Search Results (1,128)

Energy poverty is one of the major challenges to global sustainable development, while digital development, as a significant trend of the current era, is considered a key pathway to transcend traditional energy governance frameworks. Anchored in provincial panel data spanning 30 regions across China from 2003 to 2023, this study systematically examines the impact and heterogeneity of digital development on energy poverty and further explores the underlying mechanisms and nonlinear characteristics. The findings show that digital development can significantly alleviate energy poverty, and this conclusion remains valid after addressing endogeneity issues and conducting a series of robustness tests. However, the poverty reduction effect of digital development exhibits significant regional heterogeneity: the mitigation effect in central and western regions is significantly stronger than that in eastern regions, the effect in northern regions is higher than that in southern regions, and the effect in energy-disadvantaged regions is better than that in advantageous regions. Additionally, digital development alleviates energy poverty through mediating pathways such as promoting non-agricultural employment, improving human capital levels, and driving technological innovation. Notably, digital development demonstrates threshold effects and quantile heterogeneity in relation to energy poverty, characterized by diminishing marginal returns as digital development progresses; regions with higher levels of energy poverty experience more significant poverty reduction effects from digital development. This research provides a theoretical basis for energy poverty governance under the global energy crisis and offers empirical references for other countries to achieve energy sustainability goals (SDG7) through context-specific digital transformations. Full article

With rapid urbanization and industrialization in China, existing gas supply networks urgently require renewal and optimization. This paper proposes a Gray Wolf Optimizer (GWO)-based method for reducing calculation errors and a Zebra Optimization Algorithm (ZOA)-based approach for gas supply pressure distribution. For error correction, the pipe friction coefficient is adjusted to minimize the deviation between calculated and actual flows. The GWO reduces average relative error to 0.01% with satisfactory iteration speed and efficiency. For pressure distribution, supply-end pressures are optimized to reduce energy consumption and enhance system performance. The ZOA shows strong convergence and global search capabilities. These methods provide valuable theoretical and practical insights for optimizing gas supply networks, supporting green transformation and sustainable development. Full article

Despite growing interest in carbon capture and utilization (CCU), the transformation of captured CO₂ into dry ice remains poorly studied, particularly from a systems integration and energy optimization perspective. While previous works have examined individual components such as CO₂ absorption, liquefaction, or refrigerant evaluation, no existing study has modeled the full dry ice production chain from capture to solidification within a unified simulation framework. This study presents the first complete simulation and optimization of a dry ice production process, incorporating CO₂ absorption, solvent regeneration, dehydration, multistage compression, ammonia-based external liquefaction, and expansion-based solidification using Aspen HYSYS. The process features ammonia as a working refrigerant due to its favorable thermodynamic performance and zero global warming potential. Optimization of heat integration reduced total energy consumption by 66.67%, replacing conventional utilities with water-based heat exchangers. Furthermore, solvent recovery achieved rates of 75.65% for MDEA and 66.4% for piperazine, lowering operational costs and environmental burden. The process produced dry ice with 97.83% purity and 94.85% yield. A comparative analysis of refrigerants confirmed ammonia’s superiority over R-134a and propane. These results provide the first system-level roadmap for producing dry ice from captured CO₂ in an energy-efficient, scalable, and environmentally responsible manner. Full article

The hydrogen economy stands at the forefront of the global energy transition, and additive manufacturing (AM) is increasingly recognized as a critical enabler of this transformation. AM offers unique capabilities for improving the performance and durability of hydrogen energy components through rapid prototyping, topology optimization, functional integration of cooling channels, and the fabrication of intricate, hierarchical, structured pores with precisely controlled connectivity. These features facilitate efficient heat and mass transfer, thereby improving hydrogen production, storage, and utilization efficiency. Furthermore, AM’s multi-material and functionally graded printing capability holds promise for producing components with tailored properties to mitigate hydrogen embrittlement, significantly extending operational lifespan. Collectively, these advances suggest that AM could lower manufacturing costs for hydrogen-related systems while improving performance and reliability. However, the current literature provides limited evidence on the integrated techno-economic advantages of AM in hydrogen applications, posing a significant barrier to large-scale industrial adoption. At present, the technological readiness level (TRL) of AM-based hydrogen components is estimated to be 4–5, reflecting laboratory-scale progress but underscoring the need for further development, validation and industrial-scale demonstration before commercialization can be realized. Full article

With buildings accounting for 40% of global energy consumption, heating, ventilation, and air conditioning (HVAC) systems represent the single largest opportunity for emissions reduction, consuming up to 60% of commercial building energy while maintaining occupant comfort. This critical balance between energy efficiency and human comfort has traditionally relied on rule-based and model predictive control strategies. Given the multi-objective nature and complexity of modern HVAC systems, these approaches fall short in satisfying both objectives. Recently, reinforcement learning (RL) has emerged as a method capable of learning optimal control policies directly from system interactions without requiring explicit models. However, standard RL approaches frequently violate comfort constraints during exploration, making them unsuitable for real-world deployment where occupant comfort cannot be compromised. This paper addresses two fundamental challenges in HVAC control: the difficulty of constrained optimization in RL and the challenge of defining appropriate comfort constraints across diverse conditions. We adopt a safe RL with a neural barrier certificate framework that (1) transforms the constrained HVAC problem into an unconstrained optimization and (2) constructs these certificates in a data-driven manner using neural networks, adapting to building-specific comfort patterns without manual threshold setting. This approach enables the agent to almost guarantee solutions that improve energy efficiency and ensure defined comfort limits. We validate our approach through seven experiments spanning residential and commercial buildings, from single-zone heat pump control to five-zone variable air volume (VAV) systems. Our safe RL framework achieves energy reduction compared to baseline operation while maintaining higher comfort compliance than unconstrained RL. The data-driven barrier construction discovers building-specific comfort patterns, enabling context-aware optimization impossible with fixed thresholds. While neural approximation prevents absolute safety guarantees, reducing catastrophic safety failures compared to unconstrained RL while maintaining adaptability positions this approach as a developmental bridge between RL theory and real-world building automation, though the considerable gap in both safety and energy performance relative to rule-based control indicates the method requires substantial improvement for practical deployment. Full article

Breast cancer remains a leading cause of global mortality. According to international cancer data, significant progress has been made in treating breast cancer; however, metastasis and drug resistance continue to be the primary causes of mortality for many patients. This study investigated the modulation of apoptosis-related genes in response to ionizing radiation and estrogen exposure based on a human breast epithelial cell model (MCF-10F and its transformed variants: Estrogen, Alpha3, Alpha5, Tumor2) previously established, where cells were treated with high linear energy transfer alpha particles, with or without 17β-estradiol. Gene expression profiling was performed using an Affymetrix U133A microarray, and bioinformatic analyses assessed differential expression, estrogen receptor status, and correlations with overall survival. Distinct gene expression patterns emerged across cell lines and tumor subtypes. TP53 expression correlated positively with TP63, BIK, CFLAR, BIRC3, and BCLAF1. TP63, PERP, CFLAR, BCLAF1, GULP1, and BIRC3 were elevated in normal tissue, whereas BIK, PHLDA2, and BBC3 were upregulated in tumors. ER-positive tumors exhibited higher TP63, BIK, BCLAF1, and BBC3 expression, while ER-negative tumors showed increased PERP, CFLAR, BIRC3, and PHLDA2. Notably, elevated BCLAF1 expression was associated with poorer survival in Luminal A patients, and high PHLDA2 expression correlated with reduced survival in Luminal B cases. These findings indicate that resistance to apoptosis is a fundamental mechanism in breast cancer progression and therapeutic evasion. Breast tumors selectively alter the expression of key genes to promote growth, evade apoptosis, and develop therapeutic resistance. The differential expression and correlations of these apoptosis-related genes highlight their potential as molecular targets for future personalized cancer therapies and as valuable biomarkers for prognostic stratification and predicting therapeutic response. Full article

Urban roof segmentation plays a pivotal role in applications such as urban planning, infrastructure management, and renewable energy deployment. This study explores the evolution of deep learning techniques from traditional Convolutional Neural Networks (CNNs) to cutting-edge transformer-based models in the context of roof segmentation from satellite imagery. We highlight the limitations of conventional methods when applied to urban environments, including resolution constraints and the complexity of roof structures. To address these challenges, we evaluate two advanced deep learning models, Mask R-CNN and MaskFormer, which have shown significant promise in accurately segmenting roofs, even in dense urban settings with diverse roof geometries. These models, especially the one based on transformers, offer improved segmentation accuracy by capturing both global and local image features, enhancing their performance in tasks where fine detail and contextual awareness are critical. A case study on Ben Guerir City in Morocco, an urban area experiencing rapid development, serves as the foundation for testing these models. Using high-resolution satellite imagery, the segmentation results offer a deeper understanding of the accuracy and effectiveness of these models, particularly in optimizing urban planning and renewable energy assessments. Quantitative metrics such as Intersection over Union (IoU), precision, recall, and F1-score are used to benchmark model performance. Mask R-CNN achieved a mean IoU of 74.6%, precision of 81.3%, recall of 78.9%, and F1-score of 80.1%, while MaskFormer reached a mean IoU of 79.8%, precision of 85.6%, recall of 82.7%, and F1-score of 84.1% (pixel-level, micro-averaged at IoU = 0.50 on the held-out test set), highlighting the transformative potential of transformer-based architectures for scalable and precise urban imaging. The study also outlines future work in 3D modeling and height estimation, positioning these advancements as critical tools for sustainable urban development. Full article

Particulate matter (PM) concentration, especially PM_2.5, is a major culprit of environmental pollution from unreasonable energy system emissions that significantly affects visibility, climate, and public health. The prediction of PM_2.5 concentration holds significant importance in the early warning and management of severe air pollution, since it enables the provision of guidance for scientific decision-making through the estimation of impending PM_2.5 concentration. However, due to diversified human activities, seasonal factors and industrial emissions, the air quality data not only show local anomalous mutability, but also global dynamic change characteristics. This hinders existing PM_2.5 prediction models from fully capturing the aforementioned characteristics, thereby deteriorating the model performance. To address these issues, this study proposes a framework integrating multi-scale temporal convolutional networks (TCNs) and a transformer network (called MSTTNet) for PM_2.5 concentration prediction. Specifically, MSTTNet uses multi-scale TCNs to capture the local correlations of meteorological and pollutant data in a fine-grained manner, while using transformers to capture the global temporal relationships. The proposed MSTTNet’s performance has been validated on various air quality benchmark datasets in the cities of China, including Beijing, Shanghai, Chengdu, and Guangzhou, by comparing to its eight compared models. Comprehensive experiments confirm that the MSTTNet model can improve the prediction performance of 2.42%, 2.17%, 2.87%, and 0.34%, respectively, with respect to four evaluation indicators (i.e., Mean Absolute Error, Root Mean Square Error, Mean Absolute Percentage Error, and R-square), relative to the optimal baseline model. These results confirm MSTTNet’s effectiveness in improving the accuracy of PM_2.5 concentration prediction. Full article

Change points caused by extreme events in global economic markets have been widely studied in the literature. However, existing techniques to identify change points rely on subjective judgments and lack robust methodologies. The objective of this paper is to generalize a novel approach that leverages topological data analysis (TDA) to extract topological features from time series data using persistent homology. In this approach, we use Taken’s embedding and sliding window techniques to transform the initial time series data into a high-dimensional topological space. Then, in this topological space, persistent homology is used to extract topological features which can give important information related to change points. As a case study, we analyzed 26 stocks over the last 12 years by using this method and found that there were two financial market volatility indicators derived from our method, denoted as $L_1$ and $L_2$. They serve as effective indicators of long-term and short-term financial market fluctuations, respectively. Moreover, significant differences are observed across markets in different regions and sectors by using these indicators. By setting a significance threshold of 98 % for the two indicators, we found that the detected change points correspond exactly to four major financial extreme events in the past twelve years: the intensification of the European debt crisis in 2011, Brexit in 2016, the outbreak of the COVID-19 pandemic in 2020, and the energy crisis triggered by the Russia–Ukraine war in 2022. Furthermore, benchmark comparisons with established univariate and multivariate CPD methods confirm that the TDA-based indicators consistently achieve superior F1 scores across different tolerance windows, particularly in capturing widely recognized consensus events. Full article

Indoor positioning remains a recurrent and significant challenge in research. Unlike outdoor environments, where the Global Positioning System (GPS) provides reliable location information, indoor scenarios lack direct line-of-sight to satellites or cellular towers, rendering GPS inoperative and requiring alternative positioning techniques. Despite numerous approaches, indoor contexts with resource limitations, energy constraints, or physical restrictions continue to suffer from unreliable localization. Many existing methods employ a fixed number of reference anchors, which sets a hard balance between localization accuracy and energy consumption, forcing designers to choose between precise location data and battery life. As a response to this challenge, this paper proposes an energy-aware indoor positioning strategy based on Mobile Ad Hoc Networks (MANETs). The core principle is a self-adaptive control loop that continuously monitors the network’s positioning accuracy. Based on this real-time feedback, the system dynamically adjusts the number of active anchors, increasing them only when accuracy degrades and reducing them to save energy once stability is achieved. The method dynamically estimates relative coordinates by analyzing node encounters and contact durations, from which relative distances are inferred. Generalized Multidimensional Scaling (GMDS) is applied to construct a relative spatial map of the network, which is then transformed into absolute coordinates using reference nodes, known as anchors. The proposal is evaluated in a realistic simulated indoor MANET, assessing positioning accuracy, adaptation dynamics, anchor sensitivity, and energy usage. Results show that the adaptive mechanism achieves higher accuracy than fixed-anchor configurations in most cases, while significantly reducing the average number of required anchors and their associated energy footprint. This makes it suitable for infrastructure-poor, resource-constrained indoor environments where both accuracy and energy efficiency are critical. Full article

The sudden increase in global energy demand has prompted the integration of Artificial Intelligence and the Internet of Things into the utility grid. The synergy of Artificial Intelligence and the Internet of Things in renewable energy sources has emerged as a promising solution for the development of smart grids and a transformative catalyst that restructures centralized power systems into resilient and sustainable systems. The state-of-the-art of the Internet of Things and Artificial Intelligence is presented in this paper to support the design, planning, operation, management and optimization of renewable energy-based power systems. This paper outlines the benefits of smart and resilient energy systems and the contributions of the Internet of Things across several applications, devices and networks. Artificial Intelligence can be utilized for predictive maintenance, demand-side management, fault detection, forecasting and scheduling. This paper highlights crucial future research directions aimed at overcoming the challenges that are associated with the adoption of emerging technologies in the power system by focusing on market policy and regulation and the human-centric and ethical aspects of Artificial Intelligence and the Internet of Things. The outcomes of this study can be used by policymakers, researchers and development agencies to improve global access to electricity and accelerate the development of sustainable energy systems. Full article

Global efforts to achieve carbon neutrality increasingly rely on institutional green policy that reshape corporate environmental behavior. This study examines whether green policy improves corporate environmental performance (EP). Using panel data of the A-share listed firms from 2010 to 2022, we exploit the rollout of pilot cities as a quasi-natural experiment and apply a difference-in-differences (DID) framework, supplemented by double machine learning (DML) and robustness tests. The results show that the New Energy Demonstration City (NEDC) policy notably increases EP, with stronger effects for state-owned enterprises, large firms, and regulated industries. Mechanism analysis indicates that artificial intelligence innovation capacity and the stringency of regional environmental regulation amplify the policy’s effectiveness, revealing a “innovation–regulation” dual mechanism. By focusing on integrated EP rather than single outcomes, this paper extends the literature on green policy instruments. It demonstrates that structural policies combining fiscal incentives and regulatory constraints can correct market failures and foster long-term green transition. Beyond China, the findings provide insights for other developing economies where market-based instruments alone may be insufficient to trigger low-carbon transformation. Full article

The integration of artificial intelligence (AI) into bioclimatic building design is reshaping the architecture, engineering, and construction (AEC) industry by addressing critical challenges in sustainability and efficiency. By aligning structures with local climates, bioclimatic design addresses global challenges such as energy consumption, urbanization, and climate change. Complementing these principles, AI technologies—including machine learning, digital twins, and generative algorithms—are revolutionizing the sector by optimizing processes across the entire building lifecycle, from design and construction to operation and maintenance. Amid the diverse array of AI-driven innovations, this research highlights digital twin (DT) technologies as a key to AI-driven transformation, enabling real-time monitoring, simulation, and optimization for sustainable design. Applications like façade optimization, energy flow analysis, and predictive maintenance showcase their role in adaptive architecture, while frameworks like Construction 4.0 and 5.0 promote human-centric, data-driven sustainability. By bridging AI with bioclimatic design, the findings contribute to a vision of a built environment that seamlessly aligns environmental sustainability with technological advancement and societal well-being, setting new standards for adaptive and resilient architecture. Despite the immense potential, AI and DTs face challenges like high computational demands, regulatory barriers, interoperability and skill gaps. Overcoming these challenges will be crucial for maximizing the impact on sustainable building, requiring ongoing research to ensure scalability, ethics, and accessibility. Full article

A spacecraft is a typical rigid–flexible–thermal coupled multibody system, and the study of such rigid–flexible–thermal coupled systems has important engineering significance. The dissipation effect of material damping has a significant impact on the response of multibody system dynamics. Owing to the increasing multitude of computational dimensions, computational efficiency has remained a significant bottleneck hindering their practical applications in engineering. However, due to the fact that the stiffness matrix is a highly nonlinear function of generalized coordinates, traditional methods of modal truncation are difficult to apply directly. In this study, the absolute nodal coordinate formulation (ANCF) is used to uniformly describe the modeling of rigid–flexible–thermal coupled multibody systems with large-scale motion and deformation. The constant tangent stiffness matrix and damping matrix can be obtained by locally linearizing the dynamic equation and heat transfer equations, which are based on the Taylor expansion. The dynamic and heat transfer equations obtained by reducing the order of complex modes are transformed into a unified first-order equation, which is solved simultaneously. The orthogonal complement matrix of the constraint equation is proposed to eliminate the nonlinear constraints. A strategy based on energy preservation was proposed to update the reduced-order basis vectors, which improved the calculation accuracy and efficiency. Finally, a systematic method for rigid–flexible–thermal coupled viscoelastic multibody systems via modal truncation with complex global modes is developed. Full article

This study presents a comparative evaluation of post-process sensor integration in additively manufactured (AM) metal and the in-situ process for polymer structures for structural health monitoring (SHM), with an emphasis on embedded sensors. Geometrically identical specimens were fabricated using copper via metal fused filament fabrication (FFF) and PLA via polymer FFF, with piezoelectric transducers (PZTs) inserted into internal cavities to assess the influence of material and placement on sensing fidelity. Mechanical testing under compressive and point loads generated signals that were transformed into time–frequency spectrograms using a Short-Time Fourier Transform (STFT) framework. An engineered RGB representation was developed, combining global amplitude scaling with an amplitude-envelope encoding to enhance contrast and highlight subtle wave features. These spectrograms served as inputs to convolutional neural networks (CNNs) for classification of load conditions and detection of damage-related features. Results showed reliable recognition in both copper and PLA specimens, with CNN classification accuracies exceeding 95%. Embedded PZTs were especially effective in PLA, where signal damping and environmental sensitivity often hinder surface-mounted sensors. This work demonstrates the advantages of embedded sensing in AM structures, particularly when paired with spectrogram-based feature engineering and CNN modeling, advancing real-time SHM for aerospace, energy, and defense applications. Full article