Abstract Building materials derived from agricultural and industrial waste are becoming more attractive in the civil engineering and architectural applications because of their sustainability and lower environmental impact. In addition, substantial value can be added to the wastes by producing value added products from them. Therefore, four different types of locally available by-products (rice husk, wheat husk, wood fibers and textile waste fibers) were used to produce composites with a biodegradable poly(butylene adipate-co-terephthalate)/poly(lactic acid) (PBAT/PLA) blend binder by hot pressing. The morphological analysis of the composites revealed that the PBAT/PLA binder had more affinity with wood and textile fibers than with wheat and rice husks. The prepared composites showed thermal stability until 250 °C. All the prepared biodegradable composites exhibited good compressive strength (11–40 MPa) and flexural strength (0.80–2.25 MPa). The observed mechanical properties allow easy handling without risk of breaking them when positioned in the buildings. The biodegradable composites were characterized for their thermal conductivity, diffusivity, effusivity and heat capacity. The density and thermal conductivity of the produced composite was in the range of 378–488 kg/m3 and 0.08-0.14 W/m.K, respectively. The least thermal conductivity i.e. 0.08 W/m.K was observed for the rice husk composite with a density of 378 kg/m3. A minimum water absorption (42%) was found in the rice husk composites after 24 h immersion in water. The composite samples were still cohesive after 24 h immersion in the water because of the water resistance nature of the binder. The prepared biodegradable composites meet most of the required properties for the indoor building insulation applications and show great potential to replace the conventional building material in current use.
Artificial intelligence, big data, machine learning, cloud computing, and Internet of Things (IoT) are terms which have driven the fourth industrial revolution. The digital revolution has transformed the manufacturing industry into smart manufacturing through the development of intelligent systems. In this paper, a big data ecosystem is presented for the implementation of fault detection and diagnosis in predictive maintenance with real industrial big data gathered directly from large-scale global manufacturing plants, aiming to provide a complete architecture which could be used in industrial IoT-based smart manufacturing in an industrial 4.0 system. The proposed architecture overcomes multiple challenges including big data ingestion, integration, transformation, storage, analytics, and visualization in a real-time environment using various technologies such as the data lake, NoSQL database, Apache Spark, Apache Drill, Apache Hive, OPC Collector, and other techniques. Transformation protocols, authentication, and data encryption methods are also utilized to address data and network security issues. A MapReduce-based distributed PCA model is designed for fault detection and diagnosis. In a large-scale manufacturing system, not all kinds of failure data are accessible, and the absence of labels precludes all the supervised methods in the predictive phase. Furthermore, the proposed framework takes advantage of some of the characteristics of PCA such as its ease of implementation on Spark, its simple algorithmic structure, and its real-time processing ability. All these elements are essential for smart manufacturing in the evolution to Industry 4.0. The proposed detection system has been implemented into the real-time industrial production system in a cooperated company, running for several years, and the results successfully provide an alarm warning several days before the fault happens. A test case involving several outages in 2014 is reported and analyzed in detail during the experiment section.
Abstract In the context of smart factories, where intelligent machines share data and support enhanced functionalities at a factory level, workers are still seen as spectators rather than active players (Hermann, Pentek, & Otto, 2017). Instead, Industry 4.0 represents a great opportunity for workers to become part of the intelligent system; on one hand, operators can generate data to program machines and optimize the process flows, on the other hand they can receive useful information to support their work and cooperate with smart systems (Romero et al., 2016). Diversely from machines, humans are naturally smart, flexible and intelligent, so putting the operators in the digital loop can bring more powerful and efficient factories. The paper aims at defining a theoretical human-centered framework for Operator 4.0, and testing its feasibility and impact on companies, thanks to the integration of human factors in 4.0 computerized industrial contexts. The proposed framework is based on data collection about the workers’ performance, actions and reactions, with the final objective to improve the overall factory performance and organization. Data are used to assess the workers’ ergonomics performance and perceived comfort and to build a proper knowledge about the human asset of the factory, to be integrated with the knowledge derived from machine data collection. The framework is cased on the adoption of an Operator 4.0 monitoring system, which consists of an eye tracking and a wearable biosensor, combined to a proper protocol analysis to interpret data and create a solid knowledge. Virtual prototypes are used to make the workers interact with the digital factory to conveniently simulate the human–machine interaction (HMI) in order to avoid bottlenecks at the shop floor, to optimize the workflows, and to improve the workstations’ design and layout. The study represents a step toward the design of human-centred industrial systems, including human factors in the digital twin. The research approach has been successfully tested on an industrial case study, developed in collaboration with CNH Industrial, for the re-design of assembly workstations.
Linear motion is rather common in the industry, and linear electric motors (LEMs) can provide it directly (without a mechanical transmission) through electromagnetic field forces. LEMs may be considered counterparts of rotary electric machines, but specific topologies lead to characteristics that differ (in some cases notably) from those of the latter. This paper attempts an overview on recent progress of LEMs, from innovative topologies to advanced modeling, design, and control, with case studies and examples related to specific industrial applications from people movers to small compressors solenoids, speakers, and microphones.
The industrial Internet of Things (IIoT) is growing quickly due to increasing deployment and integration of smart sensors, instruments, and devices, and software using wired or wireless networks. Through this integrated hardware–software approach, industrial practices will improve significantly, resulting in industrial intelligence for more efficient manufacturing. To realize such industrial intelligence, significant developments in IIoT big data processing and analysis are required to uncover and use hidden essential and valuable information of the production process. But large-scale, streaming, multiattribute IIoT data from production processes are noisy and have redundancies. Therefore, a suitable data processing technique such as tensor-train that can handle these IIoT data is needed. However, existing tensor-train decomposition methods are inefficient and cannot meet the processing demands of the large-scale IIoT big data. In this article, we propose an advanced (improved and highly efficient) distributed tensor-train (ADTT) decomposition method with its incremental computational method for processing IIoT big data. Finally, experiments are carried out on a typical and publicly available IIoT dataset—the bearing test data to verify and measure the performances of the proposed ADTT method.
Abstract Since the first Industrial Revolution the trends in manufacturing have evolved a lot, from mechanical production to the era of smart manufacturing via technologies like Cyber Physical Systems, Internet of Things, Big Data, Cyber Security, Cloud Computing, Additive Manufacturing, Advanced robots, Modelling and Simulation and Augmented Virtual Reality. These technologies are enabling Interoperability and integration of various processes and departments in an organization because of the attribute of real-time inter-connectivity. Due to high inter-connectivity advantages like shorter development time, mass customization and modularity, configurability can be brought into existence. This will not only change the dynamics of the production lines but also add to the profit ratio of an organization by controlling over inventory via virtualization and predictive manufacturing. Due to such attributes of the Industry 4.0 paradigm, understanding them in depth is necessary. Hence, this paper aims to review many such characteristics, enablers, and main drivers of the Industry 4.0 paradigm and ultimately provides insight on the future scopes of each of the main pillars of Industry 4.0.
Robots have been part of automation systems for a very long time, and in public perception, they are often synonymous with automation and industrial revolution perse. Fueled by Industry 4.0 and Internet of Things (IoT) concepts as well as by new software technologies, the field of robotics in industry is currently undergoing a revolution on its own. This article gives an overview of the evolution of robotics from its beginnings to recent trends like collaborative robotics, autonomous robots, and human- robot interaction. Particular attention is devoted to the deep changes of the last decades, from the traditional industrial scenario based on isolated robotic cells up to the most recent coworking and collaborative robots. The role of robotics in the Industry 4.0 framework is analyzed, and the relationships with industrial communications and software technologies are also discussed. Some future directions for robotics are envisaged, focusing on the contributions coming from new materials, sensors, actuators, and technologies. Open issues are highlighted as well as the main barriers that currently limit the deployment of industrial robots in the small and medium enterprise (SME) world.
Industry 5.0, the fifth industrial revolution, consists of smart digital information and manufacturing technologies. This industrial revolution generates effective processes and makes rapid improvement in industries and healthcare. Solutions to challenges posed by COVID-19 pandemic can be identified with the deployment of Industry 5.0-based technologies. It helps to provide personalized therapy and treatment processes to the COVID-19 patients if a detailed patient’s information is available. The aim of Industry 5.0 technologies is to create a smart healthcare environment with real-time capabilities. During the COVID-19 pandemic, these technologies can provide a remote monitoring system in healthcare. This paper identifies and studies major technologies of Industry 5.0 helpful for the COVID-19 pandemic. The supportive features of Industry 5.0 for the COVID-19 pandemic are discussed diagrammatically. Finally, we identified and studied significant challenges faced in the context of Industry 5.0 technologies for the COVID-19 pandemic. The literature revealed that this technological innovation allows a high personalization level to fulfill personal specific demands of the patient and doctors. These technologies play a significant role in making the life of doctors better. Further, doctors can use this technology to focus on critically infected patients and provide proper appropriate information regarding their better treatment. Moreover, Industry 5.0 technologies can help doctors and medical students for required medical training during this COVID-19 outbreak.
Putri Ayuni Alayyannur, Muhammad Malik Al Hakim, Rr. Sri Rejeki Eviyanti Puspita Sari
Introduction: Every workplace must make an occupational health effort to avoid health problems. Many workers underestimate the risks of the job and, therefore, do not use safety equipment even when available. The most often reported case of occupational skin illnesses, contact dermatitis, accounts for more than 85% of all cases. This study was conducted to occupational dermatitis and its relationship to personal protective equipment (PPE) use. Methods: The literature search was carried out in April 2021. The research sources were taken from several databases with the keywords dermatitis, occupational health, and personal protective equipment. The Google Scholar database found 17,710 articles, ScienceDirect found 1,264 articles, ProQuest found 888 articles, and PubMed found 452 articles. Of the entire database, only 36 articles met the inclusion criteria. Results: This literature review shows that dermatitis is experienced by workers in various sectors including health workers, hairdressers, scavengers, farmers, fishermen, manufacturing industry workers, printing workers, and construction workers. The use of PPE can reduce the risk of dermatitis. However, in some conditions, the use of PPE has no effect or can even cause dermatitis due to irritation and allergies to the ingredients contained in the PPE. The limitation of this research is that the articles that are the source of this review are only from 2016–2021.Conclusion: Dermatitis still occurs in various occupational sectors. The risk of dermatitis can be decreased by using PPE; however, it can also cause the occurrence of dermatitis itself.
Newtonian fluid is ideal for lubrication purposes because the viscosity of this fluid remains as a function of the shear. Besides, the heat and mass transfer are an important study area in fluid dynamics due to its vast applications in industrial processes. The heat-mass transfer can be defined as the Soret and Dufour effect, which implemented in many industrial applications such as in chemical engineering and geosciences field. In addition, the fluid flow over an extending/compressing sheet has significant industrial applications such as the cooling of continuous strips, glass fibre production, the extrusion of plastic sheets from a die, etc. As a response, this study aims to investigate the impacts of Soret and Dufour parameters on the Newtonian fluid flow over an inclined stretching/shrinking sheet. The methodology of this mathematical model are stated as follow: 1) the transformation of partial differential equations (PDEs) to the ordinary differential equations (ODEs), and 2) The ODEs are solved using bvp4c solver in MATLAB software. The bvp4c solver is a MATLAB program directory that solves general form and multi-point boundary layer problems. The main sections of bvp4c are: 1) The solution of the ODEs, 2) The related boundary conditions that can produce the expected results, and 3) An initial guess to run the bvp4c solver. As a result, the numerical and graphical results show that the Soret effect increases the concentration profile whereas decreases the temperature profile. The vice versa occurrence is true for Dufour effect. The convective mass transfer caused by a temperature gradient is known as thermal-diffusion (Soret) effect. The convective heat transfer produced by concentration differences is known as diffusion-thermo (Dufour) effect. However, since the process of heat and mass transfers are related to each other, the Soret and Dufour effects are able to influence both of this process simultaneously. In conclusion, the convective heat transfer is enhanced by increasing Soret and Dufour number while the convective mass transfer is declined by increasing the two numbers.
Sandra Lange, Wioletta Mędrzycka-Dąbrowska, Anna Małecka-Dubiela
Introductions: Computed tomography is one of the biggest breakthroughs in diagnostic imaging. In order to more accurately assess structures and pathological changes during the examination, it is necessary to administer a contrast agent. Patients presenting for the examination, very often only find out during the examination that a contrast agent is required. This increases patients’ uncertainty when giving written consent for contrast administration, as well as anxiety about the examination. The aim of this study was to explore the experiences of patients who have contrast-enhanced CT scans, focusing primarily on anxiety, feelings, and safety. Methods: The cross-sectional study was conducted in diagnostic imaging offices in Pomeranian Voivodeship in 2019–2020. The survey was aimed at patients presenting for CT examinations with intravenous contrast injection. In total, 172 patients participated in the survey. A proprietary survey questionnaire was used to conduct the study. Results and Conclusions: During a CT scan, intravenous contrast agent administration is often necessary. Although there are few studies on patients’ experiences with this examination, the authors observe that some patients experience anxiety. The results of our study showed the following: (1) 30.8% of patients experience anxiety before a CT scan with intravenous contrast injection; (2) variables such as gender, previous experience, and searching for information about the examination influence the occurrence of anxiety; (3) the most common feelings experienced by patients during intravenous contrast injection are a sensation of warmth spreading throughout the body; (4) the most common source of information about the study used among patients was the Internet; (5) most patients feel safe during a CT scan.
Industrial safety. Industrial accident prevention, Medicine (General)
Eshagh ARIANMEHR, Zahra SABZI, Faezeh ABBAS GOHARI
et al.
Introduction: Occupational accidents not only reduce social credibility and impose a heavy economic burden on the organization, but also cause fatalities and disability among personnel. This study aims to assess risk and review safety promotion guidelines in railway construction projects.
Methods: This study investigated the safety status of Tehran-Karaj railway construction and increasing lines project in four phases, including infrastructure, technical buildings, superstructure, and joint activities. The data were gathered by reviewing project safety documentation, reported accidents, and risk assessment results by failure modes and effects analysis (FMEA) method. Risks were categorized based on control ability at three levels, including low, moderate, and high levels. Control measures were prioritized based on the risk coverage percentage, cost, implementation time, and effectiveness.
Results: In total, 377 risks were identified, 19.9%, 61.2%, and 18.9% of which were in the low, moderate, and high levels, respectively. The most frequently identified risks and the highest rate of accidents were related to technical buildings (42.2%) and superstructure (36.6%) phases, respectively. In terms of consequence, the most severe accident occurred in the infrastructure phase within the contractors' scope of action. Falling from height was identified as the greatest threat against the project, and collapsing, falling, and hitting with materials as the key cause of the accident. It was found that controlling 39.8% of the risks could avert 73.3% of the project occupational accidents.
Conclusion: The findings reveal the major role of senior management commitment to safety and emphasis on control measures, including implementing safety program training, increasing visits, and safety inspection, as well as implementing a permit to work system in all operational phases.
Industrial safety. Industrial accident prevention, Public aspects of medicine
Rizaldy Fathur Rachman, Iin Zulaiha Tuasikal, Abdul Rohim Tualeka
et al.
Introduction: Benzene is one of the pollutants in the shoe home industry that can cause cancer among the workers. The present research aimed to analyze the relationship between exposure to benzene and spmA (s-phenylmercapturic Acid) in the urine of shoe-making home industry workers in Surabaya. Methods: This was an observational study using an analytical research method where the total number of respondents in the sample was 10. The concentration of benzene was measured using Gas Chromatography-FID (Flame Ionization Detector). The data collection technique was descriptive analysis for each variable from among the worker’s characteristics. The analysis of the relationship between the level of spmA in their urine and the worker’s characteristics was performed using regression tests while the analysis of the relationship between the level of benzene in the air and the levels of workers’ spmA was performed using the Spearman correlation test. Results: The benzene I levels in the work environment were found to be between 0.06 ppm - 53.8 ppm. The average spmA was 6.68 μg/g creatinine. The p value of the relationship between the variable levels of benzene and the levels of spmA was 0.879 with a Spearman correlation coefficient of 0.056. Conclusion: The mean concentration of benzene in the air at the 6 point uptake was over the threshold. Based on the results of the spmA examination, the mean value of spmA was below the threshold value. The test results on the level of benzene in the air and the spmA indicate a very weak relationship.
Esakki Balasubramanian, Marreddy Gayatri, Ganesh M. Sai
et al.
Over the past decades, Unmanned Aerial Vehicle (UAV) have been effectively adapted to perform disaster missions, agricultural and various societal applications. The path planning plays a crucial role in bringing autonomy to the UAVs to attain the designated tasks by avoiding collision in the obstacles prone regions. Optimal path planning of UAV is considered to be a challenging issue in real time navigation during obstacle prone environments. The present article focused on implementing a well-known A* and variant of A* namely MEA* algorithm to determine an optimal path in the varied obstacle regions for the UAV applications which is novel. Simulation is performed to investigate the performance of each algorithm with respect to comparing their execution time, total distance travelled and number of turns made to reach the source to target. Further, experimental flight trails are made to examine the performance of these algorithms using a UAV. The desired position, velocity and yaw of UAV is obtained based on the waypoints of optimal path planned data and effective navigation is performed. The simulation and experimental results are compared for confirming the effectiveness of these algorithms.
Introduction: Fertilizer companies are companies that use large amounts of chemicals, natural gas as raw equipment and assist in the production process, as well as the use of factory equipment with high temperatures and pressures, making companies with high levels of risk. The purpose of this study is to determine the general picture of physical and chemical factors in the work environment and the risks that can occur to workers and companies in fertilizer companies in Indonesia. Methods: This research was conducted using descriptive methods, the object observed in this study is the physical and chemical factors that exist in fertilizer companies. Results : Lighting intensity in all areas of the operational departments of fertilizer companies in Indonesia is only 12%, which is in accordance with the Threshold Limitation Value (TLV). Intensity of noise in the area of the company, as much as 17% is above the Threshold Limitation Value (TLV). The level of dust in this fertilizer company is 33% of the area that exceeds the Threshold Limitation Value (TLV). These three things can increase the risk to health, even increase the intelligence of the workers themselves. In addition to the risks to the workers, these three things can also benefit a company's productivity. Conclusion: All areas of fertilizer companies have risks originating from physical and chemical factors. So it needs to be carried out appropriate control in order to reduce the risks that can occur both for workers and companies.
Keywords : dust, ferilizer plant, lighting, noise