Scientific progress is increasingly dependent on knowledge in computation-ready forms. In the life sciences, among others, many scientists therefore extract and structure knowledge from the literature. In a process called manual curation, they enter knowledge into spreadsheets, or into databases where it serves their and many others' research. Valuable as these curation efforts are, the range and detail of what can practically be captured and shared remains limited, because of the constraints of current curation tools. Many important contextual aspects of observations described in literature simply do not fit in the form defined by these tools, and thus cannot be captured. Here we present the design of an easy-to-use, general-purpose method and interface, that enables the precise semantic capture of virtually unlimited types of information and details, using only a minimal set of building blocks. Scientists from any discipline can use this to convert any complex knowledge into a form that is easily readable and meaningful for both humans and computers. The method VSM forms a universal and high-level language for encoding ideas, and for interacting with digital knowledge.

VSM is a recently introduced method for entering and displaying any type of knowledge, in a form that is both semantically precise for computation and intuitive for human understanding. VSM is the combination of a new semantic model, and the design for a dedicated user interface to support it. Here we present the implementation of this user interface, as a sophisticated HTML-element, <vsm-box>, that can be embedded in any web-based curation app. We show how developers can use it for biocuration projects, customize it to particular end-user needs, and contribute to its growth. Vsm-box is open-source at https://github.com/vsmjs/vsm-box under the AGPL license, as a JavaScript (ES6) Vue.js web-component that runs in all modern web browsers. It is the capstone of the Vsmjs organization at https://github.com/vsmjs that groups its supporting modules. Extensive supplementary material on VSM and vsm-box is available at https://vsmjs.github.io.

Recent progress in microfabrication technique allowed the rapid development of neural implants. They are getting categorized as effective tools for clinical practice, especially to treat traumatic and neurodegenerative disorders. Microelectrode arrays already have been used in numerous neural interface devices. Basically, almost all neural implants have been developed based on BCI (Brain Computer Interface) system. When BCI system falls under invasive technique, it is referred as BMI or Brain Machine Interface. BMIs hold promises for neurorehabilitation of motor and sensory function, cognitive state evaluation and treatment of neurological chaos. A directed overview of the field of neural implants is discussed in this article. The aim of this review is to give a brief introduction of neural prosthetics as well as their exciting applications in treating neurological disorders and a deep discussion on their functionality are mentioned. BCI system and their different types, their functionality, their pros and cons, how other neural implants developed, and their present status have been covered. Different possibilities and possible future of deep brain stimulation (DBS), Neuralink, motor and sensory neural prosthetics are further discussed.

The paper presents a study of two novel visual motion onset stimulus-based brain–computer interfaces (vmoBCI). Two settings are compared with afferent and efferent to a computer screen center motion patterns. Online vmoBCI experiments are conducted in an oddball event–related potential (ERP) paradigm allowing for “aha–responses” decoding in EEG brainwaves. A subsequent stepwise linear discriminant analysis classification (swLDA) classification accuracy comparison is discussed based on two inter–stimulus–interval (ISI) settings of 700 and 150 ms in two online vmoBCI applications with six and eight command settings. A research hypothesis of classification accuracy non–significant differences with various ISIs is confirmed based on the two settings of 700 ms and 150 ms, as well as with various numbers of ERP response averaging scenarios.The efferent in respect to display center visual motion patterns allowed for a faster interfacing and thus they are recommended as more suitable for the no–eye–movements requiring visual BCIs.

Nowadays, the study of preparing silica/rubber composites, which can be used in “green tire”, in energy saving method is fast-growing. In our work, silica modified by using alcohol polyoxyethylene ether (AEO) and 3-Mercaptopropyltriethoxysilane (K-MEPTS) together were investigated. Thermal gravimetric analyzer (TGA) result indicated that both AEO and K-MEPTS could be grafted on the silica surface. Raman spectroscopy confirmed that the AEO could generate a certain steric hindrance for the mercaptopropyl group on K-MEPTS. Silica modified by using AEO and K-MEPTS together can be completely co-coagulated with the rubber in preparing silica/natural rubber (NR) composites by latex compounding method. AEO can form a physical interface between silica and rubber; meanwhile, K-MEPTS can form a chemical interface between silica and rubber. The effects of chemical and physical interface between silica and rubber on dynamic and mechanical performances of silica/NR composites were also given in this research. A proper combination of physical and chemical interface between silica and NR can improve performances of silica/NR composites.

An effective method to inspect inaccessible nuclear power facility by interface wave which propagate along the shrink fit boundary of reactor head is proposed in this study. Reactor head is relatively thick to inspect from the outside of reactor by conventional ultrasonic testing. The proposed interface wave can propagate a long distance from the fixed transducer position. The inside of nuclear reactor is limited to access due to the high radiation, so transducers are located at outside of nuclear facility and interface wave propagates into the nuclear reactor for defect detection. The numerical simulation and experiments were carried out to validate the effectiveness of the proposed interface wave inspection method. Various defect cases simulating field failures are also presented with satisfactory detectability by the proposed technique with the features for defect classification.

The abundant mechanical vibration energy in bridge road environment can be converted into electric energy by using the piezoelectric energy harvest technology, which could be an efficient way to provide energy required by the wireless sensor network in the bridge condition monitoring system. An autonomous energy harvesting system has been designed based on cantilever beams for sensing and acquiring the bridge vibration energy. After the analysis of the dynamic properties of the piezoelectric cantilever beam in the energy conversion, three kinds of interface circuits were compared through simulation and experimental results. It was shown that the VD interface circuit has less power loss. Furthermore, the proposed closed loop control method based on the VD circuit was simple, adaptive, and self-powered, which is suitable for the road energy harvesting application. Finally, the energy harvesting system based on VD circuit was realized with harvested power of around 0.8mW.

With the rapid development of MEMS (Micro-electro-mechanical Systems) fabrication technologies, manifolds microelectrodes with various structures and functions have been designed and fabricated for applications in biomedical research, diagnosis and treatment through electrical stimulation and electrophysiological signal recording. The flexible MEMS microelectrodes exhibit multi-aspect excellent characteristics beyond stiff microelectrodes based on silicon or SU-8, which comprising: lighter weight, smaller volume, better conforming to neural tissue and lower fabrication cost. In this paper, we mainly reviewed key technologies on flexible MEMS microelectrodes for neural interface in recent years, including: design and fabrication technology, flexible MEMS microelectrodes with fluidic channels and electrode-tissue interface modification technology for performance improvement. Furthermore, the future directions of flexible MEMS microelectrodes for neural interface were described including transparent and stretchable microelectrodes integrated with multi-aspect functions and next-generation electrode-tissue interface modifications facilitated electrode efficacy and safety during implantation. Finally, the combinations among micro fabrication techniques with biomedical engineering and nanotechnology represented by flexible MEMS microelectrodes for neural interface will open a new gate to human lives and understanding of the world.

A single mode waveguide grating coupler based on multiple Si-SiO₂ pairs onto Si substrate has been designed. Numerical analysis has been carried out to calculate optimum thickness of the layers of Si-SiO₂ that ensures the constructive interference between reflected waves and actual guided wave for high coupling efficiency. Based on the results, an optimal design is developed and modeled by using a 2-D finite difference time domain (2-D FDTD) simulator that dictates a coupling efficiency of as much as 78% (-1.07 dB) at the wavelength of 1550 nm, and a 1-dB bandwidth of 77 nm. The numerical method will be useful to calculate the optimum thicknesses of the layers for any reflector based grating coupler of different materials.

S-licarbazepine was prepared by asymmetric reduction of oxcarbazepine with interface-assembled carbonyl reductase at the interface of oil/water biphasic system. The carbonyl reductase was conjugated with polystyrene in the surface of toluene/Tris-HCl biphasic system and formed a surfactant-like structure. The conversion and enantiometric excess of S-licarbazepine reached 97.39% and 99.6% when 3.97 mmol/L oxcarbazepine was reduced by interface-assembled carbonyl reductase with 60 g/L ethanol as co-substrate in toluene/Tris-HCl(12.5:10) biphasic system at 30 °C, 180 rpm for 6 h.

In this paper, a novel integrated high precision vacuum microelectronic accelerometer is put forward based on the theory of field emission, the accelerometer consists of sensitive structure and interface ASIC. The sensitive structure has a mass of a cathode cone tips array, a folded beam, an emitter electrode and a feedback electrode. The sensor is fabricated on a double side polished (1 0 0) N-type silicon wafer, the tips array of cathode are shaped by wet etching with HNA (HNO3, HF and CH3COOH) and metalized by TiW/Au thin film. The structure of sensor is released by ICP process finally. The interface ASIC was designed and fabricated based on the P-JFET high voltage bipolar process. The accelerometer is tested through static field rollover test, and the test results show the integrated vacuum microelectronic accelerometer has good performances, which sensitivity is 3.081V/g and non-linearity is 0.84% in the measuring range of −1g~1g.

This paper is focused on understanding multiscale modelling to obtain a bridge among different time and length scales of simulation techniques. These techniques are vital as it holds the potential to understand and predict the capabilities of polymer nanocomposites (PNCs). However, an appropriate approach in controlling the interfacial interaction between nanoparticle and polymer in nanocomposites structure is still needed to develop. In this review, an initial brief introduction to various trending simulation techniques has been discussed at all three levels of scale (nm, μm, mm). Later, descriptive study on fundamental issues such as thermodynamics, kinetics, mechanical properties, and morphology has been studied deeply. The multiscale modeling bridges the gaps of simulation between the different scales of models from molecular to mesoscale levels working over the broad range of length and timescale. Through the sequential, adaptive, and concurrent approaches, we can develop a system that may comprehend multiscale mode modeling adaptive resolution approach has recently added approach the molecule of the subject can shift their position freely in the domain and through this approach and studied the Brownian motion. Co-The co-current coach is also termed as handshaking path and it is linked aiming at different scale models. Covering the rigid techniques smoothly and linking them at different scales helps in normalizing the statistical behaviour.

With forty years of developments, bio-macromolecule cryo-electron microscopy has met its revolution of resolution and is playing a very important role in structural biology study. According to different specimen states, cryo-electron microscopy (cryo-EM) involves three specific techniques, single particle analysis (SPA), electron tomography and sub-tomogram averaging, and electron diffraction. All these three techniques have not realized their full potentials of solving structures of bio-macromolecules and therefore need to be developed in the future. In this review, the current existing bottlenecks of cryo-EM SPA are discussed with theoretical analysis, which includes air-water interface during specimen cryo-vitrification, bio-macromolecular conformational heterogeneity, focus gradient within thick specimen, and electron radiation damage. Besides, potential solutions of these bottlenecks are proposed and discussed, which are worthy of further investigations in the future.

Smart grid software interconnects multiple Engineering disciplines (power systems, communication, software and hardware technology, instrumentation, big data, etc.). The software architecture is an evolving concept in smart grid systems in which systematic architecture development is a challenging process. The architecture has to realize the complex legacy power grid systems and cope up with current Information and Communication Technologies (ICT). The distributed generation in smart grid environment expects the software architecture to be distributed and to enable local control. Smart grid architecture should also be modular, flexible and adaptable to technology upgrades. In this paper, the authors have made a comprehensive review on architecture for smart grids. An in depth analysis of layered and agent based architectures is presented and compared under various domains.

Viral infections, such as those caused by Herpes Simplex Virus-1 (HSV-1) and SARS-CoV-2, affect millions of people each year. However, there are few antiviral drugs that can effectively treat these infections. The standard approach in the development of antiviral drugs involves the identification of a unique viral target, followed by the design of an agent that addresses that target. Antimicrobial peptides (AMPs) represent a novel source of potential antiviral drugs. AMPs have been shown to inactivate numerous different enveloped viruses through the disruption of their viral envelopes. However, the clinical development of AMPs as antimicrobial therapeutics has been hampered by a number of factors, especially their structure as peptides. We have examined the antiviral potential of peptoid mimics of AMPs (sequence-specific N-substituted glycine oligomers). These peptoids have the distinct advantage of being insensitive to proteases, and also exhibit increased bioavailability and stability. Our results demonstrate that several peptoids exhibit potent in vitro antiviral activity against both HSV-1 and SARS-CoV-2 when incubated prior to infection. Visualization by cryo-EM shows viral envelope disruption similar to what has been observed with AMP activity against other viruses. This suggests a common or biomimetic mechanism, possibly due to the differences between the phospholipid head group makeup of viral envelopes and host cell membranes. Furthermore, we observed no cytotoxicity against primary cultures of oral epithelial cells, thus underscoring the potential of this class of molecules as safe and effective broad-spectrum antiviral agents.

Titanium dioxide (TiO₂) micro and nano architectures have been intensively studied in the past years because of many varied applications in environmental, energy conversion, and storage fields, such as heterogeneous catalysis, dye-sensitized solar cells, lithium/sodium ion batteries, lithium-sulfur (Li-S) batteries, and bio-nanotechnology, etc. Especially the surface and interface structures in the TiO₂ structures play important roles in those applications. This mini review article focuses on TiO₂ micro and nano architectures with the prevalent crystal structures (anatase, rutile, brookite, and TiO₂(B)), and summarizes major advances in the surface and interface engineering and applications in environmental and electrochemical applications. We present the principles and growth mechanisms of TiO₂ nanostructures via different strategies, with an emphasis on rational control of the surface and interface structures. We further discuss the applications of TiO₂ micro and nano architectures in photocatalysis, lithium/sodium ion batteries, and Li-S batteries. Throughout the discussion, the relationship between the device performance and the surface structures of TiO₂ micro/nano structures will be highlighted. Then we discuss the phase transitions of TiO₂ nanostructures and possible strategies of improving the phase stability. The review concludes with a perspective on the current challenges and future research directions.

Formulation is an ancient concept, although the word has been used only recently. The first formulations made our civilization advance by inventing bronze, steel, and gunpowder; then, it was used in medieval alchemy. When chemistry became a science and with the golden age of organic synthesis, the second formulation period began. This made it possible to create new chemical species and new combinations “à la carte.” However, the research and developments were still carried out by trial and error. Finally, the third period of formulation history began after World War II, when the properties of a system were associated with its ingredients and the way they were assembled or combined. Therefore, the formulation and the systems’ phenomenology were related to the generation of some synergy to obtain a commercial product. Winsor’s formulation studies in the 1950s were enlightening for academy and industries that were studying empirically surfactant-oil-water (SOW) systems. One of its key characteristics was how the interfacial interaction of the adsorbed surfactant with oil and water phases could be equal by varying the physicochemical formulation of the system. Then, Hansen’s solubility parameter in the 1960s helped to reach a further understanding of the affinity of some substances to make them suitable to oil and water phases. In the 1970s, researchers such as Shinoda and Kunieda, and different groups working in Enhanced Oil Recovery (EOR), among them Schechter and Wade’s group at the University of Texas, made formulation become a science by using semiquantitative correlations to attain specific characteristics in a system (e.g., low oil-water interfacial tension, formulation of a stable O/W or W/O emulsion, or high-performance solubilization in a bicontinuous microemulsion system at the so-called optimum formulation). Nowadays, over 40 years of studies with the hydrophilic-lipophilic deviation equation (HLD) have made it feasible for formulators to improve products in many different applications using surfactants to attain a target system using HLD in its original or its normalized form, i.e., HLDN. Thus, it can be said that there is still current progress being made towards an interdisciplinary applied science with numerical guidelines. In the present work, the state-of-the-art of formulation in multiphase systems containing two immiscible phases like oil and water, and therefore systems with heterogeneous or micro-heterogeneous interfaces, is discussed. Surfactants, from simple to complex or polymeric, are generally present in such systems to solve a wide variety of problems in many areas. Some significant cases are presented here as examples dealing with petroleum, foods, pharmaceutics, cosmetics, detergency, and other products occurring as dispersions, emulsions, or foams, that we find in our everyday lives.

The capacity and the mechanism of the adsorption of aqueous barium (Ba), cobalt (Co), strontium (Sr) and zinc (Zn) by Ecuadorian (NatAllo) and synthetic (SynAllo-1 and SynAllo-2) allophanes were studied as a function of contact time, pH and metal ion concentration using kinetic and equilibrium experiments. The mineralogy, nano-structure and chemical composition of the allophanes were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy and specific surface area analyses. The evolution of adsorption fitted to a pseudo-first-order reaction kinetics, where equilibrium between aqueous metal ions and allophane was reached within < 10 min. The metal ion removal efficiencies varied from 0.7 to 99.7 % at pH 4.0 to 8.5. At equilibrium, the adsorption behavior is better described by the Langmuir model than by the Dubinin-Radushkevich model, yielding sorption capacities of 10.6, 17.2 and 38.6 mg/g for Ba^(2+), 12.4, 19.3 and 29.0 mg/g for HCoO_2^-, 7.2, 15.9 and 34.4 mg/g for Sr^(2+) and 20.9, 26.9 and 36.9 mg/g for Zn^(2+), respectively, by NatAllo, SynAllo-2 and SynAllo-1. The uptake mechanism is based on a physical adsorption process. Allophane holds great potential to remove aqueous metal ions and could be used instead of zeolites, montmorillonite, carbonates and phosphates for wastewater treatment.

When using computer-aided translation systems in a typical, professional translation workflow, there are several stages at which there is room for improvement. The SCATE (Smart Computer-Aided Translation Environment) project investigated several of these aspects, both from a human-computer interaction point of view, as well as from a purely technological side. This paper describes the SCATE research with respect to improved fuzzy matching, parallel treebanks, the integration of translation memories with machine translation, quality estimation, terminology extraction from comparable texts, the use of speech recognition in the translation process, and human computer interaction and interface design for the professional translation environment. For each of these topics, we describe the experiments we performed and the conclusions drawn, providing an overview of the highlights of the entire SCATE project.

Coating of suitable materials having thickness of few atoms to several microns on a substrate is of great interest to the scientific community. Hard coatings develop under the significant composition of suitable-natured atoms where their force-energy behaviors when in certain transition state favour binding. In the binding mechanism of suitable atoms, electron belonging to outer ring filled state of gas-atom undertakes another clamp of energy knot belonging to outer ring unfilled state of solid-atom. Set process conditions develop the binding of different-natured atoms when processing their suitable composition in a system. Atoms of different nature develop structure in the form of hard coating by locating their ground points between the original ones. Here, gas-natured atoms increase the potential energy under decreasing levitational force of electrons, whereas, solid-natured atoms decrease the potential energy under decreasing gravitational force of electrons. In TiN coating, Ti–Ti atoms bind under the difference of expansion of their lattices, called nets of energy knots, where one atom just lands on the already landed atom. An adhered N-atom to a Ti-atom forms its position among four Ti-atoms where N-atom occupies the interstitial site of Ti-atoms. Two oppositely working force-energy behavior atoms deposit in the form of coating at substrate surface as per set conditions of the process. The rate of ejecting (or dissociating) solid-natured atoms depend on the nature of their source (target), process parameters and processing technique. In random arc-based vapor deposition system, depositing differently natured atoms at substrate surface depends on the input power. In addition to intrinsic nature of atoms, different properties and characteristics of coatings emerge as per engaged forces under their involved energy. The present study sets new trends in the field of coatings involving the diversified class of materials and their counterparts.

In this paper, the nanoscale dissipative mechanisms of a Cu pad in a Ball Grid Array (BGA) packaging structure during isothermal ageing and uniaxial tension were investigated by the molecular dynamics (MD) method and experiments. From the result of the isothermal ageing test, a nonuniform consumption of Cu and large amount of Kirkendall voids were observed at the interface of Cu and Cu₃Sn. To study the effect of pressure and orientation on this phenomenon, MD simulations were conducted on four types of Cu-Cu₃Sn interface structures with different orientations of Cu. By comparing the diffusion coefficients of atoms in those cases, it was found that the tensile stress would inhibit the diffusion of atoms, whereas compressive stress would accelerate it, and this would be more significant under a larger magnitude of stress and temperature. Note that, in the model with the (101) surface Cu at the interface, both Cu and Cu₃Sn have a higher diffusion coefficient compared with the model with (001) surface Cu. Thus, the orientation of Cu will also contribute to the uniform consumption of the pad. Uniaxial tension simulation combined with DXA and CSP analyses on those models also shows the model with (001) surface Cu has a greater mechanical reliability in our simulations and related experiments.

How can neuroscience help everyone to live and care for our confined brains? Understanding and studying human behaviour allows us to measure the impact of such confinement on each of us and to identify those who need help. The challenges are to understand the psychological repercussions following confinement and to understand how neuroscience and neurotechnology can be very interesting tools for dealing with the health crisis. Neuroscience is changing our traditional philosophical and ethical views by providing information about the biological basis of our moral behaviour. The exception of the brain is at the heart of neuroethical thinking and discourse. Neuroethics is an interdisciplinary discipline placed at the intersection between the human sciences and the neurosciences, and aims to help understand how knowledge and research in neurosciences and neurotechnologies will affect the future of society, their impact on humans, relationships, daily life, the labour market ... but also how they can help and provide solutions and answers to the questions of citizens in a health crisis. But it's a double-edged sword, collecting and analyzing brain data in real time seems to be increasingly simple and within everyone's reach, without having the hindsight of the real meaning of these data, allowing one day to read people's thoughts, control them and manipulate them. It is becoming clear that the boundaries between medical and non-medical uses of neuroscience and neurotechnology are becoming very porous, inviting us to reflect on neuroethical issues in order to put safeguards on these uses.

To improve the interfacial bonding of sisal fiber reinforced polylactide biocomposites, polylactide (PLA) and sisal fibers (SF) were melt-blended to fabricate bio-based composites via in situ reactive interfacial compatibilization with the addition of an epoxy-functionalized oligomer (ADR). The FTIR analysis and SEM characterization demonstrated that PLA molecular chain was bonded to the fiber surface and epoxy-functionalized oligomer played a hinge-like role between sisal fibers and PLA matrix, which resulted in improved interfacial adhesion between fibers and PLA matrix. The interfacial reaction and microstructures of composites were further investigated by thermal and rheological analyses, which indicated that the mobility of the PLA molecular chain in composites was restricted because of the introduction of ADR oligomer, which in turn reflected the improved interfacial interaction between SF and PLA matrix. These conclusions were further investigated by the calculated activation energies of glass transition relaxation (△Ea) of composites via dynamic mechanical analysis. The mechanical properties of PLA/SF composites were simultaneously reinforced and toughened via addition of ADR oligomer. The interfacial interaction and structure-properties relationship of composites are key points of this study.

Thermal interface material (TIM) that can exist as liquid at the service temperature enables efficient heat transfer across two adjacent surfaces in electronic applications. In this work, the thermal conductivities of different phase regions in the Ga-In system at various compositions and temperatures are measured for the first time. A modified comparative cut bar technique is used for the measurement of the thermal conductivities of In_xGa_1-x (x=0, 0.1, 0.214, 0.3, and 0.9) alloys at 40, 60, 80, and 100^oC that are the temperatures commonly encountered in consumer electronics. The thermal conductivity values for the liquid and semi-liquid (liquid+β) Ga-In alloys are higher than the TIM currently used in consumer electronics. These measured quantities, along with the available experimental data from the literature, served as input for the thermal conductivity parameter optimization using the CALPHAD (CALculation of PHase Diagram) method for the pure elements, solution phase, and two-phase region. A set of self-consistent parameters for the description of the thermal conductivity of the Ga-In system is obtained. There is good agreement between the measured and calculated thermal conductivity values for all the phases. Hence, it can be envisaged that liquid/semi-liquid Ga-In alloys can be considered as a potential TIM in consumer electronics due to its high thermal conductivity.

This paper proposes several LabVIEW applications to accomplish the data acquisition, processing, features extraction and real-time classification of the electroencephalographic (EEG) signal detected by the embedded sensor of the NeuroSky Mindwave Mobile headset. The LabVIEW applications are aimed at the implementation of a Brain-Computer Interface system, which is necessary to people with neuromotor disabilities. It is analyzed a novel approach regarding the preparation and automatic generation of the EEG dataset by identifying the most relevant multiple mixtures between selected EEG rhythms (both time and frequency domains of raw signal, delta, theta, alpha, beta, gamma) and extracted statistical features (mean, median, standard deviation, route mean square, Kurtosis coefficient and others). The acquired raw EEG signal is processed and segmented into temporal sequences corresponding to the detection of the multiple voluntary eye-blinks EEG patterns. The main LabVIEW application accomplished the optimal real-time artificial neural networks techniques for the classification of the EEG temporal sequences corresponding to the four states: 0 - No Eye-Blink Detected; 1 - One Eye-Blink Detected; 2 – Two Eye-Blinks Detected and 3 – Three Eye-Blinks Detected. Nevertheless, the application can be used to classify other EEG patterns corresponding to different cognitive tasks, since the whole functionality and working principle could estimate the labels associated with various classes.

Recent advances in recording and real-time analysis of surface electromyographic signals (sEMG) have fostered the use of sEMG human-machine interfaces for controlling personal computers, prostheses of upper limbs, and exoskeletons among others. Despite a relatively high mean performance, sEMG-interfaces still exhibit strong variance in the fidelity of gesture recognition among different users. Here we systematically study the latent factors determining the performance of sEMG-interfaces in synthetic tests and in an arcade game. We show that the degree of muscle cooperation and the amount of the body fatty tissue are the decisive factors in synthetic tests. Our data suggest that these factors can only be adjusted by a long-term training, which promotes fine-tuning of low-level neural circuits driving the muscles. A short-term training has no effect on synthetic tests, but significantly increases the game scoring. This implies that it works at a higher decision-making level, not relevant for synthetic gestures. We propose a procedure that enables quantification of the gestures’ fidelity in a dynamic gaming environment. For each individual subject the approach allows identifying “problematic” gestures that decrease gaming performance. This information can be used for optimizing the training strategy and for adapting the signal processing algorithms to individual users, which could be a way for a qualitative leap in the development of future sEMG-interfaces.

As we approach the 135th anniversary of the automobile, two industry trends, automation and digitalization, are rapidly revolutionizing the thus far, relatively unchanged automotive user experience. This paper describes the development of the Valeo MyMobius user interface concept. The goal of this project was to explore how to achieve an intuitive driving experience as the automotive industry undergoes transition from primarily analog to primarily digital interfaces and from physical buttons to multimodal interactions. To achieve the perception of intuitiveness, designers must understand their users, find and reduce physical and cognitive friction points, and bridge knowledge gaps with interface designs that facilitate discovery and learnability. The Valeo MyMobius concept featured steering wheel touch displays that supported quick, frequent menu selections using swiping gestures (common in smartphone interactions) and reinforcing icons (to facilitate learnability). Learning algorithms personalized the experience by tailoring suggestions, while more complex interactions were handled with a conversational voice assistant, which also served as a driving copilot, capable of contextually suggesting when Advanced Driving Assistance System (ADAS) features such as ACC could be utilized. The visual design aesthetic embodied Kenya Hara’s design philosophy of “Emptiness,” reducing visual clutter and creating spaces that are ready to receive inspiration and information. Altogether, the Valeo MyMobius concept demonstrated an attainable future where the perception of intuitiveness can be achieved with today’s technologies.

Joining of aluminium alloys to steels has been extensively studied, especially in the automotive sector. However, aluminium alloys are known to be difficult to join with steels when methods involving fusion welding are used because of hot cracking problem. Hence, a high strength joint between these dissimilar metals would be of benefit especially in reducing the weight of products. In this work the torch brazing method was applied to join AR500 steel with AA7075 aluminium alloy using Al-Si-Zn base filler metal at various flame times. The effect of the brazing work on the intermetallic phase formation and the mechanical strength of the joints were investigated. In this work, the maximum shear load obtained was 6460 N and the presence of the intermetallic phases had reduced the shear strength of the brazed joints. However, the torch brazing process using Al-Si-Zn filler metal had successfully facilitated the joining of these dissimilar metals.

The multidisciplinary nature of the work required for research in the Covid-19 pandemic has created new challenges for health professionals in the battle against the virus. They need to be equipped with novel tools and resources ---that have emerged during the pandemic--- to gain access to breakthrough findings, know the latest developments, and to address their specific needs for rapid data acquisition, analysis, evaluation, and reporting. Because of the complex nature of the virus, the healthcare systems worldwide are severely impacted as the treatment and the vaccine for Covid-19 disease are not yet discovered. This leads to frequent changes in regulations and policies by governments and international organizations. Our analysis suggests that given the abundance of information sources, finding the most suitable tool for a given task is one of such challenges. But health professionals and policymakers need access to the most relevant, reliable, trusted, and latest information and tools that can be used in their day-to-day tasks of Covid-19 research and analysis. In this article, we present our analysis of various novel and important tools that have been specifically developed during the Covid-19 pandemic and that can be used by the health professionals community to help in advancing their analysis and research. These tools comprise of search engines, information repositories for literature and clinical trials, data sources, dashboards, and forecasting models. We present list of the minimally essential tools to serve a multitude of purposes, from hundreds of those developed since the beginning of the pandemic. A critical analysis is provided for the selected tools based on 17 parameters that can be useful for researchers and analysts for their evaluations. These parameters make up our evaluation framework and have not been used previously for analysis and evaluation. Hence, knowledge of the tools will not only increase the productivity but will also allow to explore new dimensions for using existing tools with more control, better management, and greater outcome of their research. In addition, the parameters used in our framework can be applied for future evaluations of similar tools and health professionals can adapt them for evaluation of other tools not covered in this analysis.

Interface between matrix/coating or coating/coating in fact represents a very complicate thin interfacial layer. So interface model is necessary to avoid the difficulty on considering such a complicate thin layer in analysis. Classic interface model and cohesive model have been widely used in stress analysis of coating materials, though they cannot represent the effect of very thin interfacial layer accurately. A complete interface model has been deduced from the equivalent constitutive of interfacial layer in this paper. It is found that both classic interface model and cohesive model sometimes cannot give correct analysis results, while the complete interface model can always give a correct result. Moreover, the stress parallel to interface within the interfacial layer can also be analyzed by the new model. Besides, this model can also be used to describe the equivalent properties of interfacial layer, thereby, can provide a quantitative characterizing method for interfacial layer itself.

Artificial Intelligence (AI) experienced significant advancements in recent years, and its impact is already recognized across various industries. Yet, the rise of AI has led to a growing concern about its impact on meeting the Sustainable Development Goals (SDGs). The aim of this paper was to evaluate contributions and the potential impact of AI to sustainable development in the society domain. Furthermore, the study analyzed GPT-3 responses, as one of the largest language models developed by OpenAI, descriptively. We conducted a set of queries on the SDGs to gather infor-mation on GPT-3`s perceptions of AI impact on sustainable development. Analysis of GPT-3’s contribution potential towards the SDGs showcased its broad range of capabilities for contributing to the SDGs in areas such as education, health, and communication. The study findings provide valuable insights into the contributions of AI to sustainable development in the society domain and highlight the importance of proper regulations to promote the responsible use of AI for sustainable development. We plan subsequent research for effects on ecologic and economic domains of SDGs. We highlighted the potential for improvement in neural language processing skills of GPT-3 by avoiding imitating weak human writing styles with more mistakes in longer texts.

Additive manufacturing has already been established as a highly versatile manufacturing technique with demonstrated potential to completely transform conventional manufacturing in the future. The objective of this paper is to review the latest progress and challenges associated with the fabrication of multi-material parts using additive manufacturing technologies. Various manufacturing processes and materials used to produce functional components were investigated and summarized. The latest applications of multi-material additive manufacturing (MMAM) in automotive, aerospace, biomedical and dentistry field were demonstrated. Investigation on the current challenges were also carried out to predict the future direction of MMAM processes. It is concluded that the further research and development needed in the design of multi-material interfaces, manufacturing processes and material compatibility of MMAM parts are necessary.

Two extreme wind-driven wildfire events impacted northern and southern California in late 2017 leading to 46 fatalities and thousands of structures lost. This study describes the meteorological and climatological factors that drove and enabled these wildfire events and quantifies the rarity of such conditions over the observational record. Both extreme wildfire events featured fire-weather metrics that were unprecedented in the observational record in addition to a sequence of climatic conditions that preconditioned fuels. The North Bay fires that affected portions of northern California in early October occurred coincident with strong downslope winds. The vast majority of the fires’ devastating effects and acres burned occurred overnight and within the first twelve hours of ignition. By contrast, the southern California fires of December were characterized by the longest Santa Ana wind event on record and included the largest wildfire in California’s history. Both fire events occurred following an exceptionally wet winter that was preceded by the drought of record in California. Fuels were further preconditioned as the warmest summer and autumn on record occurred in northern and southern California, respectively. Accelerated curing of fuels coupled with the delayed onset of autumn precipitation allowed for critically low dead fuel moisture leading up to the foehn wind events. Fire weather conditions were well forecasted several days prior to the fire. However, the rarity of fire-weather conditions that occurred in the wildland urban interface, along with other societal factors were key contributors to wildfire impacts to communities.

Crimean-Congo hemorrhagic fever virus is one the most important and wide spread tick-borne viruses. Very little is known about the transmission from the tick and the early aspects of pathogenesis. Here, we generate human cutaneous antigen presenting cells: dermal dendritic cells and Langerhans cells, from umbilical cord progenitor cells. In order to mimic the environment created during tick feeding, tick salivary gland extract was generated from semi-engorged Hyalomma marginatum ticks. Our findings indicate that human dermal dendritic cells and Langerhans cells are susceptible and permissive to Crimean-Congo hemorrhagic fever virus infection, however, to different degrees. Infection leads to cell activation and cytokine/chemokine secretion, although these responses vary between the different cell types. Hyalomma marginatum salivary gland extract had minimal effect on cell responses, with some synergy with viral infection with respect to cytokine secretion. However, salivary gland extract appeared to inhibit antigen presenting cell (APC) migration. Based on the findings here we hypothesize that human dermal dendritic cells and Langerhans cells serve as early target cells. Rather affecting Crimean-Congo hemorrhagic fever virus replication, tick saliva likely immunomodulates and inhibits migration of these APC from the feeding site.

As education becomes more and more important in creating improved societies, many people who do not have access to it are falling behind. To help them catch up, many people, especially those in rural areas and developing countries, are turning to different methods of self-learning, especially those that utilize cheap technology and use interactive methods to teach. Our empirical study tests the effectiveness of an e-learning system that utilizes newer, less tested forms of interactivity and could potentially be used in these areas as a self-learning system and compares it to non-interactive video and textbook self-learning in two different topics. The results of the experiment showed that the increased interactivity provided by the e-learning system achieved significantly better learning performance over both non-interactive video and textbook self-learning. It was also found that students who learned through non-interactive video performed significantly better than those who used textbooks for self-learning.

This paper presents Tiltification, a multi modal spirit level application for smartphones. The non-profit app was produced by students in the master project “Sonification Apps” in winter term 2020/21 at the University of Bremen. In the app, psychoacoustic sonification is used to give feedback on the device’s rotation angles in two plane dimensions, allowing users to level furniture or take perfectly horizontal photos. Tiltification supplements the market of spirit level apps with the unique feature of auditory information processing. This provides for additional benefit in comparison to a physical spirit level and for more accessibility for visu- ally and cognitively impaired people. We argue that the distribution of sonification apps through mainstream channels is a contribution to establish sonification in the market and make it better known to users outside the scientific domain. We hope that the auditory display community will support us by using and recommending the app and by providing valuable feedback on the app functionality and design, and on our communication, advertisement and distribution strategy.

The environment impacts human health in profound ways, yet few theories define the form of the relationship between human physiology and the environment. Under current epidemiological constructs of causation, it is assumed that two complex systems (environment and humans) can transfer information directly. This is the underlying structure of the relationship when studies examine, for example, air pollution and brain health. In marked contrast, we conjecture that complex systems cannot interact directly, but rather such interaction requires the formation of an “interface”. Further, we contend that this dynamic, process-based interface incorporates components from all the interacting systems but exhibits operational independence. This property has many consequences, the foremost being that characteristics of the interface cannot be fully resolved by only studying the systems involved in the interaction. The interface itself must be the subject of inquiry. Without refocusing our attention on biodynamic interfaces, we cannot discern how our environment impacts health.

In order to understand the hot deformation behavior of novel Cu/Al laminated composites, isothermal hot compression tests were conducted by Gleeble-1500D thermo-mechanical simulator. And the effect of bonding interface, deformation temperature and strain rate on the deformation behavior was analyzed. Results show that under the interface constraint effect, soft Al layer trends to flow synchronously with hard Cu layer. And further microstructure examinations indicate the cooperative deformation capability of Cu/Al composites increases with increasing stain rate and decreasing deformation temperature. Strain hardening exponent, calculated based on the true stress-true strain data, also proves the effect of deformation temperature and strain rate on the cooperative deformation behavior. Meanwhile, unique composites structure allows the Al matrix to exhibit the characteristic of dynamic recrystallization during the hot deformation process. Lastly, strain compensated Arrhenius-type constitutive equation was employed to describe the coupling effect of temperature, strain rate and strain on the flow stress.

In this paper, by developing a mathematical model, the operating temperature of perovskite solar cells (PSCs) under different operating conditions has been calculated. It is found that by reducing the density of tail states at the interfaces, acting as recombination centres, through some passivation mechanisms, the operating temperature can be reduced significantly at higher applied voltages. The results show that if the density of tail states at the interfaces is reduced by three orders of magnitude through some passivation mechanisms, then the active layer may not undergo any phase change up to an ambient temperature 300 K and it may not degrade up to 320 K. The calculated heat generation at the interfaces at different applied voltages with and without passivation shows that the heat generation can be reduced by passivating the interfaces. It is expected that this study may provide a deeper understanding of the influence of interface passivation on the operating temperature of PSCs.

A new strategy to nanoengineer gold/fluorocarbon multilayer (ML) nanostructures is reported. We have investigated the morphological changes occuring at the metal-polymer interface in multilayer structures with varying volume fraction of gold (Au) and the kinetic growth aspect of the microscale properties of nano-sized Au in plasma polymer fluorocarbon (PPFC). Investigations were carried out at various temperatures and annealing time by means of grazing incidence small-angle and wide-angle X-ray scattering (GISAXS and GIWAXS). We have fabricated a series of multilayers with variying volume fraction (0.12, 0.27, 0.38) of Au and bilayer periodicity in ML structure. They show an interesting granular structure consisting of nearly spherical shaped nanoparticles within the polymer layer. The nanoparticle (NP) morphology changes due to the collective effects of NPs diffusion within ensembles in the in-plane vicinity and inter-layer with increasing temperature. The in-plane NPs size distinctly increases (from 1.9 to 4 nm) with increasing temperature. The NPs become more spherical thus reducing the surface energy. Linear growth of NPs with temperature and time shows diffusion-controled growth of NPs in the ML structure. The structural stability of the multilayer is controlled by the volume ratio of the metal in polymer. At room temperature UV-Vis shows a blueshift of the plasmon peak from 560 nm in ML Au/PTFE_1 to 437 nm in Au/PTFE_3. We have identified the fabrication and post-deposition annealing conditions to limit the Local Surface Plasmon resonance (LSPR) shift (from 〖∆λ〗_LSPR=180 nm (Au/PTFE_1) to 〖∆λ〗_LSPR=67 nm (Au/PTFE_3 ML)) and their optical response over a wide visible wavelength range. A variation in the dielectric constant of the polymer in precence of varying Au inclusion is found to be the main factor affecting the LSPR frequency. Our finding may provide insights in Nano engineering ML structure can be useful to systematically control the growth of NPs in polymer matrix.

The oil and gas industry faces increasing pressure to remove people from dangerous offshore environments. Robots present a cost-effective and safe method for inspection, repair and maintenance of topside and marine offshore infrastructure. In this work, we introduce a new immobile multi-sensing robot, the Limpet, which is designed to be low-cost and highly manufacturable, and thus can be deployed in huge collectives for monitoring offshore platforms. The Limpet can be considered an instrument, where in abstract terms, an instrument is a device that transforms a physical variable of interest (measurand) into a form that is suitable for recording (measurement). The Limpet is designed to be part of the ORCA (Offshore Robotics for Certification of Assets) Hub System, which consists of the offshore assets and all the robots (UAVs, drones, mobile legged robots etc.) interacting with them. The Limpet comprises the sensing aspect of the ORCA Hub System. We integrated the Limpet with Robot Operating System (ROS), which allows it to interact with other robots in the ORCA Hub System. In this work, we demonstrate how the Limpet can be used to achieve real-time condition monitoring for offshore structures, by combining remote sensing with signal processing techniques. We show an example of this approach for monitoring offshore wind turbines. We demonstrate the use of four different communication systems (WiFi, serial, LoRa and optical communication) for the condition monitoring process. By processing the sensor data on-board, we reduce the information density of our transmissions, which allows us to substitute short-range high-bandwidth communication systems with low-bandwidth long-range communication systems. We train our classifier offline and transfer its parameters to the Limpet for online classification, where it makes an autonomous decision based on the condition of the monitored structure.

The electrical stimulation of the visual cortices has the potential to restore vision to blind individuals. Until now, the results of visual cortical prosthetics has been limited as no prosthesis has restored a full working vision but the field has shown a renewed interest these last years thanks to wireless and technological advances. However, several scientific and technical challenges are still open in order to achieve the therapeutic benefit expected by these new devices. One of the main challenges is the electrical stimulation of the brain itself. In this review, we analyze the results in electrode-based visual cortical prosthetics from the electrical point of view. We first briefly describe what is known about the electrode-tissue interface and safety of electrical stimulation. Then we focus on the psychophysics of prosthetic vision and the state-of-the-art on the interplay between the electrical stimulation of the visual cortex and phosphene perception. Lastly, we discuss the challenges and perspectives of visual cortex electrical stimulation and electrode array design to develop the new generation implantable cortical visual prostheses.

This review summarises the main numerical models of fuel droplet heating and evaporation (DHE) in combustion engines across the different scales by accessing the nano/micro, meso and macroscopic fluid elements. The phenomena of multi-physics, multi-scale and multi-phase fluid flow and heat transfer are fully investigated when the fuel droplet (dodecane) is heating and evaporated into a background gas (nitrogen) crossing the liquid-vapour (LV) interface, kinetic region (i.e., Knudsen layer) and the bulk regions of liquid and gas in terms of molecular dynamics (MD) simulations, kinetic theory modelling (i.e., direct numerical solutions of Boltzmann equations) and convectional fluid dynamics approach, respectively. The evaporation coefficient of fuel evaporating molecules and their velocity distributions at the LV interface derived from MD simulations formulate a new kinetic boundary condition (KBC). Moreover, a novel kinetic model considering the inelastic collision between fuel molecules alongside the new KBC enables us to describe the non-equilibrium gas dynamics of fuel vapour and gas mixture in Knudsen layer (KL). Heat and mass flux analysis of the fuel droplet under combustion engine conditions can be accurately assessed by implementing the inelastic collision between fuel molecules in KL and a temperature-dependent evaporation coefficient at the LV interface into DHE. The surface temperature of fuel droplet and its evaporation time, which play a significant role in resolving the ignition delay and hence the combustion phasing in engines, can also be well estimated. The multi-scale modelling of fuel DHE will make significantly potential input into the cleaner engine targeting the low-carbon emissions and enhance the capability of the existing computational fluid dynamics (CFD) solvers.

The risk of inhalation exposure to elevated concentrations of hydrogen sulfide (H₂S) and ammonia (NH₃) during the agitation of stored swine manure is high. Once or twice a year, farmers agitate manure before pump-out and application to fields. Agitation of the swine manure causes the short-term releases of highly toxic levels of H₂S and NH₃. In our previous pilot-scale studies, the biochar powder had shown significant mitigation of H₂S and NH₃ emissions when surficially applied to manure immediately before agitation. However, fine biochar powder application poses hazards by itself and may not be practical to apply on a farm scale, especially when livestock and workers are present. We hypothesized that applying pelletized biochar to manure surface is just as effective as applying powder to protect farmers and animals from excessive exposure to H₂S and NH₃. This work reports on the lab-scale proof-of-the-concept trials with biochar pellets on the lab-scale. The objective was to compare the biochar pellets and biochar powder on their effectiveness of mitigation on H₂S and NH₃ gases during 3-hour long swine manure agitation. Three scenarios were compared in (n=3) trials (i) control, (ii) 12.5 mm thick surficial application to manure surface of biochar powder, and (iii) an equivalent (by mass) dose of pelletized biochar applied to manure surface. The biochar powder was bound with 35% (wt) water into ~5 × 10 mm (dia × length) pellets. Biochar powder was significantly (p<0.05) more effective than the biochar pellets. Still, pellets reduced total H₂S and NH₃ emissions by ~72% and ~68%, respectively (p=0.001), compared with ~99% by powder (p=0.001). The maximum H₂S & NH₃ concentrations were reduced from 48.1±4.8 ppm & 1,810±850 ppm to 20.8±2.95 ppm & 775±182 ppm by pellets, and to 22.1±16.9 ppm & 40.3±57 ppm by powder, respectively. These reductions are equivalent to reducing the maximum concentrations of H₂S and NH₃ during the 3-h manure agitation by 57% and 57% (pellets) and 54% and 98% (powder), respectively. Treated manure properties hinted at improved nitrogen retention, yet not significant due to high variability. We recommend scaling-up and trials on the farm-scales using biochar pellets to assess the feasibility of application to large manure surfaces and techno-economic evaluation.

The question of what generates conscious experience has mesmerized thinkers since the dawn of humanity, yet its origins remain a mystery. The topic of consciousness has gained traction in recent years, thanks to the development of large language models that now arguably pass the Turing test, an operational test for intelligence. However, intelligence and consciousness are not related in obvious ways, as anyone who suffers from a bad toothache can attest - pain generates intense feelings and absorbs all our conscious awareness yet nothing particularly intelligent is going on. Still, in the hard sciences, this topic is frequently met with skepticism because to date no protocol to measure the content or intensity of conscious experiences in an observer-independent manner has been agreed upon. Here we present a novel proposal: Conscious experience arises whenever a quantum mechanical superposition forms. Our proposal has several implications: First, it suggests that the structure of the superposition determines the qualia of the experience. Second, quantum entanglement naturally solves the binding problem, ensuring the unity of phenomenal experience. Finally, a moment of agency may coincide with the formation of a superposition state. We outline a research program to experimentally test our conjecture via a sequence of quantum biology experiments. Applying these ideas opens up the possibility of expanding human experience through brain-quantum computer interfaces.

In this study, a preload monitoring method using impedance signatures obtained from a piezoelectric-based smart interface is presented for bolted girder connections. Firstly, the background theory of the piezoelectric-based smart interface and its implementation into health monitoring of bolted connections are outlined. A simplified electro-mechanical (EM) impedance model of a smart interface-embedded bolted connection system is formulated to interpret mechanistic understanding of EM impedance signatures under the effect of bolt preload. Secondly, finite element modeling of a bolted connection is carried out to show the numerical feasibility of the presented method and to predetermine the sensitive frequency band of impedance signatures. Finally, impedance measurements are conducted on a lab-scaled bolted girder connection to verify the predetermined sensitive frequency range and to assess the bolt preload changes in the test structure.

The modulation instability of surface capillary-gravity water waves is analysed in a shear flow model with a tangential discontinuity of velocity. It is assumed that air blows along the surface of the water with a uniform profile in the vertical direction. Such a model, despite its simplicity, plays an important role in hydrodynamics as the reference model for investigating basic physical phenomena of wave-current interactions and acquiring insights into a series of complex phenomena. In certain cases where the wavelength of interfacial perturbations is much bigger than the width of the shear fow profile, the model with the tangential discontinuity in the velocity is adequate for describing physical phenomena at least within limited spatial and temporal frameworks. A detailed analysis of the air-flow conditions under which modulation instability sets in is presented. It is also shown that the interfacial waves are subject to dissipative or radiative instability when negativeenergy waves appear at the interface.

FPGA-based data acquisition and processing systems play an important role in modern high-speed, multichannel measurement systems, especially in High-Energy and Plasma Physics. Such FPGA-based systems require an extended control and diagnostics part corresponding to the complexity of the controlled system. Managing the complex structure of registers while keeping the tight coupling between hardware and software is a tedious and potentially error-prone process. Various existing solutions aimed at helping that task do not perfectly match all specific requirements of that application area. The paper presents a new solution based on the XML system description, facilitating the automated generation of the control system’s HDL code and software components and enabling easy integration with the control software. The emphasis is put on reusability, ease of maintenance in case of system modification, easy detection of mistakes, and the possibility of use in modern FPGAs. The presented system has been successfully used in data acquisition and preprocessing projects in High-Energy Physics experiments. It enables easy creation and modification of the control system definition and convenient access to the control and diagnostic blocks. The presented system is an open-source solution and may be adopted by the user for particular needs.

Abstract The construction of a heterostructured nanowire array allows the manipulation of the interfacial, surface, charge transport, and transfer properties simultaneously, offering new opportunities to achieve multi-functionality for various applications. Herein, we developed a facile thermal evaporation and post-annealing method to synthesize ternary-Zn2SnO4/binary-ZnO radially heterostructured nanowire arrays (HNA). Vertically aligned ZnO nanowire arrays (3.5 μm in length) were grown on a ZnO-nanoparticle-seeded fluorine-doped tin oxide substrate by a hydrothermal method. Subsequently, the amorphous layer consisting of Zn-Sn-O complex was uniformly deposited on the surface of the ZnO nanowires via the thermal evaporation of the Zn and Sn powder mixture in vacuum, followed by post-annealing at 550 °C in air to oxidize and crystallize the Zn2SnO4 shell layer. The use of a powder mixture composed of elemental Zn and Sn (rather than oxides and carbon mixture) as an evaporation source ensures high vapor pressure at a low temperature (e.g., 700 °C) during thermal evaporation. The morphology, microstructure, and charge-transport properties of the Zn2SnO4/ZnO HNA were investigated by scanning electron microscopy, X-ray diffraction, Raman spectroscopy, transmission electron microscopy, and electrochemical impedance spectroscopy. Notably, the optimally synthesized Zn2SnO4/ZnO HNA shows an intimate interface, high surface roughness, and superior charge-separation and -transport properties compared with the pristine ZnO nanowire array.

Scintillators play a key role in the detection chain of several applications which rely on the use of ionizing radiation, and it is often mandatory to extract and detect the generated scintillation light as efficiently as possible. In positron emission tomography (PET), for example, both energy resolution and coincidence resolving time, two of the key detection parameters, depend strongly on the total amount of light which reaches the photodetector surface as well as its spatial and temporal distribution. Typical inorganic scintillators do however feature a high index of refraction, which impacts light extraction efficiency in a negative way. Furthermore, several applications such as preclinical PET rely on pixelated scintillators with small pitch. In this case, applying reflectors on the crystal pixel surface, as done conventionally, can have a dramatic impact of the packing fraction and thus the overall system sensitivity. This paper presents a study on light extraction techniques, as well as combinations thereof, for two of the most used inorganic scintillators (LYSO and BGO). Novel approaches, employing distributed Bragg reflectors (DBRs), metal coatings, and a modified Photonic Crystal (PhC) structure, are described in detail and compared with commonly used techniques. The nanostructure of the PhC is surrounded by a hybrid organic/inorganic silica sol-gel buffer layer which ensures robustness while maintaining its performance unchanged. We observed in particular a maximum light gain of about 41% on light extraction and 21% on energy resolution for BGO, a scintillator which has gained interest in the recent past due to its prompt Cherenkov component and lower cost.

With global warming and accelerated degradation of permafrost, the engineering problems caused by the formation of weak zones between the shallow and permafrost layers of soil–rock mixture (S-RM) slopes in permafrost regions have become increasingly prominent. To explore the influence of rock content on the shear strength of the S-RM freezing–thawing interface, the variation in the shear strength for different rock content is studied herein using direct shear tests. In addition, a 3D laser scanner is used for obtaining the topography of the shear failure surface. Combined with the analysis results of the shear band-particle calculation model, the influence of the rock content on the shear strength of the interface is explored. It was found that the impact threshold of the rock content on the interface strength and failure mode is approximately 30%, when the rock content (R) is &gt; 30% and that the shear strength increases rapidly with increasing rock content. When R ≤ 30%, the actual shear plane is similar to waves; when R &gt; 30%, the shear plane appears as gnawing failure. The shear strength of S-RM freezing–thawing interface mainly comes from the bite force and friction between particles. The main reason for the increase in shear strength with increasing rock content is the increase in bite force between particles, which makes the ratio of bite force to friction force approximately 1:1.

Superantigens are unconventional antigens which recognise immune receptors outside the usual binding sites e.g. complementary determining regions (CDRs), to elicit a response within the target cell. T-cell superantigens crosslink T-cell receptors and MHC Class II molecules on antigen-presenting cells, leading to lymphocyte recruitment, induction of cytokine storms and T-cell anergy or apoptosis among many other effects. B-cell superantigens, on the other hand, bind immunoglobulin receptors on B-cells affecting opsonisation, IgG-mediated phagocytosis, and drive B-cells into apoptosis. Here, through a review of the structural basis for recognition of immune receptors by superantigens, we show that their binding interfaces share specific physicochemical characteristics when compared with other protein-protein interaction complexes. Given that antibody-binding superantigens have been exploited extensively in industrial antibody purification, these observations could facilitate further protein engineering to optimize the use of superantigens in this and other areas of biotechnology.

Engineered nanomaterials (ENMs) are of significant relevance due to their unique properties, which have been exploited for widespread applications. Cerium oxide nanoparticles (CeO2-NPs) are one of most exploited ENM in the industry due to their excellent catalytic and multi-enzyme mimetic properties. Thus, toxicological effects of these ENMs should be further studied. Acute and subchronic toxicity of CeO2-NPs were assessed. First an in vitro multi-dose short-term (24h) toxicological assessment was performed in three different cell lines: A549 and Calu3, representing the lung tissue, and 3T3 as an interstitial tissue model. After that, a sub-chronic toxicity assessment (90 days) of these NPs was carried out on a realistic and well stablished reconstituted primary human airway epithelial model (MucilAir™), cultured at the Air-Liquid Interface (ALI), to study long-term effects of these particles. Results showed minor toxicity of CeO2-NPs in acute exposures. However, in subchronic exposures, cytotoxic and inflammatory responses were observed in the human airway epithelial model after 60 days of exposure to CeO2-NPs. These results suggest that acute toxicity approaches may underestimate the toxicological effect of some ENM, highlighting the need of subchronic toxicological studies in order to accurately assess the toxicity of ENM and their cumulative effects in the organism.

The aim of work is to derive an explicit expression for a function of vertical mixing induced by wind-waves. To this end, in the Navier-Stokes equations, a current is decomposed into four constituents: the mean flow, the wave-orbital motion, the wave-induced turbulent and the background turbulent currents. This decomposition allows separating the wave-induced Reynolds stress, Rw, from the background one, Rb. To make a statistical closure for Rw, the Prandtl approach for the background turbulent fluctuations is used that results in an implicit expression for the wave-induced vertical mixing function, Bv. Expression for Bv is specified based on the author’s results for the eddy viscosity found earlier in the frame of the three-layer concept for a wavy air–sea interface, used for modelling wind-drift currents [1]. Finally, the explicit parameterization for Bv(a, u*, z) is found as a linear function in both the wave amplitude at depth z, a(z), and the friction velocity in the air, u*. The linear dependence of function Bv(a) on the wave amplitude provides the enhanced vertical mixing induced by wind–waves in comparison with function Bv(a) having the cubic dependence found in [2], as far as the wind-wave amplitude a(z) decays exponentially with depth.

Medical records contain many terms which are difficult to process. Our aim in this study is to allow the visual exploration of the information in medical databases where the texts presents a large number of syntactic variations and abbreviations, through an interface which facilitates content identification, navigation and information retrieval. We propose the use of multi-term tag clouds as content representation tools and as assistants for the browsing and querying tasks. The tag cloud generation is achieved through a novelty mathematical method that allows related terms to remain grouped together within the tags To evaluate this proposal, we have used a database with 24,481 records. 23 expert users in the medical field were tasked to complete a survey to evaluate the generated tag clouds properties and we obtained a precision of 0.990, a recall of 0.870 and a F1score of 0.904 in the evaluation of the tag cloud as an information retrieval tool. The main contribution of this approach is that we automatically generate a visual interface over the text capable of capturing the semantics of the information and facilitating access to medical records.

Solar Energy has enormous potential to fulfill the energy requirements of the world and can be extracted using solar cells. However, the solar cells are affected by poor efficiency and further affected by wind speed, orientation of the panel, temperature and dust deposition. There are different cleaning technologies devised by many industry experts to clean the solar panels. However, they are facing drawbacks when deployed in the solar farm. An efficient cleaning system, along with an added cooling system must be devised, so that the solar panels must be cleaned and cooled often to maximize the energy production. This paper presents a low-cost, energy-efficient, smart and innovative dust cleaning and cooling system for Photovoltaic (PV) Panels. The system is designed, fabricated, fully-automated using Programmable Logic Controller (PLC) and tested successfully. A battery-charging kiosk, capable of charging two, 24V Lead Acid Batteries embedded within this prototype, shall provide clean-energy in a sustainable manner to the rural communities of the developing nations. The user can check the status of the battery such as battery voltage, battery temperature, and state of charge on the Human Machine Interface (HMI) panel while charging the batteries

Three-phase interactions (metal-slag-argon) in ladle stirring operations have strong effects on metal-slag mass transfer processes. Specifically, the thickness of the slag controls the fluid turbulence to an extent that once trespassing a critical thickness, increases of stirring strength have not further effects on the flow. To analyze these conditions, a physical model considering the three phases was built to study liquid turbulence in the proximities of the metal-slag interface. A velocity probe placed close to the interface permitted the continuous monitoring and statistical analyses of turbulence. The slag-eye opening was found to be strongly dependent on the stirring conditions, and the mixing times decreased with thin slag thicknesses. Slag entrainment was enhanced with thick slag layers, and high flow rates of the gas phase. A multiphase model was developed to simulate these results finding a good agreement between experimental and numerical results.

Objective A trained T1 class Convolutional Neural Network (CNN) model will be used to examine its ability to successfully identify motor imagery when fed pre-processed electroencephalography (EEG) data. In theory, and if the model has been trained accurately, it should be able to identify a class and label it accordingly. The CNN model will then be restored and used to try and identify the same class of motor imagery data using much smaller sampled data in an attempt to simulate live data. Approach PyCharm, a Python platform, will be used to house and process the CNN. The raw data used for the training of the CNN will be sourced from the PhysioBank website. The EEG signal data will then be pre-processed using Brainstorm software that is a toolbox used in conjunction with MATLAB. The sample data used to validate and test the trained CNN, will be also be extracted from Brainstorm but in a much smaller size compared to the training data which is comprised of thousands of images. The sample size would be comparable to a person wearing a Brain Computer Interface (BCI), offering approximately 20 seconds of motor imagery signal data. Results The raw EEG data was successfully extracted and pre-processed. The deep learning model was trained using the extracted image data along with their corresponding labels. After training, it was able to accurately identify the T1 class label at 100 percent. The python coding was then modified to restore the trained model and feed it test sample data in which it was found to recognise 6 out of 10 lines of T1 signal image data. The result suggested that the initial training of the model required a different, more varying approach, so that it would be able to detect varying sample signal image data. The outcome of which could mean that the model could be used in applications for multiple patients wearing the same BCI hardware to control a device or interface.

The apparent metal-insulator transition (MIT) in two-dimension (2D) was discovered by Kravchenko et al. [1] more than two decades ago in strongly interacting 2D electrons residing in a Si-metal-oxide-semiconductor field-effect transistor (Si-MOSFET). Its origin remains unresolved. Recently, low magnetic field reentrant insulating phases (RIPs), which dwell between the zero-field (B=0) metallic state and the integer quantum Hall (QH) states where the Landau-level filling factor υ > 1, have been observed in strongly correlated 2D GaAs hole systems with large interaction parameter rs (~20-40) and high purity. A new complex phase diagram was proposed, which includes zero field MIT, low magnetic field RIPs, integer QH states, fractional QH states, high field RIPs and insulating phases (HFIPs) with υ < 1 in which the insulating phases are explained by the formation of Wigner crystal. Furthermore, evidences of new intermediate phases were reported. All contribute to the further understandings of the puzzle. This review article serves the purpose of summarizing those recent experimental findings and theoretical endeavors, to foster future research efforts.

Molecular causal interactions are defined as regulatory connections between biological components. They are commonly retrieved from biological experiments, and can be used for connecting biological molecules into regulatory computational models that represent biological systems. However, including a molecular causal interaction into a model requires assessing its relevance to that model, based on detailed knowledge about the biomolecules, interaction type, and biological context. In order to standardize the representation of this knowledge in ‘causal statements’, we recently developed the MI2CAST guidelines. Here we introduce causalBuilder: an intuitive web-based curation interface for the annotation of molecular causal interactions that comply with the MI2CAST standard. The causalBuilder prototype essentially embeds the MI2CAST curation guidelines in its interface, and makes its rules easy to follow by a curator. In addition, causalBuilder serves as an original application of the VSM general-purpose curation technology, and provides both curators and tool developers with an interface that can be fully configured to allow focusing on selected MI2CAST concepts to annotate. After information is entered, the causalBuilder prototype produces genuine causal statements that can be exported in different formats.

Cognitive deterioration caused by illness or aging often occurs before symptoms arise, and their timely diagnosis is crucial to reducing its medical, personal, and societal impacts. Brain-Computer Interfaces (BCIs) stimulate and analyze key cerebral rhythms, enabling reliable cognitive assessment that can accelerate diagnosis. The BCI system presented analyzes Steady-State Visually Evoked Potentials (SSVEPs) elicited in subjects of varying age to detect cognitive aging, predict its magnitude, and identify its relationship with SSVEP features (band power and frequency detection accuracy), which were hypothesized to indicate cognitive decline due to aging. The BCI system was tested with subjects of varying age to assess its ability to detect aging-induced cognitive deterioration. Rectangular stimuli flickering at theta, alpha, and beta frequencies were presented to subjects, and frontal and occipital EEG responses were recorded. These were processed to calculate detection accuracy for each subject and calculate SSVEP band power. A neural network was trained using the features to predict cognitive age. The results showed potential cognitive deterioration through age-related variations in SSVEP features. Frequency detection accuracy declined after age group 20–40 and band power, throughout all age groups. SSVEPs generated at theta and alpha frequencies, especially 7.5 Hz, were the best indicators of cognitive deterioration. Here, frequency detection accuracy consistently declined after age group 20-40 from an average of 96.64% to 69.23%. The presented system can be used as an effective diagnosis tool for age related cognitive decline.

There is a confusion in the original design concept for the tensioning of longitudinally connected reinforcement of CRTSII slab ballastless track. In order to clarify the effect of tension value of longitudinal reinforcement on mechanical characteristics of ballastless track, a three dimensional finite element model considering the nonlinear interaction between the track slab and CA mortar of CRTSII slab ballastless track was established. The mechanical characteristics of the track structure under longitudinal tension load and temperature gradient load of the longitudinal joint were calculated. A method of applying pre-stress to post-pouring concrete was proposed according to the concept of pre-stress loss of pretensioning pre-stressed concrete, reasonable tensile force value was proposed after the crack width and the reinforcement stress of the ballastless track in the operation stage were checked and calculated according to the concrete design principle. When the tension force is greater than 300 kN, it’s harmful to the bonding between the slab and mortar layer, which is prone to interlayer damage. In order to adding pre-stress to concrete of wide joints to ensure the longitudinal stability of ballastless track and the reinforcement stress and crack width to meet the design requirements. It is suggested that the tension force value should be 230 kN, and the temperature difference between reinforcement and concrete should be 30 °C before the initial curdle of wide joint concrete.

Deformation behavior of MXene based polymer composites with bioinspired brick and mortar structures is analyzed. MXene/Polymer nanocomposites are modeled at microscale using bioinspired configurations of nacre-mimetic brick-and-mortar assembly structure. MXenes (brick) with polymer matrix (mortar) are modeled using classical analytical methods and numerical methods based on Finite Elements (FE). The analytical methods provide less accurate estimation of elastic properties compared to numerical one. MXene nanocomposite models analyzed with FE method provide estimates of elastic constants in the same order of magnitude as literature reported experimental results with good consistency. Bioinspired design of MXene nanocomposites results in the effective Young’s modulus of the nanocomposite increase by 25.1 % and the strength (maximum stress capacity within elastic limits) increase by 42.3 %. The brick and mortar structure of the nanocomposites leads to interlocking mechanism between MXene fillers in polymer matrix, resulting in effective load transfer, good strength, and damage resistance. This is demonstrated in this paper by numerical analysis of MXene nanocomposites subjected to quasi-static loads.

Metal powder bed fusion (PBF) additive manufacturing (AM) builds metal parts layer by layer upon a substrate material. The strength of this interface between substrate and printed material is important to characterize, especially in applications where the substrate is retained and included in the finished part. This paper studied the tensile and torsional strengths of wrought and additively manufactured (through PBF) SS316L and compared them to specimens composed of half wrought material and half PBF material. The PBF specimens consistently exhibited higher strength and lower ductility than the wrought specimens. The hybrid PBF/wrought specimens performed similarly to the wrought material. In no specimens did any failure appear to occur at or near the interface between wrought substrate and PBF material. In addition, most of the deformation in the PBF/wrought specimens appeared to be limited to the wrought portion of the specimens. These results are consistent with microscopy showing smaller grain size in the PBF material, which often leads to increased strength in SS316L due to the Hall-Petch relationship.

The objective of this study was aimed to study the sensory processes of the “human-computer interaction” model when classifying visual images with an incomplete set of signs based on the analysis of early, middle, late and slow components of event-related potentials (ERPs). 26 healthy subjects (men) aged 20-22 years were investigated. ERPs in 19 monopolar sites according to the 10/20 system were recorded. Discriminant and factor analysis were applied. The component N450 is the most specialized indicator of the perception of unrecognizable (oddball) visual images. The amplitude of the ultra-late components N750 and N900 is also higher under conditions of presentation of the oddball image, regardless of the location of the registration points. The results of the study are discussed in the light of the paradigm of the P300 wave application in brain-computer interface systems, as well as with the peculiarities in brain pathology. Promising directions for the development of studies of the “Brain Computer Interface” (BCI) P300 systems are to increase the throughput of information flows. To extend the application of the P300 ERPs to multiple modalities, the underlying physiological mechanisms and responses of the brain for a particular sensory system and mental function must be carefully examined.

The paper describes the Field Boundary Element Method applied to the fracture analysis of a 2D rectangular plate made of Functionally Graded Material to calculate Mode I Stress Intensity Factor. The object of the Field Boundary Element Method is the transversely isotropic plane plate. Its material presents an exponential variation of the elasticity tensor depending on a scalar function of position, i.e., the elastic tensor results from multiplying a scalar function by a constant taken as a reference. Several examples using a parametric representation of the structural response show the suitability of the method that constitutes a sight of Stress Intensity Factor evaluation of Functionally Graded Materials plane plates even in the case of more complex geometries.

A developed six-port reflectometry (SPR) system was integrated to measure the relative permittivity of tumor and normal breast tissue for medical diagnostic purpose. In order to obtain an accurate and precise measurement, the calibration process was done to the SPR using the well-known three-standard technique. Next, the studied dielectric probe was connected to the calibrated measurement-port of the SPR. The open end of the probe aperture was dibbed into the normal and tumor synthetic breast tissue samples to measure the synthetic breast tissues dielectric constant, ɛrʹ, and loss factor, ɛrʺ in the frequency range of 1.5 GHz to 3.3 GHz. Finally, the comparative studies were conducted between commercial VNA with Keysight 85070E dielectric probe and the studied SPR-probe system based on the measured magnitude of the reflection coefficient, phase shift, dielectric constant, and loss factor of the synthetic breast samples. The maximum absolute errors of the measured reflection coefficient magnitude, phase shift, dielectric constant, and loss factor were found to be 0.01, 1.07°, 1.12, and 0.75, respectively. It was ascertained that the predicted dielectric constant, ɛrʹ, is able to differentiate between normal, (ɛrʹ &lt; 50) and tumor, (ɛrʹ &gt; 50) breast tissues.

Intracortical microelectrodes that can be implanted have the ability to capture fast-changing neuron action potentials in the living brain. Despite this potential, many obstacles must still be overcome for reliable, long-term, high-quality recordings and accurate analysis of brain activity. These challenges include improving recording quality, enhancing recording stability, increasing recording capacity, and adding multi-functional features. One potential approach to enhance implantable microelectrodes involves the advancement of materials, refinement of implantation methodologies, and augmentation of the number of sites for recording. However, these challenges are still being actively researched, and advancements in microelectrode technology are underway. The difficulties with implantable microelectrodes are evaluated, and solutions are presented from different perspectives. The latest advancements in microelectrode technology are reviewed and condensed, and future possibilities are explored.

The feasibility of a steady-state visual evoked potential (SSVEP) brain-computer interface (BCI) with a single flicker stimulus for multiple-target decoding has been demonstrated in a number of recent studies. The single-flicker BCIs have mainly employed the direction information for encoding the targets, i.e. different targets are placed at different spatial directions relative to the flicker stimulus. The present study explored whether visual eccentricity information can also be used to encode target for the purpose of increasing the number of targets in the single-flicker BCIs. A total number of 16 targets were encoded, placed at eight spatial directions, and two eccentricities (2.5° and 5°) relative to a 12 Hz flicker stimulus. Whereas distinct SSVEP topographies were elicited when participants gazed at targets of different directions, targets of different eccentricities were mainly represented by different signal-to-noise ratios (SNRs). Using a canonical correlation analysis-based classification algorithm, simultaneous decoding of both direction and eccentricity information was achieved, with an average offline 16-class accuracy of 66.8±16.4% averaged over 12 participants and a best individual accuracy of 90.0%. Our results demonstrate a single-flicker BCI with a substantially increased target number towards practical applications.

There always exist steel cuttings, holes and cracks at the interfaces in the explosive composite plate. Multi-pass friction stir processing (M-FSP) is proposed in this research to optimize the interface microstructure and the interface connection for 1060Al/Q235 explosive composite plate. Results show that the microstructures of 1060Al after M-FSP are fine and uniform owing to the strong stirring effect and recrystallization. Micro-defects formed by the explosive welding can be repaired by the M-FSP. However, M-FSP can also form tunnel defects in the aluminum, especially when the passes are one and two. The melting block and the melting lump in the composite plates are easy to become source of crack. The shear strengths and the bending properties for the 1060Al/Q235 explosive composite plate after M-FSP are the best when the passes are three, with the tool rotation speed of 1200rpm and the forward speed of 60mm/min. The optimized interfaces for the explosive composite plate after M-FSP are mainly by the metallurgical bondings, with a certain thickness and are discontinuous. Therefore, the crack extension stress is the largest and the mechanical properties are the best.

Functional connectivity (FC) is a potential candidate that can increase the performance of brain-computer interfaces (BCIs) in the elderly because of its compensatory role in neural circuits. However, it is difficult to decode FC by current machine learning techniques because of a lack of its physiological understanding. To investigate the suitability of FC in BCI for the elderly, we propose the decoding of lower- and higher-order FCs using a convolutional neural network (CNN) in six cognitive-motor tasks. The layer-wise relevance propagation (LRP) method describes how age-related changes in FCs impact BCI applications for the elderly compared to younger adults. Seventeen younger (24.5±2.7 years) and twelve older (72.5±3.2 years) adults were recruited to perform tasks related to hand-force control with or without mental calculation. CNN yielded a six-class classification accuracy of 75.3% in the elderly, exceeding the 70.7% accuracy for the younger adults. In the elderly, the proposed method increases the classification accuracy by 88.3% compared to the filter-bank common spatial pattern (FBCSP). LRP results revealed that both lower- and higher-order FCs were dominantly overactivated in the prefrontal lobe depending on task type. These findings suggest a promising application of multi-order FC with deep learning on BCI systems for the elderly.

To investigate the role of sulfhydryl groups and disulfide bonds in different protein-stabilized emulsions, N-ethylmaleimide (NEM) was used as sulfhydryl-blocking agent to be added in the emulsion. The addition of NEM to block the sulfhydryl groups resulted in a reduction of the content of disulfide bonds formation, which enabled destruction of the internal structure of the protein molecule, and then decreased the restriction of protein membrane on the oil droplets. Furthermore, with NEM content increasing in the emulsion, a reduction of protein emulsifying activity and emulsion stability also occurred. At the same time, the intermolecular interaction of the protein on the oil droplet interface membrane was destroyed, and the emulsion droplet size increased with the NEM content in the emulsion. Although NEM blocking sulfhydryl groups not to form disulfide bonds has similar effects on three types of protein emulsion, the degree of myofibrillar protein (MP), egg-white protein isolate (EPI), and soybean protein isolate (SPI) as emulsifier had a subtle difference.

Direct interaction with the neuronal cells is a prerequisite to deciphering useful information in understanding the underlying causes of diseases and functional abnormalities in the brain. Precisely fabricated nanoelectrodes provide the capability to interact with the brain in its natural habitat without compromising its functional integrity. Considerable research has been focused on the employment of vertical nanotubes as nanoelectrodes due to large-scale intracellular recording capability and longer-term intracellular access that arise from their unique geometry. Despite many types of nanotube structures reported in the literature, a limited subset of the nanotubes has been investigated for neural interfacing. This work reports on the fabrication and optimisation of vertically oriented titania nanotube arrays as a scalable electrode platform for neural interface application. To this end, the doping was performed by incorporating a selected group of biologically active metallic ions, including zinc, strontium, and copper, into TiO2 lattice and its effect was studied with respect to the structural, electrochemical and biological properties of the nanotube arrays. It was found that doping can change the length, diameter and wall thickness of the nanotubes. Among pure and doped samples, the copper-doped TiO2 nanotubes demonstrated the highest electrochemical and biological performance. Our results suggest that the doping can be used as a promising method to optimise the properties of nanotube arrays for the development of high-performance neural electrodes.

In this paper, we propose an imagined speech-based brain wave pattern recognition using deep learning. Multiple features were extracted concurrently from eight-channel Electroencephalography (EEG) signals. To obtain classifiable EEG data with fewer number of sensors, we placed the EEG sensors on carefully selected spots on the scalp. To decrease the dimensions and complexity of the EEG dataset and to avoid overfitting during the deep learning algorithm, we utilized the wavelet scattering transformation. A low-cost 8-channel EEG headset was used with MATLAB 2023a to acquire the EEG data. The Long-Short Term Memory Recurrent Neural Network (LSTM-RNN) was used to decode the identified EEG signals into four audio commands: Up, Down, Left, and Right. Wavelet scattering transformation was applied to extract the most stable features by passing the EEG dataset through a series of filtration processes. Filtration has been implemented for each individual command in the EEG datasets. The proposed imagined speech-based brain wave pattern recognition approach achieved a 92.50% overall classification accuracy. This accuracy is promising for designing a trustworthy imagined speech-based Brain-Computer Interface (BCI) future real-time systems. For better evaluation of the classification performance, other metrics were considered, and we obtained 92.74%, 92.50% and 92.62% for precision, recall, and F1-score, respectively.

Mental stress state recognition using electroencephalogram (EEG) signals for real-life applications needs a conventional wearable device. This requires an efficient number of EEG channels and an optimal feature set. The main objective of the study is to identify an optimal feature subset that can best discriminate mental stress states while enhancing the overall performance. Thus, multi-domain feature extraction methods were employed, namely, time domain, frequency domain, time-frequency domain, and network connectivity features, to form a large feature vector space. To avoid the computational complexity of high dimensional space, a hybrid feature selection (FS) method of minimum Redundancy Maximum Relevance with Particle Swarm Optimization and Support Vector Machine (mRMR-PSO-SVM) is proposed to remove noise, redundant, and irrelevant features and keep the optimal feature subset. The performance of the proposed method is evaluated and verified using four datasets, namely EDMSS, DEAP, SEED, and EDPMSC. To further consolidate, the effectiveness of the proposed method is compared with that of the state-of-the-art heuristic methods. The proposed model has significantly reduced the features vector space by an average of 70% in comparison to the state-of-the-art methods while significantly increasing overall detection performance.

Mutations, especially those at the protein-protein interaction (PPI) interface, have been associated with various diseases. Meanwhile, though de novo mutations (DNMs) have been proven important in neuropsychiatric disorders, such as developmental delay (DD), the relationship between PPI interface DMNs and DD has not been well studied. Here we curated developmental delay DNM datasets from the PsyMuKB database and showed that DD patients showed a higher rate and deleteriousness in DNM missense on the PPI interface than sibling control. Next, we identified 302 DD-related PsychiPPIs, defined as PPI harboring a statistically significant number of DNM missenses at their interface, and 42 DD candidate genes from PsychiPPI. We then observed that PsychiPPIs preferentially affected hub proteins in the human protein interactome network. When analyzing DD candidate genes using gene ontology and gene spatio-expression, we found that PsychiPPI genes carrying PPI interface mutations, such as FGFR3 and ALOX5, were enriched in development-related pathways and the development of the neocortex, and cerebellar cortex, suggesting their potential involvement in the etiology of DD. Our results demonstrated that DD patients carried an excess burden of PPI-truncating DNM, which could be used to efficiently search for disease-related genes and mutations in large-scale sequencing studies. In conclusion, our comprehensive study indicated the significant role of PPI interface DNMs in developmental delay pathogenicity.

Particle-mediated elasticity/viscoelasticity imaging has potential to expand the elasticity imaging field, as it can provide accurate and local tissue elastic properties as well as density, Poisson’s ratio, and viscosity. Here, we investigated elasticity imaging based on the use of small particles located within the tissue and at the tissue interface exposed to a static and dynamic external load. First, we discuss elasticity/viscoelasticity imaging methods based on the use of particles (bubbles and rigid spheres) located within the tissue. Elasticity/viscoelasticity imaging techniques based on the use of particles (bubbles, rigid and soft spheres) located at the tissue interface are then presented. Based on new advances, we updated some of the models for the responses of the particles located within the tissue and at the tissue interface available in the literature. Finally, we compared the mathematical models for the particles located within the tissue and at the tissue interface and evaluated the elasticity/viscoelasticity imaging methods based on the use of small particles. Therefore, instead of the use of the term elasticity imaging, we suggest the use of the term viscoelasticity imaging.

In this article, we report the generation of alternating current by application of constant and ramping DC voltages across oil-water interfaces. The work reported here can be broadly divided into two parts depending on the shapes of oil-water interfaces i.e. flattened and curved. In the first part, an alternating current of ~100 nA (amplitude)was generated by applying a constant DC voltage of -3V& above across a free standing and flattened oil-water interface.In another part, an alternating current of ~150 nA (amplitude) was generated by applying a ramping up DC voltage starting from -5V to 5V, then again ramping back down to -5V for the free standing and curved interface. The suggested qualitative mechanism that engenders such a phenomenon includes the oil-water interface acting like a membrane. This membrane oscillates due to the electrophoretic movement of ions present in aqueous phase by application of a DC voltage across the interface.This electrophoretic movement of ions across oil-water interfaces causes the Faraday instabilities leading to oscillations of the said interface.This method could also be used to study the stress levels in the interfacial films between two immiscible liquids. It explores more-than-Moore’s paradigm by finding a substitute to a conventional alternator/inverter that generates alternating current upon applying DC voltage input. This work would be of substantial interest to researchers exploring alternatives to conventional AC generators that can be used in liquid environments and in the design of novel integrated circuits that could be used for unconventional computing applications.

In this study we present gas-phase fluorination as a method to create a thin LiF layer on Li6.5La3Zr1.5Ta0.5O12 (LLZTO). We compare these fluorinated films with LiF films produced by RF-magnetron sputtering, where we investigated the interface between the LLZTO and the deposited LiF showing no formation of a reaction layer. Furthermore, we investigate the ability of this LiF layer as a protection layer against Li2CO3 formation in ambient air. By this we show that Li2CO3 formation is absent at the LLZTO surface after 24 h in ambient air supporting the protective character of the formed LiF films and hence could enhance the handling of LLZTO in air for battery production. With respect to the use within hybrid electrolytes consisting of LLZTO and a mixture of polyethylene oxide (PEO) and Lithium bis(trifluoromethanesulfonyl)imide (LiTFSI) we also investigated the interface between the formed LiF films and a mixture of PEO LiTFSI by X-ray photoelectron spectroscopy (XPS) showing decomposition of the LiTFSI at the interface.

In the last decades, the automatic recognition and interpretation of brain waves acquired by electroencephalographic (EEG) technologies have undergone remarkable growth, leading to a consequent rapid development of Brain Computer Interfaces (BCIs). EEG-based BCIs are non-invasive systems that allow communication between a human being and an external device interpreting brain activity directly. Thanks to the advances in neurotechnologies, and especially in the field of wearable devices, BCIs are now also employed outside medical and clinical applications. Within this context, this paper proposes a systematic review of EEG-based BCIs, focusing on one of the most promising paradigms based on Motor Imagery (MI) and limiting the analysis to applications that adopt wearable devices. This review aims to evaluate the maturity levels of these systems, both from the technological and computational points of view. The selection of papers has been performed following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), leading to 84 publications considered in the last ten years (from 2012 to 2022). Besides technological and computational aspects, this review also aims at systematically list experimental paradigms and available datasets in order to identify benchmarks and guidelines for the development of new applications and computational models.

Thin films of BiFeO3 (BFO), VO2 and BFO/VO2 were grown on SrTiO3(100) and Al2O3(0001) monocrystalline substrates using the RF and DC sputtering techniques. The surface of the films was characterized by profilometry, AFM, and XPS. The heterostructures have roughnesses between 0.2 and 16 nm and a grain size between 20 nm and 67 nm. XPS measurements show a higher proportion of the V4+ and Bi3+ oxides. In the Fe region, a higher proportion of Fe3+ is shown in the films. The homogeneous ordering, low roughnesses, and the oxidation states on the obtained surface show a good coupling in these films. The I-V curves show ohmic behavior at room temperature and change with increasing temperature. The effect of coupling these materials in a thin film shows the appearance of hysteresis cycles I-V and R-T, typical of materials with high potential in applications such as resistive memories and solar cells.

As in many parts of the world, rural-urban, forest-urban interface areas surrounding urban regions expose the natural areas they interact with to a threat of fire risk that can reach various sizes. This risk has been assessed for various regions of the world using many different methods and numerical models so far. Among these, it is seen that machine learning models have successful applications in risk assessment and risk prediction studies. For the fire risk prediction of Istanbul's yet unurbanized regions, but where the city is anticipated to potentially shift, data was collected using the opportunities provided by Geographic Information Systems and Remote Sensing technologies based on fires that occurred between 2000-2021, and the region was examined. Machine learning methods' Random Forest (RF), Extreme Gradient Boosting (XGB), and Light Gradient Boosting (LGB) models were applied for the classification of factors effective in fire. The best result was given by the RF model with 0.93 accuracy, 0.062 F1 score, and 0.753 Area Under Curve (AUC) value. In the classification of factors in the RF model, the grouping between fire-initiating factors and factors effective in spreading is evident, while this distinction is partially noticeable in the other two models.

The Brain-Computer Interface (BCI) technology has emerged as a groundbreaking innovation with profound implications across diverse domains, particularly in healthcare. By establishing a direct communication pathway between the human brain and external devices, BCI systems offer unprecedented opportunities for diagnosis, treatment, and rehabilitation, thereby reshaping the landscape of medical practice. However, despite its immense potential, the widespread adoption of BCI technology in clinical settings faces several challenges. These include the need for robust signal acquisition and processing techniques, ensuring user safety and privacy, addressing ethical considerations, and optimizing user training and adaptation. Overcoming these challenges is crucial to unleashing the complete potential of BCI technology in healthcare and realizing its promise of personalized, patient-centric care. This review work underscores the transformative potential of BCI technology in revolutionizing medical practice. This paper offers a comprehensive analysis of medical-oriented BCI applications by exploring the various uses of BCI technology and its potential to transform patient care.

Aiming at the problems that the parameters of YOLOv5s model are too large and the computing resources of development board memory are limited, a new target detection method based on deep learning YOLOv5 algorithm model is proposed. First, the light-weight module Gz-ShffleNetv2 is used to construct cotton top bud feature extraction unit, which reduces the number of parameters and improves the running speed. It can be better applied in image classification, speed up detection and meet the requirements of mobile end of development board. Secondly, in order to solve the problem of decreasing detection accuracy caused by lightweight, BotNet and C3SE attention mechanism are added to focus on specific areas of cotton terminal bud. Combined with YYG loss function XIOU boundary frame regression loss, more feature information and rich feature MAP were obtained to further improve the accuracy of target detection. Through analysis of research and experimental results, the average accuracy map reached 91.3% under the Windos system NVIDIA Geforce RTX 2060 SUPER detection. While maintaining high precision identification, the number of parameters of YOLOv6 and YOLOv7-tiny network model is reduced by 83% and 53%, respectively, and the detection accuracy is increased by 1.2% and 7.7%, respectively. Compared with YOLOv5 reasoning image, the speed of image is increased by 0.035s, and the detection accuracy of MAP_0.5:0.95 is increased by 1%. At the same time, PyQt5 and YOLOv5 target detection algorithms are used to design a cotton top bud identification system, which makes cotton top bud detection more intuitive and convenient for subsequent hardware development and use.

In the early 2000s, matrix-assisted laser desorption ionization-time of flight mass spectrometry (MALDI-TOF MS) emerged as a performant and relevant tool for identifying micro-organisms. Since then, it has become practically essential for identifying bacteria in microbiological diagnostic laboratories. In the last decade, it was successfully applied for arthropod identification allowing to distinguish vectors from non-vectors of infectious diseases. However, identification failures are not rare, hampering it widely use. Failure is generally attributed either to the absence of respective counter species MS spectra in the database, or to the insufficient quality of query MS spectra (i.e. lower intensity and diversity of MS peaks detected). To avoid matching errors due to non-compliant spectra, the development of a strategy for detecting and excluding outlier MS profiles became compulsory. To this end, we created MSProfileR, an R package leading to a bioinformatics tool through a simple installation, integrating the control quality system of MS spectra and an analysis pipeline including peak detection and MS spectra comparisons. MSProfileR can also add metadata concerning the sample that the spectra are derived from. MSProfileR has been developed in the R environment and offers a user-friendly web interface using the R Shiny framework. It is available on Microsoft Windows as a web browser application by simply navigation using the link of the package on the Github. MSProfileR is therefore accessible to non-computer specialists and is freely available to the scientific community. We evaluated MSProfileR using two datasets including exclusively MS spectra from arthropods. In addition to coherent sample classification, outlier MS spectra were detected in each dataset confirming the value of MSProfileR.

A novel phase-field method based numerical approach to modeling the compaction process of sedi- ments is presented. Water-logged rock or soil sediments are deposited on the water basins over time and the increasing volume of the sediment compacts under its own weight and external pressure. Coupled evolution of mass conservation, Darcy flow, and the viscoelastic constitutive response, in conjunction with the evolution of porosity and permeability, make this problem highly non-linear and involve moving boundaries. We adopt a phase-field approach to represent the moving sediment interface, and the underlying multiphasic model permits for studying compaction in dynamically evolving sediments. This approach does not necessitate a change of domain sizes as is the case of existing traditional models of sedimentation but rather treats sedimentation growth as a problem of interface motion. We first model a classical compaction problem in 1D to compare with existing results in the literature, and then extend the framework to 2D to model the compaction process taking place under the influence of gravity. The model is then extensively applied to understand the effect of the sediment initial state and the sediment material properties on the compaction process and its spatiotemporal evolution. Lastly, a strong validation of our phase-field treatment results against predictions from traditional methods of solving open boundary sediment compaction problems, and a good agreement between the conventional understanding and the numerical predictions of porosity dependence on the fluid pore pressure for various cases of geological interest, are used to demonstrate the applicability and scalability of this novel numerical framework to model more advanced sediment compaction problems and geometries.

Background: There is an increasing interest in the role of EEG in neurorehabilitation. We primarily aimed to identify the knowledge base through highly influential studies. Our secondary aims were to imprint the relevant thematic hotspots, research trends, and social networks within the scientific community. Methods: We performed an electronic search in Scopus looking for studies reporting on rehabilitation in patients with neurological disabilities. The most influential papers outlined the knowledge base, while a word co-occurrence analysis imprinted the research hotspots. Likewise, co-citation analyses highlighted collaboration networks between Universities, authors, and countries. The results were presented in summary tables, burst detection plots, and geospatial maps. Finally, a content review based on the top-20 most cited articles completed our study. Results: Our current bibliometric study was based on 874 records from 420 sources. There was a vivid research interest in EEG use for neurorehabilitation, with an annual growth rate as high as 14.3%. The most influential paper was the study titled "Brain-computer interfaces, a review" by Nicolas-Alfonso LF and Gomez-Gill J, with 997 citations, followed by "Brain-computer interfaces in neurological rehabilitation" by Daly J. and Wolpaw JR (708 citations). The USA, Italy, and Germany were among the most productive countries. The research hotspots shifted with time from the use of “functional magnetic imaging” to EEG-based “brain-machine interface”, “motor imagery”, and “deep learning”. Conclusions: EEG constitutes the most significant input in brain-computer interfaces (BCI) and can be successfully used in the neurorehabilitation of patients with stroke, amyotrophic lateral sclerosis, and traumatic brain and spinal injury. EEG-based BCI facilitates training, communication, and control of wheelchair and exoskeletons. However, research is limited to specific scientific groups from developed countries. Evidence is expected to change with the broader availability of BCI and improvement in EEG filtering algorithms.

Respiratory tract epithelium infection plays a primary role in Nipah virus (NiV) pathogenesis and transmission. Knowledge about infection dynamics and host responses to NiV infection in respir-atory tract epithelia is scarce. Studies in non-differentiated primary respiratory tract cells or cell lines indicate insufficient interferon (IFN) responses. However, studies are lacking to determin-ing complex host response patterns in differentiated respiratory tract epithelia to understand NiV replication and spread in swine. Here we characterized infection and spread of NiV in differenti-ated primary porcine bronchial epithelial cells (PBEC) cultivated at the air-liquid-interface (ALI). After the initial infection of only a few apical cells, lateral spread for 12 days with epithelium disruption was observed without releasing substantial amounts of infectious virus from the api-cal or basal sides. Deep time course proteomics revealed pronounced upregulation of genes re-lated to type I/II- IFN, immunoproteasomal subunits, TAP-mediated peptide transport and MHC I antigen presentation. Spliceosomal factors were downregulated. We propose a model in which NiV replication in PBEC is slowed by a potent and broad type I/II-IFN host response with con-version from 26S proteasomes to immunoproteasomal antigen processing and improved MHC I presentation for adaptive immunity priming. NiV induced cytopathic effects could reflect the focal release of cell-associated NiV, which may contribute to efficient airborne viral spread between pigs.

Bovine tuberculosis (BTB) is endemic in Ethiopian cattle. BTB is caused by Mycobacterium bovis (M. bovis) and has economic and public health significance. which has significant impact on the health of livestock and human. It has been significantly a cause for great economic loss in animal production. Associated risk factors contributed to the prevalence of the disease in cattle and its transmission. Moreover, the majority of cattle owners lack awareness about the disease and its public health significance. The presence of multiple hosts including wild animals, inefficient diagnostic techniques, absence of defined national controls and eradication programs could impede the control of bovine TB. Awareness rising about the disease, its transmission andzoonotic implication however, in Ethiopia Bovine Tuberculosis in Human-Livestock-Wildlife Interface is not well studied in the country and there were no studies concerning the burden of the disease between human ,animal and wild life which is of great importance for reduction and control measures. This paper aims to review the potential health and economic impact of bovine tuberculosis control in order to safeguard human and animal population in Ethiopia

We compare photoaligning properties of polymer layers fabricated from the same constituents: polymethyl-methacrylate (PMMA) and azo-dye Disperse Red 1 (DR1), either chemically attached to the PMMA main-chain, or physically mixed with it. Photoaligning properties depend on the preparation method drastically. This finding is supported by atomic force microscope (AFM) scans monitoring the light-induced changes at the polymer−air interface, and revealing a photoinduced mass transfer.

Maize (Zea mays) is one of the most important staple food and primary source of livestock feed in the world. As the consumption of maize grown on the selenium-enriched soils of Naore Valley is one of the apparent causes of selenosis in the area, this work collected and analyzed total Se, Se fractions, and Se species distributions in maize plant samples, including grains, leaves, stalks, roots, rhizosphere soils, and the most representative parent rock materials from Naore Valley, Ziyang County, China. The Se distribution in soils markedly correlated with the weathered Se-enriched bedrocks, but most of the Se in the analyzed soils is enclosed as recalcitrant residual Se and organic-sulfide bound Se. In contrast, Se in rocks had a comparatively higher bioavailability and is bounded mainly to organic matter and sulfides minerals, with very few of the Se enclosed in the residual fraction. Maize plants might take a large amount of Se from the organic-sulfide bound Se fraction in the Se-rich soils, the weathered products from bedrocks or plant litters. Total Se concentrations in the collected samples were observed in descending order soil>leaf>root>grain>stalk. The predominant Se species detected in maize plants was SeMet. Se inorganic forms, mainly Se(VI), decreased from root to grain and were possibly assimilated into organic forms. Se (IV) was barely present. The natural increases in Se concentration affected mainly leaf and root dry-weight biomass as they are the organs that coped with the highest Se accumulation. This paper offers an insight into the uptake, accumulation, and distribution of Se forms in natural Se-rich maize crops and an opportunity for shifting Se-rich soils from menaces to valuable resources for growing Se-rich agricultural products.

As the digital era advances, many industries continue to expand their use of digital technologies to support company operations, notably at the customer interface, bringing new commercial opportunities and increased efficiencies. However, there are new sets of responsibilities associated with the deployment of these technologies, encompassed within the emerging concept of corporate digital responsibility (CDR), which to date has received little attention in the academic literature. This exploratory paper thus looks to make a small contribution to addressing this gap in the literature. The paper adopts a qualitative, inductive research method, employing an initial scoping literature review followed by two case studies. Based on the research findings, a simple model of CDR parameters is put forward, and the article concludes with a discussion of a number of emergent issues - fair and equitable access, personal and social well-being, environmental implications, and cross-supply chain complexities - that suggest possible directions for future research.

A single paragraph of about 200 words maximum. Electroencephalography (EEG) accumulates the electrical activities of human brain. It is an easy and cost effective tool to characterize motor imagery (MI) task used in brain computer interface (BCI) implementation. The MI task is represented by short time trial of multichannel EEG. In this paper, the raw EEG is decomposed into a finite set of narrowband signals obtained from individual EEG channels using Fourier transformation based bandpass filter. Each of the subband signals represents narrowband rhythmic components which characterize the brain activities related to motor imagery. The subband signals are arranged to extent the dimension of EEG trial in spatial domain. The spatial features are extracted from the set of extended trials using common spatial pattern (CSP). An optimum number of features are used to classify the motor imagery tasks represented by EEG trials. Artificial neural network is used to classify MI tasks. The performance of the proposed method is evaluated using two publicly available benchmark datasets. The experimental results show that it performs better than the recently developed algorithms.

The internet of things and growing demand for smaller and advanced devices has created the problem of high heat production in the electronic equipment which greatly reduces the work performance and life of the electronic instruments. Thermal interface materials (TIMs) are placed in between heat generating micro-chip and the heat dissipater to conduct all the produced heat to the heat sink. Development of suitable TIMs with excellent thermal conductivity (TC) in both in-plane and through-plane directions is a very important need at present. For efficient thermal management, polymer composites are potential candidates. But in general their thermal conductivity is low compared to that of metals. The filler integration into the polymer matrix is one of the two approaches used to increase the thermal conductivity of polymer composites and is also easy to scale up for industrial production. Another way to achieve this is to change the structure of polymer chains, which fall out of the scope of this work. In this review, considering the first approach, the authors have summarized recent development in many types of fillers with different scenarios by providing multiple cases with successful strategies to improve through-plane thermal conductivity (TPTC) (K⊥). For better understanding of TC the comprehensive background is also presented. In the end, it is given a detailed conclusion which provides drawbacks of some filler, multiple significant routes recommended by other researchers to build Thermally Conductive Polymer Composites, future aspects along with direction so that the researchers can get a guideline to design an effective polymer based Thermal interface materials. A number of methods to improve the effective (out-plane) thermal conductivity of polymer composites are also discussed.

The purpose of the review is to provide new insight into the potential of surface modification of carbon fibers for enhancing the application of the carbon fibers many a fold. To this end a total of 429 papers on the subject of surface modication of carbon fibers by a variety of chemical and electrochemical methods published during the period from 2010-2022 have been reviewed. Astounding results of surface funcationalization of carbon fibers by a variety of state of the art methods resulting in the unconventional applications of the resulting modified carbon fibers are summarized in a nut-shell in schemes from 1-6 towards the end of the review. Surface modifica-tion induces functionality to carbon fibers (CFs). The vitality of CF surface modification reac-tions could only be compared to the life process of respiration that sustains the multi functional-ity of living cells. Applicability of CFs can be drastically enhanced in incomprehensible ways by surface modification. Upon surface modification, inert and non-reactive CF surface becomes chemically active and functional with utility in diverse fields, namely, health, energy, environ-ment, defense, catalysis, smart materials and many others. Surface modification methods can be broadly classified into chemical, electrochemical and physical methods. By these surface modi-fication methods, the inert FC surface becomes polar. Surface properties like roughness, wetta-bility and energy are enhanced. Modification processes like sizing, oxidation, amination, si-lanization, polymerization, nanoarchitecture induces multifunctionality on CF surface. Modi-fied CFs when used as reinforcing material in carbon fiber reinforced plastics (CFRPs), im-proved bonding at the interface with resin matrices is observed with enhanced and outstanding mechanical properties (flexural strength, flexural modulus, IFSS, ILSS, hardness, elastic modulus, bending strength and compression strength).

In the domain of financial trading, where accuracy and swift response to data discrep- ancies are paramount for maintaining clienttrust, our research paper delves into the development of a real-time alert system utilizing SpringBoot, a framework renowned for its robustness and adaptability in enterprise applications. We embark on a comprehensive journey, beginning with addressing thefoundational challenges inherent in monitoring trade inquiries - the necessity for real-time processing, unwavering data accuracy,and ensuring unwavering system reliability. The study then methodically transitions to the architectural design, where we explore howSpringBoot’s capabilities are optimally utilized to meet these challenges. Following this, we delve into the im- -plementationphase, detailing the strategic selection of compatible technologies and methods for effective data management and discrepancydetection. The paper culminates in a thorough evalu- ation of the system, where we assess performance across various operationalscenarios, scrutinize the accuracy of the discrepancy detection mechanisms, and integrate real-world feedback from users in thetrading sector. This holistic approach not only highlights the versatility and efficiency of SpringBoot in creating reliable alert systems in the high-stakes world of financial trading but also serves as a detailed guide for similar endeavors in this field

Since 2000, caffeine is used as citrate salt of caffeine for treatment of apnea of prematurity. However, it remained elusive if caffeine citrate has a direct function in the respiratory epithelium, potentially as modulator for ciliary beat frequency (CBF) regulation in human respiratory cells (hRECs). Here, we studied the mechanism of action of caffeine citrate on the respiratory epithelium on hRECs derived from healthy donors and individuals with cystic fibrosis (CF) cultured under air-liquid-interface (ALI) conditions. CBF analyses of hRECs were performed by high-speed video microscopy. In the presence of caffeine, hRECs show a significant increase in CBF over ciliary base frequency (CBFb), demonstrating a direct influence of caffeine on ciliary function. We also demonstrate a specific action of caffeine at ryanodine receptor (RYR) channels, mediating calcium efflux from intracellular stores to increase CBF. We describe a novel pharmacological effect of caffeine citrate - direct increase of CBF of primary hRECs cultured at ALI. Caffeine citrate has certain advantages over other methylxanthines: it was shown to have stable plasma concentration, longer half-life, and is better tolerated than e.g. theophylline. We conclude that caffeine citrate therapy might be suitable strategy to treat respiratory diseases with compromised mucociliary clearance.

Uncontrollable Zn dendrite formations and parasitic side reactions on Zn electrodes induce poor cycling stability and safety issues, preventing the large-scale commercialization of Zn-ion batteries. Herein, to achieve uniform Zn deposition and suppress side reactions, an electrospun ferroelectric poly(vinylidene fluoride-co-trifluoroethylene) copolymer, a [P(VDF-TrFE)] nanofiber layer, is introduced as an artificial solid-electrolyte interface on a Cu substrate acting as a current collector. The aligned molecular structure of β-P(VDF-TrFE) can effectively suppress localized current density on the Cu surface, lead to uniform Zn deposition, and suppress side reactions by preventing direct contact between electrodes and aqueous electrolytes. The half-cell configuration formed by the newly fabricated electrode can achieve an average coulombic efficiency of 99.2% over 300 cycles without short circuiting at a current density of 1 mA cm-2 and areal capacity of 1 mAh cm-2. Stable cycling stability is also maintained for 200 cycles at a current density of 0.5 A g-1 in a full-cell test using MnO2 as a cathode.

Intracortical microelectrode arrays (MEAs) can be used in a range of applications, from basic neuroscience research to providing an intimate interface with the brain as part of a Brain-Computer Interface (BCI) system aimed to restore function for people living with neurological disorders or injuries. Unfortunately, MEAs tend to fail prematurely, leading to a loss in functionality for many applications. An important contributing factor in MEA failure is oxidative stress resulting from chronically inflammatory-activated microglia and macrophages releasing reactive oxygen species (ROS) around the implant site. Antioxidants offer a means for mitigating oxidative stress and improving tissue health and MEA performance. Here, we investigate using the clinically available antioxidant dimethyl fumarate (DMF) to reduce the neuroinflammatory response and improve MEA performance in a rat MEA model. Daily treatment of DMF for 16 weeks resulted in a significant improvement in the recording capabilities of MEA devices during the sub-chronic (Weeks 5-11) phase (42% active electrode yield vs. 35% for control). However, these sub-chronic improvements were lost in the chronic implantation phase, as a more exacerbated neuroinflammatory response occurs in DMF-treated animals by 16 weeks post-implantation. Yet, neuroinflammation was indiscriminate between treatment and control groups during the sub-chronic phase. Although worse for chronic use, a temporary improvement (&lt;12 weeks) in MEA performance is meaningful. Providing short-term improvement to MEA devices using DMF can allow for improved use for limited-duration studies. Further efforts should be taken to explore the mechanism behind a worsened neuroinflammatory response at the 16-week time point for DMF-treated animals to assess its usefulness to specific applications.

This paper presents a critical review and comparison of the results of recently published studies in the fields of human-machine interface and the use of sonomyography (SMG) for the control of upper limb prothesis. For this review paper, a combination of the keywords "Human Machine Interface", "Sonomyography", "Ultrasound", "Upper Limb Prosthesis", "Artificial Intelligence" and "Non-Invasive Sensors" was used to search for articles on Google Scholar and PubMed. Sixty-one articles were found, of which 59 were used in this review. For a comparison of the different ultrasound modes, feature extraction methods, and machine learning algorithms, 16 articles were used. It was found that various modes of ultrasound devices for prosthetic control, various machine learning algorithms for classifying different hand gestures, as well as various feature extraction methods for increasing the accuracy of artificial intelligence used in their controlling systems are reviewed in this article. The results of the review article show that ultrasound sensing has the potential to be used as a viable human-machine interface in order to control bionic hands with multiple degrees of freedom. Moreover, different hand gestures can be classified by different machine learning algorithms trained with extracted features from collected data with an accuracy of around 95%.