Hasil untuk "Engineering machinery, tools, and implements"

Ricardo Huaman, Clayder Gonzalez, Sixto Prado

In recent years, LiDAR Odometry (LO) and LiDAR Inertial Odometry (LIO) algorithms for robot localization have considerably improved, with significant advancements demonstrated in various benchmarks. However, their performance in agricultural environments remains underexplored. This study addresses this gap by evaluating five state-of-the-art LO and LIO algorithms—LeGO-LOAM, DLO, DLIO, FAST-LIO2, and Point-LIO—in a blueberry farm setting. Using an Ouster OS1-32 LiDAR mounted on a four-wheeled mobile robot, the algorithms were evaluated using the translational error metric across four distinct sequences. DLIO showed the highest accuracy across all sequences, with a minimal error of 0.126 m over a 230 m path, while FAST-LIO2 achieved its lowest translational error of 0.606 m on a U-shaped path. LeGO-LOAM, however, struggled due to the environment’s lack of linear and planar features. The results underscore the effectiveness and potential limitations of these algorithms in agricultural environments, offering insights into future improvements and adaptations.

Cesare D’Amico, Fulvia Galletti, Vincenzo Ronsivalle et al.

Surgical extraction of the third molars is frequently performed because they often do not have enough space to erupt properly, resulting in partial or complete impaction and causing pain, infection, cysts, and damage to adjacent teeth. The decision to remove third molars is based on clinical and radiographic evaluations, considering factors such as angulation, depth of impaction, and presence of symptoms. In some cases, general anesthesia or sedation is required. The post-operative period may include swelling, pain, and bleeding, managed with pain relievers and antibiotics. Possible complications include infection, nerve damage, and the formation of a dry socket.

Nawras Thamer, Khattab S. Abdul-Razzaq

Even when applied at midspan, unequal loading is a major driver of deep-beam response and capacity. By contrast, with a central, equal load, the compression-strut paths that carry force from the supports to the loading node(s) are typically symmetric. However, in the present study, the load inequality causes these struts to be asymmetrical. As a result, the strut carrying the larger load failed before the one carrying the smaller load. Therefore, the deep beam fails early. Twenty-one deep beam specimens were analyzed using SAP 2000 software, which is based on the well-known finite element method.  Three patterns of load distribution between three concentrated load points were adopted: 33%-33%-33%, 50%-25%-25%, 25%-50%-25%, 67%-16.5%-16.5%, 16.5%-67%-16.5%, 75%-12.5%-12.5% and 12.5%-75%-12.5%. These load cases were studied using different concrete's compressive strength values of 20, 30, and 40 MPa. Based on these results, load capacity remained essentially unchanged, while midspan deflection and shear stresses decreased by 4.0–4.6% and 3.8–17%, respectively; in contrast, the maximum positive moments increased by 0.55–7%.

Danish Javed, Usama Arshad, Haider Irfan et al.

Early and reliable detection of skin cancer is critical for improving patient outcomes and minimizing diagnostic uncertainty in dermatological practice. This study proposes an interpretable hybrid framework that integrates ConvMixer-based deep feature extraction with gradient boosting classifiers to perform multi-class skin lesion classification on the publicly available PAD-UFES-20 dataset. The dataset contains 2298 dermoscopic and clinical images with associated patient metadata (age, gender, and anatomical site), enabling a joint evaluation of demographic and anatomical factors influencing model performance. After data augmentation, normalization, and class balancing using Borderline-SMOTE, Image embeddings extracted via ConvMixer were integrated with patient metadata and subsequently classified using CatBoost, XGBoost, and LightGBM. Among these, CatBoost achieved the highest macro-AUC of 0.94 and macro-F1 of 0.88, with a melanoma sensitivity of 0.91, while maintaining good calibration (Brier score = 0.06). Grad-CAM and SHAP analyses confirmed that the model’s attention and feature importance correspond to clinically relevant lesion regions and attributes. The results highlight that age and body-region imbalances in the PAD-UFES-20 dataset modestly influence predictive behavior, emphasizing the importance of balanced sampling and stratified validation. Overall, the proposed ConvMixer–CatBoost framework provides a compact, explainable, and generalizable solution for AI-assisted skin cancer classification.

Muhammad N. F. A. Rachman, Andri Irfan Rifai, Arief Rijaluddin et al.

Road connectivity and accessibility for the community start from the lowest level, namely rural roads. Rural road conditions reflect the condition of infrastructure in rural areas. This study aims to study the characteristics of rural road damage. The location of this study is rural roads in West Java, Indonesia, the case of Majalengka Regency. Data collection was carried out on Jalan Gunung Kuning-Sindang in May 2024. The research method and data processing used the Bina Marga–Indonesia (Directorate General of Highway) standard. The study results show that, in general, the rural roads are still damaged. Most of the damage to these village roads is due to poor road maintenance and water control along the road, especially in mountainous areas. Based on the priority order, a value of 9 was obtained to be included in the periodic maintenance program.

Liyuan Chen, Shuoling Liu, Jiangpeng Yan et al.

The advent of foundation models (FMs), large-scale pre-trained models with strong generalization capabilities, has opened new frontiers for financial engineering. While general-purpose FMs such as GPT-4 and Gemini have demonstrated promising performance in tasks ranging from financial report summarization to sentiment-aware forecasting, many financial applications remain constrained by unique domain requirements such as multimodal reasoning, regulatory compliance, and data privacy. These challenges have spurred the emergence of financial foundation models (FFMs): a new class of models explicitly designed for finance. This survey presents a comprehensive overview of FFMs, with a taxonomy spanning three key modalities: financial language foundation models (FinLFMs), financial time-series foundation models (FinTSFMs), and financial visual-language foundation models (FinVLFMs). We review their architectures, training methodologies, datasets, and real-world applications. Furthermore, we identify critical challenges in data availability, algorithmic scalability, and infrastructure constraints and offer insights into future research opportunities. We hope this survey can serve as both a comprehensive reference for understanding FFMs and a practical roadmap for future innovation.

Md Saeed Siddik, Hao Li, Cor-Paul Bezemer

Context: Jupyter Notebook has emerged as a versatile tool that transforms how researchers, developers, and data scientists conduct and communicate their work. As the adoption of Jupyter notebooks continues to rise, so does the interest from the software engineering research community in improving the software engineering practices for Jupyter notebooks. Objective: The purpose of this study is to analyze trends, gaps, and methodologies used in software engineering research on Jupyter notebooks. Method: We selected 146 relevant publications from the DBLP Computer Science Bibliography up to the end of 2024, following established systematic literature review guidelines. We explored publication trends, categorized them based on software engineering topics, and reported findings based on those topics. Results: The most popular venues for publishing software engineering research on Jupyter notebooks are related to human-computer interaction instead of traditional software engineering venues. Researchers have addressed a wide range of software engineering topics on notebooks, such as code reuse, readability, and execution environment. Although reusability is one of the research topics for Jupyter notebooks, only 64 of the 146 studies can be reused based on their provided URLs. Additionally, most replication packages are not hosted on permanent repositories for long-term availability and adherence to open science principles. Conclusion: Solutions specific to notebooks for software engineering issues, including testing, refactoring, and documentation, are underexplored. Future research opportunities exist in automatic testing frameworks, refactoring clones between notebooks, and generating group documentation for coherent code cells.

Fabian C. Peña

Large Language Models (LLMs) are revolutionizing software engineering (SE), with special emphasis on code generation and analysis. However, their applications to broader SE practices including conceptualization, design, and other non-code tasks, remain partially underexplored. This research aims to augment the generality and performance of LLMs for SE by (1) advancing the understanding of how LLMs with different characteristics perform on various non-code tasks, (2) evaluating them as sources of foundational knowledge in SE, and (3) effectively detecting hallucinations on SE statements. The expected contributions include a variety of LLMs trained and evaluated on domain-specific datasets, new benchmarks on foundational knowledge in SE, and methods for detecting hallucinations. Initial results in terms of performance improvements on various non-code tasks are promising.

Fernando Ayach, Vitor Lameirão, Raul Leão et al.

Proto-personas are commonly used during early-stage Product Discovery, such as Lean Inception, to guide product definition and stakeholder alignment. However, the manual creation of proto-personas is often time-consuming, cognitively demanding, and prone to bias. In this paper, we propose and empirically investigate a prompt engineering-based approach to generate proto-personas with the support of Generative AI (GenAI). Our goal is to evaluate the approach in terms of efficiency, effectiveness, user acceptance, and the empathy elicited by the generated personas. We conducted a case study with 19 participants embedded in a real Lean Inception, employing a qualitative and quantitative methods design. The results reveal the approach's efficiency by reducing time and effort and improving the quality and reusability of personas in later discovery phases, such as Minimum Viable Product (MVP) scoping and feature refinement. While acceptance was generally high, especially regarding perceived usefulness and ease of use, participants noted limitations related to generalization and domain specificity. Furthermore, although cognitive empathy was strongly supported, affective and behavioral empathy varied significantly across participants. These results contribute novel empirical evidence on how GenAI can be effectively integrated into software Product Discovery practices, while also identifying key challenges to be addressed in future iterations of such hybrid design processes.

Hadeel Khaleel Abd Al-Ameer, Hassan Obaid Abbas

The design of the braced excavation system is one of the important and necessary matters for the implementation of various projects. The braced excavation system is used to support excavations in temporary projects, so there are shortcomings in the study of this aspect, although sometimes there are many projects that take long periods of time especially the projects of underground tunnels and high buildings. This was the main reason for the study. Therefore, the possibility of exposing the drilling system to earthquakes is great, especially in seismically active areas. If the drilling system is exposed to an earthquake, it can cause great human and material losses, so it must be designed against earthquakes so that ensure complete collapse and failure does not occur. This study aims to investigate the behavior of braced excavations under the influence of the Ali Al-Gharbi earthquake in both x- and y- directions. A numerical study is carried out on braced excavation system of (14×6) m and depth 9m using software Plaxis 3D. The braced excavation system consists of three type of bracing system with three levels of strut and wales connected with sheet pile wall to support sides of excavation and prevent them from collapsing. The results of study showed that the horizontal displacement of braced excavation system is (100-155) % more than vertical displacement (settlement) with seismic time when system is subjected to Ali Al-Gharbi earthquake in both directions with the other factors remaining constant. The stiffness of sheet pile wall also play an important role increases and decreases lateral displacement in both direction. Also, the results showed that the movement of of braced excavation system depends on several factors like as type of soil, time acceleration and the direction of earthquake. Settlement of Ali Al-Gharbi earthquake in Y- direction is 13% more than in X-direction.  

Ryota SHIOYA, Yukio TAKEDA

In order to experimentally determine the controlled variable values realizing a quasi-zero stiffness of a cable driven continuum robot, this paper presents an experimental method for quantitatively and accurately assessing the stability of a robot in a static equilibrium state with the controlled variables being kept at their target values. Experimental stability assessment requires consideration of the difficulty for an actual robot to achieve the unstable static equilibrium states and the fact that measurement errors can greatly affect stability assessment. Thus, the proposed experimental method assesses the stability of a robot by determining the smallest eigenvalue of the Hessian matrix of the total potential energy through measuring the relationship betweensmalldisplacementandworkdonebyexternalforcesfromastaticequilibriumstatetobeassessed. This measurement sets the controlled variables on an experimental robot that stably achieve the static equilibrium state instead of the controlled variables to be assessed. Moreover, an additional small displacement is given in the same direction as the eigenvector of the smallest eigenvalue of the Hessian once obtained. Then the stability of a planar 3-DOF continuum robot consisting of a variable-length elastic rod and two cables with the controlled variables set as the rod length and the cable tensions was assessed by the derived stability analysis and the proposed experimental method. The experimental results showed that the stability of both stable and unstable static equilibrium states was quantified, with a high repeatability of the results. Additionally, the stability assessment value decreased from positive to negative with the increasing cable tensions, consistent with the theoretical results. Furthermore, giving the additional small displacement enables a more accurate assessment of the stability. Finally, an approximation of the experimental results successfully determined the controlled variable values that allow the experimental robot to realize the neutral static equilibrium state.

Shengtao He

Currently, the vast majority of locally deployed open-source large language models (LLMs) and some commercial model interfaces do not support stable tool calling functionality. The existing solution involves fine-tuning LLMs, which results in significant time and computational resource consumption. This paper proposes a method that enables LLMs to achieve stable tool calling capabilities using only prompt engineering and some ingenious code design. We conducted experiments on multiple LLMs that lack tool calling capabilities across various tool calling tasks, achieving a success rate of 100%.

James S. Wheaton, Daniel R. Herber

Traditional requirements engineering tools do not readily access the SysML-defined system architecture model, often resulting in ad-hoc duplication of model elements that lacks the connectivity and expressive detail possible in a SysML-defined model. Further integration of requirements engineering activities with MBSE contributes to the Authoritative Source of Truth while facilitating deep access to system architecture model elements for V&V activities. We explore the application of MBSE to requirements engineering by extending the Model-Based Structured Requirement SysML Profile to comply with the INCOSE Guide to Writing Requirements while conforming to the ISO/IEC/IEEE 29148 standard requirement statement patterns. Rules, Characteristics, and Attributes were defined in SysML according to the Guide to facilitate requirements definition, verification & validation. The resulting SysML Profile was applied in two system architecture models at NASA Jet Propulsion Laboratory, allowing us to assess its applicability and value in real-world project environments. Initial results indicate that INCOSE-derived Model-Based Structured Requirements may rapidly improve requirement expression quality while complementing the NASA Systems Engineering Handbook checklist and guidance, but typical requirement management activities still have challenges related to automation and support in the system architecture modeling software.

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.

James Eschrich, Sarah Sterman

We review discourses about the philosophy of science in qualitative research and evidence from cognitive linguistics in order to ground a framework for discussing the use of Large Language Models (LLMs) to support the qualitative analysis process. This framework involves asking two key questions: "is the LLM proposing or refuting a qualitative model?" and "is the human researcher checking the LLM's decision-making directly?". We then discuss an implication of this framework: that using LLMs to surface counter-examples for human review represents a promising space for the adoption of LLMs into the qualitative research process. This space is promising because it is a site of overlap between researchers working from a variety of philosophical assumptions, enabling productive cross-paradigm collaboration on tools and practices.

Ahmed Kharrufa, Sami Alghamdi, Abeer Aziz et al.

This work takes a pedagogical lens to explore the implications of generative AI (GenAI) models and tools, such as ChatGPT and GitHub Copilot, in a semester-long 2nd-year undergraduate Software Engineering Team Project. Qualitative findings from survey (39 students) and interviews (eight students) provide insights into the students' views on the impact of GenAI use on their coding experience, learning, and self-efficacy. Our results address a particular gap in understanding the role and implications of GenAI on teamwork, team-efficacy, and team dynamics. The analysis of the learning aspects is distinguished by the application of learning and pedagogy informed lenses to discuss the data. We propose a preliminary design space for GenAI-based programming learning tools highlighting the importance of considering the roles that GenAI can play during the learning process, the varying support-ability patterns that can be applied to each role, and the importance of supporting transparency in GenAI for team members and students in addition to educators.

Vishnupriya Jonnalagadda, Ji Yun Lee

Transportation systems are complex, and due to their interdependence with other essential facilities, any damage to them would pose a significant threat to the well-being of communities. Given the frequent occurrences and grave consequences of natural disasters observed in recent years, research on the resilience assessment of transportation systems has received a great deal of attention. This paper develops a dynamic Bayesian network (BN)-based resilience assessment model for a highway network subject to seismic events that can explicitly quantify uncertainties in all phases of the model and investigate the role of inspection and monitoring in uncertainty reduction. The results from this study can be used as comprehensive decision-support information so that decision makers can better assess the resilience of a highway network and associated uncertainties.

Shouryha Bhardwaj, Arnav Agarwal, Ishriit Tibdewal et al.

Magnesium alloys are commonly used in various industries such as automotive, aerospace, electrical, medical, sports, etc. The material is preferred in the making of engine blocks, transmission cases, and structural parts due to its unique material properties, like being lightweight and durable. It also offers a good strength-to-weight ratio and directly contributes to the fuel efficiency of vehicles. Due to its usage in various industries, it is essential to understand its behavior under machining. But the machining of magnesium alloys can present significant challenges compared to other conventional structural metals and alloys. The research work focuses on investigating the application of a Plug Electrical Discharge Machine (EDM) for machining the AZ-31 magnesium alloy and aims to analyze the surface quality of the machined surface for selected input parameters. The experiments were conducted on a mirror-finished flat specimen while keeping the incision depth and servo voltage constant at 0.3 mm and 45 V, respectively. A copper tool was used to make nine unique incisions on the surface using selected values of pulse on-time (T_on), pulse off-time (T_off), and current (I). A surface analysis using optical microscopy revealed that the surface roughness increased drastically with a combination of high values of I, T_on, and T_off. The tests conducted using a profilometer confirmed the proportional relationship between the input parameters and the surface roughness of the AZ-31 magnesium alloy.

Jui-Chang Chiu, Chi-Yueh Hsu, Chun-Yu Chien et al.

It is convenient to have advances in science and technology so that people obtain information without going out. We explore the intention of the use of housing apps based on the unified theory of acceptance and the use of technology. A total of 365 questionnaires were collected with 8 incomplete answers discarded. The snowball sampling method was used for confirmatory factor analysis and SEM structural equation model analysis. The research results show the following. (1) Housing app users can quickly obtain knowledge and information about houses, and it is more convenient. (2) Both effort expectancy and social influence have a direct and positive effect on behavior intention when using the housing app. (3) There is no significant impact after adding moderator variables of gender, age, and income in Unified Theory of Acceptance and Use of Technology, (UTAUT). It is convenient and helpful to use the housing app. Therefore, the use of the housing app will be an indispensable trend.

Al-Amin Abba Dabo, Olalekan Adisa, Rahul S Mor et al.

This paper presents an ongoing study that explores the influence of geopolitical and socio-economic factors on the Ukrainian food supply chain and its implications for food security. Focusing on specific sectors within Ukraine, our research aims to provide a comprehensive understanding of the intricate dynamics between geopolitics, socio-economic conditions, and the functioning of the food supply chain. To collect data, we have employed focus groups as the primary methodology, engaging key stakeholders from various sectors, including farmers, distributors, retailers, policymakers, and consumers. These focus group discussions enable us to delve into their perspectives, experiences, and challenges in relation to the influence of geopolitical and socio-economic factors on the Ukrainian food supply chain. Preliminary literature review reveals several noteworthy insights, including the impact of trade policies and regional conflicts on the availability and accessibility of specific food products in targeted regions of Ukraine. Building upon these initial findings, our ongoing study aims to propose strategies to enhance the resilience and efficiency of Ukraine’s food supply chain. By tailoring policies to address the specific needs of different regions and socio-economic groups, we anticipate mitigating the adverse effects of geopolitical dynamics on the food system. Moreover, fostering collaboration among stakeholders will be crucial in navigating the complexities and challenges inherent in managing the Ukrainian food supply chain.