Miracle-dz/UniFAR-FTUs huggingface dataset:
Little change in Palmer Drought Severity Index across global land under warming in climate projections

Abstract. Anthropogenic warming is reported to increase global drought for the 21st century when calculated using offline drought indices. However, this contradicts observations of greening and little systematic change in runoff over the past few decades and climate projections of future greening with slight increases in global runoff for the coming century. This calls into question the drought projections based on offline drought indices. To resolve this paradox, here we calculate a widely-used conventional drought index (i.e., the Palmer Drought Severity Index, PDSI) using direct outputs from 16 CMIP5 models (PDSI_CMIP5) such that the hydrologic consistency between PDSI_CMIP5 and CMIP5 models is maintained. Results show that the global PDSI_CMIP5 remains generally unchanged as climate warms, demonstrating that CMIP5 models do not actually project a general increase in PDSI drought (more reflecting soil moisture/agricultural drought) under future warming. Further analyses indicate that the projected increase in PDSI drought reported previously is primarily due to ignoring the vegetation response to elevated atmospheric CO2 concentration ([CO2]) in the offline calculations. On one hand, elevated [CO2] directly reduces stomatal opening; on the other hand, elevated [CO2] increases air temperature and thus vapor pressure deficit, which also causes partial stomatal closure. Finally, we show that the overestimation of PDSI drought can be avoided by directly using the relevant climate model outputs or by accounting for the effect of CO2 on evapotranspiration. Our findings refute the common warming leads to drying perception and highlight the importance of elevated CO2 in controlling future terrestrial hydrologic changes through vegetation responses.
The soft skills concept in organizations and project teams

Modern challenges take place in markets force the companies’ management to cope with risks and uncertainty. To forward competitive advantages, it is important to develop skills necessary to solve emerging problems. The authors attempted to reveal needed soft skills to manage organizations effectively. The purpose of scientific research is to study the degree of awareness concerning the soft skills concept among Kazakhstani employees. Due to the selected survey approach, the data are reliable but exploratory one. Thus, representative data is needed to confirm the findings.Online survey with the nonrepresentative sample of employed people working in business companies was conducted. There were two stages of the research. The first stage was exploratory secondary research. Secondary data helped to propose the hypothesis that poor soft skills lead to poor performance of any activity where people interact. Findings taken from online survey showed that 54.8 % of respondents consider that soft skills are important. 64.5% claimed that low level of awareness on soft skills leads to poor performance. There can be no doubt that awareness of employees on soft skills will increase their personal performance. Within the paper, the discussion focuses on the importance for Kazakhstani entrepreneurs, businesspersons and employees to develop soft skills to expand boundaries of career opportunities.Key words: soft and hard skills, leadership, innovators, change, project, team.
Grayscale Morphological and Unsupervised method for Human Facial Synthesis based on an Example Image

Keywords Human facial generation of example image is used as a requirement for biometric applications for the purpose of identifying individuals. In this paper, face generation consists of three main steps. In the first step, detection of significant lines and edges of the example image are carried out using grayscale morphology. Then, hair areas are identified from the face of sample. The final step combines images from previous steps. Similarity and matching between synthesized face sketch and artistic sketch are compared with two methods of extracting features, Principle Component Analysis and Linear Discriminant Analysis, and time of the process is calculated. The experiments on the pair of CUHK database images show that the proposed method compared with state-of-the-art methods such as: Eigen transformation, LLE, and MRF, has no computational complexity and creates a person's face with good quality and much less time. Matching of synthesized face sketch of the proposed method is achieved with a maximum value of 90% when Linear Discriminant Analysis is used to extract feature. The proposed method is also resistant to background effects and brightness of example images
Theoretical Characteristics of Ontology Learning Algorithm in Multi-dividing Setting

—Ontology, as a useful tool, has been widely applied in various ﬁelds, and ontology concept similarity calculation is an essential problem in these application algorithms. A recent method to get similarity between vertices on ontology is not by pairwise computation but based on a function which maps ontology graph into a line and maps every vertex in graph into a real-value, the similarity is measured by the difference of their corresponding scores. The multi-dividing method is suitable for ontology problem and plays a key role in achieving this. Such ontology function is given by learning a training sample which contains a subset of vertices with k classes from ontology graph. In this paper, we propose a new multi-dividing ontology algorithm framework, which is designed to avoid the choice of loss function. Meanwhile, there is such a vertex selection policy in new multi-dividing ontology algorithm that it guarantees that the new algorithm can be employed for an ontology graph with its structure rather than a tree. We provide some theoretical characteristics of the new multi-dividing ontology algorithm, and show that the new algorithm is convergent.
PREPARATION OF METHYL SALICYLATE USE THE ACID CATALYST BY WITHOUT SOLVENTS METHOD

Preparation of methyl salicylate use the acid catalyst by without solvents method has been done. Preparation of methyl salicylate was carried out by reacting salicylic acid with methanol and use sulfonic acid as a catalyst. The reaction was use reflux method at 60-70C for 3 hours. The result of preparation of methyl salicylate were it has a yellow clear solution (like oil) and has a distinctive smell (like gandapura oil). From the result of FT-IR analysis showed the functional groups O-H phenol, C-H sp, C-H sp, C=O ester, C=C aromatic, C-O-C ester and C-OH phenol. Based on these results it is known that the methyl salicylate has been formed.
Effective Adsorption and Separation of Lysozyme with Magnetite/Isinglass Composite Nanoparticles: Investigation of the Operational Parameters

The present study focused on the separation of lysozyme from various media using adsorption technology. Firstly, Fe3O4/isinglass nanocomposite was synthesized and then characterized by Fourier Transform Infrared Spectrometer (FTIR), Scanning Electron Microscope (SEM), Transmission Electron Microscopy (TEM) and Vibrating Sample Magnetometer (VSM). The maximum adsorbent capacity was achieved at a contact time of 90 min, pH of 6.2, the initial LYZ concentration of 0.5 mg ml-1, and the adsorbent dosage of 15 mg. The obtained experimental data suited well with the Langmuir isotherm model by prediction the maximum adsorption capacity and a correlation coefficient of 149.25 mg/g and 0.9532, respectively. The kinetic data were well fitted using a pseudo second order kinetic model. Under optimized condition, the relative standard deviation with three replicates was 3.2%. The mixture of lysozyme and bovine serum albumin was used as a mixture model protein to investigate the adsorption selectivity. The magnetite/isinglass composite was then used for the extraction of lysozyme from egg white solutions. Sodium Dodecyl Sulfate-Polyacrylamide Gel Electrophoresis (SDS-PAGE) results showed that adsorption of lysozyme from protein mixture is selective and extracted lysozyme from egg white showed high purity.
University of Birmingham The Curious Incident of Attention in Multisensory Integration

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Large Scale Mosaicing and Compositing of PROBA-V Satellite Images

The paper deals with the production of large scale vegetation products derived from PROBA-V 100 m satellite images. Image compositing and mosaicking are needed to create seamless products on a global or continental scale. In the study, we have analysed and compared two compositing methods. Evaluation of the methods was focused on the visual inspection of the product seamlessness, and efficiency in eliminating (reducing) the influence of inadequate pixels that were misclassified by a cloud detection algorithm. During the compositing procedure, we also created a raster quality map and calculated statistical properties of the mosaics in order to assess the quality of the product.
Modeling of Power Transition in Full Power Shift of Hydromechanical Transmission

This paper shall explore the dynamics of power transition in full power shift of hydromechanical transmission (HMT) and focus on the ideal target displacement ratio. An arithmetic two-range HMT is taken as the research object. A mathematical model of power transition in full power shift is established, including the hydraulic transmission unit model and the brake torque model during the double brakes overlapping. Aiming at the constant output power of HMT in the shift process, a prediction model of the displacement ratio target value is established, and the prediction method is proposed. By combining theoretical analysis and experimental research, it proves that the power transition model can describe the power transition process. And the prediction method of the displacement ratio target value proposed in this paper can complete the power transition when the double brakes overlap. In the power shift process, the output power can be transmitted normally in full power. The power transition model and the prediction method of the displacement ratio target value can provide theoretical and engineering references for the full power shift of HMT.
Localization System Supporting People with Cognitive Impairment and Their Caregivers

Localization systems are an important component of Ambient and Assisted Living platforms supporting persons with cognitive impairments. The paper presents a positioning system being a part of the platform developed within the IONIS European project. The system’s main function is providing the platform with data on user mobility and localization, which would be used to analyze his/her behavior and detect dementia wandering symptoms. An additional function of the system is localization of items, which are frequently misplaced by dementia sufferers. The paper includes a brief description of system’s architecture, design of anchor nodes and tags and exchange of data between devices. both localization algorithms for user and item positioning are also presented. Exemplary results illustrating the system’s capabilities are also included.
Uncorrected Proof ) Title : A Simulation Model of Neural Activity during Hand Reaching Movement

Neural response is a noisy random process. The neural response to a sensory stimulus is completely equivalent to a list of the spike times in the spike train. In previous studies, decrease of neuronal response variability was observed in various areas of the cortex during motor preparatory in reaching task. The reasons of reduction in neural variability (NV) is unknown whether it could be influenced by increasing the firing rate or it might be a result of the intrinsic characteristic of cells during the reaction time (RT). For this purpose, a neural response function with an underlying deterministic instantaneous firing rate signal and a random Poisson process spike generator is simulated in this paper. Neural simulation can help us understand relationships between complex data structures of cortical activities and their stability in details during motor intention in arm-reaching tasks. The our measurements showed a similar pattern of results to cortex, a sharp reduction of normalized variance of simulated spike trains across all trials. We also reported that there is a reverse relationship between activity and normalized variance. This study could be applied to neural engineering and brain machine interfaces as a means of controlling external devices such as the movement of a robot arm.
uration of untreated illness as a key to early intervention in chizophrenia: A review

The first psychotic episode is classically viewed as a critical period which management is important in determining the long-term outcome of the schizophrenia (SCZ). For this reason, the duration of untreated illness (DUI), defined as the interval between the onset of the psychiatric disorder and the administration of the first pharmacological treatment, is a clinical variable that has been increasingly investigated due to its potentially modifiable nature and its value as a predictor of outcome. DUI is poorly applicable and highly unreliable in psychosis. The present critical review examines the impact of DUI and its more operative definition of “duration of untreated psychosis” (DUP) in the course and outcome of SCZ, focusing on its epidemiologic, clinical, prognostic factors. Length of DUP has been identified as positively related to a worst treatment response, symptom control and overall functional outcome in SCZ. Negative symptoms appear to be prominently related to longer DUP. Neuroimaging correlates of DUP have not been clearly outlined: few of the studies considering first-episode patients and DUP showed structural abnormalities. A low proportion of significant associations were found mostly in cerebellum and occipital lobe of patients with longer DUP. Also, evidence of an inverse correlation between cognitive alterations and DUP is not conclusive. DUI and DUP are multidimensional constructs that imply both intrinsic, illness related (e.g. subtle symptoms at onset) and extrinsic factors (e.g. access to mental health services), so that from its study sprouted in the last decades First-Episode Units, aimed at providing secondary prevention in SCZ such as providing a timely diagnosis and treatment to patients experiencing their first psychotic episode. Early intervention seems to ensure a shortened DUP, especially for people presenting with brief limited intermittent psychotic symptoms, and, ultimately, ensure a more favorable prognosis for patients affected
RESEARCH ON HIGH RESOLUTION REMOTE SENSING IMAGE CLASSIFICATION BASED ON SEGNET SEMANTIC MODEL IMPROVED BY GENETIC ALGORITHM

Abstract. SegNet model is an improved model of Full Convolutional Networks (FCN). Its encoder, i.e. image feature extraction, is still a convolutional neural network (CNN). Aiming at the problem that most traditional CNN training uses error back propagation algorithm (BP algorithm), which has slow convergence speed and is easy to fall into local optimum solution, this paper takes SegNet as the research object, and proposes a method of extracting partial weights by using genetic algorithm (GA) to select features of SegNet model, and to alleviate the problem that SegNet is easy to fall into local optimal solution. In the training process of SegNet model, the weight of convolution layer of SegNet model used to extract features is optimized through selection, crossover and mutation of genetic algorithm, and then the improved SegNet semantic model (GA-SegNet model) is obtained by GA. In order to verify the image classification effect of the proposed GA-SegNet model, the same high-resolution remote sensing image data are used for experiments, and the model is compared with maximum likelihood (ML), support vector machine (SVM), traditional CNN and SegNet semantic model without GA improvement. The experimental results show that the proposed GA-SegNet model has the best classification accuracy and effect, which GA overcomes the problem of premature convergence of BP random gradient descent to a certain extent, and improves the classification performance of SegNet semantic model.
Classiﬁcation of Power-line Insulator Condition using Local Binary Patterns with Support Vector Machines

—Damaged insulators may affect the mechanical and electrical performance of an electric power grid, which can lead to the ﬂow of leakage currents through the line supports. This increases electrical losses and voltage drop in the power grid. It also poses a risk to human safety. Thus, it is crucial to monitor and inspect the condition of insulators to detect degradation or damage. However, the traditional method of inspection is inadequate in meeting the growth and development of the present electric power grid. Hence an automated system such as the computer vision method is presently being explored as a means to resolve this crisis safely, speedily and accurately. This paper presents a method that distinguishes between defectuous and non-defectuous power-line insulators. Active Contour model is applied for insulator segmentation in order to determine insulator region of interest. Local binary pattern is used for feature extraction from the insulator region of interest which is then fed to the support vector machine classiﬁer for classiﬁcation. An accuracy of 94.1% was achieved when morphological operation is used in combination with active contour model for segmentation based on the ground-truths. In addition, local binary patterns feature extraction method outperformed gray level co-occurrence matrix when used with support vector machines.
CHALLENgES AND DESIgN CoNSIDERATIoNS FoR RADAR oPERATIoN IN LoCAL LITToRAL

Due to Singapore’s geographical locality, its naval and airborne maritime surveillance radars operate in a unique littoral environment that poses a plethora of challenges to their system design. This article briefly describes these challenges, and focuses on relating experiences gained from operationalising radars that perform well in the local environment. These experiences include the stringent process of focusing on the system architecture design during the front-end definition phase, simulation and testing with environmental data during the development phase, and the eventual fine-tuning and optimisation phase through trials. With advancements in technology, this article also provides an overview of promising advanced radar developments and the expected benefits in the future.
Safety of selenium‐enriched biomass of Yarrowia lipolytica as a novel food pursuant to Regulation (EU) 2015/2283

Following a request from the European Commission, the EFSA Panel on Nutrition, Novel Foods and Food Allergens (NDA) was asked to deliver an opinion on selenium-enriched biomass of Yarrowia lipolytica as a novel food (NF) pursuant to Regulation (EU) 2015/2283. The NF is the dried and heatkilled selenium-enriched biomass of Y. lipolytica. This yeast species is widespread in nature, can be found in the environment and in foods, and was attributed the qualified presumption of safety (QPS) status for production purposes in 2018, including food and feed products based on biomass of the yeast. The production process, fermentation in the presence of sodium selenite, includes a heat-killing step of the yeast, resulting in the absence of viable Y. lipolytica in the NF. The maximum total selenium content in the NF is 200 lg Se/g, mainly present as organic selenium compounds. The applicant proposed to use the NF as a food supplement. The target population proposed by the applicant is the general population from 3 years of age onwards, with maximum proposed use levels of 0.2 g/day for children from 3 to 9 years of age and 1 g/day thereafter. The Panel considers that the yeast biomass is not of safety concern at the proposed use levels. The Panel also considers that the selenium provided by the NF is as safe as selenium from other dietary sources. However, the Panel notes that, at the use levels proposed by the applicant, the intake of the NF could, in combination with a background diet high in selenium, lead to total selenium intakes exceeding the UL for selenium in all target population groups, except for children from 7 to 9 years. Furthermore, the Panel considers that newly emerging data warrant a reassessment of the UL for selenium. © 2020 European Food Safety Authority. EFSA Journal published by John Wiley and Sons Ltd on behalf of European Food Safety Authority.
METHODOLOGICAL ISSUES OF MANAGEMENT REPORTING SYSTEMS DESIGN Summary

The article presents the methodological considerations of management reporting systems design. In the first part of the paper the nature and scope of management reporting were concisely explained, and moreover the role of this form of reporting information in the field of management accounting was elaborated. Then the rules of creating management dashboards and scorecards as well as the procedure of pre-implementation analysis that should be followed while designing a management reporting system were discussed. In the second part of the article the most important rules for the implementation of internal reporting with particular regard to contingent factors were described. Considerations conducted in the article are summarized with a discussion of the issues related to the design of key performance indicators starting from the identification of the business objectives and critical success factors and ending with the regular update of a set of metrics.
Addressing the Need for a Proper Legal Environment to Support Entrepreneurship in Saudi Arabia

Many studies have discussed the impact of entrepreneurial environment on the economies of developed countries. In this context, the legal environment, has been given attention. However, in developing countries, research in this area is insufficient. Furthermore, the few researchers who have discussed the legal environment in the context of the entrepreneurial environment have focused on how the legal environment affects business in general, but not entrepreneurship (Bruyat & Julien, 2001). This paper will explore the need for a proper legal environment to support entrepreneurship in Saudi Arabia. It will examine the suitability of the current legal environment for developing entrepreneurship, and it will identify weaknesses and gaps. For this research, quantitative methods were used to collect the opinions of practitioners working in the field. Finally, based on the findings, a proposed suggestion that fits the requirements of a proper legal environment for entrepreneurship in Saudi Arabia will be suggested.
Subspecialization: is this the way to go?

Subspecialization has become a global trend in many developed countries. While doctors are exposed to different branches of medicine during their undergraduate training, most are able to pursue their careers in only one specialty of interest. Within different specialties, doctors are often further directed into different subspecialities within their discipline. Whereas subspecialization can certainly improve the standard of care because specialists have more exposure in their area of interest and have more in-depth clinical and surgical skill, ‘non-specialists’ have a greater chance of treating patients with conditions outside their discipline.