Search Results (1,320)

Recent developments in unmanned aerial vehicle (UAV) activity highlight the need for advanced electromagnetic spectrum monitoring systems that can detect drones operating near sensitive or restricted areas. Such systems can identify emissions from drones even under frequency-hopping conditions, providing an early warning system and enabling a timely response to protect critical infrastructure and ensure secure operations. In this context, the present work proposes the development of a high-performance multichannel broadband monitoring system with real-time analysis capabilities, designed on an SDR architecture based on USRP with three acquisition channels: two broadband (160 MHz and 80 MHz) and one narrowband (1 MHz) channel, for simultaneous, of extended spectrum segments, aligned with current requirements for analyzing emissions from drones in the 2.4 GHz and 5.8 GHz ISM bands. The processing system was configured to support cumulative bandwidths of over 200 MHz through a high-performance hardware platform (powerful CPU, fast storage, GPU acceleration) and fiber optic interconnection, ensuring stable and lossless transfer of large volumes of data. The proposed spectrum monitoring system proved to be extremely sensitive, flexible, and extensible, achieving a reception sensitivity of −130 dBm, thus exceeding the values commonly reported in the literature. Additionally, the parallel multichannel architecture facilitates real-time detection of signals from different frequency ranges and provides a foundation for advanced signal classification. Its reconfigurable design enables rapid adaptation to various signal types beyond unmanned aerial systems. Full article

This study examines how employee benefit practices link employee well-being with financial sustainability in sustainable organization management. Focusing on Generation Z, it investigates the intersection between meaning attributed to employee benefits and managerial decision-making guided by financial rationality. Drawing on human resources management (HRM) and finance perspectives, employee benefits are conceptualized as mechanisms for balancing human-centered value creation and economic resilience. A qualitative design was used, based on semi-structured interviews with 15 Generation Z employees and 20 human resources (HR) and finance managers in Türkiye. Data were analyzed through thematic analysis and the Gioia methodology to develop an inductive, multi-level framework. The findings indicate that Generation Z employees view employee benefits as psychosocial resources reflecting justice, autonomy, psychological safety, and value alignment—core components of subjective and eudaimonic well-being—while managers assess them primarily through financial sustainability logics such as cost control and return on investment. Overall, meaning- and cost-oriented perspectives emerge as mutually reinforcing within sustainable organizational systems. The study proposes the Meaning–Cost Balance (MCB) Framework, conceptualizing employee benefits as a strategic management mechanism aligning employee well-being with financial resilience. Positioned at the intersection of HRM and financial sustainability, the framework contributes to sustainable organization management and offers a transferable basis for future comparative research. Full article

Condition monitoring of rotating machinery is critical for ensuring industrial safety and operational reliability. As a core component of intelligent diagnostic systems, domain adaptation methods have achieved notable progress in mechanical fault diagnosis. However, most existing approaches presume a fully shared label space between source and target domains, limiting their effectiveness under partial domain adaptation scenarios commonly encountered in industrial practice. In addition, they often struggle with classification uncertainty near decision boundaries. To address these challenges, this paper proposes a Selective Adversarial Augmentation Network (SAAN) for cross-domain rolling bearing fault diagnosis with partial label space alignment. The proposed framework designs a multi-level feature extraction module to enhance transferable feature representation and a Balanced Augmentation Selective Adversarial Module (BASAM) to dynamically balance class distributions and selectively filter irrelevant source classes, thereby mitigating negative transfer and achieving fine-grained class alignment. Furthermore, an uncertainty suppression mechanism is put forth to reinforce classifier boundaries by minimizing the impact of ambiguous samples. Comprehensive experiments conducted on public and proprietary bearing datasets demonstrate that SAAN consistently surpasses state-of-the-art benchmarks in diagnostic accuracy and robustness, providing an effective solution for practical applications under class-imbalanced and variable operating conditions. Full article

The efficiency of power transfer is a critical issue for wireless charging applications in electric vehicles. The misalignment between the transmitter coil and the receiver coil in wireless charging leads to a significant reduction in efficiency. This article investigates improving coil misalignment performance in wireless power transfer for electric vehicles using magnetic flux density analysis. The objective is to study the effect of the automatic alignment transmitter system’s movement on error distance. The automatic alignment transmitter system was integrated with a wireless power transfer system to realign the transmitter coil whenever lateral misalignment occurred between the transmitter and receiver coils. The experiment was performed with a horizontal misalignment of 0.35 m and was repeated three times. The gap between the coils was held constant at 0.15 m. The wireless charging system was designed according to the Society of Automotive Engineers (SAE) standard. The experimental results demonstrated that the movement error distance was 0.001 m, with an average error of 0.33%. These findings indicate that the automatic alignment transmitter system achieved an operational effectiveness of 99.67%. The maximum wireless charging efficiencies of 75.78% and 75.59% were recorded for the X-axis and Y-axis adjustments, respectively. Full article

A twin-screw multiphase pump is essential equipment for the transfer of gas-liquid multiphase mixtures in oil and gas operations. This work addresses rotor deformation in real applications by correcting the rotor profile using the arc transition approach, eliminating teeth tips, mitigating local stress concentration, and reducing the danger of rotor deformation. Simultaneously, in conjunction with the oil and gas mixed transportation requirements of the Changqing Oilfield, the MPC208-67 twin-screw mixed transportation pump was engineered, and the essential structural specifications were established. This paper employs the Mixture multiphase flow model and the SST k-ω turbulence model to simulate the internal flow field of the pump in Changqing Oilfield, aiming to examine the impact of high-gas-content conditions on the pump’s performance and ensure it aligns with design specifications. The modeling findings indicate that the pressure in the pump progressively rises along the axial direction and remains constant within the chamber. As the void fraction of the medium increases, the pressure differential between the inlet and exit of the rotor fluid domain progressively diminishes, resulting in high-velocity fluid emerging in the interstice between driving and driven rotors. The simultaneous increase in rotational speed elevates the overall fluid velocity while diminishing the pressure value. Under rated conditions, the output pressure and flow rate of the planned multiphase pump achieve 1.8 MPa and 300 m³/h, respectively, thereby fully satisfying the design specifications. This work employs the response surface approach to optimize multi-objective performance parameters, including leakage and pressurization capacity, to enhance the pump’s operational performance under high gas content situations. The optimization results indicate a 17.87% reduction in pump leakage, an 8.86% rise in pressurization capacity, and a substantial enhancement in pump performance. Full article

This paper proposes a novel method for transferable adversarial attacks from Image Quality Assessment (IQA) to Video Quality Assessment (VQA) models. Attacking modern VQA models is challenging due to their high complexity and the temporal nature of video content. Since IQA and VQA models share similar low- and mid-level feature representations, and IQA models are substantially cheaper and faster to run, we leverage them as surrogates to generate transferable adversarial perturbations. Our method, MaxT-I2VQA jointly Maximizes IQA scores and Targets IQA feature activations to improve transferability from IQA to VQA models. We first analyze the correlation between IQA and VQA internal features and use these insights to design a feature-targeting loss. We evaluate MaxT-I2VQA by transferring attacks from four state-of-the-art IQA models to four recent VQA models and compare against three competitive baselines. Compared to prior methods, MaxT-I2VQA increases the transferability of an attack success rate by 7.9% and reduces per-example attack runtime by 8 times. Our experiments confirm that IQA and VQA feature spaces are sufficiently aligned to enable effective cross-task transfer. Full article

A promising route for removing antibiotics such as penicillin from wastewater is photocatalytic degradation under UV irradiation using TiO₂ nanoparticles. However, the microscopic mechanisms governing the initial degradation steps remain poorly understood. In particular, it is still unclear whether degradation preferentially occurs in solution or upon adsorption on the oxide surface, and which molecular sites are most vulnerable to attack in solution compared to those activated on the catalyst. In this work, we introduce a unified density functional theory approach that treats penicillin V (phenoxymethylpenicillin) consistently, both isolated in solution and adsorbed on an anatase TiO₂ nanocluster, enabling a direct comparison between solution-phase and surface-mediated degradation pathways. Within this framework, we analyze the adsorption configurations, energy-level alignment, charge-transfer pathways, UV-Vis absorption properties, local reactivity descriptors, and the initial steps leading to bond breaking. The results show that the direct photoexcitation of PenV followed by electron transfer to the oxide is less likely, due to the high energy of the pollutant’s excited states. In contrast, degradation initiated by the transfer of photogenerated holes from the catalyst to the adsorbed antibiotic appears more probable, driven by the smaller energetic offset and by the hybridization between molecular and oxide states. Overall, adsorption on the oxide surface appears to be more conducive to degradation, with the carbon atom in the β-lactam ring consistently identified as a susceptible site for attack across different environments. Full article

Intensive pig production systems in China face dual challenges of heavy metal (HM) contamination and nutrient overloading from manure. However, stage-specific quantitative relationships between diet and excretion remain poorly characterized, hindering targeted mitigation. To address this, we conducted a comprehensive farm survey in the southern water network region—a major pig production hub in China—collecting 93 paired feed and manure samples from piglets, finishing pigs, and sows across 32 large-, medium-, and small-scale farms. The results revealed that essential trace elements (Cu, Zn, Fe, Mn) in feed exceeded safety guidelines by 3–19-fold, while toxic metals (As, Hg, Pb, Cd, Cr) remained below hygienic limits. Notably, Cu and Zn concentrations in manure significantly surpassed organic fertilizer standards, with piglet manure showing the highest exceedance rates (69–91%). Strong linear correlations (Pearson’s r = 0.360–0.766) were found between feed additives (Cu, Zn, As, Pb, Cd, Cr) and their excretion in manure, with Cu and Zn exhibiting the strongest relationships, especially in piglets. Feed crude protein (CP) and phosphorus (P) levels positively influenced nitrogen (N) and P excretion (r = 0.389–0.860), particularly in finishing pigs. Scenario analysis demonstrated that aligning Cu and Zn supplementation with safety guidelines could reduce HM excretion by 50–67%, while low-CP diets and precision P feeding lowered N and P losses by 10.2–10.8% and reduced feed costs by 4.1%. These findings highlight the potential of dietary interventions to mitigate environmental risks without compromising productivity, offering actionable strategies for sustainable pig production and revised feed regulations. This study provides quantitative, stage-specific evidence linking feed formulation to excretion patterns, addressing critical knowledge gaps in feed-to-manure transfer mechanisms and supporting the development of precision feeding standards and integrated manure management systems to decouple livestock intensification from environmental degradation. Full article

Cross-project software vulnerability detection must cope with pronounced domain shift and severe class imbalance, while the target project is typically unlabeled. Existing unsupervised domain adaptation techniques either focus on marginal alignment and overlook class-conditional mismatch, or depend on noisy pseudolabels, which can induce negative transfer in imbalanced settings. To address these challenges we propose DAP2ER, a progressive domain adaptation framework that couples adversarial domain confusion with entropy regularization and prototype-guided high-confidence pseudolabel optimization. Specifically, DAP2ER constructs source class prototypes, selects reliable target samples via confidence-aware pseudolabeling, and performs class-conditional alignment by pulling target features toward the corresponding prototypes. A progressive weighting schedule gradually increases the strength of domain and self-training objectives, stabilizing optimization in early epochs. Experiments on two real-world vulnerability datasets demonstrate that DAP2ER consistently outperforms strong baselines, improving the F1-score by up to 21 percentage points and achieving substantial gains in AUC for bidirectional transfer. Full article

Airport digital transformation is commonly approached through technological integration and data-driven optimization, yet such perspectives provide limited insight into system-level reasoning and governance. This paper introduces the cognitive airport paradigm (CAP) as a mathematically grounded framework that models the airport as a domain-specific cognitive digital twin within a complex aviation ecosystem. Methodologically, the study follows a conceptual–analytical and design-science research approach, combining system analysis, conceptual modeling, ontology engineering, and formal mathematical representation of cognitive transitions and governance constraints. CAP represents airport cognition as an explicit state space characterized by cognitive maturity, governance integrity, and semantic stability. Analytical reasoning, adaptive learning, and orchestration mechanisms are formalized through instrument dominance profiles and cognitive performance functionals, enabling analytical comparison of airport configurations and identification of cognitive regimes. The results include (i) a formalization of airports as cognitive digital twins with measurable cognitive and governance properties; (ii) quantitative indices such as the cognitive readiness index, governance integrity index, and ethical alignment coefficient supporting structured evaluation of airport cognitive maturity; and (iii) illustrative expert-based parameterizations and a geometric interpretation in a cognitive simplex demonstrating that governance-oriented orchestration stabilizes airport cognition under increasing system complexity. Airport development is interpreted as continuous cognitive evolution rather than discrete stages of digitalization. The paper further proposes a cognitive roadmap for guiding airport evolution through structured cognitive rebalancing. The framework contributes to the theoretical foundations of cognitive digital twins and is transferable to other safety-critical and institutionally governed socio-technical systems. Full article

Semiconductor manufacturing equipment such as vacuum pumps, wafer handling mechanisms, etching machines, and deposition systems operates for a long time under high vacuum, high temperature, strong electromagnetic, and high-precision continuous production environments. Its reliability is directly related to the yield and stability of the production line. During equipment operation, the fault signals are often weak, the noise is strong, and the working conditions are variable, so traditional methods are difficult to achieve high-precision recognition. To solve this problem, this paper proposes a fault diagnosis and health monitoring method for semiconductor manufacturing equipment based on deep learning and subspace transfer. Firstly, considering the cyclostationary characteristics of the operating signals of key equipment, the cyclic spectral analysis technology is used to obtain the cyclic spectral coherence map, which effectively reveals the feature differences under different health states. Then, a deep fault diagnosis model based on the convolutional neural network (CNN) is constructed to extract deep feature representations. Furthermore, the subspace transfer learning technology is introduced, and group normalization and correlation alignment unsupervised adaptation layers are designed to achieve automatic alignment and enhancement of the statistical characteristics of deep features between the source domain and the target domain, which effectively improves the generalization and adaptability of the model. Finally, simulation experiments based on the public bearing dataset verify that the proposed method has strong feature representation ability and high classification accuracy under different working conditions and different loads. Because the key components and experimental scenarios of semiconductor manufacturing equipment have similar signal characteristics, this method can be directly transferred to the early fault diagnosis and health monitoring of semiconductor production line equipment, which has important engineering application value. Full article

Wildfire smoke visualization using geostationary satellite imagery is essential for real-time monitoring and atmospheric analysis; however, inconsistencies in color tone across Geostationary Environment Monitoring Spectrometer (GEMS) images hinder reliable interpretation and model training. This study proposes a Standardized False Color Composite (SFCC) framework based on deep learning style transfer to enhance the visual consistency and interpretability of wildfire smoke scenes. Four tone-standardization methods were compared: the statistical Empirical Cumulative Distribution Function (ECDF) correction and three neural approaches—ReHistoGAN, StyTr², and Style Injection Diffusion Model (SI-DM). Each model was evaluated visually and quantitatively using six metrics (SSIM, LPIPS, FID, histogram similarity, ArtFID, and LSCI) and validated on three major wildfire events in Korea (2022–2025). Among the tested models, SI-DM achieved the most balanced performance, preserving structural features while ensuring consistent color-tone alignment (ArtFID = 1.620; LSCI mean = 0.894). Qualitative assessments further confirmed that SI-DM effectively delineated smoke boundaries and maintained natural background tones under complex atmospheric conditions. Additional analysis using GEMS UVAI, VISAI, and CHOCHO demonstrated that the styled composites partially reflect the optical and chemical characteristics distinguishing wildfire smoke from dust aerosols. The proposed SFCC framework establishes a foundation for visually standardized satellite smoke imagery and provides potential for future aerosol-type classification and automated detection applications. Full article

Nonintrusive Load Monitoring (NILM) models often suffer from significant performance degradation when deployed across different households and datasets, primarily because of distribution discrepancies. To address this challenge, this study proposes an adversarial hierarchical distribution alignment unsupervised domain adaptation network for nonintrusive load disaggregation. The network aims to reduce the distribution divergence between the source and target domains in both the feature and label spaces, enabling effective adaptation to transfer learning scenarios in which the source domain has limited labeled data and the target domain has abundant unlabeled data. The proposed method integrates adversarial training with a hierarchical distribution alignment strategy that uses Correlation Alignment (CORAL) to align global marginal distributions. It employs Multi-Kernel Maximum Mean Discrepancy (MK-MMD) to constrain the conditional distributions of individual appliances, thereby enhancing cross-domain generalization. Extensive experiments on three public datasets demonstrate that, in both in-domain and cross-domain settings, the proposed method consistently reduces Mean Absolute Error (MAE) and Signal Aggregation Error (SAE), outperforming baseline approaches in cross-domain generalization. Full article

AI-driven imaging is becoming central to crop monitoring, with proximal and unmanned aerial vehicle (UAV) platforms now routinely used for disease and stress detection, yield estimation, canopy structure, and fruit counting. Yet, as these models move from plots to farms, the main bottleneck is no longer raw accuracy but robustness under distribution shift. Systems trained in one field, season, cultivar, or sensor often fail when the scene, sensor, protocol, or timing changes in realistic ways. This review synthesizes recent advances on robustness and transferability in proximal and UAV imaging, drawing on a corpus of 42 core studies across field crops, orchards, greenhouse environments, and multi-platform phenotyping. Shift types are organized into four axes, namely scene, sensor, protocol, and time. The article also maps the empirical evidence on when RGB imaging alone is sufficient and when multispectral, hyperspectral, or thermal modalities can potentially improve robustness. This serves as a basis to synthesize acquisition and evaluation practices that often matter more than architectural tweaks, which include phenology-aware flight planning, radiometric standardization, metadata logging, and leave-one-field/season-out splits. Adaptation options are consolidated into a practical symptom/remedy roadmap, ranging from lightweight normalization and small target-set fine-tuning to feature alignment, unsupervised domain adaptation, style translation, and test-time updates. Finally, a benchmark and dataset agenda are outlined with emphasis on object-oriented splits, cross-sensor and cross-scale collections, and longitudinal datasets where the same fields are followed across seasons under different management regimes. The goal is to outline practices and evaluation protocols that support progress toward deployable and auditable systems, noting that such claims require standardized out-of-distribution testing and transparent reporting as emphasized in the benchmark specification and experiment suite proposed here. Full article

Emerging mid-sized cities in the Global South face growing challenges in aligning transport infrastructure with equity and safety objectives under conditions of rapid urban expansion. While accessibility metrics are widely used in transport planning, their ability to capture functional inequalities and safety-related dynamics remains limited, particularly in corridor-level assessments. This study examines structural and functional accessibility patterns along two planned BRT corridors in Querétaro, Mexico, an emerging mid-sized Latin American city. The analysis integrates spatial accessibility indicators, selected urban process proxies related to inequality and road safety, and AI-based modeling to explore non-linear spatial associations across fine-grained corridor segments. Structural accessibility is evaluated using network-based indicators, while functional accessibility reflects observed service dynamics and operational conditions. Spatial correlations and artificial neural networks are employed as exploratory tools to identify co-occurring patterns rather than causal relationships. Results reveal pronounced spatial mismatches between structural and functional accessibility, socio-spatial marginalization, and crash concentration, highlighting corridor segments where future BRT implementation may either reinforce or mitigate existing inequalities. By framing BRT corridors as test cases, the study contributes a transferable diagnostic framework for assessing accessibility–equity–safety tensions in emerging mid-sized cities. Full article