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.
Tunnelling reactions of molecules embedded on cryogenic noble-gas matrices are being used in fundamental studies of how reactivity varies with the nature of the supposedly inert matrix as well as pointers to the chemistry occurring in the interstellar medium on ice-grains. To these ends we present chemical kinetic rate constants for the \textit{cis} to \textit{trans} isomerisation of seleno-, thio- and monomeric formic acids and that of their three dimeric species, based on multidimensional calculations in the gas-phase, from 10~K to 300~K as a guide to the matrix reactions.
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.
Cameron A. B. Smith, Avinoam Bar-Zion, Qiang Wu
et al.
Genetically encodable gas-filled particles, known as gas vesicles (GVs), have shown promise as a biomolecular contrast agent for ultrasound imaging and have the potential to be used as cavitation nuclei for ultrasound therapy. In this study, we used passive acoustic mapping techniques to characterize GV-seeded cavitation, utilizing 0.5 and 1.6 MHz ultrasound insonation over peak rarefactional pressures ranging from 100 to 2200 kPa. We found that GVs produce cavitation for the duration of the first applied pulse, up to at least 5000 cycles, but that bubble activity diminishes rapidly over subsequent pulses. At 0.5 MHz, the frequency content of cavitation emissions was predominantly broadband in nature, while at 1.6 MHz, narrowband content at harmonics of the main excitation frequency dominated. Simulations and high-speed camera imaging suggest that the received cavitation emissions come not from individual GVs but instead from the coalescence of GV-released gas into larger bubbles during the applied ultrasound pulse. These results will aid the future development of GVs as cavitation nuclei in ultrasound therapy.
This study investigates the isolation and application of a Klason-lignin-based biosorbent derived from sugar palm fruit shell (SPFS) waste for the adsorption of Pb(II) and Cd(II) from toxic wastewater. The SPFS waste, rich in lignocellulose, underwent pretreatment to extract lignin-containing functional groups suitable for metal ion adsorption. The biosorbent's morphology before and after adsorption was analyzed. Adsorption effectiveness was evaluated through isotherm and kinetic models, with optimal conditions determined using Box-Behnken Design and Response Surface Methodology. Maximum adsorption capacities of 31.35 mg/g for Pb(II) and 12.5 mg/g for Cd(II) were achieved. Optimal conditions, based on Box-Behnken design, were 116.535 mins, 60 mg/L concentration, and 0.5 g adsorbent mass, resulting in adsorption efficiencies of 94.48 % and 60.98 % for Pb(II) and Cd(II), respectively. This research demonstrates the potential of Klason lignin from sugar palm fruit shell (KLSPFS) as an effective biosorbent for Pb(II) and Cd(II) removal, contributing to sustainable development goals 3, 6, and 12 by promoting efficient recycling technologies for improved health, water quality, and waste reduction.
In traditional and iterative algebraic techniques, representing fuzzy digraphs using their adjacency matrices can pose challenges, particularly when dealing with graphs featuring an extensive quantity of nodes or edges. These difficulties are especially pronounced in graphs that do not necessarily exhibit strong connectivity between nodes, which are known as reducible digraphs. In this study, we explore different structure of reducible fuzzy digraphs, employing an innovative b-metric structure. This structure enhances the embedding of massive data entities or nodes into low-dimensional realm depicted by a normed space. Therefore, our aim is to define a
novel notion of a distance function called bRD-distance between any two vertices (or nodes) in reducible fuzzy digraphs and utilize it to introduce quasi-pseudo- b-metric spaces for these digraphs. Furthermore, to minimize bRD-distance calculations, we demonstrate the process of embedding such bRD-metrics on reducible fuzzy graphs into the designated normed space ℓ∞. Ultimately, a computational example, real-world application, and comparative evaluation confirm the practicality of the suggested technique in handling reducibility, asymmetry, and vagueness in fuzzy digraphs.
Engineering (General). Civil engineering (General), Chemical engineering
Afia Yasmin, Bristy Biswas, Md. Lutfor Rahman
et al.
CoFe2O4 was synthesized at 150 °C, 180 °C, and 210 °C temperatures using hydrothermal method to find the effect on its structural, magnetic, electric, and optical properties. The saturation magnetization, coercivity and magnetic anisotropy was found using Vibrating Sample Magnetometer (VSM), ranging from 50.36 to 53.66 emu/g. XRD (X-ray Diffraction Analysis) and SEM (Scanning Electron Microscopy), FTIR (Fourier Transform Infrared Spectroscopy) was used for structural analysis verifying the spinel ferrite structure with a single phase. The crystalline size and lattice strain was found using Size-Strain Plot (SSP) and Debye-Scherrer (D-S) method which proved that as the synthesis temperature increased, the crystallite size also increased. The crystalline size ranges from 39.40 to 82.24 nm as observed by XRD. SEM analysis found the crystal size range to be from 9 to 12 nm. It was found that the optimum temperature to synthesize cobalt ferrite nanoparticles are at 180 °C for sample H2 with a crystal size of 82.24 nm and band gap energy of 2.60 eV. The Ms value was determined to be 50.36 emu/g for H2 sample with Rs value of 0.31.
As engineering applications become increasingly complex, the need for miniaturization is present in several technological fields. Places hardly reachable by traditional tools and machines, can be accessed thanks to miniaturized devices and, especially when such devices are remotely controlled or autonomous, it implies the need for miniaturized, standalone actuators. Most high-energy density actuators for these applications can not be operated if untethered from an external power-supply.In this study, we investigate the manufacturing of a mesoscale compliant expansion chamber for a miniaturized chemical-based actuator. Photopolymerization and material jetting are used for manufacturing the prototypes, exhibiting dimensions of 9.8 mm diameter, 7.5 mm height and 140 μm thickness. These dimensions are such as to allow the device to fit inside of the flapping wings micro aerial vehicle (FWMAV) that it has to power. Fabrication of such dimensions, along with the peculiar geometry of the chamber, taps into the limitations of the photopolymerization process and highlights areas of improvement for this rapidly-developing technology. The devices are successfully tested for a linear motion, mimicking that of a cylinder-piston combination, as in a conventional expansion chamber, and are actuated by a pressure pulse.
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.
We extend our measurement of the equation of state of isospin asymmetric QCD to small baryon and strangeness chemical potentials, using the leading order Taylor expansion coefficients computed directly at non-zero isospin chemical potentials. Extrapolating the fully connected contributions to vanishing pion sources is particularly challenging, which we overcome by using information from isospin chemical potential derivatives evaluated numerically. Using the Taylor coefficients, we present, amongst others, first results for the equation of state along the electric charge chemical potential axis, which is potentially of relevance for the evolution of the early Universe at large lepton flavour asymmetries.
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.
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.
Yacouba Telly, Xuezhi Liu, Tadagbe Roger Sylvanus Gbenou
Despite its immense natural resources, Angola struggles to significantly improve its economy to reduce poverty. Carbon emissions have been increasing over the years, even though the country plans to reduce them by 35% by 2030. This paper attempts to assess the carbon emissions of several sectors (industries, transport, services, and residences) on economic growth, intending to find a balance between environmental protection that requires carbon emissions reduction and economic development that may add to environmental degradation. The study employed time series data on GDP, CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O covering 1971 to 2021 and ARDL and ECM models. This is the first study at the state level in Angola on the relationship between economic development and environmental sustainability considering methane and nitrous oxide emissions. Additionally, the paper assesses the responses of GDP to deviation shock of GDP, CO<sub>2</sub>, CH<sub>4</sub>, and N<sub>2</sub>O by 2032. Phillip Perron and Augmented Dickey-Fuller tests showed that all the data are stationary at the first difference, favoring the application of the ARDL model to explore the short and long-run relationships. The result reveals that methane from agricultural activities and carbon emissions from the building sector and public services contribute to economic growth, whereas carbon emissions from industrial heat systems, non-renewable electricity production, and manufacturing industries harm economic growth. However, no relationship exists between nitrous oxide emissions and economic development. In addition, impulse response function estimates show that appropriate investments can sustain economic development over the years. Therefore, the country should diversify its economy and avoid polluting fuel sources, such as coal. Raising renewable energy’s proportion in the total energy mix can support growth while considering the environmental quality. Investments in skills training, academic projects in renewable energy technologies development, agriculture mechanization, and sustainable job creation are recommended. Additionally, investing in quality seeds adapted to climate realities might help lessen climate change’s adverse effects and promote growth. Manure manufacturing processes must be improved to reduce agriculture and livestock’s methane and nitrous oxide emissions. The country’s leaders are encouraged to promote raw material processing industries while insisting on reducing carbon emissions.
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.
Analytics corresponds to a relevant and challenging phase of Big Data. The generation of knowledge from extensive data sets (petabyte era) of varying types, occurring at a speed able to serve decision makers, is practiced using multiple areas of knowledge, such as computing, statistics, data mining, among others. In the Big Data domain, Analytics is also considered as a process capable of adding value to the organizations. Besides the demonstration of value, Analytics should also consider operational tools and models to support decision making. To adding value, Analytics is also presented as part of some Big Data value chains, such the Information Value Chain presented by NIST among others, which are detailed in this article. As well, some maturity models are presented, since they represent important structures to favor continuous implementation of Analytics for Big Data, using specific technologies, techniques and methods. Hence, through an in-depth research, using specific literature references and use cases, we seeks to outline an approach to determine the Analytical Engineering for Big Data Analytics considering four pillars: Data, Models, Tools and People; and three process groups: Acquisition, Retention and Revision; in order to make feasible and to define an organization, possibly designated as an Analytics Organization, responsible for generating knowledge from the data in the field of Big Data Analytics.
It is believed that polar regions are influenced by global warming more significantly, and because polar regions are less affected by human activities, they have certain reference values for future predictions. This study aimed to investigate the effects of climate warming on soil microbial communities in lake areas, taking Kitezh Lake, Antarctica as the research area. Below-peak soil, intertidal soil, and sediment were taken at the sampling sites, and we hypothesized that the diversity and composition of the bacterial and archaeal communities were different among the three sampling sites. Through 16S rDNA sequencing and analysis, bacteria and archaea with high abundance were obtained. Based on canonical correspondence analysis and redundancy analysis, pH and phosphate had a great influence on the bacterial community whereas pH and nitrite had a great influence on the archaeal community. Weighted gene coexpression network analysis was used to find the hub bacteria and archaea related to geochemical factors. The results showed that in addition to pH, phosphate, and nitrite, moisture content, ammonium, nitrate, and total carbon content also play important roles in microbial diversity and structure at different sites by changing the abundance of some key microbiota.
Margarita Panagiotopoulou, Sofia Papadaki, Magdalini Krokida
The objective of this work is to investigate the nanoencapsulation of valuable bioactive compounds with the innovative electrohydrodynamic technology. More specifically, electrospraying, was applied for the encapsulation of three different active agents with lipolytic, anti-aging and antioxidant activity: Deoxycholic acid (DCA), olive leaves’ enzymatically modified extract and a hybrid of Deoxycholic acid-Hydroxytyrosol (DCA-HXT) in zein (ZN) biodegradable polymer. The resulting powders were evaluated regarding the system's: compositional and chemical characteristics, thermal stability and humidity effect, morphology, shape and size, encapsulation efficiency and release kinetics from the wall material. Encapsulation led to stable nanosystems, regarding the moisture and temperature effect, with high glass Transition Temperatures (Tg between 130-139 °C), and a mean particle size of 400-600 nm. Finally, high encapsulation efficiencies (DCA= 84.01%, DCA-HXT = 79.34% and modified extract =80.75%) and long-term release profiles (DCA=192 h, DCA-HXT=228 h and mod. extract=100 h) were measured for all three systems. Based on these results, we conclude that electrospraying process is an effective methodology for the development of bioactive agents’ nanosystems with excellent physicochemical properties and increased bioavailability; a crucial factor for the formation of high added-value products such as cosmetics.