Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling mobile payment acceptance among working-age users in the emerging market

Mobile payments are poised to become the predominant method of transaction as we progress towards a society with reduced reliance on cash. Despite the rapid growth of mobile payments, not everyone is willing to accept them due to their perception of a lack of security and confidence (Aboobucker & Bao, 2018). This study aims to examine the factors influencing the acceptance of mobile payment among working-age individuals in Malaysia. Data was collected through questionnaires, generating a 49 percent response rate. Descriptive, univariate, and multivariate analyses were performed to analyze the data. Based on the underpinning of the technology acceptance model (TAM) the study discovers that perceived ease of use, personal innovativeness, and behavioral intention have a significant positive relationship with working-age acceptance of mobile payment. In contrast, perceived risk and social influence demonstrated an insignificant effect on working-age acceptance of mobile payment. Understanding these factors may enable service providers to allocate resources effectively and create a sustainable mobile payment ecosystem in the country (Moghavvemi et al., 2021). The study further holds significance for both managers and policymakers in their efforts to harness the potential of mobile payment services, in line with Bank Negara’s objective in transitioning towards a cashless society.
Bidirectional Stackable Recurrent Generative Adversarial Imputation Network for Specific Emitter Missing Data Imputation

Specific emitter identification (SEI) uses the electromagnetic pulse signal sent by emitter to determine the emitter individual. In the actual complex electromagnetic environment, due to the interference of external signals and hardware failures, it is difficult to obtain sufficient and complete transmitter signal data. The missing data imputation methods are used to impute the emitter signal data. However, the existing imputation methods need to rely on the complete signal data to train the deep learning model, and the imputation error is large due to the long sequence characteristics of the signal. Therefore, a new specific emitter missing data imputation model is proposed, which is called bidirectional stackable recurrent generative adversarial imputation network (BiSRGAIN) including a generator and a discriminator. Specifically, the bidirectional stackable recurrent (BiSR) unit is designed to be used in generators and discriminators, which simplifies the traditional recurrent neural network (RNN) structure and improves parameter utilization and inference efficiency. The novel loss function can make the training of the model independent of the true value of the missing components, so the model can be trained in incomplete data. Extensive experiments are conducted on real-world dataset. The results show that the proposed model has lower errors under the scenario of high missing rate. In addition, the proposed model has higher parameter utilization and computational efficiency. Moreover, the completed signal data after imputation is used to identify specific emitters, and the results show that the data obtained by BiSRGAIN can achieve higher recognition accuracy.
DroidExaminer: An Android Malware Hybrid Detection System Based on Ensemble Learning

Android is an open-source mobile operating system, with more than 70% of the mobile market share, widely popular on various intelligent devices. At the same time, the number of new malicious applications keeps increasing every year. In this paper, we first discuss the advantages and disadvantages of various detection methods for malicious software. A single detection method can only cover specific types of malware. Therefore, we propose a system that combines static structural analysis and dynamic detection of malware. This system has dual detection capability, which consists of a client and a server. The client is a lightweight Android application that is used to obtain the relevant data information of the installation package. The server is responsible for static analysis of APK and dynamic running of monitoring logs to get the relevant feature information. Based on the feature information, the Bagging algorithm of ensemble learning is adopted, and the decision tree and random forest are combined to identify the malware accurately. We collected 4210 Android software samples, with malicious apps accounting for about 20% of the total. Cross-testing of malware detection on this sample set showed that DroidExaminer achieved approximately 96% accuracy in detecting malware. It can resist confusion and conversion techniques, and the test performance overhead is less. In addition, DroidExaminer can alert the user to the details of malware intrusion so that the user can prevent malware intrusion.
Evolutionary Multitasking Local Community Detection on Attributed Networks

Local community detection (LCD) aims to find a community containing a given starting node using local information, which becomes a hot research topic in the area of community detection. Most mainstream studies in this field only consider network topology information, neglecting node attribute information. Some studies consider both, but these methods are limited by inflexible predefined structures. When the local community topology structure is unclear or the node attributes are heterogeneous, the performance of these methods decreases. In this paper, in order to better balance topological and attribute information, we propose an evolutionary multitasking local community detection (EMLCD) framework to solve the problem of local community detection on attributed networks. In EMLCD, the LCD problem is formulated as a multitasking optimization problem with two tasks, that is, Top-Task detects local community with tight topological connections, and Att-Task detects local community with homogeneous node attributes. The advantage of using evolutionary multitasking to detect local community on attributed networks is that the correlation between these tasks can be used to enhance detection performance. A novel knowledge transfer strategy is proposed to facilitate positive transfer of knowledge between the two tasks by leveraging the complementarity of the topology information and attribute information. Finally, a solution selection strategy is proposed to guide the decision maker in selecting the ideal solution from the set of solutions generated by the two tasks. Experimental results show that the proposed framework is very effective in solving the problem of local community detection on attributed networks.
An analysis of the lagged relationship between anomalies of precipitation and soil moisture and its potential role in agricultural drought early warning

The Standardized Precipitation Index (SPI) is the most commonly used index for detecting and characterizing meteorological droughts, and it is also extensively used as a proxy variable for Soil Moisture Anomalies (SMA) for the purpose of monitoring agricultural drought in absence of long-term soil moisture observations. However, the potential capability of SPI to warn of the time-lagged soil water deficit - following the well-known “drought cascade” effect - is often overlooked in agricultural drought studies. In this research, a time-lagged correlation analysis is used to evaluate the relationship between the SMA dataset, generated as part of the Global Drought Observatory of the EUs Copernicus Emergency Management Service, and a set of SPIs derived from the ERA5 reanalysis produced by the European Centre for Medium-Range Weather Forecasts. The possibility to achieve an optimal agreement between SPI and SMA that also preserves the early warning skills of SPI is evaluated. The results suggest that if only the correlation between SPI and SMA is considered, the maximum agreement is usually obtained with a zero lead time (almost 80% of the cases), with SPI-3 representing the best option in about 40% of the grid cells at global scale. By also accounting for the benefits of a positive lead time, short accumulation periods tend to be favored, with SPI-1 being the optimal choice in about half of the cases, and 10 to 20 days lead time in more than 90% of the grid cells is achieved without any significant reduction in either correlation or skill in drought extreme detection.
Development and integration of a capacitive sensor in a pleated woven structure using Tango chirimen production methods

This case study is an example of ongoing collaborative initiatives between the Kyoto Prefectural Institute for Northern Industry, Kyoto, Japan working with the international network created by the annual Textiles Summer School (Cassim et al, 2017) 1 in Kyoto initiated and directed by Julia Cassim to explore new weave and product possibilities using the weave, yarn and technology of Tango chirimen crêpe, a traditional kimono fabric from Kyoto and the Japanese Jacquard loom. Written from a technical standpoint, it centres on the challenges of interpreting and realising the concept of the lead designer / educator to create a hybrid silk fabric with a pleated structure with integrated conductive fibre to allow the textile to be used as a capacitive sensor. Inspired by Issey Miyake’s ‘Pleats, Please! ’ exhibition, this fabric was linked to an electric circuit and became an interactive system that changes pre-recorded sound according to the expansion and contraction of the pleats. The collaboration was facilitated exclusively online with all parties sharing ideas and the results of parallel prototyping through hybrid methods, which we designate as an “ interpretative collaboration . ” Lectures, instructional and contextual videos created for the Textiles Summer School 2022 (TSS 2022) were provided while academic papers, translated communication, and drawings were used to address the details of the project and supported the dialogue between parties which was facilitated by Cassim and the TSS 2022 team. Apart from the woven samples, a zine-like publication will be available to students interested in understanding the principles of e-textiles capacitance.
Sodium Channel β Subunits—An Additional Element in Animal Tetrodotoxin Resistance?

Tetrodotoxin (TTX) is a neurotoxic molecule used by many animals for defense and/or predation, as well as an important biomedical tool. Its ubiquity as a defensive agent has led to repeated independent evolution of tetrodotoxin resistance in animals. TTX binds to voltage-gated sodium channels (VGSC) consisting of α and β subunits. Virtually all studies investigating the mechanisms behind TTX resistance have focused on the α subunit of voltage-gated sodium channels, where tetrodotoxin binds. However, the possibility of β subunits also contributing to tetrodotoxin resistance was never explored, though these subunits act in concert. In this study, we present preliminary evidence suggesting a potential role of β subunits in the evolution of TTX resistance. We gathered mRNA sequences for all β subunit types found in vertebrates across 12 species (three TTX-resistant and nine TTX-sensitive) and tested for signatures of positive selection with a maximum likelihood approach. Our results revealed several sites experiencing positive selection in TTX-resistant taxa, though none were exclusive to those species in subunit β1, which forms a complex with the main physiological target of TTX (VGSC Nav1.4). While experimental data validating these findings would be necessary, this work suggests that deeper investigation into β subunits as potential players in tetrodotoxin resistance may be worthwhile.
Analysis of Spatiotemporal Characteristics and Influencing Factors of Electric Vehicle Charging Based on Multisource Data

Amid the global shift towards sustainable development, this study addresses the burgeoning electric vehicle (EV) market and its infrastructure challenges, particularly the lag in public charging facility development. Focusing on Wuhan, it utilizes big data to analyze EV charging behavior’s spatiotemporal aspects and the urban environment’s influence on charging efficiency. Employing a random forest regression and multiscale geographically weighted regression (MGWR), the research elucidates the nonlinear interaction between urban infrastructure and charging station usage. Key findings include (1) a direct correlation between EV charging patterns and urban temporal factors, with notable price elasticity; (2) the predominant influence of commuting distance, supplemented by the availability of fast-charging options; and (3) a strategic proposal for increasing slow-charging facilities at key urban locations to balance operational costs and user demand. The study combines spatial analysis and charging behavior to recommend enhancements in public EV charging infrastructure layouts.
Mosaic versus Melting Pot?: Immigration and Ethnicity in Canada and the United States

envisioned as one between a Canadian mosaic, where ethnic groups have maintained their distinctiveness while functioning as part of the whole, and an American melting pot, where peoples of diverse origins have allegedly fused to make a new people. There is, of course, some truth to this distinction, but it oversimplifies both the American and the Canadian experiences. It ignores the fact that the mosaic approach has not always been the prevailing attitude toward immigrant adjustment in Canada, it obscures the fact that Canada and the United States have shared very similar immigration policies (particularly on the question of which ethnic, national, and racial groups were the most desirable), and it neglects the fact that, at least with regard to immigrant groups, the history of racism, nativism, and discrimination has been very similar in the two countries.
The method of optimal resource management of a distributed dynamic system based on the algorithm of zeroing neural networks

The method of optimal resource management of a distributed dynamic system based on the algorithm of zeroing neural networks offers a new approach to effective resource management in distributed dynamic systems using advanced machine learning technologies. Research questions concern determining optimal resource management strategies in a distributed dynamic system, as well as evaluating the effectiveness of the proposed method in various scenarios. The research methods include mathematical formalization of the problem, development of an algorithm for zeroing neural networks and conducting numerical experiments based on computer modeling. The results of the study demonstrate the high efficiency of the proposed method of optimal resource management in a distributed dynamic system. The conclusions emphasize the importance of using the algorithm of zeroing neural networks in the context of optimal resource management in distributed systems and the possibility of its application to solve practical problems in various fields, such as energy, production, transport and others.
Building Landform Classification Maps from DEM: Alluvial Fan Extraction Method Taewoon KIM, Byeongpyo JEONG, Osamu TAKIZAWA and Masafumi HOSOKAWA

Landform Classification maps are used in many areas such as disaster damage assessment, developmental conformity evaluation, and environmental study. For the high resource demand of the classification processes, such maps are not available in many regions including developing countries. Our ongoing research focuses on providing Landform Classification maps for those regions using DEM which is easily obtainable by remote sensing technology. In this paper, we propose an algorithm that extracts alluvial fans, an element of our landform classification, from readily available DEM supplied by SRTM. Then we tested the algorithm over the entire area of Japan, and found that the results were consistent with existing classification maps in terms of the number, size, and location of the alluvial fans extracted, thus confirming the applicability to the regions without classification maps.
Comparison of centrifugation and solid phase extraction (SPE) methods of sample preparations in determination of residues of drugs of abuse in urine

Detection of drugs of abuse in urine samples depends on their composition, absorption,distribution, physical and chemical properties, concentrations, elimination properties, and thetechniques employed. This study compared the performance of centrifugation and solid phaseextraction (SPE) methods in the determination of levels of drugs of abuse and their metabolites inurine samples collected from drug abusers. Analysis was performed using liquid chromatographytandemmass spectrometry (LC-MS-MS). Calibration was done using both internal and externalstandards. The recovery values were generally greater in centrifugation method than in SPEmethod, but the recoveries in both methods were within an acceptable range. Linearity values (R2)ranged from 0.9789 to 0.9944 with an average of 0.99. The LOD and LOQ values weresatisfactory. The concentrations of 6-monoacetylmorphine (6-MAM), morphine and cocaine wereup to 3.37, 4.21 and 0.03 μg/mL, respectively in samples prepared by centrifugation method, whilein samples prepared by SPE method were up to 6.67, 6.66 and 0.13 μg/mL, respectively. The meanconcentrations and detection frequencies of 6-MAM and morphine were higher in samplesprepared by centrifugation method than those of SPE method. Cocaine had the same meanconcentrations and detection frequencies in samples prepared by both methods. There were nosignificant differences in the concentrations of the drugs of abuse between the samples prepared bycentrifugation and SPE methods, although the levels of the drugs in the samples prepared bycentrifugation method were slightly greater than those prepared by SPE method.
Detection of Atrial Fibrillation From PPG Sensor Data Using Variational Mode Decomposition

Atrial fibrillation (AF) is one of the most common arrhythmia. AF can be a reason for strokes and damage to heart activities. The electrocardiogram (ECG) is commonly used for AF detection. It should be noted that the burst AF does not show any symptoms and is difficult to detect using ECG. As an alternative to ECG, photoplethysmography (PPG) is used for AF diagnosis, which is easy to record and suitable for long-term monitoring. This letter proposes a new classification framework for automated AF detection based on variational mode decomposition (VMD). The proposed framework has been studied on a publicly available dataset. The proposed VMD-based classification framework outperformed other state-of-the-art methods used for AF detection and achieved the highest accuracy of 99.08% using ten-fold cross-validation. The results of the proposed methodology have shown significant improvement, and the developed sensor-based system has proved suitability to real-time clinical practices.
Factor Structure of the Bulgarian Version of FOCUS on the Outcomes of Communication Under Six

The aim of this paper is to present the Factor structure of the Bulgarian version of FOCUS on the Outcomes of Communication Under Six. The instrument is based on the World Health Organisation’s (WHO) International Classification of Functioning, Disability and Health – Children and Youth (ICF-CY), and it aims at tracing the effect of therapy on the child’s communication and participation in the real world. Our research is focused on the factor structure and reliability of the Parent’s form. The participants formed two samples, the main one consisting of 272 (mostly preschool children), males and females. The age of the subjects varied between 2.17 and 7.25 years, with a mean age of M = 4.91 years and SD = 1.10. The latent structure of the questionnaire was examined in five phases – 1-test of the assumption of one-dimensionality; 2-exploratory factor analysis was applied to establish the factor model, based on data from an empirical study; 3-three possible factor models with a fixed number of factors were analyzed; 4-the factor models were compared through confirmatory factor analysis. The aim of the analyses was to select the most appropriate final model for the Bulgarian version of the questionnaire. The reliability analysis of the scales in the two-factor model was conducted through testing their internal consistency. The two-factor model of the FOCUS questionnaire in Bulgarian shows a good orientation towards two specific functions – ‘capacity for communication’, and ‘performance’ or ‘communicative performance’. Due to its stable measurements related to functioning, this tool could be effectively used to practically assess the effect of therapy on children with development disorders.
Correlation of prosodic boundary cues in German

This study investigates how durational and F0 cues expressing intonation phrase boundaries are correlated in German. Experimental speech data is analyzed as to a correlation between pause duration, pre-boundary lengthening, and F0 range. The results reveal a negative correlation between pause duration and pre-boundary lengthening. This observation is compatible with the assumption that the durational cues are engaged in a trading relationship to fill a timing slot associated with the prosodic boundary, as has been proposed for English and Swedish in prior studies. Furthermore, we observed a positive correlation between pause duration and F0 range, which shows that these cues are implemented in congruence at the same level of the prosodic hierarchy. Altogether, our findings suggest that prosodic boundary cues should be investigated in combination rather than in isolation.
Journal of Geophysical Research: Earth Surface A process-based model for aeolian sediment transport and spatiotemporal varying sediment availability

Aeolian sediment transport is inﬂuenced by a variety of bed surface properties, like moisture, shells, vegetation, and nonerodible elements. The bed surface properties inﬂuence aeolian sediment transport by changing the sediment transport capacity and/or the sediment availability. The eﬀect of bed surface properties on the transport capacity and sediment availability is typically incorporated through the velocity threshold. This approach appears to be a critical limitation in existing aeolian sediment transport models for simulation of real-world cases with spatiotemporal variations in bed surface properties. This paper presents a new model approach for multifraction aeolian sediment transport in which sediment availability is simulated rather than parameterized through the velocity threshold. The model can cope with arbitrary spatiotemporal conﬁgurations of bed surface properties that either limit or enhance the sediment availability or sediment transport capacity. The performance of the model is illustrated using four prototype cases, the simulation of two wind tunnel experiments from literature and a sensitivity analysis of newly introduced parameters.