Miracle-dz/UniFAR-FTUs huggingface dataset:
Effects of Microwave Radiation on Human Brain: The Positives and Negatives

Several RF sources are present for the commercial as well as defense application. In this article we will focus on the neurological consequences of Microwave Radiation (MWR). Since microwaves impact so many facets of our lives, this research focuses on their health implications. The brain is perhaps the most sensitive organ to MWR, with mitochondrial damage manifesting faster and more profoundly than in other regions. The effects of MWR on brain metabolic pathways have piqued public attention. The possibility for significant numbers of people to be subjected to dynamic, multi-frequency microwave energy nowadays, is a reality. Many urban people residing in the highrise structures of the city, come in the main beam radiation of the antenna which is mounted at a comparable height. Owing to the pervasive presence of MWR, its extensive usage, and the potential for harmful effects, comprehensive analyses of the health risks are imperative. It is crucial, therefore, to assess the level of exposure that is safe for the general population so as to minimize adverse effects despite reducing the favorable uses of microwaves.
Nucleophilic Radiofluorination Using Tri-tert-Butanol Ammonium as a Bifunctional Organocatalyst: Mechanism and Energetics

We present a quantum chemical analysis of the 18F-fluorination of 1,3-ditosylpropane, promoted by a quaternary ammonium salt (tri-(tert-butanol)-methylammonium iodide (TBMA-I) with moderate to good radiochemical yields (RCYs), experimentally observed by Shinde et al. We obtained the mechanism of the SN2 process, focusing on the role of the –OH functional groups facilitating the reactions. We found that the counter-cation TBMA+ acts as a bifunctional promoter: the –OH groups function as a bidentate ‘anchor’ bridging the nucleophile [18F]F− and the –OTs leaving group or the third –OH. These electrostatic interactions cooperate for the formation of the transition states of a very compact configuration for facile SN2 18F-fluorination.
Power Allocation for Reliable and Energy-Efficient Optical LEO-to-Ground Downlinks with Hybrid ARQ Schemes

Satellites in low earth orbit (LEO) are currently being deployed for numerous communication, positioning, space and Earth-imaging missions. To provide higher data rates in direct-to-user links and earth observation downlinks, the free-space optics technology can be employed for LEO-to-ground downlinks. Moreover, the hybrid automatic repeat request (HARQ) can be adopted since the propagation latency is low for LEO satellites. In this work, a power allocation methodology is proposed for optical LEO-to-ground downlinks under weak turbulence employing HARQ retransmission schemes. Specifically, the average power consumption is minimized given a maximum transmitted power constraint and a target outage probability threshold to ensure energy efficiency and reliability, respectively. The optimization problem is formulated as a constrained nonlinear programming problem and solved for Type I HARQ, chase combining (CC) and incremental redundancy (IR) schemes. The solutions are derived numerically via iterative algorithms, namely interior-point (IP) and sequential quadratic programming (SQP), and validated through an exhaustive (brute-force) search. The numerical simulations provide insight into the performance of the retransmission schemes regarding average power. More specifically, Type I HARQ has the worst output, CC has a moderate one, and IR exhibits the best performance. Finally, the IP algorithm is a slower but more accurate solver, and SQP is faster but slightly less accurate.
Deep learning for crop yield prediction: a systematic literature review

ABSTRACT Deep Learning has been applied for the crop yield prediction problem, however, there is a lack of systematic analysis of the studies. Therefore, this study aims to provide an overview of the state-of-the-art application of Deep Learning in crop yield prediction. We performed a Systematic Literature Review (SLR) to identify and analyze the most relevant papers. We retrieved 456 relevant studies of which we selected 44 primary studies for further analysis after applying selection and quality assessment criteria to the relevant studies. A thorough analysis and synthesis of the primary studies were performed with respect to the key motivations, the target crops, the algorithms applied, the features used, and the data sources used. We observed that Convolutional Neural Network (CNN) is the most common algorithm and it has the best performance in terms of Root Mean Square Error (RMSE). One of the most important challenges is the lack of a large training dataset and thus, the risk of overfitting and as a result, lower model performance in practice. For researchers in this field, it is valuable to indicate the current challenges and the possibility for further research, because they tend to focus on the importance of missing research topics.
An efficient workflow for meshing large scale discrete fracture networks for solving subsurface flow problems

Abstract A large percentage of subsurface hydrocarbon reservoirs are characterized as very complex due to presence of small to large scale fractures. Modeling of multi-physics processes like, environmental flow, CO2 sequestration, hydrocarbon flows, and so on, through such geologically complex reservoirs is challenging. Main challenges comes from the large scale variation in the fracture scales. Use of traditional modeling approaches, based on dual-porosity/dual permeability medium, to model such complex systems is complicated and results in incorrect flow patterns. Precise and efficient modeling of the fracture networks requires fractures to be represented as lower dimensional objects, which requires an efficient gridding technique. In last decade alone modeling of flow through discrete fracture systems has attracted attention from a number of researchers. As a result few new gridding and discretization techniques have been proposed to model flow through discrete fracture network systems (DFNs). DFN’s usually involve very high or very low angle fracture-fracture intersections and sometime presence of small to very large length scale fracture networks. In this article an efficient workflow for meshing of large scale complex DFN’s networks as lower dimensional objects is presented supported by quantitative results with aim of developing a software tool box, which could be coupled to a subsurface multi-phase flow simulator.
Embodied Learning for Well-Being, Self-Awareness, and Stress Regulation: A Randomized Trial with Engineering Students Using a Mixed-Method Approach

The continuous changes in our society require adapted training that encompasses both technical and competency knowledge. There is a high level of demand, especially in areas such as engineering, which can affect the mental health of students, producing high levels of stress and psychological distress, hindering self-efficacy and academic performance. Embodied learning working on self-awareness, stress reduction and self-knowledge can help to generate healthier environments. Creative therapies can be a tool to promote the prevention of health problems in this group of the population. In particular, Dance Movement Therapy has demonstrated its effectiveness in improving health in clinical and non-clinical settings. In this work, a body awareness program based on Dance Movement Therapy is developed and implemented in engineering students. Through a mixed pre-post methodology, its impact is evaluated and analyzed in an experimental group of engineering students. Psychometric tests, physiological variables and reflective diaries are used as data sources. The results show that the experimental group, compared with the control group, increased their levels of body awareness and connectedness, well-being and life satisfaction and reduced their stress levels. The results were supported by cortisol measures. Likewise, the students acknowledged having increased their levels of self-awareness and self-knowledge and considered the inclusion of these bodily approaches in higher education to be necessary.
Genetic Diversity and Mating System of Two Mangrove Species (Rhizophora apiculata and Avicennia marina) in a Heavily Disturbed Area of China

Mangrove forests are distributed in the intertidal zones of tropical and subtropical regions, and have been severely damaged by anthropogenic activities, climate change, and stochastic events. Although much progress has been made in the conservation and restoration of mangroves in China, studies of the genetic diversity of mangroves are lacking, especially for isolated populations, yet such studies are essential for guiding conservation and restoration efforts. Here, we evaluated the genetic diversity, spatial genetic structure, and mating system of two mangrove species, Rhizophora apiculata and Avicennia marina, in a heavily disturbed area in Tielu Harbor, Sanya City, Hainan Island, China, using 18 nuclear microsatellite markers. We found that the genetic diversity of R. apiculata, which is classified as ‘Vulnerable’ in the China Red List categories, was high and similar compared with the genetic diversity estimates of other populations reported in previous studies. In contrast, the genetic diversity of A. marina, which is classified as a species of ‘Least Concern’, was low compared with the genetic diversity estimates of other populations. We then evaluated the presence of genetic bottlenecks, spatial genetic structure, and the mating system to determine the effects that habitat destruction has had on these two species. Our findings indicate that distinct conservation and restoration approaches are needed for these two species. Generally, our results provide valuable information that will aid the development of conservation and restoration strategies for the mangroves of Tielu Harbor.
Neckband-Based Continuous Blood Pressure Monitoring Device With Offset-Tolerant ROIC

This study presents a wearable neckband device for continuous cuffless blood pressure (BP) monitoring, where reliable neck-based BP measurement is achieved by utilizing the electrocardiogram (ECG) and impedance plethysmogram (IPG). Optimal composition and position of sensing electrodes around the neck were decided with consideration for physiological characteristics of the human body, and the core parameter of IPG magnitude was experimentally selected to improve the BP estimation accuracy. For its small-featured noises-tolerant detection capability, a customized readout integrated circuit (ROIC) was developed, which includes two kinds of analog front-ends (AFEs) for biopotential and bio-impedance. Both AFEs are proposed to include an attenuator-assisted hybrid DC-servo loop (DSL) structure, achieving precise pulse transit time (PTT) calculation by lowering the high-pass filtering cutoff frequency and also stable signal acquisition by widening the offset-cancellation range. Real-time PTT monitoring and BP estimation are wirelessly conducted, and its continuous operation time with 350-mAh battery capacity is longer than 5 hours. Efficacy of the proposed neckband BP device was evaluated for 8 healthy subjects under the physical stress condition, where the mean correlation coefficient and the standard deviation were achieved as 0.8508 and 4.17mmHg respectively.
Surface-Immobilized Photoinitiators for Light Induced Polymerization and Coupling Reactions

Straightforward and versatile surface modification, functionalization and coating have become a significant topic in material sciences. While physical modification suffers from severe drawbacks, such as insufficient stability, chemical induced grafting processes efficiently modify organic and inorganic materials and surfaces due to covalent linkage. These processes include the “grafting from” method, where polymer chains are directly grown from the surface in terms of a surface-initiated polymerization and the “grafting to” method where a preformed (macro)-molecule is introduced to a preliminary treated surface via a coupling reaction. Both methods require an initiating species that is immobilized at the surface and can be triggered either by heat or light, whereas light induced processes have recently received increasing interest. Therefore, a major challenge is the ongoing search for suitable anchor moieties that provide covalent linkage to the surface and include initiators for surface-initiated polymerization and coupling reactions, respectively. This review containing 205 references provides an overview on photoinitiators which are covalently coupled to different surfaces, and are utilized for subsequent photopolymerizations and photocoupling reactions. An emphasis is placed on the coupling strategies for different surfaces, including oxides, metals, and cellulosic materials, with a focus on surface coupled free radical photoinitiators (type I and type II). Furthermore, the concept of surface initiation mediated by photoiniferters (PIMP) is reviewed. Regarding controlled radical polymerization from surfaces, a large section of the paper reviews surface-tethered co-initiators, ATRP initiators, and RAFT agents. In combination with photoinitiators or photoredox catalysts, these compounds are employed for surface initiated photopolymerizations. Moreover, examples for coupled photoacids and photoacid generators are presented. Another large section of the article reviews photocoupling and photoclick techniques. Here, the focus is set on light sensitive groups, such as organic azides, tetrazoles and diazirines, which have proven useful in biochemistry, composite technology and many other fields.
Study on Internal Combustion Engine Noise Based on Fractal Theory

A method based on fractal theory was proposed to study the noise signals of internal combustion engine.According to the G-P computational method,the calculation of the fractal correlation dimension of internal combustion engine noise signals was conducted,and then the selection for some important parameters in this calculation process was discussed.From the correlation dimension analysis of noise signals of internal combustion engine,it was proved that engine noise signals have an obvious fractal characteristic.The results show that the correlation dimension of engine noise signals varies with the change of engine operating conditions,and it is correlative with engine operating condition,and it can be used as a characteristic parameter in engine monitoring and fault diagnosis to some extent.
Transcriptome and Physiological Analysis of Rootstock Types and Silicon Affecting Cold Tolerance of Cucumber Seedlings

Cucumbers grafted on rootstocks with different de-blooming capacity show varying levels of cold tolerance. The content of fruit bloom correlates with its silicon-metabolizing capacity, and rootstock grafting can alter not only the cold tolerance but also the silicon-metabolizing capacity of the scion. The molecular mechanisms responsible for resistance due to rootstocks and silicon and the pathway that affects cold tolerance, however, remain poorly understood. Therefore, we performed physiological and transcriptome analysis to clarify how rootstock types and silicon affect cold tolerance in cucumber seedlings. Then, we randomly selected eight differentially expressed genes (DEGs) for quantitative real time PCR (qRT-PCR) analysis to proof the reliability of the transcriptome data. The results showed that silicon can enhance the cold tolerance of cucumbers by boosting the phenylpropanoid metabolism, and rootstock grafting can boost the active oxygen scavenging ability and synthesis level of hormones in cucumbers and maintain the stability of the membrane structure to enhance cold tolerance. The difference in cold tolerance between the two rootstocks is because the cold-tolerant one has stronger metabolic and sharp signal transduction ability and can maintain the stability of photosynthesis, thereby contributing to the stability of the cellular system and enhancing tolerance to cold.
PVP/Highly Dispersed AgNPs Nanofibers Using Ultrasonic-Assisted Electrospinning

Silver nanoparticles (AgNPs) are novel materials with antibacterial, antifungal, and antiviral activities over a wide range. This study aimed to prepare polyvinylpyrrolidone (PVP) electrospinning composites with uniformly distributed AgNPs. In this study, starch-capped ~2 nm primary AgNPs were first synthesized using Atmospheric pressure Pulsed Discharge Plasma (APDP) at AC 10 kV and 10 kHz. Then, 0.6 wt. % AgNPs were mixed into a 10 wt. % PVP ethanol-based polymer solution and coiled through an Ultrasonic-assisted Electrospinning device (US-ES) with a 50 W and 50 kHz ultrasonic generator. At 12 kV and a distance of 10 cm, this work successfully fabricated AgNPs-PVP electrospun fibers. The electrospun products were characterized using Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), High-Resolution TEM (HR-TEM), Fourier Transform Infrared Spectroscopy (FT-IR), X-ray Diffraction (XRD), Thermogravimetric (TG), and X-ray Photoelectron Spectroscopy (XPS) methods.
Development of a Nutrition Environment Assessment Tool for Latino Ethnic Stores

The objectives were to: (1) adapt the Nutrition Environment Measures Survey for Stores (NEMS-S) to better culturally fit small Latino grocery stores (tiendas) in Iowa; (2) assess the newly adapted Latino NEMS-S for inter-rater and test-retest reliability; and (3) compare Latino and original NEMS-S summary scores. This pilot instrument, containing culturally appropriate foods from the original NEMS-S and 2015 US Dietary Guidelines for Americans, underwent two rounds of formative evaluation. The new instrument and scoring protocol were applied to a random sample of 42 of 81 possible tiendas in Iowa. Cohen’s kappa was used to assess inter-rater and test-retest reliability for availability and quality of indicator food items (total scores and food category sub scores). There were no differences in summary scores for inter-rater or test-retest reliability using paired t-tests. Inter-rater agreement was high (range 0.82–1.00; p < 0.001). Tiendas averaged 42.0 ± 7.5 of 57 possible points on the Latino NEMS-S, but only 12.0 ± 4.6 of 54 points on the original NEMS-S (p < 0.001). The Latino NEMS-S is a reliable tool for assessing the food environment within Iowa tiendas. Culturally specific instruments can describe diverse food environments more accurately and guide public health nutrition interventions within communities.
Study on Aligning and Detecting of Large Shaft Center-Line with PSD and Laser Technology

A new method for large shaft alignment was proposed based on visual laser technology and PSD(position sensitive detector) technology.The benchmark beam of light is generated by semiconductor laser and received by light targets with PSD.Finally the measure of shaft center-line alignment is finished by optimum algorithm.Through simulation experiment,it is comfirmed that the detecting principle is correct and the method has some advantages such as quick and easy operation,reduces wear and tear of large shaft and offers a new idea for shaft calibration and detection.
Chest X-ray Classification for the Detection of COVID-19 Using Deep Learning Techniques

Recent technological developments pave the path for deep learning-based techniques to be used in almost every domain of life. The precision of deep learning techniques make it possible for these to be used in the medical field for the classification and detection of various diseases. Recently, the coronavirus (COVID-19) pandemic has put a lot of pressure on the health system all around the world. The diagnosis of COVID-19 is possible by PCR testing and medical imagining. Since COVID-19 is highly contagious, diagnosis using chest X-ray is considered safe in various situations. In this study, a deep learning-based technique is proposed to classify COVID-19 infection from other non-COVID-19 infections. To classify COVID-19, three different pre-trained models named EfficientNetB1, NasNetMobile and MobileNetV2 are used. The augmented dataset is used for training deep learning models while two different training strategies have been used for classification. In this study, not only are the deep learning model fine-tuned but also the hyperparameters are fine-tuned, which significantly improves the performance of the fine-tuned deep learning models. Moreover, the classification head is regularized to improve the performance. For the evaluation of the proposed techniques, several performance parameters are used to gauge the performance. EfficientNetB1 with regularized classification head outperforms the other models. The proposed technique successfully classifies four classes that include COVID-19, viral pneumonia, lung opacity, and normal, with an accuracy of 96.13%. The proposed technique shows superiority in terms of accuracy when compared with recent techniques present in the literature.
Illegal Wildlife Trade in Traditional Markets, on Instagram and Facebook: Raptors as a Case Study

Monitoring illegal wildlife trade and how the modus operandi of traders changes over time is of vital importance to mitigate the negative effects this trade can have on wild populations. We focused on the trade of birds of prey in Indonesia (2016–2021) in bird markets (12 markets, 194 visits), on Instagram (19 seller profiles) and on Facebook (11 open groups). We link species prevalence and asking prices to body size, abundance and geographic range. Smaller species were more traded in bird markets and less so online. Abundance in trade is in part linked to their abundance in the wild. Asking prices (mean of USD 87) are positively correlated with size and negatively with their abundance in the wild. Authorities seize birds of prey according to their observed abundance in trade, but only 10% of seizures lead to successful prosecutions. The trade is in violation of national laws and the terms and conditions of the online platforms; the low prosecution rate with minimal fines shows a lack of recognition of the urgency of the threat that trade poses to already imperilled wildlife. The shift of trade from physical bird markets to the online marketplace necessitates a different strategy both for monitoring and enforcement.
Convolution neural network with edge structure loss for spatiotemporal remote sensing image fusion

ABSTRACT Spatiotemporal fusion technology provides a feasible, economical solution for generating remote sensing images with high spatiotemporal resolution. The recently proposed learning-based method achieved high accuracy; however, its network structure is relatively simple, and the deep features of the input image cannot be obtained, so that the fused image cannot restore good landform details and the quality is not very good. Moreover, most methods use a single pixel-level (MSE) loss, which makes recovering high-frequency details difficult, resulting in a reduction in the fusion accuracy. In this paper, we propose an edge structure loss, which is added to a spatiotemporal fusion network without pre training model. To fully extract the spectral information and spatial details of the image, we propose a DenseNet-BC module for image fusion tasks, which makes the features more easily transmittable in the whole network. This improvement also enables the network to perform spatiotemporal fusion with better generalizability and robustness. In addition, we propose an edge loss to further improve the accuracy of the model fusion results. Experiments with existing spatiotemporal fusion algorithms in different regions show that our proposed method is more fault tolerant and achieve a higher accuracy in terms of quality evaluation indicators and better visual effects.