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The purpose of this research is to determine the efficiency of energy usage and its role in carbon dioxide emissions (CI) and economic-environmental efficiency (EEE) for some countries Organization for Economic Co-operation and Development (OECD) economies. For environment quality assessment, data envelopment analysis (DEA) is used to assess the data cover the period from 2013 to 2017. In this study, primary energy consumption (PEC) and population are two basic inputs along with gross domestic product (GDP) and carbon dioxide emissions that are desirable and undesirable  outputs, respectively. The practical outcomes illustrate that Brunei, Australia, Singapore, and Hong Kong are the most effective and efficient states for the 5 years periods (2013–2017) in terms of energy efficiency and to reduce emission of carbon dioxide. In addition, other states in the OECD region shows greater economic proficiency than environmental proficiency. Furthermore, the results shows that energy efficiency has strong bonding with carbon emissions; however there is a weaker association between economic-environmental efficiency. Thus, the attainment of optimal level of energy efficiency could be more pivotal than economic efficiency to improve environmental efficiency in countries from the OECD region.

The transportation sector consumes 25% of world energy with 23% of the world’s total carbon emission. Therefore, it is necessary to investigate the integrated effect of fossil fuel source of energy consumption, economic development, and total population on CO 2 emission based on environmental degradation transportation sector. We employed the econometric methodologies such as a hybrid error correction model, regression coefficients, platykurtic distribution, Dickey-Fuller test, and co-integration test in order to justify empirical analysis for Pakistan transport sector. Results reveal that an increase in economic growth, urbanization, and energy consumption increased transport-based environmental degradation urbanization. Moreover, very interestingly, during this period, energy consumption has increased by 13.5%, and it shows a high dependence of economic growth on energy consumption. Further, the CO 2 emission and energy consumption per capita has a positive relationship. Finally, this study has proposed some suggestion for policy and decision-makers to mitigate environmental degradation as well as make transport sector environmentally sustainable.

Solar energy systems are a cheaper and easy solution to cope with severe energy crisis especially in emerging economies including Turkey which exerted huge efforts to enhance the existing solar power projects. However, the selection of the optimal site for the installation of solar projects needs vigorous investigation through various factors. Adequate quantitative scientific research is required for the process of site selection in Turkey. This paper categorizes various sites in Turkey through various factors such as economic, environmental, and social factors. Various major criteria have been combined through mathematical development to install the solar power project in remote areas of Turkey. The scientific evaluation of remote and rural solar projects in Turkey has been taken as a case study in the current paper. Additionally, the analytical hierarchy process (AHP) and F-VIKOR methods were used to aggregate the criteria. The results show that economic and social ratio is significant, whereas the transmission matrix, land cost, and the sun irradiance got a major score in order to generate electricity. The study results show that total sunshine time per year determined is 2741 h (a total of 7.5 h per day) and the total solar energy obtained each year is 1527 kWh per square meter per year (a total of 4.18 kWh per square meter per day).

Today, society is seeking solutions to achieve sustainable development, through association between entrepreneurship, innovation and sustainable development has become a topic of great apprehension. In this perspective, this article aims to link environmental responsive entrepreneurship with sustainable development through empirical evidences from developing country. Therefore, the purpose of this study is to validate the environmental Kuznets curve hypothesis to confirm the achievement of sustainable development goals in Pakistan. We use the combined mean estimator of the autoregressive distribution lag model and GMM model to determine the long-term relationship between the variables and analyze the environmental Kuznets curve hypothesis. We found U-shaped environmental Kuznets curves in Pakistan. Further results show long-term relationship using the PMG-ARDL estimator. Our findings indicate the presence of EKC, U-shaped EKC. This means that at a certain level of economic growth, a 1% increase in per capita income can lead to reductions in environmental pollution by 2.88%, 4.54%, and 2.48%. Therefore, governments and policy makers should strengthen policies to reduce environmental pollution and, more importantly, formulate green financing policies to encourage aspiring environmental entrepreneurs to establish environmentally driven enterprises, promote the use of environmental products to reduce environmental problems, and achieve sustainable development in Pakistan.

Acral melanoma (AM) is a rare and lethal type of skin cancer. It can be diagnosed by expert dermatologists, using dermoscopic imaging. It is challenging for dermatologists to diagnose melanoma because of the very minor differences between melanoma and non-melanoma cancers. Most of the research on skin cancer diagnosis is related to the binary classification of lesions into melanoma and non-melanoma. However, to date, limited research has been conducted on the classification of melanoma subtypes. The current study investigated the effectiveness of dermoscopy and deep learning in classifying melanoma subtypes, such as, AM. In this study, we present a novel deep learning model, developed to classify skin cancer. We utilized a dermoscopic image dataset from the Yonsei University Health System South Korea for the classification of skin lesions. Various image processing and data augmentation techniques have been applied to develop a robust automated system for AM detection. Our custom-built model is a seven-layered deep convolutional network that was trained from scratch. Additionally, transfer learning was utilized to compare the performance of our model, where AlexNet and ResNet-18 were modified, fine-tuned, and trained on the same dataset. We achieved improved results from our proposed model with an accuracy of more than 90 % for AM and benign nevus, respectively. Additionally, using the transfer learning approach, we achieved an average accuracy of nearly 97 %, which is comparable to that of state-of-the-art methods. From our analysis and results, we found that our model performed well and was able to effectively classify skin cancer. Our results show that the proposed system can be used by dermatologists in the clinical decision-making process for the early diagnosis of AM.

Background: Theintegration of artificial intelligence (AI) into clinical decision support systems (CDSSs) has significantly enhanced diagnostic precision, risk stratification, and treatment planning. AI models remain a barrier to clinical adoption, emphasizing the critical role of explainable AI (XAI). Methods: This systematic meta-analysis synthesizes findings from 62 peer-reviewed studies published between 2018 and 2025, examining the use of XAI methods within CDSSs across various clinical domains, including radiology, oncology, neurology, and critical care. Model-agnostic techniques such as visualization models like Gradient-weighted Class Activation Mapping (Grad-CAM) and attention mechanisms dominated in imaging and sequential data tasks. Results: However, there are still gaps in user-friendly evaluation, methodological transparency, and ethical issues, as seen by the absence of research that evaluated explanation fidelity, clinician trust, or usability in real-world settings. In order to enable responsible AI implementation in healthcare, our analysis emphasizes the necessity of longitudinal clinical validation, participatory system design, and uniform interpretability measures. Conclusions: This review offers a thorough analysis of the state of XAI practices in CDSSs today, identifies methodological and practical issues, and suggests a path forward for AI solutions that are open, moral, and clinically relevant.

This study evaluates the sustainable power plant cost in the outlook of global power plant efficiency to reduce fossil fuel dependency and greenhouse gas emissions. For this purpose, the Global Change Assessment Model (GCAM) applied for conducting the cost assessment of power zone technologies for all principal electricity generation. This study considers the characteristics of essential factors (cement, price of resources, possible increases in employees, and metals) that affect costs. This study suggests that the cost of electricity-generating technologies significantly affects growth efficiency, reduction in production cost, and improving environmental conditions. It also suggests that the cost of electricity-generating technologies, combined with technology mixture, is the key factor behind replacing existing technology in the electricity sector. EPRI cost assessments expanded by around 30% and 50% during 2015-2016. Factors like competition amongst power plant owners, the ambiguous marketplace, production procedures, and lack of environment-related strategies have resulted in massive environmental degradation in developing economies like Pakistan. Based on empirical findings, this study recommends designing efficient technologies, which would reduce power plant costs and ensure vertical prospects related to environmental conditions in the future.

Smart cities have emerged as a specialized domain encompassing various technologies, transitioning from civil engineering to technology-driven solutions. The accelerated development of technologies, such as the Internet of Things (IoT), software-defined networks (SDN), 5G, artificial intelligence, cognitive science, and analytics, has played a crucial role in providing solutions for smart cities. Smart cities heavily rely on devices, ad hoc networks, and cloud computing to integrate and streamline various activities towards common goals. However, the complexity arising from multiple cloud service providers offering myriad services necessitates a stable and coherent platform for sustainable operations. The Smart City Operational Platform Ecology (SCOPE) model has been developed to address the growing demands, and incorporates machine learning, cognitive correlates, ecosystem management, and security. SCOPE provides an ecosystem that establishes a balance for achieving sustainability and progress. In the context of smart cities, Internet of Things (IoT) devices play a significant role in enabling automation and data capture. This research paper focuses on a specific module of SCOPE, which deals with data processing and learning mechanisms for object identification in smart cities. Specifically, it presents a car parking system that utilizes smart identification techniques to identify vacant slots. The learning controller in SCOPE employs a two-tier approach, and utilizes two different models, namely Alex Net and YOLO, to ensure procedural stability and improvement.

The technological innovation and strict environmental protocols in the highly developed regions have become the primary sources for foreign direct investment to move in the pollution haven economies. In this regard, this study attempted to identify the role of foreign direct investment (FDI) in the developing economies of the Brazil, Russia, India, China, and South Africa (BRICS) region. For this reason, a dataset was obtained between 1995 and 2019. Chudik and Pesaran’s ( 2015 ) latest dynamic common correlated effects (DCCE) technique is used because of its new features when integrating the problems of heterogeneity and structural breaks into panel data that are general and do not encompass much recent research in this context. According to the empirical outcomes, foreign direct investment is a source of pollution haven in this region. However, the moderating effect of institutional quality on foreign direct investment has been found negative for ecological footprint. It also found the threshold point where the foreign direct investment effect becomes negative on ecological footprint. Based on these empirical results, this research suggests that foreign direct investment strategy should be maintained in the presence of good institutional efficiency as it enhances the environment and promotes economic development.

The digital transformation disrupts the various professional domains in different ways, though one aspect is common: the unified platform known as cloud computing. Corporate solutions, IoT systems, analytics, business intelligence, and numerous tools, solutions and systems use cloud computing as a global platform. The migrations to the cloud are increasing, causing it to face new challenges and complexities. One of the essential segments is related to data storage. Data storage on the cloud is neither simplistic nor conventional; rather, it is becoming more and more complex due to the versatility and volume of data. The inspiration of this research is based on the development of a framework that can provide a comprehensive solution for cloud computing storage in terms of replication, and instead of using formal recovery channels, erasure coding has been proposed for this framework, which in the past proved itself as a trustworthy mechanism for the job. The proposed framework provides a hybrid approach to combine the benefits of replication and erasure coding to attain the optimal solution for storage, specifically focused on reliability and recovery. Learning and training mechanisms were developed to provide dynamic structure building in the future and test the data model. RAID architecture is used to formulate different configurations for the experiments. RAID-1 to RAID-6 are divided into two groups, with RAID-1 to 4 in the first group while RAID-5 and 6 are in the second group, further categorized based on FTT, parity, failure range and capacity. Reliability and recovery are evaluated on the rest of the data on the server side, and for the data in transit at the virtual level. The overall results show the significant impact of the proposed hybrid framework on cloud storage performance. RAID-6c at the server side came out as the best configuration for optimal performance. The mirroring for replication using RAID-6 and erasure coding for recovery work in complete coherence provide good results for the current framework while highlighting the interesting and challenging paths for future research