Miracle-dz/UniFAR-FTUs huggingface dataset:
Protocatechualdehyde restores endothelial dysfunction in streptozotocin-induced diabetic rats

Background The present study was conducted with the aim of clarifying the effects of protocatechualdehyde (PCA) on the endothelial function in streptozotocin (STZ)-induced diabetic rats. Methods Sprague Dawley (SD) rats were intraperitoneally injected with STZ (single dose of 60 mg/kg). Diabetic model rats were given PCA (25 mg/kg/day) via gavage feeding for 6 weeks. Vascular function was studied; superoxide anion and nitrotyrosine levels were assessed; and nicotinamide adenine dinucleotide phosphate hydrogen (NADPH) oxidase as well as total superoxide dismutase (SOD) activity were detected. Protein expression of phosphorylated endothelial nitric oxide synthase (P-eNOS), total endothelial nitric oxide synthase (T-eNOS), p22phox, p47phox and Cu/Zn-SOD were measured by Western blot analysis. Results PCA treatment significantly ameliorated the impairment of acetylcholine- evoked endothelium-dependent relaxation, with no obvious effects observed on the blood glucose or body weight in the STZ-induced diabetic rats. Expression levels of aortic P-eNOS/T-eNOS and endothelial nitric oxide synthase (eNOS) activity were decreased in STZ-induced diabetic rats while they remained unchanged in PCA-treated rats. However, PCA treatment improved oxidative inactivation of nitric oxide (NO) and decreased the levels of superoxide anion and nitrotyrosine in the aorta of STZ-induced diabetic rats; these were achieved by reducing the level of nitrotyrosine and down-regulating p47phox and p22phox expression, as well as up-regulating Cu/Zn-SOD protein expression. Consistently, the effects observed were associated with a decrease in NADPH oxidase activity and an increase in total SOD activity. Conclusions Our results indicate that the administration of PCA may be protective against oxidative stress and may restore endothelial function by improving vascular NO oxidative inactivation in diabetic condition.
Grasping with kirigami shells

A robust approach to grasping combines the mechanics of kirigami with compliant, elastic shells. The ability to grab, hold, and manipulate objects is a vital and fundamental operation in biological and engineering systems. Here, we present a soft gripper using a simple material system that enables precise and rapid grasping, and can be miniaturized, modularized, and remotely actuated. This soft gripper is based on kirigami shells—thin, elastic shells patterned with an array of cuts. The kirigami cut pattern is determined by evaluating the shell’s mechanics and geometry, using a combination of experiments, finite element simulations, and theoretical modeling, which enables the gripper design to be both scalable and material independent. We demonstrate that the kirigami shell gripper can be readily integrated with an existing robotic platform or remotely actuated using a magnetic field. The kirigami cut pattern results in a simple unit cell that can be connected together in series, and again in parallel, to create kirigami gripper arrays capable of simultaneously grasping multiple delicate and slippery objects. These soft and lightweight grippers will have applications in robotics, haptics, and biomedical device design.
In-sensor reservoir computing for language learning via two-dimensional memristors

High dimensionality and fading memory for in-sensor reservoir computing are achieved via two-dimensional memristors. The dynamic processing of optoelectronic signals carrying temporal and sequential information is critical to various machine learning applications including language processing and computer vision. Despite extensive efforts to emulate the visual cortex of human brain, large energy/time overhead and extra hardware costs are incurred by the physically separated sensing, memory, and processing units. The challenge is further intensified by the tedious training of conventional recurrent neural networks for edge deployment. Here, we report in-sensor reservoir computing for language learning. High dimensionality, nonlinearity, and fading memory for the in-sensor reservoir were achieved via two-dimensional memristors based on tin sulfide (SnS), uniquely having dual-type defect states associated with Sn and S vacancies. Our in-sensor reservoir computing demonstrates an accuracy of 91% to classify short sentences of language, thus shedding light on a low training cost and the real-time solution for processing temporal and sequential signals for machine learning applications at the edge.
Analytical characteristics of plantation eucalypt woods relating to kraft pulp yields

Correlations of the chemical composition of a series of 11 Eucalyptus globulus wood samples ranging in ages from 6 to 32 years, and 21 seven year old E.nitens wood samples have been made with the yields and the composition of their kraft pulps. Analyses for extractives, lignin, pentosan, acetyl, glucuronic acid, and carbohydrates were carried out. The carbohydrates were determined as pentosans, and as their constituent sugar monomers after acid hydrolysis of the unextracted woodmeals and analysis of the hydrolysates by high performance anion exchange chromatography with pulsed amperometric detection (HPAEC-PAD). The total carbohydrates (calculated by difference) and extractives contents of E.globulus wood samples were well correlated with pulp yield at Kappa no.15, and lignin was moderately well correlated total carbohydrates for both species (measured by HPAEC-PAD) were only moderately well correlated with pulp yield. For both species, the correlation of pulp yield with cellulose and glucose was good, but was slightly inferior with hemicellulose, xylose, and pentosan. Wood samples which gave high pulp yields had cellulose as a high proportion of their polysaccharide fraction, and this trend was also evident in the pulps derived from them. Both wood samples showed variation in their oxidation products ; for the E.globulus woods the syringaldehyde/vanillin ratios were moderately well correlated with pulp yield, but for the E.nitens woods, the correlation was poor. The hydrolysis-HPAEC-PAD method for carbohydrates is proposed as a suitable basis to explore the relationship between the chemical composition of plantation eucalypt woods and their pulp yields and papermaking properties.
Randomised controlled trials in medical AI: ethical considerations

In recent years, there has been a surge of high-profile publications on applications of artificial intelligence (AI) systems for medical diagnosis and prognosis. While AI provides various opportunities for medical practice, there is an emerging consensus that the existing studies show considerable deficits and are unable to establish the clinical benefit of AI systems. Hence, the view that the clinical benefit of AI systems needs to be studied in clinical trials—particularly randomised controlled trials (RCTs)—is gaining ground. However, an issue that has been overlooked so far in the debate is that, compared with drug RCTs, AI RCTs require methodological adjustments, which entail ethical challenges. This paper sets out to develop a systematic account of the ethics of AI RCTs by focusing on the moral principles of clinical equipoise, informed consent and fairness. This way, the objective is to animate further debate on the (research) ethics of medical AI.
Systematic review of the stability of antimicrobial agents in elastomeric devices for outpatient parenteral antimicrobial therapy services based on NHS Yellow Cover Document standards

Background In order to use aseptically prepared elastomeric infusers, outpatient parenteral antimicrobial therapy (OPAT) services require extended stability data for antimicrobial agents to assign a product shelf-life. In the UK, the relevant standards for stability testing and shelf-life assignment are published in ‘A Standard Protocol for Deriving and Assessment of Stability—Part 1 (Aseptic Preparations—Small Molecules), commonly called the Yellow Covered Document (YCD). A previous systematic review published in 2017 failed to identify data on the stability of antimicrobials in elastomeric devices for OPAT services that met YCD requirements in force at the time. The aim of this review was to update that search, following a subsequent change to YCD requirements in 2017 and 2019 and expand that dataset to identify progress made in providing assurance about the stability of antimicrobial agents for OPAT services. Methods Searches were undertaken for papers relating to extended stability of antimicrobials. Citations were included when antimicrobial shelf-life was assessed using a stability-indicating method and considered a period of storage, either refrigerated or at room temperature, followed by in-use testing at a temperature at or above 32°C. Results Of 267 initial citations, six met the inclusion criteria and underwent full text review for data extraction. Included antimicrobials were cefazolin, ceftazidime, piperacillin/tazobactam, flucloxacillin and ceftolozane/tazobactam. Of these, only flucloxacillin and piperacillin demonstrated YCD compliant stability over the 24-hour infusion period while cefazolin, ceftazidime and ceftolozane/tazobactam could be infused over 12-hour period. Conclusions Contrary to the position found in 2017 review, high-quality data are now available to support the use of a number of antimicrobial agents in extended infusion in elastomeric devices for OPAT services. There is a need to expand the dataset, as well as developing international consensus on the ideal parameters for stability assessment of such infusions in elastomeric devices.
A Causal Bayesian Network Model for Resolving Complex Wicked Problems

Wicked problems are a specific class of complex problems that emerge from complex adaptive systems (CAS) and stakeholder disagreements on the definition and character of these problems and their possible resolution. Attempts at resolving wicked problems through integration and use of formal methods such as ontologies, Bayesian networks (BN), and complex systems dynamic (CSD) models have been attempted recently but wicked problems continue to defy resolution. This paper argues that this is the result of a lack of ontologically precise causal Bayesian models that adequately represent the hierarchical, dynamic, emergent characteristics and multiple perceptions of CAS and their emergent wicked problems. This paper's contribution is the incorporation of complexity systems theory concepts, namely: perspective, granularity and context, as explicit ontological constructs in a high precision ontological causal BN model, the Granular Contextual Perspectives (GCP) causal Bayesian Network model, using Hidden Markov Model (HMM) formalism to address this shortcoming. Using an illustrative example this conceptual paper shows that the (GCP) causal Bayesian Network model performs better than baseline Bayesian Network models at the visual representation, compact and retractable inference, and machine learning of CAS and their emergent wicked problems. The model is useful at supporting the exploration of possible effects of proposed alternative interventions or prototypical design strategies for resolving a given wicked problem.
Improving Accuracy of High-Speed Continuous Tap Recognition on Desk Using Acceleration and Sound Amplitude

The purpose of this study is to measure the time of tap action on a desk as accurately as possible using a mobile device, such as a smartphone and a tablet PC, placed on the desk. The existing method recognizes a tap based on whether the acceleration exceeds a threshold value. In addition, a waiting time is added after the tap judgment. However, this method often misjudges more taps when the user taps hard or taps continuously at high speed. Therefore, in this paper, we propose three new methods to solve this misjudgment problem by using the amplitude of sequential acceleration data and tap sound data: TIA (Threshold and Immediately preceding period for Amplitude), TIAA (Threshold, Immediately preceding period. and Attenuation for Amplitude), and TIAAS (Threshold, Immediately preceding period, and Attenuation for Acceleration and Sound wave). TIA uses the input wave amplitude threshold to find the time at which a tap may occur. TIA then checks the input wave for the last several milliseconds to determine if it was tapped at that time. TIAA uses the amplitude threshold of an input wave like TIA and the degree of attenuation for amplitude after the tap time. TIAAS uses the amplitude threshold of the sound for $d$ seconds after the time the tap is recognized by TIAA for z-axis acceleration. The experimental results show that TIA for sound amplitude and TIAAS recognize a tap more accurately than the existing method.
Metadata Replication with Synchronous OpCodes Writing for Namenode Multiplexing in Hadoop

A single Active Namenode (ANN) of Hadoop Distributed File System (HDFS) become a bottleneck when we require high-throughput read operations such as large-scale data analysis. Recently, various kinds of namenode schemes are proposed including asynchronous check pointing schemes to address the ANN bottleneck issue. Even if asynchronous schemes offers high throughput reading operations, they suffers in stale read problem where the latest data return is not guaranteed. In this paper, we propose a novel metadata replication scheme with synchronous OpCodes writing to achieve namenode multiplexing, where we can avoid the stale read problem. To reduce synchronization overhead, our proposed scheme conducts reduced replication only for metadata updates such as a write request, using quasi byte-level metadata operation codes. We conducted the empirical experiment to verify the effectiveness of our proposed schemes. The results show that our method reduces by 50.95% in the average required number of NNs when the number of NNs for read-only operation is 100.
Power Generation and Water Treatment Using Sediment Microbial Fuel Cells (SMFCs)

The Sediment Microbial Fuel Cell (SMFC) has been studied in terms of electrical power generation, and water treatment. The study aimed to design and test a Sediment Microbial Fuel Cell (SMFC) and for electrical power generation and water treatment. The sediment has been obtained from Al-Sonobar River bed. It has been characterized to determine the organic matter content and nutrients. The cell has been equipped with a surface aeration system. The electrical current and water pollution indicators have been observed and recorded over time for two-mode aeration systems. Results of the first mode showed that the greatest value for Short Circuit Current (ISC) was (0.905) mA while the greatest value for Open Circuit Voltage (VOC) was (0.390) V. While a rise in current and voltage values has been observed in the second mode of aeration, in which the greatest value of Short Circuit Current was (1.240) mA, and the greatest value of Open Circuit Voltage (0.430) V. When the circuit has been connected to an external resistance (R=100) Ω, the values for the current, voltage, current density and power density were (0.805) mA, (0.084) V, (3.18) mA/m2, and (0.269) mW/m2, respectively. For water analysis, an increase in pH values of (8.90) was observed. The efficiency of removing Chemical Oxygen Demand (COD), phosphates, nitrates and Total Dissolved Solids (TDS) has also increased, reaching: 72.11%, 62.70%, 35.60%, 30.61%, respectively.
Modeling Class Diagram using NLP in Object-Oriented Designing

Requirement’s analysis and design is a multifaceted and time-consuming process. The success of software projects critically relies on careful & timely analysis and modeling of system requirements. Mostly, the requirements gathered from the stakeholders are written in some language (probably English). In this regard, significant manual efforts are required for the formation of good class model which unfortunately results in time delays in the software industry. The problems associated with the requirement analysis and class modeling can be overcome by the appropriate employment of machine learning. In this paper, we propose a system, requirement engineering analysis & design (READ) to generate unified modeling language (UML) class diagram using natural language processing (NLP) and domain ontology techniques. We have implemented the READ system in Python and it successfully generates the UML class diagram i.e., class name, attributes methods, and relationships from the textual requirements written in English. To assess the performance of the proposed system, we have evaluated it on publicly available standards and the experimental results show that it outperforms the existing techniques for object-oriented based software designing.
A Digital Interfacing Circuit Based on Sliding DFT Bins for Capacitive Sensors

A digital interfacing circuit (DIC) based on sliding discrete Fourier transform (SDFT) bins has been proposed for lossy capacitive sensors. The proposed interfacing circuit consisting of an analog sensing circuit and an analog-to-digital converter (ADC)-field-programmable gate array (FPGA)-based DIC. The sliding DFT bin-1 is employed as a quadrature signal generator to obtain in-phase and quadrature signals of the input supply voltage provided to the analog sensing circuit. The output sinusoidal signal of analog sensing circuit is correlated with the in-phase and quadrature signals separately and then averaged with SDFT bin-0 for computing sensing capacitance and resistance values. The proposed method is implemented in FPGA and tested for capacitance measurement range of 11 pF–5.27 nF with the maximum relative percentage error of 1.36%. The nonlinearity error is < 0.37% FS and FSS. Moreover, the suitability of the proposed interface for humidity measurement has been explored and validated experimentally with saturated salt solutions. Furthermore, the interdigitated capacitance is fabricated and tested with the proposed digital interface for the capacitance range of 30–122 pF, which finds an application in capacitive touchpad.
Fault Diagnosis of Wind Turbines Based on a Support Vector Machine Optimized by the Sparrow Search Algorithm

Fault diagnosis technology is key to the safe and stable operation of wind turbines. An effective fault diagnosis technology for wind turbines can quickly identify fault types to reduce the operation and maintenance costs of wind farms and improve power generation efficiency. Currently, most wind farms obtain operation and maintenance data via supervisory control and data acquisition (SCADA) systems, which contain rich information related to the operation characteristics of wind turbines. However, few SCADA systems provide fault diagnosis functionality. Support vector machines (SVMs) are a popular intelligence method in the fault diagnosis of wind turbines. SVM parameter selection is key for accurate model classification. The sparrow search algorithm (SSA) is a novel and highly efficient optimization method used to optimize the penalty factor and kernel function parameter of SVM in this paper and to construct the SSA-SVM wind turbine fault diagnosis model. Data are acquired from a wind farm SCADA system and form a faulting set after preprocessing and feature selection. Experiments show that the SSA-SVM diagnostic model effectively improves the accuracy of wind turbine fault diagnosis compared with the GS-SVM, GA-SVM and PSO-SVM models and has fast convergence speed and strong optimization ability. Moreover, the SSA-SVM diagnostic model can be used to diagnose faults in practical engineering applications.
Software and Hardware Security of IoT

With the tremendous growth of IoT application, providing security to IoT systems has become more critical. In this paper, a technique is presented to ensure the safety of Internet of Things (IoT) devices. This technique ensures hardware and software security of IoT devices. Blockchain technology is used for software security and hardware logics are used for hardware security. For enabling a Blockchain, Ethereum Network is used for secure peer-to-peer transmission. A prototype model is also used using two IoT nodes to demonstrate the security logic.
Improved Deep Distributed Light Field Coding

Light fields enable increasing the degree of realism and immersion of visual experience by capturing a scene with a higher number of dimensions than conventional 2D imaging. On another side, higher dimensionality entails significant storage and transmission overhead compared to traditional video. Conventional coding schemes achieve high coding gains by employing an asymmetric codec design, where the encoder is significantly more complex than the decoder. However, in the case of light fields, the communication and processing among different cameras could be expensive, and the possibility of trading the complexity between the encoder and the decoder becomes a desirable feature. We leverage the distributed source coding paradigm to effectively reduce the encoder’s complexity at the cost of increased computation at the decoder side. Specifically, we train two deep neural networks to improve the two most critical parts of a distributed source coding scheme: the prediction of side information and the estimation of the uncertainty in the prediction. Experiments show considerable BD-rate gains, above 59% over HEVC-Intra and 17.45% over our previous method DLFC-I.
Optimized Power Control in Internet of Vehicles Based on Serial Interference Cancellation and User Matching

With the significant advantages of 5G technique such as higher transmission rate and lower transmission delay, it is more in line with the requirements of the connected vehicle scenario. In addition, the serial interference cancellation (SIC) technology in 5G technology can further improve the communication quality compared to a single one-to-one multiplexing method. Taking the dynamic communication characteristics of Internet of Vehicles into consideration, the robust optimization problem is formulated, which attempts to maximization the total throughput of vehicle users under the changing channel gains. The channel uncertainty is formulated as probability constraints, and they are transformed to the deterministic form to achieve a convex optimization problem. And the Hungarian algorithm is introduced to allocate the available channels to the V2V pairs. With the proposed joint resource allocation algorithm, the maximum throughput can be obtained with the lower algorithm complexity. Also, the robustness against the changing channel gains is guaranteed and the outage probability of users is kept below the pre-given outage threshold.
An Enhanced Gain-Bandwidth Class-AB Miller op-amp With 23,800 MHz·pF/mW FOM, 11-16 Current Efficiency and Wide Range of Resistive and Capacitive Loads Driving Capability

A compact power-efficient class-AB Miller op-amp is introduced. It uses a simple auxiliary circuit that enhances the op-amp’s gain-bandwidth product and helps to drive a wide range of capacitive and resistive loads with high static and dynamic current efficiency. Simple Miller compensation is used to obtain stability over a wide range of loading conditions. The op-amp’s simulation and experimental results in strong inversion with <inline-formula> <tex-math notation="LaTeX">$15\mu \text{A}$ </tex-math></inline-formula> bias current and in sub-threshold with 250nA bias current are shown. Its performance is measured in terms of dynamic and static current efficiency figures of merit FOM<sub>CEDyn</sub> and FOM<sub>CEStat</sub>. and using the conventional small-signal figure of merit FOM<sub>SS</sub>. Experimental results of op-amps fabricated in a 130nm CMOS technology are shown that validate the proposed approach.