Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative Trait Loci Mapping for Vigour and Survival Traits of Barley Seedlings after Germinating under Salinity Stress

Seed germination and seedling establishment are the most critical stages in the barley (Hordeum vulgare ssp. vulgare L.) life cycle that contribute substantially to grain yield. These two phases are exposed to several forms of environmental stresses such as salinity due to high level of salt accumulation in the soil rhizosphere where seed germination takes place and seedlings emerge from. Previously, we have reported genotypic variability and independent QTLs associated with salinity tolerance at seedling and germination stages. However, genotypic studies on revival of a seedling germinating under salinity stress are yet to close the lack of information between germination and seedling stages. Here, we attempt to close the genetic gap by targeting early seedling survival traits in barley after germination under salinity (NaCl) stress and the various seedling vigour indices. Seedling vigour parameters formed the basis for Quantitative trait locus (QTL) linkage mapping in 103 Doubled Haploid (DH) lines of CM72/Gairdner population, and validated the phenotypic response using a selected diverse panel of 85 barley germplasm. The results indicate that 150 mM NaCl stress significantly reduced all the recorded phenotypic traits compared to 75, 90 and 120 mM NaCl. In both DH population and diversity panel barley germplasm, the highest percentage reduction was recorded in shoot length (65.6% and 50.3%) followed by seedling vigour index length (56.5% and 41.0%), while root length (28.6% and 15.8%) and root dry weight (29.3% and 28.0%) were least reduced when control was compared to150 mM NaCl stress treatment. Six QTLs containing 13 significant markers were detected in the DH population, 3 on chromosomes 1H, 8 on 3H and 2 on 4H with LOD values ranging from 3 to 8 associated with seedling survival traits under salinity stress. Three QTLs one on 1H and two on 3H with closely linked significant markers (Bmac0032, bPb-9418 and bPb-4741), (bPb-4576 and bPb-9624) and (bPb-3623, bPb-5666 and bPb-6383) for 1H and two on 3H respectively formed the regions with high possibility of candidate genes. A QTL on 3H flanked with markers bPb-4576 and bPb-9624 that were detected in more than one salinity survival trait and were closely linked to each other will form a basis for detailed studies leading to gene functional analysis, genetic transformation and marker assisted selection (MAS).
A New Design of Z-Source Capacitors to Ensure SCR's Turn-Off for the Practical Applications of ZCBs in Realistic DC Network Protection

The installed capacity of renewable energy resources in dc format is increasing in modern power systems, and thus, the protection of high-power dc power delivery from fault is critical in HVdc transmission and MVdc distribution networks. Based on thyristors, Z-source circuit breaker (ZCB) is a promising device for high-power protection, with the capabilities of bidirectional power flow and automatic/controllable turn-off function. In this article, a new specific method for calculating Z-source capacitances is developed based on the reverse-recovery characteristics of SCR, which was not considered in the prior-art solution properly. The proposed method can ensure the turn-off action of SCR in ZCB's practice for realistic dc network protection. At first, it is revealed that the problem in the prior-art solution of ZCB parameter identification, which does not handle the SCR's reverse recovery well, and thus, could result in a failure of tripping action. To solve the problem, the specific method is proposed to adjust the Z-source capacitances, in order to accumulate enough electrons for the postfault depletion region buildup in SCR, and thus, guarantee the SCR's turn-off for a successful dc circuit protection. In the specific derivation, the required tripping time of ZCB is also considered and can remain unaltered after Z-source capacitance adjustment. Finally, experimental tests have verified the problem solving by comparing the proposed to the prior-art solution and proven its accuracy. The proposed method can be added as a supplement to the prior-art solution for the practical design of ZCB. This research article helps to increase the technology readiness level of ZCB solution and practice in realistic dc network protection.
COVID-19 Pandemic and Antecedents for Digital Transformation in the Workplace: A Conceptual Framework

The emergence of coronavirus (COVID-19) has caused us to reconsider our everyday lives around the world. While the ability to work from home is an advantage to many workers, other businesses lack the foundation of the technological infrastructure to give the capacity of “business as usual” without some compromises. The goal of this paper is to develop a conceptual framework on digital transformations antecedents of global workplaces and organizations post-pandemic outbreak. The study emphasized that the most effective transformations point to four antecedents; smart capital allocation for digital initiatives, engagement of appropriate digital tools, talent recruitment, and brand permission. The study concluded that the global pandemic has created a whole new experience for businesses and organizations around the world. Therefore, business executives cannot sit on their laurels any longer and they cannot put off a digital transformation organizational focus. Henceforth, taking the right decisions today would help ensure that businesses are better placed after the pandemic is over.
Biotechnological aspects of the use of vegetable oils in the production of meat products

Biotechnological processes are becoming an important component of the world’s economy, including agro-industrial production. They are based on the use of biological objects such as plants, animals and microorganisms; biological processes, the effective management of which is the essence of biotechnology in the traditional and modern sense. Biotechnology provides groundbreaking approaches to improving animal health and livestock productivity. This improvement is possible by improving the use of high quality feed, as well as by increasing the efficiency of their use in the body. This problem can be solved by including vegetable oils in full-component diets, which are concentrated sources of energy for the body as a whole and give the feed certain taste and structure. The paper shows the positive effect of the additional introduction of vegetable oils (soybean and linseed ones) on increasing the productive qualities of beef cattle.
Paired Offspring Generation for Constrained Large-Scale Multiobjective Optimization

Constrained multiobjective optimization problems (CMOPs) widely exist in real-world applications, and they are challenging for conventional evolutionary algorithms (EAs) due to the existence of multiple constraints and objectives. When the number of objectives or decision variables is scaled up in CMOPs, the performance of EAs may degenerate dramatically and may fail to obtain any feasible solutions. To address this issue, we propose a paired offspring generation-based multiobjective EA for constrained large-scale optimization. The general idea is to emphasize the role of offspring generation in reproducing some promising feasible or useful infeasible offspring solutions. We first adopt a small set of reference vectors for constructing several subpopulations with a fixed number of neighborhood solutions. Then, a pairing strategy is adopted to determine some pairwise parent solutions for offspring generation. Consequently, the pairwise parent solutions, which could be infeasible, may guide the generation of well-converged solutions to cross the infeasible region(s) effectively. The proposed algorithm is evaluated on CMOPs with up to 1000 decision variables and ten objectives. Moreover, each component in the proposed algorithm is examined in terms of its effect on the overall algorithmic performance. Experimental results on a variety of existing and our tailored test problems demonstrate the effectiveness of the proposed algorithm in constrained large-scale multiobjective optimization.
The effect of logistics management, supply chain facilities and competitive storage costs on the use of warehouse financing of agricultural products

This study seeks to analyze the effect of logistics management carried out by warehouse operators, the facilities and access provided to support storage, and the competitiveness of storage costs on the use of warehouse financing by suppliers in Central Java, Indonesia. The sampling method was purposive random sampling. The numbers of respondents involved in this study were 120 suppliers and farmers producing first-rate agricultural products and who are users of warehouse receipts in the Central Java region. By using linear regression analysis with assistance, the study results found that the variables of logistic management, facilities and supply chain access as well as competitive storage costs have positive and significant effects on the use of warehousing financing by suppliers and farmers who use public warehousing. This result confirms that the more precisely warehousing is managed, the higher the level of trust of users involved in the logistics business and supply chain of agricultural products to use additional services in the form of warehouse receipts to support the sustainability of agricultural businesses.
Image Sizing using Removal or Insertion of Seams based on Energy Function

The nature of the image plays a significant role in any image sizing algorithm. The technique of improving or condensing an image while preserving its key image quality is challenging. The resolutions of various display devices vary from one another in order to fit the image according to the resolution image sizing plays a vital role. In order to size the image a operator is introduced which will remove or insert the seam from an images based on the resolution of the device used. A seam in an image is related to the pixel path from up to down or right to left, the seam vector to be deleted for image size is defined by the image energy attribute. The Seam removal or insertion operator can be applied to an image and aspect ratio can be adjusted accordingly by continuously removing or adding a seam in the desired region of image. The collection and order of seams decides the picture quality, as described by the energy function. By storing the order of seams in an image that can continuously change to fit a given size in real time, multi-sized images can be produced.
Fog Network Area Management Model for Managing Fog-cloud Resources in IoT Environment

The Internet of Things (IoT) paradigm is at the forefront of the present and future research activities. The enormous amount of sensing data needing to be processed increases dramatically in volume, variety, and velocity. In response, cloud computing was involved in handling the challenges of collecting, storing, and processing the data. The fog computing technology is a model used to support cloud computing by implementing pre-processing tasks close to the end-user for achieving low latency, less power consumption, and high scalability. However, some resources in fog computing network are not suitable for some tasks, or the number of requests increases outside capacity. So, it is more efficient to reduce sending tasks to the cloud. Perhaps some other fog resources are idle, and it is better to be federated rather than forwarding them to the cloud. This issue affects the fog environment's performance when dealing with large applications or applications sensitive to time processing. This research aims to propose a holistic fog-based resource management model to efficiently discover all the available services placed in resources considering their capabilities, deploy jobs into appropriate resources in the network effectively, and improve the IoT environment's performance. Our proposed model consists of three main components: job scheduling, job placement, and mobile agent software, explained in detail in this paper.
Credit Card Fraud Detection Model Based on LSTM Recurrent Neural Networks

With the increasing use of credit cards in electronic payments, financial institutions and service providers are vulnerable to fraud, costing huge losses every year. The design and the implementation of efficient fraud detection system is essential to reduce such losses. However, machine learning techniques used to detect automatically card fraud do not consider fraud sequences or behavior changes which may lead to false alarms. In this paper, we develop a credit card fraud detection system that employs Long Short-Term Memory (LSTM) networks as a sequence learner to include transaction sequences. The proposed approach aims to capture the historic purchase behavior of credit card holders with the goal of improving fraud detection accuracy on new incoming transactions. Experiments show that our proposed model gives strong results and its accuracy is quite high.
Multipath number estimation based on QR decomposition

Accurate multipath number estimation is the prerequisite for MUSIC algorithm to achieve excellent superresolution performance. To solve the problem that the existing multipath number estimation algorithms based on eigenvalue distribution usually require a large amount of computation, this paper proposed a fast multipath number estimation algorithm based on QR decomposition under the OFDM signal model. This algorithm only needs few times QR iteration decomposition to obtain the pseudo-eigenvalues of the matrix, and uses them to estimate the multipath number through two-step comparison. Due to the avoidance of accurate eigenvalues solution, compared with the traditional algorithms based on eigenvalue distribution, proposed algorithm’s computation is smaller. Simulation results showed that the proposed algorithm has better detection performance in low SNR and few snapshots than existing algorithms.
AFGHAN STUDENTS’ PURPOSES OF USING FACEBOOK AT MIRWAIS KHAN NIKA ZABUL INSTITUTE OF HIGHER EDUCATION, ZABUL, AFGHANISTAN

The aim of this study is to investigate the students’ purposes of using Facebook at Mirwais Khan Nika Zabul Institute of Higher Education, Qalat, Zabul province, Afghanistan. In this research, the quantitative research method was employed, and the data was collected from 115 students through a designed questionnaire. In addition, the respondents were selected through random sample. Additionally, the data was analyzed by implementing IBM 24 version of Statistical Package for the Social Sciences (SPSS), and found out the frequency and percentage of the items as well reported in a table. Moreover, the findings of this research reveal that learners use Facebook for several goals such as for socializing in order to be socialized in a society, getting social information, read news and for educational purposes, but they did not practice Facebook for the aims to pass their time, and share photos with their friends on the Facebook.
Prevalence and factors associated with hypertension among people living with HIV/AIDS on antiretroviral therapy in Uganda

Introduction antiretroviral therapy (ART) has improved survival of People Living with HIV (PLWH); however, this has resulted in an increasingly high prevalence of non-communicable diseases (NCD) like hypertension. Hypertension is a major risk factor for cardiovascular and cerebral vascular disease, which are both associated with high morbidity and mortality rates. We studied the prevalence and factors associated with hypertension among PLWH on ART. Methods we conducted a retrospective data analysis of PLWH on ART enrolled between 2011 and 2014 into a randomized double-blinded placebo-controlled trial investigating the safety of discontinuing cotrimoxazole prophylaxis (COSTOP) among PLWH in Central Uganda. We used the mean blood pressure (BP) measurements of the first four monthly clinic visits to define hypertension. Patients were categorised as: having normal BP (≤120/80mmHg), elevated BP (systolic >120-129, and diastolic ≤80), Stage 1 hypertension (systolic 130-139, or diastolic >80-89) and Stage 2 hypertension (systolic ≥140 or diastolic ≥90). Multiple logistic regression was used to evaluate factors associated with hypertension. Results data from 2026 COSTOP trial study participants were analysed, 74.1% were women and 77.2% were aged 35 years and above. The overall prevalence of hypertension was 29%, of whom 19.5% had Stage 1 hypertension and 9.5% had Stage 2 hypertension. About 21.4% were overweight or obese. Factors independently associated with hypertension among PLWH on ART included increasing age (p≤0.001) and high body mass index (p≤0.001). Efavirenz (p≤0.001) and lopinavir/ritonavir (p=0.036) based regimen had lower odds of hypertension than Nevirapine based regimens. Conclusion PLWH on ART have a high prevalence of hypertension, which rises with increasing age and body mass index (BMI) and among those on nevirapine-based ART. Implementation of hypertension prevention measures among PLWH on ART and integration of NCD and HIV care to improve patients’ management outcomes are required.
Applying frequent-pattern mining and time geography to impute gaps in smartphone-based human-movement data

ABSTRACT Though GPS-based human trajectory data have been commonly used in travel surveys and human mobility studies, missing data or data gaps that are intrinsically relevant to research reliability remain a critical and challenging issue. This study proposes a novel framework for imputing data gaps based on frequent-pattern mining and time geography, which allows for considering spatio-temporal travel restrictions during imputation by evaluating the spatio-temporal topology relations between the space-time prisms of gaps and corresponding frequent activities or trips. For the validation, the proposed framework is applied to raw GPS trajectories that were collected from 139 participants in Switzerland. In the case study, the temporal and spatio-temporal gaps are artificially generated by randomly choosing activities and trips from the trajectory data. Through comparing the mobility indicators (i.e. duration and distance) calculated from raw data, imputed data, and data with gaps, we quantitatively evaluate the performance of the proposed method in terms of Pearson correlation coefficients and deviation. We further compare the framework with the shortest path interpolation method based on the generated spatio-temporal gaps. The comparison results demonstrate the performance and advantage of the proposed method in imputing gaps from GPS-based human movement data.
Asymptotic normality of the residual correlogram in the continuous-time nonlinear regression model

In a continuous time nonlinear regression model the residual correlogram is considered as an estimator of the stationary Gaussian random noise covariance function. For this estimator the functional central limit theorem is proved in the space of continuous functions. The result obtained shows that the limiting sample continuous Gaussian random process coincides with the limiting process in the central limit theorem for standard correlogram of the random noise in the specified regression model.
A Multiplexing Filtering Antenna

In this communication, a compact, highly integrated multiplexing filtering antenna operating at 4.7/5.2/6.0/6.6 GHz is proposed for the first time. Different from traditional antennas, the proposed antenna has one shared radiator but four ports working in different frequency bands, and thus, it can simultaneously support four different transmission channels. The proposed multiplexing antenna is composed of a patch with a U-shaped slot, two substrate integrated waveguide (SIW) cavities, and four resonator-based frequency-selective paths. The resonator-based paths can not only enhance the interchannel isolations but also improve the impedance bandwidth. The design principles and the methods of controlling the four operating bands are studied. Measurement results agree reasonably well with the simulations, showing four channels from 4.5 to 4.8 GHz, 5.1 to 5.3 GHz, 5.85 to 6.3 GHz, and 6.4 to 6.6 GHz, respectively. The antenna also exhibits high isolation of over 25 dB between the channels. In addition, the proposed antenna has a consistent broadside radiation pattern and polarization in the four bands, and manifesting the proposed multiplexing filtering antenna can be a promising candidate for multiservice wireless communication systems.
Effect of Real Terrain on the Lightning Magnetic Fields and Location Accuracy in Yunnan, China

The effects of real terrain on the lightning magnetic fields and location accuracy are studied by using two-dimensional finite difference time-domain method. Two estimation methods of wave arrival time (using the time corresponding to the peak field and 10% threshold of the peak field) are applied to the arrival time delay, and then using the ToA technique with three approaches of arrival time differences to evaluate the location accuracy. It is found that the effect of real terrain on the lightning magnetic fields is due to the enhancement of reflection wave and propagation attenuation of terrain topography. The arrival time of peak value method is delayed from 0.15 to 2.3 μs, while that of 10% threshold method varied from −0.11 to 0.92 μs. The lightning location accuracy using the 10% threshold method is least, about tens of meters. Furthermore, considering the specific frequency bandwidth of detection sensors, three-order 10- to 500 kHz Butterworth filter was applied. After filtering, the effect of real terrain on the lightning magnetic fields becomes more obvious. The 10% threshold method is still the best way for lightning location, and the frequency bandwidth of different sensors on the effect of the location accuracy cannot be ignored.
Chebyshev spectral methods for computing center manifolds

We propose a numerical approach for computing center manifolds of equilibria in ordinary differential equations. Near the equilibria, the center manifolds are represented as graphs of functions satisfying certain partial differential equations (PDEs). We use a Chebyshev spectral method for solving the PDEs numerically to compute the center manifolds. We illustrate our approach for three examples: A two-dimensional system, the Henon-Heiles system (a two-degree-of-freedom Hamiltonian system) and a three-degree-of-freedom Hamiltonian system which have one-, two- and four-dimensional center manifolds, respectively. The obtained results are compared with polynomial approximations and other numerical computations.
Racial Projections in Perspective: Public Reactions to Narratives about Rising Diversity

Threatened reactions to news about the approach of a racial majority-minority society have profoundly influenced Americans’ political attitudes and electoral choices. Existing research casts these reactions as responses to changing demographic context. We argue instead that they are driven in large part by the dominant majority-minority narrative framing of most public discussion about rising racial diversity. This narrative assumes the long-run persistence of a white-nonwhite binary in which the growing number of Americans with both white and non-white parents are classified exclusively as non-white, irrespective of how they identify themselves. Alternative narratives that take stock of trends toward mixed-race marriage and multiracial identification also reflect demographic fundamentals projected by the Census Bureau and more realistically depict the country’s twenty-first century racial landscape. Using three survey experiments, we examine public reactions to alternative narratives about rising diversity. The standard majority-minority narrative evokes far more threat among whites than any other narrative. Alternative accounts that highlight multiracialism elicit decidedly positive reactions regardless of whether they foretell the persistence of a more diverse white majority. Non-white groups respond favorably to all narratives about rising diversity, irrespective of whether they include the conventional majority-minority framing.