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MALL has grown in ESL classes during the previous decade. Researchers may now study the pros and cons of blended learning (BL), which combines online and in-person education. This quasi-experimental research examined how BL affects reading comprehension. This research examined how blended learning (BL) affected students' writing comprehension. 60 Pakistani secondary school pupils were split into control and experimental groups. The experimental group used "Reading Comprehension" mobile phone software to merge traditional teaching with blended learning for two months. An independent samples t-test compared pre-and post-test results. BL improved reading comprehension in the research.

Learning a language is a cognitive activity, and most of our personality traits, like extroversion and introversion, have demonstration in our language. The present study was conducted to explore the issues of language learning by science students at the University of Lahore. A mix-method was used in which both quantitative and qualitative techniques were employed on a population size of 150 participants. A survey questionnaire was prepared to collect data for achieving the objective of the present study. The questionnaire was comprised of two parts. A close-ended questionnaire and one open-ended question were used for data collection. The goal of the 12-question survey was to find out what students thought about the problems science students had learning English in four main areas: reading, writing, listening, and speaking, as well as the overall learning environment in science classes. Based on the survey results, we calculated the mean and standard deviation of science major students' views on the challenges of learning English in their science classrooms. According to the qualitative findings, the poor English language proficiency of scientific students is attributable to the traditional teaching methods employed in science classrooms.

Classification plays a critical role in machine learning (ML) systems for processing images, text and high -dimensional data. Predicting class labels from training data is the primary goal of classification. An optimal model for a particular classification problem is chosen on the basis of the model's performance and execution time. This paper compares and analyses the performance of basic as well as ensemble classifiers utilizing 10 -fold cross validation and also discusses their essential concepts, advantages, and disadvantages. In this study five basic classifiers namely Naïve Bayes (NB), Multi-layer Perceptron (MLP), Support Vector Machine (SVM), Decision Tree (DT), and Random Forest (RF) and the ensemble of all the five classifiers along with few more combinations are compared with five University of California Irvine (UCI) ML Repository datasets and a Diabetes Health Indicators dataset from kaggle repository. To analyze and compare the performance of classifiers, evaluation metrics like Accuracy, Recall, Precision, Area Under Curve (AUC) and F-Score are used. Experimental results showed that SVM performs best on two out of the six datasets (Diabetes Health Indicators and waveform), RF performs best for Arrhythmia, Sonar, Tic-tac-toe datasets, and the best ensemble combination is found to be DT+SVM+RF on Ionosphere dataset having respective accuracies 72.58%, 90.38%, 81.63%, 73.59%, 94.78% and 94.01% and the proposed ensemble combinations outperformed over the conventional models for few datasets.

This study aims to define the process of designing and manufacturing 3D printed and glass models of the pulmonary artery (PA) and utilizing them in a test bed for evaluation of devices for mechanical thrombectomy of pulmonary embolism (PE).Patient derived computed tomography angiography (CTA) images of the PA were digitally converted into a hollowed-out structure and translated into clear 3D printed and glass models. A test bed was created using a peristaltic pump and silicone tubing connected to the models. Human clot analogs were then prepared and injected within the models. Thrombectomy testing was done using clinically used predicates and baseline characteristics of the models were evaluated by independent interventionalists.The mean sizes of the main pulmonary artery (MPA) for the 3D printed model and glass model were 30.4 mm and 29.2 mm, mimicking those of the patient's PA obtained on CTA. Heterogeneous human clot analogs were created with fibrin composition ranging from 60 to 30%. Mechanical thrombectomy was successfully attempted by independent interventionalists. Both the 3D printed, and glass model were appraised as very good for multiple attributes.A complete test bed using 3D printed and glass models of the PA with human clot analogs was created for testing of mechanical thrombectomy devices for PE.

In the present study, omental adipose tissue was collected from, the animals that underwent ovariectomy and ovariohysterectomy, surgical procedures, at the age of seven months to 11/2 years of age groups. The sample was digested with 0.1% (W/V) collagenase type I and transferred to a beaker with a magnetic stirrer and kept in a stirrer with 600 rpm at 37 °C for 30 minutes. The viability of the cell was evaluated by the trypan blue exclusion method using a hemocytometer. Trypan blue had a high affinity to nuclear DNA, which traverse the member in a dead cell and dye it blue. In the present study, the cell yield of fAD-MSCs was 8.15 ± 0.68, 6.55 ± 0.26, 4.85 ± 0.42, 3.90 ± 0.34, and 3.51 ± 0.43 in different age groups viz., 7,8,9 month 1 and 1½ year respectively. In younger age groups, cell yield and viability percentage were more than in animals of higher age groups. In the younger age group, stem cells proliferation status is considered potent for therapeutic application.

Background: Menstruation, also known as the menstrual cycle, is a hormonal process that a woman's body experiences monthly to prepare for a possible pregnancy. Objective: The main aim of this study is to identify the status of knowledge and practice of menstrual hygiene among adolescent girls residing in Panauti Municipality, Kavrepalanchowk, Nepal. Methods: A descriptive cross-sectional study was carried out among 200 adolescent girls residing in ward no. one, two, and three of the Panauti Municipality. A semi-structured questionnaire was used to assess the knowledge and practice level among the respondents. Suitable statistical tools were used to reach the conclusions. Results: The level of knowledge regarding menstruation was adequate in 53.5% of respondents and the level of practice of menstrual hygiene was adequate in 59% of the respondents. The study found that factors like the age of respondents, academics, mother's occupation, annual income and household source of water were significantly associated with the level of practice of menstrual hygiene by adolescent girls. The study also inferred that there was no significant association between the level of knowledge regarding menstruation in adolescent girls and the level of practice of menstrual hygiene. Conclusion: The study concluded that 53.5% of adolescent girls were having good knowledge and 59% of girls were practicing menstrual hygiene. Socio-economic and environmental factors like age of respondents, level of education, occupation of mother, annual household income and household source of water were significantly associated with menstrual hygiene practice.

Prediction of long-term rainfall patterns is a highly challenging task in the hydrological field due to random nature of rainfall events. The contribution of monthly rainfall is important in agriculture and hydrological tasks. This paper proposes two data-driven models, namely biogeography-based extreme learning machine (BBO-ELM) and deep neural network (DNN), to predict one, two, and three month-ahead rainfall over India (All-India and six other homogeneous regions). Three other data-driven models called ELM, genetic algorithm (GA)-based ELM, and particle swarm optimization (PSO)-based ELM are used to compare the performance of the proposed models. Firstly, partial autocorrelation function (PACF) is applied in all datasets to select the optimal number of lags for input to the models. Secondly, the wavelet-based data pre-processing technique is applied in selected optimal lags and feed to the proposed models for achieving higher prediction performance. To investigate the performance of proposed models, a non-parametric statistical test, Anderson–Darling’ Normality test, is performed in all India dataset. The wavelet-based proposed hybrid models show better prediction capability compared to optimal lag-based proposed models. This study shows the successful application of time-series data using proposed techniques (optimal lags-based BBO-ELM and wavelet-based DNN) in the hydrological field which may be used for risk mitigation from dreadful natural events.

Background A scientific publication is a mirror that defines the image of a researcher in his academic and professional world. Neurosurgery and Journal of Neurosurgery (JNS) are the 2 most reputed journals in the neurosurgical community. Methods We evaluated all the original articles published in these 2 journals in the last issue of the year/December (2000–2019). We excluded all review articles and determined number of authors, institutions, and highest educational degrees. Individual abstracts were evaluated for the nature of the study, population, and citations with individual and comparative statistical analyses. Results A total of 682 original articles were analyzed. Neurosurgery had an increase in the number of authors (8.0 ± 4.11 compared with 5.65 ± 1.99), an increase in title word count (15.14 ± 5.05 from 13.2 ± 4.46), and an increase in basic science research (0.9% to 11.6%). Retrospective studies increased in JNS (35.8% to 46.8%), with fewer studies on animals (18.4% to 8.1%). An increase in number of authors from 6.1 ± 2.6 to 7.2 ± 3.2 and title count was similar. Between the 2 journals, the citation score for JNS improved faster compared with Neurosurgery, consistently. Studies of adult patients received more citations than did animal or pediatric studies. Clinical research garnered 19% more citations along with research with more collaborating institutes (coefficient = 3.5). Title count had a weak negative correlation to citations received. Conclusions Multiauthor and multi-institutional studies ensure greater expertise and thus better impact. Limited by the sample size, only a weak correlation was found with increased title count and retrospective studies with citations, although their increase is an indicator of future trends. Both journals have shown a steady increase in their impact and quality of publications. To the best of our knowledge, our study is the first to examine the correlation of various factors and citations in neurosurgery in the post-2000 era.

Objectives: The development and evaluation of percutaneous thrombectomy devices for pulmonary embolism (PE) pose a need for standardized large in vivo models with representative anatomical and physiological conditions and clots analogs. In this study, we present a swine model of PE model employing human-derived clot analogs. Material and Methods: Baseline angiographic and physiological pressure measurements were obtained in six adult Yorkshire pigs (45–65 kg) and results were benchmarked for interspecies comparison with published human data using fluoroscopic examinations, intra-arterial pressure measurements, and histologic studies. Then, clot analogs were created ex vivo employing banked human blood and a subset incubated in iodinated contrast for fluoroscopic visualization. Clot analogs were then embolized via a femoral venous access and angiographic/physiological consequences were evaluated. Results: The main, right, and left pulmonary artery diameters were 24 ± 1.1 mm, 16.5 ± 0.8 mm, and 12.6 ± 1.2 mm, respectively. The angle between the main pulmonary artery at the bifurcation point was approximately 90–95°. The clot analogs were heterogeneous and had increased fibrin content along the clot length. The overall composition was 96.63% red blood cell (RBC)/3.37% fibrin in the initial section, 48.85% RBC/51.15% fibrin in the intermediate section, and 3.44% RBC/96.56% fibrin in the final section. Embolization of the clot analogs resulted in distal occlusion of the right and left pulmonary arteries. Conclusions: This swine model coupled with clot analog is able to accurately mimic human anatomical and physiological conditions in PE making it feasible for the evaluation of pulmonary thrombectomy devices.

Global temperature rise is emerging as an alarming threat to agriculture and especially for vegetables, as they are more sensitive to high temperature because of their succulent nature. Vegetables include different edible plant parts such as leaves, stems, stalks, roots, tubers, bulbs, flowers, fruits, and seeds. An increase in temperature impairs the growth and development of vegetable plants and eventually reduces their yield. Heat tolerance is a complex quantitative trait that involves a series of physiological, biochemical, and molecular pathways. This complexity is further exacerbated by the presence of a large magnitude of genotype × environment and epistatic interactions, so breeders have to face challenges during development and selection of heat tolerant genotypes. Understanding the response of plants and resistance mechanisms involved in heat tolerance would help the breeders in formulating strategies to improve vegetable productivity under heat stress. In this review, firstly the impact of heat stress on the morphological, physiological, and molecular processes of different vegetables have been described, then discussed adaptation mechanisms employed by plants to combat heat stress. Finally, conventional and potential genomic strategies i.e. marker-assisted breeding, quantitative trait loci mapping, genome wide association, genomic selection, genetic engineering, and genome editing that are being used by the breeders to create heat resistance are presented. For vegetables, genome editing, and transgenic approaches need to be combined with conventional and marker-assisted breeding activities to develop heat tolerant varieties as these efforts will lead to tangible practical outcomes that will improve the vegetable productivity.

Every year there’s a marked increase in the number of cancer patients registered in the valley’s premiere tertiary care institute, SKIMS, where increasing number of cancer patients, especially from the poor backgrounds, continue to pour in for treatment. Sounding alarm bells, leading oncologists caution that cancer has become the number one killer disease in Kashmir and that there’s a need for more, and better, cancer awareness, and latest treatment facilities to ease the suffering of rising cancer patients in the valley. In this brief comment, going beyond the statistics, I put the spotlight on various aspects and reasons of cancer rise in the valley.

This paper proposes a deep neural network (DNN) model using the reduced input feature space of Parkinson’s telemonitoring dataset to predict Parkinson’s disease (PD) progression. PD is a chronic and progressive nervous system disorder that affects body movement. PD is assessed by using the unified Parkinson’s disease rating scale (UPDRS). In this paper, firstly, principal component analysis (PCA) is employed to the featured dataset to address the multicollinearity problems in the dataset and to reduce the dimension of input feature space. Then, the reduced input feature space is fed into the proposed DNN model with a tuned parameter norm penalty (L2) and analyses the prediction performance of it in PD progression by predicting Motor and Total-UPDRS score. The model’s performance is evaluated by conducting several experiments and the result is compared with the result of previously developed methods on the same dataset. The model’s prediction accuracy is measured by fitness parameters, mean absolute error (MAE), root mean squared error (RMSE), and coefficient of determination (R(2)). The MAE, RMSE, and R(2) values are 0.926, 1.422, and 0.970 respectively for motor-UPDRS. These values are 1.334, 2.221, and 0.956 respectively for Total-UPDRS. Both the Motor and Total-UPDRS score is better predicted by the proposed method. This paper shows the usefulness and efficacy of the proposed method for predicting the UPDRS score in PD progression.

Fog computing has emerged as an extension to the existing cloud infrastructure for providing latency-aware and highly scalable services to geographically distributed end devices. The addition of the fog layer in the cloud computing paradigm helps to improve the quality of service (QoS) in time-critical and delay-sensitive applications. Due to the continuous increase in the deployment of fog networks at large scale, energy efficiency is a significant issue in the fog computing paradigm to reduce the service cost and to protect the environment. A plethora of research has been conducted to reduce energy consumption in fog computing, majorly, focusing on the scheduling of incoming jobs to improve energy efficiency. However, node-level mechanisms have largely been neglected. Cache placement is a critical issue in fog networks for efficient content distribution to clients, which requires simultaneous consideration of many factors including quality of network connection, the demand for contents, and users’ activities. In this paper, a popularity-based caching mechanism in content delivery fog networks is proposed. In this context, two energy-aware mechanisms, i.e., content filtration and load balancing, have been applied. In the proposed approach, popular contents are found using random distribution and these contents are categorized into three classes. After finding the file popularity, an active fog node is selected based on the number of neighbors, energy level, and operational power. Further, the popular content is cached on the active node using a filtration mechanism. Moreover, a load-balancing algorithm is proposed to increase the overall system efficiency in the cached fog network. The evaluation of the proposed approach exhibits promising results in terms of energy consumption and latency. The proposed scheme consumes 92.6% and 82.7% less energy in comparison to without caching and simple caching mechanisms, respectively. Similarly, an improvement of 85.29% and 67.4% in delay has also been noticed while using advance caching against the without caching and simple caching techniques, respectively.

We investigate at decision trees that incorporate both traditional queries based on one attribute and queries based on hypotheses about the values of all attributes. Such decision trees are similar to ones studied in exact learning, where membership and equivalence queries are allowed. We present greedy algorithms based on diverse uncertainty measures for construction of above decision trees and discuss results of computer experiments on various data sets from the UCI ML Repository and randomly generated Boolean functions. We also study the length and coverage of decision rules derived from the decisiontrees constructed by greedy algorithms.

In the recent past, plasma waste technology has emerged to be an environmental friendly and beneficial technology. In this review, current status of thermal plasma, non-thermal plasma and its application for nano-scale high-tech plasmonic materials based on the scientific and technical comprehensive observation are included. Generally, thermal plasma is used for solid waste treatment but non-thermal plasma is being utilized for plasmonic materials. The current research incorporated in two phases: thermal plasma and non-thermal plasma. In the first phase, understanding and detailed information about plasma torches have been included such as DC transfer and non-transfer arc plasma torches. In addition, solid waste treatment, municipal waste, healthcare issue, steel making and treatment through plasma jet injection have been reviewed extensively. In the second phase, state-of-the-art review has been addressed for dielectric barrier discharge (DBD) and its utility for plasmonic materials. The analysis concluded that the thermal plasma is the optimal choice for treating solid waste issues and the application of non-thermal plasma such as DBD is the most useful and latest approach for plasmonic material. The prime objective of this review is not only to provide the comparison between thermal or non-thermal plasma but to recommend the ideal and most optimized suitable technique for solid waste treatment and bio-medical applications.

Social media is known to be a platform for sharing ideas, thoughts, awareness including the exchange of political awareness and political ideas, etc. Different social media tools such as Facebook, WhatsApp, Twitter, YouTube, Instagram and, IMO, etc. are being commonly used by the general public for social interaction in Pakistan. These social media tools are influential in political mobilization as well as transferrable amendments within the political setup of the country. The present study analyzes and, gauges the influence of usage of various social media tools and applications on molding the behaviors of electors during the General elections 2018 held in Punjab, Pakistan. The purpose and, major focus of the study is to measure and analyze the effect of social media on the political setup of the country, particularly in Punjab province. The findings of the study are supported by a survey conducted from the population having diverse demographics viz: gender, age group, educational qualification, profession, and, localities, etc. Results of the study depict that social media has been influential on the political behavior of the young population of Punjab province in the country.

Ultraviolet (UV) exposure to the skin causes photo-damage and acts as the primary etiological agent in photo-carcinogenesis. UV-B exposure induces cellular damage and is the major factor challenging skin homeostasis. Autophagy allows the fundamental adaptation of cells to metabolic and oxidative stress. Cellular dysfunction has been observed in aged tissues and in toxic insults to cells undergoing stress. Conversely, promising anti-aging strategies aimed at inhibiting the mTOR pathway have been found to significantly improve the aging-related disorders. Recently, autophagy has been found to positively regulate skin homeostasis by enhancing DNA damage recognition. Here, we investigated the geno-protective roles of autophagy in UV-B-exposed primary human dermal fibroblasts (HDFs). We found that UV-B irradiation to HDFs impairs the autophagy response in a time- and intensity-independent manner. However, improving autophagy levels in HDFs with pharmacological activators regulates the UV-B-induced cellular stress by decreasing the induction of DNA photo-adducts, promoting the DNA repair process, alleviating oxidative and ER stress responses, and regulating the expression levels of key cell cycle regulatory proteins. Autophagy also prevents HDFs from UV-B-induced nuclear damage as is evident in TUNEL assay and Acridine Orange/Ethidium Bromide co-staining. Salubrinal (an eIF2α phosphatase inhibitor) relieves ER stress response in cells and also significantly alleviates DNA damage and promotes the repair process in UV-B-exposed HDFs. P62-silenced HDFs show enhanced DNA damage response and also disturb the tumor suppressor PTEN/pAKT signaling axis in UV-B-exposed HDFs whereas Atg7-silenced HDFs reveal an unexpected consequence by decreasing the UV-B-induced DNA damage. Taken together, these results suggest that interventional autophagy offers significant protection against UV-B radiation-induced photo-damage and holds great promise in devising it as a suitable therapeutic strategy against skin pathological disorders.

The early identification of the influential papers is of great significance for assessing the scientific achievements of researchers and institutions as it can help in addressing the processes in an academic and scientific field, such as promotions, recruitment decisions, and funding allocation. This work evaluates features for predicting the most influential paper award that is given by several renowned conferences, ten years subsequent to their publication. The data of five renowned conferences, i.e., ICSE, ICFP, POPL, PLDI, and OOPSLA is used to predict the long-term citations to identify the most influential paper of the respective conference. GD boost model is considered to be better performing among the five different machine learning algorithms. The results show that a three to five years of the time window is good enough to evaluate the most influential paper award. Additionally, the assessment of time window and the citation trajectory of awarded and non awarded papers shows that the citation trajectory of the awarded paper vary from the Citation gain patterns of non-awarded paper.

Diagnosis, being the first step in medical practice, is very crucial for clinical decision making. This paper investigates state-of-the-art computational intelligence (CI) techniques applied in the field of medical diagnosis and prognosis. The paper presents the performance of these techniques in diagnosing different diseases along with the detailed description of the data used. This paper includes basic as well as hybrid CI techniques that have been used in recent years so as to know the current trends in medical diagnosis domain. The paper presents the merits and demerits of different techniques in general as well as application specific context. This paper discusses some critical issues related to the medical diagnosis and prognosis such as uncertainties in the medical domain, problems in the medical data especially dealing with time-stamped (temporal) data, and knowledge acquisition. Moreover, this paper also discusses the features of good CI techniques in medical diagnosis. Overall, this review provides new insight for future research requirements in the medical diagnosis domain.

Portable Document Format (PDF) is a commonly used format for the scientific publication. Currently, an input document is used to test the compliance and relevance of the document or text in Automated Compliance Engines and Natural Language Processing(NLP) based system. The whole document text is used for searching the compliance rules which is computationally expensive and slow process. For speeding up the compliance checking process and making it cost efficient, this paper purposes a method based on Table of Content(TOC) Data Structure. This work proposed the PDFparser which performs Data Indexing, separate headings text, and non-heading text, create hierarchy of headings and generates TOC to reduce the semantic-based string searching time and space. Furthermore, in the NLP based system, mostly semantic-based string matching used. The proposed PDFparser uses the Cosine Similarity method for computing semantic based similarity. Our purposed method performs 47.2% better than the previous approach of searching in the non-indexed whole document and decreases the search time and space. In the worst-case scenario, where no string match found, our purposed method performs 20.5 % better.

Coronary artery disease (CAD) is the most common cardiovascular disease (CVD) that cause millions of deaths worldwide due to heart failure, heart attack, and angina. The symptoms of the CAD do not appear in the early stage of the disease and it causes deadly conditions; therefore, accurate and early diagnosis of CAD is necessary to take appropriate and timely action for preventing or minimizing such conditions. Angiography, being the most accurate method for diagnosis of CAD, is often used by the clinicians to diagnose the CAD but this is an invasive procedure, costly, and may cause side effects. Therefore, researchers are trying to develop alternative diagnostic modalities for the efficient diagnosis of CAD. To that end, machine learning and data mining techniques have been widely employed. This paper proposes and develops hybrid Particle swarm optimization based Extreme learning machine (PSO-ELM) for diagnosis of CAD using the publicly available Z-Alizadeh sani dataset. To enhance the performance of the proposed model, a feature selection algorithm, namely Fisher, is used to find more discriminative feature subset. In the training period, the PSO algorithm is used to calibrate the ELM input weights and hidden biases. Further, the performance of the proposed model is compared with the basic ELM in terms of accuracy, Pearson correlation coefficient (R2) and Root mean square error (RMSE) goodness-of-fit functions. The results show that the performance of the proposed model is better than the basic ELM. The obtained CAD classification performance in terms of sensitivity, accuracy, specificity, and F1-measure is competitive to the known approaches in the literature.

In this paper, the facile synthesis of graphene oxide and MOF based composite material for the demanded adsorption of leading antibiotic is reported. The adsorbent showed good results for removal of norfloxacin with high adsorption capacity (1114.82 mg/g) and removal efficiency (93.61 %). In addition, Langmuir model proved most suitable with highest value of R2 = 0.99929 among three applied isothermal models. The thermodynamic study showed spontaneous and exothermic (ΔH = −1.1×10−5 Kj/ mol, ΔG = −13324.33 Kj/ mol) nature of adsorption process with reusability of 88.4 % after several cycles. Moreover, statistical analysis and mechanistic study were also performed.HIGHLIGHTSSynthesis of graphene oxide and MOF based composite materialHigh surface area, void spaces, open metal sites, and tunable morphology.Adsorption of leading antibiotic (norfloxacin).MOF/GO composite with high water stability and reuseability.Isothermal, kinetic, statistical and mechanistic study. Synthesis of graphene oxide and MOF based composite material High surface area, void spaces, open metal sites, and tunable morphology. Adsorption of leading antibiotic (norfloxacin). MOF/GO composite with high water stability and reuseability. Isothermal, kinetic, statistical and mechanistic study.

The thyroid hormone is produced by thyroid gland. This hormone regulates the body's metabolism. Hyperthyroidism and hypothyroidism are the two abnormalities which is caused by the release of too much or too little thyroid hormones respectively. In this study, Random Forest (RF), Support Vector Machine (SVM), and K-Nearest Neighbours (K-NN) classifiers are compared to assess the efficiency of these classifiers in Thyroid disease diagnoses using the thyroid disease dataset that is taken from UCI machine learning repository. The overall classification accuracy of the RF, SVM, and K-NN are 98.50%, 97.02%, and 95.81% respectively. The result shows that the RF classifier performance is better than SVM and K-NN for the diagnosis of thyroid disease using UCI dataset.

Background Detection of vestibular schwannoma in young patient demands specific attention by looking at the relatively aggressive course and natural history of the disease. Patients with syndromic variants such as NF2 suffer from multiple tumors including bilateral vestibular schwannoma, meningioma, ependymomas and peripheral nerve sheath tumors. Such patients may suffer from unprecedented complications following radio surgical treatment such as poor tumor control and functional outcome. Material and methods Authors describe their own experience with Bevacizumab as a preventive and rehabilitative treatment option in two such patients who suffered from complications of radiosurgery in the immediate post-procedure period. Literature is reviewed for the indications, dosage, safety profile, and complications profile on short and long-term of Bevacizumab in vestibular schwannomas. Results One middle-aged patient of sporadic vestibular schwannoma suffered rapid onset facial paresis within one month of GKRS. After a failed attempt with steroids, the patient regained partial improvement following Bevacizumab treatment. Another young female of phenotypic NF2 syndrome suffered rapid onset hearing deficit within two weeks of GKRS, which improved to pre GKRS level with the early introduction of Bevacizumab. Bevacizumab has shown its efficacy both as a prophylactic and rehabilitative treatment modality for syndromic patients of VS. Conclusion Though not a wonder drug, Bevacizumab is a valuable adjunct in crossroad situations. It has a proven role as a prophylactic treatment option to retard the growth of VS and spinal ependymomas with no role on meningiomas. As a rehabilitative treatment option, Bevacizumab may be used in steroid-resistant cases to prevent radiosurgery-induced complications. Long-term studies are warranted to confirm the dosing schedules, regimens, and complication profile.

Background The cognitive impairments after treatment of ruptured aneurysms have often been underestimated. This study sought to assess their prevalence and analyze various associated factors. Methods Patients who were operated on for ruptured anterior circulation aneurysms and discharged with a Glasgow Outcome Scale score of 4–5 were studied at 3 months for various cognitive impairments. Continuous scales of memory (recent, remote, verbal, visual, and overall memory), verbal fluency (phonemic and category fluency), and others were studied in relation to various factors. Univariate and multivariate analyses were performed using SPSS version 21. Results A total of 87 patients were included in our study. Phonemic fluency was the most affected, noted in 66% of patients. Although 56% had some memory-related impairments, 13 (15%) and 6 (7%) had moderate and severe deficits in recent memory and 19 (22%) and 12 (14%) had moderate and severe deficits in remote memory, respectively. Patients operated on for anterior cerebral artery (ACA) aneurysms had significantly greater impairments in recent (34% vs. 8%) and remote memory (43% vs. 28%) compared with the rest, both in univariate (P = 0.01 and 0.002, respectively) and multivariate analyses (P = 0.01 and 0.03, respectively). ACA-related aneurysms also had significantly greater independent impairments in phonemic fluency (P = 0.04), compared with others. The clinical grade had a significant independent impact only on remote memory (P = 0.01). Conclusions Cognitive impairments are frequent after treatment of ruptured anterior circulation aneurysms. Impairments in recent memory, remote memory, and phonemic fluency are significantly greater after treatment of ACA-related aneurysms, compared with others, independent of other factors.

OBJECTIVEDecompressive craniectomy is an established therapy for refractory intracranial hypertension. Cranioplasty following decompressive craniectomy not only provides protection to the brain along with cosmetic benefits, but also enhances rehabilitation with meaningful functional recovery of potentially reversible cortical and subcortical damaged areas of the affected as well as the contralateral hemisphere. The aim of the study was to assess neurological and cognitive outcome as well as cerebral blood flow after cranioplasty.METHODSThirty-four patients admitted for replacement cranioplasty after decompressive craniectomy for head injury were studied prospectively. Clinical, neurological, and cognitive outcomes were assessed by the Glasgow Outcome Scale (GOS), the Glasgow Coma Scale, and a battery of cognitive tests, respectively. Simultaneously, cerebral blood perfusion was assessed by technetium-99m ethyl cysteinate dimer (99mTc-ECD) brain SPECT imaging 7 days prior to and 3 months after cranioplasty.RESULTSPrior to cranioplasty 9 patients (26.5%) had GOS scores of 5 and 25 patients (73.5%) had GOS scores of 4, whereas postcranioplasty all 34 patients (100%) improved to GOS scores of 5. Approximately 35.3%–90.9% patients showed cognitive improvement postcranioplasty in various tests. Also, on comparison with brain SPECT, 94% of patients showed improvement in cerebral perfusion in different lobes.CONCLUSIONSCranioplasty remarkably improves neurological and cognitive outcomes supported by improvement in cerebral blood perfusion.

A biosensor as an integrated miniaturized device, exploits the modern microelectronics with specific sensing probe through signal transduction. The challenge for new generation biosensors is to achieve specific analyte detection at very low concentrations, which is possible by tailoring the materials used for fabrication of these devices based on nanoscience and nanotechnology. The new approach is explored in this thesis for fabrication of novel nanobiosensors for ultrasensitive detection of sulfite. The literature review in chapter 1 describes the long history of sulfite usage as preservative, its benefits and health concerns. This chapter also outlines the existing approaches for fabrication of sulfite nanobiosensors, and explores the new and emerging fabrication strategies based on the use of nanomaterials. The resulting new constructive matrices for electroelectrochemical detection provides amplified redox signals, enhanced direct electron transfer and an enlarged catalytic surface area for increased enzyme immobilization. The integration of all these properties certainly improved the performance of the resulting sulfite nanobiosensor with ultrahigh sensitivity and selectivity, as compared with the traditional methods used for sulfite detection. In chapter 2, the use of composite PPy and PtNPs for entrapment of sulfite oxidase (SOx) was considered for improvement of sulfite detection. The properties of the resulting nanobiosensor were dramatically improved due to increased surface area compared to those achieved in absence of PtNPs. Furthermore, the deposition of PtNPs on the electrode surface improved the chemical kinetics of redox reactions, as this nanomaterial acted in providing catalytic centres. The morphology of the exposed surface of the fabricated PtNPs-PPy-SOx demonstrated a clear difference compared to in the absence of PtNPs decoration, the electrochemical behaviour revealed by CV and potentiometric measurements further indicates fast and efficient redox kinetics. Optimization of the PtNPs-PPy-SOx biosensor enabled achievement of a detection limit of 12.35 nM, a linear range of 0.75 - 65.50 µM with a sensitivity of 57.5 mV/decade, and was successfully applied to real samples. The fabrication and use of PPy nanowires array was considered in chapter 3 to achieve better mass and electron transfer with improved surface area for SOx immobilization. Simple and low cost Anodic Aluminium Oxide (AAO) templates were prepared by a two-step anodization method. Pyrrole was deposited into the nanochannels of AAO templates by electropolymerization to obtain highly ordered PPyNWA with desired dimensions. The improved catalytic performance was achieved by decorating the nanowire surface with PtNPs. The resulting geometrical structure provided enlarged activated surface area for increased enzyme loading with free space available for the mobility of analyte. SEM images revealed the morphology of PPyNWA and their highly ordered features. Furthermore, EIS and CV were employed to gain a better understanding of the electrochemical performance in terms of sensitivity and stability for sulfite detection. The amperometric response was used for the optimization and evaluation of electrode modification. The modified electrode with PPyNWA improved detection limit (7.4 nM), and the linear concentration range (0.12 - 1200 μM) with a sensitivity of 57.33 μA mMˉ¹cmˉ². The resulting nanobiosensor was applied to real samples and also indicated improved stability of 120 days. In chapter 4, the use of AAO templates was considered for fabrication gold nanowires array. The one dimensional features of the AuNWA were modified with PtNPs to activate the surface morphology three dimensionally. The nanostructural assembly with high surface to volume ratio for SOx immobilization boosted the direct electron transfer (DET) of redox reactions and increased the sensitivity of the resulting sulfite nanobiosensor. Furthermore, cross-linking of SOx with a mixture of glutaraldehyde (GLA) and bovine serum albumin (BSA) on the AuNWA modified with PtNPs provided an opportunity to enhance the sensitivity of the nanobiosensor. Fast electrochemical kinetics achieved with the nanobiosensor enabled an improved detection limit of 3.5 nM and linear range of 4.9 µM – 1.5 mM for low concentrations and 2 mM – 5.5 mM for higher with high sensitivities of 61.5 µA mMˉ¹cmˉ² and 90.00 µA mMˉ¹cmˉ², respectively. The achieved sensitivities in low and high sulfite concentration ranges were 61.5 µA mMˉ¹cmˉ² and 90.00 µA mMˉ¹cmˉ². Long term storage stability was 140 days. A novel sulfite nanobiosensor based on the use of nanostructure composite material, involving encapsulation of SOx into Nafion membrane and PtNPs decorated hybrid matrix of MWCNTs and PPy is described in chapter 5. The large surface provided by inclusion of MWCNTs enabled immobilization of more enzyme molecules, resulting in faster direct electron transfer and enhanced response towards H₂O₂. The achieved sensitivity was strongly influenced by enzyme loading and catalytic activation of the matrix with PtNPs. The homogeneity of the resulting Naf-SOx-PtNPs-MWCNTs/PPy nanocomposite matrix was characterized by SEM, EIS and CV. The optimized sulfite nanobiosensor gave a linear range of 20 nM – 6 mM with a high sensitivity of 71 mA mMˉ¹cmˉ², detection limit of 5.4 nM, and response time of

In the present study aluminium silicon carbide (Al/SiC) composites were prepared by powder metallurgical method. The mechanical and morphological evaluation were studied upon the variation of reinforcements percentages i.e.10, 15 and 20 wt.% of SiC powder were used as the reinforcements in aluminium matrix. The comparison of powder metallurgy method with stir casting method of Al/ (SiC) composites preparation was performed and the particle reinforcements were visualized through Scanning Electron Microscopy (SEM). The results demonstrated increased hardness with increasing wt. % of SiC particles. This was attributed to efficient stress transfer and dislocation strengthening. In addition, the densification behaviour of the composites was also studied and SiC particulates were found to exhibit profound effect on composites density.

In this paper we report the results of experiments on LIFT performed using single pulses from a mode locked (40ps pulses), frequency doubled Nd:YAG laser (energy flux ≈ 13J/cm at λ = 532 nm). We studied the controlled transfer of thin films of different metallic materials to a receiving substrate. Optical and scanning electron microscopy (SEM) as well as Energy-dispersive X-ray spectroscopy (EDAX) analysis were performed, showing evidence of the production of micrometric scale patterns. One of our objectives is to investigate and develop possible applications of LIFT in jewellery industry. In order to understand the experimental results we developed a simple analytical model based on the Rankine-Hugoniot relations. Comparison between experimental and simulated results is presented. DOI:10.2961/jlmn.2011.02.0010

A definition of intelligence is given in terms of performance that can be quantitatively measured. In this study, we have presented a conceptual model of Intelligent Agent System for Automatic Vehicle Checking Agent (VCA). To achieve this goal, we have introduced several kinds of agents that exhibit intelligent features. These are the Management agent, internal agent, External Agent, Watcher agent and Report agent. Metrics and measurements are suggested for evaluating the performance of Automatic Vehicle Checking Agent (VCA). Calibrate data and test facilities are suggested to facilitate the development of intelligent systems., Comment: 5 pages, 2 figures

IceTop is an air shower array that is part of the IceCube Observatory currently under construction at the geographic South Pole. When completed, it will consist of 80 stations covering an area of 1 km2. Previous analyzes done with IceTop studied the events that triggered five or more stations, leading to an effective energy threshold of about 0.5 PeV. The goal of this study is to push this threshold lower, into the region where it will overlap with direct measurements of cosmic rays which currently have an upper limit around 300TeV.We select showers that trigger exactly three or exactly four adjacent surface stations that are not on the periphery of the detector (contained events). This extends the energy threshold down to 150TeV., Comment: 4 pages, Proceedings of the 31st ICRC, Lodz, Poland, July 2009

To determine the efficacy of antiviral therapy in hepatitis C virus associated glome-rulopathy, we studied 30 patients with HCV-associated glomerulopathy at Sheikh Zayed Hospital, Lahore, Pakistan from June 2004 to February 2007. Membranoproliferative glomerulonephritis (MPGN) was the commonest kidney lesion, being reported in 25/30 (83%), followed by membra-nous glomerulonephritis (MGN) in 3/30 (10%) and mesangioproliferative glomerulonephritis (MesGN) in 2/30 (7%). Cryoglobulinaemia was positive in 8/20 (40%) cases. Most common HCV genotype was 3a. All the patients received interferon alpha combined with ribavirin therapy for 6-12 months based on viral genotypes and doses were adjusted according to renal function. Anti-viral response was achieved in the form of aviremia at completion of 6 months treatment in 8/30 (26.6%), decreased transaminases levels from a mean of 96.4 ± 72.2 to 60.1 ± 44.3 IU/L, p= 0.005, 24-hour proteinuria decreased significantly from a mean of 4.8 g to 1.20 g, p= 0.001, and complement C3 and C4 concentrations returned to normal in those subjects who responded to treatment. The rate of relapse was 50%. We conclude that though the overall antiviral response of HCV was not high, there was a significant reduction in proteinuria suggesting indirectly an improvement in renal patho-logy. Further studies with large number of patients with follow-up renal biopsies are warranted.

We present extensive results and analysis of energy and angular distributions of diffuse UHE $\nu_{e}$, $\nu_{\mu}$, and $\nu_{\tau}$ fluxes propagated through earth, with and without augmentation of the standard model interactions by low scale gravity. With propagated fluxes in hand we estimate event rates in a $1km^{3}$ detector in ice with characteristics of ICECUBE. We determine that, at 0.5PeV energy threshold, there is a significant difference in the ratios of down shower events to upward muon events between the standard model and the low scale gravity cases with 1TeV and 2TeV mass scales. The same is true for energy threshold at 5PeV. Though the difference is large in all flux models, statistical significance of this difference depends on the flux models, especially at 5PeV and above. Both flavor assumptions, $\nu_{e}$, $\nu_{\mu}$, $\nu_{\tau}::1$, 2, 0 and $% \nu_{e}$, $\nu_{\mu}$, $\nu_{\tau}::1$, 1, 1, and all flux models show large differences. Though rates of tagged events are low, we find that $\nu_{\tau}$ regeneration by $\tau $ decay may play an important role in disclosing deviations from standard model predictions at energies in the neighborhood of 1 PeV for 1TeV-scale gravity, for example. We emphasize those analyses whose sensitivity to new physics is independent of the flux model assumed., Comment: Added two Sections and 4 Tables for event rates of taus, muons, and showers; added formalism, results, and discussion for event rates. 15 Pages, 14 Figures, and 6 Tables

Objective This study was cross sectional survey conducted to find the prevalence of Restless legs syndrome (RLS) in end stage renal disease (ESRD) patients on haemodialysis (HD). Methods Data were obtained from 250 patients on chronic maintenance HD. To assess the prevalence of RLS, Clinical diagnostic criteria for RLS was used which is established by the International RLS Study Group. Results Total 250 patients were included in this study. 153 (61.2%) patients were male and 97 (38.8%) were females. Mean age of the patients was 45.27 years. Mean duration of HD was 26.10 months. Total162 (64.8%) patients were found to be suffering from RLS. Out of 153 males, 87(56%) were suffering from RLS and among 97 females, 75(77.3%) were suffering from RLS. In our study, gender was statistically significantly associated with RLS (p-value 0.001). In age groups 159(63.6%) patients were below 51 years; among them 102(64.1%) were suffering from RLS; whereas 91(36.4%) patients were equal or above 51 years of age and among this group 60(65.9%) patients were suffering from RLS. There was no statistically significant association between RLS and age groups (p-value 0.776). Conclusions RLS is common in patients undergoing regular HD. It is more commonly seen in females.

This thesis is devoted to the development of extensions of dynamic programming to the study of decision trees. The considered extensions allow us to make multi-stage optimization of decision trees relative to a sequence of cost functions, to count the number of optimal trees, and to study relationships: cost vs cost and cost vs uncertainty for decision trees by construction of the set of Pareto-optimal points for the corresponding bi-criteria optimization problem. The applications include study of totally optimal (simultaneously optimal relative to a number of cost functions) decision trees for Boolean functions, improvement of bounds on complexity of decision trees for diagnosis of circuits, study of time and memory trade-off for corner point detection, study of decision rules derived from decision trees, creation of new procedure (multi-pruning) for construction of classifiers, and comparison of heuristics for decision tree construction. Part of these extensions (multi-stage optimization) was generalized to well-known combinatorial optimization problems: matrix chain multiplication, binary search trees, global sequence alignment, and optimal paths in directed graphs.