Extending UTAUT2 in Mobile money adoption and actual use behavior: An empirical research in Vietnam during the Covid-19

The goal of this article is to examine the antecedents of behavioral intention toward mobile money, as well as the mediating role of trust on behavioral intention and financial inclusion in Vietnam during the COVID-19 period, using an expanded unified theory of technology acceptance and use (UTAUT2). The data were collected by an online self-administered questionnaire and analyzed using SmartPLS 3.3.3. To determine the exogenous constructs’ relevance and performance, a matrix analysis of importanceperformancewas used. The findings indicate that behavioral desire to use mobile money is primarily driven by awareness, structural assurance, habit, and performance expectation. The behavioral intention of mobile money will substantially influence its adoption, and trust will not act as a mediator between behavioral intention and financial inclusion. The extended UTAUT2 was used for the first time to analyze mobile money in Vietnam. Additionally, the new research provides a more comprehensive explanation for users’ financial inclusion than past research provided.

Spatio-spectral decomposition of complex eigenmodes in subwavelength nanostructures through transmission matrix analysis

Exploiting multiple near-field optical eigenmodes is an effective means of designing, engineering, and extending the functionalities of optical devices. However, the near-field optical eigenmodes of subwavelength plasmonic nanostructures are often highly multiplexed in both spectral and spatial distributions, making it extremely difficult to extract individual eigenmodes. We propose a novel mode analysis method that can resolve individual eigenmodes of subwavelength nanostructures, which are superimposed in conventional methods. A transmission matrix is constructed for each excitation wavelength by obtaining the near-field distributions for various incident angles, and through singular value decomposition, near-field profiles and energy spectra of individual eigenmodes are effectively resolved. By applying transmission matrix analysis to conventional electromagnetic simulations, we clearly resolved a set of orthogonal eigenmodes of single- and double-slot nanoantennas with a slot width of 20 nm. In addition, transmission matrix analysis leads to solutions that can selectively excite specific eigenmodes of nanostructures, allowing selective use of individual eigenmodes.

Assessment of Knowledge, Practices, and Challenges of Pharmaceuticals Inventory Control Among Pharmacy Professionals Working in Selected Public Health Facilities of West Arsi Zone, Oromia, Ethiopia

Background: Good pharmaceutical inventory control enables health facilities (HFs) to provide complete health care by ensuring the availability of safe, effective, and affordable pharmaceuticals and related supplies of the required quality, inadequate quantity, at the required place and at all times. It boosts patients’ trust in the HFs and motivates working staff. However, it needs well-trained and skilled professionals. The aim of the current study was, therefore, assessing knowledge, practice, and challenges of pharmacy professionals conducting inventory control in selected public health facilities of West Arsi Zone, Oromia regional state for the year 2016 to 2018. Method: A mixed-methods study design was used to assess pharmacy professionals’ knowledge, skills, and challenges in applying inventory management methods. A semi-structured questionnaire was implemented for quantitative, whereas an open-ended question was employed for key informants (KIs) to explore qualitative data. Result: Ninety percent of pharmacy professionals knew about VEN analysis concepts and 70% about ABC analysis. However, none of them had a concept of FSN and XYZ analysis. Among the respondents who knew the concept, 75% had gained knowledge through formal training and 10% of them learned from on-job training. When they asked about the methods of inventory control, 60% responded as they did not hear about it. Of those who said “Yes” on being asked to mention at least 1 method of it, 80% could not able to correctly mention the methods used in inventory control. However, 44%, 62.5%, and 75% of respondents had practiced ABC, VED, and ABC-VED matrix analysis respectively. The challenges that prevented these professionals from practicing pharmaceutical inventory control in their HFs were grouped into price-related, training-related, human resource-related, and managerial-related factors. Conclusion: Inventory control is the heart of the pharmaceutical supply system. Without its healthy action, HFs’ goal attainment will not be viable. Problems of sick pharmaceutical inventory control are directly related to a lack of knowledge and appreciation of it by the concerned bodies. The current finding revealed almost all pharmacy professionals included in the study had little knowledge about how to manage their inventories. Managers’ unwillingness to cooperate and facilitate necessary resources prevented the professionals from doing inventory control.

Attentively Learning Edge Distributions for Semantic Segmentation of Remote Sensing Imagery

Semantic segmentation has been a fundamental task in interpreting remote sensing imagery (RSI) for various downstream applications. Due to the high intra-class variants and inter-class similarities, inflexibly transferring natural image-specific networks to RSI is inadvisable. To enhance the distinguishability of learnt representations, attention modules were developed and applied to RSI, resulting in satisfactory improvements. However, these designs capture contextual information by equally handling all the pixels regardless of whether they around edges. Therefore, blurry boundaries are generated, rising high uncertainties in classifying vast adjacent pixels. Hereby, we propose an edge distribution attention module (EDA) to highlight the edge distributions of leant feature maps in a self-attentive fashion. In this module, we first formulate and model column-wise and row-wise edge attention maps based on covariance matrix analysis. Furthermore, a hybrid attention module (HAM) that emphasizes the edge distributions and position-wise dependencies is devised combing with non-local block. Consequently, a conceptually end-to-end neural network, termed as EDENet, is proposed to integrate HAM hierarchically for the detailed strengthening of multi-level representations. EDENet implicitly learns representative and discriminative features, providing available and reasonable cues for dense prediction. The experimental results evaluated on ISPRS Vaihingen, Potsdam and DeepGlobe datasets show the efficacy and superiority to the state-of-the-art methods on overall accuracy (OA) and mean intersection over union (mIoU). In addition, the ablation study further validates the effects of EDA.

Gray-Level Co-occurrence Matrix Analysis for the Detection of Discrete, Ethanol-Induced, Structural Changes in Cell Nuclei: An Artificial Intelligence Approach

Gray-level co-occurrence matrix (GLCM) analysis is a contemporary and innovative computational method for the assessment of textural patterns, applicable in almost any area of microscopy. The aim of our research was to perform the GLCM analysis of cell nuclei in Saccharomyces cerevisiae yeast cells after the induction of sublethal cell damage with ethyl alcohol, and to evaluate the performance of various machine learning (ML) models regarding their ability to separate damaged from intact cells. For each cell nucleus, five GLCM parameters were calculated: angular second moment, inverse difference moment, GLCM contrast, GLCM correlation, and textural variance. Based on the obtained GLCM data, we applied three ML approaches: neural network, random trees, and binomial logistic regression. Statistically significant differences in GLCM features were observed between treated and untreated cells. The multilayer perceptron neural network had the highest classification accuracy. The model also showed a relatively high level of sensitivity and specificity, as well as an excellent discriminatory power in the separation of treated from untreated cells. To the best of our knowledge, this is the first study to demonstrate that it is possible to create a relatively sensitive GLCM-based ML model for the detection of alcohol-induced damage in Saccharomyces cerevisiae cell nuclei.

Fibrosis Protein-Protein Interactions from Google Matrix Analysis of MetaCore Network

Protein–protein interactions is a longstanding challenge in cardiac remodeling processes and heart failure. Here, we use the MetaCore network and the Google matrix algorithms for prediction of protein–protein interactions dictating cardiac fibrosis, a primary cause of end-stage heart failure. The developed algorithms allow identification of interactions between key proteins and predict new actors orchestrating fibroblast activation linked to fibrosis in mouse and human tissues. These data hold great promise for uncovering new therapeutic targets to limit myocardial fibrosis.

WINGKO INDUSTRY SUSTAINABILITY IMPROVEMENT STRATEGY USING THE BUSINESS MODEL CANVAS (BMC) METHOD

Wingko is one of Indonesian Snack from Babat of Lamongan Regency which is produced by UMKM Klapa Muda. Compete makes the production unstable, even decreased. Right now UMKM Klapa Muda must develop rapidly, because there are already many competitors selling similar products. In this study using the Business Model Canvas Development that is assisted using the Internal factor Evaluation matrix, External Factor Evaluation, Internal-External, SWOT matrix analysis, and QSPM (Quantitative Strategic Planning Matriks) analysis. For the weighting stage, 5th priority strategies used for market penetration and product development by applying Business Model Canvas (BMC) concept to UMKM Klapa Muda Babat strategy, it is 1) Customer Relationship Increasing promote activities with a value of 2,401, 2) Customer Segments establish customer loyalty with a value of 2,386, 3) Value Proposition guarantees production quality and mproves products with a value of 2.24, 4) Channels has a relationship with the government with a value of 2,015, 5) Key Resources Development of machine and equipment technology with a value of 1,308.

A SEM-NCA Approach towards Social Networks Marketing: Evaluating Consumers’ Sustainable Purchase Behavior with the Moderating Role of Eco-Friendly Attitude

This paper aims to investigate how social network marketing affects consumers’ sustainable purchase behavior (CSPB) while considering the role of Eco-friendly attitude. The statistical population of the study included Iranian users of online social networks with at least one online purchasing experience. An online questionnaire was distributed on Instagram, Telegram, and WhatsApp platforms as the most popular networks in the country. By use of convenience sampling, commonly used in quantitative studies to overcome bias, 450 out of 475 returned questionnaires were acceptable, showing a response rate of 94.7%. The results indicated that an increase in Eco-friendly attitude positively increases the effect of word of mouth on consumers’ sustainable purchase behavior. Meanwhile, Necessary Condition Analysis (NCA) revealed that to reach a 50% level of consumers’ sustainable purchase behavior, six essential necessary conditions are required: an eco-friendly consumers’ attitude at no less than 50%, the trend at no less than 57.1%, word of mouth at no less than 45.5%, interaction at no less than 42.9%, customization at no less than 35.3% and entertainment at no less than 26.7%. Furthermore, the Importance-Performance Matrix Analysis (IPMA) was investigated as a strategic tool. The results of IPMA showed that “buy products that use biodegradable material in packaging”, “buy those products that are picked up and recycled”, and “buy biodegradable products even if they belong to a less well-known company” show desirable performance and high importance and there is a great opportunity for expansion in this area.

Genome-Wide Association Study (GWAS) of Mesocotyl Length for Direct Seeding in Rice

Direct seeding is considered an efficient cultivation technology that reduces water use and labor costs. Mesocotyl length is one of the significant traits in cultivation; long mesocotyl is beneficial for the rate and uniformity of seedling emergence. In this study, we used a core collection of 137 rice accessions to identify quantitative trait loci (QTL) for mesocotyl elongation. A genome-wide association study (GWAS), combined with a principal component analysis (PCA) and a kinship matrix analysis, was conducted for the genotype analysis of 2 million, high-quality single nucleotide polymorphisms (SNPs). Through this GWAS analysis, 11 lead SNPs were confirmed to be associated with mesocotyl length, and a linkage disequilibrium (LD) decay analysis identified the 230 kb exploratory range for the detection of QTLs and candidate genes. Based on the gene expression database and haplotype analysis, five candidate genes (Os01g0269800, Os01g0731100, Os08g0136700, Os08g0137800, and Os08g0137900) were detected to be significantly associated with phenotypic variation. Five candidate gene expressions are reported to be associated with various plant hormone responses. Interestingly, two biotic stress response genes and two copper-containing redox proteins were detected as the candidate genes. The results of this study provide associated SNPs in candidate genes for mesocotyl length and strategies for developing direct seeding in breeding programs.