Technologies, Volume 9, Issue 4 (December 2021) – 34 articles

Cerebral blood flow (CBF) is significantly influenced by exposure to hypoxia, both hypobaric and normobaric. Alterations in cerebral blood flow can play a crucial role in the pathogenesis of acute mountain sickness (AMS) and its symptoms, especially headache, dizziness, and nausea. Acupuncture has been proven to be effective in treating some cerebrovascular disorders and PC6 Nei Guan stimulation seems to enhance cerebral blood flow. Therefore, we have hypothesized that PC6 Nei Guan stimulation could affect CBF in acute hypoxia and could be used to contrast AMS symptoms. We evaluated blood flow in the middle cerebral artery (MCA) in normoxia, after 15 min in normobaric hypoxia (fraction of inspired oxygen (FiO₂) 14%, corresponding to 3600 m a.s.l.) in basal conditions, and after PC6 Nei Guan stimulation, both by needle and by pressure. No comparisons with other acupuncture points and sham acupuncture were done. PC6 stimulation seemed to counteract the effects of acute normobaric hypoxia on end-diastolic velocity (EDV) in MCA, especially after acupuncture, and significantly reduced systolic and diastolic blood pressure. A rebalance of CBF could control some AMS symptoms, but further studies are necessary. Full article

A sandstorm image has features similar to those of a hazy image with regard to the obtaining process. However, the difference between a sand dust image and a hazy image is the color channel balance. In general, a hazy image has no color cast and has a balanced color channel with fog and dust. However, a sand dust image has a yellowish or reddish color cast due to sand particles, which cause the color channels to degrade. When the sand dust image is enhanced without color channel compensation, the improved image also has a new color cast. Therefore, to enhance the sandstorm image naturally without a color cast, the color channel compensation step is needed. Thus, to balance the degraded color channel, this paper proposes the color balance method using each color channel’s eigenvalue. The eigenvalue reflects the image’s features. The degraded image and the undegraded image have different eigenvalues on each color channel. Therefore, if using the eigenvalue of each color channel, the degraded image can be improved naturally and balanced. Due to the color-balanced image having the same features as the hazy image, this work, to improve the hazy image, uses dehazing methods such as the dark channel prior (DCP) method. However, because the ordinary DCP method has weak points, this work proposes a compensated dark channel prior and names it the adaptive DCP (ADCP) method. The proposed method is objectively and subjectively superior to existing methods when applied to various images. Full article

The article describes a structural and functional model of a self-driving car control system, which generates a wide class of mathematical problems. Currently, control systems for self-driving cars are considered at several levels of abstraction and implementation: Mechanics, electronics, perception, scene recognition, control, security, integration of all subsystems into a solid system. Modern research often considers particular problems to be solved for each of the levels separately. In this paper, a parameterized model of the integration of individual components into a complex control system for a self-driving car is considered. Such a model simplifies the design and development of self-driving control systems with configurable automation tools, taking into account the specifics of the solving problem. The parameterized model can be used for CAD design in the field of self-driving car development. A full cycle of development of a control system for a self-driving truck was implemented, which was rub in the “Robocross 2021” competition. The software solution was tested on more than 40 launches of a self-driving truck. Parameterization made it possible to speed up the development of the control system, expressed in man-hours, by 1.5 times compared to the experience of the authors of the article who participated in the same competition in 2018 and 2019. The proposed parameterization was used in the development of individual CAD elements described in this article. Additionally, the implementation of specific modules and functions is a field for experimental research. Full article

This paper presents the High-Level Architecture (HLA) of the European research project DEWI (Dependable Embedded Wireless Infrastructure). The objective of this HLA is to serve as a reference framework for the development of industrial Wireless Sensor and Actuator Networks (WSANs) based on the concept of the DEWI Bubble. The DEWI Bubble constitutes a set of architecture design rules and recommendations that can be used to integrate legacy industrial sensor networks with a modern, interoperable and flexible IoT (Internet-of-Things) infrastructure. The DEWI Bubble can be regarded as a high-level abstraction of an industrial WSAN with enhanced interoperability (via standardized interfaces), dependability, technology reusability and cross-domain development. The DEWI Bubble aims to resolve the issue on how to integrate commercial WSAN technology to match the dependability, interoperability and high criticality needs of industrial domains. This paper details the criteria used to design the HLA and the organization of the infrastructure internal and external to the DEWI Bubble. The description includes the different perspectives, models, or views of the architecture: the entity model, the layered perspective of the entity model and the functional model. This includes an overview of software and hardware interfaces. The DEWI HLA constitutes an extension of the ISO/IEC 29182 SNRA (Sensor Network Reference Architecture) towards the support of wireless industrial applications in different domains: aeronautics, automotive, railway and building. To improve interoperability with existing approaches, the DEWI HLA also reuses some features from other standardized technologies and architectures. The DEWI HLA and the concept of Bubble allow networks with different industrial sensor technologies to exchange information between them or with external clients via standard interfaces, thus providing consolidated access to sensor information of different industrial domains. This is an important aspect for smart city applications, Big Data, Industry 4.0 and the Internet-of-Things (IoT). The paper includes a non-exhaustive review of the state of the art of the different interfaces, protocols and standards of this architecture. The HLA has also been proposed as the basis of the European projects SCOTT (Secure Connected Trustable Things) for enhanced security and privacy in the IoT and InSecTT (Intelligent Secure Trustable Things) for the convergence of artificial intelligence (AI) and the IoT. Full article

COVID-19 has spread rapidly across the world since late 2019. As of December, 2021, there are over 250 million documented COVID-19 cases and over 5 million deaths worldwide, which have caused businesses, schools, and government operations to shut down. The most common method of detecting COVID-19 is the RT-PCR swab test, which suffers from a high false-negative rate and a very slow turnaround for results, often up to two weeks. Because of this, specialists often manually review X-ray images of the lungs to detect the presence of COVID-19 with up to 97% accuracy. Neural network algorithms greatly accelerate this review process, analyzing hundreds of X-rays in seconds. Using the Cohen COVID-19 X-ray Database and the NIH ChestX-ray8 Database, we trained and constructed the xRGM-NET convolutional neural network (CNN) to detect COVID-19 in X-ray scans of the lungs. To further aid medical professionals in the manual review of X-rays, we implemented the CNN activation mapping technique Score-CAM, which generates a heat map over an X-ray to illustrate which areas in the scan are most influential over the ultimate diagnosis. xRGM-NET achieved an overall classification accuracy of 97% with a sensitivity of 94% and specificity of 97%. Lightweight models like xRGM-NET can serve to improve the efficiency and accuracy of COVID-19 detection in developing countries or rural areas. In this paper, we report on our model and methods that were developed as part of a STEM enrichment summer program for high school students. We hope that our model and methods will allow other researchers to create lightweight and accurate models as more COVID-19 X-ray scans become available. Full article

Introduction: In chest wall reconstruction, the main objectives are the restoration of the chest wall integrity, function, and aesthetic, which is often achieved with the placement of implants. We aimed to evaluate whether 3D printed models can be useful for preoperative planning and surgical treatment in chest wall reconstruction to improve the outcome of the surgery and to reduce the rate of complications. Methods: We conducted a systematic review of literature using PubMed, Scopus, Embase, and Google Scholar databases until 8 November 2021 with the following keywords: (“3D printing” or “rapid prototyping” or “three-dimensional printing” or “bioprinting”) and (“chest wall” or “rib” or “sternum” or “ribcage” or “pectus excavatum”). Results were then manually screened by two independent authors to select studies relevant to 3D printing application in chest wall reconstruction. The primary outcome was morphological correction, and secondary outcomes were changes in operating time and procedure-related complication rate. Results: Eight articles were included in our review. Four studies were related to pectus excavatum correction, two studies were related to rib fracture stabilization, and two studies were related to chest wall tumor resection and reconstruction. Seven studies reported 3D printing of a thorax model or template implants for preoperative planning and implant modeling, and one study reported 3D printing of a PEEK prosthesis for direct implantation. Four studies reported comparison with a conventionally treated control group, and three of them detected a shorter operative time in the 3D printing model-assisted group. Satisfactory morphological correction was reported in all studies, and six studies reported a good implant fitting with minimal need for intraoperative adjustments. There were no major intraoperative or postoperative complications in any of the studies. Conclusions: The use of 3D printing models in chest wall reconstruction seems to be helpful for the production of personalized implants, reducing intraoperative adjustments. Results of morphological correction and postoperative recovery after the 3D printing-assisted surgery were satisfactory in all studies with a low rate of complication. Our literature review suggests good results regarding prosthesis fitting, accuracy of surgical planning, and reduction in operative time in 3D printing-assisted procedures, although more evidence is needed to prove this observation. Full article

Sustainable synthesis of reduced graphene oxide (rGO) is of crucial significance within the development of carbon nanomaterials. In this study, a green and eco-friendly strategy for the synthesis of rGO using lemon juice as the reducing agent for graphene oxide (GO) without using toxic and harmful chemicals was demonstrated. The reduction with lemon juice effectively eliminated the oxygen-containing functionalities of GO and regenerated the conjugated systems as confirmed by the UV-vis and FTIR spectroscopic and X-ray diffraction analyses. Microscopic evaluation showed the successful manufacturing of exfoliated and separated few layers of nano-sheets of rGO. The application of the resultant rGO as an adsorbent for organic pollutants was investigated using methylene blue (MB) as a model. The adsorption kinetics of MB on rGO is best matched with the pseudo-second-ordered kinetic model and the Langmuir model with a high adsorption capacity of 132.2 mg/g. The rGO exhibited good reusability with a removal efficiency of 80.4% in the fourth cycle. This green method provides a new prospect for the large-scale production of rGO in a cost-effective and safe manner. Full article

Squeeze casting is a process that can produce the highest quality castings. In the current study, the effect of the process settings and the in-die conditions on rejection rates is studied through a full-scale experimental study. Factors affecting the as-cast part quality were investigated in the current study from two different viewpoints. The first part of the study was to investigate the influence of the process settings on the part rejection rate, and the second was to understand the conditions in the die and the effects on the part rejection rate to understand better the reasons and sensitivity of the squeeze casting process. Full article

Floor-cleaning robots are becoming increasingly more sophisticated over time and with the addition of digital cameras supported by a robust vision system they become more autonomous, both in terms of their navigation skills but also in their capabilities of analyzing the surrounding environment. This document proposes a vision system based on the YOLOv5 framework for detecting dirty spots on the floor. The purpose of such a vision system is to save energy and resources, since the cleaning system of the robot will be activated only when a dirty spot is detected and the quantity of resources will vary according to the dirty area. In this context, false positives are highly undesirable. On the other hand, false negatives will lead to a poor cleaning performance of the robot. For this reason, a synthetic data generator found in the literature was improved and adapted for this work to tackle the lack of real data in this area. This synthetic data generator allows for large datasets with numerous samples of floors and dirty spots. A novel approach in selecting floor images for the training dataset is proposed. In this approach, the floor is segmented from other objects in the image such that dirty spots are only generated on the floor and do not overlap those objects. This helps the models to distinguish between dirty spots and objects in the image, which reduces the number of false positives. Furthermore, a relevant dataset of the Automation and Control Institute (ACIN) was found to be partially labelled. Consequently, this dataset was annotated from scratch, tripling the number of labelled images and correcting some poor annotations from the original labels. Finally, this document shows the process of generating synthetic data which is used for training YOLOv5 models. These models were tested on a real dataset (ACIN) and the best model attained a mean average precision (mAP) of 0.874 for detecting solid dirt. These results further prove that our proposal is able to use synthetic data for the training step and effectively detect dirt on real data. According to our knowledge, there are no previous works reporting the use of YOLOv5 models in this application. Full article

With the development of new methodologies for faster training on datasets, there is a need to provide an in-depth explanation of the workings of such methods. This paper attempts to provide an understanding for one such correlation filter-based tracking technology, Kernelized Correlation Filter (KCF), which uses implicit properties of tracked images (circulant matrices) for training and tracking in real-time. It is unlike deep learning, which is data intensive. KCF uses implicit dynamic properties of the scene and movements of image patches to form an efficient representation based on the circulant structure for further processing, using properties such as diagonalizing in the Fourier domain. The computational efficiency of KCF, which makes it ideal for low-power heterogeneous computational processing technologies, lies in its ability to compute data in high-dimensional feature space without explicitly invoking the computation on this space. Despite its strong practical potential in visual tracking, there is a need for an in-depth critical understanding of the method and its performance, which this paper aims to provide. Here we present a survey of KCF and its method along with an experimental study that highlights its novel approach and some of the future challenges associated with this method through observations on standard performance metrics in an effort to make the algorithm easy to investigate. It further compares the method against the current public benchmarks such as SOTA on OTB-50, VOT-2015, and VOT-2019. We observe that KCF is a simple-to-understand tracking algorithm that does well on popular benchmarks and has potential for further improvement. The paper aims to provide researchers a base for understanding and comparing KCF with other tracking technologies to explore the possibility of an improved KCF tracker. Full article

Timing closure remains one of the most critical challenges of a physical synthesis flow, especially when the design operates under multiple operating conditions. Even if timing is almost closed at the end of the flow, last-mile placement and routing congestion optimizations may introduce new timing violations. Correcting such violations needs minimally disruptive techniques such as threshold voltage reassignment and gate sizing that affect only marginally the placement and routing of the almost finalized design. To this end, we transform a powerful Lagrangian-relaxation-based optimizer, used for global timing optimization early in the design flow, into a practical incremental timing optimizer that corrects small timing violations with fast runtime and without increasing the area/power of the design. The proposed approach was applied to already optimized designs of the ISPD 2013 benchmarks assuming that they experience new timing violations due to local wire rerouting. Experimental results show that in single corner designs, timing is improved by more than 36% on average, using 45% less runtime. Correspondingly, in a multicorner context, timing is improved by 39% when compared to the fully-fledged version of the timing optimizer. Full article

With the advancement of additive manufacturing technologies in their material processing methodologies and variety of material selection, 3D printers are widely used in both academics and industries for various applications. It is no longer rare to have a portable and small desktop 3D printer and manufacture your own designs in a few hours. Desktop 3D printers vary in their functions, prices, materials used, and applications. Among many desktop 3D printers with various features, it is often challenging to select the best one for target applications and usages. In this paper, commercially available and carefully selected thermoplastic and photopolymer desktop 3D printers are introduced, and some representative models’ specifications and performances are compared with each other for user selection with respect to instructional applications. This paper aims to provide beginner-level or advanced-level end-users of desktop 3D printers with basic knowledge, selection criteria, a comprehensive overview of 3D printing technologies, and their technical features, helping them to evaluate and select the right 3D printers for a wide range of applications. Full article

Heating, ventilation and air-conditioning (HVAC) systems play a key role in shaping office environments. However, open-plan office buildings nowadays are also faced with problems like unnecessary energy waste and an unsatisfactory shared indoor thermal environment. Therefore, it is significant to develop a new paradigm of an HVAC system framework so that everyone could work under their preferred thermal environment and the system can achieve higher energy efficiency such as task ambient conditioning system (TAC). However, current task conditioning systems are not responsive to personal thermal comfort dynamically. Hence, this research aims to develop a dynamic task conditioning system featuring personal thermal comfort models with machine learning and the wireless non-intrusive sensing system. In order to evaluate the proposed task conditioning system performance, a field study was conducted in a shared office space in Shanghai from July to August. As a result, personal thermal comfort models with indoor air temperature, relative humidity and cheek (side face) skin temperature have better performances than baseline models with indoor air temperature only. Moreover, compared to personal thermal satisfaction predictions, 90% of subjects have better performances in thermal sensation predictions. Therefore, personal thermal comfort models could be further implemented into the task conditioning control of TAC systems. Full article

In this study, TiO₂ nanofibers were prepared with Polyvinylpyrrolidone (PVP) polymer using sol-gel method via electrospinning technique. Owing to the advantages of small fiber diameter, tunable porosity, low cost, large surface to volume ratio, structure control, light-weight, and less energy consumption, electrospun nanofibers are evolving as an adaptable material with a number of applications, in this case for filtration and environmental/virus protection. Different samples of TiO₂/PVP nanofibers have been prepared by changing the parameters to achieve the best result. As the polymer concentration was increased from 6 to 8 wt.% of PVP, diameter of the resultant fibers was seen to be increased, implying decrease in the pore-size of the fibers up to 1.4 nm. Surface morphology has been checked via Scanning Electron Microscope (SEM) images. Crystalline nature has been analyzed by X-ray Crystallography. Using the Bruanauer-Emmett-Teller (BET) test, surface area and porosity has been checked for the suitable application. The synthesized TiO₂/PVP nanofibers have tremendous practical potentials in filtration and environmental remediation applications. Full article

The aim of this article is to characterize the impacts of Smart Additive Manufacturing (SAM) on industrial production, digital supply chains (DSCs) and corresponding digital value chains (DVCs), logistics and inventory management. The method used consists of a critical review of the literature, enriched by the authors’ field experience. The results show that digital transformation of manufacturing is affecting business models, from resource acquisition to the end user. Smart manufacturing is considered a successful improvement introduced by Industry 4.0. Additive Manufacturing (AM) plays a crucial role in this digital transformation, changing the way manufacturers think about the entire lifecycle of a product. SAM combines AM in a smart factory environment. SAM reduces the complexity of DSCs and contributes to a more flexible approach to logistics and inventory management. It has also spurred the growth and popularization of customized mass production as well as decentralized manufacturing, rapid prototyping, unprecedented flexibility in product design, production and delivery, and resource efficiency and sustainability. SAM technology impacts all five Fletcher’s stages in DVCs. However, the need for clear definitions and regulations on 3D printing of digital files and their reproduction, as well as product health, safety, and integrity issues, cannot be ignored. Furthermore, investment in this technology is still expensive and can be prohibitive for many companies, namely SMEs. Full article

Geospatial data analysis can be improved by using data-driven algorithms and techniques from the machine learning field. The aim of our research is to discover interrelationships among topographical data to support the decision-making process. In this paper, we extracted topographical geospatial data from digital elevation model (DEM) raster images, and we discovered hidden patterns among this data based on the K-means clustering algorithm, to uncover relationships and find clusters of elevation values for the area of Jordan. We introduce a method for querying and clustering geospatial data and we built an interactive map accordingly. The method discovers hidden patterns and uncovers relationships in given large datasets. We demonstrate the applicability of the method using the Jordan map and we report on geospatial data analysis and retrieval improvements. The results show that the optimal decision is in favor of four clusters (classes). The first class includes the high elevation values, the second class includes the very low elevation values, the third class includes the medium-high elevation values, and the fourth class includes the very high elevation values. Full article

This paper proposes a novel lightweight visual perception system with Incremental Learning (IL), tailored to child–robot interaction scenarios. Specifically, this encompasses both an action and emotion recognition module, with the former wrapped around an IL system, allowing novel actions to be easily added. This IL system enables the tutor aspiring to use robotic agents in interaction scenarios to further customize the system according to children’s needs. We perform extensive evaluations of the developed modules, achieving state-of-the-art results on both the children’s action BabyRobot dataset and the children’s emotion EmoReact dataset. Finally, we demonstrate the robustness and effectiveness of the IL system for action recognition by conducting a thorough experimental analysis for various conditions and parameters. Full article

An enzyme-loaded disk-shaped cartridge was fabricated using CO₂-assisted polymer compression (CAPC), which is a polymer-bonding method that does not use heat. In this process, after the enzyme was loaded onto mesoporous silica, it was placed in a container fabricated from laminated fibrous sheets using CAPC. Subsequently, the lid and container were bonded via CAPC. Verification using the reductive decomposition reaction of an azo dye (methyl red) showed that the enzyme was not deactivated and that a reaction cartridge was created successfully. Full article

MOOCs are configured as one of the technologies that have been gaining ground in the educational field as a new approach in virtual education. In the past few years, its presence in educational institutions has increased. In addition, the level of research and publications that revolve around these technological developments is increasing. In this sense, this research focuses on the design and validation of the structure, content and tasks of a t-MOOC for the development of the Digital Competence of non-university teachers based on the DigCompEdu Framework of the European Union. For this, a Delphi-type validation design is established using an expert coefficient that has the participation of 191 people. The results demonstrate the validity of the training proposal, as well as the uniformity of criteria of the experts. In this sense, the application and benefits of t-MOOCs as tools for competence development are discussed. Full article

Neuronal cells are equipped with the function of a sensor that senses stimulation and elongates neurites to connect nearby neuronal cells in forming a neuronal network, as they are generally said to be hard to recover from physical damage, such as in the case of a spinal cord injury. Therefore, in this study, a novel in vitro simulator in which micro dynamic stimulations are applied to a damaged neuronal cell colony artificially is proposed to investigate the possibility of promoting the reconstruction of damaged neuronal cells on a colony basis. A neuronal cell colony differentiated from iPS cells is physically damaged by cutting off treatment, and micro dynamic stimulations are applied to the colony by utilizing a developed mini-vibration table system. NeuroFluor NeuO is used to establish a method for fluorescent staining of the living neuronal cells, and morphologies of the reconstructing neurons are analysed, revealing a relationship between the stimulation and the reconstructing process of the damaged neurons. It is found that significant differences are observed in the reconstructing efficiency between the statically cultured damaged neuronal cell colony and the dynamically stimulated one. The results suggest that applying appropriate micro dynamic stimulations is a promising approach to promote the reconstruction of a damaged neuronal cell colony. Full article

The fabrication of robots and their embedded systems is challenging due to the complexity of the interacting components. The integration of additive manufacturing (AM) to robotics has made advancements in robotics manufacturing through sophisticated and state-of-the-art AM technologies and materials. With the emergence of 3D printing, 3D printing materials are also being considered and engineered for specific applications. This study reviews different 3D printing materials for 3D printing embedded robotics. Materials such as polyethylene glycol diacrylate (PEGDA), acrylonitrile butadiene styrene (ABS), flexible photopolymers, silicone, and elastomer-based materials were found to be the most used 3D printing materials due to their suitability for robotic applications. This review paper revealed that the key areas requiring more research are material formulations for improved mechanical properties, cost, and the inclusion of materials for specific applications. Future perspectives are also provided. Full article

The current paper presents a novel method for reducing a multiclass confusion matrix into a $2\times 2$ version enabling the exploitation of the relevant performance metrics and methods such as the receiver operating characteristic and area under the curve for the assessment of different classification algorithms. The reduction method is based on class grouping and leads to a special type of matrix called the reduced confusion matrix. The developed method is then exploited for the assessment of state of the art machine learning algorithms applied on the net promoter score classification problem in the field of customer experience analytics indicating the value of the proposed method in real world classification problems. Full article

The durability of a platinum catalyst in a polymer electrolyte membrane fuel cell is studied at various operating conditions with respect to the different electric potential difference (called voltage) applied in accelerated stress tests. The electrochemical reactions of Pt ion dissolution and Pt oxide coverage of the catalyst lead to the degradation of platinum described by a one-dimensional Holby–Morgan model. The theoretical study of the underlying reaction–diffusion system with the nonlinear reactions is presented by numerical simulations which allow to predict a lifetime of the catalyst under applied voltage cycling. The computer simulation investigates how the Pt mass loss depends on the voltage slope and the upper potential level in cycles. Full article

A robotic technology consisting of an industrial robot mounted on an autonomous rover used to tap slash pine trees and collect their oleoresin for processing is introduced, and the technological challenges related to the robotic operations are discussed in detail. Unlike the case of industrial automated manufacturing systems where the relative position between the tool and workpiece can be controlled within a few hundredths of a millimeter accuracy, when used in highly unstructured environments characteristic to forestry or agriculture, the positioning accuracy between the industrial robot and the target on which it operates can be much lower than the accuracy required for the operation of the industrial robot. The paper focuses on presenting the robotic operations necessary for drilling three converging boreholes in the pine tree, spraying the boreholes with chemicals, inserting a plastic tube with pre-attached collection bag in one borehole and inserting two plugs in other two boreholes. The challenges related to performing these robotic operations in conditions of large variations in the actual shape of the pine tree trunk and variations in the relative position between the robot and the pine tree after the autonomous vehicle positions itself in front of the tree are presented. The technical solutions used to address these challenges are also described. The strategies used to programmatically adjust the robot toolpath based on detection of the borehole entry points and on the measurement of the insertion force are presented. Full article

To realize precision agriculture at multiple locations in the field, a low-cost measurement system should be developed for easy collection of hydrometeorological data, such as temperature, moisture, and light. In this study, a compact and low-cost hydrometeorological measurement system with a simplified wire code, which is customizable according to the purpose of observation, was built using a circuit board that connects Arduino to the sensors, which was then implemented and analyzed. The developed system measures air and soil temperatures, soil water content, and photosynthetic photon flux density using a sensor connected to Arduino Uno and saves the continuous, high-temporal-resolution output to an SD card. The results obtained from continuous measurement showed that the data collected using this system was significantly better than those collected using commercially available equipment. Anyone can easily measure the weather environments by using this fully open, highly versatile, portable, and user-friendly system. This system can contribute to the growth and expansion of precision agriculture, field management, development of crop models, and laborsaving. It can also provide a global solution to ongoing agricultural issues and improve the efficiency of farming operations, particularly in developing and low-income countries. Full article

Over the last decade, manufacturing processes have undergone significant change. Most factory activities have been transformed through a set of features built into a smart manufacturing framework. The tools brought to bear by the fourth industrial revolution are critical enablers of such change and progress. This review article describes the series of industrial revolutions and explores traditional manufacturing before presenting various enabling technologies. Insights are offered regarding traditional manufacturing lines where some enabling technologies have been included. The manufacturing supply chain is envisaged as enhancing the enabling technologies of Industry 4.0 through their integration. A systematic literature review is undertaken to evaluate each enabling technology and the manufacturing supply chain and to provide some theoretical synthesis. Similarly, obstacles are listed that must be overcome before a complete shift to smart manufacturing is possible. A brief discussion maps out how the fourth industrial revolution has led to novel manufacturing technologies. Likewise, a review of the fifth industrial revolution is given, and the justification for this development is presented. Full article

Wireless sensor networks (WSNs) are considered producers of large amounts of rich data. Four types of data-driven models that correspond with various applications are identified as WSNs: query-driven, event-driven, time-driven, and hybrid-driven. The aim of the classification of data-driven models is to get real-time applications of specific data. Many challenges occur during data collection. Therefore, the main objective of these data-driven models is to save the WSN’s energy for processing and functioning during the data collection of any application. In this survey article, the recent advancement of data-driven models and application types for WSNs is presented in detail. Each type of WSN is elaborated with the help of its routing protocols, related applications, and issues. Furthermore, each data model is described in detail according to current studies. The open issues of each data model are highlighted with their challenges in order to encourage and give directions for further recommendation. Full article

Fiber-reinforced 3D printing (3DP) technology is a recent addition to the material extrusion-based 3DP process unlocking huge potential to apply this technology for high-performance material fabrication with complex geometries. However, in order to take the full advantage of this technology, a comparative analysis with existing technologies targeting a particular application is necessary to understand its commercial applicability. Here, an applied composite part, ring-spinning travelers, has been developed using the unique design features of fiber-reinforced 3DP technology that is beyond the capability of the currently used technology; the injection molding, quality, and performance of the printed and molded travelers were investigated and compared. The results demonstrated that fiber-reinforced 3DP is a promising technology that offers a lot of flexibility regarding reinforcement patterns and materials including both short and continuous fibers to tailor the performance, although the printed travelers showed poorer surface characteristics and wear resistance than the molded travelers. Based on the present analysis, a number of recommendations have been proposed on the design of the traveler to apply the technology effectively and use the printer to improvise and manipulate the performance of the travelers. Full article

This study investigates and compares the acoustic signatures of a traditional wire-cable-pulled lure system and two alternative battery-operated lure systems jointly developed by Covey Associates Pty. Ltd. and Steriline Pty. Ltd. to eliminate the hazardous steel-wire cable and make the sport of greyhound racing safer for greyhounds, participants and spectators. The acoustical measurements of these three lure systems were conducted at the Murray Bridge greyhound racing track. The lure sounds were measured by the high-frequency Brüel & Kjær (B&K) Type 4191 microphones for the 395 m and 455 m starts at two positions: within the starting box and on the track adjacent to the starting boxes. The measurements capture the sounds that the greyhounds hear before and after the opening of the starting box gate. The frequency-domain analysis and sound quality analysis were conducted to compare the lure sounds. It was found when the battery-lure was installed with all nylon rollers, it presented less sound energy and lower frequency than the traditional wire-cable-pulled lure. When two of the nylon rollers were replaced with steel rollers, the battery-operated lure emitted a louder and higher frequency sound than the traditional wire-cable-pulled lure. The different acoustic characteristics of these lure systems suggest future research is warranted on the reaction of greyhounds to different lure sounds, particularly their excitement level within the starting box as the lure approaches. This initial research also suggests some greyhounds may not clearly hear the battery-operated lure with all nylon rollers approaching the starting boxes and the timing of these greyhounds to jump may be delayed, particularly during high wind conditions. Full article

Consideration of heat treatment in simulations of structural components and its impact on predictions of behaviour during operation is analysed here. An automotive machine element with a complex geometry and dynamic load is analysed rather than a standard laboratory specimen under controlled conditions. The heat treatment analysis of a starter clutch barrel has been performed in DANTE followed by a structural analysis in ANSYS 2019 R3 during operation simulating a load cycle due to the start of an internal combustion engine. The heat treatment simulation consisted of carburisation, quenching and tempering. First, the carbon content and its distribution have been simulated. Next, the hardness of the starter clutch barrel and its distribution have been analysed with respect to the carbon distribution and hardness-dependent material properties of the AISI/SAE 4142 steel. Finally, the stress field after the heat treatment and during the operation of the starter clutch barrel has been thoroughly evaluated and compared to the simulation without the consideration of the heat treatment. Results of the simulation show that the heat treatment introduces favourable compressive stresses at the critical location of the starter clutch barrel and reduces the effective amplitude of the equivalent stress during the operation. Furthermore, the results of the simulation prove that heat treatment should be considered already during the early stages of the R & D process as it can have a decisive effect on the operational behaviour of the structural component. Moreover, a non-consideration of the heat treatment can lead into erroneous conclusions regarding the suitability of machine elements. Full article