Search Results (857)

A system’s architectural design plays a vital role in its quality, since quality attributes are system-wide and impacted by the architecture. The evaluation of a software architecture aims to assess its suitability for the purpose of the system, making this practice a core component in the software quality assessment toolkit. Experience shows that evaluating an architecture is not straightforward, and key practical guidance for evaluation progression is a major challenge in real-world cases. This article presents a software architecture evaluation guiding and progression assessment mechanism based on the identification of five essential elements: Architecture Description, Quality Attributes, Business Goals, Architecture Decisions, and Evaluation Adoption. To describe them, this work proposes an extension of the SEMAT Kernel, where each evaluation “essential” is represented as an Alpha with a set of States that depict the (healthy) progression of architecture evaluations. The practicality and usefulness of the approach is assessed with two case studies derived from two previously executed real-world architecture evaluations. The results suggest that when using this conceptualization and description to guide and assess architecture reviews in legacy systems under classic development and maintenance approaches, architects and stakeholders can better understand how to guide and audit the progression of an architecture review; have a ground for reporting results; and regain focus on the evaluation in some scenarios. A key directly derived future research direction is to evaluate the suitability of the proposal in agile-based development contexts. The authors expect that a wider use of this principled definition of the key elements for software architecture evaluations will provide practical and concrete guidance to evaluators, allowing stakeholders to assess specific evaluation efforts, and to eventually improve teaching and learning of the evaluation practice. Full article

Sustainability has become a strategic priority in the hospitality sector, particularly in luxury hotels where environmental responsibility must be balanced with high service quality. This study explores hotel managers’ perceptions and experiences of implementing environmentally friendly practices in five-star hotels in Gauteng, South Africa. A qualitative research approach, guided by a constructivist paradigm, was employed using semi-structured interviews with seventeen middle-level managers from major departments in the hotels. Data were manually and software-coded, and thematic analysis produced nine interrelated themes: Adoption Culture, Collaboration Networks, Consumption Tracking, Guest Revenue Drivers, Operational Shifts, Operational Prioritisation, Staff Enablement, Structural Constraints, and Valued Pragmatism. The findings indicate that managers generally perceive sustainability as both an ethical responsibility and a business imperative, particularly in relation to brand reputation, guest expectations, and cost efficiency. However, implementation is constrained by infrastructural instability, high initial investment costs, limited supplier availability, and occasional resistance from staff and guests. The study highlights the importance of embedding sustainability within governance systems, staff practices, and organisational culture to support long-term adoption. This research offers context-specific insights into sustainability implementation in South African luxury hotels and provides practical value for hotel managers, policymakers, and sustainability stakeholders operating in resource-constrained environments. Full article

This article examines the impact of students’ prior experience with English on their academic success in a university English course. The study is based on a survey conducted among students majoring in Computer Science, Business Information Technology (BIT), and Software Technology and Design (STD) at the Faculty of Mathematics and Informatics (FMI), University of Plovdiv, at the beginning of their general English language course. We focus on students’ self-assessed language competence at the start of the course and examine how these self-assessments correspond to their actual test results. Using high-performance machine learning methods, we identify background factors that influence academic achievement, including the number of years spent learning English, the type of high school attended, and informal exposure to English. The findings aim to support more effective and tailored approaches to teaching English in technical and scientific disciplines. Full article

The scientific study of affect has been historically characterized by a profound lack of terminological consensus, leading to a state of conceptual fragmentation that persists in psychology, neuroscience and many other fields. This ambiguity is not merely an academic concern; it has significant consequences for the development of artificial intelligence (AI) systems designed to recognize and respond to human “emotions”. In fact, it has an influence on the entire field of affective computing. The problem is obvious. Without a distinct definition of “emotion” it is difficult to train an algorithm to recognize it. The Walla Emotion Model, also known as the ESCAPE (Emotions Convey Affective Processing Effects) model, provides a potentially helpful and neurobiologically grounded framework to resolve this impasse and to improve any discourse about it, for businesses and even lawmakers aiming at healthy societies. By establishing clear, non-overlapping definitions for affective processing, feelings, and emotions, this model offers a path toward more precise research and more ethically sound affective computing including AI-driven “emotion” recognition. It introduces a concept that allows for the detection of incongruences between internal states and external signals with a very clear terminology supporting understandable communication. This is critical for identifying feigned or socially masked inner affective states, a challenge that traditional “face-reading” AI models frequently fail to address. Even tone of voice and body postures as well as gestures can be and are often voluntarily modified. Through the separation of subcortical affective processing (evaluation of valence; neural activity) from subjective experience (feeling) and external communication (emotion), the Walla model provides a helpful framework for AI-designs meant to have the capacity to infer an internal affective state from collected signals in the wild bypassing verbal self-report. This paper is purely theoretical; it does not provide any algorithm models or other distinct suggestions to train a software package. Its main purpose is the introduction of a new emotion model, particularly a new terminology that is considered helpful in order to proceed with this endeavor. It is considered important to first enable the clearest-possible form of communication about anything related to the term emotion across all disciplines dealing with it. Only then can progress be made. Full article

Textile and fashion designers play a strategic role in sustainable development within the textile chain. Several studies highlight the decision-making role of designers, emphasizing how their choices influence the entire production sector. The aim of this research is to examine how design decisions within the Portuguese textile and fashion industry influence the implementation of sustainable development principles by exploring designers’ perceptions, practices, and the challenges they encounter throughout the design process. To achieve the proposed goal, semi-structured interviews were conducted with 11 designers from the industry. The collected data were qualitatively evaluated using NVivo software, highlighting the complexity of incorporating sustainability into the design process. The findings revealed that daily challenges are primarily related to fashion business models, greenwashing, limited knowledge of raw materials and finishing processes, cost constraints, lack of transparency and traceability in the supply chain, and low consumer awareness. By examining both the conceptual understanding and practical application of sustainability in the design process, this research provides strategic lines into designers’ decision-making processes, highlights barriers to sustainable practice, and underscores the importance of design education. The study contributes to academic debate and identifies opportunities for advancing sustainable practices and circularity in the textile and fashion industry, in alignment with the Sustainable Development Goals, especially SDGs 9, 12, and 13, to transform the current industrial and consumption models. Full article

Business Intelligence (BI) comprises methods and technologies for collecting, organizing, and analyzing data to support managerial decision-making. This study presents a systematic literature review with a two-tier scope: first, identifying the most widely adopted BI tools across organizational contexts, and second, examining the specific application of Microsoft Power BI within the civil construction sector. The review followed the PRISMA guidelines and was complemented by the snowball sampling technique. A total of 81 articles published between 2015 and 2025 were analyzed to identify the most used tools, main application sectors, benefits, and challenges in BI adoption. The analysis combines descriptive bibliometric techniques with qualitative content analysis to examine publication trends, tools, application domains, and reported challenges. Results indicate that Power BI, Tableau, and Qlik Sense are the most frequent BI tools, with Power BI standing out for its integration with diverse data sources such as spreadsheets, databases, management software, and cloud platforms, enabling the creation of dashboards. The civil construction, business management, and manufacturing industries show the highest adoption rates, mainly for cost control, performance monitoring, and sustainability indicators. Reported benefits include operational efficiency, process automation, and improved decision-making. However, gaps remain regarding data standardization, interoperability, technological infrastructure, and user resistance. As a contribution, this review advances the existing literature by explicitly distinguishing general BI tool adoption from the sector-specific use of Power BI in civil construction, systematically classifying application domains and revealing limitations in maturity that remain underexplored in prior reviews. Full article

Wi-Fi router security is a real concern for universities and research centers that rely on strong, dependable networks for everything they do. In this study, we took a close look at four popular Wi-Fi router firmwares using open-source tools such as Binwalk, CVE-Bin-Tool, and Semgrep. We carefully examined the file systems, cross-referenced them with the National Vulnerability Database (NVD), and searched for outdated software like BusyBox and OpenSSL. What we found was clear: proprietary firmwares had more Critical and High vulnerabilities, while OpenWrt stood out for being more secure, easier to update, and openly maintained by its community. Our reproducible process automates how we gather evidence and map vulnerabilities, making firmware auditing more practical and trustworthy. These results make a strong case for using open-source firmware as a safer, more manageable choice for institutional networks. Full article

The accelerating climate emergency places the built environment under increasing pressure as both a major source of greenhouse gas emissions and a system highly vulnerable to climate impacts. Buildings contribute substantially to global operational energy use and embodied carbon, while much of the existing stock remains poorly adapted to changing climatic conditions. This paper examines the role of artificial intelligence (AI) in improving energy efficiency, enabling circular material flows, and enhancing resilience across the building lifecycle. Based on a structured synthesis of recent peer-reviewed literature, institutional reports, and documented case examples, the study maps AI applications in design, construction, operation, and end-of-life stages, including generative design, predictive maintenance, digital twins, and construction and demolition waste analytics. The analysis shows how AI can reduce operational energy demand, optimize material use, and support reuse and recycling strategies, while enabling new software-driven business models in the building sector. The paper argues that AI’s effectiveness depends on data availability, interoperability, regulatory alignment, and workforce capabilities, and that its benefits are maximized when integrated with circular economy strategies and supportive policy and financial frameworks. This integrated perspective highlights pathways for reducing emissions and improving the resilience of the built environment under climate stress. Full article

According to Fortune Business Insights, the market share of the Internet of Vehicless is expected to grow from USD 95.62 billion in 2021 to USD 369.61 billion in 2028, at a compound annual growth rate of 21.4%. However, the Internet of Vehicles system still faces several challenges, including regulation, scalability, data management, connectivity, interoperability, privacy, and security. To improve communication security within the Internet of Vehicle system, we have implemented a secure cryptographic algorithm called Optimized Certificateless Key-Encapsulated Encryption, resulting from a fusion of the key-insulated cryptosystem and the cryptographic key-encapsulated mechanism. The formal security analysis of our algorithm using the AVISPA version 1.1 software shows us that our protocol is safe. Informal analysis shows that our algorithm ensures authenticity, confidentiality, integrity, and non-repudiation and resists several other attacks. Our algorithm’s computational and communicational costs are slightly better than those at which it inherits the functionalities. Full article

Previous studies, constrained by the overly rigid stability requirements, often fail to adapt to complex systems and struggle to identify stable outcomes that align with the practical context of multi-agent resource allocation. To address the three-sided matching problem in complex socio-technical and business management systems, this paper proposes a fuzzy stable matching method for three-sided agents under a framework of combinatorial preference relations, integrating network and decision theory. First, we construct a membership function to measure the degree of preference satisfaction between elements of different agents, and then define the concept of fuzzy stability. By incorporating preference satisfaction, we introduce the notion of fuzzy blocking strength and derive the generation conditions for blocking triples and fuzzy stability under the fuzzy stable criterion. Furthermore, we abstract the three-sided matching problem with combined preference relations into a shortest path problem. Second, we prove the equivalence between the shortest path solution and the stable matching outcome. We adopt Dijkstra’s algorithm for problem-solving and derive the time complexity of the algorithm under the pruning strategy. Finally, we apply the proposed model and algorithm to a case study of project assignment in software companies, thereby verifying the feasibility and effectiveness of this three-sided matching method. Compared with existing approaches, the fuzzy stable matching method developed in this study demonstrates distinct advantages in handling preference uncertainty and system complexity. It provides a more universal theoretical tool and computational approach for solving flexible resource allocation problems prevalent in real-world scenarios. Full article

The growing dominance of low-cost carriers (LCCs) in global air transport has intensified the need for airport terminal facilities that reflect their simplified, efficiency-driven operating principles. Traditional Level of Service (LOS) standards, based on International Air Transport Association’s Airport Development Reference Manual (IATA ADRM), were primarily designed for traditional air carriers or full-service network carriers (FSNCs) and may lead to over-dimensioned or misaligned airport terminal facilities when applied to airports with dominance of LCCs. This study presents the first newly developed computational tool called Airport Terminal Facilities Software (ATFS) as a methodological and conceptual advance in airport terminal planning, that integrates LOS guidelines differentiated by airline business models. The methodology integrates spatial–temporal LOS parameters, specific facility capacity formulas, and peak-hour demand calculations of airport terminal facilities. Results from the case study conducted at Pula Airport show substantial differences between IATA and LCC LOS outcomes, i.e., applying LCC LOS guidelines can significantly reduce required areas for the several airport terminal facilities. Findings confirm that new LCC LOS guidelines and the ATFS tool can optimize airport terminal facilities, reduce or reconfigure excessive or empty space, and improve passenger flow efficiency at LCC-dominant airports. Full article

Contemporary business process modeling lacks a systematic framework for converting unstructured requirements into structured models. Traditional manual approaches fail to support integrated lifecycle management from requirements elicitation to iterative model refinement. The gap severely limits the efficiency and accuracy of the alignment between requirements and business process modeling and often leads to costly rework and implementation errors in complex software projects. Therefore, this paper aims to establish a coherent modeling framework from requirements extraction to business process model verification. The framework maintains the traceability and consistency of the unstructured requirements through three tasks: (1) automatic generation of a structured requirements model from textual input to a set of designed prompts of hyperparameter-optimized large language models (LLMs); (2) establishment of a modeling routine to handle the iterative requirements via two sets of formalized mapping rules, a merging algorithm, and a toolkit; (3) detection of the obtained CBPMN model by a static flow error verification algorithm and reachability verification using CPN tools 4.0. A total of 15 sets of comparative experiments with three state-of-the-art automated modeling approaches demonstrate the superiority of our method in generating higher-quality requirements models, while an additional case study with two-step verification proves its validity. Full article

This study examines how major global hotel groups construct sustainability through corporate communication, assessing both the thematic content and the internal coherence of their Environmental-Social-Governance (ESG) narratives. The research question is How do international hotel corporations construct sustainability through their corporate communication and ESG reporting? The research applies qualitative content analysis of sustainability reports from ten international hotel corporations and a four-layer discursive coherence model (performance, operational, narrative, strategic), the study analyses 888 coded quotations and 205 sustainability-theme occurrences in ATLAS.ti version 25, a qualitative data-analysis software. Results show that while measurable, performance-based disclosures dominate—such as digital food-waste monitoring, emissions-intensity reductions, and responsible sourcing—symbolic language remains strategically deployed to reinforce identity, purpose, and legitimacy. Across the sector, sustainability discourse converges around four recurring pillars: environmental performance leadership, community resilience, responsible business governance, and inclusive economic empowerment. The study advances theoretical work on sustainability communication by conceptualizing discursive coherence as an indicator of organizational authenticity and offers actionable insights for enhancing credibility and stakeholder trust in corporate ESG reporting. Full article

Integrating environmental, social, and governance (ESG) reporting with the Sustainable Development Goals (SDGs) is important for achieving corporate sustainability. The rapid evolution of regulations like the Corporate Sustainability Reporting Directive (CSRD), and the fragmented research landscape create uncertainty for strategic planning. This paper addresses the critical gap related to the lack of predictive data into future research trends at the ESG-SDG nexus. The research begins with a bibliometric analysis using two software programs R-Biblioshiny 5.2.0 and VOSviewer 1.6.20, to process data extracted from the Web of Science (Clarivate). Selected key terms regarding sustainability reporting concepts and reporting standards, as well as the engagements of auditors were used to filter the database information. Starting from the bibliometric analysis of 361 publications completed during January 2015–September 2025, the study performs further a quantitative measurement bibliometrics using RStudio 4.5.2 and provides a novel ensemble forecasting model (AutoRegressive Integrated Moving Average, Error, Trend, Seasonal Components, and Linear regression with SDG factors) that cartograph the alignment of the current research field and forecast its evolution. The results reveal that terms regarding reporting “CSRD” and sustainability assurance, “ISSA 5000” are the most dominant research fronts, strongly aligned with SDG 12, 13 and 17. The forecasting model predicts sustained growth in this area. The study contributes by providing a forward-thinking strategic map for researchers, policymakers and businesses, transforming sustainability integration from a compliance task into systematic, data-driven approach for priority setting strategy. Full article

Early detection of breast cancer commonly relies on imaging technologies such as ultrasound, mammography and MRI. Among these, breast ultrasound is widely used by radiologists to identify and assess lesions. In this study, we developed image segmentation techniques and multiclass classification artificial intelligence (AI) tools based on pretrained models to segment lesions and detect breast cancer. The proposed workflow includes both the development of segmentation models and development of a series of classification models to classify ultrasound images as normal, benign or malignant. The pretrained models were trained and evaluated on the Breast Ultrasound Images (BUSI) dataset, a publicly available collection of grayscale breast ultrasound images with corresponding expert-annotated masks. For segmentation, images and ground-truth masks were used to pretrained encoder (ResNet18, EfficientNet-B0 and MobileNetV2)–decoder (U-Net, U-Net++ and DeepLabV3) models, including the DeepLabV3 architecture integrated with a Frequency-Domain Feature Enhancement Module (FEM). The proposed FEM improves spatial and spectral feature representations using Discrete Fourier Transform (DFT), GroupNorm, dropout regularization and adaptive fusion. For classification, each image was assigned a label (normal, benign or malignant). Optuna, an open-source software framework, was used for hyperparameter optimization and for the testing of various pretrained models to determine the best encoder–decoder segmentation architecture. Five different pretrained models (ResNet18, DenseNet121, InceptionV3, MobielNetV3 and GoogleNet) were optimized for multiclass classification. DeepLabV3 outperformed other segmentation architectures, with consistent performance across training, validation and test images, with Dice Similarity Coefficient (DSC, a metric describing the overlap between predicted and true lesion regions) values of 0.87, 0.80 and 0.83 on training, validation and test sets, respectively. ResNet18:DeepLabV3 achieved an Intersection over Union (IoU) score of 0.78 during training, while ResNet18:U-Net++ achieved the best Dice coefficient (0.83) and IoU (0.71) and area under the curve (AUC, 0.91) scores on the test (unseen) dataset when compared to other models. However, the proposed Resnet18: FrequencyAwareDeepLabV3 (FADeepLabV3) achieved a DSC of 0.85 and an IoU of 0.72 on the test dataset, demonstrating improvements over standard DeepLabV3. Notably, the frequency-domain enhancement substantially improved the AUC from 0.90 to 0.98, indicating enhanced prediction confidence and clinical reliability. For classification, ResNet18 produced an F1 score—a measure combining precision and recall—of 0.95 and an accuracy of 0.90 on the training dataset, while InceptionV3 performed best on the test dataset, with an F1 score of 0.75 and accuracy of 0.83. We demonstrate a comprehensive approach to automate the segmentation and multiclass classification of breast cancer ultrasound images into benign, malignant or normal transfer learning models on an imbalanced ultrasound image dataset. Full article