Digital Twin Platform for Water Treatment Plants Using Microservices Architecture.

The effects of climate change and the rapid growth of societies often lead to water scarcity and inadequate water quality, resulting in a significant number of diseases. The digitalization of infrastructure and the use of Digital Twins are presented as alternatives for optimizing resources and the necessary infrastructure in the water cycle. This paper presents a framework for the development of a Digital Twin platform for a wastewater treatment plant, based on a microservices architecture which optimized its design for edge computing implementation. The platform aims to optimize the operation and maintenance processes of the plant's systems, by employing machine learning techniques, process modeling and simulation, as well as leveraging the information contained in BIM models to support decision-making.

End-to-End Ultrasonic Hand Gesture Recognition.

As the number of electronic gadgets in our daily lives is increasing and most of them require some kind of human interaction, this demands innovative, convenient input methods. There are limitations to state-of-the-art (SotA) ultrasound-based hand gesture recognition (HGR) systems in terms of robustness and accuracy. This research presents a novel machine learning (ML)-based end-to-end solution for hand gesture recognition with low-cost micro-electromechanical (MEMS) system ultrasonic transducers. In contrast to prior methods, our ML model processes the raw echo samples directly instead of using pre-processed data. Consequently, the processing flow presented in this work leaves it to the ML model to extract the important information from the echo data. The success of this approach is demonstrated as follows. Four MEMS ultrasonic transducers are placed in three different geometrical arrangements. For each arrangement, different types of ML models are optimized and benchmarked on datasets acquired with the presented custom hardware (HW): convolutional neural networks (CNNs), gated recurrent units (GRUs), long short-term memory (LSTM), vision transformer (ViT), and cross-attention multi-scale vision transformer (CrossViT). The three last-mentioned ML models reached more than 88% accuracy. The most important innovation described in this research paper is that we were able to demonstrate that little pre-processing is necessary to obtain high accuracy in ultrasonic HGR for several arrangements of cost-effective and low-power MEMS ultrasonic transducer arrays. Even the computationally intensive Fourier transform can be omitted. The presented approach is further compared to HGR systems using other sensor types such as vision, WiFi, radar, and state-of-the-art ultrasound-based HGR systems. Direct processing of the sensor signals by a compact model makes ultrasonic hand gesture recognition a true low-cost and power-efficient input method.

A Novel TCN-LSTM Hybrid Model for sEMG-Based Continuous Estimation of Wrist Joint Angles.

Surface electromyography (sEMG) offers a novel method in human-machine interactions (HMIs) since it is a distinct physiological electrical signal that conceals human movement intention and muscle information. Unfortunately, the nonlinear and non-smooth features of sEMG signals often make joint angle estimation difficult. This paper proposes a joint angle prediction model for the continuous estimation of wrist motion angle changes based on sEMG signals. The proposed model combines a temporal convolutional network (TCN) with a long short-term memory (LSTM) network, where the TCN can sense local information and mine the deeper information of the sEMG signals, while LSTM, with its excellent temporal memory capability, can make up for the lack of the ability of the TCN to capture the long-term dependence of the sEMG signals, resulting in a better prediction. We validated the proposed method in the publicly available Ninapro DB1 dataset by selecting the first eight subjects and picking three types of wrist-dependent movements: wrist flexion (WF), wrist ulnar deviation (WUD), and wrist extension and closed hand (WECH). Finally, the proposed TCN-LSTM model was compared with the TCN and LSTM models. The proposed TCN-LSTM outperformed the TCN and LSTM models in terms of the root mean square error (RMSE) and average coefficient of determination (R2). The TCN-LSTM model achieved an average RMSE of 0.064, representing a 41% reduction compared to the TCN model and a 52% reduction compared to the LSTM model. The TCN-LSTM also achieved an average R2 of 0.93, indicating an 11% improvement over the TCN model and an 18% improvement over the LSTM model.

Hydrogen-rich gas production from disposable COVID-19 mask by steam gasification.

Globally, the demand for masks has increased due to the COVID-19 pandemic, resulting in 490,201 tons of waste masks disposed of per month. Since masks are used in places with a high risk of virus infection, waste masks retain the risk of virus contamination. In this study, a 1 kg/h lab-scale (diameter: 0.114 m, height: 1 m) bubbling fluidized bed gasifier was used for steam gasification (temperature: 800 °C, steam/carbon (S/C) ratio: 1.5) of waste masks. The use of a downstream reactor with activated carbon (AC) for tar cracking and the enhancement of hydrogen production was examined. Steam gasification with AC produces syngas with H2, CO, CH4, and CO2 content of 38.89, 6.40, 21.69, and 7.34 vol%, respectively. The lower heating value of the product gas was 29.66 MJ/Nm3 and the cold gas efficiency was 74.55 %. This study showed that steam gasification can be used for the utilization of waste masks and the production of hydrogen-rich gas for further applications.

Using Gesture Recognition for AGV Control: Preliminary Research.

In this paper, we present our investigation of the 2D Hand Gesture Recognition (HGR) which may be suitable for the control of the Automated Guided Vehicle (AGV). In real conditions, we deal with, among others, a complex background, changing lighting conditions, and different distances of the operator from the AGV. For this reason, in the article, we describe the database of 2D images created during the research. We tested classic algorithms and modified them by us ResNet50 and MobileNetV2 which were retrained partially using the transfer learning approach, as well as proposed a simple and effective Convolutional Neural Network (CNN). As part of our work, we used a closed engineering environment for rapid prototyping of vision algorithms, i.e., Adaptive Vision Studio (AVS), currently Zebra Aurora Vision, as well as an open Python programming environment. In addition, we shortly discuss the results of preliminary work on 3D HGR, which seems to be very promising for future work. The results show that, in our case, from the point of view of implementing the gesture recognition methods in AGVs, better results may be expected for RGB images than grayscale ones. Also using 3D imaging and a depth map may give better results.

Recent Progress of Tactile and Force Sensors for Human-Machine Interaction.

Human-Machine Interface (HMI) plays a key role in the interaction between people and machines, which allows people to easily and intuitively control the machine and immersively experience the virtual world of the meta-universe by virtual reality/augmented reality (VR/AR) technology. Currently, wearable skin-integrated tactile and force sensors are widely used in immersive human-machine interactions due to their ultra-thin, ultra-soft, conformal characteristics. In this paper, the recent progress of tactile and force sensors used in HMI are reviewed, including piezoresistive, capacitive, piezoelectric, triboelectric, and other sensors. Then, this paper discusses how to improve the performance of tactile and force sensors for HMI. Next, this paper summarizes the HMI for dexterous robotic manipulation and VR/AR applications. Finally, this paper summarizes and proposes the future development trend of HMI.

Microsurgery Robots: Applications, Design, and Development.

Microsurgical techniques have been widely utilized in various surgical specialties, such as ophthalmology, neurosurgery, and otolaryngology, which require intricate and precise surgical tool manipulation on a small scale. In microsurgery, operations on delicate vessels or tissues require high standards in surgeons' skills. This exceptionally high requirement in skills leads to a steep learning curve and lengthy training before the surgeons can perform microsurgical procedures with quality outcomes. The microsurgery robot (MSR), which can improve surgeons' operation skills through various functions, has received extensive research attention in the past three decades. There have been many review papers summarizing the research on MSR for specific surgical specialties. However, an in-depth review of the relevant technologies used in MSR systems is limited in the literature. This review details the technical challenges in microsurgery, and systematically summarizes the key technologies in MSR with a developmental perspective from the basic structural mechanism design, to the perception and human-machine interaction methods, and further to the ability in achieving a certain level of autonomy. By presenting and comparing the methods and technologies in this cutting-edge research, this paper aims to provide readers with a comprehensive understanding of the current state of MSR research and identify potential directions for future development in MSR.

Is my AV crashing? An online photo-based experiment assessing whether shared intended pathway can help AV drivers anticipate silent failures.

The shared responsibility between conditional AVs drivers demands shared understanding. Thus, a shared intended pathway (SIP)-a graphical display of the AV's planned manoeuvres in a head-up display to help drivers anticipate silent failures is proposed. An online, randomised photo experiment was conducted with 394 drivers in Australia. The photos presented traffic scenarios where the SIP forecast either safe or unsafe manoeuvres (silent failures). Participants were required to respond by selecting whether driver intervention was necessary or not. Additionally, the effects of presented object recognition bounding boxes which indicated whether a road user was recognised or not were also tested in the experiment. The SIP led to correct intervention choices 87% of the time, and to calibrating self-reported trust, perceived ease of use and usefulness. The bounding boxes found no significant effects. Results suggest SIPs can assist in monitoring conditional automation. Future research in simulator studies is recommended. Practitioner summary: Conditional AV drivers are expected to take-over control during failures. However, drivers are not informed about the AV's planned manoeuvres. A visual display that presents the shared intended pathway is proposed to help drivers mitigate silent failures. This online photo experiment found the display helped anticipate failures with 87% accuracy.

Design a protocol to investigate the effects of climate change in vivo.

Climate change has a variety of effects on communities and the environment, most of which have been directly addressed, such as floods, droughts, and fires. To date, the impacts of climate change on health in in vivo conditions have not been assessed, and no protocol has been developed in this regard. Therefore, the purpose of the current study is to develop a protocol as well as design and build a pilot to deal with climate change in vivo to show the direct effects of climate change on health. For this purpose, twenty specialists, comprising ten experts active in field climate and 10 experts in field medicine and anatomy, have been consulted to design the proposed exposure protocol using the Delphi method. According to the prepared protocol, an exposure pilot was then designed and built, which provides the climatic conditions for animal exposure with a fully automatic HMI-PLC system. The results showed the average 12:12-h day/night temperature, humidity, and circadian cycle for three consecutive ten-year periods selected for exposure of 1-month-old male rats. The duration of the exposure period is four months, which is equivalent to a ten-year climatic period. This study is a framework and a starting point for examining the effects of climate change on in vivo conditions that have not yet been considered.

Facile Pressure-Sensitive Capacitive Touch Keypad for a Green Intelligent Human-Machine Interface.

There is a great demand for human-machine interfaces (HMIs) in emerging electronics applications. However, commercially available plastic-based HMIs are primarily rigid, application-specific, and hard to recycle and dispose of due to their non-biodegradability. This results in electronic and plastic waste, potentially damaging the environment by ending up in landfills and water resources. This work presents a green, capacitive pressure-sensitive (CPS), touch sensor-based keypad as a disposable, wireless, and intelligent HMI to mitigate these problems. The CPS touch keypads were fabricated through a facile green fabrication process by direct writing of graphite-on-paper, using readily available materials such as paper and pencils, etc. The interdigitated capacitive (IDC) touch sensors were optimized by analyzing the number of electrode fingers, dimensions, and spacing between the electrode fingers. The CPS touch keypad was customized to wirelessly control a robotic arm's movements based on the touch input. A low-pressure touch allows slow-speed robotic arm movement for precision movements, and a high-pressure touch allows high-speed robotic arm movement to cover the large movements quickly. The green CPS touch keypad, as a disposable wireless HMI, has the potential to enforce a circular economy by mitigating electronic and plastic waste, which supports the vision of a sustainable and green world.

Fiber-Based Triboelectric Nanogenerator for Mechanical Energy Harvesting and Its Application to a Human-Machine Interface.

Mechanical energy harvesters including piezoelectric nanogenerators, electromagnetic generators and triboelectric nanogenerators (TENG) used to convert the mechanical motion into electricity are more and more important in the recent decades. Specifically, the fiber-based TENG (FTENG) has gained considerable favors due to its flexibility, light weight, and high environmental tolerance for the wearable devices. The traditional FTENGs made of Teflon result in better performance but are not suitable for long-term wear in person. Here, we propose a novel FTENG using a flexible micro-needle-structured polydimethylsiloxane (MN-PDMS) together with the comfortable commercially available 2D-polyester fibers, and electroless nickel-plated cotton cloth of which two are widely used in human daily life. The MN-PDMS is formed by a laser engraved mold for improving its output performance of FTENG compared to the flat-PDMS. The open-circuit voltage (Voc) and the short-circuit current (Isc) of MN-FTENG increased to 73.6 V and 36 µA, respectively, which are 34% and 37% higher than the flat-FTENG. In terms of power, the performance of MN-FTENG reaches 1.296 mW which is 89% higher than that of flat-TENG and it can also light up 90 LEDs. For application, human motion at the joints can be detected and collected with various signals that are used for the human-machine interface (HMI) through the cooperation of components for the Internet of Things (IoT). It can light up the LED bulb through MN-FTENG to potentially develop IoT HMI systems for human motion control of robot in the future.

In vitro anti-trypanosomal effect of ivermectin on Trypanosoma evansi by targeting multiple metabolic pathways.

High cytotoxicity and increasing resistance reports of existing chemotherapeutic agents against T. evansi have raised the demand for novel, potent, and high therapeutic index molecules for the treatment of surra in animals. In this regard, repurposing approach of drug discovery has provided an opportunity to explore the therapeutic potential of existing drugs against new organism. With this objective, the macrocyclic lactone representative, ivermectin, has been investigated for the efficacy against T. evansi in the axenic culture medium. To elucidate the potential target of ivermectin in T. evansi, mRNA expression profile of 13 important drug target genes has been studied at 12, 24, and 48 h interval. In the in vitro growth inhibition assay, ivermectin inhibited T. evansi growth and multiplication significantly (p < 0.001) with IC50 values of 13.82 µM, indicating potent trypanocidal activity. Cytotoxicity assays on equine peripheral blood mononuclear cells (PBMCs) and Vero cell line showed that ivermectin affected the viability of cells with a half-maximal cytotoxic concentration (CC50) at 17.48 and 22.05 µM, respectively. Data generated showed there was significant down-regulation of hexokinase (p < 0.001), ESAG8 (p < 0.001), aurora kinase (p < 0.001), casein kinase 1 (p < 0.001), topoisomerase II (p < 0.001), calcium ATPase 1 (p < 0.001), ribonucleotide reductase I (p < 0.05), and ornithine decarboxylase (p < 0.01). The mRNA expression of oligopeptidase B remains refractory to the exposure of the ivermectin. The arginine kinase 1 and ribonucleotide reductase II showed up-regulation on treatment with ivermectin. The ivermectin was found to affect glycolytic pathways, ATP-dependent calcium ATPase, cellular kinases, and other pathway involved in proliferation and maintenance of internal homeostasis of T. evansi. These data imply that intervention with alternate strategies like nano-formulation, nano-carriers, and nano-delivery or identification of ivermectin homologs with low cytotoxicity and high bioavailability can be explored in the future as an alternate treatment for surra in animals.

Smart Homes as Enablers for Depression Pre-Diagnosis Using PHQ-9 on HMI through Fuzzy Logic Decision System.

Depression is a common mental illness characterized by sadness, lack of interest, or pleasure. According to the DSM-5, there are nine symptoms, from which an individual must present 4 or 5 in the last two weeks to fulfill the diagnosis criteria of depression. Nevertheless, the common methods that health care professionals use to assess and monitor depression symptoms are face-to-face questionnaires leading to time-consuming or expensive methods. On the other hand, smart homes can monitor householders' health through smart devices such as smartphones, wearables, cameras, or voice assistants connected to the home. Although the depression disorders at smart homes are commonly oriented to the senior sector, depression affects all of us. Therefore, even though an expert needs to diagnose the depression disorder, questionnaires as the PHQ-9 help spot any depressive symptomatology as a pre-diagnosis. Thus, this paper proposes a three-step framework; the first step assesses the nine questions to the end-user through ALEXA or a gamified HMI. Then, a fuzzy logic decision system considers three actions based on the nine responses. Finally, the last step considers these three actions: continue monitoring through Alexa and the HMI, suggest specialist referral, and mandatory specialist referral.

Application Perspective on Cybersecurity Testbed for Industrial Control Systems.

In recent years, the Industry 4.0 paradigm has accelerated the digitalization process of the industry, and it slowly diminishes the line between information technologies (IT) and operational technologies (OT). Among the advantages, this brings up the convergence issue between IT and OT, especially in the cybersecurity-related topics, including new attack vectors, threats, security imperfections, and much more. This cause raised new topics for methods focused on protecting the industrial infrastructure, including monitoring and detection systems, which should help overcome these new challenges. However, those methods require high quality and a large number of datasets with different conditions to adapt to the specific systems effectively. Unfortunately, revealing field factory setups and infrastructure would be costly and challenging due to the privacy and sensitivity causes. From the lack of data emerges the new topic of industrial testbeds, including sub-real physical laboratory environments, virtual factories, honeynets, honeypots, and other areas, which helps to deliver sufficient datasets for mentioned research and development. This paper summarizes related works in the area of industrial testbeds. Moreover, it describes best practices and lessons learned for assembling physical, simulated, virtual, and hybrid testbeds. Additionally, a comparison of the essential parameters of those testbeds is presented. Finally, the findings and provided information reveal research and development challenges, which must be surpassed.

An Interactive Haptic Guidance System for Intuitive Programming CNC Machine Tool.

The human-machine interfaces in modern CNC machine tools are not very intuitive and still based on archaic input systems, i.e., switches, handwheels, and buttons. This type of solution has two major drawbacks. The pushed button activates the movement only in one direction and is insensitive to the amount of the force exerted by the operator, which makes it difficult to move the machine axes at variable speeds. The paper proposes a novel and intuitive system of manual programming of a CNC machine tool based on a control lever with strain-gauge sensors. The presented idea of manual programming is aimed at eliminating the need to create a machining program and at making it possible to move the machine intuitively, eliminating mistakes in selecting directions and speeds. The article describes the concept of the system and the principle of operation of the control levers with force sensors. The final part of the work presents the experimental validation of the proposed system and a functionality comparison with the traditional CNC control.

Surface Electromyography-Controlled Automobile Steering Assistance.

Disabilities of the upper limb, such as hemiplegia or upper limb amputation, can limit automobile drivers to steering with one healthy arm. For the benefit of these drivers, recent studies have developed prototype interfaces that realized surface electromyography (sEMG)-controlled steering assistance with path-following accuracy that has been validated with driving simulations. In contrast, the current study expands the application of sEMG-controlled steering assistance by validating the Myo armband, a mass-produced sEMG-based interface, with respect to the path-following accuracy of a commercially available automobile. It was hypothesized that one-handed remote steering with the Myo armband would be comparable or superior to the conventional operation of the automobile steering wheel. Although results of low-speed field testing indicate that the Myo armband had lower path-following accuracy than the steering wheel during a 90° turn and wide U-turn at twice the minimum turning radius, the Myo armband had superior path-following accuracy for a narrow U-turn at the minimum turning radius and a 45° turn. Given its overall comparability to the steering wheel, the Myo armband could be feasibly applied in future automobile studies.

Eye and Voice-Controlled Human Machine Interface System for Wheelchairs Using Image Gradient Approach.

Rehabilitative mobility aids are being used extensively for physically impaired people. Efforts are being made to develop human machine interfaces (HMIs), manipulating the biosignals to better control the electromechanical mobility aids, especially the wheelchairs. Creating precise control commands such as move forward, left, right, backward and stop, via biosignals, in an appropriate HMI is the actual challenge, as the people with a high level of disability (quadriplegia and paralysis, etc.) are unable to drive conventional wheelchairs. Therefore, a novel system driven by optical signals addressing the needs of such a physically impaired population is introduced in this paper. The present system is divided into two parts: the first part comprises of detection of eyeball movements together with the processing of the optical signal, and the second part encompasses the mechanical assembly module, i.e., control of the wheelchair through motor driving circuitry. A web camera is used to capture real-time images. The processor used is Raspberry-Pi with Linux operating system. In order to make the system more congenial and reliable, the voice-controlled mode is incorporated in the wheelchair. To appraise the system's performance, a basic wheelchair skill test (WST) is carried out. Basic skills like movement on plain and rough surfaces in forward, reverse direction and turning capability were analyzed for easier comparison with other existing wheelchair setups on the bases of controlling mechanisms, compatibility, design models, and usability in diverse conditions. System successfully operates with average response time of 3 s for eye and 3.4 s for voice control mode.

NLP-Based Approach for Predicting HMI State Sequences Towards Monitoring Operator Situational Awareness.

A novel approach presented herein transforms the Human Machine Interface (HMI) states, as a pattern of visual feedback states that encompass both operator actions and process states, from a multi-variate time-series to a natural language processing (NLP) modeling domain. The goal of this approach is to predict operator response patterns for n - a h e a d time-step window given k - l a g g e d past HMI state patterns. The NLP approach offers the possibility of encoding (semantic) contextual relations within HMI state patterns. Towards which, a technique for framing raw HMI data for supervised training using sequence-to-sequence (seq2seq) deep-learning machine translation algorithms is presented. In addition, a custom Seq2Seq convolutional neural network (CNN) NLP model based on current state-of-the-art design elements such as attention, is compared against a standard recurrent neural network (RNN) based NLP model. Results demonstrate comparable effectiveness of both the designs of NLP models evaluated for modeling HMI states. RNN NLP models showed higher ( ≈ 26 % ) forecast accuracy, in general for both in-sample and out-of-sample test datasets. However, custom CNN NLP model showed higher ( ≈ 53 % ) validation accuracy indicative of less over-fitting with the same amount of available training data. The real-world application of the proposed NLP modeling of industrial HMIs, such as in power generating stations control rooms, aviation (cockpits), and so forth, is towards the realization of a non-intrusive operator situational awareness monitoring framework through prediction of HMI states.

Design and Evaluation of a Surface Electromyography-Controlled Steering Assistance Interface.

Millions of drivers could experience shoulder muscle overload when rapidly rotating steering wheels and reduced steering ability at increased steering wheel angles. In order to address these issues for drivers with disability, surface electromyography (sEMG) sensors measuring biceps brachii muscle activity were incorporated into a steering assistance system for remote steering wheel rotation. The path-following accuracy of the sEMG interface with respect to a game steering wheel was evaluated through driving simulator trials. Human participants executed U-turns with differing radii of curvature. For a radius of curvature equal to the minimum vehicle turning radius of 3.6 m, the sEMG interface had significantly greater accuracy than the game steering wheel, with intertrial median lateral errors of 0.5 m and 1.2 m, respectively. For a U-turn with a radius of 7.2 m, the sEMG interface and game steering wheel were comparable in accuracy, with respective intertrial median lateral errors of 1.6 m and 1.4 m. The findings of this study could be utilized to realize accurate sEMG-controlled automobile steering for persons with disability.

A Novel Finger-Controlled Passive RFID Tag Design for Human-Machine Interaction.

Radio frequency identification (RFID) has shown its potential in human-machine interaction thanks to its inherent function of identification and relevant physical information of signals, but complex data processing and undesirable input accuracy restrict its application and promotion in practical use. This paper proposes a novel finger-controlled passive RFID tag design for human-machine interaction. The tag antenna is based on a dipole antenna with a separated T-match structure, which is able to adjust the state of the tag by the press of a finger. The state of the proposed tag can be recognized directly by the code received by the RFID reader, and no complex data processing is needed. Since the code is hardly affected by surroundings, the proposed tag is suitable to be used as a wireless switch or control button in multiple scenarios. Moreover, arrays of the proposed tag with rational tag arrangements could contribute to a series of manual control devices, such as a wireless keyboard, a remote controller, and a wireless gamepad, without batteries. A 3 × 4 array of the finger-controlled tag is presented to constitute a simple passive RFID keyboard as an example of the applications of the proposed tag array and it refers to the arrangement of a keypad and can achieve precise, convenient, quick, and practical commands and text input into machines by pressing the tags with fingers. Simulations and measurements of the proposed tag and tag array have been carried out to validate their performances in human-machine interaction.