Abstract:

The key to implementing IPM of wireworms effectively is to associate feasible, reliable and affordable sampling methods with well-defined damage thresholds. As wireworms live underground, they cannot be observed directly, thus estimating population levels can be challenging. Soil sampling to ascertain larval density is very time-consuming, and although the use of bait traps is much more time-effective, they are unable to ascertain wireworm density. The work described herein was conducted between 1993 and 1999 in two regions of Northern Italy: Veneto and Piedmont. The experimental protocol involved placing soil bait traps in a 15–30 m x 10 m grid in selected cultivated fields and taking a soil sample 3 m from the location of each bait trap. The number of monitoring points ranged from 18 to 48. Both trap contents and soil cores were put in the trap funnels to dry out, forcing the wireworms to move and fall into a vial, according to the Bernese method. Data were processed with a variety of statistical approaches. A moderate association was found between the number of wireworms (Agriotes brevis, A. sordidus and A. ustulatus) caught by the bait traps and by soil sampling, indicating a potential for reciprocal estimation between methods. In other words, bait-trap catch values can be estimated from soil sampling (e.g. when bait traps could not be used due to low temperatures or growing plants covering the field) and vice versa. The potential of bait traps for catching wireworms was shown to be 5 to 25 times higher than the potential of soil sampling. The estimated soil-sampling thresholds range from 15 to 20 larvae/m².

Abstract: Background: Intraductal papillary mucinous neoplasms (IPMN) are the most common pancreatic cystic lesions and are established precancerous entities. Side-branch IPMN (SB-IPMN) are the most prevalent subtype and generally carry a low risk of malignant transformation. The revised 2024 Kyoto guidelines define management and surveillance strategies based on high-risk stigmata and worrisome features; however, real-life adherence to these recommendations remains variable. Aim: To compare real-world management of SB-IPMN at a tertiary medical center with Kyoto guideline–based recommendations using an algorithm-based decision-support tool. Methods: SB-IPMN cases were retrospectively analyzed. An algorithm implementing the Kyoto guidelines was used to generate recommended management strategies based on imaging, clinical, and laboratory data, and these recommendations were compared with actual clinical decisions. Long-term clinical and radiological follow-up data were collected, including development of pancreatic ductal adenocarcinoma (PDAC). Results: A total of 368 patients (69% male; median age 69.5 years) were followed for a median of 48.5 months radiologically and 64 months clinically. Mean cyst size at presentation was 11 ± 6.5 mm. Only 58 patients (15.8%) were managed in accordance with Kyoto guidelines; most underwent more intensive surveillance (60.3%), while 23.9% received less intensive monitoring (p = 0.04). Larger cyst size (>2 cm) was associated with higher guideline adherence. Younger patients, including all patients under 50 years of age, were more frequently over-surveilled. Over-surveillance resulted in an excess of 0.42 MRI/MRCP examinations per patient-year. Only one PDAC case occurred, arising after more than five years of cyst stability. Conclusion: Fewer than 20% of patients with SB-IPMN were managed according to Kyoto guidelines. Over-surveillance was common, particularly in younger patients, without apparent oncologic benefit. Algorithm-based decision-support tools may help standardize care and optimize resource utilization.

Abstract: This study focuses on the ecological vulnerability and its driving mechanism of the Ebinur Lake Basin. Integrating natural factors such as annual average temperature, annual precipitation and elevation, as well as social factors including GDP and population distribution, it systematically evaluates the ecological vulnerability of the basin from 1994 to 2024 by adopting methods like the SRP model, Analytic Hierarchy Process (AHP) and Geodetector. The results show that the overall scale of ecologically vulnerable areas in the basin has presented a shrinking trend over the past 30 years: the area of severe vulnerability reached a peak of 14,270.31 square kilometers in 2004 and then decreased to 13,242.39 square kilometers; the area of slight vulnerability increased by 60.8%; and the proportion of moderate vulnerability has slightly risen since 2014. Spatially, the vulnerability exhibits significant agglomeration characteristics: severe vulnerable areas are concentrated in the mountainous areas of the basin boundary and the eastern region of Ebinur Lake, while slight vulnerable areas are distributed in woodlands and farmlands of alluvial fans in low mountains and hills. Geodetector analysis shows that, fractional vegetation cover, normalized difference vegetation index and land use type are the dominant factors, natural factors and social factors interact significantly.This study provides a scientific basis for ecological protection and sustainable development of the basin.

Abstract: Cancer remains a leading cause of morbidity and mortality worldwide, and effective strategies for cancer prevention are urgently needed to complement therapeutic ad-vances. While dietary factors are known to influence cancer risk, the molecular mecha-nisms that mediate inter-individual responses to nutritional exposures remain poorly defined. Emerging evidence identifies long non-coding RNAs (lncRNAs) as pivotal regulators of gene expression, chromatin organization, metabolic homeostasis, immune signaling, and cellular stress responses, core processes that drive cancer initiation and progression and are highly sensitive to nutritional status. In parallel, advances in pre-cision nutrition have highlighted how variability in genetics, metabolism, microbiome composition, and epigenetic landscapes shapes dietary influences on cancer susceptibility. This review integrates these rapidly evolving fields by positioning lncRNAs as molecular conduits that translate dietary exposures into transcriptional and epigenetic programs governing cancer development, progression, and therapeutic vulnerability. We provide mechanistic evidence demonstrating how dietary bioactive compounds and micronu-trients, including polyphenols (curcumin, resveratrol, EGCG), flavonoids, alkaloids such as berberine, omega-3 fatty acids, folate, vitamin D, probiotic metabolites (such as bu-tyrate and propionate), and trace elements (such as selenium, and zinc), modulate on-cogenic and tumor-suppressive lncRNAs. These nutrient-lncRNA interactions influence cancer-relevant pathways controlling proliferation, epithelial-mesenchymal transition, inflammation, oxidative stress, and metabolic rewiring. We further discuss emerging lncRNA signatures that reflect nutritional and metabolic states, their potential utility as biomarkers for individualized dietary interventions, and their integration into liquid biopsy platforms. Leveraging multi-omics datasets and systems biology, we outline AI-driven frameworks to map nutrient-lncRNA regulatory networks and identify tar-getable nodes for cancer chemoprevention. Finally, we address translational challenges, including compound bioavailability, inter-individual variability, and limited clinical validation, and propose future directions for incorporating lncRNA profiling into preci-sion nutrition-guided cancer prevention trials. Together, these insights position lncRNAs at the nexus of diet and cancer biology and establish a foundation for mechanistically informed precision nutrition strategies in cancer chemoprevention.

Abstract: Congenital erythropoietic porphyria (CEP), also known as Günther disease, is a rare autosomal recessive porphyria caused by deficiency of uroporphyrinogen III synthase, leading to accumulation of phototoxic type I porphyrins. CEP classically presents in infancy with severe photosensitivity, blistering, scarring, and hemolytic anemia; however, significant phenotypic variability has increasingly been recognized. We report 32-year-old women diagnosed with CEP in early infancy who demonstrated persistently and profoundly elevated erythrocyte porphyrin levels over more than a decade yet followed a relatively non-mutilating clinical course. Genetic testing identified a low penetrance intronic UROS variant typically associated with erythropoietic protoporphyria, underscoring diagnostic challenges and genotype-phenotype discordance. The patient experienced marked improvement in photosensitivity and burning pain after initiation of afamelanotide, without need for transfusion therapy or stem cell transplantation. This case highlights the heterogeneity of CEP, the importance of long-term biochemical follow up, and the potential role of afamelanotide in improving quality of life for selected patients with CEP.

Abstract: There are added some matters for the estimation of \( H(n,m) \) in the appendix.

Abstract: Nutraceuticals, bioactive compounds derived from foods, are increasingly investigated as interventions to promote healthy ageing. Multi-ingredient formulations may offer additive or synergistic benefits by targeting multiple ageing pathways while using low doses of each component for improved safety. However, their efficacy in mammals remains poorly understood. Here, we compared the effects of a continuous multi-ingredient nutraceutical intervention with two short-courses of senolytic regimen in naturally aged male C57Bl/6J mice. Importantly, these mice were overweight following a switch to soaked food at 20 months, a protocol that increased caloric intake and likely induced metabolic stress. This context frames the study as a model of ‘rescue’ from premature ageing rather than extension of maximum lifespan. Mice were assigned to either control, nutraceutical (12 pro-longevity natural compounds), or senolytic (Navitoclax plus BAM15) groups at 20 months of age. Lifespan and healthspan indicators were assessed longitudinally. Both interventions improved survival compared to controls (median lifespan +18–21%) and mitigated frailty progression, but with distinct patterns: nutraceutical benefits accumulated gradually, whereas senolytic effects were transient. Cognitive performance was preserved in nutraceutical-treated mice and improved shortly after senolytic treatment. In vitro, the nutraceutical lacked senolytic activity but exhibited senostatic effects, reducing nuclear size, ROS release, and IL-6 secretion in senescent fibroblasts. These findings suggest that multi-ingredient nutraceuticals can restore healthspan compromised by metabolic stress and deliver benefits comparable to senolytics when administered continuously, potentially through senostatic mechanisms. Combining senolytics to reduce senescent burden with long-term nutraceutical treatment may offer a safe, accessible strategy to optimise healthspan, particularly in the context of modern human ageing, which often occurs under conditions of caloric excess and metabolic syndrome.

Abstract: This work addresses correlation bias and causal effect confounding in advertising recommendation systems and presents a causal learning–based recommendation framework. We first examine the limitations of conventional recommendation algorithms in complex advertising environments, where confounding variables and exposure bias often prevent models from capturing users’ true preferences. To tackle these issues, we design a unified embedding architecture that jointly represents user, advertisement, and contextual features, and incorporates a structural causal graph to explicitly model dependencies among variables. During model training, causal consistency regularization and inverse propensity weighting are integrated to mitigate the impact of biased exposure mechanisms and non-uniform sampling. A joint optimization objective is further formulated to couple click-through rate prediction with causal consistency estimation, enabling robust causal effect learning without sacrificing predictive accuracy. Extensive experiments on large-scale advertising datasets demonstrate that the proposed approach consistently outperforms several representative baselines in terms of Precision@10, Recall@10, NDCG@10, and MAP, while exhibiting strong robustness under multi-dimensional sensitivity analysis. Overall, this study highlights the practical value of causal modeling and consistency-aware learning in advertising recommendation and offers a computationally grounded approach for improving both interpretability and fairness in recommendation systems.

Abstract: Hydrogen is a versatile energy carrier essential for decarbonizing hard-to-abate sectors and long-duration storage. This study presents a unified techno-economic comparison of major production pathways—grey/blue steam methane reforming, biomass gasification, thermochemical cycles, biological methods, and solar-powered electrolysis—using 2025 benchmarks. Focus is on a 100 kW off-grid PV-electrolyzer system with realistic assumptions (PV performance ratio 0.85, electrolyzer efficiency 70% LHV). In Iran's high-insolation regions (PSH ≥ 5.15 kWh/kWp/day), annual yields reach 3.2–3.4 tonnes H₂—55–60% higher than northern Europe—with round-trip efficiency of 23.8%. Solar electrolysis offers zero direct emissions and 51–55 kWh/kg H₂ consumption. Scaling to multi-MW coastal hybrids with renewable desalination projects LCOH of 3.0–4.0 USD/kg by 2030, positioning Iran as a competitive exporter. A reproducible model and phased roadmap provide actionable insights.

Abstract: The rapid evolution of data-driven fields demands educational paradigms that transition from static analysis to dynamic interaction with live information. This paper presents a novel technical framework, the Dual-Agent Curator-Tutor (DACT), which integrates Artificial Intelligence as a concurrent Real-Time Data Curator and Interactive Tutor within Immersive Analytics (IA) learning environments. The DACT framework features two synergistic AI agents: a Curation Agent that dynamically ingests, filters, and contextualizes live data streams (e.g., IoT, financial feeds) for pedagogical alignment, and a Tutoring Agent that provides adaptive, scaffolded instruction based on multimodal analysis of learner behavior within an immersive visualization space (VR/AR). This creates a closed-loop ecosystem where the data landscape and instructional guidance co-adapt in real-time to the learner’s actions. We detail a modular architecture implementing this model, utilizing perturbation-based learning for adaptive curation—inspired by recent optimization techniques—and a rule-based pedagogical engine. We propose a rigorous quantitative evaluation methodology involving controlled experiments to measure gains in analytical proficiency, cognitive load reduction, and behavioral patterns. The paper argues that this seamless integration of automated data management and personalized tutoring within an immersive context represents a transformative advancement for experiential learning, effectively leveraging technology to offload cognitive overhead and elevate higher-order analytical reasoning skills.

Abstract:

With accelerating climate change and urbanization, river catchments continue to experience structural modifications through dam construction and concrete-lining of natural channels as adaptation measures. These interventions can alter the natural hydrology. This necessitates assessment of their influence on hydrology at a catchment scale. However, such evaluations are particularly challenging in data-scarce regions such as the Chongwe River Catchment, where hydrometric records capturing conditions before and after structural modifications are limited. Therefore, we applied a 2D rain-on-grid approach in HEC-RAS to evaluate changes in high-flow characteristics in the Chongwe River Catchment in Zambia, where structural interventions have been implemented. The terrain was modified in HEC-RAS to represent 21 km of concrete drains and ten dams. Sensitivity analysis was conducted on five model parameters and showed that Manning’s roughness coefficient had by far the largest impact on peak flows. Model calibration and validation showed strong performance with R² = 0.99, NSE = 0.75 and PBIAS = – 0.68 % during calibration and R² = 0.95, NSE = 0.75, PBIAS = – 2.49 % during validation. Four scenarios were simulated to determine the hydrological effects of channel concrete-lining and dams. The results showed that concrete-lining of natural channels in the urban area increased high flows at the main outlet by approximately 4.6%, generated very high channel velocities of up to 20 m/s, increased flood depths by up to 11%, and expanded flood extents by up to 15%. The existing dams reduced peak flows by about 28%, increased lag times, reduced flood depths by about 11%, and reduced flood extents by up to 8% across the catchment. The findings demonstrate that enhancing stormwater conveyance through concrete-lining must be complemented by storage to manage high flows, while future work should explore nature-based solutions to reduce channel velocities and improve sustainable flood mitigation.

Abstract: We present the electroweak sector of Geometric Design (codename GD-313) in a form suitablefor referee audit. The framework selects the 3–13 vacuum Gr(3, 16) via a bounded integer corridordefined by two publicly tabulated anchors. On the dynamical side, we specify an explicit embeddingof SU(2)L °ø U(1)Y into the U(16) structure compatible with the (3, 13) split and computethe induced one-loop coupling ratio from the Grassmannian coset sector. Under clearly statedassumptions (background-field gauge and cancellation of the universal prefactor in the ratio), theindex computation yields sin2 θW = 3/13 at the declared matching convention. We provide anauditable appendices package: embedding generators, index computation, and a reproducibility checklist.

Abstract:

Building on our previous systematic review that synthesized eight core sustainable appetitive traits central to food behavior research, the present study extends this framework through an empirical investigation of Generation Z university students in Greece. We have established the conceptual foundation by mapping emotional, sensory, and behavioral regulation drivers of eating behavior, underscoring their relevance for nutrition and sustainability. However, empirical applications of this multidimensional framework to Generation Z remained scarce. This study addresses this gap by examining eating behaviors among approximately 800 students at the University of Ioannina using a validated post pandemic questionnaire. Results revealed heterogeneity across six domains, with consensus observed only in sensory driven eating (M = 3.88) and openness to new foods (M = 4.00). Cluster analysis identified two distinct profiles: Exploratory and Hedonic Responders and Emotionally Regulated and Satiety Oriented Responders. These clusters delineate a novel profile of Generation Z, portraying them as digitally immersed, sustainability oriented, and emotionally sensitive, yet divided between impulsive exploration and regulated satiety. The study contributes new empirical insights into post pandemic food behavior. It establishes a comprehensive evidence base for designing culturally sensitive wellness programs and targeted nutritional interventions that support sustainable dietary practices. The continuity between the two papers underscores both theoretical importance and the practical necessity of integrating emotional, sensory, and regulatory dimensions in advancing sustainable eating futures among young adults.

Abstract: Traditional reliability engineering paradigms, originally designed to prevent physical component failures, are facing a fundamental crisis when applied to today's soft-ware-intensive and autonomous systems. In critical domains like aerospace, the dom-inant risks no longer stem from the aleatory uncertainty of hardware breakdowns, but from the deep epistemic uncertainty inherent in complex systematic interactions and non-deterministic algorithms. This paper reviews the historical evolution of reliability engineering, tracing the progression through the Statistical, Physics-of-Failure, and Prognostics eras. It argues that while these failure-centric frameworks perfected the management of predictable risks, they are structurally inadequate for the "unknown unknowns" of modern complexity. To address this methodological vacuum, this study advocates for an imperative shift towards a fourth paradigm: the Resilience Era. Grounded in the principles of Safety-II, this approach redefines the engineering objec-tive from simply minimizing failure rates to ensuring mission success and functional endurance under uncertainty. The paper introduces Uncertainty Control (UC) as the strategic successor to Uncertainty Quantification (UQ), proposing that safety must be architected through behavioral constraints rather than prediction alone. Finally, the paper proposes a new professional identity for the practitioner: the system resilience architect, tasked with designing adaptive architectures that ensure safety in an era of incomplete knowledge.

Abstract:

Background: Patient safety is a top priority for healthcare organization leadership worldwide, as approximately one in ten patients experiences an adverse event, and nurses often report that the quality of the care they deliver is poor. Objectives: The present study aim was to examine the impact of work gaslighting on perceived quality of care, patient safety and quiet quitting on nursing staff. Methods: A cross-sectional study was conducted in Greece and data were collected using an online survey during October to November 2025, with 492 nurses. We used the Gaslighting at Work Scale (GWS) and the Quiet Quitting Scale to measure workplace gaslighting and quiet quitting. Perceived quality of care and perceived patient safety were measured with single items, representing the overall assessments in nurses’ unit. Results: Nurses reported low to moderate levels of workplace gaslighting and quiet quitting, as well as almost half of the participants (52.0%, n=256) evaluated the quality of care in their unit as good, and 33.1% (n=163) of nurses perceived patient safety as good. In the univariate comparisons, greater workplace gaslighting was significantly associated with lower odds of reporting perceived quality of care to be good or excellent (OR = 0.650, 95% CI: 0.527–0.803; p < 0.001). This association was still statistically significant in the multivariable model after gender, years of work experience, working in shifts and working in an understaffed department were included (adjusted OR = 0.655; 95% CI: 0.529–0.810; p < 0.001). Workplace gaslighting was also strongly related to perceived patient safety. In the univariate analysis increased workplace gaslighting was associated with decreased odds of good-to-excellent patient safety (OR = 0.553, 95% CI: 0.445–0.686, p < 0.001). This association remained after controlling for the potential confounders (adjusted OR = 0.561, 95% CI: 0.450–0.700, p < 0.001). In the multivariable model, workplace gaslighting was significantly and positively associated with quiet quitting (adjusted beta = 0.224, 95% CI = 0.163 to 0.285, p < 0.001) after adjusted for demographic and work-related characteristics. Conclusions: The present study is the first that highlighted the significant association between workplace gaslighting and the quality and safety of care, as well as nurses’ quiet quitting. A zero-tolerance stance by senior leadership, coupled with the establishment of clear policies and procedures that encourage staff to report such behaviors, is essential to dismantle the barriers created by psychological manipulation.

Abstract: The evolution of cryptocurrencies has progressed through distinct waves, from the digitization of fiat currencies in the 1980s to the emergence of independent currencies such as Bitcoin and its contemporaries, Ethereum and Ripple. While Bitcoin’s predetermined supply model offers a decentralized monetary approach, it has also resulted in extreme volatility, significantly impacting its adoption as a medium of exchange. This paper explores the rise of stablecoins—cryptocurrencies designed to maintain a stable exchange rate with fiat currencies—as a response to the challenges posed by volatility. Despite the availability of whitepapers and extensive marketing, a knowledge gap persists due to inconsistent terminology and ambiguous designations within the cryptocurrency landscape. This study aims to demystify stablecoin designs, highlighting the importance of clear definitions and precise classifications to aid in regulatory discussions and enhance user understanding. Through a comparative analysis of various stablecoin models and their implications for financial markets, we seek to provide a comprehensive overview of the challenges and opportunities that stablecoins present in the context of evolving financial ecosystems.

Abstract: This study investigates the effects of physical activity on serum cortisol levels and phagocytic capacity of the innate immune system in 8 captive bottlenose dolphins. Analysis of paired samples (n=16) revealed a significant increase in cortisol during periods of physical activity (mean increase of 1.27 µg/dL, 122% elevation), accompanied by decreased phagocytosis in granulocytes (92% reduction) and monocytes (52% reduction). Statistical analyses demonstrated consistent negative correlations between cortisol levels and phagocytic function, suggesting that physical activity influences hypothalamic-pituitary-adrenal axis activation and, consequently, innate immune system function. Sex-differentiated responses were observed, with the male showing attenuated cortisol response but maintained monocyte sensitivity. These findings highlight the complex interplay between the neuroendocrine cortisol response and immune function in cetaceans, with important implications for controlled environments management and animal welfare assessment. A multi-method statistical framework incorporating Bayesian analysis, bootstrapping, and traditional approaches ensured robust inference despite limited sample size.

Abstract: Traditional kernel fuzzers rely on coarse-grained coverage metrics that cannot reflect complex microarchitectural behaviors. We present a hardware-assisted fuzzing framework that leverages branch buffer telemetry from modern CPUs (LBR, BTB sampling) to refine fuzzing feedback. A model-based inference algorithm aggregates branch-data patterns to estimate microarchitectural novelty and guides seed prioritization. Experiments on Intel Ice Lake and AMD Zen 3 systems demonstrate 27% improvement in unique path coverage, with 11 newly identified concurrency bugs across filesystem and scheduler subsystems. Compared with coverage-only fuzzing, our method reduces time-to-crash by 46% while keeping overhead below 12%. This work shows microarchitectural-level signals can significantly boost kernel fuzzing’s effectiveness.

Abstract: This study develops an adaptive workflow allocation mechanism for anti-money laundering (AML) operations, aiming to improve the accuracy and efficiency of suspicious-transaction review. A multi-agent simulation platform was constructed to model transaction flows, alert generation, and analyst decision behaviors. The system integrates model-confidence estimation, analyst-fatigue prediction, and real-time workload signals to dynamically route alerts. Experiments were conducted using 27.3 million historical transactions and 186,000 alerts from a large commercial financial dataset. Compared with fixed allocation rules, the adaptive mechanism increased alert-escalation precision from 0.32 to 0.46 and recall from 0.70 to 0.78, while reducing average handling time by 19.4%. The proportion of high-risk alerts processed within the target time window improved by 23.8%. These results demonstrate that workflow optimization can meaningfully enhance AML performance beyond model-level improvements.

Abstract: Background/Objectives: Wilms tumor is the most common pediatric renal malignancy, and delayed or inaccurate diagnosis can significantly affect clinical outcomes. This study aimed to evaluate whether integrating traditional machine-learning and deep-learning models with computed tomography (CT) imaging could improve the accuracy of Wilms tumor detection. Methods: A large CT image dataset consisting of 18,205 kidney scans, including both normal and Wilms tumor cases, collected from publicly available medical sources. Images were preprocessed and resized to standardized dimensions before model training. Four supervised learning approaches: ResNet50, VGG16, XGBoost, and Random Forest, were developed and evaluated. The dataset was split into training (14,055 images) and independent testing (4,150 images) subsets. Model performance was assessed using accuracy, precision, recall, F1-score, and confusion matrix analysis. Results: Among the evaluated models, VGG16 demonstrated superior performance, achieving an accuracy of 99.98%, precision of 99.92%, recall of 100%, and an F1-score of 99.96%, indicating excellent sensitivity and overall classification reliability. The remaining models also performed robustly, with accuracies exceeding 94% and recall values above 90%. Conclusions: These findings suggest that deep-learning-based image classification, particularly using VGG16, can substantially enhance non-invasive detection of Wilms tumor from CT scans. The proposed approach has the potential to support clinical decision-making, reduce diagnostic delays, and improve early detection in pediatric oncology settings.