Zerubabel Desita, MD, Temesgen Tadesse, MD, Anders Solitander Bohlbro, MD
et al.
Objective: To assess whether computer-aided detection (CAD) chest X-ray (CXR) software may aid physicians in low-resource, high tuberculosis (TB) endemic settings where radiologists are scarce. Patients and Methods: A retrospective pilot study was conducted on CXR films taken between January 1, 2017, and March 30, 2018, in Guinea-Bissau and Ethiopia to compare the interpretation of CXRs regarding pulmonary TB (PTB) by CAD (qXR; Qure.ai) with that of 2 experienced Ethiopian radiologists (A and B). To improve the applicability of this method in low-resource settings, an analysis was performed on images of CXRs taken by mobile phones. Two reference standards were applied: final PTB diagnosis by clinical or laboratory findings (ie, Xpert MTB/RIF [Xpert]-confirmed PTB). Results: We included 498 CXRs from patients seeking help for TB indicative symptoms. Radiologist A identified 50, radiologist B identified 99, and the software identified 81 as indicative of TB. The overall area under the curve for the receiver-operating characteristic curve of the software was 0.84 for Xpert-confirmed cases. At the prechosen cutoff value of 0.5, the sensitivity of CAD CXR was 76.5%, and the specificity was 85.9%. Radiologist A’s assessments were 64.7% sensitive and 91.9% specific, whereas radiologist B’s assessments were 76.5% sensitive and 82.3% specific for Xpert-confirmed cases. The agreement regarding TB-related findings between the radiologists combined (κ=0.45) and each radiologist and the software (κ=0.56) was moderate. Conclusion: Our study revealed that CAD CXR performs comparably with experienced radiologists when it is applied to CXR films, photographed by mobile phones and a digital camera with similar sensor resolutions. Trial registration: PACTR201611001838365.
Computer applications to medicine. Medical informatics
Most of the current software security analysis tools assess vulnerabilities in isolation. However, sophisticated software supply chain security threats often stem from cascaded vulnerability and security weakness chains that span dependent components. Moreover, although the adoption of Software Bills of Materials (SBOMs) has been accelerating, downstream vulnerability findings vary substantially across SBOM generators and analysis tools. We propose a novel approach to SBOM-driven security analysis methods and tools. We model vulnerability relationships over dependency structure rather than treating scanner outputs as independent records. We represent enriched SBOMs as heterogeneous graphs with nodes being the SBOM components and dependencies, the known software vulnerabilities, and the known software security weaknesses. We then train a Heterogeneous Graph Attention Network (HGAT) to predict whether a component is associated with at least one known vulnerability. Since documented multi-vulnerability chains are scarce, we model cascade discovery as a link prediction problem over CVE pairs using a multi-layer perceptron neural network. This way, we produce ranked candidate links that can be composed into multi-step paths. The HGAT component classifier achieves an Accuracy of 91.03% and an F1-score of 74.02%.
Mobile Edge Computing (MEC) enhances service quality by decentralizing computational resources to network edges, thereby reducing latency and improving Quality of Service (QoS). However, the spatial distribution of edge servers critically impacts network transmission efficiency, while heterogeneous user perceptions of QoS metrics frequently lead to suboptimal Quality of Experience (QoE). Current research on Edge Server Placement (ESP) predominantly focuses on localized optimization of QoS metrics, yet fails to adequately incorporate systematic QoE modeling and coordinated optimization frameworks, leading to significant discrepancies between actual user experience and satisfaction with resource allocation. To address this gap, this study establishes a formalized QoE-aware Edge Server Placement (EESP) framework by rigorously characterizing the interdependence between QoE and QoS. We first prove the NP-completeness of the EESP problem through computational complexity analysis. Subsequently, we develop an Integer Linear Programming-based exact solver (EESP-O) for small-scale scenarios and propose an Enhanced Genetic Algorithm (EESP-EGA) for large-scale deployments. The EESP-EGA integrates adaptive crossover probability mechanisms and elite retention strategies to achieve near-optimal solutions for complex real-world configurations. Experimental evaluations conducted on a broad range of real-world datasets demonstrate that the proposed method outperforms several existing representative approaches in terms of QoE.
The mid-1990s saw the design of programming languages for software architectures, which define the high-level aspects of software systems including how code components were composed to form full systems. Our paper "Abstractions for Software Architecture and Tools to Support Them" presented a conceptual view of software architecture based on abstractions used in practice to organize software systems, a language that supported these abstractions, and a prototype implementation of this language. By invitation, we reflect on the paper's principal ideas about system-level abstractions, place the work in a historical context of steadily increasing abstraction power in software development languages and infrastructure, and reflect on how progress since the paper's 1995 publication has been influenced, directly or indirectly, by this work. We describe current manifestations of architectural ideas and current challenges. We suggest how the strategy we used to identify and reify architectural abstractions may apply to current opportunities.
In the software industry, artificial intelligence (AI) has been utilized more and more in software development activities. In some activities, such as coding, AI has already been an everyday tool, but in software testing activities AI it has not yet made a significant breakthrough. In this paper, the objective was to identify what kind of empirical research with industry context has been conducted on AI in software testing, as well as how AI has been adopted in software testing practice. To achieve this, we performed a systematic mapping study of recent (2020 and later) studies on AI adoption in software testing in the industry, and applied thematic analysis to identify common themes and categories, such as the real-world use cases and benefits, in the found papers. The observations suggest that AI is not yet heavily utilized in software testing, and still relatively few studies on AI adoption in software testing have been conducted in the industry context to solve real-world problems. Earlier studies indicated there was a noticeable gap between the actual use cases and actual benefits versus the expectations, which we analyzed further. While there were numerous potential use cases for AI in software testing, such as test case generation, code analysis, and intelligent test automation, the reported actual implementations and observed benefits were limited. In addition, the systematic mapping study revealed a potential problem with false positive search results in online databases when using the search string "artificial intelligence".
Large Language Models (LLMs) are increasingly integrated into software applications, giving rise to a broad class of prompt-enabled systems, in which prompts serve as the primary 'programming' interface for guiding system behavior. Building on this trend, a new software paradigm, promptware, has emerged, which treats natural language prompts as first-class software artifacts for interacting with LLMs. Unlike traditional software, which relies on formal programming languages and deterministic runtime environments, promptware is based on ambiguous, unstructured, and context-dependent natural language and operates on LLMs as runtime environments, which are probabilistic and non-deterministic. These fundamental differences introduce unique challenges in prompt development. In practice, prompt development remains largely ad hoc and relies heavily on time-consuming trial-and-error, a challenge we term the promptware crisis. To address this, we propose promptware engineering, a new methodology that adapts established Software Engineering (SE) principles to prompt development. Drawing on decades of success in traditional SE, we envision a systematic framework encompassing prompt requirements engineering, design, implementation, testing, debugging, evolution, deployment, and monitoring. Our framework re-contextualizes emerging prompt-related challenges within the SE lifecycle, providing principled guidance beyond ad-hoc practices. Without the SE discipline, prompt development is likely to remain mired in trial-and-error. This paper outlines a comprehensive roadmap for promptware engineering, identifying key research directions and offering actionable insights to advance the development of prompt-enabled systems.
The adoption of large language models (LLMs) and autonomous agents in software engineering marks an enduring paradigm shift. These systems create new opportunities for tool design, workflow orchestration, and empirical observation, while fundamentally reshaping the roles of developers and the artifacts they produce. Although traditional empirical methods remain central to software engineering research, the rapid evolution of AI introduces new data modalities, alters causal assumptions, and challenges foundational constructs such as "developer", "artifact", and "interaction". As humans and AI agents increasingly co-create, the boundaries between social and technical actors blur, and the reproducibility of findings becomes contingent on model updates and prompt contexts. This vision paper examines how the integration of LLMs into software engineering disrupts established research paradigms. We discuss how it transforms the phenomena we study, the methods and theories we rely on, the data we analyze, and the threats to validity that arise in dynamic AI-mediated environments. Our aim is to help the empirical software engineering community adapt its questions, instruments, and validation standards to a future in which AI systems are not merely tools, but active collaborators shaping software engineering and its study.
Orientation: Performance management aligns individual and organisational goals, enhancing employee motivation, sustaining organisational success.
Research purpose: The study aimed to investigate challenges experienced by security managers with performance management systems in a tertiary institution in Durban, South Africa.
Motivation for the study: Understanding challenges delivers insights to advance organisational effectiveness, support sustainable development, and enhance human resources (HR) practices in the higher education security sector.
Research approach/design and method: A qualitative approach was used. Twelve security managers were purposively selected for semi-structured interviews. Data were analysed using thematic analysis and NVivo software.
Main findings: The study identified three challenges, namely inadequate training, education, development and communication, technological barriers and low computer literacy, limited resource allocation, inconsistent rewards, and a lack of transparency. These challenges were found to hinder performance management system application, reducing employee engagement and productivity.
Practical/managerial implications: The study recommends the institution increase adult basic education and training in technological skills and literacy. Enhancing feedback mechanisms, conducting performance needs assessments, and implementing fair reward strategies may improve sustainable development of performance management practices.
Contribution/value-add: The study advocates for practical strategies in response to the challenges faced by security managers to enhance performance aligned with sustainable development goals 4 (quality education), 8 (decent work and economic growth), 10 (reduced inequalities) and 16 (peace, justice and strong institutions), contributing to sustainable HR, embracing contextualised performance management application in developing countries.
Abstract Eutectic alloys have garnered significant attention due to their promising mechanical and physical properties, as well as their technological relevance. However, the discovery of eutectic compositionally complex alloys (ECCAs) (e.g. high entropy eutectic alloys) remains a formidable challenge in the vast and intricate compositional space, primarily due to the absence of readily available phase diagrams. To address this issue, we have developed an explainable machine learning (ML) framework that integrates conditional variational autoencoder (CVAE) and artificial neutral network (ANN) models, enabling direct generation of ECCAs. To overcome the prevalent problem of data imbalance encountered in data-driven ECCA design, we have incorporated thermodynamics-derived data descriptors and employed K-means clustering methods for effective data pre-processing. Leveraging our ML framework, we have successfully discovered dual- or even tri-phased ECCAs, spanning from quaternary to senary alloy systems, which have not been previously reported in the literature. These findings hold great promise and indicate that our ML framework can play a pivotal role in accelerating the discovery of technologically significant ECCAs.
Materials of engineering and construction. Mechanics of materials, Computer software
Dimitrios G. Katehakis, Dimitrios Filippidis, Konstantinos Karamanis
et al.
Digital innovation can significantly enhance public health services, environmental sustainability, and social welfare. To this end, the European Digital Innovation Hub (EDIH) initiative was funded by the European Commission and national governments aiming to facilitate the digital transformation on various domains (including health) via the setup of relevant ecosystems consisting of academic institutions, research centres, start-ups, small and medium-sized enterprises, larger companies, public organizations, technology transfer offices, innovation clusters, and financial institutions. The ongoing goal of the EDIHs initiative is to bridge the gap between high-tech research taking place in universities and research centres and its deployment in real-world conditions by fostering innovation ecosystems. In this context, the smartHEALTH EDIH started its operation in Greece in 2023, offering technical consultation services to companies and public sector organizations to accelerate digitalization in precision medicine and innovative e-health services by utilizing key technologies such as artificial intelligence, high-performance computing, cybersecurity, and others. During its first 20 months of operation, over 50 prospective recipients have applied for consulting services, mainly seeking “test-before-invest” services. This paper aims to provide insights regarding the smartHEALTH initiative, preliminary outcomes and lessons learned during this first period of operation. To this end, this paper outlines smartHEALTH’s approach to attracting recipients and providing expert guidance on utilizing state-of-the-art technologies for innovative services, product development, and process creation to accelerate digital transformation.
With the advent of large language models (LLMs) in the artificial intelligence (AI) area, the field of software engineering (SE) has also witnessed a paradigm shift. These models, by leveraging the power of deep learning and massive amounts of data, have demonstrated an unprecedented capacity to understand, generate, and operate programming languages. They can assist developers in completing a broad spectrum of software development activities, encompassing software design, automated programming, and maintenance, which potentially reduces huge human efforts. Integrating LLMs within the SE landscape (LLM4SE) has become a burgeoning trend, necessitating exploring this emergent landscape's challenges and opportunities. The paper aims at revisiting the software development life cycle (SDLC) under LLMs, and highlighting challenges and opportunities of the new paradigm. The paper first summarizes the overall process of LLM4SE, and then elaborates on the current challenges based on a through discussion. The discussion was held among more than 20 participants from academia and industry, specializing in fields such as software engineering and artificial intelligence. Specifically, we achieve 26 key challenges from seven aspects, including software requirement & design, coding assistance, testing code generation, code review, code maintenance, software vulnerability management, and data, training, and evaluation. We hope the achieved challenges would benefit future research in the LLM4SE field.
Juan M. Murillo, Jose Garcia-Alonso, Enrique Moguel
et al.
As quantum computers advance, the complexity of the software they can execute increases as well. To ensure this software is efficient, maintainable, reusable, and cost-effective -key qualities of any industry-grade software-mature software engineering practices must be applied throughout its design, development, and operation. However, the significant differences between classical and quantum software make it challenging to directly apply classical software engineering methods to quantum systems. This challenge has led to the emergence of Quantum Software Engineering as a distinct field within the broader software engineering landscape. In this work, a group of active researchers analyse in depth the current state of quantum software engineering research. From this analysis, the key areas of quantum software engineering are identified and explored in order to determine the most relevant open challenges that should be addressed in the next years. These challenges help identify necessary breakthroughs and future research directions for advancing Quantum Software Engineering.
People in the modern era spend most of their lives in virtual environments that offer a range of public and private services and social platforms. Therefore, these environments need to be protected from cyber attackers that can steal data or disrupt systems. Cybersecurity refers to a collection of technical, organizational, and executive means for preventing the unauthorized use or misuse of electronic information and communication systems to ensure the continuity of their work, guarantee the confidentiality and privacy of personal data, and protect consumers from threats and intrusions. Accordingly, this article explores the cybersecurity practices that protect computer systems from attacks, hacking, and data thefts and investigates the role of artificial intelligence in this domain. This article also summarizes the most significant literature that explore the roles and effects of machine learning and deep learning techniques in cybersecurity. Results show that machine learning and deep learning techniques play significant roles in protecting computer systems from unauthorized entry and in controlling system penetration by predicting and understanding the behaviour and traffic of malicious software.
Uesli Alushi, Tomás Ramos, Juan José García-Ripoll
et al.
Quadratic light-matter interactions are nonlinear couplings such that quantum emitters interact with photonic or phononic modes exclusively via the exchange of excitation pairs. Implementable with atomic and solid-state systems, these couplings lead to a plethora of phenomena that have been characterized in the context of cavity QED, where quantum emitters interact with localized bosonic modes. Here, we explore quadratic interactions in a waveguide QED setting, where quantum emitters interact with propagating fields confined in a one-dimensional environment. We develop a general scattering theory under the Markov approximation and discuss paradigmatic examples for spontaneous emission and scattering of biphoton states. Our analytical and semianalytical results unveil fundamental differences with respect to conventional waveguide QED systems, such as the spontaneous emission frequency-entangled photon pairs or the full transparency of the emitter to single-photon inputs. This unlocks new opportunities in quantum information processing with propagating photons. As a striking example, we show that a single quadratically coupled emitter can implement a two-photon logic gate with unit fidelity, circumventing a no-go theorem derived for conventional waveguide-QED interactions.
Software Architecture, from definition to maintenance and evolution, is a complex aspect of software development and, consequently, a challenging subject when it comes to teaching it, and learning it. Many research efforts have been devoted to designing teaching approaches, strategies and tools. Most of them, however, focus on the knowledge itself and the ways to convey it to students, rather than on the different learning styles of students themselves. Teaching methods which predominantly rely on verbal and written communication, are very well aligned with some learning styles. However, students with learning styles that benefit more from physical activity or first-hand experience, need to defer to cognitive processes that are less natural to them. In this work, we propose an innovative use of role-playing as teaching strategy for architecture models of reference (i.e. layered, pipe and filter, client-server, etc.). This role-playing of different software architectures, in which students play the part of specific components in the system, intends to complement other classical teaching materials, such as in-person or recorded lectures, lab assignments, or development projects. Addressing all learning styles within a classroom is key to ensure that we favour and foster the students' different learning processes, and give everyone an even playfield in which to best develop their capabilities as Software Architects.
Ze Shi Li, Nowshin Nawar Arony, Kezia Devathasan
et al.
Capstone courses in undergraduate software engineering are a critical final milestone for students. These courses allow students to create a software solution and demonstrate the knowledge they accumulated in their degrees. However, a typical capstone project team is small containing no more than 5 students and function independently from other teams. To better reflect real-world software development and meet industry demands, we introduce in this paper our novel capstone course. Each student was assigned to a large-scale, multi-team (i.e., company) of up to 20 students to collaboratively build software. Students placed in a company gained first-hand experiences with respect to multi-team coordination, integration, communication, agile, and teamwork to build a microservices based project. Furthermore, each company was required to implement plug-and-play so that their services would be compatible with another company, thereby sharing common APIs. Through developing the product in autonomous sub-teams, the students enhanced not only their technical abilities but also their soft skills such as communication and coordination. More importantly, experiencing the challenges that arose from the multi-team project trained students to realize the pitfalls and advantages of organizational culture. Among many lessons learned from this course experience, students learned the critical importance of building team trust. We provide detailed information about our course structure, lessons learned, and propose recommendations for other universities and programs. Our work concerns educators interested in launching similar capstone projects so that students in other institutions can reap the benefits of large-scale, multi-team development
Abstract Background Single-cell DNA sequencing is getting indispensable in the study of cell-specific cancer genomics. The performance of computational tools that tackle single-cell genome aberrations may be nevertheless undervalued or overvalued, owing to the insufficient size of benchmarking data. In silicon simulation is a cost-effective approach to generate as many single-cell genomes as possible in a controlled manner to make reliable and valid benchmarking. Results This study proposes a new tool, SCSilicon, which efficiently generates single-cell in silicon DNA reads with minimum manual intervention. SCSilicon automatically creates a set of genomic aberrations, including SNP, SNV, Indel, and CNV. Besides, SCSilicon yields the ground truth of CNV segmentation breakpoints and subclone cell labels. We have manually inspected a series of synthetic variations. We conducted a sanity check of the start-of-the-art single-cell CNV callers and found SCYN was the most robust one. Conclusions SCSilicon is a user-friendly software package for users to develop and benchmark single-cell CNV callers. Source code of SCSilicon is available at https://github.com/xikanfeng2/SCSilicon .
Identity and Access Management (IAM) systems are crucial for any information system, such as healthcare information systems. Health IoT (HIoT) applications are targeted by attackers due to the high-volume and sensitivity of health data. Thus, IAM systems for HIoT need to be built with high standards and based on reliable frameworks. Blockchain (BC) is an emerging technology widely used for developing decentralized IAM solutions. Although, the integration of BC in HIoT for proposing IAM solutions has gained recent attention, BC is an evolving technology and needs to be studied carefully before using it for IAM solutions in HIoT applications. A systematic literature review was conducted on the BC-based IAM systems in HIoT applications to investigate the security aspect. Twenty-four studies that satisfied the inclusion criteria and passed the quality assessment were included in this review. We studied BC-based solutions in HIoT applications to explore the IAM system architecture, security requirements and threats. We summarized the main components and technologies in typical BC-based IAM systems and the layered architecture of the BC-based IAM system in HIoT. Accordingly, the security threats and requirements were summarized. Our systematic review shows that there is a lack of a comprehensive security framework, risk assessments, and security and functional performance evaluation metrics in BC-based IAM in HIoT applications.
La presente investigación se centra en el diseño de un nodo articulado, basado en el ensayo constructivo del Dr. Félix Escrig, quien brindó los conceptos constructivos para generar una propuesta que tiene como objetivo desarrollar sistemas plegables más rápidos, por lo cual se construyó una serie de prototipos que verifiquen y validen los diferentes alcances constructivos que podrían llegar a generar un sistema estructural transformable. Esto justificó la simulación de los modelos con el software Solid Work, el cual validó dichas conexiones estructurales plegables, y nos ayudó a verificar los modelos constructivos, lo que es el propósito de esta investigación. En consecuencia, dichas propuestas se abordaron con la finalidad de diseñar los detalles industriales de conexión por medio de Computer-aided-Design (CAD), ya que el software en su paquetería tiene la capacidad para desarrollar prototipados experimentales y realizar los detalles con mejores resultados para las conexiones constructivas, y seguir así una fase industrial más óptima para los modelos.