Hasil untuk "Industrial directories"

A. Albu-Schäffer, S. Haddadin, C. Ott et al.

Giovanni Andrea Dimauro, Salvatore Cafiso, Alessandro Di Graziano et al.

This study proposes a modelling framework for simulating cyclist–vehicle interactions at urban intersections characterised by geometric constraints and variable visibility conditions. A Digital Model (DM) of the intersection geometry was developed in SUMO, complemented by a custom behavioural model calibrated using experimental trajectory data to capture cyclists’ and drivers’ perception–reaction and braking behaviour. These two components were combined to simulate scenarios with varying visibility conditions and perception-triggered braking responses in severe conflict situations. Results show that reduced visibility significantly reduces temporal safety margins, with over 50% of all simulated interactions yielding differential time-to-arrival (TTA₂) values below 2 s. Furthermore, obstructed conditions lead to higher- and more-dispersed relative crossing speeds (DV), typically increasing by 0.5–1.0 m/s compared to unobstructed conditions. Simulation data confirmed that clear visibility promotes anticipatory and adaptive user behaviour, whereas limited sightlines reduce braking availability and increase the likelihood and severity of conflicts, with distributions conditioned by the intersection’s geometry. The ability to generate detailed synthetic datasets of cyclist–vehicle interactions, often not obtainable through field observation, demonstrates the potential of the proposed framework for safety assessment. This approach supports the evaluation of mitigation strategies, including C-ITS-based solutions, and provides a basis for developing predictive AI models to enhance the safety of vulnerable road users.

Xinghuo Yu, O. Kaynak

Damir Kralj

Mahfooz Ulhaq Bajwa, Wafaa Saleh, Grigorios Fountas

Road traffic fatalities in low- and middle-income countries (LMICs) are continuing to rise, posing significant socio-economic and public health challenges. To prevent these road deaths and avoid the corresponding costs, the World Health Organization (WHO) has recommended implementing the vision zero approach to road safety. Vision Zero aims to eliminate road deaths and reduce serious injuries. It has been adopted by many developed countries, however LMICs have faced difficulties implementing this approach due to a lack of guidance. This study aims to develop a framework for implementing vision zero in LMICs by examining the processes in India and Sweden. A qualitative research approach with a multiple-case study design was utilized, selecting 16 participants through purposive and snowball sampling. Data was collected via semi-structured interviews and analyzed using the Grounded Theory method based on Strauss and Corbin’s approach. The study identified five core implementation steps such as agenda setting, approval, planning, monitoring and evaluation, and continuous improvement. Also, a set of influencing conditions such as preconditions, objectives, strategies, intervening factors and contextual conditions were identified. Furthermore, 38 implementation proposals were suggested in the framework to guide policymakers. The proposed framework provides a road map for LMICs that is intended to act as a guide for policymakers and road safety practitioners to enhance road safety performance in LMICs.

Zhang Shaokun, Li Xueping, Wei Peng et al.

Within the discipline of structural optimization, topology optimization has emerged as a key approach, valued for its broad design flexibility and substantial innovative potential. However, existing studies have paid insufficient attention to the regulation of the global center of mass of structures. This issue is particularly pronounced in three-dimensional contexts, where a systematic and effective mechanism for controlling the center of mass remains lacking. In practical engineering applications, the position of the center of mass plays a pivotal role in determining structural stability, dynamic response, and overall system safety. To address this problem, a level-set-based structural optimization scheme is formulated, in which the regulation of the center of mass is integrated. Specifically, a boundary evolution constraint mechanism driven by center of mass sensitivity is introduced to enable precise regulation of the center of mass. Furthermore, a component-wise tolerance relaxation strategy is developed to enhance the algorithm's search flexibility while preserving target-oriented guidance. Through numerical investigations, it is shown that the developed method achieves effective and adaptable control of the center of mass in 3D structural design, underscoring its feasibility and promise for advanced structural optimization applications.

Lihong Jiang, Lida Xu, Hongming Cai et al.

Kui Yang, Christelle Al Haddad, Rakibul Alam et al.

Advanced driver assistance systems (ADASs) have recently gained popularity as they assist vehicle operators in staying within safe boundaries, helping them thereby to prevent possible collisions. However, despite their success and development, most ADAS use common and deterministic warning thresholds for all drivers in all driving environments. This may occasionally lead to the issuance of annoying inadequate warnings, due to the possible differences between drivers, the changing environments and driver statuses, thus reducing their acceptance and effectiveness. To fill this gap, this paper proposes adaptive algorithms for commonly used warnings based on real-time traffic environments and driver status including distraction and fatigue. We proposed adaptive algorithms for headway monitoring, illegal overtaking, over-speeding, and fatigue. The algorithms were then tested using a driving simulator. Results showed that the proposed adaptive headway warning algorithm was able to automatically update the headway warning thresholds and that, overall, the proposed algorithms provided the expected warnings. In particular, three or four different warning types were designed to distinguish different risk levels. The designed real-time intervention algorithms can be implemented in ADAS to provide warnings and interventions tailored to the driver status to further ensure driving safety.

Kamugisha Kajumulo, Juma Matindana, Fatma Mohamed

Issues in construction safety in Tanzania cast a long shadow, claiming lives and hindering project success. Understanding the complex dynamics at play is crucial for improving safety performance. This research aims to develop a system dynamics model to understand the factors influencing safety performance in building construction projects. Smart PLS was used to identify key variables using factor reduction, and 19 factors were determined as key factors to be used in this study from 143 variables from a questionnaire; then, STELLA was used to develop system dynamic modelling. A study was conducted on 255 building construction projects in five regions of Tanzania, Dar es Salaam, Dodoma, Arusha, Mwanza, and Mbeya, and later, the model was tested in one contractor’s company. By simulating the interactions between these diverse elements and their impact on accident rates, it was found that both safety performance and safety management processes for construction projects are not stagnant but are expected to rise at the beginning of the project and to grow steadily as the project execution approaches its end. The sensitivity analysis conducted on the system dynamics model found that the extent, frequency, and cost of incidences are positively correlated. This study contributes facts on the status of safety climate, a new definition of safety climate, and the integration of different analysis methods in evaluating safety performance in Tanzania.

Ruimin Xie, Nabil Magbool Jan, K. Hao et al.

Autoencoder (AE) is a deep neural network that has been widely utilized in process industry owing to its superior abilities of feature extraction and data reconstruction. Recently, assuming the latent variables to be random variables, a probabilistic variant of it called variational autoencoder (VAE) has achieved a major success in different applications. In this article, we develop two novel submodels based on deep VAEs (DVAE), which are further utilized to establish a soft sensor framework. By the use of our first submodel known as supervised DVAE (SDVAE), the distribution information of latent features can be obtained. This is used as a prior of the second submodel known as the modified unsupervised DVAE (MUDVAE). Then, a new soft sensor framework can be constructed by combing the encoder of SDVAE with the decoder of MUDVAE. Since our designed VAE has superior ability in data reconstruction, it also works well under the missing data situation which is common in process industries due to sensor failures. Thus, we extend the proposed soft sensor framework to handle the missing data situation. The effectiveness of our proposed soft sensor frameworks is finally demonstrated via an industrial polymerization dataset.

S. Shevchik, Giulio Masinelli, C. Kenel et al.

Additive manufacturing (AM) is considered as a revolution in manufacturing. However, the high expectations face technical difficulties that prevent further penetration into wider industries. The main reason is the lack of process reproducibility and the absence of a reliable and cost-effective process monitoring. This paper is a supplement to existing studies in this field and proposes a unique combination of highly sensitive acoustic sensor and machine learning for process monitoring. The acoustic signals from a real powder-bed fusion AM process were collected using a fiber Bragg grating. The process parameters are intentionally tuned to achieve three levels of quality categories, which are related to the porosity contents inside the workpiece. The quality categories are defined as high, medium, and poor quality and their corresponding porosity contents are 0.07%, 0.30%, and 1.42%, respectively. Wavelet spectrograms of the signals and their encoded label representations, obtained from spectral clustering, are taken as features. A deep convolutional neural network is used to classify the features from each category and the classification accuracy ranges between 78% and 91%. Hence, the proposed method has significant industrial potentials for in situ and real-time quality monitoring of AM processes since it requires minimum modifications of commercially available industrial machines.

Wu He, Lida Xu

Jingjing Huang, Yuan Zhong, Yabin Zhang

Present-day supply-side structural reform in China places an abundance of emphasis on environmental protection. In this paper, we re-measure the upstreamness of Chinese enterprises in global value chains as described by Ni Hongfu (2022). Subsequently, the impact of environmental regulations on the global value chain position of Chinese firms is studied in depth, using the cleaner production standards promulgated and implemented by the Chinese government in 2003 as a quasi-natural experiment, taking a time-varying difference-in-differences (DID) approach. The data sources employed include the Cleaner Production Standard Implementation Industry Directory, the World Input–Output Database (WIOD), the China Industrial Enterprise Database, and the China Customs Import and Export Database. This research discovered the following: First, adopting cleaner production standards significantly improves Chinese enterprises’ positions in the global value chain—a conclusion that holds up to a number of robustness tests. Second, in terms of firm size, capital intensity, ownership characteristics, and government subsidies, there exists a noticeable heterogeneity in the promotion of the adoption of cleaner production standards for the improvement of Chinese enterprises’ global value chain position. Third, the implementation of cleaner production standards stimulates the upgrading of Chinese enterprises’ global value chain position, primarily through the entry and exit impacts, product-switching effect, and innovation compensation effect. The following proposals for policy can be implemented in light of the findings of this paper: “upstream prevention” strategies in the development of future environmental protection and trade policies should be advocated; nuanced and stratified environmental policies should be meticulously constructed; a mix of policies should be employed to bolster the institutional support for green environmental regulations; the integration of environmental governance into the evaluation framework should be emphasized; the creation of an innovation-oriented environmental governance system should be expedited. In conclusion, the findings of this research provide empirical evidence on the role of environmental regulations in coordinating ecological development and strengthening the position of Chinese enterprises in global value chains, which may assist other developing nations in making the transition to a path of high-quality growth.

Chunhui Zhao, He Sun

Large-scale processes under closed-loop control are commonly subjected to frequently varying conditions due to load changes or other causes, resulting in typical nonstationary characteristics. For closed-loop control processes, the normal changes in operation conditions may distort the static and dynamic variations in a different way from real faults. In this paper, a dynamic distributed monitoring strategy is proposed to separate the dynamic variations from the steady states, and concurrently, monitor them to distinguish changes in the normal operating condition and real faults for large-scale nonstationary processes under closed-loop control. First, large-scale nonstationary process variables are decomposed into different blocks to mine the local information. Second, the static and dynamic equilibrium relations are separated by probing into the cointegration analysis solution in each block. Third, the concurrent monitoring models are constructed to supervise both the steady variations and their dynamic counterparts for each block. Finally, the local monitoring results are combined by Bayesian inference to obtain global results, which enable description and monitoring of both static and dynamic equilibrium relations from the global and local viewpoints. The feasibility and performance of the proposed method are illustrated with a real industrial process, which is a 1000-MW ultra-supercritical thermal power unit.

Godspower Oke Omokaro, Zipporah Simiyu Nafula

Microalgae, as photosynthetic microorganisms, possess significant potential in addressing environmental challenges and promoting sustainable practices. This review explores the wide-ranging applications of microalgae, emphasizing their pivotal roles in bioremediation, biofuel production, bioethanol generation, and biofertilizer development. Microalgae’s ability to thrive in various wastewater types, including municipal, agricultural, and industrial, signifies a sustainable approach to wastewater treatment. By efficiently removing nitrogen, phosphorus, carbon, and heavy metals, microalgae make substantial contributions to environmental sustainability. Furthermore, their integration into wastewater treatment processes not only reduces operational costs but also yields valuable biomass for various applications. Microalgae’s capacity to sequester carbon dioxide, coupled with their role in enhancing soil fertility, renders them invaluable tools in mitigating climate change and promoting sustainable agriculture. This review underscores the importance of ongoing research to fully harness microalgae’s potential, paving the way for a greener and more resilient future. It summarizes the effects of microalgae’s potential on agricultural soil and wastewater treatments, among other areas, by examining relevant works related to the topic. To achieve this, databases such as Google Scholar, Frontier in Microbiology, Microbial Cell Factory (MCF), Scopus, Web of Science, ScienceDirect, and Directory of Open Access Journals (DOAJ) were explored to identify studies on microalgae’s potential in various fields.

Mila Tejamaya, Amelia Anggarawati Putri, Sapto Budi Nugroho et al.

Introduction: In line with the increasing number of COVID-19 cases from July to early August 2022, this paper aimed to analyze the perception of COVID-19 among Indonesians. Methods: A cross-sectional online study on COVID-19 risk perception was conducted in the first week of July 2022. A questionnaire adapted from ECOM (Effective Communication in Outbreak Management for Europe) was distributed online through social media to obtain information about the respondents’ knowledge, behavior, and risk perceptions on COVID-19. Results: There were 775 respondents. Most of them were female (61.3%), lived in the eight most targeted areas (84.1%), were unmarried (52.5%), held a bachelor’s degree (38.5%), and were Muslims (80.8%). The percentages of respondents who had been infected with COVID-19 were (43.8%). Most participants believed that their knowledge level of the disease was average and above average (>91%). Of the respondents, 83.6% perceived the seriousness of COVID-19 as serious and very serious. However, the anxiety level among these respondents was moderate (slightly and quite anxious). This indicates that even though most respondents still see COVID-19 as a serious disease, their level of fear is decreasing. Compared to a previous study, most respondents in the current study were more confident of their ability to control the risks associated with the transmission of the virus. Nevertheless, they still believe that outdoor activity and not using a face mask can significantly increase the probability of getting infected. Conclusion: The risk perception of COVID-19 in Indonesian community among our study population was appropriate.

Mykhailo Strelbitskyi, Valentyn Mazur , Volodymyr Lemeshko

У статті, на підставі класифікації інформації з обмеженим доступом, визначені передумови порушення її конфіденційності у ході дотримання вимог політики безпеки інформаційно-телекомунікаційної системи. Визначено, що збільшення кількості інформації може призводити до підвищення рівня обмеження доступу. Для множин інформації такого типу дано визначення як «агрегованої». Наведені умови порушення безпеки інформації, рівень конфіденційності якої залежить від її кількості. Визначені можливі канали прихованого витоку інформації з вузлів мережі без порушення політики безпеки інформаційно-телекомунікаційної системи. Запропоновано спосіб попередження несанкціонованого доступу суб’єктів інформаційної системи шляхом запровадження нового протоколу обміну між вузлами інформаційно-телекомунікаційних систем. Розроблений протокол обміну «агрегованої» інформації між вузлами мережі передбачає використання контейнеру «агрегованої» інформації, який забезпечує формування інформації з вищим рівнем доступу на захищеному вузлі інформаційно-телекомунікаційної системи.

Mohamad Iyad Al-Khiami, Martin Jaeger

Virtual Reality (VR) construction safety training modules have reached a level of maturity which renders them as a serious alternative to traditional safety training modules. The purpose of this study is to investigate the usability of a particular safety training module related to “Working at heights” for blue-collar construction workers in Kuwait. A mixed study approach was applied based on a semi-quasi experimental research design, utilizing a control group/experimental group with pre-/post-test measurements, supplemented by observations. The findings indicate a statistically insignificant higher learning effectiveness of the workers exposed to the VR approach. Observations confirmed that trainees require an extended time of preparation to become familiar with moving within the virtual environment and using the related hardware. Furthermore, younger users with less work experience reported a higher usability than older users with more work experience. VR content developers are encouraged to investigate the possibilities of simplifying the virtual environment to make it more relevant for blue-collar workers, reduce the complexity of the hardware, and intensify the feeling of the consequences resulting from users’ choices. Construction companies and educational institutions training construction blue-collar workers can benefit from the VR approach to safety training if they allow sufficient time for familiarization with the virtual training module.

Jingjing Wang, Chunxiao Jiang, Kai Zhang et al.

To achieve the goal of “Industrial 4.0,” cellular network with wide coverage has gradually become an intensely important carrier for industrial Internet of Things (IIoT). The fifth generation cellular network is expected to be a unifying network that may connect billions of IIoT devices for the sake of supporting advanced IIoT business. In order to realize wide and seamless information coverage, heterogeneous network architecture becomes a beneficial method, which can also improve the near-ceiling network capacity. In order to guarantee the quality of service (QoS) as well as the fairness of different IIoT devices with limited network resources, the network association in IIoT should be performed in a more intelligent manner. In this article, we propose a distributed $Q$-learning aided power allocation algorithm for two-layer heterogeneous IIoT networks. Moreover, we discuss the spirit of designing reward functions, followed by four delicately defined reward functions considering both the QoS of femtocell IoT user equipments and macrocell IoT user equipments and their fairness. Also, both fixed and dynamic learning rates and different kinds of multiagent cooperation modes are investigated. Finally, simulation results show the effectiveness and superiority of our proposed $Q$-learning based power allocation algorithm.

D. You, Xiangdong Gao, S. Katayama