Search Results (468)

This article presents a spatial analysis of projects selected by city residents and implemented in five successive editions (2015–2019) of the participatory budgeting in Częstochowa, Poland. The study examines the relationship between the type of hard projects (small investments in public infrastructure and landscaping) and the pre-existing characteristics of the land use of each district. Kernel density estimation and Spearman correlation analysis were used. The highest spatial density occurred in projects related to the modernization of roads and sidewalks, recreation, and greenery, indicating a relatively high number of proposals within or near residential areas. Key correlations included the following: (1) greenery projects were more common in districts lacking green areas; (2) recreational infrastructure was more frequently chosen in areas with significant water features; (3) street furniture projects were mostly selected in districts with sparse development, scattered buildings, and postindustrial sites; (4) educational infrastructure was often chosen in low-density, but developing districts. The selected projects often reflect local deficits in specific land use or public infrastructure, but also stress the predestination of the recreational use of waterside areas. Full article

Actuators and power electronics are fundamental components of modern control systems, enabling high-precision functionality, enhanced energy efficiency, and sophisticated automation. This study investigates evolving research trends and thematic developments in these areas spanning the last two decades (2005–2024). This study analyzed 1840 peer-reviewed abstracts obtained from the Web of Science database using BERTopic modeling, which integrates transformer-based sentence embeddings with UMAP for dimensionality reduction and HDBSCAN for clustering. The approach also employed class-based TF-IDF calculations, intertopic distance visualization, and hierarchical clustering to clarify topic structures. The analysis revealed a steady increase in research publications, with a marked surge post-2015. From 2005 to 2014, investigations were mainly focused on established areas including piezoelectric actuators, adaptive control, and hydraulic systems. In contrast, the 2015–2024 period saw broader diversification into new topics such as advanced materials, robotic mechanisms, resilient systems, and networked actuator control through communication protocols. The structural topic analysis indicated a shift from a unified to a more differentiated and specialized spectrum of research themes. This study offers a rigorous, data-driven outlook on the increasing complexity and diversity of actuator and power electronics research. The findings are pertinent for researchers, engineers, and policymakers aiming to advance state-of-the-art, sustainable industrial technologies. Full article

Agriculture is the foundational industry for human survival, profoundly impacting economic, ecological, and social dimensions. In the face of global challenges such as rapid population growth, resource scarcity, and climate change, achieving technological innovation in agriculture and advancing smart farming have become increasingly critical. In recent years, deep learning and computer vision have developed rapidly. Key areas in computer vision—such as deep learning-based image processing, object detection, and multimodal fusion—are rapidly transforming traditional agricultural practices. Processes in agriculture, including planting planning, growth management, harvesting, and post-harvest handling, are shifting from experience-driven methods to digital and intelligent approaches. This paper systematically reviews applications of deep learning and computer vision in agricultural growth management over the past decade, categorizing them into four key areas: crop identification, grading and classification, disease monitoring, and weed detection. Additionally, we introduce classic methods and models in computer vision and deep learning, discussing approaches that utilize different types of visual information. Finally, we summarize current challenges and limitations of existing methods, providing insights for future research and promoting technological innovation in agriculture. Full article

Carbon capture technologies are largely considered to play a crucial role in meeting the climate change and global warming target set by Net Zero Emission (NZE) 2050. These technologies can contribute to clean energy transitions and emissions reduction by decarbonizing the power sector and other CO₂ intensive industries such as iron and steel production, natural gas processing oil refining and cement production where there is no obvious alternative to carbon capture technologies. While the progress of carbon capture technologies has fallen behind expectations in the past, in recent years there has been substantial growth in this area, with over 700 projects at various stages of development. Moreover, there are around 45 commercial carbon capture facilities already in operation around the world in different industrial processes, fuel transformation and power generation. Carbon capture technologies including pre/post-combustion, oxyfuel and chemical looping combustion have been widely exploited in the recent years at different Technology Readiness level (TRL). Although, a large number of review studies are available addressing different carbon capture strategies, however, studies related to the commercial status of the carbon capture technologies are yet to be conducted. In this review article, we summarize the state-of-the-art of different carbon capture technologies applied to different emission sources, focusing on emission reduction, net-zero emission, and negative emission. We also highlight the commercial status of the different carbon capture technologies including economics, opportunities, and challenges. Full article

Since the early 21st century, urban shrinkage has become a significant global phenomenon. Dongguan, in Guangdong Province, China, is known as a “world factory”. It experienced notable urban shrinkage following the 2008 financial crisis. However, the city demonstrated remarkable recovery and ongoing development in subsequent years. On that basis, this study focuses on the following three points: (1) identifying the spatiotemporal factors contributing to the growth and shrinkage of manufacturing cities, taking Dongguan as an example; (2) explaining the influencing factors of the growth and shrinkage of Dongguan City during three critical periods, 2008–2014 (post-crisis), 2015–2019 (as machinery replaced human work), and 2020–2023 (the COVID-19 pandemic and recovery); and (3) selecting representative towns and streets for on-site observation and investigation, analyzing the measures they have taken to cope with growth and shrinkage during different periods. The key findings include the following: (1) The spatial dynamics of growth and shrinkage in Dongguan show significant temporal patterns, with traditional manufacturing areas shrinking from 2008 to 2014, central urban areas recovering from 2015 to 2019, and renewed shrinkage from 2020 to 2023. However, some regions maintained stability through strategic innovations. (2) Various factors, particularly industrial upgrading and technological innovation, drove the urban dynamics, enhancing economic resilience. (3) The case study of Houjie Town revealed successful adaptive mechanisms supported by policy while facing challenges like labor mismatches and inadequate R&D investment. This research offers insights for improving urban resilience and promoting sustainable development in Dongguan. Full article

Air pollution monitoring in ungauged zones presents unique challenges yet remains critical for understanding environmental health impacts and socioeconomic dynamics in the Eastern Mediterranean region. This study investigates air pollution patterns in northwestern Syria during 2019–2024, analyzing NO₂ and CO concentrations using Sentinel-5P TROPOMI satellite data processed through Google Earth Engine. Monthly concentration averages were examined across eight key locations using linear regression analysis to determine temporal trends, with Spearman’s rank correlation coefficients calculated between pollutant levels and five meteorological parameters (temperature, humidity, wind speed, atmospheric pressure, and precipitation) to determine the influence of political governance, economic conditions, and environmental sustainability factors on pollution dynamics. Quality assurance filtering retained only measurements with values ≥ 0.75, and statistical significance was assessed at a p < 0.05 level. The findings reveal distinctive spatiotemporal patterns that reflect the region’s complex political-economic landscape. NO₂ concentrations exhibited clear political signatures, with opposition-controlled territories showing upward trends (Al-Rai: 6.18 × 10⁻⁸ mol/m²) and weak correlations with climatic variables (<0.20), indicating consistent industrial operations. In contrast, government-controlled areas demonstrated significant downward trends (Hessia: −2.6 × 10⁻⁷ mol/m²) with stronger climate–pollutant correlations (0.30–0.45), reflecting the impact of economic sanctions on industrial activities. CO concentrations showed uniform downward trends across all locations regardless of political control. This study contributes significantly to multiple Sustainable Development Goals (SDGs), providing critical baseline data for SDG 3 (Health and Well-being), mapping urban pollution hotspots for SDG 11 (Sustainable Cities), demonstrating climate–pollution correlations for SDG 13 (Climate Action), revealing governance impacts on environmental patterns for SDG 16 (Peace and Justice), and developing transferable methodologies for SDG 17 (Partnerships). These findings underscore the importance of incorporating environmental safeguards into post-conflict reconstruction planning to ensure sustainable development. Full article

Small Language Models (SLMs) have emerged as a critical area of study within natural language processing, attracting growing attention from both academia and industry. This systematic literature review provides a comprehensive and reproducible analysis of recent developments and advancements in SLMs post-2023. Drawing on 70 English-language studies published between January 2023 and January 2025, identified through Scopus, IEEE Xplore, Web of Science, and ACM Digital Library, and focusing primarily on SLMs (including those with up to 7 billion parameters), this review offers a structured overview of the current state of the art and potential future directions. Designed as a resource for researchers seeking an in-depth global synthesis, the review examines key dimensions such as publication trends, visual data representations, contributing institutions, and the availability of public datasets. It highlights prevailing research challenges and outlines proposed solutions, with a particular focus on widely adopted model architectures, as well as common compression and optimization techniques. This study also evaluates the criteria used to assess the effectiveness of SLMs and discusses emerging de facto standards for industry. The curated data and insights aim to support and inform ongoing and future research in this rapidly evolving field. Full article

This study aimed to investigate land-use and land-cover change and the associated change in the ecological stability of the model area Dobrá–Opatová (district of Trenčín, Slovakia), where increasing landscape transformation has raised concerns about declining ecological resilience. Despite the importance of sustainable land management, few studies in this region have addressed long-term landscape dynamics in relation to ecological stability. This research fills that gap by evaluating historical and recent LULC changes and their ecological consequences. Four time horizons were analysed: 1850, 1949, 2009, and 2024. Although the selected time periods are irregular, they reflect key milestones in the region’s land development, such as pre-industrial land use, post-war collectivisation, and recent land consolidation. These activities significantly altered the structure of the landscape. To assess future trends, we used the MOLUSCE plug-in in QGIS to simulate ecological stability for the future. The greatest structural landscape changes occurred between 1850 and 1949. Significant transformation in agricultural areas was observed between 1949 and 2009, when collectivisation reshaped small plots into large block structures and major water management projects were implemented. The 2009–2024 period was marked by land consolidation, mainly resulting in the construction of gravel roads. These structural changes have contributed to a continuous decrease in ecological stability, calculated using the coefficient of ecological stability derived from LULC categories. To explore future trends, we simulated ecological stability for the year 2030 and the simulation confirmed a continued decline in ecological stability, highlighting the need for sustainable land-use planning in the area. Full article

As part of the post-conflict reconstruction and recovery, the development of Kabul New City aims to bring relief to the existing capital city, Kabul, which has experienced exponential population growth, putting heavy pressure on its existing resources. Kabul New City is divided into four subsectors, and each of them is being developed and is expected to reach a target population by 2025, as defined by the master plan. The study’s objective is to determine which of the four zones are in demand and need to be prioritized for development, as per the model results. The data collection involves an online questionnaire, and the responses are collected from residents of Kabul and Herat. Agent-based modeling (ABM) is an emerging method of simulating urban dynamics. Cities are evolving continuously and are forming unique spatial patterns that result from the movement of residents in search of new locations that accommodate their needs and preferences. An agent-based model is developed using the weighted random selection process based on household size and income levels. The agents are the residents of Kabul and Herat, and the environment is the land use classification image using the Sentinel 2 image of Kabul New City. The barren class is treated as the developable area and is divided into four sub-sectors. The model simulates three alternative growth rate scenarios, i.e., ambitious, moderate, and steady. The results of the simulation reveal that the sub-sector Dehsabz South, being closer to Kabul city, is in higher demand. Barikab is another sub-sector high in demand, which has connectivity through the highway and is an upcoming industrial hub. Full article

The effective adoption of quality assurance and traceability systems is increasingly recognised as a critical enabler of circular economy (CE) outcomes in the plastics sector. This study examines the factors that influence the implementation of such systems within Australia’s recycled plastics industry, with a focus on how these factors vary by company size, supply chain role, and adoption of CE strategy. Recycled plastics are defined here as post-consumer or post-industrial polymers that have been reprocessed for reintegration into manufacturing applications. A mixed-methods survey was conducted with 65 stakeholders across the Australian plastics value chain, comprising recyclers, compounders, converters, and end-users. Respondents assessed a structured set of regulatory, technical, economic, and systemic factors, identifying whether each currently operates as an enabler or barrier in their organisational context. The analysis employed a comparative framework adapted from a 2022 European study, enabling a cross-regional interpretation of patterns and a comparison between CE-aligned and non-CE firms. The results show that firms with CE strategies report greater alignment with innovation-oriented enablers such as digital traceability, standardisation, and closed-loop models. However, these firms also express heightened sensitivity to systemic weaknesses, particularly in areas such as infrastructure limitations, inconsistent material quality, and data fragmentation. Small- and medium-sized enterprises (SMEs) highlighted compliance costs and operational uncertainty as primary barriers, while larger firms frequently cited frustration with regulatory inconsistency and infrastructure underperformance. These findings underscore the need for differentiated policy mechanisms that account for sectoral and organisational disparities in capacity, scale, and readiness for traceability. The study also cautions against the direct transfer of European circular economy models into the Australian context without consideration of local structural, regulatory, and geographic complexities. Full article

This study explores the use of Large Language Models (LLMs), specifically GPT, for different safety management applications in the construction industry. Many studies have explored the integration of GPT in construction safety for various applications; their primary focus has been on the feasibility of such integration, often using GPT models for specific applications rather than a thorough evaluation of GPT’s limitations and capabilities. In contrast, this study aims to provide a comprehensive assessment of GPT’s performance based on established key criteria. Using structured use cases, this study explores GPT’s strength and weaknesses in four construction safety areas: (1) delivering personalized safety training and educational content tailored to individual learner needs; (2) automatically analyzing post-accident reports to identify root causes and suggest preventive measures; (3) generating customized safety guidelines and checklists to support site compliance; and (4) providing real-time assistance for managing daily safety tasks and decision-making on construction sites. LLMs and NLP have already been employed in each of these four areas for improvement, making them suitable areas for further investigation. GPT demonstrated acceptable performance in delivering evidence-based, regulation-aligned responses, making it valuable for scaling personalized training, automating accident analyses, and developing safety protocols. Additionally, it provided real-time safety support through interactive dialogues. However, the model showed limitations in deeper critical analysis, extrapolating information, and adapting to dynamic environments. The study concludes that while GPT holds significant promise for enhancing construction safety, further refinement is necessary. This includes fine-tuning for more relevant safety-specific outcomes, integrating real-time data for contextual awareness, and developing a nuanced understanding of safety risks. These improvements, coupled with human oversight, could make GPT a robust tool for safety management. Full article

Micro, Small, and Medium Enterprises (MSMEs) are vital drivers of economic growth in Kenya, yet they face persistent barriers, including limited capacity, financial exclusion, and weak market integration. This study assessed the potential of universities as strategic hubs for MSME capacity building through a collaborative initiative between Egerton University and the KCB Foundation. Using the International Labour Organization’s Start and Improve Your Business (SIYB) methodology, 481 entrepreneurs from Egerton, Njoro, and Gilgil were trained in a business development bootcamp. This study evaluated the training effectiveness, participant demographics, confidence in skill application, networking outcomes, and satisfaction levels. The results showed high participant confidence (over 95% across all regions), strong financial management uptake (85%), and mobile banking adoption (70%). Gilgil led in inclusivity and peer engagement, while Njoro showed stronger gender representation. However, logistical challenges caused 25% absenteeism in rural areas, and only 23% accessed post-training mentorship. These findings underscore the transformative role of HEIs in fostering sustainable entrepreneurship through localized, inclusive, and industry-aligned training. Policy recommendations include hybrid delivery models, tiered curricula for diverse skill levels, and institutionalized mentorship through public–private partnerships. This case demonstrates the value of embedding entrepreneurship support within university mandates to advance national MSME development agendas. Full article

Italy supplies about one-seventh of the European Union’s total glass production, and the sector’s sizeable resource demands make it a linchpin of national industrial strategy. With growing environmental regulations and the push for resource efficiency, Material Flow Accounting has become essential for companies to stay compliant and advance sustainability. The investigation concentrates on Italy’s glass industry to clarify its material requirements, ecological footprint, and overall sustainability performance. STAN software v2, combined with an Economy-Wide Material Flow Accounting (EW-MFA) framework, models the national economy as a single integrated input–output system. By tracking each material stream from initial extraction to end-of-life, the analysis delivers a cradle-to-grave picture of the sector’s environmental impacts. During the 2021 production year, Italy’s glass makers drew on a total of 10.5 million tonnes (Mt) of material inputs, supplied 76% (7.9 Mt) from domestic quarries, and 24% (2.6 Mt) via imports. Outbound trade in finished glass removed 1.0 Mt, leaving 9.5 Mt recorded as Domestic Material Consumption (DMC). Within that balance, 6.6 Mt (63%) was locked into long-lived stock, whereas 2.9 Mt (28%) left the system as waste streams and airborne releases, including roughly 2.1 Mt of CO₂. At present, the post-consumer cult substitutes only one-third of the furnace batch, signalling considerable scope for improved circularity. When benchmarked against EU-27 aggregates for 2021, Italy registers a NAS/DMI ratio of 0.63 (EU median 0.55) and a DPO/DMI ratio of 0.28 (EU 0.31), indicating a higher share of material retained in stock and slightly lower waste generated per ton of input. A detailed analysis of glass production identifies critical stages, environmental challenges, and areas for improvement. Quantitative data on material use, waste generation, and recycling rates reveal the industry’s environmental footprint. The findings emphasise Economy-Wide Material Flow Accounting’s value in evaluating and improving sustainability efforts, offering insights for policymakers and industry leaders to drive resource efficiency and sustainable resource management. Results help scholars and policymakers in the analysis of the Italian glass industry context, supporting in the data gathering, while also in the use of this methodology for other sectors. Full article

Purpose: The acquirer’s corporate environmental performance (CEP) in mergers and acquisitions has been a subject of debate, yielding mixed results. This paper uses the US firm-level data of 1437 M&A deals from 2002–2019 to examine the impact of overall CEP, resource use, emissions, and innovation on the acquirers’ post-merger market value. Design/methodology/approach: This study employs multi-level fixed effects panel regression using Ordinary Least Squares (OLS) and the instrumental variable (IV) 2SLS method to estimate the models and compare the results with those from robust estimation. Absorbing the multiple levels of fixed effects (i.e., firm, industry, and year) offers a novel and robust algorithm for efficiently accounting for unobserved heterogeneity. The results from IV (2SLS) are more convincing, as the method overcomes the problem of endogeneity due to reverse causality and sample selection bias. Findings: The authors find that CEP has a significant impact on market value, particularly in the long term. While both resource use and emissions performance have positive effects, emissions performance has a stronger impact, presumably because external stakeholders and market participants are more concerned about emissions reduction. The performance of environmental innovation is relatively weak compared to other pillars. Descriptive analysis shows low average scores in environmental innovation compared to the resource use and emissions performance of the acquirers. However, large deals yield significant returns from investing in environmental innovation in both the short and long term compared to small deals. Practical implications: This paper offers several practical implications. First, environmental performance can help improve the acquirer’s long-term market value. Second, managers can focus on the strategic side of environmental performance, based on its pillars, and benchmark their relative position against peers. Third, environmental innovation can be considered a new potential, as the market as a whole in this area is still lagging. Given the growing pressure to improve environmental technology and innovation, prospective acquirers should confidently prioritise actions on green revenue, product innovation, and capital expenditure now rather than ticking these boxes later. Originality value: The key contribution is offering valuable insights into the impact of acquirers’ environmental performance on long-term value creation in mergers and acquisitions (M&A). These results fill the gap in the literature focusing mainly on the effect of environmental pillar and sub-pillar scores on acquirer’s firm value. The authors claim that analysing sub-pillar-level granularity is crucial for accurately measuring the effects on firm-level performance. Full article

Background: The integration of mining and urban spaces in coal-resource-based cities holds significant implications for urban transformation and sustainable development. However, existing research lacks an in-depth analysis of its characteristics and driving factors. Methods: This study takes the central urban area of Huaibei City as a case, utilizing historical documents, POI data, and spatial analysis methods to explore the evolution patterns and influencing factors of mining–urban spatial integration. Standard deviation ellipse analysis was employed to examine historical spatial changes, while a binary logistic regression model and principal component analysis were constructed based on 300 m × 300 m grid units to assess the roles of 11 factors, including location, transportation, commerce, and natural environment. Results: The results indicate that mining–urban spatial integration exhibits characteristics of lag, clustering, transportation dominance, and continuity. Commercial activity density, particularly leisure, dining, and shopping facilities, serves as a core driving factor. Road network density, along with the areas of educational and residential zones, positively promotes integration, whereas water surface areas (such as subsidence zones) significantly inhibit it. Among high-integration areas, Xiangshan District stands as the most economically prosperous city center; Lieshan–Yangzhuang mining area blends traditional and modern elements; and Zhuzhuang–Zhangzhuang mining area reflects the industrial landscape post-transformation. Conclusions: The study reveals diverse integration patterns under the synergistic effects of multiple factors, providing a scientific basis for optimizing spatial layouts and coordinating mining–urban development in coal-resource-based cities. Future research should continue to pay attention to the dynamic changes of spatial integration of mining cities, explore more effective integrated development models, and promote the rational and efficient use of urban space and the sustainable development of cities. Full article