adopted metrics such as target registration error (TRE), pose error, and reprojection error. The survey also highlights representative clinical applications and analyzes the practical challenges that remain in real-world deployment, including robustness to imaging artifacts, variations in imaging dose, and real-time computational constraints. Finally, we discuss emerging research directions, such as differentiable rendering, deep learning based pose estimation, and multi-view registration frameworks, which are expected to further improve the accuracy, robustness, and clinical applicability of 2D-3D registration methods.

Abstract: Background/Objectives: Transradial access (TRA) is the preferred route for coronary catheterization, yet its consequences for radial artery vasoreactivity and hemodynamic parameters remain incompletely characterized. We prospectively quantified TRA-induced functional impairment, its clinical determinants, and the predictive val-ue of baseline parameters. Methods: Ninety-four consecutive patients undergoing elective TRA were assessed at baseline, 24 hours, and one month using high-resolution Doppler ultrasound. Nine vascular parameters were measured: flow-mediated dilation (FMD), nitroglycerin-mediated dilation (NMD), peak systolic velocity (PSV), resistive index (RI), pulsatility index (PI), resting and hyperemic velocity-time integral, hyper-emic blood flow volume, and lumen diameter. Non-parametric methods were applied throughout. Results: FMD declined at 24 hours (−31.2%; p< 0.001) and showed no sig-nificant recovery at one month (p=0.08 vs 24 hours). NMD showed a greater acute de-cline (−36.6%; p< 0.001) with partial but statistically significant recovery at one month (p< 0.001). PSV recovered fully by one month; RI fell below baseline, consistent with compensatory microvascular vasodilation. Radial artery lumen diameter remained significantly below baseline at one month. Radial artery occlusion occurred in 4 patients (4.3%), all with spontaneous recanalization. Female sex selectively predicted greater NMD reduction (ΔNMD −8.3% vs −5.8%; p=0.005) without a corresponding FMD difference (p=0.40). Older age correlated with impaired FMD recovery at one month (ρ=−0.62; p< 0.001) but not with NMD outcomes. Baseline PSV demonstrated the highest discriminatory performance for significant FMD decline (AUC=0.73). Conclu-sions: TRA causes multidomain, persistent radial artery functional impairment at one month, with distinct recovery trajectories for endothelial and smooth muscle function. Female sex and advanced age are selective determinants of injury and recovery, re-spectively. A pre-procedural phenotype comprising baseline diameter, PSV, RI, and age predicts post-procedural outcomes and supports further investigation of pre-procedural phenotyping for risk stratification.

Abstract: The Tilapia (Oreochromis aureus) sustains more than 90% of the fishery value and volume in the Vicente Guerrero Reservoir (VGR), Northeast Mexico, but stock status is uncertain due to lack of assessments. A total of 1,792 individuals (2020-2021) were analyzed. Von Bertalanffy growth, total (Z), natural (M) and fishing (F) mortality, and exploitation rate (E) were estimated. Under a data-limited framework, four complementary approaches were applied: the LBB model, length-based indicators, empirical reference points, and ecological risk assessment. Growth was negatively allometric (b=2.89). Estimated parameters were: L∞=464 mm, K=0.2275 yr⁻¹, Z=3.591 yr⁻¹, M=0.3894 yr⁻¹, F=3.302 yr⁻¹, E=0.892. The LBB model estimated a relative biomass B/B₀=0.057 (95% CI: 0.042-0.072) and an F/M ratio of 8.48. Only 7.5% of individuals exceeded maturity length, 4.8% were at optimal length, and 2.6% were mega-spawners. Estimated fishing mortality exceeded the reference points (F_MSY=0.339 yr⁻¹; F_limit=0.508 yr⁻¹; F_crash=0.678 yr⁻¹) by 9.7, 6.5, and 4.9 times, respectively, classifying the stock as extreme high risk. The O. aureus stock in VGR is in biological collapse (B/B₀=5.7%; F/M=8.48). Increasing minimum capture length to at least 290 mm and reducing fishing effort by 80-90% is urgently required. The convergence of independent methods validates data-limited approaches for artisanal fisheries.

Abstract: Urbanisation is reshaping ecosystems and increasing human–wildlife interactions. Wild boar (Sus scrofa), a highly adaptable species, is increasingly common in European cities, where it exploits natural and anthropogenic resources, often leading to conflict. Predicting when and where wild boar enters urban areas remains challenging, particularly using scalable tools such as remote sensing. Here we show that temporal and spatial drivers of urban presence are decoupled in Barcelona over a 14-year period. Seasonal vegetation dynamics influenced the timing of urban incursions, with peaks in spring and late summer associated with changes in vegetation moisture and likely reinforced by increased energetic demands during reproduction and early lactation. However, remotely sensed vegetation indices captured these dynamics only partially, limiting their predictive power when used alone. Spatial variation in urban green area use was primarily explained by landscape structure, with proximity to streams and habitat fragmentation contributing similarly. Green areas near natural corridors concentrated higher and more variable presence, while heterogeneous landscapes likely facilitated repeated use by increasing access to foraging and refuge. Integrating remote sensing with landscape metrics can improve anticipation and management of human–wildlife conflicts.

Abstract: This work deals with the time-domain analysis of asymmetrical faults in three-phase systems. Conventional three-phase analysis provides steady-state solutions for asymmetrical faults. Transient analysis, however, is usually performed by resorting either to oversimplified approximate circuits, or to numerical methods. In this paper, a rigorous analytical methodology based on the time-domain Clarke transformation is presented for the most common asymmetrical faults in three-phase systems. In particular, it is shown that asymmetrical faults result in circuit coupling in the Clarke equivalent circuits. Circuit representation of coupling is also derived in the paper. Coupled equivalent circuits allow rigorous analytical solution of transients in case of asymmetrical faults. The analytical results derived in the paper are validated through proper numerical simulation of faulted radial systems.

Abstract: Background: Epigenomics has emerged as an essential technique of modern molecular biology, providing a critical layer of gene regulation. DNA methylation, histone modi-fications and alterations to the chromatin accessibility of DNA have been widely associated with complex diseases including cancer. The most recent developments in high-throughput sequencing technology have made it possible to profile epigenetic landscapes genomically on a large scale. However, these techniques often mask endogenous cellular heterogeneity essential for understanding complex disease stages. Objective: The purpose of the review is to assemble accessible tools and algorithms to conduct an Epigenomic study in the field of biomedical research, from bulk tissue analysis to the high-resolution frontier of single-cell epigenomics. Methods: We performed a comparative analysis of common methods used to analyze DNA methylation, chromatin immunoprecipitation, sequencing analysis and chromatin accessibility profiling. We described the standardized bioinformatics tools and pipelines required to transform raw sequencing data into mechanistic biological understanding, highlight-ing the role of quality control, peak calling, and differential analysis. Furthermore, we explore the integration of epigenomic with other "omics" layers through advanced computational frameworks, including machine learning and network-based modeling. Results: These advanced multi-omics techniques demonstrate efficient clinical utility by enabling biomarker discovery, disease subtyping, and the identification of novel therapeutic targets. Conclusions: Despite challenges with data complexity, the fusion of Artificial Intelligence (AI) and single-cell technologies will speed up the transition toward precision medicine. This review serves as a blueprint for providing the technical and computational complexities of epigenomics to uncover the mechanisms controlling human health and disease.