Search Results (22,074)

The Four-Leg Three-Phase Voltage Source Inverter (4LVSI) is a versatile solution for integrating renewable energy sources (RESs) into distribution networks, as it compensates unbalanced voltages and currents while providing a path for zero-sequence components. Accurate current control is essential to ensure power quality and reliable operation under these conditions. Conventional controllers such as proportional–integral, resonant, or feedback-linearization methods achieve acceptable tracking under static dc-link conditions, but their performance degrades when dc-link voltage dynamics arise due to renewable-source fluctuations. This paper proposes a data-driven model reference neural control (MRNC) strategy for a four-leg inverter connected to RESs, explicitly accounting for dc-link voltage variations. The proposed controller reformulates the classical Model Reference Adaptive Control (MRAC) as a lightweight single-layer neural network whose adaptive weights are updated online using the Recursive Least Squares (RLS) algorithm. In this framework, the dc-link variations are not modeled explicitly but are implicitly learned through the data-driven adaptation process, as their influence is captured in the neural network regressors formed from real-time input–output measurements. This allows the controller to continuously identify the inverter dynamics and compensate the effect of dc-link fluctuations without requiring additional observers or prior modeling. The proposed approach is validated through detailed time-domain simulations and real-time Hardware-in-the-Loop (HIL) experiments implemented at a 10 kHz switching frequency. The results indicated that the RLS-based MRNC controller achieved the lowest steady-state current error, reducing it by approximately 1.85% and 1% compared to the Proportional-Resonant (PR) and One-Step-Ahead (OSAC) controllers, respectively. Moreover, under dc-link voltage variations, the proposed controller significantly reduced the current overshoot, achieving decreases of 5.9 A and 6.36 A relative to the PR controller. Full article

As global climate change intensifies and extreme weather events become more frequent, understanding the historical spatial distribution of vegetation is of critical importance. However, most vegetation studies are temporally limited to the post-1980 period due to satellite data constraints. To bridge this gap, we integrated tree-ring width chronologies from the International Tree-Ring Databank with Landsat-derived Enhanced Vegetation Index (EVI) data and evaluated three machine learning models—Random Forest (RF), Support Vector Machine (SVM), and Convolutional Neural Network (CNN)—to reconstruct annual, spatially explicit EVI for the period 1850–1985 in Diqing, Yunnan, China. RF regression was the best among the three with highest adjusted R² (0.90) and lowest Root Mean Square Error (0.032). The RF-based reconstruction indicated a consistent increase in regional EVI from 1991 to 2005. Breakpoint analysis identified three distinct sub-periods, each with unique spatiotemporal variation patterns. In current times, the EVI value shows a significant positive correlation with average temperatures in June, July, August, and December. In the contemporary period, it also correlates significantly and positively with winter average temperatures, March average precipitation, and spring average precipitation. The spatial pattern for the past 100 years reflects the succession of the local vegetation ecosystem and provides an insight into the influences of natural disturbances (low-temperature damages and droughts) on vegetation growth. This study demonstrates the feasibility of reconstructing high-resolution, long-term vegetation spatial dynamics using tree-ring proxies and machine learning. Full article

In response to the widespread adoption of hybrid work models, organizations must strategically address the challenges of knowledge transfer and organizational learning in distributed environments. We extend March’s computational model of organizational learning by initially incorporating three variables: the ratio of tacit-to-explicit knowledge, the proportion of remote workers, and structured shift arrangements. The extended model incorporates distinct subgroups for remote and on-site workers, organizational memory mechanisms for tacit knowledge exchange, and alternating work location schedules designed to foster interaction. Simulation results reveal that under non-contact scheduling, the interaction effect between learning from code/memory ($p_1$) and the proportion of tacit knowledge ($q$) is insignificant, while the coefficient of interaction effect $p_2\times q$ between learning by code/memory ($p_2$) and $q$ is twice that under partial-contact or full-contact scheduling. Moreover, under full-contact scheduling, the interaction effect between $p_1$ and the proportion of work-from-home employees ($wfh$) is insignificant ($p>0.1$), whereas the interaction effect between $p_2$ and $wfh$ is significant ($p<0.05$). Aligning with March’s findings that a low $p_1$ and high $p_2$ contribute to higher organizational knowledge, our simulation results indicate that non-contact scheduling preserves knowledge diversity, and full-contact scheduling promotes small-world network effects, thereby enhancing organizational knowledge equilibrium. These findings position hybrid work scheduling as a data-driven managerial decision, and the proposed model offers analytical insights for optimizing knowledge processes within business analytics contexts. Full article

Rural freight mobility and logistics face persistent challenges, including inadequate road infrastructure, high transportation costs, safety risks, tolls at link access points, and dispersed demand. Traditional inventory routing models often fail to address these complexities, especially in rural contexts where alternative routing options and integrated in-haul/back-haul operations are essential for improving efficiency and reducing empty miles. This study proposes a bi-objective mathematical model for the inventory routing problem in rural logistics, incorporating multiple routing attributes (transportation costs, risks, link-access tolls, and distances) and inventory dynamics (integrated in-haul and back-haul visits). The model aims to minimize total logistics costs and accident risk while balancing operational expenses and safety considerations. Risk estimation is derived from crash data along rural road links connecting distribution nodes. A real-world case study involving Walmart distribution centers in Macclenny, Baker County, Florida, and several rural Supercenters is conducted to validate the model. A modified Non-Dominated Sorting Genetic Algorithm II (NSGA-II) is developed and compared with CPLEX for solution efficiency across small and large-scale problem instances. Results indicate that the proposed approach outperforms classical methods, improves routing decisions in rural logistics systems, and achieves cost savings of up to 17% for the evaluated objectives, emphasizing the importance of using multi-attribute, multi-route network structures in rural logistics optimization. Full article

The agrarian sector, as the key source of livelihood in Sub-Saharan Africa (SSA), has become highly vulnerable to changes in extension service deliveries. Farmers mainly lack access to technical advice, financial credits, farming inputs and mechanization tools while environmental challenges reinforce the adaptation of sustainable management practices. Therefore, an understanding how multi-functional actor relationships determine agricultural knowledge and information (AKI) sharing is required. This study contributes to filling this gap by characterizing horizontal and vertical interactions. By applying a social network analysis, we mapped actor relations along public–private-community co-operations to provide insights into structural dependencies at different administrative levels. Related to three sites distributed over Burkina Faso and Ghana, local perceptions were collected in stakeholder workshops to generate social network narratives. These narratives were analyzed by various metrics to identify patterns of partnerships and key actors. Study results reveal for Burkina Faso a slight shared network topology, while both sites in Ghana reflect a top-down flow of AKI. The statistical findings indicate that agricultural extension services are primarily delivered to farmers through a few key actors such as NGOs and farm-based organizations/cooperatives. Especially at the community level, the results show many reciprocal links between farmers, business actors and NGOs. This highlights a shift toward a pluralistic agricultural extension service system and underpins the demand for policies to support the long-term viability of these actors, in particular for regions where public extension agents are under-represented. Full article

YouTube is a de facto learning environment for athletes seeking fast, actionable nutritional guidance, yet platform dynamics may favor simplified or testimonial narratives over evidence-aligned messages. This study maps Spanish-language sports-nutrition videos to clarify who is most visible, how advice is framed, and what users encounter first. We conducted a cross-sectional, mixed-methods study of 558 YouTube videos on pre/post-exercise nutrition and supplementation. Data was coded for video types (divulgation/testimonial), claim presence, evidence links, and creator status (professional/non-professional). Exposure-adjusted metrics (View Ratio, Viewer Interaction) and nonparametric tests summarized distributions. An undirected network generated centrality rankings to select qualitative samples. Thematic analysis of titles and descriptions identified recurring rhetorical patterns and discourse modes. Divulgation videos predominated (97.3%). Evidence links were rare (0.2%). Exposure and interaction were right-skewed, indicating concentrated visibility. Non-professionals produced most videos, with older uploads and higher daily view accrual; however, interaction per view was similar across groups. Qualitative synthesis revealed two dominant discourse modes, scientific–cautious and experience–testimonial. Oversimplification and motivational cues clustered in testimonial/non-professional items; instructional language and scarce evidence links concentrated in professional/divulgation items. In Spanish sports-nutrition content, visibility is concentrated, and creator identity shapes advice framing. Evidence-aligned messages can compete when expressed with clear athletic framing, explicit caveats, and links to trustworthy sources. Full article

The integration of distributed renewable energy sources into distribution networks is a key approach to achieving sustainable and low-carbon power systems. However, high renewable penetration significantly increases the volatility and uncertainty of distribution systems, posing challenges to renewable energy accommodation and reliable operation. To address these challenges, active control of distribution networks is required, which in turn relies on accurate system states. In practice, the limited number and accuracy of measurement devices in distribution networks make dynamic state estimation a critical technology for sustainable distribution systems. In this paper, a novel dynamic state estimation method for sustainable distribution systems is proposed, incorporating spatiotemporal data correlation and adaptiveness to process and measurement noise. A CNN-BiGRU-Attention model is developed to reconstruct high-accuracy real-time pseudo-measurements, compensating for insufficient sensing infrastructure. Furthermore, a noise adaptive dynamic state estimation method is proposed based on an improved unscented Kalman filter. An amplitude modulation factor (AMF) is applied to track time-varying process noise, while an evaluation method based on robust Mahalanobis distance (RMD) is embedded to deal with non-Gaussian measurement noise. Finally, simulation studies on the IEEE 33-bus three-phase unbalanced distribution network demonstrate the effectiveness and robustness of the proposed method. Full article

Firm requirements on electromagnetic compatibility (EMC) of electronic devices demand low electromagnetic emissions (EMI) of high-speed circuits, especially in the automotive industry. To be able to apply cost-effective anti-perturbative measures that reduce noise emission, critical signal integrity and power integrity (SI/PI) tools are needed for developing high-speed printed circuit board (PCB) designs. This paper presents an efficient method for modeling and analyzing the current drawn by digital ICs based on SPICE modeling data. The profile of the current drawn by the ICs from the power supply is composed of the static supply current and the dynamic supply current. This method enables power integrity engineers, in particular, PhD students and researchers who aim to develop an intuitive understanding of PI phenomena during the pre-layout phase, to see the hidden impact of the supply current on the power rail noise through time domain simulations, using a complex simulation model that integrates the Finite-Difference Time-Domain (FDTD) method of modeling the power and ground plane, with Voltage Regulator Modules (VRMs) and decoupling capacitors. A comparison of simulation results between the proposed models and SPICE IC models is also included to validate the proposed model. Full article

This article presents an investigation of the thermomechanical properties of silicone elastomer-based polymer composites modified with carbon nanotubes (CNTs) and mixed fillers (CNTs, bronze, graphite). The primary technique employed was the composite piezoelectric oscillator (CPO) method at approximately 100 kHz. This approach enabled precise measurements of the polymers’ forced oscillation frequency and logarithmic damping decrement (internal friction) across a wide temperature range (80–300 K). The application of this method is novel for this specific class of materials. Scanning electron microscopy confirmed the uniform distribution of the fillers within the polymer matrix. Differential scanning calorimetry (DSC) showed that the fillers modify the thermal stability of the composite. The systematic decrease in the enthalpy of the endothermic decomposition peak suggests a retardation of degradation kinetics, most likely due to a barrier effect of the filler network. Electrical measurements revealed a distinct contrast: the hybrid composite exhibited a frequency-independent conductivity plateau (~1.8 × 10⁻¹ S/m), confirming a robust percolating network, unlike the strong frequency dependence observed for the CNT-only composite. Research shows that the fillers effectively suppress relaxation processes linked to crystallization (205–215 K) and glass transition (165–170 K), as evidenced by a significant reduction in the amplitude of the corresponding internal friction peaks. The most pronounced effect was observed in the composite with mixed fillers, attributable to a synergistic effect between constituents. Furthermore, amplitude-dependent internal friction was found to occur predominantly below the glass transition temperature. The primary objective of the present study is to investigate the dynamic mechanical and damping behavior of CNT-filled silicone composites with mixed fillers under high-frequency loading, using the CPO method. These findings demonstrate the potential for tailoring the stiffness and damping characteristics of these composites for advanced applications in soft robotics and portable electronics. Full article

Electroencephalography (EEG), as a typical non-invasive biosensing signal, reflects individual emotional changes by recording the brain’s neural activity in response to various external stimuli. However, the significant differences in brain activity among individuals and the complex interrelationships between EEG channels notably hinder the accuracy of emotion decoding in non-invasive biosensing scenarios. To address this challenge, this paper proposes a two-discriminator domain adversarial neural network method (TD-DANN). The proposed method aims to obtain more generalized and individualized emotion feature representations through adversarial learning. Specifically, graph convolution is utilized to extract features from EEG signals. By modeling the EEG channels as graph nodes, the adjacency matrix can be dynamically learned to capture the complex relationships between different channels during emotion generation. Moreover, we design a domain discriminator and an individual discriminator. The domain discriminator is used to minimize the difference in feature distribution between the source and target domains. It is able to obtain discriminative features with universality. The individual discriminator is used to learn discriminative features consistent with the individual’s brain activity. It can enhance the adaptability to the individual’s emotion. The experimental results show that the TD-DANN achieves promising recognition accuracies of (98.45 ± 2.38)% and (89.45 ± 5.87)% for subject-dependent and subject-independent experiments on the SEED dataset, respectively. The proposed method attains recognition accuracies of (84.40 ± 8.70)% and (77.13 ± 7.97)% for subject-dependent and subject-independent experiments on the SEED-IV dataset, respectively. These results validate the effectiveness of the TD-DANN in the emotion decoding problem. Full article

The scientific establishment of ecological security pattern and identification of ecological restoration priority areas are key for territorial space ecological restoration and people’s well-being enhancement. Although numerous studies have addressed this topic, most focused on regional and urban scales. As the most basic administrative units in China, townships serve as a crucial link between macro-ecological protection strategies and micro-ecological restoration practices and are essential for effectively implementing ecological restoration and supporting rural revitalization practices, but research at this scale is currently lacking. Therefore, taking a typical township in Shanghai as an example, this study incorporated the Integrated Valuation of Ecosystem Services and Trade-offs (InVEST) model, Morphological Spatial Pattern Analysis (MSPA), landscape connectivity analysis, and circuit theory to construct an ecological security pattern and identify ecological restoration priority areas at the township scale, as well as to discuss corresponding ecological restoration strategies. The results showed that: (1) The study area contained 19 significant ecological sources (area of approximately 4.85 km²), exhibiting a spatial pattern characterized by “north–south concentration, central dispersion”. High-resistance areas were mainly distributed in areas with dense human activity and high development intensity, reflecting the significant impact of human activities on ecological processes. There were 32 main ecological corridors with a total length of 58.06 km, showing significant spatial imbalance, with some northern ecological sources at the risk of forming ecological isolated islands. (2) The ecological restoration priority areas mainly consisted of 41 ecological pinch points (area of approximately 27.24 ha) and 30 ecological barrier points (area of approximately 25.67 ha), which were crucial for enhancing ecological network connectivity and maintaining ecological security. (3) Based on the current land use status and spatial distribution characteristics of key ecological restoration areas, a hierarchical and categorized ecological restoration strategy was formulated. This study can strengthen research on identifying ecological restoration priority areas at the township scale. The methodological system established can provide a theoretical framework for ecological restoration research in similar areas. Moreover, this study pinpointed key areas and the spatial layout for ecological restoration, which helped to enhance the level of refined ecological governance at the township level and can also provide precise spatial decision-making basis for ecological restoration of the township territorial space. Full article

Value is the result of the combined, conscious, and creative actions of caring, which promote sustainable prosperity. Despite its centrality in marketing theory, value is treated in the literature as a self-evident, abstract term denoting concepts as diverse as the desire to acquire goods or enjoy services, the benefits derived from using a product, the price of an object, or a customer’s contribution to business profits. This approach leads to amoral marketing decision-making focused on extracting value from stakeholders and accumulating it in the form of shareholder wealth. In this framework, the negative consequences of marketing actions for society and the natural environment are simply dismissed as externalities. This is not sustainable as it degrades the environment and increases wealth and human welfare disparities between individuals, groups, and societies. Drawing on conceptualisations of value from the fields of philosophy, semiotics, and economics, value is here defined as the result of the combined, conscious, and creative actions of caring which promote sustainable prosperity. As such, value is understood to be co-created by the interactions of various stakeholders and positioned as the link between individuals, companies, markets, society, and the natural environment. Marketing theory has traditionally viewed value creation and exchange as the result of dyadic interactions. The socioeconomic and technological milieu of the 21st century, however, creates a business ecosystem characterised by digitalisation, interconnectivity, and decentralisation which means that, the number of participants in value co-creation networks is increasing and potentially tending towards infinity. Consequently, marketing is reconceptualised as the values-driven mechanism for value formation, valuation, symbolism, exchange facilitation, and integration of the resources required for value co-creation and distribution aiming at contributing to sustainable prosperity. Virtuous marketers and mindful marketing practice can ensure the optimal use of resources and the maximisation and equitable distribution of welfare in the present without compromising the ability of future generations to continue to generate and enjoy value. Thus, by placing value at the centre of the business ecosystem, marketing contributes to sustainable prosperity. Full article

The integration of renewable energy sources into modern power systems requires advanced control strategies to maintain stability, reliability, and efficiency. This paper presents a comprehensive review of the application of artificial intelligence techniques, including machine learning, deep learning, and reinforcement learning, in improving droop control for renewable energy integration. These artificial intelligence-based methods address key challenges such as frequency and voltage regulation, power sharing, and grid compliance under conditions of high renewable penetration. Machine learning approaches, such as support vector machines, are used to optimize droop parameters for dynamic grid conditions, while deep learning models, including recurrent neural networks, capture complex system dynamics to enhance the stability of distributed energy systems. Reinforcement learning algorithms enable adaptive, autonomous control, improving multi-objective optimization within microgrids. In addition, emerging directions such as transfer learning and real-time data analytics are explored for their potential to enhance scalability and resilience. Overall, this review synthesizes recent advances to demonstrate the growing impact of artificial intelligence in droop control and outlines future pathways toward more intelligent and sustainable power systems. 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 prediction of chiller performance parameters is crucial for optimal control and fault diagnosis. Numerous efficient and accurate data-driven models have been developed and implemented. These models are normally trained on historical operational data of chiller units. However, the distribution of operational data may shift due to accumulated operating hours or changes in control strategies. Under new operating conditions, models trained on historical data often generalize poorly, leading to prediction deviations. To address this issue, this study integrates a one-dimensional convolutional neural network with a domain adaptation method that extracts features from both the source and target domains and aligns their inverse Gram matrices in terms of angle and scale. A predictive model applicable to multiple chiller performance parameters is established using limited historical data, enhancing the model’s generalization ability. Compared to the baseline model (MLP), the proposed method achieves an average reduction of 74.3% in mean absolute error (MAE) and 76.1% in root mean square error (RMSE), while the R² values exceed 0.96 (for certain scenarios). Additionally, this paper analyzes the data distribution between the source and target domains, investigates key factors affecting the model’s generalization capability, and provides insights for evaluating the quality of modeling data. Full article