Miracle-dz/UniFAR-FTUs huggingface dataset:
Measurement-Based FET Analytical Modeling Using the Nonlinear Function Sampling Approach

A novel and fast method for the measurement-based identification of an analytical field-effect transistor (FET) compact model from large-signal waveforms is presented. Based on a two-tone two-port experiment, a recently published nonlinear function sampling (NFS) operator providing the samples of the FET state functions in the voltage domain is here exploited, for the first time, to extract an equivalent-circuit model. The approach is demonstrated on a 250-nm gallium nitride (GaN)-on-silicon carbide (SiC) high-electron-mobility transistor (HEMT) at 2.5 and 5 GHz.
A multimodal-signals-based gesture recognition method for human machine interaction

As the capacity for machines to extend human capabilities continues to grow, the communication channels used also need to expand. Allowing machines to translate gestural command into robot movements can make the communication between humans and machines more similar to interpersonal communication. Yet for operating requirements, instantaneity and precision of the interaction must reach the application level in realistic scenarios, and the accuracy of results cannot be guaranteed by thejudgement of a single motion sensor. Therefore, exploring a gesture detection technology that integrates multisensor information is very necessary. The presented work takes a step towards real-time gesture detection by fusing multiple physiological signals with wearable motion sensors. An algorithm is presented for processing and extracting motion signal acquired via inertial measurement unit (IMU) and electromyography (EMG) with a high error-tolerant way of wearing, and it is applied to the gesture recognition model established in reasonable threshold and logical judgment. This enables real-time gesture detection of 24 various assembled movements such as rotating palms while bending arms, clenching fist when unwinding upper limb. The result of involving intentional behavior by analyzing electroencephalogram (EEG) is also added to the gesture recognition process for eliminating unconscious actions. Together, these pipelines offer efficient gesture vocabulary suitable for remotely controlling robots. Experiments evaluate classifier performance and interface efficacy. The system successfully detected 95.6% of 360 commands, and the average processing time was 25.4ms among all the 15 trials of the experiment.
NIH Workshop Report: sensory nutrition and disease

Abstract In November 2019, the NIH held the “Sensory Nutrition and Disease” workshop to challenge multidisciplinary researchers working at the interface of sensory science, food science, psychology, neuroscience, nutrition, and health sciences to explore how chemosensation influences dietary choice and health. This report summarizes deliberations of the workshop, as well as follow-up discussion in the wake of the current pandemic. Three topics were addressed: A) the need to optimize human chemosensory testing and assessment, B) the plasticity of chemosensory systems, and C) the interplay of chemosensory signals, cognitive signals, dietary intake, and metabolism. Several ways to advance sensory nutrition research emerged from the workshop: 1) refining methods to measure chemosensation in large cohort studies and validating measures that reflect perception of complex chemosensations relevant to dietary choice; 2) characterizing interindividual differences in chemosensory function and how they affect ingestive behaviors, health, and disease risk; 3) defining circuit-level organization and function that link and interact with gustatory, olfactory, homeostatic, visceral, and cognitive systems; and 4) discovering new ligands for chemosensory receptors (e.g., those produced by the microbiome) and cataloging cell types expressing these receptors. Several of these priorities were made more urgent by the current pandemic because infection with sudden acute respiratory syndrome coronavirus 2 (SARS-CoV-2) and the ensuing coronavirus disease of 2019 has direct short- and perhaps long-term effects on flavor perception. There is increasing evidence of functional interactions between the chemosensory and nutritional sciences. Better characterization of this interface is expected to yield insights to promote health, mitigate disease risk, and guide nutrition policy.
Kimia farmasi Analisis

Buku ini membahas berbagai macam metode analisis konvensional ( volumetri dan gravimetri )dan metode-metode modern yakni potensiometri, polarografi, spektrofotometri ( UV-Vis. fluoresen, dan sarapan atom ), serta kromatografi ( lapis tipis, cair kinerja tinggi, dan gas )disertai dengan penggunaan masing-masing metode untuk analisis senyawa kimia/obat. Sertadisajikan juga cara/protokol validasi metode analisis.
Mathematical Modeling of Influence of the Genotype on Cell Processes by Statistical Analysis

This article describes the mathematical modeling of the relationship between genotype and phenotype using statistical methods. The results of the study of cells of laboratory mice are used as the initial data. To build a relationship model, methods of correlation analysis, cluster analysis, ANOVA and its modification - QTL analysis are used. Graph theory is used to model the interaction of proteins in cellular processes. The QTL mapping idea is to phenotype observation and identification of the genome region on which the genotype is associated with the phenotype. With the help of molecular-genetic markers, molecular maps of individual chromosomes and genomes are made, genes and QTLs mapping are performed on them. Thus, genes with the greatest connectivity to phenotype were identified. The correlation between genotype and phenotype is studied across the full genome of the individual in this study. The data provided by the Laboratory of Molecular Genetics of the innate immunity of Petrozavodsk State University were the initial data in this research. The essence of the project, carried out jointly with the laboratory, is to study the arrays of genetic information to identify and model the relationships between the genotype and the phenotype of biological organisms. Genotyping and phenotyping were conducted based on sequencing data (determination of amino acid and nucleotide sequence) of matrix RNA. The result of the study is the construction of a mathematical model of the relationship between phenotype and genotype and the identification of groups of significant phenotypes that affect the cells processes.
3D Spacer Fabric Structure for the Prevention and Care of Pressure Ulcers

Many pressure wound protection products, such as pressure redistribution supports and exuding wound dressings, are made of polyurethane sponge. However, polyurethane sponge does not possess a desirable combination of air permeability, moisture management and mechanical properties required by pressure redistribution supports and exuding wound dressings. This study explores the optimization of a 3D warp-knitted spacer fabric (WKSF) structure for pressure ulcer protection. The influence of construction and material parameters on the compression performance and air permeability of WKSFs was investigated by numerical simulation and then validated with experiments. The moisture transport and wetting properties of WKSFs were characterized by a moisture management tester. The numerical simulation and experimental results indicate that the WKSF structure can be tuned to achieve compressional properties, air permeability, and moisture management performance that are more suitable for pressure ulcer protection than polyurethane sponge. WKSFs constructed with a low-density surface layer, coarser spacer yarns, and larger spacer yarn inclination-angle are more suitable for pressure redistribution support surfaces, while WKSFs constructed with dense surface layers and coarse spacer yarn can better meet the performance requirements of exuding wound dressings.
Broadband Wired and Wireless Access System with Novel Sub-Nyquist Sampling-Rate ADC Receiver

To meet the broadband requirement of future wired and wireless access systems, we proposed the delay-division-multiplexing scheme with sub-Nyquist sampling ADC to simplify the user receivers without performance degradation. We successfully demonstrated both broadband multi-users OFDMA-PONs and 60-GHz OFDM 2x2 MIMO wireless systems.
GOAMLP: Network Intrusion Detection With Multilayer Perceptron and Grasshopper Optimization Algorithm

In this paper, an intrusion detection system is introduced that uses data mining and machine learning concepts to detect network intrusion patterns. In the proposed method, an artificial neural network (ANN) is used as a learning technique in intrusion detection. The metaheuristic algorithm with the swarm-based approach is used to reduce intrusion detection errors. In the proposed method, the Grasshopper Optimization Algorithm (GOA) is used for better and more accurate learning of ANNs to reduce intrusion detection error rate. The role of the GOAMLP algorithm is to minimize the intrusion detection error in the neural network by selecting useful parameters such as weight and bias. Our implementation in MATLAB software and using the KDD and UNSW datasets show that the proposed method detects abnormal, malicious traffic and attacks with high accuracy. The GOAMLP method outperforms and is more accurate than the existing state-of-the-art techniques such as RF, XGBoost, and embedded learning of ANN with BOA, HHO, and BWO algorithms in network intrusion detection.
The use of Convolutional Neural Network for Malware Classification

Digital security is confronting an immense risk from malware or malicious software. In recent years, there has been an increase in the volume of malware, reaching above 980 million in 2019*. To identify and classify this pernicious software, complex details and patterns among them are to be gathered, segregated, and analyzed. In this regard, Convolutional Neural Networks (CNN) – an architecture of Deep Neural Networks (DDN) can offer a more efficient and accurate solution than Conventional Neural Network (NN) systems.In this paper, we have looked into the consequences of using conventional NN systems and the benefits of using CNN on a sample of malware provided by Microsoft. In 2015, Microsoft announced a malware classification challenge and released more than 21,000 malware samples. Many interesting solutions were put forward by scientists and students around the world. Inspired by their efforts we also have put forward a method. We have converted the malware binary files into images and then trained a CNN model for identification and categorization of this malware to their respective families. From this method, we achieved a high percentage accuracy of 98.88%.
A Geometrical Wavelet Framework for the Time-Series Analysis of Full-Polarimetric Features

Polarimetric SAR (PolSAR) image time series have been employed for the analysis of temporal patterns of natural features in terms of the extended polarimetric scattering properties. However, the time series provide a rich scattering information that can be used for tracking and analyzing the evolution of targets, individuating smooth and/or abrupt changes. In this work we propose a wavelet framework that exploits the information from polarimetric features and analyze them to both mitigate the speckle effect on the multi-temporal information and improve the targets homogeneity using the multi-temporal information. The framework combines the powerful description from the main polarimetric decomposition features and the temporal analysis using geometrical wavelet transform. The analysis is applied on a multi-temporal polarimetric dataset of Radarsat-2 images acquired over the Argentière glacier site.
Low Loss Polymer Wavelength (De)multiplexer Interposing Si Waveguide and Single-mode Fiber Using Topology Optimization

We demonstrate an ultra-compact polymer wave-length (de)multiplexer also function as interposer between Si and optical fiber using gradient-based topology optimization and downhill simplex method with an excess waveguide propagation loss of of 0.84 dB and 1.06 dB at 1310 nm and 1550 nm, respectively.
Utilizing Multimedia-Based Learning Materials in Scouting Education Program for Deaf Students

Based on the difficulties of special education teachers to teach basic scouting learning materials for deaf students, this study is intended to analyze the results of the trial use of multimedia-based teaching materials of scouting skills for deaf students in Special Schools (Sekolah Dasar Luar Biasa / SDLB). This quasi-experimental study with pre-posttest design purposively uses the sample of 41 deaf students from special schools located in the Greater Malang. The quantitative research result shows a significant increase of up to 25.62% in the cumulative scores after the implementation of multimedia based material for scouting skills. It is concluded that there is a significant contribution to the use of multimedia-based material in scouting education programs for deaf students in special schools.
The Role of Satisfaction and Ease of Use on User Attitudes of E-Learning: Case Study MI Mumtaza

Satisfaction, and ease of use of e-learning are very important for user needs. E-learning is an application where system users interact with others via the web. The use of e-learning supports the implementation of the teaching and learning process at schools, which will increase students' absorption of the material being taught. It can also support the ability to display information with information technology devices, where it is difficult to do with ordinary devices. This paper examines the convenience and ease of using e-learning at MI Mumtaza Islamic School. The method used is quantitative, which measures the comfort and ease of use of e-learning. This study uses six variables, including Quality (KTS), Satisfaction (KPS), Ease (KMD), Familiar (FML), Intention (NAT), and Attitude (SKP). The results obtained from 10 hypotheses, there are two that are rejected, which means Familiar (FML) is not influence Intention (NAT), and Ease of Use (KMD) is not influenced Attitude (SKP).
Polymer Nanofiber Deposition in Lab-on-a-Chip Devices By Electrospinning

Electrospinning is a relatively cheap and simple method to deposit polymer nanofiber mats. The mats can be made of biocompatible materials and can be used to immobilize cells, proteins, enzymes or drug molecules on a surface or inside the fibers, such as in a microreactor inside a Lab-on-a-Chip (LoC) device. For self-contained, portable analytical devices, it is essential to have chemical and biological components immobilized in the device, so the system can be packaged and transported. We developed a method to repeatedly deposit a controlled amount of polylactic acid (PLA) nanofibers on a carrier substrate, which could then be sliced and transferred into a microfluidic chip. In this paper, we demonstrate repeatable PLA deposition on aluminum foils using a standardized electrospinning setup with humidity and temperature control. Furthermore, we demonstrate the deposition of PLA fibers with encapsulated biomolecules by mixing the lipase enzyme with the PLA precursor. Finally, we demonstrate the transfer of the enzyme-filled fibers into 3D printed microfluidic chips (a.k.a. electrospun microreactors) and evaluate flow characteristics of the created system.
Image Super-Resolution Reconstruction Based on a Generative Adversarial Network

In the field of computer vision, super-resolution reconstruction techniques based on deep learning have undergone considerable advancement; however, certain limitations remain, such as insufficient feature extraction and blurred image generation. To address these problems, we propose an image super-resolution reconstruction model based on a generative adversarial network. First, we employ a dual network structure in the generator network to solve the problem of insufficient feature extraction. The dual network structure is divided into an upsample subnetwork and a refinement subnetwork, which upsample and optimize a low-resolution image, respectively. In a scene with large upscaling factors, this structure can reduce the negative effect of noise and enhance the utilization of high-frequency details, thereby generating high-quality reconstruction results. Second, to generate sharper super-resolution images, we use the perceptual loss, which exhibits a fast convergence and excellent visual effect, to guide the generator network training. We apply the ResNeXt-50- $32\times 4\text{d}$ network, which has few parameters and a large depth, to calculate the loss to obtain a reconstructed super-resolution image that is highly realistic. Finally, we introduce the Wasserstein distance into the discriminator network to enhance the discrimination ability and stability of the model. Specifically, this distance is employed to eliminate the activation function in the last layer of the network and avoid the use of the logarithm in calculating the loss function. Extensive experiments on the DIV2K, Set5, Set14, and BSD100 datasets demonstrate the effectiveness of the proposed model.
Influence of Officially Ordered Restrictions during the First Wave of COVID-19 Pandemic on Physical Activity and Quality of Life in Patients after Kidney Transplantation in a Telemedicine Based Aftercare Program—A KTx360° Sub Study

Guidelines recommend a healthy lifestyle and regularly physical activity (PA) after kidney transplantation (KTx). The KTx360° program is a multicenter, multisectoral, multimodal, telemedicine-based follow-up care program. Effects of the first COVID-19 wave restrictions on health-related quality of life and PA of supervised KTx360° patients were evaluated using an online questionnaire. Six hundred and fifty-two KTx360° patients were contacted via email and were asked to complete the Freiburg questionnaire of physical activity and the Short form 12 Health Survey (SF-12) online. Pre-pandemic and lockdown data were compared in 248 data sets. While sporting activity decreased during the COVID-19 pandemic, basic and leisure activity increased, resulting in increased overall activity. The physical component scale of the SF-12 was in the low normal range before as well as during the pandemic, with a small but significant increase during the pandemic. The mental component scale showed normal values before and during pandemic with a small but statistically significant decrease. Our study supports the effectiveness of a telemedicine based program for KTx patient care in maintaining PA and quality of life during the first peak of the COVID-19 pandemic. However, further research and observation during the ongoing pandemic are required.
Retinal Disease Classification from Optical Coherence Tomographical Scans using Multilayered Convolution Neural Network

Classification of retinal diseases using Optical Coherence Tomographical (OCT) scans is a crucial task. Accurate detection and classification of these diseases is necessary for patient’s survival. Presently, the analysis of retinal diseases are carried out by doctors by examining the OCT images regularly. However the manual diagnosis procedure is tedious. Therefore, in this paper, an automatic detection and classification technique of retinal diseases has been proposed to assist doctors in their diagnosis. A deep multilayered convolutional neural network (CNN) has been used to detect and classify the retinal abnormalities using OCT scans. The proposed technique has been applied on an open source retinal OCT dataset containing 59,142 images and 96.5% blind test accuracy has been achieved.
Quercetin alleviates styrene oxide-induced cytotoxicity in cortical neurons in vitro via modulation of oxidative stress and apoptosis

Abstract Styrene 7,8-oxide (SO) is the principal metabolite of styrene, an industrial neurotoxic compound which causes various neurodegenerative disorders. The present study aimed to explore the mechanisms of SO cytotoxicity (0.5 − 4 mM) in primary cortical neurons and to evaluate the neuroprotective potential of quercetin (QUER). Our results showed that exposure to SO decreased viability of cortical neurons in a concentration-dependent manner. In the presence of QUER, cell viability was increased significantly. The neuroprotective effects of QUER were associated with the reduction of intracellular Reactive Oxygen Species (ROS), the decrease in calcium overload and the restoration of mitochondrial membrane depolarization caused by SO. Additionally, to evaluate neuronal death mechanisms triggered by SO, cells were incubated with Ac-DEVD-CHO, Calpeptin and Necrostatin-1, pharmacological inhibitors of caspase-3, calpains and necroptosis respectively. The data showed that the three inhibitors reduced cell death induced by SO and suggested the implication of apoptotic, necrotic and necroptotic pathways. However, western blot analysis showed that QUER attenuated the activation of caspase-3 but did not prevent calpain activity. Taken together, these data indicated that the cytotoxicity of SO was mediated by oxidative stress and apoptosis, necrosis and necroptosis mechanisms, while the neuroprotection provided by QUER against SO depended mainly on its anti-apoptotic activity. Graphical Abstract
Country of birth and mortality risk in hypertension with and without diabetes: the Swedish primary care cardiovascular database.

OBJECTIVES Hypertension and diabetes are common and are both associated with high cardiovascular morbidity and mortality. We aimed to investigate associations between mortality risk and country of birth among hypertensive individuals in primary care with and without concomitant diabetes, which has not been studied previously. In addition, we aimed to study the corresponding risks of myocardial infarction and ischemic stroke. METHODS This observational cohort study of 62 557 individuals with hypertension diagnosed 2001-2008 in the Swedish Primary Care Cardiovascular Database assessed mortality by the Swedish Cause of Death Register, and myocardial infarction and ischemic stroke by the National Patient Register. Cox regression models were used to estimate study outcome hazard ratios by country of birth and time updated diabetes status, with adjustments for multiple confounders. RESULTS During follow-up time without diabetes using Swedish-born as reference, adjusted mortality hazard ratios per country of birth category were Finland: 1.26 (95% confidence interval 1.15-1.38), high-income European countries: 0.84 (0.74-0.95), low-income European countries: 0.84 (0.71-1.00) and non-European countries: 0.65 (0.56-0.76). The corresponding adjusted mortality hazard ratios during follow-up time with diabetes were high-income European countries: 0.78 (0.63-0.98), low-income European countries: 0.74 (0.57-0.96) and non-European countries: 0.56 (0.44-0.71). During follow-up without diabetes, the corresponding adjusted hazard ratio of myocardial infarction was increased for Finland: 1.16 (1.01-1.34), whereas the results for ischemic stroke were inconclusive. CONCLUSION In Sweden, hypertensive immigrants (with the exception for Finnish-born) with and without diabetes have a mortality advantage, as compared to Swedish-born.
Communication Network Modeling Taking into Account Different Kinds of Inhomogeneities

The application efficiency of communication networks, which use the resources of cyberspace, as well as organizational needs and technical capabilities, predetermine various types of inhomogeneities that emerge in the entire set of network characteristics and properties. The applied communication network modeling methods do not take into account the inhomogeneities arising during the communication network development, which reduces modeling adequacy. The proposed method allows simulating sections of communication networks taking into account structural and topological inhomogeneities.