Miracle-dz/UniFAR-FTUs huggingface dataset:
A novel LSTM based deep learning approach for multi-time scale electric vehicles charging load prediction

Short-term load forecasting is an important issue in energy management system and a key measure to maintain the stable and effective operation of power systems, providing reasonable future load curve feeding to the unit commitment and economic load dispatch. Specifically, load forecasting at different time scales has various and corresponding roles in power systems. In recent years, plug-in electric vehicles have gradually become popular in major cities, and the number of electric vehicles is growing rapidly. However, the mass roll out of EVs may cause severe problems to the power system due to the huge charging power and stochastic charging behaviours of the EVs drivers. The accurate model of EVs charging load forecasting is therefore an emerging topic. In this paper, artificial neural networks and a long short term memory model based deep learning approaches are employed and compared in forecasting the EVs charging load from the charging station perspective. Numerical results show that the long short term memory model has demonstrated better performance and provided a model of higher accuracy in short-term EVs load forecasting comparing with the traditional artificial neural networks.
Deep Tree Net-Vector of Locally Aggregated Descriptor (VLAD) Model

In this work, we combined both a tree and a NetVLAD (vector of locally aggregated descriptors) in order to design new deep model. In developing this model, we studied the impact of tree hyperparameters and found that branch factors had major effects on the parameter utilization as major indicator and the accuracy of the model. The new architecture presented herein exposes a novel hyperparameter called the tree branch factor, which grants additional control over model complexity and on the maps codependency. Deep Tree Net-Vector provide the flexibility to combine two very famous techniques, namely the tree-based technique and VLAD. The former reduces the number of parameters, whereas the latter provides better feature representation inside the model. This work aimed to demonstrate the integration of the strong image descriptor, VLAD, with the tree module, to gain additional control on model size, rather than obtaining better results than state-of-the-art models, and to enhance image representation inside model layers, which could then be invested towards several tasks such as image classification and retrieval. We performed experiments on the Canadian Institute for Advanced Research-10 dataset and were able to show that the proposed models were superior—in terms of information density and accuracy—to many well-known networks.
Electromagnetic modelling of the SKA-LOW AAVS1.5 prototype

The numerical modelling of the Square Kilometre Array (SKA) Aperture Array Verification System Version 1.5 is discussed. The role of this SKA prototype is placed within the perspective of the overall SKA project. The dual-polarized log-periodic SKALA4.1 antenna elements comprising a station are briefly described, along with some considerations regarding the station. Computational simulation aspects are considered, and preliminary results shown. An appraisal of the implications for station-level calibration concludes the paper.
Calculation of Rotor Currents for Doubly-Fed Induction Generation under Short Circuits in Power Grids

the doubly-fed wind power generator is more sensitive to the voltage of the machine falls, with the increasing of wind power installed capacity, power grid failure of doubly-fed wind power generator rotor current analysis and it is very important to evaluate accurately, relationship with doubly-fed wind power generator through the implementation of the low voltage and the analysis and calculation of fault electric parameters. Starting from the principle of doubly-fed induction generator, mathematical model is established, when grid short-circuit fault of its rotor galvanometer formula is derived, by time domain simulation results verify the validity of the expression and an example. The proposed expression using the voltage of the machine and the grid inverter control parameters can be calculated from short circuit at any time when the rotor current, has clear physical meaning and good practicability.
Effect of Ethanolic Extract of Pisonia alba Span. Leaves on Blood Glucose levels and Histological Changes in Tissues of Alloxan-Induced Diabetic rats

Summary: The ethanolic extract of Pisonia alba Span (Nyctaginaceae) (PAEt) was found to have antidiabetic activity that reduces blood sugar level in alloxan induced diabetic rats. In this study, a 15 days antihyperglycemic effect of the two different doses (250 and 500 mg/kg b.w.p.o) of PAEt was investigated. Glibenclamide was used as a reference drug at a dose of 0.6 mg/kg b.w. Treatment of alloxan diabetic wistar rats with the extract caused a significant (P<0.01) reduction in the blood glucose levels when compared with control. PAEt also showed significant lipid peroxide scavenging in vitro, besides exhibiting significant activity in quenching 1,1-diphenyl-2 picryl hydrazyl (DPPH) radicals, thereby indicating its potent antioxidant effects. Treatment with PAEt of the diabetic animals helped to maintain the body weight at near normal values and maintain the glucose levels of urine below 2% like glibenclamide. Treatment with the ethanol extract of Pisonia alba Span (Nyctaginaceae) (PAEt) was found to almost restore the normal histological architecture of the liver, kidney and pancreas of alloxan-induced diabetic rats. Industrial relevance: Herbal medicines are getting more importance in the treatment of Diabetes mellitus and other ailments because of the hazardous adverse effects of the current therapy used to treat these ailments using synthetic drugs. Diabetes can be controlled by allopathic medicine as well as herbal medicine. In case of allopathic medicine, complete cure and tolerance are major problems. Herbal medicine is free from side effects and less costly when compared to synthetic hypoglycemic agents. Nowadays a lot of herbal formulations are available as tablets and other dosage forms. The present study will help the industry to produce herbal drugs with fewer side effects, which are affordable and more effective in the treatment of Diabetes mellitus.
Women Parliamentarians Impact on Economic Growth: A Cross-Country Analysis Evidence

Women constitute half of the global population, and consequently half of the potential creators, talents, and innovators. However, they are still under-represented in national parliaments and other political bodies. In this paper, the relationship between economic growth of high-income countries and the proportion of women candidates in nation parliaments is investigated through a cross-country statistical analysis. Other included predictors are population growth, initial GDP/capita, and foreign direct investment. To obtain a long-term view, panel data of 20 high-income economies are used in the form of averages of twelve years starting 2006. Development indicators in the cross-country data exhibit complex interactions. Advanced Machine-Learning tools are used to uncover and capture the complex relationships. By the aid of these tools, interactions are identified and relationships are constructed in a non-linear fashion. The generated nonlinear regression model that best fits the data has a high coefficient of determination that exceeds 0.93. Since the aim of this study is to quantify the relationship between shares of parliamentarian women and economic growth independently, Principle Component Analysis is also employed to unveil the desired link. A principle component regression based model is developed. The results reveal a positive statistical significant effect at level of 0.05, where a 10% increase in proportion of women parliamentarians may, on average, increase economic growth by 0.306%. This study provides an empirical evidence on the positive political role of women with respect to stimulating economic growth.
Multi-beam Transmitarray Antennas for 5G Radio Access at Ka-Band

A beam scanning transmitarray antenna (TA) working in the 27.5-29.5 GHz frequency band is presented here. The unit-cell is made of three stacked meshed circular rings printed on Diclad substrates, and provides a 300° phase-shift for an insertion loss lower than 1 dB at 28.5 GHz. The TA is illuminated by a $2\times 2$-element array of aperture-coupled stacked microstrip patch (ACMP), whose location is shifted in the focal plane to scan the antenna beam in H-plane from −30° to 30°. A TA prototype with a diameter of 9.9 wavelengths has been designed, fabricated and tested. A broadside gain of 25 dBi and −1 dB gain bandwidth of 1.8 GHz have been obtained experimentally.
Existence and Global Exponential Stability of Pseudo Almost Periodic Solution for Clifford- Valued Neutral High-Order Hopfield Neural Networks With Leakage Delays

In this paper, we are concerned with a class of Clifford-valued neutral high-order Hopfield neural networks with leakage delays. Although the multiplication of Clifford numbers does not satisfy the commutativity, which brings great difficulties to the study of Clifford-valued systems, we have found a method that does not decompose Clifford-valued systems into real-valued systems to study the existence and global exponential stability of pseudo-almost periodic solutions of this class of neural networks. Our results are completely new. Finally, two examples are given to illustrate the effectiveness and feasibility of our main results.
Double fortified salt and deworming”- game changers in the battle against iodine and iron malnutrition in Indian school children

Background Controlling malnutrition among school children has been a priority issue of current nutrition strategies in India. However, meeting majority of the MDGs by controlling micronutrient deficiencies among school children demands attention and intense efforts by the country. Various state governments have implemented different strategies in school health programs, but the results are not very promising. Thus as an universal strategy, double fortified salt (DFS) can be implemented as a measure to combat deficiencies of two micronutrients. Aims and Objectives to assess impact of NIN-DFS supplementation on micronutrient status (iron and iodine) of rural school children with deworming and NHE. Methods and materials School children (N=947) (6-15 years) from rural villages of Vadodara, Gujarat were randomly selected and were assigned into 4 case control groups using DFS and deworming as intervention strategies. Results After 9 months of intervention, median UIC improved significantly (p<0.001) in both the groups and the prevalence of iodine deficiency decreased significantly (p<0.001). Mean Hb improved (+0.60 g/dl) significantly (p<0.001) among supplemented group (DFS+DW) compared to decrease (-0.54 g/dl) among control group (DW). IQ/cognition scores also improved significantly among experimental groups (p<0.01, Memory and draw-a-man test scores) compared to control groups. However, NHE provided to the parents of the school children, helped to meet major proportion of RDA of the children. The micronutrient status of the children was observed to improve significantly, who were benefited by multiple approaches. Conclusion DFS production and consumption should be promoted as universal strategy at policy level and multiple strategies should be used to benefit the targeted population in simple and most effective manner.
DeepCrash: A Deep Learning-Based Internet of Vehicles System for Head-On and Single-Vehicle Accident Detection With Emergency Notification

Most individuals involved in traffic accidents receive assistance from drivers, passengers, or other people. However, when a traffic accident occurs in a sparsely populated area or the driver is the only person in the vehicle and the crash results in loss of consciousness, no one will be available to send a distress message to the proper authorities within the golden window for medical treatment. Considering these issues, a method for detecting high-speed head-on and single-vehicle collisions, analyzing the situation, and raising an alarm is needed. To address such issues, this paper proposes a deep learning-based Internet of Vehicles (IoV) system called DeepCrash, which includes an in-vehicle infotainment (IVI) telematics platform with a vehicle self-collision detection sensor and a front camera, a cloud-based deep learning server, and a cloud-based management platform. When a head-on or single-vehicle collision is detected, accident detection information is uploaded to the cloud-based database server for self-collision vehicle accident recognition, and a related emergency notification is provided. The experimental results show that the accuracy of traffic collision detection can reach 96% and that the average response time for emergency-related announcements is approximately 7 s.
Two stage SVD-based method for QRST waves cancellation in atrial fibrillation detection

The paper presents the results of studies devoted to analysis of performance of two-stage SVD-based (singular value decomposition) method for cancellation of QRST waves in ECG (electrocardiogram) signals. In our studies, the purpose of cancellation of QRST (related to ventricular activity) is to extract the main atrial activity (AA) from ECG signal. A special feature of the analyzed method is SVD-based estimation and subtraction of QRS-complexes and T-waves in two consequent stages instead of one. To improve the quality of QRST cancellation we proposed upsampling of ECG signal to increase the time resolution. The results of preliminary experiments show that two stage SVD-based method with appropriately chosen parameters can allow achieving better performance than the method based on single-stage SVD-based QRST cancellation, in applications related to atrial activity analysis, e.g. detection of atrial fibrillation (AFib) symptoms on the basis of atrial activity.
ABCD Rule Implementation for the Skin Melanoma Assesment – A Study

Examination of the Skin-Melanoma (SM) is an essential practice to verify and authenticate the phase of the cancer in skin fragment. If the cancer phase, such as the Benign/Malignant is recognized through the screening process, a possible treatment procedure can be implemented to cure the patient. This work employs a soft-computing assisted procedure to threshold and segment the SM to identify the phase. The thresholding is executed with the Bat Algorithm (BA) and Otsu’s thresholding and the extraction is employed with the Watershed scheme. After extracting the SM slice, a relative study is applied to find the similarity level among the ground-truth and the SM to validate the performance. Further, the ABCD rule is also applied to identify the crucial SM parameters, such as Asymmetry (A), Border abnormality (B) and Diameter (D) to classify the considered SM images into Benign/Malignant. The outcome of this work confirms that, implemented practice works well on the chosen SM images.
Expression of P63 in Oral Mucosa Covering Impacted Teeth: An Immunohistochemical Study

Objective: The purpose of the study was to examine the expression of p63 gene in asymptomatic enclosed lower third molar covering oral mucosa. Design: : 50 samples of normal oral mucosa taken from 50 patients who underwent third molar surgery were included into the study. The samples were subdivided into two groups. One group was made of oral mucosa covering completely impacted teeth, while other group was made of oral mucosa covering partially impacted teeth. Only nuclear staining of the cells was evaluated by percentage of positive cells. Results: : Immunoexpression of p63 was almost equal in submucosa cells of both groups , showing a faint to intermediate positivity. Basal layer of oral mucosa covering completely enclosed teeth have shown a stronger expression of p63 than it was the case with the other group. Conclusion: Expression of p63 is noticed in basal and suprabasal layers of oral mucosa covering impacted teeth. Weak expression of p63 in basal layer of oral mucosa covering partially impacted teeth, migh be associated with teeth eruption, lesser number of stem cells and thinner mucosa patch than it is a case with oral mucosa covering completely impacted teeth.
A Simplified Model for the Thermal Behavior Prediction of Inflatable Hangar

The necessary condition for safe use of the inflatable hangar is the proper internal gas pressure. The alternating temperature between day and night will cause the change of gas pressure inside the inflatable hangar, which will affect the load-bearing performance of the inflatable hangar. However, the thermal performance analysis of the inflatable hangar is very few. This paper develops a simplified theoretical model consisting of direct solar radiation model, infrared radiation model, reflected radiation model and convection heat transfer model to analyze the thermal properties of the inflatable hangar. The feasibility of the model is verified by comparing the calculation results with the results of other researchers. Then, the temperature distribution of outer skin and inner gas of the inflatable hangar in summer solstice is analyzed and discussed in detail. The simulation results showed that the theoretical model is reliable and the inner gas temperature of inflatable hangar fluctuates about 27K during the day and night. The theoretical model developed in this paper would provide a useful guide to solve the problem of load-bearing performance change of inflatable hangar under the condition of temperature alternation.
Comparative Studies on the Efficacy of Different Vaccination Methods in the Treatment of Bovine Papillomatosis

The aim of this study was to compare the efficacy of autogenous, species-specific and herd-specific vaccination in the treatment of bovine papillomatosis. This study was performed on 24 female Holstein cattle aged between 8 and 17 months in the same herd. The animals were divided into three vaccination groups (eight cattle in each group) as autogenous, species-specific and herd-specific. A dose of 10 ml per animal was inoculated subcutaneously every 10 days, for a total of three times. The recovery status of the animals was checked for 3 months at 15-day intervals. In this study, all the animals fully recovered within 1.5-3 months in the species-specific group, and 1.5-2 months in the autogenous and herd-specific groups. In conclusion, all the vaccines were found to be successful against bovine papillomatosis. However, recovery in the autogenous and herd-specific vaccination groups occurred earlier than in the species-specific vaccination group.
Potential Reducing CO2 Emission for Ice Cream Industry through Energy Efficiency Improvement in Indonesia

Food needs in Indonesia increase unanimously in line with population growth. The large number of population indicates certain behavioral food consumption especially the preference in ice cream. Ice cream is relatively well balanced, wholesome, easily digestible and delicious food. Sector industry is the third largest sector for using energy under household and transportation. Hence, implementing energy efficiency programs for industry is necessity. The study aims to determine the energy efficiency and its effect in reducing CO2 emission. The study had used the combination of two methods, quantitative and qualitative method. Quantitative method was conducted by using analysis data from field survey, while the qualitative method by interview and study literature. The study discusses about energy efficiency implemented in one of ice cream factory in Indonesia, which can reduce 3 percent of energy consumption and CO2 emission. This attempt gives advantages in economical aspect, environmental aspect, employee morals, compliance, and corporate image.
Combining short and long-term models for predicting blood glucose concentrations on diabetic patients

This work presents a study about identification techniques applicable to predict the blood glucose concentration (glycaemia) taking into account data of two free-living patients with type 1 diabetes (T1D). The main objective is to have an efficient way to model the inter-patient and intra-day variability to improve the design of control algorithms to be implemented in the context of artificial pancreas (AP). Historically, multiple models have been proposed to attack this kind of problems. The novelty here is new since we propose to generate a family of long and short term models which predictions are selected according to specific conditions closely related with the historical data of the patient. Therefore, a systematic methodology is developed firstly obtaining an average long term prediction model (ALTPM), considering insulin and carbohydrates as inputs and glycaemia as output. Secondly, the ALTPM glycaemia predictions together with historical data allows to obtain statistically an interval long term prediction model (ILTPM), to be able to define the range of blood glucose variations at least for the 70% of the time considered for the experiments. To improve the short term predictions we use a particular approach based on Kalman Filter which helps us to tracking glycaemia every five minutes and estimate two weight parameters. These weights affect the predictions of the ILTM to improve the quality of the estimations which are evaluated through the commonly used Root Mean Square Error (RMSE) metric.
Argatroban Versus Bivalirudin in the Treatment of Suspected or Confirmed Heparin-Induced Thrombocytopenia

Background: Argatroban and bivalirudin are direct thrombin inhibitors (DTIs) used for the treatment of heparin-induced thrombocytopenia (HIT). The purpose of this study was to determine whether either agent offered an advantage in efficacy and ability to remain within the targeted therapeutic anticoagulation range. Methods: This was a single-center, retrospective, observational cohort study at a large academic medical center. The primary efficacy outcome was time to therapeutic anticoagulation, defined as total number of hours to achieve 2 consecutive activated partial thromboplastin time (aPTT) values in goal range. Results: A total of 91 patients were included in the analysis. Average time to initial therapeutic anticoagulation was 4.71 hours and 9.8 hours for the argatroban and bivalirudin groups, respectively (P < .01). Conclusions: Argatroban may be advantageous compared to bivalirudin in achieving initial therapeutic anticoagulation goals among patients with suspected or confirmed HIT.