Abstract: Water pollution is one of the most critical societal, environmental challenges and remains a persisting problem worldwide. The origin of this pollution is diverse while organic matter occupies a significant portion originating from different sources. This creates major environmental and health risks, requiring reliable and sensitive analytical tools for effective monitoring. The permanganate index stands as a conventional assessment method for organic pollution, but it demonstrates compound non-specificity toward compounds and limited sensitivity to various contaminant structures. This research introduces cyclic voltammetry as a standalone electrochemical method which provides sensitive detection and characterization of organic oxidizing compounds. Six organic compounds including gallic acid, phenol, oxalic acid, ascorbic acid, salicylic acid and p-benzoquinone were used as model compounds and studied in aqueous media. These compounds were analyzed individually, in single-compound mode, to characterize its redox behavior and to identify the voltammetric peaks. Subsequently, a multi-compound analysis was studied to check for the validity of the concept in a more complex matrix. Notably, a strong linear correlation was observed between the measured charge and the theoretical permanganate index, highlighting the quantitative reliability of the electrochemical method. Comparing the obtained results with the permanganate index method confirmed the superiority of cyclic voltammetry in terms of response time and detection capability. The outcomes demonstrate that cyclic voltammetry functions as a robust alternative to the classical chemical oxidation method for environmental water assessment.

Abstract: In this paper we study the performance and feasibility of integrating a novel key encapsulation protocol into Quantum Key Distribution (QKD). The key encapsulation protocol includes a challenge-response pair (CRP). In our design, Alice and Bob derive identical cryptographic tables from shared challenges, allowing the ephemeral key to be encoded and recovered without disclosing helper data. Software simulations show error-free key recovery for quantum channel bit error rates up to 40% when using longer response lengths. Additionally, we designed the protocol to detect eavesdropping solely from the statistics of the received quantum stream, without sacrificing key bits for public comparison. We formalize the encoding and decoding model, analyze trade-offs between response length and latency, and report key recovery and error detection performance across different noise levels. The results indicate that this CRP-based multi-wavelength QKD protocol can reduce the reliance on classical reconciliation while preserving security in noisy settings.

Abstract: AI-enabled fitness services rely on continuous collection of activity, physiological, and location data to support monitoring and personalized feedback, which raises persistent privacy and security concerns and ethical tensions regarding data use and user autonomy. Nevertheless, sustained engagement with these services remains common, indicating a divergence between privacy concern and continued use. Using online survey data from 596 adults aged 18 years and above, this study examines AI fitness use from an AI ethics perspective grounded in bounded rationality. A Deviation index is constructed as the standardized difference between privacy concern and risk acceptance. High willingness to use AI fitness services is analyzed using a parsimonious probability-based approach. Logistic regression models examine how the likelihood of high use varies across the Deviation range, while accounting for perceived transparency and safety, measured as Information Control Level, and stated privacy trade-off attitudes. The results show that continued use varies systematically across the Deviation spectrum. Higher Deviation values are not associated with a collapse in use probability. Instead, predicted probabilities change gradually across the observed range. Privacy concern and continued AI fitness use therefore coexist within this adult user sample. This pattern supports a descriptive AI ethics interpretation of privacy satisficing under bounded rationality rather than a binary privacy paradox.

Abstract: Evacuation planning is increasingly challenged by compound hazards in which interacting threats degrade infrastructure, influence human behavior, and destabilize transportation systems. Although agent-based models and dynamic traffic simulations have advanced substantially, much of the evacuation literature remains hazard-specific, case-bound, or difficult to transfer across regions. In parallel, transportation resilience research shows that multi-hazard effects are often non-additive and that cascading infrastructure failures can amplify disruption beyond directly affected areas. These realities motivate the development of evacuation modeling frameworks that are modular, adaptable, and able to represent co-evolving behavioral and network processes under compound conditions. This review synthesizes advances in evacuation agent-based modeling, dynamic traffic assignment, hazard-induced network degradation, and compound disaster research to propose an adaptable compound-hazard evacuation framework integrating three interdependent layers: hazard processes, transportation network dynamics, and agent decision-making. The proposed framework is organized around four principles: (1) modular hazard representation, (2) decoupling behavioral decision logic from hazard physics, (3) dynamic network state evolution, and (4) neighborhood-scale performance metrics. The framework prioritizes planning-relevant, spatially resolved outputs, including neighborhood clearance time, isolation probability, and shelter demand imbalance. By prioritizing modularity, configurability, and policy-aligned metrics, this review bridges the gap between methodological advances in evacuation modeling and the operational needs of local multi-hazard planning.

Abstract: The El Niño–Southern Oscillation (ENSO) is the main driver of interannual climate variability, strongly influencing precipitation, temperature, and extreme events worldwide. In South America, its impacts are well documented. However, studies examining different ENSO types—Eastern Pacific (EP), Central Pacific (CP), and Mixed (MX), defined according to the location of sea surface temperature (SST) anomalies in the tropical Pacific—remain limited, particularly for the Brazilian subtropical climate. This study investigates rainfall variability in the Brazilian subtropical region associated with different ENSO types. Composite analyses of precipitation, wind, and SST anomalies were performed, and monthly rainfall data from 703 stations were used to identify homogeneous regions. The results show the intensity and spatial coherence of rainfall anomalies vary according to El Niño type, with EP events favoring widespread wet conditions and CP events producing more heterogeneous or locally negative anomalies. For La Niña, the intensity and seasonal distribution of negative rainfall anomalies vary by ENSO type: stronger impacts occur in summer (EP), spring (MX), and autumn (CP). These findings improve the understanding of ENSO-related rainfall variability in the Brazilian subtropical region and provide valuable insights for the management of climate-related risks in a region frequently affected by rainfall extremes.

Abstract: Indonesia's E10 blending mandate presents a strategic opportunity for decarbonization and inclusive rural development, contingent on a robust supply chain integrating smallholder farmers. This study developed a novel supply-chain framework for corn products in Sumenep to facilitate sustainable ethanol production. Methods involved comprehensive data collection, mathematical modeling using the p-median method, and farmer clustering techniques. Findings reveal that Sumenep Regency's substantial corn harvest of 8,475,914.5 tons, yielding 1,271,387.175 tons of kernels, can produce 381,416.1525 liters of bioethanol. By applying clustering supply chain model, the farmers' group profit is Rp 205,693,725,826, while Rp 177,394,823,353 profit for non-clustering model. It increasing profit 16% compared to the model without clustering. This localized production, enabled by a simplified, decentralized supply-chain architecture, significantly enhances national energy security, reduces greenhouse gas emissions, and improves the economic stability of smallholder farmers through equitable value capture and minimized logistical costs. The framework offers a practical, implementable strategy for Indonesia's energy transition, fostering environmental sustainability and inclusive socio-economic development.

Abstract: Multi-agent LLM systems face communication bottlenecks using natural language tokens, lacking end-to-end differentiability. While dense vector communication helps, existing methods are inflexible due to fixed topologies and static transformations. We propose the Adaptive Sparse Dense Communication Network (ASDNet), a novel framework for efficient, flexible, and context-aware dense communication. ASDNet employs a dynamic Communication Hub per agent, intelligently selecting sparse partners and adaptively generating optimal dense vector transformations. This end-to-end differentiable architecture enables joint optimization of communication and inference. Experiments with an ASDNet variant, built on a foundational LLM, demonstrate consistent outperformance against state-of-the-art dense communication baselines and other open-source LLMs across diverse benchmarks, with efficient training. Ablation studies confirm dynamic target selection and adaptive transformations are critical. Further analyses highlight ASDNet's enhanced efficiency, superior qualitative outputs, and robust low-data performance, showcasing its potential for scalable multi-agent collaboration.

Abstract: The intensive use of synthetic pesticides and fertilizers has raised environmental concerns. Sustainable alternatives, such as plant biostimulants and plant resistance inducers, offer promising solutions by enhancing growth, yield, stress tolerance, or activating defense responses against pathogens. However, the physiological impacts and combined effects of these products remain poorly understood, limiting evidence-based application strategies. Here, we evaluated the effects of a biostimulant and a plant defense inducer on durum wheat (Triticum turgidum ssp. durum), a key cereal crop in the Mediterranean Basin. Using controlled experiments, we assessed plant growth, chlorophyll content, and resistance to Zymoseptoria tritici, while considering potential trade-offs between growth promotion and defense activation. As expected, our results indicate that the biostimulant improved growth and photosynthetic performance, whereas the plant resistance inducer enhanced protection against Z. tritici. But the combination of these two treatments can trigger mitigated interaction effects, influenced by varietal genetic background. This study provides novel insights into the interactions between plant growth promotion and defense induction in durum wheat. Understanding these multi-factorial effects (in particular genotype effect) enables the identification of optimal treatment strategies, supporting the development of sustainable crop management practices that reduce chemical inputs while maintaining productivity and resilience under biotic stress.

Abstract: Background/Objectives: Age-related changes in body composition (bone, muscle, and adipose tissue) are often assumed to follow linear, sex-specific patterns. Some evidence suggests that these trajectories are nonlinear, and their timing and dynamics remain poorly characterized. Osteosarcopenic adiposity/obesity (OSA), defined by the coexistence of osteopenia/osteoporosis, sarcopenia, and excess/redistributed adiposity, is recognized as a body composition disorder associated with multiple morbidities, but its impact on age-related body composition trajectories has not been fully explored. We aimed to delineate sex-specific, age-related trajectories of bone, muscle, fat mass, and intramuscular adipose tissue (IMAT), identify inflection points across adulthood, and compare patterns in individuals without and with OSA. Methods: Cross-sectional data from 9717 healthy Caucasian adults (aged 20–90 years) enrolled in a multicenter Italian study were analyzed. Body composition was measured using validated bioelectrical impedance analysis. LOESS regression was employed to identify age-related inflection points. Standard diagnostic criteria defined osteopenia/osteoporosis, sarcopenia, adiposity, and OSA. Results: Men exhibited earlier peaks and midlife stability in bone and muscle mass, followed by later decline. Women showed lower baseline values, multiple early-life inflection points, and sharper midlife downturns, particularly around menopause. Fat mass increased steadily in men but followed a multi-phasic pattern in women. IMAT rose progressively with age in both sexes. Adults with OSA, identified in participants even as young as 20 years, demonstrated destabilized trajectories, earlier downturns in bone and muscle, and more complex body fat and IMAT patterns. Conclusions: Distinct sex-specific patterns and mitigating effect of OSA on body composition trajectories were identified. Early detection of OSA may be crucial for preventing acceleration of musculoskeletal decline and rise in adiposity.

Abstract: Manilkara (Sapotaceae) includes tropical tree species of high ecological and socio-economic value, yet genetic and phylogenetic evidence remains uneven across taxa and eco-geographic regions. Here, we synthesize studies conducted between 1999 and 2025 which summarize the use of molecular markers to infer genetic diversity, connectivity, population structure, and evolutionary relationships within this genus. The studies are dominated by PCR-based marker systems, including dominant markers (like RAPD and SCoT) and microsatellites from the nuclear genome and plastid genome. Other studies rely on PCR-amplified sequence loci, such as ITS and chloroplast regions, while others use high-throughput technologies, including NGS-assisted SSR development, sequences of complete plastomes, and targeted nuclear sequencing. Overall, studies using SSRs provide the most informative estimates for within-species diversity and fine-scale structure, whereas plastid datasets (cpSSR/cpDNA) mainly support inference on maternal lineages and plastid-based relationships but can be constrained by uniparental inheritance and limited variation, especially under small sampling. Some limitations found include heterogeneous sampling, inconsistency in reporting the methodological parameters, and limited connection with ecological or phenotypic parameters which restricts chances of inferences on demography and adaptation. Based on this review, future research in Manilkara would benefit from setting up a broader taxonomic and geographic coverage, incorporating genome-wide technology where feasible to strengthen conservation management, and breeding opportunities in Manilkara.

Abstract: While climate change creates the overarching biophysical stress on Ethiopian agriculture, institutional and governance structures primarily mediate agrobiodiversity outcomes, often trading evolutionary resilience for short-term productivity. This review synthesizes cross-sectoral evidence from Ethiopia’s major highland and rangeland systems to demonstrate that climate change acts as a systemic stress test, exposing latent vulnerabilities in agricultural policy, seed regulation, and land tenure systems. The widespread loss of agrobiodiversity, documented by genetic erosion rates ranging from 56% in barley to over 65% in teff and wheat, including total displacement in certain districts, is largely driven by a structural conflict between productivity imperatives and ecological stewardship. Our synthesis reveals that policy silos, top-down extension models, and regulatory biases toward genetic uniformity collectively erode the functional heterogeneity required for climate adaptation. This institutional failure necessitates a governance-centered framework that formalizes pluralistic seed systems and empowers decentralized farmer innovation. Realigning governance incentives to treat agrobiodiversity as a strategic national asset is essential for securing Ethiopia’s genetic capital against accelerating climatic stress.

Abstract: Cancer remains the leading cause of death in domestic dogs. Conventional therapeutic approaches, including surgery, chemotherapy, and radiotherapy frequently fail to achieve sustained remission or stabilization. Oncolytic virotherapy, a rapidly advancing therapeutic modality in human oncology, is emerging as a novel strategy in veterinary medicine. This systematic review summarizes current knowledge on the application of oncolytic viruses (OVs) in canine cancer treatment, focusing on their mechanisms of action, safety profiles, and clinical efficacy. We evaluate diverse OV platforms, including myxoma virus, reovirus, vesicular stomatitis virus, canine adenoviruses, vaccinia virus, Sendai virus, and Newcastle disease virus, across preclinical and clinical studies in dogs with various malignancies. While several OVs have demonstrated favorable tolerability and modest antitumor activity, key challenges such as pre-existing immunity, optimization of dosing regimens, and rational combination strategies, remain to be addressed. This review emphasizes the translational significance of canine studies for both veterinary and human oncology, underscoring the critical need for rigorously designed clinical trials to refine virotherapy protocols and expand therapeutic options for canine cancer patients.

Abstract: Static linear lumped circuits (conductances, independent sources, and linear dependent sources, with no storage) can be studied through their boundary behavior: the set of boundary voltage–current pairs consistent with internal circuit laws. Fixing a set of accessible boundary nodes B of size n, and assuming standard well-posedness conditions for modified nodal analysis (MNA), we show that the boundary current injection vector iB depends affinely on the boundary voltage vector vB on an admissible affine set: iB = Yeq vB + i0 for all vB ∈ VB . We then provide a canonical boundary normal form that realizes this law using only indepen- dent current sources and voltage-controlled current sources (VCCS) connected directly to the boundary nodes. The construction is deterministic and idempotent, and it yields a complete classification: two circuits are behaviorally equivalent on the same boundary if and only if their normal-form parameters agree (modulo boundary constraints). A worked example (including a dependent source), an explicit VCCS synthesis list, and an exact numerical spot-check are included.

Abstract: The burning candle discussed in Faraday's lectures is used as an example to discuss the relationships between physical theories at different levels of organization: continuum mechanics at the macro level and statistical and quantum mechanics at the micro level. The first part of the paper examines the connections between theoretical and experimental physics. Physics theory serves as the foundation of the research program, but experimental research is a measure of ongoing development. Reasonable extrapolationism denotes a situation where the ideas of physical theory contribute to the development of experimental research. On the other hand, radical extrapolationism is used for a situation where the ongoing discussion goes far beyond experimental physics. In the second part of the paper, the arrows of explanation between continuum mechanics and statistical mechanics are considered and classified within the framework of the proposed terminology. The estimation of the properties of a substance from molecular constants and the derivation of continuum mechanics equations from statistical mechanics are considered. Qualitative explanations and emergence are also discussed.

Abstract: Knowledge-intensive organizations undergoing Industry 4.0 transformations face unprecedented behavioral and cognitive challenges as algorithmic systems increasingly mediate decision-making processes. This qualitative study examines how managers in knowledge-intensive organizations interpret, integrate, and respond to algorithmic knowledge within decision contexts characterized by technological complexity and data volatility.Through thematic analysis of secondary qualitative data from thirty organizational case studies, interviews, and practitioner narratives across digital consulting, advanced analytics, and technology-enabled service organizations, we identify critical behavioral dynamics shaping human-algorithm collaboration. Our findings reveal that managerial decision-making operates through three interconnected behavioral processes: interpretive sensemaking of algorithmic outputs, oscillation between algorithm appreciation and aversion contingent on organizational and decision contexts, and organizational adaptation mechanisms spanning cognitive supports, psychological safety, and distributed learning structures. Drawing on bounded rationality theory, cognitive load theory, and socio materiality perspectives, we develop an analytical framework explaining how technological complexity and data volatility mediate the relationship between managerial cognition and decision quality through psychological safety and organizational learning. We conclude that successful Industry 4.0 adoption in knowledge-intensive organizations requires deliberate attention to behavioral and organizational context factors, particularly psychological safety enabling risk-taking in algorithmic engagement, cognitive diversity fostering critical evaluation of algorithmic recommendations, and organizational learning structures supporting the development of algorithmic literacy. This research contributes to organizational behavior theory by articulating how digital complexity reshapes managerial cognition and organizational practice, and to practitioners by offering evidence-based strategies for managing human–algorithm complementarity during technological transitions.

Abstract: The Central Dogma has provided a foundational framework for understanding biological information flow, yet it does not fully explain how living systems maintain stable identity, functional robustness, and recoverability under continuous molecular noise and environmental perturbation. Here, I propose the Central Homeostatic Principle (CHP) as a complementary first-principle framework that shifts the explanatory center from information execution alone to the physical constraint architecture that makes biological execution possible. The CHP posits that, in living cells, a central homeostatic state functions as a system-level coordinating layer that defines the feasible space within which genetic and biochemical programs can operate.This framework is motivated by convergent evidence across mechanical confinement, electrophysiological coupling, membrane contact-site transduction, phase-state regulation, and non-genetic phenotypic heterogeneity, all of which indicate that global physical states can gate, reshape, or buffer molecular outcomes. Building from systemic prerequisites and material constraints, I further argue, through an exclusionary, first-principle analysis, that lipid-based boundary systems occupy a near-irreplaceable physical position in implementing this central homeostatic constraint in aqueous cellular life, not as exclusive causal authorship, but as the dominant substrate of feasibility control.To render the theory scientifically actionable, the manuscript provides a formal articulation of CHP, a three-tier realization model, operational corollaries, and a rule typology distinguishing strong and weak forms. It then derives a set of falsifiable hypotheses spanning temporal commitment dynamics, non-genetic resistance, aging-related resilience loss, state-engineering-based reprogramming, and evolutionary primacy in prebiotic systems. By reframing life as a problem of constrained state maintainability rather than information flow alone, the CHP offers a testable theoretical scaffold for integrating molecular biology, biophysics, systems biology, and translational state engineering.

Abstract: Flue gases generated by residential solid fuel combustion sources contain both particulate matter and gaseous pollutants. Their concentrations can be effectively reduced using emission control devices, which have traditionally been applied in small- and large-scale combustion sources. However, in residential combustion sources, such emission control devices remain unimplemented, except for electrostatic precipitators. This mini review provides a coherent summary of the current state of knowledge regarding emission control devices experimentally tested on residential combustion sources. It also outlines the technical and measurement challenges that must be addressed to enable effective, standards-compliant integration into residential combustion sources.

Abstract: Caffeine is widely consumed among health care workers (HCWs) as a coping mechanism for occupational demands and may influence depressive symptoms. This study investigated this relationship after adjustment for perceived stress and sleep quality. In this cross-sectional study on licensed HCWs at a tertiary hospital in Jeddah, Saudi Arabia, habitual caffeine intake was assessed using a validated caffeine food frequency questionnaire. Depressive symptoms were assessed using Patient Health Questionnaire (PHQ-9), with clinically relevant symptoms defined as PHQ-9 ≥10. Perceived stress and sleep quality were measured. Logistic and linear regression models evaluated the as-sociations with PHQ-9 ≥10 and PHQ-9 score respectively. Among 298 HCWs (mean age 37.5 years; 66.1% women), 18.5% had PHQ-9 ≥10. Mean caffeine intake was 216 mg/day (median 125 mg/day). HCWs with PHQ-9 ≥10 reported a higher caffeine intake than those without (Mean 282 vs. 201 mg/day and median 169 vs. 114 mg/day; p = 0.038). Caffeine intake was significantly associated with PHQ-9 score but not with PHQ-9 ≥10 after full adjustment (β = 0.331; p = 0.014 per twofold increase). Perceived stress and sleep quality were independently associated with PHQ-9 ≥10. Higher caffeine intake may reflect response to occupational strains rather than a primary depression risk driver.

Abstract: We study time-generalization in neural networks by training a shared iterative cell under explicit supervision of computation length. Rather than treating depth as fixed or learned implicitly, we provide a deterministic target step schedule and penalize deviations from the prescribed execution length, enabling controlled evaluation beyond the training horizon.We perform experiments on three representative dynamical regimes: contracting (Euclidean GCD), attractor-aligned (Log-Fibonacci), and expanding additive (Log-Factorial). We find that models can generalize to larger inputs when the effective iteration depth remains within the trained regime, yet often fail when required computation length increases, even if input magnitudes are moderate. Failure modes track the stability properties of the underlying update dynamics: contraction dampens errors, attractor alignment bounds them over finite horizons, and additive accumulation induces systematic drift.These results suggest that algorithmic generalization depends not only on function approximation but on the stability of learned update rules under composition. Explicit step conditioning improves interpretability and stability of computation depth, but does not by itself guarantee robust extrapolation to longer iterative chains.

Abstract: Recent large-scale simulations demonstrate that LLMs exhibit systematic performance degradation in multi-turn conversations, with unreliability increasing 112% across 200,000+ conversations (Laban et al., 2025). However, this "Lost in Conversation" phenomenon lacks mechanistic explanation. We present an entanglement framework for understanding context sensitivity in large language models using embedding-level variance analysis across 12 model-domain runs (4 philosophy, 8 medical) and 360 position-level measurements. We demonstrate that ΔRCI—a measure of context sensitivity introduced in Papers 1–2—tracks variance reduction in response embeddings. The correlation between ΔRCI and the Variance Reduction Index (VRI = 1 − Var_Ratio) is strong and highly significant (r = 0.76, p = 2.37 × 10⁻⁶⁸, N = 360). This relationship reveals bidirectional context coupling: convergent entanglement (Var_Ratio < 1, ΔRCI > 0), where context narrows the response distribution, and divergent entanglement (Var_Ratio > 1, ΔRCI < 0), where context widens it. The "Lost in Conversation" effect corresponds specifically to divergent entanglement. Two medical models (Llama 4 Scout: Var_Ratio = 7.46; Llama 4 Maverick: Var_Ratio = 2.64) exhibit extreme divergent entanglement at the summarization position (P30), producing highly unstable outputs when task enablement is expected. We introduce the Entanglement Stability Index (ESI) to predict which models will exhibit instability in multi-turn settings, transforming the descriptive observation that "LLMs get lost" into a predictive science of human-AI relational dynamics.