Miracle-dz/UniFAR-FTUs huggingface dataset:
A Selective Ensemble Approach for Accuracy Improvement and Computational Load Reduction in ANN-based PV power forecasting

Day-ahead power forecasting is an effective way to deal with the challenges of increased penetration of photovoltaic power into the electric grid, due to its non-programmable nature. This is significantly beneficial for smart grid and micro-grids application. Machine learning and hybrid approaches are well assessed techniques, able to provide effective forecasting with a data-driven approach based on previous measurements from existing power plants. Ensemble methods can be employed to increase solar power forecasting accuracy, by running several independent forecasting models in parallel. In this paper, a novel selective approach is proposed and assessed, where independently trained neural networks are evaluated in terms of accuracy, in order to properly select a suitable forecasting. Moreover, in order to reduce the associated computational burden, suitably developed new normalization approaches are proposed and evaluated. The considered experimental case study shows that the combination of the proposed procedures is able to increase accuracy and to mitigate the overall computational load, resulting in a simple and lightweight algorithm. Additionally, a comparison with other commonly used techniques has shown that the proposed approach is robust with respect to dataset limited size and discontinuities.
CALCULATING THE START-UP COSTS OF HYDROPOWER GENERATORS

This paper presents several models for calculation of hydropower generator start-up costs. All start-up costs are assumed to be either directly proportional to the number of starts or increasing with the number of operating hours of the unit. Focusing on the latter, simplified models for both average and marginal start-up costs are developed and compared. Finally, an improved model is developed which takes into account both the direct start-up cost and the effect of the unit's refurbishment intervals. As for the simplified models, the start-up cost is assumed to increase with the number of operating hours (age) of the unit. As each start is assumed to cause abrasion equivalent to a certain number of operating hours, however, additional costs are incurred as each start will reduce the remaining service life of the unit and thus shift the refurbishment intervals closer in time.
EU Insights study on consumers and dietary sugars

EFSA undertook consumer research on nutrition and dietary sugars awareness, knowledge and risk perception, preferred information sources and trust in different actors, plus additional research on stakeholder perspectives. The research results informed EFSA’s communications and engagement for a public consultation on its draft scientific assessment of a Tolerable Upper Intake Level for dietary sugars, and upon its subsequent final publication. Interviews with the representatives of nine stakeholder organisations and two institutional partners were analysed for qualitative insights, which were used to validate and reinforce EFSA’s outreach to partners and civil society organisations. A consumer survey was conducted among a representative sample of 7,469 citizens (aged between 18 and 76) across EU Member States, Iceland and Norway in October and November 2020. The analysis of the quantitative data using segments derived from knowledge and risk perception indexes delivered important improvements for audience identification, messaging and choice of formats and channels for communication to the general public. The production of tips for risk communication on this topic used in combination with country-specific data, provided both accessible and comprehensive support to risk communicators at the national competent authorities in EU Member States, Iceland and Norway.
Accurate Modeling of Cryogenic Temperature Effects in 10-nm Bulk CMOS FinFETs Using the BSIM-CMG Model

In this letter, we have proposed modifications to the existing BSIM-CMG compact model to enhance its ability to model the behavior of short channel bulk FinFETs (both n and p-type) from room temperature down to cryogenic temperatures (10K). The proposed model is highly accurate in capturing the subthreshold swing, threshold voltage, and effective mobility trends observed in FinFET cryogenic operation. For efficient optimization of the proposed model parameters, we have proposed an adequate modeling strategy. We have compared convergence time between the existing BSIM-CMG model and the proposed model by simulating a reasonably large circuit using pseudo-inverters.
Statistical optimization of fermentation conditions for bioactive compounds production by marine Acinetobacter sp. EM11 against the fish pathogen Vibrio alginolyticus MK170250

The Vibrio species represent a large percentage of the natural microbiota in marine ecosystems (Urakawa & Rivera, 2006). This is mainly due to its wide host range as it may exist in a relationship with zooplankton, shellfish, shrimp and fishes. The Vibrio species coexist with the host as a mutualistic symbiont or an opportunistic pathogen. In the latter case, the drawbacks are intense on the aquaculture industry (Austin & Austin, 2007). Moreover, Vibriosis, a human illness, may develop post consumption of contaminated seafood meal. In Egypt, where aquaculture production is considered the largest in Africa and 11 th of the global market (El-Sayed et al., 2015; Abd El Tawab et al., 2018) there is a constant need to identify natural marine pathogens of Vibrio sp. , which forms the top threat of aquaculture industry. Diverse Vibrio species are reported as ARTICLE INFO ABSTRACT Article History: Received: Nov. 3, 2021 Accepted: Jan. 17, 2022 Online: Jan. 24, 2022 _______________
Dual-Arm Box Grabbing With Impact-Aware MPC Utilizing Soft Deformable End-Effector Pads

Safely generating impacts is challenging due to subsequent discontinuous velocity jumps and high impact forces. For this reason, state-of-the-art multi-robot controllers performing collaborative object manipulation typically approach contacts with low relative velocities. We instead aim for high-speed bi-manual impact-aware swift grabbing tasks, generating intentionally maximum feasible impacts. Therefore, we propose that the robot end-effector tips be equipped with deformable soft pads. The hardware modification partially absorbs the shock and enables us to control the deformation state during the prolonged impact duration. Mapping the robot’s structural hardware constraints from the high-dimensional configuration-space to the low-dimensional contact-space enables us to model the bounded deformation dynamics explicitly. Exploiting constrained model-predictive control (MPC), we maximize the impact velocity within the feasible limits for dual-arm setups with deformable end-effector tips. Our control paradigm is assessed with real-robot experiments on two redundant Panda manipulators, demonstrating high pre-impact velocities for boxes with varying weights and size. The approach also applies to grabbing soft objects with rigid end-effectors.
Optimal design and techno-economic analysis of a hybrid solar-wind power resource: a case study at Al Baha University, KSA

This study presents a feasibility analysis of supplying the measured load of al baha university in Saudi arabia by renewable resources including solar photovoltaic (PV), wind turbine (WT), and storage banks instead of the current conventional grid. The objective of this paper is to find the optimum system that has the lowest net present cost (NPc) and greenhouse emission cO2. The metrological data and load profile are collected at the desired location. The simulation results show that NPc of a proposed combination of grid/PV/WT system, at the current grid’s tariff of 0.085$/kWh, is more efficient than other configurations with power load shortage (<0.1%), with more than 30% reduction in cO2 emission, and lower cost of energy (cOE). The results also show that the integration of PV and WT sources with the main grid is the best configuration that leads to the minimum cOE of 0.0772 $/kWh, 0.075 $/ kWh, and 0.048 $/kWh at the educational building, administration building, and total campuses’ load, respectively. The developed methods conclude that the objective function and simulation results are feasible for the selected loads at al baha university. The current analyses can be adopted to install the real renewable energy system at the desired university.
Putrescine: A Key Metabolite Involved in Plant Development, Tolerance and Resistance Responses to Stress

Putrescine (Put) is the starting point of the polyamines (PAs) pathway and the most common PA in higher plants. It is synthesized by two main pathways (from ornithine and arginine), but recently a third pathway from citrulline was reported in sesame plants. There is strong evidence that Put may play a crucial role not only in plant growth and development but also in the tolerance responses to the major stresses affecting crop production. The main strategies to investigate the involvement of PA in plant systems are based on the application of competitive inhibitors, exogenous PAs treatments, and the most efficient approaches based on mutant and transgenic plants. Thus, in this article, the recent advances in understanding the role of this metabolite in plant growth promotion and protection against abiotic and biotic stresses will be discussed to provide an overview for future research.
Machine Learning Model-Based Simple Clinical Information to Predict Decreased Left Atrial Appendage Flow Velocity

Background: Transesophageal echocardiography (TEE) is the first technique of choice for evaluating the left atrial appendage flow velocity (LAAV) in clinical practice, which may cause some complications. Therefore, clinicians require a simple applicable method to screen patients with decreased LAAV. Therefore, we investigated the feasibility and accuracy of a machine learning (ML) model to predict LAAV. Method: The analysis included patients with atrial fibrillation who visited the general hospital of PLA and underwent transesophageal echocardiography (TEE) between January 2017 and December 2020. Three machine learning algorithms were used to predict LAAV. The area under the receiver operating characteristic curve (AUC) was measured to evaluate diagnostic accuracy. Results: Of the 1039 subjects, 125 patients (12%) were determined as having decreased LAAV (LAAV < 25 cm/s). Patients with decreased LAAV were fatter and showed a higher prevalence of persistent AF, heart failure, hypertension, diabetes and stroke, and the decreased LAAV group had a larger left atrium diameter and a higher serum level of NT-pro BNP than the control group (p < 0.05). Three machine-learning models (SVM model, RF model, and KNN model) were developed to predict LAAV. In the test data, the RF model performs best (R = 0.608, AUC = 0.89) among the three models. A fivefold cross-validation scheme further verified the predictive ability of the RF model. In the RF model, NT-proBNP was the factor with the strongest impact. Conclusions: A machine learning model (Random Forest model)-based simple clinical information showed good performance in predicting LAAV. The tool for the screening of decreased LAAV patients may be very helpful in the risk classification of patients with a high risk of LAA thrombosis.
Validation of the Serbian Adaptation of the Self-Absorption Scale

McKenzie and Hoyle made the first Self-Absorption Scale. Since this scale is not available in Serbian, we designed two studies aimed at its adaptation (Study 1, N = 400), and validation of the instrument (Study 2, N = 212). Results of Study 1 confirmed original two-factorial structure, but without two items on each subscale. The reliability of the public self-absorption was α = 0.75; and α = 0.72 for the private self-absorption. The factors in both, the original and adapted version, are congruent (measured by Tucker`s congruence coefficient). Results of Study 2 showed that self-absorption was positively correlated to the most aspects of pathological narcissism, self-consciousness, depression, stress and anxiety. Also, it was negatively correlated to self-esteem. We can conclude that the Self-Absorption Scale is short, reliable, and valid measure for assessing the pathological aspect of self-focusing on Serbian population.
Locomotion Control of Snake-like Robot utilizing Friction Forces: Stability Verification of Model Following Servo Controller

The purpose of this paper is to verify the e ﬀ ectiveness of Model Following Servo Control (MFSC) as a stabilizing control measure for systems with uncontrollable disturbances. Both the gravity compensation control system of the 1-link manipulator and the head-position control system of the two-wheeled robot is designed. In gravitational compensation control, gravitational acceleration is deﬁned as an uncontrollable state. In the head-position control, the frictional force in the axial direction of the skidding wheel is deﬁned as an uncontrollable state. The e ﬀ ectiveness of the MFSC as a stabilizing control system for systems containing uncontrollable states is veriﬁed via numerical simulations.
Ciri-Ciri Pembelajaran Kolaboratif Menerusi Google Classroom yang Meningkatkan Kemahiran Berfikir Aras Tinggi Pelajar

Kajian ini bertujuan untuk mengenal pasti ciri-ciri pembelajaran kolaboratif disokong komputer (Computer-supported collaborative learning, CSCL) yang digunakan di dalam e-pembelajaran Sains bagi tajuk Kereaktifan Logam menerusi Google Classroom dapat meningkatkan kemahiran berfikir aras tinggi pelajar (KBAT). Seramai 32 orang murid tingkatan tiga yang dipilih berdasarkan teknik persampelan rawak mudah terlibat dalam kajian ini. Instrumen kajian ini merupakan soal selidik ciri-ciri dari pendekatan Pembelajaran Kolaboratif disokong Komputer (CSCL). Semua dapatan dalam kajian ini dianalisis dan diproses oleh komputer menggunakan program SPSS versi 22.0 untuk mendapatkan nilai min dan sisihan piawai. Dapatan kajian ini menunjukkan bahawa semua pelajar setuju dimana ciri-ciri persekitaran pembelajaran CSCL yang digunakan dalam e-pembelajaran Sains ini membantu proses peningkatan KBAT pelajar semasa pembelajaran dan pengajaran Sains Tingkatan 3 bagi topik Kereaktifan Logam. Kajian ini telah memberikan sumber maklumat kepada guru Sains bagaimana caranya untuk melaksanakan proses pengajaran dan pembelajaran melalui penggunaan e-pembelajaran Sains yang mengandungi ciri-ciri pembelajaran CSCL yang telah ditetapkan.
Electrical impedance tomography for simulated muscle contraction: a viability analysis

Muscles problems could be studied by analysis of their electrical properties. This work realized EIT simulations for muscles under contractions. The EIT images can differentiate muscles positions and sizes by their contractions. Some electrical methods to evaluate the muscle conditions, analyses the muscle as a geometric structure, the bioimpedance analysis (BIA), and other analyses the muscle as a specific part, the electrical impedance myography (EIM). Both methods are already practical methods to evaluate muscles conditions but they don’t evaluate muscles as the image techniques, like magnetic resonance and sonography not always accessible. Regarding these techniques an alternative is presented: electrical impedance tomography (EIT). Accessible and well commented for other applications like brain activity imaging, breast imaging, liver fat detection, and bladder volume estimation. The EIT prospects to become an alternative muscle evaluation method alongside BIA and EIM. The forward problem solution method is briefly commented on, also the simulated model construction with your tissues and electrodes. Current patterns usage and the Inverse Problem solution through Electrical Impedance and Diffuse Optical Tomography Reconstruction Software (EIDORS) library. As result, the numerical EIT model permitted EIT images were achieved from different muscles contractions. An EIT model of transfemoral tomography was made, providing some EIT images. Each EIT image was derived from forward problem solutions. Comparisons permitted to conclude the advantages of a more realistic EIT numerical model. A numerical model permits to adapt and reorganize the model easier than a real phantom. Muscle contractions were simulated, even deep muscles were detected through EIT.
Brain pH Measurement Using AACID CEST MRI Incorporating the 2 ppm Amine Resonance

Many pathological conditions lead to altered intracellular pH (pHi) disrupting normal cellular functions. The chemical exchange saturation transfer (CEST) method, known as Amine and Amide Concentration Independent Detection (AACID), can produce image contrast that is predominantly dependent on tissue intracellular pHi. The AACID value is linearly related to the ratio of the 3.5 ppm amide CEST effect and the 2.75 ppm amine CEST effect in the physiological range. However, the amine CEST effect at 2 ppm is often more clearly defined in vivo, and may provide greater sensitivity to pH changes. The purpose of the current study was to compare AACID measurement precision utilizing the 2.0 and 2.75 ppm amine CEST effects. We hypothesized that the 2.0 ppm amine CEST resonance would produce measurements with greater sensitivity to pH changes. In the current study, we compare the range of the AACID values obtained in 24 mice with brain tumors and in normal tissue using the 2 ppm and 2.75 ppm amine resonances. All CEST data were acquired on a 9.4T MRI scanner. The AACID measurement range increased by 39% when using the 2 ppm amine resonance compared to the 2.75 ppm resonance, with decreased measurement variability across the brain. These data indicate that in vivo pH measurements made using AACID CEST can be enhanced by incorporating the 2 ppm amine resonance. This approach should be considered for pH measurements made over short intervals when no changes are expected in the concentration of metabolites that contribute to the 2 ppm amine resonance.
When CNN Praises Trump: Effects of Content and Source on Hostile Media Perception

Research on hostile media perception (HMP) has suggested that both news slants and partisan source cues influence individuals’ perception of news bias. Yet, relatively little attention has been paid to the possibility that the two message features may interact. Extending the literature on HMP, the present experiment investigates the content-source interaction in the context of President Trump’s policy on immigration, with two audience characteristics as potential moderators: political ideology strength (PIS) and need for cognition (NFC). Results show that (1) the effect of news slants on HMP is greater when the news is from an in-group source and (2) such interaction is more pronounced for those with higher levels of PIS and lower levels of NFC. Implications for our understanding of HMP and for public opinion in an increasingly fragmented and partisan media environment will be discussed.
تأثیر المیزة التنافسیة علی الأداء الاستراتیجی فی شرکات القطاع الخاص الصناعی المصری The impact of competitive advantage on strategic performance in the Egyptian industrial Private Sector Companies

This study attempts to explain the impact of competitive advantage on strategic performance in Egyptian private industrial sector companies, and for that the researcher reviewed previous studies that addressed the impact of competitive advantage and strategic performance in Western environments, and the study relied on four research hypotheses that test the impact of cost , quality, flexibility on strategic performance. A sample of (255) companies from the Egyptian industrial private sector companies, four hypotheses were formed, and a questionnaire was used in data collection pertaining to the variables of the study, Multivariate analysis techniques were used in data analysis. and the study concluded that there is a positive significant effect between competitive advantage in its three dimensions on strategic performance, and that demographic variables have no effect on competitive advantage and strategic performance. A discussion of the results and their implications is presented along with suggestions for further research.
Average volume-assured pressure support for patients with obstructive sleep apnea with failed CPAP titration

ABSTRACT Objectives: Obstructive sleep apnea (OSA) is a common disease, often treated using continuous positive airway pressure (CPAP) therapy. In many cases, patients fail a CPAP titration study due to inadequate control of the apnea-hypopnea index (AHI, events/hour) or due to treatment-emergent central sleep apnea (TE-CSA). We report our experience using a mode of non-invasive ventilation for alternative treatment of these patients. Material and Methods: We reviewed records of adults who had OSA with AHI≥15 diagnosed on polysomnography (PSG) with failed CPAP titration and in whom titrations with average volume-assured pressure support (AVAPS) with auto-titrating expiratory positive airway pressure were performed. Results: Forty-five patients, age 57.9±13.1 y, 26 males, body mass index (BMI) 40.2±8.7kg/m 2 . Reasons for CPAP titration failure included: TE-CSA (25, 55.6%) and inadequate control of AHI at maximum CPAP of 20cm H2O (20, 44.4%). Changes noted from baseline PSG to AVAPS titration: AHI: 65.3±29.3 decreased to 22.3±16.1 (p<0.001). Median time SpO2 ≤88%: 63.7 to 6.9min (p<0.001). In 16 patients the AHI was reduced to <15 and in 16 additional patients the AHI was reduced to <30. Improvement in AHI was not related to gender, age, or opioid use, but was correlated with BMI: ∆AHI=12.2 - (1.4 * BMI); p=0.05. AVAPS resulted in improved sleep architecture: median N3 sleep increased: 1.4% to 19.6% total sleep time (TST) (p<0.001), and median R sleep increased: 6.4% to 13.6% TST (p<0.01). Discussion: For patients with OSA for whom CPAP titration failed, titration with AVAPS may be an effective treatment.
Modeling Communicable Diseases, Human Mobility, and Epidemics: A Review

The spatiotemporal propagation patterns of recent infectious diseases, originated as localized epidemic outbreaks and eventually becoming global pandemics, are highly influenced by human mobility. Case exportation from endemic areas to the rest of the countries has become unavoidable because of the striking growth of the global mobility network, helping to overcome the physical distance existing between faraway regions. In this context, understanding the features driving contagions upon the arrival of an index case in local environments constitutes an essential task to devise policies aimed at avoiding the community transmission of these diseases and the subsequent case exportation to other unaffected areas. In this review, an overview of the different models addressing this topic is given, focusing on the movement–interaction–return model and different subsequent frameworks introduced to explain the complex interplay between the recurrent movements and contagion dynamics.