PurposeThe purpose of this research is twofold: first, to investigate the impact of the adoption of green supply chain practices (GSCPs) on various parameters of competitive operational capabilities; ...second, to investigate the parameters that influence the market performance.Design/methodology/approachStructural equation modeling analysis was done based on the collected data through a self-administrated questionnaire from managers of 120 manufacturing firms.FindingsIn this study, we suggest that the relationship of GSCPs is positively related to all competitive operational capabilities. Further, we recommend that operational capabilities are directly related to market performance.Originality/valueThis paper investigates the relationship between GSCPs, Operational Competitive Capabilities and market performance, a relatively unexplored area in the developing economy. Moreover, it also adds value to the nascent literature on GSCPs in developing countries.
Traditional distance and density-based anomaly detection techniques are unable to detect periodic and seasonality related point anomalies which occur commonly in streaming data, leaving a big gap in ...time series anomaly detection in the current era of the IoT. To address this problem, we present a novel deep learning-based anomaly detection approach (DeepAnT) for time series data, which is equally applicable to the non-streaming cases. DeepAnT is capable of detecting a wide range of anomalies, i.e., point anomalies, contextual anomalies, and discords in time series data. In contrast to the anomaly detection methods where anomalies are learned, DeepAnT uses unlabeled data to capture and learn the data distribution that is used to forecast the normal behavior of a time series. DeepAnT consists of two modules: time series predictor and anomaly detector. The time series predictor module uses deep convolutional neural network (CNN) to predict the next time stamp on the defined horizon. This module takes a window of time series (used as a context) and attempts to predict the next time stamp. The predicted value is then passed to the anomaly detector module, which is responsible for tagging the corresponding time stamp as normal or abnormal. DeepAnT can be trained even without removing the anomalies from the given data set. Generally, in deep learning-based approaches, a lot of data are required to train a model. Whereas in DeepAnT, a model can be trained on relatively small data set while achieving good generalization capabilities due to the effective parameter sharing of the CNN. As the anomaly detection in DeepAnT is unsupervised, it does not rely on anomaly labels at the time of model generation. Therefore, this approach can be directly applied to real-life scenarios where it is practically impossible to label a big stream of data coming from heterogeneous sensors comprising of both normal as well as anomalous points. We have performed a detailed evaluation of 15 algorithms on 10 anomaly detection benchmarks, which contain a total of 433 real and synthetic time series. Experiments show that DeepAnT outperforms the state-of-the-art anomaly detection methods in most of the cases, while performing on par with others.
PurposeBy extending the service robot acceptance model (sRAM), this study aims to explore and enhance the acceptance of chatbots. The study considered functional, relational, social, user and ...gratification elements in determining the acceptance of chatbots.Design/methodology/approachBy using the purposive sampling technique, data of 321 service customers, gathered from millennials through a questionnaire and subsequent PLS-SEM modeling, was applied for hypotheses testing.FindingsFindings revealed that the functional elements, perceived usefulness and perceived ease of use affect acceptance of chatbots. However, in social elements, only perceived social interactivity affects the acceptance of chatbots. Moreover, both user and gratification elements (hedonic motivation and symbolic motivation) significantly influence the acceptance of chatbots. Lastly, trust is the only contributing factor for the acceptance of chatbots in the relational elements.Practical implicationsThe study extends the literature related to chatbots and offers several guidelines to the service industry to effectively employ chatbots.Originality/valueThis is one of the first studies that used newly developed sRAM in determining chatbot acceptance. Moreover, the study extended the sRAM by adding user and gratification elements and privacy concerns as originally sRAM model was limited to functional, relational and social elements.
The blue whale, Balaenoptera musculus (Linnaeus, 1758), is the biggest animal recognized to exist today throughout world’s oceans. High commercial value of lipids has made this species vulnerable. ...Blubber, a crucial adaptation for mammals living in water, serves as energy reservoir. Surplus energy is deposited in the form of fatty acids (FA) and therefore have been analysed. The compositional analysis also helps in understanding the dietary and structural role of FAs in blubber. Lipid analysis of blubber from stranded, dead blue whale through Thin layer chromatography (TLC) has resulted in identifying 6 constituents. These constituents are a triacyl glyceride (TAG), 2 steroids, and 3 FAs. Approximate analysis of waxy constituents has also been attempted exploiting TLC. Gas chromatography-mass spectrometry (GC-MS) analyses has resulted in identification of 86 compounds, which were further confirmed through the Retention Indices. Altogether 17 SFAs including 4 Branched FAs, 5 MUFAs, and a PUFA were identified. These accounted to a total concentration of 85.7, 86.1, 84.8, and 89.7 % in jaw, abdomen, peduncle, and fluke, respectively. The main reasons for the low quantitative and qualitative content of PUFAs were susceptibility of PUFAs towards oxidation. Thus 8 FAlds, 4 FAlcs, and 3 other oxygenated FAs, which made a total of 2.7, 0.9, 1.3, and 5.2% in jaw, abdomen, peduncle, and fluke, respectively were justified. Further the chromatographic region where PUFAs are expected to resolve has been found masked with significant concentration of anthropogenic compounds, which accounted to 43.4, 35.6, 34.6, and 30.7% in jaw, abdomen, peduncle, and fluke, respectively. These pollutants included 25 hydrocarbons, 4 phthalates, 2 siloxanes, 2 bisphenols, and diphenyl carbonate. 4 natural Prenols were also identified. Altogether 16 constituents with concentration of 14.2, 8.0, 15.7, and 10.8% in jaw, abdomen, peduncle, and fluke, respectively, were remained unidentified. Few constituents were justified through food chain.
The need for robust unsupervised anomaly detection in streaming data is increasing rapidly in the current era of smart devices, where enormous data are gathered from numerous sensors. These sensors ...record the internal state of a machine, the external environment, and the interaction of machines with other machines and humans. It is of prime importance to leverage this information in order to minimize downtime of machines, or even avoid downtime completely by constant monitoring. Since each device generates a different type of streaming data, it is normally the case that a specific kind of anomaly detection technique performs better than the others depending on the data type. For some types of data and use-cases, statistical anomaly detection techniques work better, whereas for others, deep learning-based techniques are preferred. In this paper, we present a novel anomaly detection technique, FuseAD, which takes advantage of both statistical and deep-learning-based approaches by fusing them together in a residual fashion. The obtained results show an increase in area under the curve (AUC) as compared to state-of-the-art anomaly detection methods when FuseAD is tested on a publicly available dataset (Yahoo Webscope benchmark). The obtained results advocate that this fusion-based technique can obtain the best of both worlds by combining their strengths and complementing their weaknesses. We also perform an ablation study to quantify the contribution of the individual components in FuseAD, i.e., the statistical ARIMA model as well as the deep-learning-based convolutional neural network (CNN) model.
This article proposes a simple yet fully adaptive particle swarm optimization (PSO) algorithm to find the global peak (GP) of a photovoltaic array under partial shading condition. It exploits a ...constrained optimization approach having a very simple PSO structure with two adaptive parameters. The first one that controls the magnitude of velocity is adaptively varied according to the absolute distance of each particle with respect to best particle's position. The other parameter controls the search space, has been varied based on a penalty condition that decides the participation of particle in the next iteration. Regardless of a wide range of population size, the proposed scheme precisely locates the GP without jeopardizing the tracking speed-consumes a maximum of 16 perturbations. The algorithm is implemented on a Cuk converter and compared to two well-known tracking methods. It is also validated through run length distribution (RLD) test. The obtained RLD results reveal that proposed PSO outperforms other two methods, in terms of convergence speed and success rate. It always obtains 100% rate by utilizing fewer perturbations of voltage. When tested for a whole day environmental profile, the average efficiency of proposed algorithm is found to be 99.65%.
With the advancement of powerful image processing and machine learning techniques, Computer Aided Diagnosis has become ever more prevalent in all fields of medicine including ophthalmology. These ...methods continue to provide reliable and standardized large scale screening of various image modalities to assist clinicians in identifying diseases. Since optic disc is the most important part of retinal fundus image for glaucoma detection, this paper proposes a two-stage framework that first detects and localizes optic disc and then classifies it into healthy or glaucomatous.
The first stage is based on Regions with Convolutional Neural Network (RCNN) and is responsible for localizing and extracting optic disc from a retinal fundus image while the second stage uses Deep Convolutional Neural Network to classify the extracted disc into healthy or glaucomatous. Unfortunately, none of the publicly available retinal fundus image datasets provides any bounding box ground truth required for disc localization. Therefore, in addition to the proposed solution, we also developed a rule-based semi-automatic ground truth generation method that provides necessary annotations for training RCNN based model for automated disc localization.
The proposed method is evaluated on seven publicly available datasets for disc localization and on ORIGA dataset, which is the largest publicly available dataset with healthy and glaucoma labels, for glaucoma classification. The results of automatic localization mark new state-of-the-art on six datasets with accuracy reaching 100% on four of them. For glaucoma classification we achieved Area Under the Receiver Operating Characteristic Curve equal to 0.874 which is 2.7% relative improvement over the state-of-the-art results previously obtained for classification on ORIGA dataset.
Once trained on carefully annotated data, Deep Learning based methods for optic disc detection and localization are not only robust, accurate and fully automated but also eliminates the need for dataset-dependent heuristic algorithms. Our empirical evaluation of glaucoma classification on ORIGA reveals that reporting only Area Under the Curve, for datasets with class imbalance and without pre-defined train and test splits, does not portray true picture of the classifier's performance and calls for additional performance metrics to substantiate the results.
Over 70 different missense mutations, including a dominant mutation, in RPE65 retinoid isomerase are associated with distinct forms of retinal degeneration; however, the disease mechanisms for most ...of these mutations have not been studied. Although some mutations have been shown to abolish enzyme activity, the molecular mechanisms leading to the loss of enzymatic function and retinal degeneration remain poorly understood. Here we show that the 26 S proteasome non-ATPase regulatory subunit 13 (PSMD13), a newly identified negative regulator of RPE65, plays a critical role in regulating pathogenicity of three mutations (L22P, T101I, and L408P) by mediating rapid degradation of mutated RPE65s via a ubiquitination- and proteasome-dependent non-lysosomal pathway. These mutant RPE65s were misfolded and formed aggregates or high molecular complexes via disulfide bonds. Interaction of PSMD13 with mutant RPE65s promoted degradation of misfolded but not properly folded mutant RPE65s. Many mutations, including L22P, T101I, and L408P, were mapped on non-active sites. Although their activities were very low, these mutant RPE65s were catalytically active and could be significantly rescued at low temperature, whereas mutant RPE65s with a distinct active site mutation could not be rescued under the same conditions. Sodium 4-phenylbutyrate and glycerol displayed a significant synergistic effect on the low temperature rescue of the mutant RPE65s by promoting proper folding, reducing aggregation, and increasing membrane association. Our results suggest that a low temperature eye mask and sodium 4-phenylbutyrate, a United States Food and Drug Administration-approved oral medicine, may provide a promising “protein repair therapy” that can enhance the efficacy of gene therapy by reducing the cytotoxic effect of misfolded mutant RPE65s.
Background: Disease-causing mutations reduce RPE65 protein levels with unknown mechanisms.
Results: Interaction of 26 S proteasome non-ATPase regulatory subunit 13 with mutant RPE65s mediates degradation of misfolded RPE65s via the ubiquitin-proteasome pathway.
Conclusion: Many mutant RPE65s with non-active site mutations are catalytically active and can be rescued.
Significance: Low temperature and chaperones that rescue enzymatic function of mutant RPE65s are therapeutic candidates.
Orchids are basically ornamental, and biological functions are seldom evaluated. This research investigated the effects of Acampe ochracea methanol extract (AOME) in ameliorating the paracetamol ...(PCM) induced liver injury in Wistar albino rats, evaluating its phytochemical status through UPLC-qTOF-MS analysis. With molecular docking and network pharmacology, virtual screening verified the inevitable interactions between the UPLC-qTOF-MS-characterized compounds and hepatoprotective drug receptors. The AOME has explicit a dose-dependent decrease of liver enzymes acid phosphatase (ACP), aspartate aminotransferase (AST), alanine aminotransferase (ALT), alkaline phosphatase (ALP), gamma-glutamyl transferase (GGT), lactate dehydrogenase (LDH), total bilirubin, as well as an increase of serum total protein and antioxidant enzymes catalase (CAT), superoxide dismutase (SOD), glutathione reductase (GSH) with a virtual normalization (p < 0.05-p < 0.001) and the values were almost equivalent to the reference drug silymarin. After pretreatment with AOME, PCM-induced malondialdehyde (MDA) levels were considerably decreased (p < 0.001). Histopathological examinations corroborated the functional and biochemical findings. The AOME upregulated the genes involved in antioxidative (CAT, SOD, β-actin, PON1, and PFK1) and hepatoprotective mechanisms in PCM intoxicated rats. An array of 103 compounds has been identified from AOME through UPLC-qTOF-MS analysis. The detected compounds were substantially related to the targets of several liver proteins and antioxidative enzymes, according to an in silico study. Virtual prediction by SwissADME and admetSAR showed that AOME has drug-like, non-toxic, and potential pharmacological activities in hepatic damage. Furthermore, VEGFA, CYP19A1, MAPK14, ESR1, and PPARG genes interact with target compounds impacting the significant biological actions to recover PCM-induced liver damage.
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•Acampe ochracea exhibited significantly improved PCM induced liver-damage in Wistar albino rats.•Biochemical markers related to liver ALT, GGT, AST ACP and ALP have been remarkably reduced.•A. ochracea upregulated the relevant CAT, SOD, β-actin, PON1 and PFK1 genes.•A. ochracea showed promising in silico effects against respective receptors.•Effect of A. ochracea on liver function regulating genes including VEGFA, CYP19A1, MAPK14, ESR1, and PPARG.
Background
In view of importance for competency-based education (CBE), we undertook a self-study to elicit the available operative surgical workload and supervision for residents in the general ...surgical residency program at the teaching hospital in Karachi.
Methodology
This was a cross-sectional study spanning a 5-year period between January 2015 and December 2019. The numbers of surgical residents during this period were identified. Five procedures were selected as core general surgical procedures: incision and drainage of superficial abscess, laparoscopic appendectomy, laparoscopic cholecystectomy, open inguinal hernia repair, and perianal procedures. Trends of the number of residents per year and the numbers of procedures per year were determined. The mean number of core procedures per eligible resident during their entire training was calculated to represent potential operative surgical experience and were benchmarked. The ratio of the average number of residents rotating in general surgery per year to the number of attending surgeons was determined as a measure of available supervision.
Result
The mean total number of general surgical residents per year was 31.2 (range 28–35). The numbers of core general surgical procedures were consistent over the years of study. Potential exposure of eligible residents to each core procedure during their entire training was: 19.5 cases for incision and drainage of superficial abscess; 89 cases for laparoscopic appendectomy; 113.6 for inguinal hernia repair, 267.5 for laparoscopic cholecystectomy and 64.5 for perianal procedures. The average yearly residents to full-time attending surgeons’ ratio was 2.5. The workload of core general surgical procedures at AKUH was higher than the Accreditation Council for Graduate Medical Education (ACGME) recommended volumes for operative surgical experience for residents in the US.
Conclusion
This method of assessing the potential of a surgical program for transitioning to CBE appears practical and can be generalized.
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