Sign in to use this feature.

Years

Between: -

Subjects

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Journals

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Article Types

Countries / Regions

remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline
remove_circle_outline

Search Results (1,251)

Search Parameters:
Keywords = country research performance model

Order results
Result details
Results per page
Select all
Export citation of selected articles as:
25 pages, 2133 KiB  
Article
Blockchain-Enabled Self-Autonomous Intelligent Transport System for Drone Task Workflow in Edge Cloud Networks
by Pattaraporn Khuwuthyakorn, Abdullah Lakhan, Arnab Majumdar and Orawit Thinnukool
Algorithms 2025, 18(8), 530; https://doi.org/10.3390/a18080530 - 20 Aug 2025
Viewed by 138
Abstract
In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent [...] Read more.
In recent years, self-autonomous intelligent transportation applications such as drones and autonomous vehicles have seen rapid development and deployment across various countries. Within the domain of artificial intelligence, self-autonomous agents are defined as software entities capable of independently operating drones in an intelligent transport system (ITS) without human intervention. The integration of these agents into autonomous vehicles and their deployment across distributed cloud networks have increased significantly. These systems, which include drones, ground vehicles, and aircraft, are used to perform a wide range of tasks such as delivering passengers and packages within defined operational boundaries. Despite their growing utility, practical implementations face significant challenges stemming from the heterogeneity of network resources, as well as persistent issues related to security, privacy, and processing costs. To overcome these challenges, this study proposes a novel blockchain-enabled self-autonomous intelligent transport system designed for drone workflow applications. The proposed system architecture is based on a remote method invocation (RMI) client–server model and incorporates a serverless computing framework to manage processing costs. Termed the self-autonomous blockchain-enabled cost-efficient system (SBECES), the framework integrates a client and system agent mechanism governed by Q-learning and deep-learning-based policies. Furthermore, it incorporates a blockchain-based hash validation and fault-tolerant (HVFT) mechanism to ensure data integrity and operational reliability. A deep reinforcement learning (DRL)-enabled adaptive scheduler is utilized to manage drone workflow execution while meeting quality of service (QoS) constraints, including deadlines, cost-efficiency, and security. The overarching objective of this research is to minimize the total processing costs that comprise execution, communication, and security overheads, while maximizing operational rewards and ensuring the timely execution of drone-based tasks. Experimental results demonstrate that the proposed system achieves a 30% reduction in processing costs and a 29% improvement in security and privacy compared to existing state-of-the-art solutions. Full article
(This article belongs to the Section Algorithms for Multidisciplinary Applications)
Show Figures

Figure 1

17 pages, 671 KiB  
Article
Price Integration of the Ukrainian and EU Corn Markets in the Context of the Russian—Ukrainian War
by Mariusz Hamulczuk and Denys Cherevyk
Agriculture 2025, 15(16), 1777; https://doi.org/10.3390/agriculture15161777 - 19 Aug 2025
Viewed by 231
Abstract
Russia’s full-scale aggression against Ukraine has led to profound disruptions in local and global agri-food markets. Since Ukraine is one of the world’s largest maize exporters, the war also contributed to considerable changes in trade reallocation, as well as an increase in the [...] Read more.
Russia’s full-scale aggression against Ukraine has led to profound disruptions in local and global agri-food markets. Since Ukraine is one of the world’s largest maize exporters, the war also contributed to considerable changes in trade reallocation, as well as an increase in the significance of the European Union in Ukrainian exports. This study analyses the effects of the Russian–Ukrainian war on horizontal maize price transmission between Ukraine and the EU countries. The panel autoregressive distributed lag model (ARDL) was applied to investigate the impact of the war on the price pass-through between those countries. The econometric analysis was performed on a weekly feed maize export price series for Ukraine and 14 selected EU countries. The time frame of research, January 2019 to December 2024, was split into pre-war and war periods. The study indicates that with the outbreak of the war, the long-term relationship between Ukraine and the EU’s maize prices has weakened. At the same time, there was an increase in the short-run maize price transmission between Ukraine and the Eastern EU countries. This proves that in the face of the conflict, market participants in these countries are increasingly guided by the market situation in Ukraine when making economic decisions. Full article
(This article belongs to the Special Issue Price and Trade Dynamics in Agricultural Commodity Markets)
Show Figures

Figure 1

20 pages, 328 KiB  
Article
Sectoral Contributions to Financial Market Resilience: Evidence from GCC Countries
by Khaled O. Alotaibi, Mohammed A. Al-Shurafa, Meshari Al-Daihani and Mohamed Bouteraa
J. Risk Financial Manag. 2025, 18(8), 460; https://doi.org/10.3390/jrfm18080460 - 19 Aug 2025
Viewed by 237
Abstract
This study investigates the contributions of five key sectors—insurance, materials, utilities, real estate, and transport—to the financial markets of six Gulf Cooperation Council (GCC) countries from 2004 to 2023. Grounded in the Sectoral Linkage Theory and Endogenous Growth Theory, the study employs a [...] Read more.
This study investigates the contributions of five key sectors—insurance, materials, utilities, real estate, and transport—to the financial markets of six Gulf Cooperation Council (GCC) countries from 2004 to 2023. Grounded in the Sectoral Linkage Theory and Endogenous Growth Theory, the study employs a Panel Autoregressive Distributed Lag (Panel ARDL) model to examine both short-term and long-term sectoral impacts on financial market resilience. The findings reveal that the insurance and transport sectors offer short-term market stimulation, but lack persistent effects. Conversely, the materials, utilities, and real estate sectors exhibit strong, long-run contributions to financial stability and economic diversification. These results highlight the asymmetric impact of sectoral dynamics on market performance in resource-rich contexts. This research contributes to the literature by providing empirical evidence on sectoral interdependence in oil-dependent economies and highlights the importance of structural diversification for sustainable financial resilience. The study provides actionable insights for policymakers and investors seeking to enhance market resilience and reduce reliance on hydrocarbon revenues through targeted sectoral development. Full article
(This article belongs to the Section Financial Markets)
21 pages, 20253 KiB  
Article
Study on Stress Testing and the Evaluation of Flood Resilience in Mountain Communities
by Mingjun Yin, Hong Huang, Fucai Yu, Aizhi Wu, Yingchun Tao and Xiaoxiao Sun
Sustainability 2025, 17(16), 7463; https://doi.org/10.3390/su17167463 - 18 Aug 2025
Viewed by 270
Abstract
The increasing frequency and intensity of extreme weather events pose significant challenges to mountain communities, particularly in terms of flash flood risks. This study presents a framework for stress testing and evaluating flood resilience in mountain communities through the integration of high-resolution InfoWorks [...] Read more.
The increasing frequency and intensity of extreme weather events pose significant challenges to mountain communities, particularly in terms of flash flood risks. This study presents a framework for stress testing and evaluating flood resilience in mountain communities through the integration of high-resolution InfoWorks ICM two-dimensional hydrodynamic modeling and systematic resilience assessment. The framework makes three key innovations: (1) multi-scale temporal stress scenarios combining short-duration extreme events (1–2 h) with long-duration persistent events (24 h) and historical extremes; (2) integrated infrastructure–drainage stress analysis that explicitly models roads’ dual role as critical infrastructure and emergency drainage channels; and (3) dynamic resilience quantification under multiple stressors across 15 systematically designed stress conditions. Using Western Beijing as a case study, the model is validated, achieving Nash–Sutcliffe efficiency values exceeding 0.9, demonstrating its robust capability in simulating complex mountainous terrain flood processes. Through systematic analysis of fifteen rainfall scenarios designed based on Chicago rainfall patterns and historical events (including the July 2023 Haihe River basin flood), encompassing various intensities (30–200 mm/h), durations (1 h, 2 h, 24 h), and return periods (10, 50, 100 years), the key findings include the following: (1) A rainfall intensity of 60 mm/h represents a crucial threshold for system performance, beyond which significant impacts on community infrastructure emerge, with built-up areas experiencing inundation depths of 0.27–0.4 m that exceed safe passage limits. (2) Road networks become primary drainage channels during intense precipitation, with velocities exceeding 5 m/s in village roads and exceeding 5 m/s in country road sections, creating significant hazard potential. (3) Four major risk spots were identified with distinct waterlogging patterns, characterized by maximum depths ranging from 0.8 to 2.0 m and recovery periods varying from 2 to 12 hours depending on the topographic confluence effects and drainage efficiency. (4) The system demonstrates strong recovery capability, achieving >90% recovery within 3–6 hours for short-duration events, while showing vulnerability to extreme scenarios, with performance declining to 0.75–0.80, highlighting the coupling effects between water depth and flow velocity in steep terrain. This research provides quantitative insights for flood risk management and for enhancing community resilience in mountainous regions, offering valuable guidance for infrastructure improvement, emergency response optimization, and sustainable community development. This study primarily focuses on physical resilience aspects, with socioeconomic and institutional dimensions representing important directions for future research. Full article
Show Figures

Figure 1

19 pages, 2569 KiB  
Article
CNN-Random Forest Hybrid Method for Phenology-Based Paddy Rice Mapping Using Sentinel-2 and Landsat-8 Satellite Images
by Dodi Sudiana, Sayyidah Hanifah Putri, Dony Kushardono, Anton Satria Prabuwono, Josaphat Tetuko Sri Sumantyo and Mia Rizkinia
Computers 2025, 14(8), 336; https://doi.org/10.3390/computers14080336 - 18 Aug 2025
Viewed by 243
Abstract
The agricultural sector plays a vital role in achieving the second Sustainable Development Goal: “Zero Hunger”. To ensure food security, agriculture must remain resilient and productive. In Indonesia, a major rice-producing country, the conversion of agricultural land for non-agricultural uses poses a serious [...] Read more.
The agricultural sector plays a vital role in achieving the second Sustainable Development Goal: “Zero Hunger”. To ensure food security, agriculture must remain resilient and productive. In Indonesia, a major rice-producing country, the conversion of agricultural land for non-agricultural uses poses a serious threat to food availability. Accurate and timely mapping of paddy rice is therefore crucial. This study proposes a phenology-based mapping approach using a Convolutional Neural Network-Random Forest (CNN-RF) Hybrid model with multi-temporal Sentinel-2 and Landsat-8 imagery. Image processing and analysis were conducted using the Google Earth Engine platform. Raw spectral bands and four vegetation indices—NDVI, EVI, LSWI, and RGVI—were extracted as input features for classification. The CNN-RF Hybrid classifier demonstrated strong performance, achieving an overall accuracy of 0.950 and a Cohen’s Kappa coefficient of 0.893. These results confirm the effectiveness of the proposed method for mapping paddy rice in Indramayu Regency, West Java, using medium-resolution optical remote sensing data. The integration of phenological characteristics and deep learning significantly enhances classification accuracy. This research supports efforts to monitor and preserve paddy rice cultivation areas amid increasing land use pressures, contributing to national food security and sustainable agricultural practices. Full article
(This article belongs to the Special Issue Machine Learning Applications in Pattern Recognition)
Show Figures

Figure 1

23 pages, 2690 KiB  
Article
Harmonizing the Interplay Between SDG 3 and SDG 10 in the Context of Income Inequality: Evidence from the EU and Ukraine
by Zoriana Dvulit, Liana Maznyk, Natalia Horbal, Olga Melnyk, Tetiana Dluhopolska and Bartłomiej Bartnik
Sustainability 2025, 17(16), 7442; https://doi.org/10.3390/su17167442 - 18 Aug 2025
Viewed by 309
Abstract
This paper investigates how Sustainable Development Goals SDG 3 (Health and Well-being) and SDG 10 (Reducing Inequality) interacted during the period 2009–2021 within the context of income disparities in the European Union and Ukraine. The central assumption is that lowering income inequality improves [...] Read more.
This paper investigates how Sustainable Development Goals SDG 3 (Health and Well-being) and SDG 10 (Reducing Inequality) interacted during the period 2009–2021 within the context of income disparities in the European Union and Ukraine. The central assumption is that lowering income inequality improves overall population health. The research proposes a conceptual model with four main elements: classifying countries according to their Gini index along with their performance on SDG 3 and SDG 10; analyzing how income inequality and progress on SDG 10 influence health outcomes (SDG 3); categorizing countries based on the strength of links between inequality measures and well-being indicators; and interpreting these results in the context of Ukraine’s European integration aspirations. Methodologically, cluster analysis, correlation and regression models, and semantic differentiation are applied. The findings show that a reduction in income inequality positively affects health and well-being. Nonetheless, Ukraine continues to face considerable structural and institutional hurdles. From a governance standpoint, the study highlights the need for cohesive policies that integrate economic, health, and social dimensions. Effective public management should coordinate national reforms to match EU healthcare and social policy standards. Strengthening institutions, ensuring fair access to healthcare services, and adopting inclusive policy instruments remain crucial to advancing both SDG 3 and SDG 10 targets, as well as supporting Ukraine’s broader integration with the European Union. Full article
Show Figures

Figure 1

24 pages, 791 KiB  
Article
Herding Behavior, ESG Disclosure, and Financial Performance: Rethinking Sustainability Reporting to Address Climate-Related Risks in ASEAN Firms
by Ari Warokka, Jong Kyun Woo and Aina Zatil Aqmar
J. Risk Financial Manag. 2025, 18(8), 457; https://doi.org/10.3390/jrfm18080457 - 16 Aug 2025
Viewed by 317
Abstract
This study examines the intersection of environmental, social, and governance (ESG) disclosure (operationalized through sustainability reporting), corporate financial performance, and the behavioral dynamics of herding in capital structure decisions among non-financial firms in five ASEAN countries. As ESG and sustainability finance gain prominence [...] Read more.
This study examines the intersection of environmental, social, and governance (ESG) disclosure (operationalized through sustainability reporting), corporate financial performance, and the behavioral dynamics of herding in capital structure decisions among non-financial firms in five ASEAN countries. As ESG and sustainability finance gain prominence in addressing climate change and climate risk, understanding the behavioral factors that relate to ESG adoption is crucial. Employing a quantitative approach, this research utilizes a purposive sample of 125 non-financial firms from Indonesia, Malaysia, the Philippines, Singapore, and Thailand, gathered from the Bloomberg Terminal spanning 2018–2023. Managerial Herding Ratio (MHR) is used to assess herding behavior, while Sustainability Report Disclosure Index (SRDI) measures ESG disclosure. Partial Least Squares Structural Equation Modeling (PLS-SEM) and Multigroup Analysis (MGA) were applied for data analysis. This research finds that while sustainability reporting enhances return on assets (ROA) and Tobin’s Q, it does not significantly relate to net profit margin (NPM). The findings also confirm that herding behavior—where companies mimic the financial structures of peers—moderates the relationship between sustainability reporting and performance outcomes, with leader firms gaining more from transparency efforts. This highlights the double-edged nature of herding: while it can accelerate ESG adoption, it may dilute the strategic depth of climate action if firms merely follow rather than lead. The study provides actionable insights for regulators and corporate strategists seeking to strengthen ESG finance as a driver for climate resilience and long-term stakeholder value. Full article
Show Figures

Figure 1

21 pages, 4164 KiB  
Article
Geostatistical Analysis and Delineation of Groundwater Potential Zones for Their Implications in Irrigated Agriculture of Punjab Pakistan
by Aamir Shakoor, Imran Rasheed, Muhammad Nouman Sattar, Akinwale T. Ogunrinde, Sabab Ali Shah, Hafiz Umar Farid, Hareef Ahmed Keerio, Asim Qayyum Butt, Amjad Ali Khan and Malik Sarmad Riaz
World 2025, 6(3), 115; https://doi.org/10.3390/world6030115 - 15 Aug 2025
Viewed by 388
Abstract
Groundwater is essential for irrigated agriculture, yet its use remains unsustainable in many regions worldwide. In countries like Pakistan, the situation is particularly pressing. The irrigated agriculture of Pakistan heavily relies on groundwater resources owing to limited canal-water availability. The groundwater quality in [...] Read more.
Groundwater is essential for irrigated agriculture, yet its use remains unsustainable in many regions worldwide. In countries like Pakistan, the situation is particularly pressing. The irrigated agriculture of Pakistan heavily relies on groundwater resources owing to limited canal-water availability. The groundwater quality in the region ranges from good to poor, with the lower-quality water adversely affecting soil structure and plant health, leading to reduced agricultural productivity. The delineation of quality zones with respect to irrigation parameters is thus crucial for optimizing its sustainable use and management. Therefore, this research study was carried out in the Lower Chenab Canal (LCC) irrigation system to assess the spatial distribution of groundwater quality. The geostatistical analysis was conducted using Gamma Design Software (GS+) and the Kriging interpolation method was applied within a Geographic Information System (GIS) framework to generate groundwater-quality maps. Semivariogram models were evaluated for major irrigation parameters such as electrical conductivity (EC), residual sodium carbonate (RSC), and sodium adsorption ratio (SAR) to identify the best fit for various Ordinary Kriging models. The spherical semivariogram model was the best fit for EC, while the exponential model best suited SAR and RSC. Overlay analysis was performed to produce combined water-quality maps. During the pre-monsoon season, 17.83% of the LCC area demonstrated good irrigation quality, while 42.84% showed marginal quality, and 39.33% was deemed unsuitable for irrigation. In the post-monsoon season, 17.30% of the area had good irrigation quality, 44.53% exhibited marginal quality, and 38.17% was unsuitable for irrigation. The study revealed that Electrical Conductivity (EC) was the primary factor affecting water quality, contributing to 71% of marginal and unsuitable conditions. In comparison, the Sodium Adsorption Ratio (SAR) accounted for 38% and Residual Sodium Carbonate (RSC) contributed 45%. Therefore, it is recommended that groundwater in unsuitable zones be subjected to artificial recharge methods and salt-tolerated crops to enhance its suitability for agricultural applications. Full article
Show Figures

Figure 1

19 pages, 650 KiB  
Article
Algorithmic Efficiency Analysis in Innovation-Driven Labor Markets: A Super-SBM and Malmquist Productivity Index Approach
by Chia-Nan Wang and Giovanni Cahilig
Algorithms 2025, 18(8), 518; https://doi.org/10.3390/a18080518 - 15 Aug 2025
Viewed by 300
Abstract
Innovation-driven labor markets play a pivotal role in economic development, yet significant disparities exist in how efficiently countries transform innovation inputs into labor market outcomes. This study addresses the critical gap in benchmarking multi-stage innovation efficiency by developing an integrated framework combining Data [...] Read more.
Innovation-driven labor markets play a pivotal role in economic development, yet significant disparities exist in how efficiently countries transform innovation inputs into labor market outcomes. This study addresses the critical gap in benchmarking multi-stage innovation efficiency by developing an integrated framework combining Data Envelopment Analysis (DEA) Super Slack-Based Measure (Super-SBM) for static efficiency evaluation and the Malmquist Productivity Index (MPI) for dynamic productivity decomposition, enhanced with cooperative game theory for robustness testing. Focusing on the top 20 innovative economies over a 5-year period, we analyze key inputs (Innovation Index, GDP, trade openness) and outputs (labor force, unemployment rates), revealing stark efficiency contrasts: China, Luxembourg, and the U.S. demonstrate optimal performance (mean scores > 1.9), while Singapore and the Netherlands show significant underutilization (scores < 0.4). Our results identify a critical productivity shift period (average MPI = 1.325) driven primarily by technological advancements. This study contributes a replicable, data-driven model for cross-domain efficiency assessment and provides empirical evidence for policymakers to optimize innovation-labor market conversion. The methodological framework offers scalable applications for future research in computational economics and productivity analysis. Full article
Show Figures

Figure 1

26 pages, 554 KiB  
Article
Industrial Robots and Green Productivity: Evidence from Global Panel Data on High-Quality Economic Development
by Bongsuk Sung, Yu-Cheng Lin and Sang-Do Park
Sustainability 2025, 17(16), 7257; https://doi.org/10.3390/su17167257 - 11 Aug 2025
Viewed by 379
Abstract
Amid escalating concerns over air pollution and demographic shifts, industrial robots have emerged as a key solution to enhancing energy efficiency, reducing emissions, and fostering economic growth. However, existing research often overlooks their role in shaping green total factor productivity (GTFP), a critical [...] Read more.
Amid escalating concerns over air pollution and demographic shifts, industrial robots have emerged as a key solution to enhancing energy efficiency, reducing emissions, and fostering economic growth. However, existing research often overlooks their role in shaping green total factor productivity (GTFP), a critical measure of environmentally sustainable economic performance. This study investigates the relationship between industrial robot applications (IRAs) and high-quality economic development (HQED) by integrating theoretical modeling and empirical analysis. Using panel data from 32 countries (16 developed and 16 developing) over the period of 1993–2019, classified according to the 2023 International Monetary Fund (IMF) standards, this study employs fixed-effects models, system generalized method of moments (SYS-GMM), and threshold regression models to assess IRA-induced impacts on HQED. The findings reveal that IRAs significantly contribute to HQED, with a stronger effect observed in developing economies. Moreover, a threshold effect exists, wherein environmental regulations (ERs) mediate the effectiveness of IRAs in improving GTFP. Additionally, IRAs drive HQED through foreign direct investment (FDI) and technological innovation (TI). These results provide empirical evidence and policy insights for leveraging industrial automation to promote sustainable economic growth across different national contexts. Full article
Show Figures

Figure 1

19 pages, 440 KiB  
Article
Contextual Study of Technostress in Higher Education: Psychometric Evidence for the TS4US Scale from Lima, Peru
by Guillermo Araya-Ugarte, Miguel Armesto-Céspedes, Nicolás Contreras-Barraza, Alejandro Vega-Muñoz, Guido Salazar-Sepúlveda and Nelson Lay
Sustainability 2025, 17(15), 6974; https://doi.org/10.3390/su17156974 - 31 Jul 2025
Viewed by 485
Abstract
Sustainable education requires addressing the challenges posed by digital transformation, including technostress among university students. This study evaluates technostress levels in higher education through the validation of the TS4US scale and its implications for sustainable learning environments. A cross-sectional study was conducted with [...] Read more.
Sustainable education requires addressing the challenges posed by digital transformation, including technostress among university students. This study evaluates technostress levels in higher education through the validation of the TS4US scale and its implications for sustainable learning environments. A cross-sectional study was conducted with 328 university students from four districts in Lima, Peru, using an online survey to measure technostress. Confirmatory factor analysis (CFA) was performed to assess the psychometric properties of the TS4US scale, resulting in a refined model with two latent factors and thirteen validated items. Findings indicate that 28% of students experience high technostress levels, while 5% report very high levels, though no significant associations were found between technostress and sociodemographic variables such as campus location, employment status, gender, and academic level. The TS4US instrument had been previously validated in Chile; this study confirms its structure in a new sociocultural context, reinforcing its cross-cultural applicability. These results highlight the need for sustainable strategies to mitigate technostress in higher education, including institutional support, digital literacy programs, and policies fostering a balanced technological environment. Addressing technostress is essential for promoting sustainable education (SDG4) and enhancing student well-being (SDG3). This study directly contributes to the achievement of Sustainable Development Goals 3 (Good Health and Well-being) and 4 (Quality Education) by providing validated tools and evidence-based recommendations to promote mental health and equitable access to digital education in Latin America. Future research should explore cross-country comparisons and targeted interventions, including digital well-being initiatives and adaptive learning strategies, to ensure a resilient and sustainable academic ecosystem. Full article
(This article belongs to the Section Sustainable Education and Approaches)
Show Figures

Figure 1

31 pages, 11619 KiB  
Article
Experimental Verification of Innovative, Low-Cost Method for Upgrading of Seismic Resistance of Masonry Infilled Rc Frames
by Jordan Bojadjiev, Roberta Apostolska, Golubka Necevska Cvetanovska, Damir Varevac and Julijana Bojadjieva
Appl. Sci. 2025, 15(15), 8520; https://doi.org/10.3390/app15158520 - 31 Jul 2025
Viewed by 236
Abstract
For the past few decades, during each disastrous earthquake, severe damage and poor seismic performance of masonry infilled RC frames, including many newly designed ones, have been reported extensively. Inherent problems related to analysis and design methods for tight-fit infilled frame structures have [...] Read more.
For the past few decades, during each disastrous earthquake, severe damage and poor seismic performance of masonry infilled RC frames, including many newly designed ones, have been reported extensively. Inherent problems related to analysis and design methods for tight-fit infilled frame structures have not yet been solved and are recognized as being far from satisfactory in terms of completeness and reliability. The primary objective of this research was to propose and test an innovative method that can effectively mitigate undesirable interaction damage to masonry infilled RC frame structures. This proposed technical solution consists of connection of the infill panel to the bounding columns with steel reinforcement connections deployed in mortar layers and anchored to the columns. This is practical, cheap and easy to implement without any specific technology, which is especially important for developing countries. A three story, two bay RC building model with the proposed connection implemented on the infill walls was designed and tested on the shake table at IZIIS in Skopje, N. Macedonia. The test results and design guidelines/recommendations from the proposed research are also expected to benefit the infrastructural development in other countries threatened by earthquakes, preferably in the Balkan and the Mediterranean region. Full article
Show Figures

Figure 1

25 pages, 878 KiB  
Article
Impact of Environmental, Social, and Governance Risks and Mitigation Strategies of Innovation and Sustainable Practices of Host Country on Project Performance of CPEC
by Iqtidar Hussain, Sun Zhonggen, Jaffar Aman and Sunana Alam
Sustainability 2025, 17(15), 6861; https://doi.org/10.3390/su17156861 - 28 Jul 2025
Viewed by 433
Abstract
This research examines the relationship between environmental, social safety and governance risks, and the mitigation strategies of the host country to enhance project performance in the China–Pakistan Economic Corridor (CPEC). The study concludes that the timely and effective completion of CPEC projects is [...] Read more.
This research examines the relationship between environmental, social safety and governance risks, and the mitigation strategies of the host country to enhance project performance in the China–Pakistan Economic Corridor (CPEC). The study concludes that the timely and effective completion of CPEC projects is challenged by environmental, social safety, and governance (ESG) risks, including environmental degradation, security threats, and governance issues. Based on the data of 618 respondents from Pakistan and using Structural Equation Modeling (SEM) through SMART PLS 4, the study investigates the impact of sustainable environmental practices, safety and security measures, governance risk mitigation actions, and project management systems on the project performance of CPEC projects. The results show that mitigation efforts implemented by the host country reduce the ESG investment risk and yield a positive effect on the project performance. Hence, this paper will show the importance of proactive measures such as sustainable development practices, security risk management systems, and transparent governance practices in matching challenges and enhancing project benefits. This research reinforces the potential for these risks to be mitigated through the adoption of innovative technologies. Innovation in environments, social protection, and governance frameworks can greatly mitigate the negative impacts of risks, directly improving the outcomes of project delivery. Infrastructure projects are extremely challenging to manage, and this study gives key hints for enhancing project safety and risk management in those types of infrastructure projects for practitioners, policymakers, project managers, and other stakeholders to establish innovative, sustainable strategies. Full article
Show Figures

Figure 1

26 pages, 3526 KiB  
Article
All Roads Lead to Excellence: A Comparative Scientometric Assessment of French and Dutch European Research Council Grant Winners’ Academic Performance in the Domain of Social Sciences and Humanities
by Gergely Ferenc Lendvai, Petra Aczél and Péter Sasvári
Publications 2025, 13(3), 34; https://doi.org/10.3390/publications13030034 - 24 Jul 2025
Viewed by 656
Abstract
This study investigates how differing national research governance models impact academic performance by comparing European Research Council (ERC) grant winners in the social sciences and humanities from France and the Netherlands. Situated within the broader context of centralized versus decentralized research systems, the [...] Read more.
This study investigates how differing national research governance models impact academic performance by comparing European Research Council (ERC) grant winners in the social sciences and humanities from France and the Netherlands. Situated within the broader context of centralized versus decentralized research systems, the analysis aims to understand how these structures shape publication trends, thematic diversity, and collaboration patterns. Drawing on Scopus and SciVal data covering 9996 publications by 305 ERC winners between 2019 and 2023, we employed a multi-method approach, including latent Dirichlet allocation for topic modeling, compound annual growth rate analysis, and co-authorship network analysis. The results show that neuroscience, climate change, and psychology are dominant domains, with language and linguistics particularly prevalent in France and law and political science in the Netherlands. French ERC winners are more likely to be affiliated with national or sectoral institutions, whereas in the Netherlands, elite universities dominate. Collaboration emerged as a key success factor, with an average of four co-authors per publication and network analyses revealing central figures who bridge topical clusters. International collaborations were consistently linked with higher visibility, while single-authored publications showed limited impact. These findings suggest that institutional context and collaborative practices significantly shape research performance in both countries. Full article
Show Figures

Figure 1

26 pages, 1378 KiB  
Article
Effects of Electricity Price Volatility, Energy Mix and Training Interval on Prediction Accuracy: An Investigation of Adaptive and Static Regression Models for Germany, France and the Czech Republic
by Marek Pavlík and Matej Bereš
Energies 2025, 18(15), 3893; https://doi.org/10.3390/en18153893 - 22 Jul 2025
Viewed by 458
Abstract
Electricity markets in Europe have undergone major changes in the last decade, mainly due to the increasing share of variable renewable energy sources (RES), changing demand patterns, and geopolitical factors—particularly the war in Ukraine, tensions over energy imports, and disruptions in natural gas [...] Read more.
Electricity markets in Europe have undergone major changes in the last decade, mainly due to the increasing share of variable renewable energy sources (RES), changing demand patterns, and geopolitical factors—particularly the war in Ukraine, tensions over energy imports, and disruptions in natural gas supplies. These changes have led to increased electricity price volatility, reducing the reliability of traditional forecasting tools. This research analyses the potential of static and adaptive linear regression as electricity price forecasting tools in the context of three countries with different energy mixes: Germany, France and the Czech Republic. The static regression approach was compared with an adaptive approach based on incremental model updates at monthly intervals. Testing was carried out in three different scenarios combining stable and turbulent market periods. The quantitative results showed that the adaptive model achieved a lower MAE and RMSE, especially when trained on data from high-volatility periods. However, models trained under turbulent conditions performed poorly in stable environments due to a shift in market dynamics. The results supported several of the hypotheses formulated and demonstrated the need for localised, flexible and continuously updated forecasting. Limitations of the adaptive approach and suggestions for future research, including changing the length of training windows and the use of seasonal models, are also discussed. The research confirms that modern markets require adaptive analytical approaches that account for changing RES dynamics and country specificities. Full article
Show Figures

Figure 1

Back to TopTop