Activity Based Costing (ABC): Implementation and Success in Pakistani Companies

The main quest for this research is to examine the actual implementation experience of the Activity-Based Costing system in the corporate sector of Pakistan. The study investigates the benefits received that are associated with the successful implementation of Activity-based Costing. The study aims to investigate two dimensions of ABC system: 1) what are the key internal organizational success factors for ABC's successful Implementation? 2) Whether ABC system successful implementation is helpful or not in the realization of the associated benefits? Path regression analysis based on Structural Equation Modelling Technique (AMOS) has been used to verify these hypothetical relationships.  Our results verify that internal organizational factors such as management commitment, top management support, implementation training, and resource adequacy have a positive impact on ABC's successful implementation.  Consequently, ABC's successful implementation ensures the attainment of associated benefits such as accurate product costing and better overhead cost allocation. However, the study fails to link it with cost reduction and a better budgeting process.&nbsp

Efficient quantitative assessment of facial paralysis using iris segmentation and active contour-based key points detection with hybrid classifier

BACKGROUND: Facial palsy or paralysis (FP) is a symptom that loses voluntary muscles movement in one side of the human face, which could be very devastating in the part of the patients. Traditional methods are solely dependent to clinician’s judgment and therefore time consuming and subjective in nature. Hence, a quantitative assessment system becomes apparently invaluable for physicians to begin the rehabilitation process; and to produce a reliable and robust method is challenging and still underway. METHODS: We introduce a novel approach for a quantitative assessment of facial paralysis that tackles classification problem for FP type and degree of severity. Specifically, a novel method of quantitative assessment is presented: an algorithm that extracts the human iris and detects facial landmarks; and a hybrid approach combining the rule-based and machine learning algorithm to analyze and prognosticate facial paralysis using the captured images. A method combining the optimized Daugman’s algorithm and Localized Active Contour (LAC) model is proposed to efficiently extract the iris and facial landmark or key points. To improve the performance of LAC, appropriate parameters of initial evolving curve for facial features’ segmentation are automatically selected. The symmetry score is measured by the ratio between features extracted from the two sides of the face. Hybrid classifiers (i.e. rule-based with regularized logistic regression) were employed for discriminating healthy and unhealthy subjects, FP type classification, and for facial paralysis grading based on House-Brackmann (H-B) scale. RESULTS: Quantitative analysis was performed to evaluate the performance of the proposed approach. Experiments show that the proposed method demonstrates its efficiency. CONCLUSIONS: Facial movement feature extraction on facial images based on iris segmentation and LAC-based key point detection along with a hybrid classifier provides a more efficient way of addressing classification problem on facial palsy type and degree of severity. Combining iris segmentation and key point-based method has several merits that are essential for our real application. Aside from the facial key points, iris segmentation provides significant contribution as it describes the changes of the iris exposure while performing some facial expressions. It reveals the significant difference between the healthy side and the severe palsy side when raising eyebrows with both eyes directed upward, and can model the typical changes in the iris region

China–Pakistan Economic Corridor, Logistics Developments and Economic Growth in Pakistan

The study aims to analyze the impact of China–Pakistan Economic Corridor (CPEC) logistics-related developments on economic growth in Pakistan. The study defined a Cobb–Douglas type of research framework in which the country’s real income level relates to four factor inputs, e.g., employed labor force, logistics development, financial development, and energy consumption in an economy. The study utilized the time series data set for the period 1972–2018. To estimate the long run relationship and short run adjustment mechanism, the study used Johansen’s method of co-integration and error correction model. Estimated results showed that the country’s logistics developments have a significant positive impact on economic growth in both the long run and the short run. It implies that China–Pakistan collaborative efforts for logistics developments will have a strong positive impact on economic growth in Pakistan

Detection and Identification of Demagnetization and Bearing Faults in PMSM Using Transfer Learning-Based VGG

Predictive maintenance in the permanent magnet synchronous motor (PMSM) is of paramount importance due to its usage in electric vehicles and other applications. Recently various deep learning techniques are applied for fault detection and identification (FDI). However, it is very hard to optimally train the deeper networks like convolutional neural network (CNN) on a relatively fewer and non-uniform experimental data of electric machines. This paper presents a deep learning-based FDI for the irreversible-demagnetization fault (IDF) and bearing fault (BF) using a new transfer learning-based pre-trained visual geometry group (VGG). A variant of ImageNet pre-trained VGG network with 16 layers is used for the classification. The vibration and the stator current signals are selected for the feature extraction using the VGG-16 network for reliable detection of faults. A confluence of vibration and current signals-based signal-to-image conversion approach is also introduced for exploiting the benefits of transfer learning. We evaluate the proposed approach on ImageNet pre-trained VGG-16 parameters and training from scratch to show that transfer learning improves the model accuracy. Our proposed method achieves a state-of-the-art accuracy of 96.65% for the classification of faults. Furthermore, we also observed that the combination of vibration and current signals significantly improves the efficiency of FDI techniques

Diagnostic accuracy of intraoperative brain smear: A meta-analysis of studies from resource-limited settings

Background: Intraoperative brain smear is an easy, rapid, and cost-effective technique for immediate diagnosis of brain tumors. Earlier studies have gauged its application on a limited number of samples, but its diagnostic accuracy especially in low-resource settings, where its practice would be extremely helpful is still undetermined.Objective: To investigate the diagnostic accuracy of intraoperative brain smear in resource-limited settings for diagnosis of brain tumors.Methods: A systematic search was conducted on PubMed, Google Scholar, Scopus, Cumulative Index to Nursing and Allied Health Literature (CINAHL), and Embase for all articles utilizing intraoperative brain smears that were extracted. Studies from low- and middle-income countries (LMIC) with test performance characteristics were selected and subsequent values were summarized using a hierarchical summary receiver operating characteristic (ROC) curve via STATA and pooled using a random-effects model on MetaDiSc 2.0.Results: Twelve studies consisting of 1124 patients were identified. Six studies included both adult and pediatric population groups, while four investigated adults and two included pediatric patients. The pooled diagnostic odds ratio was calculated to be 212.52 (CI: [104.27 - 433.13]) of Bivariable pooled specificity and sensitivity were 92% (CI: [86%-96%]) and 96% (CI: [93%-98%]), respectively.Conclusion: Our study shows that intraoperative brain smear is not only an accurate and sensitive diagnostic modality in resource-rich settings, but it is also equally useful in resource-limited settings, making it an ideal method for rapid diagnosis