Miracle-dz/UniFAR-FTUs huggingface dataset:
Exploring users' adoption intentions in the evolution of artificial intelligence mobile banking applications: the intelligent and anthropomorphic perspectives

PurposeThe development of mobile technology has changed the traditional financial industry and banking sector. While traditional banks have adopted artificial intelligence (AI) techniques to deepen the development of mobile banking applications (apps), the current literature lacks research on the use of AI-based constructs to explore users' mobile banking app adoption intentions. To fill this gap, based on stimulus-organism-response (SOR) theory, two AI feature constructs as stimuli are considered, namely, perceived intelligence and anthropomorphism. This study then develops a research model to investigate how intelligence and anthropomorphism affect task-technology fit (TTF), perceived cost, perceived risk and trust (organism), which in turn influence users' AI mobile banking app adoption (response). Design/methodology/approachThis study used a convenience nonprobability sampling approach; a total of 451 responses were collected to examine the model. The partial least squares technique was utilized for data analysis.FindingsThe results show that intelligence and anthropomorphism increase users' willingness to adopt mobile banking apps through TTF and trust. However, higher levels of anthropomorphism enhance users' perceived cost. In addition, both intelligence and anthropomorphism have insignificant effects on perceived risk. The results provide theoretical contributions for AI-based mobile banking app adoption and offer practical guidance for bank planning to use AI to retain users.Originality/valueBased on SOR theory, this study reveals that as features, AI-enabled intelligence and anthropomorphism help us further understand users' perceptions regarding cost, risk, TTF and trust in the context of AI-enabled app adoption intentions.
Qualitative Perceptions of an Anticipated Fresh Food Prescription Program.

Objective Food insecurity (FI) is a growing public health problem. Produce prescriptions are known to improve healthy eating and decrease FI; however, few studies have incorporated community voice prior to its implementation. In this study, we aimed to elicit perspectives of individuals at risk for FI and the potential impact of a fresh food prescription (FFRx) program. Methods We conducted this qualitative descriptive study through an academic medical center in collaboration with community partners. We conducted focus groups involving Latinx (N = 16) and African-American (N = 8) adults in community settings. Data were interpreted using an inductive thematic analysis. Results Three overarching themes emerged: (1) fresh food accessibility was limited by cost, household size, and transportation but enhanced by food pantries, budgeting, and education; (2) cooking behaviors were curbed by time constraints and unfamiliarity but propagated by passion, traditions, and communal practices; and (3) health and wellness deterrents included unhealthy diets driven by cultural and familial norms; however, weight loss and awareness of comorbidities were positive motivators. Participants shared their preference for local produce and cooking classes as components of a FFRx program while raising concerns about low participation due to the stigma of receiving aid. Conclusions Our findings illuminated interest in engaging in a FFRx program and learning ways to prepare healthy foods. A program distributing fresh produce and healthy lifestyle education could close gaps identified in African-American and Latinx communities at risk for FI.
Harmonic detection based on independent component analysis

For a long time,harmonic control has been an important part of power quality control and the accurate detection of harmonic is also the basic condition for the correct analysis.This paper views the problem of harmonic detection as blind source separation(BSS). The independent component analysis(ICA) algorithm is applied in this paper,which is widely used in the filed of BSS.In order to apply ICA algorithm correctly,it is necessary to construct proper number of virtue observed channels.The simulation validates that harmonic can be separated effectively with this method in the circumstances of low demand for the real time.
Flexible Learning of Sparse Neural Networks via Constrained L0 Regularizations

We propose to approach the problem of learning L 0 -sparse networks using a constrained formulation of the optimization problem. This is in contrast to commonly used penalized approaches, which combine the regularization terms additively with the (surrogate) empirical risk. Our experiments demonstrate that we can obtain approximate solutions to the constrained optimization problem with comparable performance to state-of-the art methods for L 0 -sparse training. Finally, we discuss how this constrained approach provides greater (hyper-)parameter interpretability and accountability from a practitioner’s point of view
IAES International Journal of Artificial Intelligence (IJ-AI)

Received Jun 26, 2021 Revised Dec 23, 2021 Accepted Jan 8, 2022 Sound event detection (SED) assists in the detainment of intruders. In recent decades, several SED methods such as support vector machine (SVM), K-Means clustering, principal component analysis, and convolution neural network (CNN) on urban sound have been developed. Advanced work on SED in a rare sound event is challenging because it has limited exploration, especially for surveillance in a forest environment. This research provides an alternative method that uses informative features of sound event data from a natural forest environment and evaluates the CNN capabilities of the detection performances. A hybrid CNN and random forest (RF) are proposed to utilize a distinctive sound pattern. The feature extraction involves mel log energies. The detection processes include refinement parameters and postprocessing threshold determination to reduce false alarms rate. The proposed CNN-RF and custom CNN-RF models have been validated with three types of sound events. The results of the suggested approach have been compared with well-regarded sound event algorithms. The experiment results demonstrate that the CNN-RF assesses the superiority with remarkable improvement in performance, up to a 0.82 F1 score with a minimum false alarms rate at 10%. The performance shows a functional advantage over previous methods.
The role of left angular gyrus in the representation of linguistic composition relations

Language comprehension is compositional: individual words are combined structurally to form larger meaning representations. The neural basis for compositionality is at the center of a growing body of recent research. Previous work has largely used univariate analysis to investigate the question, a technique that could potentially lead to the loss of fined‐grained information due to the procedure of averaging over neural responses. In a functional magnetic resonance imaging experiment, the present study examined different types of composition relations in Chinese phrases, using a 1‐back composition relation probe (CRP) task and a 1‐back word probe (WP) task. We first analyzed the data using the multivariate representation similarity analysis, which better captures the fine‐grained representational differences in the stimuli. The results showed that the left angular gyrus (AG) represents different types of composition relations in the CRP task, but no brain areas were identified in the WP task. We also conducted a traditional univariate analysis and found greater activations in the bilateral inferior frontal gyrus in the CRP task relative to the WP task. We discuss the methodological and theoretical implications of our findings in the context of the larger language neural network identified in previous studies. Our findings highlight the role of left AG in representing and distinguishing fine‐grained linguistic composition relations.
The Urban-Rural Divide and Depression During COVID-19 Pandemic Among Filipino Emerging Adults

Mental health inequalities run along geographic and demographic lines. As the Philippines enters its second year of the coronavirus crisis, the country still struggles to manage the COVID-19 pandemic. The health and societal impacts of the pandemic has also increased mental health challenges among Filipinos, including depression and other affective disorders (Tee et al., 2020). While there is equivocal evidence demonstrating mental health disparities existing between urban and rural populations, most of the studies were conducted outside the Philippines (Solmi et al., 2017). There is reason to suspect that these mental health gaps could be accentuated by the pandemic and the differences between urban and rural areas in terms of access to basic needs, available health resources and COVID responses. Hence, I conducted this study to examine the association of urban/rural living and depression among Filipino emerging adults, whose psycho-emotional development has been interrupted by COVID-19 (Cleofas, 2021). This cross-sectional study draws from an online survey from a larger research project on social media health conducted during the third quarter of 2021, wherein 1087 Filipino emerging adults (18–29 years old) participated. The independent variable, area of residence, was measured by inquiring whether they lived in an urban (n1⁄4 506) or rural (n1⁄4 581) area. The dependent variable, depression, was determined using the Warwick-Edinburgh Mental Wellbeing Scale (WEMWBS) which is a 14-item, 5-point likert scale that can be used as a screening tool for depression (Bianco, 2012). Respondents were categorized as “with probable depression” (WEMWBS 1⁄4 14–44; n1⁄4 558) and “no depression” (WEMWBS 1⁄4 45–70; n1⁄4 529). Age (Mean 1⁄4 20.081 ± 1.728, Mode 1⁄4 21 years old) and (females 1⁄4 661, 60.810%) were also measured and included as covariates for the regression test. More than half (51.334%) of emerging adult respondents of the study reported scores within the range of probable depression. Logistic regression results suggest that when age and sex are kept constant, emerging adult respondents living in rural areas have 1.421 times greater odds for depression than those who live in urban areas (B1⁄4 0.352, p1⁄4 0.004, 95%CI 1⁄4 0.111–0.592). Consistent with previous research in the Philippines (Tee et al., 2020), the findings of the present study indicate high prevalence of depression among Filipino emerging adults. This high rate of depressive symptoms in this sub-population can be attributed to unmet developmental milestones of emerging adulthood such as finishing college, entering the workforce and self-exploration (Cleofas, 2021). Moreover, results indicate that rural dwellers have a higher risk of acquiring depression compared to urban counterparts. The lack of health care facilities and human resources in rural areas could have contributed to COVID-19-related depression. Likewise, mental health services are also highly concentrated in urban areas of the
Complexity analysis of human-machine interaction based on principles of axiomatic design.

ion levels are typically represented in the form of a hierarchy, which is defined as an ordered structure (Timpf, 1999). Different types of hierarchical structures have been used frequently to model complex systems (Vicente, 1999). Cognitive systems engineering typically uses a combination of two hierarchical structures to representation work domains: a decomposition hierarchy and an abstraction hierarchy (Rasmussen et al., 1994) (see Figure A1.3). Figure A1.3. Cognitive systems engineering typically uses a combination of a wholepart hierarchy and a means-ends hierarchy to represent work domains (from Rasmussen et al., 1994). ATTENTION: The Singapore Copyright Act applies to the use of this document. Nanyang Technological University Library Complexity Analysis of Human-Machine Interaction based on Principles of Axiomatic Design 146 The decomposition hierarchy specifically refers to a structure that has a part-whole relationship between levels, and an abstraction hierarchy specifically refers to a structure that has a means-ends relationship between levels (Vicente, 1999). The meansends hierarchy displays a strong triadic characteristic; when one studies a phenomenon at a particular abstraction level, the mechanistic understanding comes from the next lower level, whereas the significance of that phenomenon can only be revealed at the next higher level (Wu, 1999). Therefore, each three adjacent levels in the abstraction hierarchy can be described as having a “why-what-how” relationship (see Figure A1.4). This characteristic of the means-ends hierarchy has been reported to support goaldirected problem solving (Vicente, 1999). Figure A1.4. The triadic characteristic of a means-ends hierarchy. In AD, between each pair of adjacent design domains, there exists a “what-how” relationship (Suh, 1990). Since this is a means-ends hierarchy, based on the above discussion, one can also say that between each triplet of interlinked design domains there is a “why-what-how” relationship (see Figure A1.5). What? Why?
Managing 5FU Cardiotoxicity in Colorectal Cancer Treatment

Abstract Fluorouracil (5FU) is the backbone chemotherapy agent in the treatment of colorectal cancer (CRC). Cardiotoxicity represents an uncommon but serious side effect of treatment with 5FU. Here, we review the current literature on 5FU-cardiotoxicity in the setting of CRC specifically, with a focus on data from the modern era of combination chemotherapy. Despite decades of study, there is little consensus on risk factors and biomarkers for 5FU-cardiotoxicity, nor how patients with CRC should be managed following a cardiotoxicity event. Given the elevated risk of recurrent cardiotoxicity on rechallenge, the use of alternative regimens that do not contain 5FU is a critical aspect of management. Data on the cardiotoxicity risk and efficacy of non-5FU regimens in CRC are therefore reviewed in detail.
Green Buildings: An Assessment of Life Cycle Cost

Modern building practices show little regard for energy efficiency, environmental or social impact of the built environment over their entire life cycle. Resources such as ground cover, forests, water and energy are depleted to give way to buildings. On an average, worldwide building construction consumes approximately 17% fresh water, 25% wood stock and 40% material and energy produce. This indiscriminate use of natural resources puts pressure on the ecosystem. During building construction, vast quantities of waste material is created, and during building operations, large amount of energy is consumed, contributing extensively to environmental pollution. This paper presents the various aspects and benefits of green buildings along with building environment assessment tools and rating systems followed worldwide. Life cycle cost analysis is performed to prove the economics of green buildings vis-a-vis ordinary buildings.
Development and validation of parental alert and reminder assistant: a prenatal expert system prototype;

The Prenatal Alert and Reminder Assistant (PARA) was developed at the LDS Hospital at Salt Lake City, Utah, as part of the HELP hospital information system. PARA is the third expert system prototype to computerize the prenatal record and provide decision support to the obstetric team with patient specific alerts and reminders. Its development and validation are presented, as well as the studies into (1) the control of error propagation and (2) data modeling of pregnancy outcome. The experimental knowledge base with 25 rules was validated using a retrospective study of 50 prenatal charts (25 normal and 25 hypertensive pregnancies). PARA's knowledge base was validated with a mean score of 98.9% correct as compared to 97.8% for the obstetric team (p. = 0.0216). The difference of 1.1 % represents a 50% reduction in the error rate (2.2%) as compared to the obstetric team. Propagation of erroneous decision-support messages in a large clinical database was controlled through the use of Truth Maintenance techniques. These techniques included tagging messages as "'active" and "inactive," limiting the reasoning to active messages, and requiring each Medical Logic Module (MLM) in PARA's knowledge base to have rules that would assert the message was false, unless it could be asserted true. The patient charge for a labor-and-delivery admission was used as a crude index of pregnancy outcomes. A model of the relationships of certain pregnancy complications to this pregnancy outcome variable was developed. The regression model's 17 independent variables had a strong, positive correlation (R = 0.70) with the dependent variable, log hospital charges. The association was statistically significant (12. 0.0001). Fortyeight percent of the variance in the hospital charges were explained by the 17 variables (adjusted multiple R-square = 0.482).
Compact microstrip band-pass filter for EMI reduction

A compact microstrip band-pass filter design using parallel coupled lines is presented in this work. The parallel coupled resonator's dimensions are calculated theoretically and the equivalent circuit is analyzed. The frequency response is then approximated using CST software. The filter is integrated with Defected Ground Structure (DGS) to alter current distribution over the ground plane and hence changing the resonance properties of the filter's element. The proposed structure offers a simple and compact design while exhibiting wide stop-band characteristics in comparison with conventional coupled microstrip line filter design. The fabrication and measurements of 2GHz prototype DGS integrated parallelcoupled bandpass filter are presented. The measured results agree well with the simulations and calculations data. The introduction of DGS suppresses the second, third and fourth harmonic responses enhancing the stop band criteria. In addition, the DGS causes resonance frequency shift on some resonator elements, miniaturizing the length and make the overall size 20.8% more compact compared to the conventional ones.
Pantograph/catenary contact force control

In this paper, a new continuum-based pantograph/catenary model based on the absolute nodal coordinate formulation (ANCF) is proposed and used to develop an effective method to control the contact force which arises from the pantograph/catenary interaction. In the proposed new model, only one ANCF gradient vector is used in the formulation of the pantograph/catenary contact conditions, thereby allowing for using the proposed approach for both fully parameterized and gradient deficient ANCF finite elements. A three-dimensional multibody system (MBS) model of a pantograph mounted on a train is developed using a nonlinear augmented MBS formulation. In order to take into account the catenary large deformation, ANCF finite elements are used. The contact between the pantograph and the catenary system is ensured using a sliding joint constraint whereas the contact between the rail vehicle wheels and the train track is modelled using an elastic contact formulation. In addition to the use of the new MBS approach to model the pantograph/catenary interaction, the contact force between the pantograph and the catenary is computed using a simpler lumped parameter model which describes the pan-head and the plunger subsystem dynamics. In order to reduce the standard deviation of the contact force without affecting its mean value, a control actuator is used between the pan-head and the plunger. To this end, three types of control laws for the control action are designed to improve the contact quality both in the transient phase and in the steady state phase of the pantograph/catenary interaction. The first control law proposed features a feedback structure whereas the second and the third control strategies employ a feedback plus feed-forward architecture. In order to demonstrate the effectiveness of the proposed method, the results of a set of numerical simulations with and without the controllers are presented.Copyright © 2015 by ASME
RBF and ARIMA Combined for Time Series Forecasting

Permasalahan yang ada dalam dunia nyata seringkali adalah masalah yang kompleks di mana satu metode saja mungkin tidak mampu mengatasi masalah tersebut dengan baik. Dalam penelitian ini, dua buah metode, yaitu ARIMA dan RBF, akan digabungkan. Alasan penggabungan kedua model ini adalah karena adanya asumsi bahwa model tunggal tidak dapat secara total mengidentifikasi semua karakteristik dari time series. Pada penelitian ini dilakukan peramalan terhadap data Indeks Harga Perdagangan Besar (IHPB), data tersebut berada pada rentang tahun 2006 hingga beberapa bulan di tahun 2012, serta memiliki 6 variabel. Hasil peramalan metode ARIMA-RBF dibandingkan dengan metode ARIMA dan metode RBF secara individual. Hasil analisa menunjukkan bahwa dengan metode penggabungan ARIMA dan RBF, model yang diberikan memiliki hasil yang lebih baik dibandingkan dengan model ARIMA saja atau model RBF saja. Hal ini terlihat dalam MAPE, RMSE, dan waktu komputasi dari semua variabel yang ada pada data uji coba tersebut.
Diverse experiences and approaches to tele neuropsychology: Commentary and reflections over the past year of COVID-19

Abstract Objectives: In response to the coronavirus disease 2019 (COVID-19) pandemic, neuropsychologists rapidly adopted teleneuropsychology (TeleNP) services to ensure continued clinical care. Prior to COVID-19, TeleNP was not widely used nor was it included in the majority of traditional practice or training models across graduate, internship, and postdoctoral programs. Out of necessity, the pandemic was a catalyst that promoted greater adoption of TeleNP services. In response, neuropsychological guidelines for modified assessments were developed and further empirical studies have been published. Numerous surveys in response to service delivery changes during COVID-19 now exist, but what follows is a commentary based on neuropsychologists’ experiences with adapting clinical practice to TeleNP. Methods: Co-authors represent settings across academic medical centers, Veterans Affairs hospitals, and private practices that serve multiculturally diverse pediatric, adult, and geriatric populations in the United States. Results: The perspectives within this commentary aim to highlight the growth of TeleNP and highlight lessons learned from implementation across practice settings. Conclusions: Our goal is to help foster the development of further empirical studies through candid discussion of various TeleNP experiences and approaches. Through this reflective process, TeleNP presents both opportunities and challenges but it ultimately has potential to reduce healthcare disparities and enhance patient care.