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.
The rapid advance of Generative AI into software development prompts this empirical investigation of perceptual effects on practice. We study the usage patterns of 147 professional developers, examining perceived correlates of AI tools use, the resulting productivity and quality outcomes, and developer readiness for emerging AI-enhanced development. We describe a virtuous adoption cycle where frequent and broad AI tools use are the strongest correlates of both Perceived Productivity (PP) and quality, with frequency strongest. The study finds no perceptual support for the Quality Paradox and shows that PP is positively correlated with Perceived Code Quality (PQ) improvement. Developers thus report both productivity and quality gains. High current usage, breadth of application, frequent use of AI tools for testing, and ease of use correlate strongly with future intended adoption, though security concerns remain a moderate and statistically significant barrier to adoption. Moreover, AI testing tools' adoption lags that of coding tools, opening a Testing Gap. We identify three developer archetypes (Enthusiasts, Pragmatists, Cautious) that align with an innovation diffusion process wherein the virtuous adoption cycle serves as the individual engine of progression. Our findings reveal that organizational adoption of AI tools follows such a process: Enthusiasts push ahead with tools, creating organizational success that converts Pragmatists. The Cautious are held in organizational stasis: without early adopter examples, they don't enter the virtuous adoption cycle, never accumulate the usage frequency that drives intent, and never attain high efficacy. Policy itself does not predict individuals' intent to increase usage but functions as a marker of maturity, formalizing the successful diffusion of adoption by Enthusiasts while acting as a gateway that the Cautious group has yet to reach.
Spatial intensity modulation in amplified laser beams, particularly hot spots, critically constrains attainable pulse peak power due to the damage threshold limitations of four-grating compressors. This study demonstrates that the double-smoothing grating compressor (DSGC) configuration effectively suppresses modulation through directional beam smoothing. Our systematic investigation validated the double-smoothing effect through numerical simulations and experimental measurements, with comprehensive spatiotemporal analysis revealing excellent agreement between numerical and practical pulse characteristics. Crucially, the DSGC enables a 1.74 times energy output boost compared to conventional compressors. These findings establish the DSGC as a pivotal advancement for next-generation ultrahigh-power laser systems, providing a viable pathway toward hundreds of PW output through optimized spatial energy redistribution.
David Idiata, Ngozi Kayode - Ojo, Ehizonomhen Okonofua
This study investigates the geochemical and geotechnical properties of soils from Uwelu, Benin City, Nigeria (6.3861°N, 5.5827°E, 107 m altitude), to assess their engineering relevance. Samples from two sites underwent tests including specific gravity, sieve analysis, Atterberg limits, compaction, and California Bearing Ratio (CBR), following ASTM and AASHTO standards. X-ray fluorescence (XRF) was used to determine the presence of major oxides and trace elements. The soils, classified as A-2-4 and A-2-6 by AASHTO, had specific gravities of 2.55 and 2.54. The optimum moisture content was 10%, with Maximum Dry Densities (MDD) of 2.01 and 2.06 g/cm³. CBR results showed higher strength in unsoaked samples (20.11%, 6.38%) than soaked ones (9.69%, 3.24%). SiO₂ dominated the geochemistry (57.33%, 48.36%), with notable Al₂O₃ and Fe₂O₃. The findings emphasize the value of integrating geochemical and geotechnical analyses in construction.
Abstract The 10 000‐m ultradeep dolomite reservoir holds significant potential as a successor field for future oil and gas exploration in China's marine craton basin. However, major challenges such as the genesis of dolomite, the formation time of high‐quality reservoirs, and the preservation mechanism of reservoirs have always limited exploration decision‐making. This research systematically elaborates on the genesis and reservoir‐forming mechanisms of Sinian–Cambrian dolomite, discussing the ancient marine environment where microorganisms and dolomite develop, which controls the formation of large‐scale Precambrian–Cambrian dolomite. The periodic changes in Mg isotopes and sedimentary cycles show that the thick‐layered dolomite is the result of different dolomitization processes superimposed on a spatiotemporal scale. Lattice defects and dolomite embryos can promote dolomitization. By simulating the dissolution of typical calcite and dolomite crystal faces in different solution systems and calculating their molecular weights, the essence of heterogeneous dissolution and pore formation on typical calcite and dolomite crystal faces was revealed, and the mechanism of dolomitization was also demonstrated. The properties of calcite and dolomite (104)/(110) grain boundaries and their dissolution mechanism in carbonate solution were revealed, showing the limiting factors of the dolomitization process and the preservation mechanism of deep buried dolomite reservoirs. The in situ laser U‐Pb isotope dating technique has demonstrated the timing of dolomitization and pore formation in ancient carbonate rocks. This research also proposed that dolomitization occurred during the quasi‐contemporaneous or shallow‐burial periods within 50 Ma after deposition and pores formed during the quasi‐contemporaneous to the early diagenetic periods. And it was clear that the quasi‐contemporaneous dolomitization was the key period for reservoir formation. The systematic characterization of the spatial distribution of the deepest dolomite reservoirs in multiple sets of the Sinian and the Cambrian in the Chinese craton basins provides an important basis for the distribution prediction of large‐scale dolomite reservoirs. It clarifies the targets for oil and gas exploration at depths over 10 000 m. The research on dolomite in this study will greatly promote China's ultradeep oil and gas exploration and lead the Chinese petroleum industry into a new era of 10 000‐m deep oil exploration.
Engineering geology. Rock mechanics. Soil mechanics. Underground construction
This paper investigates the coupled relationship between solid-phase temperature fields and droplet evaporation, focusing on the effects of substrate thermal conduction properties on droplet evaporation behavior. A mathematical model is developed to analyze the impacts of substrate thermal conductivity, thickness, and lower-surface temperature on evaporation rate, surface temperature, and evaporation flux. A dimensionless relative evaporation rate (HCs) is introduced to characterize the influence of substrate thermal conduction. Results show that increasing substrate thermal conductivity enhances droplet surface temperature and evaporation flux, thereby monotonically increasing evaporation rate until it approaches the rate of the evaporative cooling model. Conversely, increasing substrate thickness lengthens the heat transfer path, reducing heat conducted to the solid–liquid interface and decreasing evaporation rate. Changes in substrate lower-surface temperature significantly affect evaporation rate, but HCs remains nearly unaffected. The concept of equivalent substrates is proposed and verified through dimensionless analysis and simulations. It is found that different combinations of substrate thickness and thermal conductivity exhibit consistent effects on droplet evaporation, with minimal relative errors in evaporation rate and total heat transfer at the solid–liquid interface. This confirms the existence of the equivalent substrate phenomenon. Additionally, the effects of droplet properties, such as contact angle and evaporative cooling coefficient (<i>Ec</i>), on the equivalent substrate phenomenon are explored, revealing negligible impacts. These findings provide theoretical guidance for optimizing droplet evaporation processes in practical applications, such as micro/nanoscale thermal management systems.
Countries worldwide are increasingly focused on addressing the imbalance between the supply and demand for EV charging infrastructure, with the community-shared charging post (CSCP) co-construction project emerging as a promising solution. The broad participation and investment support of the residents are the keys to the success of the CSCP co-construction project. This study, grounded in the theory of planned behavior (TPB) from social psychology, incorporated factors such as community identity, perceived green value, economic benefit, uncivil behaviors, and perceived risk to construct a structural model explaining community residents’ intention to invest in the CSCP co-construction project. This research confirmed that (1) 85.73% of respondents expressed strong recognition of the CSCP co-construction project, with a mean recognition score of 5.56 out of a possible 7; (2) an individual’s social-related perceptions, including the subjective norms and community identity are the strongest determinant of the intention to invest in the CSCP co-construction project; (3) the willingness to invest in CSCP co-construction project differs significantly between the EV group and the non-EV group. Economic benefit was significant only for the non-EV group, while uncivil behaviors were significant only for the EV group. These results provide valuable guidelines for governments and corporations that are promoting or pursuing sharing community for the residents.
Yining Hong, Christopher S. Timperley, Christian Kästner
Machine learning (ML) components are increasingly integrated into software products, yet their complexity and inherent uncertainty often lead to unintended and hazardous consequences, both for individuals and society at large. Despite these risks, practitioners seldom adopt proactive approaches to anticipate and mitigate hazards before they occur. Traditional safety engineering approaches, such as Failure Mode and Effects Analysis (FMEA) and System Theoretic Process Analysis (STPA), offer systematic frameworks for early risk identification but are rarely adopted. This position paper advocates for integrating hazard analysis into the development of any ML-powered software product and calls for greater support to make this process accessible to developers. By using large language models (LLMs) to partially automate a modified STPA process with human oversight at critical steps, we expect to address two key challenges: the heavy dependency on highly experienced safety engineering experts, and the time-consuming, labor-intensive nature of traditional hazard analysis, which often impedes its integration into real-world development workflows. We illustrate our approach with a running example, demonstrating that many seemingly unanticipated issues can, in fact, be anticipated.
Hashini Gunatilake, John Grundy, Rashina Hoda
et al.
Empathy, defined as the ability to understand and share others' perspectives and emotions, is essential in software engineering (SE), where developers often collaborate with diverse stakeholders. It is also considered as a vital competency in many professional fields such as medicine, healthcare, nursing, animal science, education, marketing, and project management. Despite its importance, empathy remains under-researched in SE. To further explore this, we conducted a socio-technical grounded theory (STGT) study through in-depth semi-structured interviews with 22 software developers and stakeholders. Our study explored the role of empathy in SE and how SE activities and processes can be improved by considering empathy. Through applying the systematic steps of STGT data analysis and theory development, we developed a theory that explains the role of empathy in SE. Our theory details the contexts in which empathy arises, the conditions that shape it, the causes and consequences of its presence and absence. We also identified contingencies for enhancing empathy or overcoming barriers to its expression. Our findings provide practical implications for SE practitioners and researchers, offering a deeper understanding of how to effectively integrate empathy into SE processes.
Abstract The growing focus on enhancing color quality in liquid crystal displays (LCDs) and organic light-emitting diodes (OLEDs) has spurred significant advancements in color-conversion materials. Furthermore, color conversion is also important for the development and commercialization of Micro-LEDs. This article provides a comprehensive review of different types of color conversion methods as well as different types of color conversion materials. We summarize the current status of patterning process, and discuss key strategies to enhance display performance. Finally, we speculate on the future prospects and roles that color conversion will play in ultra-high-definition micro- and projection displays.
Climate change has become a global issue affecting all countries in the last decades. This phenomenon poses a concern to Indonesia as it is one of the climate change’s epicenters. Various studies have shown that climate change can harm multiple community activities, such as unstable agricultural production, decreased people’s health, and global warming. This study tried to model and analyze climate change topics discussed in the media. Finding hidden topics from texts can provide clues and information regarding public conversation surrounding climate change, such as public thoughts, perceptions, and readiness to mitigate the possible adverse effects of climate change. In order to identify hidden subjects from the corpus, this work modeled climate change issues in Indonesia using the latent Dirichlet allocation (LDA) algorithm to analyze texts from Indonesian media headlines. As many as 7,000 headline data from five online media were collected from 2017 to 2021 using web scraping techniques. The proposed approach produced eight topics related to climate change, which were determined by the highest coherence value of 0.560. Those topics were renewable energy, carbon emissions, environmental management, development economics, international cooperation, policy/regulation, rehabilitation, and disaster. Based on the results, the model could sufficiently describe the theme of discussion in society and photograph public thoughts and the government’s readiness in the form of policies and regulations in dealing with the climate change phenomenon.
Case studies are a popular and noteworthy type of research study in software engineering, offering significant potential to impact industry practices by investigating phenomena in their natural contexts. This potential to reach a broad audience beyond the academic community is often undermined by deficiencies in reporting, particularly in the context description, study classification, generalizability, and the handling of validity threats. This paper presents a reflective analysis aiming to share insights that can enhance the quality and impact of case study reporting. We emphasize the need to follow established guidelines, accurate classification, and detailed context descriptions in case studies. Additionally, particular focus is placed on articulating generalizable findings and thoroughly discussing generalizability threats. We aim to encourage researchers to adopt more rigorous and communicative strategies, ensuring that case studies are methodologically sound, resonate with, and apply to software engineering practitioners and the broader academic community. The reflections and recommendations offered in this paper aim to ensure that insights from case studies are transparent, understandable, and tailored to meet the needs of both academic researchers and industry practitioners. In doing so, we seek to enhance the real-world applicability of academic research, bridging the gap between theoretical research and practical implementation in industry.
Safety on construction sites is now a top priority for the construction industry all around the world. Construction labor is often seen as hazardous, putting employees at risk of serious accidents and diseases. The use of Industrial Revolution (IR) 4.0 advanced technologies such as robotics and automation, building information modelling (BIM), augmented reality and virtualization, and wireless monitoring and sensors are seen to be an effective way to improve the health and safety of construction workers at the job site, as well as to ensure construction safety management in general. The main aim of this research was to analyze the IR-4.0-related technologies for improving the health and safety problems in the construction industry of Malaysia by utilizing the analytical hierarchy process (AHP) technique. IR-4.0-related technologies show great potential in addressing the construction industry’s existing health and safety problems from the perspective of civil engineering practitioners and industry experts. This research adopted the analytical hierarchy process (AHP) for quantitative analysis of data collected through the survey questionnaire approach. The findings of the study indicate that from matrix multiplication, the highest importance among the criteria and the alternatives was for BIM with a score of 0.3855, followed by wireless monitoring and sensors (0.3509). This research suggests that building information modelling (BIM) and integrated systems had the greatest potential as advanced technology and should be prioritized when it comes to introducing it to the construction industry to improve the current health and safety performances.
Nowadays, services related to IT technologies have assumed paramount importance in most sectors, creating complex systems involving different stakeholders. Such systems are subject to unpredictable risks that differ from what is usually expected and cannot be properly managed using traditional risk assessment approaches. Consequently, ensuring their reliability represents a critical task for companies, which need to adopt resilience engineering tools to reduce the occurrence of failures and malfunctions. With this goal in mind, the current study proposes a risk assessment procedure for cloud migration processes that integrates the application of the Functional Resonance Analysis Method (FRAM) with tools aimed at defining specific performance requirements for the suppliers of this service. In particular, the Critical-To-Quality (CTQ) method was used to define the quality drivers of the IT platform customers, while technical standards were applied to define requirements for a security management system, including aspects relevant to the supply chain. Such an approach was verified by means of its application to a real-life case study, which concerns the analysis of the risks inherent to the supply chain related to cloud migration. The results achieved can contribute to augmenting knowledge in the field of IT systems’ risk assessment, providing a base for further research.
The digitization of engineering drawings is crucial for efficient reuse, distribution, and archiving. Existing computer vision approaches for digitizing engineering drawings typically assume the input drawings have high quality. However, in reality, engineering drawings are often blurred and distorted due to improper scanning, storage, and transmission, which may jeopardize the effectiveness of existing approaches. This paper focuses on restoring and recognizing low-quality engineering drawings, where an end-to-end framework is proposed to improve the quality of the drawings and identify the graphical symbols on them. The framework uses K-means clustering to classify different engineering drawing patches into simple and complex texture patches based on their gray level co-occurrence matrix statistics. Computer vision operations and a modified Enhanced Super-Resolution Generative Adversarial Network (ESRGAN) model are then used to improve the quality of the two types of patches, respectively. A modified Faster Region-based Convolutional Neural Network (Faster R-CNN) model is used to recognize the quality-enhanced graphical symbols. Additionally, a multi-stage task-driven collaborative learning strategy is proposed to train the modified ESRGAN and Faster R-CNN models to improve the resolution of engineering drawings in the direction that facilitates graphical symbol recognition, rather than human visual perception. A synthetic data generation method is also proposed to construct quality-degraded samples for training the framework. Experiments on real-world electrical diagrams show that the proposed framework achieves an accuracy of 98.98% and a recall of 99.33%, demonstrating its superiority over previous approaches. Moreover, the framework is integrated into a widely-used power system software application to showcase its practicality.
In this review, our aim is to make a brief description about technology of Wireless Sensor Network (WSN) and its capability to pave the way in order to make connection between physical and virtual world based on Internet worldwide network. Hence, in this technology, sensor nodes play an important role to transmit data from a node to other defined nodes in its broaden range. Due to gain most optimal state from WSNs, subject of localization for radio frequency networks has a great importance in many technical applications such as military devices to detect specified local points to attack or defend, civil engineering and in general sensor networks. The main technology to obtain direct locations is GPS (Global Positioning System). After expressing a brief history on introduction part, we will go through in order to interrogate on main structure of WSNs regarding mathematical formulations and algorithms to find best and optimal access points based on Localization action. Then, we summarize algorithms and approaches to develop in order to introduce the best strategy in order to access nodes in the best possible state in WSNs. As a result, we conclude about the mentioned issues in order of comparison and reaching a final result. Therefore, final aim of this review is to explain efficiency and reliability of localization based on different opinions. Results show this overwhelming technology can be completely modified in order to find new solutions to find nodes in most optimal nodes based on spontaneous structure of WSNs.
Djamila Boukhelkhal, Mohamed Guendouz, Alexandra Bourdot
et al.
Use of olive core wastes as sand in self-compacting mortar (SCM). The behavior of SCM with olive core waste is evaluated by the physico-mechanical and thermal properties of different mixes. The bulk density and thermal conductivity are improved by using of olive core wastes. Use of olive core wastes as sand in self-compacting mortar (SCM). The behavior of SCM with olive core waste is evaluated by the physico-mechanical and thermal properties of different mixes. The bulk density and thermal conductivity are improved by using of olive core wastes. The recycling of organic wastes in the field of civil engineering is a very important process as long as the products to be obtained are not subjected to stringent quality standards. This research is a part of the general policy of saving energy and protecting the environment. Its aim is to study the possibility of developing a new insulating building material by recycling vegetable waste from the olive processing industry (olive core) that discarded in nature. After having been sorted, dried and then extruded in the form of grains, these wastes are incorporated as fine aggregate (sand) in the manufacturing of self-compacting mortar (SCM) by substituting the mass of sand with different percentages (10, 20, 30 40 and 50%). The physico-mechanical and thermal properties of the obtained SCMs are analyzed and compared to the control. The results of this study show a decrease in density and compressive strength of SCM by increasing the content of olive core wastes. However, the thermal properties of SCM are improved through replacing sand by such wastes, which could allow using olive waste core based SCM in various types of nonstructural components with intriguing insulating properties.