Miracle-dz/UniFAR-FTUs huggingface dataset:
Machine Learning for Conservation Planning in a Changing Climate

Wildlife species’ habitats throughout North America are subject to direct and indirect consequences of climate change. Vulnerability assessments for the Intermountain West regard wildlife and vegetation and their disturbance as two key resource areas in terms of ecosystems when considering climate change issues. Despite the adaptability potential of certain wildlife, increased temperature estimates of 1.67–2 °C by 2050 increase the likelihood and severity of droughts, floods, heatwaves and wildfires in Utah. As a consequence, resilient flora and fauna could be displaced. The aim of this study was to locate areas of habitat for an exemplary species, i.e., sage-grouse, based on current climate conditions and pinpoint areas of future habitat based on climate projections. The locations of wildlife were collected from Volunteered Geographic Information (VGI) observations in addition to normal temperature and precipitation, vegetation cover and other ecosystem-related data. Four machine learning algorithms were then used to locate the current sites of wildlife habitats and predict suitable future sites where wildlife would likely relocate to, dependent on the effects of climate change and based on a timeframe of scientifically backed temperature-increase estimates. Our findings show that Random Forest outperforms other competing models, with an accuracy of 0.897, and a sensitivity and specificity of 0.917 and 0.885, respectively, and has great potential in Species Distribution Modeling (SDM), which can provide useful insights into habitat predictions. Based on this model, our predictions show that sage-grouse habitats in Utah will continue to decrease over the coming years due to climate change, producing a highly fragmented habitat and causing a loss of close to 70% of their current habitat. Priority Areas of Conservation (PACs) and protected areas might be deemed insufficient to halt this habitat loss, and more effort should be put into maintaining connectivity between patches to ensure the movement and genetic diversity within the sage-grouse population. The underlying data-driven methodical approach of this study could be useful for environmentalists, researchers, decision-makers, and policymakers, among others.
Modeling Starch Digestograms: Computational Characteristics of Kinetic Models for in vitro Starch Digestion in Food Research.

Starch digestion is mostly investigated with in vitro techniques, and time-course measurements are common. These yield digestograms that are modeled by theoretical, semitheoretical, and empirical kinetic equations, many of which are reviewed here. The Duggleby model has Michaelis-Menten functions, and its dependent variable is on both sides of the equation with no apparent parameter for maximum digestible starch (D∞ ). The Gaouar and Peleg models are equivalent. They predict both the initial digestible starch (D0 ) and D∞ , and an average digestion rate, but they can reveal "biratial" digestions. The first-order kinetic model exhibits diverse predictabilities and, when linearized, D∞ is sometimes equated to 100 g/100 g dry starch (100%), it yields an average rate of digestion and can predict negative D0 . The log of slope (LOS) model is unique in revealing the rapid-to-slow digestion rate phenomenon, but without guidelines to identify such. The LOS model does not sometimes use all the digestogram data, can predict D∞ greater than 100%, and returns zero digestion rate for some digestograms. However, some starchy materials exhibit a slow-to-rapid digestion rate phenomenon, as demonstrated with an example. The modified first-order kinetic model uses all the digestogram data with practical constraints (D0 ≥ 0 g/100 g dry starch; D∞ ≤ 100 g/100 g dry starch), describes all digestograms, and yields an average digestion rate, but it can also be used for "biratial" digestions. In addition, the logistic and Weibull models are discussed. Using some published data, the computational characteristics of these commonly used models are presented with objective parameters to guide choices.
Impact of Agricultural Exports on Agricultural Economic Growth in Ecuador: Case of Banana and Cocoa

The contribution of this paper is to investigate the impact of agricultural exports on agricultural economic growth in Ecuador with the case of banana and cocoa. Annual time series data from the period between 1987 to 2017 was tested applying some econometrics techniques such as: Johansen co-integration test, Vector Auto Regression Model and Granger Causality test. The findings show there is no long run relationship between agricultural exports and agricultural economic growth on Ecuador, positive results were found in the short run regarding with banana exports, cocoa exports and agricultural capital formation It is concluded that policies should be implemented which aim to increase the quality and productivity of these crops. Keywords: Agricultural economic growth, Banana exports, Cocoa exports DOI: 10.7176/JESD/11-12-04 Publication date: June 30th 2020
Sensorial, Microbiological, and Physico-Chemical Analysis of Minas Frescal Cheese with Oregano Essential Oil (Origanum Vulgare) Addition

This study aims to evaluate, from production to shelf-life, the physico-chemical, sensorial, and microbiological characteristics of minas frescal cheese made with oregano essential oil. Three samples F1, F2, and F3 were prepared with different levels of essential oil concentration and evaluated using the hedonic scale for attributes such as color, flavor, texture, odor, and overall evaluation. The physico-chemical analysis included the determination of moisture, total fat, ash, protein, and carbohydrates which were evaluated by ANOVA and Tukey test (p<0.05). The samples were analysed for Staphylococcus coagulase positive, mesophilic, psychrotrophic, coliforms, and Salmonella at 0, 7, 14, 21, and 28 days. Results for Staphylococcus coagulase positive, aerobic mesophilic, psychrotrophic, coliforms, and salmonella were found within the limits established by applicable Brazilian legislation. The sensory acceptance rates were satisfactory, being greater than 70 % at all concentrations, suggesting that the insertion of oregano essential oil cheese in the consumer market is feasible at the evaluated concentrations.
Refractive Multi-view Stereo

In this article we show how to extend the multi-view stereo technique when the object to be reconstructed is inside a transparent – but refractive – material, which causes distortions in the images. We provide a theoretical formulation of the problem accounting for a general, non-planar shape of the refractive interface, then a discrete solving method, which are validated by tests on synthetic and real data.
Improved Dynamic Response Design for Proportional Resonant Control Applied to Three-Phase Grid-Forming Inverter

For grid-forming inverters, abilities of both disturbance rejection and reference tracking are important for voltage controls, which act as inner loops of power controls. In this article, a complex variable-based dominant pole elimination (DPE) algorithm is applied to the voltage control that includes both positive- and negative-sequence complex variable resonant controllers. The algorithm is effective for both αβ- and dq-frame voltage control. Then, the DPE algorithm is extended for conventional dual-loop proportional resonant control to improve the dynamic response. Impedance-based analysis also indicates that the proposed DPE algorithm results in a suitable impedance to enhance the robustness in grid-connected mode. The effectiveness of the DPE method is verified through an experimental implementation in a three-phase hardware prototype with a TMS320F28379D digital signal processor and simulations in MATLAB/Simulink.
Quasi-Optimization of Uplink Power for Enabling Green URLLC in Mobile UAV-Assisted IoT Networks: A Perturbation-Based Approach

Efficient resource allocation can maximize power efficiency, which is an important performance metric in future fifth-generation (5G) communications. The minimization of sum uplink power in order to enable green communications while concurrently fulfilling the strict demands of ultrareliability for short packets is an essential and central challenge that needs to be addressed in the design of 5G and subsequent wireless communication systems. To address this challenge, this article analyzes the joint optimization of various unmanned aerial vehicle (UAV) systems parameters, including the UAV’s position, height, beamwidth, and the resource allocation for uplink communications between ground Internet-of-Things (IoT) devices and a UAV employing short ultrareliable and low-latency (URLLC) data packets. Toward achieving the aforesaid task, we proposed a perturbation-based iterative optimization to minimize the sum uplink power in order to determine the optimal position for the UAV, its height, beamwidth of its antenna, and the blocklength allocated for each IoT device. It is shown that the proposed algorithm has lower time complexity, yields better performance than other benchmark algorithms, and achieves similar performance to exhaustive search. Moreover, the results also demonstrate that Shannon’s formula is not an optimum choice for modeling sum power for short packets as it can significantly underestimate the sum power, where our calculations show that there is an average difference of 47.51% for the given parameters between our proposed approach and Shannon’s formula. Finally, our results confirm that the proposed algorithm allows ultrahigh reliability for all the users and converges rapidly.
Can the issue of corruption be solved? An analysis of corruption in the Balkans and potential solutions

This research paper analyses how countries in the western Balkans have been affected by corruption and how, despite significant efforts, corrupt practices continue to present barriers to their long-standing goal of joining the European Union. Corruption has severely negative impacts on economic growth and a country’s international reputation, and so we infer that a different approach is required. Ten expert interviews were conducted in an explorative research approach in order to examine the concept of anti-bribery compliance incentives. Interview partners were asked to address how anti-bribery incentives could be employed by multinational corporations in order to eliminate corruption. Subsequent discussion illustrates how our findings could be combined with other measures towards eliminating corruption across all sectors. The overall purpose of this research, and its contribution, lies in exploring a new method of tackling corruption and developing innovative theory to gain a more profound understanding of the concept, its implications for the Balkans and how it could yet be addressed more effectively.
Distributed Dynamic Event-Based Control for Nonlinear Multi-Agent Systems

This brief studies the synchronization problem for a class of QUAD and interconnected nonlinear multi-agent systems (MASs). A dynamic event-based control scheme is designed, and two event-based synchronization conditions are constructed by utilizing stability theory. Moreover, the Zeno-behavior can be excluded in the MASs. An example and its simulation are given to verify the applicability of the designed dynamic event-based protocol for MASs.
Preliminary prediction of the potential distribution and consequences of Haemaphysalis longicornis using a simple rule-based climate envelope model

Haemaphysalis longicornis, the Asian longhorned (or bush) tick has been detected on a sheep in August 2017 in Hunterdon County, New Jersey. By October 26, 2018, this tick has been detected in 44 counties in 9 states along the Atlantic coast of the United States, with the first detection backdated to 2010. Here, I use a simple rule-based climate envelope model, based on a prior analysis in New Zealand, to provide a preliminary analysis of the potential range of this introduced tick species in North America. After validating this model against the counties where the tick has been already detected, I highlight the counties where this tick might cause considerable economic harm. I discuss the many limitations of this simple approach, and potential remedies for these limitations, and more sophisticated approaches. Finally, I conclude that substantial areas of the US, especially along the Gulf and Atlantic coast, are suitable for the establishment of this tick, putting millions of heads of livestock potentially at risk.
Hybrid-Domain Neural Network Processing for Sparse-View CT Reconstruction

X-ray computed tomography (CT) is one of the most widely used tools in medical imaging, industrial nondestructive testing, lesion detection, and other applications. However, decreasing the projection number to lower the X-ray radiation dose usually leads to severe streak artifacts. To improve the quality of the images reconstructed from sparse-view projection data, we developed a hybrid-domain neural network (HDNet) processing for sparse-view CT (SVCT) reconstruction in this study. The HDNet decomposes the SVCT reconstruction problem into two stages and each stage focuses on one mission, which reduces the learning difficulty of the entire network. Experiments based on the simulated and clinical datasets are performed to demonstrate the performance of the proposed method. Compared with other competitive algorithms, quantitative and qualitative results show that the proposed method makes a great improvement on artifact suppression, tiny structure restoration, and contrast retention.
Varietal Analysis and Quantification of Resveratrol in Mulberry Fruits

Resveratrol is naturally occurring phytoalexin compounds produced by grape berries, peanuts, and their products in response to stress such as fungal infection, heavy metal ions or UV irradiation. The objective of this study was to develope a reliable high performance liquid chromatographic (HPLC) method for the quantitative determination of trans-resveratrol in mulberry fruit. Samples were extracted in 80% MeOH and filtered with syringe filter. The transresveratrol was separated Waters column, using a mobile phase containing 0.025% trifluroacetic acid in 5% acetonitril and 0.035% trifluroacetic acid in 50% acetonitril, detected by photodiode array detector (PDA) at 254 nm and the flow rate was 1ml/min. Under this analytical condition, the mean content of mulberry fruits (fifty varieties) was . Among the tested samples, 'Mansaengbaekpinosang (II)' was the highest level in 3450.6 ppm. However four accessions including 'Gukbu', 'Sabangso (I)', 'Simseol' and yield mulberry fruit were not able to detected. Eight suitable varieties selected for the production of fruit were 'Jeolgokchosaeng (Chungbuk)' 777.8 ppm, 'Dangsang 7' 771.1 ppm, 'Jangsosang' 133.9 ppm, 'Susungppong' 31.1 ppm, 'Suwonnosang' 639.7 ppm, 'Palcheongsipyung' 1475.9 ppm, 'Kangsun' 864.0 ppm, and 'Jukcheonchosaeng' 1458.5 ppm. 'Daesungppong' which was the first authorized variety for the production of mulberry fruit was 1236.7 ppm. In conclusion, these results suggest that mulberry including fruit and leaf may a good new resource for resveratrol production.
Activity of fishers at multiple temporal scales

Ecologically relevant multi-temporal-scale frameworks elucidate whether patterns of animal activity (movement) are distinct at different temporal scales. We studied fishers (Pekania pennanti) fitted with transmitter collars that measured activity at high temporal resolution during winter in Wisconsin. A multi-temporal-scale framework with two scales, day (number of activity bouts per 24 h) and decision (time spent exploring a small area), revealed that the best models of fisher activity were different at different temporal scales. At the day scale, the fishers that we studied increased activity when temperatures were warmer and increased activity more when it was windy and cold than when it was windy and warm. At the decision scale, when wind speeds increased, female fishers reduced activity more than male fishers. The role of temperature on activity was only detected at the day scale, and intersexual variation was only detected at the decision scale. Results at both scales supported the prey synchrony hypothesis for activity, but support was mixed at the day scale. Assessing activity using a multi-temporal-scale analysis framework leads to a better understanding of factors that influence activity and biological fitness.
Description and Attribution Analysis of the 2017 Spring Anomalous High Temperature Causing Floods in Kazakhstan

It is speculated that floods in many areas of the world have become more severe with global warming. This study describes the 2017 spring floods in Kazakhstan, which, with about six people dead or missing, prompted the government to call for more than 7,000 people to leave their homes. Then, based on the Climatic Research Unit (CRU), the NCEP/NCAR Reanalysis 1, and the Coupled Model Intercomparison Project 5 (CMIP5) simulations, the seasonal trends of temperature were calculated using the linear least-squares regression and the Mann–Kendall trend test. The correlation between the surface air temperature and atmospheric circulation was explored, and the attributable risk of the 2017 spring floods was evaluated using the conventional fraction of the attributable risk (FAR) method. The results indicate that the north plains of Kazakhstan had a higher (March–April) mean temperature anomaly compared to the south plains, up to 3°C, relative to the 1901-2017 average temperature. This was the primary cause of flooding in Kazakhstan. March and April were the other months with a higher increasing trend in temperature from 1901 to 2017 compared with other months. In addition, a positive anomaly of the geopotential height and air temperature for the March–April 2017 period (based on the reference period 1961-1990) was the reason for a warmer abnormal temperature in the northwest region of Kazakhstan. Finally, the FAR value was approximately equal to 1, which supported the claim of a strong anthropogenic influence on the risk of the 2017 March–April floods in Kazakhstan. The results presented provide essential information for a comprehensive understanding of the 2017 spring floods in Kazakhstan and will help government officials identify flooding situations and mitigate damage in future.
Exploring Factors That Impact Faculty Decisions to Teach Languages Online: Is It Worth the Individual Return on Investment?.

Over the past decade, scholars have explored factors that motivate or impede faculty decisions to teach online in the broader context of higher education (Mitchell & Geva-May, 2009; Shea, Pickett, & Li, 2005; Tabata & Johnsrud, 2008; Wright, 2014; Zhen, Garthwait, & Pratt, 2008). However, comparable research in speciﬁc, academic disciplines is limited, especially as it relates to online language learning (OLL). This study investigates the role of faculty demographics, experience, and their perceptions of OLL as they relate to motivating factors, barriers, and the perception of quality. The results identiﬁed seven interrelated themes that shaped the participants’ (n = 24) decisions whether to participate in online instruction. Findings related to the personal nature of the decision to teach online and perceived return on investment distinguish this study from others. The article concludes with a discussion of reframing faculty decisions to teach online in terms of individual return on investment and with suggestions for future research.
VikNGS: a C++ variant integration kit for next generation sequencing association analysis

Motivation Integration of next generation sequencing data (NGS) across different research studies can improve the power of genetic association testing by increasing sample size and can obviate the need for sequencing controls. Unfortunately, if differential genotype uncertainty across studies is not accounted for, combining data sets can also produce spurious association results. The robust variance score statistic (RVS) for genetic association of rare and common variants has been shown to effectively adjust for bias caused by the differences in read depth in case-control genetic association studies when the two groups were sequenced using different experimental designs. To enable consortium research, the aggregation of several data sets for genetic association analysis of quantitative and binary traits with covariate adjustment is required, and we developed the Variant Integration Kit for NGS (VikNGS) that expands the functionality of RVS (vRVS) for this purpose. Results VikNGS is a fast and computationally efficient cross-platform software package that provides an implementation for vRVS, as well as conventional rare and common variant genotype-based association analysis approaches. The package includes a graphical user interface that contains power simulation functionality and data visualization tools. Availability and Implementation The VikNGS package can be downloaded at http://www.tcag. ca/tools/index.html Documentation can be found at https://VikNGSdocs.readthedocs. io/en/latest/ Contact lisa.strug@sickkids.ca Supplementary information Supplementary data are available at Bioinformatics online.
Development of antibody-based assays for the detection of aflatoxins and organophosphates

The work described in this thesis involves the production and characterisation of antibodies and recombinant antibodies to aflatoxin Bi (AFBi), a potent mycotoxin and carcinogen. The antibodies produced were then applied for use in a surface plasmon resonance-based biosensor (BIAcore), which measures biomolecular interactions in ‘real-time’. Production of hapten-protein conjugates of the organophosphate pesticides chlorpyrifos and pirimiphos, and characterisation of antichlorpyrifos antibodies were also successfully performed. Rabbit polyclonal and single chain Fv (scFv) antibodies were produced to aflatoxin Bi. The scFv’s were produced from an established phage display system, which incorporated a range of different plasmids for scFv expression. Both sets of antibodies were used in the development of competitive ELISA’s and also for the development of BIAcore-based inhibition immunoassays. The antibodies were also used in the detection of AFBi in spiked grain samples by ELISA and BIAcore. Both the polyclonal and scFv antibodies were found to be suitable for the detection of AFBi, and the assays were reproducible. The production of an activated form of the organophosphate pirimiphos for the production of a pirimiphos-protein conjugate was attempted. However it was not possible to conjugate this pesticide by any suitable means. Chlorpyrifos-BSA conjugates were produced with different molar substitution ratios, and were used for the development of a competitive ELISA with polyclonal anti-chlorpyrifos antibodies.
A Design Method to Implement ZVS for Electric Vehicle Wireless Charging System With Double-Side LCC Compensation

In this article, the zero voltage switching (ZVS) of the inverter in the electric vehicle wireless charging system is realized through the parameter optimization design of compensation components, which is conducive to the high efficient operation of the system. The effects of the parameter adjustment of each compensation component on the input impedance angle and output current of the double-side LCC compensation topology were analyzed under the conditions of different output voltages and coupling coefficients, which provides a basis for the component optimization design of the inverter ZVS realization. The quantitative relationship between the optimized component parameter and the soft switching implementation conditions was analyzed. Then, a specific parameter design method was proposed, according to the results analyzed earlier. At last, the effectiveness of the proposed method was verified by a 3.3-kW wireless charging system prototype. Experimental results show that the optimized system is always able to achieve soft switching, the system efficiency is up to 95.2% at the rated power, and it is higher than 93% even under the condition where the coupling coefficient and output voltage are both reduced by half.