State/event fault trees provide an expressive formalism for modeling software-controlled and dynamic safety-critical systems, but their analysis may suffer from state-space explosion. Building on the top-down reachability perspective proposed for state/event fault trees and on the combinatorial insight of conservative Petri-net state-space estimation, this paper addresses the related problem of estimating the state-space size before constructing the reachability graph. The proposed method formulates the estimation as a combinatorial counting problem: the initial universe of possible configurations is represented as the Cartesian product of component-state sets, whereas known unreachable state combinations are treated as exceptions. The Subtraction Principle is used to remove configurations containing these exceptions, and the Inclusion-Exclusion Principle is applied to avoid over-subtraction when exceptions overlap. The method can be viewed as a state/event-fault-tree-specific refinement of conservative-Petri-net state-space size estimation: it preserves the structural counting advantage while incorporating mutual-exclusion constraints between component states. If the supplied exclusion constraints are sound and complete, the computation gives the exact count; if they are sound but incomplete, it gives an upper-bound estimate. The formulation provides a compact and a-priori way to reason about the expected size of a state/event fault tree state space before full reachability generation is attempted.

Artificial Intelligence (AI) has achieved remarkable progress through deep learning, enabling machines to excel at perception-based tasks such as image recognition and natural language processing. However, these purely data-driven systems remain limited in reasoning, explainability, and generalization. Conversely, symbolic AI excels at logical inference but lacks adaptability to complex, unstructured data. This research bridges these paradigms by developing a unified NeuroSymbolic AI (NSAI) framework that integrates neural perception modules with symbolic reasoning engines through a differentiable representation alignment layer. The proposed model was implemented and tested on benchmark datasets—CLEVR, bAbI, ConceptNet, and MedQA—to evaluate its performance in terms of accuracy, data efficiency, and interpretability. Experimental results demonstrated that NSAI achieved an average of 31% higher reasoning accuracy, 35% improvement in explainability, and 28% reduction in training data requirements compared to traditional neural architectures. The study establishes that integrating symbolic logic constraints enhances both the interpretability and trustworthiness of AI systems without compromising predictive performance. By aligning perception and reasoning, the proposed framework moves toward creating transparent, explainable, and domain-adaptive intelligent systems, marking a significant step toward human-like artificial general intelligence and sustainable, trustworthy AI.

The major problems that SMEs experience in carrying out their financial management tasks include fragmentation of accounting system, lack of technological capacity and lack of intelligent financial decision making tools. Even though cloud computing and artificial intelligence technologies have individually contributed significantly towards the development of effective financial management strategies, there still lacks a comprehensive solution that brings together automated accounting, financial analysis and intelligent decision making. In this research paper, the proposal of a Cloud Based Intelligent Financial Management System (CBIFM) is discussed where cloud computing, cloud accounting, AI and financial analytics have been combined within a single platform to help SMEs in their financial management. Some of the functionalities that can be achieved using CBIFM include automated transactions processing, budget management, expense tracking, cash flow predictions and dashboard visualization among others. The proposed framework incorporates cloud based financial management modules, artificial intelligence engines and databases. Performance of the proposed framework was evaluated using the Accounting Data for Financial Management dataset while comparing its performance with a conventional cloud accounting system using metrics including financial reporting accuracy, accuracy in predicting cash flows, effectiveness of budget usage, processing time of transactions, and response time of the system. The experimental results show that the suggested CBIFM framework was successful in reaching an accuracy of 97.6% in financial reporting, 95.8% in cash flow prediction, and 96.3% in budget utilization, while decreasing the transaction processing time from 245 ms to 168 ms. It can be said that the integration of cloud computing along with AI-based financial analysis leads to a more effective financial management and decision-making process, which will help ensure sustainability of SMEs.

Effective teamwork is essential in Agile software development, so software engineering education now often includes Agile and DevOps practices to help students work well together. Despite this trend, there is limited empirical evidence regarding the progression of teamwork quality across successive Agile sprint iterations in undergraduate software engineering projects and its relationship to sprint performance. This study looks at how teamwork quality evolves during Agile sprints and how it relates to sprint performance in an undergraduate Agile and DevOps course. The empirical study involved 120 final-year undergraduate students working in 14 Agile teams across three successive one-week sprints. Teamwork quality was assessed after each sprint using a 34-item questionnaire derived from the Teamwork Quality and Big Five Teamwork models, while sprint performance was assessed separately using sprint execution measures. Factor analysis resulted in three interpretable components: Leadership Style, Working Environment, and Organizational Support, which together accounted for 41.89% of the variance. The components demonstrated acceptable internal consistency, with Cronbach’s alpha values ranging from 0.765 to 0.997. Teamwork quality also showed strong consistency across successive sprint assessments, with correlations of 0.851 between the first and second sprints and 0.864 between the second and third sprints. The results also showed that as teamwork quality improved, sprint execution improved. Overall, the findings suggest that repeated Agile and DevOps practices enable students to develop better teamwork, and stronger teamwork is linked to better sprint performance. This study offers evidence for including regular teamwork assessments in Agile and DevOps education and highlights key areas that can help educators support teamwork growth in student teams.

Cloud computing has become the fundamental platform for offering scalable and on-demand computing services. However, the widespread use of cloud computing technology makes it vulnerable to cyber-attacks. Conventional intrusion detection techniques are prone to low detection accuracy because of redundant feature selection and higher computation complexity. In this paper, a Probabilistic Optimized Feature Voting Classification (POFVC) model is proposed to address the problem of intrusion detection in cloud computing. The proposed POFVC model incorporates data pre-processing, optimized feature selection, weighted feature voting, and probabilistic classification to increase attack detection efficiency and minimize the impact of irrelevant features on the system. The relevance of the features is calculated by means of Information Gain, Mutual Information, and Gini Importance, which allows selecting the most relevant network attributes. The proposed approach is evaluated using the UNSW-NB15 standard dataset and compared with conventional machine learning algorithms based on Accuracy, Precision, Recall, F1-Score, and ROC-AUC metrics. The experimental results show that the proposed model performs better in terms of accuracy and computational complexity reduction.

In teleoperated surgery, robotic telesurgery uses the concept of master-slave robotic configuration to provide remote surgical intervention; however, the safety of teleoperation is influenced by factors such as communication delay, jitter, packet loss, and varying network quality. The literature on telesurgery techniques based on digital twins has largely focused on virtual synchronization, visual feedback, buffering, replay, or fixed-delay operations. In this study, we propose a Network State-Aware Digital Twin Trajectory Prediction and Safety-Constrained Compensation for Robotic Telesurgery. Within the proposed framework, the characteristics of the surgical robot kinematics and network communication, such as end-to-end delay, jitter, packet loss ratio, and bandwidth state, are utilized to predict the end-effector trajectory of the future delayed teleoperation. This digital twin augments the state of the robot and performs command compensation based on workspace constraints, confidence estimation of predictions, and velocity limitation while ensuring safety. We applied our approach to a simulation of a teleoperated robotic surgery environment while considering surgical kinematics and network impairments. The performability of telesurgical robotics is enhanced through the joint improvement of trajectory accuracy, synchronization, command/service continuity, and safety in the presence of delays, jitters, and packet loss using the proposed approach.