Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the Potential Global Distribution of Honeybee Pest, Galleria mellonella under Changing Climate

Simple Summary The greater wax moth (GWM) is a common pest of bee colonies throughout the world. This study highlighted the global habitat suitability of GWM using the statistical power of Maxent to model its current and future distribution under climate-change scenarios in 2050 and 2070. Our study shed light on the major environmental factors that manage the habitat suitability of GWM. To the best of our knowledge, this is the first modeling study of wax moths. In brief, this pest can cause severe indirect damage to the global honey market totalling millions of dollars; therefore, developing prompt monitoring or control strategies is advised. Abstract Beekeeping is essential for the global food supply, yet honeybee health and hive numbers are increasingly threatened by habitat alteration, climate change, agrochemical overuse, pathogens, diseases, and insect pests. However, pests and diseases that have unknown spatial distribution and influences are blamed for diminishing honeybee colonies over the world. The greater wax moth (GWM), Galleria mellonella, is a pervasive pest of the honeybee, Apis mellifera. It has an international distribution that causes severe loss to the beekeeping industry. The GWM larvae burrow into the edge of unsealed cells that have pollen, bee brood, and honey through to the midrib of the wax comb. Burrowing larvae leave behind masses of webs that cause honey to leak out and entangle emerging bees, resulting in death by starvation, a phenomenon called galleriasis. In this study, the maximum entropy algorithm implemented in (Maxent) model was used to predict the global spatial distribution of GWM throughout the world. Two representative concentration pathways (RCPs) 2.6 and 8.5 of three global climate models (GCMs), were used to forecast the global distribution of GWM in 2050 and 2070. The Maxent models for GWM provided a high value of the Area Under Curve equal to 0.8 ± 0.001, which was a satisfactory result. Furthermore, True Skilled Statistics assured the perfection of the resultant models with a value equal to 0.7. These values indicated a significant correlation between the models and the ecology of the pest species. The models also showed a very high habitat suitability for the GWM in hot-spot honey exporting and importing countries. Furthermore, we extrapolated the economic impact of such pests in both feral and wild honeybee populations and consequently the global market of the honeybee industry.
An empirical study on the effect of education on labor productivity

This study investigates the effect of education on labor productivity using data from the Jordanian economy. Jordan has the world’s second-highest share of refugees in comparison to its population. This fact outlines the main challenges facing efforts to provide quality education to Jordanian citizens and refugees. This study shed light on the obstacles and challenges facing education in Jordan to serve as a wake-up call for decision-makers. The study employs the autoregressive distributed lag (ARDL) model bounds testing procedure developed by Pesaran, Shin, and Smith (2001), using data ranging from 1984 to 2018. The results showed that in the long run, education has a positive and statistically significant effect on labor productivity. Inversely, in the short run, education has a negative and statistically significant effect on labor productivity. The estimation results indicate that a 1% increase in education in the long run increases labor productivity by 1.15%, and a 1% increase in education in the short run decreases labor productivity by 1.25%. In addition, the study found that both labor share of capital and foreign direct investment (FDI), have a positive and significant effect on labor productivity. The study concluded with several recommendations.
Study on multi-body dynamics simulation of linkage based on ADAMS

Taking flexible linkage as research object,simulation research on crank-block mechanism by applying multi-body system dynamics is conducted.An ADAMS-based model of mechanism prototype machine is established and influence of linkage flexibility under high-speed and heavy-load on system performance is obtained by dynamic simulation.Through vibration mode of linkage,principle of system performance change is analyzed and then calculation and analysis of dynamic stress distribution on linkage are carried out by utilizing FEA method.This makes mechanism service life prediction during structural design stage possible and implements the design of mechanism virtual prototype machine.
Designing inclusive playgrounds in Switzerland: why is it so complex?

ABSTRACT Playgrounds designed with the intention to be inclusive are one approach to creating equal opportunities for all children, including those with disabilities, in terms of their right to play. However, when building inclusive playgrounds, the focus is often limited to the physical environment. Yet, studies investigating children’s play in inclusive playgrounds have shown that other aspects of inclusion, such as social inclusion, are equally as important as the physical environment. Nevertheless, there is a lack of knowledge about how inclusion is considered in the design of inclusive playgrounds. Therefore, this study aimed to explore the design and use of inclusive playgrounds among people involved in the provision of inclusive playgrounds and advocates of children with disabilities from a Swiss context. Four focus groups were conducted with 26 participants involved in providing inclusive playgrounds or having a professional or personal relationship with children with disabilities. Results revealed no uniform understanding of inclusive playgrounds. Barriers to inclusive playground provision included negative attitudes, lack of knowledge about inclusion and the absence of policies for inclusion. Through the focus group discussions, it was proposed that a community network is needed, to bring together children with disabilities and their families with playground providers when designing inclusive playgrounds. In this context, user involvement can inform the design of playgrounds and support the understanding of the needs of people with disabilities in playgrounds, among other things. To enhance inclusion for children with disabilities on inclusive playgrounds, design approaches that consider social inclusion, like Universal Design, are proposed.
An Analysis of the Digital Transformation-Based Learning Implementation System in the Era of Disruption

The purpose of this paper is to complete the shortcomings of previous studies related to the challenges of implementing learning in the current era of disruption. In an effort to find alternative solutions that are more effective, this study seeks to analyze and examine how educators transform themselves to face various challenges in education in accordance with the spirit of the times. The existence of educational disruption has not been followed by an emphasis on the importance of digital-based transformative education and strengthening the competence of educators as an increasingly ideal basis for transformation. This paper is based on the argument that no matter how strong the influence of the pace of development of digital information technology, which tends to be disruptive, the spillover of which has shaken the education sector, creative and dynamic efforts will increasingly find momentum during the Covid-19 pandemic. Thus, educators continue to play a vital role in the educational process that cannot simply be replaced by technological sophistication. Within the limits of this understanding, basically all achievements of scientific and technological progress are rooted in the educational process by placing the basis of digital transformation as a starting point for change to a better, civilized and quality direction.
Volatility of Exchange Rate in Nigeria: An Investigation of Risk on Investment

The contribution of investment in currency trading in economic growth of a nation cannot be over emphasized. Hence, the examination of the risk involved in such trading because of volatility in foreign exchange rate. Time series data and model were used in this study. The monthly exchange rate of four major foreign currencies in Nigeria namely the US Dollars (USD), the Great Britain Pounds (GBP), the EURO and CFA Francs against Nigerian Naira (NGN) from January, 2004 to December, 2019 were extracted from website of Central Bank of Nigeria (CBN) with an open access to the public. Due to the volatile nature of the exchange rate, the Generalized Autoregressive Conditional Hetroscedastic (GARCH) model was a suitable model used at order 1 for parsimony to determine volatility used in computing Value at Risk (VaR). It was discovered that the maximum loss (risk), measured by VaR, that can occur at 95% confidence interval for twelve months forecast of trading with GBP was the highest, among the four currencies, with percentage loss of between 15.5% to 16.2%. While the CFA has the lowest risk with VaR between 0.02% to 0.03%. Based on the findings, the risk of investing in foreign exchange is the highest when trading in GBP with attendant high returns due to large fluctuations up and down of the exchange rate. This study will help investors to study the pattern of risk and returns and decide on the foreign currency to trade on. The government should put policies in place to encourage existing investors and new investors that want to invest on foreign exchange to create employment, since the risk involved can be determined as it is done in this study.
Classification of Ancient Buddhist Architecture in Multi-Cultural Context Based on Local Feature Learning

Ancient Buddhist architecture plays an important role in the development of Chinese architectural culture. Under the background of multiculturalism, the ancient Buddhist architectural style has also been influenced to varying degrees. In order to realize automatic classification of ancient Buddhist architecture under multi-cultural background, this paper proposes an automatic classification algorithm based on local feature learning. Firstly, the ancient Buddhist architecture images are gridded, so that the backbone network can obtain relatively flat ancient Buddhist architecture image blocks. At the same time, the backbone network can learn more local details. Then, the grid reconstruction module is designed to strengthen the connection between the features of each block and highlight the distinguishing detail features. The accuracy of ancient Buddhist architecture classification can be effectively improved through image meshing and mesh reconstruction. Experiment and analysis are carried out by using the dataset of ancient Buddhist architecture images on the Internet. Experimental results show that the proposed algorithm has better recognition accuracy and robustness than other comparison algorithms.
Design and testing of high temperature micro-ORC test stand using Siloxane as working fluid

Organic Rankine Cycle is a mature technology for many applications e.g. biomass power plants, waste heat recovery and geothermal power for larger power capacity. Recently more attention is paid on an ORC utilizing high temperature heat with relatively low power. One of the attractive applications of such ORCs would be utilization of waste heat of exhaust gas of combustion engines in stationary and mobile applications. In this paper, a design procedure of the ORC process is described and discussed. The analysis of the major components of the process, namely the evaporator, recuperator, and turbogenerator is done. Also preliminary experimental results of an ORC process utilizing high temperature exhaust gas heat and using siloxane MDM as a working fluid are presented and discussed. The turbine type utilized in the turbogenerator is a radial inflow turbine and the turbogenerator consists of the turbine, the electric motor and the feed pump. Based on the results, it was identified that the studied system is capable to generate electricity from the waste heat of exhaust gases and it is shown that high molecular weight and high critical temperature fluids as the working fluids can be utilized in high-temperature small-scale ORC applications. 5.1 kW of electric power was generated by the turbogenerator.
Antiferromagnetic skyrmion based shape-configured leaky-integrate-fire neuron device

Spintronic devices based on antiferromagnetic (AFM) skyrmion motion on the nanotracks have gained significant interest as a key component of neuromorphic data processing systems. AFM skyrmions are favorable over the ferromagnetic (FM) skyrmions as they follow the straight trajectories and prevent its annihilation at the nanotrack edges. In this paper, the AFM skyrmion-based neuron device that exhibits the leaky-integrate-fire functionality is proposed for the first time. It exploits the current-driven skyrmion dynamics on the shape-configured nanotracks that are linearly decreasing and exponentially decaying. The device structure creates the regions from lower to higher energy states for the AFM skyrmions during its motion from the wider to narrower region. This causes the repulsion force from the nanotrack edges to act on the AFM skyrmion thereby, drifting it in the backward direction in order to minimize the system energy. This provides the leaking functionality to the neuron device without any external stimuli and additional hardware cost. The average velocities during the integration and leaky processes are in the order of 103 and 102 m s−1, respectively, for the linearly and exponentially tapered nanotracks. Moreover, the energy of the skyrmion is in the order 10−20 J. Hence, the suggested device opens up the path for the development of high-speed and energy-efficient devices in AFM spintronics for neuromorphic computing.
Effects of Near-Fault Pulses on Nonlinear Soil-Structure Systems

ABSTRACT This paper is focused on effects of near-fault pulse characteristics on seismic performance of soil-structure systems considering foundation uplifting and soil yielding. To this end, an extensive parametric study is conducted. Mid-to-high-rise buildings of different aspect ratios (SR) resting on shallow mat foundations are investigated. Different vertical load-bearing safety factors (FS) of foundation as well as different soil types (i.e. soft to very dense) are considered in this study. Finite element method is used for numerical modeling. The underlying soil is simply modeled with a set of nonlinear springs and dashpots beneath the foundation. Mathematical near-fault pulse models of fling step and forward directivity are used as input ground motions. The results show that reduction in structural drift demands due to nonlinear soil-structure interaction (SSI) is more considerable in the case of short-period pulses compared to long-period ones. In more precise words, significant reduction occurs when pulse-to-fixed-base period ratio falls within 0.7–1.5 in the case of directivity pulses and 0.5–1.4 in the case of fling pulses. It is also demonstrated that the beneficial effects of nonlinear SSI reduce when the number of stories increases.
Double-sided photonic crystal fiber (PCF) temperature and refractive index (RI) sensor based on surface plasmon resonance (SPR)

Abstract A double-sided photonic crystal fiber (PCF) temperature and refractive index (RI) sensor based on surface plasmon resonance (SPR) is reported to simultaneously measure temperature and RI. An arc groove covered with gold is filled with chloroform for temperature detection. The D-shaped plane coated with silver is in direct contact with the analyte to provide the RI. Two independent channels distinguish temperature and RI changes, thus completely solving the cross-sensitivity problem. The sensing characteristics of the plane coating, arc coating, and inner ring coating are discussed. The influence of gold film closure on the optimum RI measurement range was identified, and the best sensing structure of chloroform as a temperature-sensitive material is obtained. The sensing characteristics of different types of metal films are investigated. It is concluded that the band separation may be achieved by plating gold film and silver film in the arc groove and D-plane, respectively. The influence of the central angle of the arc groove on the sensor characteristics was investigated. High-order resonance may be avoided at a 180° center angle. The influence of the thickness of the metal film on the sensitivity of the sensor is studied numerically, and the optimal coating thickness is 50 nm. This work simplifies the selection of the RI range of sensing materials and provides a new approach to solve the high-order resonance and band interference in SPR multi-parameter sensors.
Fuzzy-Ant Colony based Routing on Road Networks

Route selection is essential in everyday life. We have several algorithms for detecting efficient route on Large Road Networks. This paper introduces the hierarchical community, is presented. It splits large road networks into hierarchical structure. It introduces a multi parameter route selection system which employs Fuzzy Logic (FL) and ant’s behavior in nature is applied to the dynamic routing. The important rates of parameters such as path length and traffic are adjustable by the user. The purposes of the new hierarchical routing algorithm significantly reduce the search space. We develop a community-based hierarchical graph model that supports Dynamic, efficient route computation on large road networks.
Reliability life evaluation of bipolar transistors based on deep learning

Quickly predicting the remaining useful life of bipolar transistors is an effective means of assessing their health and reliability. Aiming at the problems of high modeling difficulty and poor fitting accuracy in the traditional physical model method and data fitting method, this paper proposes a reliability life prediction method for bipolar transistors based on deep learning. Firstly, the experiment is carried out with the accelerated storage experimental data of transistors in the Microelectronics Reliability Laboratory of Beijing University of Technology, and the reverse leakage current ICBO is selected as the failure sensitive parameter, and a reasonable failure judgment is set. The lifetime of the sample is calculated; Secondly, the average lifetime under three common distributions is given, and combined with the Peak temperature and humidity model, the storage lifetime of the transistor under natural storage conditions is extrapolated; finally, the mean absolute error (MAE) and mean square error (MSE) are selected as evaluation functions, and the life prediction results of the deep learning model and the data fitting method are compared. The results show that compared with the data fitting method, the MAE of the life prediction results of the deep learning model decreases by 82.00%, and the MSE decreases by 98.16%, which verifies the effectiveness of the method.
Oscillometry-based Blood Pressure Estimation using Convolutional Neural Networks

Blood pressure measurement is required to monitor the cardiovascular state of a person, and it is commonly conducted in a noninvasive way using oscillometry-based blood pressure monitors (BPM). Blood pressure can be estimated by analyzing the oscillometric waveform (OMW) in the BPM, and many methods have been examined to increase their estimation accuracy. In this study, we proposed a new method that enhances estimation accuracy and requires no external user information, such as age and gender, in the test phase. In the method, the entire OMW was considered as an input to reduce information loss via feature extraction, and convolutional neural networks were utilized to effectively analyze the high-dimensional input. Additionally, the proposed method included a novel ensemble method to further increase the estimation accuracy. The performance of the proposed method was evaluated and compared with other studies via subject-independent tests considering real situations in which it is difficult to obtain preliminary information on a test subject. Data from 64 subjects were used in the test. The mean absolute error of the proposed method was 3.12 and 3.98 mmHg for systolic and diastolic blood pressure, respectively, which was superior to those reported in other studies conducted in similar conditions. Individuals can measure their blood pressure with higher precision using the proposed method with improved estimation performance. This can aid in reducing the risk of cardiovascular diseases.
PSegNet: Simultaneous Semantic and Instance Segmentation for Point Clouds of Plants

Phenotyping of plant growth improves the understanding of complex genetic traits and eventually expedites the development of modern breeding and intelligent agriculture. In phenotyping, segmentation of 3D point clouds of plant organs such as leaves and stems contributes to automatic growth monitoring and reflects the extent of stress received by the plant. In this work, we first proposed the Voxelized Farthest Point Sampling (VFPS), a novel point cloud downsampling strategy, to prepare our plant dataset for training of deep neural networks. Then, a deep learning network—PSegNet, was specially designed for segmenting point clouds of several species of plants. The effectiveness of PSegNet originates from three new modules including the Double-Neighborhood Feature Extraction Block (DNFEB), the Double-Granularity Feature Fusion Module (DGFFM), and the Attention Module (AM). After training on the plant dataset prepared with VFPS, the network can simultaneously realize the semantic segmentation and the leaf instance segmentation for three plant species. Comparing to several mainstream networks such as PointNet++, ASIS, SGPN, and PlantNet, the PSegNet obtained the best segmentation results quantitatively and qualitatively. In semantic segmentation, PSegNet achieved 95.23%, 93.85%, 94.52%, and 89.90% for the mean Prec, Rec, F1, and IoU, respectively. In instance segmentation, PSegNet achieved 88.13%, 79.28%, 83.35%, and 89.54% for the mPrec, mRec, mCov, and mWCov, respectively.
Thermal uniformity enhancement of the motorcycle exhaust thermoelectric generator—Part 2: Muffler outlet optimization

Following the development of the muffler with the optimal guide fins (MOGF) in the companion paper (Part 1), this study optimizes the muffler outlet in terms of structure and geometry to further improve the thermal uniformity of the hot‐side heat exchanger (HHE). Two outlet structures and two outlet geometry parameters (i.e., outlet angle φ and outlet position y) are proposed. The exhaust flow uniformity of HHE serves as an assessment criterion in numerous computational fluid dynamics simulations to determine the optimal outlet structure and optimal values of φ and y. The outlet of the MOGF is then modified based on the simulation results to create the muffler with the optimal guide fins and outlet (MOGFO). Various experiments on the MOGFO are conducted to verify the simulation results and evaluate the improvement of the HHE's thermal uniformity. The simulation results prove that the optimal outlet improves the exhaust flow uniformity by 1.78% on average compared with the original outlet. In experiments, the combination of optimal muffler's outlet and optimal muffler's guide fins on the MOGFO raises the thermal uniformity of HHE by an average of 22.71% and 50% compared with the MOGF and the muffler model with nonoptimal guide fins and outlet in Part 1, respectively. The remarkable outcomes of this paper, together with Part 1, are expected to enhance the efficiency and lifespan of the thermoelectric generator unit.