Search

The lubricants mostly used in industry have non-Newtonian properties and adhere to a variety of constitutive connections. The power law lubricant, which follows the Ostwald model and is widely used in engineering lubrication, is one of them. In this study the flow analysis of nanofluid in the vicinity of a stagnation point over a lubricated stretching surface has been investigated. The lubrication is provided by a thin layer of variable thickness of power-law fluid. Impact of thermophoresis and Brownian motion is also investigated. To acquire the dimensionless governing equations utilized an appropriate transformation. A newly developed powerful numerical procedure known as Legendre wavelet spectral collocation method (LWSCM) has been employed to acquire the similar solution. A comparison in the special cases between the published and present results substantiate the obtained solutions. Addition of the lubrication enhances the fluid velocity and reduces the temperature of the nanofluid at the stretchable surface.

Selecting the perfect area of specialization can be a vital step when one is pursuing a degree of BS or M.Phil. in English. This seems what an individual must aim to follow to assist them to get ahead in their professional life and career. One can regard it as a game changer or an important decision that a student needs to consider and it is at times difficult to identify as to which specific area best suits the needs and interests of a given learner. Students of BS and M.Phil. levels in English are perplexed either to opt for Linguistics or Literature as their area of specialization. In this regard, adequate and timely career planning/counseling is highly significant and is needed on an emergent basis to overcome the prevailing ambiguity among the students. Lack of career counseling is leading to numerous problems. The primary objective of this study has been to identify and highlight the lack of awareness among our students about their decision-making and career selection, which in turn possibly leads to numerous problems for the students in their career prospects. The studys respondents include the BS, M.Phil. English students from the Okara district of Pakistan’s Punjab. This study uses the quantitative paradigm with survey research being its research design. Data has been gathered through a Close-ended questionnaire. The sample from study respondents from an Okara-based university has been derived based on Convenience sampling. Data collected from the population was analyzed via Microsoft Excel and represented in bar diagrams. There must be courses of at least 2 or 3 credit hours on career selection and decision-making in our BS-level curriculum to combat the currentscenario of poor planning and decision-making in terms of career selection.

Degenerative lumbar disc disease is a condition in which an intervertebral disk and surrounding spinal components are weakened. This might be due to age or a pathogenic cause. Individuals may complain of low back pain. Objective: To determine the pattern of lumbar spine and disc degenerative diseases amongst patients having or not having Low Back Pain with or without radiculopathy diagnosed on Magnetic Resonance Imaging. Methods: Data was collected following inclusion criteria using convenient sampling technique. A total of 266 Patients undergone Lumber MRI scans on MRI Machine 1.5T Phillips. Data was collected from Radiology Department of Tertiary Care Hospital in Lahore, Pakistan. Data was entered and analyzed on SPSS version 20. Age Groups, Gender frequencies were mentioned. Cross Tabulation of Lumber Disc Degenerative Diseases with age and Gender was done and Chi square was applied. P-value ≤0.05 was considered significant. Results: The most prevalent age group for disc degeneration was 40-59 having 56.8 %. Females with disc degeneration have the highest frequency of 143 (53.8%) while males were 123 (48.2%). Lumber Disc degeneration was 101 (38%), Disc Herniation 54 (20.3%), and last with lowest presence of Spinal Canal Stenosis 18 (6.8%). Patients with Radiculopathy with Low back Pain was 61 (22.93%) and only LBP was the most common between the patients having 205 (77.1%). Cross Tabulation of Age Groups with Lumber Disc Pathologies was Significant having P value 0.05. Conclusion: In conclusion MRI can detect Disc Degeneration, Disc Herniation & Buldges, and Spinal Canal Stenosis & Nerve Root Compression. It can be considered significant and accurate to avoid any other interventional procedures. It is also concluded the LBP is an important factor in Disc Degenerative changes & radiculopathy is less seen.

This research is aimed at identifying the positive impacts of Covid-19 pandemic in relation to the education sector of Pakistan. Generally, individuals have a lot to talk about the devastating effects of the pandemic but this work of research identifies the positive aspects of the Corona virus on the education sector of the country. The data has been gathered from various educational institutions of the province of the Punjab. Population of the study includes both the students as well as the tutors and it has been located as to how the students and their tutors have both become tech-savvy by engaging themselves in online teaching-learning process through different web-based utilities such as Zoom, Google Classroom, WhatsApp, Microsoft Teams, Moodle etc.

Background: The hypertensive disorders affect pregnant women and can create complications for the women in last trimester of pregnancy. To assess the impact of high hypertension, doppler scan is recommended in the third trimester. Objective: Present explores values of PSV, EDV, S/D ratio, RI and PI among normotensive and hypertensive pregnant women during third trimester of pregnancy in Islamabad, Pakistan. Material and Methods: A Cross-sectional study was conducted at PIMS hospital Islamabad with objectives to evaluate doppler indices in fetal umbilical artery among hypertensive pregnant women in the third trimester of pregnancy. Data were collected from 1st February 2022 to 15th May 2022. A total of 93 pregnant women were recruited from already registered at maternal child health care center of PIMS and recommended for doppler indices scan. Toshiba Aplio 300 Ultrasound System was used for the assessment. Data about S/D ratio, RI and PI was analyzed by using IBM SPSS® software platform. Results: Mean Age of pregnant women in our study conducted was 26.722 ± 4.133. Also the mean gestational week for pregnant women was 32.163 ± 2.35. Doppler indices were significantly raised in hypertensive women as compared to normotensive women. Doppler indices i.e. Pulsatility Index (Mean 1.049 ± 0.312 vs 1.360 ± 0.421), Resistivity Index (Mean 0.631 ± 0.114 vs 0.743 ± 0.01), Systolic/Diastolic flow (Mean 2.672 ± 0.512 vs 3.818 ± 0.986) were increased in third trimester of hypertensive women as compared to normotensive patients. Conclusion: In this study, among doppler indices pulsatility index and resistivity index were identified as significant after applying null hypothesis test. Doppler ultrasound should be used to identify vascular resistance and evaluating degree of fetoplacental blood flow in hypertensive pregnant women.

Ba1-xPbxFe12O19 (x = 0.0 to 1.0) was synthesized by a co-precipitation method at room temperature. Parent compounds used were oxides of iron and lead, while barium was used in a carbonated form. Acids and Di-H2O were used as a solute. Na-Alkaline solution with molarity 5 was used for fertilization. Iron to barium ratio was kept 12. Washing played a vital role to minimize the impurities. It also improved the homogeneity. Semi-liquid-type material was obtained after overnight drying in an oven. It was further transformed into powder form which further processed, and pellets were formed for characterizations. These pellets were sintered for 3 h in a box furnace at 965 ± 5 °C. Growth along with its surface and dimensional modifications were analyzed by X-ray diffractometry (XRD) and scanning electron microscopy (SEM). It showed almost 70% phase purity. Lead physical properties like higher mobility, ionic radii (Pb2+ = 1.76 A & Ba = 1.37 A), and occupancy preference were the important reasons for the investigation during heat treatment. Lead doping in R blocks was another big reason for these variations. SEM showed modification in all respect in each successive composition. Frequency-based analysis of whole composition from room temperature to 973 K with a step of 100 has discussed. Frequency precession analyzer was used from 20 Hz to 3 MHz for each sample. Obtained data was used to calculate dielectric constant, dielectric loss and dielectric loss factor, and AC conductivity. Determined parameters were further studied with respect to respective temperatures during whole applied frequency spectrum. The study contains almost different behaviors from each other. Prime factors were heat treatment, impurities, dopant (x), and variation of applied electric field. These behaviors and trends could be useful for today’s modern IT applications and other related electrical and electronic components like capacitors. It could also be useful for high frequency microwave and related network applications.

In order to provide benchmark performance for Urdu text document classification, the contribution of this paper is manifold. First, it provides a publicly available benchmark dataset manually tagged against 6 classes. Second, it investigates the performance impact of traditional machine learning-based Urdu text document classification methodologies by embedding 10 filter-based feature selection algorithms which have been widely used for other languages. Third, for the very first time, it assesses the performance of various deep learning-based methodologies for Urdu text document classification. In this regard, for experimentation, we adapt 10 deep learning classification methodologies which have produced best performance figures for English text classification. Fourth, it also investigates the performance impact of transfer learning by utilizing Bidirectional Encoder Representations from Transformers approach for Urdu language. Fifth, it evaluates the integrity of a hybrid approach which combines traditional machine learning-based feature engineering and deep learning-based automated feature engineering. Experimental results show that feature selection approach named as normalized difference measure along with support vector machine outshines state-of-the-art performance on two closed source benchmark datasets CLE Urdu Digest 1000k, and CLE Urdu Digest 1Million with a significant margin of 32% and 13%, respectively. Across all three datasets, normalized difference measure outperforms other filter-based feature selection algorithms as it significantly uplifts the performance of all adopted machine learning, deep learning, and hybrid approaches. The source code and presented dataset are available at Github repository https://github.com/minixain/Urdu-Text-Classification .

In order to accelerate the performance of various Natural Language Processing tasks for Roman Urdu, this article for the very first time provides 3 neural word embeddings prepared using most widely used approaches namely Word2vec, FastText, and Glove. The integrity of generated neural word embeddings is evaluated using intrinsic and extrinsic evaluation approaches. Considering the lack of publicly available benchmark datasets, it provides a first-ever Roman Urdu public dataset which consists of 3241 sentiments annotated against positive, negative, and neutral classes. To provide benchmark baseline performance over the presented dataset for Roman Urdu sentiment analysis, we adapt diverse machine learning (Support Vector Machine, Logistic Regression, Naive Bayes), deep learning (convolutional neural network, recurrent neural network), and hybrid deep learning approaches. Performance impact of generated neural word embeddings based representation is compared with other most widely used bag of words based feature representation approaches using diverse machine and deep learning classifiers. In order to improve the performance of Roman Urdu sentiment analysis, it proposes a novel precisely extreme multi-channel hybrid methodology which makes use of convolutional and recurrent neural networks along with pre-trained neural word embeddings. The proposed hybrid approach outperforms adapted machine learning approaches by the significant figure of 9% and deep learning approaches by the figure of 4% in terms of F1-score.

A new approach is adopted to grow cadmium zinc telluride (CdZnTe) thin films using the close spaced sublimation (CSS) technique. The deposition parameters for the growth of cadmium telluride (CdTe) thin films onto the glass substrate were optimized. A zinc telluride (ZnTe) thin film layer was deposited onto already-deposited CdTe thin film to fabricate the CdZnTe (CZT) thin film sample as a ternary compound. Annealing was done after the successful deposition of CZT thin films before further characterization of the CZT thin film samples. The structures of the CZT thin film samples were studied using X-ray diffraction (XRD) and cubic phases were found. A spectrophotometer was used to study the optical parameters, and the energy band gap was found to be in the range of 1.45 eV to 1.75 eV after annealing. The nature of the direct band gap predicts that it might be an ideal component in second-generation solar cells. A Hall measurement system was used to find that the electrical conductivity was in the range of 4.6 × 10

Thin films of lead sulfide (PbS) are being extensively used for the fabrication of optoelectronic devices for commercial and military applications. In the present work, PbS films were fabricated onto a soda lime glass substrate by using an electron beam (e-beam) evaporation technique at a substrate temperature of 300 °C. Samples were annealed in an open atmosphere at a temperature range of 200–450 °C for 2 h. The deposited films were characterized for structural, optical, and electrical properties. Structural properties of PbS have been studied by X-ray diffraction (XRD), field emission scanning electron microscopy (FESEM), energy dispersive spectroscopy (EDS), and Rutherford backscattering spectrometry (RBS). The results of XRD showed that the PbS thin film was crystalline in nature at room temperature with cubic crystal structure (galena) and preferential (111) and orientation (022). The morphology of the thin films was studied by FESEM, which also showed uniform and continuous deposition without any peel-off and patches. EDS analysis was performed to confirm the presence of lead and sulfur in as-deposited and annealed films. The thickness of the PbS film was found to be 172 nm, which is slightly greater than the intended thickness of 150 nm, determined by RBS. Ultraviolet-Visible-Near-Infrared (UV-Vis-NIR) spectroscopy revealed the maximum transmittance of ~25% for as-deposited films, with an increase of 74% in annealed films. The band gap of PbS was found in the range of 2.12–2.78 eV for as-deposited and annealed films. Hall measurement confirmed the carriers are p-type in nature. Carrier concentration, mobility of the carriers, conductivity, and sheet resistance are directly determined by Hall-effect measurement. The as-deposited sample showed a conductivity of 5.45 × 10−4 S/m, which gradually reduced to 1.21 × 10−5 S/m due to the composite nature of films (lead sulfide along with lead oxide). Furthermore, the present work also reflects the control of properties by controlling the amount of PbO present in the PbS films which are suitable for various applications (such as IR sensors).

Currently, thermoelectric devices are receiving an immense attention from the renewable energy community due to their environment friendliness. We report a single step facile arc-melting method for developing Cu2Se thermoelectric material, which eliminates the need of expensive and time taking sintering process. The structural and thermoelectric properties measurement indicates that the proposed method is an alternative to the other reported methods for instance ball-milling followed by spark plasma sintering (SPS) with a great improvement in their efficiency. Based upon the optimized power factor and decreased total thermal conductivity, a higher figure-of-merit (ZT) value of 1.46 at 873 K was obtained from the arc-melted sample which is about 70% larger than that of its counterpart prepared through ball-milling followed by SPS process. This remarkable enhancement in the ZT value through a facile synthesis method opens up an avenue for cheaper and fast processing of Cu2Se and related materials.

Zinc-doped lithium ferrite nanoparticles with general formula Li0.46Zn0.04Fe2.5O4 were prepared by Co-precipitation method and their thin films were grown by spin coating method. X-ray diffraction (XRD) confirmed the spinel structure of the synthesized samples. Fine morphology of the films was obtained by controlling the parameters. AC electrical properties including dielectric constant (έ), dielectric loss tangent (tanδ), AC electrical conductivity (σac), and impedance (Z) were studied as a function of frequency at different temperatures and at 1 MHz frequency in a temperature range of 40–500 °C. Dielectric constant, dielectric loss tangent, and ac electrical conductivity showed the decreasing trend with increase in temperature while opposite trend was observed for impedance. Zinc-doped Li-ferrite has better absorption properties in the studied frequency and temperature range as compared to that of Li-ferrites.

Web contains a vast amount of data, which are accumulated, studied, and utilized by a huge number of users on a daily basis. A substantial amount of data on the Web is available in an unstructured format, such as Web pages, books, journals, and files. Acquiring appropriate information from such humongous data has become quite challenging and a time-consuming task. Trivial keyword-based information retrieval systems highly depend on the statistics of data, thus facing word mismatch problem due to inevitable semantic and context variations of a certain word. Therefore, this marks the desperate need to organize such massive data into a structured format so that information can be easily processed in a large context by taking data semantics into account. Ontologies are not only being extensively employed in the semantic Web to store unstructured information in an organized and structured way but it has also raised the performance of diverse information retrieval approaches to a great extent. Ontological information retrieval systems retrieve data based on the similarity of semantics between the user query and the indexed data. This paper reviews modern ontology-based information retrieval methods for textual, multimedia, and cross-lingual data types. Furthermore, we compare and categorize the most recent approaches used in the above-mentioned information retrieval methods along with their major drawbacks and advantages.

Biomedical experts and bio-curators are unable to quickly find short and precise information using typical search engines as the amount of biomedical literature is increasing exponentially. The research community is focusing on biomedical question answering (QA) systems so that anyone can find precise information nuggets from the massive amount of biomedical literature. Generally, the user queries fall under different categories such as factoid, list, yes/no, or summary. The existing state-of-the-art question answering systems deal with most of these question types. However, the research to improve the performance of individual question types is also on the rise. To improve QA system performance, question classification plays a vital role for factoid and list type questions as it allows the answer processing stage to narrow down the candidate answer space and assigns a higher rank to the correct answers. A single biomedical answer or entity may be associated with more than one biomedical category or semantic type, e.g., Coenzyme Q(10) is classified under two categories in Unified Medical Language System (UMLS): organic chemical and biologically active substance. This inherent characteristic of biomedical entities makes question classification in the biomedical domain a multi-label classification problem, where one question might expect answers belonging to more than one semantic type. To the best of our knowledge, several QA systems deal with question classification as a multi-class classification problem and only one state-of-the-art system – OAQA – deals with it as a multi-label classification problem. In this paper, we analyze the pipeline of the OAQA system for factoid and list type questions, emphasizing the multi-label question classification. We use an improved question classification dataset with the copy transformation technique to improve the performance of list type questions. Moreover, we introduce a binary transformation in the pipeline of factoid questions to increase its performance. Our modified methodology enhances the performance of both list and factoid type questions by a margin of 2% and 3% evaluated on standard $F_{1}$ and Mean Reciprocal Rank measures, respectively.

Early diagnosis of Alzheimer’s disease (AD) allows individuals and their health managers to manage healthier medication. We proposed an approach for classification of AD stages, with respect to principal component analysis (PCA)-based algorithm. The PCA has been extensively applied as the most auspicious face-recognition algorithm. For the proposed algorithm, 100 images of 10 children were transformed for feature extraction and covariance matrix was constructed to obtain eigenvalues. The eigenvector provided a useful framework for face recognition. For the classification of AD stages, magnetic resonance imaging (MRI) and functional magnetic resonance imaging (fMRI) data were obtained from Alzheimer’s Disease Neuroimaging Initiative database. Hippocampus is one of the most affected regions by AD. Thus, we selected clusters of voxels from the “hippocampus” of AD screening stage (mild cognitive impairment), AD stage 1, AD stage 2, and AD stage 3. By using eigenvectors corresponding to maximum eigenvalues of fMRI data, the purposed algorithm classified the voxels of AD stages effectively.

A low-temperature co-precipitation technique was employed to synthesize Pb-doped Ba-hexaferrite (x = 0.0–1.0) for memory- and sensor-type applications. X-ray diffraction was used for structural study showing impurity phases generated and enhanced in successive composition. This was due to stresses and distortions by Pb due to its volatile nature and difference in ionic radius (1.76 A) than that of Ba. The nanomaterial has cation ratio Fe3+/Ba2+. Impurity phases enhanced to 30%, and dopant also caused the change in structural parameters. Scanning electron microscopy was employed for morphological study, and it confirmed variations and modification in crystallization process. DC electrical properties ‘Idc’ were measured from room temperature to 758 K, and that showed the decreasing trends in resistivity with increase in temperature. These physical properties of synthesized material could be useful for smart and sensitive applications like memory devices.

Zinc telluride thin films can be used in a large variety of applications such as optoelectronics and microelectronics tools. In this paper, the growth of ZnTe films prepared by electrodeposition, thermal evaporation and closed space sublimation method with various precursors and some its physical properties of obtained zinc telluride films are discussed.

The evolution of Internet of Things (IoT) has increased the appetite for the energy efficient wireless infrastructures. Most of the IoT devices are inherently resource constrained and heterogeneous in respect of their traffic demand. Moreover, these devices need to be made adaptable to the varying environmental conditions. However, existing traffic scheduling and duty cycling algorithms lack the capability to meet the dynamic service quality requirements of IoT applications with variable context information. Specifically, they are infeasible for IoT use-cases where multi-hop communication is required. This paper strives to realize efficient resource allocation to the Wi-Fi based IoT devices in multi-hop IoT infrastructures. Firstly, IoT applications are characterized as per their heterogeneous traffic demand and mapped to the distinct weighted quality classes. Then, context awareness is introduced for IoT devices and an optimization model constrained by their service quality requirements and context priorities is presented. Further, an energy efficient context aware traffic scheduling (EE-CATS) algorithm is proposed wherein the convergence of model is specified by a sub-gradient projection method. The EE-CATS algorithm efficiently allocates resources to multi-hop IoT devices and reduce their total awake time by employing adaptive duty cycling. The performance evaluation is done for various IoT use-cases through simulations in Network Simulator (ns-2) and testbed implementation in Contiki operating system. The results suggest substantial improvements in the energy efficiency of IoT devices while ensuring their dynamic service requirements. In addition, the proposed algorithm minimizes the end-to-end delays in overall IoT networks.

Background Arteriovenous fistula (AVF) formation is the most common vascular access procedure for patients requiring haemodialysis. However, it is associated with high failure rates, influenced by vessel diameter and arterial inflow. Mode of anaesthesia may affect these factors, and subsequently AVF maturation rates. Objective To perform a systematic review and meta-analysis to assess the effect of anaesthesia type for autologous primary radiocephalic or brachiocephalic AVF creation on subsequent fistula failure rates. Methods The online databases of Medline, EMBASE, CINAHL, The Cochrane Database of Systematic Reviews, ClinicalTrials.gov , and Google Scholar as well as vascular and anaesthesiology conference abstracts were searched on August 1, 2016. Randomised control trials (RCTs) that reported the effect of anaesthesia type on subsequent failure rates during autologous AVF creation were included. Two independent reviewers performed methodological assessment and data extraction. Random effects models were used to calculate pooled effect size estimates. A sensitivity analysis was also carried out. Results Four RCTs (286 patients) were identified with 286 autologous AVFs. There were 48 fistula failures. Most of the studies suffered from significant methodological flaws. There was a significantly lower failure rate among patients undergoing regional (12/143) compared with local (36/143) anaesthesia (OR 0.28, 95% CI 0.14–0.57). On sensitivity analysis, having excluded the most heavily weighted study, the results remained significant (OR 0.20, 95% CI 0.05–0.75). Conclusions The use of regional anaesthesia is associated with lower AVF failure rates when compared with local anaesthesia in patients undergoing primary forearm AVF formation for haemodialysis.

Compute intensive programs generally consume significant fraction of execution time in a small amount of repetitive code. Such repetitive code is commonly known as hotspot code. We observed that compute intensive hotspots often possess exploitable loop level parallelism. A JIT (Just-in-Time) compiler profiles a running program to identify its hotspots. Hotspots are then translated into native code, for efficient execution. Using similar approach, we propose a methodology to identify hotspots and exploit their parallelization potential on multicore systems. Proposed methodology selects and parallelizes each DOALL loop that is either contained in a hotspot method or calls a hotspot method. The methodology could be integrated in front-end of a JIT compiler to parallelize sequential code, just before native translation. However, compilation to native code is out of scope of this work. As a case study, we analyze eighteen JGF (Java Grande Forum) benchmarks to determine parallelization potential of hotspots. Eight benchmarks demonstrate a speedup of up to 7.6x on an 8-core system

Stabilized un-doped Zinc Telluride (ZnTe) thin films were grown on glass substrates under vacuum using a closed space sublimation (CSS) technique. A dilute copper nitrate solution (0.1/100 mL) was prepared for copper doping, known as an ion exchange process, in the matrix of the ZnTe thin film. The reproducible polycrystalline cubic structure of undoped and the Cu doped ZnTe thin films with preferred orientation (111) was confirmed by X-rays diffraction (XRD) technique. Lattice parameter analyses verified the expansion of unit cell volume after incorporation of Cu species into ZnTe thin films samples. The micrographs of scanning electron microscopy (SEM) were used to measure the variation in crystal sizes of samples. The energy dispersive X-rays were used to validate the elemental composition of undoped and Cu-doped ZnTe thin films. The bandgap energy 2.24 eV of the ZnTe thin film decreased after doping Cu to 2.20 eV and may be due to the introduction of acceptors states near to valance band. Optical studies showed that refractive index was measured from 2.18 to 3.24, whereas thicknesses varied between 220 nm to 320 nm for un-doped and Cu doped ZnTe thin film, respectively, using the Swanepoel model. The oxidation states of Zn+2, Te+2, and Cu+1 through high resolution X-ray photoelectron spectroscopy (XPS) analyses was observed. The resistivity of thin films changed from ~107 &Omega, &middot, cm or undoped ZnTe to ~1 &Omega, cm for Cu-doped ZnTe thin film, whereas p-type carrier concentration increased from 4 &times, 109 cm&minus, 2 to 1.4 &times, 1011 cm&minus, 2, respectively. These results predicted that Cu-doped ZnTe thin film can be used as an ideal, efficient, and stable intermediate layer between metallic and absorber back contact for the heterojunction thin film solar cell technology.

Site occupancy plays an important role in the properties of the materials in general, and in the case of ferrites, this makes a significant effect. Barium ferrite has a wide range of applications ranging from everyday applications to sophisticated communication technologies. Two compositions Ba1−xPbxFe12O19 and BaPbxFe12−xO19 (x = 0.0–1.0) were synthesized by co-precipitation method using similar synthesis conditions. The aim was to understand the different behaviors when Pb replaced Ba in R and R* blocks in the first composition, while in the second composition it replaced iron in RSR*S* blocks. Microstructural and morphological variations in both synthesized compositions because of Pb dopant were responsible for affecting all properties. Higher ionic radius (1.76 A), lower melting point (950 °C) of lead and gradually increased concentration were responsible for generating stresses and distortions of different magnitudes in both compositions. Magnetic and DC electrical characterizations were investigated by applying similar conditions. Higher phase purity and better morphology are obtained when Ba is replaced by Pb. Strong variation in coercivity due to decrease in anisotropic energy is useful in high-density storage devices. For the other composition, when Fe is replaced by Pb, higher resistivity is obtained. The obtained range of resistivity is useful against eddy current losses in high-frequency devices.

Stabilized un-doped Zinc Telluride (ZnTe) thin films were grown on glass substrates under vacuum using closed space sublimation (CSS) technique. A dilute copper nitrate solution (0.1/100 ml) was prepared for copper doping known as ion exchange process in the matrix of ZnTe thin film. The reproducible polycrystalline cubic structure of undoped and Cu doped ZnTe thin films with preferred orientation (111) was confirmed by X-rays diffraction (XRD) technique. Lattice parameter analyses verified the expansion of unit cell volume after incorporation of Cu species into ZnTe thin films samples. The micrographs of scanning electron microscopy (SEM) were used to measure the variation in crystal sizes of samples. The energy dispersive X-rays was used to validate the elemental composition of undoped and Cu-doped ZnTe thin films. The bandgap energy 2.24 eV of ZnTe thin film decreased after doping Cu to 2.20 eV may be due to the introduction of acceptors states near to valance band. Optical studies showed that refractive index was measured from 2.18 to 3.24 whereas thicknesses varied between 220 nm to 320 nm for un-doped and Cu doped ZnTe thin film respectively using Swanepoel model. The oxidation states of Zn+2, Te+2 and Cu+1 through high resolution X-ray photoelectron spectroscopy (XPS) analyses was observed. The resistivity of thin films changed from ~107 Ω-cm for undoped ZnTe to ~1 Ω-cm for Cu-doped ZnTe thin film, whereas p-type carrier concentration increased from to respectively. These results predicted that Cu-doped ZnTe thin film can be used as an ideal, efficient and stable intermediate layer between metallic and absorber back contact for the heterojunction thin film solar cell technology.

ObjectivesTo use high-resolution imaging to characterise palindromic rheumatism (PR) and to compare the imaging pattern observed to that seen in new-onset rheumatoid arthritis (NORA).MethodsUltrasound (US) assessment of synovitis, tenosynovitis and non-synovial extracapsular inflammation (ECI) was performed during and between flares in a prospective treatment-naive PR cohort. MRI of the flaring region was performed where possible. For comparison, the same US assessment was also performed in anticyclic citrullinated peptide (CCP) positive individuals with musculoskeletal symptoms (CCP+ at risk) and patients with NORA.ResultsThirty-one of 79 patients with PR recruited were assessed during a flare. A high frequency of ECI was identified on US; 19/31 (61%) of patients had ECI including 12/19 (63%) in whom ECI was identified in the absence of synovitis. Only 7/31 (23%) patients with PR had synovitis (greyscale ≥1 and power Doppler ≥1) during flare. In the hands/wrists, ECI was more prevalent in PR compared with NORA and CCP+ at risk (65% vs 29 % vs 6%, pConclusionPR has a distinct US pattern characterised by reversible ECI, often without synovitis. In patients presenting with new joint swelling, US may refine management by distinguishing relapsing from persistent arthritis.

Exponential growth of biomedical literature and clinical data demands more robust yet precise computational methodologies to extract useful insights from biomedical literature and to perform accurate assignment of disease-specific codes. Such approaches can largely enhance the effectiveness of diverse biomedicine and bioinformatics applications. State-of-the-art computational biomedical text classification methodologies either solely leverage discrimintaive features extracted through convolution operations performed by deep convolutional neural network or contextual information extracted by recurrent neural network. However, none of the methodology takes advantage of both convolutional and recurrent neural networks. Further, existing methodologies lack to produce decent performance for the classification of different genre biomedical text such as biomedical literature or clinical notes. We, for the very first time, present a generic deep learning based hybrid multi-label classification methodology namely GHS-NET which can be utilized to accurately classify biomedical text of diverse genre. GHS-NET makes use of convolutional neural network to extract most discriminative features and bi-directional Long Short-Term Memory to acquire contextual information. GHS-NET effectiveness is evaluated for extreme multi-label biomedical literature classification and assignment of ICD-9 codes to clinical notes. For the task of extreme multi-label biomedical literature classification, performance comparison of GHS-Net and state-of-the-art deep learning based methodology reveals that GHS-Net marks the increment of 1%, 6%, and 1% for hallmarks of cancer dataset, 10%, 16%, and 11% for chemical exposure dataset in terms of precision, recall, and F1-score. For the task of clinical notes classification, GHS-Net outperforms previous best deep learning based methodology over Medical Information Mart for Intensive Care dataset (MIMIC-III) by the significant margin of 6%, 8% in terms of recall and F1-score. GHS-NET is available as a web service at1 and potentially can be used to accurately classify multi-variate disease and chemical exposure specific text.

Zinc Telluride (ZnTe) powder of 99.9% purity was used to deposit ZnTe thin films on ultra-sonically cleaned slides of glass. A locally manufactured Closed Space Sublimation (CSS) technique was used to deposit these thin films. As-deposited ZnTe thin films were further exposed to tellurium (Te) for different deposition times under high vacuum. Annealing was carried out after the deposition of Tellurium (Te) on as-deposited ZnTe thin films for diffusion of Te into ZnTe thin films. Cubic phases were found before and after enrichment of Te into as-deposited ZnTe thin films via X-ray Diffraction (XRD) studies. Preferred orientation was [111] and the crystallite size increased after enrichment of Te. The surface morphology was studied using scanning electron microscopy (SEM) which confirmed that grain sizes significantly increased after the enrichment of Te into ZnTe thin films. Energy dispersive X-rays (EDX) confirmed the elemental composition of zinc (Zn) and tellurium (Te). The transmittance decreased after the enrichment of Te into ZnTe thin films. A small change in energy band gap was calculated using Tauc relation. Hall measurement was carried out to study the electrical behavior of Te-enriched ZnTe thin films and found that electrical resistivity of as-deposited ZnTe thin films was of the order of 106 Ω-cm. The electrical resistivity reduced to 104 Ω-cm after Te enrichment. X-rays photoelectron spectroscopy (XPS) study was carried out to check the chemical bonding and states before and after Te-enrichment. Te-enriched ZnTe thin films showed better properties and found suitable for buffer layer of II generation solar cells.

Functional magnetic resonance imaging (fMRI) data analysis is developing rapidly in a fast emerging society, because of temporal and spatial resolution and the inoffensive feature of their acquisition in human brains. Spatial resolution governs how “sharp” the image is in appearance, whereas temporal resolution denotes the precision of a measurement with respect to time. The goal of fMRI technique is to identify the activation pattern and functional connectivity in the brain regions. In our study, artificial neural network technique was used for the identification and classification of decision-making voxels of fMRI data based on Brodmann areas 10 and 47 from the prefrontal cortex of human brain. The total number of voxels of Brodmann areas was 159, and we determined that some particular voxels played dominant role for decision-making process while performing a visual task. We also analyzed true positive and false positive classification rates between two decisions in the context of a well-known receiver operating characteristic curve (ROC).

Metal chalcogenides especially Cu2- x Se has gained much attention in thermoelectric community due to its complex crystal structure and superionic behavior. Here, we report a facile method to improve the thermoelectric efficiency by introducing ZnTe nanoinclusions into the matrix of Cu2- x Se. As a result, a substantial improvement of 32% in electrical conductivity of Cu2- x Se-ZnTe composite is observed. The increase in electrical conductivity is at the expense of Seebeck coefficient, which slightly decreases the power factor of the composite samples than that of pure Cu2- x Se. Furthermore, the introduction of secondary phase facilitates in declining the total thermal conductivity of Cu2- x Se-ZnTe composite up to 34% by suppressing the lattice thermal contributions. Thus, the moderate power factor and lower thermal conductivity values result in an improved figure of merit ( zT ) value of ~0.40 in mid-range temperature (750 K) for Cu2- x Se-ZnTe composite with 10 wt.% of ZnTe, which is about 40% higher than that of its pure counterpart. Hence, it is believed that the incorporation of ZnTe nanoinclusions in the matrix of Cu2- x Se may be an important route to improve the thermoelectric properties of Cu2- x Se based compounds.

The successful use of intelligent agents in healthcare has attracted researchers to apply this emerging software engineering paradigm in more advanced and complex applications. Main success factor is the natural mapping of real world medical problems into cyber world. Multi-agent architecture can easily model the heterogeneous, distributed and autonomous health care systems. The multi agent systems have been applied from single healthcare activity like knowledge based medical system to complex, multi-component based systems like complete healthcare unit. The use of multi agent systems in health care domain has also opened the ways to find out new applications like personalized and socialized health care systems. This versatile use of multi agent systems has also posed new problems for researchers like; security, communication, and different social issues. This work reviews recent years' research and applications of multi agent systems in healthcare published in different research journals, international conferences, and implemented practically. We reviewed five subdomains and three systems in each subdomain. A set of common parameters of these systems has been extracted and compared to analyze systems' merits and deficiencies. Based on our analysis, we have provided recommendations for multi agent systems applied in healthcare domain. Future research directions for interested researchers and practitioners are also discussed. As our own future research work, we intend to study healthcare and multi agent systems in e-commerce.

The article [Benchmarking performance of machine and deep learning-based methodologies for Urdu text document classification], written by [Muhammad Nabeel Asim, Muhammad Usman Ghani, Muhammad Ali Ibrahim, Waqar Mahmood, Andreas Dengel and Sheraz Ahmed], was originally published.

Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

Pakistan has a key problem of power supply in urban as well as rural areas having very limited supply of electricity. Being an agricultural country, Pakistan produces a significant volume of agro-waste having little economic use at the moment. The increased consciousness in green house emission has motivated to develop new technologies to accommodate the detention and confiscation of CO 2 . Keeping in mind the scenario, the combustion and pyrolysis behavior of Pakistani lignite (PL), rice husk (RH) and their blends of PL50/RH50 wt.%, PL40/RH60 wt.% and PL30/RH70 wt.% were examined in oxy-fuel and nitrogen environments for their probable use as sustainable and alternative energy resource. Combustion profiles of rice husk and lignite blends lie between those of individual fuels. Thermal stability of the lignite was observed to be decreased as increase of rice husk and suggest favoring when more energy is needed in short interval of time. Energy potential of PL50/RH50 blend was better due to the presence of higher amount of carbon and calorific value, low amount of ash and sulfur contents. Activation energy found to be decreased with the increase of rice husk quantity in pyrolysis reaction and associated with carbon content of lignite. The reduction in activation energy indicated the decrease in the operational temperature and suggests that gasification process will start at lower temperature for blends of lignite and rice husk than individual lignite.

Introduction: A case control analytical study , carried out among the patients of chronic kidney disease (CKD) to find out the casual factors of the disease. Objective of the study was to identify major casual factors of the disease to determine association of those factors with formation and progression of the disease i.e. CKD. Methodology: A case control analytical study was carried out among patients of chronic kidney disease (CKD) to find out the casual factors of the disease. Study included all patients ( all ages and both sex) , visiting dialysis unit, minimum duration of diseases 6 months and fifth stage of five stages of kidney disease is looked for. A sample of 32 patients of CKD from Nephrology Unit Bahawal Victoria Hospital , Bahawalpur is selected & information gathered through filling ofstructured Questionnaire to find out the factors leading to the formation of the disease. The whole study took time duration of approximately 6 months. Results: Total32 conveniently selected patients were inducted in the study. Only 5 (15.6%) out of32 had a family history of CKD, 25 were suffering from hypertension, 7 were suffering from Diabetes & just 3 had taken Hakeem medication for their disease and 9 patients had kidney inflammation and urinary obstruction. Conclusion: According to our study, there are two major causes of Kidney disease. One of them is Diabetes. High blood sugar levels caused bydiabetes damage blood vessels in the kidneys. If the blood sugar level remains high over many years, this damage gradually reduces the function of the kidneys leading to CKD. Second major cause of CKD is High blood pressure (hypertension). Uncontrolled high blood pressure damages blood vessels, which can lead to damage in the kidneys. Also, long-term use of medicines can damage the kidneys.

Question classification is considered one of the most significant phases of a typical Question Answering (QA) system. It assigns certain answer types to each question which leads to narrow down the search space of possible answers for factoid and list type questions. The process of assigning certain answer types to each question is also known as Lexical Answer Type (LAT) Prediction. Although much work has been done to enhance the performance of question classification into coarse and fine classes in diverse domains, it is still considered a challenging task in the biomedical field. The difficulty in biomedical question classification stems from the fact that one question might have more than one label or expected answer types associated with it (also, referred to as a multi-label classification). In the biomedical domain, only preliminary work is done to classify multi-label questions by transforming them into a single label through copy transformation technique. In this paper, we have generated a multi-labeled corpus (MLBioMedLAT) by exploring the process of Open Advancement of Question Answering (OAQA) system for the task of biomedical question classification. We use 780 biomedical questions from BioASQ challenge and assign them appropriate labels. To annotate these labels, we use the answers for each question and assign the question semantic type labels by leveraging an existing corpus and utilizing OAQA system. The paper introduces a data transformation approach namely Label Power Set with logistic regression (LPLR) for the task of multi-label biomedical question classification and compares its performance with Structured SVM (SSVM), Restricted Boltzmann Machine (RBM), and copy transformation based logistic regression (CLR) (previously used for a similar task in the OAQA system). To evaluate the integrity of the introduced data transformation technique, we use three prominent evaluation measures namely Micro F 1 , Accuracy, and Hamming Loss. Regarding MicroF 1 , our introduced technique coupled with a new feature set surpasses CLR, SSVM, and RBM with a margin of 7%, 8%, and 22% respectively.

Introduction: Breast lumps are one of the most prevalent presenting complaints in an outpatient department ( OPD) in Pakistan. About 90% are benign and without any grave consequences but malignant lumps contribute to a consequential percentage of all breast lumps. With growing vigilance in the general population , specially about breast pathologies and the associated solicitude and stress that this condition may lead to breast cancer with grin consequences , compels patients to seek medical advice. Materials & Methods: This is a Cross sectional study performed at surgical OPD of Bahawal Victoria Hospital , Bahawalpur. Study was conducted from 15th April to 15th May 2018. A total of 95 patients were included by convenience sampling method. Data was analyzed manually and elaborated with the help of tables and charts. Conclusion: This study concludes that the frequency of breast cancer, inflammatory diseases and fibro adenoma is higher in our set up as compared to the world literature. Breast cancer was the most common disease. This maybe because the study was carried out on a small scale and limited number of cases were included and the duration was short. However the early age of diagnosis shows that there is need of breast care clinics and population screening programs to detect breast cancer as soon as possible. It is recommended that the surgeon managing a case of breast lump in a patient above 30 years should be highly suspicious and cautious so that early detection and management ofmalignant lumps be carried out.

Cadmium sulphide (CdS) is the n-type, wide band gap II-VI semiconducting material by offering applications in photovoltaics. Among the range of applications offered by CdS thin films, it has found application as a window layer in second generation solar cells by creating photon traps, thereby quantum efficiency. Closed space sublimation (CSS) offers a trivial yet effective approach for the synthesis of thin films for solar cell applications at moderate temperatures. CdS thin films of controlled thicknesses were synthesized by CSS technique by varying exposure time. X-rays diffraction data of these thin films revealed polycrystalline nature with a preferred orientation along (002) direction. The scanning electron microscopy (SEM) based morphological studies showed grain size variation with an increase in thickness of deposited thin films in the range of 300–500 nm. The electrical studies revealed high resistivity of the order of 106 Ω-cm. Spectrophotometric studies performed for CdS thin films concluded with the calculation of the optical parameters such as refractive index, absorption coefficient employing the Swanepoel model, and energy band gap of ∼2.42 eV using the Tauc relation in addition to thin film thickness confirmation. The variations in thickness affect the structure, surface, optical (Khan et al. 2018), electrical properties.

Biomedical information retrieval systems are becoming popular and complex due to massive amount of ever-growing biomedical literature. Users are unable to construct a precise and accurate query that represents the intended information in a clear manner. Therefore, query is expanded with the terms or features that retrieve more relevant information. Selection of appropriate expansion terms plays key role to improve the performance of retrieval task. We propose document frequency chi-square, a newer version of chi-square in pseudo relevance feedback for term selection. The effects of pre-processing on the performance of information retrieval specifically in biomedical domain are also depicted. On average, the proposed algorithm outperformed state-of-the-art term selection algorithms by 88% at pre-defined test points. Our experiments also conclude that, stemming cause a decrease in overall performance of the pseudo relevance feedback based information retrieval system particularly in biomedical domain. Database URL: http://biodb.sdau.edu.cn/gan/

Brain Computer Interfaces (BCI) are a natural extension to Human Computer Interaction (HCI) technologies. BCI is especially useful for people suffering from diseases, such as Amyotrophic Lateral Sclerosis (ALS) which cause motor disabilities in patients. To evaluate the effectiveness of BCI in different paradigms, the need of benchmark BCI datasets is increasing rapidly. Although, such datasets do exist, a comparative study of such datasets is not available to the best of our knowledge. In this paper, we provided a comprehensive overview of various BCI datasets. We briefly describe the characteristics of these datasets and devise a classification scheme for them. The comparative study provides feature extractors and classifiers used for each dataset. Moreover, potential use-cases for each dataset are also provided.

Cadmium sulfide (CdS) has been investigated extensively for II generation solar cell as an efficient window layer with n type conductivity. The significance of CdS is because of its large energy band gap tune-ability and native n-type conductivity. In this report, energy band gap engineering of CdS films was carried out by means of controlled silver (Ag) doping to modify their optical properties along with electrical properties. Closed space sublimation (CSS) technique has been employed to synthesize as-deposited CdS thin films. One of the advantages of CSS grown films is roughness which offers more trapping/absorbing of light. Ion exchange solution process was carried out for Ag-doping, which resulted in widening of the band gap from 2.15 eV to a value of 2.45 eV, showing the increased capability of solar energy harvesting. Un-doped CdS thin films showed a high resistivity of the order of ∼106 Ω-cm, which reduced to only ∼102 Ω-cm with the Ag-doping, as probed by four-point probe, offering great advantage for the system to be used in II-VI solar cells. Un-doped and Ag-doped films showed n-type conductivity. We studied the Ag-doping effects on the surface morphological and correlated the morphological modification with changing optical and electrical behaviour of CdS thin films. It is believed that enhanced properties offer great potential for these films to be used for the solar cell applications.

Internet of Things (IoT) systems cannot successfully realize the notion of ubiquitous connectivity of everything if they are not capable to truly include 'multimedia things'. However, the current research and development activities in the field do not mandate the features of multimedia objects, thus leaving a gap to benefit from multimedia content based services and applications. In this paper, we analyze this issue by contemplating the concept of IoT and drawing an inspiration towards the perspective vision of 'Internet of Multimedia Things' (IoMT). Therein, we introduce IoMT as a novel paradigm in which smart heterogeneous multimedia things can interact and cooperate with one another and with other things connected to the Internet to facilitate multimedia based services and applications that are globally available to the users. Some applications and use-cases for IoMT are presented to reflect the possibilities enabled by this new paradigm. An IoMT architecture is then presented which is segregated into four distinct stages; (i) multimedia sensing, (ii) reporting and addressability, (iii) multimedia-aware cloud, and (iv) multi-agent systems. Instead of proposing specific technical solutions for each individual stage of the presented architecture, we survey the already existing technologies, providing a synthesis for the realization of the vision of IoMT. Subsequently, various requirements and challenges as well as the feasibility of existing solutions for each stage of proposed IoMT architecture are comprehensively discussed.

Bismuth iron oxide (BiFeO3) is amongst the class of multiferroic materials that has attracted world attraction because of its antiferromagnetic Neel temperature and high ferroelectric Curie temperature of 643K and 1103K respectively. However, synthesis of pure phase BiFeO3 is extremely difficult due to volatile nature of Bi2O3. This results in the presence of bismuth deficient and bismuth rich phases in pure bismuth iron oxide. In the past attention was focused on obtaining pure BiFeO3 phase with variation in annealing/calcination temperature in the range 400-700˚C, and very little consideration is given to the changes in Bi/Fe ratio during synthesis. We here report synthesis of pure phase bismuth iron oxide thin films using cost effective sol-gel method. The ratio of Bi/Fe is varied as 0.90, 1.0 and 1.10. The films are annealed at 300˚C in vacuum under the application of 500 Oe magnetic field. XRD results show that with Bi/Fe ratio of 1.10 pure phase BiFeO3 can be obtained at low temperature of 300˚C. Bi/Fe ratios of 0.9 and 1.0 result in the presence of bismuth impurity phase. The films show ferromagnetic behavior as opposed to antiferromagnetic nature of bulk BiFeO3 due to suppression of spiral spin structure.

Closed space sublimation (CSS) technique was employed to deposit thin films of zinc telluride (ZnTe) on a glass substrate under high vacuum. Two sets of ZnTe thin films and Zn enriched ZnTe thin films were prepared for comparative study. The enrichment for Zn onto the as-deposited ZnTe thin films was done by the novel manner of layer by layer deposition with subsequent annealing. X-ray diffraction (XRD) studies revealed before and after the enrichment of Zn the preferred orientation is [1 1 1] having cubic phase. The lattice constant was found to be increased and the crystallite size decreased 28 nm to 24 nm after the enrichment of Zn. A morphological study was carried out through a scanning electron microscope (SEM). For Zn enriched samples the average grain size is smaller as compared to ZnTe thin films. The local compositions of Zn and Te were confirmed by energy dispersive x-rays (EDX) from 51 atomic % of as-deposited ZnTe thin films to 68 atomic % in Zn enriched ZnTe thin films. The Zn enriched samples have a slight decrease in optical transmission in UV-VIS-NIR range as compared to the as-deposited ZnTe thin films. Due to the deposition of Zn there is a very small change in optical band gap energy. A four-probe technique was used to study electrical properties of as-deposited and Zn-enriched ZnTe thin films. These results shows that the as-deposited samples had the resistivity of 106 Ω • cm. For Zn enrichment samples resistivity increases from 106 Ω • cm to 108 Ω • cm, which shows that Zn-enriched samples are not suitable for back contact of II-VI solar cells. X-rays photoelectron spectroscopy (XPS) was used to confirm the elemental compositions and its bonding strength before and after the enrichment of Zn.

The massively ever increasing amount of textual and linked biomedical data available online poses many challenges for information seekers. So, the focus of information retrieval community has shifted to precise information retrieval, i.e. providing exact answer to a user question. In recent years, many datasets related to Biomedical Question Answering (BioQA) have emerged which the researchers can use to evaluate the performance of their systems. We reviewed these biomedical datasets and analyzed their characteristics. The survey in this paper covers these datasets for BioQA and has a two fold purpose: to provide an overview of the available datasets in this domain and to help researchers select the most suitable dataset for benchmarking their system.