Miracle-dz/UniFAR-FTUs huggingface dataset:
Potential risks of Invasive Alien Plant Species on native plant biodiversity in Sri Lanka due to climate change

ABSTRACT There have been few studies investigating the implications of the potential distribution of plant invasions on native biodiversity due to climate change. In this study, we used combined climatic suitability maps of 14 priority Invasive Alien Plant Species (IAPS) in Sri Lanka under the current climate and under Representative Concentration Pathway (RCP) 4.5 and RCP 8.5 climate scenarios for 2050 to examine the potential risks of plant invasions on native plant biodiversity. We used three types of layers defining plant biodiversity patterns for Sri Lanka; (i) nine zones of plant endemism (zones of high floristic richness and endemicity), (ii) eleven threatened endemic taxa and (iii) eight forest-related ecosystems. Our results reveal that the biodiversity-rich zones of endemism are at potentially high-risk under climate change. The potential risks on threatened endemic plants are likely to reduce slightly under an RCP 4.5 low-emissions scenario and be intensified under an RCP 8.5 high-emissions scenario. Forest-related ecosystems are vulnerable to IAPS to varying degrees; dry zone ecosystems are predicted to increase the risks of IAPS, while those in the wet zone are envisioned to decrease. Overall, our findings suggest that the potential risks of plant invasions on native plant biodiversity differ significantly under projected climate change. Greater understanding of the potential risks of IAPS at an early stage is important in prioritising future conservation measures for effective protection of native biodiversity.
A machine learning approach for implementing data-driven production control policies

ABSTRACT Given the extensive data being collected in manufacturing systems, there is a need for developing a systematic method to implement data-driven production control policies. For an effective implementation, first, the relevant information sources must be selected. Then, a control policy that uses the real-time signals collected from these sources must be implemented. We analyse the production control policy implementation problem in three levels: choosing the information sources, forming clusters of information signals to be used by the policy and determining the optimal policy parameters. Due to the search-space size, a machine-learning-based framework is proposed. Using machine learning speeds up optimisation and allows utilising the collected data with simulation. Through two experiments, we show the effectiveness of this approach. In the first experiment, the problem of selecting the right machines and buffers for controlling the release of materials in a production/inventory system is considered. In the second experiment, the best dispatching policy based on the selected information sources is identified. We show that selecting the right information sources and controlling a production system based on the real-time signals from the selected sources with the right policy improve the system performance significantly. Furthermore, the proposed machine learning framework facilitates this task effectively.
Depressive symptoms as a mediator between impulsiveness and suicidal ideation among Chinese medical college students

Background: Suicidal ideation is thought to be of high risk for attempts and behaviours. Researches have suggested that depressive symptoms and impulsiveness can affect suicidal ideation, attempts and behaviours independently. But how they work together in influencing suicidal ideation remains unclear. This study is aiming to investigate the reciprocal associations among impulsiveness, depressive symptoms, and suicidal ideation and to confirm the mediating effect of depressive symptoms on impulsiveness and suicidal ideation through cross-sectional and longitudinal analyses.Methods: The Self-rating Depression Scale (SDS), Barratt Impulsiveness Scale 11th version (BIS-11) and Self-rating Idea of Suicide Scale (SIOSS) were applied, as well as a list of demographic information. Multilinear regression and parallel process latent growth curve models were applied to conduct cross-sectional and longitudinal analyses, respectively.Results: (1) Participants with suicidal ideation scored higher in depressive symptoms and impulsiveness than those without suicidal ideation (P<10-5). (2) The three variables and the three dimensions of impulsiveness were significantly reciprocally correlated with each other (P<0.01). (3) For the impulsiveness total score, non-planning, motor and attention, the multilinear regression models fit well (adjusted R2>0.3, SRMR<0.05). Their indirect effects on suicidal ideation through depressive symptoms were all statistically significant (BIS-SDS-SIOSS, indirect effect coefficient=0.557, P<0.001; non-planning-SDS-SIOSS, indirect effect coefficient=0.480, P<0.001; motor-SDS-SIOSS, indirect effect coefficient=0.486, P<0.001; attention-SDS-SIOSS, indirect effect coefficient=0.504, P<0.001). (4) Four parallel process latent growth curve models fit well (RMSEA<0.05). The total indirect effect of impulsiveness on the growth rate of suicidal ideation through the initial level and growth rate of depressive symptoms was significant (indirect effect coefficient=-0.450, P=0.001). The indirect effects of non-planning and attention impulsiveness were -0.336 (P=0.005) and -0.314 (P=0.002), respectively. However, the indirect effect of motor function on the growth rate of suicidal ideation was not significant (coefficient=-0.379, P=0.067). Conclusions: This study mainly revealed that individuals with impulsive personalities might be at higher risk for and experience more rapid changes in suicidal ideation. The effects were indirect and mediated by depressive symptoms. This implies that impulsive personality with depressive symptoms is critical for suicide prevention and needs more attention.
Limitations to community participation in tourism from local people’s perspectives: Manicaland Province, Zimbabwe

ABSTRACT This article, examines the perspectives of rural people in Manicaland on the barriers to community participation in tourism. A qualitative approach was selected to give local people from the four districts of Manicaland a voice. In-depth interviews, observations and informal conversations were used to collect data. Data was collected from the various levels of the local communities. Data was collected from 43 local people in Manicaland and twenty more interviews were done with key informants. The findings indicate that local people are limited to participate in tourism by their lack of tourism knowledge, limited finance, peripherality, the marginalisation of women, and elite domination. Suggestions are made to mitigate these barriers. These findings may expose the strengths and weaknesses of the current strategies and approaches used to promote community participation in Zimbabwe’s community-based tourism development, making way for the best policies and approaches to boost community participation in tourism.
A feasibility study of metal artifacts reduction using weighted iterative reconstruction algorithm for frequency split metal artifact reduction

The sinogram inpainting based methods such as normalized metal artifact reduction (NMAR) shows good performance in reducing the metal artifacts. However, these methods wipe away the structures near the metal which are severely corrupted by artifacts. The proposed method using a weight to lower metal corrupted pixels during the iteration instead of using an inpainting method to conserve the structures. Then, the proposed method was complementarily used with NMAR to generate a corrected image using the frequency split method. The titanium inserted XCAT phantoms were simulated from the 80kVp of energy. The metals were segmented from the filtered backprojection image using the threshold from the projection data. Afterwards, they were projected to acquire the metal sinogram index and the metal subtracted projection data. A weight map is generated from the scale factor and the log of the original raw data which has the metal index. The metal subtracted projection data were iteratively reconstructed. During the iteration, the step size of the update term was affected by a weight map, and the contributions of the metal present sinogram were lowered. Finally, using the frequency split approach, the corrected image was generated from the proposed and NMAR results. It was possible to lower the artifacts while conserving the structures in the proposed method. This work indicates the weighted iterative reconstruction can be complementarily used with the existing FSMAR approach.
Paperchain: Distributed Way of Publishing Paper on the Blockchain﻿

— Decentralization is the process by which the activities are distributed in such a way that there is no central, authoritative location or any central server where all records are stored. A blockchain is one such decentralized, distributed, a public network comprising of records called blocks which keeps track of transactions across many devices so that any involved block cannot be changed or tampered, without the alteration of all blocks. [1] Paperchain is a platform that integrates both these concepts. Paperchain is a decentralized platform that leverages the power of blockchain technology in empowering authors to take full ownership of their work and finances. Our platform is built on a transparent Peer-to-Peer network powered by programmable smart contracts to enable fair remuneration for all research work and journals. The long-term vision is to develop an open ecosystem where every geek is allowed to read and publish their research work on top of the Paperchain platform.
Quantitative Recognition and Characteristic Analysis of Production-Living-Ecological Space Evolution for Five Resource-Based Cities: Zululand, Xuzhou, Lota, Surf Coast and Ruhr

The accurate identification of PLES changes and the discovery of their evolution characteristics is a key issue to improve the ability of the sustainable development for resource-based urban areas. However, the current methods are unsuitable for the long-term and large-scale PLES investigation. In this study, a modified method of PLES recognition is proposed based on the remote sensing image classification and land function evaluation technology. A multi-dimensional index system is constructed, which can provide a comprehensive evaluation for PLES evolution characteristics. For validation of the proposed methods, the remote sensing image, geographic information, and socio-economic data of five resource-based urbans (Zululand in South Africa, Xuzhou in China, Lota in Chile, Surf Coast in Australia, and Ruhr in Germany) from 1975 to 2020 are collected and tested. The results show that the data availability and calculation efficiency are significantly improved by the proposed method, and the recognition precision is better than 87% (Kappa coefficient). Furthermore, the PLES evolution characteristics show obvious differences at the different urban development stages. The expansions of production, living, and ecological space are fastest at the mining, the initial, and the middle ecological restoration stages, respectively. However, the expansion of living space is always increasing at any stage, and the disorder expansion of living space has led to the decrease of integration of production and ecological spaces. Therefore, the active polices should be formulated to guide the transformation of the living space expansion from jumping-type and spreading-type to filling-type, and the renovation of abandoned industrial and mining lands should be encouraged.
Research on fatigue detection based on visual features

The high incidence of trafﬁc accidents brings immeasurable losses to life. In order to avoid such crises, researchers and automakers have used many methods to solve this problem. Among them, technology based on visual features is widely used in driver fatigue detection. As fatigue detection plays a vital role in the driving process, the high accuracy of fatigue monitoring is very important. This paper focuses on the method based on convolutional neural network to detect driver fatigue. First, in the face detection part, the Single-Shot Multi-Box Detector algorithm is used to improve the speed and accuracy of face detection to extract the eye and mouth regions; second, the VGG16 network is used to learn fatigue features, which is performed on the NTHU-Drowsy Driver Detection (NTHU-DDD) data set and the other two modiﬁed data sets Training test. The main result of this work is that the accuracy of fatigue monitoring is higher than other methods including the original method, with an accuracy rate of over 90%. And it has better generalization ability than the multi-physical feature fusion detection method. At the same time, we propose the fatigue detection method based on convolutional neural network to improve the advanced driver assistance system (ADAS) to make it more robust and reliable decision making.
Silicon photonic devices for wavelength/mode-division-multiplexing

Silicon photonics is recognized as one of the most promising platforms for on-chip optical interconnects. In order to further enhance the on-chip optical-interconnect link capacity, silicon photonic devices for advanced multiplexing technologies have been widely investigated, including wavelength-division-multiplexing (WDM), mode-divisionmultiplexing (MDM), as well as the hybrid multiplexing. This paper gives a review for our recent progresses in the development of silicon-based on-chip wavelength/mode-division-multiplexers.
Disturbances in Sporadic‐E During the Great Solar Eclipse of August 21, 2017

The great solar eclipse traveled eastward through the American Continent from coast to coast on August 21, 2017 and provided a rare opportunity to investigate the ionospheric responses to the rapid solar radiation variations. We have analyzed the peak electron density and density gradient variations in Sporadic E (Es) and applied the data of four Digisondes close to the eclipse path. All the four Digisondes have recorded the same interesting phenomena. One is the depletion on the fbEs (blanketing frequency of Es layer) curve during the solar eclipse and the other is the enhancements on the semi‐transparency parameter of Es layer Δf after the solar eclipse, where Δf = ftEs−fbEs, and ftEs is the top frequency of Es layer. The wind disturbances on the tidal wind and the photoionization decrease during the eclipse are put forward as a possible reason for the peak plasma density reduce. The periodic gravity waves have been observed in both ionospheric E and F region. However, the Δf enhancements occurred after the gravity waves had passed through the Es layer. Thus, we suggest that the solar induced waves did not raise the plasma density gradient directly. Gravity waves breaking may occur in the Mesosphere/Lower Thermosphere region to deposit momentum and produce turbulence, and so the accompanying instabilities may be generated to enhance the electron density gradient in E‐region.
Cloud-Based Fusion of Residual Exploitation-Based Convolutional Neural Network Models for Image Tampering Detection in Bioinformatics

Cloud computing has evolved in various application areas such as medical imaging and bioinformatics. It raises the issues of privacy and tampering in the images especially related to the medical field and bioinformatics for various reasons. The digital images are quite vulnerable to be tampered by the interceptors. The credibility of individuals can transform through falsified information in the images. Image tampering detection is an approach to identifying and finding the tampered components in the image. For the efficient detection of image tampering, the sufficient number of features are required which can be achieved by a deep learning architecture-based models without manual feature extraction of functions. In this research work, we have presented and implemented a cloud-based residual exploitation-based deep learning architectures to detect whether or not an image is being tampered. The proposed approach is implemented on the publicly available benchmark MICC-F220 dataset with the - fold cross-validation approach to avoid the overfitting problem and to evaluate the performance metrics.
Comprehensive study of SIR: Leading SUT repository for software testing

Software testing is performed by executing test cases against the system under test (SUT). For researchers, it is slightly challenging to find real case SUT. As an alternative, researchers utilize the existing SUT from repositories as their research objects. Software-artifact Infrastructure Repository (SIR) provides some reasonably comprehensive SUTs that had been used by over 50% of software testing literature. Nevertheless, it is still rare among researchers to conduct profound studies of SIR’s SUTs. This paper aims to fill the gap by explaining SIR’s SUTs specification and how it’s organized. Each of the C-language SUT was exposed and recorded its features comprising the size of SUT, lines of code (LOC), number of test cases, as well as the number and types of faults. SIR’s SUTs divided into two parts namely “older” and “newer” objects which both provided with various versions of the test program as well as predefined test sets along with its specification. These results indicate that SIR’s SUTs is carefully prepared to conduct software testing research.
Suppressing Buffer-Induced Current Collapse in GaN HEMTs with a Source-Connected p-GaN (SCPG): A Simulation Study

Carbon doping in the buffer of AlGaN/GaN high-electron-mobility transistors (HEMTs) leads to the notorious current collapse phenomenon. In this paper, an HEMT structure with a source-connected p-GaN (SCPG) embedded in the carbon-doped semi-insulating buffer is proposed to suppress the buffer-induced current collapse effect. Two-dimensional transient simulation was carried out to show the successful suppression of buffer-induced current collapse in the SCPG-HEMTs compared with conventional HEMTs. The mechanism of suppressing dynamic on-resistance degradation by ejecting holes from the SCPG into the high resistive buffer layer after off-state stress is illustrated based on energy band diagrams. This paper contributes an innovative device structure to potentially solve the buffer-induced degradation of the dynamic on-resistance in GaN power devices.
From science to sensational headline: a critical examination of the “sugar as toxic” narrative

ABSTRACT Over the past few decades, some scholars have labeled sugar “toxic” and portrayed it as the primary cause of diet-related health conditions. In this paper, this sugar as toxic rhetoric – which is widely echoed in the public sphere – is critically examined in terms of its physiological basis and wider social consequences. We argue that the claims of the narrative are oversimplified and partly based on a particular interpretation of scientific studies. By exaggerating the hazards of sugar consumption often using emotive language, the claims generate public concern about it being a threat to their health, which, in the process, contributes to a sense of moral panic. The resultant anxieties give emphasis to feelings of individual responsibility for regulating sugar consumption, which can further generate feelings of guilt and shame especially among those with limited agency for altering their consumption. We contend that the science on which the sugar as toxic narrative builds tends to be ideologically driven in a way that deflects attention away from a more serious debate about food environments. We conclude that the challenges are considerable for those whose role it is to communicate public health messages, especially if the issue is complex and the science incomplete.
Impact of Product Knowledge and Product Characteristics on Consumers Purchase Decision of Laptop Computers among Students of Ekiti State University, Ado Ekiti

The study to examine the impact of product knowledge and product characteristics on consumers’ purchase decision of laptop computers among students of Ekiti State University. However the specific objectives of the research are to examine the impact of product knowledge; product characteristics; product familiarity and perceived product quality influence consumers’ purchase decision of laptop computers among students in Ekiti State University. The population of this study was 13,753 full time undergraduate students of Ekiti State University. The sample size is 390 respondents using stratified sampling techniques. Data gathered were sorted, coded, and analysed using descriptive and inferential statistics. The descriptive statistics were mainly frequency table. Inferential statistics used was regression analysis. The result showed that product knowledge (t = 12.373, p = 000 < 0.05); product characteristics (t = 4.750, p = 000 < 0.05); product familiarity (t = 14.235; p = 000 < 0.05) and product knowledge (t = 1.449; p = 0.148 > 0.05). The results showed that product familiarity and product knowledge has moderate positive impact on consumer purchase decision than does product characteristics and perceived product quality influence on consumer purchase decision. The study concluded that product familiarity is the most potent motivating factors influencing consumers’ choice of brand of laptop computers meaning that the student purchase their laptop computers base on the familiar product.
Asymptotic stability and continuity of nonlinear hybrid stochastic differential equation with randomly occurring delay

This paper concentrates on discussing several types of asymptotic stability and continuity for the nonlinear hybrid stochastic differential equation (NHSDE) with randomly occurring delay. Different from the time delay considered in previous studies, we discuss the randomly occurring time-varying delay characterized by the random variable subject to the Bernoulli distribution. The existence and uniqueness of the maximal local solution and the global solution to the NHSDE are investigated by using some classical inequalities and the stochastic analysis theory. Then, by means of the delay-dependent Lyapunov functional method and the M-matrix technique, some novel sufficient conditions are given to ensure that the trivial solution can achieve the pth moment asymptotic stability, the almost surely asymptotic stability, the pth moment continuity and the continuity in probability. The stability and continuity criteria established in this paper include the upper bound of the time-varying delay and are less conservative. In the end, two numerical examples and corresponding computer simulations are provided to verify the validity of the presented theoretical results.