Hasil untuk "Mechanical engineering and machinery"

Ana B. M. Bett, Thais S. Nepomuceno, Edson OliveiraJr et al.

Context: The empirical software engineering (ESE) community has contributed to improving experimentation over the years. However, there is still a lack of rigor in describing controlled experiments, hindering reproducibility and transparency. Registered Reports (RR) have been discussed in the ESE community to address these issues. A RR registers a study's hypotheses, methods, and/or analyses before execution, involving peer review and potential acceptance before data collection. This helps mitigate problematic practices such as p-hacking, publication bias, and inappropriate post hoc analysis. Objective: This paper presents initial results toward establishing an RR template for Software Engineering controlled experiments using the Open Science Framework (OSF). Method: We analyzed templates of selected OSF RR types in light of documentation guidelines for controlled experiments. Results: The observed lack of rigor motivated our investigation of OSF-based RR types. Our analysis showed that, although one of the RR types aligned with many of the documentation suggestions contained in the guidelines, none of them covered the guidelines comprehensively. The study also highlights limitations in OSF RR template customization. Conclusion: Despite progress in ESE, planning and documenting experiments still lack rigor, compromising reproducibility. Adopting OSF-based RRs is proposed. However, no currently available RR type fully satisfies the guidelines. Establishing RR-specific guidelines for SE is deemed essential.

Ashwani Kumar, Anish Pandey, TCSM Gupta et al.

Evaluating lubricant life is crucial for maintaining equipment reliability and preventing failures. Conventional methods often depend on original equipment manufacturer recommendations for lubricant changes, which may result in the premature disposal of operationally effective lubricants, leading to economic costs and degrading overall efficiency. The chemical properties of these samples are evaluated by calculating multiple parameters such as total acid number, total base number, oxidation index, soot level, and water contamination. In addition, rheological properties through viscosity index analysis and the tribological properties via friction-wear analysis are determined. In this study, an artificial neural network (ANN) (a four-layer perceptron) and an adaptive neuro-fuzzy inference system (ANFIS) are applied to predict oil conditions based on multiple calculated parameters. Performance and comparison of these advanced mathematical models are evaluated using statistical indices. Overall, the artificial intelligence (AI)-powered approach proved effective in predicting lubricant life for HEMM. Among the AI models, the ANN model demonstrated particularly strong performance, with a correlation coefficient of 0.99 compared to 0.98 for the ANFIS model. Implementing the ANN model could lead to a potential 19% reduction in current engine oil expenses, which would lower operating costs and decrease environmental impact by reducing the frequency of oil disposal.

Aashish K Moses, Srinath Ranjan Tripathy, Saroj Sundar Baral

Abstract The existing energy-wastewater nexus may be resolved using metal oxide semiconductor photocatalysts in photocatalytic hydrogen production and pollutant degradation, which is a clean and sustainable process. SnO2 is one such well-researched and proven photocatalyst that is now in use, although it only works with ultraviolet light, which only makes up 4% of the total solar energy received. The present research aims to use iron as a dopant to make SnO2 active under visible light, enhancing reactions like water splitting and dye degradation. The sol-gel method was used to synthesize the photocatalysts. XRD, BET, UV diffuse reflectance spectra, PL spectra, XPS, and SEM micrographs were used to characterize the synthesized photocatalysts. For 7.5 wt% Fe-doped SnO2, a remarkable hydrogen generation rate of 18.81 µmol/hr under sunlight was achieved, nearly three times that of pure SnO2 (5.71 µmol/h). The nanocomposites display excellent photoreactivity towards RhB dye degradation with an optimal concentration of 7.5 wt% Fe-doped SnO2. This optimal composite photocatalyst removes 93% of RhB dye on 0.1 g/L photocatalysts in only 60 min under sunlight. Pristine SnO2 removes 36% of the dye under similar reaction conditions. The photoluminescence spectra of Fe-doped SnO2 had lower peak locations than the pristine SnO2, indicating a decreased rate of charge recombination and increased life duration of the active species. As a result, hydrogen generation rates and dye degradation efficiencies have increased significantly. The photocatalyst’s recyclability study revealed that the photocatalysts can be used efficiently for four cycles without significant reduction in the yield.

Songyuan Yan, Sara Louise Pacheco, Asa K. Laskie et al.

Maintaining favorable biological productivities in photosynthetic biomanufacturing systems, especially when the risk of contamination with competing microbes is high, remains a challenge to achieve while maintaining economic feasibility. This study presents a dielectrophoresis (DEP)-based microfluidic approach for isolating a desired strain within a co-culture. The cyanobacterium <i>Synechocystis</i> sp. PCC 6803 (a strain capable of producing the bioplastic precursor polyhydroxybutyrate, or PHB) was enriched from mixed cultures containing the competing cyanobacterium Synechococcus elongatus PCC 7942 (which does not naturally produce PHB). A DEP cascade electrode system was established to increase purification efficiency through sequential enrichment, which leveraged inherent differences in cell morphology and dielectric properties, to achieve the selective separation of these strains under physiological conditions. A substantial increase in the relative abundance of PHB-producing cells was assessed by optical microscopy and flow cytometry characterization, confirming more than five-fold reduction of the Synechococcus fraction in the refined cell mix. The presented electrokinetic platform offers a scalable and effective approach for selectively enhancing desired microbial components within microbial biomanufacturing systems, leading towards improved product yields.

Roham Koohestani, Philippe de Bekker, Begüm Koç et al.

Benchmarks are essential for unified evaluation and reproducibility. The rapid rise of Artificial Intelligence for Software Engineering (AI4SE) has produced numerous benchmarks for tasks such as code generation and bug repair. However, this proliferation has led to major challenges: (1) fragmented knowledge across tasks, (2) difficulty in selecting contextually relevant benchmarks, (3) lack of standardization in benchmark creation, and (4) flaws that limit utility. Addressing these requires a dual approach: systematically mapping existing benchmarks for informed selection and defining unified guidelines for robust, adaptable benchmark development. We conduct a review of 247 studies, identifying 273 AI4SE benchmarks since 2014. We categorize them, analyze limitations, and expose gaps in current practices. Building on these insights, we introduce BenchScout, an extensible semantic search tool for locating suitable benchmarks. BenchScout employs automated clustering with contextual embeddings of benchmark-related studies, followed by dimensionality reduction. In a user study with 22 participants, BenchScout achieved usability, effectiveness, and intuitiveness scores of 4.5, 4.0, and 4.1 out of 5. To improve benchmarking standards, we propose BenchFrame, a unified framework for enhancing benchmark quality. Applying BenchFrame to HumanEval yielded HumanEvalNext, featuring corrected errors, improved language conversion, higher test coverage, and greater difficulty. Evaluating 10 state-of-the-art code models on HumanEval, HumanEvalPlus, and HumanEvalNext revealed average pass-at-1 drops of 31.22% and 19.94%, respectively, underscoring the need for continuous benchmark refinement. We further examine BenchFrame's scalability through an agentic pipeline and confirm its generalizability on the MBPP dataset. All review data, user study materials, and enhanced benchmarks are publicly released.

Paloma Guenes, Rafael Tomaz, Bianca Trinkenreich et al.

Research shows that more than half of software professionals experience the Impostor Phenomenon (IP), with a notably higher prevalence among women compared to men. IP can lead to mental health consequences, such as depression and burnout, which can significantly impact personal well-being and software professionals' productivity. This study investigates how IP manifests among software professionals across intersections of gender with race/ethnicity, marital status, number of children, age, and professional experience. Additionally, it examines the well-being of software professionals experiencing IP, providing insights into the interplay between these factors. We analyzed data collected through a theory-driven survey (n = 624) that used validated psychometric instruments to measure IP and well-being in software engineering professionals. We explored the prevalence of IP in the intersections of interest. Additionally, we applied bootstrapping to characterize well-being within our field and statistically tested whether professionals of different genders suffering from IP have lower well-being. The results show that IP occurs more frequently in women and that the prevalence is particularly high among black women as well as among single and childless women. Furthermore, regardless of gender, software engineering professionals suffering from IP have significantly lower well-being. Our findings indicate that effective IP mitigation strategies are needed to improve the well-being of software professionals. Mitigating IP would have particularly positive effects on the well-being of women, who are more frequently affected by IP.

Hans H Falk, Stefanie Eckner, Konrad Ritter et al.

The chalcopyrite alloy (Ag,Cu)(In,Ga)Se _2 is a highly efficient thin film solar cell absorber, reaching record efficiencies above 23%. Recently, a peculiar behavior in the bond length dependence of (Ag,Cu)GaSe _2 was experimentally proven. The common cation bond length, namely Ga–Se, decreases with increasing Ag/(Ag + Cu) ratio even though the crystal lattice expands. This is opposite to the behavior observed for Cu(In,Ga)Se _2 , where all bond lengths increase with increasing lattice size. To better understand this peculiar bond length behavior, element-specific bond lengths of (Ag,Cu)InSe _2 and Ag(In,Ga)Se _2 alloys are determined using extended x-ray absorption fine structure spectroscopy. They show that the peculiar bond length dependence occurs only for (Ag,Cu) alloys, independent of the species of common cation (In or Ga). The bond lengths are used to determine the anion displacements and to estimate their contribution to the bandgap bowing. Again, both behaviors differ significantly depending on the type of alloyed cation. A valence force field approach, relaxing bond lengths and bond angles, is used to describe the structural distortion energy for a comprehensive set of I–III–VI _2 and II–IV–V _2 chalcopyrites. The model reveals bond angle distortions as main driving factor for the tetragonal distortion and reproduces the literature values with less than 10% deviation. In contrast, the peculiar bond length dependence is not reproduced, demonstrating that it originates from electronic effects beyond the scope of this structural model. Thus, a fundamental understanding of bond length behavior and tetragonal distortion is achieved for chalcopyrite materials, benefiting their technological applications such as high efficiency thin film photovoltaics.

Jialiang Wei, Anne-Lise Courbis, Thomas Lambolais et al.

Graphical User Interfaces (GUIs) are central to app development projects. App developers may use the GUIs of other apps as a means of requirements refinement and rapid prototyping or as a source of inspiration for designing and improving their own apps. Recent research has thus suggested retrieving relevant GUI designs that match a certain text query from screenshot datasets acquired through crowdsourced or automated exploration of GUIs. However, such text-to-GUI retrieval approaches only leverage the textual information of the GUI elements, neglecting visual information such as icons or background images. In addition, retrieved screenshots are not steered by app developers and lack app features that require particular input data. To overcome these limitations, this paper proposes GUing, a GUI search engine based on a vision-language model called GUIClip, which we trained specifically for the problem of designing app GUIs. For this, we first collected from Google Play app introduction images which display the most representative screenshots and are often captioned (i.e.~labelled) by app vendors. Then, we developed an automated pipeline to classify, crop, and extract the captions from these images. This resulted in a large dataset which we share with this paper: including 303k app screenshots, out of which 135k have captions. We used this dataset to train a novel vision-language model, which is, to the best of our knowledge, the first of its kind for GUI retrieval. We evaluated our approach on various datasets from related work and in a manual experiment. The results demonstrate that our model outperforms previous approaches in text-to-GUI retrieval achieving a Recall@10 of up to 0.69 and a HIT@10 of 0.91. We also explored the performance of GUIClip for other GUI tasks including GUI classification and sketch-to-GUI retrieval with encouraging results.

Marvin Wyrich, Marvin Muñoz Barón, Justus Bogner

Software engineering (SE) is full of abstract concepts that are crucial for both researchers and practitioners, such as programming experience, team productivity, code comprehension, and system security. Secondary studies aimed at summarizing research on the influences and consequences of such concepts would therefore be of great value. However, the inability to measure abstract concepts directly poses a challenge for secondary studies: primary studies in SE can operationalize such concepts in many ways. Standardized measurement instruments are rarely available, and even if they are, many researchers do not use them or do not even provide a definition for the studied concept. SE researchers conducting secondary studies therefore have to decide a) which primary studies intended to measure the same construct, and b) how to compare and aggregate vastly different measurements for the same construct. In this experience report, we discuss the challenge of study selection in SE secondary research on latent variables. We report on two instances where we found it particularly challenging to decide which primary studies should be included for comparison and synthesis, so as not to end up comparing apples with oranges. Our report aims to spark a conversation about developing strategies to address this issue systematically and pave the way for more efficient and rigorous secondary studies in software engineering.

Yu Miao, Alexandre Yokochi, Goran Jovanovic et al.

Non-thermal plasma as a tool in chemical reaction engineering has been studied for many years. The temperature of electrons in non-thermal plasma far exceeds other particles, which leads to its high efficiency. Besides the well-studied destruction of volatile organic compounds (VOCs), the reaction environment generated by non-thermal plasma is also suitable for the activation of many significant gas-phase chemical reactions, e.g., as methane coupling, reduction of carbon dioxide, ammonia synthesis, nitrogen fixation, as well as some liquid phase chemical reactions such as the treatment of contaminated water. Material synthesis is another target field of non-thermal plasma. Plasma in micro scale with several enhanced properties makes it an even more promising tool for plasma-chemical processing. This work summarizes different types of non-thermal plasmas and their performance in commonly studied chemical reactions. The advantages gained by generating non-thermal plasma in micro scale with constricted spaces, reduced timescales, and micro-/nano-structured electrodes are also discussed.

Zhiyu Fan, Yannic Noller, Ashish Dandekar et al.

The growing number of students enrolling in Computer Science (CS) programmes is pushing CS educators to their limits. This poses significant challenges to computing education, particularly the teaching of introductory programming and advanced software engineering (SE) courses. First-year programming courses often face overwhelming enrollments, including interdisciplinary students who are not CS majors. The high teacher-to-student ratio makes it challenging to provide timely and high-quality feedback. Meanwhile, software engineering education comes with inherent difficulties like acquiring industry partners and the dilemma that such software projects are often under or over-specified and one-time efforts within one team or one course. To address these challenges, we designed a novel foundational SE course. This SE course envisions building a full-fledged Intelligent Tutoring System (ITS) of Programming Assignments to provide automated, real-time feedback for novice students in programming courses over multiple years. Each year, SE students contribute to specific short-running SE projects that improve the existing ITS implementation, while at the same time, we can deploy the ITS for usage by students for learning programming. This project setup builds awareness among SE students about their contribution to a "to-be-deployed" software project. In this multi-year teaching effort, we have incrementally built an ITS that is now deployed in various programming courses. This paper discusses the Intelligent Tutoring System architecture, our teaching concept in the SE course, our experience with the built ITS, and our view of future computing education.

Randi Wang, Vadim Shapiro, Morad Behandish

Engineering design often involves representation in at least two levels of abstraction: the system-level, represented by lumped parameter models (LPMs), and the geometric-level, represented by distributed parameter models (DPMs). Functional design innovation commonly occurs at the system-level, followed by a geometric-level realization of functional LPM components. However, comparing these two levels in terms of behavioral outcomes can be challenging and time-consuming, leading to delays in design translations between system and mechanical engineers. In this paper, we propose a simulation-free scheme that compares LPMs and spatially-discretized DPMs based on their model specifications and behaviors of interest, regardless of modeling languages and numerical methods. We adopt a model order reduction (MOR) technique that a priori guarantees accuracy, stability, and convergence to improve the computational efficiency of large-scale models. Our approach is demonstrated through the model consistency analysis of several mechanical designs, showing its validity, efficiency, and generality. Our method provides a systematic way to compare system-level and geometric-level designs, improving reliability and facilitating design translation.

Xiaofeng Wang, Henry Edison, Dron Khanna et al.

[Context] Systematic Literature Review (SLR) has been a major type of study published in Software Engineering (SE) venues for about two decades. However, there is a lack of understanding of whether an SLR is really needed in comparison to a more conventional literature review. Very often, SE researchers embark on an SLR with such doubts. We aspire to provide more understanding of when an SLR in SE should be conducted. [Objective] The first step of our investigation was focused on the dataset, i.e., the reviewed papers, in an SLR, which indicates the development of a research topic or area. The objective of this step is to provide a better understanding of the characteristics of the datasets of SLRs in SE. [Method] A research synthesis was conducted on a sample of 170 SLRs published in top-tier SE journals. We extracted and analysed the quantitative attributes of the datasets of these SLRs. [Results] The findings show that the median size of the datasets in our sample is 57 reviewed papers, and the median review period covered is 14 years. The number of reviewed papers and review period have a very weak and non-significant positive correlation. [Conclusions] The results of our study can be used by SE researchers as an indicator or benchmark to understand whether an SLR is conducted at a good time.

Srijoni Majumdar, Soumen Paul, Debjyoti Paul et al.

The Information Retrieval in Software Engineering (IRSE) track aims to develop solutions for automated evaluation of code comments in a machine learning framework based on human and large language model generated labels. In this track, there is a binary classification task to classify comments as useful and not useful. The dataset consists of 9048 code comments and surrounding code snippet pairs extracted from open source github C based projects and an additional dataset generated individually by teams using large language models. Overall 56 experiments have been submitted by 17 teams from various universities and software companies. The submissions have been evaluated quantitatively using the F1-Score and qualitatively based on the type of features developed, the supervised learning model used and their corresponding hyper-parameters. The labels generated from large language models increase the bias in the prediction model but lead to less over-fitted results.

Wouter Groeneveld, Laurens Luyten, Joost Vennekens et al.

Creativity is a critical skill that professional software engineers leverage to tackle difficult problems. In higher education, multiple efforts have been made to spark creative skills of engineering students. However, creativity is a vague concept that is open to interpretation. Furthermore, studies have shown that there is a gap in perception and implementation of creativity between industry and academia. To better understand the role of creativity in software engineering (SE), we interviewed 33 professionals via four focus groups and 10 SE students. Our results reveal 45 underlying topics related to creativity. When comparing the perception of students versus professionals, we discovered fundamental differences, grouped into five themes: the creative environment, application of techniques, creative collaboration, nature vs nurture, and the perceived value of creativity. As our aim is to use these findings to install and further encourage creative problem solving in higher education, we have included a list of implications for educational practice.

Wankai Yao, Junbin Yan, Xiangcheng Li et al.

In order to improve the mechanical properties of B4C ceramics, B4C@TiB2 composite powders with core-shell structure are prepared by molten salt method using B4C and Ti powders as raw materials. And B4C ceramics were prepared from B4C@TiB2 composite powders by spark plasma sintering (SPS). The results show that the B4C@TiB2 composite powders exhibit intact core-shell structure. The B4C@TiB2 composite powders improves the mass transfer during spark plasma sintering. When the molar ratio of B4C/Ti is 2/1, the relative density, Vickers hardness, fracture toughness and flexural strength of the BT1/2 sample are 94.2%, 26.9 GPa, 5.34 MPa·m1/2 and 570 MPa, respectively, which is best comprehensive properties.

Ian A. Cosden, Kenton McHenry, Daniel S. Katz

As software has become more essential to research across disciplines, and as the recognition of this fact has grown, the importance of professionalizing the development and maintenance of this software has also increased. The community of software professionals who work on this software have come together under the title Research Software Engineer (RSE) over the last decade. This has led to the formalization of RSE roles and organized RSE groups in universities, national labs, and industry. This, in turn, has created the need to understand how RSEs come into this profession and into these groups, how to further promote this career path to potential members, as well as the need to understand what training gaps need to be filled for RSEs coming from different entry points. We have categorized three main classifications of entry paths into the RSE profession and identified key elements, both advantages and disadvantages, that should be acknowledged and addressed by the broader research community in order to attract and retain a talented and diverse pool of future RSEs.

Md Jobair Hossain Faruk, Santhiya Subramanian, Hossain Shahriar et al.

Software Engineering is the process of a systematic, disciplined, quantifiable approach that has significant impact on large-scale and complex software development. Scores of well-established software process models have long been adopted in the software development life cycle that pour stakeholders towards the completion of final software product development. Within the boundary of advanced technology, various emerging and futuristic technology is evolving that really need the attention of the software engineering community whether the conventional software process techniques are capable to inherit the core fundamental into futuristic software development. In this paper, we study the impact of existing software engineering processes and models including Agile, and DevOps in Blockchain-Oriented Software Engineering. We also examine the essentiality of adopting state-of-art concepts and evolving the current software engineering process for blockchain-oriented systems. We discuss the insight of software project management practices in BOS development. The findings of this study indicate that utilizing state-of-art techniques in software processes for futuristic technology would be challenging and promising research is needed extensively towards addressing and improving state-of-the-art software engineering processes and methodology for novel technologies.

Ufaith Qadiri

In this contribution two Alternative fuels in fixed proportions were compared with conventional 100% gasoline fuel on a constant Speed single cylinder based generator. This work defines the complete state of the art work done on computational Simulation Software on AVL Boost. In this work, we have compared the performance and emission characteristics of single cylinder spark Ignition engine constant speed of 3000 rpm fuelled conventional Gasoline 100% with blended Alternative fuel Ethanol15% with 85% Gasoline, and water-Ethanol based micro-emulsion fuel Gasoline 85% Ethanol 10% and H2O 5% on licence based Simulation Software AVL Boost. The performance parameters were checked for all the three types of fuels and emission characteristics were compared with all the three types of fuels. The results were very promising for water-Ethanol based micro-emulsion fuel as far as the emission characteristics are concerned. Ethanol 15% blends with 85% Gasoline also showed very less emissions as compared to conventional 100% Gasoline. The power &amp; Torque has shown slightly more increase for conventional 100% Gasoline fuel as compared to other two Alternative Fuels. However, emissions were far lesser for water-Ethanol based micro-emulsion and Ethanol blended fuel. The main aim of this investigation is to reduce the emissions and trying to meet the future emission standards Euro 7.