Productive efficiency of tea industry: A stochastic frontier approach

In an economy where recourses are scarce and opportunities for a new technology are lacking, studies will be able to show the possibility of raising productivity by improving the industry’s efficiency. This study attempts to measure the status of technical efficiency of tea-producing industry for panel data in Bangladesh using the stochastic frontier production function, incorporating technical inefficiency effect model. It was observed that Translog Production Function is more preferable than Cobb-Douglas Production Function. The study estimates that the average technical efficiency of tea producing industries in Bangladesh is 59%. Therefore, the results indicated that there is a great potential exists fortea industry to further increase the value added by 41% using the available input, technology and efficiency improvement, thereby reducing the cost of production. The study identifies that the mean efficiency of tea industries for value added vary among the regions and year-wise mean efficiency seems to be unstable during the study period and therefore, continued efforts to update technologies and equipment are required in pursuit of efficiency in tea industry

Cost Efficiency Measurement of Leasing Companies with SFA and DEA Approach

Nowadays the leasing industry is a successful industry in economic conditions, but there are few studies that deal with the assessment of leasing companies' efficiency in Bangladesh. Previous research for leasing company's' efficiency measurement usually adopts either DEA or SFA method, but not both of them. Therefore, this paper is aimed to measure the cost efficiencies of 15 leasing companies from 2002 to 2008 by applying both SFA and DEA models with four inputs and two outputs. The results suggest that leasing companies have generally less experience in the allocation of resources and in the improvement of cost efficiency. INTRODUCTION Performance evaluation of the leasing companies is a most important issue because leasing has a vital role in economic development and growth and also contributes a major share in the gross domestic production (GDP) [4] developed a model for the evaluation of service quality in auto-industry leasing in Iran. They used the factor analysis to assess service quality and customer satisfaction in the form of performance. Majazi Dalfard et al. [16] applied the super efficiency DEA model to rank efficient leasing companies due to the failure of basic DEA models, there are other methods that can be used to rank the DMUs [19] in future studies. There are only a few literatures available in the subject of leasing companies in Bangladesh. For instance, Khanam The measure of inefficiency derived from micro-economic theory [1], Charnes, Cooper, and Rhodes [9,10], and Banker, Charnes, and Cooper The paper is organized as follows. Section 2 discusses the background and literatures for leasing companies in Bangladesh. Section 3 presents the methodologies of SFA and DEA. Section 4 assesses the efficiency ratings of leasing companies. Finally, the main results and suggestions for future research are summarized

Context-Aware Prediction of User Engagement on Online Social Platforms

The success of online social platforms hinges on their ability to predict and understand user behavior at scale. Here, we present data suggesting that context-aware modeling approaches may offer a holistic yet lightweight and potentially privacy-preserving representation of user engagement on online social platforms. Leveraging deep LSTM neural networks to analyze more than 100 million Snapchat sessions from almost 80.000 users, we demonstrate that patterns of active and passive use are predictable from past behavior (R2=0.345) and that the integration of context information substantially improves predictive performance compared to the behavioral baseline model (R2=0.522). Features related to smartphone connectivity status, location, temporal context, and weather were found to capture non-redundant variance in user engagement relative to features derived from histories of in-app behaviors. Further, we show that a large proportion of variance can be accounted for with minimal behavioral histories if momentary context information is considered (R2=0.44). These results indicate the potential of context-aware approaches for making models more efficient and privacy-preserving by reducing the need for long data histories. Finally, we employ model explainability techniques to glean preliminary insights into the underlying behavioral mechanisms. Our findings are consistent with the notion of context-contingent, habit-driven patterns of active and passive use, underscoring the value of contextualized representations of user behavior for predicting user engagement on social platforms

Machine learning prediction of sleep stages in dairy cows from heart rate and muscle activity measures

\ua9 2021, The Author(s). Sleep is important for cow health and shows promise as a tool for assessing welfare, but methods to accurately distinguish between important sleep stages are difficult and impractical to use with cattle in typical farm environments. The objective of this study was to determine if data from more easily applied non-invasive devices assessing neck muscle activity and heart rate (HR) alone could be used to differentiate between sleep stages. We developed, trained, and compared two machine learning models using neural networks and random forest algorithms to predict sleep stages from 15 variables (features) of the muscle activity and HR data collected from 12 cows in two environments. Using k-fold cross validation we compared the success of the models to the gold standard, Polysomnography (PSG). Overall, both models learned from the data and were able to accurately predict sleep stages from HR and muscle activity alone with classification accuracy in the range of similar human models. Further research is required to validate the models with a larger sample size, but the proposed methodology appears to give an accurate representation of sleep stages in cattle and could consequentially enable future sleep research into conditions affecting cow sleep and welfare

The microbiome of diabetic foot ulcers : a comparison of swab and tissue biopsy wound sampling techniques using 16S rRNA gene sequencing

Background: Health-care professionals need to collect wound samples to identify potential pathogens that contribute to wound infection. Obtaining appropriate samples from diabetic foot ulcers (DFUs) where there is a suspicion of infection is of high importance. Paired swabs and tissue biopsies were collected from DFUs and both sampling techniques were compared using 16S rRNA gene sequencing. Results: Mean bacterial abundance determined using quantitative polymerase chain reaction (qPCR) was significantly lower in tissue biopsies (p = 0.03). The mean number of reads across all samples was significantly higher in wound swabs X = 32,014) compared to tissue (X = 15,256, p = 0.001). Tissue biopsies exhibited greater overall diversity of bacteria relative to swabs (Shannon’s H diversity p = 0.009). However, based on a presence/ absence analysis of all paired samples, the frequency of occurrence of bacteria from genera of known and potential pathogens was generally higher in wound swabs than tissue biopsies. Multivariate analysis identified significantly different bacterial communities in swabs compared to tissue (p = 0.001). There was minimal correlation between paired wound swabs and tissue biopsies in the number and types of microorganisms. RELATE analysis revealed low concordance between paired DFU swab and tissue biopsy samples (Rho = 0.043, p = 0.34). Conclusions: Using 16S rRNA gene sequencing this study identifies the potential for using less invasive swabs to recover high relative abundances of known and potential pathogen genera from DFUs when compared to the gold standard collection method of tissue biopsy. SOME OF THE SCIENTIC SYMBOLS CAN NOT BE REPRESENTED CORRECTLY IN THE ABSTRACT. PLEASE READ WITH CAUTION AND REFER TO THE ORIGINAL PUBLICATION

Cost efficiency measurement of leasing companies with SFA and DEA approach

Nowadays the leasing industry is a successful industry in economic conditions, but there are few studies that deal with the assessment of leasing companies' efficiency in Bangladesh. Previous research for leasing company's' efficiency measurement usually adopts either DEA or SFA method, but not both of them. Therefore, this paper is aimed to measure the cost efficiencies of 15 leasing companies from 2002 to 2008 by applying both SFA and DEA models with four inputs and two outputs.The results suggest that leasing companies have generally less experience in the allocation of resources and in the improvement of cost efficiency.The coefficients of Interest revenue and Non Interest revenue are found significant and positive effects on the cost efficiency of the leasing company in case of stochastic cost frontier model. The results of technical efficiency and allocative efficiency are combined to provide a measure of total cost efficiency in case of cost data envelopment analysis.The lowest cost efficiency (39.6%) for Union Capital Limited, and highest cost efficiency (89.7%) for International Leasing & Financial S.Ltd., is observed in case of cost data envelopment analysis

Goat Genomic Resources: The Search for Genes Associated with Its Economic Traits

Goat plays a crucial role in human livelihoods, being a major source of meat, milk, fiber, and hides, particularly under adverse climatic conditions. The goat genomics related to the candidate gene approach is now being used to recognize molecular mechanisms that have different expressions of growth, reproductive, milk, wool, and disease resistance. The appropriate literature on this topic has been reviewed in this article. Several genetic characterization attempts of different goats have reported the existence of genotypic and morphological variations between different goat populations. As a result, different whole-genome sequences along with annotated gene sequences, gene function, and other genomic information of different goats are available in different databases. The main objective of this review is to search the genes associated with economic traits in goats. More than 271 candidate genes have been discovered in goats. Candidate genes influence the physiological pathway, metabolism, and expression of phenotypes. These genes have different functions on economically important traits. Some genes have pleiotropic effect for expression of phenotypic traits. Hence, recognizing candidate genes and their mutations that cause variations in gene expression and phenotype of an economic trait can help breeders look for genetic markers for specific economic traits. The availability of reference whole-genome assembly of goats, annotated genes, and transcriptomics makes comparative genomics a useful tool for systemic genetic upgradation. Identification and characterization of trait-associated sequence variations and gene will provide powerful means to give positive influences for future goat breeding program