Search Results (5,181)

Recently, poultry production has increased worldwide to address the increasing demand of affordable animal-sourced protein. To meet this requirement, poultry production operations have become more concentrated, introducing management challenges related to disease control, productivity, and animal welfare. However, manual flock monitoring and management have become impractical in such cases, creating a need for automatic data-driven management approaches. In this context, the Internet of Things (IoT) has emerged as a potential technological solution for continuous flock monitoring, data sharing, and decision-making. Despite this, its adoption in poultry production is limited compared with its widespread use in crop production, transportation, and manufacturing industrial sectors. Furthermore, advanced analytical techniques such as artificial intelligence (AI), applied to data gathered by IoT-enabled devices, have shown promising results by generating actionable information. Existing literature suggests that the integration of IoT and AI can address the major challenges associated with modern large-scale poultry production systems. While most applications remain at the research scale, such technologies have the potential for improving flock monitoring, enhancing productivity, and ensuring proper animal welfare. This narrative review examines the current state of IoT and AI based technologies, together or in part identifies the limitations, research gaps, and opportunities for future development. Full article

Ship propulsion electrification is an important enabler towards a sustainable shipping industry. Ship power systems are turning into modern microgrids integrating different generation/storage resources, converter technologies and electric propulsion, utilizing different control levels and communication systems. The definition of comprehensive test requirements, set-ups and procedures is critical to ensure that the equipment will behave as expected in the ship system context. Comprehensive testing is becoming increasingly challenging due to complex interactions at the system level, attributed to electrical, mechanical/hydrodynamic, control, protection, and information and communication systems present in modern and future ships. Standardization has addressed the testing of several individual components, as well as specific system tests for marine applications; however, a holistic testing approach is missing. This paper reviews the generic and maritime standards for testing ship electric power propulsion systems and equipment, focusing on generators/motors, power electronic drives and onshore power supply systems. A review of the scientific literature is performed, classifying the publications according to the testing method, such as pure hardware tests, co-simulation and hardware in the loop simulation (HIL). The need for holistic testing of shipboard microgrids is explained. A holistic HIL testing approach is proposed, which integrates hardware controllers and power equipment of different manufacturers and functions, in order to reduce the complexity and cost of sea trials. The proposed approach is accompanied by example implementation and application guidelines. Full article

Background: School feeding programs aim to improve child nutrition, and they may influence weight outcomes insofar as program modalities and household responses alter children’s total energy intake. This is especially relevant in countries facing the double burden of malnutrition, where undernutrition and micronutrient deficiencies coexist with rising overweight and obesity. This study estimates the effect of Peru’s former National School Feeding Program on obesity and excess weight among children aged 36 to 59 months under a selection-on-observables identification strategy and assesses whether impacts differ across operational modalities, particularly breakfast-only versus breakfast plus lunch and ready-to-eat rations versus foods delivered for preparation. Methods: We use repeated cross-sectional microdata from the Demographic and Health Survey (ENDES) pooled over 2014 to 2018 and link them to administrative information. The sample includes 18,959 children aged 36 to 59 months. To improve comparability, we estimate propensity score weights targeting the average treatment effect on the treated (ATT) using a machine learning generalized boosted model (GBM), and assess covariate balance using standardized mean differences and Kolmogorov–Smirnov statistics. Identification assumes conditional independence given observed covariates and overlap (common support). Main estimates rely on weighted probit models with fixed effects, progressively adding exposure duration, modality indicators, and controls. Distributional effects are examined using quantile regression on the continuous weight-for-height z-score. Results: Without differentiating modalities, beneficiary status is not associated with a statistically significant change in obesity, while pooled baseline estimates indicate a statistically significant higher probability of excess weight. Modality-specific results show that obesity declines only when Qali Warma is delivered as breakfast plus lunch through products to be prepared (approximately −1.0 percentage point in parsimonious models and −0.4 percentage points after controls). Evidence for excess weight is directionally consistent by modality but less conclusive once controls are included. Conclusions: Qali Warma’s effects on early-childhood weight outcomes depend on implementation modality. Evaluations of school feeding programs should incorporate operational heterogeneity, particularly during program redesign. Full article

We propose a proposal to achieve strong mechanical squeezing in an optomechanical system through the joint effect of a weak squeezed vacuum field and Duffing nonlinearity. The squeezing of the cavity field induced by the squeezed vacuum field is transferred to the mechanical oscillator, which has already been squeezed via Duffing nonlinearity. This joint effect significantly enhances the degree of mechanical squeezing, enabling it to exceed the 3 dB strong mechanical squeezing limit. Moreover, the resulting mechanical squeezing exhibits remarkable robustness against thermal noise. The joint effect proposed in this scheme can be directly observed through homodyne detection of the cavity output field. This novel approach opens up a new avenue for generating a strong mechanical squeezed state and provides a promising pathway for the applications of macroscopic quantum control in quantum sensing and quantum information processing. Full article

The Mobile Reliable Opportunistic Routing (MROR) protocol improves data-forwarding reliability in Cognitive Radio Sensor Networks (CRSNs) through mobility-aware virtual contention groups and handover zoning. However, its heuristic decision logic is difficult to optimize under highly dynamic spectrum access and random node mobility. To address this limitation, we present DRL-MROR, a refined routing framework that incorporates deep reinforcement learning (DRL) to enable intelligent and adaptive forwarding decisions. In DRL-MROR, the secondary users (SUs) act as autonomous agents that observe local state information, including primary-user activity, link quality, residual energy, and neighbor-mobility patterns. Each agent learns a forwarding policy through a Deep Q-Network (DQN) optimized for long-term network utility in terms of throughput, delay, and energy efficiency. We formulate routing as a Markov Decision Process (MDP) and use experience replay with prioritized sampling to improve learning stability and convergence. The DQN used at each node is intentionally lightweight, requiring 5514 trainable parameters, about 21.5 kB of weight storage in 32-bit precision, and approximately 5.4k multiply-accumulate operations per inference, which supports practical deployment on edge-capable CRSN nodes. Extensive simulations show that DRL-MROR outperforms the original MROR protocol and representative AI-based routing baselines such as AIRoute under diverse operating conditions. The results indicate gains of up to 38% in throughput, 42% in goodput, a 29% reduction in energy consumed per packet, and an approximately 18% improvement in network lifetime, while maintaining high route stability and fairness. DRL-MROR also reduces control overhead by about 30% and average end-to-end delay by up to 32%, maintaining strong performance even under elevated PU activity and higher node mobility. These results show that augmenting opportunistic routing with lightweight DRL can substantially improve adaptability and efficiency in next-generation IoT-oriented CRSNs. Full article

Uncertainty is a central feature of sport and has been extensively examined in sport science, primarily from performance-oriented perspectives such as anticipation, decision-making, and motor control. However, less attention has been given to how athletes subjectively perceive and experience uncertainty and how these interpretations shape their responses. The present study addresses this gap by exploring athletes’ lived experiences of uncertainty in sport. Using a qualitative design, semi-structured interviews were conducted with former youth athletes from various sports. Data were analyzed using thematic analysis, generating 15 themes organized across key dimensions of uncertainty, including unpredictability, lack of information, internal versus external sources, and the appraisal of uncertainty as a threat or a challenge. Findings indicate that uncertainty is experienced as a multifaceted and subjective phenomenon shaped by perceived control, prior experience, and situational context. Athletes differed in how they interpreted uncertainty, with some perceiving it as threatening and others as an opportunity for growth, which in turn influenced emotional responses and coping strategies. Key coping mechanisms included communication, information seeking, social support, and focusing on controllable aspects of performance. These findings extend existing sport science literature by integrating experiential and interpretative dimensions of uncertainty with established performance-based approaches. Furthermore, the results suggest conceptual links with the construct of intolerance of uncertainty (IU), highlighting the potential value of examining individual differences in how athletes appraise and manage uncertainty. The study provides an exploratory foundation for future research integrating IU within sport contexts and underscores the importance of addressing both subjective and performance-related aspects of uncertainty in sport psychology. Full article

Background/Objectives: Beyond glycemic control, oral antidiabetic drugs (OADs) may exert class-specific effects on muscle mass and hematologic parameters. However, real-world evidence comparing these effects across OAD classes remains limited. This study aimed to evaluate the differential effects of commonly prescribed OADs on skeletal muscle mass (SMM) and hemoglobin (Hb) levels in adults with type 2 diabetes mellitus (T2DM). Methods: In this prospective observational cohort study, 60 adults with newly initiated OAD therapy were followed for six months at a tertiary care center in Türkiye. Patients were classified according to the OAD class newly added to their regimen (metformin, sulfonylureas, dipeptidyl peptidase-4 inhibitors, pioglitazone, or sodium–glucose cotransporter-2 inhibitors [SGLT2-i]). Multi-frequency bioelectrical impedance analysis was used to evaluate body composition, and hematologic parameters including Hb were obtained at both time points. To account for potential confounders—including age, sex, BMI, baseline Hb, and eGFR—binary logistic regression analyses were performed. Results: Patients initiated on pioglitazone (n = 11) demonstrated a borderline within-group increase in SMM in unadjusted analysis (median delta +0.17 kg, IQR −0.55 to +0.50; p = 0.050); however, this association was attenuated and no longer statistically significant after multivariable adjustment (OR 2.16, 95% CI 0.60–7.83; p = 0.240). In contrast, SGLT2-i users (n = 28) showed a significant increase in Hb (median delta +0.10 g/dL, IQR −0.30 to +0.50; p = 0.022), which remained significant after adjustment (OR 4.22, 95% CI 1.32–13.44; p = 0.015). Other OAD classes were not associated with meaningful changes in SMM or Hb. Conclusions: In this real-world prospective cohort, pioglitazone showed a trend toward increased SMM in unadjusted analysis that did not reach significance after adjustment, suggesting a hypothesis-generating signal warranting further investigation. SGLT2 inhibitors were independently associated with increased Hb levels, though the observed median increment was modest in absolute terms. These findings highlight potentially clinically relevant, non-glycemic effects of OAD classes and may inform individualized treatment selection, particularly in patients at risk of sarcopenia or anemia. Adequately powered, prospective studies are needed to validate and extend these preliminary observations. Full article

Collaborative operation of heterogeneous UAV swarms in dense urban environments remains challenging because right-of-way allocation is often rigid, frequent replanning consumes considerable onboard computation, and paths obtained by purely mathematical optimization may not be easy to execute under real dynamic constraints. This paper presents a physics-informed, event-triggered path planning and control framework, termed Physics-Informed SSA-MPC. Its global search layer is built on the Sparrow Search Algorithm (SSA), whose search mechanism originates from sparrow foraging and anti-predatory behaviors. On this basis, the method combines an event-triggered Stackelberg game for airspace coordination, a physically constrained SSA for global path generation, and an event-triggered MPC for local replanning. Battery State of Health (SoH) is incorporated into the adaptive search process, while Lévy-flight updates are limited by the maximum available acceleration to avoid infeasible path mutations. Local replanning is activated only when predicted safety ellipsoids overlap or tracking errors exceed prescribed thresholds, which helps reduce redundant computation. Simulations in a digital twin of Lujiazui, Shanghai, show that the proposed method shortens path length by 3.3% to 14.9%, reduces obstacle-avoidance latency to 45 ms, and achieves a 100% engineering feasibility rate. Full article

Sensing audio using non-acoustic modalities such as millimeter-wave radar and laser-based systems has emerged as an active research area with significant implications for privacy, security, and robust speech processing. These approaches recover speech-related information from vibration measurements captured by non-acoustic sensing modalities. Prior work spans a wide range of techniques, from classical signal-processing pipelines to modern machine-learning and deep-learning models, enabling applications such as speech reconstruction, eavesdropping, automatic speech recognition, and noise-robust enhancement. Some systems rely on radar or laser sensing as a standalone audio surrogate, while others fuse radar-derived features with microphone signals to improve robustness in noisy or non-line-of-sight environments. Experimental results across the literature demonstrate that recovering intelligible speech or discriminative speech features from radar or laser-sensed vibrations is feasible under controlled conditions. However, performance remains sensitive to practical factors including sensing distance, object material and geometries, environmental interference, multipath effects, and task complexity. Not all speech-related tasks are reliably solved, particularly in unconstrained real-world scenarios. Overall, the field is rapidly evolving, with open challenges in robustness, generalization, and deployment, offering several promising directions for future research. Full article

Semantic communications have emerged as a key paradigm for intelligent sixth-generation (6G) wireless networks, which aim to convey the meaning of information rather than accurate bit sequences. However, in open-space low Earth orbit (LEO) satellite links, the broadcast nature and wide beam coverage expose semantic transmissions to severe eavesdropping risks. This paper establishes a unified theoretical and algorithmic framework for secure semantic downlink transmission in satellite networks. In particular, we first develop an integrated mathematical model that couples the semantic representation process, physical-layer satellite propagation characteristics, and information-theoretic secrecy into a single analytical formulation. By defining a joint semantic security cost function, the antagonistic trade-off between semantic fidelity and secrecy capacity is quantitatively characterized under realistic power, beamforming, and propagation constraints. To balance semantic fidelity and information secrecy, a reinforcement-learning-based optimization framework is proposed, wherein an actor–critic agent learns optimal power allocation and semantic weighting strategies through continuous interaction with the environment. This learning-based optimization approach enables autonomous control without requiring explicit channel distribution knowledge or offline parameter tuning. Extended simulation results show that the proposed approach consistently enhances both semantic fidelity and secrecy performance compared with conventional power-control schemes and demonstrate its potential as a foundational architecture for secure and intelligent semantic communications in next-generation satellite networks. Full article

This paper examines whether Open Government Data (OGD) can improve urban air quality in China. Using the staggered rollout of city-level OGD platforms as a quasi-natural experiment, it estimates the effect of platform launches on annual average PM_2.5 concentration at the prefecture-city level. The results show that OGD significantly reduces PM_2.5 concentration. This finding remains robust after replacing the dependent variable, conducting event-study tests, applying the Goodman–Bacon decomposition, using a heterogeneity-robust estimator, and carrying out a placebo test. The analysis also shows that controlling for the prior influence of provincial platforms is important, because ignoring this factor may lead to an underestimation of the city-level policy effect. Further analysis suggests that OGD may improve air quality by promoting green innovation, strengthening the government’s orientation toward high-quality development, and increasing public environmental attention. The effect is stronger in cities with better digital infrastructure and stronger government governance capacity, and is generally more pronounced in major urban agglomerations. Overall, the findings suggest that OGD is not only a tool for information disclosure, but also a policy instrument with broader value for environmental governance. Full article

Ginkgo biloba products have been used for decades for cognitive symptoms, yet the clinical evidence in Alzheimer’s disease (AD) remains modest and heterogeneous. This review revisits key symptomatic and prevention trials and summarizes how systematic reviews and meta-analyses have informed ongoing clinical skepticism, often citing small effect sizes, limited patient-centered meaningfulness, short follow-up, and repeated trial designs. We suggest that long-standing ambiguity reflects multiple, overlapping sources of heterogeneity, including mixed-pathology recruitment, variable dosing and exposure duration, inconsistent outcome frameworks, and limited integration of biological readouts; differences across preparations and characterization practices may further contribute to variability. In the biomarker era, AD is increasingly defined biologically, and amyloid PET-confirmed cohorts offer a clearer test by reducing diagnostic noise and enabling mechanism-adjacent interpretation. Recent studies in amyloid PET-positive MCI/AD report clinical preservation alongside directional changes in plasma oligomerization tendency (MDS-OAβ), with decreases in treated groups compared with increases in controls. While such findings cannot, by design, establish disease-modifying effects, they provide a biologically anchored context for interpreting modest clinical signals. We conclude with practical recommendations to align cohort biology, stage, exposure certainty, duration, endpoints, and biomarker panels in next-generation trials of Ginkgo preparations in early AD-spectrum disease. Full article

Generative artificial intelligence (AI) is increasingly used in higher education as an interactive tutoring partner rather than a passive information tool. While AI offers opportunities to support learning, concerns remain regarding cognitive offloading, reduced engagement, and unreflective use. Although instructional scaffolding is a well-established design principle for supporting complex learning, its role in shaping cognitive and metacognitive processes in AI-supported settings remains underexplored. This quasi-experimental pre–post study examined how varying levels of scaffolding influence learning outcomes and motivational, cognitive and metacognitive processes during AI-tutored learning. A total of 175 first-semester students from two faculties and diverse academic backgrounds completed the same academic task within a four-hour university session under one of three conditions: (1) full scaffolding, including a structured prompting template based on the Goal–Context–Constraints (GCC) strategy, iterative refinement, and reflective guidance; (2) light scaffolding, including the GCC prompting template; or (3) no scaffolding template as the control condition. Measures included knowledge gain, motivation, cognitive load, critical thinking, and reflective use. Data were analysed using ANOVAs, ANCOVAs, regression models, and PROCESS moderation and mediation analyses. Across the conditions, students showed significant gains in knowledge, critical thinking, and reflective use, while motivation remained stable and intrinsic and extraneous cognitive load decreased; no significant differences between scaffolding conditions were observed. The scaffolding conditions did not produce significant interaction effects, although descriptive trends suggested higher gains in higher-order knowledge under scaffolded conditions. Overall, the findings suggest that short-term learning gains in AI-supported settings may not depend on scaffolding intensity alone, but rather on how learners engage with AI during the learning process. Full article

Extended reality (XR), encompassing augmented reality (AR), virtual reality (VR), and mixed reality (MR), is a key enabling technology for virtual worlds, and XR-related patents continue to grow rapidly. However, patent-based XR technology analysis faces a fundamental challenge: document–keyword matrix (DKM) built from patent titles and abstracts are typically high dimensional, sparse, and often exhibit excess zeros, which can distort inference when conventional text mining pipelines are applied without a generative count perspective. In this study, we propose a statistically grounded XR technology analysis framework that combines likelihood-based count modeling with interpretable structure mining to map XR sub-technologies from a patent DKM. Using an XR patent–keyword matrix, we fit Poisson regression (PR), negative binomial regression (NBR), and zero-inflated negative binomial regression (ZINBR) models via maximum likelihood estimation (MLE), controlling for document-length effects. Model selection by Akaike information criterion (AIC) consistently favored NBR for both target keywords, indicating substantial overdispersion in XR patent counts. We interpret exponentiated coefficients as incidence rate ratios (IRRs) and construct a technology relatedness network from significant IRR edges, revealing a dual-axis XR structure: reality is anchored in an AR or VR experience and content axis such as virtual and augment, whereas extend is embedded in a structure and integration axis for example, surface, edge, layer, and connectivity-related terms. To show how the proposed method can be applied to real domains, we searched the XR patent documents, and analyzed them for XR technology analysis. Full article

The development of high-performance and sustainable carbon electrodes is increasingly important for next-generation supercapacitors, yet controlling heteroatom doping and hierarchical pore evolution in biomass-derived carbons remains a key challenge. Lignin, as an abundant aromatic biopolymer, offers a structurally rich platform for designing functional carbons, but its rigid cross-linked architecture limits precise pore regulation and efficient nitrogen incorporation. In this work, nitrogen-doped hierarchical porous carbons were engineered from enzymatically treated lignin through a synergistic urea-assisted nitrogen doping and KOH activation strategy. The urea–KOH co-activation drives the coordinated evolution of micropores and mesopores. This approach yields an optimized carbon material possessing a high BET surface area of 2569 m² g⁻¹, an interconnected micro–mesoporous architecture, and a favorable distribution of pyridinic, pyrrolic, and graphitic nitrogen species. The engineered pore hierarchy is correlated with enhanced ion transport kinetics, as evidenced by a high b value of 0.99 and a capacitive contribution of 98.5% at 100 mV s⁻¹; nitrogen functionalities introduce redox-active sites and improve interfacial wettability. As a result, the selected material delivers a high specific capacitance of 221 F g⁻¹ at 0.5 A g⁻¹, strong rate capability with 84.4% retention at 20 A g⁻¹, and excellent cycling durability with 90.7% capacitance retention after 50,000 cycles. This study demonstrates a potentially mechanistically informed, scalable pathway for coupling enzymatic structural regulation with chemical activation, offering a sustainable route for transforming lignin into high-value carbon electrodes suitable for advanced supercapacitor applications. Full article