Hasil untuk "Industrial engineering. Management engineering"

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.

Md Javeed Khan, Mohammed Raahil Ahmed, Mohammed Abdul Aziz Taha et al.

Tshepo Sithole, Vasudeva Rao Veerdhi, Thembelani Sithebe

This paper presents a comprehensive review of static transfer switch (STS) applications in AC power systems, with a focus on enhancing reliability and fault tolerance. The review outlines the fundamental requirements for effective STS deployment, including the necessity of two truly independent and nominally synchronized AC power sources, optimal placement of the STS near protected loads, and proper grounding practices to minimize single points of failure. The analysis synthesizes recent literature on STS topologies, control mechanisms, and integration with uninterruptible power supplies (UPS), highlighting the importance of redundancy and the persistent challenges of achieving source independence and synchronization. Empirical studies and case analyses are discussed to demonstrate the impact of STS design and deployment on minimizing risks to sensitive loads. The paper concludes by providing practical recommendations and identifying future research directions for further improving STS solutions in resilient AC power systems.

Ye Wang, Yaling Deng, Ge Wang et al.

Modern Large Language Models (LLMs) exhibit complexity and granularity similar to humans in the field of natural language processing, challenging the boundaries between humans and machines in language understanding and creativity. However, whether the semantic network of LLMs is similar to humans is still unclear. We examined the representative closed-source LLMs, GPT-3.5-Turbo and GPT-4, with open-source LLMs, LLaMA-2-70B, LLaMA-3-8B, LLaMA-3-70B using semantic fluency tasks widely used to study the structure of semantic networks in humans. To enhance the comparability of semantic networks between humans and LLMs, we innovatively employed role-playing to generate multiple agents, which is equivalent to recruiting multiple LLM participants. The results indicate that the semantic network of LLMs has poorer interconnectivity, local association organization, and flexibility compared to humans, which suggests that LLMs have lower search efficiency and more rigid thinking in the semantic space and may further affect their performance in creative writing and reasoning.

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.

Hashini Gunatilake, John Grundy, Rashina Hoda et al.

Empathy plays a critical role in software engineering (SE), influencing collaboration, communication, and user-centred design. Although SE research has increasingly recognised empathy as a key human aspect, there remains no validated instrument specifically designed to measure it within the unique socio-technical contexts of SE. Existing generic empathy scales, while well-established in psychology and healthcare, often rely on language, scenarios, and assumptions that are not meaningful or interpretable for software practitioners. These scales fail to account for the diverse, role-specific, and domain-bound expressions of empathy in SE, such as understanding a non-technical user's frustrations or another practitioner's technical constraints, which differ substantially from empathy in clinical or everyday contexts. To address this gap, we developed and validated two domain-specific empathy scales: EmpathiSEr-P, assessing empathy among practitioners, and EmpathiSEr-U, capturing practitioner empathy towards users. Grounded in a practitioner-informed conceptual framework, the scales encompass three dimensions of empathy: cognitive empathy, affective empathy, and empathic responses. We followed a rigorous, multi-phase methodology, including expert evaluation, cognitive interviews, and two practitioner surveys. The resulting instruments represent the first psychometrically validated empathy scales tailored to SE, offering researchers and practitioners a tool for assessing empathy and designing empathy-enhancing interventions in software teams and user interactions.

Metwally Rashad, Doaa M. Alebiary, Mohammed Aldawsari et al.

The expressions on human faces reveal the emotions we are experiencing internally. Emotion recognition based on facial expression is one of the subfields of social signal processing. It has several applications in different areas, specifically in the interaction between humans and computers. This study presents a simple CCNN-SVM automated model as a viable approach for FER. The model combines a Convolutional Neural Network for feature extraction, certain image preprocessing techniques, and Support Vector Machine (SVM) for classification. Firstly, the input image is preprocessed using face detection, histogram equalization, gamma correction, and resizing techniques. Secondly, the images go through custom single Deep Convolutional Neural Networks (CCNN) to extract deep features. Finally, SVM uses the generated features to perform the classification. The suggested model was trained and tested on four datasets, CK+, JAFFE, KDEF, and FER. These datasets consist of seven primary emotional categories, which encompass anger, disgust, fear, happiness, sadness, surprise, and neutrality for CK+, and include contempt for JAFFE. The model put forward demonstrates commendable performance in comparison to existing facial expression recognition techniques. It achieves an impressive accuracy of <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>99.3</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the CK+ dataset, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>98.4</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the JAFFE dataset, <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>87.18</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the KDEF dataset, and <inline-formula><math xmlns="http://www.w3.org/1998/Math/MathML" display="inline"><semantics><mrow><mn>88.7</mn><mo>%</mo></mrow></semantics></math></inline-formula> on the FER.

Joseph Heeley, Samuel Boobier, Jonathan D. Hirst

Abstract Selecting greener solvents during experiment design is imperative for greener chemistry. While many solvent selection guides are currently used in the pharmaceutical industry, these are often paper-based guides which can make it difficult to identify and compare specific solvents. This work presents a stand-alone version of the solvent flashcards that were developed as part of the AI4Green electronic laboratory notebook. The functionality is an intuitive and interactive interface for the visualisation of data from CHEM21, a pharmaceutical solvent selection guide that categorises solvents according to “greenness”. This open-source software is written in Python, JavaScript, HTML and CSS and allows users to directly contrast and compare specific solvents by generating colour-coded flashcards. It can be installed locally using pip, or alternatively the source code is available on GitHub: https://github.com/AI4Green/solvent_flashcards . The documentation can also be found on GitHub or on the corresponding Python Package Index webpage: https://pypi.org/project/solvent-guide/ . Scientific Contribution This simple and easy-to-use digital tool provides a visualisation of solvent greenness data through a novel intuitive interface and encourages green chemistry. It offers numerous advantages over traditional solvent selection guides, allowing users to directly customise the solvent list and generate side-by-side comparisons of only the most important solvents. The release as a standalone package will maximise the benefit of this software. Graphical Abstract

Lichong WANG, Chenhua SUN, Weisong ZHAO et al.

At present, the frequency of non-geostationary orbit(NGSO) mobile satellite service is becoming more and more tense.Aimed at the complicated problem of frequency coordination of mobile direct connection to NGSO satellite service, firstly, an analysis was conducted on the current frequency usage status and trends of mobile direct connection to NGSO satellite service.Secondly, the frequency coordination scheme between mobile direct connection to NGSO satellite service and other same frequency space services in extended L-band was analyzed and studied.By analyzed the frequency coordination of mobile direct connection to NGSO satellite service in extended L-band, the space services that need to carry out frequency coordination work with mobile direct connection to NGSO satellite service were determined.The basic frequency coordination scheme of mobile direct connection to NGSO satellite service with each space service was studied and the coordination scheme suggestions were given, at the same time, the interference analysis and calculation involved in the specific coordination scheme were listed for example.The above research can be used as reference for the research of frequency coordination of mobile direct connection to NGSO satellite service system, as well as for the next step research of key technologies related to mobile direct connection to NGSO satellite service.

B.I. Andrew, A. Anuradha

Hydrocarbons are one of the subclasses of organic compounds that comprise exactly of hydrogen and carbon. Alkanes are one of the types of hydrocarbons that have chemical formula CnH2n+2. Isomers are molecules with identical chemical formula but different structural arrangements, leading to variations in their spectral properties as their corresponding molecular graphs also differ in structure. This exploration is motivated by the understanding that variations in structural configurations manifest as differences in spectral properties, as evidenced by alterations in their respective molecular graphs. Alkanes with ten carbon atoms are called decanes. Our study employs a multifaceted approach, encompassing the determination of spectral properties and the calculation of eigenvalue-based entropy for the C10H22 decane isomers. This analysis is undertaken with the goal of unravelling the intricate relationships between structural variations and corresponding spectral bounds. Notably, our investigation extends beyond the realm of molecular structures to draw connections with physico-chemical properties. Through meticulous comparison of the obtained spectral data with the known characteristics of C10H22 isomers, we unveil interesting correlations among the characteristics. We establish that the spectral gap, a key parameter in our study, intriguingly exhibits a maximal correlation with the refractive index of the isomers. This finding not only enhances our understanding of the spectral intricacies of decane isomers but also underscores the practical implications of such research. The correlation between spectral gap and refractive index opens avenues for predicting and manipulating the optical properties of hydrocarbons, offering potential applications in diverse fields, from materials science to optics. In essence, this study bridges the gap between molecular structure and macroscopic properties, shedding light on the intricate interplay between isomeric variations and their consequential effects on spectral characteristics.

Isaiah Lahr, Saghir Alfasly, Peyman Nejat et al.

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.

Madhava Krishna, Bhagesh Gaur, Arsh Verma et al.

The creation of a Software Requirements Specification (SRS) document is important for any software development project. Given the recent prowess of Large Language Models (LLMs) in answering natural language queries and generating sophisticated textual outputs, our study explores their capability to produce accurate, coherent, and structured drafts of these documents to accelerate the software development lifecycle. We assess the performance of GPT-4 and CodeLlama in drafting an SRS for a university club management system and compare it against human benchmarks using eight distinct criteria. Our results suggest that LLMs can match the output quality of an entry-level software engineer to generate an SRS, delivering complete and consistent drafts. We also evaluate the capabilities of LLMs to identify and rectify problems in a given requirements document. Our experiments indicate that GPT-4 is capable of identifying issues and giving constructive feedback for rectifying them, while CodeLlama's results for validation were not as encouraging. We repeated the generation exercise for four distinct use cases to study the time saved by employing LLMs for SRS generation. The experiment demonstrates that LLMs may facilitate a significant reduction in development time for entry-level software engineers. Hence, we conclude that the LLMs can be gainfully used by software engineers to increase productivity by saving time and effort in generating, validating and rectifying software requirements.

Jinqi Luo, Tianjiao Ding, Kwan Ho Ryan Chan et al.

Large Language Models (LLMs) are being used for a wide variety of tasks. While they are capable of generating human-like responses, they can also produce undesirable output including potentially harmful information, racist or sexist language, and hallucinations. Alignment methods are designed to reduce such undesirable outputs via techniques such as fine-tuning, prompt engineering, and representation engineering. However, existing methods face several challenges: some require costly fine-tuning for every alignment task; some do not adequately remove undesirable concepts, failing alignment; some remove benign concepts, lowering the linguistic capabilities of LLMs. To address these issues, we propose Parsimonious Concept Engineering (PaCE), a novel activation engineering framework for alignment. First, to sufficiently model the concepts, we construct a large-scale concept dictionary in the activation space, in which each atom corresponds to a semantic concept. Given any alignment task, we instruct a concept partitioner to efficiently annotate the concepts as benign or undesirable. Then, at inference time, we decompose the LLM activations along the concept dictionary via sparse coding, to accurately represent the activations as linear combinations of benign and undesirable components. By removing the latter ones from the activations, we reorient the behavior of the LLM towards the alignment goal. We conduct experiments on tasks such as response detoxification, faithfulness enhancement, and sentiment revising, and show that PaCE achieves state-of-the-art alignment performance while maintaining linguistic capabilities.

A. Mannu, S. Garroni, Jesus Ibanez Porras et al.

Recently, the interest in converting waste cooking oils (WCOs) to raw materials has grown exponentially. The driving force of such a trend is mainly represented by the increasing number of WCO applications, combined with the definition, in many countries, of new regulations on waste management. From an industrial perspective, the simple chemical composition of WCOs make them suitable as valuable chemical building blocks, in fuel, materials, and lubricant productions. The sustainability of such applications is sprightly related to proper recycling procedures. In this context, the development of new recycling processes, as well as the optimization of the existing ones, represents a priority for applied chemistry, chemical engineering, and material science. With the aim of providing useful updates to the scientific community involved in vegetable oil processing, the current available technologies for WCO recycling are herein reported, described, and discussed. In detail, two main types of WCO treatments will be considered: chemical transformations, to exploit the chemical functional groups present in the waste for the synthesis of added value products, and physical treatments as extraction, filtration, and distillation procedures. The first part, regarding chemical synthesis, will be connected mostly to the production of fuels. The second part, concerning physical treatments, will focus on bio-lubricant production. Moreover, during the description of filtering procedures, a special focus will be given to the development and applicability of new materials and technologies for WCO treatments.

M. J. Regufe, Ana Pereira, A. Ferreira et al.

An essential line of worldwide research towards a sustainable energy future is the materials and processes for carbon dioxide capture and storage. Energy from fossil fuels combustion always generates carbon dioxide, leading to a considerable environmental concern with the values of CO2 produced in the world. The increase in emissions leads to a significant challenge in reducing the quantity of this gas in the atmosphere. Many research areas are involved solving this problem, such as process engineering, materials science, chemistry, waste management, and politics and public engagement. To decrease this problem, green and efficient solutions have been extensively studied, such as Carbon Capture Utilization and Storage (CCUS) processes. In 2015, the Paris Agreement was established, wherein the global temperature increase limit of 1.5 °C above pre-industrial levels was defined as maximum. To achieve this goal, a global balance between anthropogenic emissions and capture of greenhouse gases in the second half of the 21st century is imperative, i.e., net-zero emissions. Several projects and strategies have been implemented in the existing systems and facilities for greenhouse gas reduction, and new processes have been studied. This review starts with the current data of CO2 emissions to understand the need for drastic reduction. After that, the study reviews the recent progress of CCUS facilities and the implementation of climate-positive solutions, such as Bioenergy with Carbon Capture and Storage and Direct Air Capture. Future changes in industrial processes are also discussed.

L. Blank, T. Narančić, J. Mampel et al.

The envisaged circular economy requires absolute carbon efficiency and in the long run abstinence from fossil feedstocks, and integration of industrial production with end-of-life waste management. Non-conventional feedstocks arising from industrial production and societal consumption such as CO2 and plastic waste may soon enable manufacture of multiple products from simple bulk chemicals to pharmaceuticals using biotechnology. The change to these feedstocks could be faster than expected by many, especially if the true cost, including the carbon footprint of products, is considered. The efficiency of biotechnological processes can be improved through metabolic engineering, which can help fulfill the promises of the Paris agreement.

Le Zhang, K. Loh, Jingxin Zhang

Abstract Solid organic wastes (SOWs) are abundant resources that can be used for conversion to biofuels. Among various conversion technologies, anaerobic digestion (AD) is one of the most promising for management of SOWs because of the production of methane rich biogas and recycling of nutrients. This review consolidates and summarizes research associated with characterization of SOWs, and the advantages/limitations of various performance-enhancing strategies associated with SOWs with the hope to further promote development and industrial applications of the AD technology. Performance-enhancing strategies surveyed include: (1) parameter optimization, (2) physical, chemical, and biological pretreatments, (3) additives, (4) co-digestion, (5) bioreactor design, (6) genetic engineering of enzymes and microbial strains, and (7) coupling technologies of multiple enhancing techniques. Industrial applications of these strategies in AD are also discussed. Finally, current state of AD of SOWs to energy is compiled and several future development prospects of strategies in enhancing biogas production are highlighted.

Chlistier Flovenzia Glorya, Widya Setiafindari

PT Batik Danar Hadi Pabelan merupakan perusahaan tekstil yang memproduksi dan menjual batik tulis dan batik cap. Permasalahan yang terjadi diperusahan ialah perusahaan mendapatkan tingkat hasil kinerja rantai pasok yang berada dibawah rata-rata sejak tahun 2020-2022, yaitu kurang (<) dari 40% yang berarti sangat buruk, sedangkan target yang di inginkan perusahaan yaitu 70%-90% kategori good. Tujuan penelitian adalah untuk mengetahui penyebab terjadinya penurunan tingkat kinerja rantai pasok. Metode yang digunakan yaitu SCOR untuk mencari hasil pengukuran kinerja supply chain dan metode analytical hierarchy process yang digunakan untuk mencari matrik kategori yang menyebabkan penurunan kinerja rantai pasok. Hasil dari penelitian didapatkan nilai akhir rantai pasok adalah sebesar 1,82 dimana nilai tersebut masih berada didalam kategori sangat buruk (poor). Key performance indicator yang sangat mempengaruhi kinerja rantai pasok perusahaan adalah indicator perencanaan pemesanan material, terdapat motif batik yang baru, efisiensi penggunaan mesin dalam proses produksi, dan penanganan kerusakan mesin.   Kata kunci: AHP, Kinerja rantai pasok, KPI, SCOR

Harun Pirim, Morteza Nagahi, Oumaima Larif et al.

Abstract Systems Thinking (ST) has become essential for practitioners and experts when dealing with turbulent and complex environments. Twitter medium harbors social capital including systems thinkers, however there are limited studies available in the extant literature that investigate how experts' systems thinking skills, if possible at all, can be revealed within Twitter analysis. This study aims to reveal systems thinking levels of experts from their Twitter accounts represented as a network. Unraveling of latent Twitter network clusters ensues the centrality analysis of their follower networks inferred in terms of systems thinking dimensions. COVID-19 emerges as a relevant case study to investigate the relationship between COVID-19 experts’ Twitter network and their systems thinking capabilities. A sample of 55 trusted expert Twitter accounts related to COVID-19 has been selected for the current study based on the lists from Forbes, Fortune, and Bustle. The Twitter network has been constructed based on the features extracted from their Twitter accounts. Community detection reveals three distinct groups of experts. In order to relate system thinking qualities to each group, systems thinking dimensions are matched with the follower network characteristics such as node-level metrics and centrality measures including degree, betweenness, closeness and Eigen centrality. Comparison of the 55 expert follower network characteristics elucidates three clusters with significant differences in centrality scores and node-level metrics. The clusters with a higher, medium, lower scores can be classified as Twitter accounts of Holistic thinkers, Middle thinkers, and Reductionist thinkers, respectfully. In conclusion, systems thinking capabilities are traced through unique network patterns in relation to the follower network characteristics associated with systems thinking dimensions.

Rudrajit Choudhuri, Dylan Liu, Igor Steinmacher et al.

Conversational Generative AI (convo-genAI) is revolutionizing Software Engineering (SE) as engineers and academics embrace this technology in their work. However, there is a gap in understanding the current potential and pitfalls of this technology, specifically in supporting students in SE tasks. In this work, we evaluate through a between-subjects study (N=22) the effectiveness of ChatGPT, a convo-genAI platform, in assisting students in SE tasks. Our study did not find statistical differences in participants' productivity or self-efficacy when using ChatGPT as compared to traditional resources, but we found significantly increased frustration levels. Our study also revealed 5 distinct faults arising from violations of Human-AI interaction guidelines, which led to 7 different (negative) consequences on participants.