Miracle-dz/UniFAR-FTUs huggingface dataset:
Estimation of Heritability, Genes Number and Multivariate Analysis Using Non- segregation and Segregation Generations in Two Cotton Crosses

This study was conducted to evaluate the heritability methods, the genes number equations and comparison between them, as well as multivariate analysis of cotton yield and its components traits in the two crosses G.85 x TNB and G.86 x Suvin. Broad sense heritability (BSH) using Mahmud & Kramer (1951), Burton (1951), Weber & Moorthy (1952), Modified Weber & Moorthy (1952), Briggs & Krowles (1967), Mather & Jinks (1971), Lawrence & Jinks (1973) and Kotecha & Zimmerman (1978) methods, as well as narrow sense heritability using Warner (1952) and Modified Warner (1952) methods were calculated. The methods of BSH and NSH showed high values (BSH < 0.60) and significant for yield and yield components traits in the two crosses. For BSH estimations, the highest values by Mahmood & Kramer and Burton methods and the lowest values by Mod.Weber & Moorthy were registered for most studied traits. Estimates of genes number affecting traits were obtained with Chen & Line (1995). The genes number values by equation N3 were much higher than the other equations (close to each other or slightly different) for all studied traits in the two crosses. Based on the ranks method and cluster analysis suggested that there are differences between most the methods of BSH and genes number estimations. The methods of Mather & Jinks and Lawrence & Jinks gave the same values, the two methods of Mahmud & Kramer and Briggs & Krowles as well as the two methods Weber & Moorthy and Kotecha & Zimmerman showed equal or close values for the studied traits in the two crosses. While, the others methods of BSH had difference with these methods and with from each other. These methods are calculated based on the components of variance, so a change in each component of the variance can affect it. Thus, these methods differ with respect to the calculation of an environmental variance. According to principal component analysis (PC), the PC1 and PC2 had mainly distinguished the generations in different groups. The PC1 and PC2 contributed towards 86.29% and 92.36% of cumulative variability in the two crosses G.85 x TNB and G.86 x Suvin, respectively, and the PC1 exhibited Eigen value >1 for all studied traits in the six populations. According to biplot and based on the all populations, the PCs with the highest variability showed positive correlation to yield and its components, but, they differed in their degree of significance/insignificant and consistency in quantity. The PC of the relationship between the six generations revealed that the most appropriate generations for selecting these traits were BC1 generation in the cross G.85 x TNB as well as F2 and BC1 generations in the cross G.86 x Suvin. Backcrossing may be done for 2–5 cycles (BC2 – BC5) at Suvin parent for improving cotton yield in Egypt.
Dengue Cases Forecasting Based on eXtreme Gradient Boosting Ensemble with Coyote Optimization

Dengue is considered a public health problem in tropical regions, periodically affecting an increasing number of citizens. Consequently, the development of efficient models is essentials to short and long-term forecasting, supporting health care officials to optimally disseminate available resources in the dengue-prone areas. Hybridization of two or more models is a common solution to this problem where one can take advantage of diversity among models to reduce both the bias and variances of the prediction error obtained using single models. Fortunately, the use of ensemble approaches becomes attractive. In this paper, we propose a novel ensemble learning approach combining the eXtreme Gradient Boosting (XGBoost) and Coyote Optimization Algorithm (COA) to capture the nonlinearity in a dataset and perform dengue cases forecasting. The performance of the XGBoost model depends upon the appropriate choice of its hyperparameters. In this study, COA has been employed to tune the XGBoost hyperparameters. The proposed hybrid COA-XGBoost model is applied to predicting dengue time-series dataset from Parana, Brazil. Averages of precipitation, temperature, thermal amplitude, relative humidity, and previous dengue cases are considered as input variables as well as dengue cases are used as output variables. The performance of the proposed COA-XGBoost model has been compared with XGBoost when hyperparameters are obtained using other optimization techniques like Differential Evolution, Genetic Algorithm, Cuckoo Search Optimization, Grey Wolf Optimizer, and Firefly Algorithm. The results indicate that the proposed COA–XGBoost can be competitive model when compared to other classical techniques.
Design features of high speed PM machines

The high-speed permanent magnet (PM) machine has been widely investigated since it has small size, high efficiency and power density. However, due to the high rotor speed and high frequency of the stator flux variation, the design of a high-speed machine is quite different from designing a conventional machine with low speed and low frequency. As a example, the design features of a high-speed PM generator driven by micro turbines are introduced in this paper. The choice of rotor structure, the design of stator core and winding, the bearing structure and the control system are discussed in some details.
Can ride-sharing become attractive? A case study of taxi-sharing employing a simulation modelling approach

Improved urban mobility can be attained through more efficient vehicle usage and better road network utilisation, namely through increased vehicle occupancy and new operation modes. In this study, the authors focus on a dynamic and distributed taxi-sharing system that takes advantage of nowadays widespread availability of communication and distributed computation to provide a cost-efficient, door-to-door and flexible service, offering a quality of service similar to conventional taxis. This system has been evaluated following a simulation modelling approach, including a realistic and accurate replication of the taxi operation in the city of Porto using empirical data (real origin/destination data and average occupancy rates). Simulation results show improved performance in terms of reduced fares (up to 8%), reduced total travel distance (up to 9%) and smaller operation costs. Furthermore, they proposed that several trade-offs (e.g. service performance against passengers' transit times) should be considered during the system deployment and operation. In this study, it was also shown that increased system penetration rate and demand level can even further improve the system performance.
Understanding and intervening in the personal challenges and social relationships risk categories to early leaving

ABSTRACT Early leaving studies demonstrate it is possible to improve young people’s opportunities through education and training. The current research evidence has yet to agree a definitive answer as to where the primary policy focus to improve their life chances should be. That is, which risk factors are the most impactful for minimising early school leaving rates. This paper reports on the qualitative findings from the Orienta4YEL project – conducted via interviews and focus groups administered to 711 educational agents in five countries – Spain, Portugal, Romania, Germany and UK. The goal of the study was to contribute to the development of initiatives addressed to early leaving through orientation and tutorial action – as key points of intervention by schools, colleges and training providers. The analysis indicates that the categories of young peoples’ personal challenges and social relationships are key mediators of the broader risk categories (family, institutional and structural) and therefore an opportune focus to target intervention. The close interconnection found between the micro categories of risk (social relationships/family/institutional) and in their impact upon the personal challenges that young people face, highlights the importance of individual support and underscores the affordances of orientation and tutorial actions, as key mechanisms to tackle early leaving.
On the uniqueness of inverse problems for the reduced wave equation with unknown embedded obstacles

In this paper, we consider inverse problems associated with the reduced wave equation on a bounded domain Ω⊂RN(N⩾2) for the case where unknown obstacles are embedded in the domain Ω. We show that, if both the leading and 0-order coefficients in the equation are a priori known to be piecewise constant functions, then both the coefficients and embedded obstacles can be simultaneously recovered in terms of the local Dirichlet-to-Neumann map defined on an arbitrary small open subset of the boundary ∂Ω. The method depends on a well-defined coupled PDE-system constructed for the reduced wave equations in a sufficiently small domain and the singularity analysis of solutions near the interface for the model.
The Food-as-a-Commons Discourse: Analyzing the Journey to Policy Impact

Increasingly, a discussion is emerging on new framings for food beyond food as a commodity. Several initiatives deem food a human right or a common good in the context of a variety of food issues, along the entire food system. This paper focuses on the development of discourses on food-as-a-commons and their success in influencing policy. We explore in detail four discourses: “Open source inputs in agriculture”, “Joint responsibility for food products”, “Reducing food waste” and “Safeguarding food culture and knowledge”. We examine and classify case studies in Germany based on a semi-systematic literature review including policy documents of 12 initiatives that apply and inform these four discourses. This allows us to present various levels of policy uptake, working at different speeds. Identifying characteristics based on commons theory helps us to describe the initiatives better, and especially explain the success of some discourses over others in influencing policy. Results show that discourses that invoke ideas of core human values and are aimed at changing relatively feasible goals (changing resource allocation, but not changing governance or institutions), may be the most likely new food discourses to have policy impact. A prime example of this is the discourse “Reducing food waste”.
Influence of Dominant Body Somatotype and Sex Difference on Q-angle and Selected Skeletal Measures Among Undergraduates in a South-Eastern Nigerian University

The body somatotype is expressed in a three-number rating representing the endomorphy, mesomorphy and ectomorphy components respectively. Endomorphy is the relative fatness; mesomorphy is the relative musculoskeletal robustness; and ectomorphy is the relative slenderness of a physique.The purpose of this study was to investigate the influence of dominant body somatotype and sex difference on Q-angle and selected skeletal measures (hip width and femur length) of undergraduates in a south-eastern Nigerian university. A total of 250 young adults (125 males and 125 females) aged 18 - 30 years participated in this study. The Heath- Carter Anthropometric Body Somatotyping method was used to measure the body somatotype of each of the participants. A goniometer and flexible tape were also used to measure the Q-angle, hip width and femur length of each of the participants. Data was summarized using mean, standard deviation, frequency and percentages; and analysed using the student’s t-test, ANOVA and Pearson’s correlation at alpha level of 0.05. The mean ages of the male and female participants were 22.61 ± 2.56yrs and 21.92 ± 2.61yrs respectively. The results show that the most prevalent dominant body somatotype in males and females was mesomorphy. It can be concluded that each dominant somatotype has different values for Q-angle, hip width and femur length; thus establishing that dominant body somatotype and sex difference influences Q- angle, hip width and femur length. KEY WORDS: body somatotype, q-angle, hip width, femur length
Role of Adiabatic Flame Temperature on Controlling Operability of a Micromixer-Based Gas Turbine Combustor Holding Premixed Oxy-Flames for Carbon Capture

The study presents an experimental and numerical investigation to determine the role of adiabatic lame temperature on controlling the operability of a micromixer-based gas turbine model combustor holding premixed CH4/CO2/O2 non-swirl jet flame for carbon capture. The experimental test rig consists of a mixing pipe and multihole/micromixer-like burner of 61 tubes. The numerical aspect of the study is achieved using the computational fluid dynamic (CFD) approach. Models of the Ansys Fluent are used to solve elliptical governing equations involved in the combustion system. Since flames under consideration are jet premixed releasing into a quiescent atmosphere, the partially premixed combustion of species model is used. Large eddy simulation (LES) is used as the turbulence model while radiative transfer equation (RTE) is solved using discrete ordinate (DO). A good agreement between experimental and numerical results is achieved. Results indicated that; adiabatic flame temperature (Tad) controls the flame macrostructure, oxygen fraction (OF) controls the peak of the product formation rate rather than Tad, positions of OH radical reaffirmed that Tad controls the flame macrostructure and increasing the OF leads to reaction rate dominancy and, hence, increases the Damkohler number.
The Al-Invest Contribution to the Resilience of the Chemical Company: Development Cooperation

This article aims to identify the contribution of the Al-Invest program to resilience and economic growth in companies in the chemical sector. The study sample consists of 596 European and Latin American companies that benefited from Al-Invest. It was found that Knowledge Transfer was not the main interest in the collaborations. The economic dynamics of the chemical industry pressure companies to compete, having a low impact on local development when it comes to knowledge transfer. Al-Invests contribution is oriented to the company in four aspects: relational capital with foreign agents, geographic openness, open innovation, and greater technological and marketing capacity. These four elements increase the resilience and economic growth of the company, accompanied by a favorable impact on economic development in the region. Received: 2 September 2021 / Accepted: 5 October 2021 / Published: 5 November 2021
Sugarcane Yield Forecast in Ivory Coast (West Africa) Based on Weather and Vegetation Index Data

One way to use climate services in the case of sugarcane is to develop models that forecast yields to help the sector to be better prepared against climate risks. In this study, several models for forecasting sugarcane yields were developed and compared in the north of Ivory Coast (West Africa). These models were based on statistical methods, ranging from linear regression to machine learning algorithms such as the random forest method, fed by climate data (rainfall, temperature); satellite products (NDVI, EVI from MODIS Vegetation Index product) and information on cropping practices. The results show that the forecasting of sugarcane yield depended on the area considered. At the plot level, the noise due to cultivation practices can hide the effects of climate on yields and leads to poor forecasting performance. However, models using satellite variables are more efficient and those with EVI alone may explain 43% of yield variations. Moreover, taking into account cultural practices in the model improves the score and enables one to forecast 3 months before harvest in 50% and 69% of cases whether yields will be high or low, respectively, with errors of only 10% and 2%, respectively. These results on the predictive potential of sugarcane yields are useful for planning and climate risk management in this sector.
Down-Regulation of Phosphoenolpyruvate Carboxylase Kinase in Grapevine Cell Cultures and Leaves Is Linked to Enhanced Resveratrol Biosynthesis

In grapevine, trans-Resveratrol (tR) is produced as a defence mechanism against stress or infection. tR is also considered to be important for human health, which increases its interest to the scientific community. Transcriptomic analysis in grapevine cell cultures treated with the defence response elicitor methyl-β-cyclodextrin (CD) revealed that both copies of PHOSPHOENOLPYRUVATE CARBOXYLASE KINASE (PPCK) were down-regulated significantly. A role for PPCK in the defence response pathway has not been proposed previously. We therefore analysed the control of PPCK transcript levels in grapevine cell cultures and leaves elicited with CD. Moreover, phosphoenolpyruvate carboxylase (PPC), stilbene synthase (STS), and the transcription factors MYB14 and WRKY24, which are involved in the activation of STS transcription, were also analysed by RT-qPCR. The results revealed that under CD elicitation conditions PPCK down-regulation, increased stilbene production and loss of PPC activity occurs in both tissues. Moreover, STS transcripts were co-induced with MYB14 and WRKY24 in cell cultures and leaves. These genes have not previously been reported to respond to CD in grape leaves. Our findings thus support the hypothesis that PPCK is involved in diverting metabolism towards stilbene biosynthesis, both for in vitro cell culture and whole leaves. We thus provide new evidence for PEP being redirected between primary and secondary metabolism to support tR production and the stress response.
Impacts of Covid‐19 pandemic on consumer behavior in Turkey: A qualitative study

Abstract The current study aims to examine consumer behavior in Turkey during the COVID‐19 pandemic and how consumers adapt to this new normal. In this regard, 78 Turkish consumers were interviewed online. Data were analyzed through grounded theory with a stimulus‐organism‐response framework and constant comparative methods. This study showed that environmental stimuli affecting consumer behavior during the pandemic are economic downturn, partial lockdown regulations, restrictions on some services, and social media messages. Organism consists of fear, boredom, and perceived risk. Consumers' behavioral responses to the pandemic are changes in purchasing of consumer goods, avoidance from physical stores, a decline in leisure activities, a decline in shopping frequency, planned vs. impulse buying, stockpiling, and prosumption. Overall, this study provides a more general framework regarding multiple aspects of the pandemic on consumer behavior.
Domain-based Comparative Analysis of Bacterial Proteomes: Uniqueness, Interactions, and the Dark Matter

Background: Proteins may have none, single, double, or multiple domains, while a single domain may appear in multiple proteins. Their distribution patterns may have impacts on bacterial physi-ology and lifestyle. Objective: This study aims to understand how domains are distributed and duplicated in bacterial prote-omes, in order to better understand bacterial physiology and lifestyles. Methods: In this study, we used 16712 Hidden Markov Models to screen 944 bacterial reference prote-omes versus a threshold E-value<0.001. The number of non-redundant domains and duplication rates of redundant domains for each species were calculated. The unique domains, if any, were also identified for each species. In addition, the properties of no-domain proteins were investigated in terms of physico-chemical properties. Results: The increasing number of non-redundant domains for a bacterial proteome follows the trend of an asymptotic function. The domain duplication rate is positively correlated with proteome size and in-creases more rapidly. The high percentage of single-domain proteins is more associated with small pro-teome size. For each proteome, unique domains were also obtained. Moreover, no-domain proteins show differences with the other three groups for several physicochemical properties analysed in this study. Conclusion: The study confirmed that a low domain duplication rate and a high percentage of single-domain proteins are more likely to be associated with bacterial host-dependent or restricted niche-adapted lifestyle. In addition, the unique lifestyle and physiology were revealed based on the analysis of species-specific domains and core domain interactions or co-occurrences.
APLIKASI TECHNOLOGY ACCEPTANCE MODEL PADA MINAT NASABAH UNTUK MENGGUNAKAN INTERNET BANKING

Tujuan utama dari penelitian ini adalah untuk menganalisis faktor-faktor yang mempengaruhi minat seorang nasabah untuk mau menggunakan Sistem Informasi Akuntansi Perbankan berupa Internet Banking dengan menggunakan Technology Acceptance Model (TAM). Selain itu, peneliti juga menambahkan konstruk laten berupa persepsi keamanan kedalam TAM. Objek dalam penelitian ini adalah nasabah Bank Central Asia yang ada di kota Palembang dengan pertimbangan bahwa Bank Central Asia merupakan Bank pelopor Internet Banking di Indonesia sejak tahun 2000 dan mempunyai volume transaksi perbankan yang tertinggi pada akhir tahun 2012 dibandingkan dengan Bank lainnya. Sebanyak 194 sampel diambil dan dianalisis dengan menggunakan Structual Equation Model (SEM). Hasilnya menunjukkan bahwa persepsi kegunaan, persepsi kemudahan penggunaan, persepsi keamanan, dan sikap berhubungan positif secara signifikan dengan minat nasabah Bank Central Asia di Kota Palembang untuk menggunakan Internet Banking. Selain itu juga ditemukan fenomena bahwa ternyata persepsi kegunaan tidak berpengaruh secara langsung terhadap minat, melainkan berpengaruh secara tidak langsung melalui sikap. Fenomena ini sebenarnya disebabkan oleh faktor demografis berupa perbedaan tempat dan waktu dilakukannya penelitian menjadi penyebab utama terjadinya perbedaan hasil penelitian ini dengan dasar teori yang ada dan penelitian terdahulu. Kata Kunci : Sistem Informasi Akuntansi, E- Banking , TAM, SEM
Protocol for a scoping review: A review of Internet-delivered of mindfulness-based interventions

Depression is a common mental illness affecting more than 264 million people worldwide (James et al., 2018). It can cause the affected person to suffer much and function poorly at work, school, and the family, leading to suicide in the worst case (World Health Organization, 2020). Rates of depression have increased during the COVID-19 outbreak, with prevalence ranging from 7.45% to 48.30% and an average or pooled prevalence rate of about 25%(Bueno-Notivol et al., 2021). The prevalence rate of depression is seven times higher than a global estimate, accounting for 3.44% in 2017(Bueno-Notivol et al., 2021). It seems that the COVID-19 pandemic has also resulted in anxiety and depression in people with and without a history of mental illnesses (Gulliver et al., 2021). Generally, psychological therapies can reduce stress and depression symptoms severity and improve well-being by teaching psychotherapeutic strategies(Gulliver et al., 2021).
Loss-contribution-based in situ Visualization for Neural Network Training

This paper presents an in situ visualization algorithm for neural network training. As each training data item leads to multiple hidden variables when being forward-propagated through a neural network, our algorithm ﬁrst estimates how much each hidden variable contributes to the training loss. Based on linear approximation, we can approximate the contribution mainly based on the forward-propagated value and the backward-propagated derivative per hidden variable, both of which are available during the training with no cost. By aggregating the loss contribution of hidden variables per data item, we can detect difﬁcult data items that contribute most to the loss, which can be ambiguous or even incorrectly labeled. For convolution neural networks (CNN) with images as inputs, we extend the estimation of loss contribution to measure how different image areas impact the loss, which can be visualized over time to see how a CNN evolves to handle ambiguous images.