Hasil untuk "Systems engineering"

Michael L. Lovett, Kyongbum Lee, A. Edwards et al.

J. Duffie, W. Beckman, W. Worek

P. Carayon, A. Hundt, B. Karsh et al.

H. Miras, Jun Yan, D. Long et al.

Junwei Yu, Mufeng Yang, Yepeng Ding et al.

The proliferation of AI-powered search engines has shifted information discovery from traditional link-based retrieval to direct answer generation with selective source citation, creating new challenges for content visibility. While existing Generative Engine Optimization (GEO) approaches focus primarily on semantic content modification, the role of structural features in influencing citation behavior remains underexplored. In this paper, we propose GEO-SFE, a systematic framework for structural feature engineering in generative engine optimization. Our approach decomposes content structure into three hierarchical levels: macro-structure (document architecture), meso-structure (information chunking), and micro-structure (visual emphasis), and models their impact on citation probability across different generative engine architectures. We develop architecture-aware optimization strategies and predictive models that preserve semantic integrity while improving structural effectiveness. Experimental evaluation across six mainstream generative engines demonstrates consistent improvements in citation rate (17.3 percent) and subjective quality (18.5 percent), validating the effectiveness and generalizability of the proposed framework. This work establishes structural optimization as a foundational component of GEO, providing a data-driven methodology for enhancing content visibility in LLM-powered information ecosystems.

Shuvechha Chakraborty, Indumathi Palanikumar, Yash Gune et al.

Abstract Candida albicans, responsible for approximately 70% of all Candida infections, is a leading cause of invasive candidiasis and poses a significant global health threat. With the emergence of drug-resistant strains, mortality rates have reached a staggering 63.6% in severe cases, complicating treatment options and demanding the discovery of novel therapeutic targets. To address this pressing need, using a unique multidisciplinary approach, we attempted to identify some the critical metabolic pathways that can be targeted to modulate the virulence of CAL. Condition-specific genome-scale metabolic models (GSMMs), along with a novel integrated host-CAL model developed in this study, highlighted the central role of arginine (Arg) metabolism and uncovered ALT1, an arginine biosynthesis enzyme, as a critical metabolic vulnerability in CAL virulence. Heightened expression of arginine biosynthesis genes indicated that increased arginine synthesis mainly occurred through proline intermediates during host interaction. Significantly impaired virulence and in vivo pathogenicity of ALT1-deleted CAL highlighted the potential of targeting arginine metabolism as a novel strategy to combat antifungal resistance and underscored the power of integrating systems biology with experimental approaches in identifying new therapeutic targets.

Shuangzhe Li, Le Xu, Yitong Li et al.

ABSTRACT Soil salinization is a substantial environmental stressor that limits plant growth and development. Soil salinization has, therefore, emerged as a substantial barrier to crop production, particularly affecting soybean production in arable regions. Cultivating soybean varieties with high salt tolerance is an efficient approach for improving soybean production on arable land with soil salinization. The growth and development of soybean plants exposed to salt stress involve numerous physical and molecular regulation networks. Therefore, a comprehensive understanding of the molecular mechanisms underlying soybean salt tolerance is a prerequisite for improving the salt tolerance of current soybean varieties or for breeding new soybean varieties with higher salt tolerance. This review provides a general overview of recent knowledge that may help to understand the molecular mechanisms of soybean responses to salt stress and discusses the potential challenges in salt‐tolerant soybean breeding, as well as possible strategies. We emphasize the importance of different genetic resources, especially wild soybeans, for mining new advantageous alleles. Additionally, pyramiding superior alleles and genome editing technologies are excellent tools for accelerating the cultivation of salt‐tolerant soybeans.

Abdullah Addas, Abdullah Addas, Muhammad Nasir Khan et al.

IntroductionThe regional disparity in higher education access can only be met when there are strategies for sustainable development and diversification of the economy, as envisioned in Saudi Vision 2030. Currently, 70% of universities are concentrated in the Central and Eastern regions, leaving the Northern and Southern parts of the country with limited opportunities.MethodsThe study created a framework with sensors and generative adversarial networks (GANs) that optimize the distribution of medical universities, supporting equity in access to education and balanced regional development. The research applies an artificial intelligence (AI)-driven framework that combines sensor data with GAN-based models to perform real-time geographic and demographic data analyses on the placement of higher education institutions throughout Saudi Arabia. This framework analyzes multisensory data by examining strategic university placement impacts on regional economies, social mobility, and the environment. Scenario modeling was used to simulate potential outcomes due to changes in university distribution.ResultsThe findings indicated that areas with a higher density of universities experience up to 20% more job opportunities and a higher GDP growth of up to 15%. The GAN-based simulations reveal that redistributive educational institutions in underrepresented regions could decrease environmental impacts by about 30% and enhance access. More specifically, strategic placement in underserved areas is associated with a reduction of approximately 10% in unemployment.DiscussionThe research accentuates the need to include AI and sensor technology to develop educational infrastructures. The proposed framework can be used for continuous monitoring and dynamic adaptation of university strategies to align them with evolving economic and environmental objectives. The study explains the transformative potential of AI-enabled solutions to further equal access to education for sustainable regional development throughout Saudi Arabia.

Khalid S. Mohammad, Fatimah Hussain Bu Izran

Bone remains one of the most hospitable—and devastating—destinations for metastatic cancer cells. At the center of this unwelcome alliance is transforming growth factor‑β (TGF‑β), a cytokine stored in the mineralized matrix and unleashed during osteoclastic bone resorption. Once activated, TGF‑β fuels a self‑reinforcing “vicious cycle”: it co‑opts tumor cells to undergo epithelial‑to‑mesenchymal transition, recruits and primes osteoclasts, suppresses osteoblast function, and shapes an immunosuppressive niche that shields malignant clones. The result is a micro‑environment exquisitely tuned for tumor survival, skeletal destruction, and therapy resistance. This review traces the molecular choreography of TGF‑β signaling within the bone tumor microenvironment (TME), detailing its crosstalk with osteogenic, immune, and stromal compartments across breast, prostate, and lung cancer metastases. We synthesize pre‑clinical and clinical efforts to interrupt this pathway, ranging from ligand-neutralizing antibodies and activin receptor-like kinase 5 (ALK5) kinase inhibitors to antisense oligonucleotides and tumor-selective ligand traps—and examine why benefits observed in early trials are tempered by dose‑limiting toxicities and adaptive resistance. Beyond TGF‑β itself, we highlight parallel targets in the TME, including receptor activator of nuclear factor kappa-B ligand (RANKL)‑driven osteoclastogenesis, vascular endothelial growth factor/fibroblast growth factor (VEGF/FGF)‑mediated angiogenesis, and immune checkpoints such as PD‑1, TIM‑3, and LAG‑3, arguing that multi‑pronged combinations guided by real‑time TME profiling offer the most promising path forward. We outline pressing research priorities: mapping the spatiotemporal dynamics of TGF‑β activation, identifying predictive biomarkers for patient stratification, and engineering bone‑targeted delivery systems that preserve normal tissue repair. By decoding and disrupting the TGF‑β‑centered circuitry of bone metastasis, we can move closer to therapies that not only palliate skeletal complications but also prolong life for patients with advanced cancer.

Akihiro Shimba, Akihiro Shimba, Satoru Munakata et al.

The (pro)renin receptor (PRR) is a multifunctional transmembrane protein that enhances β-catenin/TCF1 signaling and V-ATPase-mediated lysosomal acidification. Emerging evidence indicates that it may also regulate potential roles in regulating T cell development, survival, and immune responses. Here, we demonstrated that PRR promotes the maturation and survival of T cells within the thymus. In particular, PRR-deficient mice exhibited a significant reduction in iNKT cells in the thymus and periphery. PRR promoted the energy synthesis process in mitochondria, as evidenced by increased mitochondrial amount and membrane potential. This phenomenon was accompanied by an increase in TCF1 expression and lysosomal acidification. Furthermore, PRR enhanced the survival of naive T and iNKT cells in the periphery, while simultaneously suppressing inflammatory cytokine-producing T cells, thereby preventing colitis. In contrast, PRR enhanced resistance against tumor growth by increasing the number of tumor-infiltrating Th1 and iNKT cells, which in turn promoted NK cell recruitment. This study indicates that PRR is critical for supporting T cell maintenance, suppressing excessive inflammation, and enhancing anti-tumor immunity.

Mia Mohammad Imran, Tarannum Shaila Zaman

Large Language Models (LLMs) are increasingly used in empirical software engineering (ESE) to automate or assist annotation tasks such as labeling commits, issues, and qualitative artifacts. Yet the reliability and reproducibility of such annotations remain underexplored. Existing studies often lack standardized measures for reliability, calibration, and drift, and frequently omit essential configuration details. We argue that LLM-based annotation should be treated as a measurement process rather than a purely automated activity. In this position paper, we outline the \textbf{Operationalization for LLM-based Annotation Framework (OLAF)}, a conceptual framework that organizes key constructs: \textit{reliability, calibration, drift, consensus, aggregation}, and \textit{transparency}. The paper aims to motivate methodological discussion and future empirical work toward more transparent and reproducible LLM-based annotation in software engineering research.

Nazanin Ahmadi, Qianying Cao, Jay D. Humphrey et al.

Physics-informed machine learning (PIML) is emerging as a potentially transformative paradigm for modeling complex biomedical systems by integrating parameterized physical laws with data-driven methods. Here, we review three main classes of PIML frameworks: physics-informed neural networks (PINNs), neural ordinary differential equations (NODEs), and neural operators (NOs), highlighting their growing role in biomedical science and engineering. We begin with PINNs, which embed governing equations into deep learning models and have been successfully applied to biosolid and biofluid mechanics, mechanobiology, and medical imaging among other areas. We then review NODEs, which offer continuous-time modeling, especially suited to dynamic physiological systems, pharmacokinetics, and cell signaling. Finally, we discuss deep NOs as powerful tools for learning mappings between function spaces, enabling efficient simulations across multiscale and spatially heterogeneous biological domains. Throughout, we emphasize applications where physical interpretability, data scarcity, or system complexity make conventional black-box learning insufficient. We conclude by identifying open challenges and future directions for advancing PIML in biomedical science and engineering, including issues of uncertainty quantification, generalization, and integration of PIML and large language models.

Daniel N. Wilke

This paper introduces Design for Sensing and Digitalisation (DSD), a new engineering design paradigm that integrates sensor technology for digitisation and digitalisation from the earliest stages of the design process. Unlike traditional methodologies that treat sensing as an afterthought, DSD emphasises sensor integration, signal path optimisation, and real-time data utilisation as core design principles. The paper outlines DSD's key principles, discusses its role in enabling digital twin technology, and argues for its importance in modern engineering education. By adopting DSD, engineers can create more intelligent and adaptable systems that leverage real-time data for continuous design iteration, operational optimisation and data-driven predictive maintenance.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy plays a crucial role in software engineering (SE), influencing collaboration, communication, and decision-making. While prior research has highlighted the importance of empathy in SE, there is limited understanding of how empathy manifests in SE practice, what motivates SE practitioners to demonstrate empathy, and the factors that influence empathy in SE work. Our study explores these aspects through 22 interviews and a large scale survey with 116 software practitioners. Our findings provide insights into the expression of empathy in SE, the drivers behind empathetic practices, SE activities where empathy is perceived as useful or not, and the other factors that influence empathy. In addition, we offer practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.

Ghi Tran Nha, Trung Nguyen Tan, Long Nguyen Thanh et al.

This study is conducted to explain entrepreneurial support resources of firms based on social network theory in developing countries, the case of Vietnam. Partial Least Squares Structural Modeling (PLS-SEM) was conducted with a sample size of 220 entrepreneurs in SMEs. The results supported the positive link between formal and informal networks and entrepreneurial firm performance. Second, the study explored the partial mediating role of access to entrepreneurial resources between formal networks, informal networks, and entrepreneurial firm performance. In addition, the results also provide practical value to entrepreneurs in actively building relationship networking in the entrepreneurship ecosystem. Finally, the study proposed some implications for entrepreneurs, limitations, and further research.

G. Stober, G. Stober, S. L. Vadas et al.

<p>The Hunga Tonga–Hunga Ha′apai volcano erupted on 15 January 2022, launching Lamb waves and gravity waves into the atmosphere. In this study, we present results using 13 globally distributed meteor radars and identify the volcanogenic gravity waves in the mesospheric/lower thermospheric winds. Leveraging the High-Altitude Mechanistic general Circulation Model (HIAMCM), we compare the global propagation of these gravity waves. We observed an eastward-propagating gravity wave packet with an observed phase speed of 240 <span class="inline-formula">±</span> 5.7 m s<span class="inline-formula">⁻¹</span> and a westward-propagating gravity wave with an observed phase speed of 166.5 <span class="inline-formula">±</span> 6.4 m s<span class="inline-formula">⁻¹</span>. We identified these waves in HIAMCM and obtained very good agreement of the observed phase speeds of 239.5 <span class="inline-formula">±</span> 4.3 and 162.2 <span class="inline-formula">±</span> 6.1 m s<span class="inline-formula">⁻¹</span> for the eastward the westward waves, respectively. Considering that HIAMCM perturbations in the mesosphere/lower thermosphere were the result of the secondary waves generated by the dissipation of the primary gravity waves from the volcanic eruption, this affirms the importance of higher-order wave generation. Furthermore, based on meteor radar observations of the gravity wave propagation around the globe, we estimate the eruption time to be within 6 min of the nominal value of 15 January 2022 04:15 UTC, and we localized the volcanic eruption to be within 78 km relative to the World Geodetic System 84 coordinates of the volcano, confirming our estimates to be realistic.</p>

Claudio Di Sipio, Riccardo Rubei, Juri Di Rocco et al.

Software engineering (SE) activities have been revolutionized by the advent of pre-trained models (PTMs), defined as large machine learning (ML) models that can be fine-tuned to perform specific SE tasks. However, users with limited expertise may need help to select the appropriate model for their current task. To tackle the issue, the Hugging Face (HF) platform simplifies the use of PTMs by collecting, storing, and curating several models. Nevertheless, the platform currently lacks a comprehensive categorization of PTMs designed specifically for SE, i.e., the existing tags are more suited to generic ML categories. This paper introduces an approach to address this gap by enabling the automatic classification of PTMs for SE tasks. First, we utilize a public dump of HF to extract PTMs information, including model documentation and associated tags. Then, we employ a semi-automated method to identify SE tasks and their corresponding PTMs from existing literature. The approach involves creating an initial mapping between HF tags and specific SE tasks, using a similarity-based strategy to identify PTMs with relevant tags. The evaluation shows that model cards are informative enough to classify PTMs considering the pipeline tag. Moreover, we provide a mapping between SE tasks and stored PTMs by relying on model names.

Johannes Schleiss, Aditya Johri

In this practice paper, we propose a framework for integrating AI into disciplinary engineering courses and curricula. The use of AI within engineering is an emerging but growing area and the knowledge, skills, and abilities (KSAs) associated with it are novel and dynamic. This makes it challenging for faculty who are looking to incorporate AI within their courses to create a mental map of how to tackle this challenge. In this paper, we advance a role-based conception of competencies to assist disciplinary faculty with identifying and implementing AI competencies within engineering curricula. We draw on prior work related to AI literacy and competencies and on emerging research on the use of AI in engineering. To illustrate the use of the framework, we provide two exemplary cases. We discuss the challenges in implementing the framework and emphasize the need for an embedded approach where AI concerns are integrated across multiple courses throughout the degree program, especially for teaching responsible and ethical AI development and use.

Bohui Zhang, Valentina Anita Carriero, Katrin Schreiberhuber et al.

Ontology engineering (OE) in large projects poses a number of challenges arising from the heterogeneous backgrounds of the various stakeholders, domain experts, and their complex interactions with ontology designers. This multi-party interaction often creates systematic ambiguities and biases from the elicitation of ontology requirements, which directly affect the design, evaluation and may jeopardise the target reuse. Meanwhile, current OE methodologies strongly rely on manual activities (e.g., interviews, discussion pages). After collecting evidence on the most crucial OE activities, we introduce \textbf{OntoChat}, a framework for conversational ontology engineering that supports requirement elicitation, analysis, and testing. By interacting with a conversational agent, users can steer the creation of user stories and the extraction of competency questions, while receiving computational support to analyse the overall requirements and test early versions of the resulting ontologies. We evaluate OntoChat by replicating the engineering of the Music Meta Ontology, and collecting preliminary metrics on the effectiveness of each component from users. We release all code at https://github.com/King-s-Knowledge-Graph-Lab/OntoChat.

Loiy Al-Ghussain, Adnan Darwish Ahmad, Ahmad M. Abubaker et al.