Abstract: Zucchini plants are prone to powdery mildew, a fungal disease that can hamper plant growth and yield. This study investigated whether silicon supplementation, applied via fertigation or foliar sprays, could enhance plant resilience and reduce powdery mildew disease severity. Two independent greenhouse experiments were conducted, testing six treatments that varied in both silicon (Si) concentration and application method. The most pronounced effect was observed when fertigation with 1.5 mmol/L Si was combined with weekly foliar spraying (12.5–25 g/L Fertigro Sil), resulting in the lowest disease severity. Microscopic analysis further revealed significantly thicker cell walls in silicon-treated plants, suggesting a mechanical defense mechanism that may hinder fungal penetration. These findings highlight the potential of silicon as a sus-tainable and effective component in integrated crop management strategies for zuc-chini cultivation.

Abstract: Protein language models (pLMs) have become indispensable tools in computational biology, driving advances in variant effect prediction, functional annotation, structure prediction, and engineering. However, their rapid expansion from millions to tens of billions of parameters introduces significant computational, accessibility, and sustainability challenges that limit practical application in environments constrained by GPU memory, hardware availability, and energy budgets. This survey presents the first comprehensive review of efficient pLMs, synthesizing recent advancements across four key dimensions. We first examine (1) dataset efficiency through meta-learning-based few-shot and scaling-law-guided data allocation; and (2) architecture efficiency via lightweight alternatives including quantized transformers, embedding compression, and convolution-based designs. Furthermore, we review (3) training efficiency through scaling-law-informed pretraining, structure-integrated multimodal approaches, and low-rank adaptations with diverse distillation strategies; and (4) inference efficiency via quantization, dense-retrieval, and structure-search methods. By providing a structured taxonomy and practical guidance, this survey enables the development of high-performance, scalable, yet sustainable next-generation pLMs.

Abstract: Current evaluation paradigms for agentic AI focus predominantly on task success rates under nominal conditions, creating a critical blind spot: agents may succeed under ideal circumstances while exhibiting catastrophic failure modes under stress. We propose HB-Eval, a rigorous methodology for measuring behavioral reliability through three complementary metrics: Failure Resilience Rate (FRR) quantifying recovery from systematic fault injection, Planning Efficiency Index (PEI) measuring trajectory optimality against oracle-verified paths, and Traceability Index (TI) evaluating reasoning transparency via calibrated LLM-as-a-Judge (κ = 0.82 with human consensus). Through systematic evaluation across 500 episodes spanning three strategically selected domains (logistics, healthcare, coding), we demonstrate a 42.9 percentage point reliability gap between nominal success rates and stressed performance for baseline architectures. We introduce an integrated resilience architecture combining Eval-Driven Memory (EDM) for selective experience consolidation, Adaptive Planning for PEI-guided recovery, and Human-Centered Explainability (HCI-EDM) for trust calibration. This closed-loop system achieves 94.2% ±2.1% FRR with statistically significant improvements over base lines (Cohen’s d = 3.28, p < 0.001), establishing a rigorous methodology for transitioning agentic AI from capability demonstrations to reliability-certified deployment. We conclude by proposing a three-tier certification framework and identifying critical research directions for community validation.

Abstract: Abnormal repetitive behaviours ('ARBs') in captive animals are a heterogeneous group of troubling activities (e.g. stereotypic pacing by Carnivora; feather-plucking by poultry). To assess and improve their construct validity as welfare indicators, we reviewed their responsiveness to mood-improving drugs; links with imprisonment, stress and self-reported poor well-being in humans; and in animals, the impact of welfare-compromising (e.g. aversive) treatments. Considerable evidence links ARB development with negative moods or mood disorders, and early/prolonged/recurrent negative experiences (potentially via dose-response-like effects). Findings also indicate effects of cumulative affective experience ('cumulative stress'). Furthermore, if ARBs transiently help subjects cope, such effects are only partial. Therefore, whenever husbandry or housing causes ARB-prone phenotypes, negative affect can reliably be inferred (with more severe ARBs indicating poorer welfare). However, ARBs are rather prone to false nulls as welfare indicators: prolonged negative affect does not always cause the emergence or increase of ARBs, primarily due to threshold effects, ceiling effects, and inactivity being an alternate response. Furthermore, in ARB-prone subjects, the onset/offset of bouts appears not to reliably track moment-by-moment levels of negative emotion. Additionally, because variation in activity, behavioural flexibility and stress-response style are potential confounds, ARBs are not advised for comparing welfare across individuals, strains, species or prenatal treatments. Overall, ARBs have strong construct validity as indicators of negative moods/mood disorders; and our additional rules-of-thumb should further refine their accuracy. Future research should investigate underlying mechanisms (e.g. those suggested by human and biomedical findings), especially to clarify the boundaries and biological sub-types of ARBs.

Abstract: A novel poly(ionic liquid) nanofiber membrane (PIL NF) was synthesized by the cyclization of polyacrylonitrile (PAN) with piperazine, converting the nitrile groups into imidazoline units, followed by quaternization with 1-bromobutane. The resulting PIL NF was further functionalized by loading the photocatalyst, phosphomolybdic acid (PMo), via anion exchange, forming a new type of photocatalytic material, PM-PIL. Under visible light irradiation, the PM-PIL photocatalyst achieved an impressive methyl blue degradation rate of 98%. Additionally, the nanofiber membrane morphology facilitates the efficient recovery of the catalyst, with 98% of the initial degradation efficiency maintained after five photocatalytic cycles. This robust, highly efficient, and recyclable material provides a new approach for catalyst support. To the best of our knowledge, PM-PIL is the first reported photocatalyst of this kind. This cost-effective, functionalized membrane material utilizes solar light as an economical and clean energy source, offering promising potential for sustainable environmental applications.

Abstract: Adapting to evolving resource landscapes, nonprofit organizations increasingly embrace hybrid models to ensure sustainability and impact. This paper investigates the leadership and strategic innovations driving traditional nonprofits to evolve into market-engaged social ventures. Through indepth qualitative analysis of organizational transformations, we identify pivotal entrepreneurial practices that foster commercial viability, professionalize operations, and legitimize a blended socio-economic mission. Our findings offer a practical framework for nonprofit leaders navigating organizational redesign and fostering sustainable social entrepreneurship.

Abstract: The rise of digital media has intensified "context-mismatched" news, where image-text discrepancies erode veracity and trust. Cross-modal Entity Consistency (CEC) verification is crucial, yet existing Large Vision-Language Models struggle with complex entity ambiguity, fine-grained event associations, and insufficient explicit reference information. To address these challenges, we propose an Adaptive Multi-modal Contextual Verifier (AMCV). AMCV incorporates a Fine-grained Entity-Context Extractor, a Dynamic Evidence Retrieval and Augmentation module leveraging external knowledge, and a Multi-stage Adaptive Verification framework. This framework integrates LVLM-based alignment with evidence-fusion reasoning and adversarial training for confidence aggregation. Evaluated zero-shot across benchmark datasets, AMCV consistently outperforms state-of-the-art baselines, showing significant improvements. Ablation studies confirm each module's critical role, and human evaluations validate AMCV's predictions align better with human judgment in challenging scenarios. Our work offers a robust framework for CEC, substantially advancing cross-modal reasoning by intelligently leveraging fine-grained contextual understanding and dynamic external knowledge.

Abstract:

Unmanned Aerial Vehicles (UAVs) are essential tools for high-resolution urban remote sensing; however, maximizing their operational efficiency is often hindered by the Size, Weight, and Power (SWaP) constraints inherent to aerial platforms. High-end sensors (e.g., LiDAR) provide dense data but reduce flight endurance and require extensive post-processing, delaying actionable intelligence. To address the challenge of maximizing data utility through cost-effective means, this study evaluates an adaptive multi-modal monitoring framework utilizing high-resolution RGB imagery. Using a DJI Matrice 300 RTK, we assessed the performance of RGB-based advanced AI architectures across varying urban density zones. We stress-tested End-to-End Deep Learning models (Mask R-CNN, YOLOv8-seg) and a Hybrid approach (U-Net++ fused with RGB-derived Canopy Height Models) to determine their viability for replacing active sensors in precision analysis. Results indicate that the RGB-based Hybrid model achieved superior Semantic IoU (0.551), successfully demonstrating that optical imagery combined with deep learning can substitute for heavy active sensors in area-based estimation tasks. Crucially for autonomous UAV operations, YOLOv8-seg achieved inference speeds of 3.89 seconds per tile, approximately 1.86 times faster than Mask R-CNN, validating its suitability for onboard inference on embedded systems. This study establishes a protocol for high-precision analysis using standard RGB sensors, offering a strategic pathway for deploying scalable, consumer-grade UAV fleets in complex urban environments.

Abstract:

Background and Objectives: This study aimed to investigate the effects of explaining the perioperative process to pediatric patients scheduled for adenotonsillectomy using pictures on their anxiety levels. Materials and Methods: A prospective, randomized controlled trial was conducted, enrolling 58 patients. The patients were divided into two groups: Group 1 (n=29), where the perioperative process was explained using pictures, and Group 2 (n=29), the control group, where no pictures were used. Child anxiety was assessed using the modified Yale Preoperative Anxiety Scale Short Form (mYPAS-SF) at five observation time points before anesthesia induction. Parents’ anxiety was measured using the Visual Analog Scale for Anxiety. Results: Patients in Group 1 had significantly lower heart rates during induction and the intraoperative period compared to Group 2 (p = 0.031, p = 0.025, respectively). In terms of anxiety and RSAS scores, patients in Group 1 had significantly lower mYPAS-SF scores at time points t2, t3, t4, and t5 compared to Group 2 (t2: p = 0.001; t3-t5: p < 0.001). No significant difference was observed at t1 (p = 0.068). The mean RSAS scores were also significantly lower in Group 1 (p = 0.029). Parents’ anxiety was significantly lower in Group 1 at all three time points (t1: p = 0.017; t2: p = 0.006; t3: p = 0.036). Conclusion: Our study results demonstrate that illustrating the perioperative process in children undergoing adenotonsillectomy can significantly reduce preoperative anxiety and prevent awakening agitation. Given its ease of implementation, we believe that using visual aids to explain the perioperative process to pediatric patients can facilitate process management for patients, parents, and physicians.

Abstract: Accurate estimation of hematoma age remains a major challenge in forensic practice, where assessments rely largely on subjective visual interpretation. Hyperspectral imaging (HSI) captures rich spectral signatures that may reflect the biochemical evolution of hematomas. We evaluate whether integrating spectral and spatial information with a convolutional neural network (CNN) improves hematoma age estimation and whether a reduced, physiologically motivated subset of wavelengths can maintain performance. A forearm hematoma dataset from 25 participants was processed using radiometric normalization, SAM-based segmentation, and extraction of 64 × 64 × 204 hyperspectral patches. Using leave-one-subject-out cross-validation, the CNN achieved substantially lower mean absolute error (MAE 2.29 days) compared to a spectral-only Lasso baseline (MAE 3.24 days). Bandimportance analysis combining SmoothGrad and occlusion sensitivity identified 20 highly informative wavelengths, and using only these bands matched or exceeded the accuracy of the full 204-band model across early, middle, and late hematoma stages. These results show that spectral-spatial modeling and physiologically grounded band selection can significantly enhance hematoma age estimation while reducing data dimensionality, supporting the development of compact multispectral systems for objective clinical and forensic evaluation.

Abstract: Background and Objectives: The prevalence of heart failure and atrial fibrillation is increasing because of population aging. There are important sex-related differences in epidemiology, pathophysiology, treatment, and prognosis of patients with both heart failure and atrial fibrillation. While the overall lifetime risk of both diseases is similar between women and men, women tend to be older when diagnosed and to have more comorbidities. Materials and Methods: A narrative review was conducted by analyzing studies published across databases such as PubMed, SCOPUS, Web of Science and Google Scholar. The review focused on research about sex-related differences in patients with heart failure and atrial fibrillation, emphasizing the peculiarities in women regarding drug treatment and prognosis after cardiac device implantation. Results: Current evidence highlights the sex-related differences in patients with both heart failure and atrial fibrillation, regarding pathophysiology, clinical manifestations, and echocardiographic findings. There are data regarding possible sex-related differences also in mortality and therapy, as women tend to have longer hospital stays, but fewer reevaluations after discharge. Conclusions: Women with both atrial fibrillation and heart failure are at increased risk of stroke and other adverse outcomes which negatively affect their quality of life. Females with atrial fibrillation and heart failure tend to be less treated with rhythm control strategy and ablation, which may have a great impact on symptom burden in women compared to men.

Abstract: Automating ICD-10 coding from discharge summaries remains demanding because coders analyze clinical narratives while justifying decisions. This study compares three automation patterns: PLM-ICD as a standalone deep learning system emitting 15 codes per case, LLM-only generation with full autonomy, and a hybrid approach where PLM-ICD drafts candidates for an agentic LLM filter to accept or reject. All strategies were evaluated on 19,801 MIMIC-IV summaries using four LLMs spanning compact (Qwen2.5-3B, Llama-3.2-3B, Phi-4-mini) through large scale (Sonnet-4.5). Precision guided evaluation because coders still supply any missing diagnoses. PLM-ICD alone reached 55.8% precision while always surfacing 15 suggestions. LLM-only generation lagged severely (1.5--34.6% precision) and produced inconsistent output sizes. The agentic filter delivered the best trade-off: compact LLMs reviewed the 15 candidates, discarded weak evidence, and returned 2--8 high-confidence codes. Llama-3.2-3B, for example, improved from 1.5% as a generator to 55.1% as a verifier while trimming false positives by 73%. These results show that positioning LLMs as quality controllers, rather than primary generators, yields reliable support for clinical coding teams, while formal recall/F1 reporting remains future work for fully autonomous implementations.

Abstract: We state a compact result within Newell's Unified Scientific Framework (USF): when dissipative scale evolution is retained as a first-class component of the dynamics (the omni-term), spacetime curvature need not be treated as a fundamental primitive of the external (meta-dynamical, scale-space) description. Instead, curvature arises as an observer-relative effective structure induced by projection onto internal spacetime coordinates (clocks, rulers) and by boundary/coarse-grained reductions. In the internal limit where the scale-flow is suppressed, integrated out, or treated as an effective closure, Einstein-type curvature language is recovered as the appropriate representation of the projected dynamics. The framework therefore distinguishes gravity-as-mechanism (dissipative entropy-driven evolution) from curvature-as-representation (observer-embedded geometry), while preserving standard relativistic phenomenology in the internal description. Finally we describe the mathematical perspective, detailing the cosmological clarification, and it's physical \& mathematical realization.

Abstract: This paper investigates the advantages of employing mobile phones as key components in wireless health monitoring systems. It argues that mobile phones offer superior connectivity, user interaction, and real-time communication capabilities compared to traditional hub-based systems. An information architecture is proposed to effectively manage data flow from wearable health sensors to mobile devices, enabling features such as direct user feedback and automated alerts. The architecture’s design and a practical implementation for a fall detection system are presented, highlighting the potential of mobile phones to advance healthcare delivery.

Abstract: Large Language Model (LLM) agents represent a paradigm shift in artificial intelligence, combining the remarkable reasoning capabilities of foundation models with the ability to perceive environments, make decisions, and take actions autonomously. This comprehensive survey provides an in-depth examination of LLM-based agents across multiple dimensions. We first establish a formal definition of LLM agents and trace their evolution from early language models to today's sophisticated autonomous systems. We then present a novel taxonomy that organizes the field into four fundamental categories: reasoning-enhanced agents that leverage chain-of-thought and tree-structured deliberation; tool-augmented agents that extend LLM capabilities through external APIs and knowledge bases; multi-agent systems that enable collaborative problem-solving through inter-agent communication; and memory-augmented agents that maintain persistent context across interactions. For each category, we analyze representative architectures, discuss key innovations, and evaluate their relative strengths and limitations. We further examine diverse applications spanning software engineering, scientific research, embodied robotics, and web automation, supported by systematic comparisons on established benchmarks including SWE-bench, WebArena, and AgentBench. Our analysis reveals that while current agents achieve impressive performance on structured tasks, significant challenges remain in areas such as long-horizon planning, hallucination mitigation, and safe deployment. We conclude by identifying promising research directions, including neuro-symbolic integration, multi-modal perception, and human-agent collaboration frameworks, providing a roadmap for advancing this rapidly evolving field.

Abstract: Since 2019, Spain has experienced one of the largest minimum-wage increases among developed economies, providing a valuable natural experiment to evaluate the effects of this policy. This article aims to conduct a systematic review using the PRISMA 2020 methodology to compile all available evidence from the Spanish case —including hard-to-access studies not published in academic journals— and to critically discuss the methodologies employed and the results obtained. The results reveal an increase in the lowest wages and less wage inequality, without substantial adverse effects on employment.

Abstract: Scanning electron microscopy (SEM) is a powerful tool for the morphological characterization of multiscale nanomaterials, including two-dimensional (2D) systems such as graphene and molybdenum disulfide (MoS₂). However, conventional SEM imaging often struggles to resolve nanoscale features due to limited contrast and depth sensitivity, especially when dealing with ultrathin layers. In this work, we propose and demonstrate a simple yet effective strategy to overcome these limitations by exploiting grazing-incidence (radent) observation, achieved through a controlled tilting of the sample close to 90°. This approach significantly enhances the emission of secondary electrons from near-surface regions, thereby increasing image contrast and revealing morphological details, such as edges, ripples, defects, and overlapping layers, that remain hidden under standard imaging conditions. Optical characterization of the prepared MoS₂ colloids further supports the formation of monolayer and few-layer sheets, validating the structural information obtained from SEM. Interestingly, this approach recalls natural strategies observed in living organisms, where grazing-angle vision improves edge perception and surface recognition and therefore it can be considered as bio-inspired. Beyond its use with MoS₂, this biomimetic methodology offers a versatile and broadly applicable solution for improving morphological analysis of 2D nanomaterials and thin films, providing deeper insights into their structural characterization.

Abstract: This review synthesizes four decades of scientific and industrial developments in pack-aging glass, integrating structural, technological, and sustainability perspectives. Glass remains the benchmark material for inert, transparent, and fully recyclable contain-ment, yet its scope has expanded from conventional bottles and vials to advanced func-tional and electronic encapsulation. Packaging glasses are classified into five main fami-lies—soda-lime, borosilicate, aluminosilicate, recycled (cullet-rich), and function-al/electronic—and compared across key domains: mechanical, thermal, chemical, opti-cal, barrier, and hermetic. Quantitative tables and normalized diagrams illustrate how compositional and processing trends govern structure, processability, and performance. Advances in forming, surface engineering, and melting practice are analyzed for their contributions to lightweighting, durability, and decarbonization. Sustainability is ad-dressed through cullet utilization, energy demand, life-cycle indicators, and regulatory alignment, defining pathways toward circular and low-carbon production. Overall, packaging glass emerges as a circular, chemically stable, and traceable material sys-tem, while advances in high-integrity glass formulations now support hermetic encap-sulation for diagnostic, electronic, and energy devices.

Abstract: In this paper we investigate the weighted $L^p$ boundedness of generalized Marcinkiewicz integrals $\mathcal{M}^{(\varepsilon)}_{\mathbf{K}}$ over multiple symmetric domains. Under the conditions $\mathbf{K}\in L^{q}( \mathbb{B}^{{{m}}-1}\times \mathbb{B}% ^{{{n}}-1})$, $q>1$, we stablish suitable weighted $L^p$ bounds for the integrals $\mathcal{M}^{(\varepsilon)}_{\mathbf{K}}$. These bounds are combined with an extrapolation argument of Yano so we obtain the weighted $L^p$ boundedness of $\mathcal{M}^{(\varepsilon)}_{\mathbf{K}}$ from the Triebel-Lizorkin space $\overset{.}{F}_{p}^{0,\varepsilon}(\omega_1,\omega_2)$ to the space $L^p(\omega_1,\omega_2)$ under the weak conditions $\mathbf{K}$ lie in the space $ B_q^{(0,\frac{2}{\varepsilon}-1)}(\mathbb{B}^{m-1}\times\mathbb{B}% ^{n-1})$ or in the space $L(\log L)^{2/\varepsilon}(\mathbb{B}^{m-1}\times\mathbb{B}% ^{n-1})$. Our findings are essential improvements and extension of several known findings in the literature.

Abstract: Visual Impression in Architectural Space (VIAS) plays a central role in how users intuitively respond to surrounding environment, where visual stimuli such as signage, layout, and spatial density immediately shape attention, movement, and engagement. While designers intentionally deploy these visual attractors, the resulting perceptual and behavioural responses remain uncertain and vary across cultural and methodological contexts. To address this challenge, this study reframes urban public space, taking event-space as a case study, by integrating architecture and data-science into a framework that combines VIAS theory, behaviour-perception analysis, and sentiment-aware linguistic modelling. Firstly, we introduce a visual behavioural layer that identifies how spatial attractors such as advertising banners, product displays and event layouts. Secondly, we construct an expanded dataset from previous research comprising eight native participants interviewed in their native language, enabling linguistically accurate and culturally grounded comparison with the previous English-based mixed cohort. Thirdly, we develop a multi-modal sentiment-weighted keyword extraction algorithm that captures participant-initiated perceptual themes while suppressing interviewer influence and modality-specific bias, enabling alignment between verbal impressions and visual-behavioural evidence. Finally, we compare three interview modalities (onsite, video-based and virtual-environment) against behavioural observation data collected at a small-scale event in Matsue City, Japan. Results demonstrate that onsite participants exhibit systematic positive bias driven by the festive atmosphere, while remote modalities elicit more balanced assessments of visual clarity, signage effectiveness, stall arrangement, and missing spatial amenities. Furthermore, cross-linguistic analysis reveals cultural differences: native participants emphasise holistic spatial atmosphere, whereas international participants identify discrete visual focal points. By integrating visual attractors, behavioural metrics, and sentiment-aware linguistic patterns, the proposed framework provides a replicable method for explaining how designed visual elements trigger, reinforce, or contradict actual user behaviour. The findings offer evidence-based guidance for designing inclusive temporary event spaces, highlighting how architectural visual elements can be validated and refined through multi-modal computational analysis.