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This study delineates the effect of cover thickness on reinforced concrete (RC) columns and beams under an elevated fire scenario. Columns and beams are important load-carrying structural members of buildings. Under all circumstances, the columns and beams were set to be free from damage to avoid structural failure. Under the high-temperature scenario, the RC element may fail because of the material deterioration that occurs owing to the thermal effect. This study attempts to determine the optimum cover thickness for beams and columns under extreme loads and fire conditions.

Cover thicknesses of 30, 40, 45, 50, 60 and 70 mm for the columns and 10, 20, 25, 30, 35, 40, 50, 60 and 70 mm for the beams were adopted in this study. Both steady-state and transient-state conditions under thermomechanical analysis were performed using the finite element method to determine the heat transfer through the RC section and to determine the effect of thermal stresses.

The results show that the RC elements have a greater influence on the additional cover thickness at extreme temperatures and higher load ratios than at the service stages. The safe limits of the structural members were obtained under the combined effects of elevated temperatures and structural loads. The results also indicate that the compression members have a better thermal performance than the flexural members.

Numerical investigations concerning the high-temperature behavior of structural elements are useful. The lack of an experimental setup encourages researchers to perform numerical investigations. In this study, the finite element models were validated with existing finite element models and experimental results.

The obtained safe limit for the structural members could help to understand their resistance to fire in a real-time scenario. From the safe limit, a suitable design can be preferred while designing the structural members. This could probably save the structure from collapse.

There is a lack of both numerical and experimental research works. In numerical modeling, the research works found in the literature had difficulties in developing a numerical model that satisfactorily represents the structural members under fire, not being able to adequately understand their behavior at high temperatures. None of them considered the influence of the cover thickness under extreme fire and loading conditions. In this paper, this influence was evaluated and discussed.

This study assesses the relevance of foreign aid to the incidence of capital flight and unemployment in 20 countries in sub-Saharan Africa.

The study is for the period 1996–2018, and the empirical evidence is based on interactive quantile regressions in order to assess the nexuses throughout the conditional distribution of the unemployment outcome variable.

From the findings, capital flight has a positive unconditional incidence on unemployment, while foreign aid dampens the underlying positive unconditional nexus. Moreover, in order for the positive incidence of capital flight to be completely dampened, foreign aid thresholds of 2.230 and 3.964 (% of GDP) are needed at the 10th and 25th quantiles, respectively, of the conditional distribution of unemployment. It follows that the relevance of foreign aid in crowding out the unfavourable incidence of capital flight on unemployment is significantly apparent only in the lowest quantiles or countries with below-median levels of unemployment. The policy implications are discussed.

The study complements the extant literature by assessing the importance of development assistance in how capital flight affects unemployment in sub-Saharan Africa.

The purpose of this paper is to systematically map research on the effect of classroom acoustics and noise on high school students’ listening, learning and well-being, as well as identify knowledge gaps to inform future research.

This scoping review followed the PRISMA-ScR protocol. A comprehensive search of four online databases (ERIC, PubMed, Scopus and Web of Science) was conducted. Peer-reviewed papers were included if they conducted a study on the effect of classroom acoustics or noise on students’ listening, learning or well-being; had a clear definition of the noise level measurement; were conducted with high school students; and had the full text in English available.

In total, 14 papers met the criteria to be included in the review. The majority of studies assessed the impact of noise on students’ listening, learning or well-being. Overall, the results showed that higher noise levels have a negative effect on students’ listening, learning and well-being. Effects were even more pronounced for students who were non-native speakers or those with special educational needs such as hearing loss. Therefore, it would be beneficial to limit unnecessary noise in the classroom as much as possible through acoustic insulation, acoustic treatment and classroom management strategies.

This paper is the first review paper to synthesize previous research on the effect of classroom acoustics and noise on high school students’ listening, learning and well-being. It provides an analysis of the limitations of existing literature and proposes future research to help fill in these gaps.

This study aims at evaluating the technical efficiency (TE) of healthcare systems in the Arab region and exploring the key factors that affect the efficiency performance.

The study applies a two-stage Data Envelopment Analysis (DEA) approach to a sample of 20 Arab countries. In the first stage, a DEA model is used to calculate the TE scores of the examined healthcare systems in 2019 and 2010, following both the output and input orientations of efficiency. In the second stage, a censored Tobit model is estimated to investigate the determinants of healthcare efficiency.

DEA results of 2019 indicate that achievable efficiency gains of the Arab countries range from 0.4% to 16% under the output and input orientations, respectively. Six countries are efficient under both orientations. Although the average efficiency scores of the Arab countries have deteriorated between 2010 and 2019, Djibouti and Sudan had the greatest efficiency improvements between the two years. Bahrain, Mauritania, Morocco and Qatar proved to be efficient in 2010 and 2019 under the two orientations of efficiency and according to the two DEA specifications followed. The Tobit model reveals that corruption and government health expenditure tend to have an adverse impact on healthcare efficiency.

The author evaluates healthcare efficiency and healthcare's efficiency determinants in the Arab countries. Regardless Arab countries' diversity, these countries are facing common health challenges, including diminishing role of governments in healthcare financing; increased out-of-pocket healthcare spending; poor healthcare outputs and prevalence of health inequities resulting from weak governance institutions. Comparing the efficiency of healthcare systems between 2010 and 2019 gives insights on the potential impact of the Arab spring uprisings on healthcare efficiency. Moreover, examining the determinants of healthcare efficiency allows for better understanding of how to improve the efficiency of healthcare systems in the region.

This study investigates the effect of unproven energy reserve news on the volatility of energy firms' stocks. Thus, investors' perception of unproven energy reserves is revealed. Additionally, the study aims to determine whether the effect of the news changes according to time and volatility level.

The general autoregressive conditional heteroskedasticity (GARCH) and exponential generalized autoregressive conditional heteroskedasticity (EGARCH) models consist of the energy reserve exploration news in Turkey for the period 2009–2022 and the volatility of 14 energy stocks.

The results indicate energy exploration news's negative and significant effect on volatility. According to empirical results, energy stock volatility is most affected in the first ten days. Besides, the results show that the significant models of energy reserve news in low-volatility stocks are proportionally higher than in high-volatility stocks.

Only unproved reserve news is included in the analysis, as sufficient confirmed reserves could not be reached during the sampling period. Further studies can compare proven and unproved reserve news effects. Additionally, a similar analysis can be conducted between Turkey and another country with a similar socio-economic character to examine different investor behaviors.

This research includes indications on managing investors' reactions to unproven energy reserve news.

This study contributes to the literature by analyzing unproven reserves. Contrary to previous studies, examining stock volatility also makes the study unique.

This paper presents the experimental and numerical results of the bending properties of low-height prestressed T-beams. The purpose is to study the bearing capacity, failure state and strain distribution of low-height prestressed T-beams.

First, two 13 m-long full-size test beams were fabricated with different positions of prestressed steel bundles in the span. The load–deflection curves and failure patterns of each test beam were obtained through static load tests. Secondly, the test data were used to validate the finite element model developed to simulate the flexural behavior of low-height prestressed T-beams. Finally, the influence of different parameters (the number of prestressed steel bundles, initial prestress and concrete strength grade) on the flexural performance of the test beams is studied by using a finite element model.

The test results show that when the distance of the prestressed steel beam from the bottom height of the test beam increases from 40 to 120 mm, the cracking load of the test beam decreases from 550.00 to 450.00 kN, reducing by 18.18%, and the ultimate load decreases from 1338.15 to 1227.66 kN, reducing by 8.26%, therefore, the increase of the height of the prestressed steel beam reduces the bearing capacity of the test beam. The numerical simulation results show that when the number of steel bundles increases from 2 to 9, the cracking load increases by 183.60%, the yield load increases by 117.71% and the ultimate load increases by 132.95%. Therefore, the increase in the number of prestressed steel bundles can increase the cracking load, yield load and ultimate load of the test beam. When the initial prestress is from 695 to 1,395 MPa, the cracking load increases by 69.20%, the yield load of the bottom reinforcement increases by 31.61% and the ultimate load increases by 3.97%. Therefore, increasing the initial prestress can increase the cracking load and yield load of the test beam, but it has little effect on the ultimate load. The strength grade of concrete increases from C30 to C80, the cracking load is about 455.00 kN, the yield load is about 850.00 kN and the ultimate load is increased by 4.90%. Therefore, the improvement in concrete strength grade has little influence on the bearing capacity of the test beam.

Based on the experimental study, the bearing capacity of low-height prestressed T-beams with different prestressed steel beam heights is calculated by finite element simulation, and the influence of different parameters on the bearing capacity is discussed. This method not only ensures the accuracy of bearing capacity assessment, but also does not require a large number of samples and has a certain economy. The study of prestressed low-height T-beams is of great significance for understanding the principle and application of prestressed technology. Research on the mechanical behavior and performance of low-height prestressed T beams can provide a scientific basis and technical support for the design and construction of prestressed concrete structures. In addition, the study of prestressed low-height T-beams can also provide a reference for the optimization design and construction of other structural types.

The smoothness of the high-speed railway (HSR) on the bridge may exceed the allowable standard when an earthquake causes vibrations for HSR bridges, which may threaten the safety of running trains. Indeed, few studies have evaluated the exceeding probability of rail displacement exceeding the allowable standard. The purposes of this article are to provide a method for investigating the exceeding probability of the rail displacement of HSRs under seismic excitation and to calculate the exceeding probability.

In order to investigate the exceeding probability of the rail displacement under different seismic excitations, the workflow of analyzing the smoothness of the rail based on incremental dynamic analysis (IDA) is proposed, and the intensity measure and limit state for the exceeding probability analysis of HSRs are defined. Then a finite element model (FEM) of an assumed HSR track-bridge system is constructed, which comprises a five-span simply-supported girder bridge supporting a finite length CRTS II ballastless track. Under different seismic excitations, the seismic displacement response of the rail is calculated; the character of the rail displacement is analyzed; and the exceeding probability of the rail vertical displacement exceeding the allowable standard (2mm) is investigated.

The results show that: (1) The bridge-abutment joint position may form a step-like under seismic excitation, threatening the running safety of high-speed trains under seismic excitations, and the rail displacements at mid-span positions are bigger than that at other positions on the bridge. (2) The exceeding probability of rail displacement is up to about 44% when PGA = 0.01g, which is the level-five risk probability and can be described as 'very likely to happen'. (3) The exceeding probability of the rail at the mid-span positions is bigger than that above other positions of the bridge, and the mid-span positions of the track-bridge system above the bridge may be the most hazardous area for the running safety of trains under seismic excitation when high-speed trains run on bridges.

The work extends the seismic hazardous analysis of HSRs and would lead to a better understanding of the exceeding probability for the rail of HSRs under seismic excitations and better references for the alert of the HSR operation.

In the analysis of structures in a fire situation by simplified and analytical methods, one assumption is that the fire resistance time is greater than or equal to the required fire resistance time. Among the methodologies involving the fire resistance time, the most used is the tabular method, which associates fire resistance time values to structural elements based on minimum dimensions of the cross section. The tabular method is widely accepted by the technical-scientific community due to the fact that it is safe and practical. However, its main criticism is that it results in lower fire resistance times than advanced thermal and thermostructural analysis methods. The objective of this study was to evaluate the fire resistance time of reinforced concrete beams and compare it with the required fire resistance time recommended by the tabular method of NBR 15200 (ABNT, 2012).

The fire resistance time and required fire resistance time of reinforced concrete beams were evaluated using, respectively, numerical models developed based on the finite element method and the tabular method of NBR 15200 (ABNT, 2012). The influence of the following parameters was investigated: longitudinal reinforcement cover, characteristic compressive strength of concrete, beam height, longitudinal reinforcement area and arrangement of steel bars.

Among the evaluated parameters, the covering of the longitudinal reinforcement proved to be more relevant for the fire resistance time, justifying that the tabular method of NBR 15200 (ABNT, 2012) being strongly and directly influenced by this parameter. In turn, more resistant concretes, higher beams and higher steel grades have lower fire resistance time values. This is because beams in these conditions have greater resistance capacity at room temperature and, consequently, are subject to external stresses of greater magnitude. In some cases, the fire resistance time was even lower than the required fire resistance time prescribed by NBR 15200 (ABNT, 2012). Both the fire resistance time and the required fire resistance time were not influenced by the arrangement of the longitudinal reinforcements.

The present paper innovates by demonstrating the influence of other important design variables on the required fire resistance time of the NBR 15200 (ABNT, 2012). Among several conclusions, it was found that the load level to which the structural elements are subjected considerably affects their fire resistance time. For this reason, it was recommended that the methods for calculating the required fire resistance time consider the load level. In addition, the article quantifies the security degree of the tabular method and exposes some situations for which the tabular method proved to be unsafe. Moreover, in all the models analyzed, the relationship between the span and the vertical deflection associated with the failure of the beams in a fire situation was determined. With this, a span over average deflection relationship was presented in which beams in fire situations fail.

There is a need to predict whether the consumers liked the stay in the hotel rooms or not, and to remove the aspects the customers did not like. Many customers leave a review after staying in the hotel. These reviews are mostly given on the website used to book the hotel. These reviews can be considered as a valuable data, which can be analyzed to provide better services in the hotels. The purpose of this study is to use machine learning techniques for analyzing the given data to determine different sentiment polarities of the consumers.

Reviews given by hotel customers on the Tripadvisor website, which were made available publicly by Kaggle. Out of 10,000 reviews in the data, a sample of 3,000 negative polarity reviews (customers with bad experiences) in the hotel and 3,000 positive polarity reviews (customers with good experiences) in the hotel is taken to prepare data set. The two-stage feature selection was applied, which first involved greedy selection method and then wrapper method to generate 37 most relevant features. An improved stacked decision tree (ISD) classifier) is built, which is further compared with state-of-the-art machine learning algorithms. All the tests are done using R-Studio.

The results showed that the new model was satisfactory overall with 80.77% accuracy after doing in-depth study with 50–50 split, 80.74% accuracy for 66–34 split and 80.25% accuracy for 80–20 split, when predicting the nature of the customers’ experience in the hotel, i.e. whether they are positive or negative.

The implication of this research is to provide a showcase of how we can predict the polarity of potentially popular reviews. This helps the authors’ perspective to help the hotel industries to take corrective measures for the betterment of business and to promote useful positive reviews. This study also has some limitations like only English reviews are considered. This study was restricted to the data from trip-adviser website; however, a new data may be generated to test the credibility of the model. Only aspect-based sentiment classification is considered in this study.

Stacking machine learning techniques have been proposed. At first, state-of-the-art classifiers are tested on the given data, and then, three best performing classifiers (decision tree C5.0, random forest and support vector machine) are taken to build stack and to create ISD classifier.

The purpose of the study is to optimize the blending ratio of Arecanut and cotton fibers to create yarn with the best quality for various applications, particularly home furnishings. The study aims to determine the effect of different blend ratios on the physical and mechanical properties of the yarn.

The study involves blending Arecanut and cotton fibers in various ratios (90:10, 75:25, 50:50, 25:75 and 10:90) at two different yarn counts (10/1 and 5/1). Various physical and mechanical properties of the blended yarn are analyzed, including unevenness, coefficient of mass variation (cvm%), imperfection, hairiness, breaking strength, elongation, tenacity and breaking work.

The research findings suggest that the blend ratio of 10:90 (10% cotton and 90% Arecanut fiber) produced the best results in terms of physical and mechanical properties for both yarn counts. This blend ratio resulted in reduced unevenness, cvm% and imperfection, while also exhibiting good mechanical properties such as breaking strength, elongation, tenacity and breaking work. The blend with a higher concentration of cotton generally showed better properties due to the coarseness of Arecanut fiber. As the goal of the study was to determine the best blend ratio that included the most Arecanut fiber based on its physical and mechanical properties, which is suitable for home furnishing applications, 75:25 Areca cotton blend ratio of yarn count 5/1 proved to be the best.

The study acknowledges that Arecanut fiber must be blended with other commercially used fibers like cotton due to its coarseness. While the study provides insights into optimizing blend ratios for home furnishings and packaging, further research may be needed to make the material suitable for clothing applications.

The research has practical implications for industries interested in utilizing Arecanut and cotton blends for various applications, such as home furnishings and packaging materials. It suggests that specific blend ratios can result in yarn with desirable properties for these purposes.

The study mentions that the increased use of Arecanut fibers can benefit the growers of Arecanut, potentially providing economic opportunities for communities engaged in Arecanut farming.

The research explores the utilization of Arecanut fibers, an underutilized resource, in combination with cotton to create sustainable yarn. It assesses various blend ratios and their impact on yarn properties, contributing to the understanding of eco-friendly textile materials.