Exploring SAFe Framework Adoption for Autism-Centered Remote Engineering with Secure CI/CD and Containerized Microservices Deployment

To meet the ever-changing market demands, more efficient strategies are required due to the complexity of today's software development. Because it can combine Agile concepts with organizational structures to manage large-scale projects involving multiple teams and departments, the Scaled Agile Framework (SAFe) has been widely adopted. This research investigates how Flow Metrics from the SAFe framework can be used as a tool to improve productivity, efficiency and alignment of the software development process. This research examines how measurements such as flow velocity, flow efficiency, flow time, and flow load can be used to pinpoint bottlenecks, streamline processes, and improve the value delivered to clients. This research uses a qualitative methodology to examine the use of Flow Metrics in two interdependent Program Increments (PIs) by combining interviews with Agile practitioners and a literature survey. The analysis highlights how the Continuous Delivery (CD) Pipeline, backlog synchronization, and program increment planning—three critical components of SAFe—interact with each other. By highlighting the importance of metrics-based performance evaluation, collaborative planning, and continuous improvement, the findings of this research are intended to offer a useful foundation for businesses looking to implement SAFe for large-scale software development. This research advances a more comprehensive understanding of how SAFe and Flow Metrics can facilitate increasingly complex software development while guaranteeing adaptability to changing business needs.


 
 
 
 Several years ago software development has become critical to the global market. In the past decade, as a reflection of globalization, software companies began to distribute their development processes in different places, creating distributed software development (DSD). With the growing of Distributed Software Development, organizations have attempt to sketch the best possible development structure, in order to improve productivity and quality. The aim of this paper is to present a Systematic Review of Literature to identify which ways of collaboration are commonly used by software organizations where teams are temporal and geographically dispersed, with also different native languages and culture. Further, the research was based on the basic life cycle of the traditional development, and where phases of the project are performed: onsite, distributed/offshore and multi-site. The systematic review have examined 868 papers published between 2000 and 2010.
 
 
 

This article presents a comprehensive analysis of integration patterns for Salesforce-centric enterprise architectures in multi-cloud environments. It examines the design considerations, implementation strategies, and architectural patterns that enable seamless data flow between Salesforce and other major cloud platforms. The article explores both synchronous and asynchronous integration approaches, with particular emphasis on event-driven architectures and their application in distributed systems. The article evaluates integration technologies native to the Salesforce ecosystem, including MuleSoft, Salesforce Connect, and Platform Events, alongside considerations for RESTful and SOAP API implementations. Security aspects of cross-cloud integration are addressed through examination of authentication mechanisms, data encryption strategies, and compliance frameworks. Furthermore, the article presents resilience engineering techniques for ensuring fault tolerance in integration solutions. The article provides enterprise architects and Salesforce professionals with a structured methodology for designing robust multi-cloud integration strategies that balance performance, scalability, and maintainability while addressing the inherent challenges of distributed cloud environments.

The exponential growth of social media platforms has created new avenues for communication, brand engagement, and information sharing. However, this digital expansion has also increased vulnerabilities to identity fraud, phishing, misinformation, and a wide range of cyber threats. These risks are especially critical for industries and organizations that operate under strict regulatory and compliance frameworks, including finance, healthcare, and government sectors. This paper proposes the development of compliance-oriented social media risk management models tailored to address identity fraud and cyber threats in real-time. The proposed models integrate regulatory requirements with cybersecurity best practices to create a structured approach for identifying, assessing, and mitigating risks originating from social media activities. The models utilize advanced data analytics, natural language processing (NLP), and machine learning algorithms to detect anomalies, suspicious patterns, and unauthorized access or impersonation attempts. Compliance alignment is achieved by embedding regulatory controls such as GDPR, HIPAA, and ISO/IEC 27001 into the risk assessment matrix to ensure accountability, data privacy, and operational integrity. Furthermore, the framework incorporates real-time monitoring, automated incident response, and threat intelligence integration to enhance situational awareness and reduce response latency. A modular design allows for scalability and adaptability across sectors and platforms. The effectiveness of the proposed models is demonstrated through simulation scenarios involving identity theft and brand impersonation attacks on social media. Results show improved threat detection accuracy, reduced false positives, and enhanced compliance adherence. By bridging the gap between regulatory compliance and cybersecurity resilience, the study offers a proactive solution to emerging threats in the digital communication landscape. This research underscores the importance of incorporating compliance and governance principles into cybersecurity strategies for social media, thus enabling organizations to safeguard their digital presence, protect sensitive information, and maintain public trust.

Digital Twins are becoming central enablers of Europe’s digital and green transitions, yet their data-intensive and autonomous nature exposes them to one of the most complex regulatory environments in the world. This article presents a comprehensive scoping review of how six principal European digital laws—the General Data Protection Regulation, Data Governance Act, Data Act, Artificial Intelligence Act, NIS2 Directive, and Cyber Resilience Act—jointly govern the design, deployment, and operation of Digital Twin systems. Building on the PRISMA-ScR methodology, the study constructs a Unified Digital Twin Compliance Framework (UDTCF) that consolidates overlapping obligations across data governance, privacy, cybersecurity, transparency, interoperability, and ethical responsibility. The framework is operationalised through a Digital Twin Compliance Evaluation Matrix (DTCEM) that enables qualitative assessment of compliance maturity in research and innovation projects. Applying these tools to representative European cases in Smart Cities, Industrial Manufacturing, Transportation, and Energy Systems reveals strong convergence in data governance, security, and interoperability, but also persistent gaps in the transparency, explainability, and accountability of AI-driven components. The findings demonstrate that European digital legislation forms a coherent yet fragmented ecosystem that increasingly requires integration through compliance-by-design methodologies. The article concludes that Digital Twins can act not only as regulated technologies but also as compliance infrastructures themselves, embedding legal, ethical, and technical safeguards that reinforce Europe’s vision for trustworthy, resilient, and human-centric digital transformation.

Ensuring secure and energy‐efficient authentication of resource‐constrained devices has become a critical challenge owing to the rapid expansion of the Internet of Things (IoT) ecosystem. Physically unclonable functions (PUFs) that exploit inherent manufacturing variations to generate device‐unique keys have emerged as a promising hardware‐based security primitive. In this study, a PUF architecture is proposed that utilizes multi‐level programming of flash memory capacitors. Reliable and reproducible binary responses are generated across multiple programmed states by extracting flat‐band voltage variations from capacitance–voltage measurements. Performance is evaluated using the uniformity, P ‐value, inter‐Hamming distance, and intra‐Hamming distance, which indicated strong randomness, uniqueness, and stability. To further enhance the entropy and cryptographic strength, a hash‐based message authentication code (HMAC)‐based key derivation function is integrated, which transformed the PUF outputs into high‐entropy pseudorandom keys. The final architecture supported key generation with zero standby power and is compatible with commercially available memory structures, thereby enabling low‐cost and scalable deployment in secure IoT systems. These results highlight the potential of multi‐level memory‐based PUFs as a lightweight and robust solution for next‐generation hardware security.

One of the major challenges in commercializable micro-nano systems development is the high cost and turnaround that are incurred through multiple product-optimization iterations and expensive fabrication processes for specific systems. Development of complex and heterogeneous micro-nano systems, that are only possible through assembly and not by conventional surface machining approaches, are further impeded by lack of standard design rules and off-the-shelf robotic manipulation systems. Dedicated hardware and system specific component designs, although possible, are not commercially viable for addressing the wide range of opportunities that exists in the prevailing micro-nano domain. In this paper, we present an alternative and holistic top-down approach for micro-nano manufacturing using modular part designs and flexible assembly systems. We incorporate, seamlessly, multiple novel algorithms related to microrobotics and scaling of physics, obtained both analytically as well as experimentally; in order to predict, track and control the uncertainty propagation in a typical manufacturing process, in micro-nano scale, throughout production steps including design, machining, setup, assembly, testing etc. We demonstrate, through multiple examples, the implementation of the proposed framework in micro-nano scale manufacturing.

In recent years, agile software development methods have gained increasing popularity. Distributed software development have been becoming a common business reality also. Software development organizations are striving to blend agile development methods like Scrum and distributed development to reap the benefits of both. However, agile and distributed development approaches differ significantly in their key tenets. While agile methods mainly rely on informal processes to facilitate coordination, distributed development typically relies on formal mechanisms. This paper aims at implementing modular design of software products to identify feature clusters that can be implemented co-located to minimize the communication needs between distributed teams. Presented method is evaluated with simulations that demonstrate how this method can produce 1) lower-risk feasible plans, 2) balanced workload on teams, and 3) provide higher quality feature distributions. Finally, the paper analyzes benefits and issues from the use of this method.Keywordsdistributed software developmentagile release planning

As more software development organizations are increasingly distributing their operations spatially, information systems development researchers are taking perspectives such as transactions costs and resource dependency to explain the effects of spatial distribution on coordination. This paper argues that these perspectives are limited because they do not address all the key relationships between software development resources in a unified and systemic manner. The interactions between people, information, and technology, which are the key software development resources, are characterized by four key relationships – interdependencies, uncertainties, conflicts, and technology representations. Based on the premise that spatial distribution of the resources exposes their relationships to direct environmental stimuli, the paper proposes a complementary resource relationships perspective to explain the spatial effects on the dynamics of coordination. An empirical example of software development distributed between sites in USA and Republic of Ireland is used to illustrate these issues. Analyzing the effects of spatial distribution in terms of resource relationships leads to a more systemic, in-depth and phenomenological understanding of the dynamics of coordination. Implications of this perspective for the analysis of coordination in distributed software development are discussed.

Fault detection, isolation and recovery (FDIR) functions are essential components of complex engineering systems. The design, validation, implementation, deployment and maintenance of FDIR systems are extremely information intensive tests requiring in-depth knowledge of the engineering system. The paper gives an overview of a model-integrated toolset supporting FDIR from design, into post deployment. The toolset is used for development of large, complex spacecraft systems during the engineering design phase.

It is suggested to build a 9-level pencil-shaped (PS) inverter with a minimum of two DC supplies. Low component multilevel inverters (MLIs) employ extra conducting switches in addition to the DC supply. • This suggested MLI is more effective since it uses fewer power electronic switches. The architecture enables a higher voltage level inverter with a modular design that uses fewer DC supplies and does it appropriately without the need for an additional H-bridge circuit. Additionally, a control plan is suggested for distributing loads equally in a nine-level architecture. • The potential for equitable load distribution in higher-level designs and fundamental frequency switching of switches carrying larger voltage strains are also being looked into. Through simulations and experimental experiments, several notions are validated. The simplified formulae for the suggested inverter parameters are built to determine their optimal capabilities. • Additionally, an optimal PSMLI design is created using the expanded model of the suggested architecture to reduce the inverter's total standing voltage (TSV). Comparison studies are provided to support the proposed inverter and show its benefits over recent MLIs of comparable sorts. • Through accurate simulation and lab tests, the MLI was able to achieve a greater efficiency of 95.54%. The improved 17-level version of PSMLI, on the other hand, achieved IEEE 519 standard performance with total harmonic distortions (THD) of just 5.15%. Key Word: Multi-level inverter, power electronics, switched capacitor, voltage boost, nearest level control.

IT Systems in Pharmaceutical Operations: Ensuring Data Integrity, Compliance, and Public Safety

Security in wireless body area networks via anonymous authentication: Comprehensive literature review, scheme classification, and future challenges

Cloud Computing provides a new landscape for application development and infrastructure management, and introduces a dynamic thread space due to its distributed and elastic nature. Classic security methods are not enough to fight off cyber-threats despite modern agile and cloud-native environments. This paper explores how to integrate Artificial Intelligence (AI) in an efficient manner across the Software Development Life-Cycle (SDLC) that would enhance cloud security. Add more-attractive threat detection, anomaly discovery, and policy-based automation into the development, testing, deployment, and runtime life cycles, so that you can detect threats more quickly, as well as more proactively break down risk. In this work we explore the state of the art and tools of security automation, with a particular focus on AI models for anomaly detection for security and case demonstrations of its real applications. A holistic framework for AI-driven security orchestration in Cloud application Pipelines: Towards Reducing Attack Surfaces, Compliance and Resilience.

To aid the development and deployment of complex software systems, techniques are needed to ensure effective communication between human factors and cognitive engineers, the software system engineers, and other stakeholders (e.g., users of the system under development, project managers). In this paper, we present a novel approach to addressing this need. Our approach is based on the view that systems development is a holistic process and that human factors and cognitive engineering can best be integrated as a component of that process, rather than explicitly altering the systems engineering process to incorporate human factors and cognitive engineering. We describe this approach in more detail, and present a tool that embodies this approach by facilitating communication among stakeholders by leveraging existing software tools used in systems development.