Search Results (44,694)

Employing a 4 (opponent type) × 2 (communication condition) between-subjects design, the study recruited 194 valid human participants to complete three rounds of game tasks. Results revealed: (1) The type of game counterpart exerted a significant main effect on participants’ remaining funds (F(3, 185) = 3.179, p = 0.025). Human participants retained significantly more funds when the counterpart was a real large model compared to other groups. (2) A significant interaction existed between the type of game counterpart and communication conditions (F(3, 185) = 3.318, p = 0.021). Specifically, when the opponent was a fake AI model (presented as human but actually an AI), human participants’ remaining funds were significantly higher under the communication condition than without communication (p = 0.012). This indicates that communication can promote rational decision-making in identity mismatch scenarios by providing additional behavioral cues. In the fake-human group (informed as human but actually AI), a numerical trend toward increased funds was also observed under communication conditions, though it did not reach statistical significance (p = 0.159); (3) The moderating effect of social value orientation did not reach significance. These findings extend the application of the theory of mind in human–machine games, revealing the complex influence mechanism of identity perception and communication dynamics on rational decision-making. Full article

Stevia is a natural source of high-intensity sweeteners, collectively known as steviol glycosides (SG), which are approximately 300 times sweeter than sucrose and widely used as sugar substitutes. This study examines the impact of five different red-to-blue (R:B) light ratios on SG content and yield in hydroponic Stevia across four growth stages. Results indicate that the highest and lowest leaf dry weights were recorded in the R1B0 (R:B = 1:0) and R0B1 (R:B = 0:1) groups, at 2.88 and 1.98 g/plant, respectively, reflecting a 45.45% difference. The total SG content in dried leaves was highest in R0B1 (196.32 mg/g) and lowest in R1B0 (115.16 mg/g), with a 70.48% variation. The highest and lowest total SG yields (YSG) per square meter were observed in R0B1 (46.56 g/m²) and R50B37 (35.70 g/m²), differing by 30.42%. Stage-specific optimal YSG values were identified, with designated growth stages P1 (early vegetative growth phase), P2 (early leaf development phase), and P3 (late leaf development phase) favoring R4B1 and P4 (leaf senescence phase) favoring R0B1. These findings suggest an optimized lighting strategy for the four growth stages of hydroponic Stevia, sequentially applying R4B1, R4B1, R4B1 and R0B1 to enhance biomass accumulation and SG production at different developmental stages. Full article

Background/Objectives: Blood glucose prediction (BGP) for individuals with type 1 diabetes (T1D) is a clinically essential yet highly challenging task in time series forecasting (TSF) and an important problem in personalised medicine. Accurate bespoke BGP is crucial for individualised T1D management, reducing complications, and supporting patient-specific glycaemic risk mitigation. However, the pronounced volatility of glycaemic fluctuations in T1D, combined with the need for mathematical rigor and clinical relevance, hampers reliable prediction. This complexity underscores the demand to explore and enhance more advanced techniques. While adversarial learning is adept at modelling intricate data variability, its potential for BGP remains largely untapped. Methods: This work presents a novel approach for BGP by addressing a key limitation in conventional adversarial learning when applied to this task. Typically, these methods optimise prediction accuracy within a set horizon by minimising adversarial loss. This focus overlooks how predictions align with longer-term patterns, which are critical for clinical relevance in BGP, thereby yielding suboptimal results. To overcome this limitation, we introduce collaborative augmented adversarial learning, designed to improve the model’s temporal awareness. Incorporating collaborative interaction optimisation, this approach enables the model to reflect extended time dependencies beyond the immediate horizon, thereby improving both the clinical reliability of predictions and overall predictive performance. We develop and evaluate four learning systems for BGP: independent learning, adversarial learning, collaborative learning, and adversarial collaborative learning. The proposed systems were evaluated for two clinically relevant prediction horizons, namely 30 min and 60 min ahead. Results: The interdependent collaboratively augmented learning frameworks, validated using the well-established Ohio T1D datasets, demonstrate statistically significant superior performance in both clinical and mathematical evaluations. Conclusions: Beyond advancing BGP accuracy and clinical reliability, the proposed approach supports personalised medicine by improving subject-specific glucose forecasting from CGM data, with potential relevance for more individualised diabetes monitoring and decision support. The proposed approach also opens new avenues for advancements in other complex TSF domains, as outlined in our future work. Full article

Wireless sensor networks using battery-powered, low-cost sensors, due to their non-rechargeability and strictly limited energy resources, are more sensitive to energy efficiency than other networks of this type. Clustered wireless sensor networks address this problem. In these networks, the most energy-intensive communication, i.e., a long-range one, is carried out via designated nodes, called cluster head nodes, while other cluster nodes communicate with their cluster heads. Cluster head node selection is handled by appropriate routing protocols, and newly designed protocols are first tested in simulations. Among the simulators of cluster head selection protocols, those implemented in a MATLAB environment play an important role, and among these, those implementing a first-order radio model to estimate the energy cost of transmission, both at the transmitter and at the receiver, play a particularly important role. This paper presents and discusses the energy aspects of MATLAB-based open-source wireless sensor network environments that employ the first-order radio model for the simulation of cluster head selection protocols. Current MATLAB-based open-source simulators of cluster head selection protocols were inventoried and analyzed. The review results showed that the first-order radio model had been used in its classic form for years, with the same default parameters. Although the simulators were written using different programming paradigms, precluding simple copy-and-paste, the first-order radio model was generally similar. However, there were exceptions to this rule. A hard exception is the simulator for a body-area wireless sensor network, which only implements a version of the first-order radio model specific to that environment. Soft exceptions are two simulators of the popular cluster head selection protocol, which implemented only half the functionality of the classic first-order radio model. On the one hand, this demonstrates both the widespread use of a conservative approach to the model, which ensures relatively easy repeatability of simulation results, and, on the other hand, the flexibility of the model, which allows its extension to other environments. Finally, the limitations of the model are presented and directions for future research are indicated. Full article

Permanent magnet synchronous motor (PMSM)-based servo actuators are fundamental to high-performance electromechanical systems. However, in energy-sensitive aerospace applications, the impact of tracking error on system-level efficiency remains insufficiently quantified. This paper establishes an energy-oriented analytical framework linking PMSM tracking accuracy to vehicle-level energy consumption and flight range. By employing a specific mechanical energy formulation, we demonstrate that tracking deviations modify aerodynamic drag and introduce additional dissipative work. Specifically, the accumulated dissipation is shown to admit a lower bound proportional to the integral of the squared tracking error, from which a range degradation bound is derived. These results reveal that “tracking-error energy” imposes a fundamental limit on achievable flight distance. A Lyapunov-based analysis further proves that minimizing this error energy reduces total aerodynamic dissipation without requiring modifications to propulsion scheduling or guidance laws. Numerical simulations comparing a conventional sliding mode controller with an advanced fuzzy-adaptive nonsingular terminal sliding mode controller confirm that enhanced servo precision directly improves velocity retention and range performance. This framework offers practical insights for designing energy-aware PMSM control strategies in energy-constrained aerospace platforms. Full article

Code smells are indicators of potential design problems in software systems and are commonly used to guide refactoring activities. Recent advances in representation learning have enabled the use of embedding-based models for analyzing source code, offering an alternative to traditional approaches based on manually engineered metrics. However, the effectiveness of different embedding representations for multiclass code smell detection remains insufficiently explored. This study presents an empirical comparison of embedding models for the automatic detection of three widely studied code smells: Long Method, God Class, and Feature Envy. Using the Crowdsmelling dataset as an empirical basis, source code fragments were extracted from the original projects and transformed into vector representations using two embedding approaches: a general-purpose embedding model and the code-specialized CodeBERT model. The resulting representations were evaluated using several machine learning classifiers under a stratified group-based validation protocol. The results show that CodeBERT consistently outperforms the general-purpose embeddings across multiple evaluation metrics, including balanced accuracy, macro F1-score, and Matthews correlation coefficient. Dimensionality reduction analyses using PCA and t-SNE further indicate that CodeBERT organizes code smell instances in a more structured latent representation space, which facilitates the separation of smell categories. In particular, CodeBERT achieved a macro F1-score of 0.8619, outperforming general-purpose embeddings (0.7622) and substantially surpassing a classical TF-IDF baseline (0.4555). These findings highlight the value of this study as a controlled multiclass evaluation of embedding representations and demonstrate the practical value of domain-specific representations for improving automated code smell detection and class separability in real-world software engineering scenarios. Full article

Remote sensing object detection is fundamental to Earth observation, yet remains challenging when relying on a single sensing modality. While optical imagery provides rich spatial and textural details, it is highly sensitive to illumination and adverse weather; conversely, Synthetic Aperture Radar (SAR) offers robust all-weather acquisition but suffers from speckle noise and limited semantic interpretability. To address these limitations, we leverage the potential of foundation models for optical–SAR object detection via a novel gated–guided fusion approach. By integrating transferable and generalizable representations from foundation models into the detection pipeline, we enhance semantic expressiveness and cross-environment robustness. Specifically, a gated–guided fusion mechanism is designed to selectively merge cross-modal features with foundational priors, enabling the network to prioritize informative cues while suppressing unreliable signals in complex scenes. Furthermore, we propose a dual-stream architecture incorporating attention mechanisms and State Space Models (SSMs) to simultaneously capture local and long-range dependencies. Extensive experiments on the large-scale M4-SAR dataset demonstrate that our method achieves state-of-the-art performance, significantly improving detection accuracy and robustness under challenging sensing conditions. Full article

Against the backdrop of accelerating climate change, extreme weather events have increasingly caused yield losses in agricultural crops. Meanwhile, they undermine the stability of production systems, posing an increasingly severe threat to agriculture. This study draws on the “diversity–stability” hypothesis to construct a country-level measure of agricultural production resilience in China (ARES). Using output time series for multiple agricultural products, we capture the co-movements of shocks and system resilience through output stability and volatility. By combining ARES with climate exposure measures, we assemble a panel dataset covering 1343 counties over the period 2000–2023 and employ a dynamic panel threshold model to jointly account for persistence in ARES and state-dependent nonlinearities in climate impacts. The results reveal significant path dependence in ARES and pronounced threshold effects across climate dimensions. In the full sample, extreme high-temperature days become significantly detrimental after crossing the threshold, whereas extreme low-temperature days become significantly beneficial in the high-exposure regime. Extreme rainfall days and extreme drought days generally exhibit positive effects that weaken markedly beyond their respective thresholds, indicating diminishing marginal gains in ARES under severe exposure. The comprehensive climate physical risk index significantly suppresses ARES when it is below the threshold value; however, after surpassing the threshold, its marginal effect becomes significantly weaker. Heterogeneity analyses across hilly, plain, and mountainous areas, as well as nationally designated key counties for poverty alleviation and development, further show that threshold locations and regime-specific effects differ substantially by terrain and development conditions. These findings highlight the need for “threshold-based” climate adaptation governance, emphasizing targeted investments and risk-financing instruments to prevent ARES collapse under tail-risk regimes. Full article

Improving energy efficiency is critical for tackling environmental issues and achieving sustainable development. Understanding how digital technology affects energy efficiency and its underlying mechanisms can deepen our comprehension of the economic consequences of digital innovation. This study adopts a dictionary-based method to identify digital technology patents from a large-scale patent dataset and employs a comprehensive evaluation approach incorporating both subjective and objective weights to measure digital technology advancement. Building on this framework, the research uses city-level data from China and applies panel data models alongside mediation effect models as core analytical tools to investigate the impact mechanisms and effects of digital technology on energy efficiency. Key findings reveal that digital technology has developed rapidly, exhibiting distinct phase-specific characteristics, especially after 2010, though notable regional disparities remain. Robust tests confirm that digital technology significantly enhances energy efficiency. Nonlinear regression results indicate that the marginal effect of digital technology changes dynamically across different stages of energy efficiency development. Heterogeneity tests demonstrate that the impact of digital technology on energy efficiency exhibits typical heterogeneous characteristics. Mechanism analysis shows that digital technology enhances energy efficiency primarily through two pathways: green technology innovation and industrial structure upgrading. Further analysis suggests that regional convergence in energy efficiency is objectively present, and digital technology actively accelerates this convergence process. These findings offer practical insights to guide policymakers in designing and implementing digital technology-driven strategies aimed at enhancing energy efficiency. Full article

Heat stress is a major challenge for broiler production worldwide and is expected to intensify with more frequent heatwaves. This study focuses on mechanically ventilated broiler houses in South Korea, where heatwaves have become increasingly frequent. Three regression meta-models were developed to predict the indoor temperature–humidity index (THI) directly from weather forecast data, using simulated results from a validated building energy simulation (BES) model. A TRNSYS-based BES model was validated against field measurements from four rearing cycles in a commercial broiler house (RMSE 1.31–2.16; MAPE < 2.00%). Using 3072 simulation cases that combined multiple sites, thermal-transmittance levels, cooling conditions, building sizes, and broiler body weights, three regression meta-model approaches were evaluated: a condition-specific regression meta-model for each condition set, a unified regression meta-model with categorical predictors, and a single variable meta-model using only external THI as a predictor. All three showed strong predictive performance, and the unified regression meta-model achieved R² = 0.978, RMSE = 0.817, and MAPE = 0.829, providing the best balance between accuracy and simplicity. This unified model offers a practical tool to link weather forecasts with broiler-house design and environmental-control decisions for heat-stress risk management. Full article

The liver’s anatomic position and immune specialization make it both a major target and a major filter for systemically delivered therapeutics. Because portal venous inflow exposes the liver early to gut-derived molecules and exogenous compounds, many intravenously administered agents, including gene-based medicines and their viral and non-viral delivery systems, preferentially enter and accumulate in hepatic tissue. This review synthesizes how core liver physiology and immunobiology influence the performance, safety, and clinical translation of genomic medicines in hepatology, and outlines near-term practice and research shifts likely to define a genomics-driven future in liver disease care. We review the hepatic microarchitecture relevant to therapeutic trafficking, including sinusoidal transit, the space of Disse, hepatocyte uptake, and hepatobiliary elimination, and highlight the gatekeeping roles of liver sinusoidal endothelial cells and Kupffer cells in clearing particulate material and shaping inflammatory signaling. We then discuss how these same features create both opportunities, such as efficient hepatic targeting, and constraints, including innate immune activation, vector clearance, and variable intrahepatic distribution, for DNA- and RNA-based platforms. Finally, we propose five actionable developments poised to move genomics from a niche tool to a routine component of hepatology practice: earlier genomic testing in unexplained liver disease, multidisciplinary hepatology genome rounds, a centralized liver-specific gene resource, genetics-aware clinical trial design, and expansion of genetic therapies. Integrating liver biology with genomic medicine is essential to improve diagnostic yield, personalize therapy, and accelerate translation of gene-based treatments while mitigating immunologic and delivery-related barriers. Full article

Citrus trees exhibit severe alternate bearing, resulting in significant annual yield fluctuations and posing substantial challenges to orchard management planning. Accurate citrus bud counting provides an effective solution by supplying essential data for tree-level and orchard-level yield prediction. However, citrus buds are extremely small (5–10 mm in diameter) and are frequently occluded by leaves during the flowering stage, which makes precise detection highly challenging in complex orchard environments. To address these challenges, this paper proposes a Contextual and Frequency-Aware Detector (CFADet) for robust citrus bud detection. Specifically, an Enhanced Feature Fusion (EFF) module is introduced in the neck to refine multi-scale feature aggregation and strengthen information flow for small targets. A Contextual Boundary Enhancement Module (CBEM) is designed to capture surrounding contextual cues and enhance boundary representation through dimensional interaction and max-pooling operations. To suppress background interference, a Frequency-Aware Module (FAM) is developed to adaptively recalibrate frequency components in the amplitude spectrum, thereby enhancing target features while reducing background noise. In addition, Spatial-to-Depth Convolution (SPDConv) is employed to reconstruct the backbone to preserve fine-grained bud features while reducing model parameters. Experimental results show that CFADet achieves 81.1% precision, 80.9% recall, 81.0% F1-score, and 87.8% mAP, with stable real-time performance on mobile devices in practical orchard scenarios. This study presents a preliminary investigation into robust citrus bud detection in real-world orchard environments and provides a promising technical foundation for intelligent orchard monitoring and early yield estimation, while further validation on larger and more diverse datasets is still required. Full article

Injection molding process optimization has predominantly relied on simulation-generated data, which cannot capture machine-specific variability and stochastic process noise inherent in real manufacturing environments. This paper presents an experiment-driven machine learning framework for multi-objective optimization of injection molding process parameters targeting volumetric shrinkage, warpage, cycle time, and part weight. Physical experiments were conducted on an industrial injection molding machine using high-density polyethylene with a face-centered central composite design. Systematic benchmarking of four machine learning algorithms under identical cross-validation protocols identified Gaussian process regression as the best-performing surrogate model for the majority of quality metrics, while warpage prediction remained challenging across all algorithms due to its complex thermo-mechanical origins. Permutation-based feature importance analysis established a clear parameter hierarchy, identifying holding time as the dominant factor governing multiple quality responses. Constrained Bayesian optimization with progressive constraint tightening was employed to identify optimal parameter sets and fundamental process capability boundaries. The resulting parameter configurations were validated against a held-out test set. This work demonstrates that rigorous, data-driven optimization using exclusively experimental data provides a viable and practically achievable alternative to simulation-based approaches, contributing to experiment-centric smart manufacturing in polymer processing. Full article

The fall armyworm (Spodoptera frugiperda) is a major global migratory pest. Its increasing insecticide resistance poses a severe threat to food security. Developing biopesticides such as SfMNPV is critical for sustainable control. Nevertheless, the early molecular mechanisms underlying the S. frugiperda midgut response to oral SfMNPV challenge remain poorly understood. This study utilized high-throughput transcriptome sequencing to systematically characterize the dynamic transcriptional profiles of the larval midgut at 1, 12, and 24 h after oral SfMNPV inoculation. Results showed that the midgut transcriptional response to SfMNPV is time and stage-specific. During this period, the physical midgut barrier underwent remodeling, with core components of the peritrophic matrix downregulated at 1 h, followed by the basal lamina at 12 h, alongside the activation of cytoskeleton genes during 12–24 h. Concurrently, sustained endoplasmic reticulum stress, autophagy, and ubiquitin system responses occurred from 12 to 24 h. At the metabolic level, the defense system exhibited a functional succession, shifting from ABC transporters and UDP-glycosyltransferases at 1 h to glutathione S-transferases and superoxide dismutase at 12–24 h. Additionally, the midgut tissue exhibited a cascade transition from pro-apoptotic signaling at 1 h to compensatory regenerative repair mediated by the Wnt, mTOR, and Hippo pathways at 12–24 h. This study elucidates the molecular process of barrier damage, homeostatic imbalance, and tissue remodeling during early oral SfMNPV challenge. These findings provide a global perspective on baculovirus-host interactions and establish a theoretical foundation for designing novel biopesticides targeting the midgut interaction. Full article

To address the challenges associated with laser powder bed fusion (LPBF) of overhanging structures—namely warping deformation, powder adhesion, and inadequate forming accuracy—this study investigates the optimization of the support–part contact interface using Inconel 625 alloy. The objective is to achieve high-quality part formation with minimal support structures. A Taguchi experimental design was employed to systematically evaluate the effects of key block support parameters—tooth height, tooth top length, tooth base length, and tooth base spacing—on the forming performance of overhanging structures, with forming accuracy and support removability as the optimization targets. The results reveal that tooth top length significantly influences both the forming accuracy of overhanging specimens and the ease of support removal. Specifically, an increase in tooth top length leads to a rapid reduction in specimen deformation, but simultaneously increases the difficulty of support removal. When the tooth top length was set to 0.1 mm, all overhanging specimens failed to form successfully. Tooth base length also plays a critical role in support removability, with removal difficulty initially decreasing and then stabilizing as the tooth base length increases. Based on the trade-off between forming quality and support removability, the optimal parameter combination was identified as: tooth height of 0.4 mm, tooth top length of 0.7 mm, tooth base length of 1.0 mm, and tooth base spacing of 0.3 mm. A validation experiment conducted using this optimized configuration demonstrated good forming accuracy in the support contact area, with a deformation value of −0.208 mm, confirming the effectiveness and reliability of the proposed parameters. This study not only provides a theoretical foundation for the optimal design of block supports in LPBF but also offers experimental data and practical guidance for selecting support parameters in the fabrication of overhanging structures. Full article