Miracle-dz/UniFAR-FTUs huggingface dataset:
Association between Total and Individual PCB Congener Levels in Maternal Serum and Birth Weight of Newborns: Results from the Chiba Study of Mother and Child Health Using Weighted Quantile Sum Regression

Maternal exposure to polychlorinated biphenyls (PCBs) during pregnancy is associated with a low birth weight; however, the congener-specific effects of PCB congeners are not well defined. In this study, we used maternal serum samples from the Chiba Study of Mother and Child Health (C-MACH) cohort, collected at 32 weeks of gestational age, to analyze the effects of PCB congener exposure on birth weight by examining the relationship between newborn birth weight and individual PCB congener levels in maternal serum (n = 291). The median total PCB level in the serum of mothers of male and female newborns at approximately 32 weeks of gestation was 39 and 37 ng g−1 lipid wt, respectively. The effect of the total PCB levels and the effects of PCB congener mixtures were analyzed using a linear regression model and a generalized weighted quantile sum regression model (gWQS). The birth weight of newborns was significantly associated with maternal exposure to PCB mixtures in the gWQS model. The results suggest that exposure to PCB mixtures results in low newborn birth weight. However, specific impacts of individual PCB congeners could not be related to newborn birth weight.
Serological Positivity against Selected Flaviviruses and Alphaviruses in Free-Ranging Bats and Birds from Costa Rica Evidence Exposure to Arboviruses Seldom Reported Locally in Humans

Arboviruses have two ecological transmission cycles: sylvatic and urban. For some, the sylvatic cycle has not been thoroughly described in America. To study the role of wildlife in a putative sylvatic cycle, we sampled free-ranging bats and birds in two arbovirus endemic locations and analyzed them using molecular, serological, and histological methods. No current infection was detected, and no significant arbovirus-associated histological changes were observed. Neutralizing antibodies were detected against selected arboviruses. In bats, positivity in 34.95% for DENV-1, 16.26% for DENV-2, 5.69% for DENV-3, 4.87% for DENV-4, 2.43% for WNV, 4.87% for SLEV, 0.81% for YFV, 7.31% for EEEV, and 0.81% for VEEV was found. Antibodies against ZIKV were not detected. In birds, PRNT results were positive against WNV in 0.80%, SLEV in 5.64%, EEEV in 8.4%, and VEEV in 5.63%. An additional retrospective PRNT analysis was performed using bat samples from three additional DENV endemic sites resulting in a 3.27% prevalence for WNV and 1.63% for SLEV. Interestingly, one sample resulted unequivocally WNV positive confirmed by serum titration. These results suggest that free-ranging bats and birds are exposed to not currently reported hyperendemic-human infecting Flavivirus and Alphavirus; however, their role as reservoirs or hosts is still undetermined.
Impact of Nurse–Physician Collaboration, Moral Distress, and Professional Autonomy on Job Satisfaction among Nurses Acting as Physician Assistants

Although there is considerable literature on job satisfaction among nurses in various settings, there is little research about contributing factors, including moral distress to job satisfaction among a certain group of nurses, such as nurses acting as physician assistants. The purpose of this study was to verify the impact of nurse–physician collaboration, moral distress, and professional autonomy on job satisfaction among nurses acting as physician assistants. Descriptive and correlational research was conducted on a convenience sample of 130 nurses from five general hospitals in South Korea. In the final regression model, the adjusted R square was significant, explaining 38.2% of the variance of job satisfaction (F = 8.303, p < 0.001), where ‘cooperativeness’ (β = 0.469, p = 0.001) from nurse–physician collaboration, ‘institutional and contextual factor’ from moral distress (β = −0.292, p = 0.014), and professional autonomy (β = 0.247, p = 0.015) were included. In hospital environments, a more cooperative inter-professional relationship between nurses and physicians led to less moral distress caused by organisational constraints. A higher level of professional autonomy among nurses acting as physician assistants is required to increase their job satisfaction.
Visitor Comments about Botanic Parks and Gardens in the Context of Botanical Tourism

Botanical parks and gardens present different natural beauties together and allow the recognition of flora varieties. Within the scope of botanical tourism, botanical parks and gardens constitute one of the important visiting points of tourists. In this study, visitor comments on botanical parks / gardens are analyzed. Qualitative research method was used in the study. The study data consists of visitor comments on TripAdvisor website for "Ankara Botanical Park", "Atatürk Botanical Park", "Gaziantep Botanical Park", "Karaca Botanical Garden" and "Nezahat Gökyiğit Botanical Garden" in Turkey between May 2014 and November 2020. In the study, when the visitor's comments were evaluated, it was concluded that the comments on the botanical parks and gardens consisted of panoramic, peaceful / relaxing, lush place, nostalgic, natural wonder, tree museum codes. In addition to these, it was concluded that activities such as walking and sports, picnics, photographing, viewing the scenery, resting, and obtaining information about flora can be done by visitors in these parks and gardens.
Dispositional Mindfulness and Psychotic Symptoms in Schizophrenia Spectrum Disorders: The Mediating Roles of Rumination and Negative Emotion

Introduction Studies have shown that rumination plays a significant mediating role between dispositional mindfulness (DM) and psychopathological symptoms in both clinical and non-clinical populations. However, no studies have examined this pathway in people with schizophrenia spectrum disorders (SSDs). Methods A cross-sectional, clinician-administered survey was conducted among people with SSDs (n = 52) in a community setting. Participants completed the Chinese versions of the Depression Anxiety Stress Scale, Five Facet Mindfulness Questionnaire-Short Form, Psychotic Symptom Rating Scale and Scale for the Assessment of Negative Symptoms. Structural equation modelling was performed to examine the pathways of mindfulness facets, rumination, negative emotions and psychotic symptoms. Results Two facets of mindfulness (nonjudging and acting with awareness) reduced rumination and negative emotional status. Rumination fully mediated the relationship between nonjudging and negative emotions and partially mediated the relationship between acting with awareness and negative emotions. Furthermore, rumination and negative emotions sequentially mediated the relationship between acting with awareness and hallucination (B = −0.44, 95% confidence interval [CI] = −0.338 to −0.045, p = 0.01) and between nonjudging and hallucination (B = −0.356, 95% CI = −0.255 to −0.008, p = 0.034). The model fit the data well (χ2(2) = 1.318, p = 0.517, Tucker–Lewis index = 1.075, comparative fit index = 1, standardised root mean residual = 0.0251, root mean square error of approximation = 0.0001). Conclusion Rumination and negative emotions serially mediated the relationship between DM and hallucination. The findings support the contribution of changes across transdiagnostic mediators underlying the therapeutic effects of mindfulness training. Further research examining the transdiagnostic processes of DM in influencing clinical outcomes in SSDs is warranted.
Spatiotemporal Evolution Analysis and Future Scenario Prediction of Rocky Desertification in a Subtropical Karst Region

Landscape change is a dynamic feature of landscape structure and function over time which is usually affected by natural and human factors. The evolution of rocky desertification is a typical landscape change that directly affects ecological environment governance and sustainable development. Guizhou is one of the most typical subtropical karst landform areas in the world. Its special karst rocky desertification phenomenon is an important factor affecting the ecological environment and limiting sustainable development. In this paper, remote sensing imagery and machine learning methods are utilized to model and analyze the spatiotemporal variation of rocky desertification in Guizhou. Based on an improved CA-Markov model, rocky desertification scenarios in the next 30 years are predicted, providing data support for exploration of the evolution rule of rocky desertification in subtropical karst areas and for effective management. The specific results are as follows: (1) Based on the dynamic degree, transfer matrix, evolution intensity, and speed, the temporal and spatial evolution of rocky desertification in Guizhou from 2001 to 2020 was analyzed. It was found that the proportion of no rocky desertification (NRD) areas increased from 48.86% to 63.53% over this period. Potential rocky desertification (PRD), light rocky desertification (LRD), middle rocky desertification (MRD), and severe rocky desertification (SRD) continued to improve, with the improvement showing an accelerating trend after 2010. (2) An improved CA-Markov model was used to predict the future rocky desertification scenario; compared to the traditional CA-Markov model, the Lee–Sallee index increased from 0.681 to 0.723, and figure of merit (FOM) increased from 0.459 to 0.530. The conclusions of this paper are as follows: (1) From 2001 to 2020, the evolution speed of PRD was the fastest, while that of SRD was the slowest. Rocky desertification control should not only focus on areas with serious rocky desertification, but also prevent transformation from NRD to PRD. (2) Rocky desertification will continue to improve over the next 30 years. Possible deterioration areas are concentrated in high-altitude areas, such as the south of Bijie and the east of Liupanshui.
CNN Inference Costs Estimation on Microcontrollers: the EST Primitive-based Model

Neural network inference on embedded devices will have an important industrial impact on our society. Embedded devices are ubiquitous in many fields, like human activity recognition or visual object detection. As a matter of fact, Convolutional Neural Networks (CNNs) are now the best modality to solve most of computer vision problems. Although, the accuracy offered by these algorithms has a cost: an important energy consumption, a high execution time, and a significant memory footprint. This cost is a major challenge to implement CNNs within embedded devices with limited computational power, memory space and energy available. This makes prior estimation about the impact of a CNN on a given microcontroller, a design key point before applying neural network compression techniques. We introduce the EST primitive-based model to estimate the impact of a CNN on a microcontroller, regarding the latency, the power consumption and the needed memory space. The target hardware is the STM32L496ZG with CPU ARM Cortex M4 running at 14 different frequencies. Our model shows an average estimation error of 13.66% on latency, 5.52% on power consumption and 2.09% on needed memory space.
Fused Deposition Modeling as a Possible Approach for the Preparation of Orodispersible Tablets

Additive manufacturing technologies are considered as a potential way to support individualized pharmacotherapy due to the possibility of the production of small batches of customized tablets characterized by complex structures. We designed five different shapes and analyzed the effect of the surface/mass ratio, the influence of excipients, and storage conditions on the disintegration time of tablets printed using the fused deposition modeling method. As model pharmaceutical active ingredients (APIs), we used paracetamol and domperidone, characterized by different thermal properties, classified into the various Biopharmaceutical Classification System groups. We found that the high surface/mass ratio of the designed tablet shapes together with the addition of mannitol and controlled humidity storage conditions turned out to be crucial for fast tablet’s disintegration. As a result, mean disintegration time was reduced from 5 min 46 s to 2 min 22 s, and from 11 min 43 s to 2 min 25 s for paracetamol- and domperidone-loaded tablets, respectively, fulfilling the European Pharmacopeia requirement for orodispersible tablets (ODTs). The tablet’s immediate release characteristics were confirmed during the dissolution study: over 80% of APIs were released from printlets within 15 min. Thus, this study proved the possibility of using fused deposition modeling for the preparation of ODTs.
Atom species energy dependence on magnetic configurations in the perovskite yttrium orthoferrite

The article represents the results of studying of the influence of atom species in the perovskite multiferroic yttrium orthoferrite YFeO3 on magnetic configurations by ab-initio methods. Four magnetic configurations possible in magnetic sublattice that were formed by iron atoms were analyzed. It is shown that different magnetic orderings change the lattice parameters and the ions occupied positions while preserving symmetry of the unit cell, the lowest state responds G-AFM type magnetic ordering. The lattice parameters are in good relevant published experimental data. The atom species energy dependence shows that the main role in magnetic properties goes to iron and oxygen. In the ground state, magnetic properties relate with Dzyaloshinskii – Moriya interaction, while in other configurations, these relate with superexchange through Fe- O-Fe chains. Obtained results are useful for analyzing and designing straintronics devices. Also, the results can be interesting for interpretation and predicting magnetic properties of partially or fully substituted orthoferrites including substitution on rare-earth elements.
Three-Dimensional Sparse SAR Imaging with Generalized Lq Regularization

Three-dimensional (3D) synthetic aperture radar (SAR) imaging provides complete 3D spatial information, which has been used in environmental monitoring in recent years. Compared with matched filtering (MF) algorithms, the regularization technique can improve image quality. However, due to the substantial computational cost, the existing observation-matrix-based sparse imaging algorithm is difficult to apply to large-scene and 3D reconstructions. Therefore, in this paper, novel 3D sparse reconstruction algorithms with generalized Lq-regularization are proposed. First, we combine majorization–minimization (MM) and L1 regularization (MM-L1) to improve SAR image quality. Next, we combine MM and L1/2 regularization (MM-L1/2) to achieve high-quality 3D images. Then, we present the algorithm which combines MM and L0 regularization (MM-L0) to obtain 3D images. Finally, we present a generalized MM-Lq algorithm (GMM-Lq) for sparse SAR imaging problems with arbitrary q0≤q≤1 values. The proposed algorithm can improve the performance of 3D SAR images, compared with existing regularization techniques, and effectively reduce the amount of calculation needed. Additionally, the reconstructed complex image retains the phase information, which makes the reconstructed SAR image still suitable for interferometry applications. Simulation and experimental results verify the effectiveness of the algorithms.
Numerical Simulation for Dynamic Characteristics of Bouc-Wen Hysteretic System

One of the most widely accepted smoothly varying differential models in the engineering field is Bouc-Wen hysteresis model. With different parameters of Bouc-Wen hysteresis model, the vibrating system is nonlinear for its soft or hard responses. The relationship between amplitude and frequency of a SOF Bouc-Wen hysteretic system with different parameters is investigated by means of numerical simulation. The influence of hysteretic parameters on dynamic responses of vibrating system is studied, and some new phenomena are detected.
Gyroscope Failure Trend Prediction Based on CPSO-LSSVM Algorithm

In order to improve the learning performance and generalization ability of the least squares support vector machine (LSSVM),Chaos particle swarm optimization algorithm (CPSO) combined with k -fold cross validation (CV) was proposed for selecting the optimal parameters of LSSVM.Chaotic search was introduced to PSO algorithm to generate the initial chaotic particles and chaotic interrupt was added to the particles in the motions for the selection of LSSVM parameters automatically.CV error was used to construct the fitness function of particles as the assessing criteria of the LSSVM parameters choice.Gyroscope random drift was the main factors affecting the reliability of the gyroscope performance.LSSVM regression model based on CPSO-CV algorithm was used to establish time series prediction model of gyroscopes random drift for gyroscope failure trend prediction.The results showed that the proposed method is an effective approach for LSSVM parameter selection and the regression model has a better prediction precision.
Blockchain-Based Security Model for LoRaWAN Firmware Updates

The Internet of Things (IoT) is changing the way consumers, businesses, and governments interact with the physical and cyber worlds. More often than not, IoT devices are designed for specific functional requirements or use cases without paying too much attention to security. Consequently, attackers usually compromise IoT devices with lax security to retrieve sensitive information such as encryption keys, user passwords, and sensitive URLs. Moreover, expanding IoT use cases and the exponential growth in connected smart devices significantly widen the attack surface. Despite efforts to deal with security problems, the security of IoT devices and the privacy of the data they collect and process are still areas of concern in research. Whenever vulnerabilities are discovered, device manufacturers are expected to release patches or new firmware to fix the vulnerabilities. There is a need to prioritize firmware attacks, because they enable the most high-impact threats that go beyond what is possible with traditional attacks. In IoT, delivering and deploying new firmware securely to affected devices remains a challenge. This study aims to develop a security model that employs Blockchain and the InterPlanentary File System (IPFS) to secure firmware transmission over a low data rate, constrained Long-Range Wide Area Network (LoRaWAN). The proposed security model ensures integrity, confidentiality, availability, and authentication and focuses on resource-constrained low-powered devices. To demonstrate the utility and applicability of the proposed model, a proof of concept was implemented and evaluated using low-powered devices. The experimental results show that the proposed model is feasible for constrained and low-powered LoRaWAN devices.
Unconventional Monetary Policy through Open Market Operations: A Principal Component Analysis*

Abstract This paper examines the unconventional monetary policies of the Bank of Japan from 2002 to 2019 with a focus on open market operations. We apply a principal component analysis to investigate the complexity of monetary policy. Our results identify four principal components that explain the variance of measures taken by the Bank of Japan and its operations of various facilities: asset purchase measures including Japanese Government Bonds (JGBs), Exchange-Traded Funds (ETFs), Japanese Real Estate Investment Trusts (J-REITs), and three different liquidity supply measures. Complexity differs substantially among different governorships of Fukui, Shirakawa (most complex), and Kuroda. We derive some conclusions from the increased complexity with implications for the economy.
Cuckoo Search Algorithm and Artificial Neural Network-based MPPT: A Comparative Analysis

Solar PV system has exponentially gained its popularity and applications in residential, industrial, battery charging, and solar-powered vehicles. Because of its certain shortcomings such as low conversion efficiency, non-linear characteristic, high cost of installation, and dependability on environmental conditions that is ambient temperature and solar radiation, it drew the researcher’s attention to overcome these limitations. Numerous MPPT (maximum power point tracking) algorithms have been presented in this regard in order to capture the maximum power from a solar PV module. Among these MPPT techniques, only a few can effectively work under varying atmospheric conditions. In this article, artificial neural network-based MPPT is presented and cuckoo search algorithm-based MPPT has been used and their comparative analysis has been done. The simulation is performed and the outcome is then validated using MATLAB/Simulink.
An Overview of Molecular Dynamics Simulation for Food Products and Processes

Molecular dynamics (MD) simulation is a particularly useful technique in food processing. Normally, food processing techniques can be optimized to favor the creation of higher-quality, safer, more functional, and more nutritionally valuable food products. Modeling food processes through the application of MD simulations, namely, the Groningen Machine for Chemical Simulations (GROMACS) software package, is helpful in achieving a better understanding of the structural changes occurring at the molecular level to the biomolecules present in food products during processing. MD simulations can be applied to define the optimal processing conditions required for a given food product to achieve a desired function or state. This review presents the development history of MD simulations, provides an in-depth explanation of the concept and mechanisms employed through the running of a GROMACS simulation, and outlines certain recent applications of GROMACS MD simulations in the food industry for the modeling of proteins in food products, including peanuts, hazelnuts, cow’s milk, soybeans, egg whites, PSE chicken breast, and kiwifruit.
Modulation of Sleep Architecture by Whole-Body Static Magnetic Exposure: A Study Based on EEG-Based Automatic Sleep Staging

A steady increase in sleep problems has been observed along with the development of society. Overnight exposure to a static magnetic field has been found to improve sleep quality; however, such studies were mainly based on subjective evaluation. Thus, the presented data cannot be used to infer sleep architecture in detail. In this study, the subjects slept on a magneto-static mattress for four nights, and self-reported scales and electroencephalogram (EEG) were used to determine the effect of static magnetic field exposure (SMFE) on sleep. Machine learning operators, i.e., decision tree and supporting vector machine, were trained and optimized with the open access sleep EEG dataset to automatically discriminate the individual sleep stages, determined experimentally. SMEF was found to decrease light sleep duration (N2%) by 3.51%, and sleep onset latency (SOL) by 15.83%, while it increased deep sleep duration (N3%) by 8.43%, compared with the sham SMFE group. Further, the overall sleep efficiency (SE) was also enhanced by SMFE. It is the first study, to the best of our knowledge, where the change in sleep architecture was explored by SMFE. Our findings will be useful in developing a non-invasive sleep-facilitating instrument.
A Burst-Mode TIA with Adaptive Response and Stable Operation for in-Vehicle Optical Networks

In-vehicle optical network systems have been studied eagerly for realizing self-driving cars. To provide a stable operation of the optical receiver in the system, we propose an appropriate-time-constant control circuit for a burst-mode transimpedance amplifier (TIA) which provides adaptive response in single-ended to differential conversion circuit in accordance with input-data patterns. We present operating principles, the design procedures, and the post-layout simulation results of the proposed circuit in 65-nm CMOS process. We verify the fast response within 4 ns and the high tolerance to consecutive identical digit length of 66 bits in the proposed circuit.