Abstract: Common bunt of wheat (Tilletia spp.) remains a significant threat to wheat production in low-input and organic farming systems, where chemical seed treatments are restricted or avoided. Host resistance represents a key component of sustainable disease control, but it’s effective deployment requires detailed knowledge of race-specific virulence and the genetic basis of resistance. In this study, we analysed the reaction of a large and diverse wheat germplasm collection to current European populations of common bunt and mapped the underlying resistance genes using SNP-based approaches. A total of 2,731 wheat accessions were phenotyped from 2012 to 2025 using up to 42 purified bunt races with well-defined virulence profiles. Based on phenotypic responses to race-specific resistance patterns , accessions were grouped, and compared with established differential lines. A total of 1504 selected accessions were genotyped using Illumina 26k SNP arrays, and resistance loci were identified by genome-wide association studies followed by fine mapping using recombination analysis. All classical Bt resistance genes from Bt1 to Bt10 and Bt13 were mapped to defined physical intervals, and the genomic positions of 16 additional race-specific resistance genes were identified in the panel of germplasm. Our results confirm that several historically defined Bt genes including Bt11 and Bt12 represent multi-gene resistance complexes rather than single loci. Also, genes established as separate genes may possibly be identical, including Bt4 being identical to Bt6, Bt10 being identical BtZ and Bt9 possibly being identical to one of the genes in the Bt11 complex. These finding highlights the need for revised nomenclature of genes and differetial set of varieties. The identified resistance haplotypes provide an improved tool for marker-assisted selection, and support the development of wheat cultivars with durable resistance to common bunt.

Abstract: Background/Objectives: Delirium is an acute state of confusion associated with impaired consciousness and a decline in cognitive function. Delirium has significant clinical importance due to its substantial impact on morbidity and mortality. The primary objective of this systematic review is to assess Dexmedetomidine’s potential efficacy for delirium in critically ill and elderly patients, addressing a significant need in this high-risk population, and to evaluate its safety as a secondary objective. Methods: A systematic review (2018–2024) searched PubMed, Embase, and Cochrane for English-language studies on dexmedetomidine and delirium in older intensive care unit (ICU) patients. Dual reviewers independently screened, extracted data, and resolved disagreements. Eligible randamized control trials (RCTs) and observational studies were assessed using Risk of Bias 2 ( RoB-2) and Risk Of Bias In Non-randomized Studies - of Interventions (ROBINS-I) tools. Results: Dexmedetomidine emerges as a promising drug because of its unique pharmacological profile, which provides sedation without modulating gamma-aminobutyric acid (GABA) receptors, potentially lowering the risk of delirium. Its additional analgesic, anti-inflammatory, and organ-protective characteristics could provide broader clinical benefits in the ICU. However, the aged population’s heightened susceptibility to Dexmedetomidine’s hemodynamic effects may limit some of its potential benefits. Conclusions: Although existing research suggests short-term neuroprotective effects, these effects do not always translate into better long-term survival. As a result, further large-scale, well-designed randomized controlled trials are needed to determine the optimal use of Dexmedetomidine in this population and to understand its overall impact on morbidity and mortality in the ICU.

Abstract: Objective: This study aimed to estimate the trends, projections, and determinants of standalone and Coexisting Forms of Malnutrition (CFM) at global, regional, national, and individual level among children under five in low- and middle-income countries (LMICs). The study also assessed the projection trajectory towards the 2030 GNTs (GNT) for child growth. Methods: Data from 48 LMICs were analysed using the Multiple Indicator Cluster Surveys (MICS) and Demographic & Health Surveys (DHS). Children with complete anthropometry were included for national and individual-level descriptive analyses. Projected prevalence of each form of malnutrition, including CFM, was calculated using the Annual Rate of Change (ARR). Inferential analyses employed generalized linear regression models (GzLM) with two-way interaction terms to identify determinants of each malnutrition type. Findings: By 2030, 22 of 48 LMICs are projected to achieve all three GNT, up from 10 countries currently, while Yemen and Zimbabwe are expected to remain off-track. Stunting is the most prevalent form, affecting 42 countries, with nine nations projected to have over 50% of children affected by any form of malnutrition. Wasting, obesity, and CFM are rising in several countries. Maternal education and household wealth were the strongest determinants, with children of uneducated mothers and from poorest households at highest risk. Inequalities are narrowing slowly, by 1–2% per year, and marked regional disparities persist. Conclusion: Many LMICs are off track to meet child growth targets when CFM in considered alongside standalone indicators. The government and global health partners must strengthen nutrition surveillance systems and equity-focused policies and programs to routinely capture CFM and prevent as well as manage all forms of malnutrition at national and individual levels.

Abstract: Reinforcement learning is increasingly deployed in domains where reward feedback is sparse, delayed, and entangled with long-horizon constraints, making reliable credit assignment difficult. A central development in recent work is the insertion of large language model modules directly into the reinforcement learning loop, not as peripheral interfaces but as components that alter trajectory generation and supervision. In these systems, language modules provide planning priors, structured reward shaping, process verification, synthetic world traces, and tool-memory context that reconfigure optimization at trajectory level. This survey develops a mechanism-first synthesis of that shift. We formalize intervention operators for planning, reward and verifier channels, world construction, and tool-memory mediation; analyze how each operator changes update targets, bias pathways, and stability conditions; and organize the field into a unified taxonomy grounded in optimization effects rather than model branding. We then examine evaluation practice across embodied control, web interaction, games, continuous control, and multi-agent settings, highlighting reproducibility gaps and protocol confounds. Finally, we synthesize recurring failure modes and propose a concrete research agenda on calibration, module authority arbitration, uncertainty-aware simulation, and benchmark design. The resulting perspective positions LLM-in-the-loop reinforcement learning as a systems and optimization discipline centered on trustworthy credit assignment under heterogeneous supervision.

Abstract: The rapid proliferation of agentic AI, autonomous software systems capable of executing transactions, accessing sensitive data, and acting on behalf of human users, has created an unprecedented security challenge. The existing authentication systems which developers created to authenticate human users and fixed system accounts, face their most significant authentication challenge because they need to establish the identity and access rights and operational purpose of AI agents. Deepfake technology has developed to the point where it can generate synthetic identities that perfectly mimic actual human beings. The first complete framework for AI agent authentication in environments with widespread deepfake usage appears for the first time in this research paper. We propose a verification model that uses multiple security layers to establish machine identity through cryptography while holding users accountable through human identification and measuring user behavior against expected patterns with risk assessment based on transaction details. Drawing on emerging industry concepts including "Know Your Agent" frameworks (Rasmussen, 2026; Sumsub, 2026), agentic AI orchestration platforms (Veritas AI, 2025), and multi-modal deepfake detection research (Bank Rakyat Indonesia & Telkom University, 2025; Kubam, 2024), we present a unified architecture for establishing trust in autonomous digital entities. The framework we developed establishes a complete system which enables people to establish trust in autonomous digital entities. Our framework addresses the fundamental question of our era: when an AI agent appears at the digital gate requesting access, how do we know it is who it claims to be, acting for a legitimate purpose, and not a deepfake in disguise?

Abstract: This study investigates a multidimensional "choir–maqām–meaning" model for Qur’anic memorization (hifz) integrated with formal undergraduate education, ana-lyzed through the lens of Qira’at science and cognitive pedagogy. Departing from tra-ditional individualistic methods, this research evaluates the effectiveness of a collec-tive, melodic approach in sustaining student commitment. Utilizing a mixed-methods design at Kırşehir Ahi Evran University, data from a cohort of 20 students were ana-lyzed through open-ended questionnaires, thematic analysis, and descriptive statistics. Findings indicate that despite the high cognitive and physical demands of dual curric-ula, the integration of choir and maqām enhances long-term retention and minimizes phonetic errors while maintaining peak motivation through peer support. Crucially, this research serves as a pilot phase for an expansive interdisciplinary project. By es-tablishing a theoretical and practical foundation, it aims to pave the way for subse-quent neuroimaging stages utilizing fMRI, DTI, and EEG methodologies to investigate the impacts of this model on neuroplasticity and cognitive reserve. Thus, the study of-fers a novel perspective on how specialized religious training can contribute to brain-based learning and cognitive development within the higher education ecosys-tem.

Abstract: In this work, we present a generic classification of a class of n-dimensional piecewise smooth vector fields known as refractive systems. Our purpose is to characterize the local structural stability of refractive fold-folds of elliptic type. To this end, we reduce the study of such piecewise smooth vector fields to the analysis of the first return maps and their structural stability. Normal forms for such systems are also provided.

Abstract: Large Language Models face significant challenges in biomedical multi-hop reasoning, including noise interference, context sensitivity, and ensuring factual consistency. To address these limitations, we propose the Robust Biomedical Reasoning Agent (RBRA), a novel agent framework designed to significantly improve the robustness and accuracy of LLMs in this critical domain. RBRA integrates core mechanisms such as hierarchical query decomposition, dynamic context filtering and aggregation, and iterative fact verification and refinement, underpinned by Robustness-aware Metric Optimization. Zero-shot evaluations on BioMultiHopQA-Dynamic, a challenging dataset designed to rigorously assess robustness, confirm its efficacy. RBRA-GPT4o achieves state-of-the-art average accuracy (70.1\%) and robust accuracy (63.5\%). Crucially, RBRA significantly reduces the Robustness Gap to 6.6\% (RBRA-GPT4o) and 6.5\% (RBRA-Llama3-70B), marking a substantial improvement compared to baseline methods' gaps exceeding 12\%. Furthermore, RBRA empowers open-source models, enabling RBRA-Llama3-70B to surpass leading proprietary LLMs in robust accuracy. Ablation studies and detailed analyses confirm the critical contribution of each RBRA component and its superior resilience to various perturbations. RBRA thus represents a significant step towards more reliable and trustworthy AI systems in biomedical applications.

Abstract: Redefining of the notions of econophysics based on the Landauer principle is suggested. Economic temperature is defined via the economic Landauer principle. The economic temperature is proportional to the minimal monetary cost associated with erasing or transmitting one bit of information in a given economic system. The introduced definition is useful for high-frequency trading. Clausius formulation of the Second Law of Thermodynamics for economic systems is formulated as follows: energy/money cannot spontaneously flow from a colder economic system to a hotter economic system. Carnot and Szilard’s economical engines are addressed. The Carathéodory formulation of the Second Law of Thermodynamics is re-shaped as follows: in every neighborhood of any equilibrium economic state, there exist states that cannot be reached by the process, which does not spend money or information. Optimal-power Curzon–Ahlborn economic engine is discussed.

Abstract: Accurate wind speed forecasting is critical for renewable energy planning and meteorological studies. However, wind behaviour is complex due to the presence of synoptic systems, terrain effects, and turbulence. This paper proposes a new model that uses a linear regression mean model and a Gaussian process for residual modelling. The monitoring stations were clustered by geographic coordinates and elevation. The Hopkins statistic was employed for cluster validation, while silhouette values were employed for cluster quality validation. It was found that for stations at high elevation located in the interior (Cluster 2), the GP model for residual modelling consistently improved wind forecast accuracy by up to 16.3%. However, for coastal stations at low elevation (Cluster 1), the GP model was not effective for residual modelling. This proves that the accuracy of GP residual modelling depends to a large extent on the wind regime.

Abstract: This technical note reports an exploratory, AI-assisted in silico proof of concept implementing a “signaling first, killing later” discovery paradigm: prioritizing compounds with high predicted affinity for bacterial quorum sensing (QS) pathways, then refining them for bactericidal potency. Using Claude Opus 4.6 (Anthropic), a custom SMILES-based descriptor calculator (170+ features) and a four-model ensemble (Random Forest, Gradient Boosting, SVM-RBF, Logistic Regression) were trained on 150 compounds (87 QS modulators, 63 negatives), achieving cross-validated AUC of 0.954 ± 0.024. Screening 218 ZINC15 CEBB tranche compounds identified 101 Tier 1 hits (46.3%), of which 91.1% were nitroaromatic. Bioisosteric modifications rescued 9/15 analogs (60%) as PAINS-clean. An orthogonal antibiotic-likeness model (44 antibiotics vs. 49 non-antibiotics, AUC = 0.809) identified a diacetyl hexahydroxytriphenylene prodrug as dual-high (P_QS = 0.849, P_Abx = 0.876). Six iterative optimization cycles across two phases—structural alert reduction followed by scaffold simplification—produced the final lead M6-12 (SMILES: CNCc1c(F)cc(OC)c2c(OC)c3C(O)CNCC3c(O)c12), a partially saturated fluorinated piperidine-fused tricyclic scaffold. M6-12 achieved: dual-high ML convergence (P_QS = 0.928, P_Abx = 0.792, Joint = 0.735, 4/4 ABX models >0.5), zero PAINS, zero Brenk alerts, zero violations across all five drug-likeness filters, zero CYP inhibition (SwissADME 0/5, pkCSM 0/7), AMES-negative, high GI absorption, and “Very soluble” classification. RDKit validation confirmed: MW = 340.40, Crippen LogP = 0.48, TPSA = 82.98 Ų, HBD = 4, HBA = 6, Fraction Csp3 = 0.647. ChEMBL similarity: 0% at 95% threshold. Property-space MIC estimation: 2–32 μg/mL (Gram-positive), 1–11 μg/mL (Escherichia coli), 33–333 μg/mL (Pseudomonas aeruginosa), with 5/5 Richter rule compliance for Gram-negative penetration. A single pkCSM hepatotoxicity flag—contextualized by zero CYP inhibition, AMES-negative status, and low lipophilicity—probably constitutes the principal limitation requiring in vitro resolution. The signaling-first approach may enrich for molecules operating within biologically relevant chemical spaces, potentially offering a reduction in attrition compared to conventional MIC-first screening. All results require experimental validation.

Abstract: In this study we engineered bilayered electrospun scaffolds consisting of hydrophobic PDLLA and hydrophilic PVP layer which incorporate either native HA or semi-synthetic HA-Gly-OH at concentrations of 1% and 3% w/w. Generally, bilayer scaffolds electrospun on different days delaminated, while herein they maintained their integrity because electrospun on the same day. Sequential electrospinning enabled the bilayer structure, characterized via Scanning Electron Microscopy (SEM), Atomic Force Microscopy (AFM), and Young’s modulus measurements to assess morphology and mechanics. In vitro cytotoxicity and cell viability assays with fibroblast cells confirmed good biocompatibility for both the individual layers and bilayer system. Among the tested formulations, the bilayer PDLLA/PVP–HA-Gly-OH 1% showed the most promising performance, attributed to the synergistic effects of HA and Gly-OH in promoting adhesion and proliferation.

Abstract: In public DC fast-charging scenarios, protocol inconsistencies, current-limiting variations, and communication anomalies often lead to handshake failures, current oscillations, voltage overshoot, and delayed fault recovery. Under high-power conditions, mishandling these issues can cause prolonged high-temperature, high-stress battery operation, elevating safety risks. To address this, a fast-charging safety framework is proposed, integrating hierarchical control, fault diagnostics, and staged recovery for high-voltage battery systems. A charging state machine is designed to cover phases such as handshake, pre-charge, CC/CV transition, derating, disconnection, and recovery. Transition nodes include consistency checks to handle packet loss, timing errors, and abnormal responses. Charging current is generated through a constrained optimization model incorporating cell voltage, temperature rise, predicted power limits, protection boundaries, equipment constraints, and diagnostics-based disconnection triggers. The system enables smooth, recoverable current control and active fault response. Tests across 3,000 sessions show a 38% drop in interruption rate, recovery time cut from 6.5 s to 2.1 s, voltage overshoot reduced by 45%, and peak temperature rise lowered by 0.8–1.3 °C. This validates the framework’s effectiveness for safe, stable fast charging in complex, interoperable networks.

Abstract: This study investigates the effect of progressive CaO/SrO substitution on the structure, crystallisation behaviour, and coordination chemistry of fluorapatite-forming glass-ceramics in the SiO₂–Al₂O₃–P₂O₅–CaO/SrO–CaF₂ system. Differential scanning calorimetry (DSC), X-ray diffraction (XRD), ATR-FTIR spectroscopy, ³¹P and ¹⁹F MAS-NMR, and transmission electron microscopy (TEM) were employed to probe both crystallisation and local structural environments. Increasing SrO content reduced the glass transition temperature and suppressed homogenous nucleation, promoting surface-nucleated fluorapatite (Ca5−x​Srx​(PO4​)3​F) formation. XRD confirmed fluorapatite as the primary crystalline phase and revealed systematic lattice expansion consistent with partial Sr²⁺ incorporation. ¹⁹F MAS-NMR indicated limited Sr substitution at Ca(II) sites (solid-solution), while ³¹P MAS-NMR demonstrated pronounced phosphorus deshielding, reflecting sensitivity of phosphate tetrahedra to local coordination distortion rather than extensive Sr occupancy. Integrating these findings provides a comprehensive framework for interpreting structural and coordination changes in Sr-fluorapatite glass-ceramics.

Abstract: This conceptual research note aims to draw the attention of researchers to what the author defines as ‘everydayness’ in tourism. It brings together initial examples of current ‘everydayness’ tourism developments. We posit that leisure travel is no longer necessarily an escape from everyday life but also a way of engaging with it. Unfortunately, this ‘everydayness’ appears to be largely absent from tourism research thus far. Therefore, we aim to encourage a broader, less binary (ordinary/extraordinary), more intertwined tourism perspective where ‘everydayness’ enters leisure and tourism. What is needed is a paradigm shift and an expansion of the tourism concept in the postmodern reality, along with a proposal of methods to research ‘everydayness’.

Abstract: Background and Objectives: The relationship between mental health and sports injuries is increasingly relevant in soccer, given the psychological vulnerability of young athletes to training and competition demands. This study aimed to examine the association between injury history and mental health indicators (anxiety, stress, and depression), and to ex-plore whether injury history differed according to gender, competitive category, and play-ing position. Materials and Methods: A total of 146 soccer players (79 males, 67 females) aged 12–30 years from Under-14, Under-16, Under-18, and Senior categories completed the STAI-T and DASS-21 questionnaires. Mental health levels were compared across inju-ry history groups (no injuries, 1–2 injuries, >2 injuries), and associations with gender, cat-egory, and position were examined. Results: Overall, 73.3% of players reported at least one injury, with no significant differences by gender, category, or playing position. Significant differences were found for anxiety: players with a higher number of injuries reported higher levels of trait anxiety (STAI-T) and anxiety symptoms (DASS-21). No significant group differences were observed for stress and depression, although scores showed an in-creasing trend with greater injury history. Conclusions: Injury history in soccer players was primarily associated with anxiety indicators. These findings highlight the importance of considering the emotional impact of injuries, especially anxiety, and support the inclusion of targeted psychological strategies within injury prevention programs and during reha-bilitation.

Abstract: Ultrasound evaluation of the median nerve in carpal tunnel syndrome (CTS) is inherently operator-dependent. Automating CTS severity grading from ultrasound using deep learning may improve objectivity, reproducibility, and scalability in clinical practice. Background/Objectives: To develop convolutional neural network (CNN) models for automated CTS severity classification using B-mode ultrasound and electrodiagnostic results as the reference standard, and to compare performance between ultrasound scans. Methods: Fifty participants with suspected CTS provided 94 wrists, each classified into four severity levels (normal, mild, moderate, severe) through standardized nerve conduction studies. Ultrasound videos in transverse and longitudinal planes were acquired under a uniform protocol. From 11,895 frames, expert quality control produced a dataset of 2,518 valid images. Two CNN classifiers (transverse and longitudinal) were trained on nerve-centred crops using a 75/25 training–validation split, Adam optimization, and categorical cross-entropy loss. Performance was assessed through accuracy, class-wise precision, and confusion matrices. Results: Both models showed stable convergence. The transverse classifier achieved 0.92 validation accuracy, with precision values of 0.88 (normal), 0.94 (mild), 0.93 (moderate), and 0.97 (severe). The longitudinal classifier achieved 0.94 accuracy, with precision values of 0.98 (normal), 0.94 (mild), 0.96 (moderate), and 0.85 (severe). Misclassifications occurred mainly between adjacent categories, and reduced performance for severe longitudinal cases reflected limited sample representation. Conclusions: CNN-based classifiers can automatically predict CTS severity from B-mode ultrasound with high agreement to electrodiagnostic labels. These operator-independent models may support standardized diagnostic pathways and ultrasound-based screening. Future work should expand and balance datasets, incorporate multi-center data, and conduct external validation.

Abstract: Pitch is a powerful cue for segregating sound sources in complex acoustic scenes, yet the neural mechanisms through which it guides selective attention remain unclear. In this review, we synthesise behavioural and neurophysiological evidence from humans and animal models to examine how pitch supports selective listening in a two-stage process: bottom-up pitch-based feature binding, followed by top-down enhancement of an attended sound source. Behavioural studies demonstrate that even modest pitch differences substantially improve listeners’ segregation of harmonic sounds, tone streams, and competing talkers. Human EEG, MEG, fMRI and ECoG studies show enhancement of target sound representations in auditory cortex during selective listening, but understanding this process at the level of individual neurons requires further study in animals that are trained in pitch-based selective listening tasks. Other key questions in this field include the relative roles of resolved and unresolved harmonic cues, the neural circuit mechanisms underlying target enhancement versus masker suppression, and how attention can target distributed cortical pitch representations. We argue that cross-species, naturalistic paradigms are essential for answering these questions and for addressing the listening difficulties associated with ageing and hearing loss.

Abstract: Deubiquitinases or DUBs have emerged as pivotal regulators in cellular homeostasis, coordinating the delicate balance of protein ubiquitination and deubiquitination. Their versatile roles span from controlling protein turnover to modulating signal transduction pathways, thereby influencing diverse cellular processes such as DNA damage repair, apoptosis, and immune responses. This review comprehensively explores the current understanding of DUBs, elucidating their structural diversity, catalytic mechanisms, physiological functions, and implications in human diseases. Moreover, we discuss the therapeutic potential of targeting DUBs in various pathological conditions, highlighting recent advancements and challenges in developing DUB-specific inhibitors.

Abstract: The yellow mealworm, Tenebrio molitor (T. molitor), is increasingly considered a promising protein and lipid source for circular bioeconomy strategies in food and feed. Interest is driven by the need to diversify protein supplies and reduce environmental footprints, but feasibility depends on safety, regulation, and scalable operating conditions. Alongside industrial systems, low-input models adapted to arid conditions have been proposed, yet evidence remains heterogeneous and context-dependent. This review covers developments between 2020 and 2025, a period that coincides with accelerated EU novel food assessments and a rapid expansion of applied research on processing, safety, and valorization, with a focus on scientific progress and regulatory approvals such as those issued by EFSA in Europe. Several new applications have emerged, including enzymatic hydrolysates, lipid recovery, and the extraction of chitosan from exuviae. Uses now span animal nutrition, biodegradable materials, and bioactive food ingredients. Life-cycle assessments often report lower greenhouse gas emissions and land use than conventional livestock, but outcomes are sensitive to energy inputs, feed substrates, and system boundaries. Key constraints include variable frass composition, allergenicity and cross-reactivity risks, regulatory and compliance constraints, and mixed consumer acceptance. For research, priority needs include longer-term safety datasets and field-relevant validation of bioactive claims beyond in vitro assays. For policy and industry, priorities include harmonised criteria for substrate safety and traceability, and transparent supply-chain controls that enable reproducible quality at scale.