Influence of the Supervision Related Background Variables on the Supervisees’ Supervision Experiences at Postgraduate Level

This research aims to investigate the influence of some supervision related background variables on the supervisees’ supervision experiences during their research at postgraduate level. We conducted a survey from (N= 422) supervisees using Supervisor-Supervisee. Relationship Questionnaire (SSRQ, 65 items) scored on six point scale. The items of SSRQ were developed on six supervision aspects to find the supervision related experiences in addition to the selected background variables. The subscale wise content validity and reliability of the SSRQ was ensured. Inferential statistics were applied to achieve the main objectives of the research. The findings of the research highlighted the importance of supervisors’ expertise and research skills in the supervisees’ area of research. The supervisees who were not given choice for the selection of the supervisor have reported negative supervision experiences. Giving choice to the selection of a supervisor can improve the supervision experiences of supervisees and can minimize the potential personality and research interest related anomalies. Supervisees from the social sciences disciplines reported the problem of workload management during the supervision process. On the basis of findings it is suggested that supervision allotment procedure, alignment between supervisors’ area of specialization and supervisee research topic and discipline specific supervision trainings may be initiated

Assessing the Quality of Supervision Experiences in the Different Research Stages at Postgraduate Level

The nature of supervisory relationship is dynamic that changes over the course of candidacy. Subsequently, the supervision demands of supervisees also change to help them in performing certain tasks at specific research stages. In this context, this research is proposed to investigate the supervision experiences of supervisees in four different research stages (i.e. Stage 1: developing synopsis, Stage 2: collecting data, Stage 3: writing thesis and Stage 4: submitted thesis). In the light of six supervision aspects (i.e. Project management, Intellectual support, pertinent research skills, Inter-personal communication skills, Workload management and Supportive skills) the quality of supervision was assessed from supervisees’ perspective. Cross-sectional survey design was used to assess the supervision experiences of (N=422) supervisees in four distinct stages of research supervision. The data were collected from 12 public and private universities of the Punjab. For this study, a multi-sectioned, self-constructed Supervisor-Supervisee Relationship Questionnaire (Saleem, 2014) was used. The findings of the study highlighted the need to train the supervisors to manage their time not just in terms of teaching and supervision of research students, but also keeping the stages of their research in mind. By addressing the stage specific needs of supervisees the quality of supervision could be improved.Keywords: quality of supervision, research stages, supervision dynamics, supervision experience

Spectral Restoration Based Speech Enhancement for Robust Speaker Identification

Spectral restoration based speech enhancement algorithms are used to enhance quality of noise masked speech for robust speaker identification. In presence of background noise, the performance of speaker identification systems can be severely deteriorated. The present study employed and evaluated the Minimum Mean-Square-Error Short-Time Spectral Amplitude Estimators with modified a priori SNR estimate prior to speaker identification to improve performance of the speaker identification systems in presence of background noise. For speaker identification, Mel Frequency Cepstral coefficient and Vector Quantization is used to extract the speech features and to model the extracted features respectively. The experimental results showed significant improvement in speaker identification rates when spectral restoration based speech enhancement algorithms are used as a pre-processing step. The identification rates are found to be higher after employing the speech enhancement algorithms

Deep Neural Networks for Speech Enhancement in Complex-Noisy Environments

In this paper, we considered the problem of the speech enhancement similar to the real-world environments where several complex noise sources simultaneously degrade the quality and intelligibility of a target speech. The existing literature on the speech enhancement principally focuses on the presence of one noise source in mixture signals. However, in real-world situations, we generally face and attempt to improve the quality and intelligibility of speech where various complex stationary and nonstationary noise sources are simultaneously mixed with the target speech. Here, we have used deep learning for speech enhancement in complex-noisy environments and used ideal binary mask (IBM) as a binary classification function by using deep neural networks (DNNs). IBM is used as a target function during training and the trained DNNs are used to estimate IBM during enhancement stage. The estimated target function is then applied to the complex-noisy mixtures to obtain the target speech. The mean square error (MSE) is used as an objective cost function at various epochs. The experimental results at different input signal-to-noise ratio (SNR) showed that DNN-based complex-noisy speech enhancement outperformed the competing methods in terms of speech quality by using perceptual evaluation of speech quality (PESQ), segmental signal-to-noise ratio (SNRSeg), log-likelihood ratio (LLR), weighted spectral slope (WSS). Moreover, short-time objective intelligibility (STOI) reinforced the better speech intelligibility

Effects Of Locus Of Control On Gender

The study was intended to determine the gender differences in the locus of control with academic achievement among secondary school science students of Wah Cantonment area. Locus of control means expectancy whether perceived reinforcement is under internal or external control. It is individual variations in his belief that what control the events in his life, himself or others. 270 students were chosen from the Federal Government High School for Girls and Boys and private secondary schools of Wah Cantonment area. 35 items self-developed questionnaire was administered to determine the locus of control. The instrument was validated by expert suggestions of concerned area. t-test was applied for numerical analysis by statistical package for social sciences (SPSS). It was concluded from the study results that the students have belief that they can or can’t control events that affect their academic achievement. The significant difference found between the mean locus of control scores of boys and the mean locus of control scores of girls. The boys were found superior to girls in their mean locus of control scores which shows that boys have comparatively strong belief then girls about the control of some events that affect their academic achievement

Noninvasive Follicular Thyroid Neoplasms with Papillary Like Nuclear Features (NIFTP) Birth of a New Entity-An Attempt to Prevent Cancer Overdiagnosis

There is a worldwide emphasis on preventing overdiagnosis of disease. Overdiagnosis is defined as the diagnosis of a disease or condition that is unlikely to ever cause harm.1 Overdiagnosis is a huge problem which has serious consequences for the patients as well as health care system. For the patients, these include unnecessary treatment with consequent adverse effects and unnecessary monetary burden, as well as the psychological trauma. For health care system, it causes waste of precious resources and unduly increased work load for the health professionals. Such is the global concern for this problem that since 2013, health professionals from all around the world gather for “Preventing Overdiagnosis” conference, held every year. Cancer overdiagnosis has a much greater potential for harm due to its enormous psychological trauma, arduous treatment protocol, rife with life threatening complications and staggering cost for the patient and healthcare system.

Big data and artificial intelligence in earth science: recent progress and future advancements

[abstract not available

Efficient Gated Convolutional Recurrent Neural Networks for Real-Time Speech Enhancement

Deep learning (DL) networks have grown into powerful alternatives for speech enhancement and have achieved excellent results by improving speech quality, intelligibility, and background noise suppression. Due to high computational load, most of the DL models for speech enhancement are difficult to implement for realtime processing. It is challenging to formulate resource efficient and compact networks. In order to address this problem, we propose a resource efficient convolutional recurrent network to learn the complex ratio mask for real-time speech enhancement. Convolutional encoder-decoder and gated recurrent units (GRUs) are integrated into the Convolutional recurrent network architecture, thereby formulating a causal system appropriate for real-time speech processing. Parallel GRU grouping and efficient skipped connection techniques are engaged to achieve a compact network. In the proposed network, the causal encoder-decoder is composed of five convolutional (Conv2D) and deconvolutional (Deconv2D) layers. Leaky linear rectified unit (ReLU) is applied to all layers apart from the output layer where softplus activation to confine the network output to positive is utilized. Furthermore, batch normalization is adopted after every convolution (or deconvolution) and prior to activation. In the proposed network, different noise types and speakers can be used in training and testing. With the LibriSpeech dataset, the experiments show that the proposed real-time approach leads to improved objective perceptual quality and intelligibility with much fewer trainable parameters than existing LSTM and GRU models. The proposed model obtained an average of 83.53% STOI scores and 2.52 PESQ scores, respectively. The quality and intelligibility are improved by 31.61% and 17.18% respectively over noisy speech

Management of Post Traumatic Epilepsy in Pediatric Population in Pakistan

Objective:  To investigate the efficacy of seizure prophylaxis in the prevention of early and late-onset seizures after the traumatic brain injury known as post-traumatic epilepsy (PTE). Material and Methods:  A retrospective study was performed on children aged 0 to < 12 years who were presented to a level 1 trauma center during the six months with the diagnosis of mild to severe TBI. Data included is of 66 patients from Children’s Hospital, Lahore. It was analyzed according to a patient’s demographic data, mechanism of injury, clinical and radiological presentation, management, and follow-up. History of seizures was tracked through guardian referral or staff witnesses. Results:  Among 66 pediatric cases of acute brain injury from June 2019 to December 2019, 45 were males (68%) and 21 were females (32%) with a male to female ratio of 2:1. The mean age in our study was 3.8 years. 60% of children were managed under observation, 30% of children required medical pharmacological treatment, 9% of children needed surgical intervention, and 13% of children required artificial ventilation. Overall mortality is 4.5%. In our study, we found a considerable relationship between residual neurological deficits and severity of injury (p = 0.3), there is no noteworthy relationship between mechanism of injury and outcome (p = 0.5). The mean length of stay was 3.9 days but 60% of patients had stayed less than 3 days. Conclusion:  Analyzing the underlying mechanisms of post-traumatic epilepsy can lead us to propose effective treatments to prevent seizures following traumatic brain injury