Hasil untuk "Industrial engineering. Management engineering"

M. Kendall

Asadullah Shaikh, Wahidur Rahman, Kaniz Roksana et al.

Bangladesh has plentiful water, which is essential to its freshwater fish traditions. Environmental concerns and other causes have reduced the country's water resources, threatening many native freshwater fish species. Thus, the younger generation deficiencies recognition of local freshwater fish and struggles to recognize them. Traditional methods are very insufficient to overcome these issues. To address these research gaps, the research proposes an automatic system for categorizing Bangladesh's freshwater fish. The proposed methodology involves several key steps, including building a comprehensive dataset, extracting features from fish images using pre-trained Convolutional Neural Network (CNN) models, and employing typical ML approaches. Initially comprising eight classes, the dataset undergoes feature extraction using CNN algorithms, followed by the utilization of various feature selection methods such as Support Vector Classifier, Principal Component Analysis, Linear Discriminant Analysis, and optimization models like Particle Swarm Optimization, Bacterial Foraging Optimization, and Cat Swarm Optimization. In the final phase, seven conventional ML techniques are applied to classify the images of local fishes. Empirical measurements are gathered and analyzed to assess the proposed framework's performance. Particularly, the present approach achieves the highest accuracy of 98.71% through the utilization of the ML mechanism Logistic Regression with Resnet50, SVC, and CSO models.

Shaniff Esmail, Brendan Concannon

BackgroundImmersive virtual reality (VR) and artificial intelligence have been used to determine whether a simulated clinical exam setting can reduce anxiety in first-year occupational therapy students preparing for objective structured clinical examinations (OSCEs). Test anxiety is common among postsecondary students, leading to negative outcomes such as increased dropout risk, lower grades, and limited employment opportunities. Students unfamiliar with specific testing environments are particularly prone to anxiety. VR simulations of OSCEs may allow students to become familiar with the exam setting and reduce anxiety. ObjectiveThis study aimed to assess the efficacy of a VR simulation depicting clinical settings to reduce student anxiety about a clinical exam while gathering perspectives on their first-year coursework experiences to better understand their learning environment. MethodsAn experimental, nonrandomized controlled trial compared state anxiety, trait test anxiety, and OSCE grades in 2 groups of first-year occupational therapy students analyzed using independent t tests (2-tailed). Group 1 (NoVR) was not exposed to the VR simulation and acted as a control group for group 2 (YesVR), who were exposed to the VR simulation. The VR used artificial intelligence in the form of a generative pretrained transformer to generate responses from virtual patients as students interacted with them in natural language. Self-reported psychometric scales measured anxiety levels 3 days before the OSCE. YesVR students completed perceived preparation surveys at 2 time points—3 weeks and 3 days before the OSCE—analyzed using dependent t tests. Semistructured interviews and focus groups were conducted within 1 week after the OSCE. Student perspectives on their classes and VR experiences were summarized using interpretative thematic analysis. ResultsIn total, 60 students—32 (53%) in the NoVR group and 28 (47%) in the YesVR group—participated in the study, and the YesVR group showed a significant reduction in state anxiety (t58=3.96; P<.001; Cohen d=1.02). The mean difference was 11.96 units (95% CI 5.92-18.01). Trait test anxiety and OSCE scores remained static between groups. There was an increase in all perceived preparedness variables in the YesVR group. In total, 42% (25/60) of the participants took part in interviews and focus groups, providing major themes regarding factors that affect OSCE performance, including student experience and background, feedback and support, fear of unknown, self-consciousness, and knowledge of the exam environment. ConclusionsIntolerance of uncertainty may lead students to interpret ambiguous exam situations as overly precarious. Findings suggest that VR simulation was associated with reduced state anxiety, although results from this small, nonrandomized sample should be interpreted cautiously. Qualitative data indicated that VR helped students gain familiarity with clinical exam settings, potentially decreasing uncertainty-based anxiety. Future research with larger or randomized samples is needed to confirm these findings and explore advanced VR tools offering feedback to enhance learning.

Hassan Mishmast Nehi, Faranak Hosseinzadeh Saljooghi, Amir Rahimi et al.

Data Envelopment Analysis with inaccurate data poses a significant challenge in data science and analytics due to the inherent uncertainties and discrepancies present in real-world data. This article investigates the performance of units evaluated with inaccurate data and presents modeling approaches, including fuzzy and interval methodologies. In other words, by examining the effectiveness of units evaluated with interval data with fuzzy or interval-based bounds, novel approaches for modeling data coverage issues are introduced. Various mathematical techniques and analytical processes are utilized to solve problems and prove theorems. The primary focus is on modeling data coverage issues with fuzzy or interval bounds, which facilitates the creation of more accurate and effective representations of uncertain data. The findings of this article indicate that these modeling approaches lead to improvements in data-driven decision-making. Practical applications of these methods include information management and decision-making for DMU sets in fuzzy and interval environments, enabling analysts to make better decisions. This research contributes to advancing the field of data analytics by providing systematic methods for managing and analyzing inaccurate data, thereby enhancing the reliability and applicability of insights based on data foundations.

Seongil Han, Haemin Jung, Paul D. Yoo

Abstract The Basel Accord emphasizes the necessity of employing internal data models to manage key credit risk components, including Probability of Default (PD), Loss Given Default (LGD), and Exposure At Default (EAD). Among these, internal datasets are critical for estimating PD, a fundamental measure of borrower creditworthiness. Nevertheless, practical application often faces challenges due to incomplete datasets, which can skew analyses and undermine the accuracy of credit scoring models. Traditional approaches to addressing missing data, such as sample deletion or mean imputation, are widely used; however, they often prove insufficient for accurate prediction. Consequently, imputation methods are typically favored over deletion, as they allow for the full utilization of available data. Recent advancements have introduced more sophisticated techniques, such as Generative Adversarial Imputation Networks (GAIN), which utilize a generative adversarial network to model data distributions and impute missing values with greater precision than conventional methods. Building on these developments, this study proposes a novel imputation approach, SMART (Structured Missingness Analysis and Reconstruction Technique) for credit scoring datasets. SMART consists of two primary stages: first, it normalizes and denoises the dataset using randomized Singular Value Decomposition (rSVD), followed by the implementation of GAIN to impute missing values. Experimental results demonstrate that SMART significantly outperforms existing state-of-the-art methods, particularly in high missing data contexts (20%, 50%, and 80%), with improvements in imputation accuracy of 7.04%, 6.34%, and 13.38%, respectively. In conclusion, SMART represents a substantial advancement in handling incomplete credit scoring datasets, leading to more precise PD estimation and enhancing the robustness of credit risk management models.

Weidong WANG, Hui GAO, Xin SU et al.

For a multi-user integrated sensing and communication system in the network of vehicles, a robust sensing-assisted communication massive multiple-input multiple-output (MIMO) orthogonal time frequency space (OTFS) transmission scheme was proposed.Due to the limited sensing accuracy of the radar, errors existed in the channel state information (CSI) reconstructed based on sensing parameters.The transmission performance decreased as a result.To address this issue, the CSI in the delay doppler domain was reconstructed based on the sensing parameters by the transmitter firstly.And a robust beam forming scheme was designed considering the CSI error.Secondly, the channel estimation error and inter user interference were perceived by receivers based on sensing parameters.Then the robust receiver was designed by incorporating the perceived interference errors into the signal detector in an analytical way.Finally, numerical simulation results show that the proposed method effectively reduces the system bit error rate and increases the data reception rate of users.The proposed method improves the overall system performance in this situation.

Hale Pamukçu, Pelin Soyertaş Yapıcıoğlu, Mehmet İrfan Yeşilnacar

This study majorly aimed to determine the effect of optimization on transport routes on the reduction of greenhouse gas (GHG) emissions from municipal solid waste management (MSM) within the scope of European Union (EU) Green Deal. Optimization of collection and transportation routes has been regarded as an effective methodology in order to mitigate the GHG emissions of municipal waste management, recently. Optimization of routes has been obtained using ant colony algorithm (ACA) and Monte Carlo simulation, in this study. In this context, this study investigated to reduce GHG emissions from municipal waste management using optimization of transportation routes in Diyarbakir city in Turkey. Firstly, GHG emissions which are carbon dioxide (CO2), methane (CH4) and nitrous oxide (N2O) emissions from waste collection and transport have been calculated using a new developed model based on Tier-I method. Then, Monte Carlo simulation has been used to figure out the GHG emissions. Finally, life cycle assessment (LCA) approach has been applied to determine the GHG emissions. According to the route optimization resulting ACA methodology, nearly 47.43% of reduction on each GHG emissions. Approximately, 58%, 38% and 51% of reduction on CO2, CH4 and N2O emissions respectively has been achieved, in the result of the route optimization using Monte Carlo simulation. The results of LCA methodology revealed that the reduction reached up 45.71% on GHG emissions in terms of Global Warming Potential (GWP). The reduction amounts have been overlapped with each other.

Venelin Todorov, Slavi Georgiev, Ivan Dimov et al.

Jan C. Brammer, Gerd Blanke, Claudia Kellner et al.

Abstract TUCAN is a canonical serialization format that is independent of domain-specific concepts of structure and bonding. The atomic number is the only chemical feature that is used to derive the TUCAN format. Other than that, the format is solely based on the molecular topology. Validation is reported on a manually curated test set of molecules as well as a library of non-chemical graphs. The serialization procedure generates a canonical “tuple-style” output which is bidirectional, allowing the TUCAN string to serve as both identifier and descriptor. Use of the Python NetworkX graph library facilitated a compact and easily extensible implementation. Graphical Abstract

Yanis Belkheyar, Joan Daemen, Christoph Dobraunig et al.

Recently, a memory safety concept called Cryptographic Capability Computing (C3) has been proposed. C3 is the first memory safety mechanism that works without requiring extra storage for metadata and hence, has the potential to significantly enhance the security of modern IT-systems at a rather low cost. To achieve this, C3 heavily relies on ultra-low-latency cryptographic primitives. However, the most crucial primitive required by C3 demands uncommon dimensions. To partially encrypt 64-bit pointers, a 24-bit tweakable block cipher with a 40-bit tweak is needed. The research on low-latency tweakable block ciphers with such small dimensions is not very mature. Therefore, designing such a cipher provides a great research challenge, which we take on with this paper. As a result, we present BipBip, a 24-bit tweakable block cipher with a 40-bit tweak that allows for ASIC implementations with a latency of 3 cycles at a 4.5 GHz clock frequency on a modern 10 nm CMOS technology.

Amirreza Asnafi, Mohsen Haji Zeinolabedini, Faezeh Ahmadipour

Access to the required information in all available scientific disciplines is one of the most important factors in the survival of that field. In the architecture field, the type of information format differs from other disciplines. The purpose of this study was to identify the behavior of images in the architecture of Shahid Beheshti University. The present study is an applied target and uses a descriptive survey method. The statistical population of the study consists of two groups of students and professors in the architecture major of Shahid Beheshti University. To determine the sample size, the Cochran formula was used and the sample size in this formula was 296 people. The results showed that the architects mainly used images for identifying creative ideas and taking advantage of the details of architectural structures. The type of image content they used was mostly photos, maps, and charts, which could be found in engines and image databases by limiting the size of the image and following related links as long as the image was taken. One of the major obstacles in finding images for architects was the lack of familiarity with the way they were searched. Creativity, proximity to the subject, credibility, and quality of the images were the criteria for selecting content. Considering the library's share in retrieving research-based images, it is suggested that library and library librarians conduct awareness-raising activities at the university's research groups such as brochures, conferences, library visits, and workshops.

Tolga Erol, A. Mendi, Dilara Doğan

Technologies were being developed in laboratories for military purposes in the past, and its spread in daily life was a result of a long-term process. However, because of the rapid spread of technological developments and the facilitation of information access processes, we can become aware of new technologies in a short time and shape our daily lives with the advantages of the use of these technologies. The Digital Twin concept, which is expected to cause changes in many areas of our lives in the near future, has entered our lives with the industry 4.0 industrial revolution and is defined as a digital copy of a physical system. Digital Twins have started to take place in our lives in other civil fields as well as in industrial and engineering fields, with the advantages they offer in terms of time and cost. We will discuss the current works and future opportunities in health, industrial, smart city management systems applications where this promising technology will be seen directly reflecting on daily lives.

J. Marmolejo-Saucedo

Arash Badakhsh, Young-Min Lee, K. Rhee et al.

Abstract Improving the utilization of industrial thermal waste and abundant solar thermal energy is of immense significance in energy management and thermal engineering. Latent heat thermal storage is one of the emerging methods that employ the large caloric density of materials mainly as a result of its constant-temperature phase-change process. Herein, paraffin was selected as the phase-change matrix which was reinforced with length controlled-carbon nanotubes (LCCNTs) as the primary filler and graphene nanoplatelets (GNPs) as the secondary reinforcing nanoparticles. Electrical conductivity (EC) of samples was tested, and carbon nanotube (CNT) was proved to be more effective in the increase of EC, than GNP. Furthermore, the thermal conductivity of the fabricated composite phase-change material was measured, and at the filler ratio of 5 phr an enhancement of about 148.0% was found compared with that of pristine paraffin. Optimal CNT/GNP ratios were also determined at the maximum enhancement achieved for each property. To observe the effect of LCCNTs on the mechanical properties of composites, polyester resin-based composites were prepared, and the tensile strength results are reported.

Themis Palpanas, V. Beckmann

The analysis of time-series data associated with modernday industrial operations and scientific experiments is now pushing both computational power and resources to their limits. In order to analyze the existing and (more importantly) future very large time series collections, new technologies and the development of more efficient and smarter algorithms are required. The two editions of the Interdisciplinary Time Series Analysis Workshop brought together data analysts from the fields of computer science, astrophysics, neuroscience, engineering, electricity networks, and music. The focus of these workshops was on the requirements of different applications in the various domains, and also on the advances in both academia and industry, in the areas of time-series management and analysis. In this paper, we summarize the experiences presented in and the results obtained from the two workshops, highlighting the relevant state-ofthe- art-techniques and open research problems.

Viacheslav Kopylov, Oleg Kuzin, Nickolai Kuzin

Borse Pranjal, Gaikwad Aishwarya, Dabhade Nachiket et al.

Nowadays, a large network of cameras is predominantly used in public places which provide enormous video data. These data are monitored manually and may be utilized only when the need arises to ascertain the facts. Automating the system can improve the quality of surveillance and be useful for high-level surveillance tasks like person identification, suspicious activity detection or undesirable event prediction for timely alerts. In this paper, we proposed a model that can Re-identify a person from a single camera tracking environment. This system will automatically extract face features of the person and generate the Unique Id for each person when it enters for the first time in the monitored area. Its face features are stored in the database which will help to Re-identify the person whenever the same person appears again. The challenges faced by the system are occlusion, pose, light conditions, and face orientation. The proposed system highlights, effect of different deep neural networks for Person Re-identification and compares based on the accuracy, GPU usage, Speed, Number of faces detected by overcoming the challenges like illumination and occlusion. The advantage of the system is it doesn′t require the database of people in advance for recognition and it will be helpful for criminal identification for crime control and prevention.

D. Beverungen, Christoph F. Breidbach, J. Poeppelbuss et al.

Science , Marketing , Information Systems the University of Muenster on developing methods and tools for assessing and improving business process management capabilities in service networks. His main research are in the areas of managing and innovating industrial services as well as business process management. His work has published in peer-reviewed academic journals (including IEEE Transactions on Engineering Management , Communications of the AIS, Scandinavian Journal of Information Systems and Business & Information Systems Engineering presented at major IS conferences. Poeppelbuss has been as associate editor and track chair (Service Innovation, Engineering, and Management) for ECIS conferences since 2012 and as minitrack for AMCIS conferences ICT and digital creative use of

Lim, Su Lin, Johal, Jolyn, Ong, Kai Wen et al.

BackgroundThe prevalence of nonalcoholic fatty liver disease (NAFLD) reaches up to 30% in the Asian adult population, with a higher prevalence in obese patients. Weight reduction is typically recommended for patients at high risk or diagnosed with NAFLD, but is a challenge to achieve. ObjectiveWe aimed to evaluate the effect of a lifestyle intervention with a mobile app on weight loss in NAFLD patients. MethodsThis prospective randomized controlled trial included 108 adults with NAFLD confirmed by steatosis on ultrasound and a body mass index ≥23 kg/m2 who were recruited from a fatty liver outpatient clinic. The patients were randomly allocated to either a control group (n=53) receiving standard care, consisting of dietary and lifestyle advice by a trained nurse, or an intervention group (n=55) utilizing the Nutritionist Buddy (nBuddy) mobile app in addition to receiving dietary and lifestyle advice by a dietitian. Body weight, alanine aminotransferase (ALT), aspartate aminotransferase (AST), waist circumference, and blood pressure were measured at baseline, and then at 3 and 6 months. Intention-to-treat and per-protocol analyses were used for statistical comparisons. ResultsThe intervention group had a 5-fold higher likelihood (relative risk 5.2, P=.003, 95% CI 1.8-15.4) of achieving ≥5% weight loss compared to the control group at 6 months. The intervention group also showed greater reductions in weight (mean 3.2, SD 4.1 kg vs mean 0.5, SD 2.9 kg; P<.001), waist circumference (mean 2.9, SD 5.0 cm vs mean –0.7, SD 4.4 cm; P<.001), systolic blood pressure (mean 12.4, SD 14.8 mmHg vs mean 2.4, SD 12.4 mmHg; P=.003), diastolic blood pressure (mean 6.8, SD 8.9 mmHg vs mean –0.9, SD 10.0 mmHg; P=.001), ALT (mean 33.5, SD 40.4 IU/L vs mean 11.5, SD 35.2 IU/L; P=.004), and AST (mean 17.4, SD 27.5 U/L vs mean 7.4, SD 17.6 IU/L, P=.03) at 6 months. ConclusionsLifestyle intervention enabled by a mobile app can be effective in improving anthropometric indices and liver enzymes in patients with NAFLD. This treatment modality has the potential to be extended to a larger population scale. Trial RegistrationAustralian New Zealand Clinical Trials Registry ACTRN12617001001381; https://tinyurl.com/w9xnfmp

M. Rosa, H. Reijers, Wil M.P. van der Aalst et al.

Business process models are becoming available in large numbers due to their widespread use in many industrial applications such as enterprise and quality engineering projects. On the one hand, this raises a challenge as to their proper management: how can it be ensured that the proper process model is always available to the interested stakeholder? On the other hand, the richness of a large set of process models also offers opportunities, for example with respect to the re-use of existing model parts for new models. This paper describes the functionality and architecture of an advanced process model repository, named APROMORE. This tool brings together a rich set of features for the analysis, management and usage of large sets of process models, drawing from state-of-the art research in the field of process modeling. A prototype of the platform is presented in this paper, demonstrating its feasibility, as well as an outlook on the further development of APROMORE.