Skyline University College

Remote cochlear implant (CI) programming is increasingly used to expand access to follow-up care. This systematic review evaluated the clinical effectiveness and implementation outcomes of synchronous, asynchronous and hybrid remote CI programming in adults and children. PubMed, Embase, Scopus, CINAHL and the Cochrane Library were searched from January 2010 to July 2025. Eligible studies were randomised or observational designs reporting clinical outcomes (speech perception, aided thresholds, impedances, threshold/comfort levels) and/or implementation outcomes (feasibility, satisfaction, technical performance). Two reviewers independently screened, extracted data and assessed risk of bias. Given heterogeneity in design and outcomes, a narrative synthesis was conducted. Eleven studies (n = 356 CI recipients) met inclusion criteria. Across comparator studies, remote programming produced no clinically meaningful differences relative to in-person programming for speech perception (7/7 studies) or aided thresholds (5/5), and programming parameters remained stable (5/5). Feasibility was high, with session-completion rates typically ≥ 85%. CI recipients and clinicians reported favourable satisfaction, citing reduced travel burden and increased flexibility. Key challenges included intermittent connectivity, need for additional preparation or troubleshooting, limited digital literacy among older adults, and caregiver dependency in paediatric sessions. Remote CI programming yields clinical outcomes comparable to in-person programming and is feasible and well accepted across delivery models. However, small sample sizes, limited paediatric evidence, inconsistent technical reporting and a lack of cost-effectiveness data limit confidence in large-scale adoption. Standardised technical endpoints and rigorous multicentre evaluations are required to support scalable and equitable implementation.

This research aims to explore how Machine Learning (ML), Predictive Analytics (PA), and Data Utilisation (DU) help hospitals in the healthcare sector of Houston, considering the mediating role of DU. Since there are around 180 hospitals in the area, the aim of this research is to determine how these technologies influence decision-making and flow of activities in hospitals. An explorative, quantitative approach is used for this research, and 120 operations managers in hospitals are included as participants. Quantitative data is gathered from the participants using structured Likert-scale questionnaires and analysed through Structural Equation Modelling (SEM) to determine the relationship between ML, PA, and DU and Mediating role of DU. Based on the results, the proposed ML method considerably increases PA by increasing predictive precision and dependability. Through centralised data management, DU facilitates this relationship by ensuring that data integration and real-time processing are made possible and improving the quality of data that goes into the process. These outcomes demonstrate what the application of such tools as advanced analytics can bring to the healthcare realm in terms of operations. Regarding the research recommendations, the research suggests increasing capital expenditure allocation to data infrastructure and offering relevant continuing education to operations managers to increase the effectiveness of analytics. Further research should establish how these technologies might be implemented in other healthcare institutions.

This study investigates how multinational enterprises strategically integrate the United Nations Sustainable Development Goals (SDGs) into corporate strategy to attain sustainable competitive advantage. Studies emphasize that SDG alignment enables firms to balance profitability with environmental and social imperatives. The research adopts a qualitative methodology anchored in interpretivism, employing a 90-day multiple case study of Unilever, IKEA, and Safaricom, selected via purposive sampling based on demonstrated SDG integration and global operations. Content analysis of sustainability reports, peer-reviewed publications, and industry frameworks provided robust secondary data. Findings reveal that firms operate the SDGs through ESG-aligned metrics, circular business models, digital innovations, and multi-stakeholder partnerships. For example, IKEA’s circular economy strategy and Safaricom’s mobile financial inclusion platform significantly advanced SDGs 7, 12, and 8, respectively. Key challenges include regulatory fragmentation, short-termism, and supply chain inconsistencies. The study concludes that SDG integration is a strategic imperative and recommends performance-based metrics, cross-sector alliances, and capacity-building for sustainable transformation. These findings offer a replicable framework for global firms navigating sustainable development imperatives.

Structural Health Monitoring is a process that involves the use of various sensing technologies to assess the condition of structures such as buildings and other infrastructures. The primary aim of Structural Health Monitoring is to detect changes in the performance of these structures over time and to provide data that can help ensure their safety and integrity. This study is mainly proposed six subsystem. The first subsystem consists of a structure of sensors, placed at selected points of the structure. Secondly, there is a data acquisition system to collect data from sensors. Third one is data transmission network. The fourth subcomponent is the data processing and analysis center, which is used to obtain meaningful information about the current state of the structure. Fifth, there is a decision support system designed to utilize analysis results to determine the necessary maintenance or intervention for the structure. And finally, there is a user interface that allows users to access the system, view reports, and monitor analysis results. This study also aims to send information to the structure’s electrical power system when it detects critical values from a structural stability perspective, triggering the power system to enter an emergency mode.

Credit card behavior scoring is indispensable for banks which are eager to evaluate the credit worthiness of consumers, lower the risk, and adjust the lending strategy. The traditional methods are based on historical data and rule-based systems, which could overlook new types of consumer spending. In this paper, we have applied machine learning techniques to Kaggle credit card behavior score dataset. PCA is used in dimensionality reduction without loss of significant characteristics and to reduce redundancy. A k-means clustering is then carried out to classify customers according to their credit creation behavior and to identify three clusters of customers that represent low, medium and high users of risk. The findings of the research yield valuable information regarding customer segmentation and can assist financial institutions in developing or fine-tuning credit policies while allowing for early identification of a high-risk portfolio. It leads to more accurate and flexible credit scoring models and consequently to improved decision making under the financial risk assessment aspect.

Sustainability has long been an issue of concern to society, countries, and the economy.In this context, circular supply chain management is a good strategy for reducing material consumption, reducing waste and carbon footprints, and creating recycling opportunities throughout the supply chain.To ensure the successful integration of the circular supply chain practices by the Malaysian Micro, Small and Medium Enterprises (MSMEs) businesses, it is imperative to investigate the key structures to the barriers and the main enablers that can drive its successful adoption.Thus, this study used the scoping review procedure based on the preferred reporting items for systematic reviews and meta-analyses extension for scoping reviews (PRISMA-ScR) involving 188 shortlisted articles.Five interconnected influential structures which are the government agencies, industry stakeholders, MSMEs management, customers and consumers, and financing institutions were identified as key roles in ensuring the successful implementation of circular supply chain practices.Subsequently, 33 barriers and 27 key enablers were identified as the catalysts for the successful adoption of circular supply chain practices by MSMEs.The circular supply chain framework is envisaged to motivate policymakers in Malaysia, as well as other interested countries, to implement the proposed framework through a greater number of supportive legislative measures as well as practical promotion and education policies to further enhance its acceptance.

The rise of electric and autonomous vehicles in smart cities poses challenges in vehicular energy management due to un-optimized consumption, inefficient grid use, and unpredictable traffic patterns. Traditional centralized machine learning models and cloud-based Energy Management Systems (EMSs) struggle with real-time adaptability, high-dimensional data processing, and data privacy risks. These issues lead to high costs, excessive energy waste, and regulatory concerns. Federated Learning (FL) offers a decentralized approach where multiple edge devices collaboratively train models without sharing raw data. This enhances privacy, reduces communication overhead, and is well-suited for smart city applications. However, FL’s black-box nature limits interpretability, reducing trust in AI-driven decisions. Explainable AI (XAI) addresses this by enhancing transparency, interpretability, and regulatory compliance. This research introduces Explainable FL (XFL) for optimizing vehicular energy management in smart cities. The proposed XFL framework integrates distributed learning with explainability techniques for interpretable and accountable decision-making. Using a real-world AEV telemetry dataset of approximately 1,219,567 records with features like speed, energy consumption, and traffic density, it employs a hierarchical FL architecture to ensure secure and decentralized learning. It efficiently analyzes real-time traffic, vehicle energy states, and grid load balancing while preserving privacy. Experimental results show that the proposed Multi-Layer Perceptron (MLP)-based global model achieves superior predictive accuracy, with R² values of 94.73% for energy consumption and 99.83% for traffic density, significantly outperforming previous methods.

This article explores the integration of advanced Artificial Intelligence (AI) enabled deep learning methods with accurate crop yield prediction. The objective of the work is to enhance the accuracy, sensitivity, and specificity of crop yield prediction. Also, false positive and false negative cases are minimized in crop yield prediction. AI-enabled Barilai–Blinder–Oaxaca–Bernoulli Deep Classifier (BBO-BDC) is proposed including preprocessing, feature selection, and crop yield prediction. First, the raw samples were collected from the crop yield prediction dataset. Barilai–Borwein Gradient Min–max Normalization-based preprocessing is applied to eliminate all missing values. Second, to provide fine-grained feature subsets, the Blinder–Oaxaca Statistical Decomposition-based feature selection method is used. Finally, an AI-enabled Bernoulli Deep Belief Network is designed to predict the crop yield. The empirical results demonstrate that the BBO-BDC technique significantly improves the accuracy up to 12%, specificity up to 15%, and sensitivity up to 3% with feature selection. Furthermore, the BBO-BDC technique realizes a substantial reduction in convergence speed by 29% and 51% reduction in overhead compared to conventional methods with feature selection. The study of innovative AI method integrated with Min–max Normalization, Barilai–Borwein gradient, Blinder–Oaxaca decomposition function, Deep Belief Network, Xavier Initialization function, Bernoulli distribution function, and Principal Components for achieving better performance in crop yield prediction.

The paper examines ethical leadership and corporate governance in Nigeria, challenging the existing perspectives. The objectives of the study are to reveal the unethical leadership characteristics that have negative effects on corporate governance in Nigeria. The research methodology is qualitative and expository, as it involves a deep level of analysis of secondary sources to critically explore the literature and comprehend complex phenomena, providing a nuanced interpretation of the research inquiries. The study employs agency theory, which best fits into this study. The findings revealed that a weak institutional structure is one of the fundamental predicaments of ethical leadership and corporate governance in Nigeria. The conclusion provided recommendations for the study challenges.