Shaanxi Polytechnic Institute

South Asia faces a critical challenge in balancing economic growth with environmental sustainability, especially amid mounting global concerns over climate change. This study examines the long-term relationships between digital trade, green technology investment, financial inclusion, natural resource rent, and carbon dioxide emissions in South Asian economies from 2003 to 2023. Using the Generalized Method of Moments–Panel Vector Auto Regression (GMM-PVAR) model, the study captures both dynamic interactions and causal linkages among the variables. The findings reveal that lagged carbon emissions significantly reduce current emissions, indicating a corrective mechanism influenced by regulatory and economic adjustments. Both green technology investment and digital trade substantially reduce carbon emissions. However, financial inclusion tends to increase emissions due to greater economic activities in carbon-intensive industries. Natural resource rent does not significantly impact carbon emissions. Causality analysis confirms both unidirectional and bidirectional causal relationships, highlighting the significance of legislative actions to balance environmental sustainability and economic prosperity. These findings provide valuable insights for policymakers in South Asia and beyond, emphasizing the need for strategic investments in green innovation and digital infrastructure to achieve sustainable development goals.

The rural ecological environment constitutes a critical component of the "three farmers" agenda, with the remediation of black and odorous water bodies being a pivotal element within this domain. This paper elucidates the definition of black and odorous water bodies in rural settings, examines their formation mechanisms and prevailing challenges, and proposes an integrated water body treatment technology encompassing "source control and pollution interception + internal remediation + ecological purification." Additionally, it advocates for enhancing the operational and maintenance models as well as governance systems to provide actionable insights for addressing black and odorous water bodies in rural areas.

The integration of electric vehicles (EVs) into power systems via vehicle-to-grid (V2G) technology offers new opportunities for bidirectional energy exchange and resource allocation. However, time delays and adversarial attacks in communication networks can undermine the coordination of EV aggregators and power systems. To address this challenge, this paper presents a hybrid counterfactual learning approach for control of EV aggregators in the multi-area power systems V2G and load frequency control (LFC) framework. The proposed hybrid approach integrates counterfactual multi-agent learning, adversarial training, and monotonic neural network (CMA-HMNN). The multi-agent counterfactual learning marginalizes the impact of individual actions on the overall reward, thereby better coordinating controllable resources and reducing variances in adversarial multi-agent training. By enforcing deviation-command monotonicity constraints within the neural network architecture, the proposed approach can satisfy Lyapunov stability conditions and improve the stability of power systems with integrated EVs. Adversarial training based on the fast gradient sign method (FGSM) is applied to enhance the robustness of the networks against perturbations. Even under concurrent time-varying communication delays and malicious threats from communication networks, the method effectively coordinates multiple generation resources and EV aggregators. Compared with four DRL-based control methods, the superiority of the proposed method is verified on the three-area power system and IEEE 39-bus power system with wide EVs integrations.

Rolling element bearings are commonly used in rotating machines. However, reliable and real-time bearing condition monitoring and fault diagnosis is a challenging problem in industry, due to limitations associated with reliable real-time data acquisition (DAQ) and signal processing in noisy environments where bearings are often housed. In this article, a smart sensor-based bearing fault detection technology is proposed. The proposed method introduces a DAQ design for high resolution vibration signal measurement. A wavelet swarm decomposition (WSD) technique is also developed for stand-alone bearing fault detection. The proposed WSD technique adopts a new wavelet filter to adaptively determine center frequencies in the time-frequency domain. Moreover, an ensemble function is introduced to select decomposition modes and improve processing efficiency. The effectiveness of the proposed smart sensor bearing fault detection method is validated experimentally to detect different types of bearing faults. Test results show that the proposed method powered by the developed WSD technique performs better (6% higher) than other related techniques in bearing fault detection and diagnosis.

Chiral stereochemistry modulates the crystal packing to rationally regulate the photophysical properties of nonconventional luminophores.

A gestão inadequada de resíduos orgânicos representa um desafio significativo para a sustentabilidade ambiental, especialmente em áreas urbanas. Este estudo visa avaliar a eficácia da compostagem doméstica na produção de adubo orgânico e na redução da poluição do solo e do meio ambiente. A metodologia adotada compreendeu três etapas principais: uma revisão bibliográfica, para fundamentar teoricamente a compostagem doméstica e seus benefícios ambientais; uma pesquisa experimental, onde foi construída e adaptada uma composteira doméstica para o tratamento de resíduos orgânicos como frutas, verduras e grãos; e uma análise quantitativa, com monitoramento do processo de compostagem ao longo de 45 dias, incluindo medições de temperatura, humidade e análises das características físicas do composto. O composto final apresentou características desejáveis, como cor escura, odor terroso e textura solta, resultando em um crescimento significativo das plantas após a aplicação do adubo em uma horta doméstica. Em função dos resultados conclui-se que a compostagem doméstica é uma prática eficaz para a gestão de resíduos orgânicos, contribuindo para a redução de resíduos em aterros sanitários e promovendo uma agricultura mais sustentável.

Visible and infrared image fusion (VIF) aims to integrate multi-spectral information to generate a fused image with enhanced content richness and discriminability, thereby improving visual perception in complex environments. Existing approaches, however, still face several challenges, including insufficient use of frequency-domain information, difficulty in balancing structural preservation and detail enhancement, limited multi-scale fusion capability, and lack of explicit frequency-domain constraints during training. To address these issues, we propose a Frequency-Aware Invertible Fusion Network (FAIFN), which innovatively combines Haar wavelet transform with invertible neural networks to perform lossless frequency decomposition and reconstruction. The model introduces a bidirectional cross-frequency attention mechanism to adaptively enhance interactions between low-frequency and high-frequency features, a multi-scale adaptive fusion module to capture contextual information at different scales, and a frequency-aware loss function to provide joint supervision in both spatial and frequency domains. Extensive experiments on multiple public datasets demonstrate that FAIFN achieves superior performance across various evaluation metrics while maintaining high inference efficiency and low model complexity.