Population Density, Habitat Characteristics and Preferences of Red Fox (Vulpes vulpes) in Chakwal, Pakistan

The Red fox (Vulpes vulpes) is a least concern carnivore according to the IUCN Red List of Threatened Species (2016). However, in Pakistan Red fox is considered as Near Threatened (NT), due to habitat destruction and depletion of food resources. The objective of the study was to identify habitat preferences and population density of Red fox in District Chakwal, Pakistan. Line transect census method was used to estimate the population density of Red fox through direct sighting and indirect method of burrow counting, presence of footprints and scats. A total of 10 transects were carried out at three potential sites: Devi, Photaki and Chumbisurla Wildlife Sanctuary (CWS) in Chakwal based on preliminary surveys. Habitat preference was estimated by comparing three different study sites by quadrat method and found that CWS area is preferred habitat for Red fox. A total of 24 plant species were recorded in the study areas, among them Cynodon dactylon is major herb found to provide shelter to Red fox in all study sites based on Importance value Index (I.V.I) at CWS (IVI=208.8) followed by Devi (IVI=185.93) and Photaki (IVI=142.33). The maximum population density of Red fox through direct sighting at CWS having 0.26 individuals/km2 compared to Devi and Photaki having 0.16 and 0.13 individuals/km2, respectively. The indirect estimation method revealed that maximum dens were found in CWS area compared to Devi and Photaki, while footprints and scats were found maximum in Devi and Photaki, respectively. It is concluded that Red fox preferred habitat is CWS site. Habitat destruction and conflicts with fox are causing the population of the Red fox to dwindle in Chakwal, Pakistan

Population Density, Habitat Characteristics and Preferences of Red Fox (Vulpes vulpes) in Chakwal, Pakistan

The Red fox (Vulpes vulpes) is a least concern carnivore according to the IUCN Red List of Threatened Species (2016). However, in Pakistan Red fox is considered as Near Threatened (NT), due to habitat destruction and depletion of food resources. The objective of the study was to identify habitat preferences and population density of Red fox in District Chakwal, Pakistan. Line transect census method was used to estimate the population density of Red fox through direct sighting and indirect method of burrow counting, presence of footprints and scats. A total of 10 transects were carried out at three potential sites: Devi, Photaki and Chumbisurla Wildlife Sanctuary (CWS) in Chakwal based on preliminary surveys. Habitat preference was estimated by comparing three different study sites by quadrat method and found that CWS area is preferred habitat for Red fox. A total of 24 plant species were recorded in the study areas, among them Cynodon dactylon is major herb found to provide shelter to Red fox in all study sites based on Importance value Index (I.V.I) at CWS (IVI=208.8) followed by Devi (IVI=185.93) and Photaki (IVI=142.33). The maximum population density of Red fox through direct sighting at CWS having 0.26 individuals/km2 compared to Devi and Photaki having 0.16 and 0.13 individuals/km2, respectively. The indirect estimation method revealed that maximum dens were found in CWS area compared to Devi and Photaki, while footprints and scats were found maximum in Devi and Photaki, respectively. It is concluded that Red fox preferred habitat is CWS site. Habitat destruction and conflicts with fox are causing the population of the Red fox to dwindle in Chakwal, Pakistan

Does Rural–Urban Migration Improve Employment Quality and Household Welfare? Evidence from Pakistan

Urban migration unlocks new employment opportunities for rural dwellers in a productive manner. This study assessed the quality of employment of migrant workers, and its effect on rural households’ welfare. To this end, we used primary data collected from the four major districts of Lahore, Gujranwala, Faisalabad, and Sialkot in Punjab, Pakistan. These data include 504 immigrant and non-immigrant families in rural areas, and 252 migrant workers in urban destinations. We use IV probit and two-step sequential estimation methods for the empirical analysis. The study provides new insights for migration in Pakistan. First, migrant workers are better off in their new urban settings in terms of improved incomes and living conditions, but their social protection status is still poor. Second, the results of the employment quality models show that migration is a successful strategy for rural households to improve the quality of their employment. In addition, the characteristics of migrants and native households affect the relative improvement in the quality of employment and migrants’ conditions. Third, the results of the propensity score matching technique suggest that migration has a positive impact on rural households’ income, and these impacts are more pronounced in large cities. Based on the findings, the study recommends that the government should invest in quality education in rural areas, and ensure that social security schemes are provided for migrant workers in urban areas

Analyzing Classification Performance of fNIRS-BCI for Gait Rehabilitation Using Deep Neural Networks

This research presents a brain-computer interface (BCI) framework for brain signal classification using deep learning (DL) and machine learning (ML) approaches on functional near-infrared spectroscopy (fNIRS) signals. fNIRS signals of motor execution for walking and rest tasks are acquired from the primary motor cortex in the brain’s left hemisphere for nine subjects. DL algorithms, including convolutional neural networks (CNNs), long short-term memory (LSTM), and bidirectional LSTM (Bi-LSTM) are used to achieve average classification accuracies of 88.50%, 84.24%, and 85.13%, respectively. For comparison purposes, three conventional ML algorithms, support vector machine (SVM), k-nearest neighbor (k-NN), and linear discriminant analysis (LDA) are also used for classification, resulting in average classification accuracies of 73.91%, 74.24%, and 65.85%, respectively. This study successfully demonstrates that the enhanced performance of fNIRS-BCI can be achieved in terms of classification accuracy using DL approaches compared to conventional ML approaches. Furthermore, the control commands generated by these classifiers can be used to initiate and stop the gait cycle of the lower limb exoskeleton for gait rehabilitation

LASSO Homotopy-Based Sparse Representation Classification for fNIRS-BCI

Brain-computer interface (BCI) systems based on functional near-infrared spectroscopy (fNIRS) have been used as a way of facilitating communication between the brain and peripheral devices. The BCI provides an option to improve the walking pattern of people with poor walking dysfunction, by applying a rehabilitation process. A state-of-the-art step-wise BCI system includes data acquisition, pre-processing, channel selection, feature extraction, and classification. In fNIRS-based BCI (fNIRS-BCI), channel selection plays a vital role in enhancing the classification accuracy of the BCI problem. In this study, the concentration of blood oxygenation (HbO) in a resting state and in a walking state was used to decode the walking activity and the resting state of the subject, using channel selection by Least Absolute Shrinkage and Selection Operator (LASSO) homotopy-based sparse representation classification. The fNIRS signals of nine subjects were collected from the left hemisphere of the primary motor cortex. The subjects performed the task of walking on a treadmill for 10 s, followed by a 20 s rest. Appropriate filters were applied to the collected signals to remove motion artifacts and physiological noises. LASSO homotopy-based sparse representation was used to select the most significant channels, and then classification was performed to identify walking and resting states. For comparison, the statistical spatial features of mean, peak, variance, and skewness, and their combination, were used for classification. The classification results after channel selection were then compared with the classification based on the extracted features. The classifiers used for both methods were linear discrimination analysis (LDA), support vector machine (SVM), and logistic regression (LR). The study found that LASSO homotopy-based sparse representation classification successfully discriminated between the walking and resting states, with a better average classification accuracy (p < 0.016) of 91.32%. This research provides a step forward in improving the classification accuracy of fNIRS-BCI systems. The proposed methodology may also be used for rehabilitation purposes, such as controlling wheelchairs and prostheses, as well as an active rehabilitation training technique for patients with motor dysfunction

Correlation of Smart Phone Addiction with Poor Sleep Quality and Low Academic Score in Medical Students of Nishtar Medical University, South Punjab

Background: Smart phone addiction can disturb sleep quality in medical students by upsetting internal biological clock (SCN) and melatonin level which in turn adversely affects academic performance of medical students. Objectives: To correlate smart phone addiction (SPA), poor sleep quality and low academic score in medical students and its association with gender. Methodology: A Cross- sectional descriptive study was conducted on medical students of 4th and final year MBBS (who were mobile phone addict for more than one year). The percentage of last professional exam was taken as academic score. A proposed SPA diagnostic criterion was used to diagnose smart phone addict students. For SPA severity and sleep quality assessment Problematic Mobile Phone Use Questionnaire (PMPU-Q) and Pittsburgh Sleep Quality Index (PSQI) were used. Results: A total of 74 subjects having mean (SD) age of 22.24 (1.929) years presented with negative correlation between academic score and PSQI (p&lt; 0.05) were included in the study. There was no significant association between academic score and smart phone addiction. The male students had worse score in dependency and dangerous use on PMUQ scale. The female students were worse in dangerous and problematic use of smart phone on PMUQ scale. Conclusion: The SP dependency and poor subjective sleep quality of male students were negatively associated with their academic score. The female students with low academic score were worse at PSQI score, their academic score was not significantly correlated with their smart phone addiction. Keywords: Smartphone addiction, Sleep Quality, Academic Scor

Advancements, Trends and Future Prospects of Lower Limb Prosthesis

Amputees with lower limb loss need special care during daily life activities to make the movement natural as before amputation. No such work exists covering the main aspects from causes of amputation to the psycho-social impact of the amputees after using the prosthetic device. This review presents for lower limb prosthesis; the study of lower limb amputation, design &#x0026; development, control strategies &#x0026; machine learning algorithms, the psycho-social impact of prosthetic users, and design trends in patents. Research articles, review papers, magazines, letters, study reports, surveys, and patents, etc. have been used as sources for this review. Traumatic injuries and different diseases have been found as common causes of amputation. Design &#x0026; development section illustrates design mechanisms, the categories of passive, active, &#x0026; semi-active prostheses, an overview of a subset of commercially available prosthetic devices, and 3D printing of the accessories. The control section provides information about control techniques, sensors used, machine learning algorithms, and their key outcomes. Quality of life, phantom limb pain, and psycho-social impact of prosthetic users have been summarized for different countries that are believed to attract the interest of the readers. We have also developed an open-source database &#x201C;FAKH-50&#x201D; for patents to emphasize the design trends and advancements in lower limb prostheses from 1970 to 2020. Overall trend analysis determined is in the descending order as the knee (48&#x0025;) &#x003E; ankle (28&#x0025;) &#x003E; foot (22&#x0025;) &#x003E; hip (2&#x0025;) patents in the current version of our database. The forthcoming section highlights the challenges and prospects of the domain. A mutual observation demands the design of a bio-compatible, lightweight, and economic prosthesis to track the normal human gait by eliminating phantom limb pain. This will empower the amputees to live a quality life in society. This work may be beneficial for researchers, technicians, clinicians, and amputees

Enhancing Classification Performance of fNIRS-BCI by Identifying Cortically Active Channels Using the z-Score Method

A state-of-the-art brain&ndash;computer interface (BCI) system includes brain signal acquisition, noise removal, channel selection, feature extraction, classification, and an application interface. In functional near-infrared spectroscopy-based BCI (fNIRS-BCI) channel selection may enhance classification performance by identifying suitable brain regions that contain brain activity. In this study, the z-score method for channel selection is proposed to improve fNIRS-BCI performance. The proposed method uses cross-correlation to match the similarity between desired and recorded brain activity signals, followed by forming a vector of each channel&rsquo;s correlation coefficients&rsquo; maximum values. After that, the z-score is calculated for each value of that vector. A channel is selected based on a positive z-score value. The proposed method is applied to an open-access dataset containing mental arithmetic (MA) and motor imagery (MI) tasks for twenty-nine subjects. The proposed method is compared with the conventional t-value method and with no channel selected, i.e., using all channels. The z-score method yielded significantly improved (p &lt; 0.0167) classification accuracies of 87.2 &plusmn; 7.0%, 88.4 &plusmn; 6.2%, and 88.1 &plusmn; 6.9% for left motor imagery (LMI) vs. rest, right motor imagery (RMI) vs. rest, and mental arithmetic (MA) vs. rest, respectively. The proposed method is also validated on an open-access database of 17 subjects, containing right-hand finger tapping (RFT), left-hand finger tapping (LFT), and dominant side foot tapping (FT) tasks.The study shows an enhanced performance of the z-score method over the t-value method as an advancement in efforts to improve state-of-the-art fNIRS-BCI systems&rsquo; performance

Treatment Efficacy of Sofosbuvir and Ribavirin Combination at Two Weeks in Chronic Hepatitis C

Background:To determine the effectiveness of sofosbuvir plus ribavirin in terms of frequency of negative qualitative PCR at 2nd week of treatment in chronic hepatitis C patients with genotype 3. Methods: In this case control study &nbsp;60 patients with hepatitis C who were planned to receive sofobuvir and ribavarin combination therapy were included . Patients included&nbsp; were&nbsp; chronically infected with hepatitis C virus genotype 3 for whom treatment with peg-interferon is not an option and have contraindication for their use like decompensated liver disease, and patients are either non responder or relapsers to previous interferon based therapy. Pregnant or breast-feeding women, patients taking any of the medications which had interactions with sofosbuvir and patients who had not been compliant to sofosbuvir plus ribavirin therapy were excluded. Sofosbuvir was given in dose 400 mg once daily and ribavirin was given in dose of 400 or 600 twice daily(if weight &lt;75kg or &gt;75kg respectively). Patients were followed at 2nd week of treatment and qualitative PCR for HCV RNA was carried out Results:Total sixty patients fulfilling the inclusion criteria were included in this study. Overall efficacy of sofosbuvir and ribavirin combination was 91. 7%. Majority of patients 55(91.7%) attained negative PCR for HCV RNA at2nd week of treatment). Conclusion: Sofosbuvir plus ribavirin is an effective Treatment regimen as far as viral clearance at 2nd week of treatment is considered