Hasil untuk "Industrial engineering. Management engineering"

D. Boud, G. Feletti

J. Dutton, A. Thomas

Agnieszka Gębczyńska-Janowicz, Elżbieta Marczak, Anna Zapiec et al.

The global phenomenon of an aging population calls for the modernization of public spaces to better accommodate the needs of older individuals. Beaches in coastal cities are popular destinations for seniors; therefore, this article will examine effective design practices for public spaces in coastal areas. The study focused on eleven pedestrian routes that connect public transport stops to the main beach entrances in the coastal zone of Gdańsk, Poland. Field observations assessed the pedestrian and cycling infrastructure, route lengths, surface quality, availability of small urban elements (such as benches, restrooms, and water fountains), and safety conditions (including lighting, signage, barriers, and general cleanliness). The analysis of these spaces led to the development of guidelines for the architectural and urban design of senior-friendly coastal urban zones.

Tianze Sun, Xiwang He, Xueguan Song et al.

There is a growing need for precise diagnosis and personalized treatment of disease in recent years. Providing treatment tailored to each patient and maximizing efficacy and efficiency are broad goals of the healthcare system. As an engineering concept that connects the physical entity and digital space, the digital twin (DT) entered our lives at the beginning of Industry 4.0. It is evaluated as a revolution in many industrial fields and has shown the potential to be widely used in the field of medicine. This technology can offer innovative solutions for precise diagnosis and personalized treatment processes. Although there are difficulties in data collection, data fusion, and accurate simulation at this stage, we speculated that the DT may have an increasing use in the future and will become a new platform for personal health management and healthcare services. We introduced the DT technology and discussed the advantages and limitations of its applications in the medical field. This article aims to provide a perspective that combining Big Data, the Internet of Things (IoT), and artificial intelligence (AI) technology; the DT will help establish high-resolution models of patients to achieve precise diagnosis and personalized treatment.

J. Klemeš, P. Varbanov, T. Walmsley et al.

Abstract The emergence of Pinch Analysis from more than four decades ago opened a new area of intense research development that has even accelerated in recent years. Initially, Pinch Analysis (PA) provided a systematic thermodynamic-based approach to address the need for large energy savings around the 1970s oil crises. Since inception, the Pinch Methodology (PM) has flourished considerably, finding meaningful application to a wide range of industrial, regional, and global challenges well beyond heat – it’s most well-known and first application. This review represents an attempt to identify and substantiate future directions of research for the most significant implementations of Pinch Methodology. Reported applications in the literature range from Heat Integration, Total Site and Water Integration through to Emergy and even Financial Investment Planning; cutting across multiple engineering fields – Mechanical, Chemical, Process, Power, and Environmental Engineering – as well as entering the research domains of Management and Finance. Key findings of this review include: (1) the need for more awareness within the engineering and science research communities of the latest and continuing developments of the Pinch Methodology; (2) a need for complete tool sets covering targeting through to engineering design for many of the Pinch Methodology applications; and, (3) the full benefits of Pinch Methodology can only be achieved in developing design solutions with an appreciation for the most recent developments.

Pablo Becerra, Josefa Mula, Raquel Sanchis

S.A. Hosseini

This paper introduces a direct quadrature method for the numerical solution of Volterra integral equations of the first kind, utilizing a composite quadrature scheme based on the Floater–Hormann family of linear barycentric rational interpolants. The convergence of the proposed method is rigorously proved, and the order of convergence is explicitly derived in terms of the parameters of the method, thereby providing a clear theoretical framework for its performance. Several numerical experiments are provided to demonstrate both the efficiency and accuracy of the method, as well as to verify the excellent agreement between the implementation results and the theoretically predicted convergence rates.

Francisco-Jose Alvarado-Alcon, Rafael Asorey-Cacheda, Antonio-Javier Garcia-Sanchez et al.

The Internet of Things (IoT) is gaining significant attention for its ability to digitally transform various sectors by enabling seamless connectivity and data exchange. However, deploying these networks is challenging due to the need to tailor configurations to diverse application requirements. To date, there has been limited focus on examining and enhancing the carbon footprint (CF) associated with these network deployments. In this study, we present an optimization framework leveraging machine learning techniques to minimize the CF associated with IoT multi-hop network deployments by varying the placement of the required gateways. Additionally, we establish a direct comparison between our proposed machine learning method and the integer linear program (ILP) approach. Our findings reveal that placing gateways using neural networks can achieve a 14% reduction in the CF for simple networks compared to those not using optimization for gateway placement. The ILP method could reduce the CF by 16.6% for identical networks, although it incurs a computational cost more than 250 times higher, which has its own environmental impact. Furthermore, we highlight the superior scalability of machine learning techniques, particularly advantageous for larger networks, as discussed in our concluding remarks.

Jurrijn A. Koelen, Lisa de Koning, Matilda K. Nottage et al.

Online cognitive behavioral therapy (iCBT) is a promising treatment for depression and anxiety among university students but faces high dropout rates. Understanding the reasons behind dropout or completion can help improve the implementation of iCBT in educational settings. Semi-structured phone interviews were conducted with 32 students who dropped out early (n = 9), midway (n = 12), or completed (n = 11) guided or unguided iCBT in the context of a randomized controlled trial. Data were analyzed using Braun and Clarke's (2012) thematic analysis. Common themes among dropouts included personal factors (like competing priorities), perceived difficulty or redundancy of the intervention, and lack of human interaction. Early dropouts uniquely cited disbelief in the intervention's efficacy and preference for other mental health support. Midway dropouts mentioned issues with the interactivity, feedback, content, perceived effectiveness, and lack of personalization. Completers had positive initial impressions, valued the online format, found the exercises and guidance helpful, and felt cared for. The themes identified among participants who dropped out from or completed the iCBT intervention provide valuable insights into factors which may be of importance for retention. Implications regarding setting expectations, participant selection, interactive functionalities, personalized feedback, and the role of therapist guidance are discussed.

ZHANG Wanxiang, ZHANG Xianyong, YANG Jilin et al.

Attribute reduction relies on knowledge granulation and uncertainty measurement， thus facilitating intelligent recognition. For incomplete continuous data， neighborhood decision rough sets induce attribute reduction. However， the related neighborhood relation deserves optimal improvements， while the existing decision cost deserves integrated reinforcements. In this paper， a new neighborhood relation is proposed， and three decision-cost fusion measures are constructed， so new incomplete neighborhood decision rough sets are established and the attribute reduction is systematically researched. At first， an improved distance is introduced to produce an incomplete neighborhood relation， so improved rough sets on incomplete neighborhood are proposed. Then， the dependence degree and neighborhood entropy are introduced based on decision costs， so three fusion measures on decision costs are obtained by multiplication fusion， thus acquiring granulation non-monotonicity. Furthermore， eight heuristic reduction algorithms based on attribute importances are designed from two neighborhood relations and four relevant measures of decision costs. As finally verified by data experiments， the five algorithms out of the seven new algorithms have good performance of classification learning， thus improving the basic reduction algorithm.

Gebrail Bekdaş, Yaren Aydın, Umit Işıkdağ et al.

Shear wave velocity (V_s) is an important soil parameter to be known for earthquake-resistant structural design and an important parameter for determining the dynamic properties of soils such as modulus of elasticity and shear modulus. Different V_s measurement methods are available. However, these methods, which are costly and labor intensive, have led to the search for new methods for determining the V_s. This study aims to predict shear wave velocity (V_s (m/s)) using depth (m), cone resistance (q_c) (MPa), sleeve friction (f_s) (kPa), pore water pressure (u₂) (kPa), N, and unit weight (kN/m³). Since shear wave velocity varies with depth, regression studies were performed at depths up to 30 m in this study. The dataset used in this study is an open-source dataset, and the soil data are from the Taipei Basin. This dataset was extracted, and a 494-line dataset was created. In this study, using HyperNetExplorer 2024V1, V_s prediction based on depth (m), cone resistance (q_c) (MPa), shell friction (f_s), pore water pressure (u₂) (kPa), N, and unit weight (kN/m³) values could be performed with satisfactory results (R² = 0.78, MSE = 596.43). Satisfactory results were obtained in this study, in which Explainable Artificial Intelligence (XAI) models were also used.

Lixin Tang, Y. Meng

Rodrigo Rodríguez-Gutiérrez, Francisco Hernandez-Cabrera, Francisco Javier Almaguer-Martínez et al.

Saulo Cézar Seiffert Santos, Malena Albuquerque Oliveira, Mirlane Maria Moura Matos

A Amazônia é conhecida por sua biodiversidade, formas culturais e tecnológicas, em exposições científico-culturais as audiências urbanas, turísticas e autóctones. Há instituições de Ciência e Tecnologia amazônicas que realizam atividade de divulgação científica das suas próprias pesquisas e tecnologias. O objetivo desse trabalho é conhecer a proposta comunicativa da Revista de Divulgação Científica (RDC) de uma instituição de Ciência e Tecnologia, o Instituto Nacional de Pesquisa da Amazônia – INPA, o seu Discurso de Divulgação Científica (DDC). Para isso, analisaremos 12 volumes publicados em cerca de seis anos, pela Pró-Reitoria de Extensão/INPA, a partir do aporte da leitura do Círculo de Bakhtin, visando construir uma interpretação fundamentada na Análise Discursiva. Nossos resultados - foram levantados 129 textos em 12 números de RDC, no qual se destacaram temas de desenvolvimento, pesquisa, educação, saúde, entre outros. Percebeu-se uma distinção entre o discurso da divulgação científica (65%) e divulgação institucional científica (34%) dos textos selecionados. Desse último grupo, selecionou-se dois textos na área de Educação Ambiental para análise detalhada, e percebeu-se uma perspectiva institucional de apresentação de projeto extensionista voltada ao público escolar, utilizou-se dos objetos de pesquisa biológica para ser atrativo para divulgação científico-ambiental com proposito de conservação da Amazônia.

Ashokkumar Palanivinayagam, Robertas Damaševičius

The existence of missing values reduces the amount of knowledge learned by the machine learning models in the training stage thus affecting the classification accuracy negatively. To address this challenge, we introduce the use of Support Vector Machine (SVM) regression for imputing the missing values. Additionally, we propose a two-level classification process to reduce the number of false classifications. Our evaluation of the proposed method was conducted using the PIMA Indian dataset for diabetes classification. We compared the performance of five different machine learning models: Naive Bayes (NB), Support Vector Machine (SVM), k-Nearest Neighbours (KNN), Random Forest (RF), and Linear Regression (LR). The results of our experiments show that the SVM classifier achieved the highest accuracy of 94.89%. The RF classifier had the highest precision (98.80%) and the SVM classifier had the highest recall (85.48%). The NB model had the highest F1-Score (95.59%). Our proposed method provides a promising solution for detecting diabetes at an early stage by addressing the issue of missing values in the dataset. Our results show that the use of SVM regression and a two-level classification process can notably improve the performance of machine learning models for diabetes classification. This work provides a valuable contribution to the field of diabetes research and highlights the importance of addressing missing values in machine learning applications.

Binoy Debnath, Md Shihab Shakur, Fahmida Tanjum et al.

<i>Background:</i> Additive manufacturing (AM) applications in producing spare parts are increasing day by day. AM is bridging the digital and physical world as a 3D computer-aided manufacturing (CAM) method. The usage of AM has made the supply chain of the aviation spare parts industry simpler, more effective, and efficient. <i>Methods:</i> This paper demonstrates the impacts of AM on the supply chain of the aircraft spare parts industry following a systematic literature review. Hence, centralized and decentralized structures of AM supply chains have been evaluated. Additionally, the attention has been oriented towards the supply chain with AM technologies and industry 4.0, which can support maintenance tasks and the production of spare parts in the aerospace industry. <i>Results:</i> This review article summarizes the interconnection of the industry findings on spare parts. It evaluates the potentiality and capability of AM in conceptualizing the overall supply chain. Moreover, MROs can adopt the proposed framework technologies to assist decision-makers in deciding whether the logistics hub with AM facilities is centralized or decentralized. <i>Conclusions:</i> Finally, this review provides an overall view to make critical decisions on the supply chain design of spare parts driven by new and disruptive technologies of industry 4.0. The next-generation supply chain may replace the logistics barriers by reducing waste and improving capability and sustainability by implementing AM technologies.

Ion Sococol, Petru Mihai, Tudor-Cristian Petrescu et al.

In the first part of the current study, the effectiveness of the transversal cross-section reduction method for RC beams in marginal areas (by means of mechanical drilling) was validated. The said method “encourages” the formation of plastic hinges at the beam ends and, at the same time, allows for taking into account the bending stiffness of RC slabs, which is exerted upon the RC beams. In these conditions, the second part of the current research study (i.e., the current manuscript) highlights the real mode of reducing the lateral stiffness of the slabs upon the RC beams. These elements form a common body, together with the beam–column frame node. The same method as in the first part of the study—“weakening” the plates in the corner area through vertical drilling, without affecting the integrity of the reinforcing elements—was used. The analytical MR RC frame model, studied by means of the comparative method, highlights the efficiency of the transversal cross-section reduction method for RC slabs. Basically, the directing of the plastic deformations from the weakened slab areas towards the marginal areas of the reinforced concrete beams takes place. The beams rotate as far as the weakened slab areas allow its plastic deformation, thus being possible to observe the partial conservation effect of the beam–column frame joint. Furthermore, for the analytical model with the maximum number of vertical holes in the corner areas of the concrete plate, minimal plastic deformations are recorded for the marginal areas of the concrete columns. A partial conservation of the formation mechanism of the “beam-slab-frame node” common rigid block is also noted. Consequently, the dissipation of the seismic energy is made in a partially controlled and directed manner, in the “desired” areas, according to the “Strong Columns—Weak Beams” (SCWB) ductile mechanism of the lateral behavior to seismic actions for reinforced concrete frame structures. The mechanism is specified in current design norms for RC frame systems. The effectiveness of the method for reducing the transversal section of the RC plates in the corner areas by means of transversal drilling is highlighted and validated from the perspective of the local and global ductile seismic response of reinforced concrete frame structures. A significant reduction in the bending stiffness of the slabs upon the beams and a real development of the plastic hinges in the marginal areas of the beams (together with partial implications and plastic deformations) were observed.

Sheng Zheng, Wenwen Tang

The carbon emissions vary over space and time in China, as well as driving forces. It is particularly important to analyze the spatiotemporal variations and driving factors of China’s per capita carbon emissions. This study adopted global Moran’ I and local indicators of spatial association to analyze the spatial autocorrelation of per capita carbon emissions in China during 2004–2019 and discussed the driving factors of per capita carbon emissions by geographically and temporally weighted regression model. The results demonstrated a positive spatial correlation between inter-provincial per capita carbon emissions, but this correlation has gradually decreased since 2008. The High-High clusters were concentrated in the Bohai Economic Rim and the Low-Low clusters were mainly located in the south. Driving factors of per capita carbon emission at the provincial level have spatiotemporal heterogeneity. From 2004 to 2019, per capita GDP, urbanization rate, and energy intensity are the main contributors to per capita carbon emissions, and the role of per capita GDP is weakening, while urbanization rate and energy intensity change in the opposite direction. Foreign direct investment is the main disincentive in most regions. These findings provided a reference for emission reduction policies implemented in different regions.

Inês Angélica Andrade Freire, Janice Cassia Lando, Eliene Barbosa Lima

Na década de 1960, na Bahia, um grupo de professores de matemática tanto do ensino superior como da educação básica elaborou um programa curricular para o ensino de matemática no secundário, o qual foi viabilizado, em caráter experimental, por meio de apostilas e livros didáticos em sala de aula e, posteriormente, publicados e utilizados em maior escala. Assim, este artigo analisou historicamente a proposta do programa curricular de autoria do grupo de professores da Bahia, materializada na produção Coleção Matemática Moderna com saberes matemáticos e metodológicos norteados pelas recomendações internacionais do Movimento da Matemática Moderna, de forma local, historicamente situados e com uma dinâmica social que acomodou as diferentes concepções sobre os processos de ensino e de aprendizagem em matemática. A legitimidade, nesse período, de constituição de classes experimentais, possibilitou experimentações e avaliações desse programa curricular de matemática em escolas da educação básica, na cidade de Salvador, Bahia.