Search Results (1,801)

The prevailing view is that ESG performance contributes to corporate financial stability, particularly regarding long-term sustainability objectives. However, there is a notable lack of critical research exploring its short-term financial effects, especially within capital-intensive sectors experiencing green transformation. This study examines the theoretical gap by investigating whether increased ESG performance may unintentionally heighten the financial burden and default risk in the short run. To verify the stability of each variable in the series, we employed the short-panel unit root test on panel data from 234 Chinese energy industry companies covering the years 2015 to 2023. Including enterprise fixed effects as well as time fixed effects, we find that higher ESG ratings increase the possibility of default risk in the Chinese energy sector. This effect remains robust after controlling firm size, financial leverage, return on assets, return on equity, earnings per share, beta and firm age. In addition, we conduct robustness checks using alternative default risk measures, both endogeneity- and component-based, and the outcomes demonstrate that the impact is substantial and consistent. Consequently, we may draw the conclusion that raising the ESG rating has an adverse effect on reducing corporate default risk, which fills the knowledge gap regarding the influence of listed companies’ default risk on China’s energy sector. Moreover, it has been found that green innovation plays a strengthening role in the analysis of the interaction term between green innovation and ESG on default risk. This suggests that while green innovation is a strategic initiative aimed at long-term sustainability, it requires a significant amount of capital and resources in the short term, which may result in higher default risk in the beginning. Full article

Although epsilon-near-zero (ENZ) media have emerged as a promising platform for power dividers, the majority of existing designs are confined to fixed power splitting. In this work, two dynamically tunable power dividers using waveguide ENZ media are proposed by precisely modulating the internal magnetic field and the widths of the output waveguides. The first approach features a mechanically reconfigurable ring-shaped ENZ waveguide. By continuously re-distributing the magnetic field within the ENZ tunneling channels utilizing rotatable copper plates, arbitrary power division among multiple output ports is constructed. The second design integrates a rectangular-loop ENZ cavity into a substrate-integrated waveguide, with four positive–intrinsic–negative diodes embedded to dynamically activate specific output ports. This configuration steers electromagnetic energy toward output ports with varying cross-sectional areas, enabling on-demand control over both the power division and the number of output ports. Both analytical and full-wave simulation results confirm dynamic power division, with transmission efficiencies exceeding 93%. Despite differences in structure and actuation mechanisms, both designs exhibit flexible field control, high reconfigurability, and excellent transmission performance, highlighting their potential in advanced applications such as real-time wireless communications, multi-input–multi-output systems, and reconfigurable antennas. Full article

White clover (Trifolium repens L.) is known for its ability to fix nitrogen biologically, its high nutritional value, and its adaptability to livestock systems. However, excessive fertilization with synthetic nitrogen alters its symbiosis with Rhizobium and reduces the protein content of the forage. The objective of this study was to evaluate the interaction between nitrogen fertilization (0 and 60 kg N ha⁻¹), cutting time, and post-cutting evaluation on the morphology, yield, and nutritional composition of white clover. A completely randomized block experimental design with three factors, distributed in three blocks, was used. Within each block, three replicates of each treatment were assigned (six interactions), giving a total of 54 experimental units. The data were analyzed using a three-way analysis of variance and Tukey’s multiple comparison test. Exponential models and generalized additive models (GAMs) were applied to the morphology and yield data to identify the best fit. The treatment with 60 kg N ha⁻¹ and cutting at 30 days showed significant increases in plant height (47.42%), fresh weight (59.61%), dry weight (98.41%), and leaf width (27.55%) compared to the control. It also produced the highest protein content (28.44%) compared to the other treatments with fertilization, without negatively affecting digestibility. The GAMs best fit most morphological and yield parameters (except leaf height and width). All fertilized treatments had higher fresh and dry weight yields. In conclusion, applying 60 kg N ha⁻¹ after cutting at 30 days, followed by harvesting between 54 and 60 days, improved both the quality and yield of white clover, which favored sustainable pasture management and reduced excessive nitrogen use. Full article

Random packed beds (RPBs) of various particles are widely used in chemical reactors to enhance the contact between the reactants or the catalyst. This numerical study investigates the prospects of using a helical flow deflector spanning the whole cross-section of the reactor and the height of the random packing to control residence time distribution (RTD) in RPBs of spherical particles. The packed bed geometry is generated via sequential particle deposition, while flow equations are solved for the real geometry of the packing without additional modelling terms. The results demonstrate that in laminar conditions the flow deflector significantly increases flow tortuosity and residence time (even a few times for small helix pitches) when the effective velocity in the RPB is kept fixed. The relationship between the helix pitch and tortuosity, pressure drop, and RTD is quantified, revealing that residence time scale similarly to tortuosity while the increase in pressure drop is more pronounced. The study provides a validated framework for optimising helical deflector designs in RPBs (at least in the laminar regime), with implications for reactor efficiency. Full article

The rapid expansion of broadband wireless communication systems, including 5G, satellite networks, and next-generation IoT platforms, has created a strong demand for antenna architectures capable of real-time beam control, compact integration, and broad frequency coverage. Traditional reflectarrays, while effective for narrowband applications, often face inherent limitations such as fixed beam direction, high insertion loss, and complex phase-shifting networks, making them less viable for modern adaptive and reconfigurable systems. Addressing these challenges, this work presents a novel wideband planar metasurface that operates as a polarization rotation reflective metasurface (PRRM), combining 90° polarization conversion with 1-bit reconfigurable phase modulation. The metasurface employs a mirror-symmetric unit cell structure, incorporating a cross-shaped patch with fan-shaped stub loading and integrated PIN diodes, connected through vertical interconnect accesses (VIAs). This design enables stable binary phase control with minimal loss across a significantly wide frequency range. Full-wave electromagnetic simulations confirm that the proposed unit cell maintains consistent cross-polarized reflection performance and phase switching from 3.83 GHz to 15.06 GHz, achieving a remarkable fractional bandwidth of 118.89%. To verify its applicability, the full-wave simulation analysis of a 16 × 16 array was conducted, demonstrating dynamic two-dimensional beam steering up to ±60° and maintaining a 3 dB gain bandwidth of 55.3%. These results establish the metasurface’s suitability for advanced beamforming, making it a strong candidate for compact, electronically reconfigurable antennas in high-speed wireless communication, radar imaging, and sensing systems. Full article

To address the issues of low efficiency, insufficient coverage, and high energy consumption in wheat sowing path planning for large-scale irregular farmland, this study proposes an improved hybrid particle swarm optimization algorithm (TLG-PSO) for autonomous operational path planning. Building upon the standard PSO, the proposed method introduces a Tent chaotic mapping initialization mechanism, a Logistic-based dynamic inertia weight adjustment strategy, and adaptive Gaussian perturbation optimization to achieve precise control of the agricultural machinery’s driving orientation angle. A comprehensive path planning model is constructed with the objectives of minimizing the effective operation path length, reducing turning frequency, and maximizing coverage rate. Furthermore, cubic Bézier curves are employed for path smoothing, effectively controlling path curvature and ensuring the safety and stability of agricultural operations. The simulation experiment results demonstrate that the TLG-PSO algorithm achieved exceptional full-coverage operation performance across four categories of typical test fields. Compared to conventional fixed-direction path planning strategies, the algorithm reduced average total path length by 6228 m, improved coverage rate by 1.31%, achieved average labor savings of 96.32%, and decreased energy consumption by 6.45%. In large-scale comprehensive testing encompassing 1–27 field plots, the proposed algorithm reduced average total path length by 8472 m (a 5.45% decrease) and achieved average energy savings of 44.21 kW (a 5.48% reduction rate). Comparative experiments with mainstream intelligent optimization algorithms, including GA, ACO, PSO, BreedPSO, and SecPSO, revealed that TLG-PSO reduced path length by 0.16%–0.74% and decreased energy consumption by 0.53%–2.47%. It is worth noting that for large-scale field operations spanning hundreds of acres, even an approximately 1% path reduction translates to substantial fuel and operational time savings, which holds significant practical implications for large-scale agricultural production. Furthermore, TLG-PSO demonstrated exceptional performance in terms of algorithm convergence speed and computational efficiency. The improved TLG-PSO algorithm provides a feasible and efficient solution for autonomous operation of large-scale agricultural machinery. Full article

This study systematically investigated water-soluble inorganic ions (WSIIs) and their sources in PM2.5 in mountainous urban areas of Chongqing City. PM2.5 monitoring was conducted throughout 2023, spanning one year. The two districts under discussion are the Liang Jiang New Area (LJ) and He Chuan District (HC). The ion chromatography (Dionex Integrion HPIC) method was utilized to quantify eight ions (Cl⁻, SO₄²⁻, NO₃⁻, Na⁺, K⁺, Mg²⁺, Ca²⁺, NH₄⁺). The results obtained were then analyzed in conjunction with the EPA PMF 5.0 source apportionment model. The following key findings are presented: the data demonstrate that there is significant seasonal fluctuation in PM2.5 concentrations. The mean winter concentration (64 ± 27 μg/m³) was found to be 3.25 times higher than the mean summer concentration (19.7 ± 2 μg/m³). These fluctuations were primarily influenced by basin topography and unfavorable meteorological conditions. The proportion of PM2.5 mass attributable to WSII ranges from 31 to 33 percent, with the majority of this mass being attributed to secondary inorganic aerosols (SNA: SO₄²⁻, NO₃⁻, NH₄⁺; accounting for 47–85% WSII). The annual NO₃⁻/SO₄²⁻ ratio (0.69–0.80, <1) indicates that fixed sources (coal/industry) dominate, but a winter ratio >1 suggests increased contributions from mobile sources under low-temperature conditions. The sulfur oxidation rate (SOR: 0.35–0.37) is significantly higher than the nitrogen oxidation rate (NOR: 0.08–0.13), reflecting the efficient conversion of SO₂ through wet, low-temperature pathways. PMF identified six sources, with secondary formation (43.8–44.3%) being the primary contributor to the overall process. In urban LJ, transportation (26.1%) and industry (13.6%) have been found to contribute significantly, while in suburban HC, combustion (15.4%) and dust (8.8%) have been determined to have notable impacts. This study recommends the implementation of synergistic control of SNA precursors (SO₂, NOx, NH₃), the strengthening of transportation and industrial management in LJ, and the enhancement of biomass combustion and dust control in HC. Full article

Background/Objectives: Ovarian cancer (OC) is frequently diagnosed at an advanced stage due to the nonspecific nature of its symptoms. While population-wide screening has failed to reduce mortality, timely diagnosis in symptomatic women remains a promising and underutilized strategy to improve clinical outcomes. The aim of this study was to develop a sensitive, scalable biomarker assay to improve early-stage detection in symptomatic women. Methods: A multi-omic diagnostic model was developed using serum samples from symptomatic women. Lipidomic profiles were generated by liquid chromatography–mass spectrometry (LC-MS), and protein levels were measured using immunoassays. Statistical and machine learning approaches were applied to assess diagnostic performance across disease stages and subtypes. Results: The multi-omic model demonstrated robust performance across a clinically challenging population, with both lipid and protein data necessary for detecting OC across a range of stages and subtypes. The model achieved 98.7% sensitivity in early-stage OC and 98.6% across a range of OC subtypes and stages at 70% fixed specificity, which represented significant improvements over CA125 in the same cohort. In addition, in a small subset of samples, lipid and protein profiles from OC patients undergoing treatment differed from untreated patients and controls, suggesting that this approach may also be useful in other aspects of clinical management, such as treatment monitoring. Conclusions: This multi-omic assay offers a promising solution to accelerate diagnosis, improve early detection, and potentially reduce OC mortality. Full article

Digital twins represent a transformative innovation for battery energy storage systems (BESS), offering real-time virtual replicas of physical batteries that enable accurate monitoring, predictive analytics, and advanced control strategies. These capabilities promise to significantly enhance system efficiency, reliability, and lifespan. Yet, despite the clear technical potential, large-scale deployment of digital twin-enabled battery systems faces critical governance barriers. This study identifies three major challenges: fragmented standards and lack of interoperability, weak or misaligned market incentives, and insufficient cybersecurity safeguards for interconnected systems. The central contribution of this research is the development of a comprehensive governance framework that aligns these three pillars—standards, market and regulatory incentives, and cybersecurity—into an integrated model. Findings indicate that harmonized standards reduce integration costs and build trust across vendors and operators, while supportive regulatory and market mechanisms can explicitly reward the benefits of digital twins, including improved reliability, extended battery life, and enhanced participation in energy markets. For example, simulation-based evidence suggests that digital twin-guided thermal and operational strategies can extend usable battery capacity by up to five percent, providing both technical and economic benefits. At the same time, embedding robust cybersecurity practices ensures that the adoption of digital twins does not introduce vulnerabilities that could threaten grid stability. Beyond identifying governance gaps, this study proposes an actionable implementation roadmap categorized into short-, medium-, and long-term strategies rather than fixed calendar dates, ensuring adaptability across different jurisdictions. Short-term actions include establishing terminology standards and piloting incentive programs. Medium-term measures involve mandating interoperability protocols and embedding digital twin requirements in market rules, and long-term strategies focus on achieving global harmonization and universal plug-and-play interoperability. International examples from Europe, North America, and Asia–Pacific illustrate how coordinated governance can accelerate adoption while safeguarding energy infrastructure. By combining technical analysis with policy and governance insights, this study advances both the scholarly and practical understanding of digital twin deployment in BESSs. The findings provide policymakers, regulators, industry leaders, and system operators with a clear framework to close governance gaps, maximize the value of digital twins, and enable more secure, reliable, and sustainable integration of energy storage into future power systems. Full article

Time-varying lumped disturbance and measurement noise are primary obstacles that restrict the control performance of permanent magnet synchronous motor (PMSM) drives. To tackle these obstacles, an adaptive nonsingular terminal sliding mode (ANTSM) algorithm is combined with augmented recursive sliding mode observer (ARSMO) for PMSM speed regulation system in this paper. Generally, conventional nonsingular terminal sliding mode (NTSM) controller adopts a fixed and conservative control gain to suppress the time-varying disturbance, which will lead to unsatisfactory steady-state performance. Without requiring any information of the time-varying disturbance in advance, a novel barrier function adaptive algorithm is utilized to adjust the gain of NTSM controller online according to the amplitude of disturbance. In addition, the ARSMO is emoloyed to estimate the total disturbance and motor speed simultaneously, thereby alleviating the negative impact of measurement noise and excessive control gain. Comprehensive experimental results verify that the proposed enhanced ANTSM strategy can optimize the dynamic performance of PMSM system without sacrificing its steady-state performance. Full article

To improve the tracking accuracy and the adaptability of intelligent vehicles in various road conditions, an adaptive model predictive controller combining reinforcement learning is proposed in this paper. Firstly, to solve the problem of control accuracy decline caused by a fixed prediction time domain, a low-computational-cost adaptive prediction horizon strategy based on a two-dimensional Gaussian function is designed to realize the real-time adjustment of prediction time domain change with vehicle speed and road curvature. Secondly, to address the problem of tracking stability reduction under complex road conditions, the Deep Q-Network (DQN) algorithm is used to adjust the weight matrix of the Model Predictive Control (MPC) algorithm; then, the convergence speed and control effectiveness of the tracking controller are improved. Finally, hardware-in-the-loop tests and real vehicle tests are conducted. The results show that the proposed adaptive predictive horizon controller (DQN-AP-MPC) solves the problem of poor control performance caused by fixed predictive time domain and fixed weight matrix values, significantly improving the tracking accuracy of intelligent vehicles under different road conditions. Especially under variable curvature and high-speed conditions, the proposed controller reduces the maximum lateral error by 76.81% compared to the unimproved MPC controller, and reduces the average absolute error by 64.44%. The proposed controller has a faster convergence speed and better trajectory tracking performance when tested on variable curvature road conditions and double lane roads. Full article

With the increasing integration of renewable energy sources and power electronic converters, Grid-Forming (GFM) technologies such as Virtual Synchronous Generators (VSGs) have emerged as key enablers of future power systems. However, conventional VSG control strategies with fixed parameters often fail to maintain transient stability under dynamic grid conditions. This paper proposes a novel adaptive GFM control framework based on physics-informed reinforcement learning, targeting transient power angle stability in systems with high renewable penetration. An adaptive controller, termed the 3N-D controller, is developed to periodically update the virtual inertia and damping coefficients of VSGs based on real-time system observations, enabling anticipatory adjustments to evolving operating conditions. The controller leverages a reinforcement learning architecture embedded with physical priors, which captures the high-order differential relationships between rotor angle dynamics and control variables. This approach enhances generalization, reduces data dependency, and mitigates the risk of local optima. Comprehensive simulations on the IEEE-39 bus system with varying VSG penetration levels validate the proposed method’s effectiveness in improving system stability and control flexibility. The results demonstrate that the physics-embedded GFM strategy can significantly enhance the transient stability and adaptability of future power grids. Full article

In recent years, machine learning (ML) techniques have gained significant attention in time series classification tasks, particularly in industrial applications where early detection of abnormal conditions is crucial. This study proposes an intelligent monitoring framework based on a multimodal convolutional neural network (CNN) to classify normal and abnormal copper ion (Cu²⁺) concentration states in the etching process in the printed circuit board (PCB) industry. Maintaining precise control Cu²⁺ concentration is critical in ensuring the quality and reliability of the etching processes. A sliding window approach is employed to segment the data into fixed-length intervals, enabling localized temporal feature extraction. The model fuses two input modalities—raw one-dimensional (1D) time series data and two-dimensional (2D) recurrence plots—allowing it to capture both temporal dynamics and spatial recurrence patterns. Comparative experiments with traditional machine learning classifiers and single-modality CNNs demonstrate that the proposed multimodal CNN significantly outperforms baseline models in terms of accuracy, precision, recall, F1-score, and G-measure. The results highlight the potential of multimodal deep learning in enhancing process monitoring and early fault detection in chemical-based manufacturing. This work contributes to the development of intelligent, adaptive quality control systems in the PCB industry. Full article

A fast terminal sliding mode control based on a fixed-time sliding surface is proposed for a permanent magnet arc motor (PMAM), effectively improving speed response, control accuracy, and disturbance rejection capability. Due to its piecewise structure and advanced logarithmic characteristics, a PMAM is subject to high-frequency disturbances. Additionally, it is also influenced by external disturbances. To address this, a sliding mode reaching law that combines terminal terms, linear terms, and switching terms is designed to reduce chattering and enhance robustness. Furthermore, to improve the convergence speed of the sliding mode and disturbance rejection ability, a novel fixed-time converging sliding surface based on a variable exponent terminal term is introduced. Numerical simulations verify the convergence and disturbance rejection capabilities of the proposed sliding surface. Stability based on the Lyapunov theorem is strictly proven. Experimental results validate the effectiveness and superiority of the proposed algorithm. Full article

We present a novel encoder-only Transformer model for symbolic music harmony generation, based on a fixed time-grid representation of melody and harmony. Inspired by denoising diffusion processes, our model progressively unmasks harmony tokens over a sequence of discrete stages, learning to reconstruct the full harmonic structure from partial context. Unlike autoregressive models, this formulation enables flexible, non-sequential generation and supports explicit control over harmony placement. The model is stage-aware, receiving timestep embeddings analogous to diffusion timesteps, and is conditioned on both a binary piano roll and a pitch class roll to capture melodic context. We explore two unmasking schedules—random token revealing and midpoint doubling—both requiring a fixed and significantly reduced number of model calls at inference time. While our approach achieves competitive performance with strong autoregressive baselines (GPT-2 and BART) across several harmonic metrics, its key advantages lie in controllability, structured decoding with fixed inference steps, and alignment with musical structure. Ablation studies further highlight the role of stage awareness and pitch class conditioning. Our results position this method as a viable and interpretable alternative for symbolic harmony generation and a foundation for future work on structured, controllable musical modeling. Full article