Miracle-dz/UniFAR-FTUs huggingface dataset:
Group threat and voter turnout: Evidence from a refugee placement program

We study the impact of refugee inflows on voter turnout in Sweden in a period when shifting immigration patterns made the previously homogeneous country increasingly heterogeneous. Analyzing individual ‐ level panel data and exploiting a national refugee placement program to obtain plausibly exogenous variation in immigration, we find that refugee inflows significantly raise the probability of voter turnout. Balancing tests on initial turnout as well as placebo tests regressing changes in turnout on future refugee inflows support the causal interpretation of our findings. The results are consistent with group ‐ threat theory, which predicts that increased out ‐ group presence spurs political mobilization among in ‐ group members.
A Framework to Model and Analyze Electric Grid Cascading Failures to Identify Critical Nodes

The goal of this work is to identify critical nodes in a bulk electric system for grid resilience to a specified threat. We present a cascading outage framework and an analytical framework for identifying electric grid failure trends and critical components. We create thousands of threat scenarios to be modeled in a dynamic electric grid cascading outage model. Each threat scenario determines which major grid components are removed from service due to the threat. The cascading outage model runs transient dynamic simulations which allow for secondary transients to affect the relays/protection leading to cascading outages. The results of the cascading model feed an analytics model to identify trends and critical components whose failure is more likely to cause serious systemic effects. Information on which system components are most critical to electric grid resilience can significantly assist grid planning and reduce grid consequences of large-scale disasters.
K Nearest Neighbor and Flexible Neural Tree Based IDS in Mobile Ad- hoc Network

Mobile ad-hoc networks (MANETs) have brought about a lot of importance to recent researches because of its popularity and developing benefit. However, they seem to be defenseless against the various security threats that decrease their efficiency in comparison to other networks, by all accounts. Intrusion Detection Systems (IDS) creates a second line of safeguard against the various vulnerabilities to MANETs, as they scan the network for malicious activities performed by attackers. Because of the distributed characteristic of MANET, traditional cryptography mechanisms cannot shield MANETs entirely as far as novel attacks and vulnerabilities are concerned. Hence by applying machine learning strategies for IDS, these difficulties can be resolved. In this paper, numerous research works based on ML techniques over four types of attacks (DOS, Probe, U2R and R2L) have been reviewed and their performance is observed. From the above reviewed work, it can be inferred that KNN and FNT algorithm would perform better than the rest. After applying KNN and FNT in the proposed intrusion detection MANET model, KNN performed better than FNT. Accuracy of KNN for detection of DOS, Probe, U2R and R2L was 99.24%, 99.13, 98.89% and 98.42% respectively, whereas accuracy of FNT for detection of the same attacks was 98.35%, 98.07%, 97.84% and 98.01% respectively. So the detection accuracy of KNN is better than FNT. For KNN, the TP, FP, FN and precision value is 0.931, 0.015, 0.063 and 0.983 respectively , whereas for FNT, the TP, FP, FN and precision value is 0.815, 0.153, 0.274 and 0.826 respectively. From these above results, it is clear that KNN is better than FNT as the True Positive value and Precision is higher while the False Positive value and False Negative value is lower for KNN.
Investigating 5th-grade students' opinions and attitudes towards using an Arabic pedagogical agent

Due to covid-19, educational institutions all around the world resorted to e-learning regardless of the readiness of e-learning tools used or the students' attitude and readiness towards using them. In this paper, we developed a pedagogical conversational agent, which is an e-learning tool and considered as a solution for the lack of social interaction that e-learning suffers from, to know the attitude of 5th-grade students in an elementary school in Tunisia towards learning « Fractions» with the help of a pedagogical conversational agent as learning support. The attitude towards using the conversational agent was favorable with the suggestions of providing a more natural interaction that focuses on making the conversational agent have more personality and emotionally intelligent and making the conversations longer with including multi-media elements.
Unvoicing a field's expertise: A two-pronged citation and language analysis

The field of Developmental Education (DE) draws from a distinct and multidisciplinary body of research and scholarship to facilitate students' transitions to college and to support their postsecondary academic success. However, highly cited scholars and policymakers external to the field perpetuate negative perceptions of DE, arguing for reform or elimination without inclusion of field experts. Through a combination of Citation Content Analysis and Transitivity Analysis, this study examined citation trends and verb transitivity to uncover the voices privileged as experts within an influential publication by the Community College Research Center and aimed to uncover how the authors (re)presented the DE field, literature, scholarship, its members, and its students. Findings revealed a single citation of an in-field DE scholar and repeated erasure of developmental educators and DE scholarship through passivation of DE professionals as social actors. Implications are discussed for the importance of including inside-field experts’ voices in discussions about their field as a necessary component of developing a critical praxis of integrating scholarship and practice in support of students.
Monetary policy during the wartime: How to ensure macroeconomic stability

In peacetime, the main contribution of monetary policy to macroeconomic stability is to ensure the stability of price dynamics through regulating money supply. During the war, the market principles of the economy and the formation of its prices are violated, monetary transmission mechanisms do not work adequately, the role of the state in ensuring the proper functioning of commodity-money relations increases. Therefore, the purpose of this paper is to generalize approaches to the formulation of monetary policy during the wartime and to substantiate the relevant recommendations for contemporary situation in Ukraine. Theoretical sources, advisory and research materials of international organizations and national macroeconomic regulators, statistical databases were used to achieve the stated aim. The generally accepted principle of modifying monetary policy during the wartime is the use by the central bank of instruments that expand the money supply – purchasing assets on the open market, outright purchase of government bonds on the primary market, special targeted refinancing of credit institutions. The paper suggest the design of the monetary regime of the war period, which provides for the modification of such aspects of the central bank performance as the target of monetary policy, the composition of interest rates on basic operations of the central bank, foreign exchange market regulation and regulation of capital flows, the relationships of the central bank and fiscal authority. It is argued that in the conditions of military economy, the main contribution of monetary policy to macroeconomic stability is achieved through ensuring the stable functioning of the government borrowing market and controlling capital flows.
Ca2+ Signalling and Hypoxia/Acidic Tumour Microenvironment Interplay in Tumour Progression

Solid tumours are characterised by an altered microenvironment (TME) from the physicochemical point of view, displaying a highly hypoxic and acidic interstitial fluid. Hypoxia results from uncontrolled proliferation, aberrant vascularization and altered cancer cell metabolism. Tumour cellular apparatus adapts to hypoxia by altering its metabolism and behaviour, increasing its migratory and metastatic abilities by the acquisition of a mesenchymal phenotype and selection of aggressive tumour cell clones. Extracellular acidosis is considered a cancer hallmark, acting as a driver of cancer aggressiveness by promoting tumour metastasis and chemoresistance via the selection of more aggressive cell phenotypes, although the underlying mechanism is still not clear. In this context, Ca2+ channels represent good target candidates due to their ability to integrate signals from the TME. Ca2+ channels are pH and hypoxia sensors and alterations in Ca2+ homeostasis in cancer progression and vascularization have been extensively reported. In the present review, we present an up-to-date and critical view on Ca2+ permeable ion channels, with a major focus on TRPs, SOCs and PIEZO channels, which are modulated by tumour hypoxia and acidosis, as well as the consequent role of the altered Ca2+ signals on cancer progression hallmarks. We believe that a deeper comprehension of the Ca2+ signalling and acidic pH/hypoxia interplay will break new ground for the discovery of alternative and attractive therapeutic targets.
PENGUNGKAPAN SUKARELA OLEH PERUSAHAAN KELUARGA DI INDONESIA: SEBELUM DAN SETELAH PENGADOPSIAN IFRS

This research is a research development of Darmadi Sodikin (2013). This study confirms differences in voluntary disclosure by family ownership before the adoption of IFRS (in 2008) and after the adoption of IFRS (in 2013). This study also provides additional analysis to determine the effect of IFRS adoption and voluntary disclosure on family companies in Indonesia. This study uses 23 samples of family companies in Indonesia. The dependent variable (voluntary disclosure) is accepted by the 49 disclosure index checklist method (Komsiah, 2005; Darmadi Sodikin, 2013). The independent variable (adoption of IFRS) is supported by dummy variables. This study provides evidence of the average voluntary disclosure presented by family companies after the adoption of IFRS more than before the adoption of IFRS. Disclosure items are presented more than disclosing more family company information. In addition, this study also proves that the adoption of IFRS has a positive impact on voluntary disclosures presented by family companies. Keywords: Voluntary Disclosure, Family Companies, Adoption of IFRS
Evaluation of the mycoflora and aflatoxins from the pre-harvest to storage of peanuts: a case study doi: 10.4025/actasciagron.v36i1.16972

Aflatoxins are carcinogens produced by Aspergillus flavus , A. parasiticus and A. nomius . In the present study, peanut samples were collected at different phenological stages of the plant during the 2007/2008 and 2008/2009 seasons and from stored peanuts harvested in 2007/2008. The mycoflora and aflatoxins in the peanuts were evaluated. The results showed the presence of Fusarium spp. , Macrophomina spp. , Trichoderma spp. , Aspergillus spp. and Cladosporium spp. during the period of peanut maturation (39.8, 17.9, 8.2, 2.7 and 1.7%, respectively) and storage (49.8, 27.8, 12.5, 8.8 and 1.0%, respectively). However, aflatoxins were not detected in the samples. Of the 25 Aspergillus spp. isolates, 24 (96%) were producers of aflatoxin B 1 (96%), 10 (40%) of aflatoxin B 2 , 17 (68%) of aflatoxin G 1 , and 10 (40%) of aflatoxin G 2 . The isolation of Aspergillus spp. during storage was not influenced by the temperature, relative humidity or water activity (p > 0.05). The detection of aflatoxin-producing Aspergillus spp. in the samples analysed at different phenological stages, aerial gynophore, pod filling (seeds), mature fruits (pod), and dry fruits (harvest), indicates the importance of good agricultural practices from the cultivation to storage of peanuts in southern Brazil.
The Untold Impact of Learning Approaches on Software Fault-Proneness Predictions

Software fault-proneness prediction is an active research area, with many factors affecting prediction performance extensively studied. However, the impact of the learning approach (i.e., the specifics of the data used for training and the target variable being predicted) on the prediction performance has not been studied, except for one initial work. This paper explores the effects of two learning approaches, useAllPredictAll and usePrePredictPost , on the performance of software fault-proneness prediction, both within-release and across-releases. The empirical results are based on data extracted from 64 releases of twelve open-source projects. Results show that the learning approach has a substantial, and typically unacknowledged, impact on the classification performance. Specifically, using useAllPredictAll leadstosignificantlybetterperformancethanusing usePrePredictPost learning approach, both within-release and across-releases. Furthermore, this paper uncovers that, for within-release predictions, this difference in classification performance is due to different levels of class imbalance in the two learning approaches. When class imbalance is addressed, the performance difference between the learning approaches is eliminated. Our findings imply that the learning approach should always be explicitly identified and its impact on software fault-proneness prediction considered. The paper concludes with a discussion of potential consequences of our results for both research and practice.
Alteration in Asymmetry of White Matter Network of Parkinson's Disease

Parkinson's disease (PD) is manifest clinically by an asymmetrical presentation of motor dysfunction. A large number of previous neuroimaging research studies have stated the alteration in the hemispheric asymmetry of morphological features in PD disease. Diffusion Magnetic Resonance Imaging (MRI), which is noninvasive, has been widely used to quantify the white matter network in the human brain of both healthy subjects and patients. Besides, graph theory analysis is widely used to quantify the topological architecture of the human brain network. Lately, researchers have discovered that the topological architecture of the white matter network significantly differs in PD compared with healthy controls (HC). Nevertheless, the asymmetry of the topological architecture of the white matter network for PD patients remains unclear. To clarify this, the diffusion-weighted images and tractography technique were used to reconstruct the hemispherical white matter networks for 22 bilateral PD patients and 18 HC subjects. Network-based statistical analysis and graph theory analysis approaches were employed to estimate the asymmetry at both the connectivity level and the hemispheric topological level for PD patients. We found that the PD group showed atypically right-higher-than-left asymmetry in hemispheric brain global and local efficiencies. The detected right-higher-than-left asymmetry was driven by the atypically topological changes in the left hemispheric brain in the PD group. Findings from these studies might provide new insights into the asymmetric features of hemispheric disconnectivity and emphasize that the topological asymmetry of the hemispheric brain could be used as a biomarker to identify PD individuals.
In-Situ and High-Throughput Determination of Antibiotics in Pork Using Electro-Filter Paper Spray Ionization Tandem Miniature Ion Trap Mass Spectrometry

Abstract In the recent years, the issue of antibiotic residues in food has become an important topic. Most current methods of detecting antibiotics are time-consuming, laborious, and cannot meet the requirements of field testing. In this study, electro-filter paper spray ionization was coupled with a portable linear ion trap mass spectrometer for the rapid determination of four antibiotics (norfloxacin, tetracycline, oxytetracycline, and chlortetracycline) in pork and milk samples. The experimental setup is not only structurally simple but enables analysis of both solid and liquid samples. The samples require only a simple pretreatment and the complete analysis of a sample requires less than 1 min. The linear range for the determination of the four antibiotic standards was from 1 to 100 mg/L (R 2≥0.960). The limits of detection (LOD) for the analytes were 0.100 mg/L, with recoveries from 72.4% to 11%. The relative standard deviations (RSDs) were between 2.70% and 16.5%. This study expands the capability for rapid field testing of antibiotics in meat using portable mass spectrometers, which is important for food safety, environmental protection, and criminal investigations.
Classification-assisted Deep Sparse Image Recognition

This paper proposes a classification-assisted deep sparse network (CDSRC) model to achieve the purpose of image classification. The proposed algorithm consists of four parts: Encoder, Self-representer, Decoder and Classifier. The Encoder part can extract the high-level feature map of the input image, and Self-representer can establish the representational relationship between the test set and the training set image, so as to reconstruct the image of the test set. The Decoder can restore the reconstructed sample to the original image in the form of deconvolution, which is used to supervise the Self-representer. Next, the Encoder can effectively extract the feature map of the original image and the reconstruction of the test set sample. In addition, in order to increase the robustness of image recognition, a Classifier part is added after the Encoder. The Classifier is mainly used to classify training samples while extracting features in the training phase. This will increase the feature similarity of images of the same category, increase the difference of image features of different categories, and reduce the noise interference formed by individual samples. After the algorithm training is completed, the test sample is imported into the Encoder to extract the feature map, the feature map is combined with the sparse matrix of the Self-representer part, and then the test sample category is predicted. Experiments show that the algorithm(CDSRC) in this paper has better results than the SRC-related algorithms that have been proposed.
A New Input Grouping and Sharing Method to Design Low Complexity FFT Implementation

FFT is a commonly applied algorithm in digital signal processing and communications. In this brief, a new low power and low complexity FFT architecture design is proposed. An input grouping method is used to reduce the multiplications of the inputs and FFT twiddle factor coefficients. In addition, a new input partial sum sharing scheme is proposed to reuse the hardware resources to further reduce the adder cost. Logic synthesis results in ASIC show that the proposed 16-point FFT architecture can save area and power cost by at least 19.1% and 19.0% respectively compared with the recently published designs. Similarly, the proposed 32-point FFT architecture can reduce both power and delay by at least 6.91% and 5.35%.
Secure Data Storage and Sharing Techniques for Data Protection in Cloud Environments: A Systematic Review, Analysis, and Future Directions

A large number of researchers, academia, government sectors, and business enterprises are adopting the cloud environment due to the least upfront capital investment, maximum scalability, and several other features of it. Despite the multiple features supported by the cloud environment, it also suffers several challenges. Data protection is the primary concern in the area of information security and cloud computing. Numerous solutions have been developed to address this challenge. However, there is a lack of comprehensive analysis among the existing solutions and a necessity emerges to explore, classify, and analyze the significant existing work for investigating the applicability of these solutions to meet the requirements. This article presents a comparative and systematic study, and in-depth analysis of leading techniques for secure sharing and protecting the data in the cloud environment. The discussion about each dedicated technique includes: functioning for protecting the data, potential and revolutionary solutions in the domain, the core and adequate information including workflow, achievements, scope, gaps, future directions, etc. about each solution. Furthermore, a comprehensive and comparative analysis of the discussed techniques is presented. Afterward, the applicability of the techniques is discussed as per the requirements and the research gaps along with future directions are reported in the field. The authors believe that this article’s contribution will operate as a catalyst for the potential researchers to carry out the research work in the area.
Impact of Gas System Modelling on Uncertainty Management of Gas-Electric Systems

Gas fired power plants can provide flexible generation that can help to balance intermittent renewable energy sources as the world moves towards using a more sustainable energy infrastructure. The increasing use of gas-fired power plants for balancing renewable generation creates an interdependence between the electricity and gas networks, where uncertainty can propagate between the two systems, causing insecure operating conditions and disruptions in both gas and electricity supply. Unfortunately, these interactions are computationally challenging to analyze due to the relatively longer transient dynamics of the gas network models. Recent work have provided efficient methods for solving problem as a two stage stochastic optimization problem. In this paper we investigate the importance of using a dynamic gas network model, as compared to a simplified steady-state network model which ignores gas system transients. We find that using the dynamic model allows the networks to better manage the uncertainty of the renewable energy sources and results in lower costs and reduced load shedding.
The 4W Framework of the Online Social Community Model for Satisfying the Unmet Needs of Older Adults

—Human's cherished and respectable desires could be fulfilled by social integration through interaction with their friends and families. These kinds of interactions are critical for the elderly, particularly for someone who has retired. Online social communities could assist them and offer a beneficial impact on the elderly. However, because the elderly people are hesitant to use new technology, researchers have attempted to integrate specially built social networking applications into simple user-interface gadgets for the elderly through the context aware systems. A proper understanding amongst the aged and the supporting community people is needed for optimal execution of the platform. The study presents a 4W framework (Who, What, Where, When) to effectively comprehend and portray the online social interaction community model's application in assisting the elderly in satisfying their unmet needs, as well as to improve the system's efficiency in addressing the elderly's unfulfilled demands. It is essential to discover what the users are keen on and provide a chance for the community group to take good decisions by utilizing the insights gained from these events.