Volume 8.
Issue 09.

The transition toward sustainable regional development has become a strategic priority as governments, industries, and communities seek to address environmental degradation, resource scarcity, economic inequality, and technological transformation. Regional eco-systems are increasingly recognized as dynamic socio-technical environments in which sustainable technologies, institutional governance, community participation, and innovation networks interact to create long-term economic, environmental, and social value. However, many existing development initiatives remain fragmented because technological implementation often progresses independently from social innovation mechanisms, resulting in reduced adoption rates, limited stakeholder collaboration, and uneven regional benefits. Consequently, there is a growing need for an integrated systems perspective capable of synchronizing technological advancement with inclusive social innovation.

This review research proposes an Integrated Systems Approach (ISA) that conceptualizes sustainable regional development as a multi-layered interaction among technological infrastructure, governance systems, innovation ecosystems, stakeholder engagement, environmental stewardship, and adaptive policy mechanisms. Rather than examining sustainable technologies or social innovation independently, the proposed framework emphasizes their interdependence within regional ecosystems to improve resilience, sustainability, and long-term competitiveness. The study synthesizes existing literature to identify major technological enablers, institutional factors, collaborative governance structures, and innovation pathways that collectively influence regional sustainability performance. Furthermore, the review highlights how systems thinking enables policymakers to coordinate cross-sector collaboration while reducing implementation barriers and improving adaptive capacity.

The findings suggest that sustainable technology adoption achieves greater regional impact when accompanied by participatory governance, knowledge-sharing networks, inclusive institutional arrangements, and continuous innovation. The proposed framework demonstrates how technological infrastructure, digital transformation, environmental management, and social innovation reinforce one another through feedback mechanisms that improve ecosystem resilience. The study contributes a comprehensive conceptual model capable of supporting policymakers, researchers, industry practitioners, and regional planners in designing integrated sustainability strategies suitable for diverse economic and institutional contexts. Future research should validate the framework empirically using cross-regional comparative studies and quantitative systems modelling.

This study investigates the impact of social media on the development of communicative compensation strategies among Lung fibrosis, particularly idiopathic pulmonary fibrosis (IPF), involves chronic, progressive scarring of lung tissue driven by dysregulated extracellular matrix (ECM) remodelling and persistent fibroblast activation. This study presents a comprehensive bioinformatics investigation of the structural biology and protein–protein interaction (PPI) mapping of six key fibrosis associated proteins: MMP2, MMP9, TGFB1, ACTA2, FBLN1, and COL1A1. Three dimensional structural superimposition was performed using US align and MM align to identify conserved domains, TM scores, and RMSD values across all pairwise and multi way combinations. An expanded structural alignment matrix covering ten pairwise combinations reveals TM scores ranging from 0.18 (ACTA2–COL1A1; distinct folds) to 0.48 (MMP9–MMP2; shared gelatinase superfamily), with highest inter family similarity between FBLN1 and TGFB1 (TM score = 0.34), attributable to the structural homology between fibulin cbEGF repeats and TGF β finger loops  a finding with implications for LTBP1 mediated TGF β sequestration in the ECM. PPI networks constructed using STRING v12 (2025) identify TGFB1 as the dominant signalling hub (degree = 38; MCC rank = 1), forming a dense interaction triangle with MMP2 and MMP9 governing ECM degradation. KEGG and Disease Ontology enrichment analysis confirms significant involvement of these proteins in the TGF β signalling pathway (hsa04350), ECM receptor interaction (hsa04512), regulation of actin cytoskeleton (hsa04810), and pathways shared with oncogenic processes (Proteoglycans in cancer, hsa05205). A detailed domain architecture analysis of all six proteins identifies key fibrosis relevant structural features including the MMP catalytic Zn²⁺ binding HEXGHXXGXXH motif, the TGF β cystine knot growth factor fold, the FBLN1 calcium binding EGF repeat (cbEGF) arrays, and the COL1A1 Gly X Y triple helix repeat with 4 hydroxyproline water bridges. Virtual screening analysis against the six hub proteins identifies druggable cavities and lead compounds including fresolimumab, galunisertib, marimastat, halofuginone, BAPN, and nintedanib. These structural and interaction based insights provide a rigorous mechanistic framework for the molecular architecture of lung fibrosis and constitute a high quality foundation for anti fibrotic drug design, biomarker validation, and experimental follow up.

For organisations that have multiple locations, there is a need to be consistent, efficient and responsive, and this is where digital transformation is a strategic necessity. Integration of business processes, real-time communication and data-driven decision making across geographically distributed units are all enabled by information technology (IT), which is key to operational excellence. This paper aims to review and synthesize the current literature to understand the impact of digital transformation in improving operational performance in multi-location business environments. It delves into the role of enterprise systems, cloud computing, data analytics, Internet of Things (IoT) technologies, artificial intelligence (AI), and digital collaboration platforms in the process of standardisation, optimisation of resources, improvement of customer services, and agility of organisations. The review also emphasizes the strategic value of digital integration in enhancing the coordination of the supply chain, human resources management, performance tracking and monitoring, and decision-making, all of which allow organizations to better adapt to market conditions. Furthermore, the paper explores the key organisational, technological and human aspects that can impact digital transformation projects, such as leadership support, employee readiness, cybersecurity, investing in infrastructure, and managing change. The results show that, besides using advanced technologies, multi-location companies need to be able to properly connect their digital capabilities to their organizational strategy and business goals to become operational exemplary. The paper concludes that the advantages that an integrated digital ecosystem brings to an organisation are sustainable competitive advantages for organisations, as it provides them with operational visibility, better cooperation, less inefficiency and continuous innovation. The study adds to the current body of knowledge on digital transformation by offering a comprehensive view of the relationship between information technology and operational excellence in geographically distributed business operations.

Quality in the laboratory is closely related to the results of the analysis test. The data from the analysis test is said to be high if the data can make customers/patients feel satisfied by considering technical values so as to achieve accuracy and precision. Quality assurance of health laboratories is all activities that are shown to ensure the accuracy and precision of laboratory examination results. Several factors that influence internal quality assurance include commitment to achieve quality results, facilities, funds, competent staff, control measures for pre-analytical, analytical and post-analytical factors, statistical control monitoring and the existence of problem-solving mechanisms. This article aims to analyze the results of internal quality assurance that have been carried out in hematology laboratories, whether they have been running well and correctly. Data collection of PMI results for a month was observed using the Westgard multirules system rules, Levey-Jennings charts that have been routinely programmed on each laboratory tool. The results obtained from the PMI hematology laboratory that has been carried out are good and accurate, so that the laboratory can produce examinations that can be trusted -and accounted for.

The effective identification of potential loan defaults is crucial for banking risk management, enabling informed lending decisions and financial stability. In this paper, a credit risk assessment framework that integrates both predictive modeling and risk interpretability is developed, which leverages Explainable AI (XAI) to enhance banking risk management to meet Basel III requirements. The proposed approach utilizes the probability outputs of the Random Forest (RF) and XGBoost (XGB) models which are combined using a Hybrid Model of Random Forest-XGBoost (RF-XGB). The Kaggle Credit Risk Dataset is preprocessed using duplicate remover, missing value imputer, outlier capping, categorical encoding, stratified data splitting and SMOTE data balancing. The performance of the Hybrid RF-XGBoost model is validated through experimental results with accuracy of 93.14%, precision of 94.19%, recall of 73.13%, F1 of 82.33%, ROC-AUC of 93.28%, and PR-AUC of 88.48%, for the default prediction task. In order to increase transparency of the model, explanations of individual predictions and influential credit-risk factors are provided by means of SHAP and LIME. The predicted Probability of Default (PD) is then translated to five risk categories (Very Low Risk through Very High Risk) and combined with Loss Given Default (LGD), Exposure at Default (EAD), Expected Loss (EL), risk-weighted assets (RWA) and capital requirements. Additionally, a stress testing process is conducted to assess for possible rise in credit losses during adverse conditions. The framework therefore offers a holistic solution which integrates predictive accuracy with explanations, borrower-level risk stratification and Basel III oriented risk assessment. The findings suggest that combining XAI with ensemble credit risk models can boost the transparency and usability of automated banking credit decisions.

The prevalence, diagnostic complexity, and visual similarity of nail disorders in different pathological conditions make them a widespread healthcare challenge that requires early and accurate identification to support timely intervention and improve healthcare accessibility, particularly in environments with limited access to specialized dermatological services. The performance of different deep learning approaches in automated nail disease classification has been better, but the existing convolutional neural network (CNN)-based methods remain constrained by limited feature representation, insufficient generalization capability, class imbalance, and lack of model interpretability. Hence, the purpose of this work is to create a novel Hybrid EfficientNet–Swin Transformer Network (HEFT-Net) that relies on medical images for automated multi-class nail disease diagnosis; EfficientNet-based convolutional feature extraction is combined with Swin Transformer-based global contextual representation learning in the proposed network to improve the ability to differentiate between different/complex nail disease patterns. The proposed HEFT-Net also incorporates attention-based feature fusion for improved feature extraction (strictly via adaptive combination of complementary representations and self-supervised learning) under limited image availability; focal loss optimization is also considered for better handling of the issue of imbalance among disease categories. Additionally, the proposed framework incorporates an explainable artificial intelligence (XAI) module for visual interpretation of disease-related regions, as well as to improve clinical transparency. Performance evaluation of the new framework was done for multi-class classification of nail diseases, where it performed exceedingly well in diagnostic capability compared with traditional CNN-based approaches. The proposed HEFT-Net recorded around 92.5 % accuracy, 92.1 % precision, 91.8 % recall, and 91.9% F1-score values. Therefore, the robustness, interpretability, and efficiency of the proposed framework as a computer-aided diagnostic framework were demonstrated, making it a significant potential for automated nail disease screening and intelligent healthcare applications.

Artificial Intelligence (AI) is gradually changing the landscape of product management by improving how PMs gather, process, and evaluate data and options to manage products across their lifecycle. This review explores the new paradigm of Human-AI collaboration in the product management domain and looks at how AI functions effectively on the side of the product manager. It merges a variety of machine learning, predictive analytics, NLP, recommender systems, generative AI, and emerging agentic AI applications in customer and market discovery, product ideation, strategy and prioritization, road mapping, product development, product launch, and post-launch optimization. Specific focus is placed on task distribution, responsibilities and decision-making power between product manager and AI system. The study suggests that AI can offer many benefits in areas such as data analysis, pattern recognition, prediction, information synthesis, content creation, recommendations, and automation, while human involvement is still crucial for aspects of context interpretation, strategic decision-making, creativity, empathy, ethical considerations, and accountability. Yet, collaboration is hindered by issues such as algorithmic bias, hallucination, opacity, automation bias, over-reliance, privacy issues, deskilling and unclear accountability. The review thus suggests a framework of integration between product lifecycle activities, AI capabilities, human expertise, collaboration mechanisms, governance and organizational outcomes. The framework also emphasizes how product managers will be moving away from AI-enabled product management and toward agentic systems where product managers will take on more supervisory, orchestrating, evaluating and governing roles. Lastly, the review pinpoints research priorities related to human-AI task allocation, trust calibration, autonomous agents, measurement of collaboration, responsible AI, and end-to-end empirical evaluation.