Miracle-dz/UniFAR-FTUs huggingface dataset:
Classification of Customer Reviews on E-commerce Platforms Based on Naive Bayesian Algorithm and Support Vector Machine

Customer reviews on e-commerce platforms contain valuable information, while sifting through them manually tends to dismay people because of the huge amount of data. Ideally, the identification classifier would analyze the emotional disposition of product reviews (positive, negative) and aggregate opinions about each of them. Previous literature has demonstrated extensive research conclusions on opinion extraction and semantic classification of product reviews on websites. However, analysis on the universality of machine learning algorithms in identifying the emotional tendency of different Chinese e-commerce reviews is not yet studied. This study uses a method, which is based on general machine learning algorithms, to classify feedbacks. Our classifier extracts Chinese word segmentation and text frequency for feature extraction and scoring, and implements the classification with methods of Naive Bayesian and Support Vector Machines. Experimental results on the Alibaba product review sentiment datasets show that our model based on two machine learning algorithms, though results in different performances, can provide suggestions on the selection of the identification classifier using a trade-off strategy and helps obtain fast and accurate classification on reviews of different categories.
Health Insurance Ownership among Moluccans in Indonesia

Background: Expanding the reach of health insurance in Maluku aims to increase public access with the archipelago topography to healthcare facilities. Objective: The study aimed to analyze factors related to health insurance ownership among Moluccans. Materials and Methods: The study employed 788 respondents. The variables analyzed included health insurance, age, gender, education, and employment. The study used multinomial logistic regression in the final stage. Results: The age group ≤19 years was 0.182 times more likely than the ≥50 years of age group to have government-run type health insurance. The 20–29 years of age group was 0.219 times more likely than the ≥50 years of age group to have government-run health insurance. On the other hand, Moluccans with primary education were 0.196 times more likely than Moluccans with higher education to have a government-run type of health insurance. Moluccans with secondary education were 0.415 times more likely than Moluccans with higher education to have government-run health insurance. Unemployed have a probability of 0.358 times than employed to have the government-run health insurance type. Finally, unemployed is 0.056 times more likely than employed to have private-run health insurance. Conclusions: Three variables prove associated with health insurance ownership among Moluccans in Indonesia, namely age, education, and employment.
Lifetime Enhancement to Improve Data Transmitted for Clustering Protocol in Heterogeneous Wireless Sensor Networks

The Routing Protocols in Sensor Networks come from many different ways from mandatory registration in fixed networks: there is no architecture in WSN, wireless connections are inaccurate, sensor networks may fail and routing protocols have to meet stringent energy saving criteria. There have been several routing algorithms commonly set up WSN. In the sensor network, a large number of inexpensive sensors with small batteries are deployed in the area to be monitored, which has to survive for a long time, where it is difficult or even impossible to implement the sensor battery replacement. Consequently, power consumption is an important factor to consider when designing WSN. We proposed a new scheme to investigate the cluster in heterogeneous WSN, the clustering by using Fuzzy-Logic Stable Election Protocol (SEP_ FUZZY ), since the Fuzzy Logic Inference System (FIS)is used in the clustering process. FIS work with two input (nodes’ remaining-energy(R-energy), and its distance from the base station(D2BS)). Results from the simulation indicate that the lifespan of the network reached by the suggested approach will be improved by almost (47 %, 35.7 %, and 23.8 %) more than that gained by the protocols “(LEACH, PEGASIS, and SEP)”.
Research on recognition of safety helmet wearing of electric power construction personnel based on artificial intelligence technology

The traditional safety helmet wearing recognition is only based on the color, shape, texture and other characteristics of the image, which is greatly affected by the external environment, and has the problem of unstable recognition accuracy. In view of the above problems, this paper studies the recognition method of power construction workers’ safety helmet based on artificial intelligence technology. After preprocessing the construction monitoring image, such as graying and denoising, the construction personnel in the detection image are located, that is, on the basis of detecting the construction personnel area, the head position of the construction personnel is located, and finally the safety helmet wearing recognition is realized by using YOLO algorithm. The simulation results show that the average recognition accuracy is 95.2%, the recognition effect is stable and has good robustness.
Performance of organic thin-film transistors

An investigation of the performance of organic and polymeric thin-film transistors (OTFTs) made of several organic semiconducting and insulating materials in the last two decades is presented, in an attempt to capture the state-of-the-art experimental values for effective mobility, threshold voltage, on/off ratio, and subthreshold slope in OTFTs made of different materials by different fabrication approaches. The analysis of published data demonstrates that the effective mobility decreases when the product of the semiconducting film thickness (tO) and gate capacitance per unit area (CI) increases. The decrease is given by a power-law function with parameters for several organic semiconductors, and examples are provided on how the deduced trend can be used in the practical design of organic circuits. The second observation is that the polarity of the OTFTs’ threshold voltage VT is random, but the spread in magnitudes of ∣VT∣ decreases, when CI increases. This trend, together with the third observation that.
Impact of Land-Use Changes on Sediment Load and Capacity Reduction of Lake Ziway, Ethiopia

Land-use change has been a factor that alters the hydrologic response of the watersheds leading to influencing on sediment yield changes. This study is mainly focusing on the assessment of the impacts of the land-use changes on sediment load and lake depth reduction on Lake Ziway, Ethiopia using an integrated approach of Remote Sensing (RS), GIS and SWAT model. ERDAS IMAGINE 14 model was used to generate land-use maps from Landsat TM, ETM+, and Ls8 acquired, in 1988, 2002 and 2015 as representative for the periods of (1988-1998), (1998-2008) and (2008-2018), respectively. The maximum likelihood algorithm of supervised classification applied to classify the basin land-use into seven land-use classes. The SWAT hydrological model with ArcGIS interface setup for the basin to evaluate the flow and sediment load with calibration and validation performance of the model range R2 (0.71 - 0.89) and NSE (0.57 - 0.87). As a result, the total average annual sediment yield from the sub-basins estimated as 3.59 t/ha/yr, 4.36 t/ha/yr, and 4.89 t/ha/yr for three consecutive decadal periods 1988-1998, 1998-2008, and 2008-2018 respectively. The increasing trend of sediment yield in the Lake Ziway watershed through one period to another justified as due to land-use. Similarly, the net sediment volume deposited in the lake also showed incremental trained with the land-use changes as 1.5 mcm/yr, 1.81 mcm/yr, and 2.033 mcm/yr for the period of 1988-1998, 1999-2008, and 2009-2018, respectively. The depth and water holding capacity of the lake reduced by 4.3 m and 25.76 mcm, respectively, from the depth and capacity recorded on the 2006 bathymetric survey, which was the effect of deposited sediment over the last 12 years.
Different conformational responses of the β2-adrenergic receptor-Gs complex upon binding of the partial agonist salbutamol or the full agonist isoprenaline

GPCRs are responsible for most cytoplasmic signaling in response to extracellular ligands with different efficacy profiles. Various spectroscopic techniques have identified that agonists exhibiting varying efficacies can selectively stabilize a specific conformation of the receptor. However, the structural basis for activation of the GPCR-G protein complex by ligands with different efficacies is incompletely understood. To better understand the structural basis underlying the mechanisms by which ligands with varying efficacies differentially regulate the conformations of receptors and G proteins, we determined the structures of β2AR-Gαs$\beta $γ bound with partial agonist salbutamol or bound with full agonist isoprenaline using single-particle cryo-electron microscopy at resolutions of 3.26 Å and 3.80 Å, respectively. Structural comparisons between the β2AR-Gs-salbutamol and β2AR-Gs-isoprenaline complexes demonstrated that the decreased binding affinity and efficacy of salbutamol compared with those of isoprenaline might be attributed to the weakened hydrogen bonding interactions, attenuated hydrophobic interactions in the orthosteric binding pocket and different conformational changes in the rotamer toggle switch in TM6. Moreover, the observed stronger interactions between the intracellular loop 2 or 3 (ICL2 or ICL3) of β2AR and Gαs with the binding of salbutamol versus isoprenaline might decrease phosphorylation in the salbutamol-activated β2AR-Gs complex. From the observed structural differences between these complexes of β2AR, a mechanism of β2AR activation by partial and full agonists is proposed to shed structural insights for β2AR desensitization.
Statistical Inference of Spot Correlation and Spot Market Beta under Infinite Variation Jumps*

Empirical evidence has revealed that the jumps in financial markets appear to be very frequent. This study considers the statistical inference of the spot correlation and the spot market beta between two different assets using high-frequency data, in a setting where both the cojumps and the individual jumps in the underlying driving processes could be of infinite variation. Starting from the estimation of the spot covariance, we propose consistent estimators of the spot correlation and the spot market beta when the jump processes involved are general semimartingales. The second-order approximation for the estimators, namely, the central limit theorems, is established under the assumption that the jumps around zero are of stable Lévy type. Our estimation procedure is based on the empirical characteristic function of the increments of the processes and the application of the polarization identity; the bias terms stemming from the jumps are removed iteratively. The finite sample performances of the proposed estimators and other existing estimators are assessed and compared by using datasets simulated from various models. Our estimators are also applied to some real high-frequency financial datasets.
The 14N quadrupole hyperfine structure in the rotational spectrum of laser vaporized urea observed by molecular beam Fourier transform microwave spectroscopy

The microwave spectrum of urea has been recorded in the frequency range 8-19 GHz using a laser ablation molecular beam Fourier transform microwave spectrometer. The analysis of the nuclear quadrupole hyperfine splitting caused by two equivalent 14N nuclei yields the quadrupole coupling constants χaa = 2·205(26) MHz and χ bb = 1·8884(29) MHz. Seven different transitions transitions are reported. Distortion analysis is made with these seven plus 41 transitions previously published. We determine the rotational constants A = 11233·3161(87) MHz, B = 10369·4074(88) MHz, and C = 5416·6392(86) MHz and Watson's A reduced centrifugal distortion constants ΔJ = 10·58(37) kHz, Δ JK = -20·982(53) kHz, Δ K = 10·80(29) kHz, Δ J = 0·2208(43) kHz, and δ K = -14·6439(73) kHz.
Use of Social Media as an Effective Marketing Tool for Fashion Startups in Saudi Arabia

In Saudi Arabia, the social media has encouraged and facilitated promotion and creation of various business, resulting in the development of many startup fashion businesses on platforms such as Instagram and Facebook. However, current literature does not offer an in-depth analysis to this topic and the potential of this digital market. Therefore, the following research covers the topic of effect of social media fashion marketing for startup fashion brands in Saudi Arabia. Utilizing many scientific articles on social media marketing and general literature on fashion marketing, this research aims to improve the understanding of the benefits of use of social media as an effective marketing tool for fashion startups by means of examining existing knowledge on social media marketing, and explains how fashion startups can use social media to benefit their businesses. It tries to familiarize targeted audience, including potential fashion startup entrepreneurs, with many concepts and strategies related to social media marketing such as content marketing, consumer engagement, impact of influencers, user generated content, consumer purchasing behavior of fashion brands.
Elemental analysis of micro-samples of liquids or slurries by coiled-filament in-torch vaporization-inductively coupled plasma atomic emission spectrometry (ITV-ICP-AES)

In coiled-filament in-torch vaporization (ITV) sample introduction for inductively coupled plasma atomic emission spectrometry (ICP-AES), a sample is placed onto a coiled filament (e.g., a Re wire) and the sample carrying filament is inserted into a vaporization chamber which is attached to an ICP torch. In this study, six vaporization chambers were tested using in-situ generated smoke. Of these, a 6.5 cm 3 (internal volume) chamber was chosen. An Ar-H 2 (≈3% v/v) carrier gas was used and the effect of flow rate and filament insertion position was determined for several elements (e.g., Zn, Pb, Cd, Mn, Mg, V, Be and Sr) that covered a range of wavelengths and vaporization characteristics. Analytical performance characteristics were also obtained and calibration graphs were linear over several orders of magnitude. Detection limits were in the pg to sub-pg range. Coiled-filament ITV-ICP-AES was also briefly tested with powdered solids as slurries using a biological standard reference material and Mn and Zn as test elements.
A Cell-Based Biosensor System for Listeria monocytogenes Detection in Food

Listeria monocytogenes is an intracellular bacterium that causes serious epidemic and sporadic food-borne illnesses in humans. Rapid and trustworthy methods are necessary for the detection of the pathogen to prevent potential food contamination. The aim of this study was to test a newly developed L. monocytogenes biosensor on actual food samples and validate its ability to detect the presence of pathogens robustly and accurately. The newly developed method uses a cell-based biosensor technology (BERA) and a portable device developed by EMBIO Diagnostics called B.EL. D, and provides results within 3 min. Tests were conducted on ready-to-eat lettuce salads, milk and halloumi cheese and the results indicate that the novel system was able to identify inoculated samples with 98%, 90%, and 91% accuracy, respectively. Furthermore, the limit of detection was determined to be as low as 0.6 log CFU mL−1 or g−1 in all food types. Classification of the samples Above or Below the detection limit was accessed through a newly developed algorithm for each food substrate. Samples were also analyzed with the ISO 11290-1:2017 and 11290-2:2017, in parallel. Thus, it was concluded that the newly developed biosensor can be a useful tool in the food supply chain, decreasing the required time for the detection of pathogens and increasing the number of tested samples before they reach the market.
Differential responses to fertilization and competition among invasive, noninvasive alien, and native Bidens species

Abstract Comparative studies of invasive, noninvasive alien, and native congenic plant species can identify plant traits that drive invasiveness. In particular, functional traits associated with rapid growth rate and high fecundity likely facilitate invasive success. As such traits often exhibit high phenotypic plasticity, characterizing plastic responses to anthropogenic environmental changes such as eutrophication and disturbance is important for predicting the invasive success of alien plant species in the future. Here, we compared trait expression and phenotypic plasticity at the species level among invasive, noninvasive alien, and native Bidens species. Plants were grown under nutrient addition and competition treatments, and their functional, morphological, and seed traits were examined. Invasive B. frondosa exhibited higher phenotypic plasticity in most measured traits than did the alien noninvasive B. pilosa or native B. bipinnata. However, differential plastic responses to environmental treatments rarely altered the rank of trait values among the three Bidens species, except for the number of inflorescences. The achene size of B. frondosa was larger, but its pappus length was shorter than that of B. pilosa. Two species demonstrated opposite plastic responses of pappus length to fertilization. These results suggest that the plasticity of functional traits does not significantly contribute to the invasive success of B. frondosa. The dispersal efficiency of B. frondosa is expected to be lower than that of B. pilosa, suggesting that long‐distance dispersal is likely not a critical factor in determining invasive success.
Detecting Alzheimer’s Disease by The Decision Tree Methods Based On Particle Swarm Optimization

In this study aims to determine the classification of Alzheimer’s disease, this disease is a dangerous disease that can eliminate memory loss and can even result in a loss of ability to remember. For this reason, early detection of this disease is needed so that it can prepare for medical treatment. In this study the proposed method is to compare several decision tree methods with feature or attribute selection using the Particle Swarm Optimization (PSO) algorithm with the Alzheimer OASIS 2 dataset: Longitudinal Data from kaggle.com. The results of experiments with ten-fold cross validation, by testing the decision tree algorithm before the feature or attribute selection is performed, the highest accuracy value is obtained from the random forest algorithm with a value of 91.15%. The feature selection process is carried out using the PSO algorithm and the experiment is repeated using the Decision tree, the PSO-based random forest algorithm has the highest accuracy value of 93.56% with a kappa value of 0.884. Feature or attribute selection using the PSO algorithm is proven to be able to improve the accuracy of the decision tree algorithm, and is included in the algorithm with a very good range of values.
Simultaneous fluid and solid density measurements in swelling clay using X‐ray microtomography and 3D particle tracking

In order to demonstrate the safety of engineered barriers for radwaste geological repositories, rich characterisations of bentonite hydro‐mechanical behaviour are essential. X‐ray computed tomography was used as the single input to measure both the full displacement field and water content in a heterogeneous bentonite sample resaturated in quasi‐isochoric conditions. Since large texture changes do not allow the use of digital volume correlation, this was achieved using 3D particle tracking to provide the kinematics in combination with calibrated and beam hardening‐corrected grey levels. Matching the grey level sampling with kinematics using a mesh‐based analysis, results show the development of heterogeneous swelling inside the cell and a final solid density gradient in the direction of water flow.
Clinical Features and Mortality Associated with Severe Malaria in Adults in Southern Mauritania

Severe malaria in adults is not well-studied in Sahelian Africa. Clinical features and mortality associated with severe Plasmodium falciparum malaria in adult patients hospitalized in Kiffa, southern Mauritania, were analysed. Patients over 15 years old admitted for severe malaria between August 2016 and December 2019 were included in the present retrospective study. The World Health Organization (WHO) criteria were used to define severe malaria. The presenting clinical characteristics and outcome were compared. Of 4266 patients hospitalized during the study period, 573 (13.4%) had a positive rapid diagnostic test for malaria, and 99 (17.3%; mean age, 37.5 years; range 15–79 years; sex-ratio M/F, 2.1) satisfied the criteria for severe malaria. On admission, the following signs and symptoms were observed in more than one-fourth of the patients: fever (98%), impairment of consciousness (81.8%), multiple convulsions (70.7%), cardiovascular collapse (61.6%), respiratory distress (43.4%), severe anaemia ≤ 80 g/L (36.4%), haemoglobinuria (27.3%), and renal failure (25.3%). Patients were treated with parenteral quinine or artemether. Fourteen (14.1%) patients died. Multiple convulsions, respiratory distress, severe anaemia, haemoglobinuria, acute renal failure, jaundice, and abnormal bleeding occurred more frequently (p < 0.05) in deceased patients. Mortality due to severe falciparum malaria is high among adults in southern Mauritania. An adoption of the WHO-recommended first-line treatment for severe malaria, such as parenteral artesunate, is required to lower the mortality rate associated with severe malaria.
The release rate of indomethacin from solid dispersions with Eudragit E

AbstractThe coprecipitates were prepared by a solvent technique using Eudragit E as carrier and indomethacin as a model drug.X-Ray diffractometry, differential scanning calorimetry (DSC) and wettability tests were employed to investigate the physical state of the studied formulations. Up to 50% of indomethacin can be dispersed in an amorphous state in Eudragit E.The influence of the pH on the in vitro release of solid dispersions has been evaluated. Because of the good solubility of Eudragit E at pH 1.2 a fast dissolution rate of the drug was observed while a marked delay was noticed at pH 7.5 where the polymer is only permeable to water. At pH 5.8 the kinetics of drug release can be modulated by the drug/polymer ratio. The dissolution rate of the drug can be increased by decreasing its amount in the coevaporate.
Energy, Equipment and Cost Savings by Using a Membrane Unit in an Amine-Based Absorption Process for CO2 Removal

A hybrid system consisting of a membrane unit and an amine-based process for CO2 removal in the oxidative coupling of methane is investigated experimentally and in simulations. Absorption processes with two different absorbents for removing 90 % of CO2 from the feed stream are regarded as benchmarks and compared to the membrane process with respect to potential energy, equipment, and cost reductions. The results show that a membrane unit using polyimide could assist with CO2 capture and lead to a reduction of the consumed energy per kilogram of CO2 by 40 % and also reduce the size of the absorption columns. Alternative processes with a two-step membrane cascade to enhance the recovery of product ethylene are also discussed.