Search Results (11,559)

This study addresses the decarbonisation of the glass industry from an integrated energy system perspective, analysing the role of renewable electricity, furnace electrification, and hydrogen in meeting the high and continuous thermal demands of glass melting. While direct electrification represents the most energy-efficient option, its implementation is challenged by the intermittent nature and limited operating hours of renewable generation, scale constraints, and technological limitations in replacing fossil-based processes, highlighting a potential complementary role for hydrogen. A general methodological framework is first developed and then applied to a representative oxyfuel glass furnace using mixed-integer linear programming (MILP) optimisation that minimises melting costs while accounting for variable solar and wind generation, battery storage, and hydrogen production and storage. The results show that high levels of furnace electrification combined with wind-dominated renewable supply yield the lowest decarbonisation costs, which can become negative at moderate decarbonisation levels. Under the current solar–wind capacity expansion mix, the integration of battery and hydrogen storage extends achievable emission reductions from around 50% to 80%, with hydrogen acting as a complementary solution to electrification. Sensitivity analysis of energy and carbon prices, as well as technology investment costs, identifies the economic conditions in which storage-based solutions become cost-effective, highlighting the strategic role of hydrogen under conditions of low electricity prices and high fuel prices. The findings demonstrate viable pathways for deep decarbonisation of the glass sector and provide a transferable methodological framework for optimal renewable energy integration in other hard-to-abate industrial sectors facing similar constraints. Full article

The global transition towards sustainable and resilient energy systems has emphasized the need for efficient utilization of renewable energy sources (RESs) and rapid electrification of transportation. However, smart grids must address the intermittency of solar and wind power while accommodating the growing demand from electric vehicles (EVs). Hence, in this paper, a data-driven energy management system (EMS) is proposed that combines multivariable forecasting, generation scheduling, and EV charging coordination in a dual-level decentralized framework to increase the efficiency, reliability, and scalability of modern power grids. First, short-term forecasts of solar irradiance, wind speed, and load demand are addressed via five machine learning models ranging from nonlinear to ensemble models. Accordingly, a unified CatBoost-based platform for forecasting these three variables is selected because of its better performance and accuracy. These forecasts are subsequently utilized in a mixed-integer linear programming (MILP) framework for optimal generation scheduling in the considered network, fulfilling load demand at reduced electricity and emission costs while maintaining grid stability. Finally, a priority-based scheme is proposed for charging/discharging coordination of the aggregated EVs, minimizing demand variability while fulfilling vehicles’ charging needs and maintaining their batteries’ lifetime. The superiority of the proposed method lies in integrating a multivariable forecasting pipeline, linear MILP generation scheduling, and battery-health-aware V2G coordination in a unified decoupled framework, unlike many recent frontier works that treat these capabilities independently. Simulation results, under different scenarios, confirm that the proposed intelligent EMS can significantly reduce operational fluctuations, satisfy load and EV demands, optimize RES utilization, and support system cost-effectiveness, sustainability, and resilience. Full article

Solar photovoltaic (PV) systems can provide a clean and sustainable alternative to fossil fuel energy, but the uptake of solar PV systems is often constrained by perceived and actual barriers. Despite South Africa’s very high solar generation potential, solar PV adoption remains low, accounting for less than 10% of the country’s energy mix. Central to renewable energy debates is making sure that the transition is equitable, affordable, and inclusive. Yet, despite high levels of inequality, there is no empirical standing on the factors hindering solar PV adoption among a socially differentiated household gradient. Using household interviews, the study investigated barriers to solar PV adoption across an income gradient in four towns located in the Eastern Cape province of South Africa. Results showed that significantly more high-income (82%) than low-income (63%) households had considered solar PV adoption, primarily to mitigate power cuts (89%). Both income groups were constrained by financial, institutional, and social barriers, but low-income households were disproportionately affected. While 86% of high-income households cited initial financial constraints, low-income households were nearly twice as likely to worry about high maintenance costs (48% vs. 26%) and system unfamiliarity (36% vs. 14%). High-income respondents were more concerned with institutional and technical barriers, such as a lack of supportive policies and grid-feed difficulties. Nearly all (98%) low-income households lacked knowledge regarding solar PV system capacity, compared to 60% for high-income households. Altogether, the results highlight that heterogeneity matters in our understanding of barriers to solar PV adoption and should be a key consideration in designing differentiated approaches to address barriers and ensure equity in renewable energy adoption. Full article

The large-scale grid integration of distributed renewable energy enhances the flexible regulation capacity of the power system. However, the inherent randomness and volatility of its output, coupled with weak coupling access characteristics, pose severe challenges to the safe and stable operation of the power system. To address these issues, this paper proposes a power system planning method suitable for urban power grids. To accurately characterize the uncertainty of renewable energy output, the method incorporates the concept of multi-scenario stochastic optimization and introduces a dynamic scenario generation method for wind and solar power based on nonparametric kernel density estimation and standard multivariate normal distribution sequence sampling. This method generates a set of typical daily dynamic output scenarios for wind and solar power that closely match actual output characteristics. Considering the spatiotemporal response characteristics of flexible resources, the Soft Open Point (SOP) DC link enables flexible cross-node power transmission and spatiotemporal coupling regulation of flexible resources. Therefore, this paper constructs a mathematical model for the grid integration of flexible resources based on the SOP DC link. By integrating operational constraints such as power flow constraints in the power grid and source-load uncertainty constraints, a power system planning model is established. However, traditional convex optimization methods require approximate simplifications of the model, which can easily lead to a loss of accuracy. Although the Particle Swarm Optimization (PSO) algorithm is suitable for nonlinear optimization, it is prone to getting trapped in local optima. Therefore, this paper introduces an improved PSO algorithm based on refraction opposite learning, which enhances the algorithm’s global optimization capability by expanding the particle search space and increasing population diversity. Finally, simulation verification is conducted based on an improved IEEE-39 bus test system, and the results show that the proposed scenario generation method achieves a sum of squared errors of only 4.82% and a silhouette coefficient of 0.94, significantly improving accuracy compared to traditional methods such as Monte Carlo sampling. Full article

In China’s stock-based renewal agenda, many old residential communities display pronounced intergenerational overlap, in which grandparental childcare becomes a dominant pattern of outdoor-space use. Against the backdrop of age-structure shifts, population ageing, and persistently low fertility, community-level outdoor-space supply, distributive equity, and environmental adaptability have become key concerns in renewal practice. Yet, practitioners still lack a rankable, low-cost, and implementable evaluation-to-decision workflow. Using Xingshe Community in Dalian, China as an empirical case, this study establishes and tests an integrated “NLP–AHP–GBDT” assessment framework. Guided by policy discourse and planning theory, over 50 semi-structured interviews were processed via NLP-based semantic analysis and keyword mining to derive a two-tier indicator set (criterion and indicator layers). Seven specialists then applied the analytic hierarchy process to elicit indicator weights, and a resident survey was administered to generate weighted performance scores for diagnosing deficiencies. In the feedback-validation stage, we adopted both a qualitative Framework Method and a quantitative GBDT approach, first using the Framework Method to conduct feedback validation based on community residents’ open-ended evaluations. Subsequently, gradient boosting decision trees were used for supervised verification with renewal-scenario data, providing empirical backing for the weighting scheme and the resulting priority order for interventions. The findings suggest that outdoor spaces are broadly serviceable but fall short in intergenerational friendliness, reflecting a structural misalignment between intergenerational activity patterns and spatial provision. Based on the validated priorities, the study proposes modular, incremental micro-renewal measures focusing on safety and emergency accessibility, environmental comfort and caregiving–recreation coupling, and place identity with community organizational mobilization. Full article

With the growing significance of renewable energy resources and energy storage systems, the number of grid-connected inverters has been rising at an increasingly rapid pace. Generally, these inverters are directly integrated with the distribution network by synchronizing with the grid voltage at the point of common coupling. However, the low grid strength and varying R/X ratios, as the common characteristics of most distribution networks or weak grids, can lead to dynamic interactions that comprise stability and limit the power transfer capacity of grid-connected inverters. To ensure stable operation of the inverters, researchers must determine the stability boundary, described as the maximum power transfer capacity of grid-connected inverters under the premise of maintaining system small-signal stability. For this purpose, we propose to formulate a state-space model of the system in the synchronously rotating dq-frame of reference and perform eigenvalue analysis to determine the stability boundary. With a detailed model of the control structure and parameters of the grid-connected inverters, the stability boundary is identified as a surface with respect to different grid strengths and R/X ratios. Case study results of proposed eigenvalue analysis are compared with those of admittance model-based stability analysis as well as time-domain simulation using a switching model in Matlab/Simulink, validating the effectiveness and accuracy of the proposed eigenvalue analysis for stability boundary identification. Full article

Assessing environmental impacts across the full life cycle of buildings is essential for advancing toward a net-zero and regenerative built environment. However, life cycle inventory generation and impact assessment remain methodologically complex and time-intensive, limiting their integration into early design decision-making. This study aims to quantify and reduce the embodied carbon of a regenerated building while optimizing material selection based on environmental performance and circularity potential. An integrated Building Information Modeling–Life Cycle Assessment (BIM–LCA) framework combined with Sensitivity Analysis (SA) was applied within a circular economy perspective. A regenerative building was modeled using BIM, and Industry Foundation Classes (IFC) data were employed to conduct a detailed life cycle assessment to quantify embodied carbon and identify emission hotspots across life cycle stages. The results indicate that material extraction, processing, and manufacturing dominate environmental impacts, contributing more than 85% of total CO₂ emissions. Sensitivity analysis further demonstrates the influence of material choices on overall carbon performance. The findings underscore the importance of evaluating embodied carbon at early design stages to support informed decisions regarding material efficiency, renewability, and recyclability. The proposed BIM–LCA framework provides a scalable, data-driven approach to support early-stage decarbonization strategies and contributes to reducing the carbon footprint of buildings in alignment with net-zero and regenerative design objectives. Full article

Type 1 diabetes (T1D) results from autoimmune-mediated destruction of pancreatic β-cells, leading to insulin deficiency and chronic hyperglycemia. β-cell replacement represents a promising therapeutic strategy, yet the identification of a sustainable and immune-compatible cell source remains a major challenge. Here, we explore the potential of the gastrointestinal (GI) epithelium as an alternative source of β-cells through in vivo cellular reprogramming. Given the large size and highly regenerative nature of the GI tract, partial reprogramming could provide a renewable source of insulin-producing (insulin⁺) cells. We demonstrate that ectopic expression of Pax4 is sufficient to convert gut endocrine L-cells into insulin⁺ cells in vivo. Phenotypic analyses reveal that these gut-derived cells express key β-cell markers, components of the glucose-sensing machinery, and properly process proinsulin into mature insulin. Functional studies using organoids derived from Pax4-expressing gut epithelium further demonstrate that these cells display glucose-responsive insulin secretion. Collectively, our findings highlight the plasticity of gut endocrine cells and support the feasibility of generating β-like cells from the GI epithelium, providing a potential avenue for the development of alternative cell-based therapies for T1D. Full article

Recent revisions of personality disorder (PD) classifications have moved from categorical diagnoses toward dimensional models, raising renewed questions about the nosological status and clinical utility of borderline personality disorder (BPD). This narrative review traces the development of the borderline construct from early descriptions of patients positioned between neurosis and psychosis, through its theoretical consolidation within the concept of borderline personality organization, to the operationalization of BPD in DSM-III and subsequent diagnostic revisions. A central section summarizes contemporary controversies regarding the validity and utility of BPD features. Arguments for abandoning the diagnosis emphasize the absence of a distinct borderline factor in factor analytic studies, the tendency of the construct to capture fluctuating symptoms and patterns of behaviour rather than stable maladaptive personality traits, the stigmatizing and non-selective use of the label, and the lack of disorder-specific treatment approaches. In contrast, converging evidence supports the view that core borderline symptoms frequently function as markers of general PD pathology and of the severity of impairments in self and interpersonal functioning. The paper integrates the concept of the borderline level of personality functioning, conceptualizing borderline pathology as a dynamic dimension of dysfunction with potential transient regressions, and links this concept to the Level of Personality Functioning (LPF, Criterion A) within the DSM 5 Alternative Model for Personality Disorders (AMPD). Retaining borderline pathology as a dimension may support contemporary PD assessment by offering a clinically recognizable marker of overall dysfunction, a guide for rating severity, an indicator of personality structure and need for psychotherapy, without disrupting continuity with an extensive clinical and research tradition. Full article

The design of sustainable power systems requires planning tools that jointly account for economic, environmental, and social dimensions. However, multi-objective energy system models typically prioritize economic–environmental trade-offs, while the social dimension is still rarely included as an explicit optimization objective. Furthermore, many formulations adopt a low temporal resolution (e.g., annual time steps) and assume fully flexible power plants, potentially overlooking temporal variability and operational constraints. This paper presents a three-objective optimization model for sustainable power system design that minimizes (i) costs, (ii) greenhouse gas (GHG) emissions, and (iii) social opposition (i.e., the public resistance to certain energy technologies). Temporal variability and operational detail are preserved using weighted representative periods with intra-period time steps and a clustered unit commitment (CUC) formulation. The Pareto frontier is generated using the normalized normal constraint (NNC) method, highlighting the space of efficient economic, environmental, and social solutions. A case study focused on the Italian electricity system exemplifies the model application by providing the cost-optimal, emissions-optimal, and social-optimal solutions, together with trade-off solutions. Among the trade-off solutions, the selected best balance solution achieves a significant reduction in emissions (−20%) compared to the cost-optimal solution, with a limited cost increase (+5%) and a marginal increase in social opposition (+0.7%). Overall, the proposed model enables transparent quantification of multi-dimensional trade-offs to support decision-making in sustainable power system design. Full article

Lithium–sulfur (Li–S) batteries have emerged as a promising next-generation energy storage solution as the capacity demands on lithium-ion systems begin to exceed practical limits. In a global push for renewable energy and sustainable practices, Li–S technology offers several compelling advantages. Both lithium and sulfur are relatively inexpensive (especially compared to the transition metals used in lithium-ion cells), and Li–S batteries are easier and less costly to recycle. Moreover, Li–S chemistry carries a theoretical energy density about five times greater than that of current lithium-ion batteries, making it attractive for high-energy-density applications. Because of these advantages, research interest in Li–S batteries remains high despite significant challenges that still limit their performance and lifespan. However, despite these advantages, several fundamental challenges limit the practical deployment of Li–S batteries, including the polysulfide shuttle effect, large volume expansion of sulfur during cycling, low intrinsic electrical conductivity of sulfur and its discharge products, and instability of the lithium metal anode caused by dendrite formation. This paper explains the working principles of Li–S batteries, analyzes the key challenges and recent achievements in their development, and surveys various mechanical engineering applications for which Li–S batteries are being explored, as well as prospects for their future commercialization and sustainability. Full article

This paper investigates the electromagnetic performance of an outer-rotor spoke-type permanent magnet synchronous generator intended for small wind turbine applications below 5 kW. The study focuses on the influence of structural ferromagnetic components on magnetic flux distribution and overall machine performance. The generator was initially designed and optimized using 2D finite element analysis, followed by a comprehensive 3D model to account for axial flux leakage and structural details; particular attention was given to the fastening screws used. Experimental validation on a dedicated laboratory test bench confirms the accuracy of the 3D model, mainly at lower wind speeds. The results highlight the necessity of including structural components in three-dimensional electromagnetic modeling for accurate performance prediction of flux-concentrating wind turbine generators. Full article

Phosphorus is a non-renewable resource critical for global food security, yet its natural reserves are rapidly depleting. Meanwhile, the poultry industry generates vast amounts of nutrient-rich waste that pose serious environmental risks if not managed properly. While valorizing these wastes offers a sustainable raw material alternative, investigating the environmental impacts of recovering them as a phosphorus source is crucial. This study evaluates phosphorus recovery from poultry litter via acid leaching following Hydrothermal Carbonization (HTC) and pyrolysis processes holistically. By conducting a Life Cycle Assessment (LCA) using this specific substrate and method combination, this work aims to provide comprehensive environmental insights. The impact assessment reveals that the total Global Warming Potential (GWP) is 6.00 kg CO₂ eq for the pyrolysis scenario and 4.18 kg CO₂ eq for the HTC scenario. Methodologically, a ‘system expansion’ approach was applied to integrate the avoided burdens from poultry manure management into the system boundaries. Furthermore, the inventory analysis revealed that chemical consumption (specifically NaOH and H₂SO₄) in the production process is the dominant factor not only for Global Warming Potential (GWP) but also across other environmental impact categories evaluated. The findings clearly indicate that chemical intensity predominantly determines the environmental performance across carbon footprint, acidification and other environmental impact categories. Full article

This paper presents a hybrid control strategy for robust voltage regulation of a DC–DC boost converter used in a renewable-rich DC microgrid. The DC microgrid may comprise batteries, photovoltaic, and wind energy sources connected to a common DC bus, where voltage fluctuations arise due to variable generation and dynamic load profiles. To ensure optimal and efficient output voltage regulation under these conditions, a novel Linear Quadratic Regulator (LQR) driven self-regulating Sliding Mode Control (SMC) approach is developed. The proposed scheme is realized by combining the optimal performance of an LQR voltage-reference tracking controller with the robustness of a tangent-hyperbolic-based-sliding-mode reaching law defined over an LQR-driven sliding surface. To reduce chattering and improve adaptability to bounded disturbances, the waveform of the hyperbolic switching function in the reaching law is adaptively modulated via an online indirect supervised learning law. The control parameters are tuned offline using numerical optimization. Simulation results under different scenarios, including input voltage disturbances, load variations, and model uncertainties, show that the proposed method achieves superior voltage regulation, reduced chattering, and enhanced dynamic response compared to conventional controllers. The framework ensures reliable EV integration into intelligent DC microgrids. Full article

The prosperity of the Maritime Spice Route in China during the Song–Yuan dynasties (960–1368) propelled Quanzhou into a global hub for maritime trade and cultural integration. A large number of Muslims settled in Quanzhou via maritime routes, living and multiplying over generations—their journey fully documenting the localization trajectory of the immigrant group. To explore the relationship between the evolution of their traditional residence types and immigration localization, this study takes 185 “one bright hall and two dark rooms” traditional residences of the Ding’s Hui ethnic group in Chendai as an example, constructing a “4 × 6” matrix framework via the spatiotemporal biaxial coordinate classification method, with an integrated application of statistics, field surveying and mapping, Space Syntax, and genealogical document analysis. Results reveal that 15 of the 24 theoretical residence types are effectively preserved, forming a “prototype + combined type” evolutionary chain. Residence-type acceptance presents distinct traits, Class A as the foundational form, Class D as the mainstream, and Classes B and C as transitional types, a pattern reflecting the comprehensive influence of construction land conditions, living patterns, and local construction concepts on residence-type selection. Significant disparities in average connectivity between the central courtyard and various core public spaces embody the multi-branch small-family cohabitation mode and verify the localization development trajectory of residential space. The evolution of this residence-type system is confirmed to feature three core characteristics—nonlinearity, integrated and diversified fusion, and spatial constraint—and proposes preservation strategies for double-standard dimensional, multicultural and identifiability qualities, which provide a scientific reference for the protection and renewal of architectural heritage in Hui ethnic communities and similar immigrant settlements on China’s southeast coast. Full article