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The fifth-generation (5G) network is a fast-growing technology that impacts personal devices for both society and the economy. With the widespread Internet of Things (IoT) devices in such networks, powering and exploring energy to operate them is vital in the 5G era. Therefore, projecting and forecasting how to power IoT devices in the 5G network have already begun, with an aspiration to provide self-powered and green future communication services. Here, we provide a vision for energy harvesting technologies for IoT devices (e.g., emerging wearable and implantable devices employed in healthcare and consumer technologies) that can aid the research line in the 5G era. We propose that various power harvesting and management techniques at the circuit, device, and system levels will be the most crucial segment of IoT devices. Thus, self-power and sustainability should be key features of IoT devices in the 5G network, and ought to be given close attention by electronics and communications communities. To envision this concept, we provide a systematic outline in which prospective power harvesting scenarios of IoT devices in the 5G network are expected. We successively describe vital potential landscapes of 5G and evaluate the energy harvesting and power management requirements. We additionally investigate the challenges beyond energy technologies that could impede the 5G enhancement. [ABSTRACT FROM AUTHOR]

Magnetism‐based systems are widely utilized for sensing and imaging biological phenomena, for example, the activity of the brain and the heart. Magnetomyography (MMG) is the study of muscle function through the inquiry of the magnetic signal that a muscle generates when contracted. Within the last few decades, extensive effort has been invested to identify, characterize and quantify the magnetomyogram signals. However, it is still far from a miniaturized, sensitive, inexpensive and low‐power MMG sensor. Herein, the state‐of‐the‐art magnetic sensing technologies that have the potential to realize a low‐profile implantable MMG sensor are described. The technical challenges associated with the detection of the MMG signals, including the magnetic field of the Earth and movement artifacts are also discussed. Then, the development of efficient magnetic technologies, which enable sensing pico‐Tesla signals, is advocated to revitalize the MMG technique. To conclude, spintronic‐based magnetoresistive sensing can be an appropriate technology for miniaturized wearable and implantable MMG systems. [ABSTRACT FROM AUTHOR]

Electronics that conform to 3D surfaces are attracting wider attention from both academia and industry. The research in the field has, thus far, focused primarily on showcasing the efficacy of various materials and fabrication methods for electronic/sensing devices on flexible substrates. As the device response changes are bound to change with stresses induced by bending, the next step will be to develop the capacity to predict the response of flexible systems under various bending conditions. This paper comprehensively reviews the effects of bending on the response of devices on ultra-thin chips in terms of variations in electrical parameters such as mobility, threshold voltage, and device performance (static and dynamic). The discussion also includes variations in the device response due to crystal orientation, applied mechanics, band structure, and fabrication processes. Further, strategies for compensating or minimizing these bending-induced variations have been presented. Following the in-depth analysis, this paper proposes new mathematical relations to simulate and predict the device response under various bending conditions. These mathematical relations have also been used to develop new compact models that have been verified by comparing simulation results with the experimental values reported in the recent literature. These advances will enable next generation computer-aided-design tools to meet the future design needs in flexible electronics. [ABSTRACT FROM AUTHOR]

The field of flexible electronics is rapidly evolving. The ultrathin chips are being used to address the high-performance requirements of many applications. However, simulation and prediction of changes in response of device/circuit due to bending induced stress remains a challenge as of lack of suitable compact models. This makes circuit designing for bendable electronics a difficult task. This paper presents advances in this direction, through compressive and tensile stress studies on transistors and simple circuits such as inverters with different channel lengths and orientations of transistors on ultrathin chips. Different designs of devices and circuits in a standard CMOS 0.18- \mu \textm technology were fabricated in two separated chips. The two fabricated chips were thinned down to 20~\mu \textm using standard dicing-before-grinding technique steps followed by post-CMOS processing to obtain sufficient bendability (20-mm bending radius, or 0.05% nominal strain). Electrical characterization was performed by packaging the thinned chip on a flexible substrate. Experimental results show change of carrier mobilities in respective transistors, and switching threshold voltage of the inverters during different bending conditions (maximum percentage change of 2% for compressive and 4% for tensile stress). To simulate these changes, a compact model, which is a combination of mathematical equations and extracted parameters from BSIM4, has been developed in Verilog-A and compiled into Cadence Virtuoso environment. The proposed model predicts the mobility variations and threshold voltage in compressive and tensile bending stress conditions and orientations, and shows an agreement with the experimental measurements (1% for compressive and 0.6% for tensile stress mismatch). [ABSTRACT FROM PUBLISHER]

Current study aimed to explore the factors related to the high school students' low achievement in English course from teachers' perspectives in Ardebil city, Iran. Cluster random sampling was utilized and the participants of the study were 27 English teachers teaching in high schools of an EFL context during the fall term of 2012. The required data were gathered during the fall term of 2012 through a researcher-made questionnaire. The instrument was first piloted before it was administered and it showed the reliability of 0.81. The findings were analyzed using descriptive statistics and statistical indicators like frequency, percentage, and mean. SPSS software (version 20) was used to run t-tests and ANOVA to analyze the closed items of the questionnaire. The findings revealed that from the teachers' viewpoints, the teachers-based factors were identified to be the most relevant reasons for high school students' low achievement in English course. [ABSTRACT FROM AUTHOR]

Climate change, with warming temperatures and shifting precipitation patterns, may increase natural-caused forest fire activity. Increasing natural-caused fires throughout western United States national forests could place people, property, and infrastructure at risk in the future. We used the fine K nearest neighbor (KNN) method coupled with the downscaled Multivariate Adaptive Constructed Analogs (MACA) climate dataset to estimate changes in the rate of natural-caused fires in western United States national forests. We projected changes in the rate of minor and major forest fires from historical (1986–2015) to future (2070–2099) conditions to characterize fire-prone national forests under a range of climate change scenarios. The results indicate that climate change can add to the occurrence of forest fires in western United States national forests, particularly in Rocky Mountain, Pacific Southwest, and Southwestern United States Forest Service regions. Although summer months are projected to have the highest rate of natural-caused forest fire activity in the future, the rate of natural-caused forest fires is likely to increase from August to December in the future compared to the historical conditions. Improved understanding of altered forest fire regimes can help forest managers to better understand the potential effects of climate change on future fire activity and implement actions to attenuate possible negative consequences. [ABSTRACT FROM AUTHOR]

They summarize the state-of-the-art requirements for brain-implanted devices to realize the wireless interface and address the operating principles and applications of wireless interfaces based on modalities. Keywords: neurotechnology; biomedical engineering; neural engineering EN neurotechnology biomedical engineering neural engineering 1 4 4 06/23/22 20220725 NES 220725 In the nervous system, huge amounts of neurons constantly induce and transmit electrophysiological signals to communicate between neurons and brain regions. In a thought-provoking article by Luca Selmi and colleagues [2], after discussing recent progress in electronic sensor architecture for I in vitro i neural recording, authors modelled the neuron/sensor interface and readout electronics with a mixed-mode finite-element/circuit approach. [Extracted from the article]

Miniaturized, flexible, and biocompatible neural probes have the potential to circumvent the brain's foreign body response, but the problem of surgical implantation remains. Herein, a probe intended for implantation in the rat hippocampus is coated in four bioresorbable stiffeners to determine which is most effective in aiding insertion. The stiffeners (sucrose, maltose, silk fibroin, and alginate) are evaluated through mechanical, chemical, and dissolution tests. After coating with silk fibroin, the buckling force of the neural probe increases from 0.31 to 75.99 mN. This goes in accordance with subsequent successful insertion tests. Fourier transform infrared spectroscopy results demonstrate the increase in β‐sheet content of silk fibroin samples after treatment (e.g., water annealing) and show relevant changes due to dehydration of the alginate hydrogel. Both qualitative and quantitative dissolution studies in artificial cerebrospinal fluid illustrate that alginate and silk fibroin outlasts the disaccharide stiffeners. In this work, a variety of multidisciplinary analyses are carried out to find the best bioresorbable stiffener for deep brain implantable devices with the highest buckling force, longest dissolution time, and the most tunable structure. For the first time, an alginate hydrogel is used as a stiffener to aid insertion, expanding its usefulness beyond neural tissue engineering. [ABSTRACT FROM AUTHOR]

In the past few years, magnetoelectronics has emerged as a promising new platform technology in various biosensors for detection, identification, localisation and manipulation of a wide spectrum of biological, physical and chemical agents. The methods are based on the exposure of the magnetic field of a magnetically labelled biomolecule interacting with a complementary biomolecule bound to a magnetic field sensor. This Review presents various schemes of magnetic biosensor techniques from both simulation and modelling as well as analytical and numerical analysis points of view, and the performance variations under magnetic fields at steady and nonstationary states. This is followed by magnetic sensors modelling and simulations using advanced Multiphysics modelling software (e.g. Finite Element Method (FEM) etc.) and home-made developed tools. Furthermore, outlook and future directions of modelling and simulations of magnetic biosensors in different technologies and materials are critically discussed. [ABSTRACT FROM AUTHOR]

Quantitative and qualitative knowledge about the potential impacts of climate change on extreme hydrological events is crucial for water resource management and extreme risk management under climate change. This has theoretical and realistic implications to study and couple the climate system with hydrologic processes, to understand the system and solve related problems in water resources and extreme hydrology, such as decision making, plan management, environmental protection, and ecological balance. This paper reviews recent studies investigating climate change impact on hydrological extremes using a perspective of the integrated modeling framework comprising climate change scenarios, climate models, bias correction methods, hydrological modeling (model structure and parameterization), and reducible uncertainty arising from these sources characterized by a paucity of knowledge. The available research outcomes show the extreme high flows are likely to increase under climate change in the most parts of Europe, Asia, and the USA, but greatly vary and decrease in Africa and Latin America, which is highly variable and uncertain in space and time. Each component in integrated modeling has an important role in shifting and producing uncertainty in projected extreme flow. Among them, the climate model's discrepancy and hydrological models' structure are the more dominant source of uncertainty in projection of extreme high and low (or mean) flow in most of the regions, respectively. However, the quality of input data and hydrological model structures are the most dominant source of uncertainty in Africa, Latin America, and some parts of Asia. This indicated that these regions have strong hydrological cycle and higher physiographic heterogeneity. We believe that our existing knowledge and skills need to be improved and transformed into an accurate mathematical and physical representation, to minimize the uncertainty due to the effect of choices in the methodology chain. So, disentangling the aggregated uncertainty in the cascade modeling chain can be done by using variances, which can help understand the interaction effect and identify their contribution to the projected extreme flow. This comprehensive review can help modelers to identify and reduce uncertainty in projecting hydrological extremes and policy makers for full awareness of the various uncertainties to make a robust decision for water resources management under climate change. Article Highlights: This study provides a comprehensive review on reducing uncertainty in projecting hydrological extremes from the perspective of an integrated modeling framework Existing knowledge and skills need to be improved and transformed into an accurate mathematical representation The impact of climate change on hydrological extremes is highly variable and uncertain in space and time Uncertainties of the integrated modeling framework can be reduced by decomposing the aggregated uncertainty using variance analysis and/or the modeling chain [ABSTRACT FROM AUTHOR]

Implantable electronic neural interfaces typically take the form of probes and are made with rigid materials such as silicon and metals. These have advantages such as compatibility with integrated microchips, simple implantation and high-density nanofabrication but tend to be lacking in terms of biointegration, biocompatibility and durability due to their mechanical rigidity. This leads to damage to the device or, more importantly, the brain tissue surrounding the implant. Flexible polymer-based probes offer superior biocompatibility in terms of surface biochemistry and better matched mechanical properties. Research which aims to bring the fabrication of electronics on flexible polymer substrates to the nano-regime is remarkably sparse, despite the push for flexible consumer electronics in the last decade or so. Cleanroom-based nanofabrication techniques such as photolithography have been used as pattern transfer methods by the semiconductor industry to produce single nanometre scale devices and are now also used for making flexible circuit boards. There is still much scope for miniaturizing flexible electronics further using photolithography, bringing the possibility of nanoscale, non-invasive, high-density flexible neural interfacing. This work explores the fabrication challenges of using photolithography and complementary techniques in a cleanroom for producing flexible electronic neural probes with nanometre-scale features. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'. [ABSTRACT FROM AUTHOR]

Careful design and material selection are the most beneficial strategies to ensure successful implantation and mitigate the failure of a neural probe in the long term. In order to realize a fully flexible implantable system, the probe should be easily manipulated by neuroscientists, with the potential to bend up to 90°. This paper investigates the impact of material choice, probe geometry, and crucially, implantation angle on implantation success through finite-element method simulations in COMSOL Multiphysics followed by cleanroom microfabrication. The designs introduced in this paper were fabricated using two polyimides: (i) PI-2545 as a release layer and (ii) photodefinable HD-4110 as the probe substrate. Four different designs were microfabricated, and the implantation tests were compared between an agarose brain phantom and lamb brain samples. The probes were scanned in a 7 T PharmaScan MRI coil to investigate potential artefacts. From the simulation, a triangular base and 50 µm polymer thickness were identified as the optimum design, which produced a probe 57.7 µm thick when fabricated. The probes exhibit excellent flexibility, exemplified in three-point bending tests performed with a DAGE 4000Plus. Successful implantation is possible for a range of angles between 30° and 90°. This article is part of the theme issue 'Advanced neurotechnologies: translating innovation for health and well-being'. [ABSTRACT FROM AUTHOR]

There is a growing interest in neuromorphic hardware since it offers a more intuitive way to achieve bio-inspired algorithms. This paper presents a neuromorphic model for intelligently processing continuous electrocardiogram (ECG) signal. This model aims to develop a hardware-based signal processing model and avoid employing digitally intensive operations, such as signal segmentation and feature extraction, which are not desired in an analogue neuromorphic system. We apply delay-based reservoir computing as the information processing core, along with a novel training and labelling method. Different from the conventional ECG classification techniques, this computation model is a end-to-end dynamic system that mimics the real-time signal flow in neuromorphic hardware. The input is the raw ECG stream, while the amplitude of the output represents the risk factor of a ventricular ectopic heartbeat. The intrinsic memristive property of the reservoir empowers the system to retain the historical ECG information for high-dimensional mapping. This model was evaluated with the MIT-BIH database under the inter-patient paradigm and yields 81% sensitivity and 98% accuracy. Under this architecture, the minimum size of memory required in the inference process can be as low as 3.1 MegaByte(MB) because the majority of the computation takes place in the analogue domain. Such computational modelling boosts memory efficiency by simplifying the computing procedure and minimizing the required memory for future wearable devices. [ABSTRACT FROM AUTHOR]

Cardiovascular disease continues to be one of the dominant causes of global mortality. One effective treatment is to utilize cardiovascular implantable devices (cIMDs) with multi‐functional cell sensing and monitoring features that have the potential to manipulate cardiovascular hyperplasia disorders as well as provide therapy. However, batteries with a fixed capacity entail high‐risk surgeries for battery‐replacement, which causes health hazards and imposes significant costs to patients. This review accesses comprehensive power solutions for cIMDs, from conventional batteries to state‐of‐the‐art energy harvesters and wireless power transfer (WPT) schemes. In particular, WPT has great potential to eliminate the percutaneous wires and overcome frequent battery removal. Here, the fundamentals, power transfer efficiency, antenna design and miniaturization, and operating frequencies in various WPT schemes are presented. Moreover, the power loss attenuation and bio‐safety standard (specific absorption rate) for implants are also considered in WPT design envelope. In addition, wireless data transmission of implantable devices from external to internal milieu (and vice versa) along with different modulation and demodulation techniques are investigated. The last advanced power solutions for cIMDs in in‐vivo and in vitro research are illustrated throughout. Finally, specifications and future potential of WPT systems in cIMDs are highlighted. [ABSTRACT FROM AUTHOR]

The single electron transistor (SET) is a nanoscale switching device with a simple equivalent circuit. It can work very fast as it is based on the tunneling of single electrons. Its nanostructure contains a quantum dot island whose material impacts on the device operation. Carbon allotropes such as fullerene (C60), carbon nanotubes (CNTs) and graphene nanoscrolls (GNSs) can be utilized as the quantum dot island in SETs. In this study, multiple quantum dot islands such as GNS-CNT and GNS-C60 are utilized in SET devices. The currents of two counterpart devices are modeled and analyzed. The impacts of important parameters such as temperature and applied gate voltage on the current of two SETs are investigated using proposed mathematical models. Moreover, the impacts of CNT length, fullerene diameter, GNS length, and GNS spiral length and number of turns on the SET’s current are explored. Additionally, the Coulomb blockade ranges (CB) of the two SETs are compared. The results reveal that the GNS-CNT SET has a lower Coulomb blockade range and a higher current than the GNS-C60 SET. Their charge stability diagrams indicate that the GNS-CNT SET has smaller Coulomb diamond areas, zero-current regions, and zero-conductance regions than the GNS-C60 SET. [ABSTRACT FROM AUTHOR]

[Display omitted] • Preparation of g-C 3 N 4 /TiO 2 as a S-scheme system in order to degrade MB and RhB dyes from aqueous solution. • Design and Fabrication of parabolic trough collectors as a continuous flow-loop photoreactor for photo-degradation. • Natural solar light irradiation and blue LED light irradiation are compared for the photocatalytic process. • Examine the effects of different operational parameters such as the amount of photocatalyst, dyes concentration and time. Developing photocatalytic systems by larger design to achieve degradation of dye pollutants by using solar light is highly desirable. Present work is devoted to the synthesis of step-scheme (S-scheme) g-C 3 N 4 /TiO 2 heterojunction which subsequently employed for simultaneous degradation of a binary mixture of Methylene blue (MB) and Rhodamine B (RhB) solution in parabolic trough collectors (PTC) as continuous flow loop photoreactor. The as-prepared g-C 3 N 4 /TiO 2 was analyzed by various techniques such as FE-SEM, EDS, XRD, FT-IR, BET, elements mapping and DRS. The composite central design (CCD) was applied to express mathematical relationships among variables such as process time, catalyst mass and initial concentrations of MB and RhB in the degradation process. The photocatalytic activity of the as-prepared composite is higher than pure TiO 2 and g-C 3 N 4 that is attributed to the positive synergetic effect of S-scheme between g-C 3 N 4 and TiO 2 nanostructure. Under solar irradiation in PTC, g-C 3 N 4 /TiO 2 was able to degrade about 94.92 and 93.07% of binary mixture MB and RhB, respectively. [ABSTRACT FROM AUTHOR]

Liquid crystals with a varying phase profile enable reconfigurable and intelligent devices to be designed, which are capable of manipulating incident electromagnetic fields in display, telecommunications as well as wearable applications. The active control of defects in these devices is becoming more important, especially since the electrodes used to manipulate them are shrinking to nanometer length scales. In this paper, a simple subwavelength, 1D, interdigitated metal electrode structure that can be reconfigured using nematic liquid crystals aligned in the homeotropic, planar, and hybrid methods are demonstrated. Accurate electro‐optic modeling of the directors and the defects are shown, which are induced by the fringing electric fields. Applied voltages result in liquid crystal reorientation near the bottom surface, such that defects are induced between the electrodes. The height of the electrodes does not affect the lateral position of these defects. Rather, this can be achieved by increasing the biasing voltage on the top electrode, which also leads to greater splay‐bend in the bulk of the material. These results therefore aim to generalize the control of defects in complex anisotropic nematic liquid crystals using simple interdigitated structures for a range of reconfigurable intelligent surface applications. [ABSTRACT FROM AUTHOR]

Neurological diseases are a prevalent cause of global mortality and are of growing concern when considering an ageing global population. Traditional treatments are accompanied by serious side effects including repeated treatment sessions, invasive surgeries, or infections. For example, in the case of deep brain stimulation, large, stiff, and battery powered neural probes recruit thousands of neurons with each pulse, and can invoke a vigorous immune response. This paper presents challenges in engineering and neuroscience in developing miniaturized and biointegrated alternatives, in the form of microelectrode probes. Progress in design and topology of neural implants has shifted the goal post toward highly specific recording and stimulation, targeting small groups of neurons and reducing the foreign body response with biomimetic design principles. Implantable device design recommendations, fabrication techniques, and clinical evaluation of the impact flexible, integrated probes will have on the treatment of neurological disorders are provided in this report. The choice of biocompatible material dictates fabrication techniques as novel methods reduce the complexity of manufacture. Wireless power, the final hurdle to truly implantable neural interfaces, is discussed. These aspects are the driving force behind continued research: significant breakthroughs in any one of these areas will revolutionize the treatment of neurological disorders. [ABSTRACT FROM AUTHOR]

This article presents a fast-transient radiation-hardened low-dropout (LDO) voltage regulator integrated in a standard 0.6- $\mu \text {m}$ BiCMOS technology for space and other harsh radiation environment applications. The fabricated LDO consumes 150- $\mu \text {A}$ quiescent current at 6-A maximum output current. An LDO voltage of 300 mV which corresponds to an ultralow $R_{\text {DS}(\text {ON})}$ resistance of 50 $\text{m}\Omega $ is realized by accurate modeling. A separate fast-transient response circuit under narrow-bandwidth condition is proposed to improve the output voltage transient speed. It operates from an input voltage range of 2.8–5.5 V and provides an output voltage of 2.5 V, with output voltage accuracy of ±2%. The proposed LDO achieves a successful line regulation of 1 mV/V, and a load regulation of 2.16 mV/A. Effective radiation-hardened layout techniques are applied to realize high area-efficient LDO chip, whose total area including pads is 5.35 mm2, which represents only about one-third area of similar radiation-hardened products. Furthermore, a special set of total ionizing dose (TID) experiments and single-event latch-up (SEL) experiments were performed. The measurement results show that the LDO can tolerate up to a TID of 150 krad (Si) and SEL immunity at a linear energy transfer (LET) of 99.8 MeV/(mg/cm2) with proposed layout design. [ABSTRACT FROM AUTHOR]

Neurological diseases are a prevalent cause of global mortality and are of growing concern when considering an ageing global population. Traditional treatments are accompanied by serious side effects including repeated treatment sessions, invasive surgeries, or infections. For example, in the case of deep brain stimulation, large, stiff, and battery powered neural probes recruit thousands of neurons with each pulse, and can invoke a vigorous immune response. This paper presents challenges in engineering and neuroscience in developing miniaturized and biointegrated alternatives, in the form of microelectrode probes. Progress in design and topology of neural implants has shifted the goal post toward highly specific recording and stimulation, targeting small groups of neurons and reducing the foreign body response with biomimetic design principles. Implantable device design recommendations, fabrication techniques, and clinical evaluation of the impact flexible, integrated probes will have on the treatment of neurological disorders are provided in this report. The choice of biocompatible material dictates fabrication techniques as novel methods reduce the complexity of manufacture. Wireless power, the final hurdle to truly implantable neural interfaces, is discussed. These aspects are the driving force behind continued research: significant breakthroughs in any one of these areas will revolutionize the treatment of neurological disorders. [ABSTRACT FROM AUTHOR]

A Hall sensor array system for magnetic field detection and analysis is realized in X-FAB 0.18 μm CMOS technology. Magnetic field detection is attributed to the magnetization of metal coils to metal particles and the sensing characteristics of the Hall sensor array. The system puts forward a complete solution from Hall sensors, analog front-end circuit, analog-to-digital converter (ADC) to microcontroller unit. Using Ansoft Maxwell and COMSOL Multiphysics software for simulation verification, the minimum diameter of magnetic particles that can be detected in the system is 2 μm. The measured signal to noise and distortion ratio, spurious free dynamic range, and effective number of bits of the proposed ADC are 70.61 dB, 90.08 dB, and 11.44-bit, respectively. The microsystem based on STM32 combines hardware and software design, which can effectively adjust the motion parameters and realize the real-time display in the LCD screen of the magnetic field and voltage information. Compared to the prior system, the portability, cost, and efficiency have been considerably improved, which is aimed at the rapid measurement of heavy metal particles such as Fe, Co, and Ni in ambient air and blood. [ABSTRACT FROM AUTHOR]

In this article, we investigate the effect of various antidote defects on the electronic properties and current characteristics of an armchair Germanene nanoribbon (AGeNR) using density functional theory (DFT) and non-equilibrium Green's function (NEGF) method. The defected AGeNRs are introduced by setting antidote topologies in the pristine nanoribbons, resulting in antidote super-lattice of AGeNRs. It is found that new electronic properties appear due to the presence of defects. The obtained results indicate that bandgap of the defected AGeNRs can be increased or decreased in different cases. Moreover, the transport properties are analyzed based on the various defect locations in the AGeNR when the ribbon is utilized as the channel of a tunneling field effect transistor (TFET). Based on our results, it is found that the presence of antidote defects leads to reduction or increase in the current, drifting the Dirac point, and decreasing or increasing the minimum or off-state current. • First principles study is carried out to investigate impact of antidotes on transport properties of Germanene nanoribbons. • Four topologies of antidotes are introduced and bandstructures are obtained and compared with the pristine case. • The bandgap size and the carrier effective mass is extracted, analyzed and compared from the bandstructures. • The transport properties are analyzed when defected ribbon is utilized as the channel of a tunneling field effect transistor. • The impact of position of antidotes on the electronic properties is investigated both in lateral and transverse direction. [ABSTRACT FROM AUTHOR]

Multiple bioresorbable insertion aids are investigated in article number 2200117 by Hadi Heidari and co-workers through mechanical, chemical and dissolution characterizations to determine their viability as stiffening coatings for novel flexible neural probes. B Implantable Probes b As miniaturized, flexible, and biocompatible implantable neural probes seek to circumvent the foreign body response, the problem of surgical implantation due to buckling remains. [Extracted from the article]

In this paper, with taking advantage of electrical properties of a germanene nanoribbon, we propose a germanene nanoribbon field effect transistor (GeNR-FET). Here by tuning the width and geometry of the germanene nanoribbon in the source, drain and channel regions, we investigate theoretically the transistor characteristics, analog and digital performances of these several different GeNR-FETs at room temperature. Our simulations are obtained using density functional theory (DFT) combined withnon-equilibrium Green's function (NEGF) method. The simulation results show that for digital applications, by tuning the width of the germanene nanoribbon a GeNR-FET with a finite band gap in the channel region and small band gap in the source and drain regions shows a better I on /I off ratio in transfer characteristics. However, for the analog applications, if the band gap of the channel region has small value and the band gap of the source and drain regions have a finite value, the output characteristic shows a higher peak to valley (PVR) ratio which is an important figure of merit in analog applications. Also from the output characteristics, we find that the T-shape channel shows more desirable (PVR) compared with other devices and it reaches to 17.28 in this case. [ABSTRACT FROM AUTHOR]

A versatile lossy mode resonance (LMR) sensor is developed for Epinephrine monitoring based on the state-of-the-art molecular imprinting technique over an optical fiber. The optical absorption concept of the LMR-based sensor is proven by incorporating an amino silane-functionalized graphene oxide (SiO 2 -NH 2 @GO) as an adhesive layer for grafting Epinephrine imprinting polydopamine (Ep-IPDA) as a sensing layer onto the optical fiber curved surface. The accurate structural and morphological characterization confirmed the rough crystalline and spherical particles for Ep-IPDA and SiO 2 -NH 2 -GO onto optical fiber curved surface, while spectroscopic analysis confirms the formation of imprinting polymer and desirable absorbance characteristics. The optimized probe exhibits an excellent performance with the maximum sensitivity within two intervals of 0.3–1 µM and 1–90 µM along with the linearity coefficient of R2 = 0.99 possessing a low limit of detection of 0.07 µM. Additionally, this sensor selectively detects Ep in the presence of other species and showed good recovery in human urine and injection samples. This work opens a new avenue for developing bio-inspired LMR-based optical fiber biosensors and could be further extended to detect other catecholamine neurotransmitters. • A layer of SiO 2 -NH 2 @GO was used for grafting Ep-IPDA onto the optical fiber. • Characterization confirmed coating of Ep-IPDA and SiO 2 -NH 2 -GO onto optical fiber. • Operating fabrication parameters were fully optimized at different parameters. • Sensitivity for 0.3–1 µM and 1–90 µM Ep obtained with R2 = 0.99 and LOD of 2.8 nM. [ABSTRACT FROM AUTHOR]

Please find the correct acknowledgements here: Acknowledgements This work was supported by the European Union's Horizon 2020 Hybrid Enhanced Regenerative Medicine Systems (HERMES) project (GA n. 824164). B 2021 b , I 8 i , 2002693 DOI: 10.1002/advs.202002693 In the originally published article, the funding information in the acknowledgment is not correct. The work of EM is also supported by the UK Engineering and Physical Sciences Research Council (EPSRC) DTP under grant number 2279645. [Extracted from the article]

With the popularity of smart wearable systems, sensor signal processing poses more challenges to machine learning in embedded scenarios. For example, traditional machine-learning methods for data classification, especially in real time, are computationally intensive. The deployment of Artificial Intelligence algorithms on embedded hardware for fast data classification and accurate fall detection poses a huge challenge in achieving power-efficient embedded systems. Therefore, by exploiting the associative memory feature of Hopfield Neural Network, a hardware module has been designed to simulate the Neural Network algorithm which uses sensor data integration and data classification for recognizing the fall. By adopting the Hebbian learning method for training neural networks, weights of human activity features are obtained and implemented/embedded into the hardware design. Here, the neural network weight of fall activity is achieved through data preprocessing, and then the weight is mapped to the amplification factor setting in the hardware. The designs are checked with validation scenarios, and the experiment is completed with a Hopfield neural network in the analog module. Through simulations, the classification accuracy of the fall data reached 88.9% which compares well with some other results achieved by the software-based machine-learning algorithms, which verify the feasibility of our hardware design. The designed system performs the complex signal calculations of the hardware's feedback signal, replacing the software-based method. A straightforward circuit design is used to meet the weight setting from the Hopfield neural network, which is maximizing the reusability and flexibility of the circuit design. [ABSTRACT FROM AUTHOR]

For the information transmission and application field of the bus, it is necessary to design a bus transceiver with high reliability and hot-swap ability. In order to achieve this requirement, this paper proposes a novel transceiver circuit with hot-swap structure. This structure effectively improves the transistor utilization efficiency and reduces the chip area on the premise of achieving the hot-swap performance. Furthermore, the traditional Schottky diode is avoided, and the hot-swap circuit proposed in this work can be implemented in any CMOS process. A prototype was fabricated in 0.5 μm CMOS technology, and the measurement results match well with the simulation results show that the chip meets the design requirements. [ABSTRACT FROM AUTHOR]

Background: The demand for effective use of water resources has increased because of ongoing global climate transformations in the agriculture science sector. Cost-effective and timely distributions of the appropriate amount of water are vital not only to maintain a healthy status of plants leaves but to drive the productivity of the crops and achieve economic benefits. In this regard, employing a terahertz (THz) technology can be more reliable and progressive technique due to its distinctive features. This paper presents a novel, and non-invasive machine learning (ML) driven approach using terahertz waves with a swissto12 material characterization kit (MCK) in the frequency range of 0.75 to 1.1 THz in real-life digital agriculture interventions, aiming to develop a feasible and viable technique for the precise estimation of water content (WC) in plants leaves for 4 days. For this purpose, using measurements observations data, multi-domain features are extracted from frequency, time, time–frequency domains to incorporate three different machine learning algorithms such as support vector machine (SVM), K-nearest neighbour (KNN) and decision-tree (D-Tree). Results: The results demonstrated SVM outperformed other classifiers using tenfold and leave-one-observations-out cross-validation for different days classification with an overall accuracy of 98.8%, 97.15%, and 96.82% for Coffee, pea shoot, and baby spinach leaves respectively. In addition, using SFS technique, coffee leaf showed a significant improvement of 15%, 11.9%, 6.5% in computational time for SVM, KNN and D-tree. For pea-shoot, 21.28%, 10.01%, and 8.53% of improvement was noticed in operating time for SVM, KNN and D-Tree classifiers, respectively. Lastly, baby spinach leaf exhibited a further improvement of 21.28% in SVM, 10.01% in KNN, and 8.53% in D-tree in overall operating time for classifiers. These improvements in classifiers produced significant advancements in classification accuracy, indicating a more precise quantification of WC in leaves. Conclusion: Thus, the proposed method incorporating ML using terahertz waves can be beneficial for precise estimation of WC in leaves and can provide prolific recommendations and insights for growers to take proactive actions in relations to plants health monitoring. [ABSTRACT FROM AUTHOR]