TSP is one of the most famous problems in graph theory, as well as one of the typical NP-hard problems in combinatorial optimization. Its applications range from how to plan the most reasonable and efficient road traffic to how to better set up nodes in the Internet environment to facilitate information flow, among others. Reinforcement learning has been widely regarded as an effective tool for solving combinatorial optimization problems. This paper attempts to solve the TSP problem using different reinforcement learning algorithms and evaluated the performance of three RL algorithms (Q-learning, Sarsa, and Double Q-Learning) under different reward functions, ε-greedy decay strategies, and running times. The results show that the Double Q-Learning algorithm is the best algorithm, as it could produce results closest to the optimal solutions, and by analyzing the results, better reward strategies and epsilon-greedy decay strategies are obtained.

Edge wave detection is a key technology in steel plate quality inspection, which is beneficial to improve the production quality of steel products and the efficiency of enterprises. In this letter, a novel algorithm based on visual projection and pixel statistics is proposed to detect the edge wave using 3D camera. The proposed algorithm firstly uses a series of image preprocessing to effectively remove the influence of irrelevant interference, and transforms the colour image into the binary image. Subsequently, three key positions of the edge wave in range image are precisely determined based on visual projection and pixel statistics. Finally, two 3D cameras are used to acquire the range images to evaluate the proposed algorithm's performance. The results show that the proposed algorithm can accurately detect the edge wave instead of human eye detection.

Microbial infections are considered as one of the most important concerns of the world community. Developing drug delivery systems based on formulation of nanoparticles with antimicrobial agents have shown beneficial effectiveness against microbial infections and related antimicrobial resistance. In this study we prepared and characterized a chitosan based hydrogel loaded with zinc oxide nanoparticles for controlling the release of vancomycin and also improving its antibacterial effect. Characterization studies demonstrated that the developed biopolymeric hydrogel was able to sustained and controlled the release of vancomycin in response to acidic media for 96 hours. Furthermore, antimicrobial studies showed siginificant and efficient antibacterial activity of prepared hydrogel against S. aureus and P. aeruginosa. Based on obtained results, it can be concluded that the prepared chitosan hydrogel containing ZnO nanoparticles has a desirable activity for controlling the release of vancomycin and improving its antibacterial properties.

This study applied the World Health Organization (WHO) guidelines to redesign the Portable Health Clinic (PHC), as a Remote Healthcare System (RHS), for the spread of COVID-19 containment. Additionally, the proposed system not only collects people data but also classifies the case according to the the main symptoms of coronavirus using the COVID-19 triage process (CT-process) based on the analysis of measurement readings taken from patients, where drones are used as a PHC platform and are equipped with the required sensors and essential COVID-19 medications for testing and treating people at their doorstep autonomously when a full curfew is imposed.

Electric Vehicles (EV) have the potential to completely transform the transportation industry, slash carbon emissions significantly, and pave the road for significant climate progress. According to surveys, worries regarding range and charging accessibility are a significant barrier to EV customer adoption. The electricity grid will be significantly impacted by EV charging. A coordinated charging approach that facilitates EV charging while avoiding the power grid from overloading is required to manage EV charging. In this work, the EVs are classified based on their charging capacity. This classification includes Car, truck and bus. With this a chargingdischarging scheme has been established using Real Time Pricing (RTP) for each hour for a 20 min block. Augmenting this, in the present work the power loss is analyzed when Distributed Generation (DG) units are installed after the EVs are located in the system for charging and discharging. The DG units are installed considering the Voltage Stability Index (VSI) and the power losses incurred due to the presence of EVs. The Monetary benefit of such an investment is analyzed for different topologies of IEEE-33 and 69 bus systems and compared with the existing methods where DGs are installed before the EVs are located in the system for charging and discharging.

The integration of Augmented Reality (AR) into daily surgical practice is withheld by the correct registration of pre-operative data. This includes intelligent 3D model superposition whilst simultaneously handling real and virtual occlusions caused by the AR overlay. Occlusions can negatively impact surgical safety and as such deteriorate rather than improve surgical care. Robotic surgery is particularly suited to tackle these integration challenges in a stepwise approach as the robotic console allows for different inputs to be displayed in parallel to the surgeon. Nevertheless, real-time de-occlusion requires extensive computational resources which further complicates clinical integration. This work tackles the problem of instrument occlusion and presents, to our best knowledge, the first-in-human on edge deployment of a real-time binary segmentation pipeline during three robot-assisted surgeries: partial nephrectomy, migrated endovascular stent removal and liver metastasectomy. To this end, a state-of-the-art real-time segmentation and 3D model pipeline was implemented and presented to the surgeon during live surgery. The pipeline allows real-time binary segmentation of 37 non-organic surgical items, which are never occluded during AR. The application features real-time manual 3D model manipulation for correct soft tissue alignment. The proposed pipeline can contribute towards surgical safety, ergonomics and acceptance of AR in minimally invasive surgery.

Drug treatment in elderly patients is a challenge for healthcare systems. This challenge is exacerbated by age-related changes in pharmacokinetics and pharmacodynamics, multimorbidity, frailty and cognitive impairment. Moreover, old patients are commonly exposed to polypharmacy, leading to increased risk of drug interactions, adverse drug reactions, and higher costs for the healthcare systems. Thus, the complex task of prescribing medications to older polymedicated patients encourages the use of Clinical Decision Support Systems (CDSS). This paper evaluates the CDSS miniQ for identifying potentially inappropriate prescribing in older adults with polypharmacy and to assesses the usability and acceptability of miniQ in health care professionals, patients, and caregivers. It describes how useful is CDSS miniQ system for Primary Care physicians, as well as for patients and their caregivers to know their medications. The discussion emphasizes how this system is useful to improve the prescribing process and reduce errors.

The health monitoring for disease diagnosis and prognosis in a desired smart medical structure is realized by interpreting the health data. The advances in sensor technologies and biomedical data acquisition tools have led to the new era of big data, where different sensors collect massive medical data every day. This special issue explores the latest development in emerging technologies of biomedical engineering, including big medical data, artificial intelligence, cloud/fog computing, federated learning, ubiquitous computing and communication, internet of things, wireless technologies, and, security and privacy. The biological wearable sensors can enhance the decision-making and early disease diagnosis processes by intelligently investigating and collecting large amounts of biomedical data (i.e., big health data). Hence, there is a need for scalable advanced learning, and intelligent algorithms that lead to reliable and interoperable solutions to make effective decisions in emergency medicine technologies. The optimization algorithms can be used in order to acquire the sensor data from multiple sources for fast and accurate health monitoring.

The fully passive noise shaping (NS) successive approximation register (SAR) analog-to-digital converters (ADCs) are simple, OTA-free and scaling friendly. Previous passive NS-SAR ADCs rely on the multi-path-input comparator or capacitors stacking to realize the passive gain for compensating the signal attenuation during passive integration. However, the former causes high comparator power consumption, and the latter suffers from additional signal attenuation due to the parasitics and is hard to extend to high-order systems. This work proposes a new fully passive NS-SAR technique, it can realize 2× gain with a simple structure, leading to the reduced comparator power and less parasitics. This technique is also easy to extend to high-order NS-SAR ADCs.

In this paper, an adaptive sliding mode observer (ASMO) associated with a phase locked loop (PLL) is assessed for the sensor-less control of a rotor-tied doubly-fed induction generator (RDFIG). In the proposed PLL-ASMO estimator, the ASMO utilizes the stator current, the stator voltage and the back electromotive force (EMF) as state variables. The proposed ASMO is used in order to estimate the back-EMF from which the slip position/speed is extracted using a PLL. The design of the ASMO gains is based on the Lyapunov stability criteria to ensure the convergence of the proposed observer in a finite time. Therefore, the main contribution of this paper is to propose a PLL-based ASMO estimator that aims to improve the estimation by reducing the chattering effect. A comparative study between the standard PLL-SMO estimator and the ASMO-PLL estimator is presented. Also, For the first time, an adaptive sliding mode observer is used for the sensor-less control of a RDFIG. The performance of the proposed sensor-less control strategy is validated through simulation and experimental measurements under various operating conditions. Furthermore, the estimator is shown to be robust to machine parameter variation.

Affective video content analysis is an active topic in the field of affective computing. In general, affective video content can be depicted by feature vectors of multiple modalities, so it is important to effectively fuse information. In this work, a novel framework is designed to fuse information from multiple stages in a unified manner. In particular, a unified fusion layer is devised to combine output tensors from multiple stages of the proposed neural network. With the unified fusion layer, a bidirectional residual recurrent fusion block is devised to model the information of each modality. Moreover, the proposed method achieves state-of-the-art performances on two challenging datasets, i.e., the accuracy value on the VideoEmotion dataset is 55.8%, and the MSE values on the two domains of EIMT16 are 0.464 and 0.176 respectively. The code of UMFN is available at: https://github.com/yunyi9/UMFN.

A common 400V dc bus for industrial motor drives advantageously allows the use of high-performance 600V power semiconductor technology in the inverter drive converter stages and to lower the rated power of the supplying rectifier system. Ideally, this supplying rectifier system features unity power factor operation, bidirectional power flow and nominal power operation in the three-phase and the single-phase grid. This paper introduces a novel bidirectional universal single-/three-phase-input unity power factor differential ac-dc converter suitable for the above mentioned requirements: The basic operating principle and conduction states of the proposed topology are derived and discussed in detail. Then, the main power component voltage and current stresses are determined and simulation results in PLECS are provided. The concept is verified by means of experimental measurements conducted in both three-phase and single-phase operation with a 6kW prototype system employing a switching frequency of 100 kHz and 1200V SiC power semiconductors.

This paper proposes an intelligent approach based on the empirical Fourier decomposition (EFD) to identify harmonic sources at the point of common coupling (PCC) when different inverter-based distributed generations (DGs) like microturbine (MT), Battery energy storage system (BESS), photovoltaic (PV), superconducting magnetic energy storage (SMES), wind turbine with a permanent magnet synchronous generator (PMSG), and doubly-fed induction generator (DFIG) wind turbine are presented. In order to decrease memory storage and computational burden, strife feature selection is used. Applying just voltage signals consumes less processing time and decreases measurement devices. Moreover, the whale optimization algorithm (WOA) as the optimizer of the parameters of the support vector machine (SVM) classifier is used. Consequently, the results from the proposed method can be helpful for both engineers and researchers to plan and develop a better strategy to mitigate harmonic distortion.

Liben Building, constructed in 1822 AD, is located in Xinnan Village, Hukeng Town, Yongding District, Longyan City, Fujian Province, China. This square earthen building covers an area of about 2,100 square meters. In 1931 AD, the building was sacked and burned down by bandits during a war, leaving behind only the remnants of the walls of the main building. In the meta-universe environment, firstly, this study adopted blockchain DAO technology to conduct a study on the digital collection, storage, processing, display and dissemination of Liben Building, revealing the problems with digital regeneration of cultural heritage. Then, the questionnaire with 20 questions was designed, and 158 valid completed copies of the questionnaire were collected. Combining influence relationship with sample clustering analysis methods, this paper explored the findings of a study of the historical sites under blockchain digital heritage preservation and protection in the metaverse.

5G network slicing is a promising solution to prioritize time-critical protection communication in wireless networks. However, recent trends indicate that a 5G slice could encompass all smart grid applications lacking the necessary granularity. At the same time, while substation communication standards recommend prioritization of protection communication traffic to improve reliability, these recommendations are only for wired connections. Therefore, this paper investigates traffic shaping and uplink (UL) bitrate adaptation of video stream based on existing commercial solutions as methodologies for prioritizing the protection communication in a 5G slice. These methodologies are validated in an experimental setup combining controller-hardware-in-the-loop (CHIL) simulation with a quality of service (QoS) measurement system. The system under test consists of commercial 5G networks, commercial intelligent electronic devices (IEDs), and merging units to validate the methodologies on three smart grid applications: fault location, line differential, and intertrip protection. The results show improvement in protection communication when traffic shaping and UL bitrate adaptation are applied. Traffic shaping even improves prioritization with a wired connection.

A novel frequency-to-voltage converter (FVC) based phase-locked loop (PLL) is proposed to overcome the inability of an FVC-based frequency-locked loop (FLL) to lock phase. The proposed dual-loop PLL adds variable phase-locking capability, such that the phase locking angle can vary from 0o – 360o. The additional variable phase-locking can be applied in data communication in the form of phase modulation. The design is targeted for a 0.5-µm CMOS process. The proposed design generates a 480MHz clock from a reference clock of 15MHz. In simulation, the proposed PLL locks within 3.56 µs while consuming 1.61 mW of power.

As self-driving cars perform more tasks, new challenges arise. One of these challenging tasks is autonomous driving decision-making due to the uncertainty of the vehicle’s complex environment. This paper provides an overview of decision-making technology and trajectory control for autonomous vehicles. The main common goal in decision-making is to consider uncertainties, unpredictable situations, and driving tasks to propose a global and robust solution adapted to each situation. The main concern is safety. Decision-making falls into three categories. The first is the traditional approach, which often consists of building a rule system and deriving optimal operations. The advantages of such an approach are well known for being easy to understand and applicable to small problems. The second category of decision-making is based on a probabilistic process and, due to its efficiency, has several applications in this area. The third category is learning-based approaches. Once a decision has been made, manipulate the steering angle or accelerator/brake pedals to perform the appropriate action. Two approaches are existing to designing autonomous driving controllers. Either based on imitating human drivers that includes approaches based on the use of driver models such as AI, or the use of approach-based models

Chronic lesion has become a major biological burden for individual patients and health organizations. Using nanoparticles as drug delivery systems is remarkable nowadays. The unique properties of chitosan without any toxicity for living creations make it a suitable option for drug delivery. Epidermal growth factor (EGF) is one of the important agents for wound healing, cellular proliferation, extracellular matrix formation, and skin remodeling. A combination of these properties can accelerate the wound healing process. In this study, rh-EGF is embedded into the chitosan nanoparticles by the Ion-gelation method. Nanoparticles are characterized by TEM microscopy and the DLS method and conjugation efficacy is measured by FT-IR radiation. The antibacterial effect of manipulated nanoparticles was estimated by MIC/MBC methods. The cytotoxicity and proliferation were measured by MTT assay on the HFF-1 human fibroblast cell line. Migration assay was accomplished by in vitro scratch model and the gene expression analysis for TGF-β, VEGF, and PDGF were manipulated by the real time-PCR method. The obtained results were considered statistically significant with P < 0.05. Obtained results illustrated no toxic effect on the HFF-1 cell line treated with Chitosan-EGF (CS-EGF). In cellular proliferation and migration assays, CS-EGF nanoparticles demonstrated a better effect than free rh-EGF. For the duration of 72h of the experiment, the whole scratch was covered by fibroblasts. The real time-PCR analysis also showed upregulation of all TGF-β, VEGF, and PDGF genes. As CS-EGF nanoparticles in the acceleration of the skin remodeling process showed promising results, subsequent studies might be useful.