Abstract: Background: Anastomotic leakage (AL) remains one of the most feared complications after colorectal surgery. This study aims to identify preoperative risk factors for AL using a five-year dataset from two Romanian surgical clinics. Matherial and Methods: A retrospective cohort of 155 patients undergoing colorectal resection with primary anastomosis (105 from “Colentina” Hospital and 50 from “Dr. I. Cantacuzino” Hospi-tal) was analyzed. Preoperative demographic, clinical, and laboratory data were ex-tracted and assessed using univariate and multivariable logistic regression. Statistical analyses were performed using IBM SPSS. Results: The overall AL rate was 10.3%. Multivariable analysis identified high ASA class (OR 17.6; p = 0.001), emergency sur-gery (OR 32.2; p = 0.0007), and heavy alcohol use (OR 15.3; p = 0.004) as independent predictors of leakage. While low preoperative albumin and smoking were associated with leakage in bivariate analysis, these did not remain significant after adjustment. Notably, all laboratory markers were based on preoperative values, distinguishing our approach from prior studies that commonly evaluate postoperative biomarkers. No statistically significant effect was found for neoadjuvant chemotherapy or radiother-apy after controlling for other covariates. Conclusions: High ASA score, alcohol abuse, and emergency surgery were the strongest independent predictors of AL in our cohort. The lack of predictive power of certain widely reported factors, such as low albumin, may reflect our dataset’s focus on preoperative optimization. These findings support the use of individualized risk assessment and reinforce the role of preoperative prepa-ration in reducing leak incidence in colorectal surgery.

Abstract: In this paper we prove that, on the one hand, a duality relationship between different types of modular T-transitive relations and the reciprocal modular generalized metrics exists and, on the other hand, that based on this duality a construction of functions that aggregate modular T-transitive relations can be made from functions aggregating generalized metrics. Furthermore, we provide a guide of the families of that type of functions that can be used to the aforementioned purpose. Finally, illustrative examples of how to create such functions via the duality are given.

Abstract: Natural waves are widely used in seismology and seismic exploration as tools for nondestructive testing of the surface layer. The study examines longitudinal and transverse vibrations of a polymer pipeline transporting petroleum products, which is modelled as a viscoelastic cylindrical shell filled with a viscous fluid. This work examines the longitudinal–transverse vibrations of a viscoelastic cylindrical shell filled with a viscous fluid, considering the viscous properties of both the fluid and the cylindrical shell during longitudinal–transverse oscillations. The differential equations governing the longitudinal–transverse vibrations of a cylindrical shell in contact with a viscous fluid are derived based on thin-shell equations satisfying the Kirchhoff–Love hypotheses, while the motion of the viscous fluid obeys the Navier–Stokes equations. The viscoelastic properties of the shell are described using the Boltzmann–Volterra hereditary integral. After applying the “freezing method” to the system of integro-differential equations, we obtain ordinary differential equations with complex coefficients, which are subsequently solved by the method of separation of variables and Godunov’s orthogonal sweep combined with Müller’s and Gauss’s methods in complex arithmetic. It is established that for small viscosity, the frequencies of both modes are close to each other in the low-frequency region, while at high frequencies, the phase velocity of the first mode tends toward the velocity of the dry shell.

Abstract: Intrusion Detection Systems (IDSs) have evolved to safeguard networks and systems from cyber attacks. Anomaly-based Intrusion Detection Systems (A-IDS) have been commonly employed to detect known and unknown anomalies. However, conventional anomaly detection approaches encounter substantial challenges when dealing with complex, large-scale, and heterogeneous data sources. These challenges include high False Positive Rates (FPRs), imbalanced data behavior, complex data handling, resource constraints, limited interpretability, and difficulties with encrypted networks. This survey reviews Graph-based Anomaly Detection (GBAD) approaches, highlighting their ability to address these challenges by utilizing the inherent structure of graphs to capture and analyze network connectivity patterns. GBAD approaches offer flexibility for handling diverse data types, scalability to analyze large datasets, robustness detection capabilities, and enhanced interpretability through visualizations. We present a phased graph-based anomaly detection methodology for intrusion detection. This includes phases of data capturing, graph construction, graph pre-processing, anomaly detection, and post-detection analysis. Furthermore, we examine the evaluation methods and datasets employed in GBAD research and provide an analysis of the types of attacks identified by these methods. Lastly, we outline the key challenges and future directions that require significant research efforts in this area and offer some recommendations to address them.

Abstract: Background/Objectives: Obesity is a growing global health concern with significant implications for female reproductive health, notably contributing to menstrual irregu-larities, anovulation, and infertility. This review explores the relationship between fe-male obesity and reproductive dysfunction, with a particular focus on the impact of bariatric surgery on fertility outcomes. Methods: Drawing from 116 original research studies, the analysis examines the two bariatric surgical techniques, sleeve gastrecto-my (SG) and Roux-en-Y gastric bypass (RYGB), and their effects on weight loss, hor-monal profiles, ovulatory function, and conception rates. Conclusions: Results con-sistently demonstrate improvements in menstrual regularity, resolution of polycystic ovary syndrome (PCOS) symptoms, including improved insulin sensitivity and re-duced hyperandrogenism, increased spontaneous ovulation and enhanced fertility following significant post-surgical weight reduction. Additionally, timing of pregnan-cy after surgery, type of surgical intervention, and degree of weight loss are identified as critical determinants of reproductive success. The review also addresses gaps in long-term data and emphasizes the need for individualized preconception counseling in women with obesity pursuing fertility.

Abstract: Stimuli-responsive textiles are a rapidly advancing class of functional fiber-based materials able to sense and adapt to environmental triggers. Within these enabling technologies hydrogels and microcapsules are very representative, both of which offer complementary mechanisms for moisture management, controlled release, and adaptive performance. Hydrogels provide soft, water-rich polymer networks with tunable swelling, permeability, and mechanical properties, while microcapsules offer protection and targeted delivery of active agents through engineered shell architectures. When integrated into fibrous networks, these systems can impart dynamic responses moisture, temperature, pH, mechanical stress, light, and chemical or biological agents. This review critically examines progress in the design, synthesis, and textile integration of hydrogel- and microcapsule-based systems, with particular emphasis on materials that exhibit true stimuli-responsive behavior rather than passive or extended-release functionality. Strategies for incorporating bulk hydrogels, micro- and nanogels, and stimuli-responsive microcapsules into fibers, yarns, and fabrics are discussed in addition to key application areas such as smart apparel, medical and hygienic textiles, controlled drug delivery, antimicrobial fabrics, and adaptive filtration media. Current challenges related to durability, washability, response kinetics, scalability, and sustainability are highlighted, while future research directions are proposed to advance the development of robust, intelligent textile systems at the nexus of soft matter science and fiber engineering.

Abstract: This paper presents a set of survey-style notes linking core themes of pure algebra with central topics in algebraic and analytic number theory. We begin with finite extensions of Q and describe algebraic number fields through their realization as finite-dimensional Q-algebras (via multiplication operators and matrix representations), leading naturally to the arithmetic invariants trace, norm, and discriminant, and to the ring of integers, ideals, Dedekind domains, and the ideal class group. We then develop the classical theory of cyclotomic fields, emphasizing their Galois structure and their role in abelian extensions of Q. Next, we discuss ramification in general extensions, including decomposition and inertia groups, Frobenius element and the Chebotarev density theorem. The exposition continues with a concise algebraic introduction to elliptic curves and their L-functions, and it places key conjectural links (including Birch and Swinnerton-Dyer) in context. Finally, a collection of examples highlights a common operational backbone between fractional calculus and number theory: Laplace and Mellin transforms turn convolution-type operators into multiplication, clarifying the appearance of Γ-factors, Dirichlet series, and zeta/L-function structures in both settings.

Abstract: Existing methods for power load anomaly detection suffer from several limitations, including insufficient extraction of multi-scale temporal features, difficulty in capturing long-range dependencies, and inefficient fusion of heterogeneous spatiotemporal information. To address these issues, this study proposes the TGCformer, an enhanced Transformer-based model designed for dynamic spatiotemporal feature fusion. First, a dual-path spatiotemporal feature extraction module is constructed. The temporal path utilizes TSFresh to enhance the explicit pattern representation of the load sequences, while the spatial path employs an improved GATv2 to model dynamic correlations among grid nodes. Together, these two paths provide more interpretable and structured inputs for the Transformer encoder. Subsequently, a multi-head cross-attention mechanism is designed, where temporal features serve as the Query and graph embeddings as the Key and Value, to guide the feature fusion process. This design ensures the effective integration of complementary information while suppressing noise. Experimental results on the public Irish dataset demonstrate the effectiveness of the proposed model. Specifically, TGCformer achieves average F1-score improvements of 0.35 and 0.53 compared with InceptionTime and XceptionTime, respectively.

Abstract: Addressing the issue of high cleaning loss rates in practical operations, this study designed adevice for detecting cleaning losses. Three-dimensional models of wheat kernels were constructed using Blender software. Subsequently, the discrete element method software EDEM was employed to simulate the impact process of wheat kernels and straw dropped from varying heights onto a sensing plate, obtaining the contact force history and particle trajectories. The results revealed a significant difference in the impact force between the two material types on the sensing plate, enabling material identification and loss rate calculation through signal acquisition.Based on this, a detection device comprising mechanical structures and a control system was designed. An ESP32 microcontroller was used to read data from a piezoelectric ceramic vibration sensor. After processing with a Kalman filter, material classification thresholds were determined based on the normal distribution pattern of the signals. Experimental parameters were initially identified through a three-factor, three-level experiment and subsequently optimized using response surface methodology. The experimental results indicated that the threshold discriminability and loss rate calculation accuracy were optimal when the sensing plate was installed at a height of 550mm with a tilt angle of 40°, and the conveyor belt speed was 8 meters per minute.Bench test verification demonstrated that the device achieved an overall error of less than 3%, with recognition rates for both wheat kernels and straw exceeding 97%.

Abstract: Current multimodal large language model (MLLM)-based autonomous driving systems struggle with deep contextual understanding, fine-grained personalization, and transparent risk assessment in complex real-world scenarios. This paper introduces ConsciousDriver, a novel context-aware multimodal personalized autonomous driving system designed to address these limitations. Our system integrates a Context-Awareness Module for richer environmental understanding and a Dynamic Risk Adaptation Mechanism to flexibly modulate driving behaviors based on real-time user prompts and situational risks. Built upon an extended MLLM architecture, ConsciousDriver processes environmental inputs and user prompts to generate deep contextual understanding, context-adaptive danger levels, optimal action decisions, and explicit decision intent explanations. Evaluated on an extended PAD-Highway benchmark, ConsciousDriver demonstrates superior performance in driving safety, efficiency, lane-keeping, and traffic density adaptation. Furthermore, it exhibits robust adaptability to diverse personalized prompts and enhanced performance in challenging traffic scenarios, with lower collision and higher completion rates. Human evaluation confirms the high quality of its explanations. ConsciousDriver represents a significant advancement towards intelligent, adaptive, and trustworthy autonomous driving.

Abstract:

Background: Hormonal fluctuations across the menstrual cycle may influence cognitive and neuromuscular performance in female athletes. Caffeine is a widely used ergogenic aid, yet its phase-specific effects remain unclear. This study investigated the acute effects of caffeine supplementation on cognitive and physical performance across menstrual cycle phases in eumenorrheic female athletes. Methods: Twelve trained female athletes (mean age: 24.4 ± 2.7 years) with regular menstrual cycles participated in a randomized, double-blind, placebo-controlled study. Each participant completed a battery of cognitive (reaction time [RT], vigilance test [VT]) and physical performance tests (countermovement jump \[CMJ], repeated sprint test [RST], and time to exhaustion test [TTE]) during the early follicular (EFP), late follicular (LFP), and mid-luteal (MLP) phases. Caffeine (CAF) (400 mg) or placebo (PLA) was ingested one hour before the testing session. Results: CAF significantly improved vigilance performance across all menstrual phases compared with placebo (p<0.01), with no phase effect (p=0.26). RT score was significantly reduced following CAF ingestion during the LFP (p=0.02) and MLP (p<0.01), whereas no significant effect was observed during the EFP (p=0.22). Regarding CMJ, in the PLA condition, jump height was higher during the LFP than EFP and MLP (p<0.01), while CAF significantly increased jump performance during the MLP compared with PLA condition (p<0.01). During repeated sprint exercise, peak power declined across sprints (p<0.01); however, CAF significantly increased peak power output (p=0.02), particularly during later sprints in the MLP (p<0.05). Time-to-exhaustion was not influenced by CAF or menstrual phase (p>0.48). Conclusion: CAF supplementation consistently enhances cognitive performance across the menstrual cycle and selectively improves neuromuscular performance during the mid-luteal phase, supporting its targeted ergogenic use to mitigate menstrual phase–related performance fluctuations in female athletes.

Abstract: In Mediterranean dryland agroecosystems, conservation tillage is increasingly adopted, yet its long-term effects on weed biomass within cereal–legume rotations remain insufficiently quantified. This study evaluated the effects of conventional tillage (CT), minimum tillage (MT), and no-tillage (NT) on aboveground weed biomass over seven years (2011–2017) within a long-term (established in 1985) cereal–legume rotation experiment in central Spain. Weed biomass was sampled annually prior to herbicide application and analyzed using linear mixed-effects models to assess the effects of crop type, tillage system, year, and their interactions. A total of 36 weed species were recorded, with annual broadleaf species accounting for 94% of total biomass. Mean weed biomass was greater in legume phases than in wheat, and tillage system significantly affected biomass. Minimum tillage resulted in higher weed biomass than CT or NT, particularly in legume crops. A significant crop type × tillage interaction indicated that tillage effects on weed biomass were crop-dependent, with stronger differences among tillage systems in legumes than in wheat. These results demonstrate that weed biomass responses to tillage cannot be generalized across crops, highlighting the importance of considering crop–tillage combinations when designing weed management strategies in Mediterranean cereal–legume rotations.

Abstract:

Bacterial cellulose (BC) is an attractive biopolymeric scaffold for the development of functional membranes due to its high purity, nanofibrillar network, mechanical robustness, and biocompatibility. In this work, we report the production and characterization of BC membranes functionalized with silver nanoparticles (AgNPs) generated through a plant-mediated green synthesis strategy, with particular emphasis on maximizing nanoparticle incorporation within the BC matrix. Mint (Mentha spicata) and avocado (Persea americana) extracts were employed as dual reducing and stabilizing agents for AgNP formation, enabling nanoparticle synthesis under mild and environmentally benign conditions. AgNP formation was first investigated in aqueous media as a function of silver precursor concentration, pH, and temperature, and monitored by UV–Vis spectroscopy through localized surface plasmon resonance (LSPR) features. Neutral pH (pH 7) and moderate temperature (23 °C) were identified as optimal conditions, yielding well-defined LSPR indicative of efficient and controlled nanoparticle formation. Two strategies for BC functionalization were subsequently compared: post-synthesis immersion of BC membranes in AgNP suspensions and in situ synthesis of AgNPs directly within the BC network. Spectroscopic analysis demonstrated that in situ synthesis enables significantly higher effective nanoparticle loading and a more homogeneous distribution throughout the BC scaffold, compared with the immersion approach.The resulting BC–AgNP composite membranes were subsequently evaluated for their antibacterial efficacy against Escherichia coli. Antibacterial performance was assessed using two complementary experimental stups. In the first, composite membranes were placed on agar surfaces uniformly seeded with E. coli, and the diameter of the resulting inhibition zones was measured following a defined incubation period as an indicator of bacteriostatic and bactericidal activity. In the second model, the BC–AgNP membranes were directly introduced into liquid cultures of E. coli, and bacterial growth was quantified by measuring the optical density (OD) of the cultures after incubation. This dual assay approach allowed for evaluation of both surface- mediated inhibition and the effects of AgNP release on planktonic bacterial growth. Membranes functionalized via in situ synthesis exhibited markedly enhanced antibacterial activity, with larger growth-inhibition zones and the absence of bacterial regrowth in both solid and liquid assays, confirming a predominantly bactericidal effect. Overall, this study demonstrates that combining bacterial cellulose with in situ green synthesis of silver nanoparticles is an effective strategy to maximize nanoparticle incorporation and produce robust antimicrobial membranes, offering strong potential for applications in wound dressings, filtration systems, antimicrobial packaging, and other sustainable functional materials.

Abstract: D-galactose (D-gal) induced accelerated aging is a popular and widely used experimental method in the field of aging and aging-related degenerative disorders. It has been shown that the major characteristics of D-gal induced aging process are increased oxidative stress, decreased antioxidant enzymes, elevated cell death, increased tissue fibrosis and accumulation of inflammatory mediators. This review focuses on D-gal induced kidney aging in mice and rats with discussions on both kidney aging mechanisms and anti-kidney aging regimens using this model. It is our belief that D-gal induction of accelerated kidney aging will continue to be used as a convenient platform for elucidating kidney aging mechanisms and exploring novel anti-kidney aging targets that may slow down kidney aging and retard the development of aging related renal disorders.

Abstract: The type of AI used to design video game enemies greatly affects the gameplay experience of speed, difficulty, and enjoyment. In most cases, the majority of developers who create 2D platforming games will choose to implement a simple but efficient AI design over an advanced AI model that learns based on experience. One of these simpler AI models that is frequently utilized by 2D platforming game developers is the Finite State Machine (FSM) model. The FSM model creates an organization of the enemy's actions into a limited number of well-defined behaviours, while also indicating how these behaviours relate to one another. We look at how AI uses the FSM method in the 2D platform game "Kirby: Nightmare in Dreamland," which first came out on the Game Boy Advance. The analysis of FSM models enemy AI behaviors and how those behaviors change and when they do so affects how hard the game is. Simulated experiments were conducted on how state time is spread out and how to make things harder by changing the attack cooldown. The results show that FSM-based AI is easy to control, doesn't need a lot of processing power, and has behavior that can be predicted. This makes it a good choice for platform games that are easy to get into. The results show that FSMs are still important in AI research and game design today.

Abstract: Large Language Models (LLMs) frequently struggle with factual accuracy and the precise handling of uncertain information, often leading to hallucinations or misinterpretations. Existing methods like Chain-of-Thought (CoT) prompting fail to explicitly distinguish between facts and assumptions within complex contexts. To address these challenges, we introduce the Probabilistic Chain-of-Evidence (PCE) method, a novel prompt engineering strategy designed to enhance LLMs' Factual Boundary Recognition and Uncertainty Reasoning Accuracy. PCE guides LLMs through a meta-cognitive process comprising Evidence Identification, Probabilistic Assessment, and Weighted Inference, enabling explicit quantification and integration of evidence certainty throughout reasoning. Implemented purely through sophisticated prompt design without model modifications, PCE was rigorously evaluated across diverse tasks including Factual Question Answering with Ambiguity, Medical Report Interpretation, and Legal Text Analysis. Our experiments demonstrate that PCE consistently and significantly outperforms traditional CoT prompting, achieving substantial improvements in Factual Boundary Recognition Accuracy and Uncertainty Expression Precision, while drastically reducing the Hallucination Rate. Human evaluations further corroborate these findings, indicating superior Overall Answer Quality. An ablation study confirms the crucial contribution of each PCE stage, and an analysis highlights the efficacy of a conservative "minimum" approach for robust uncertainty propagation. PCE offers a highly adaptable and practical solution for generating more reliable, transparent, and trustworthy responses from LLMs in complex, ambiguous information environments.

Abstract: Whereas there is a growing body of literature on the impact of Covid-19 pandemic, limited evidence exists on the impact of the pandemic on informal female-owned enterprises, and especially those that are located in urban informal settlements. In this study, we explore the adverse impacts of COVID-19 pandemic on women food vendors enterprises and their coping strategies across four informal settlements in Nairobi, Kenya. The study is based on a quantitative survey of 448 women vendors selected through stratified random sampling. Our findings show that women food vendors face numerous challenges which intensified during the COVID-19 pandemic, leading to increased costs of business operations, spoilage of perishable products, and oscillating daily sales and profits, largely due to the unpredictable market supply and demand forces. The vendors adopted a number of strategies to cushion their business enterprises and households, including price and stock adjustments, use of mobile phones and hygiene measures at business enterprises, reliance on credit, loans, savings and social networks for survival, temporary closure of business, and relocation of household members to the rural home. These results underscore the critical need for context-specific strategies to support and foster resilience of informal economies during future global pandemics.

Abstract: Due to its molecular structure we find a specific reaction mechanism of ozone in biological systems, which requires low doses. By contrast to disinfection, where ozone is added over a certain period of time (ct concept) until disinfection becomes effective, we must pay particular attention to concentrations and dosages so as not to increase oxidative stress in patients, e.g., those with chronic inflammatory diseases and high oxidative stress. Here we start with a deeper insight into the effect of ozone on Red Bood Cells (RBC) and the glutathione system which can be blocked at higher concentrations if needed, such as is the case in reducing the plasmodium falciparum growth. At low ozone concentrations, the RBC metabolism is activated, 2,3-diphosphoglycerate (2.3-DPG) increases and oxygen is easily released from hemoglobin, which is helpful in diabetes and sporting activities. In mononuclear cells low dose ozone acts as a redox bioregulator, e.g., by downregulating proinflammatory cytokines and oxidative stress.

Abstract: We present a novel framework for quantifying and tracking conceptual evolution in temporal document collections through multi-metric semantic analysis. Our methodology introduces three key innovations: (1) ensemble clustering validation combining silhouette coefficient, Calinski-Harabasz index, and Davies-Bouldin score for optimal semantic prototype discovery, (2) permutation-based statistical testing for establishing significant conceptual continuity across time periods, and (3) multi-dimensional conceptual change quantification through centroid shift analysis, distribution divergence via Wasserstein distance, and semantic space transformation measurement. Applied to sustainability discourse spanning 2018-2023, our framework reveals statistically significant paradigm shifts (p < 0.05) with centroid shift magnitudes ranging from 0.142 to 0.387, demonstrating the transition from Corporate Social Responsibility to ESG integration and finally to regulatory-driven net-zero frameworks. The system achieves 94.7% inter-annotator agreement on prototype classification and identifies semantic prototypes with mean intra-cluster coherence of 0.823. Our contributions include rigorous statistical foundations for semantic evolution analysis, automated prototype discovery with validated clustering, and a comprehensive framework for longitudinal discourse analysis applicable across domains from scientific literature to policy documents.

Abstract: This paper presents a comprehensive policy framework to position New Jersey as a national leader in artificial intelligence (AI) education and workforce development. Through analysis of current state initiatives—including the NJ AI Hub, AI Task Force reports, apprenticeship programs, and regulatory guidance—we identify strategic gaps and opportunities across K-12, higher education, and workforce development sectors. We propose a multi-layered approach visualized through interconnected frame works: an integrated AI education ecosystem, phased implementation roadmaps for K-12 AI literacy, a statewide AI curriculum consortium structure, multi-track workforce development pathways, and equity and access frameworks. Quantitative analysis reveals that while 25%+ of New Jersey’s workforce already uses AI technology daily, only 20-25% of educators feel prepared for AI integration. Our policy recommendations address this gap through a $165 million annual investment strategy with projected 3.8x return on investment, creating pathways for 15,000-20,000 new AI jobs by 2030. This framework provides actionable guidance for lawmakers, educators, and industry stakeholders to enhance New Jersey’s competitiveness, ensure ethical AI deployment, and foster inclusive economic growth in the AI era. Drawing from over recent sources including state publications, academic research, and industry reports, this paper offers concrete ecommendations for lawmakers, regulators, educators, and industry stakeholders to enhance New Jersey’s competitiveness, ensure ethical AI deployment, and foster inclusive economic growth in the AI era. Recommendations include establishing AI literacy standards for all K-12 students, creating specialized AI high schools, expanding community college AI programs, developing industry-aligned university curricula, and implementing statewide AI teacher training. We also address equity considerations, funding mechanisms, and implementation timelines.