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On-time recovery and treatment of disease is always desirable. The use of Machine learning in health-care has grown very fast to diagnosis the different kinds of diseases in the past few years. In such a diagnosis, past and real-time data are playing very crucial role in using data mining techniques. Still, we are lacking in diagnosing the emotional mental disturbance accurately in the early stages. Thus,the initial diagnosis of depression expressively stances a great problem for both,researchers and clinical professionals. We have addressed the said problem in our proposed work using Pipeline Machine Learning technique where people based on emotional stages have been effectively classified into different groups in e-healthcare. To implement Hybrid classification, a well known machine learning multi-feature hybrid classifier is used by having the emotional stimulation in form of negative or positive people. In order to improve classification, an Ensemble Learning Algorithm is used which helps in choosing the more suitable features from the available genres-emotion data on online media. Additionally, Hold out validation method has been to split the dataset for training and testing of the predictive model. Further, performance evaluation measures have been applied to check the proposed system evaluation. This study is done on Genres-Tags MovieLens dataset. The experimental results show that applied ensemble method provides optimal classification performance by choosing the best subset of features. The said results proved the excellency of the proposed system which comes from the choosing most related features selected by the Integrated Learning algorithm. Additionally, suggested approach is used to accurately and effectively diagnose the depression in its early stage. It will help in recovery and treatment of depressed people. We conclude that use of the suggested method is highly suitable in all aspects of e-healthcare for depress stimulation. [ABSTRACT FROM AUTHOR]

In the present article, we give new techniques for proving general identities of the Popoviciu type for discrete cases of sums for two dimensions using higher-order ∇-divided difference. Also, integral cases are deduced by different methods for differentiable functions of higher-order for two variables. These identities are a generalization of various previously established results. An application for the mean value theorem is also presented. [ABSTRACT FROM AUTHOR]

Novel mutant camelina has become a crop of interest inspired by its short growing season, low harvesting costs and high oil composition. Despite those advantages, limited research has been done on novel mutant lines to determine applicability for biodiesel production. Jatropha is an extremely hardy, frugal and high oil yielding plant species. The major aim of the present study was not only to compare biodiesel production from jatropha and camelina but was also to test the efficacy of camelina mutant lines (M6 progenies) as superior feedstock. The biodiesel yield from camelina oil and jatropha oil was 96% and 92%, respectively. The gas chromatographic analysis using flame ionization detector (GC-FID) showed that mutant camelina oil biodiesel sample contain major amount of oleic acid (46.54 wt%) followed by linolenic acid (20.41 wt%) and linoleic acid (16.55 wt%). Jatropha biodiesel found to contain major amount of oleic acid (45.03 wt%) followed by linoleic acid (25.07 wt%) and palmitic acid (19.31 wt%). The fuel properties of produced biodiesel were found in good agreement with EN14214 and ASTM D6751 standards. The mutant camelina lines biodiesel have shown comparatively better fuel properties than jatropha. It has shown low saponification value (120.87–149.35), high iodine value (130.2–157.9) and better cetane number (48.53–59.35) compared to jatropha biodiesel which have high saponification value (177.39–198.9), low iodine value (109.7–123.1) and lesser cetane number (47.76–51.26). The results of the present student of utilizing novel mutant camelina lines for biodiesel production are quite promising and are helpful in turning out the outcomes of the previous studies suggesting that C. sativa biodiesel presents serious drawbacks for biodiesel applications. [ABSTRACT FROM AUTHOR]

Auricularia cornea, jelly mushroom, is a popular ingredient of traditional Chinese cuisine. This study aimed at evaluating the growth, yield, biological efficiency, total phenolic and flavonoid contents, the antioxidant activity, elemental composition, and molecular structure of the wild and domesticated strain Ac24 and the commercially cultivated edible mushroom A. cornea strains Ac1, Ac3 and Ac15. Based on the weight of the fresh fruiting bodies of A. cornea strains, the maximum yield was obtained from commercial strain Ac1 (237.10 g), followed by Ac3 (224.47 g), Ac15 (158 g) and Ac24 (132.37 g), while the biological efficiency range of A. cornea strains was 52.94–94.84%, with significant differences among the A. cornea strains. Our results revealed that Ac24 contained the highest phenolic content (20.10 mg GAE · g−1), while the highest flavonoid content was found in Ac1 (35.13 mg CE · g−1). The maximum mineral contents and the strains were as follows: copper (7.2 mg · kg−1) and zinc (310 mg · kg−1) in Ac1, manganese (788 mg · kg−1) in Ac3 and iron (310 mg · kg−1) in Ac24. DPPH assay found maximum antioxidant activity in Ac24 (IC50 0.233 mg TX · mL−1), FRAP (591 mg TX · g−1) in Ac15, and erythrocyte haemolysis in Ac24. SEM-EDX and FTIR analyses verified the differences among A. cornea strains. The results revealed that wild, domesticated A. cornea strain Ac24 is a promising dietary source of natural antioxidants and is of high nutritional value, compared to commercially cultivated strains. [ABSTRACT FROM AUTHOR]

Traditional memory hierarchy designs, primarily based on SRAM and DRAM, become increasingly unsuitable to meet the performance, energy, bandwidth, and area requirements of modern embedded and high-performance computer systems. Racetrack memory (RTM), an emerging nonvolatile memory technology, promises to meet these conflicting demands by offering simultaneously high speed, higher density, and nonvolatility. RTM provides these efficiency gains by not providing immediate access to all storage locations, but by instead storing data sequentially in the equivalent to nanoscale tapes called tracks. Before any data can be accessed, explicit shift operations must be issued that cost energy and increase access latency. The result is a fundamental change in memory performance behavior: the address distance between subsequent memory accesses now has a linear effect on memory performance. While there are first techniques to optimize programs for linear-latency memories, such as RTM, existing automatic solutions treat only scalar memory accesses. This work presents the first automatic compilation framework that optimizes static loop programs over arrays for linear-latency memories. We extend the polyhedral compilation framework Polly to generate code that maximizes accesses to the same or consecutive locations, thereby minimizing the number of shifts. Our experimental results show that the optimized code incurs up to 85% fewer shifts (average 41%), improving both performance and energy consumption by an average of 17.9% and 39.8%, respectively. Our results show that automatic techniques make it possible to effectively program linear-latency memory architectures such as RTM. [ABSTRACT FROM AUTHOR]

Background and Objective: Multiple Choice Questions (MCQs) can sample broad domains of knowledge efficiently and reliably. The MCQs of lower order C1(Cognitive Level1=Recall of knowledge) do not fulfill this purpose and those of higher cognitive order C2 (Cognitive Level2=Interpret) &C3(Cognitive Level3=Analyze) are better at assessing the problem solving capabilities of the student. Every good educational activity must be supported by quality examination to complete the objectives of a curriculum. The objective of the study was to evaluate MCQs presently being used in internal examinations of medical colleges in Lahore. Methods: Papers consisting of MCQs from Orthopedics other specialties were collected in June 2019 from different medical colleges of Lahore and reviewed by a senior medical teacher without blinding and without his knowing the scores the students had been awarded before. Question statement, clinical scenarios, options and other mistakes were assessed in each item on predetermined criteria. Cognitive level of the item was determined if it was asking for a recall/identify/analyze response. The results were tabulated and compared in two groups i.e. Miscellaneous and Orthopedics. Results: Most of the items(total=589) in both groups were of C1 cognitive level though Orthopedics (229) were slightly better (X2 = 49.882 P-Value = 0.000 (Statistically Significant). Miscellaneous group (360) was better in quality in making clinical scenarios (X2 = 29.952 P-Value = 0.000 (Statistically Significant) and writing a question statement without confusion. Options were better written in both groups. A good percentage of items needed to be corrected for mistakes in spellings, grammar and segregation into under graduate level. Conclusions: The cognitive level of assessment tool s MCQs is quiet low in both groups especially clinical scenario construction can be improved. Mistakes in spellings, grammar and conceptual mediocrity is common in both groups. [ABSTRACT FROM AUTHOR]

We use Riemann-Liouville fractional integral to provide generalization of Ostrowski type inequality with bounded derivatives. Our results improved the inequalities of [19]. [ABSTRACT FROM AUTHOR]

The aim of this article is to give the refinement of Niezgoda's inequality with its applications to Ky Fan inequality and cyclic mixed symmetric means. [ABSTRACT FROM AUTHOR]

Politics is one of the hottest and most commonly mentioned and viewed topics on social media networks nowadays. Microblogging platforms like Twitter and Weibo are widely used by many politicians who have a huge number of followers and supporters on those platforms. It is essential to study the supporters' network of political leaders because it can help in decision making when predicting their political futures. This study focuses on the supporters' network of three famous political leaders of Pakistan, namely, Imran Khan (IK), Maryam Nawaz Sharif (MNS), and Bilawal Bhutto Zardari (BBZ). This is done using social network analysis and semantic analysis. The proposed method (1) detects and removes fake supporter(s), (2) mines communities in the politicians' social network(s), (3) investigates the supporters' reply network for conversations between supporters about each leader, and, finally, (4) analyses the retweet network for information diffusion of each political leader. Furthermore, sentiment analysis of the supporters of politicians is done using machine learning techniques, which ultimately predicted and revealed the strongest supporter network(s) among the three political leaders. Analysis of this data reveals that as of October 2017 (1) IK was the most renowned of the three politicians and had the strongest supporter's community while using Twitter in a very controlled manner, (2) BBZ had the weakest supporters' network on Twitter, and (3) the supporters of the political leaders in Pakistan are flexible on Twitter, communicating with each other, and that any group of supporters has a low level of isolation. [ABSTRACT FROM AUTHOR]

The main intent of this paper is to innovate a new construction of modified Lupaş-Jain operators with weights of some Beta basis functions whose construction depends on σ such that σ 0 = 0 and inf x ∈ 0 , ∞ σ ′ x ≥ 1. Primarily, for the sequence of operators, the convergence is discussed for functions belong to weighted spaces. Further, to prove pointwise convergence Voronovskaya type theorem is taken into consideration. Finally, quantitative estimates for the local approximation are discussed. [ABSTRACT FROM AUTHOR]

Using an extension of Montgomery's identity and the Green's function, we obtain new identities and related inequalities for weighted averages of n-convex functions, i.e. the sum ∑ i = 1 m ρ i h (λ i) and the integral ∫ a b ρ (λ) h (γ (λ)) d λ where h is an n-convex function. [ABSTRACT FROM AUTHOR]

We state new general linear identities and inequalities involving n-convex functions using Hermite interpolation polynomials and Green functions. We also study positivity conditions of our stated inequalities. Moreover, we state related inequalities for n-convex functions at a point. Bounds for the reminders in new generalizations of general linear inequalities are given by using Čeby-sev functional, Ostrowski- and Grüss- types inequalities. [ABSTRACT FROM AUTHOR]

We have proved a generalized integral inequality of Ostrowski's type using weights with parameters for differentiable mapping whose second order derivatives are bounded and first order derivatives are absolutely continuous. Further, we obtained number of new results as special cases. We also stated applications of our work in Numerical Quadrature rules and Probability Theory. [ABSTRACT FROM AUTHOR]

This Letter investigates the cryogenic behavior of NbO2 threshold switching devices. Pt/NbO2/Pt devices are demonstrated to be well functional as threshold switching devices at ultra-low temperature (4 K). When the temperature decreases, the OFF-state resistance of NbO2 increases and the switching voltage increases. With the extracted characteristics of NbO2 ranging from 4 K to 300 K, we continue to study the neuromorphic system using the crossbar array with resistive memories as resistive synapses and NbO2 as oscillation neurons at different temperatures through SPICE simulation. The simulation results show that the oscillation systems could still work properly at 4 K. The oscillation amplitude decreases as temperature increases. The oscillation frequency depends on both the temperature and the input voltage. [ABSTRACT FROM AUTHOR]

Summary: This paper presents link to system (L2S) interfacing technique for multiple input and multiple output (MIMO) iterative receivers. In L2S interfacing, usually the post detection signal to noise ratio (SNR)‐based frame error rate lookup tables (LUT) are used to predict the link level performance of receivers. While L2S interfacing for linear MIMO receivers can be conveniently implemented, it is more challenging for MIMO iterative receivers due to unavailability of the closed form SNR expressions. In this paper, we propose three methods for post detection SNR estimation for MIMO iterative receivers. The first is based on the QR decomposition of the channel matrix, the second relies on the residual noise calculation based on the soft symbols, and the third exploits the closed form SNR expressions for linear receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. It is shown that linear and residual noise‐based SNR approximations result in a very good prediction performance whereas the performance of QR decomposition‐based method degrades for higher order modulations and coding schemes. This paper presents link to system interfacing technique for MIMO iterative receivers. A link to system interface model for iterative receivers is developed for evaluating the reference curves for different modulation and coding schemes, and results are validated by comparing the simulated and predicted frame error rates. Three post detection SNR evaluation schemes have been proposed for link to system interfacing all of which give good prediction performance especially at lower order modulation. [ABSTRACT FROM AUTHOR]

Cut-off grade classifies the available supply of ore (valuable) and waste material within a mineralised deposit. Given the mining, processing and refining limitations of a mining operation, an optimal cut-off grade policy ensures that the flow of ore from the mine to the processing and refining facilities is maintained at the maximum possible throughput. This policy defines a schedule of cut-off grades along with corresponding quantities of mineralised material to be mined, processed and metal refined in each period of the scheduling horizon. The criteria that controls the development of cut-off grade policy aligns with the strategic objectives of an operation in order to maximise the discounted value (net present value or NPV) over the life of operation. This paper proposes a new mixed integer linear programming (MILP) based model that maximises NPV subject to the mining, processing, refining capacity constraints and develops an optimal cut-off grade policy for an open pit mining operation with multiple processing streams. An implementation of the proposed method on hypothetical and realistic data promises a relatively higher NPV as compared to the traditional Lane's approach practiced in the mining industry. [ABSTRACT FROM AUTHOR]

Zinc (Zn) is needed for plants growth and human's balanced diet. The non-availability or less mobilization of Zn affects crop yield and nutritional quality and when such produce is consumed, it causes malnutrition in the consumers. Biofortification of staple cereals and vegetables like tomato (Solanum lycopersicum L.) is one of the strategies to fight such type of hidden hunger, particularly to crumble Zn deficiency. This needs the understanding of the molecular mechanism of Zn assimilation in plants. ZIP genes have not been annotated and characterised earlier. Therefore, the genes of ZIP family were identified from genome database by homology search. After carrying out the phylogenetic analysis, the 10 diverse genes of tomato ZIP family were selected for the transcript profiling in two selected genotypes; one with the highest Zn assimilation (LA-2662) and the other with the lowest one (NTH-242) was done. The RT-PCR results showed that in genotype LA-2662, genes SLZIPL, SLZIP3 SLZIP5L and SLZIP5 were upregulated in roots and leaf tissues, while their transcript level was the lowest in genotype NTH- 242. In addition, the genes; SLZIPL, SLZIP3, LeIRT1 and LeIRT2 were upregulated under Zn deficient environment and downregulated in high Zn environment. The better performance of LA-2662 for Zn assimilation might be linked to the upregulation of SLZIPL and SLZIP3 genes. It showed that the attenuation of these candidate genes might improve the capability of tomato for the development of Zn fortified tomato genotypes. It further showed that tomato can be bred for biofortification of various nutritional elements to fight hidden hunger. [ABSTRACT FROM AUTHOR]

The most common use of robots is to effectively decrease the human's effort with desirable output. In the human-robot interaction, it is essential for both parties to predict subsequent actions based on their present actions so as to well complete the cooperative work. A lot of effort has been devoted in order to attain cooperative work between human and robot precisely. In case of decision making , it is observed from the previous studies that short-term or midterm forecasting have long time horizon to adjust and react. To address this problem, we suggested a new vision-based interaction model. The suggested model reduces the error amplification problem by applying the prior inputs through their features, which are repossessed by a deep belief network (DBN) though Boltzmann machine (BM) mechanism. Additionally, we present a mechanism to decide the possible outcome (accept or reject). The said mechanism evaluates the model on several datasets. Hence, the systems would be able to capture the related information using the motion of the objects. And it updates this information for verification, tracking, acquisition, and extractions of images in order to adapt the situation. Furthermore, we have suggested an intelligent purifier filter (IPF) and learning algorithm based on vision theories in order to make the proposed approach stronger. Experiments show the higher performance of the proposed model compared to the state-of-the-art methods. [ABSTRACT FROM AUTHOR]

Opioids are the mainstay therapy in burned adults. Little data in the pediatric burn population exists that elucidates opioid prescribing practices. The primary purpose of this report is to quantify opioid and non-opioid analgesic use in pediatric burn patients admitted to a tertiary referral burn center. A retrospective audit of hospital charts and discharge records for patients <18 years old from March 2016 to March 2017 was performed. Opioid amounts were converted to either oral morphine miligram equivalents (MME) or oral MME per day and subsequently adjusted for age in kilograms (kg). Of the 226 patients, 223 (98.7%) were administered an opioid during admission. The median total opioid amount administered during admission was 0.4 (IQR: 0.3-0.6) mg oral MME per kilogram per day. Anecdotally, doses above 1 mg/kg/day are considered high risk for opioid tolerance. The median total opioid amount prescribed upon discharge was high at 3.9 (IQR: 2.3, 5.6) mg of oral MME per kilogram. Hydrocodone (96.0%) was the most common opioid administered, followed by morphine (88.1%). The most commonly prescribed discharge opioid was hydrocodone (95.4%). Non-opoioid analgesia during admission was used in 112 patients (49.6%). This study provides novel insight into the opioid practices at a tertiary burn center for pediatric patients, with our analysis showcasing high usage of opioids during admission and discharge for burn analgesia. It emphasizes the need to expand beyond opioids for burn analgesia and the importance of promoting non-opioid, multimodal analgesia in the pediatric burn population. [ABSTRACT FROM AUTHOR]

Marshall Herbert McLuhan (1911-1980) was a Canadian writer and communication expert. He was quite famous for a lot of quotes and aphorisms, and, in 1960s, was celebrated for his catchphrase "the medium is the message."McLuhan's reasoning about the medium that the information it carries or disseminates can influence or control society was much debated, discussed and criticised as well. His work is referred to be the cornerstone of media theory and his term "global village" lived its fame and is still transcending. This paper attempts to study the relevance and influence of McLuhan's philosophy and tries to correlate the significance of his various aphorisms, i.e., the terms he used. His contribution as a theorist and media/communication expert as well as an influential advocate of media determinism, remains ever persuasive. Essentially, his key concepts provide a revised outlook as proven by the time and technology we see today. Nothing truly could push him aside and this outwardly pop-cult-scientist-cum-metaphysician seems ever accurate and relevant in the times we live in. [ABSTRACT FROM AUTHOR]

The main purpose of this work is to establish relationship of functions with non-decreasing increments with other functions of high importance. Especially, we pay attention to the role of representation and connection among functions with non-decreasing increments and arithmetic integral mean, Wright convex functions, convex functions, ∇−convex functions, Jensen m−connvex functions, m−convex functions, m−∇−convex functions, k−monotonic functions, absolutely monotonic functions, completely monotonic functions, Laplace Transform and exponentially convex functions, by using the method of finite difference operator as different cases of ∆m h f(y). We also consider function with non-decreasing increments of order three and obtain the generalisation of the Levinson’s-type inequality and Jensen-Mercer’s-type inequality by using Jensen-Boas inequality and also deduce some results. [ABSTRACT FROM AUTHOR]

Background: The sequence diversity of dengue virus (DENV) is one of the challenges in developing an effective vaccine against the virus. Highly conserved, serotype-specific (HCSS), immune-relevant DENV sequences are attractive candidates for vaccine design, and represent an alternative to the approach of selecting pan-DENV conserved sequences. The former aims to limit the number of possible cross-reactive epitope variants in the population, while the latter aims to limit the cross-reactivity between the serotypes to favour a serotype-specific response. Herein, we performed a large-scale systematic study to map and characterise HCSS sequences in the DENV proteome. Methods: All reported DENV protein sequence data for each serotype was retrieved from the NCBI Entrez Protein (nr) Database (txid: 12637). The downloaded sequences were then separated according to the individual serotype proteins by use of BLASTp search, and subsequently removed for duplicates and co-aligned across the serotypes. Shannon's entropy and mutual information (MI) analyses, by use of AVANA, were performed to measure the diversity within and between the serotype proteins to identify HCSS nonamers. The sequences were evaluated for the presence of promiscuous T-cell epitopes by use of NetCTLpan 1.1 and NetMHCIIpan 3.2 server for human leukocyte antigen (HLA) class I and class II supertypes, respectively. The predicted epitopes were matched to reported epitopes in the Immune Epitope Database. Results: A total of 2321 nonamers met the HCSS selection criteria of entropy < 0.25 and MI > 0.8. Concatenating these resulted in a total of 337 HCSS sequences. DENV4 had the most number of HCSS nonamers; NS5, NS3 and E proteins had among the highest, with none in the C and only one in prM. The HCSS sequences were immune-relevant; 87 HCSS sequences were both reported T-cell epitopes/ligands in human and predicted epitopes, supporting the accuracy of the predictions. A number of the HCSS clustered as immunological hotspots and exhibited putative promiscuity beyond a single HLA supertype. The HCSS sequences represented, on average, ~ 40% of the proteome length for each serotype; more than double of pan-DENV sequences (conserved across the four serotypes), and thus offer a larger choice of sequences for vaccine target selection. HCSS sequences of a given serotype showed significant amino acid difference to all the variants of the other serotypes, supporting the notion of serotype-specificity. Conclusion: This work provides a catalogue of HCSS sequences in the DENV proteome, as candidates for vaccine target selection. The methodology described herein provides a framework for similar application to other pathogens. [ABSTRACT FROM AUTHOR]

Water is mainly used in moving vehicles as a coolant to remove the engine's heat mostly through forced convection heat transfer. However, mixtures of water and anti-freezing materials like ethylene glycol (EG) are also used as coolant. In the present research work, a mixture of EG and water (50:50) mixed with ZnO nanoparticle, called nanofluid, is utilized in a car radiator. Different volume concentrations of ZnO nanoparticles (0.01%, 0.02%, 0.03% and 0.04%) have been added in base fluid. It has been observed that overall heat transfer coefficient and heat transfer rate enhanced with different volume concentrations by mixing ZnO particle in base fluid, while flow rate varied in a range of 4–12 LPM (litre per minute). A maximum enhancement in heat transfer rate up to 36% has been observed experimentally at 0.04% volume concentration of ZnO nanoparticle. Greater heat transfer rate has been observed at low flow rate, i.e. at 4 LPM and 6 LPM, as compared to higher flow rate, and contribution of inlet temperature is found to be minimum in transferring the heat. [ABSTRACT FROM AUTHOR]

We present an extension of Jensen-Mercer inequality for functions with nondecreasing increments along with its refinements. A Mercer type variant is given for the Burkill-Mirsky-Pečarić’s result and related inequalities for the finite sequence of k−tuples monotone in means. [ABSTRACT FROM AUTHOR]

BACKGROUND: This study aimed to evaluate the possibility of cotton waste enrichment with glycine betaine (GB) for production of two strains (P9, P10) of king oyster (Pleurotus eryngii). Cotton waste was used as (100%) control (T0 = cotton waste) and augmented with various combinations of GB, (T1 = 2 mmol L−1, T2 = 4 mmol L−1, T3 = 6 mmol L−1, T4 = 8 mmol L−1 and T5 = 10 mmol L−1). The response of king oyster to GB was evaluated by earliness, yield, biological efficiency (BE), minerals (nitrogen, phosphorus, potassium, zinc (Zn), copper (Cu), magnesium (Mg), manganese (Mn), iron (Fe), sodium (Na), calcium (Ca)), total sugars, total soluble solids, reducing sugars, non‐reducing sugars, ascorbic acid, proximate (crude protein, carbohydrates, crude fibers, ash, fats) content of fruiting body and Fourier‐transform infrared (FTIR) spectroscopy analysis compared with the control substrate (cotton waste). RESULTS: The earliness, yield, and BE were higher as compared to control substrate and increased with an augmentation in the concentration of GB within the cotton waste. Two strains showed (on dry weight basis) 33.9–54.9 mg g−1 nitrogen, 6.8–12.5 mg g−1 phosphorus, 16.9–25.1 mg g−1 potassium, 40.5–64.2 mg kg−1 Zn, 17.1–37.3 mg kg−1 Cu, 1174–1325 mg kg−1 Mg, 20.1–29.1 mg kg−1 Mn, 129–265 mg kg−1 Fe, 779–835 mg kg−1 Ca), 6.3%–11.3% total sugars, 7.3–14.9 °Brix total soluble solids, 2.1–7.3% reducing sugars, 10.4–18.1% crude protein, 3.6–4.4% crude fiber and 5.6–16.7 mg (100 g)‐1 on various concentration of GB enrich cotton waste. Cotton waste enriched with GB significantly affected nutritional profile of king oyster mushroom. CONCLUSION: The results revealed that GB enriched cotton waste can be used as an innovative substrate to enhance the yield and quality of king oyster mushroom. © 2019 Society of Chemical Industry [ABSTRACT FROM AUTHOR]

Nanofluids are suspensions of nanoparticles with small concentration spread in base fluids such as water, oil and ethylene glycol. Nanofluid boiling is an important research area which provides many chances to explore new frontiers but also poses great challenges. Over the last decade, various studies have been carried out on pool boiling of nanofluids for the enhancement of critical heat flux which is otherwise limited by the use of base fluids. Several efforts have been made in the literature on nanofluid boiling, however, data on the boiling heat transfer coefficient and the critical heat flux have been unpredictable. Current study is a review of the status of research work on effects of nanofluids on heat transfer coefficient and critical heat flux. An emphasis is put in a review form on the recent progresses in nanofluid heat transfer coefficient and critical heat flux of pool boiling. This study also focuses on advancements in nanofluids, their properties and various parameters affecting boiling critical heat flux and heat transfer coefficient. At the end correlations used by different researchers to find out the critical heat flux and heat transfer coefficient are listed. [ABSTRACT FROM AUTHOR]

A study of the evolution of wave function penetration effects on ballistic drain current (ID) in nanoscale double gate (DG) metal-oxide-semiconductor field-effect-transistors (MOSFET) with the downscaling of device dimensions is presented. The electrostatics of the devices is calculated through the self-consistent solution of two dimensional Schrödinger and Poisson equations. HfO2/SiO2 stack is considered as the gate-dielectric material. It is observed that wave function penetration increases drain current in DG MOSFETs fabricated on (110) silicon, and the off-state current is more sensitive to the penetration effects than the on-state current. Numerical results show that the magnitude of the relative increase in ID due to wave function penetration increases sharply with the downscaling of silicon body thickness. On the other hand, the impact of the downscaling of gate length on the penetration effects depends on the gate bias. The relative increase in the off-state current due to wave function penetration decreases with the scaling of the gate length, while the variation in the relative increase in the on-state current with the gate length is insignificant. Drain-induced barrier lowering plays an important role in determining the effects of wave function penetration on the ballistic drain current with device scaling. Wave function penetration effect decreases the threshold voltage and increases the on-state transconductance. This phenomena becomes stronger with the scaling of the silicon body thickness. However, gate length scaling has little influence on these parameters. With the simultaneous scaling of the body thickness and the gate length, wave function penetration effects on all the parameters become more dominant. Physical explanation for these observations are provided. [ABSTRACT FROM AUTHOR]

[Display omitted] • The suggested approach BC-AI is utilized to determine the prediction of breast cancer using computer vision. • A data-driven computer-aided diagnostic (CAD) method for identifying patients as malignant, non-cancerous, or neither. • Feature extraction using the GLCM and HOG-based approach. • Self-constructing ensemble learning fuzzy algorithm (S-ELFA) blends a fuzzy methodology with an advanced neural network for optimum breast cancer illness detection and diagnosis. Breast cancer is a leading cause of mortality among women. Early detection will increase the chances of successful treatment and minimize the death rate. Even though many studies have been conducted to detect breast cancer, medical experts still face difficulty distinguishing between malignant and benign tumors. Hence, a technique enabling medical practitioners to effectively identify breast cancer was developed in this study. A computer-aided diagnostic (CAD) tool is used for classifying and diagnosing patients. The input images are pre-processed at the initial stage and a algorithm based on histogram of oriented gradients (HOG) and gray level co-occurrences matrix (GLCM) is applied to extract key features from pre-processed images. Then, shuffle shepherded optimization (SSO) selects the best features from the extracted features. Finally, the proposed self-constructing ensemble learning fuzzy algorithm (S-ELFA) identifies benign and malignant tumors. ROC, sensitivity, specificity, precision, and accuracy metrics were used to evaluate the developed method. [ABSTRACT FROM AUTHOR]

We consider the positivity of the sum Σi=1n ρiF(ξi), where F is a convex function of higher order, as well as analogous results involving the integral ∫aobo ρ(ξ)F(g(ξ))dξ . We use a representation of the function F via the Fink identity and the Green function that leads us to identities from which we obtain conditions for positivity of the above-mentioned sum and integral. We also obtain bounds for the integral remainders which occur in these identities, as well as corresponding mean value results. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes a Convolutional Neural Network (CNN) based approach for the classification and prediction of various types of in-plane and through-the-thickness delamination in smart composite laminates using low-frequency structural vibration outputs. An electromechanically coupled mathematical model is developed for the healthy and delaminated smart composite laminates, and their structural vibration responses are obtained in the time domain. Short Time Fourier Transform (STFT) is employed to transform the transient responses into two-dimensional spectral frame representation. A convolutional neural network is incorporated to distinguish between the damaged and undamaged states, as well as various types of damage of the laminated composites, by automatically extracting discriminative features from the vibration-based spectrograms. The CNN showed a classification accuracy of 90.1% on one healthy and 12 delaminated cases. The study of the confusion matrix of CNN provided further insights into the physics of the problem. The predictive performance of a pre-trained CNN classifier was also evaluated on unseen cases of delamination, and physically consistent results were obtained. [ABSTRACT FROM AUTHOR]

Silver nanoparticles (AgNPs) have been reported to penetrate the central nervous system (CNS) and induce neurotoxicity. However, there is a paucity of understanding of the toxicity of AgNPs and their effect on the blood-brain barrier (BBB) including the underlying molecular mechanism(s) of action. Such information is important for the formulation of new strategies for delivery of biological therapeutics to central nervous system (CNS) targets. Using an in vitro BBB model and mass spectrometry-based proteomics, we investigated alterations in the proteomes of brain endothelial cells and astrocytes at different time points after AgNPs exposure (24 and 48 h). Our data showed that several proteins involved in neurodisorders and neurodegeneration were significantly upregulated in endothelial cells (e.g. 7-dehydrocholesterol reductase, zinc transporters 1 and 6), while proteins responsible for maintaining brain homeostasis were significantly downregulated (e.g anti-oxidative proteins glutathione peroxidase 1 and glutathione peroxidase 4). Many inflammatory pathways were significantly upregulated at 24 h post-AgNPs exposure (C9 pathway), while at 48 h proteins involved in BBB damage and anti-inflammatory responses were upregulated (quinoneoxidoreductase1 and glutamate cysteine ligase catalytic subunit) suggesting that by the later time point, cellular protection pathways had been activated to rescue the cells from AgNPs-induced toxicity. Our study suggests that in the initial stage of exposure, AgNPs exerted direct cellular stress on the endothelial cells by triggering a pro-inflammatory cascade. This study provides detailed insight into the toxic potency of AgNPs on in vitro BBB model and adds to the understanding of the adaptive role of BBB with regards to AgNPs-mediated toxicity. [ABSTRACT FROM AUTHOR]

Abstract Very few qualitative studies focusing on teaching and learning practices at the tertiary level have emerged from Pakistan. The present study, which applied a qualitative approach, examined those contextual and institutional factors that have a direct link with teaching/learning practices at the tertiary level in Pakistan. While exploring the said phenomenon, the study exclusively focused on three factors: 1) reading material, 2) course syllabi, and 3) teaching practices. The study noted that although there was a consensus among the university students and instructors regarding the satisfactory level of teaching and learning practices, some fundamental elements related to teaching and learning, were either ignored or missing. [ABSTRACT FROM AUTHOR]

In the world of journalism and new age media, the essence of convergence is on and in constant use. As technology continues to make inroads into the field of journalism, it becomes rather quintessential to understand the impact of such technology on this profession. In this paper an attempt has been made to examine the emergence of process of convergence through various connecting technology driven processes, delineated in the form of a model. The model is based on premise that in today's world of journalism, the process of 'convergence and its effect' is absolutely inevitable. This irrevocability of convergence in the profession of journalism or media needs to be understood taking into consideration the influence of the term 'Digital-Clout' - which basically involves media convergence, online medium impact, organizational set up and professional/job transformation. While Internet being central force in this clout; assumes vital role in this model. The paper proposes and elaborates this concept in the shape of model-Digital Clout - Convergence-Effect Model. [ABSTRACT FROM AUTHOR]

Latent fingerprint segmentation involves marking out all the foreground regions accurately in a latent fingerprint image, but due to poor quality images and complex background, segmentation of latent fingerprint images is one of the most difficult tasks in automatic latent fingerprint recognition systems. In this article, we propose a patch-based technique for segmentation of latent fingerprint images, which uses Convolutional Neural Network (CNN) to classify patches. CNN has recently shown impressive performance in the field of pattern recognition, classification, and object detection, which inspired us to use CNN for this complex task. We trained the CNN model using SGD to classify image patches into fingerprint and non-fingerprint classes followed by proposed false patch removal technique, which uses "majority of neighbors" to remove the isolated and miss-classified patches. Finally, based on the final class of patches, an ROI is constructed to mark out the foreground from the background of latent fingerprint images. We tested our model on IIIT-D latent fingerprint database and the experimental results show improvements in the overall accuracy compared to existing methods. [ABSTRACT FROM AUTHOR]

Abstract This paper proposes the synergetic integration of system identification and artificial intelligence for the detection and assessment of delamination damages in smart composite laminates. An electromechanically coupled mathematical model is developed for the healthy and delaminated smart composite laminates on the basis of improved layerwise theory, higher order electric potential field and finite element method. A discriminative feature space is constructed for the healthy and delaminated structures via system identification from their structural vibration responses. The discriminative features are used for the training and cross-validation of various supervised machine learning classifiers and an optimal classifier is identified. The optimal classifier is employed to make predictions on unseen test delamination cases, and its predictions are validated via a dimensionality reduction tool. The obtained results show that the proposed technique could be employed as a reliable tool for nondestructive evaluation of smart composite laminates. [ABSTRACT FROM AUTHOR]

Abstract Polyindole–tin (IV) molybdophosphate (PIn-SMP), an organic-inorganic nanocomposite ion exchanger, was synthesized by a sol-gel process and exhibited excellent ion exchange capacity (2.1 meq g−1). A heterogeneous ion exchange membrane of PIn-SMP (ion exchange capacity 0.95 meq g−1) was also prepared by a solution casting method. The materials were characterized by various instrumental methods, such as scanning electron microscopy, transmission electron microscopy, Fourier transform IR spectroscopy, and thermogravimetric analysis. PIn-SMP exhibits high electrical conductivity (6.1 × 10−2 S/cm) and is stable up to 120 °C under ambient conditions. A Cu2+-selective membrane electrode was fabricated, and its linear working range (1.8 × 10−7–1.0 × 10−1 M), response time (20 s), Nernstian slope (24.07 mV dec−1), and working pH range (4–7) were calculated. Moreover, it was used as an indicator electrode in the potentiometric titration of Cu2+. Graphical abstract Image 1 Highlights • Polyindole–tin (IV) molybdophosphate nanocomposite ion exchanger (IEC-2.1 meq g−1), was synthesized by a sol-gel process. • The nanocomposite displayed high DC electrical conductivity (6.1 × 10−2 S cm−1) at room temperature. • A heterogeneous ion exchange membrane (IEC-0.95 meq g−1) was prepared by a solution casting method. • The materials were characterized by various instrumental methods, such as SEM, TEM, FT-IR, and TGA analysis. • A Cu2+-selective membrane electrode was fabricated and exhibited a wide linear response range (1.8 × 10−7–1.0 × 10−1 M). [ABSTRACT FROM AUTHOR]

Objective: To identify the prevalence and predictors of metabolic syndrome using NCEP ATP III guidelines in patients with CAD who underwent angiography at Karachi Institute of Heart Disease (KIHD). Methodology: This cross-sectional study was conducted at the Department of Cardiology; Karachi Institute of Heart Disease from 1st March to 31st December 2016. Patients with confirmed diagnosis of CAD after satisfying the inclusion and exclusion criteria were recruited in this study through non-probability consecutive sampling technique. Data was collected on demographics (i.e. age, gender, and ethnicity) and co-morbidities (i.e. diabetes and hypertension). Metabolic syndrome in patients with CAD was assessed according to the 2011 NCEP ATP III guidelines. Moreover, severity of CAD in patients was also recorded as well as systolic and diastolic blood pressure and biochemical parameters. The data was analysed using SPSS version 21. Objective: To identify the prevalence and predictors of metabolic syndrome using NCEP ATP III guidelines in patients with CAD who underwent angiography at Karachi Institute of Heart Disease (KIHD). Methodology: This cross-sectional study was conducted at the Department of Cardiology; Karachi Institute of Heart Disease from 1st March to 31st December 2016. Patients with confirmed diagnosis of CAD after satisfying the inclusion and exclusion criteria were recruited in this study through non-probability consecutive sampling technique. Data was collected on demographics (i.e. age, gender, and ethnicity) and co-morbidities (i.e. diabetes and hypertension). Metabolic syndrome in patients with CAD was assessed according to the 2011 NCEP ATP III guidelines. Moreover, severity of CAD in patients was also recorded as well as systolic and diastolic blood pressure and biochemical parameters. The data was analysed using SPSS version 21. Results: Among 400 participants, majority (60%) of the participants with CAD were diagnosed with metabolic syndrome. Significantly greater proportion of females (51.2% vs. 21.9%; p = 0.001) were diagnosed with metabolic syndrome. The mean weight, height, BMI and waist circumference was significantly higher in CAD patients diagnosed with metabolic syndrome. Significant difference was not observed in severity of CAD between patients with and without metabolic syndrome. Conclusion: The study highlighted a higher prevalence of metabolic syndrome evaluated using the 2011 NCEP ATP III guidelines among patients with CAD. [ABSTRACT FROM AUTHOR]

In this paper, statistical approximation properties of Lupa,s (p; q)-analogue of Bernstein operators are studied. Rate of statistical convergence by means of modulus of continuity and Lipschitz type maximal functions has been investigated. Further Limit Lupa,s (p; q)-Bernstein operators are defined and some results are investigated. [ABSTRACT FROM AUTHOR]

Background: Hospital acquired infections (HAIs) are one of the most common problems encountered in health care. It is a major cause of concern for hospitals throughout the world. Methods: The study design was cross sectional and data was obtained from health care workers currently employed in hospitals. We selected tertiary care hospitals by convenient sampling technique. Data were analyzed through SPSS version 20. Results: A total of 330 health care workers were included in the study out of which 54.4% (180) were males and the rest 45.4% (150) were females. When the participants were asked of the minimum time required for alcohol based hand rub to eradicate the microorganisms, only 42% (138) were able to correctly answer that it takes 20 seconds. Conclusion: Our study identified considerable gaps in knowledge and practice in some key areas regarding hand hygiene which need re-emphasis through regular training sessions aim at keeping abreast with standard protocols of hand hygiene. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) are amongst the leading causes of death worldwide. The Internet of Things (IoT) is an emerging technology that enables the healthcare system to identify cardiovascular diseases. In this article, a novel cardiovascular disease prediction framework combining Predator Crow Optimization (PCO) and Deep Neural Network (DNN) is designed. In the proposed PCO-DNN framework, DNN is used to predict cardiac disease, and the PCO is utilized to optimize the DNN parameters, thereby maximizing the prediction performances. The proposed framework aims to predict and classify cardiovascular diseases accurately. Further, an intensive comparative analysis is performed to validate the obtained results with the existing classification models. The results show that the proposed framework achieves an accuracy of 96.6665%, a precision of 97.5256%, a recall of 97.0953%, and an F1-measure of 96.4242% and can outperform the existing CVD predictors. [ABSTRACT FROM AUTHOR]

We present a case of tension gastrothorax in a 27 year old primigravida with the complaints of respiratory distress. She was hypoxic and tachycardiac. Left thorax was resonant to percussion and had no air entry. Chest X ray was not helpful to reach the diagnosis. Later on tracheal deviation was noted and thoracostomy was done. The patient aborted overnight. Greenish fluid in the chest drain led to investigation with computed tomography which revealed massive vicerothorax. Review of her history revealed that she had blunt abdominal injury as child. Laparotomy confirmed the above findings along with a gastric perforation. Diaphragm and stomach were repaired, gastropexy and caecopexy was done. Occult diaphragmatic injury can complicate the pregnancy. High intra-abdominal pressure during the pregnancy can push much of abdominal contents through the diaphragmatic defect. Creation of angulation at gastro esophageal junction acts as a one way valve and thus leads to progressive dilation of the stomach, resulting in tension gastrothorax. [ABSTRACT FROM AUTHOR]

New identities and inequalities are given for weighted majorization theorem for n-convex functions by using extension of the Montgomery identity and Green function. Various bounds for the reminders in new generalizations of weighted majorization formulae are provided using Čebyšev type inequalities. Mean value theorems are also discussed for functional related to new results. [ABSTRACT FROM AUTHOR]

The study, which examines how a foreign-funded teachers' preparation program was implemented, primarily focuses on policy-related matters and curriculum implementation. Despite some of the challenges faced by the instructors and prospective teachers, the two stakeholders acknowledged that the new program is the correct approach for addressing the issues of quality education in Pakistan. The instructors and students agreed that the new Advance Degree in Education (ADE) program, unlike the traditional teachers' preparation programs that have questionable productivity, helped them to broaden their understanding about such concepts as student-centered learning, practice-oriented teaching, multiple methods of teaching, and the integration of technology in the teaching and learning processes. However, the study maintains that certain issues, such as questionable recruitment policies, limited capacities of teacher educators, and lack of proper orientation about the ADE program, seem to compromise the productivity of this important intervention. [ABSTRACT FROM AUTHOR]

The incidence of Cotton Leaf Curl Disease (CLCuD) and low crop stand are major impediments to sustainable cotton production in Pakistan. Early sowing provides protection against CLCuD but causes low seed germination. Early cotton planting overlaps with wheat harvesting, thus causing conflict with Wheat-Cotton-Wheat cropping pattern. To address these problems and suggest improvements, a series of field experiments was conducted to optimize cotton planting technology. Three sowing methods viz., transplanting cotton nursery in May, direct seeding in March, and direct seeding in May were compared for physiological indices, CLCuD incident and yield of seed cotton. For the production of seedlings, four nursery media were evaluated for their efficiency in terms of germination percentage, seedling survival, and convalescent period. On the basis of these parameters, SSM (Soil+Sand+Manure) media produced better seedlings. The early sown (transplanted) cotton gave good escape from CLCuD. The maximum incidence was observed in May sowing (22.2%) while the severity was maximum in the month of August. Maximum seed cotton yield was recorded in March sown-transplanted cotton. Income generated from March sowing was more than transplanting due to labor cost in transplanted cotton. The income from transplanted cotton was 60% more than the May sowing, on account of better production due to improved crop stand, number of growing days and escape from CLCuD. Therefore, early planting of cotton through seedlings transplantation is suggested to improve cotton production through better crop stand and escape from CLCuD in a cotton-wheat production cropping system. [ABSTRACT FROM AUTHOR]

Wise phosphorus (P) management is paramount to ensure food and environmental security and prolong the life of P resources. Long-term experiments under winter wheat–summer maize (with irrigation) and winter wheat–summer fallow (rain-fed) cropping systems were undertaken to investigate P efficiency, soil P availability and P downward movement in the deep profile of a loess soil in China. The four nutrient management regimes in each experiment were: a control without any nutrient input; P and K combined (PK); N, P and K combined (NPK), and organic manure (M) combined with NPK (MNPK). The P efficiency ranged from 20 to 54% under the double cropping system, and from 13 to 33% in the single cropping system over the 25 years studied, and the highest P efficiency was in the NPK treatment in both systems. Application of P significantly increased soil total-P, Olsen-P and CaCl 2 -P over the years, especially in the MNPK treatment. Two segment regression analyses indicated that Olsen-P was about 14 mg kg −1 or 16 mg kg −1 for optimal crop yield under double cropping or single cropping. In addition, Olsen-P content above 37 mg kg −1 or 57 mg kg −1 led to a significant increase in CaCl 2 -P content under double cropping or single cropping, which might indicate risk of P leaching. After 24 years of P applications, total P accumulated at various depths depending on treatment under double cropping, but there was no accumulation under single cropping. While Olsen-P leached down to a depth of 300 cm in the MNPK treatments under both cropping systems, and higher concentrations were recorded in the deeper layers in single cropping than in double cropping systems. The Olsen-P leached to a shallower depth in the PK and NPK treatments under single cropping compared to double cropping. We conclude that P supply in excess of the crop’s requirement (e.g. MNPK) or an unbalanced nutrient supply (e.g. PK) resulted in not only low P efficiency and massive accumulation of P in the topsoil but also leaching out of root zone under both irrigated and rain-fed conditions in the loess soil. Thus, P fertilization recommendations need to be adjusted based on changes in soil P over time. [ABSTRACT FROM AUTHOR]

Background: Ebolavirus (EBOV) is responsible for one of the most fatal diseases encountered by mankind. Cellular T-cell responses have been implicated to be important in providing protection against the virus. Antigenic variation can result in viral escape from immune recognition. Mapping targets of immune responses among the sequence of viral proteins is, thus, an important first step towards understanding the immune responses to viral variants and can aid in the identification of vaccine targets. Herein, we performed a large-scale, proteome-wide mapping and diversity analyses of putative HLA supertype-restricted T-cell epitopes of Zaire ebolavirus (ZEBOV), the most pathogenic species among the EBOV family. Methods: All publicly available ZEBOV sequences (14,098) for each of the nine viral proteins were retrieved, removed of irrelevant and duplicate sequences, and aligned. The overall proteome diversity of the non-redundant sequences was studied by use of Shannon's entropy. The sequences were predicted, by use of the NetCTLpan server, for HLA-A2, -A3, and -B7 supertype-restricted epitopes, which are relevant to African and other ethnicities and provide for large (~86%) population coverage. The predicted epitopes were mapped to the alignment of each protein for analyses of antigenic sequence diversity and relevance to structure and function. The putative epitopes were validated by comparison with experimentally confirmed epitopes. Results & discussion: ZEBOV proteome was generally conserved, with an average entropy of 0.16. The 185 HLA supertype-restricted T-cell epitopes predicted (82 (A2), 37 (A3) and 66 (B7)) mapped to 125 alignment positions and covered ~24% of the proteome length. Many of the epitopes showed a propensity to co-localize at select positions of the alignment. Thirty (30) of the mapped positions were completely conserved and may be attractive for vaccine design. The remaining (95) positions had one or more epitopes, with or without non-epitope variants. A significant number (24) of the putative epitopes matched reported experimentally validated HLA ligands/T-cell epitopes of A2, A3 and/or B7 supertype representative allele restrictions. The epitopes generally corresponded to functional motifs/domains and there was no correlation to localization on the protein 3D structure. These data and the epitope map provide important insights into the interaction between EBOV and the host immune system. [ABSTRACT FROM AUTHOR]

System identification is an inverse algorithm of developing/improving mathematical representation of a physical system from input-output responses. This paper assessed a mathematical model of smart composite laminate identified with spurious output data due to sensor debonding failure. Improved layerwise theory and higher-order electric potential field were incorporated to develop electromechanically coupled finite element based mathematical model of the smart composite laminate with or without sensor debonding failure. The input-output data of the developed model were fed into the direct system identification algorithm to identify the state-space model of the smart structure. The developed theory was numerically implemented on a 16-layer cross-ply laminate with surface bonded piezoelectric sensor and actuator. Results showed that system identification algorithm misapprehended the true dynamic behavior of the smart structure in the presence of sensor debonding failure. In addition, principal component analysis was used to detect the presence and severity of partial sensor debonding in the identified state-space model. [ABSTRACT FROM AUTHOR]

Summary: Successful integration of wind energy in an electrical grid system requires prior information of the generated wind power from a turbine or a wind farm. That is a challenging task given the unpredictable, intermittent, and nonlinear nature of wind speed. We present a novel method for short‐term wind speed prediction by using a hybrid approach based on variational mode decomposition (VMD) in conjunction with both linear and nonlinear prediction models. The VMD is a fully data‐adaptive signal decomposition method that offers significant improvement over existing data‐adaptive techniques, such as empirical mode decomposition, in terms of its sound mathematical framework and accuracy of its decomposition. In our work, VMD is used to decompose wind speed data into multiple intrinsic narrow band components to facilitate their forecasting/prediction. We report results from extensive experiments performed on a large wind speed data set obtained from multiple sites in Pakistan to demonstrate the efficacy of the proposed method. [ABSTRACT FROM AUTHOR]

Background: Viral vaccine target discovery requires understanding the diversity of both the virus and the human immune system. The readily available and rapidly growing pool of viral sequence data in the public domain enable the identification and characterization of immune targets relevant to adaptive immunity. A systematic bioinformatics approach is necessary to facilitate the analysis of such large datasets for selection of potential candidate vaccine targets. Results: This work describes a computational methodology to achieve this analysis, with data of dengue, West Nile, hepatitis A, HIV-1, and influenza A viruses as examples. Ourmethodology has been implemented as an analytical pipeline that brings significant advancement to the field of reverse vaccinology, enabling systematic screening of known sequence data in nature for identification of vaccine targets. This includes key steps (i) comprehensive and extensive collection of sequence data of viral proteomes (the virome), (ii) data cleaning, (iii) large-scale sequence alignments, (iv) peptide entropy analysis, (v) intra- and inter-species variation analysis of conserved sequences, including human homology analysis, and (vi) functional and immunological relevance analysis. Conclusion: These steps are combined into the pipeline ensuring that a more refined process, as compared to a simple evolutionary conservation analysis, will facilitate a better selection of vaccine targets and their prioritization for subsequent experimental validation. [ABSTRACT FROM AUTHOR]

We demonstrate that a ferroelectric can cause a differential voltage amplification without needing an external energy source. As the ferroelectric switches from one polarization state to the other, a transfer of energy takes place from the ferroelectric to the dielectric, determined by the ratio of their capacitances, which, in turn, leads to the differential amplification. This amplification is very different in nature from conventional inductor-capacitor based circuits where an oscillatory amplification can be observed. The demonstration of differential voltage amplification from completely passive capacitor elements only has fundamental ramifications for next generation electronics. [ABSTRACT FROM AUTHOR]