Abstract: Background: Work disability in fibromyalgia is only partially explained by symptom severity, suggesting a relevant contribution of cognitive–behavioral mechanisms. Objective: This study aimed to determine whether kinesiophobia is associated with fibromyalgia impact and work-related disability, and to assess whether pain catastrophizing mediates these relationships within a hierarchical biopsychosocial framework. Methods: This cross-sectional study included 2,096 women with fibromyalgia recruited through a nationwide online survey. Participants completed validated instruments assessing fibromyalgia impact (FIQ), pain catastrophizing (PCS), depressive symptoms (PHQ-9), central sensitization (CSI), and kinesiophobia (Tampa Scale). Pain-related work disability was defined using the Graded Chronic Pain Scale–Revised (GCPS-R). Hierarchical logistic regression models identified factors independently associated with work disability. Mediation was tested using bootstrapped analyses (5,000 resamples). Results: Kinesiophobia demonstrated a robust independent association with work disability (OR 1.03; 95% CI 1.02–1.05) after adjustment for sociodemographic factors, clinical pain phenotype, systemic burden, pain severity, psychocognitive load, and medication burden. Other relevant contributors included pain severity (OR 1.96; 95% CI 1.70–2.27), psychocognitive burden (OR 1.35; 95% CI 1.15–1.58), use of benzodiazepines (OR 1.74; 95% CI 1.33–2.28), and opioid use (OR 1.29; 95% CI 1.06–1.56). Mediation analysis indicated a significant indirect effect of kinesiophobia on work disability through pain catastrophizing (β = 0.131; 95% CI 0.078–0.188). Conclusion: Kinesiophobia is a proximal determinant of work disability in fibromyalgia, exerting direct and cognitively mediated effects through pain catastrophizing, reinforcing the fear-avoidance framework and the need for psychologically informed rehabilitation.

Abstract: Existing research on quality gain-loss functions predominantly focuses on single variables or separable quality characteristics, overlooking the correlations among multiple quality attributes and the complexity of spatiotemporal factors. This paper employs the Matérn kernel to construct spatiotemporal interaction terms, incorporates Kalman filtering and smoothing algorithms to enhance computational efficiency, and establishes joint gain-loss weights using the signal-to-noise ratio method. Consequently, a multivariate multidimensional quality gain-loss function model based on the Non-Separable Gaussian Process (NSGP) is developed. The NSGP model is applied to simulation cases and dam concrete production scenarios. Comparative optimization with machine learning methods such as Gaussian processes and linear regression validates the robustness of the NSGP model. Crucially, it eliminates the computational requirement for determining covariance separability, thereby reducing computational costs. This provides robust case support for quality management in hydraulic concrete construction.

Abstract: Accurate vessel speed prediction is essential for maritime traffic supervision, navigational safety, and intelligent coastal management. However, due to the nonlinear, time-varying, and context-dependent characteristics of vessel motion in nearshore waters, conventional single-model approaches often fail to provide sufficiently accurate forecasts. To address this issue, this study proposes a hybrid deep learning framework for AIS-based nearshore vessel speed prediction and risk warning, integrating a temporal convolutional network (TCN), an attention mechanism, and a bidirectional long short-term memory network (BiLSTM) into a unified architecture. In the proposed framework, TCN is used to extract local temporal patterns and multi-scale sequence features from historical AIS observations, the attention mechanism is introduced to adaptively emphasize informative representations, and BiLSTM is employed to model bidirectional contextual dependencies in vessel motion sequences. On this basis, a speed-risk warning process is constructed by combining the predicted speed with electronic-fence threshold constraints. Experiments conducted on real AIS data from coastal waters show that the proposed method outperforms several benchmark models in terms of mean absolute error (MAE), mean squared error (MSE), root mean square error (RMSE), and coefficient of determination (R2). The results demonstrate that the proposed framework can effectively improve vessel speed prediction accuracy and provide practical support for proactive maritime supervision and nearshore safety management.

Abstract: We introduced a possible electric charge forming mechanism, which includes the quasi normal mode calculations for a Kerr black hole with area of and spin 2 and the states generation by the preon model. We think the electric charge may exist as energy in the 3+1 space time with no need for additional dimension. And we think there might possibly be a tiny Kerr black hole net in our universe, which is sparse for electric charges and will select out the energies corresponding to electric charges as the only possible propagating wave energies. This net may at least be another possible way to have electric charge quantization except confinement, especially when we have to treat it as quantized energy propagating in the 3+1 space time. We also showed that electric charge may be the source of a Berry curvature to curve the 3+1 space time to form the conventional electromagnetic field. Observation considerations have also been given. Future gravitational wave detections may offer opportunities to check the ideas proposed here.

Abstract: Abdominal radiographs remain a widely used first-line investigation for both acute and chronic abdominal conditions. In routine clinical practice, they often reveal findings unrelated to the patient’s presenting complaint. While many of these are benign or reflect normal anatomical variation, they can sometimes resemble significant disease and lead to unnecessary investigations, patient anxiety, and added healthcare costs. This review presents a practical approach to interpreting such incidental findings, using a simple classification based on their radiographic appearance. These include calcifications, gas patterns, soft tissue and organ-related findings, as well as foreign bodies and procedure-related materials. Common examples, such as phleboliths, costal cartilage calcifications, gallstones, and vascular calcifications, are discussed, along with important mimics, such as pseudopneumoperitoneum. Normal variants, including Riedel’s lobe, renal anomalies, and bowel malposition, are also described. Attention is given to recognising typical imaging appearances and avoiding common sources of error in interpretation. The continued importance of abdominal radiography in settings with limited access to advanced imaging is also acknowledged. Selected radiographic examples are included to support pattern recognition and day-to-day clinical application. A clear, structured approach allows incidental findings to be interpreted with greater confidence and guides appropriate clinical decisions. This reduces unnecessary imaging, limits patient anxiety, and supports more effective and focused patient care.

Abstract: Agapé is a light-touch relationship enhancement smartphone app. This study used data from a longitudinal study couples using the Agapé app to link change in an array of behavioral processes into mechanistic chains, thereby providing some of the first quantitative insights into how various relationship processes might be linked as they shape the course of relationships. A sample of 405 couples in long-term relationships (810 partners, 50% women, 75% white, together M=4.5yrs, 50% living together, 33% currently dissatisfied) completed assessments across their first month of using Agapé. Men and women significantly improved on 15 of the 16 relationship processes assessed. Network analyses highlighted increases on three processes (quality time spent together, perceived partner responsiveness, and gratitude toward partner) as the processes most proximally linked to increases in relationship quality. The network findings also uncovered a number of indirect mechanistic pathways to be explored in future studies (e.g., increases in couples talking about their relationships to increases in awareness within those relationships to increases in gratitude and quality time to increases in relationship quality). Thus, the results offer a tentative blueprint for the inner workings of relationship dynamics and guidance toward optimizing the benefits of Agapé.

Abstract: The construction sector faces a dual challenge: meeting growing global demand while achieving deep decarbonisation in line with the European Green Deal and the EU Bioeconomy Strategy. Bio-based construction materials offer significant potential to reconcile these objectives through carbon sequestration, reduced embodied emissions, improved indoor environmental quality, and compatibility with circular economy principles. However, their transition from niche applications to mainstream specification remains limited. This paper provides a comprehensive review of bio-based construction materials and examines the systemic barriers constraining their large-scale adoption. The analysis identifies four interrelated categories of constraints—structural, economic, technical, and enabling—and emphasises the conditional relationships between them, highlighting the implications for policy prioritisation and sequencing. The strategic urgency of this transition has been reinforced by the 2026 Strait of Hormuz crisis, which triggered severe disruptions to global petrochemical supply chains and exposed the structural vulnerability of European construction to fossil-derived material inputs, reframing bio-based alternatives as a supply security imperative alongside an environmental one. The findings show that the primary obstacles to adoption are not technological, but institutional and economic, particularly regulatory fragmentation, the absence of harmonised standards, supply chain limitations, and persistent market failures that disadvantage bio-based solutions.The paper concludes that scaling bio-based construction materials requires coordinated action across governance, market design, and industrial policy. Without addressing these systemic constraints, advances in material innovation and performance are unlikely to translate into widespread adoption.

Abstract: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive cancer type in which therapeutic options are limited and the disease is characterized by a poor prognosis. In the development of pancreatic cancers, dysregulated calcium signaling plays a key role, due to the regulation of proliferation, survival, metabolic adaptation, and tumor–microenvironment interactions. Among the calcium channels, TRPV6 has emerged as a key regulator since this channel is highly selective for calcium and frequently overex-pressed in different types of cancers. The aim of this review is to summarize our cur-rent knowledge on the structure, regulation, and function of TRPV6, with emphasis on its cell type–specific roles within the pancreas. We describe the mechanisms by which TRPV6-mediated calcium influx activates oncogenic signaling pathways, such as NFAT, AKT/mTOR, and NF-κB, and how this channel plays a role in intra- and extra-cellular pH regulation. In addition, the clinical relevance and potential contribution of TRPV6 to therapy resistance are discussed. Finally, we review pharmacological strate-gies and future perspectives regarding TRPV6 in PDAC.

Abstract:

Background: There is a critical need for effective therapeutics for Alzheimer’s. Personalized, precision medicine approaches represent a potentially effective strategy, and proof-of-concept trials have provided supportive data. Objective: To determine whether a precision medicine approach to Alzheimer’s at the mild cognitive impairment or early dementia stage is effective in a randomized controlled clinical trial. Methods: Seventy-three patients with mild cognitive impairment or early dementia were evaluated for biochemical, microbiological, genetic, epigenetic, and imaging parameters associated with cognitive decline, then assigned randomly to a precision medicine approach or standard of care treatment. Results: Statistically significant effects of the precision medicine approach were observed for overall neurocognitive functioning (d=1.12; 95% CI, 0.56-1.66; p<0.001), memory (d=0.94; 95% CI, 0.40-1.46; p<0.001), executive function (d=0.89; 95% CI, 0.35-1.43; p=0.001), processing speed (d=0.67; 95% CI, 0.14-1.19; p=0.012), self-reported cognitive symptom severity (d=-1.05; 95% CI, -1.60, -0.49, p<0.001), and partner-reported cognitive symptom severity (d=1.26; 95% CI, 0.70-1.81; p<0.001), with MoCA scores showing a trend to improvement (p=0.154). Furthermore, overall health was enhanced, with improvements in blood pressure, body mass index, glycemic index, lipid profiles, and methylation status. Treatment effect size on overall cognitive function exceeded previous trials, being 2-3 times larger than effects of lifestyle interventions and 4-7-times larger than those of anti-amyloid therapies. Conclusion: A personalized, precision medicine approach represents an effective treatment for patients with mild cognitive impairment or early-stage dementia. This treatment improves cognition and overall health rather than simply retarding decline, without significant negative side effects such as brain edema, microhemorrhage, or atrophy.

Abstract: This technical note discusses, at system level, a fully analog control architecture in which a programmable spintronic crossbar can generate rapid mismatch-handling signals and a maximum-power-control signal for a photovoltaic source operating under rapidly varying shading conditions. The note is intentionally technology-agnostic and focuses on architectural principles rather than on fabrication details, device-specific programming workflows, or implementation-specific optimization procedures. The main value of the approach is the possibility of forming protection and control signals in parallel, with very low decision latency in the fast path, while overall operating-point convergence remains governed by the source and converter dynamics. The discussion is framed as a pedagogical technical note associated with already filed patent applications. Its purpose is to explain the conceptual role of a spintronic crossbar in analog control, the relationship between crossbar decision latency and converter response time, and the practical distinction between system-level architectural advantage and device-level maturity.

Abstract: Soil salinity is a major abiotic stressor that inhibits plant growth. Arbuscular mycorrhizal fungi (AMF) form symbiotic relationships with plants that can enhance their tolerance to such stresses. This study evaluated the efficacy of AMF in mitigating salt stress in three plant species. Sorghum bicolor, Sesbania sesban, and Cassia tora were cultivated under greenhouse conditions for five months. Plants were subjected to four salinity levels (0, 2.5, 5.0, and 7.5 dS m^-1) with or without AMF inoculation. Growth parameters (plant height, leaf number, fresh and dry weight of shoots and roots, relative growth rate (RGR), and root-to-shoot ratio (RSR)) were measured. The percentage of root colonization by AMF structures (mycelium, vesicles, arbuscules) was also assessed. AMF colonization rates were highest at the lowest salinity level (2.5 dS m^-1) and declined significantly at 7.5 dS m^-1. Sesbania sesban showed the highest colonization rate (90%), followed by Sorghum bicolor and Cassia tora. Inoculation with AMF significantly improved all growth parameters under salt stress, particularly at 2.5 dS m^-1. Sorghum bicolor demonstrated the highest tolerance, with AMF-inoculated plants showing remarkable improvements in RGR and biomass even at 7.5 dS m^-1. AMF symbiosis significantly enhances salt stress tolerance in the studied species, with the effectiveness being species-dependent and inversely correlated with salinity levels. Sorghum bicolor exhibited the greatest potential for AMF-assisted cultivation in saline soils.

Abstract: Background: A significant percentage of patients with major depressive disorder (MDD) fail to achieve remission with antidepressant monotherapy and frequently experience residual mood and cognitive symptoms that impair their functional recovery. Thus, an augmentation with vortioxetine, a multimodal antidepressant with reported cognitive benefits, might be a useful strategy for such patients. Methods: We conducted a 12-week naturalistic, prospective observational study in a Malaysian university hospital. 40 adults with MDD and inadequate response to at least eight weeks of antidepressant therapy received either adjunctive vortioxetine or optimization of their existing antidepressant as part of treatment-as-usual care. Depressive symptoms were assessed using the Montgomery–Åsberg Depression Rating Scale (MADRS), cognitive symptoms using the Perceived Deficits Questionnaire-5 (PDQ-D5), and global improvement using the Clinical Global Impressions–Improvement (CGI-I) scale. Results: Both groups demonstrated significant improvements in MADRS and PDQ-D5 scores over 12 weeks (p < 0.001). Remission rates at Week 12 were high in both groups (93.8% adjunctive vortioxetine vs 86.7% control). While between-group differences were not statistically significant, patients receiving vortioxetine showed earlier improvement in several core depressive symptoms, including apparent sadness, suicidal ideation, and appetite disturbance. Greater clinician-rated global improvement was observed in the vortioxetine group at Week 12 (87.5% vs 40.0%, p < 0.001). Conclusions: In this outpatient clinical setting, adjunctive vortioxetine was associated with earlier improvement of core depressive symptoms and greater global clinical improvement compared with optimization of existing antidepressant therapy. Collectively, these findings suggest adjunctive vortioxetine as a clinically relevant option for patients with MDD who show an inadequate response to antidepressant monotherapy.

Abstract: Psychological empowerment is associated with women's well-being, yet how it translates into life satisfaction in high-informality Latin American settings remains untested — as does whether empowerment must cross a threshold before any benefit appears. We tested a moderated mediation model with 251 women aged 18–44 from three northern Peruvian regions using PLS-SEM with 5,000 bootstrap resamples. Coping engagement fully mediated the empowerment–life satisfaction relationship (indirect β = .134, 95% CI [.065, .213]; VAF = 87.6%; R²[engagement] = .070, R² [life satisfaction] = .285); the direct path was non-significant (β = .019, p = .754). In exploratory threshold analyses, empowerment predicted life satisfaction only above a normative cut-point (≥136; β = .382, p < .001) below it, the association was flat (β = .047, p = .547). Age moderated the engagement–satisfaction link (β = −.239, p = .031), with stronger effects among younger women; motherhood amplified the negative impact of disengagement on satisfaction (β = −.272, p = .021). Model fit was good (SRMR = .078, at threshold; NFI = .942). Engagement is the mechanism that converts empowerment into well-being, but it only activates once empowerment is high. Incremental, single-dimension programs are unlikely to shift life satisfaction. Tailored design for mothers and younger women is warranted.

Abstract: We deploy an intrusion detection classifier on the STM32N6570-DK, a Cortex-M55 MCU with the Neural-ART NPU. Using the approximate T = 1 SNN–INT8 ANN equivalence, we compile a lightweight MLP to the NPU and evaluate four datasets: NSL-KDD (5-class), UNSW-NB15 (10-class), CICIDS2017 (15-class), and IoT-23 (5-class). Results are reported as mean ± std over multi-seed runs (5–20 seeds), with paired Wilcoxon signed-rank tests and Holm–Bonferroni correction. Across all datasets, INT8 NPU inference runs in 0.29–0.46 ms (2.7–4.2× faster than the same model on Cortex-M55 CPU), with estimated energy 44–69 μJ per inference and Flash 105–138 KB. Compared with recent MCU-class deployments on STM32F7 (31 ms, 7.86 mJ) and Raspberry Pi 3B+ (27 ms), our path delivers 59–107× lower latency; the estimated energy envelope implies 114–179× lower energy than STM32F7. QCFS and ReLU are statistically indistinguishable on all four datasets (p ≥ 0.227), supporting practical T = 1 near-equivalence under commodity MCU deployment constraints. Energy is estimated from STMicroelectronics application note AN5946 rather than direct on-board measurement, and UNSW-NB15 shows greater INT8 quantization fragility than NSL-KDD. We frame this as a deployment case study on a commodity Cortex-M-class MCU paired with a general-purpose NPU (Neural-ART), bounded by a documented systematic literature search (Supplementary File S1).

Abstract: Attainable yields represent the yields that can be achieved under production conditions and are used to determine the exploitable yield gap. However, these yields are constrained by multiple factors, including soil properties, which vary at different spatial scales, even withing a single field. Thus, the attainable yields should be adjusted to specific soil units. This study uses results from multi-environment cultivar testing trials conducted by COBORU in Poland to estimate winter wheat attainable yields depending on arable land quality classes (ALQCs) and arable land suitability groups (ALSGs). The database comprises 10 years of observations from 18 locations and 156 experiments.. The results indicate a clear relationship between the scores assigned to particular ALQCs and ALSGs in 1981. In contrast, the relationship between the average scores assigned to ALQCs within ALSGs was weaker. Attainable yields were estimateddirectly based on experimental data, using the third quartile (Q3) of yields, for well-represented soil units, and regression analysis between Q3 yields and point scores for less-represented soil units. The results could be improved by using a more extensive dataset, particularly for underrepresentedsoils. The proposed method may be applied to estimate soil-adjusted attainable yields for other crops whose cultivars are tested by COBORU in multi-environment trials.

Abstract: This research answers the knowledge gap regarding the explanation of the quantum jump of the electron. This scientific paper aims to complete Einstein’s research regarding general relativity and attempt to link general relativity to quantum laws.

Abstract: Transthyretin amyloidosis (ATTR) is a progressive disease caused by the dissociation of the transthyretin (TTR) tetramer, leading to amyloid fibril formation. Although pharmacological stabilizers have been developed, preventive strategies for wild-type ATTR (ATTRwt) have not been established. This study developed a computational model to predict TTR binding activity from chemical structure and to apply the model to screen food-derived compounds as potential preventive candidates. A machine learning model was constructed using TTR-8-anilino-1-naphthalenesulfonic acid displacement assay data from the Tox24 Challenge. The model achieved root mean square error and R² values of 21.34 and 0.64, respectively, on an external test dataset. Using an integrated dataset compiled from multiple literature sources, the predicted TTR binding activity exhibited a significant positive correlation with amyloid fibril formation inhibition (Spearman’s ρ = 0.602, p &lt; 0.001). The model was then applied to the PhytoHub database, identifying 63 candidate compounds with high predicted binding activity, predominantly polyphenols, found in 126 food sources. These results suggest that the proposed in silico method is useful for identifying potential TTR stabilizers from food-derived compounds and may contribute to the exploration of effective preventive strategies for ATTRwt.

Abstract: The systematic construction of expansive fictional universes, known as worldbuilding, faces significant challenges in maintaining long-range structural consistency, particularly within generative AI architectures prone to "ontological drift". This paper introduces WorldPT, a novel framework and dataset that formalizes worldbuilding through Directed Multilayer Attributed Graphs. By implementing a Grounding Directionality Axiom and a hexapartite layering system (Structural, Causal, Temporal, Social, Ontological, and Symbolic), we transition from unstructured text-centric models to machine-verifiable narrative structures. The dataset is uniquely curated in Portuguese, aiming to democratize access to advanced computational narratology resources for the Lusophone community. To evaluate the framework, we applied Social Network Analysis (SNA) metrics to a case study of Tolkien's Middle-Earth universe. Results reveal a "Small-World" topology (average path length of 2.68) and a predominant structural layer (48.7% of connections), quantitatively fingerprinting the setting as a structural-driven worldbuilding. Furthermore, we propose the Cross-Layer Coupling (CLC) metric to identify "lore-shifters" entities whose multidimensionality transcends individual layers. Our findings demonstrate that WorldPT provides a robust foundation for building ontologically stable and interconnected narrative experiences, bridging the gap between graph-based knowledge representation and creative storytelling.

Abstract: Background/Objectives: We still do not clearly know whether COVID-19 continues to impose a greater clinical burden than influenza in the “post-pandemic” era. Our study quantified and compared monthly COVID-19 and influenza hospitalization incidence among adult subgroups from October 2022 through December 2024. We assessed vaccine coverage trends and examined vaccination status among those hospitalized. Methods: Using the Veradigm linked claims and electronic health record dataset, we conducted a non-interventional, retrospective cohort study; three monthly cohorts included individuals aged 65+, high-risk (HR) adults (defined as adults 18+ with HR conditions and/or aged 65+), and adults aged 50-64 years who were enrolled with both medical and pharmacy coverage. We estimated monthly cumulative incidence of COVID-19 and influenza-related hospitalizations, vaccination coverage rates, and the proportion of hospitalized individuals who had received seasonal vaccines. Results: COVID-19 hospitalizations consistently exceeded those of influenza across months and populations. Among adults aged 65+, COVID-19 hospitalization rates were 2–3 times higher than influenza in winter and 20–30 times higher during off-season months, with similar trends observed in high risk adults. COVID-19 incidence surged in summer, while influenza remained seasonally confined. Vaccination coverage for influenza peaked near 50% annually; COVID-19 coverage was lower, peaking at ~26% by December each year. Most hospitalizations occurred among unvaccinated individuals, particularly for COVID-19. Conclusions: COVID-19 continues to impose a substantial, year-round burden, particularly in older and high-risk adults, exceeding that of influenza. The high proportion of unvaccinated hospitalizations highlight a critical gap in prevention efforts and underscore the need for improved public health messaging and vaccine adoption.

Abstract: This study presents a comprehensive assessment of longwave radiation variability in the Arctic based on unique measurements collected at the North Pole drifting station SP‑28 in 1987. The primary objective is to compare these historical observations with modern datasets from the Surface Heat Budget of the Arctic Ocean (SHEBA, 1997–1998) and the Multidisciplinary drifting Observatory for the Study of Arctic Climate (MOSAiC, 2019–2020) to evaluate long‑term changes in the Arctic radiation regime. Continuous longwave radiation measurements were obtained using high‑precision spectral pyrgeometers to identify Arctic haze. The results show that in 1987, Arctic haze layers enhanced the downward longwave flux by 15–20 W·m-2 and increased atmospheric emissivity. In contrast, MOSAiC observations reveal emissivity values that closely match aerosol‑free model calculations, indicating a substantial decline in Arctic haze and the disappearance of radiatively significant aerosol layers. This shift is in alignment with the long-term reduction of global anthropogenic sulfur dioxide emissions across the Northern Hemisphere.