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.
Phenological parameters extracted from time series (TS) of spectral indices are essential to characterizing crops. However, the lack of data in the TS can affect their accuracy. The Copernicus Land Monitoring Service (CLMS) provides these parameters and their temporal quality. This paper evaluates the impact of missing vegetation index data on phenological parameters, namely, SOS, EOS, and MAX, for extensive arable crop between 2018 and 2023. The TSGenerator package was developed to download, process, and analyze the data. We used 252 images from the BIOPAR-VI module, 6 phenology parameters, and 2025 plots of barley and maize in Monegros and Zaidín, Spain. In barley, SOS and MAX showed 42.9% and 40.9% of missing data, while in maize, SOS and EOS showed 36.6% and 41.0%. The correlation between the Copernicus VPP quality parameter and the proposed one was r = 0.89 for barley and r = 0.74 for maize. This study advances the understanding of the effect of missing data on SOS, EOS, and MAX.
We evaluated and predicted the quality of financial services and professional management using cluster analysis. Using K-prototype clustering analysis and TF-IDF word frequency methods, the differences in different evaluations of job positions and vocational skill requirements of college graduates were analyzed. The graduates with better school curricula and higher rationality tended to have more knowledge-based skills. Professional knowledge learning ability, theoretical knowledge level, project execution ability, and organizational coordination ability were important in learning skill requirements. The ability to analyze data and conduct research and development is important in the development of digital finance technology. It is necessary to build a professional foundation, teach workplace skills, keep up with recent technology, and optimize the standards to improve educational effectiveness in educating financial services and management.
Ismail El Gaabouri, Mostafa Belkasmi, Mohamed Senhadji
et al.
From hieroglyphic writing in ancient Egypt to the post-quantum edge, cryptology is usually seen as an immortal concept that evolves within the enhancement of human civilization. However, modern cryptography primitives try to make revealing ciphered information as tough as possible for attackers. As a sort of enhancement, substitution boxes play an important role in leveraging security, especially for symmetric-based algorithms. The S-box concept is integrated internally into the encryption process for block ciphers and added as a strengthened layer for stream ciphers. Consequently, in-depth analytical considerations are always needed to gather the required information if any S-box wants to be integrated. For this reason, this paper is about providing a scrutiny cryptanalysis for these S-boxes and, more precisely, size ones, since they are not widely investigated.
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.
A retaining wall was practically developed to provide lateral support for soil, and it is widely used in underground projects, highway barriers, and mines as well as for aesthetic considerations and slope stabilization. This type of earth structure member can carry machine foundation load simultaneously with traditional static load. This study carried out using the finite element program PLAXIS 3D. The linear elastic model for retaining walls and the Mohr-Coulomb model for soil layers were used in this numerical analysis. The study included three layers of soils under the wall with dry condition. The high of the wall was 4m and the dimensions of machine foundation were 3x3m. It can be concluded that the vertical settlement, horizontal displacement and velocity increased when the duration of the machine load increases. Usually, the horizontal displacement increases to highest value and reached to 10 times the original static value when the machine was closed to the wall with 0.5m and 75Hz. This can be taken into account in the design for such geotechnical system in the design stages.
Engineering machinery, tools, and implements, Mechanics of engineering. Applied mechanics
The present study introduces a two-step extraction methodology that integrates cloud point extraction (CPE) with magnetic solid-phase extraction (MSPE) for the extraction and quantification of amaranth dye. Initially, the dye is extracted using CPE in the micellar phase of the non-ionic surfactant Triton X-114. Subsequently, hydrophobic tetraethyl orthosilicate (TEOS)-modified Fe<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow><mspace width="-2.pt"></mspace><mo> </mo></mrow><mn>3</mn></msub></semantics></math></inline-formula>O<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msub><mrow><mspace width="-2.pt"></mspace><mo> </mo></mrow><mn>4</mn></msub></semantics></math></inline-formula> magnetic nanoparticles (MNPs) are employed to recover the micellar phase. A comprehensive evaluation was conducted to optimize the key parameters influencing the efficacy of both CPE and MSPE techniques, as well as signal enhancement. Under optimized conditions, the proposed methodology exhibited a linear response in the concentration range of 10 to 90 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>g Kg<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow><mspace width="-2.pt"></mspace><mo> </mo></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>, with a correlation coefficient (<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mi>R</mi><mn>2</mn></msup></semantics></math></inline-formula>) of 0.9945. The detection limit was determined to be 8.443 <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mi mathvariant="sans-serif">μ</mi></semantics></math></inline-formula>g g<inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><msup><mrow><mspace width="-2.pt"></mspace><mo> </mo></mrow><mrow><mo>−</mo><mn>1</mn></mrow></msup></semantics></math></inline-formula>. This robust and environmentally friendly approach offers a promising avenue for the accurate and efficient determination of amaranth dye in various applications.
Róbert Dzurňák, Gustáv Jablonský, Katarína Pauerová
et al.
This paper presents the results of increasing the hydrogen concentration in natural gas distributed within the territory of the Slovak Republic. The range of hydrogen concentrations in the mathematical model is considered to be from 0 to 100 vol.% for the resulting combustion products, temperature, and heating value, and for the scientific assessment of the environmental and economic implications. From a technical perspective, it is feasible to consider enriching natural gas with hydrogen up to a level of 20% within the Slovak Republic. CO<sub>2</sub> emissions are estimated to be reduced by 3.76 tons for every 1 TJ of energy at an operational cost of EUR 10,000 at current hydrogen prices.
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.
Searching for similar images in archives of histology and histopathology images is a crucial task that may aid in patient matching for various purposes, ranging from triaging and diagnosis to prognosis and prediction. Whole slide images (WSIs) are highly detailed digital representations of tissue specimens mounted on glass slides. Matching WSI to WSI can serve as the critical method for patient matching. In this paper, we report extensive analysis and validation of four search methods bag of visual words (BoVW), Yottixel, SISH, RetCCL, and some of their potential variants. We analyze their algorithms and structures and assess their performance. For this evaluation, we utilized four internal datasets ($1269$ patients) and three public datasets ($1207$ patients), totaling more than $200,000$ patches from $38$ different classes/subtypes across five primary sites. Certain search engines, for example, BoVW, exhibit notable efficiency and speed but suffer from low accuracy. Conversely, search engines like Yottixel demonstrate efficiency and speed, providing moderately accurate results. Recent proposals, including SISH, display inefficiency and yield inconsistent outcomes, while alternatives like RetCCL prove inadequate in both accuracy and efficiency. Further research is imperative to address the dual aspects of accuracy and minimal storage requirements in histopathological image search.
The integration of artificial intelligence into agricultural practices, specifically through Consultation on Intelligent Agricultural Machinery Management (CIAMM), has the potential to revolutionize efficiency and sustainability in farming. This paper introduces a novel approach that leverages large language models (LLMs), particularly GPT-4, combined with multi-round prompt engineering to enhance decision-making processes in agricultural machinery management. We systematically developed and refined prompts to guide the LLMs in generating precise and contextually relevant outputs. Our approach was evaluated using a manually curated dataset from various online sources, and performance was assessed with accuracy and GPT-4 Scores. Comparative experiments were conducted using LLama-2-70B, ChatGPT, and GPT-4 models, alongside baseline and state-of-the-art methods such as Chain of Thought (CoT) and Thought of Thought (ThoT). The results demonstrate that our method significantly outperforms these approaches, achieving higher accuracy and relevance in generated responses. This paper highlights the potential of advanced prompt engineering techniques in improving the robustness and applicability of AI in agricultural contexts.
Digitalization technologies have been identified as enablers for the adoption of Circular Economy practices. The machinery value chain, addressed in this study, is affected by the introduction of digital technologies that enable real-time monitoring data on product condition and control optimization deploying predictive analytics techniques, as well as the offerings of circular-based services. Machinery lifetime extension can be digitally enabled on both old and new machines. The research objectives were to investigate how digital technologies enable the adoption of circular economy-based business models in manufacturing companies and provide an answer about i) what Life Cycle Extension Strategy is suitable for digital circular business model adoption and ii) how digitalization of machines enables manufacturing companies to innovate their business model. The correlation matrix is the tool developed from the proposed approach; it aims at supporting manufacturers in the very first contact with circular business models. In the context of the European RECLAIM project, two manufacturers apply the approach, proving its validity and wide applicability. The next steps are expected to introduce quantitative indicators to define thresholds for the steps toward circularity, without replacing the qualitative approach, as it guarantees the wide applicability of the approach in context that never considered circularity yet.
The inherent complexities of Artificial Intelligence (AI) and machine learning (ML) technologies expose autonomous ships to a wide range of multifaceted interconnected risks. However, very few studies have aimed at the holistic risk assessment of autonomous ships. To this end, this study employs an expert-opinion-based integrated machine learning approach amalgamating logistic regression and Bayesian network to conduct risk assessment for autonomous ships. The results reveal human factor interactions and operational issues as the prominent accident causation factors. The findings of this study will contribute significantly to the existing literature on autonomous ships and the complexities involved in their operational systems. By identifying critical factors causing accidents and their impact on autonomous ship safety and resilience, stakeholders such as autonomous ship manufacturers, port authorities, shipping companies, and governments can develop more efficient and effective operational and safety systems.
Jason Aebischer, Matteo Fael, Javier Fuentes-Martín
et al.
In recent years, theoretical and phenomenological studies with effective field theories have become a trending and prolific line of research in the field of high-energy physics. In order to discuss present and future prospects concerning automated tools in this field, the SMEFT-Tools 2022 workshop was held at the University of Zurich from 14th-16th September 2022. The current document collects and summarizes the content of this workshop.
Marc Arnela, Mariona Ferrandiz-Rovira, Marc Freixes
et al.
Environmental noise and air pollution, as well as poor green infrastructure quality, are major concerns for the European population due to their impacts on citizens’ health, especially for those citizens living in urban environments, which materializes in a rising number of complaints to public administration. This issue is further stressed for urban areas located close to aggressive sources of such pollutants, such as airports, railways, highways, or leisure areas. To attend to this situation from the viewpoint of citizens’ everyday lives, this paper proposes a hybrid methodology in the form of a collective campaign in which citizens, especially those from environments that have a stronger impact, cooperate with scientists to collect high quality acoustic, chemical, and biodiversity data. The campaign consists of a conscious walk that considers acoustic measurements conducted by both experts and citizens, coupled with air quality measurements and biodiversity descriptions. The final goal of the method is to obtain subjective and objective data on the soundscape, air quality, and biodiversity in order to evaluate a pre-designed route in an urban location, namely, in the surroundings of Parc de la Ciutadella, Barcelona, Spain.
Gilda Santos, Rita Marques, Francisca Marques
et al.
Nowadays, despite the evolution of personal protective equipment (PPE), the number of firefighters injured and burned during fire extinguishing operations is still very high, leading in some cases to loss of life. Therefore, the research and development of new solutions to minimize firefighters’ heat load and skin burns, with consecutive improvements of commercial firefighters’ suits, is of extreme importance. The integration of phase change materials (PCMs) in a protective clothing system has been used to significantly reduce the incoming heat flux from the fire environment. This study consists in the development of a protective clothing system composed by a vest, specially designed to protect the torso (back, chest and abdomen) with a layer of PCM pouches, to be worn over a fire-resistant jacket – selection and design based on numerical models’ predictions. Therefore, several mockups were made, varying the number of PCM pouches and their distribution in the vest, allowing the creation of air ducts to increase the breathability of the vest. The most promising solutions are being evaluated in a real controlled environment, at a Portuguese National School of Firefighters (ENB) simulation site, using a fire manikin and thermocouples to monitor vest temperature during heat and flame exposure, and consequently to verify PCMs influence in heat protection. Results regarding the development of a PCM vest will be presented, focusing on the integration of PCM pouches and the thermal performance of the most promising solutions.
Textile bleaching, dyeing, printing, etc., Engineering machinery, tools, and implements
Recruiting participants for software engineering research has been a primary concern of the human factors community. This is particularly true for quantitative investigations that require a minimum sample size not to be statistically underpowered. Traditional data collection techniques, such as mailing lists, are highly doubtful due to self-selection biases. The introduction of crowdsourcing platforms allows researchers to select informants with the exact requirements foreseen by the study design, gather data in a concise time frame, compensate their work with fair hourly pay, and most importantly, have a high degree of control over the entire data collection process. This experience report discusses our experience conducting sample studies using Prolific, an academic crowdsourcing platform. Topics discussed are the type of studies, selection processes, and power computation.
Ontologies serve as a one of the formal means to represent and model knowledge in computer science, electrical engineering, system engineering and other related disciplines. Ontologies within requirements engineering may be used for formal representation of system requirements. In the Internet of Things, ontologies may be used to represent sensor knowledge and describe acquired data semantics. Designing an ontology comprehensive enough with an appropriate level of knowledge expressiveness, serving multiple purposes, from system requirements specifications to modeling knowledge based on data from IoT sensors, is one of the great challenges. This paper proposes an approach towards ontology-based requirements engineering for well-being, aging and health supported by the Internet of Things. Such an ontology design does not aim at creating a new ontology, but extending the appropriate one already existing, SAREF4EHAW, in order align with the well-being, aging and health concepts and structure the knowledge within the domain. Other contributions include a conceptual formulation for Well-Being, Aging and Health and a related taxonomy, as well as a concept of One Well-Being, Aging and Health. New attributes and relations have been proposed for the new ontology extension, along with the updated list of use cases and particular ontological requirements not covered by the original ontology. Future work envisions full specification of the new ontology extension, as well as structuring system requirements and sensor measurement parameters to follow description logic.
Mahmoud Kheir-Eddine, Michael Banf, Gregor Steinhagen
Milling machines form an integral part of many industrial processing chains. As a consequence, several machine learning based approaches for tool wear detection have been proposed in recent years, yet these methods mostly deal with standard milling machines, while machinery designed for more specialized tasks has gained only limited attention so far. This paper demonstrates the application of an acceleration sensor to allow for convenient condition monitoring of such a special purpose machine, i.e. round seam milling machine. We examine a variety of conditions including blade wear and blade breakage as well as improper machine mounting or insufficient transmission belt tension. In addition, we presents different approaches to supervised failure recognition with limited amounts of training data. Hence, aside theoretical insights, our analysis is of high, practical importance, since retrofitting older machines with acceleration sensors and an on-edge classification setup comes at low cost and effort, yet provides valuable insights into the state of the machine and tools in particular and the production process in general.
Sharing research artifacts is known to help people to build upon existing knowledge, adopt novel contributions in practice, and increase the chances of papers receiving attention. In Model-Driven Engineering (MDE), openly providing research artifacts plays a key role, even more so as the community targets a broader use of AI techniques, which can only become feasible if large open datasets and confidence measures for their quality are available. However, the current lack of common discipline-specific guidelines for research data sharing opens the opportunity for misunderstandings about the true potential of research artifacts and subjective expectations regarding artifact quality. To address this issue, we introduce a set of guidelines for artifact sharing specifically tailored to MDE research. To design this guidelines set, we systematically analyzed general-purpose artifact sharing practices of major computer science venues and tailored them to the MDE domain. Subsequently, we conducted an online survey with 90 researchers and practitioners with expertise in MDE. We investigated our participants' experiences in developing and sharing artifacts in MDE research and the challenges encountered while doing so. We then asked them to prioritize each of our guidelines as essential, desirable, or unnecessary. Finally, we asked them to evaluate our guidelines with respect to clarity, completeness, and relevance. In each of these dimensions, our guidelines were assessed positively by more than 92\% of the participants. To foster the reproducibility and reusability of our results, we make the full set of generated artifacts available in an open repository at \texttt{\url{https://mdeartifacts.github.io/}}.