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This article reviews soft pneumatic actuators (SPAs) that are manufactured entirely via additive manufacturing methods. These actuators are known as four-dimensional (4D)-printed SPAs and can generate bending motions in response to either pressurised or vacuum (negative pressure) air stimulus after fabrication. They are characterised by geometrical and material factors that determine their motion trajectory, and the force they exert on manipulated soft objects in delicate applications such as food handling and non-invasive surgery. Here, we review various 3D printers and materials used for the fabrication of the pressurised air bending-type SPAs. The reported approaches for modelling and control of these actuators are presented and compared. General discussions, as well as future directions and challenges of these actuators, are given.

This paper presents the design of an extended parameterisations of H ∞ controller for off grid operation of a microgrid. The microgrid consists of distributed generation units, filters and local loads. The filters are used to achieve accurate sinusoidal output voltage. However, loads which are connected to the microgrid are parametrically uncertain. Hence, it undergoes with unknown loads uncertainties. These unknown loads may create unknown loads harmonics, non-linearities which may reduce the voltage and current profile of the microgrid. As a result, the sudden rise and fall of voltage current profile damages the domestic and commercial loads. The proposed controller provides robust stability against various unknown loads and uncertainties. The design of the controller is presented using linear matrix inequality approach and satisfies the Lyapunov stability criterion. Moreover, it provides lower closed-loop H ∞ norm and has better tracking accuracy than other. For justification, several load conditions have been tested in MATLAB/SimPowerSystem Toolbox to ensure the robust stability of the proposed controller. All the results presented in the paper indicate high performance of the controller.

Cloud computing gained much popularity in the recent past due to its many internet-based services related to data, application, operating system, and eliminating the need for central hardware access. Many of the challenges associated with cloud computing can be specified as network load, security intrusion, authentication, biometric identification, and information leakage. Numerous algorithms have been proposed and evaluated to solve those challenges. Among those, bio-inspired algorithms such as Evolutionary, Swarm, Immune, and Neural algorithms are the most prominent ones which are developed based on nature’s ecosystems. Bio-inspired algorithms’ adaptability allows many researchers and practitioners to utilize them to solve many security-related cloud computing issues. This paper aims to explore previous research, recent studies, challenges, and scope for further analysis of cloud security. Therefore, this study provides an overview of bio-inspired algorithms application and evaluations, taking into account cloud security challenges, such as Identity and Authentication, Access Control Systems, Protocol and Network Security, Trust Management, Intrusion Detection, Virtualization, and Forensic.

Robotics is one of the many tools that is making a substantial difference as the world is experiencing the fourth industrial revolution. To ease control over this engineering marvel substantially, Reinforcement Learning (RL) has paved its way in recent years quite remarkably. RL enables robots to become self-aware towards carrying out a specific task followed by user operations. For decades of rigorous endeavor, this research field has gone through numerous groundbreaking developments and it will be the same for the coming days. Therefore, this paper steps in to enlighten the scientific community with a systemic review of the published research papers within the past decade. The bibliographic data that is extracted from the papers are analyzed using an automated tool named Vosviewer with respect to some parameters. Substantial excerpts from the most influential papers are highlighted in this work. Furthermore, this paper points out the global research practice in this field. The paper also generates some intriguing questions and answers them in regards to the research topic. After reading this paper, future researchers will have a firm idea in the RL-based robotics and will be able to incorporate in their own research.

In this article, a method of winding optimization of the linear generator is proposed to increase the electrical power generation from the oceanic wave. A linear generator is designed in the ANSYS/Maxwell environment to analyze the proposed winding optimization method. The electrical and magnetic properties of the generator are extensively analyzed. The cross-sectional area and the number of turns of the copper conductor are optimized to maximize the output power. Load characteristics are also considered for different conductor sizes and turn numbers to determine a suitable operating point. A cooling system is also incorporated in the optimized linear generator to extract the thermal energy produced during the operation, which eventually enhances the electrical power generation. It is found that the optimized linear generator finally generates around 42% more electrical power compared to that of the conventional one. Therefore, the volumetric power density of the linear generator is noticeably increased with the proposed optimization method that also results in the minimization of material cost of the generator. A downscale prototype of the linear generator is constructed in the laboratory. It is expected that the proposed winding optimization method can be used in designing other linear generators.

In the face of a global epidemic of drug addiction, neglecting to develop new effective therapies will perpetuate the staggering human and economic costs of substance use. This review aims to summarize and evaluate the preclinical and clinical studies of deep brain stimulation (DBS) as a novel therapy for refractory addiction, in hopes to engage and inform future research in this promising novel treatment avenue. An electronic database search (MEDLINE, EMBASE, Cochrane library) was performed using keywords and predefined inclusion criteria between 1974 and 6/18/2021 (registered on Open Science Registry). Selected articles were reviewed in full text and key details were summarized and analyzed to understand DBS’ therapeutic potential and possible mechanisms of action. The search yielded 25 animal and 22 human studies. Animal studies showed that DBS of targets such as nucleus accumbens (NAc), insula, and subthalamic nucleus reduces drug use and seeking. All human studies were case series/reports (level 4/5 evidence), mostly targeting the NAc with generally positive outcomes. From the limited evidence in the literature, DBS, particularly of the NAc, appears to be a reasonable last resort option for refractory addictive disorders. We propose that future research in objective electrophysiological (e.g., local field potentials) and neurochemical (e.g., extracellular dopamine levels) biomarkers would assist monitoring the progress of treatment and developing a closed-loop DBS system. Preclinical literature also highlighted the prefrontal cortex as a promising DBS target, which should be explored in human research.

Objectives Despite recent advances in depression treatment, many patients still do not respond to serial conventional therapies and are considered "treatment resistant." Deep brain stimulation (DBS) has therapeutic potential in this context. This comprehensive review of recent studies of DBS for depression in animal models identifies potential biomarkers for improving therapeutic efficacy and predictability of conventional DBS to aid future development of closed-loop control of DBS system. Materials and methods A systematic search was performed in Pubmed, EMBASE, and Cochrane Review using relevant keywords. In overall, 56 animal studies satisfied the inclusion criteria. Results Outcomes were divided into biochemical/physiological, electrophysiological, and behavioral categories. Promising biomarkers include biochemical assays (in particular, microdialysis and electrochemical measurements), which provide real-time results in awake animals. Electrophysiological tests, showing changes at both the target site and downstream structures also revealed characteristic changes at several anatomic targets (such as the medial prefrontal cortex and locus coeruleus). However, the substantial range of models and DBS targets limits the ability to draw generalizable conclusions in animal behavioral models. Conclusions Overall, DBS is a promising therapeutic modality for treatment-resistant depression. Different outcomes have been used to assess its efficacy in animal studies. From the review, electrophysiological and biochemical markers appear to offer the greatest potential as biomarkers for depression. However, to develop closed-loop DBS for depression, additional preclinical and clinical studies with a focus on identifying reliable, safe, and effective biomarkers are warranted.

Insertion of flexible microprobes into the brain requires withstanding the compressive penetration force by the microprobes. To aid the insertion of the microprobes, most of the existing approaches use pushing mechanisms to provide temporary stiffness increase for the microprobes to prevent buckling during insertion into the brain. However, increasing the microprobe stiffness may result in acute neural tissue damage during insertion. Moreover, any late or premature removal of the temporary stiffness after insertion may lead to further tissue damage due to brain micromotion or inaccuracy in the microprobe positioning. In this study, a novel pneumatic-based insertion mechanism is proposed which simultaneously pulls and pushes a flexible microprobe toward the brain. As part of the brain penetration force in the proposed mechanism is supplied by the tensile force, the applied compressive force, which the microprobe must withstand during insertion, is lower compared with the existing approaches. Therefore, the microprobes with a critical buckling force less than the brain penetration force can be inserted into the brain without buckling. Since there is no need for temporary stiffness increment, neural tissue damage during the microprobe insertion will be much lower compared with the existing insertion approaches. The pneumatic-based insertion mechanism is modeled analytically to investigate the effects of the microprobe configuration and the applied air pressure on the applied tensile and compressive forces to the microprobe. Next, finite element modeling is conducted, and its analysis results not only validate the analytical results but also confirm the efficiency of the mechanism.

Closed loop optogenetic brain stimulation enhances the efficacy of the stimulation by adjusting the stimulation parameters based on direct feedback from the target area of the brain. It combines the principles of genetics, physiology, electrical engineering, optics, signal processing and control theory to create an efficient brain stimulation system. To read the underlying neuronal condition from the electrical activity of neurons, a sensor, sensor interface circuit, and signal conditioning are needed. Also, efficient feature extraction, classification, and control algorithms should be in place to interpret and use the sensed data for closing the feedback loop. Finally, a stimulation circuitry is required to effectively control a light source to deliver light based stimulation according to the feedback signal. Thus, the backbone to a functioning closed loop optogenetic stimulation device is a well-built electronic circuitry for sensing and processing of brain signals, running efficient signal processing and control algorithm, and delivering timed light stimulations. This paper presents a review of electronic and software concepts and components used in recent closed-loop optogenetic devices based on neuro-electrophysiological reading and an outlook on the future design possibilities with the aim of providing a compact and easy reference for developing closed loop optogenetic brain stimulation devices.

Innovation in wireless communications and microtechnology has progressed day by day, and this has resulted in the creation of wireless sensor networks. This technology is utilised in a variety of settings, including battlefield surveillance, home security, and healthcare monitoring, among others. However, since tiny batteries with very little power are used, this technology has power and target monitoring issues. With the development of various architectures and algorithms, considerable research has been done to address these problems. The adaptive learning automata algorithm (ALAA) is a scheduling machine learning method that is utilised in this study. It offers a time-saving scheduling method. As a result, each sensor node in the network has been outfitted with learning automata, allowing them to choose their appropriate state at any given moment. The sensor is in one of two states: active or sleep. Several experiments were conducted to get the findings of the suggested method. Different parameters are utilised in this experiment to verify the consistency of the method for scheduling the sensor node so that it can cover all of the targets while using less power. The experimental findings indicate that the proposed method is an effective approach to schedule sensor nodes to monitor all targets while using less electricity. Finally, we have benchmarked our technique against the LADSC scheduling algorithm. All of the experimental data collected thus far demonstrate that the suggested method has justified the problem description and achieved the project’s aim. Thus, while constructing an actual sensor network, our suggested algorithm may be utilised as a useful technique for scheduling sensor nodes.

Presented here is the synthesis of a 3D printable nano-polysaccharide self-healing hydrogel for flexible strain sensors. Consisting of three distinct yet complementary dynamic bonds, the crosslinked network comprises imine, hydrogen, and catecholato-metal coordination bonds. Self-healing of the hydrogel is demonstrated by macroscopic observation, rheological recovery, and compression measurements. The hydrogel was produced via imine formation of carboxyl methyl chitosan, oxidized cellulose nanofibers, and chitin nanofibers followed by two subsequent crosslinking stages: immersion in tannic acid (TA) solution to create hydrogen bonds, followed by soaking in Fe

In this paper, an unmanned aerial vehicle (UAV)-aided multi-antenna configured downlink mmWave cooperative generalized frequency division multiplexing (GFDM) system is proposed. To provide physical layer security (PLS), a 3D controlled Lorenz mapping system is introduced. Furthermore, the combination of T-transformation spreading codes, walsh Hadamard transform, and discrete Fourier transform (DFT) techniques are integrated with a novel linear multi-user multiple-input multiple-output (MU-MIMO) gyre precoding (GP) for multi-user interference reduction. Furthermore, concatenated channel-coding with multi-user beamforming weighting-aided maximum-likelihood and zero forcing (ZF) signal detection schemes for an improved bit error rate (BER) are also used. The system is then simulated with a single base station (BS), eight massive machine-type communications (mMTC) users, and two UAV relay stations (RSs). Numerical results reveal the robustness of the proposed system in terms of PLS and an achievable ergodic rate with signal-to-interference-plus-noise ratio (SINR) under the implementation of T-transformation scheme. By incorporating the 3D mobility model, brownian perturbations of the UAVs are also analyzed. An out-of-band (OOB) reduction of 320 dB with an improved BER of 1×10−4 in 16-QAM for a signal-to-noise ratio, Eb/N0, of 20 dB is achieved.

The need for renewable energy resources is ever-increasing due to the concern for environmental issues associated with fossil fuels. Low-cost high-power-density manufacturing techniques for the thermoelectric generators (TEG) have added to the technoeconomic feasibility of the TEG systems as an effective power generation system in heat recovery, cooling, electricity, and engine-efficiency applications. The environment-dependent factors such as the nonuniform distribution of heat, damage to the heat-transfer coating between sinks and sources, and mechanical faults create nonuniform current generation and impedance mismatch causing power loss. As a solution to this nonlinear multisolution problem, an improved MPPT control is presented, which utilizes the improvised barnacle mating optimization (BMO). The case studies are formulated to gauge the performance of the proposed BMP MPPT control under nonuniform temperature distribution. The results are compared to the grey wolf optimization (GWO), particle swarm optimization (PSO), and cuckoo search (CS) algorithm. Faster global maximum power point tracking (GMPP) within 381 ms, higher power tracking efficiency of up to 99.93%, and least oscillation ≈0.8 W are achieved by the proposed BMO with the highest energy harvest on average. The statistical analysis further solidifies the better performance of the proposed controller with the least root mean square error (RMSE), RE, and highest SR.

RF power is broadly available in both urban and semi-urban areas and thus exhibits as a promising candidate for ambient energy scavenging sources. In this research, a high-efficiency quad-band rectenna is designed for ambient RF wireless energy scavenging over the frequency range from 0.8 to 2.5 GHz. Firstly, the detailed characteristics (i.e., available frequency bands and associated power density levels) of the ambient RF power are studied and analyzed. The data (i.e., RF survey results) are then applied to aid the design of a new quad-band RF harvester. A newly designed impedance matching network (IMN) with an additional L-network in a third-branch of dual-port rectifier circuit is familiarized to increase the performance and RF-to-DC conversion efficiency of the harvester with comparatively very low input RF power density levels. A dual-polarized multi-frequency bow-tie antenna is designed, which has a wide bandwidth (BW) and is miniature in size. The dual cross planer structure internal triangular shape and co-axial feeding are used to decrease the size and enhance the antenna performance. Consequently, the suggested RF harvester is designed to cover all available frequency bands, including part of most mobile phone and wireless local area network (WLAN) bands in Malaysia, while the optimum resistance value for maximum dc rectification efficiency (up to 48%) is from 1 to 10 kΩ. The measurement result in the ambient environment (i.e., both indoor and outdoor) depicts that the new harvester is able to harvest dc voltage of 124.3 and 191.0 mV, respectively, which can be used for low power sensors and wireless applications.

Four-dimensional printing has set the stage for a new generation of soft robotics. The applications of rigid planar parallel robotic manipulators are also significant because of their various desirable characteristics, such as lower inertia, higher payload, and high accuracy. However, rigid planar parallel robots are heavy and require different actuators and components. This study introduces a novel technique to produce a light three degrees of freedom soft parallel manipulator at a low cost, which can be stimulated easily. This technique allows researchers to customize the actuator’s design based on the requirement. The robot is made by 3D printing based on fused deposition modelling and a direct ink writing process. The design, development, and additive manufacturing of a soft parallel robot electrothermally driven by a linear silicon-based actuator and polylactic acid parts are presented. Silicon-based soft actuators replace the rigid conventional linear actuators in this study to drive the planar parallel manipulator. The actuation of actuators is conducted using simple heating compared to the conventional rigid actuator. Various heating approaches and configurations are compared and analysed to find the most suitable one for the effective linear stroke of the soft actuator. The finite element model is used to analyse the performance of the electrothermally silicon-ethanol soft actuators in ABAQUS. The kinematics of the planar parallel robotic manipulator are simulated in MATLAB to achieve its workspace. The final soft parallel robot mechanism and the active and passive links are fabricated and tested experimentally.

Coronavirus Disease 2019 (COVID-19) has emerged as a global pandemic since late 2019 and has affected all forms of human life and economic developments. Various techniques are used to collect the infected patients’ sample, which carries risks of transferring the infection to others. The current study proposes an AI-powered UAV-based sample collection procedure through self-collection kits delivery to the potential patients and bringing the samples back for testing. Using a hypothetical case study of Islamabad, Pakistan, various test cases are run where the UAVs paths are optimized using four key algorithms, greedy, intra-route, inter-route, and tabu, to save time and reduce carbon emissions associated with alternate transportation methods. Four cases with 30, 50, 100, and 500 patients are investigated for delivering the self-testing kits to the patients. The results show that the Tabu algorithm provides the best-optimized paths covering 31.85, 51.35, 85, and 349.15 km distance for different numbers of patients. In addition, the algorithms optimize the number of UAVs to be used in each case and address the studied cases patients with 5, 8, 14, and 71 UAVs, respectively. The current study provides the first step towards the practical handling of COVID-19 and other pandemics in developing countries, where the risks of spreading the infections can be minimized by reducing person-to-person contact. Furthermore, the reduced carbon footprints of these UAVs are an added advantage for developing countries that struggle to control such emissions. The proposed system is equally applicable to both developed and developing countries and can help reduce the spread of COVID-19 through minimizing the person-to-person contact, thus helping the transformation of healthcare to smart healthcare.

Integrating disruptive technologies within smart cities improves the infrastructure needed to potentially deal with disasters. This paper provides a perspective review of disruptive technologies such as the Internet of Things (IoT), image processing, artificial intelligence (AI), big data and smartphone applications which are in use and have been proposed for future improvements in disaster management of urban regions. The key focus of this paper is exploring ways in which smart cities could be established to harness the potential of disruptive technologies and improve post-disaster management. The key questions explored are a) what are the gaps or barriers to the utilization of disruptive technologies in the area of disaster management and b) How can the existing methods of disaster management be improved through the application of disruptive technologies. To respond to these questions, a novel framework based on integrated approaches based on big data analytics and AI is proposed for developing disaster management solutions using disruptive technologies.

Concrete is an important construction material. Its characteristics depend on the environmental conditions, construction methods, and mix factors. Working with concrete is particularly tricky in a hot climate. This study predicts the properties of concrete in hot conditions using the case study of Rawalpindi, Pakistan. In this research, variable casting temperatures, design factors, and curing conditions are investigated for their effects on concrete characteristics. For this purpose, water–cement ratio (w/c), in-situ concrete temperature (T), and curing methods of the concrete are varied, and their effects on pulse velocity (PV), compressive strength (fc), depth of water penetration (WP), and split tensile strength (ft) were studied for up to 180 days. Quadratic regression and artificial neural network (ANN) models have been formulated to forecast the properties of concrete in the current study. The results show that T, curing period, and moist curing strongly influence fc, ft, and PV, while WP is adversely affected by T and moist curing. The ANN model shows better results compared to the quadratic regression model. Furthermore, a combined ANN model of fc, ft, and PV was also developed that displayed higher accuracy than the individual ANN models. These models can help construction site engineers select the appropriate concrete parameters when concreting under hot climates to produce durable and long-lasting concrete.

In this paper, a coordinated multipoint joint transmission (CoMP-JT) framework at mmWave for a cyclic prefix (CP)-free multiuser OFDM wireless communication system is developed and analyzed. The aim is to provide high-quality service to cell-edge users, otherwise, the cell-users would suffer from significant signal degradation due to undesired interference. The impact of complex Hadamard transform with block diagonalization channel precoding for multiuser interference reduction and designed subcarrier mapping for out-of-band (OOB) reduction are investigated. In addition, the paper studied the input back-off-aided high-power amplifier for peak-to-average power ratio (PAPR) reduction and forward error correction channel coding for improved bit error rate (BER) for cell-edge users at mmWave frequencies. Moreover, signal-to-interference-noise ratio and ergodic achievable rate are estimated both in the presence and absence of CoMP-JT-based transmission technique to verify their significance in terms of transmitted power. Numerical investigations showed an OOB reduction of 312 dB, PAPR reduction from 17.50 dB to 7.66 dB, and improved BER of 1×10−3 in 16-QAM for a signal-to-noise ratio of −6 dB. Hence, the simulation results demonstrated the effectiveness of the developed system.

Defective PV panels reduce the efficiency of the whole PV string, causing loss of investment by decreasing its efficiency and lifetime. In this study, firstly, an isolated convolution neural model (ICNM) was prepared from scratch to classify the infrared images of PV panels based on their health, i.e., healthy, hotspot, and faulty. The ICNM occupies the least memory, and it also has the simplest architecture, lowest execution time, and an accuracy of 96% compared to transfer learned pre-trained ShuffleNet, GoogleNet, and SqueezeNet models. Afterward, ICNM, based on its advantages, is reused through transfer learning to classify the defects of PV panels into five classes, i.e., bird drop, single, patchwork, horizontally aligned string, and block with 97.62% testing accuracy. This proposed approach can identify and classify the PV panels based on their health and defects faster with high accuracy and occupies the least amount of the system’s memory, resulting in savings in the PV investment.

Detecting buildings from high-resolution satellite imagery is beneficial in mapping, environmental preparation, disaster management, military planning, urban planning and research purposes. Differentiating buildings from the images is possible however, it may be a time-consuming or complicated process. Therefore, the high-resolution imagery from satellites needs to be automated to detect the buildings. Additionally, buildings exhibit several different characteristics, and their appearance in these images is unplanned. Moreover, buildings in the metropolitan environment are typically crowded and complicated. Therefore, it is challenging to identify the building and hard to locate them. To resolve this situation, a novel probabilistic method has been suggested using local features and probabilistic approaches. A local feature extraction technique was implemented, which was used to calculate the probability density function. The locations in the image were represented as joint probability distributions and were used to estimate their probability distribution function (pdf). The density of building locations in the image was extracted. Kernel density distribution was also used to find the density flow for different metropolitan cities such as Sydney (Australia), Tokyo (Japan), and Mumbai (India), which is useful for distribution intensity and pattern of facility point f interest (POI). The purpose system can detect buildings/rooftops and to test our system, we choose some crops with panchromatic high-resolution satellite images from Australia and our results looks promising with high efficiency and minimal computational time for feature extraction. We were able to detect buildings with shadows and building without shadows in 0.4468 (seconds) and 0.5126 (seconds) respectively.

Dynamic hydrogels are prepared by either dynamic covalent bonds or supramolecular chemistry. Herein, we develop a dynamic hydrogel by combining both dynamic covalent bonds and supramolecular chemistry that exhibits environmentally adaptive self-healing and pH-tuning properties. To do so, we prepared a gelatin–nanopolysaccharide mixed hydrogel containing pyrogallol/catechol groups and trivalent metal ions. The as-prepared hydrogels are able to heal damage inflicted on them under acidic (pH 3 and 6), neutral (pH 7), and basic (pH 9) environments. The mechanism of healing at acidic and neutral pHs is dominated by coordination bonds between pyrogallol/catechol groups of tannic acid and ferric ions, whilst Schiff-base reaction between amines from gelatin and dialdehyde-modified cellulose nanocrystals dominates the formation of dynamic hydrogels at basic pH. Self-healing mechanism of the hydrogel at all pHs occurred at ambient temperature without any external stimuli. The hydrogels also showed different mechanical, electrical, self-healing, and self-adhesiveness properties in different pH levels. Furthermore, the hydrogels showed printability and injectability at pH 6.

Driven by the recent growth in the fields of internet of things (IoT) and deep neural networks (DNNs), DNN-powered IoT devices are expected to transform a variety of industrial applications. DNNs, however, involve many parameters and operations to process the data generated by IoT devices. This results in high data-processing latency and energy consumption. New approaches are thus being souhgt to tackle these issues and deploy real-time DNNs into resource-limited IoT devices. This paper presents a comprehensive review on hardware-and-software-co-design approaches developed to implement DNNs on low-resource hardware platforms. These approaches explore the trade-off between energy consumption, speed, classification accuracy, and model size. First, an overview of DNNs is given. Next, available tools for implementing DNNs on low-resource hardware platforms are described. Then, the memory hierarchy designs together with dataflow mapping strategies are presented. Furthermore, various model optimization approaches, including pruning and quantization, are discussed. In addition, case studies are given to demonstrate the feasibility of implementing DNNs for IoT applications. Finally, detailed discussions, research gaps, and future directions are provided. The presented review can guide the design and implementation of the next generation of hardware and software solutions for real-world IoT applications.

Reconfigurable antennas are capable of dynamically altering their frequency, polarization, and radiation properties in a controlled and reversible manner. They modify their geometry and behaviour to maximize the antenna performance in response to changes in their surrounding conditions. To implement a dynamical response, they employ different mechanisms such as PIN diodes, varactors, radio-frequency microelectromechanical systems (RF-MEMS), field effect transistors (FETs), parasitic pixel layers, photoconductive elements, mechanical actuators, metamaterials, ferrites, and liquid crystals. These mechanisms enable intentional distribution of current on the antenna surface producing reversible modification of their properties. This paper presents the design process and applications of reconfigurable antennas. The activation mechanisms of reconfigurable antennas, and their design and operation optimization are discussed. The latest advances on reconfigurable metamaterial engineering, and the current trends and future directions relating to reconfigurable antennas are reviewed. Finally, the applications of reconfigurable antennas in cognitive radio, multi-input multi-output (MIMO) systems, satellite communications, and biomedical devices are highlighted.

Epilepsy affects nearly 1% of the world’s population. A third of epilepsy patients suffer from a kind of epilepsy that cannot be controlled by current medications. For those where surgery is not an option, neurostimulation may be the only alternative to bring relief, improve quality of life, and avoid secondary injury to these patients. Until recently, open loop neurostimulation was the only alternative for these patients. However, for those whose epilepsy is applicable, the medical approval of the responsive neural stimulation and the closed loop vagal nerve stimulation systems have been a step forward in the battle against uncontrolled epilepsy. Nonetheless, improvements can be made to the existing systems and alternative systems can be developed to further improve the quality of life of sufferers of the debilitating condition. In this paper, we first present a brief overview of epilepsy as a disease. Next, we look at the current state of biomarker research in respect to sensing and predicting epileptic seizures. Then, we present the current state of open loop neural stimulation systems. We follow this by investigating the currently approved, and some of the recent experimental, closed loop systems documented in the literature. Finally, we provide discussions on the current state of neural stimulation systems for controlling epilepsy, and directions for future studies.

The current research investigates the vibration of single-walled boron nitride nanotube (SWBNNT) induced by a moving nanoparticle. In order to decrease the forced vibration of SWBNNT under a moving nanoparticle, a linear classical optimal control procedure is used to manipulate the movement of a nanoparticle as a drug material inside a nano-structure with consideration of various size-dependent procedures based on Rayleigh beam model. The Pasternak substrate is utilized to model the elastic medium. Hamilton, Galerkin and Newmark methods are also employed to solve the motion equations. Different size-dependent beam theories have been considered, such as the classical beam theory, nonlocal beam theory, strain gradient beam theory and nonlocal strain gradient beam theory, in order to reveal small-scale effects. The effects of the small length scale considered herein, the velocity of a moving nanoparticle, electrical potential field, the number of vibration modes and actuators and also resultant control force on the vibration behavior of the SWBNNT are investigated in detail.

Developing rationally designed dynamic hydrogels and polymers as inks for 3D printing is in the limelight today. They would enable us to precisely fabricate complex structures in high resolutions and modular platforms with smart functions (e.g., self-healing and self-recovery), as well as tunable mechanical, chemical, and biological properties. In this paper, we explore recent developments in dynamic hydrogels and polymers as inks for 3D printing and discuss their properties and applications in tissue engineering, biomedicine, soft robotics, and sensors. The main scope of the paper is to give a deeper understanding of the field in terms of chemistry, physics, and associated properties. Moreover, the challenges and prospects of hydrogel/polymer inks will be discussed. We envisage that 3D printed dynamic hydrogels and polymers will provide unprecedented opportunities in designing and fabricating smarter structures.

Introduction of 3-dimensional (3D) printing in fabrication and increasing applications of intriguing products in soft robotics have led to studies on controllable 3D printed soft actuators. Therefore, a demand for a precise and computationally efficient model for bending control of the 3D printed soft actuators has arisen. This study initially used a grey box strategy for dynamic modeling of a 3D printed soft actuator which undergoes large bending deformations. Yet, the primary model estimated results deviated from experimental results due to uncertainties such as hysteresis and time varying characteristics of the soft actuator in presence of electric field. Thus, a robust feedback control needed for more accurate bending control of the 3D printed polyelectrolyte actuator. In this paper a sliding mode control scheme is developed with the incorporation of Takagi–Sugeno (T-S) fuzzy modeling of a class of complex 3D printed soft actuator system. A set of extreme fuzzy subsystems are derived for modeling purpose of the actuator; then, a sliding mode control law is employed to ensure the stability of the closed-loop fuzzy system and deal with model uncertainties. Finally, the proposed approach is verified by experimental results.

Digital electromechanical system (DES), consisting of automatic sample collection, on-site microwave drying and weighing is a novel technology for soil bulk density measurement. The study investigates the potential use of the DES for in situ measurement of bulk density, and evaluates the thermal efficiency and specific energy consumption during measurement. The experiment was conducted in eight fields with different soil textures of a silty clay, loamy, clay loam, silty loam, and silty clay loam, at three soil depths of 0–10, 10–20 and 20–30 cm. Data management and analysis used include split-plot with systematic plot arrangement. Least significant difference (LSD) analyses at 0.05 confidence were applied to compare between the mean of groups. Results indicate that the dry bulk density (ρb) and wet bulk density (ρn) of soil can be measured successfully by the DES, and accuracy of measurement is significantly influenced by soil texture type and depth. The largest microwave drying time of 37 min was needed for the silty clay soil, while 17 min was the lowest drying time recorded for the sandy loam soil. Thermal efficiency was increased with increased soil depth for different soil textures and moisture content strongly influenced the thermal efficiency. Furthermore, the specific energy consumption decreased with increased soil depth for different soil textures. An inversely proportional relationship between specific energy consumption and soil depth was found and was attributed to soil moisture evaporation. A significant relationship between soil ρb and microwave penetration depths in different soil textures was demonstrated, which was due to changes in soil dielectric constant.