Abstract: Background/Objectives: Prolonged stays in the emergency department (ED) may contribute to an increase in the rate of healthcare-associated infections (HAIs) with an increased risk of mortality. Early identification of the risk profile of these patients could reduce both complications and adverse outcomes. The study aimed to verify whether the development of an HAI was associated with mortality. Design, settings and participants: This prospective multicentre study involved all subjects, who required urgent admission to an acute care hospital from the ED, between 2023 and 2024. Outcome measures: The primary endpoint was 30-day mortality. A Cox proportional hazards model was used to test the hypothesis. Results: Of the 27,516 patients included in the analysis, with a mean age of 79 [20] years (median [IQR]), 1,575 (7.8%) died. The main features, in order of importance, selected for predicting mortality were: diagnosis of neoplasm; older age; NEWS; diagnosis of infectious diseases; HAIs; diagnosis of respiratory diseases; CCI; priority level on arrival; and male gender, yielding an accuracy of 0.804 ± 0.012. The development of a nosocomial infection was associated with a mortality risk ratio of 1.518 (95% confidence interval (CI): 1.338–1.721; p < 0.001), particularly high for bloodstream infections (2.54; 2.12–3.06) and pneumonia (1.44; 1.20–1.73). Conclusion: In patients admitted to acute care hospital from the ED, the development of HAIs is associated with an increased risk of mortality. This risk is particularly elevated in cases of bloodstream infections and pneumonia.

Abstract: Absolute Newtonian time—as a continuous, universal parameter external to physical reality—contradicts the emergent, discrete temporal structure observed in chaotic systems. This paper provides numerical validation for the hypothesis that objective time emerges discretely from ordinal patterns rather than being imposed a priori. The Discrete Extramental Clock Law, defined by tn+1 = tn +∆t·g(τs) with universal gating g(τs) rooted in Kendall’s τ variance thresholds and Feigenbaum scaling, is tested across classical and non-classical chaotic attractors. Extensive simulations reveal empirical support for three core predictions: fractal inheritance in emergent time tn (Dtn ≈ 1.98 from D ≈ 2.06), trimodal stochastic dynamics in g(τs) with high variance (σ2 ≈ 0.85) and autocorrelation (ρ1 ≈ 0.85), and ∼ 50% variance reduction in weakly coupled networks, yielding smoother collective temporality. These results demonstrate time as a fractal-stochastic emergent phenomenon, providing quantitative evidence against Newtonian absolutism and supporting Polo’s transcendental view of extramental persistence. The findings bridge physics and metaphysics, offering empirical tools for modeling synchronization in biological collectives and human agency in critical regimes, where local retrocausality enables kairos—opportune moments—from chaotic physis.

Abstract: We propose a non-perturbative quantum gravity framework using quantum vortices (statistical average topological structures of microscopic particles) embedded in AdS/CFT holographic duality, resolving black hole singularities without renormalization. Thus, this constitutes a singularity-resolution mechanism grounded in physical processes rather than mathematical techniques. The quantum vortex field generates a repulsive potential within the critical radius r∗ ≈ 8.792 × 10⁻¹¹m, dynamically preventing matter from reaching r = 0 and avoiding curvature divergence. The derived Huang metric (Schwarzschild metric with quantum corrections) enables parameter-free prediction of black hole shadow angular diameters, without post-observation fitting of Kerr black hole spin. Observational verification shows: the theoretical shadow of Sgr A* is 53.3 μas (EHT: 51.8 ± 2.3 μas), and that of M87* is 46.2 μas (EHT: 42 ± 3 μas), resolving contradictions of the Kerr model. This framework unifies singularity elimination, information conservation, and shadow prediction, providing a testable quantum gravity paradigm.

Abstract: A nutritional supplement was formulated for hyperprolific gilts to support metabolic adaptation and reproductive performance during the peripartum period. A total of 126 gilts were randomly assigned to a control (C) or a treatment (T) group. Control gilts received standard commercial diets, whereas treatment gilts received the same diets supplemented during the last 35 days of gestation and the first 5 days of lactation. The multi-nutrient supplement contained calcium (Ca; 4.1%), sodium (Na; 4.0%), lysine (Lys; 1.96%), methionine (Met; 1.32%), vitamin B₁₂ (0.3 mg/kg), choline chloride (600 mg/kg), betaine (475 mg/kg), and L-carnitine (500 mg/kg). The treatment group showed a reduction in stillbirth rate (p = 0.001), a lower incidence of neonatal diarrhea (p < 0.001), and a lower prevalence of postpartum hypophagia (p = 0.014). In addition, β-hydroxybutyrate (BHBA) and creatinine (CREA) concentrations at day 107 of gestation were significantly lower in the T group (p < 0.001). Higher piglet body weight at birth (p = 0.011) and at 15 days of lactation (p < 0.001), as well as greater maternal backfat thickness and longissimus muscle depth at 26 days of lactation (p < 0.001), were also observed in supplemented gilts. More-over, hypophagia was associated with elevated BHBA concentrations (p < 0.001), whereas neona-tal diarrhea was associated with higher BHBA (p = 0.001) and CREA (p = 0.005) concentrations. Overall, peripartum multi-nutrient supplementation could represent a practical nutritional strategy to support reproductive efficiency and early litter performance in hyperprolific gilts.

Abstract: [¹⁸F]FDG positron emission tomography (PET) is extensively utilized to assess brain glucose metabolism, typically through static images reflecting radiotracer accumulation of up to one hour. In comparison, functional PET (fPET) enables investigation of [¹⁸F]FDG dynamics occurring within seconds. However, the physiological mechanisms supporting these rapid changes in metabolism necessitate further attention to allow accurate interpretation of brain function and their clinical implications. This work highlights candidate mechanisms driving [¹⁸F]FDG signal changes at high temporal resolution, offering complementary insights to existing interpretations.At rest, metabolic demands are closely matched by glucose supply across the blood-brain barrier (BBB), regulated by glucose transporter 1 (GLUT1). During neuronal activation, both glucose transport and phosphorylation by hexokinase are elevated to meet increased energy requirements. Simulations indicate that rapid [¹⁸F]FDG signal increases are primarily driven by BBB transport (K₁ and k₂ in a two-tissue compartment model), with subsequent increases in hexokinase activity (k₃). Mechanisms supporting increased BBB transport include elevated glucose concentration gradient towards the brain and changes in GLUT1 intrinsic properties, but only minor effects of blood flow. Conversely, moment-to-moment fluctuations in [¹⁸F]FDG, as examined using molecular connectivity, reflect temporally synchronized supply in response to metabolic demand, mediated jointly by blood flow and BBB transport.We emphasize that, particularly during neuronal activation, the strong coupling between BBB transport and metabolism underpin the [¹⁸F]FDG fPET signal. Considering alterations of GLUT1 and subsequent metabolism in numerous brain disorders, stimulation-induced changes of energy demands and moment-to-moment fluctuations of molecular connectivity represent a promising opportunity to investigate the underlying pathophysiological processes.

Abstract: In order to solve the dynamic analysis and interactive imaging control problems in the deformation process of bionic soft lenses, dielectric elastomer (DE) actuators are separated from the convex lenses, and data-driven eye-controlled motion technology is investigated. According to the DE properties which are consistency with the deformation characteristics of hydrogel electrodes, the motion and deformation effects of eye-controlled lenses under film prestretching, lens size and driving voltage, are studied. The results show that when the driving voltage increases to 7.8 kV, the focal length of the lens, whose prestreching λ is 4, and the diameter d is1 cm, varies in the range of 49.7 mm and 112.5 mm. And the maximum focal‒length change could reach to 58.9%. In the process of eye controlling design and experimental verification, the high DC voltage supply was programmed and the eye movement signals for controlling the lens were analyzed by MATLAB software. The eye-controlled interactive real-time motion and tunable imaging of the lens were realized. The response efficiency of soft lenses could reach over 93%. The adaptive lens system developed in this research has the potential to be applied to medical rehabilitation, exploration, augmented reality (AR) and virtual reality (VR) in the future.

Abstract: In electronic product manufacturing, quality control and production cost management pose challenges for enterprises. First, key factors for production decisions are identified: component inspection, assembly, inspection of semi-finished and finished products, and handling defective goods at different stages. Next, dynamic programming and genetic algorithms optimise sampling inspection. A production decision model covers multiple processes and component combinations. The relationship between the inspection and the final product quality is explored, showing that different decision paths affect the total cost and defect rate. Real-time monitoring and a dynamic Bayesian network guide production strategy adjustments to boost efficiency and reduce defects. This study proposes adaptable inspection and disassembly strategies that reduce costs and optimise resource use across production scenarios.

Abstract:

Immuno modulation and metabolism are crucial for survival, with host metabolites and microbiota influencing immune responses. This study explores the role of immune stimulation on insulin sensitivity, lipid management, and glucose regulation, highlighting tissue-specific effects in muscles, liver, adipose tissue, and the gut. It also examines human milk-derived bioactives on early metabolism and immunity, as well as how microbial substrates, postbiotics, and dysbiosis impact immune function and contribute to metabolic diseases, including obesity and inflammation. Prenatal immunometabolism changes significantly impact pregnancy outcomes and are linked to long-term metabolic issues. This review explores various factors, including the microbiome, autophagy, epigenetics, organokine signaling, and immunological dysfunction from undernutrition, along with advancements in metabolomics. It assesses therapeutic strategies aimed at restoring metabolic-immune balance, particularly focusing on anti-inflammatory and nutritional interventions such as IL-1β antagonism, omega-3 fatty acids, and intermittent fasting. The findings highlight the importance of understanding immune-metabolic interactions to improve health and develop personalized treatments for metabolic syndrome and related disorders.

Abstract: Climate change is a major global challenge, negatively affecting agriculture, economies, and societies. This research focuses on its impact on crop productivity and farmers' adaptation strategies in Tuli Guled woreda, Ethiopia. Data from 255 household heads reveal that 60% of farmers report significant life impacts, with 67% experiencing income declines due to reduced crop yields. Most farmers (87.5%) are considering relocating. Preferred adaptation strategies include improved seeds (48.4%) and crop diversification (37.2%). Meteorological data from 1993-2022 show decreased precipitation and increased temperatures. The study indicates deadly impacts of climate change on agriculture, necessitating training on sustainable practices and adapt.

Abstract: Pooled proof-of-work ecosystems inherit an expanding attack surface from plaintext or weakly authenticated transport, centralized job selection, and latency-sensitive job propagation, enabling adversaries to siphon hash power, manipulate workloads, or degrade availability at scale. This paper systematizes threats against mining coordination protocols, including man-in-the-middle share redirection, extranonce/job injection, and routing-layer disruption that silently wastes miner effort, then maps each vector to concrete transport- and protocol-layer countermeasures such as AEAD-secured sessions, authenticated key agreement, header-only job separation to curb emptyblock incentives, and decentralized template declaration to resist transaction-level coercion. A practitioner-focused implementation pathway demonstrates secure role composition with a pool service, translation proxy, template provider, and negotiator, hardening legacy endpoints while progressively enabling encrypted binary framing, version rolling controls, and minerdriven template workflows. The result is a defense-in-depth blueprint that both mitigates known Stratum-era attacks and reduces single points of failure in pooled mining, providing actionable guidance for secure-by-default deployment of nextgeneration coordination under adversarial network conditions.

Abstract: This study explores the adoption of artificial intelligence as a strategic pathway to achieving and sustaining competitive advantage, drawing on in-depth qualitative insights from industry practitioners. As artificial intelligence increasingly reshapes business environments, organizations are under growing pressure to understand not only its technical potential but also its strategic and organizational implications. This research adopted a qualitative, interpretive approach to capture practitioners’ lived experiences, perceptions, and sensemaking related to artificial intelligence adoption across diverse industries, including manufacturing, services, retail, and technology-driven sectors. Data were collected through semi-structured interviews with professionals directly involved in artificial intelligence initiatives, enabling a rich exploration of motivations, implementation processes, challenges, and perceived outcomes. The findings reveal that artificial intelligence adoption was widely perceived as a strategic necessity rather than a discretionary innovation. Practitioners emphasized that competitive advantage emerged when artificial intelligence initiatives were closely aligned with organizational strategy, supported by leadership commitment, and embedded into everyday decision-making processes. Rather than replacing human expertise, artificial intelligence was viewed as augmenting managerial judgment by enhancing analytical depth, speed, and confidence, particularly in complex and uncertain contexts. The study further highlights that the competitive benefits of artificial intelligence unfolded over time, with early gains centered on operational efficiency and later advantages linked to improved decision quality, innovation, and customer-centric value creation. Importantly, the research underscores that artificial intelligence adoption is as much a human and organizational challenge as it is a technological one. Issues such as resistance to change, skills gaps, data quality, and trust in AI outputs shaped adoption trajectories and influenced competitive outcomes. Organizations that invested in learning, cultural openness, and change management were better positioned to translate artificial intelligence investments into sustained competitive advantage. By foregrounding practitioner perspectives, this study contributes a human-centered understanding of artificial intelligence adoption and offers practical insights for organizations seeking to leverage AI as a dynamic and adaptive source of competitive advantage in an increasingly data-driven economy.

Abstract: Driven by global initiatives to mitigate climate change, the offshore wind power industry is experiencing rapid growth. Personnel transfer between service operation vessels (SOVs) and offshore wind turbines under complex sea conditions remains a critical factor governing the safety and efficiency of operation and maintenance (O&amp;M) activities. This study establishes a fully coupled dynamic response and control simulation framework for an SOV equipped with an active motion-compensated gangway. A numerical model of the SOV is first developed using potential flow theory and frequency-domain multi-body hydrodynamics to predict realistic vessel motions, which serve as excitation inputs to a co-simulation environment (MATLAB/Simulink coupled with MSC Adams) representing the Stewart platform-based gangway. To address system nonlinearity and coupling, a composite control strategy integrating velocity and dynamic feedforward with three-loop PID feedback is proposed. Simulation results demonstrate that the composite strategy achieves an average disturbance isolation degree of 21.81 dB, significantly outperforming traditional PID control. Validation is conducted using a ship motion simulation platform and a combined wind-wave basin with a 1:10 scaled prototype. Experimental results confirm high compensation accuracy, with heave variation maintained within 1.6 cm and a relative error between simulation and experiment of approximately 18.2%.

Abstract: This work develops the transfer mechanism by which fluctuations of a light spectator scalar field carrying long-range correlation structure are converted into observable curvature perturbations during inflation. It serves as the theoretical companion to recent falsification-oriented viability studies (A Quantum Memory Field: Inflationary Local-Like Non-Gaussianity and Non-Markovian Coherence Recovery), providing the explicit modulation mechanism underlying the phenomenological signatures tested there. Using a $\delta N$ background-modulation framework combined with quasi-single-field mode coupling, it is shown that derivative spectator couplings can yield squeezed-enhanced, local-like non-Gaussianity when the spectator field remains light and its correlations persist beyond horizon exit. The resulting bispectrum is local-like but not identical to the pure local template, motivating the conservative use of existing observational constraints as proxies rather than exact shape-matched bounds. The analysis clarifies the assumptions underlying parameter viability scans and provides a consistent transfer picture linking memory-bearing spectator dynamics to curvature perturbations.

Abstract: Porous NiCo₂O₄ nanomaterials were prepared by using in-situ synthesized polyacrylamide as template, and cobalt nitrate, nickel nitrate and urea as raw materials. XRD and FESEM results show the spinel type NiCo2O4 electrode materials with 3D macroporous/mesoporous structure and an average grain size of about 8.1 nm had been synthesized by calcining the amorphous precursor at 300 °C. The electrochemical results of as-calcined NiCo2O4 showed that the specific capacitance at 10 A g^-1 is equivalent to 88.9% of 1 A g^-1, indicating good rate characteristics. After 3000 cycles, the specific capacity gradually increases from 275.2 F g^-1 to 678.4 F g^-1, and the capacitance retention rate is up to 246.5%, suggesting excellent cycling stability and capacity retention rate.

Abstract: The SARS-Cov-2, the causative organism of COVID-19 pandemic, is a highly transmissible, enveloped, single stranded, RNA virus that has mutated into several variants, complicating vaccine strategies and drug resistance. Novel treatment modalities targeting conserved structural vulnerable points are essential to combat these challenges. A true experimental pretest and post-test study design was applied in order to assess the effect of high frequency ultrasound on SARS-Cov-2 viral load under controlled laboratory biosafety setup. Since detailed imaging tools are unavailable for analysis, viral disruption was indirectly measured with the real time PCR cycle threshold (Ct) values. Ct values increased significantly after high-frequency ultrasound exposure, indicating a reduction in detectable viral RNA. Paired t-test indicated a significant difference between pre-test and post-test Ct (P &lt; 0.001), which is supported by Monte Carlo test results that revealed statistically significant shifting in viral load categories (P = 0.001, two-sided). As, 85.7% of high-viral-load samples converted to low or moderate content, 46.7% of low or moderate samples were shifted to negative content. This intervention produced a massive effect size (Cohen’s d = 2.422). These results indicate that ultrasound may offer a promising non-pharmacological approach to target any enveloped viruses.

Abstract: Artificial intelligence (AI) is increasingly embedded in education through adaptive platforms, intelligent tutoring systems, and generative tools. While these technologies promise efficiency and personalization, they also raise concerns about pedagogical deskilling, reduced teacher autonomy, and ethical risks. This paper conceptualizes the potential impacts of AI on teaching expertise and instructional design through the lens of Cognitive Load Theory (CLT). The aim is to conceptualize how AI may reshape the management of intrinsic, extraneous, and germane cognitive loads. The study proposes that AI may effectively scaffold intrinsic load and reduce extraneous distractions but displace teacher judgment in ways that undermine germane learning and reflective practice. Additionally, opacity, algorithmic bias, and inequities in access may create new forms of cognitive and ethical burden. The conceptualization presented in this paper contributes to scholarship by foregrounding teacher cognition, an underexplored dimension of AI research, conceptualizing the teacher as a cognitive orchestrator who balances human and algorithmic inputs, and integrating ethical and equity considerations into a cognitive framework. Recommendations are provided for teacher education, policy, and AI design, emphasizing the need for pedagogy-driven integration that preserves teacher expertise and supports deep learning.

Abstract: Few-shot object detection (FSOD) in high-resolution remote sensing (RS) imagery remains challenging due to scarce annotations, large intra-class variability, and high visual similarity between categories, which together limit the generalization ability of convolutional neural network (CNN)-based detectors. To address this issue, we explore leveraging large vision-language models (LVLMs) for FSOD in RS. We propose a two-stage, parameter-efficient fine-tuning framework with hierarchical prompting that adapts Qwen3-VL for object detection. In the first stage, low-rank adaptation (LoRA) modules are inserted into the vision and text encoders and trained jointly with a Detection Transformer (DETR)-style detection head on fully annotated base classes under three-level hierarchical prompts. In the second stage, the vision LoRA parameters are frozen, the text encoder is updated using K-shot novel-class samples, and the detection head is partially frozen, with selected components refined using the same three-level hierarchical prompting scheme. To preserve base-class performance and reduce class confusion, we further introduce knowledge distillation and semantic consistency losses. Experiments on the DIOR and NWPU VHR-10.v2 datasets show that the proposed method consistently improves novel-class performance while maintaining competitive base-class accuracy and surpasses existing baselines, demonstrating the effectiveness of integrating hierarchical semantic reasoning into LVLM-based FSOD for RS imagery.

Abstract: Efficient placement of Virtual Machines (VMs) iscritical for optimizing resource utilization and ensuring servicereliability in cloud computing infrastructures. Existing validationmethods for VM placement algorithms, such as limited in-vivoexperiments and ad hoc simulators, often fail to reflect real-worldcomplexities and provide fair comparisons. This paper introducesVMPlaceS, a simulation framework built on SimGrid, designed toaddress these shortcomings by enabling the robust evaluation andcomparison of VM placement strategies. VMPlaceS facilitateslarge-scale scenario modeling with customizable parameters torepresent dynamic workloads and realistic platform conditions.By simulating centralized, hierarchical, and distributed algo-rithms, this study highlights the framework’s capability to assessscalability, reactivity, and SLA adherence in various deploymentscenarios. VMPlaceS emerges as a valuable tool for researchersand practitioners to explore innovative VM placement solutionsand advance the field of cloud computing resource management.

Abstract: How people perceive their neighborhoods can impact their aging trajec-tories, with less known regarding neighborhood perceptions among older adults from minoritized groups. This study examined the impacts of be-havioral and psychosocial factors on neighborhood perceptions among Non-Latino (NL) Black and Latino older adults. Participants (N=506) were NL Black (n=372) and Latino (n=134) older adults (?̅ age=79 years) without dementia. Participants completed a modified Perceptions of Neighbor-hood Environments Scale (mPNES; higher scores indicate more favorable perceptions) and measures of behavioral and psychosocial factors. We performed fully saturated linear regression analyses to assess how each factor related to the mPNES, followed by stepwise linear regression analyses to determine final predictive models for the full sample and each ethnoracial group. For the full sample, higher purpose in life, more physical activity, less discrimination, and higher income were associated with higher mPNES scores. For NL Black older adults, more physical ac-tivity, less discrimination, and higher income were associated with higher mPNES scores. For older Latinos, more purpose in life and a larger social network size were associated with higher mPNES scores. Distinct associ-ations exist by ethnoracial group and suggest unique considerations to facilitate positive neighborhood perceptions among NL Black and Latino older adults.

Abstract: Chronic diseases—including diabetes mellitus, cardiovascular disease, chronic kidney disease, and autoimmune disorders—remain the leading causes of global morbidity and mortality. While biomedical pathophysiology defines the etiology and progression of these conditions, cultural factors significantly modulate how patients perceive illness, engage in treatment, and adhere to medical recommendations. This review synthesizes evidence from cross-cultural studies, with a specific focus on medical manifestations and therapeutic challenges, to examine how sociocultural determinants intersect with biological disease processes. We highlight nuanced case comparisons between South Asian, East Asian, Middle Eastern, African, Latinx, and Indigenous populations, illustrating how cultural constructs such as collectivism, fatalism, stigma, traditional medicine reliance, and health literacy directly influence outcomes in chronic disease management. Importantly, we integrate evidence-based recommendations for healthcare professionals, emphasizing culturally tailored interventions, precision medicine approaches, and the role of interdisciplinary care teams.