In recent decades, rapid advances in Internet technology have led to numerous innovative smart technologies. This research investigates the customer acceptance of and resistance to smart technologies ...in the retail sector, by integrating the literature on technology acceptance model, system characteristics, technology readiness, and store reputation. Data were collected using a quantitative survey and analysed using symmetrical PLS path modelling and asymmetrical fuzzy set qualitative comparative analysis (fsQCA). Results show complex relationships among perceived technology readiness, perceived ease of use, perceived usefulness, superior functionality, perceived adaptiveness, and store reputation in determining customers’ attitudes and behavioural intentions towards smart retail technologies. The findings also show that technology readiness does not directly affect customer attitude but does indirectly through perceived innovation characteristics. The findings indicate that retail stores should focus on smart technologies that are simple, yet offer enhanced customer value through improved shopping efficiency. Findings also suggest that retail stores can engage in brand management strategies to improve customers’ acceptance of smart technologies.
Lumpy skin disease (LSD) has devastating economic impact. During the last decade, LSD had spread to climatically new and previously disease-free countries, which also includes its recent emergence in ...the Indian subcontinent (2019). This study deals with the LSD outbreak(s) from cattle in Ranchi (India). Virus was isolated from the scabs (skin lesions) in the primary goat kidney cells. Phylogenetic analysis based on nucleotide sequencing of LSD virus (LSDV) ORF011, ORF012 and ORF036 suggested that the isolated virus (LSDV/Bos taurus-tc/India/2019/Ranchi) is closely related to Kenyan LSDV strains. Further, we adapted the isolated virus in Vero cells. Infection of the isolated LSDV to Vero cells did not produce cytopathic effect (CPE) until the 4th blind passage, but upon adaptation, it produced high viral titres in the cultured cells. The kinetics of viral DNA synthesis and one-step growth curve analysis suggested that Vero cell-adapted LSDV initiates synthesizing its genome at ~24 hours post-infection (hpi) with a peak level at ~96 hpi whereas evidence of progeny virus particles was observed at 36-48 hours (h) with a peak titre at ~120 h. To the best of our knowledge, this study describes the first successful isolation of LSDV in India, besides providing insights into the life cycle Vero cell-adapted LSDV.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Purpose
The purpose of this paper is twofold: first, to determine the role of emotions in customer evaluation of service failures; and second, to examine how customers’ emotion regulation impacts ...customer satisfaction and behavioural responses (e.g. repurchase intentions and negative word-of-mouth).
Design/methodology/approach
A scenario-based survey was used to elicit responses in a hospitality setting. Structural equation modelling and hierarchical regression analysis were used to test the proposed hypotheses.
Findings
Results show that both positive and negative emotions mediate the relationship between perceived injustice and customer satisfaction. The emotion regulation of customers through suppression and reappraisal influences the effects of satisfaction on both negative word-of-mouth and repurchase intentions.
Practical implications
This study advances service managers’ understanding of customer experience during service failure by demonstrating how emotion regulation influences customer response behaviours. With a better understanding of customers’ emotion regulation strategies, managers and frontline employees can more effectively develop and execute recovery strategies which adapt to customer emotions while eliciting more satisfying outcomes.
Originality/value
This research is one of the first to examine the moderating role of customers’ emotion regulation strategies in determining their behavioural responses. Conducted in the hospitality services context, this study provides support for relationships among perceived injustice, customer emotions, emotion regulation, customer satisfaction, negative word-of-mouth and repurchase intentions.
In the present study, we have integrated the spectral anomaly map derived from airborne visible-infrared imaging spectrometer-next generation (AVIRIS-NG) data and residual ground geophysical anomaly ...maps (gravity and magnetic) for identifying the mafic cumulates and chromitites in the Sittampundi Layered-Complex (SLC), Tamil Nadu, India. A spectral map of chromite bearing mafic cumulates was prepared by implementing the matched filtering (MF) algorithm on the selected spectral bands of AVIRIS-NG data using the mixed image spectra of mafic cumulate and chromitite as an end member. A low pass filter was applied in the spectral map to extract the clusters of coherently distributed spectrally anomalous pixels representing the surface exposure of mafic cumulates. Concurrently, the ground gravity and magnetic data were processed for deriving the residual gravity and magnetic anomaly maps. Subsequently, an integration of spectral map, residual gravity, and magnetic anomaly maps was attempted using the support vector machine (SVM) algorithm to identify the potential sites of chromitite occurrence. Results were verified using the geological map and the field data on surface exposures of mafic cumulates. This research demonstrates that integration of remote sensing data and geophysical anomaly maps has immense potential to detect the surface distribution of chromite bearing mafic cumulates in the layered complex and the same methods can be replicated for carrying out mineral prospecting in the other parts of the world.
Background The use of computers and other Visual Display Terminal (VDT) screens is increasing in Nepal. However, there is a paucity of evidence on the prevalence of Computer Vision Syndrome (CVS) and ...other occupational health concerns among employees working in front of VDT screens in the Nepalese population. Objectives This study aims to estimate the prevalence of CVS, musculoskeletal and work-related stress among VDT screen users in the office, as well as their understanding and usage of preventive measures. Methods The study was a cross-sectional descriptive study among 319 VDT users in office settings in Kathmandu Metropolitan City, Nepal, using a semi-structured self-administered questionnaire. Multivariate logistic regression analysis was conducted to identify the associated factors at 95% CI. P-value <0.05 was considered as statistically significant. Results The prevalence of CVS was 89.4%. More than eight out of ten study participants reported at least one visual and musculoskeletal symptom. Work-related stress, which was moderate-difficult to handle, was present in 36.7% of the study population. The mean±SD computer usage per day was 7.9±1.9 hours. Tired eye (63.3%), feeling of dry eye (57.8%), headache (56.9%) were the common visual symptoms of CVS reported. Total computer use/day > = 8 hours OR 2.6, improper viewing distance OR 3.2, Not using an anti-glare screen OR 2.6, not using eye-drops, and not wearing protective goggles OR 3.1 were significantly associated with the presence of CVS. There was no statistically significant association between visual symptoms of CVS, musculoskeletal symptoms, and stress with gender. Conclusion CVS was substantially related to not employing preventive measures, working longer hours, and having an incorrect viewing distance. With more hours per day spent in front of a VDT screen, work-related stress and musculoskeletal complaints were also found to be important correlates. Similarly, work-related stress was found more among those who had less than five years of job.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
As a result of their easy availability in enantiomerically enriched form and their possession of synthetically transformable diverse functional groups, amino acids have been extensively used by ...synthetic organic and medicinal chemists as a chiral pool for access to heterocycles (monocycles, bicycles or polycycles, either bridged or fused). This review describes the syntheses of diverse asymmetric heterocycles with various membered rings (
n
= 3-9) followed by benzo or heteroannulated ones, for the period from 1996 to Dec. 2013. It details solution phase synthetic methodologies in which the naturally occurring α-amino acid is incorporated, totally or partially, into the final product.
This review describes diverse asymmetric heterocycles with various membered rings (
n
= 3-9) for the period from 1996 to Dec. 2013.
The rapid assessment of building damage in earthquake-stricken areas is of paramount importance for emergency response. The development of remote sensing technology has aided in deriving reliable and ...precise building damage assessments of extensive areas following disasters. It is well documented that convolutional neural network methods have superior performance in earthquake building damage assessment compared with traditional machine learning methods. However, deep learning models require a large number of samples, and sufficient numbers of samples are usually not available in the newly earthquake-stricken areas rapidly enough. At the same time, the historical samples inevitably differ from the new earthquake-affected areas due to the discrepancy of regional building characteristics. For this purpose, this study proposes a data transfer algorithm for evaluating the impact of a single historical training sample on the model performance. Then, beneficial samples are selected to transfer knowledge from the historical data for facilitating the calibration of the new model. Four models are designed with two earthquake damage building datasets and the performance of the models is compared and evaluated. The results show that the data transfer algorithm proposed in this work improves the reliability of the building damage assessment model significantly by filtering samples from the historical data that are suitable for the new task. The performance of the model built based on the data transfer method on the test set of new earthquakes task is approximately 8% higher in overall accuracy compared with the model trained directly with the new earthquake samples when the training data for the new task is only 10% of the historical data and is operating under the objective of four classes of building damage. The proposed data transfer algorithm has effectively enhanced the precision of the seismic building damage assessment in a data-limited context. Thus, it could be applicable to the building damage assessment of new disasters.
Abstract In river research, forecasting flow velocity accurately in vegetated channels is a significant challenge. The forecasting performance of various independent and hybrid machine learning (ML) ...models are thus quantified for the first time in this work. Utilizing flow velocity measurements in both natural and laboratory flume experiments, we assess the efficacy of four distinct standalone machine learning techniques—Kstar, M5P, reduced error pruning tree (REPT) and random forest (RF) models. In addition, we also test for eight types of hybrid ML algorithms trained with an Additive Regression (AR) and Bagging (BA) (AR-Kstar, AR-M5P, AR-REPT, AR-RF, BA-Kstar, BA-M5P, BA-REPT and BA-RF). Findings from a comparison of their predictive capabilities, along with a sensitivity analysis of the influencing factors, indicated: (1) Vegetation height emerged as the most sensitive parameter for determining the flow velocity; (2) all ML models displayed outperforming empirical equations; (3) nearly all ML algorithms worked optimal when the model was built using all of the input parameters. Overall, the findings showed that hybrid ML algorithms outperform regular ML algorithms and empirical equations at forecasting flow velocity. AR-M5P (R 2 = 0.954, R = 0.977, NSE = 0.954, MAE = 0.042, MSE = 0.003, and PBias = 1.466) turned out to be the optimal model for forecasting of flow velocity in vegetated-rivers.
Snake venoms are cocktails of enzymes and non‐enzymatic proteins used for both the immobilization and digestion of prey. The most common snake venom enzymes include acetylcholinesterases, l‐amino ...acid oxidases, serine proteinases, metalloproteinases and phospholipases A2. Higher catalytic efficiency, thermal stability and resistance to proteolysis make these enzymes attractive models for biochemists, enzymologists and structural biologists. Here, we review the structures of these enzymes and describe their structure‐based mechanisms of catalysis and inhibition. Some of the enzymes exist as protein complexes in the venom. Thus we also discuss the functional role of non‐enzymatic subunits and the pharmacological effects of such protein complexes. The structures of inhibitor–enzyme complexes provide ideal platforms for the design of potent inhibitors which are useful in the development of prototypes and lead compounds with potential therapeutic applications.
Snake venoms are cocktails of enzymes and non‐enzymatic proteins. Here, we describe the structures of the common snake venom enzymes, namely, acetylcholinesterase, L‐amino acid oxidase, phospholipase A2, serine proteinase, and metalloproteinase, and their complexes. We discuss the contribution of these structures in understanding the mechanisms of catalysis and inhibition as well as in the structure‐based design of new, potent inhibitors
The post-coronavirus disease 2019 (COVID-19) syndrome is defined as the persistence of symptoms after viral clearance and the emergence of new symptoms after a few months following recovery from ...COVID-19. This study aimed to assess the prevalence of post-COVID-19 syndrome and the risk factors that contribute to its development. This study was conducted prospectively in Tribhuvan University Teaching Hospital (TUTH), located in Maharajgunj, Kathmandu. The patients were followed up for three months. The post-COVID status of 300 patients admitted to the COVID emergency of TUTH was studied. The mean age of the patients was 46.6±15.7 years, and the proportion of male (56%) was slightly higher than female (44%). Most of the patients (81.7%) had fever on their presentation to the emergency which was followed by fatigue (81.3%) and cough (78.3%). During the post-COVID phase, fatigue was the most common persistent symptom, with 34% experiencing fatigue after 60 days and 28.3% even after 90 days from the onset of symptoms. Univariate logistic regression showed sore throat (OR 4.6; 95% CI (2.8-7.6)), rhinitis (OR 3.6; 95% CI (2.1-5.9)), fatigue (OR 3.7; 95% CI (1.8-7.6)), diarrhea (OR 4.1; 95% CI (2.4-6.9)), anosmia (OR 6.7; 95% CI (3.9-11.3)), ageusia (OR 7.8; 95% CI (4.5-13.4)) and shortness of breath (OR 14.9; 95% CI (1.8-119.6)) at admission were all predictors of post-COVID syndrome after three months. Even after recovering from COVID-19, people with COVID-19 may develop symptoms. As a result, COVID-19's long-term consequences should not be neglected, as they may lead to increased morbidity among patients, consumption of financial resources, and added burden on the health system.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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