Antibiotic contamination in aquatic ecosystems drives the dissemination of antibiotic-resistant bacteria (ARB) and antibiotic resistance genes (ARGs). While heterogeneous photocatalysis using metal-free porous organic polymers (POPs) and covalent organic frameworks (COFs) offers a sustainable remediation strategy, current literature predominantly focuses on parent pollutant disappearance rather than fundamental degradation bottlenecks. This review critically addresses this gap by evaluating the inherent molecular trade-offs governing COF/POP design, the mechanistic pathways of real-water matrix interference, and the ecotoxicity of transformation products. Furthermore, we provide a critical Life Cycle Assessment (LCA) and Techno-Economic Analysis (TEA) perspective on synthesis versus performance, concluding with a standardized reporting checklist to guide future research toward practical, pilot-scale implementation.

Modern enterprises increasingly depend on interconnected information systems, cloud services, software applications, third party platforms, and digital business processes. This dependence creates exposure to cyber incidents, abnormal behavior, service failures, and operational disruption. Existing cybersecurity approaches often focus on threat identification without translating technical risk into business impact or actionable response priorities. This study proposes an Explainable AI Driven Decision Intelligence framework for cyber resilient enterprise operations under uncertainty. The framework integrates cybersecurity telemetry, enterprise context, dual stream predictive modeling, service criticality, dependency analysis, Business Impact Score assessment, explainable reasoning, resource constrained prioritization, and resilience response. The predictive layer processes temporal technical signals and contextual severity attributes to estimate malicious activity and disruption probabilities. TreeSHAP and rule-based reasoning provide interpretable factors, affected services, estimated consequences, and recommended actions. The framework is designed for comparative evaluation against XGBoost, SVM, standard BiLSTM, and conventional threshold alerting using predictive, operational, explainability, resource allocation, and uncertainty measures. The study provides an integrated approach for connecting cyber threat prediction with transparent operational decisions and enterprise resilience.

Healthcare organizations face challenges in workforce allocation because fixed staffing approaches may not adequately reflect changing demand, workflow bottlenecks, staff qualifications, workload, and operational constraints. This study develops an Explainable Process Intelligence framework for Workforce Allocation and Operational Governance in Digital Healthcare Systems to connect healthcare process analysis with transparent staff-to-task recommendations. A quantitative, simulation-based design was used with a simulated healthcare event log and workforce records. The framework integrates process discovery, conformance analysis, bottleneck identification, demand estimation, constrained workforce allocation, explainable artificial intelligence, and governance monitoring. The allocation model was compared with a historical baseline roster using identical demand periods and eligibility rules. The proposed approach increased demand coverage from 78.1% to 88.7%, reduced median waiting time from 1.70 to 1.29 hours, decreased workload coefficient of variation from 0.31 to 0.21, and reduced overtime exposure from 12.4% to 8.9%. Explanations were available for accepted and rejected recommendations, while human reviewers retained authority to modify or reject recommendations. The findings indicate that integrating process intelligence, explainable allocation, and governance can support more transparent workforce planning, workload management, and operational oversight in digital healthcare environments.