Miracle-dz/UniFAR-FTUs huggingface dataset:
Precision luminosity measurement with proton-proton collisions at the CMS experiment in Run 2

A precise luminosity measurement is critical to determine fundamental parameters of the standard model and to constrain or to discover beyond-the-standard-model phenomena at LHC. The luminosity determination at the LHC interaction point 5 with the CMS detector, using proton-proton collisions at 13 TeV during Run 2 of the LHC (2015–2018), is reported. The absolute luminosity scale is obtained with the Van der Meer method using beam-separation scans. The dominant sources of systematic uncertainty are related to the knowledge of the scale of the beam separation provided by LHC magnets and the nonfactorizability between the spatial components of the proton bunch density distributions in the transverse direction. When applying the Van der Meer calibration to the entire data-taking period, a substantial contribution to the total uncertainty in the integrated luminosity originates from the measurement of the detector linearity and stability. The reported integrated luminosity in 2016–2018 is among the most precise luminosity measurements at bunched-beam hadron colliders.
Application of XGBoost Algorithm in The Detection of SARS-CoV-2 Using Raman Spectroscopy

The novel coronavirus (SARS-CoV-2), which was first discovered in late 2019 and rapidly spread to many countries around the world in a short period of time, is highly contagious and poses a significant threat to global public safety. How to quickly and efficiently detect whether a human is infected by the novel coronavirus is a crucial step in dealing with this public health emergency. Therefore, the collected Raman spectral data are preprocessed by normalization, smoothing denoising and feature extraction in this paper. A novel coronavirus detection method based on XGBoost and Raman spectroscopy is proposed. The experiments demonstrated the feasibility and accuracy of the method in the detection of novel coronavirus with an accuracy of 93.548%.
NEW TECHNIQUE FOR EXTRACTION AND CHARACTERIZATION OF BIOACTIVE COMPOUNDS FROM IRAQI VITIS-VINIFERA LEAVES

ABSRACT Natural products from medicinal plants either as pure or as standardized extract provide unlimited opportunities for new drug leads because of the unmatched availability of chemical diversity. The aqueous extracts of Vitis-Vinifera Leaves were prepared by taking the fresh leaves from Iraqi Vitis-Vinifera tree and washed under running tap water then choped by knife to small pieces. Air dried at room temperature(25 o C) and dark place to avoid the effectivenees of high temperature and sun light . The chopped leaves were collected and ground to fine powder and stored in airtight dark bottles. For aqueous extract, 50 g of air-dried powder was added to 200ml distilled water and gently heated on slow rate rising temperature reached to 47 0 C for 10 hours with continuous stirring then leave on punch to cool. The mixture was collected in airtight ember color flask and filtered through filter paper ( wattman-5). The filtration process was repeated at least three times. The supernatant was collected to ember color flask. The final (150 ml) was concentrated to one-tenth of the original volume by heating gently to 40-45 0 C using rotary evaporator. The aqueous extract was analyzed the phenolic compounds in crude extract by using Ultra Violet-Visible "UVVIS" " spectrophotometer and Fourier Transform Infrared Spectroscopy"FTIR". Aqueous extract was evaluated for antibacterial activity against some types of Gram-positive and Gram-negative bacteria. The invitro antibacterial activity was performed by agar well diffusion method and the minimum inhibitory concentration (MIC) was determined by microWORLD JOURNAL OF PHARMACY AND PHARMACEUTICAL SCIENCES
Wind Turbine Prognosis Models Based on SCADA Data and Extreme Learning Machines

In this paper, a method to build models to monitor and evaluate the health status of wind turbines using Single-hidden Layer Feedforward Neural networks (SLFN) is presented. The models are trained using the Extreme Learning Machines (ELM) strategy. The data used is obtained from the SCADA systems, easily available in modern wind turbines. The ELM technique requires very low computational costs for the training of the models, and thus allows for the integration of a grid-search approach with parallelized instances to find out the optimal model parameters. These models can be built both individually, considering the turbines separately, or as an aggregate for the whole wind plant. The followed strategy consists in predicting a target variable using the rest of the variables of the system/subsystem, computing the error deviation from the real target variable and finally comparing high error values with a selection of alarm events for that system, therefore validating the performance of the model. The experimental results indicate that this methodology leads to the detection of mismatches in the stages of the system’s failure, thus making it possible to schedule the maintenance operation before a critical failure occurs. The simplicity of the ELM systems and the ease with which the parameters can be adjusted make it a realistic option to be implemented in wind turbine models to work in real time.
The impact of WTO reform on china’s economy: some numerical simulation results

Abstract Using a global general equilibrium numerical model system with endogenous trade imbalance, this paper simulates the potential economic impacts of different WTO reform scenarios on China. The simulation results show that ending China’s ‘special and differential treatment’ (SDT) alone would have a negative impact on China’s social welfare and GDP, but the impact on manufacturing employment and foreign trade would be positive. The results of a simultaneous withdrawal of SDT from China, Brazil, India, and South Africa would be in line with the effect of China’s separate exit, but China’s damage would be lessened. If all developing countries were to withdraw from SDT, China’s losses would be further reduced. If the WTO reform goals were to promote and achieve a new round of global trade liberalisation, China’s welfare, output, manufacturing employment, and foreign trade would all improve significantly.
Beliefs about Medicines among a Sample of Iraqi patients with Psoriasis

Abstract Objectives: This study aimed to evaluate beliefs about use of medications for a sample of Iraqi psoriasis patients, and to examine the association between these beliefs and selected patient’s related factors. Methods: This cross-sectional study included 300 patients with diagnosed psoriasis. Participants were recruited at the center of Dermatology and Venereology, Medical City in Baghdad, the capital city of Iraq. Patients’ mean age was 35.15years (±10.54). Beliefs about medicines were measured by the Arabic version of Beliefs about Medicines Questionnaire. Results: Most the patients (76.7%) had strong beliefs in the need (acceptance beliefs) for their psoriasis medicines (specific-necessity score higher than specific-concern), whereas 15.0% of patients had specific-concern score higher than specific-necessity and 8.3% of patients had specific-necessity score equal to specific-concern. At the same time, 74.4% of the patients believed that the medicines disrupt their lives and (35.6%) of them had concerns about the possibility of becoming addicted on these medicines. Many other patients were worried about the long-term consequences of the medicines (58.7%). In addition, 31.0% of the participants believe that all medicines are poisoning, and that they do more harm than good. Finally, many of the participants believed that physicians prescribe too many medicines (46.7%), and they can minimize the number of prescribed medicines by spending more time with their patients (32.6%). Conclusions: Female gender and longer disease duration have direct association with specific necessity, while psoriasis severity has a direct association with specific concern. In conclusion, Beliefs about medications and habit strength are important modifiable drivers to enhance adherence and clinical outcomes in the control of psoriasis.
Enhancing High-concentrated Wastewater Quality on Evaporation Rate from Five-Consecutive Oxidation Ponds as Located in Phetchaburi, Southerly Thailand

This research aimed to examine the environmental factors determining the rates of evaporation, a natural phenomenon contributing to the treatment of wastewater of 5-consecutive oxidation ponds of the King’s Royally Initiated Laem Phak Bia Environmental Research and Development Project. Data collected from the 17th of April to 30th of May 2019 by US Class A Evaporation Pan revealed that the sedimentation pond (Pond 1) has the highest rate, 7.22 mm d-1, the oxidation pond 1 (Pond 2), 5.70 mm d-1, the oxidation pond 3 (Pond 4), 5.56 mm d-1, the stabilization pond (Pond 5), mm d-1, the reference pond at 5.07 mm d-1 and the oxidation pond 2 (Pond 3), 3.59 mm d-1. Concluding the evaporation in domestic wastewater treatment plants is characterized by 1) heat generated from short and long wave radiation emitted by earth and the sun, 2) local wind profiles of the area affected the height differences of the roughness length, and 3) heat generated by the respiration and digestion process of microbial activities and other grey body contaminants. Presenting the day and night variations made for the analysis, the day evaporation was significantly higher resulted by the net radiation were accountable. Wind profile generated from the measurement of speeds and directions at two different sites at 3 and 10 m has explained for the roughness length heights over each pond as lower roughness height have cause the increased in the rates of evaporation in Pond 4 and 5 however, these process were also suppressed by high ionic bonding molecules effected suggested by the high TDS and EC values. The vertical temperature profile has conveyed the movement in the heat flux that dominated an upward flux movement in Pond 1. This is the exothermic reaction from the digestion process have suggested that extra heat has been added.
Comparative Study of Optimization of Plate Fin Heat Exchanger and Pressure Vessel Design using mTLBO Algorithm

Teaching Learning Based Optimization (TLBO) algorithm has been proved beneficial in many engineering applications. This algorithm is free from any algorithm specific parameters and can be adapted to all types of design problems. However, there are some drawbacks like convergence to local optimal solution, large computational time and slow convergence rate for complex functions. Some modifications were introduced to overcome these drawbacks in modified Teaching Learning Based Optimization (mTLBO) algorithm. In this paper mTLBO has been applied to optimize plate fin heat exchanger and pressure vessel design. The performance of mTLBO algorithm was compared with original algorithm and other population based techniques such as Particle Swarm Optimization, Generic Algorithm and Artificial Bee Colony. It was found that mTLBO gives the least value of entropy generation units that is 7.22% less than the value obtained using TLBO. Also cost of pressure vessel design using mTLBO is 3.2% lower than that of TLBO design.
The Democratic Significance of Everyday News Use: Using Diaries to Understand Public Connection over Time and beyond Journalism

Abstract This article argues that journalism scholars should strive to understand the democratic significance of everyday news use, and that such an interest requires us to tackle some methodological and conceptual challenges. We utilize the concept of public connection as a starting point for prioritizing aspects of everyday news use that are most relevant to democracy. Based on a review of existing methods—surveys, interviews and digital trace data—we propose an approach that facilitates analysis beyond the moment, and beyond single platforms or providers, and which situates journalism and news as part of intricate media experiences in everyday life. Our approach centres on media diaries to achieve these aims, and we illustrate its relevance for public connection research with a case from Norway.
Eye Diseases Classification using Back Propagation with Welch Estimation based-Learning Rate

Human eye disease classification adapted by many pieces of research in the last decade due to the importance of eye organ for humans and the evolution in classification techniques. External eye diseases classified in this paper using backpropagation with non-linear cyclic learning rate based on welch estimation as a step for improving the performance of backpropagation. As a result, Classification accuracy achieved in this paper is (93.22%).
Elderly fall detection using IoT and image processing

Abstract The falls due to accidents or loss of consciousness in older people can turn out to be fatal if the required assistance is not received with in time. The fear of falling puts constraints on the free movement of the elderly and forces them to always be accompanied by a caretaker. This paper deals with the problem to seek help as and when required rather than always being watched over. The key contribution of this paper belongs to its novelty to detect elderly fall and even to cross validate it(that increase the trust for the model). To find the fall, algorithm fallows 2-way approach. In first way the sensors collect all the data related to fall such as Net acceleration of body, Pitch and Roll, based upon these values various activity is identified. Secondly, to further increases the trust over the model (remove false positive scenarios), this paper has also used image processing. Here, the images of various finger sign made by elderly person are processed at the time of fall. Thus, our work in this paper (Algorithm for fall detection using IoT and Image Processing(AFDI2) works over all the possible conditions and gives 99% accuracy mostly in all the possible conditions whereas SVM model only able to reach an accuracy of 97%, 96%, 98% while walking, walking downstairs and walking upstairs.
Corrosion Inhibition of Aluminium in Gas and Acid Media by Some Chalcone-Based N-(3-Aminopropyl)Imidazoles: TD-DFT-Based FMO, Conceptual DFT, QTAIM and EDA Studies

The efficacy and mode of action of five chalcone-based imidazole derivatives as corrosion inhibitors of aluminium metal in gas-phase and acidic medium have been investigated herein via quantum chemical calculations. Dispersion-corrected DFT (DFT-D3) and time-dependent DFT (TD-DFT) calculations were performed at PBE0/def2-TZVP//PBEh-3c and CAM-B3LYP/def2- TZVP levels of theory, respectively. Conceptual DFT, the quantum theory of atoms-in-molecules (QTAIM) and local energy decomposition (LED) analyses have been performed. The LED analysis was performed at the coupled-cluster singles and doubles with perturbative triples (CCSD(T))/def2-SVP level of theory. Frontier molecular orbital energy gaps calculated using the TD-DFT method are found to lie in the range 3.574 - 4.444 eV, indicative of good adsorption and corrosion inhibition efficacies of the investigated molecules. The interactions between aluminium and the inhibitor molecules studied are found to be energetically favorable, owing to negative computed interaction energy values. Furthermore, QTAIM analysis revealed metal-carbon, metal-oxygen and metal-nitrogen interactions in the inhibitor-aluminium complexes, which are predominantly electrostatic in character, according to LED analysis results. Calculated proton affinities (PAs) have revealed the anticorrosion potentials of the investigated inhibitors in acidic medium, with a noticeable dependency on temperature within the range 273.15 - 343.15 K.
English Medium Instruction and the Internationalization of Higher Education in Latin America: A Case Study from a Colombian University

This study discusses the implementation of English medium instruction (EMI) at a Colombian university. First, the paper reviews the rise of EMI in the internationalization of higher education. Second, it illustrates how one university incorporated EMI as part of an internationalization process. Third, it identifies the perceptions that a group of administrators, faculty, and students have regarding the EMI initiative. Questionnaires, semi-structured interviews, and document analysis were conducted. Findings suggest that EMI is tied to the structuring of an internationalization office, curricular reforms, and English language learning support. Participants’ perceptions are associated with their imaginaries, identities, experiences, and obligations in relation to the English language. The study concludes that the implementation of EMI within the internationalization of universities is inevitable, yet a sustainable EMI strategy requires contextual awareness and articulation amongst its participants. Received: 28/02/2020 Accepted: 18/09/2020
Helix: A Fair Blockchain Consensus Protocol Resistant to Ordering Manipulation

We present Helix, a blockchain-based consensus protocol for fair ordering of transactions among nodes in a distributed network. Helix advances in rounds, in each an elected primary node proposes a potential block (a successive set of transactions). For being included in the blockchain, a block must pass validation by an elected committee of nodes. Nodes have two primary preferences. First, to be elected as committee members. Additionally, because each transaction is associated with one of the network nodes, nodes would like to prioritize their own transactions over those of others. Our definition of fairness incorporates three key elements. First, the process of electing nodes to committees is random and unpredictable. Second, a correlated sampling scheme is used to guarantee random selection and ordering of pending transactions in blocks. Third, transactions are encrypted to hide their associations with nodes and prevent censorship. Through the corresponding threshold decryption process we obtain an unpredictable and non-manipulable randomness beacon, which serves both the election process and the correlated sampling scheme. We define a quantitative measure of fairness in the protocol, prove theoretically that fairness manipulation in Helix is significantly limited, and present experiments evaluating fairness in practice.
The Evolution of Arrears among US Households 1995–2013

This paper looks at arrears among US households between 1995 and 2013. It uses household data from the Survey of Consumer Finances (SCF) where arrears occur when a household reports it “sometimes got behind or missed a payment”. The key contribution is that it decomposes the change in arrears into a behavioural part and a compositional part. Older poorer households increased arrears between 1995 and 2001 (this reversed in 2004). Younger middle-income households increased arrears in 2004. Following bankruptcy reform, wealthier households under 50 reduced their arrears between 2004 and 2007. During the sub-prime recession, everyone except younger low income households increased their arrears. The decomposition exercise shows that most of the changes over time are attributed to changes in arrears once the loan is given and not to the change in the composition of the pool of borrowers.
Survey of DoS/DDoS attacks in IoT

The term internet of things (IoT) has gained much popularity in the last decade, which can be defined as various connected devices over the internet. IoT has rapidly spread to include all aspects of our lives. For instance, smart houses, smart cities, and variant wearable devices. IoT devices work to do their desired goals, which is to develop a person's living with his/her minimal involvement. At the same time, IoT devices have many weaknesses, which attackers exploit to affect these devices' security. Denial of Service (DoS) and Distributed Denial of Service (DDoS) are considered the most common attacks that strike IoT security. The main aim of these attacks is to make victim systems down and inaccessible for legitimate users by malicious malware. This paper's objective is to discuss and review security issues related to DoS/DDoS attacks and their countermeasures i.e. prevention based on IoT devices' layers structure.
Does Competition from Informal Firms Hurt Job Creation by Formal Firms? Evidence using Firm-Level Survey Data

The informal sector is an important source of livelihoods and jobs for a vast majority of people in developing countries. However, there is concern that the informal sector may undermine job creation in the formal sector. According to the â€œparasiteâ€ view of informality, informal firms can compete against formal firms, and often "unfairly" so as they do not have to comply with costly regulations and pay taxes. This "unfair" advantage makes it difficult for formal firms to compete against informal firms, implying a significant loss of formal sector jobs. Using firm-level survey data for manufacturing small and medium-size enterprises in 109 mostly developing countries, this study estimates the impact of competition from informal firms on the growth rate of employment among formal sector small and medium-size enterprises. The results show that the growth rate of employment declines significantly as competition from informal firms rises. According to the baseline specification, for each one standard deviation increase in informal competition, the employment growth rate declines by 1 percentage point. Consistent with the parasite view of informality, the negative impact on job growth is much larger when the business environment is less conducive to operating formally versus informally due to factors such high corruption, weak rule of law, more burdensome regulations, and high profit tax rate. Several checks are provided against endogeneity concerns.
Multi‐input transformer‐less four‐wire microinverter with distributed MPPT for PV systems

This paper proposes three‐phase microinverter able to process the power of three PV modules simultaneously and independently; thus, efficient utilization of PV modules through distributed maximum power point tracking (DMPPT) is realized. The proposed microinverter employs switched capacitors (SCs) networks combined with three‐phase voltage source inverter; it is named switched capacitor buck‐boost voltage source inverter and abbreviated as SC‐BBVSI. The connections of SCs networks are determined to fulfill the high boosting gain along with minimized leakage currents. In addition, low capacitances values can be employed for power decoupling purpose. Thus, in addition to DMPPT feature, three features of high boosting gain, high‐reliable product, and minimized leakage current are realized without using the bulky and costly isolation transformer. In this paper, the operation modes of SC‐BBVSI for single and multi‐input scenarios are explained. The modulation strategy to realize these modes along with the control system design is presented as well. The relations required to design the passive elements and identify the device's stresses are derived. The features of the proposed microinverter are confirmed through detailed comparison with other recently reported topologies. Both simulation and experimental results are provided to verify the features of the proposed topology.