Abstract: Background and Objectives: Sepsis is a life-threatening organ dysfunction, and specific biomarkers could improve prognostic assessment in septic patients. The Sequential Organ Failure Assessment (SOFA) score is the standard tool for clinical sepsis monitoring. Recent studies highlight the need for its revision and the identification of rapid, specific, sensitive predictors of sepsis mortality. The aim of this study was to determine the significance of cardiac biomarkers alone or combined with the SOFA score for evaluating sepsis-related mortality. Materials and Methods: This is a retrospective, single-center study with a relatively small sample size of 73 septic patients (Sepsis-3 criteria) hospitalized in an intensive care unit (ICU) and intermediate care unit (IMCU). All patients had standard laboratory parameters, cardiac biomarkers, and the SOFA score available upon admission. Statistical analyses included non-parametric Mann–Whitney U test, ROC (Receiver Operating Characteristic) curve analysis, Hanley & McNeil method and Hosmer–Lemeshow goodness-of-fit test. Results: Lactate (p < 0.001) and SOFA (p < 0.001) showed the highest area under the curve (AUC) values, and all cardiac biomarkers had statistically significant AUCs (p < 0.05) for sepsis mortality prediction. A comparison of all ROC curves was conducted, but no statistically significant differences were observed. Adding hs-cTn (high-sensitivity cardiac troponin) and lactate to the SOFA score increased its AUC from 0.767 to 0.827 (p = 0.421). Conclusions: The results highlight the potential role of cardiac biomarkers alone or in combination with the SOFA score as useful clinical tool for predicting sepsis mortality. Further research with a larger sample size is required to validate and generalize the findings.

Abstract: The current article explores how people in contemporary America understand George Washington’s national authority through their daily cultural activities. It examines how individuals understand national symbol through ethical beliefs. Using thirty-three semi-structured online interviews and qualitative narrative analysis, the findings reveal that participants see Washington as a national symbol through personal understanding, emotional ties and their judgment of his moral character. Washington serves as a national figure whom people learn about in schools and see in popular culture yet his historical connection to slavery creates discomfort and mixed feelings for many individuals. Participants resolve this conflict through selective reverence, distancing, qualification, and informal critique. These practices illustrate a process of symbolic governance in which national authority persists because individuals continuously negotiate their understanding of national symbols through emotional and moral evaluation. Consequently, Washington is revealed as a figure whose authority is culturally constructed, maintained through varied cultural readings of his persona. This research shows how national symbols persist in modern culture, a process shaped by the continuous interplay between individual interpretation and historical awareness.

Abstract: A zoonotic disease caused primarily by Mycobacterium bovis (M. bovis), bovine tuberculosis (bTB), remains a considerable global concern. The intradermal tuberculin test (ITT) is a primary global screening tool for infected animals through their cellular immune response. However, ITT fails to identify all bTB-infected animals. Serological enzyme-linked immunosorbent assays (ELISAs), which detect humoral immune responses are a potential complementary approach for bTB diagnosis. Herein, 86 serum samples collected from a bTB-free herd were analyzed using three bTB serological ELISA kits: the IDEXX M. bovis antibody test (IDEXX), the BIONOTE BTB antibody ELISA 2.0 kit (BTB), and an in-house ELISA using MPB70 and MPB83 as antigens (termed homemade). Antibody responses were monitored before and after ITT administration for 21 weeks. All serum samples collected before ITT administration tested negative with all three ELISA kits. However, 1 week after ITT administration, samples tested positive using the IDEXX, BTB, and homemade ELISA kits. Week 9, all samples tested negative with the BTB and homemade ELISA kits, whereas for IDEXX they remained negative until week 21. ITT-induced a serological response against M. bovis, engendering false-positive results. Therefore, collecting serum samples for bTB antibody testing should be avoided for at least 21 weeks following ITT.

Abstract: Diagnostic latency limits time-sensitive care and early detection, and exhaled breath provides a rapid, repeatable window into metabolic and inflammatory chemistry. We review real-time breath sampling and analytical technologies and evaluate their readiness for clinical adoption, with emphasis on molecular pathways reflected in the breath volatilome and in exhaled breath condensate. Real-time mass spectrometry enables kinetic VOC profiling and targeted quantification, while humidity-aware sensors and wearable condensate platforms extend monitoring beyond the laboratory. Pathway-anchored interpretation links breath readouts to ketone handling, isoprenoid metabolism, nitric oxide signaling, lipid peroxidation, uremic nitrogen handling, and microbiome-host co-metabolism, but performance remains vulnerable to confounding, drift, and non-representative comparators. Translation requires standardized breath fraction control, traceable features, robust quality systems, and governed device algorithm stacks so that breath outputs change decisions and outcomes.

Abstract: We ask for ultrametric version of following three: (1) Bourgain-Figiel-Milman Theorem, (2) Enflo Type, (3) Mendel-Naor Cotype.

Abstract: This qualitative study investigates how AI applications that support or replace instructional tasks influence teachers’ professional judgment, cognitive load management, and sense of agency. Drawing on interviews with 23 high school teachers from multiple countries using diverse AI platforms, the study explores teachers’ lived experiences of working in AI-mediated environments. Data were analyzed thematically using Cognitive Load Theory (CLT) as an analytical lens to examine shifts in intrinsic, extraneous, and germane cognitive load. The findings indicate that while AI tools reduce workload and streamline planning and assessment, they also displace diagnostic reasoning, instructional sequencing, and evaluative judgment. Teacher agency persists but becomes conditional, shaped by institutional pressures, algorithmic opacity, and professional confidence. Ethical and equity concerns related to transparency and authority emerged as everyday cognitive and emotional challenges. By extending CLT to teachers’ work, the study highlights the need for AI integration that preserves reflective practice, professional judgment, and sustainable teacher agency.

Abstract: This paper reports thermophysical-property data for binary dielectric mixtures of hy-drofluoroether (HFE) fluids and ethyl acetate (EA) and applies a correlation-based workflow to compare their single-phase forced-convection performance in rectangular minichannels. Density, viscosity, thermal conductivity, and isobaric heat capacity were measured at three temperature levels (293.1, 313.1, and 328.1 K) for selected compositions of HFE-7100/EA, HFE-7300/EA, and HFE-73DE/EA. Using these meas-ured properties, Reynolds and Prandtl numbers were evaluated and a laminar ther-mally developing correlation was employed to obtain Nusselt numbers and corre-sponding heat transfer coefficients. The assessment was performed for two geometries representing a long reference minichannel module and a short multi-minichannel module. A validation dataset for pure HFE-7100 in the short module, derived from IR thermography and an energy-balance data reduction, indicates a systematic deviation between correlation-based estimates and experimental values, which should be con-sidered when interpreting absolute predictions. The presented dataset and workflow support transparent down-selection of candidate mixtures prior to extended experi-mental campaigns.

Abstract: In this study, we developed a chatbot-based system for detecting early signs of depression and verified its effectiveness through experimental evaluations and user surveys. Em-phasizing that it does not rely on medical checklists, the system is designed to auto-matically extract three linguistic features associated with depression—frequent use of first-person pronouns, pessimistic expressions, and obsessive-compulsive writing styles—from natural user conversations. Multiple models were constructed for these features, and an ensemble layer integrates their outputs for a comprehensive judgment. The implemented system analyzes input sentences obtained through chat, extracts the three categories of features, calculates a final score through an ensemble layer, and visualizes potential signs of depression based on the total score. We conducted performed an evaluation experiment with 20 participants. In the test data evaluation, the system demonstrated over 76% accuracy in each of the three classification categories: first-person usage, pessimistic tendency, and obsessive-compulsive tendency.

Abstract: Environmental attitudes and behaviour play a vital role in developing a responsible and environmentally sustainable culture. Objective: To determine the relationship between attitudes and environmental behavior in nursing students of a public uni-versity in Peru. Material and Methods: Non-experimental, quantitative, correla-tional and prospective study, population made up of 450, sample of 207 students, using as instrument 2 nationally validated questionnaires, adapted to our reality, applying a pilot test to 15% of the sample, reporting the Cronbach's alpha coeffi-cient of (0.784) and (0.873), (attitudes and behavior), respectively. Results: The age group of 20-24 years (46.2%), and female sex (79.7%) predominated, X ̅=20.56 SD = 2.875, the second cycle was 19.3%. The descriptive results show that the environ-mental attitudes of the students are predominantly characterized by an "unfavora-ble" attitude (52.7%), when analyzing by dimensions, it is observed that the cogni-tive attitude is favorable (75.8%), the behavioral and affective attitude is unfavora-ble (79.2%) and (61.4%) respectively. The environmental behavior of students is predominantly evaluated as "good" (55.1%), "fair" behavior (42.5%), and "poor" be-havior (2.4%). Conclusion: Pearson's correlation analysis revealed a moderately and statistically significant positive relationship between environmental attitudes and environmental behavior in students of the Peruvian public university (r = 0.469, p < 0.001). This result indicates that, as students' environmental attitudes be-come more favorable, their environmental behavior also tends to improve.

Abstract: CAR-T cell therapy remains ineffective in most solid tumours because effector cells infiltrate poorly, undergo exhaustion, and face antigen escape within an immunosuppressive microenvironment. To address this, we developed a hybrid framework that combines a mechanistic spatiotemporal model with machine learning for limited patient-specific calibration. At its core, we employed a reaction-diffusion-chemotaxis model describing functional and exhausted CAR-T cells, antigen-positive and antigen-negative tumour subpopulations, a chemoattractant, an immunosuppressive factor, and hypoxia. Gradient boosting combined with nested cross-validation was used as the primary method for parameter inference. Parameters characterising the tumour microenvironment and CAR-T cell exhaustion were recovered most robustly, whereas antigen escape and individualised initial conditions were identified substantially less accurately. As an auxiliary reference point, we also considered a direct empirical baseline for binary clinical outcomes. This baseline indicated that the observed clinical features contained a more stable signal for disease control than for objective response. A favourable response was associated with high CAR-T cell infiltration and cytotoxic potency, whereas resistance was linked to exhaustion, antigen escape, and a suppressive microenvironment. Overall, the proposed approach constitutes an interpretable proof-of-concept platform for limited patient-specific inference of latent parameters and for stratifying the mechanisms underlying response and resistance in CAR-T cell therapy for solid tumours.

Abstract: Agriculture faces compounding pressures from food insecurity, climate change, and resource scarcity, creating urgent demand for scalable analytical tools. This PRISMA 2020-compliant systematic review synthesises 582 peer-reviewed studies on machine learning (ML) and deep learning (DL) applications in agriculture, drawn from Scopus for the period January 2019 to March 2026. The 2026 data cover only the first quarter (January–March) and are therefore not directly comparable to full-year counts. Publication volume grew exponentially — from 6 papers in 2019 to 251 in 2025 — driven by the adoption of convolutional neural networks (CNNs), Vision Transformers (ViT), and YOLO-based object detectors. Plant disease detection (27.0%) and crop yield prediction (13.7%) dominated the application landscape. South Asia and East Asia together contributed 59.3% of the corpus, while Sub-Saharan Africa and Latin America each accounted for only 1.4%, revealing a profound mismatch between research output and global food insecurity burden. Median reported classification accuracy was 98.1% for disease detection, largely reflecting controlled laboratory datasets rather than field conditions. Median R² was 0.823 for yield prediction, based on 22 of 80 yield studies reporting this metric. Unit heterogeneity, dataset artefacts, and inconsistent evaluation practices limit cross-study comparability and the real-world interpretability of these figures. Open science practices remain critically low: only 7.7% of papers shared code and 14.1% shared data openly. Explainable AI, federated learning, and physics-informed modelling represent emerging frontiers. The review identifies benchmark standardisation, smallholder-relevant design, and geographic equity as the field's most pressing unresolved challenges.

Abstract: Cognitive radio networks (CRNs) face significant challenges in dynamic spectrum access due to the complex interactions among multiple secondary users, sparse reward signals, and poor cross-domain generalization. Existing approaches, ranging from traditional optimization to single-agent deep reinforcement learning (DRL), struggle to balance spectral efficiency, collision avoidance, and adaptability in heterogeneous wireless environments. In this paper, we propose FedMA-DRL, a federated multi-agent deep reinforcement learning framework that integrates centralized training with decentralized execution (CTDE), graph neural network (GNN)-augmented Q-value prediction, age-aware federated aggregation (FedAge), and attention-based domain adaptation for joint channel selection and power control in CRNs. The GNN module captures topological relationships among secondary users through attention-weighted message passing on the interference graph, while the FedAge strategy enables privacy-preserving knowledge sharing with staleness-aware weighting. Extensive experiments on a CRN testbed with 10 PU channels and 15 heterogeneous SUs demonstrate that FedMA-DRL achieves 14.87 Mbps SU throughput, 0.038 collision probability, 4.35 bits/Joule energy efficiency, and 6.23 bits/s/Hz spectrum efficiency, outperforming existing methods including R2D2 and C-DRL. Ablation studies and cross-domain evaluations further confirm the effectiveness of each proposed component.

Abstract: Background/Objectives: Vaccine development pipelines are forward-looking indicators of public health preparedness, reflecting the capacity to address unmet medical needs and emerging threats. This analysis aims to characterise the 2025 clinical-stage pipeline of infectious disease vaccines and prophylactic monoclonal antibodies developed by Vaccines Europe member companies and to assess how it aligns with evolving public health priorities. Methods: A descriptive analysis was conducted using publicly available data compiled in the Vaccines Europe Pipeline Review 2025, with validation by participating companies. Candidates in clinical development or regulatory review were classified using a standardised framework by pathogen, target population, public health priority, and technology platform. Results: The pipeline comprises 91 candidates across clinical development phases predominantly targeting respiratory pathogens (75%), with a strong life-course focus (85% evaluated in adults and/or older adults), and sustained activity in bacterial pathogens relevant to antimicrobial resistance. Notably, 41% of candidates address diseases for which there is no immunisation solution available. The pipeline shows high technological diversity (12 technologies), dominated by RNA approaches and multivalent candidates, with growing focus on climate-sensitive, zoonotic, and pandemic-prone pathogens. Conclusions: This analysis reflects a shift toward broader, prevention-oriented, platform-based innovation for long-term preparedness. As a structured and recurring assessment, the Vaccines Europe Pipeline Review supports horizon scanning and evidence-based dialogue between industry and vaccine ecosystem stakeholders. Translating this innovation into public health impact requires predictable investment, streamlined trial and regulatory pathways, strong surveillance and real-world data systems, coordinated decision-making to enable timely and equitable access, and complementary incentive and procurement reforms.

Abstract: Purpose: Hospital out-patient departments (OPDs) in India face severe queue inefficiencies with average waiting times of 90+ minutes and poor patient communication. Methodology: This study presents MediQueue — a full-stack intelligent queue management system built with React.js, Node.js, MySQL, Socket.io, and a self-learning ML engine. A dual-prediction architecture (Random Forest + equal-distribution fallback) predicts per-department wait times. A nightly recalibration scheduler updates slot capacities from verified treatment records. Findings: The system achieves a Mean Absolute Error (MAE) of 2.3 minutes after accumulating five verified samples per department. All role dashboards (patient, doctor, admin) show identical wait time estimates using the equal-distribution formula. Conclusion: MediQueue demonstrates that a self-bootstrapping ML system — requiring no pre-labelled dataset — can significantly improve OPD efficiency, patient communication, and clinical workflow management.

Abstract: The Compiled Edition of the Yuan History Bibliography《元史藝文志輯本》, compiled by Luo Zhuyun 雒竹筠and Li Xinqian李新乾, represents the definitive modern reconstruction of the Yuan dynasty's scattered literary corpus, synthesizing and correcting earlier Qing supplements. Moving beyond a simple catalogue of errors, this study employs a critical historical epistemology to analyze the persistent inaccuracies within its Buddhist categories. It argues that these mistakes—including paleographic corruptions, misreadings of monastic institutions and anachronistic inclusions of Ming works—are not random oversights. Rather, they are systematic artifacts that reveal the inherent limitations of reconstructing a lost bibliographic tradition. These limitations manifest as a knowledge disjunction between later compilers and Yuan-specific socio-religious structures, and as temporal stratification, where later historical layers and scholarly contexts inadvertently permeate the reconstructed past. By examining these entries as a palimpsest—a composite text bearing traces of its own production—this paper demonstrates that the Compiled Edition is both an indispensable scholarly achievement and a historically mediated construct. The conclusion emphasizes the necessity of using this foundational reference work with source-critical awareness, understanding it as much for the insights it offers into Yuan textual history as for what it reveals about the perennial challenges of historical reconstruction itself.

Abstract: Dominant paradigms across biology, artificial intelligence, and cognitive science define intelligence through its observable expressions—gene activation, computational output, and decision-making behavior. While this perspective has enabled significant advances, it systematically overlooks a fundamental architectural dimension: the preservation of structured potential in non-executing states.Across biological systems, large portions of the genome remain transcriptionally inactive yet structurally conserved, suggesting the existence of preserved functional capacity beyond immediate expression. In artificial intelligence, knowledge is encoded within high-dimensional latent spaces that guide outputs without continuous activation. In human systems, cognition depends on layers of unexpressed interpretation and perceptual structure that shape decision-making beyond observable behavior.Here, we propose that this shared phenomenon reflects a universal principle, which we define as latency: the structured preservation of encoded potential in a non-executing state with conditional accessibility across time. Within this framework, intelligence is not solely a function of execution, but of the dynamic balance between preservation and activation. We introduce the concept of a latency spectrum, in which elements vary in depth, stability, and activation cost, providing a graded architecture of temporal accessibility rather than a binary distinction between active and inactive states.We further identify a critical failure mode—the Meaning Gap—which arises when the velocity of system output exceeds the depth of latent structure. This misalignment manifests as incoherent outputs in artificial intelligence, dysregulated activation in biological systems, and loss of interpretive coherence in human decision-making environments.Extending this framework, we introduce Cognitive Sovereignty as the capacity of individuals and institutions to interpret, contextualize, and assume authorship over decisions in increasingly automated environments. We argue that this capacity depends fundamentally on the preservation of cognitive latency. As intelligent systems accelerate decision cycles, the compression of latency risks reducing interpretive depth, undermining autonomy, and destabilizing system coherence.By integrating genomic memory, computational latent representations, and human cognitive frameworks, this study advances a unified theory in which latency emerges as the hidden architecture of intelligence. This perspective reframes intelligence from a purely kinetic phenomenon to a temporally structured system of preserved potential, with implications for biological theory, artificial intelligence design, and the governance of complex sociotechnical systems.We conclude that the central challenge is no longer the acceleration of intelligence, but the preservation and regulation of latency itself. Designing latency-aware systems will be essential for sustaining meaning, coherence, and human agency in the age of intelligent machines.

Abstract: Technological and social development is desirable and even indispensable, which necessarily involves the restriction of new life situations within a legal framework. European legislation has been visibly struggling with this problem in recent years, but the established/ongoing regulation may be an obstacle to development. Among other things, this includes the issue of regulating platform work. The emergence and spread of platform work has numerous advantages from an economic point of view, but from a legal point of view, the cautious regulation of this relatively new employment construction is not acceptable to the majority dealing with labour law. In our opinion, the relevant EU legislation is fundamentally flawed, as it basically seeks to answer the question of whether a given legal relationship is an employment relationship or not. This is similar to trying to decide whether a mule is a horse or a donkey. Obviously, neither. Similarly, in the case of platform work, we can start from this and treat it accordingly. Thus, the present study examines why platform work can be considered a special construction and what are the labour law guarantees that are justified to be extended – at least as a rule – in this regard. Our aim is to examine whether it is possible to develop a minimum guarantee system that allows for easier transparency, greater legal certainty and a more uniform application of the law, unlike the current regulation.

Abstract: Community reintegration of formerly incarcerated individuals is one of the most pressing challenges confronting criminal justice systems worldwide. High recidivism rates, fragmented service delivery, stigma, and inadequate coordination among correctional agencies, social service providers, and communities collectively undermine successful reintegration outcomes. Artificial intelligence (AI) offers transformative potential to address these systemic deficiencies through data-driven risk assessment, personalised service matching, and continuous behavioural monitoring. However, no comprehensive, ethically grounded architectural framework currently exists that integrates these capabilities into a unified community reintegration platform. This paper proposes the AI-based Community Reintegration Integration Platform (AI-CRIP), a five-layer architectural framework designed to support the full reintegration lifecycle—from prerelease assessment through post-release community stabilisation. The proposed framework integrates machine learning-based risk classification, natural language processing (NLP) for needs extraction, K-nearest neighbour (KNN) service matching, predictive recidivism analytics, blockchain-based audit trails, and a human-in-the-loop caseworker review mechanism. A formal pseudo-algorithm details the core plan-generation pipeline, demonstrating how structured offender profiles are transformed into personalised, milestone-driven reintegration plans. The framework is evaluated against fifteen representative studies from the existing literature spanning risk assessment models, digital reintegration tools, fairness in algorithmic decision-making, and technology-assisted supervision. The proposed architecture advances the state of the art by synthesising these disparate research threads into a coherent, deployable platform that prioritises fairness, transparency, and individual dignity. Critically, while AI-based tools such as emotive robots, digital avatars, and immersive virtual reality environments have emerged as low-stakes social surrogates for individuals experiencing isolation and withdrawal, they remain limited in their capacity to cultivate genuine human intimacy. Lasting reintegration therefore demands that technological aids be balanced by structural reforms addressing work-life balance, social inclusion, and community belonging, recognising that even highly personalised AI cannot substitute for the human connection that effective rehabilitation ultimately requires. Key technical, ethical, and policy challenges—including algorithmic bias, data privacy, digital inclusion, and stakeholder trust—are also discussed, with directions for future empirical validation. This work contributes a blueprint for practitioners, policymakers, and technology developers seeking to harness AI responsibly in post- carceral rehabilitation.

Abstract: Chikungunya poses a tough challenge because of its varied symptoms and frequent misdiagnosis. Inappropriate treatment can affect long-term patient outcomes. This review aims to identify clinical features, current management and therapeutic strategies, and long-term clinical outcomes of Chikungunya disease. This scoping review, reported according to the JBI and PRISMA-ScR guidance. This study was conducted from three databases as primary data sources and several of gray literatures. From 19 studies were included in this review, fever and joint symptoms were reported as the main clinical features of acute Chikungunya infection, which may persist and become chronic, then affecting the patient’s quality of life. The primary management of Chikungunya infection, include symptomatic and long-term therapy. Preventions and public health management also play a role. In conclusion, most cases of Chikungunya may improve with treatment, but some have long-term complications that require rehabilitation, support, prevention, and public health management.

Abstract:

Let