Abstract: During cholera outbreaks in Zambia, diagnostic strategies that rely on single-plex or targeted assays risk overlooking concomitant infections with other clinically important enteric pathogens. We estimated the prevalence of rotavirus and described co-detected enteropathogens and rotavirus genotypes among patients admitted with clinically suspected cholera during Zambia’s 2023–2024 cholera outbreak.We conducted a sub-analysis of diarrhoeal specimens collected from patients admitted to five cholera treatment centres who met the syndromic suspected cholera case definition. Stool samples were tested using the Bosphore® Gastroenteritis Panel v2, a multiplex PCR enteric panel, to detect rotavirus and other gastrointestinal pathogens. Rotavirus-positive specimen with sufficient viral load were further characterised by RT-PCR genotyping and Sanger sequencing targeting VP7 and VP4 genes. Among 319 suspected cholera admissions, rotavirus was detected in 18 patients, yielding a prevalence of 5.6% (95% CI 3.4%, 8.8%). Rotavirus detections occurred predominantly in children aged < 5 years (87.5%) and 6-15 years (80.0%). Co-infection was common - 93.7%, (15/16) of rotavirus-positive samples showed co-infection with at least one additional enteric pathogen, primarily Campylobacter. Genotyping was successful in five samples and showed heterogenous circulating strains, including G1P[8], G2P[4], G3P[6], G12P[6], and a rare G1P[6] reassortant. During a large 2023–2024 cholera outbreak in Zambia, rotavirus accounted for a modest but clinically important fraction of the suspected cholera admissions and was typically identified within mixed enteric infections. These findings highlight the limitations of syndromic diagnosis in outbreak settings and support integrating multi-pathogen diagnostics and sustained molecular surveillance to improve case management, antimicrobial stewardship, and vaccine-era monitoring.

Abstract: Melanoma remains the most lethal form of skin cancer, responsible for the majority of skin cancer-related fatalities despite its relatively low incidence \cite{milton2018}. Melanoma can spread to different parts of the body if it is not identified and treated in its early stages. In \cite{kumar2021, kumarVatsa2022} previous work on melanoma classification, the VGG16 architecture of the CNN performed better on the cancer dataset than any other popular network. % The early detection of melanoma by analyzing skin lesion images aims to enhance early diagnosis and accessibility. However, a significant challenge arises when incorporating demographic factors as biases in training data, as they can lead to disparities in model performance across different populations. These biases often stem from the underrepresentation of certain demographic groups in medical datasets, leading to lower accuracy for underserved communities. Such disparities can have serious consequences, including delayed diagnoses and inadequate treatment recommendations, further increasing healthcare inequities \cite{dietterich2000}. Therefore, this study examines the effects of demographic factors such as age, gender, and data scalability on the early detection of melanoma. It also evaluates and compares two distinct deep learning approaches for classifying melanoma from dermoscopic images and associated patient metadata. The first experiment establishes a baseline using a VGG-16 convolutional neural network (CNN) trained via transfer learning. The second, expanded experiment introduces a novel multimodal ensemble model that synergistically combines an EfficientNetB0 CNN with a Multi-Layer Perceptron (MLP) to process both image and tabular data concurrently.

Abstract: Multi-environment trials (METs) are central to plant breeding programs for evaluating genotype performance and adaptation, yet spatial field variability and genotype × environment interaction (GEI) often reduce the precision of genotype assessment. This study aimed to improve genotype evaluation by integrating spatial linear mixed models, GGE biplot analysis, and parametric and non-parametric stability statistics. Grain yield data from seven environments were analysed using linear mixed models fitted by restricted maximum likelihood. Non-spatial randomized complete block design (RCBD) model was compared with two-dimensional first-order autoregressive spatial model on an environment-specific basis. Spatial model provided a superior fit in three environments, while non-spatial model was adequate in the remaining environments, demonstrating that spatial dependence was not uniform across environments. Genotypic differences for grain yield were detected in most environments, with BLUEs ranging from 0.75 to 0.95 t ha⁻¹ and an overall mean of 0.84 t ha⁻¹. The average-environment coordination view identified Genotypes 3 and 5 as closest to the ideal genotype. Parametric and non-parametric stability analyses supported the GGE results. Overall, the study demonstrates that integrating spatial modelling, GGE biplots, and stability statistics provides a robust framework for accurate genotype evaluation and reliable varietal recommendation in plant breeding programs.

Abstract: Ultrasound-assisted germination (UAG) has been proposed as a process intensification strategy to enhance seed performance while improving resource efficiency. This study combines thermoacoustic multiphysics modeling with controlled experimental validation to evaluate resonance-driven UAG in Cucurbita pepo. Frequency-domain analysis identified 40 kHz as the resonance condition of the seed system, enabling localized acoustic energy concentration. Thermoacoustic simulations demonstrated that temperature increases remained below 46 ◦C across all exposure times, ruling out bulk thermal effects and supporting a predominantly mechanical activation mechanism associated with enhanced permeability and mass transfer. Experimental treatments (40 kHz, 1.5 MPa, 5–25 min) revealed a non-linear germination response to acoustic exposure. A 10 min treatment produced the optimal outcome, increasing final germination from 20% in untreated seeds to 47% and reducing the time required to reach steady state from 13 to 10 days. Longer exposure times did not generate proportional improvements, indicating the presence of a finite acoustic energy window beyond which diminishing returns occur. Because daily water (0.45 L·day−1) and electrical (0.438 kWh·day−1) consumption remained constant across treatments, the shortened germination period directly reduced cumulative resource demand. Under optimal conditions, total water consumption decreased by approximately 1.35 L and electricity use by 1.31 kWh per germination cycle relative to the control. When normalized per percentage point of germination achieved, energy and water intensity were reduced by nearly threefold. The integration of multiphysics modeling with biological experimentation establishes a mechanistically validated and energy-optimized framework for UAG, supporting its application in resource-efficient controlled-environment agricultural systems.

Abstract: The equation of state of crystals under external stress, derived years ago based on the principles of statistical physics, was re-derived in the same way, but for NON-crystals under general external stress and temperature. Its relationship with the macroscopic mechanical equilibrium condition was also discussed.

Abstract: Background/Objectives: Near-infrared photoimmunotherapy (NIR-PIT) provides tumor-selective cytotoxicity with minimal collateral tissue damage and has emerged as a novel treatment option for recurrent head and neck squamous cell carcinoma (HNSCC). However, biomarkers that predict treatment response to NIR-PIT remain poorly defined. Therefore, this study aimed to determine whether baseline nutritional and inflammatory composite biomarkers are associated with complete response to NIR-PIT in patients with recurrent HNSCC. Methods: Fifteen non-surgical candidates with recurrent HNSCC underwent NIR-PIT between January 2022 and December 2025. Baseline composite nutritional indices and inflammatory markers, including the systemic inflammation response index (SIRI), were assessed before and 4–8 weeks post-treatment. Tumor response was evaluated according to RECIST version 1.1. Exploratory comparisons between complete response (CR) and non-CR groups were performed using Wilcoxon rank-sum tests with effect size estimation. Results: Five of 15 patients achieved CR (33.3%). Baseline SIRI was significantly lower in the CR group than in the non-CR group (median 70.7 vs. 120.2; p = 0.03), with a large effect size (r = 0.55). In contrast, baseline composite nutritional indices and other inflammatory markers showed no significant association with treatment response. Nutritional status remained stable after NIR-PIT, as reflected by preserved nutritional index values. SIRI tended to increase post-treatment in patients who achieved CR. Conclusions: NIR-PIT achieved encouraging local tumor responses in recurrent HNSCC while preserving early nutritional status. Baseline SIRI may represent an inflammation-based correlate of CR, reflecting the balance between systemic inflammation and host immune status, and warrants validation in larger prospective cohorts.

Abstract: Overexposure, and severe noise in aerial images taken by monocular UAVs under complex lighting conditions (such as dusk and backlight) . A three-stage adaptive enhancement and restoration algorithm based on a "divide and conquer" strategy is proposed. The core innovation of this scheme lies in firstly, using a lightweight U-Net network to perform precise semantic segmentation of the illumination component of the input image, generating a mask that divides the image pixels into four regions: underexposed, normal, exposed , and overexposed. This mask serves as a navigation map for subsequent differential processing. For underexposed regions , the algorithm employs a Retinex -guided illumination decomposition method, decomposing them into reflectance and illumination maps, and then correcting them through reflectance recovery and illumination adjustment networks respectively to improve brightness and restore details. To address the noise introduced during the enhancement process, a two-stage trained Generative Adversarial Network (GAN) is specifically designed as an image enhancement module, effectively denoising and improving visual realism. For severely overexposed regions , they are treated as occlusions, and image restoration is performed using contextual information through another complex GAN framework to intelligently reconstruct lost textures. Experimental results show that the proposed algorithm performs excellently on both self-built datasets and multiple public datasets in terms of objective metrics (such as NIQE) and subjective visual quality, especially demonstrating significant advantages in noise suppression and overexposed area restoration. This provides higher-quality image input for subsequent tasks such as object detection and 3D reconstruction. Ablation experiments further validated the effectiveness of each module.

Abstract: This study introduces the LESG index as a composite measure of national systemic read-iness for sustainable development, integrating logistics performance, governance quality, and sustainability outcomes within a unified data-driven analytical framework. Moving beyond outcome-oriented approaches based primarily on income metrics, the framework conceptualizes development as a structural condition emerging from the coherence of interdependent dimensions that also condition the effectiveness of digitally enabled sup-ply chain systems. Using cross-country data for 123 countries, the index is constructed through normalization procedures and Principal Component Analysis, and externally validated against GDP per capita within a diagnostic, non-causal framework. Cluster analysis is subsequently employed to identify distinct systemic development readiness regimes. The results reveal substantial cross-country heterogeneity that remains obscured in income-based or single-indicator assessments, highlighting coherent structural configurations of logistics capability, institutional quality, and sustainability alignment. These regimes differentiate countries beyond conventional development classifications and provide insight into varying readiness conditions for resilient and sustainable logis-tics and supply chain environments. Overall, the LESG index functions as a transparent diagnostic tool for comparative analysis and policy interpretation, reframing development in terms of systemic readiness rather than ex post economic performance and offering a macro-structural perspective relevant to data-driven and digital supply chain transformation.

Abstract: Quantum electrodynamics (QED) provides extraordinarily accurate predictions for charged lepton properties, although its formalism offers limited intuitive insight into the geometrical and energetic scales associated with vacuum effects. In this work, a phenomenological representation is introduced to describe the leading-order contribution to the anomalous magnetic moment of charged leptons. By combining characteristic length and energy scales associated with the Compton radius and rest energy with geometric arguments, the Schwinger correction to the electron magnetic moment is recovered. Within this framework, the fine-structure constant acquires the meaning of a characteristic angular scale associated with the effective vacuum dressing of the particle. The construction naturally extends to the muon, indicating the universality of the angular structure underlying anomalous magnetic moments. The model does not replace quantum electrodynamics but tries to provide an effective geometric representation of its lowest-order result, offering an intuitive picture of vacuum dressing and interaction scales.

Abstract: Soil salinity severely constrains strawberry production by disrupting ion homeostasis and provoking oxidative injury. Here, we investigated whether soluble silicon (Si) and activated carbon (AC) act synergistically to enhance salt tolerance in strawberry (Fragaria × ananassa). Under NaCl stress, plants showed pronounced growth inhibition, increased Na⁺ accumulation and a deteriorated K⁺/Na⁺ balance, accompanied by elevated reactive oxygen species (ROS) and lipid peroxidation. In contrast, combined Si and AC treatment consistently provided the strongest protection, improving seedling vigor and survival, limiting Na⁺ build-up while maintaining a higher K⁺/Na⁺ ratio, and attenuating oxidative damage as reflected by reduced ROS and MDA levels together with enhanced activities of antioxidant enzymes (SOD, POD and CAT). Beyond plant responses, AC-containing treatments alleviated salt-induced increases in soil electrical conductivity and improved soil nutrient availability, coinciding with a clear restructuring of the rhizosphere bacterial community and enrichment of putatively beneficial taxa. Transcriptome profiling further supported coordinated reprogramming of ion transport, redox control and stress-responsive signaling pathways under the Si+AC regime. Collectively, our results indicate that Si and AC co-application enhances strawberry salt tolerance through an integrated soil–plant–microbiome mechanism that stabilizes ion homeostasis and reinforces redox homeostasis.

Abstract: Limited fossil fuels have created a societal energy crisis necessitating the use of renewable energy. However, existing renewable energy sources are problematic and incur high costs. To overcome these issues, we propose a new renewable energy source with a divergent current density and highly symmetric circuits. This circuit comprises two voltage sources and two identical loads that output a few energies. In this circuit, stray capacitors in the vacuum play an important role to generate a divergent current density. This divergent current generates large electric power. This paper verified this fact theoretically and experimentally. In the theory, a simple Hamiltonian of Schrodinger equation results in a unique current–voltage characteristic, allowing for the current to flow along a large load without the Joule heating. During our experiments, a considerably large divergent current flowed into a huge resistance, boosting the output electric power to a level almost equal to that of a nuclear power station. In addition, the experimental results were consistent with the theoretical expectations. In conclusion, this paper has succeeded to present a novel system that generates considerably large energy in the theory and experiments.

Abstract: Psychological constructs such as anxiety, depression, fatalism, divine control, luck, helplessness, and internality are pressing subjects in the United States (US). Although several studies have explored how specific covariates influence these constructs, like depression and anxiety, less is known about how predictors interact to predict the different subscales of fatalism, namely, fatalism, luck, divine control, helplessness, and internality. This study addresses the gap by using Conditional Inference Trees (CIT) to explore how interactions among predictors influence these constructs. Using a nationally representative survey of 2,000 respondents, CIT was employed to investigate how covariates, including Adverse Childhood Experiences (ACE), age, gender, race, education, and urbanicity, interact to predict each construct. Our analyses revealed that for the scales of fatalism, education, age, and race were key predictors, but their effects varied across the subscales of fatalism- fatalism, divine control, luck, helplessness, and internality. For instance, higher levels of education and younger age were associated with higher levels of fatalism and internality. ACE interacted with race to provide different levels of helplessness and divine control. In addition, by leveraging CIT, we were able to identify subtle interactions between covariates in predicting psychological constructs, primarily those related to the multi-fatalism scale.

Abstract: Background: The optimal frequency of EEG biofeedback sessions for elite athletes remains unclear, despite growing adoption of neurofeedback in high-performance sport. Methods: This randomized, controlled study compared three EEG biofeedback protocols (daily, every-other-day, every-third-day) in 24 national-level male judo athletes stratified into three phenotypic groups. Each protocol comprised 15 standardized sessions. Pre- and post-intervention assessments included functional indices (strength, power) and neuro-physiological measures (Frontal Alpha Index, EMG amplitude/RMS, corrected strength sum). Biosensor performance was validated via signal quality metrics. Results: Daily EEG biofeedback produced superior improvements in strength, FAI, and fatigue resistance. Although LRG showed the largest pre-post RMS increase (+17.44 μV vs. +16.54 μV in HRG), HRG maintained the highest post-intervention RMS values and best fatigue re-sistance (MF_drop = −2.15 Hz). Significant group × time interactions were observed for FAI (p = 0.027) and RMS (p = 0.019). Every-other-day protocols yielded moderate gains, while every-third-day protocols produced minimal or maladaptive EMG–load dynamics. A ro-bust dose-response relationship was evident. Conclusions: Session frequency is critical for optimizing neurofeedback interventions in elite athletes. Daily EEG biofeedback confers superior adaptation compared to less frequent dosing.

Abstract: This paper develops the institutional implications of the Noble Person Test, a framework for evaluating justice proposed in [Shuhao Z., Beyond the Veil of Ignorance: The Noble Person Test as a Framework for Justice]. The Noble Per- son Test evaluates institutional arrangements by asking whether a hypothetical agent—default self-interested, intellectually honest, and persuadable under strict conditions—would accept the arrangement from every position within it. This pa- per argues that the test is best operationalised not as individual thought experiment but as structured adversarial debate: a red team representing those bearing the costs of an arrangement defaults to refusal, while a blue team representing those proposing the arrangement bears the burden of proving necessity and the absence of less costly alternatives. The paper derives four structural features that just insti- tutions must possess, examines the relationship between the Noble Person Test and democratic governance, applies the framework to three domains of legal and pol- icy controversy, and proposes concrete institutional mechanisms for implementing adversarial review. The paper draws on existing practices in military red-teaming, intelligence analysis, and judicial adversarial procedure to argue that the proposed mechanism is not utopian but an extension of proven institutional designs.

Abstract: Contemporary organizations face the complex challenge of integrating diverse workplace resources—spanning digital tools, inclusive practices, sustainability initiatives, and well-being programs—into coherent systems that simultaneously support employee flourishing and organizational effectiveness. This theoretical paper develops the concept of Employee Experience Capital (EEC), defined as the integrated configuration of organizational resources that shape employees' holistic work experiences and generate sustainable competitive advantage. Drawing on Job Demands–Resources theory and the Resource-Based View of the firm, this paper identifies seven illustrative dimensions of EEC—digital autonomy, psychological safety climate, sustainability alignment, human–AI collaboration, restorative work design, learning climate, and well-being support systems—and provides systematic theoretical justification for their selection based on Self-Determination Theory's basic psychological needs framework. The paper proposes that EEC influences organizational performance through two empirically distinguishable mediating pathways: work meaningfulness (a cognitive-evaluative pathway) and experienced vitality (an affective-energetic pathway). Specific propositions predict which EEC dimensions more strongly affect each pathway. The framework advances theory by demonstrating how ostensibly disparate organizational practices function as an integrated resource system with emergent properties. The paper acknowledges boundary conditions, engages with competing theoretical perspectives, and discusses potential tensions and dark sides of EEC dimensions. Implications for research methodology and organizational practice are discussed.

Abstract: DNA-encoded chemical libraries (DECLs/DELs) enable the pooled synthesis and selection of millions to billions of DNA-barcoded small molecules, providing an efficient route to discover binders and early leads against diverse biological targets. As DEL-derived programs advance toward identifying clinical candidates, the asset surface of a DEL platform expands from a small set of optimized hits to include library designs, building-block combinations, DNA tags, selection data, and physical library stocks, thus creating new challenges in registration, traceability, and scalable ownership in transfer practices. Non-fungible tokens (NFTs) are unique blockchain-native tokens that can represent digital assets that can be coupled to smart contracts to enable traceable transactions and programmable rights management, which inspire proposals to tokenize intellectual-property (IP) assets such as patents. Here, we review (i) the scientific and commercial value of DEL in modern drug discovery, (ii) NFT/blockchain concepts, specifically in reported biomedical-IP and supply-chain use cases, and (iii) a conceptual architecture for NFT-enabled registration and controlled transfer of DEL libraries or sublibraries using on-chain identifiers with off-chain encrypted metadata and legal agreements.

Abstract: Six dentin adhesives were tested in vitro regarding their cytotoxicity on human fibroblasts. Hybrid Bond, One-up Bond F Plus, AdheSE, Clearfil SE Bond, Optibond Solo Plus and Syntac were tested by using a cell culture model. The several components of dentin adhesives like primer and bonding were analyzed as single and additive applied components as specified by the manufacturer for the application in-vivo. 75 petri dishes were produced per group and all petri dishes (480 ones) were evaluated triangulated. This unique assessment is following our first investigation and the observation period is extended from 24 hours to 48 hours. AdheSE, Clearfil SE Bond, One-up Bond F Plus and Optibond Solo Plus showed statistically significant less amounts of viable cells compared to the cell control. All dentin adhesives except Clearfil SE Bond showed a statistically significant difference regarding the reactivity index in the application comparison. In conclusion, the test materials showed a moderate grade of cytotoxicity with no statistically significant difference regarding the cytotoxicity between the tested self-etch and etch-and-rinse dentin adhesives. However, the results show differences between sequentially and single applied adhesive parts.

Abstract: The security crisis in Burkina Faso has displaced over two million people, disproportionately affecting women and children, who are exposed to multiple forms of violence. This study assesses the resilience capacity of internally displaced women in the Boucle du Mouhoun region. A mixed-methods approach was employed with 1,056 participants, combining questionnaires administered via KoboToolbox and semi-structured interviews, in compliance with ethical standards. Findings reveal statistically significant correlations between year of displacement and both physical (r = 0.150, p = 0.017) and psychological violence (r = 0.072, p = 0.022). Nearly 46.74% of respondents lost relatives in atrocious circumstances (summary executions, throat-slitting, immolation), generating post-traumatic disorders including chronic insomnia, flashbacks, and psychosis. Despite psychosocial support from NGOs, prayer (39.74%) and silence (23.36%) remain the predominant coping strategies. These findings underscore the imperative for psychosocial interventions grounded in the victims’ cultural habitus to enhance their effectiveness.

Abstract: Background: Benign paroxysmal positional vertigo (BPPV) is the most common cause of peripheral vertigo and is diagnosed clinically, yet many patients initially present in primary care. Early identification may optimize referral and management. Objective: To perform a pilot Phase 1 validation of the BPPV-SQ, a brief screening questionnaire designed for future use in general practice, assessing its ability to identify BPPV, suggest canal involvement, and support progression to Phase 2 validation. Methods: In this prospective observational study, 108 patients with positional vertigo and no neurological signs were evaluated in a specialist setting. The 7-item dichotomous questionnaire (score 0–3 for diagnostic core) was administered prior to bedside examination, which served as the reference standard. Results: Confirmed BPPV increased with higher scores. Among patients with score 3, BPPV was confirmed in 73.5%, with 69.4% lateralization concordance. Lower scores (0–1) were associated with low confirmation rates (14.3%). Conclusions: In this pilot Phase 1 validation, the BPPV-SQ demonstrated score-dependent diagnostic reliability and acceptable lateralization agreement in high-score patients, supporting progression to Phase 2 validation in primary care.

Abstract: Decoding affective states and personal preferences from physiological responses remains a fundamental challenge in affective computing due to strong heterogeneity across neural, autonomic, and attentional signals, as well as the coupling between transient emotions and long-term preferences. Most existing methods address these factors independently and lack explicit mechanisms to preserve the intrinsic structural regularities and invariances of physiological affective responses, limiting their applicability in real-world scenarios such as music therapy. In this paper, we propose a symmetry-aware and structured multi-modal physiological modeling framework for joint affective state and preference inference. The framework integrates electroencephalography (EEG), peripheral physiological signals (GSR, BVP, EMG, respiration, and temperature), and eye-movement data (EOG) within a unified temporal modeling paradigm. At its core, a Dynamic Token Feature Extractor (DTFE) converts raw physiological time series into compact token representations without handcrafted features, and explicitly decomposes representation learning into cross-series symmetry and intra-series symmetry. These two complementary symmetry dimensions are realized through Cross-Series Intersection (CSI) and Intra-Series Intersection (ISI) mechanisms, enabling structured and interpretable physiological representations. A hierarchical cross-modal fusion strategy further integrates modality-level tokens in a symmetry-consistent manner, capturing dependencies among neural, autonomic, and attentional modalities. Extensive experiments on the DEAP dataset demonstrate consistent improvements over state-of-the-art methods under both single-task and multi-task settings. The proposed model achieves 98.32% and 98.45% accuracy for valence and arousal prediction, respectively, and 97.96% accuracy for quadrant-based emotion classification in single-task evaluation, while attaining 92.8%, 91.8%, and 93.6% accuracy for valence, arousal, and liking prediction in joint multi-task settings. Additional robustness analyses under reduced training data confirm that symmetry-aware structured decomposition improves data efficiency and generalization. Overall, this work establishes a principled symmetry-preserving representation learning framework for robust affective decoding and intelligent, feedback-driven music therapy systems.