Brain tumors constitute a heterogeneous and life-threatening group of neurological disorders in which timely and accurate diagnosis is critical to improving patient outcomes. Conventional diagnostic practices, which rely heavily on manual interpretation of medical imaging, remain constrained by inter-observer variability, subjective judgment, and limited reproducibility, particularly when assigning tumor grades according to the World Health Organization (WHO) classification system. In recent years, machine learning (ML) and deep learning (DL) have emerged as transformative computational paradigms capable of automating complex pattern recognition in neuroimaging and enhancing diagnostic precision, efficiency, and consistency. A comprehensive review of ML/DL-based approaches for brain tumor analysis is presented in this study, encompassing key methodologies developed for tumor detection, segmentation, and classification across WHO grades. Despite notable research advances, clinical adoption remains impeded by several critical challenges, including insufficient dataset size and heterogeneity, a lack of model interpretability, limited generalizability across imaging acquisition protocols, and barriers associated with clinical integration and regulatory approval. Addressing these obstacles will require the development of large-scale, standardized, and multi-institutional datasets; the advancement of explainable artificial intelligence (XAI) frameworks to enhance clinical trust; and the incorporation of multi-modal patient data to improve diagnostic robustness. Furthermore, the convergence of ML/DL with emerging technologies such as blockchain and the Internet of Things (IoT) holds promise for enabling privacy-preserving, interoperable, and real-time diagnostic platforms. With continued advancements in algorithmic robustness, interpretability, and cross-institutional validation, ML/DL-based frameworks hold the potential to revolutionize brain tumor diagnosis and classification, ultimately improving diagnostic precision, prognostic assessment, and personalized treatment planning.
Based on panel data of A-share listed companies in China from 2015 to 2023, this study empirically examines the impact of corporate digital transformation on the quality of information disclosure, focusing on the underlying mechanism of the information effect. The findings reveal that digital transformation significantly improves the quality of information disclosure, and this effect remains robust after addressing endogeneity and conducting a series of robustness checks. Further analysis suggests that digital transformation enhances the transparency and reliability of information disclosure by improving internal control quality and reducing information asymmetry, thereby exerting a significant information effect. Moreover, heterogeneity analysis indicates that the positive impact of digital transformation on disclosure quality is more pronounced among state-owned enterprises, non-high-tech firms, and large-scale enterprises. This study provides empirical evidence for policymakers and corporate managers on leveraging digital transformation to enhance information disclosure quality.
The objective of this work is to analyze the environmental sustainability performance of deposit banks traded in Borsa Istanbul (BIST) through the application a novel integrated grey Multi-Criteria Decision-Making (MCDM) approach. The grey combined model proposed for the assessment of environmental performance in the banking sector integrates the Logarithmic Objective Weighting Based on Percentage Change (LOPCOW) and Proximity Indexed Value (PIV) algorithms. In the first stage, the importance weights of the criteria were determined using the Grey LOPCOW objective weighting technique, which enables a comprehensive and robust weighting system. Following this, the Grey PIV method was employed to assess the banks' environmental sustainability performance. To demonstrate the robustness and applicability of the suggested MCDM framework, several sensitivity analyses and comparative assessments were conducted. The empirical findings imply that the most significant environmental performance indicator affecting the environmental sustainability performance of deposit banks is “amount of disposed waste”. Moreover, Yapı Kredi was identified to be the bank with the highest environmental sustainability performance compared to its competitors in the BIST banking industry. The findings obtained through sensitivity and comparative analyses indicate that the introduced hybrid decision model in the existing work constitutes a robust, defendable, and effective framework for assessing the environmental sustainability performance of banking institutions. Lastly, the findings have important implications for bank management, regulators, and policymakers, offering valuable insights for the enhancement of sustainability practices within the banking industry. This work contributes to the growing body of literature on environmental performance measurement in the financial sector and provides a methodological foundation for future sustainability assessments in similar contexts.
As a rapidly developing nation, India faces an urgent need to diversify its energy portfolio to ensure long-term sustainability and energy security. Biomass energy, as a renewable and sustainable resource, has the potential to play a crucial role in achieving these objectives. Its integration into the national energy framework, however, is hindered by multiple challenges, including technological limitations, socio-economic constraints, and environmental concerns. Despite its advantages—such as reducing greenhouse gas emissions, promoting economic growth, managing waste, and preserving biodiversity—several barriers must be systematically analyzed to facilitate its widespread adoption. In this study, a structured approach is employed to identify and evaluate the key challenges associated with biomass energy adaptation in India. The Decision-Making Trial and Evaluation Laboratory (DEMATEL) methodology is applied to determine the relative importance of these challenges, offering insights into the most critical criteria that require focused intervention. The findings of this study are expected to provide a strategic foundation for policymakers and stakeholders in formulating effective policies and technological solutions to enhance the viability of biomass energy in India's energy transition.
Fixed assets are signs of a significant source of carbon emissions in intensive carbon sectors. This study aims to investigate the impact of asset structure and asset utilization efficiency on the carbon emissions reduction in high polluting industry in Indonesia. The study uses the high-polluting industries in Indonesia in the period 2018-2022, as the sample. Secondary data were collected from the company’s annual and sustainability report from the company’s website. To test the hypotheses, the study used logistic regression. The results show asset structure does not have a significant effect on carbon emissions reduction, however, asset utilization efficiency has a negative effect on carbon emissions reduction. This study’s results highlight the critical need for the government and research organizations to define the carbon emissions capacity of various fixed assets. As a result, it is easier for high-carbon industries to implement more detailed carbon management strategies and maximize their carbon advantages.
Power-domain non-orthogonal multiple access (NOMA) is one of the key technologies in 5G communica-tions, enabling efficient multi-user transmission over the same time-frequency resources through power multiplexing. In this study, an improved max-min relay selection strategy was proposed for NOMA cooperative communication systems to address the issue of insufficient channel fairness in conventional strategies. The proposed strategy optimizes the relay selection process with the objective of ensuring channel fairness. Theoretical derivations and simulation analyses were conducted to comprehensively evaluate the proposed strategy from the perspectives of user throughput and system outage probability. The results demonstrate that, compared to the conventional max-min strategy and other commonly used relay selection methods, the proposed strategy significantly reduces the system outage probability while enhancing user throughput, thereby verifying its superiority in improving system reliability and stability.
Under the "dual carbon" goals, green finance, as a financial activity aimed at optimizing resource allocation and protecting the environment, holds significant scientific importance for the rational adjustment and upgrading of regional industrial structures. Hebei Province, a key industrial hub in China, faces an urgent need for industrial structure adjustment and optimization. This paper employs time series data from Hebei Province spanning 2001 to 2023 to measure the development levels of green finance and industrial structure, and constructs a coupling coordination model to analyze their interactions. Furthermore, the study uses the GM (1,1) grey model to predict future trends. The results indicate that the coupling coordination degree between green finance and industrial structure upgrading in Hebei Province has steadily improved but remains at a moderate coupling stage. It is projected that the coupling coordination degree will continue to rise, entering a high coordination stage by 2032.
The existence of the Panglima Laot (sea commander) to sustainably manage coastal areas in Aceh Province faces various problems. Moreover, the role of the Panglima Laot institution is sub-optimal in implementing customary maritime (adat laot) rules for managing coastal areas. This study aims to analyze the institutional performance of Panglima Laot in the sustainable management of coastal areas. This study was conducted in Aceh, Indonesia. Observation and structured interviews (questionnaires) were utilized to gather primary data. Secondary data were acquired from various agencies. Data were analyzed through multiple linear regression analysis, and qualitative data was analyzed by scoring the Likert scale. The performance of the Panglima Laot institution, ranked from highest to lowest, was as follows: (1) the application of customary maritime law, (2) fishermen's compliance with the customary maritime law, (3) the implementation of roles and functions by the Panglima Laot, and (4) dispute resolution among fishermen. The role of Panglima Laot is significant in preserving coastal ecosystems. Enhancing the performance requires developing a strong synergy with the government for effective fisheries supervision and management.
This study presents a novel image restoration method, designed to enhance defective fuzzy images, by utilizing the Fuzzy Einstein Geometric Aggregation Operator (FEGAO). The method addresses the challenges posed by non-linearity, uncertainty, and complex degradation in defective images. Traditional image enhancement approaches often struggle with the imprecision inherent in defect detection. In contrast, FEGAO employs the Einstein t-norm and t-conorm for non-linear aggregation, which refines pixel coordinates and improves the accuracy of feature extraction. The proposed approach integrates several techniques, including pixel coordinate extraction, regional intensity refinement, multi-scale Gaussian correction, and a layered enhancement framework, thereby ensuring superior preservation of details and minimization of artifacts. Experimental evaluations demonstrate that FEGAO outperforms conventional methods in terms of image resolution, edge clarity, and noise robustness, while maintaining computational efficiency. Comparative analysis further underscores the method’s ability to preserve fine details and reduce uncertainty in defective images. This work offers significant advancements in image restoration by providing an adaptive, efficient solution for defect detection, machine vision, and multimedia applications, establishing a foundation for future research in fuzzy logic-based image processing under degraded conditions.
Diabetic foot infection (DFI) represents a severe and potentially limb-threatening complication of long-standing and poorly controlled diabetes mellitus, a condition currently affecting over 422 million individuals globally and associated with more than 2 million annual deaths. This retrospective observational study was conducted at Hayatabad Medical Complex (HMC), Peshawar, with the objective of characterizing the clinical features, comorbidities, antibiotic regimens, and management outcomes of patients diagnosed with DFI. Clinical records of 341 patients admitted over a three-month period were reviewed. A male predominance was observed, with the highest prevalence noted among individuals aged 40–60 years. The majority of cases involved insulin-dependent diabetes mellitus, and an extended disease duration was identified as a major predisposing factor for DFI development. The mean hospitalization period was 25 days. Notably, complications such as peripheral neuropathy, diabetic nephropathy, and peripheral vasculopathy were more frequently documented in patients aged 65 years and older. Empirical treatment commonly involved poly-antibiotic regimens, which were administered in 64.81% of cases, underscoring the polymicrobial nature and severity of infections encountered. An amputation rate of 44.07% was recorded, which exceeds figures reported in comparable regional studies and is likely attributable to delayed clinical presentation and advanced stages of infection at the time of admission. The findings underscore the urgent need for enhanced early screening protocols, timely initiation of pathogen-targeted antimicrobial therapy, and multidisciplinary surgical intervention to reduce the risk of lower extremity amputation and the associated socio-economic burden.
Accurate monitoring of turbine speed is essential for ensuring operational stability and efficiency in power generation systems, particularly within the context of low-carbon and renewable energy integration. This study evaluates the performance of three Variable Reluctance Sensors (VRSs)—VRS1, VRS2, and VRS3—used for real-time speed monitoring of the Steam Turbine Generator (STG) 1.0 at the Tambak Lorok Combined Cycle Power Plant (CCPP). The evaluation was conducted using statistical methods, including Root Mean Square Error (RMSE), standard deviation, and two-factor Analysis of Variance (ANOVA) without replication, to assess the accuracy and consistency of the sensors under varying operational conditions. The operational conditions were simulated through a motor controlled by a Variable Speed Drive (VSD), which allows for precise control over speed variations. The results indicate that the VRSs exhibit high accuracy and reliability, with RMSE values ranging from 0.08% to 0.28%. Among the three sensors, VRS3 demonstrated the highest performance, achieving minimal variability, with a standard deviation of 0.000 at a frequency of 50.00 Hz. ANOVA revealed no significant differences in performance between the three sensors (P-value = 1.000), suggesting uniformity in their measurement capabilities. These findings substantiate the suitability of VRSs for turbine speed monitoring in power plants, ensuring operational stability and supporting the integration of renewable energy technologies. The results reinforce the potential of VRSs as a reliable tool for improving the efficiency of sustainable energy systems
Open Access
