ESSAYS ON EVASION AND ENFORCEMENT IN VALUE ADDED TAX (VAT)

Value added tax (VAT) based on credit invoice system is the most common consumption tax in the world. Despite its self-regulating nature, VAT faces challenges in developing countries who have limited state capacity to check evasion and enforce tax on informal sectors of the economy. The tax authorities introduce policy interventions that can target the evasive behavior of firms interacting with informal sectors. My dissertation seeks to provide insight into three such policy reforms in Pakistan’s VAT regime. Therefore, this dissertation is composed of three essays. In first essay of my dissertation, titled “Using Computerization to enforce VAT: Evidence from Pakistan”, I study a policy intervention which empowered a computerized system to check invoices and reject input tax claims based on risk-based criteria. I use administrative tax data for the universe of VAT returns filed in Pakistan from tax year 2009 to 2016 to estimate the impact of this reform on the firms operating domestically. Using the exporters not subject to the reform as a control group, I find that the input tax claims fell by 2.36 million Pak Rs. per treated firm, representing a decline in input tax claims to the tune of Pak Rs. 86 billion. Firm heterogeneity analysis by business activity and firm structure shows a decline ranging from 30% to 90%. Surprisingly, the corporations and partnerships also show significant reduction in input tax claims from 50-70%. Contrary to the expectations, the huge volume of evasion shows that VAT implementation in limited tax capacity regimes may not yield the expected revenue efficiency gains. Second essay of my dissertation titled, “Is Minimum the Maximum? Tax Burden on Informal Sector in VAT: Evidence from Pakistan”, analyzes another policy reform. In developing countries, a substantial amount of revenue at import stage is now collected from VAT instead of traditional import tariffs. This modern approach assumes negligible VAT evasion at post-importation stage. I test this assumption through universe of monthly VAT returns filed in Pakistan for tax years 2009 to 2016 to estimate evasion by firms exclusively engaged in imports. I utilize kinks produced by minimum value addition thresholds to estimate evasion of VAT post-importation. I estimate an average evasion rate of nearly 78%. Using changes in thresholds over years, I provide evidence that this minimum tax collection is the best-case scenario for revenue efficiency. The firms show strong bunching at or below threshold with about 40-60% of the firms showing bunching behavior. My results support the view that, absent deviations from standard, replacing high import tariffs with VAT would decrease welfare. Third essay of my dissertation titled, “The Deterrence Value of Tax Audits: Estimates from a Randomized Audit Program”, analyzes a randomized audit program. It is a joint project with Michael Best and Mazhar Waseem. In modern tax systems audit is the sole instrument through which the tax authority can detect noncompliance and create deterrence. We exploit a national program of randomized audits covering the entire population of VAT filers from Pakistan to study how much evasion audit uncovers and how much evasion it prevents by changing behavior. While audit uncovers a substantial amount of evasion (the evasion rate among firms in the bottom three size quartiles is more than 100%), it does not deter future cheating. Examining more than ten intensive and extensive margin outcomes, we detect no effect of audit on proximate or distant firm behavior. Our results suggest audits are sub optimally utilized in checking mechanical violations of law instead of creating deterrence against evasion

Inventory Management System for a General Items Warehouse of the Textile Industry

This research is based on Inventory Management System for a General Items Warehouse of the Textile Industry. The overall inventory is managed by applying classification tools such as ABC, FSN & HML that categorize inventory based on consumption value, issuance rate and unit price respectively. Also, it helps to appropriately position the items on the desired rack and position. The optimized layout is designed that reduces the retrieval time, uplift the storage capacity, and have cross aisles that reduce the retrieval time of any item from the warehouse. The system for proper traceability & tracking of the items is also studied that is based on the 1D Barcode. This whole study improves the overall operation of the Supply Chain

Advanced sensing techniques for intelligent human activity recognition using machine learning

No abstract available

Design of Portable Exoskeleton Forearm for Rehabilitation of Monoparesis Patients Using Tendon Flexion Sensing Mechanism for Health Care Applications

Technology plays a vital role in patient rehabilitation, improving the quality of life of an individual. The increase in functional independence of disabled individuals requires adaptive and commercially available solutions. The use of sensor-based technology helps patients and therapeutic practices beyond traditional therapy. Adapting skeletal tracking technology could automate exercise tracking, records, and feedback for patient motivation and clinical treatment interventions and planning. In this paper, an exoskeleton was designed and subsequently developed for patients who are suffering from monoparesis in the upper extremities. The exoskeleton was developed according to the dimensions of a patient using a 3D scanner, and then fabricated with a 3D printer; the mechanism for the movement of the hand is a tendon flexion mechanism with servo motor actuators controlled by an ATMega2560 microcontroller. The exoskeleton was used for force augmentation of the patient’s hand by taking the input from the hand via flex sensors, and assisted the patient in closing, opening, grasping, and picking up objects, and it was also able to perform certain exercises for the rehabilitation of the patient. The exoskeleton is portable, reliable, durable, intuitive, and easy to install and use at any time

Privacy-Preserving Wandering Behavior Sensing in Dementia Patients Using Modified Logistic and Dynamic Newton Leipnik Maps

The health status of an elderly person can be identified by examining the additive effects of aging along disease linked to it and can lead to the ’unstable incapacity’. This health status is essentially determined by the apparent decline of independence in Activities of Daily Living (ADLs). Detecting ADLs provide possibilities of improving the home life of elderly people as it can be applied to fall detection systems.. This article looks at Radar images to detect large scale body movements. Using a publicly available Radar spectogram dataset, Deep Learning and Machine Learning techniques are used for image classification of Walking, Sitting, Standing, Picking up Object, Drinking Water and Falling Radar spectograms. The Machine Learning algorithm used were Random Forest, K Nearest Neighbours and Support Vector Machine. The Deep Learning algorithms used in this article were Long Short Term Memory, Bi-directional Long Short-Term Memory and Convolutional Neural Network. In addition to using Machine Learning and Deep Learning on the spectograms, data processing techniques such as Principal Component Analysis and Data Augmentation is applied to the spectogram images. The work done in this article is divided into 4 experiments. The first experiment applies Machine and Deep Learning to the the Raw images data, the second experiment applies Principal Component Analysis to the Raw image Data, the third experiment applies Data Augmentation to the Raw image data and the fourth and final experiment applies Principal Component Analysis and Data Augmentation to the Raw image data. The results obtained in these experiments found that the best results were obtained using the CNN algorithm with Principal Component Analysis and Data Augmentation together to obtain a result of 95.30 % accuracy. Results also showed how Principal Component Analysis was most beneficial when the training data was expanded by augmentation of the available data

Chaos‐based privacy preserving vehicle safety protocol for 5G Connected Autonomous Vehicle networks

There is a high demand for secure and reliable communications for Connected Autonomous Vehicles (CAVs) in the automotive industry. Privacy and security are key issues in CAVs, where network attacks can result in fatal accidents. The computational time, cost, and robustness of encryption algorithms are important factors in low latency 5G‐enabled secure CAV networks. The presented chaotic Tangent‐Delay Ellipse Reflecting Cavity‐Map system and PieceWise Linear Chaotic Map‐based encryption on short messages exchanged in a CAV network provide both robustness and high speed encryption. In this work, we propose a 5G radio network architecture, which leverages multiple radio access technologies and utilizes Cloud Radio Access Network functionalities for privacy preserved and secure CAV networks. The proposed Vehicular Safety Message identifier algorithm meets transmission requirements with a high probability of 85% for low round trip delay of ≤50 milliseconds. The proposed chaos‐based encryption algorithm exhibits faster speeds with a computational time of 2 to 3 milliseconds, showcasing its lightweight properties ideal for time critical applications

Genetic dissection of Ni toxicity in a spring wheat diversity panel by using 90 K SNP array

Excess Ni intake has harmful implications on human health, which include chronic bronchitis, reduced lung function, and cancer of lung and nasal sinuses. Like other toxic metals, higher Ni accumulation in grains leads to excess intake by humans when the contaminated grains are consumed as food. There is little information about the genetic factors that regulate Ni uptake in plants. To investigate genetic architecture of Ni uptake in leaf and translocation to grain, we performed a genome-wide association study with genotyping from 90 K array in a historical bread wheat diversity panel from Pakistan. We observed that Ni toxicity caused more than 50 % reductions in biological yield and grain yield, other agronomic traits were also partly or severely affected. Genetic association study helped identify 23 SNP-trait associations involved in Ni uptake in leaf and translocation to grains. These 23 SNPs covered 15 genomic loci at chromosomes 1A, 2D, 3B, 4A and 4B of wheat. The favorable alleles of these SNPs were randomly distributed in subpopulations indicating no selection pressure for this trait during breeding improvement. These regions had 283 low-confidence and 248 high-confidence protein coding genes. Among these, 156 were annotated using databases of wheat and closely related grass species. Since there is no previous report on genetic information of Ni uptake and translocation, these results provide sufficient grounds for further research of candidate genes and varietal development.Peer reviewe

WiFreeze: Multiresolution Scalograms for Freezing of Gait Detection in Parkinson's Leveraging 5G Spectrum with Deep Learning

Freezing of Gait (FOG) is an episodic absence of forward movement in Parkinson's Disease (PD) patients and represents an onset of disabilities. FOG hinders daily activities and increases fall risk. There is high demand for automating the process of FOG detection due to its impact on health and well being of individuals. This work presents WiFreeze, a noninvasive, line of sight, and lighting agnostic WiFi-based sensing system, which exploits ambient 5G spectrum for detection and classification of FOG. The core idea is to utilize the amplitude variations of wireless Channel State Information (CSI) to differentiate between FOG and activities of daily life. A total of 225 events with 45 FOG cases are captured from 15 patients with the help of 30 subcarriers and classification is performed with a deep neural network. Multiresolution scalograms are proposed for time-frequency signatures of human activities, due to their ability to capture and detect transients in CSI signals caused by transitions in human movements. A very deep Convolutional Neural Network (CNN), VGG-8K, with 8K neurons each, in fully connected layers is engineered and proposed for transfer learning with multiresolution scalogram features for detection of FOG. The proposed WiFreeze system outperforms all existing wearable and vision-based systems as well as deep CNN architectures with the highest accuracy of 99.7% for FOG detection. Furthermore, the proposed system provides the highest classification accuracies of 94.3% for voluntary stop and 97.6% for walking slow activities, with improvements of 9% and 23%, respectively, over the best performing state-of-the-art deep CNN architecture

Burnout among surgeons before and during the SARS-CoV-2 pandemic: an international survey

Background: SARS-CoV-2 pandemic has had many significant impacts within the surgical realm, and surgeons have been obligated to reconsider almost every aspect of daily clinical practice. Methods: This is a cross-sectional study reported in compliance with the CHERRIES guidelines and conducted through an online platform from June 14th to July 15th, 2020. The primary outcome was the burden of burnout during the pandemic indicated by the validated Shirom-Melamed Burnout Measure. Results: Nine hundred fifty-four surgeons completed the survey. The median length of practice was 10 years; 78.2% included were male with a median age of 37 years old, 39.5% were consultants, 68.9% were general surgeons, and 55.7% were affiliated with an academic institution. Overall, there was a significant increase in the mean burnout score during the pandemic; longer years of practice and older age were significantly associated with less burnout. There were significant reductions in the median number of outpatient visits, operated cases, on-call hours, emergency visits, and research work, so, 48.2% of respondents felt that the training resources were insufficient. The majority (81.3%) of respondents reported that their hospitals were included in the management of COVID-19, 66.5% felt their roles had been minimized; 41% were asked to assist in non-surgical medical practices, and 37.6% of respondents were included in COVID-19 management. Conclusions: There was a significant burnout among trainees. Almost all aspects of clinical and research activities were affected with a significant reduction in the volume of research, outpatient clinic visits, surgical procedures, on-call hours, and emergency cases hindering the training. Trial registration: The study was registered on clicaltrials.gov "NCT04433286" on 16/06/2020