Miracle-dz/UniFAR-FTUs huggingface dataset:
Corporate Social Responsibility Disclosure and Financial Performance: The Mediating Role of Financial Statement Comparability

The aim of this study is to investigate the relationship between corporate social responsibility (CSR) disclosure and financial performance with the consideration of the mediating role of financial statement comparability (FSC) for a sample of Vietnamese listed firms. We used content analysis of the information related to the GRI Standards on annual reports in order to construct CSR disclosure score. We used a dataset of 1125 firm-year observations, covering 225 firms listed on Vietnam’s stock market in the period 2014–2018. Applying OLS and GMM estimation methods, Sobel test, and using different proxies of the mediator variable to increase the robustness, we obtained two remarkable conclusions. First, CSR disclosure has a positive impact on the financial performance of listed companies in Vietnam. Second, there is a complementary mediation effect of financial statement comparability in the above relationship. Our results suggest that it is necessary to develop a legal framework for the practice and disclosure of CSR as well as to apply the international accounting standards in the Vietnamese stock market.
Effects of a Dual-Purpose Inoculant on the Quality and Aerobic Stability of Corn Silage at the Laboratory and Field Scales

This study investigated the effects of a dual-purpose inoculant (DPI) on the fermentation profile, nutritive value, and aerobic stability of silage. The inoculant effect was first examined with minisilos, and the results were later validated with 400-kg silo bales and a 40-t bunker silo. Briefly, whole-plant corn harvested at the one-half to two-thirds milk line stage was chopped and then treated with or without inoculant containing Lactobacillus plantarum LP1028 and Lactobacillus buchneri LBC1029 at application rates of 2.5 × 105 cfu and 5.0 × 105 cfu per gram of fresh forage, respectively. The results showed that applying DPI had no effect on the nutritive value in all trials. DPI inoculation also slowed yeast and mold growth in silage under aerobic exposure. Inoculation may double the aerobic stability time after 105 d of ensiling (53.25 vs. 113.20 h) in a bunker silo. This study successfully examined the effectiveness of DPI in minisilos, and the results were consistent when moving from the laboratory to the field. Applying DPI made the fermentation more heterolactic without compromising the silage nutritive value, and increasing acetic acid acted as an antifungal agent to inhibit spoilage microbial growth and improve silage aerobic stability.
Improved Low-Order Thermal Model for Critical Temperature Estimation of PMSM

The low-order lumped parameter thermal models (LPTMs) are one of the most competitive online approaches to estimate the critical temperatures in electrical machines. Firstly, this paper explores the correlation between the conventional high-order and low-order LPTMs and then presents some critical modelling guidelines of low-order LPTM together with an improved four-order LPTM. Secondly, a simple dynamic quasi-linear model (DQM) is proposed to define convection thermal resistances, which can be used in both conventional high-order and low-order LPTMs. The application of DQM can improve the estimation accuracy by indirectly introducing more degrees of freedom, which is especially beneficial for the magnet temperature estimation in the low-order LPTM due to its highly abstract rotor thermal modelling. Finally, a prototype permanent magnet synchronous machine is used for experimental validation under different operation profiles.
Accurate and robust sparse-view angle CT image reconstruction using deep learning and prior image constrained compressed sensing (DL-PICCS).

BACKGROUND Sparse-view CT image reconstruction problems encountered in dynamic CT acquisitions are technically challenging. Recently, many deep learning strategies have been proposed to reconstruct CT images from sparse-view angle acquisitions showing promising results. However, two fundamental problems with these deep learning reconstruction methods remain to be addressed: 1) limited reconstruction accuracy for individual patients and 2) limited generalizability for patient statistical cohorts. PURPOSE The purpose of this work is to address the previously mentioned challenges in current deep learning methods. METHODS A method that combines a deep learning strategy with prior image constrained compressed sensing (PICCS) was developed to address these two problems. In this method, the sparse-view CT data were reconstructed by the conventional filtered backprojection (FBP) method first, and then processed by the trained deep neural network to eliminate streaking artifacts. The outputs of the deep learning architecture were then used as the needed prior image in PICCS to reconstruct the image. If the noise level from the PICCS reconstruction is not satisfactory, another light duty deep neural network can then be used to reduce noise level. Both extensive numerical simulation data and human subject data have been used to quantitatively and qualitatively assess the performance of the proposed DL-PICCS method in terms of reconstruction accuracy and generalizability. RESULTS Extensive evaluation studies have demonstrated that: 1) quantitative reconstruction accuracy of DL-PICCS for individual patient is improved when it is compared with the deep learning methods and CS based methods; 2) the false positive lesion-like structures and false negative missing anatomical structures in the deep learning approaches can be effectively eliminated in the DL-PICCS reconstructed images; and 3) DL-PICCS enables a deep learning scheme to relax its working conditions to enhance its generalizability. CONCLUSIONS DL-PICCS offers a promising opportunity to achieve personalized reconstruction with improved reconstruction accuracy and enhanced generalizability. This article is protected by copyright. All rights reserved.
Lower secondary school students’ perceptions of science assessment feedback and their conceptions of science assessment: a transitional phase of developing an understanding of assessment

ABSTRACT Background In the field of science education, the importance of assessment in students’ learning process has been recognized. Understanding of how students perceive science assessment and feedback may be an important issue that science education researchers need to pay attention to in order to achieve meaningful learning. Purpose The purpose of this study is to investigate the relationships between lower secondary students’ perceptions of science assessment feedback and their conceptions of science assessment. Sample The participants of this survey study were 313 eighth-grade students (161 males and 152 females) in Taiwan. Design and methods Two instruments were adapted and implemented to investigate the aim of this study (i.e. the perceptions of science assessment feedback and conceptions of science assessment). To examine the structural relations between the students’ perceptions of science assessment feedback and their conceptions of science assessment, partial least squares structural equation modeling (PLS-SEM) analysis was performed. Results The results showed that students who perceived the science assessment feedback as knowledge of the results, reports, and grades (i.e. outcome feedback) tended to highlight the surface conceptions of science assessment (i.e. Reproducing knowledge). Students with the perceptions of assessment feedback as the provision of the correct answer (i.e. corrective feedback) were prone to hold summative conceptions (i.e. Rehearsing and Accountability). In particular, those students who regarded the science assessment feedback as process feedback which focused on improving learning may have had both summative and formative purposes of science assessment (i.e. Improving learning, Problem solving, and Critical judgment). Conclusions The findings revealed that the lower secondary students’ perceptions of science assessment feedback made a significant contribution to their conceptions of science assessment and may be in a transitional phase of shaping an understanding of assessment. The practical implications and suggestions for future research are discussed.
Emission Abatement Technology Selection, Routing and Speed Optimization of Hybrid Ships

This paper evaluates the effect of a large-capacity electrical energy storage, e.g., Li-ion battery, on optimal sailing routes, speeds, fuel choice, and emission abatement technology selection. Despite rapid cost reduction and performance improvement, current Li-ion chemistries are infeasible for providing the total energy demand for ocean-crossing ships because the energy density is up to two orders of magnitude less than in liquid hydrocarbon fuels. However, limited distance zero-emission port arrival, mooring, and port departure are attainable. In this context, we formulate two groups of numerical problems. First, the well-known Emission Control Area (ECA) routing problem is extended with battery-powered zero-emission legs. ECAs have incentivized ship operators to choose longer distance routes to avoid using expensive low sulfur fuel required for compliance, resulting in increased greenhouse gas (GHG) emissions. The second problem evaluates the trade-off between battery capacity and speed on battery-powered zero-emission port arrival and departure legs. We develop a mixed-integer quadratically constrained program to investigate the least cost system configuration and operation. We find that the optimal speed is up to 50% slower on battery-powered legs compared to the baseline without zero-emission constraint. The slower speed on the zero-emission legs is compensated by higher speed throughout the rest of the voyage, which may increase the total amount of GHG emissions.
The Impact Of The Covid-19 Crisis On Consumer Behavior Online Shopping In Wisma Atlet 2 Quarantine Area, Jakarta

This study aims to see whether there is an impact during the Covid 19 pandemic on online shopping behaviour, especially for volunteers and people who are in the quarantine area of Wisma Atlet 2. Where the independent variable of this study is crisis covid 19, which will affect the dependent variable, namely consumer behavior towards online shopping. This research was conducted because currently, Indonesia is experiencing the Corona Virus or more. They are known as Covid 19. Handling and prevention efforts continue to be carried out to fight COVID-19, one of which is by carrying out quarantine both independently and in bulk. One of the places in Jakarta that is used as a place for quarantine is Wisma Atlet 2. Therefore, researchers want to see whether this pandemic can affect someone in shopping online. This research is a quantitative study that uses a questionnaire from Icek Ajzen (Ajzen, 1991) as the main technique of data collection.The results show that online shopping behaviour has increased during the quarantine period.
Soil greenhouse gas emissions, organic carbon and crop yield following pinewood biochar and biochar–manure applications at eroded and depositional landscape positions: A field trial in South Dakota, USA

Soil amendments can help reduce greenhouse gas (GHG) emissions and increase soil organic carbon (SOC) and crop yield. However, most biochar studies have been conducted on single soil type under controlled conditions. To address this limitation, the aim of this research was to investigate how field biochar and manure applications affect soil quality, plant productivity, and GHG emissions at eroded (sandy loam) and depositional (clay loam) positions in a climate transition zone (udic to ustic and mesic to frigid temperature). A field study was established in 2013 in South Dakota, USA, under a corn–soya bean rotation. Soil treatments included biochar, manure, a manure and biochar mixture, and a control (untreated soil). Soil properties (pH, electrical conductivity (EC), SOC, available nitrogen, available phosphorus and available potassium) were measured in 2017. Plant productivity parameters in 2016 and 2017 and GHG fluxes were measured during the 2016 and 2017 growing seasons. Compared with the control, SOC increased under all treatments at the eroded position (biochar 26%, manure 24%, and manure–biochar mixture 15%) and increased under biochar (25%) and the manure–biochar mixture (25%) at the depositional position. Plant parameters were similar under all treatments at both positions. Area‐scaled CO2 fluxes were lower in soils treated with biochar compared with the control at the eroded landscape position but not at the depositional landscape position. Area‐scaled N2O fluxes were lower in soils treated with biochar at both positions. Furthermore, the biochar–manure mixture treatment emitted lower area‐scaled N2O fluxes compared with manure alone at both positions. This study suggests that for eroded and depositional landscape positions, biochar can improve soil organic carbon and the effects of the biochar and biochar–manure mixture on GHG emissions vary based on the soil texture.
The top‐down pattern of social innovation and social entrepreneurship. Bricolage and agility in response to COVID‐19: cases from China

Social innovation and social entrepreneurship usually follow a bottom‐up pattern. Companies and entrepreneurs decide to focus their business effort on meeting critical and urgent social needs. However, what happens when institutions promote or push top‐down initiatives? The outbreak of COVID‐19 is redefining, for many aspects, entrepreneurial dynamics. By creating a critical shortage of resources and medical supplies, the pandemic drew central and local institutions to push companies to cover the increasing social and medical needs. This study explores how companies reacted to top‐down‐initiated social innovation and social entrepreneurship activities. In doing so, the study focuses on the first heavily hit country, China, and it collects data from companies involved in the production of medical masks and the provision of solutions for nucleic acid tests. Our findings reveal that companies answer to top‐down pushes by implementing two main strategies in a time of crisis. First, the social bricolage by exploiting available and local resources. Second, companies react with agility by re‐thinking their internal innovation, relying on past similar experiences, and making their resource fluid. Our study adds the literature regarding social innovation and entrepreneurship in a crisis time by providing implications for institutions and organizations in setting and responding to strategies for future crises.
Comparative Dynamics of Nutrient Release through Litter Decomposition in Eucalyptus grandis Hill ex Maiden and Pinus taeda L. Stands

The installation of commercial stands with exotic forest species on low fertility soils originally covered by native pastures is an unusual situation worldwide. In recent years, the area occupied by forest systems designed for pulp or wood production with immediate replanting has increased strongly in the Pampean region of South America. In this context, the study of nutrient recycling from forest litter decomposition acquires particular relevance. This work seeks to evaluate and compare the nutrient release from the decomposition of forest litter produced by 14-year-old Eucalyptus grandis Hill ex Maiden and Pinus taeda L. stands and test the applicability of a new sampling methodology in the nutrient recycling assessment. For two years, the evaluation of N, P, K, Ca, Mg, Fe, Mn, Cu and Zn dynamics during litter decomposition was carried out. In general, K concentration decreased through decomposition, meanwhile, all other nutrients showed some degree of immobilization, but this was counteracted by biomass loss for most of them. This mainly resulted in net nutrient release from litter. A higher release rate of all nutrients from P. taeda forest litter compared to E. grandis, with the exception of Mn, was verified. Fe immobilization was observed in both species showing a higher immobilization rate in E. grandis compared to P. taeda. Finally, Zn exhibited immobilization processes in E. grandis and releases in P. taeda. This might suggest higher temporal and quantitative availability of nutrients in P. taeda, due their faster return to the soil. These findings could be relevant in the development of models for sustainable management, adapting the demand for nutrients to the supply during forest rotations.
Printed Band-Stop Filter for Mitigation of Electromagnetic Interference

A new printed microstrip band-stop filter is proposed and implemented. A systematic design approach is presented to realize the filter for a specific band of operation employing the concept of SIR and embedded stub. The proposed filter design is suitable for multiple stop band applications. Moreover, the filter configuration can be easily incorporated in the microstrip feed line connected with a radiating element to reject the signal at the desired band for mitigating the electromagnetic interference. A prototype model of the proposed design is developed, and characteristics are measured and compared with the simulation result. A good agreement between the two is evident.
Pengaruh Ekspektasi Guru terhadap Prestasi Belajar Peserta Didik Kelas VI Semester I di Madrasah Ibtidaiyah (MI) Al-Mu’awwanah Jombang

This study aims to dig deep information about the effect of positive and negative teacher expectations on the learning achievement of students in MI. The method used is a survey-associative quantitative research method. The population as well as the research sample was 20 people from the teacher for variable X and 30 students for variable Y. Data analysis was carried out by using multiple regression analysis techniques. From the data analysis conducted, several research findings indicate that positive expectations (X1) and negative expectations (X2) simultaneously do not have a significant effect on student achievement (Y). The strength of the relationship between Variable X1 (Teacher Positive Expectations) and X2 (Teacher Negative Expectations) simultaneously to Y (Student Learning Achievement) is 0.304 = 30.4% with the influence of variable X on Variable Y of 9.3% and 90, 7% is influenced by variables other than the expectation variable. The regression equation that can be formed from the results of data processing is as follows: Y '= 5.310 + 0.006X1 - 0.094X2. From the above equation it can be seen that the effect of positive expectations (X1) on student learning achievement (Y) is positive. This means that if teachers' positive expectations increase, the learning achievement of students will also increase. However, if the positive expectations of the teacher decrease, the learning achievement will also decrease. While the direction of the negative expectation variable (X2) on learning achievement (Y) is negative. That is, if negative expectations increase, learning achievement will decrease. But, on the contrary, if the teacher's negative expectations decrease, the learning achievement of students will increase
Self-Attentive Graph Convolution Network With Latent Group Mining and Collaborative Filtering for Personalized Recommendation

The remarkable progress of machine learning has led to some state-of-the-art algorithms in personalized recommendation. Previous recommendation algorithms generally learn users’ and items’ representations based on a user-item rating matrix. However, these methods only consider a user's own preference, but ignore the influence of the user's social circles. In this paper, we propose a novel recommendation algorithm, Self-Attentive Graph Convolution Network with Latent Group Mining and Collaborative Filtering, which consists of Latent Group Mining (LGM) module, Collaborative Embedding (CE) module and Self-Attentive Graph Convolution (SAGC) module. The LGM module analyzes users’ social circles by exploring their latent groups and generates group embedding for users and items. The CE module uses a graph embedding method to provide semantic collaborative embedding for users and items. The SAGC module fuses users’ (items’) collaborative embedding and group embedding by a self-attentive graph convolution network to learn their fine-grained representations for rating prediction. We conduct experiments on different real-world datasets, which validates that our algorithm outperforms the state-of-the-art algorithms.
The Icing Distribution Characteristics Research of Tower Cross Beam of Long-Span Bridge by Numerical Simulation

The cross beam of a long-span bridge will freeze in low temperature. When the temperature rises, the ice on the cross beam will thaw and fall off. If the ice is too heavy, it may cause vehicle damage and casualty. In order to reduce the risk of falling ice, a scale model of the cross beam was taken as an example, and a kind of numerical simulation method is presented to study the icing distribution characteristics on surface of the cross beam. This paper simulates the ice accretions process of the cross beam by Fluent module and FENSAP-ICE module of ANSYS and investigates the influence of wind and temperature in the process. This is a new numerical simulation method for studying ice accretions of buildings. The results indicate that water freezes mainly on the windward surface, and the thicker ice is near the top and bottom edge of windward surface. According to the results of numerical simulation, a measure of ice melting based on electric heating method is proposed in this paper, and the feasibility and effectiveness of this method are verified by numerical simulation. The results show that the icing distribution characteristics are accord with the fact and the ice-melting measure is feasible and effective.
Reply on RC1

We are very grateful to the reviewer’s for his deep and thorough review of our manuscript, we also thank him for the effort and time put into the review of the manuscript. We revised our present research in the light of their useful remarques; the comment has been carefully considered and responded. Hope our revision has improved to a level of their satisfaction, and our manuscript will be accepted in this new form. Enclosed a document with all the requested modifications.
The Combined Use of SLAM Laser Scanning and TLS for the 3D Indoor Mapping

An efficient 3D survey of a complex indoor environment remains a challenging task, especially if the accuracy requirements for the geometric data are high for instance in building information modeling (BIM) or construction. The registration of non-overlapping terrestrial laser scanning (TLS) point clouds is laborious. We propose a novel indoor mapping strategy that uses a simultaneous localization and mapping (SLAM) laser scanner (LS) to support the building-scale registration of non-overlapping TLS point clouds in order to reconstruct comprehensive building floor/3D maps. This strategy improves efficiency since it allows georeferenced TLS data to only be collected from those parts of the building that require such accuracy. The rest of the building is measured with SLAM LS accuracy. Based on the results of the case study, the introduced method can locate non-overlapping TLS point clouds with an accuracy of 18–51 mm using target sphere comparison.