Abstract: To investigate the impacts of combining controlled-release urea (CRU) with controlled-release potassium chloride (CRK) on nutrient leaching and use efficiency in wheat fields, we carried out experiments spanning three consecutive years from 2022 to 2024, utilizing a split-plot design. In this study, the control plot received neither nitrogen nor potassium applications (Control). The main plots were designated based on nitrogen fertilizer types: controlled-release urea (CRU) and conventional urea (Urea). The sub-plots were assigned potassium fertilizer rates using CRK, specifically 50 kg ha-1 (LCRK), 75 kg ha-1 (MCRK), and 100 kg ha-1 (HCRK). The findings revealed that the nutrient release pattern of CRU combined with CRK aligned well with wheat's nutrient uptake requirements. Notably, the wheat yields in CRU treatments witnessed a significant average increase of 2.2% from 2022 to 2024 compared to ordinary urea treatments. In the final season, nitrogen recovery efficiency augmented by 10.9%. Furthermore, CRU treatments significantly boosted the number of effective wheat spikes and grains per spike but had no notable influence on wheat's thousand-grain weight (TGW). Consequently, the yield enhancement observed in CRU treatments was primarily attributed to an increase in wheat's effective tiller count. CRU also markedly elevated inorganic nitrogen levels in the plow layer soil during wheat's mid to late growth stages, effectively mitigating nitrate nitrogen leaching into deeper soil layers. The application of CRU×MCRK notably and significantly improved wheat leaf photosynthesis during its mid to late growth stages, yielding substantial economic benefits and theoretical significance.

Abstract: Elementary particles exhibit intrinsic phase recurrences, so each can serve as a reference clock. From this perspective, Rovelli's ``timeless'' viewpoint is best read not as denying time, but as denying the fundamentality of any preferred external time coordinate: time persists as internal cyclic variables carried by particles, covariantly modulated by energy exchange and relativistic transformations. Macroscopic flow arises from records and thermodynamic coarse-graining. Intrinsic temporal periodicity, supported by theoretical and phenomenological results published in previous works, constitutes the fundamental principle at the base of Elementary Cycles Theory, which may be regarded as a minimal, purely four-dimensional string-like framework.

Abstract: Oropouche fever (OF), caused by Oropouche virus (OROV), has expanded beyond its Amazonian range into Minas Gerais (MG), Brazil, raising concern about transmission in extra‑Amazonian Atlantic Forest landscapes. Critical gaps persist regarding Culicoides vector communities, anthropophily, and climate-sensitive transmission risk in these newly affected regions. We conducted targeted entomological surveys in five MG outbreak communities using CDC light traps and Protected Human Attraction (PHA) to characterize Culicoides composition. Females underwent RT-qPCR for OROV (n = 819) and physiological assessment (n=312). We developed an entomological alert framework that integrates blood-fed abundance, minimum infection rate (MIR) upper confidence bounds, and environmental drivers via generalized additive mixed models, which explained 68% of the variability in Culicoides abundance and the alert index across communities. We collected 1,171 Culicoides representing five species, C. leopoldoi (79.1%) and the primary vector C. paraensis (20.3%) predominated. C. paraensis was documented for the first time in all five outbreak areas and dominated PHA captures (90%), confirming anthropophily. Although no specimens tested OROV-positive (consistent with expected field infection rates of 0.01–1%), MIR upper bounds reached 132/1,000 in low-sample settings and humidity and temperature strongly modulated abundance. This operational baseline and alert index transform virologically negative, sparse surveillance data into prioritized targets for in-tensified sampling and vector control during early, low‑prevalence phases, when con-tainment of OROV’s extra‑Amazonian spread is still achievable.

Abstract: Purpose The aim of the study was to evaluate and shorten a psychometric scale measuring quiet quitting and passive quitting, while maintaining the quality of measurement and the predictive utility of the instrument. Design / Methodology / Approach A hybrid approach was applied, integrating structural equation modeling (SEM) and supervised machine learning (ML). A two-factor measurement model with regression on organizational engagement (UWES-9) was estimated using a sample of 1,040 working respondents. Simultaneously, the predictive validity of the scale items was assessed using regression algorithms within a cross-validation procedure. The scale was shortened iteratively by eliminating only those items whose removal did not significantly worsen SEM model fit or ML predictive performance. Findings The scale was reduced from 14 to 9 items. The reduction led to improved SEM fit indices (increased CFI and TLI with stable RMSEA) and only a slight decrease in the predictive validity of the ML models. The results confirm that integrating SEM and ML enables effective shortening of psychometric tools while maintaining their reliability and diagnostic functionality. Research Limitations / Implications The study was based on a single external criterion (organizational engagement) and one research sample, which limits the generalizability of the results. Future studies should include other criteria (e.g., burnout, turnover) and independent validation samples. Practical Implications The shortened scale reduces respondent burden, shortens survey time, and lowers measurement costs while retaining predictive utility relevant for HR practice and organizational diagnostics. Social Implications Improved and more efficient measurement of quiet and passive quitting can support early identification of declining employee engagement, contributing to enhanced quality of work life and human resource management policies. Originality / Value The originality of the study lies in proposing an integrated procedure for shortening psychometric scales by combining measurement and predictive criteria, which constitutes a methodological contribution to research on the development and optimization of measurement tools in management and quality sciences.

Abstract: Introduction: Wound healing, tissue repair, and regeneration are biological processes essential for restoring tissue integrity following injury. Disorders in these processes can lead to complications and the formation of scars, impacting both physical and psychological well-being. Methods: This review synthesizes recent advancements in understanding of the molecular and cellular mechanisms governing these processes. We explore the sequential phases of wound healing, the key cellular and molecular players involved, factors influencing healing outcomes, and emerging therapeutic strategies. Special emphasis is placed on novel biomaterials, cell-based therapies, gene therapies, and physical modalities. Modern therapeutic approaches aim to accelerate healing while minimizing complications such as scarring, infection, or chronic inflammation. Among the commercially available topical agents, Dermatix Ultra, Epicyn, Flosteron, and Contractubex are widely used and studied. Results: This review provides a contemporary analysis in the context of wound healing, tissue repair, and scar management, with an evidence-based comparison of these agents, focusing on their composition, mechanisms of action, clinical applications, and a comparative perspective on their efficacy in improving scar outcomes. Conclusion: Thus, this review aims to provide clinicians and patients with an up-to-date understanding of these treatments to facilitate informed decision-making in scar management. Finally, we discuss current challenges and future directions in the field, highlighting the potential for personalized medicine and translational research.

Abstract: Recommender systems are increasingly exposed to anomalous user behavior that can distort recommendation outcomes and compromise system reliability. In real-world settings, explicit labels identifying malicious activity are rarely available, motivating the adoption of unsupervised detection approaches. This study presents a comparative analysis of classical machine learning and deep learning techniques for anomaly detection in recommender systems. Using the MovieLens 1M dataset, we construct a user-level behavioral representation based on statistical, temporal, and interaction-based features derived from explicit rating data. Three unsupervised detection models are evaluated: Isolation Forest, One-Class Support Vector Machine, and an autoencoder-based neural network. To address the absence of ground truth labels, evaluation is conducted using label-free protocols, including score distribution analysis, percentile-based thresholding, and inter-model agreement. Results indicate that individual models capture complementary aspects of anomalous behavior, exhibiting low to moderate agreement. An ensemble scoring strategy improves ranking stability and provides a consistent mechanism for identifying highly deviant user profiles. The findings suggest that ensemble-based unsupervised detection constitutes a practical and interpretable first-layer screening approach for recommender system monitoring.

Abstract: Multimodal Named Entity Recognition (MNER) leverages both textual and visual information to improve entity recognition, particularly in unstructured scenarios such as social media. While existing approaches predominantly rely on raster images (e.g., JPEG, PNG), Scalable Vector Graphics (SVG) offer unique advantages in resolution independence and structured semantic representation—an underexplored potential in multimodal learning. To fill this gap, we propose MNER-SVG, the first framework that incorporates SVG as a visual modality and enhances it with ChatGPT-generated auxiliary knowledge. Specifically, we introduce a Multimodal Similar Instance Perception Module that retrieves semantically relevant examples and prompts ChatGPT to generate contextual explanations. We further construct a Full Text Graph and a Multimodal Interaction Graph, which are processed via Graph Attention Networks (GATs) to achieve fine-grained cross-modal alignment and feature fusion. Finally, a Conditional Random Field (CRF) layer is employed for structured decoding. To support evaluation, we present SvgNER, the first MNER dataset annotated with SVG-specific visual content. Extensive experiments demonstrate that MNER-SVG achieves state-of-the-art performance with an F1 score of 82.23%, significantly outperforming both text-only and existing multimodal baselines. This work validates the feasibility and potential of integrating vector graphics and large language model–generated knowledge into multimodal NER, opening a new research direction for structured visual semantics in fine-grained multimodal understanding.

Abstract: A laboratory–scale mechanical draft cooling tower equipped with eight sections of perforated inclined plates was designed to determine the effect of operating conditions on the volumetric mass transfer coefficient (kya) between water and air. A three–factor, three–level design of experiments (DOE) was implemented, considering liquid mass flow rate L (120, 240, and 360 kg/h), gas mass flow rate G (36, 57, and 75 kg/h), and top water temperature TL2 (50, 60, and 70◦C). A total of 54 runs were performed, and the global volumetric mass transfer coefficient was calculated by combining energy and mass balances with the Mickley method. The experimental data were fitted to a power–law correlation using multivariable regression. The ANOVA showed that TL2 is the dominant factor, followed by L, whereas the influence of G is comparatively small in the studied range. The selected correlation, based on the nominal gas flow rate, achieved R2=0.869 and a RMSE of 5930 kg/(m3h). The kya values were found in the range from 4600 to 62000 kg/(m3h). Vertical temperature profiles of water and air along the column revealed that, for high liquid flow rates, most of the cooling occurs in the lower stages, suggesting that the upper sections are underutilized.

Abstract: This study proposes a Natural Language Processing (NLP)-based evaluation framework to examine the linguistic consistency of Large Language Model (LLM)-generated personalized texts over time. NLP metrics were used to quantify and compare linguistic patterns across repeated generations produced using identical prompts. In Experiment 1, internal reliability was examined across 10 repeated generations from four LLMs (Claude, Llama, Gemini, and ChatGPT) applied to 10 scientific texts tailored for a specific reader profile. Linear mixed-effects models showed no effect of repeated generation on linguistic features (e.g., cohesion, syntactic complexity, lexical sophistication), suggesting short-term consistency across repeatedly generated outputs. Experiment 2 examined linguistic variation across model updates of GPT-4o (October 2024 vs. June 2025) and GPT-4.1 (June 2025). Significant variations were observed across outputs from different model versions. GPT-4o (June 2025) generated more concise but cohesive texts, whereas GPT-4.1 (June 2025) generated outputs that are more academic, lexically sophisticated and complex syntax. Given the rapid evolution of LLMs and the lack of standardized methods for tracking output consistency, the current work demonstrates one of the applications of NLP-based evaluation approaches for monitoring meaningful linguistic shifts across model updates over time.

Abstract: Control charts are widely used in the industrial world to monitor the average and variability of production processes. Max-Half-Mchart is a multivariate control chart that is less effective in handling many outliers. This research aims to develop a control chart that is more resistant to outliers by using Minimum Regularized Covariance Determinant (MRCD). MRCD is a development of the MCD method which is better at dealing with 'fat data', namely situations where the number of variables is greater than the number of observations. The performance evaluation of the robust Max-Half-Mchart control chart based on MRCD using Average Run Length (ARL) against shifts in process mean, process variance, and simultaneous shifts. In addition, a comparison is made of the outlier detection accuracy between the robust Max-Half-Mchart based on MRCD and the standard Max-Half-Mchart. The research results show that the MRCD-based Robust Max-Half-Mchart provides better accuracy and Area Under Curve (AUC) in detecting outliers compared to the traditional Max-Half-Mchart, especially at outlier levels of 10%, 20%, 30%, and 40%. Application of this method to cement quality data also shows superiority in detecting outliers.

Abstract: Objective: The role of bronchodilators in bronchiolitis remains unclear, yet they are commonly used. We evaluated their impact in children based on family history of atopy and viral etiology (RSV and rhinovirus). Methods: This was a single-center, retrospective study of children ≤ 2 years admitted to the PICU, step-down ICU, or cardiothoracic ICU who required high-flow nasal cannula, non-invasive ventilation, or invasive ventilation. Patients were categorized by bronchodilator use and stratified by family history of atopy. Primary outcomes were ICU and hospital length of stay (LOS) and length of respiratory support (LRS). The Critical Bronchiolitis Score (CBS) was used to adjust for illness severity by calculating predicted outcomes and comparing them with the observed values. Secondary analysis evaluated outcomes based on family history of atopy and RSV/ rhinovirus positivity. Results: Of 105 included patients, 56 (53.3%) received bronchodilators. The no-bronchodilator group had shorter ICU-LOS (1.7 vs. 2.5 days; p = 0.0005) and hospital LOS (2.1 vs. 3.4 days; p = 0.0038) compared to the bronchodilator group. Predicted ICU outcomes did not differ between groups. Secondary analyses suggested differences in ICU-LOS (p = 0.007) and hospital LOS (p = 0.02) based on family history of atopy. In rhinovirus-positive patients, both ICU and hospital LOS were shorter without bronchodilators, while no differences were observed in RSV-positive patients. Conclusions: Bronchodilators in critical bronchiolitis were associated with longer inpatient LOS, despite similar predicted illness severity. Neither a family history of atopy nor rhinovirus/RSV positivity affected bronchodilator outcomes. Future prospective research is needed to identify targeted subgroups of patients who may benefit from this therapy.

Abstract: Debates on ontological underdetermination—from scientific realism to Bayesian epistemology—typically assume that epistemic agents remain structurally intact even when evidence fails to determine theory. This paper argues that such debates tacitly preserve a “posture of mastery”: indeterminacy is treated as a problem for theory selection rather than as a destabilization of epistemic agency itself. I introduce the concept of epistemic anti-mastery to describe a rational reconfiguration of epistemic posture under conditions of radical opacity. Through a structural reading of Marguerite Porete’s account of annihilation in The Mirror of Simple Souls, I demonstrate that: (i) Bayesian conditionalization presupposes an architectural stability that radical underdetermination undermines; (ii) scientific realism’s convergence rhetoric depends on an untenable mastery-orientation; and (iii) under structural opacity, epistemic anti-mastery is rationally required. The aim is conceptual intervention: rational engagement requires revision of epistemic stance rather than refinement of theoretical control.

Abstract: Large language models exhibit context-dependent behavioral patterns that vary systematically across task domains, yet standardized cross-domain measurement frameworks remain lacking. This study addresses methodological limitations in prior work by applying a rigorous 50-trial protocol uniformly across 14 models (25 model-domain runs) spanning medical (closed-goal) and philosophical (open-goal) reasoning domains using a three-condition protocol (TRUE/COLD/SCRAMBLED). Key findings: (1) domain means show no significant difference (philosophy 0.317 vs medical 0.308; Mann-Whitney U=51, p=0.149), but variance differs markedly (medical SD=0.131 vs philosophy SD=0.045); (2) 23 of 25 model-domain runs show positive ΔRCI, with Gemini Flash medical as the sole negative outlier (ΔRCI=-0.133), suggesting safety filtering interference; (3) vendor signatures show significant differentiation when excluding the Gemini Flash anomaly (F(7,16)=3.55, p=0.017), with Moonshot (Kimi K2) showing highest context sensitivity and Google lowest; (4) the expected information hierarchy (ΔRCI_COLD > ΔRCI_SCRAMBLED) holds in 24/25 runs (96%), validating the measurement framework; (5) position-level analysis reveals prompt-specific variation with a strong P30 summarization spike in medical domain (z=3.74). These results establish ΔRCI as a robust, domain-general metric for context sensitivity and provide the foundation for deeper analyses of temporal dynamics and information-theoretic mechanisms.

Abstract: Chirality is a pervasive and functionally critical feature of biological macromolecules, yet its distributed and emergent forms remain poorly quantified in complex systems such as membrane proteins. We present Chirobiophore, a novel paradigm for capturing biochirality across scales—from atomic geometries to global structural asymmetries. Unlike traditional stereochemical metrics, Chirobiophore employs a multidimensional model-independent vector comprising Local Tetrahedral Asymmetry (LTA), Helical Path Curvature (HPC), Asymmetric Environment Score (AES), Directional Density Profile (DDP), Leaflet Asymmetry Index (LAI), and Orientation Twist Score (OTS). This framework enables coordinate-invariant comparisons of structurally diverse proteins in a continuous chirality space. We demonstrate its application to canonical, GPCR, and topologically complex membrane proteins, revealing distinct chirality signatures and functional clustering. Furthermore, we map Chirobiophore descriptors to tissue-level asymmetry indices, providing a bridge between molecular structure and morphogenetic patterning. Chirobiophore offers a unified, extensible platform for structural biology, synthetic design, and developmental modeling of chirality.

Abstract: Background: Experimental research on workplace motivation faces significant practical and ethical constraints. Random assignment to motivational interventions, manipulation of organizational contexts, and testing across diverse populations are often infeasible in field settings.Objective: This paper investigates whether AI-simulated worker personas can serve as a complementary methodological tool for early-stage exploration of motivational interventions. We examine this question using beneficiary impact and job crafting interventions in a simulated fundraising context.Method: We generated 240 diverse worker personas using three large language models (Claude, GPT-4, Llama), varying systematically in personality traits, cultural backgrounds, age, and prior experiences. Personas were randomly assigned to three conditions: (1) control training, (2) beneficiary impact intervention, or (3) job crafting reflection intervention. We measured motivation, anticipated performance, and response quality through both quantitative ratings and qualitative text analysis.Results: Across all three LLMs, beneficiary impact and job crafting conditions showed substantially higher motivation ratings than control (Cohen's d = 3.15 and 3.97 respectively, using within-condition residual SD as standardizer). AI simulations showed consistent patterns across model architectures, with LLM choice explaining only 3-4% of variance. Individual differences moderated intervention effects in theoretically predictable ways, with collectivist personas and those high in agreeableness showing stronger responses. Qualitative analysis revealed distinct psychological mechanisms and generated testable hypotheses about intervention processes.Conclusions: AI-simulated personas can rapidly generate hypotheses and enable iterative exploration of motivational interventions. The method shows promise for early-stage intervention design, mechanism exploration, and boundary condition testing. However, important questions remain about whether AI-generated effect sizes, moderator patterns, and psychological processes accurately reflect human responses. We provide recommendations for appropriate uses of AI simulation and identify critical needs for human validation research.

Abstract: Glypican-1 (GPC1) has emerged as a critical mediator of malignant tumor progression. GPC1 plays essential roles in regulating various signaling pathways involved in tumor cell proliferation, invasiveness, and tumorigenesis. Overexpression of GPC1 in tumors mediates oncogenic transformation, epithelial-to-mesenchymal transition, metastatic dissemination, and therapeutic resistance. Accordingly, GPC1-targeted therapeutic strategies have been investigated in clinical and preclinical studies. However, the clinical efficacy has been limited. We previously developed an anti-GPC1 monoclonal antibody (mAb), G1Mab-28 (mouse IgG1, κ), which exhibits high affinity and specificity for GPC1. In the present study, we generated recombinant isotype-converted G1Mab-28, including G1Mab-28-mG2a (mouse IgG2a) and G1Mab-28-hG1 (human IgG1). Both mAbs recognized GPC1-expressing human tumor cell lines, including lung squamous cell carcinoma PC10 and pancreatic ductal adenocarcinoma PK-45H, by flow cytometry. Moreover, both mAbs exerted antibody-dependent cellular cytotoxicity and complement-dependent cytotoxicity against those cell lines. In mouse xenograft models, treatment with the mAbs resulted in potent antitumor efficacy against PC10 and PK-45H tumors. Collectively, these findings support the therapeutic potential of G1Mab-28 for the treatment of GPC1-positive tumors.

Abstract: This paper addresses distributed formation control for multiple unmanned aerial vehicles (UAVs) operating in obstacle-dense environments under directed switching communication topologies. A leader–follower architecture is adopted, wherein the leader performs online trajectory replanning while followers rely on delayed and intermittently available neighbor information. To simultaneously tackle collision avoidance, formation feasibility under narrow passages, and communication intermittency, we propose an integrated deformable formation navigation framework. The framework couples Safe Flight Corridor (SFC)-constrained Bézier trajectory planning with a dynamic formation scaling mechanism, allowing the swarm to adaptively shrink or expand its geometric configuration when traversing constricted spaces, thereby ensuring all agents remain within certified collision-free corridors. A nonlinear distributed consensus-based estimator is designed to propagate leader reference states under directed switching graphs with bounded delays. Using a max-min contraction analytical approach, we establish guaranteed practical convergence for both leader tracking and inter-follower agreement without requiring persistent connectivity. Extensive simulations in complex cluttered environments demonstrate that the proposed approach enables flexible and real-time formation reshaping, enhancing navigational safety and robustness while maintaining cohesive swarm behavior under challenging communication and spatial constraints.

Abstract: COVID-19 still poses a global public health challenge, exerting pressure on radiology services. Chest X-ray (CXR) imaging is widely used for respiratory assessment due to its accessibility and cost effectiveness. However its interpretation is often challenging because of subtle radiographic features and inter-observer variability. Although recent deep learning (DL) approaches have shown strong performance in automated CXR classification, their black-box nature limits interpretability. This study proposes an explainable deep learning framework for COVID-19 detection from chest X-ray images. The framework incorporates anatomically guided preprocessing, including lung-region isolation, contrast-limited adaptive histogram equalization (CLAHE), bone suppression, and feature enhancement. A novel four-channel input representation was constructed by combining lung-isolated soft-tissue images with frequency-domain opacity maps, vessel enhancement maps, and texture-based features. Classification was performed using a modified Xception-based convolutional neural network, while Gradient-weighted Class Activation Mapping (Grad-CAM) was employed to provide visual explanations and enhance interpretability. The framework was evaluated on the publicly available COVID-19 Radiography Database, achieving an accuracy of 95.3%, an AUC of 0.983, and a Matthews Correlation Coefficient of approximately 0.83. Threshold optimisation improved sensitivity, reducing missed COVID-19 cases while maintaining high overall performance. Explainability analysis showed that model attention was primarily focused on clinically relevant lung regions.

Abstract: The widescale deployment of radars, distributed across a platform and across multiple platforms for reliable 360° situational awareness (SA), introduces the challenge of radar interference. Interference can broadly be categorised as self-interference (between radars mounted on the same platform) and mutual interference (signals received from radars on other platforms). Both types of interference impede the reliability of SA delivered by such systems, particularly in dense environments where numerous radars operate simultaneously within the same frequency band. This work presents a comprehensive evaluation of a multi-modal beamforming approach that combines unfocused synthetic aperture radar with the traditional Multiple-Input, Multiple-Output beamformer to enhance radar resolution and suppress interference. Additionally, various aspects of sensor configurations defining hardware and software capabilities of state-of-the-art radars are discussed, and a systematic analysis of signal-to-interference-plus-noise ratio at each step of the processing is presented. Extensive simulations and experimental results in both automotive and maritime environments are shown to validate the effectiveness of the proposed approach.

Abstract: Chimeric antigen receptor (CAR)-T cell therapy has transformed the management of hematologic malignancies but faces obstacles, including severe treatment-related toxicities, highly suppressive tumor microenvironment (TME), inadequate long-term persistence, and poor trafficking/infiltration into solid tumor. This review summarizes recent genetic engineering strategies designed to overcome these barriers and to improve the safety, durability, and spatial effectiveness of CAR-T cell therapy. To mitigate cytokine release syndrome and neurotoxicity, approaches such as affinity-tuned and humanized scFvs, hinge/TM optimization, ITAM calibration have been developed alongside programmable “switch-off” and “switch-on” systems incorporating suicide genes, antibody-bridging switches, and optogenetic or hypoxia-gated circuits. TME remodelling strategies leverage nanomaterials for localized cytokine delivery, cell-surface “backpack” systems, and oncolytic viruses engineered to release cytokines or checkpoint-blocking biologics. Enhancing durability and resistance to exhaustion increasingly relies on precision genome engineering, including CRISPR-based editing and multiplexed shRNA platforms targeting inhibitory receptors and exhaustion-driving transcriptional programs. Finally, chemokine-receptor engineering and local biomaterial-based delivery systems are discussed as routes to improve CAR-T trafficking and intratumoral persistence. We also highlight the remaining translational challenges including checkpoint redundancy, in vivo payload dilution, vector capacity limits, and the safety of multiplex genome editing. Collectively, these interdisciplinary innovations point towards integrated, patient-tailored CAR-T platforms that combine safety control, metabolic and transcriptional resilience, and improved TME navigation to enable broader clinical application.