Nonprofit Organizations (NPOs) are adopting technology to improve their quality of services, scale up, or reduce operation costs. However, due to the heterogeneity of systems they use, NPOs face system-integration challenges when collaborating with other organizations. Middleware is an intermediary software that assists dissimilar systems in working together. This paper explores middleware applications, opportunities, and challenges within the sector. It extensively reviewed the current state of research on middleware usage in the nonprofit sector for all papers published in Scopus and Web of Science (WoS) until 2023. Out of 127 papers returned, only 31 remained after removing duplicates, invalid entries, and out-of-scope publications. Then, we synthesized insights from a thorough survey of these selected papers. In light of the survey results, we observed that NPOs primarily use middleware in a few of the Sustainable Development Goals (SDGs), namely, health (SDG 3), NPO operations (SDG 8 and 9), NPO collaborations (SDG 17), development of sustainable cities (SDG 11), security and disaster management (SDG 16), and education (SDG 4). We also identified several challenges related to using middleware in the nonprofit sector, which include privacy, security, system development and performance, data processing and transfer, and volunteer attrition.

Cognitive cities integrate IoT, AI, and machine learning to enhance urban efficiency, yet lack dedicated cybersecurity frameworks. In this paper, we develop a taxonomy of cognitive city cybersecurity challenges through multivocal literature review (86 academic papers, 29 grey literature sources) and prioritize them using Fuzzy Analytic Hierarchy Process (FAHP) with 23 experts. The taxonomy identifies six challenge domains: resource constraints, system complexity and integration, security design deficiencies, data management and advanced technology challenges, human and organizational factors, and economic and regulatory constraints. FAHP analysis ranks resource constraints most critical (0.239), with computational limitations as top concern (0.107). This framework provides a foundation for understanding and addressing cognitive cities' complex security landscape, supporting holistic approaches by integrating technical, organizational, and policy dimensions.

Context: Agile methodologies have gained immense popularity in software development organizations for their numerous advantages, like increased flexibility and better customer satisfaction. However, integrating security measures into Agile practices presents significant challenges and complexities. Objectives: The primary goal is to identify and analyze challenges associated with developing secure software in Agile environments. This entails evaluating the validity of these challenges concerning Agile principles and secure software assurance practices. Methodology: A systematic literature review methodology was used to identify these challenges. Fifteen publications were scrutinized to identify potential challenges, resulting in the identification of 26 challenges. Results: The literature review revealed significant challenges in creating secure software within Agile contexts. Five were invalidated among the 26 potential challenges, while the remaining 21 were deemed valid and categorized. The challenges span various aspects of the development process, indicating the multifaceted nature of the issue. Conclusion: Addressing the identified challenges necessitates a dual focus on Agile methodologies and the requisites for secure software development. By acknowledging and confronting these challenges, practitioners can devise effective strategies to enhance the security of Agile software development practices. Furthermore, the findings serve as a foundation for formulating best practices in secure software development within Agile frameworks, thereby facilitating the advancement of secure software engineering methodologies.

Software maturity models can be utilized by organizations to evaluate and enhance their development processes. Established and recognized models such as the Capability Maturity Model Integrated (CMMI) and ISO/IEC 15504 (Software Process Improvement and Capability Determination (SPICE)) have proven their value. However, many new software maturity models exist, and their quality and potential value remain questionable until they are properly assessed before adoption. Without such an assessment, organizations can implement poor or ineffective models, resulting in wasted resources and failed improvement initiatives. Our research aims to address this challenge by developing a measurement framework based on ISO/IEC 15504-3 standards to assess the quality of developed software maturity models. We derived our quality assessment criteria through literature analysis, analyzing four main categories: basic model information, structural design, assessment methods, and implementation support. After developing this framework, we validated it with expert reviews to assess its design and usability and through a series of case studies. Feedback from academics and industry practitioners confirmed the framework’s utility, especially recognizing its clear structure and comprehensiveness of evaluation criteria. Case studies also revealed the framework’s effectiveness in identifying strengths and areas of improvement, finding that evaluated models had quality scores ranging from 83.3% to 93.2%. Our study enhances software maturity models’ practical utility and adoption across different software contexts, providing professionals and academics with a structured way to evaluate and enhance maturity models.

Introduction: Cognitive cities integrate autonomous decision-making, machine learning, and Internet of Things networks to create adaptive urban environments. These technologies introduce cybersecurity challenges that existing enterprise and smart city frameworks do not adequately address. This study develops an evidence-based readiness model to assess and advance cybersecurity preparedness in cognitive cities. Methods: A three-phase methodology develops the model. Phase 1 elicits cybersecurity best practices through a focused review mapped to documented cognitive city security challenges, Phase 2 organizes the practices into knowledge areas and readiness levels, and Phase 3 evaluates the result through a panel of ten cybersecurity experts and an initial application case study in a large-scale real-world cognitive city. Results: The resulting Cognitive City Cybersecurity Readiness Index (C3RI) comprises 117 best practices organized into eight Cybersecurity Knowledge Areas and distributed across five progressive readiness levels (Foundational, Structured, Intelligent, Autonomous, and Adaptive). A Motorola-based assessment scores each practice across Approach, Deployment, and Results, and a 7.0 advancement gate governs progression between levels. In the case study, level means range from 7.04 to 7.52 and exceed the gate at every readiness level. The expert review supports the clarity, coverage, and practical applicability of the model.