Miracle-dz/UniFAR-FTUs huggingface dataset:
A systematic review of consumer information search in online and offline environments

Purpose The purpose of this study is to identify major themes and potential research opportunities in online and offline consumer search. Design/methodology/approach A systematic review was conducted based on 118 articles identified from prevalent journal databases. Keywords frequency analysis was carried out to identify the major themes. An inductive thematic analysis was carried out to verify the generated themes. Findings Results show that uncertainty, knowledge, perceived risk, price, experience and involvement are the major themes associated with consumer information search. Uncertainty, one of the major themes of offline search, has not been studied in the online search context. Similarly, the previous experience needs to be explored in the context of the offline search. Finally, potential research opportunities for future research has been summarized based on the retrieved themes. Research limitations/implications The systematic review provides an in-depth understanding on the current research on information search literature with future research directions. Practical implications This study helps retailers to understand the key elements that motivate consumers to perform external information searches from online and offline sources and to curate targeted information provision strategies to influence purchase decisions. Social implications Consumers with limited internet availability may access channels prior to decision-making. The themes identified in this study can aid policymakers to design affordable access to these channels. Originality/value This study adds to the sparse literature on systematic reviews on consumer search for online and offline channels.
Polysaccharides as Edible Films and Coatings: Characteristics and Influence on Fruit and Vegetable Quality—A Review

There has been a significant increase in the development of edible films and coatings in recent times, and this is expected to have a significant impact on the quality of fruit and vegetables in the coming years. Consumers expect fresh fruit and vegetables free from pesticide residues, with high quality, nutritional value and an extended shelf life. The application of coatings and edible films to fruits and vegetables represents an environmentally friendly approach to an innovative solution to this problem. Coatings and edible films can act as ecological and biodegradable packaging. The coating strategy involves a combination of natural biopolymers and appropriate preservation methods. The article presents the applicability, trends and perspectives of polysaccharide coatings and edible films and their impact on the quality of fruit and vegetables, providing an understanding of their main functions and benefits. Numerous studies show that natural polysaccharides are well suited for use as packaging material for fresh fruit and vegetables and can often be an important alternative to synthetic compounds. Natural polymer materials are a good barrier to oxygen and carbon dioxide; however, they are characterised by excessive solubility in the water environment, water vapour permeability and low extensibility. The properties of edible coatings can be modified by the addition of plasticisers, surfactants, cross-linkers, antimicrobial agents, functional additives, nanosilver particles or fruit and vegetable residues. The use of an electric field is also a promising technology here. The use of polysaccharides for the preparation of edible films and coatings is justified not only by the possibility of reducing the consumption of packaging made of synthetic polymer materials but also by the fact that the production of some natural polymers can be made using waste products generated during the processing of food raw materials.
Physicochemical study essential oils of Laurus nobilis according toits conservation

Our work aims to study a very aromatic and medicinal plant known by the locals called Laurus nobilis (laurel). The extraction of essential oil of the plant was carried by a drive to steam. The yield is attractive even in terms of industrial exploitation. A number of physicochemical characteristics were determined on freshly extracted oils. The study of the degradation of these oils as a function of time showed a high instability when stored at room temperature and in the presence of light and rather good preservation of the low temperature and protected from light.
Prediction of Tensile Strain Capacity for X52 Steel Pipeline Materials Using the Extended Finite Element Method

Strain-based design (SBD) plays an important role in pipeline design and assessment of pipelines subjected to geo-hazards. Under such hazards, a pipe can be subjected to substantial plastic strains, leading to tensile failure at locations of girth weld flaws. For SBD, the finite element method (FEM) can be a reliable tool to calculate the tensile strain capacity (TSC) for better design in pipelines. This study aims to investigate the ductile fracture properties for specific vintage pipeline steel (API 5L grade of X52) using the extended finite element method (XFEM). Eight full-scale tests were simulated using the commercial finite element analysis software ABAQUS Version 6.17. Maximum principal strain is used to assess the damage initiation using the cohesive zone model (CZM) when the crack evolution is evaluated by fracture energy release. A proper set of damage parameters for the X52 materials was calibrated based on the ability of the model to reproduce the experimental results. These experimental results included the tensile strain, applied load, endplate rotation, and crack mouth opening displacement (CMOD). This study describes a methodology for validation of the XFEM and the proper damage parameters required to model crack initiation and propagation in X52 grades of pipeline.
Research on Intelligent Blending Method of Matcha Based on Improved PID Control Algorithm

Most of the existing Matcha blending is done manually, which is inefficient and leads to uneven tea products. To solve this problem, this paper proposes an intelligent blending method based on improved PID control algorithm. In this paper, considering the various factors that affect the quality of tea, without affecting the quality of the product, taking the lowest cost of the enterprise as the constraint goal, the basic combination model is constructed by using PID control algorithm. Since the selection of PID parameters will affect the output of the model, this paper introduces particle swarm optimization algorithm to optimize the PID controller parameters globally, so that the PID controller can achieve the ideal control index, and finally determine the optimal combination of Matcha. The experimental results show that the method proposed in this paper can effectively realize the intelligent blending of Matcha, and maximize the cost of tea factory, effectively improve the income of Matcha products, which is conducive to the further development of tea companies.
Mortality 30 and 90 days after hospitalisation for COVID-19: prognostic factors on admission to hospital

Introduction: Severe acute respiratory syndrome (SARS-CoV-2) infection causes substantial mortality in hospitalised patients. The purpose of this study was to investigate mortality and identify prognostic factors from the data collected on hospital admission. Methods: This was a retrospective cohort study of patients hospitalised with clinically suspected and/or laboratory confirmed SARS-CoV-2 infection. The primary outcome was mortality 30 and 90 days after admission. Risk factors for death were identified by multivariable logistic regression. Results: Three hundred and thirty-four patients were included; 93.4% of patients had positive SARS-CoV-2 RT-PCR swab; median (IQR) age 75 (63–84) years, male 54.2%, smoking (≥ 5 CPD/ ≥5 pack years) 37.1%, obesity 24.8% and frailty 42%. Mortality was 30.8% at 30 days after admission and 34% at 90 days after admission. Mortality was greatest in older patients (age >65 years; 36.7 vs. 16.5%; P 65 years; male 42.5 vs. female 30%; P = 0.046); smoking (41.1 vs. 24.8%; P = 0.003); younger smokers (age ≤ 65 years; 35.3 vs. non-smoker 6.5%; P < 0.001); with chronic kidney disease (CKD: 50 vs. 27.8%; P = 0.003), chronic neurological disease (43.8 vs. 27.2%; P = 0.007), COPD (41.3 vs. 28.8%; P = 0.048), cardiac disease (40.7 vs. 25.1%; P = 0.003), frailty (44.3 vs. 21%; P < 0.001), and with chest x-ray changes of COVID-19 (39 vs. 13.2%; P < 0.001). Mortality was associated with raised C-reactive protein (CRP) and lymphopaenia on admission. Independent predictors of mortality were (adjusted OR; 95% CI): age (1.05; 1.02–1.08), smoking (2.08; 1.19–3.63); CKD (2.32; 1.09–4.92), chronic neurological disease (2.27; 1.17–4.40), frailty (1.92; 1.047–3.53); chest x-ray changes of COVID-19 (4.31; 2.12–8.79) and Log-CRP (3.21; 1.43–7.22); ROC analysis AUC 0.811 (0.765–0.852). Conclusion: Mortality for patients hospitalised with SARS-CoV-2 infection is high (>30%) with greatest risk in older-age males with chronic disease and frailty. A history of moderate–heavy smoking is a major risk factor, particularly in younger patients (≤ 65 years). Chest x-ray changes of COVID-19 and raised CRP are clinically valuable prognostic indicators.
Application of team‐based learning for teaching food analysis

Food analysis courses introduce the theory and application of analytical proce-dures to characterize foods and their ingredients. Students learn the principles behind analytical techniques commonly used to analyze foods in the industry and evaluate their suitability for the analysis of specific food products. Food analysis courses have traditionally been taught in a lecture-based format. The objec-tive of this study was to evaluate the effectiveness of utilizing team-based learning (TBL), an established collaborative learning technique, in an undergraduate food analysis course to enhance student learning. Post-course student surveys, quizzes, and exam scores were used to assess the effectiveness of the TBL implementation. TBL uses pre-reading assignments, individual and team quizzes, and problem solving in teams to create an active learning environment in the classroom that increases student engagement and long-term learning. The results from surveys, quizzes, and exam scores from the Food Analysis course show a successful implementation in the classroom: no significant difference in the average midterm exam scores was observed between the TBL and lecture formats ( p ≤ 0.05), indicating that TBL maintained performance on midterm exams, students reported being engaged in the course, and the deliberately designed group work encouraged students to prepare for class and participate in the activities. The students also reported that they were able to recall information better, and that the effectiveness of TBL activities helped them strengthen concepts. In conclusion, the results suggest that TBL is beneficial for students in food analysis, and that implementation in additional food science courses could help improve student learning.
‘All social work takes place in a macro context’: The gap between international social work training and practice

This explanatory sequential mixed-methods study combined data from an online survey with international social workers (N = 44), and key informant qualitative interviews (N = 6), to identify gaps and synergies between what is taught in graduate social work programmes and expected by employers. Findings suggested that although social work values align well with international social work, gaps exist between the macro knowledge and skills required for international work and that which graduate training offers. Findings further suggested that if unaddressed, these incompatibilities may contribute to the invisibility of social work as a viable training ground for practice in international aid agencies.
Intelligent Scheduling with Reinforcement Learning

In this paper, we present and discuss an innovative approach to solve Job Shop scheduling problems based on machine learning techniques. Traditionally, when choosing how to solve Job Shop scheduling problems, there are two main options: either use an efficient heuristic that provides a solution quickly, or use classic optimization approaches (e.g., metaheuristics) that take more time but will output better solutions, closer to their optimal value. In this work, we aim to create a novel architecture that incorporates reinforcement learning into scheduling systems in order to improve their overall performance and overcome the limitations that current approaches present. It is also intended to investigate the development of a learning environment for reinforcement learning agents to be able to solve the Job Shop scheduling problem. The reported experimental results and the conducted statistical analysis conclude about the benefits of using an intelligent agent created with reinforcement learning techniques. The main contribution of this work is proving that reinforcement learning has the potential to become the standard method whenever a solution is necessary quickly, since it solves any problem in very few seconds with high quality, approximate to the optimal methods.
Electronic and optical properties of ASc2S4 (A = Ca, Sr) compounds

This study investigates the ASc2S4 (A = Ca, Sr) compounds having an AB2O4-type structure of orthorhombic crystal nature. The calculated formation enthalpies reveal the thermodynamic stability of these compounds. In addition, the mechanical and dynamical stabilities are found as well. The detailed electronic properties are studied using Perdew-Burke-Ernzerhof functional within the Generalized Gradient Approximation (GGA-PBE) and DFT + U methods. The ASc2S4 compounds are found to be semiconductor due the electronic band structures. The band gaps are determined as 1.11 eV and 1.25 eV using the GGA-PBE method, and 1.54 eV and 1.64 eV using the DFT + U method for CaSc2S4 and SrSc2S4, respectively. The orbital contributions to the partial density of states are investigated in detail. Furthermore, the optical properties of these compounds are obtained using the Bethe-Salpeter Equation (BSE) to consider the electron-hole interactions. The optical properties of the ASc2S4 compounds such as refractive indexes, extinction coefficients, absorption coefficients and loss functions are determined. This study fills the gap in the literature concerning the compounds that have S atoms instead of O atoms in AB2O4, while also revealing the electronic and optical properties of ASc2S4 (A = Ca, Sr) compounds in detail.
Integration of Expectation Maximization using Gaussian Mixture Models and Naïve Bayes for Intrusion Detection

Article history Received: 27 February 2021 Accepted: 17 March 2021 Published Online: 20 April 2021 Intrusion detection is the investigation process of information about the system activities or its data to detect any malicious behavior or unauthorized activity. Most of the IDS implement K-means clustering technique due to its linear complexity and fast computing ability. Nonetheless, it is Naïve use of the mean data value for the cluster core that presents a major drawback. The chances of two circular clusters having different radius and centering at the same mean will occur. This condition cannot be addressed by the K-means algorithm because the mean value of the various clusters is very similar together. However, if the clusters are not spherical, it fails. To overcome this issue, a new integrated hybrid model by integrating expectation maximizing (EM) clustering using a Gaussian mixture model (GMM) and naïve Bays classifier have been proposed. In this model, GMM give more flexibility than K-Means in terms of cluster covariance. Also, they use probabilities function and soft clustering, that’s why they can have multiple cluster for a single data. In GMM, we can define the cluster form in GMM by two parameters: the mean and the standard deviation. This means that by using these two parameters, the cluster can take any kind of elliptical shape. EM-GMM will be used to cluster data based on data activity into the corresponding category.
Preparation and Properties of Bio-Based Polyurethane Foam Prepared from Modified Natural Rubber and Poly(ε-caprolactone)

The aim of this research was to prepare a bio-based polyurethane foam (PUF) containing hydroxyl telechelic oligomers from natural rubber (HTNR) and waste tire crumbs (HTWT) and polycaprolactone diol (PCL) as soft segments. The studied parameters included type of polyols and molar ratio between HTNR/PCL and HTWT/PCL. The molecular weight of HTNR and HTWT derived from 1H-NMR spectra were 1,800 and 1,400 g/mol, respectively. The molecular weight of PCL diol was 2000 g/mol. The effect of HTNR/PCL and HTWT/PCL molar ratio (1/0, 1/0.5, 1/1 and 0.5/1) on the foam formation rate and physical and chemical properties of the resulting PUF was investigated. The chemical structure of HTNR, HTWT and PUF were confirmed by FTIR and 1H-NMR. PCL diol provided faster reaction, thus higher PCL diol content showed higher foam formation rate. The foam density slightly changed with the molar ratio whereas the specific tensile strength of all samples was in the same range. The average diameter of cell increased with increasing contents of PCL diol. The addition of PCL diol resulted in reduced elongation at break and compressive strength. The cellular structure observed by SEM micrographs of HTNR based foams showed an almost closed cell. The biodegradability was assessed according to a modified Sturm test. Low density polyethylene and sodium benzoate were used as a negative and positive control sample, respectively. PUF samples showed an induction time of 33 days in which the percentage of biodegradation was  7-11%. The biodegradation of PUF containing only HTNR was 8.4% and 31.89% at 28 days and 60 days of testing respectively whereas the PUF containing 1/0.5 HTNR/PCL (by mole) showed a higher biodegradation: 11.31% and 45.6% at 28 days and 60 days of testing respectively. The molar ratio of HTWT/PCL strongly affected the kinetic rate of foam formation and foam morphology. According to SEM micrographs, polyhedral closed cells were observed. The addition of the PCL diol increased the thermal degradation temperature of the PUF based on TGA results. A low kinetic rate provided PUF with a high density, small cell size and a broad cell size distribution. The biodegradation of PUF containing only HTWT was 31.2% and 51.3% at 28 days and 60 days of testing respectively whereas the PUF containing 1/0.5 HTWT/PCL diols (by mole) showed a higher biodegradation: 39.1% and 64.3% at 28 days and 60 days of testing respectively. The foam formation rate of HTWT based PUF was higher than the one of HTNR based PUF. All HTWT based PUF have a higher density than HTNR based PUF. The HTWT based PUF had an inferior cell size in comparison to HTNR based PUF. The cellular structure of HTNR based and HTWT based PUF were different, but all PUFs showed almost closed cells. The HTWT based PUF had a higher thermal degradation temperature and biodegradation properties than foams from HTNR.
Depth Control of Autonomous Underwater Vehicle Using Robust Tracking Control

Since the behavior of an autonomous underwater vehicle (AUV) is influenced by disturbances and moments that are not accurately known, the depth control law of AUVs must have the ability to track the input signal and to reject disturbances simultaneously. Here, we proposed robust tracking control for controlling the depth of an AUV. An augmented closed-loop system is represented by an error dynamic equation, and we can easily show the asymptotic stability of the overall system by using a Lyapunov function. The robust tracking controller is consisted of the internal model of the command signal and a state feedback controller, and it has the ability to track the input signal and reject disturbances. The closed-loop control system is robust to parameter uncertainties. Simulation results showed the control performance of the robust tracking controller to be better than that of a P + PD controller.
Computationally accelerated discovery of high entropy pyrochlore oxides

High entropy ceramics provide enhanced flexibility for tailoring a wide range of physical properties, emerging from the diverse chemical and configurational degrees of freedom. Expanding upon the endeavors of recently synthesized high entropy ceramics in rock salt, fluorite, spinel and perovskite structures, we explore the relative feasibility of formation of high entropy pyrochlore oxides, A2B2O7, with multi-cation occupancy of the B-site, estimated from first principles based thermodynamic descriptors. Subsequently, we used Monte Carlo simulations to estimate the phase composition, oxygen vacancy concentration and local ionic segregation as a function of temperature and oxygen partial pressure. In parallel, we have investigated the synthesis of several multicomponent oxides with a pyrochlore composition, related to our computational investigations, resulting in several high purity pyrochlore oxides, in some cases with minor impurity phases. Ultimately, our approach allows us to evaluate potential impurity phases, ionic disorder and oxygen vacancy concentration in response to the experimental variables, thereby making realistic predictions that can direct and accelerate experimental synthesis of novel multicomponent ceramics.
Non-intrusive DC voltage measurement based on resonant electric field microsensors

Based on the resonant electric field microsensors with coplanar electrodes, a novel non-intrusive measurement scheme of DC power-line voltage suitable for actual product is presented in this paper. Compared with using microsensors directly without some protection, employing the proposed scheme can avoid the influence of humidity and electromagnetic disturbance on non-contact voltage measurement. A theoretical model is developed to analyze the influence of the structural parameters of the proposed measurement scheme on its sensitivity and to predict the voltage response. Furthermore, the theoretical analysis reveals that the proposed scheme obtains the DC power-line voltage non-intrusively by measuring the voltage of a floating electrode. A prototype of the non-intrusive measurement apparatus of DC power-line voltage has been developed, calibrated, and tested. The prototype shows a favorable linear response characteristic with a linearity of 0.86%. And the experimental results are in good agreement with the theoretical results. The maximum relative deviation noticed is −2.16% over a range of voltages from −1000 V to 1000 V.
Social capital from sport event participation: scale development and validation

ABSTRACT Participatory sport events can generate substantial social benefits for the participants. A key benefit derived from participatory sport events is the development of social capital. The purpose of this study was to develop a reliable and valid instrument that measures social capital among active sport event participants. Using three different samples of running event participants, the proposed Sport Event Participation Social Capital Scale (SEPSCS) was tested, purified, and validated. The final scale consisted of three dimensions: trust (three items), reciprocity (three items), and network (three items). Results from factor, correlation and regression analyses supported the convergent and nomological validity of SEPSCS. Overall, SEPSCS provided researchers a psychometrically sound tool to better understand the social impacts of participatory sport events.