Modeling the Role of Maternal Care in the Educational and Health Development of the Children

Mothers play different roles in their family which has an effect on  health and well being of all members of the family specially on children. Almost in all different societies around the world, they have been assigned to be primary care-givers to infants and children. The present study investigates the relationship of maternal care and its effects on the educational performance and health of children in Pakistan from a statistical perspective. The modeling and significance has been established by measures of association and automated linear regression

Code-Switched Urdu ASR for Noisy Telephonic Environment using Data Centric Approach with Hybrid HMM and CNN-TDNN

Call Centers have huge amount of audio data which can be used for achieving valuable business insights and transcription of phone calls is manually tedious task. An effective Automated Speech Recognition system can accurately transcribe these calls for easy search through call history for specific context and content allowing automatic call monitoring, improving QoS through keyword search and sentiment analysis. ASR for Call Center requires more robustness as telephonic environment are generally noisy. Moreover, there are many low-resourced languages that are on verge of extinction which can be preserved with help of Automatic Speech Recognition Technology. Urdu is the $10^{th}$ most widely spoken language in the world, with 231,295,440 worldwide still remains a resource constrained language in ASR. Regional call-center conversations operate in local language, with a mix of English numbers and technical terms generally causing a "code-switching" problem. Hence, this paper describes an implementation framework of a resource efficient Automatic Speech Recognition/ Speech to Text System in a noisy call-center environment using Chain Hybrid HMM and CNN-TDNN for Code-Switched Urdu Language. Using Hybrid HMM-DNN approach allowed us to utilize the advantages of Neural Network with less labelled data. Adding CNN with TDNN has shown to work better in noisy environment due to CNN's additional frequency dimension which captures extra information from noisy speech, thus improving accuracy. We collected data from various open sources and labelled some of the unlabelled data after analysing its general context and content from Urdu language as well as from commonly used words from other languages, primarily English and were able to achieve WER of 5.2% with noisy as well as clean environment in isolated words or numbers as well as in continuous spontaneous speech.Comment: 32 pages, 19 figures, 2 tables, preprin

Domestic Violence against Women: Statistical Analysis and Legislative Solutions

Violence against women contains many facets and beating wives is one of the most prevalent in Pakistan. The issue has been justified on state and private social level based upon religious and social arguments. Women empowerment and equal protection before law is guaranteed on both constitutional and legislative levels but effective implementation of these laws to gain equal living standards to wives is still a far cry. This paper aims to study the issue at different levels. It will define the domain of issue by statistical analysis based upon data provided by United Nation Global Data Base on violence against women and will probe into factors hindering the enforcement of state aspirations to protect women from violence at family level

Domestic Violence against Women: Statistical Analysis and Legislative Solutions

Violence against women contains many facets and beating wives is one of the most prevalent in Pakistan. The issue has been justified on state and private social level based upon religious and social arguments. Women empowerment and equal protection before law is guaranteed on both constitutional and legislative levels but effective implementation of these laws to gain equal living standards to wives is still a far cry. This paper aims to study the issue at different levels. It will define the domain of issue by statistical analysis based upon data provided by United Nation Global Data Base on violence against women and will probe into factors hindering the enforcement of state aspirations to protect women from violence at family level

Recent advances in traumatic brain injury

Abstract: Traumatic brain injury (TBI) is the most common cause of death and disability in those aged under 40 years in the UK. Higher rates of morbidity and mortality are seen in low-income and middle-income countries making it a global health challenge. There has been a secular trend towards reduced incidence of severe TBI in the first world, driven by public health interventions such as seatbelt legislation, helmet use, and workplace health and safety regulations. This has paralleled improved outcomes following TBI delivered in a large part by the widespread establishment of specialised neurointensive care. This update will focus on three key areas of advances in TBI management and research in moderate and severe TBI: refining neurointensive care protocolized therapies, the recent evidence base for decompressive craniectomy and novel pharmacological therapies. In each section, we review the developing evidence base as well as exploring future trajectories of TBI research

Endophytic Potential of Entomopathogenic Fungi for the Remediation of Wastewater

Phytoremediation has the potential to significantly reduce water contamination caused by excessive harmful chemicals. The degradative properties of fungi are used in fungal phytoremediation to eliminate or neutralise the hazardous pollutants present in water. The goal of the current study was to endophytize water lettuce with the two entomopathogenic fungus Metarhizium anisopilae and Trichoderma harazium. The plant is inoculated with the fungus using the root-dipping procedure. There were two main treatments and a control all with five replications. The analysis of plant and wastewater were analyzed initially like frequency of fungus remained in plant weight, root length and for water that was Biological oxygen demand (BOD), Chemical Oxygen demand (COD), and heavy metals (Copper, Nickle, Zinc and Cadmium). The data were taken for 3rd 5th and 7th day of the experiment. The results exhibit that T. harazium exhibited the 82.67 % followed by the M. anisopilae with 65.33 % as compared to control with 1.33 % mean frequency the 10th day of inoculation. Maximum weight 295.98 and 265.13 g and root length were maximum recorded 15.18 and 18.12 cm respectively at the end of the experiment. Performance of T. harazium endophytic plant found to be 90.7 % for BOD, 73.82 % for COD. The removal % of Cu, Zn, Ni, Cd exhibited 75.13, 96.58, 87.14, 61.17 % after 7d of treatment. In case of M. anisopilae, 85.2 % for BOD, 69.38 % for COD. The removal % of Cu, Zn, Ni, Cd exhibited 66.48, 89.43, 77.42, 52.4 % after 7d of treatment. The treatments exhibited the remarkable reduction in pollutants and increase in plant weight and root length

Role of Medicine Patent Pool (MPP) in Resolving Conflict between Patents and Access to Essential Medicines

This paper analyses the issue from both international intellectual property law and access to medicine with reference to contributions made by Medicine Patent Pool (MPP), an alternate model of resolving conflict between patent protection of medicines and access to medicine. Adoption of Trade-Related Aspects of Intellectual Property Rights (TRIPS) Agreement, under framework of World Trade Organisation (WTO), has significantly altered the enforcement standards of intellectual property rights, especially patent rights (Halewood, 1997).  Although, TRIPS Agreement introduces minimum standards of intellectual property rights protection but in case of pharmaceutical patents, they have impact on access to essential medicines because of strict standards (Kojo, 2018). This paper aims at analysing role of MPP towards solving conflict between patents on medicines and access to medicine

Reducing Prediction volatility in the surgical workflow recognition of endoscopic pituitary surgery

PURPOSE: Workflow recognition can aid surgeons before an operation when used as a training tool, during an operation by increasing operating room efficiency, and after an operation in the completion of operation notes. Although several methods have been applied to this task, they have been tested on few surgical datasets. Therefore, their generalisability is not well tested, particularly for surgical approaches utilising smaller working spaces which are susceptible to occlusion and necessitate frequent withdrawal of the endoscope. This leads to rapidly changing predictions, which reduces the clinical confidence of the methods, and hence limits their suitability for clinical translation. METHODS: Firstly, the optimal neural network is found using established methods, using endoscopic pituitary surgery as an exemplar. Then, prediction volatility is formally defined as a new evaluation metric as a proxy for uncertainty, and two temporal smoothing functions are created. The first (modal, Mn) mode-averages over the previous n predictions, and the second (threshold, Tn) ensures a class is only changed after being continuously predicted for n predictions. Both functions are independently applied to the predictions of the optimal network. RESULTS: The methods are evaluated on a 50-video dataset using fivefold cross-validation, and the optimised evaluation metric is weighted-F1 score. The optimal model is ResNet-50+LSTM achieving 0.84 in 3-phase classification and 0.74 in 7-step classification. Applying threshold smoothing further improves these results, achieving 0.86 in 3-phase classification, and 0.75 in 7-step classification, while also drastically reducing the prediction volatility. CONCLUSION: The results confirm the established methods generalise to endoscopic pituitary surgery, and show simple temporal smoothing not only reduces prediction volatility, but actively improves performance