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Abstract In the contemporary landscape of smart cities, ensuring the security and confidentiality of transmitted data has become paramount. The proliferation of Internet of Things (IoT) devices, mobile communication platforms, and wireless sensor networks has magnified the need for robust cryptographic solutions to safeguard sensitive information from malicious adversaries. In response to these challenges, we introduce ChessCrypt—a novel approach to enhancing wireless communication security through the development of a new S-Box algorithm inspired by the nonlinear movement patterns of chess pieces. ChessCrypt leverages the dynamic and unpredictable nature of chess piece movements, namely knights, kings, and bishops, to introduce a high degree of nonlinearity and confusion into the encryption process. By incorporating elements of randomness and unpredictability, our proposed S-Box algorithm offers robust protection against a wide range of cyber threats, including eavesdropping, data interception, and cryptographic attacks. We demonstrate the effectiveness and resilience of ChessCrypt through rigorous testing and analysis, showcasing its superiority over traditional cryptographic methods in enhancing the security of wireless communication networks. Our results underscore the significance of ChessCrypt as a promising solution for fortifying wireless communication security in an increasingly interconnected world of ours.

China's status as the world's largest energy consumer highlights its pivotal role in global energy consumption and the urgency of enhancing its energy efficiency and renewable energy development. This study evaluates the influence of environmental regulations on green economic growth in China, with a focus on renewable energy and energy efficiency, from 2000 to 2023 across 30 provinces. Using the Metafrontier-Global-SBM super-efficient DEA model, the research measures green economic development through the lens of energy efficiency. An enhanced fuzzy comprehensive assessment gauges the stringency of environmental regulations. To explore non-linear impacts, regional spillover, and feedback effects, a spatial econometric model was applied. The results reveal a U-shaped non-linear relationship between environmental regulations and green economic growth, with significant spatial feedback and regional spillover effects. Environmental regulations are also shown to have a substantial positive influence on the development of renewable energy, a critical driver of green economic growth. Policy implications suggest that strengthening environmental regulations could accelerate renewable energy adoption and foster sustainable economic growth across regions in China.

Abstract RNA modifications are pivotal in the development of newly synthesized structures, showcasing a vast array of alterations across various RNA classes. Among these, 5-hydroxymethylcytosine (5HMC) stands out, playing a crucial role in gene regulation and epigenetic changes, yet its detection through conventional methods proves cumbersome and costly. To address this, we propose Deep5HMC, a robust learning model leveraging machine learning algorithms and discriminative feature extraction techniques for accurate 5HMC sample identification. Our approach integrates seven feature extraction methods and various machine learning algorithms, including Random Forest, Naive Bayes, Decision Tree, and Support Vector Machine. Through K-fold cross-validation, our model achieved a notable 84.07% accuracy rate, surpassing previous models by 7.59%, signifying its potential in early cancer and cardiovascular disease diagnosis. This study underscores the promise of Deep5HMC in offering insights for improved medical assessment and treatment protocols, marking a significant advancement in RNA modification analysis.

This study investigates the sensitivity of stock returns to changes in oil prices and tax returns in net-oil exporting economies, focusing on the Gulf Cooperation Council (GCC) countries from March 30, 2003 to March 30, 2022. The problem statement addresses the challenge of understanding the interplay between oil price fluctuations, tax policies, and stock market dynamics in these economies, which are heavily reliant on oil revenues. The aim of the study is to provide insights into how variations in oil prices and tax returns impact the cross-section of stock returns in the GCC region. The objectives include analyzing the econometric relationships between oil prices, tax policies, and stock market performance using an appropriate econometric model. The results reveal a significant sensitivity of stock returns to changes in oil prices and tax returns in the GCC countries during the specified period. The econometric model employed indicates strong correlations between fluctuations in oil prices, tax policies, and stock market movements, highlighting the intricate connections between these variables. Moreover, the study identifies specific sectors within the stock market that are particularly sensitive to oil price changes and tax policy adjustments. Policymakers in the GCC countries can use these insights to design more effective fiscal and economic policies aimed at enhancing market stability and investor confidence amidst dynamic oil market conditions.

Probability distributions are widely utilized in applied sciences, especially in the field of biomedical science. Biomedical data typically exhibit positive skewness, necessitating the use of flexible, skewed distributions to effectively model such phenomena. In this study, we introduce a novel approach to characterize new lifetime distributions, known as the New Flexible Exponent Power (NFEP) Family of distributions. This involves the addition of a new parameter to existing distributions. A specific sub-model within the proposed class, known as the New Flexible Exponent Power Weibull (NFEP-Wei), is derived to illustrate the concept of flexibility. We employ the well-established Maximum Likelihood Estimation (MLE) method to estimate the unknown parameters in this family of distributions. A simulation study is conducted to assess the behavior of the estimators in various scenarios. To gauge the flexibility and effectiveness of the NFEP-Wei distribution, we compare it with the AP-Wei (alpha power Weibull), MO-Wei (Marshal Olkin Weibull), classical Wei (Weibull), NEP-Wei (new exponent power Weibull), FRLog-Wei (flexible reduced logarithmic Weibull), and Kum-Wei (Kumaraswamy Weibull) distributions by analyzing four distinct biomedical datasets. The results demonstrate that the NFEP-Wei distribution outperforms the compared distributions.

This study presents an analysis and evaluation of gait asymmetry (GA) based on the temporal gait parameters identified using a portable gait event detection system, placed on the lateral side of the shank of both lower extremities of the participants. Assessment of GA was carried out with seven control subjects (CS), one transfemoral amputee (TFA) and one transtibial amputee (TTA) while walking at different speeds on overground (OG) and treadmill (TM). Gait cycle duration (GCD), stance phase duration (SPD), swing phase duration (SwPD), and the sub-phases of the gait cycle (GC) such as Loading-Response (LR), Foot-Flat (FF), and Push-Off (PO), Swing-1 (SW-1) and Swing-2 (SW-2) were evaluated. The results revealed that GCD showed less asymmetry as compared to other temporal parameters in both groups. A significant difference (p

Cancer is a chronic disease, and recent cases of cancer cause a challenge for the whole world of medicine. Current studies have shown that T-cell antigens play a significant role in cancer treatment. Therefore, identifying tumor T cell antigens is vital for cancer detection and drug development. Several computational methods have been proposed to identify tumor T cell antigens. These models have produced promising results; however, they ignored the sequence’s physiochemical structure and correlation information during the feature transformation process. This study proposes a robust model considering sequence structure and correlation information for identifying tumor T-cell antigens. The proposed model uses three feature encoding (transformation) techniques and a multi-layer deep learning model. Additionally, the proposed model employed a feature fusion approach to consider sequence structure and correlation information during feature transformation. The fusion approach added redundant and noisy features in the feature vectors, which were removed during the feature selection phase. The performance of the proposed model is extensively evaluated by considering different measurement metrics using 10-fold cross-validation. The experimental results showed that the proposed model achieved 99.02% and 98.62% accuracies, improving an average of 14.66% and 23.75% using training and independent datasets, respectively.

The S-Box (Substitution Box) plays a crucial role in cryptographic security by introducing non-linearity and confusion into the encryption process. It enhances the resistance against cryptanalytic attacks, such as differential and linear cryptanalysis, by providing a complex and unpredictable transformation of input data. This study has exploited the constructs of Delannoy Numbers and the chaotic map to come up with a novel and secured S-Box. The non-linearity and confusion effects have been poured into the potential S-Box by swapping the randomly selected non-overlapping vectors of numbers from arbitrary rows and columns of the S-Box. A filter has been embedded in the proposed algorithm to avoid the swapping of vectors in case they overlap with each other. This process has been iterated a number of times to boost the security effects. 5D multi-wing hyperchaotic map has been used to spawn the streams of random numbers. By means of extensive testing and analysis, we demonstrate the improved cryptographic characteristics of the designed S-boxes over traditional designs. In particular, advanced security considerations have been used, including non-linearity (NL), bit-independence criteria (BIC), strict-avalanche criteria (SAC), differential probability (DP), and bijectivity. This study unquestionably contributes significantly to the ever-evolving field of network security.

In an era characterized by ever-increasing data exchange across different networks, the importance of robust network security measures cannot be overemphasized. Cryptographic algorithms, on the other hand, perform a vital role in imparting security to the sensitive information during transmission, with S-Boxes (Substitution Boxes) serving as a fundamental component of symmetric-key ciphers. This study has endeavored to engineer a novel S-Box algorithm to bolster the network security. Moreover, the core focus of the suggested algorithm lies in enhancing the confidentiality, defiance to nefarious designs of cryptanalysis savvy, and overall cryptographic strength of data encryption in network communication. Random numbers spawned by the 5D multi-wing hyperchaotic system have been employed to design the proposed box. Apart from that, Rook —a chess piece has been used in this regard. Particularly, the random walk of Rook on the $16 \times 16$ hypothetical chessboard has been capitalized to sufficiently permute the data of the potential S-Box. Through a comprehensive performance evaluation and cryptographic analyses, the proposed S-Box algorithm exhibits superior resilience against diverse threats thus fortifying network security effectively. In particular, the state of the art security parameters like bijectivity, non-linearity (NL), strict-avalanche criterion (SAC), bit-independence criterion (BIC), linear probability (LP) and differential probability (DP) have been used. This work, no doubt, contributes significantly to the ever-evolving field of network security.

The security of images has become a hot research area due to the widespread usage of the digital images all over the world. Literature review on chaotic image cryptography, on the other hand, indicates that many constructs have been employed for the realization of confusion operation while developing the image ciphers. To enhance the computational time, this study proposes a novel image cipher based on two chaotic maps and rectangle as a scrambler. As the given grayscale image is input, it is scrambled through dynamically generated rectangles. As a modus operandi, these rectangles are created in the given image with various dimensions and different location. The pixels lying on the boundary of the rectangle are rotated clockwise and anticlockwise with an arbitrary amount. These operations have been repeated a number of times to get the scrambling effects. The scrambled image has been further subjected to an XOR operation for embedding the diffusion effects in it. Two chaotic maps of Hénon and piecewise linear chaotic map have been used for the generation of random numbers. These numbers help in deciding the size, location, clockwise/anticlockwise movement of the pixels and the amount with which these pixels have to be rotated. The performance analysis and the machine simulation validate that the suggested cipher is sufficient enough to thwart the varied cryptanalytic attacks. In particular, we gained the computational speed of 0.5156 seconds and the information entropy of 7.9975. Besides, we posit that it has the ample prospects for its real world application.

Plethora of image encryption schemes exist in literature based on the construct of magic square for realizing the purpose of image obfuscation. This magic square carries out the scrambling project of the encryption. In these schemes, normally single and static magic square is implied. To render greater scrambling effects, this study proposes a novel image encryption scheme using all order-4 magic squares whose frequency reaches to the tune of 880. These magic squares have been dynamically selected to carry out the scrambling project. As the color image is input, it is broken into its gray scale red, green and blue components. These components are joined together to make a big gray scale image. Intertwining logistic map (ILM) has been used for the generation of random data. Besides, one more stream has been created through the arithmetic manipulation of the generated three streams. Streams generated by ILM has been used to realize the effects of confusion and diffusion. First and second streams out of the four streams randomly select the address from the big gray scale image to apply the randomly selected magic square by the third stream, in order to create the scrambling effects. The fourth and last stream of random numbers is used to create the diffusion effects in the scrambled image. Plaintext senstivity has been introduced by tempering the one initial value of the chaotic system through the usage of a characteristic of the given input color image. The experimentation and security analyses sections vividly demonstrate the strength, immunity from the diverse attacks and prospects for the real world application of the proposed image cipher. In particular, we got very promising stats of information entropy (7.9974) and computational time (0.9865 seconds). No doubt, they suggest the potential application of the proposed image cipher in some real world setting.

Teratomas are usually seen in gonads but they do occur in other extra gonadal regions such as sacrococcygeal region, mediastinum, head and neck, and retroperitoneum. Rarely in the retroperitoneal area, such tumours mostly develop in the pararenal area and usually on the left side. They have bimodal presentation at the age of six months and then in early adulthood. They originate from the germ cells that have failed to migrate to normal anatomical destinations. Many of such patients are diagnosed incidentally. Here, we report a case of symptomatic primary retroperitoneal mature teratoma in a young lady managed at Pakistan kidney and Liver Institute, Lahore. Key Words: Benign Mature Cystic Teratoma; Kidney; Surgery; Retroperitoneal teratoma.

Sumoylation is a post-translation modification (PTM) mechanism that involves many critical biological processes, such as gene expression, localizing and stabilizing proteins, and replicating the genome. Moreover, sumoylation sites are associated with different diseases, including Parkinson’s and Alzheimer’s. Due to its vital role in the biological process, identifying sumoylation sites in proteins is significant for monitoring protein functions and discovering multiple diseases. Therefore, in the literature, several computational models utilizing conventional ML methods have been introduced to classify sumoylation sites. However, these models cannot accurately classify the sumoylation sites due to intrinsic limitations associated with the conventional learning methods. This paper proposes a robust computational model (called Deep-Sumo) for predicting sumoylation sites based on a deep-learning algorithm with efficient feature representation methods. The proposed model employs a half-sphere exposure method to represent protein sequences in a feature vector. Principal Component Analysis is applied to extract discriminative features by eliminating noisy and redundant features. The discriminant features are given to a multilayer Deep Neural Network (DNN) model to predict sumoylation sites accurately. The performance of the proposed model is extensively evaluated using a 10-fold cross-validation test by considering various statistical-based performance measurement metrics. Initially, the proposed DNN is compared with the traditional learning algorithm, and subsequently, the performance of the Deep-Sumo is compared with the existing models. The validation results show that the proposed model reports an average accuracy of 96.47%, with improvement compared with the existing models. It is anticipated that the proposed model can be used as an effective tool for drug discovery and the diagnosis of multiple diseases.

The COVID-19 pandemic is dangerous to people’s lives and livelihoods, creating immediate obstacles for organizations that support impacted populations. This research concentrates on the consequences for local microfinance institutions in Pakistan, which is a well-developed sector that has pulled many households out of the poverty trap. Microfinance programs in Pakistan provide financial resources to vulnerable and deprived people to engage in income-generating practices on more favorable terms. As a result, this study addressed and assessed the financial dimensions of managing poverty reduction in rural Pakistan through the microfinance segment and its effectiveness on poverty-reduction programs in Pakistan during the COVID-19 pandemic. The primary data were collected through a questionnaire survey to determine the views of the households, beneficiaries, and non-beneficiaries on the outcome and efficacy of poverty-reduction programs during the pandemic to meet the study objectives. The Mann-Whitney U test of the non-parametric method and Cronbach’s alpha of the data reliability test have been applied for the empirical analysis. According to the non-parametric findings, programs, marital status, working women members, and resources such as land, livestock, business assets, shares, and loans have all been affected during the COVID-19 pandemic. Education, wages, gender, size, child dependency, and district variables are significant factors related to poverty, but they fell into second position during COVID-19. These findings suggests that the small loan system must be improved and made efficient during the pandemic. This could be a practical tool to maintain poor people’s current economic and poverty position.

With recent advancements in computational biology, high throughput Next-Generation Sequencing (NGS) has become a de facto standard technology for gene expression studies, including DNAs, RNAs, and proteins; however, it generates several millions of sequences in a single run. Moreover, the raw sequencing datasets are increasing exponentially, doubling in size every 18 months, leading to a big data issue in computational biology. Moreover, inflammatory illnesses and boosting immune function have recently attracted a lot of attention, yet accurate recognition of Anti-Inflammatory Peptides (AIPs) through a biological process is time-consuming as therapeutic agents for inflammatory-related diseases. Similarly, precise classification of these AIPs is challenging for traditional technology and conventional machine learning algorithms. Parallel and distributed computing models and deep neural networks have become major computing platforms for big data analytics now required in computational biology. This study proposes an efficient high-throughput anti-inflammatory peptide predictor based on a parallel deep neural network model. The model performance is extensively evaluated regarding performance measurement parameters such as accuracy, efficiency, scalability, and speedup in sequential and distributed environments. The encoding sequence data were balanced using the SMOTETomek approach, resulting in a high-accuracy performance. The parallel deep neural network demonstrated high speed up and scalability compared to other traditional classification algorithms study’s outcome could promote a parallel-based model for predicting anti-Inflammatory Peptides.

In this article, a new hybrid time series model is proposed to predict COVID-19 daily confirmed cases and deaths. Due to the variations and complexity in the data, it is very difficult to predict its future trajectory using linear time series or mathematical models. In this research article, a novel hybrid ensemble empirical mode decomposition and error trend seasonal (EEMD-ETS) model has been developed to forecast the COVID-19 pandemic. The proposed hybrid model decomposes the complex, nonlinear, and nonstationary data into different intrinsic mode functions (IMFs) from low to high frequencies, and a single monotone residue by applying EEMD. The stationarity of each IMF component is checked with the help of the augmented Dicky–Fuller (ADF) test and is then used to build up the EEMD-ETS model, and finally, future predictions have been obtained from the proposed hybrid model. For illustration purposes and to check the performance of the proposed model, four datasets of daily confirmed cases and deaths from COVID-19 in Italy, Germany, the United Kingdom (UK), and France have been used. Similarly, four different statistical metrics, i.e., root mean square error (RMSE), symmetric mean absolute parentage error (sMAPE), mean absolute error (MAE), and mean absolute percentage error (MAPE) have been used for a comparison of different time series models. It is evident from the results that the proposed hybrid EEMD-ETS model outperforms the other time series and machine learning models. Hence, it is worthy to be used as an effective model for the prediction of COVID-19.

Outsourcing has been getting a significant growth for the last few years. Organizations tend to outsource Information Technology (IT), primarily to take advantage of the availability of qualified, trained and skilled workforce in low cost countries across the globe. Outsourcing of IT and software maintenance seem very promising, but a number of factors, risks, and challenges associated with the outsourcing process that make the sourcing decision very complicated. The present study aimed at gaining in-depth understanding of the three aspects of outsourcing, namely; perceived benefits of IT outsourcing, influencing factors of IT outsourcing and software maintenance offshoring. The findings of the current study will lead us to develop a sourcing framework for outsourcing decision as well as a decision support system for software maintenance. A systematic literature review is performed that presents perceived benefits of IT outsourcing, the influencing factors of IT outsourcing and software maintenance. Furthermore, the identified factors are analyzed based on their occurrences in literature as well as chi square test is performed to derive the significant differences amongst the factors based on decades. Similarly, critical success factors are derived both for IT outsourcing and software maintenance offshoring. Our article shows that how the critical success factors impact the IT as well the software maintenance in global delivery perspective. The findings of the current study will help the IT experts and decision makers in making suitable sourcing decisions.

Enhancers are short DNA regulatory elements which play a vital role in gene expression. Due to their important roles in genomics, several computational models have been proposed in the literature for identification of enhancers and their strengths using traditional machine learning algorithms, however, the proposed models are unable to identify enhancers and their strength with reasonable accuracy because of high non-linearity in DNA sequences. This article proposes a two-level intelligent model based on Deep Neural Network (DNN) along with multiple feature extraction methods. Firstly, the proposed model represents the given DNA sequences into feature vectors using Pseudo K-tuple Nucleotide Composition (PseKNC) and FastText methods. Secondly, the features vectors are fused to make a heterogeneous features vector that considered the local and global correlation amongst the given sequences along with internal structure information. Finally, the heterogeneous feature vector is given to a DNN model to make final predictions. The proposed iEnhancer-DHF is developed using two-layer approach. The first layer predicts whether the given DNA samples are enhancers or non-enhancers whereas the second layer identifies either the enhancers are strong enhancers or weak enhancers. The outcome of the proposed model was rigorously assessed using both training and independent datasets via 10-fold cross validation method. The validation outcome demonstrated that the iEnhancer-DHF model yielded accuracies 86.07% and 69.60% at first layer and second layer respectively utilizing the training dataset. Similarly, the model yielded accuracies 83.21% and 67.54% at first layer and at second layer respectively by using the independent dataset. Additionally, the outcomes of the proposed model was initially compared with widely applied classifiers such as Support Vector Machine, Random Forest and K-nearest Neighbor and subsequently the performance is compared with the existing models using both the training and independent datasets. The comparison results exhibited that the iEnhancer-DHF model performed superior than the recently published models.

Underwater Sensor Networks (UWSNs) are deployed to monitor various phenomena in marine environment such as pollution control, fuel exploration and underwater seismic activities. Various challenges such as, limited and non-replaceable batteries in sensor nodes, high path loss and high propagation delay exist for UWSNs, to name a few. Successful design deployment of an energy efficient routing scheme is an intense need of the day for successful operation of UWSNs. In this paper we have presented an energy efficient routing protocol by the name of Energy Harvesting Intelligent Relay Selection Protocol (EH-IRSP). The scheme uses task-specific energy harvested relay nodes using piezoelectric technique utilizing dynamic transmission radius incorporated in all sensor nodes. EH-IRSP protocol is compared with existing UWSNs protocols Cooperative UWSN (Co-UWSN) and Energy Harvested Analytical approach towards Reliability with Cooperation for Underwater WSNs (EH-ARCUN). The Co-UWSN focuses on strengthening the sound-to-noise ratio on the minimum distance communication channel in order to reduce the path loss. The EH-ARCUN scheme selects relay nodes based on energy harvesting level in combination with Amplify and Forward (AF) technique. The proposed scheme employs a Euclidean distance between the source-destination and source-relay nodes pairs. Each source node selects the most feasible energy harvested relay node by computing cosine of the angles between itself, relay node, and destination nodes and sends the data using cooperative communication. Based on these computed parameters, each source node adjusts its transmission radius hence conserving energy. Performance parameters for this comparison are based on stability period, packet delivery ratio, end-to-end delay and path loss. Simulation results show enhanced performance of proposed scheme EH-IRSP in contrast to Co-UWSN and EH-ARCUN.

Graph-based methods have been widely used by the document image analysis and recognition community, as the different objects and the content in document images is best represented by this powerful structural representation. Designing of novel computation tools for processing these graph-based structural representations has always remained a hot topic of research. Recently, Graph Neural Network (GNN) have been used for solving different problems in the domain of document image analysis and recognition. In this article we take forward the state of the art by presenting a new approach to gather the symbolic and numeric information from the nodes and edges of a graph. We use this information to learn a Graph Neural Network (GNN). The experimentation on the recognition of handwritten letters and graphical symbols shows that the proposed approach is an interesting contribution to the growing set of GNN-based methods for document image analysis and recognition.

The Wnt-activated $\beta $ -catenin and $Ca^{2+}$ ions play a critical role in the regulation of physiology of cardiomyocytes. The dysregulation of both the components causes the replacement of myocardial mass right ventricle with fibrous and adipose tissue which results in the condition of Arrhythmogenic Right ventricular Cardiomyopathy (ARVC). The major hurdle in ARVC treatment is lack of effective therapies targeting the underlying root molecular biomarkers of pathogenesis. Despite major advancements in interpreting the mechanism of ARVC etiology, the dynamics of molecular links in underlying biological machinery are still being delineated. Previously, formal methods based computational modeling techniques including kinetic logic, Petri Nets, hybrid automata and static analysis have greatly contributed in increasing our comprehension to decipher the molecular systems dynamics. It has allowed the identification of biomarkers which can be utilized for target-based therapies owing to meticulous biological abstractions along with implementing reference map that confines together the discrete biological insights. In this study, we have performed the static analysis of the Biological Regulatory Networks (BRNs) of the Wnt/ $\beta $ -catenin and Wnt/Ca 2+ signaling pathways to identify the significant biomarkers for ARVC. The abstracted qualitative models of afore mentioned BRNs are first constructed in GINsim software tool and then these models are converted into Automata Network (ANs) Models using Pint software tool. The fix point analysis is performed which contributed in pinpointing the possible therapeutic strategies for ARVC treatment by identification of drug targets such as Gsk3, Ck1 and Axin in Wnt/ $\beta $ -catenin AN and Bak & Bax, Parp, mCalpain, JNk and CIn in Wnt/Ca 2+ AN. Moreover, the Bcl2 gene is identified as novel therapeutic remedy in both the ANs of ARVC. The Bcl2 gene prevents the cardiac apoptosis via positive regulation of Wnt in Wnt/ $\beta $ -catenin AN and through inhibition of Bak & Bax (apoptotic component) in Wnt/Ca 2+ AN. The current study tends to fulfill the scientific gap between wet lab studies and provides cost effective and time saving computational strategies for an effectual treatment for deadly diseases like ARVC.

Hundreds of image encryption algorithms have been developed for the security and integrity of images through the combination of DNA computing and chaotic maps. This combination of the two instruments is not sufficient enough to thwart the potential threats from the cryptanalysis community as the literature review suggests. To inject more robustness and security stuff, a novel image encryption scheme has been written in this research by fusing the chaotic system, DNA computing and Castle—a chess piece. As the plain image is input, its pixels are shifted to the scrambled image at the randomly chosen pixel addresses. This scrambling has been realized through the routine called Image Scrambler using Castle (ISUC). Castle randomly moves on the hypothetical large chessboard. Pixels taken from the plain image are shifted to the addresses of the scrambled image, where Castle lands in each iteration. After the plain image is scrambled, it is DNA encoded. Two mask images are also DNA encoded. Then to throw the diffusion effects in the cipher, DNA Addition and DNA XOR operations between the DNA encoded pixels data and the DNA encoded mask images have been conducted. Next, the pixels data are converted back into their decimal equivalents. Four dimensional chaotic system has been used to get the chaotic vectors. The hash codes given by the SHA-256 function have been used in the cipher to introduce the plaintext sensitivity in its design. We got an information entropy of 7.9974. Simulation carried out through the machine, and the thorough security analyses demonstrate the good security effects, defiance to the varied attacks from the cryptanalysis community, and the bright prospects for some real world application of the proposed image cipher.

Weeds are one of the most important biological constraint to cotton production, and resulting in a yield losses of up to 90%. The evolution of hundreds of resistant weed species, the lack of new herbicide chemistries, and the increase in weed management costs are all making weed management more arduous for the growers. Hence, a field experiment was conducted to investigate the efficacy of allelopathic crop water extracts (ACWEs) alone and in combination with one third rate of S-metolachlor (717 g a.i. ha−1) for effective weed management in cotton. The treatments investigated were; weedy check, sorghum + brassica water extract (WE) at 1.5 L ha−1, sorghum + sunflower WE at 1.5 L ha−1, sorghum + brassica + sunflower WE at 1.5 L ha−1, sorghum + brassica WE at 1.5 L ha−1 + S-metolachlor at 717 g a.i.ha−1, sorghum + sunflower WE at 1.5 L ha−1 + S-metolachlor at 717 g a.i.ha−1, sorghum + brassica WE + sunflower WE at 1.5 L ha−1 + S-metolachlor at 717 g a.i.ha−1, and S-metolachlor at recommended rate of 2.15 kg a.i. ha−1. Results revealed that pre-emergent application of sorghum + brassica water extract (WE) at 1.5 L ha−1 was the best treatment in terms of effective dry biomass reduction (40%) of Trianthema portulacastrum and Cyperus rotundus, and increase in seed cotton yield (12%). The second best treatment was sorghum + sunflower WE at 1.5 L ha−1 + S-metolachlor at 717 g a.i. ha−1 with yield increase of 11% over the weedy control. In comparison, S-metolachlor at recommended rate 2.15 kg a.i. ha−1 recorded only 4% decrease in weeds dry biomass reduction and 8% increase in seed cotton yield. Hence, it can be predicted that binary combination of sorghum and brassica WE at 1.5 L ha−1 or binary combination of sorghum + sunflower WE at 1.5 L ha−1 with one third dose of S-metolachlor (717 g a.i. ha−1) can be used for effective weed management and increase in seed cotton yield. Furthermore, adoption of this technique will also reduce the herbicide application, which is not only beneficial for the ecosystem but, also minimize the evolution of herbicide-resistant weed species. Keywords: Allelopathy, Chemical weed control, Herbicide, Weeds, Biomass, Seed cotton yield, Environment

Functioning of ecosystems depends on the nutrient dynamics across trophic levels, largely mediated by microbial interactions in the soil food web. The present study investigated the use of phosphate solubilizing bacteria (PSB) and poultry manure (PM) for maintaining labile P in the soil for an extensive fertility enhancement and as a substitution of chemical fertilizers. Based on the different P solubilizing capabilities of Bacillus and Pseudomonas, a quadruple consortium of Bacillus subtilis, Bacillus cereus, Bacillus thuringiensis and Pseudomonas fluorescens, and their grazer nematodes (soil free living) supplemented with PM were studied. This study was carried out on the trophic levels of soil communities to assess the growth and availability of P to the wheat plants. Experiment was performed for 90 days. Comparing the unamended and amended predator results showed that nematode addition beyond bacterial treatment substantially increased the net available P by ≈2 times, and alkaline phosphatase (ALP) activity by 3.3 times. These results demonstrated the nematodes association with increasing nutrient availability or P mineralization. The interactive effect of PM as substrate and biological drivers was more noticeable on plant dry biomass (1.6 times) and plant P concentration (3.5times) compared to the similar unamended treatment. It is concluded that the biological drivers significantly enhanced the soil ALP and available P while the substrate and biological drivers enhanced dry biomass and plant P concentration. Bacterivore nematodes enhanced the effect of PSB for P mineralization via microbial loop and could be used for the enhancement of wheat production.

This study evaluates the role of information in the environmental performance index (EPI) in different energy-consuming sectors in Pakistan through a novel slack-based data envelopment analysis (DEA). The index combines energy consumption as the primary input and gross domestic product (GDP) as the desirable output and CO2 emissions as the undesirable output. Yale’s EPI measures the efficiency of the sectoral level environmental performance of primary energy consumption in the country. Performance analysis was conducted from 2009 to 2018. The sectors were assigned scores between one and zero, with zero indicating maximum decision-making unit (DMU) inefficiency and one indicating maximum DMU efficiency. Despite being in the top-performing sector, agriculture scored only 0.51 in 2018, and the electricity sector obtained 0.412. Results also show that even the best-performing sector operates below the efficiency level. The mining and quarrying sector ranked second by obtaining 0.623 EPI and 0.035 SBEPI. Results also show that much of the energy supply of Pakistan (60.17%) is focused on fossil fuels, supplemented by hydropower (33%), while nuclear, wind, biogas, and solar power account for 5.15%, 0.47%, 0.32%, and 0.03%, respectively. Nonetheless, the overall results for both measures remained reasonably consistent. According to the literature and the energy crisis and climate instability dilemma, the authors conclude that changes to a diverse green power network are a possibility and an imminent need. Similarly, the government should penalize companies with poor performance. Furthermore, to ensure the capacity development and stability of environmental management and associated actions in the country, providing access to knowledge and training to groom human resources and achieve the highest performance is crucial.

The real-time availability of the Internet has engaged millions of users around the world. The usage of regional languages is being preferred for effective and ease of communication that is causing multilingual data on social networks and news channels. People share ideas, opinions, and events that are happening globally i.e., sports, inflation, protest, explosion, and sexual assault, etc. in regional (local) languages on social media. Extraction and classification of events from multilingual data have become bottlenecks because of resource lacking. In this research paper, we presented the event classification task for the Urdu language text existing on social media and the news channels by using machine learning classifiers. The dataset contains more than 0.1 million (102,962) labeled instances of twelve (12) different types of events. The title, its length, and the last four words of a sentence are used as features to classify the events. The Term Frequency-Inverse Document Frequency (tf-idf) showed the best results as a feature vector to evaluate the performance of the six popular machine learning classifiers. Random Forest (RF) and K-Nearest Neighbor (KNN) are among the classifiers that out-performed among other classifiers by achieving 98.00% and 99.00% accuracy, respectively. The novelty lies in the fact that the features aforementioned are not applied, up to the best of our knowledge, in the event extraction of the text written in the Urdu language.

In real time scenarios, the single image encryption schemes are not that efficient when a bunch of color images is to be encrypted. This problem should be addressed due to computational cost incurred by multiple executions of single color-image encryption scheme. The objective of this research work is to provide a new and efficient multiple color images cipher scheme. This cipher is based on chaotic system, circular shift operations and SHA-384 hash codes. First of all, an arbitrary number of RGB images are input. They are combined in a rectangular fashion to get their hash code. Then this big color-image is decomposed into its three basic components, i.e., red, green and blue. To scramble the pixels of these components, they are shifted circularly both the row-wise and column-wise. Further, XOR operation has been performed to realize the effects of diffusion in these components. Lastly, these components are joined together to get the final RGB cipher image. Apart from the 384-hash codes, a salt key of 384-bit has also been used in the proposed cipher to heighten the security effects. The SHA-384 hash codes render the required key space and plaintext sensitivity. The simulation results and the performance analyses carried out at the end clearly prove the efficiency, effectiveness, robustness and the real-world applicability of the proposed multiple RGB images cipher. Besides, the encryption throughput 7.716 Mb/s of the proposed scheme outperforms many of the existing ones.

Single image encryption schemes are not efficient enough when a bunch of images is to be encrypted in some real-world setting. To overcome this problem, an efficient and secured multiple images encryption scheme is proposed in this study using two chaotic maps and simple row and column swapping operations in a 3D image space. The N input images are piled to make a 3D image. To confuse the given pixel data, two images are chosen randomly from this pile. The randomly chosen two rows from the two randomly chosen images are swapped with each other. In the same way, two randomly chosen columns are swapped with each other. The operation of randomly chosen two images, two rows, and two columns have been iterated an arbitrary number of times to throw the confusion effects in the pixels data. Intertwining Logistic Map (ILM) and Improved Piecewise Linear Chaotic Map (MPWLCM) have been used to get the four streams of random numbers. The three streams of the former map have been used to create the confusion effects, whereas the fourth stream of random numbers given by the latter map has been used for the diffusion effects. SHA-256 hash codes have been used to throw the plaintext sensitivity in the proposed cipher. Besides, a 256-bit user key has been employed to increase the key space. Both the simulation and the exhaustive security analyses carried out at the end vividly prove the security, resistance to the varied attacks, and the real-world applicability of the proposed cipher.

In this study, a novel technique using a hyper chaotic dynamical system and DNA computing has been designed with high plaintext sensitivity. In order to reduce cost, a selection procedure using tent map has been employed for generating different key streams from the same chaotic data obtained from the iterations of chaotic dynamical system. After separating the three channels from the input color image, they are both confused and diffused. First of all, these channels are diffused on a decimal level. Then they are permuted. Further, DNA encoding is performed upon these channels. Moreover, DNA level diffusion is performed to further increase the degree of randomness in the image. Lastly, the DNA encoded image is converted into decimal to get the final cipher image. Both the experimental results and security analysis strongly demonstrate the robustness of the proposed scheme. A comparison of the proposed scheme has also been made with other recently developed schemes to show that this scheme outperforms the others in terms of computational cost, time and memory efficiency. Additionally, with the large key space, the proposed scheme can resist any brute force, plaintext and statistical attacks, therefore it is a good fit for the real world applications of the image security.

With recent advancement in computational biology, high throughput next generation sequencing technology has become a de facto standard technology for genes expression studies including DNAs, RNAs and proteins. As a promising technology, it has significant impact on medical sciences and genomic research. However, it generates several millions of short DNA and RNA sequences with several petabytes size in single run. In addition, the raw sequencing datasets such as RNAs are increasing exponentially leading to a big data analytics issue in computational biology. Due to the explosive growth of RNA sequences, the timely classification of RNAs sequence into piRNAs and non-piRNAs have become a challenging issue for traditional technology and conventional machine learning algorithms. Parallel and distributed computing models along with deep neural network have become a major computing platform for big data analytics now required in the field of computational biology. This paper presents a computational model based on parallel deep neural network for timely classification of large number of RNAs sequence into piRNAs and non-piRNAs, taking advantages of parallel and distributed computing platform. The performance of the proposed model was extensively evaluated using two-fold performance metrics. In the first fold, the performance of the proposed model was assessed using accuracy-based metrics such as accuracy, specificity, sensitivity and Matthews's correlation coefficient. In the second fold, computational-based metrics such as computation times, speedup and scalability were observed. Moreover, initially the performance of the proposed model was assessed using real benchmark dataset and subsequently the performance was assessed using replicated benchmark dataset. The evaluation results in both cases showed that the proposed model improved computation speedup in order of magnitude in comparison with sequential approach without affected accuracy level.

The recent success of graph neural networks (GNNs) in the area of pattern recognition (PR) has increased the interest of researchers to use these frameworks in non-euclidean structures. This non-euclidean structure includes graphs or manifolds that are called geometric deep learning (GDL). It has opened a new direction for researchers to deal with graphs using deep learning in document processing, outperforming conventional methods. We propose a Deep Graph Neural Network (DGNN) classifier-based on additive angular margin loss for the classification task in document analysis. Another contribution of this work is to investigate the performance of a DGNN as a classifier using different loss functions, which helps to minimize the loss for the document analysis problem. We compare additive angular margin loss, Cosine angular margin loss, and multiplicative angular margin loss. Furthermore, we give a comparison between the mentioned loss functions and the Softmax loss function. We also present the comparisons of results using different graph edit distance (GED) methods. Our quantitative results suggest, that by applying the additive angular marginal loss function makes more compact intra-class ability and increases the inter-class discrepancy which enhances the discriminating power of the DGNN. Enhancing the decision boundaries between the classes increase the intra-class compactness and inter-class discrimination power of the model.

In the past, many image encryption schemes have been developed through the swapping operations at the different levels of granularity. These levels span bits, Deoxyribonucleic acid (DNA) molecules, pixels, blocks of pixels. In this study, a new scheme for the encryption of color images based on the DNA strands level scrambling (DNASLS) and chaotic system has been proposed. After a color image is input, it is decomposed into the red, green and blue components. After it, these components are merged to form a big single image. Intertwining logistic map (ILM) has been used for the random data which generates three streams of random numbers. These streams have been further manipulated in such a way that nine streams are spawned out of them. One stream out of these nine streams has been used for the generation of a key image. Two streams have been used to DNA-encode the big single image and the key image. Afterwards, the strands of DNA-encoded single image are swapped with each other. Four streams determine the addresses of two DNA encoded pixels for the selection. A yet another stream is being used to select a particular strand from the DNA strands. To create the diffusion effects, an XOR operation has been done between the DNA encoded image after the swapping of strands and the DNA encoded key image. Finally, the last and ninth stream has been used to decode the DNA-encoded pixels into the decimal form. Purely random numbers with no inter-dependence have been employed in the entire encryption process. The effects of plaintext sensitivity have been achieved through the incorporation of Secure Hash Algorithm-256 or SHA-256 hash codes. In the end, the experiment and the security analysis have been performed. The results of the validation metrics like information entropy(7.9973), average key sensitivity(99.61%) and mean absolute error(84.7158) demonstrate the security, defiance to the number of attacks and a potential for real world application of the proposed image cipher.

Hundreds of image encryption schemes have been conducted (as the literature review indicates). The majority of these schemes use pixels as building blocks for confusion and diffusion operations. Pixel-level operations are time-consuming and, thus, not suitable for many critical applications (e.g., telesurgery). Security is of the utmost importance while writing these schemes. This study aimed to provide a scheme based on block-level scrambling (with increased speed). Three streams of chaotic data were obtained through the intertwining logistic map (ILM). For a given image, the algorithm creates blocks of eight pixels. Two blocks (randomly selected from the long array of blocks) are swapped an arbitrary number of times. Two streams of random numbers facilitate this process. The scrambled image is further XORed with the key image generated through the third stream of random numbers to obtain the final cipher image. Plaintext sensitivity is incorporated through SHA-256 hash codes for the given image. The suggested cipher is subjected to a comprehensive set of security parameters, such as the key space, histogram, correlation coefficient, information entropy, differential attack, peak signal to noise ratio (PSNR), noise, and data loss attack, time complexity, and encryption throughput. In particular, the computational time of 0.1842 s and the throughput of 3.3488 Mbps of this scheme outperforms many published works, which bears immense promise for its real-world application.

The objective of this research paper is to check the impact of openness on inflation volatility. The time period ranges from 1986-2014 by utilizing Panel data for SAARC countries. Four proxy variables are used to calculate openness by using Generalized Method of Moment (GMM) and Impulse Response for the existence of short run and long run relationship. GMM results indicates that the relation depend on the proxy variable being used in the model. Export to GDP and KOF index shows negative impact on inflation volatility while Trade to GDP ratio has significant positive impact on inflation volatility. The empirical study suggests that exports should be encouraged as it resulted in decreasing rate of inflation as along with imports inflation is also imported in terms of commodity price.

Price stability are important for macroeconomic stability, especially for the economies, which are facing macroeconomic instability. Contractionary monetary policy can play an important role in minimizing the frequency of price change, so for effective monetary policy, it is necessary to identify the determinants of price setting behaviour. The objective of the study is to determine the patterns of price setting of firms and responses of firms to economic shocks, i.e. changes in demand and supply side factors, the role of firm characteristics, institutional and other factors in determining the channel of adjustment after these shocks. The data is collected through stratified frandom samplling from 342 firms, located in four Industrial estates of Khyber Pakhtunkhwa. Price setting behavior is measured through importance of demand and cost shocks for price change. To estimate the effects of determinants of price change, models are estimated through logistic technique. Firms’ likelihood of price increases higher than that to price decrease in response to both demand and supply shocks. Furthermore, supply-side factors lead to higher frequency of price change than demand-side factors do. The cost of raw material and cost of energy are the most important causes of price change, both for the price increase and decrease. Demand and cost shocks are important determinants of price change for imperfectly competitive firms, backward-looking firms, firms run by managers having more information about economic conditions, while credibility of central bank is important determinant of price change in case of demand shocks only. Size of firms and information set regarding expected inflation do not have any effect. For effective monetary policy of Pakistan, credibility of central bank has to be established to stabilize prices and pre-emptive measures should be taken on the part of central bank to counter supply shocks.

Meiotic recombination is the driving force of evolutionary development and an important source of genetic variation. The meiotic recombination does not take place randomly in a chromosome but occurs in some regions of the chromosome. A region in chromosomes with higher rate of meiotic recombination events are considered as hotspots and a region where frequencies of the recombination events are lower are called coldspots. Prediction of meiotic recombination spots provides useful information about the basic functionality of inheritance and genome diversity. This study proposes an intelligent computational predictor called iRSpots-DNN for the identification of recombination spots. The proposed predictor is based on a novel feature extraction method and an optimized deep neural network (DNN). The DNN was employed as a classification engine whereas, the novel features extraction method was developed to extract meaningful features for the identification of hotspots and coldspots across the yeast genome. Unlike previous algorithms, the proposed feature extraction avoids bias among different selected features and preserved the sequence discriminant properties along with the sequence-structure information simultaneously. This study also considered other effective classifiers named support vector machine (SVM), K-nearest neighbor (KNN), and random forest (RF) to predict recombination spots. Experimental results on a benchmark dataset with 10-fold cross-validation showed that iRSpots-DNN achieved the highest accuracy, i.e., 95.81%. Additionally, the performance of the proposed iRSpots-DNN is significantly better than the existing predictors on a benchmark dataset. The relevant benchmark dataset and source code are freely available at: https://github.com/Fatima-Khan12/iRspot_DNN/tree/master/iRspot_DNN.

This article introduces a quantile penalized regression technique for variable selection and estimation of conditional quantiles of counts in sparse high-dimensional models. The direct estimation and variable selection of the quantile regression is not feasible due to the discreteness of the count data and non-differentiability of the objective function, therefore, some smoothness must be artificially imposed on the problem. To achieve the necessary smoothness, we use the Jittering process by adding a uniformly distributed noise to the response count variable. The proposed method is compared with the existing penalized regression methods in terms of prediction accuracy and variable selection. We compare the proposed approach in zero-inflated count data regression models and in the presence of outliers. The performance and implementation of the proposed method are illustrated by detailed simulation studies and real data applications.

The application of confusion and diffusion processes on the three individual components of an RGB image is not secure and efficient, so this problem needs to be addressed. In this paper, a novel RGB image cipher is proposed using chaotic systems, 15-puzzle artificial intelligence problem and DNA computing. First of all the given color image is decomposed into its red, green and blue gray scale images. Then these gray scale images are concatenated to make a single gray scale image. This single gray scale image is further divided into different blocks. A block level permutation (BLP) is proposed on this gray scale image by using the 15-puzzle problem. A pixel level permutation is applied to further randomize the image pixels. This confused image is then DNA encoded. Afterwards, a diffusion process is applied on this DNA encoded image. Lastly this DNA diffused image is converted back into the decimal. Further, this single gray scale image is broken into three gray scale images. These three images are combined to get the final color cipher image. To create the plaintext sensitivity, SHA 256 hash function has been used. Both the simulation and a comprehensive security analyses suggest the robustness and the impregnability of the proposed scheme which in turn signals towards the real world applicability of the scheme.

Hybrid maize farmers have to face diverse kinds of climate, biological, price and financial risks. Farmers' risk perceptions and risk attitudes are essential elements influencing farm operations and management decisions. However, this important issue has been overlooked in the contemporary studies and therefore there is a dearth of literature on this important issue. The present research is therefore, an attempt to fill this gap. This study aims to quantify hybrid maize farmers' perceptions of disastrous risks, their attitudes towards risk and to explore the impacts of various farm and farm household factors on farmers' risk attitudes and risk perceptions. The present study is conducted in four hybrid maize growing districts of Punjab Province, Pakistan, using cross-sectional data of 400 hybrid maize farmers. Risk matrix and equally likely certainty equivalent (ELCE) method are used to rank farmers' perceptions of four catastrophic risk sources including climate, biological, price and financial risks and to investigate farmers' risk aversion attitudes, respectively. Furthermore, probit regression is used to analyze the determinants affecting farmers' risk attitudes and risk perceptions. The results of the study showed that majority of farmers are risk averse in nature and perceive price, biological and climate to be potential sources of risks to their farm enterprise. In addition, analysis divulges that distance from farm to main market, off-farm income, location dummies for Sahiwal and Okara, age, maize farming experience, access to extension agent, significantly (either negatively or positively) influence farmers' risk attitudes and risk perceptions. The study delivers valuable insights for farmers, agricultural insurance sector, extension services researchers and agricultural policy makers about the local understanding of risks to hybrid maize crop in developing countries, like Pakistan, and have implications for research on farmers' adaptation to exposed risks.

The objective of this study is to explore how investors execute decisions in persistence of Islamic anomalies in context of Pakistan stock exchange. Applying interpretive approach, data was acquired from PSX investor by asking a series of questions through an interview protocol. Interviews were conducted by using semi-structured interview approach. The responses were explored using NVivo 10.0 plus software. The results of the study indicated Islamic anomalies are changing emotions and perceptions of investors due to their past experience and perceived market consequences, and biased behaviors. Due to Islamic anomalies investors decisions are found to be biased and depict a self-serving, anchoring and overconfident behavior. Moreover, various conventional market factors (e.g. market capitalization, price earning and capita market line fluctuation etc.) are constant and reshaping investor decisions. Promotion of corporate culture, controlling mispricing, conducting awareness/ training sessions on rationality, capital gain than price gain is seriously required in local context. This is the first qualitative study investigating Islamic Calendar anomalies and PSX investors, leading to the formulation of sound policy implications.

Assessment of working capital management is mandatory to ensure managerial implication and decision making therefore the study contains an assessing role of manager’s Islamic associations, religious sentiments and biased behaviors affecting working capital management in Islamic Banks of organization of Islamic countries. For empirical assessment positivist approach is incorporated by using systematic sampling in parallel to belief congruence and prospect theories. The results show the considerable differences among Islamic association of managers, their religiously factors and working capital management. Furthermore the results show that Islamic association of managers and their religiously factors have significant mediating moderated impact on working capital management. The dire need is to enhance corporate culture in sample countries for better financial sustainability in the light of Islamic codes. The study provides valuable information for policy and decision makers.

Most of the researchers are focusing to bring automation in mechanical systems. So, current research work presents a mechatronic system called active hydrostatic journal bearing that is a higher demand of fast-growing industrial needs. The objective is to improve the dynamics and static characteristics of hydrostatic journal bearing. To achieve the objective, a feedback servo controlled system is presented. A mathematical model is proposed for newly proposed active hydrostatic journal bearing with servo control. A comparison has been performed between the active hydrostatic journal bearing and traditional/conventional hydrostatic journal bearing under the influence of different dynamic conditions of load, viscosity, speed, and pressure. The results demonstrate that proposed active hydrostatic journal bearing has better performance, including controllability, stability, faster response, higher stiffness as well as better disturbance rejection. To further enhance the performance of proposed active hydrostatic journal bearing, two different control strategies are proposed such as proportional–integral–derivative and intelligent control. The results show that fuzzy logic–based intelligent control has faster response, greater amplitude reduction, and good stiffness against load. So, it shows that role of active lubrication in hydrostatic journal bearing will have potential applications in high load and high speed.

Annual ryegrass (Lolium rigidum Gaud.), traditionally utilised as a pasture species, has become the most problematic and difficult-to-control weed across grain production regions in Australia. Annual ryegrass has been favoured by the adoption of conservation tillage systems due to its genetic diversity, prolific seed production, widespread dispersal, flexible germination requirements and competitive growth habit. The widespread evolution of herbicide resistance in annual ryegrass has made its management within these systems extremely difficult. The negative impacts of this weed on grain production systems result in annual revenue losses exceeding $93 million (AUD) for Australian grain growers. No single method of management provides effective and enduring control hence the need of integrated weed management programs is widely accepted and practiced in Australian cropping. Although annual ryegrass is an extensively researched weed, a comprehensive review of the biology and management of this weed in conservation cropping systems has not been conducted. This review presents an up-to-date account of knowledge on the biology, ecology and management of annual ryegrass in an Australian context. This comprehensive account provides pragmatic information for further research and suitable management of annual ryegrass.

An elevated atmospheric carbon dioxide (CO2) concentration and frequent droughts are two anticipated climate change scenarios in which certain invasive weeds may develop competitive advantages over crops and adversely impact productivity and herbicide efficacy. Hence, a study was conducted to explore the effect of different climatic scenarios on the growth and management of Sesbania cannabina (Retz.) Pers with glyphosate. The variables investigated were two CO2 concentrations (400 and 700 ppm), two soil moisture levels (100% and 50% of field capacity (FC)), and three glyphosate rates (0 (control), 517 (50% of recommended rate), and 1034 g ae ha−1 (recommended rate)). CO2 concentrations and soil moisture levels had different effects on the growth and management of S. cannabina. Overall, 100% FC and elevated [CO2] of 700 ppm recorded the maximum plant height (38 cm), leaves per plant (20), growth index (60), chlorophyll content (SPAD value 37), and dry biomass (3 g) in comparison with ambient [CO2] of 400 ppm and 50% FC treatment. The recommended glyphosate application gave 100% weed biomass reduction; however, efficacy was reduced (63%) when applied at 50% of the recommended rate under elevated [CO2] of 700 ppm and 50% FC conditions.

In South Asia, soil health degradation is affecting the sustainability of the rice-wheat cropping system (RWCS). Indeed, for the sustainability of the soil quality, new adaptive technologies, i.e., conservation tillage and straw management resource conservation, are promising options. This investigation was focused on the interaction of tillage and straw management practices and their effects on Aridisols, Yermosols soil quality, and nutrients dynamics with different soil profiles within RWCS. The long-term field experiment was started in 2014 with the scenarios (i) conventional tillage (SC1), (ii) residue incorporation (SC2), (iii) straw management practices (SC3 and SC4) and conservation tillage (SC5). Conservation tillage practice (SC5) showed significant impact on properties of soil and availability of nutrients in comparison with that of conventional farmers practice (SC1) at the studied soil depths. The SC5 showed significant results of gravitational water contents (25.34%), moderate pH (7.4), soil organic-matter (7.6 g kg−1), total nitrogen (0.38 g kg−1), available phosphate (7.4 mg kg−1), available potassium (208 mg kg−1) compared to SC1 treatment at 0 to 15 cm soil depth. Whereas, DTPA-extractable-Cu, Mn, and Zn concentration were significantly higher, i.e., 1.12 mg kg−1, 2.14 mg kg−1, and 4.35 mg kg−1, respectively under SC5 than conventional farmer’s practices, while DTPA (diethylene triamine pentaacetic acid) extractable Fe (6.15 mg kg−1) was more in straw management practices (SC4) than conventional and conservation tillage. Therefore, conservation tillage (SC5) can surge the sustainability of the region by improving soil assets and nutrients accessibility and has the potential to minimize inorganic fertilizers input in the long run.

The intent of the study is to assess the impact of Islamic events on Pakistani Stock exchange returns. The study is assessing Islamic events i-e Moharram, Rabi ul Aawal event, 1stRamzan event, 1st Shiwal event and 10th Zil Hajj event respectively on market returns. For this, regression, correlation and Jarque-Bera test are used. The results have concluded that Moharram, 10th Zil Hajj have negative impact while Rabi ul Awal, Ramazan start and first Shiwal has significant positive impact on stock returns as these are positive sentiment. The study is providing a valuable guideline for investors and decision makers in a way to control market practices and arbitrage roles to control such extremeness of key stakeholders during these events regarding market trading.

Background: To compare vitamin D levels in patients of dengue hemorrhagic fever with dengue fever Methods: A total of 50 patients with diagnosed dengue fever, who fulfilled the inclusion criteria were enrolled in the study. Patients were divided into two groups having 25 participants each; one group had Dengue fever (DF) while the other was suffering from dengue hemorrhagic fever (DHF). Vitamin D was estimated by the chemiluminescence method. Pearson’s Chi-square was applied to compare the proportion of patients in each study group. Relative Risk was measured along with 95% confidence interval. Significant p-value was < 0.05. Results: Mean age of patients was 37.79 ± 15.2 years (range: 16-90 years). There were 74% males and 26% females. Mean vitamin D levels in dengue fever patients were higher (21.5 ± 13.6 ng/ml) as compared to Dengue hemorrhagic fever (12.4 ± 5.6 ng/ml). The difference was statistically significant (p=0.003). Conclusions: Vitamin D may have a role in the management of dengue fever. Low levels of vitamin D might be associated with dengue hemorrhagic fever.

Active hydrostatic journal bearings represent a mechatronic answer to the fast-growing industrial needs to high-performance rotary machineries. The aim of this research is to study and improve the dynamic characteristics of hydrostatic bearing, with four pockets or recess. In order to improve dynamic characteristics for hydrostatic journal bearing, this research presents an active hydrostatic journal bearing with servo control. The results of active hydrostatic journal bearing with servo control were compared with those of traditional hydrostatic journal bearing with capillary throttling. The dynamic characteristics of the newly proposed bearing and traditional bearing are studied and compared under different dynamic conditions such as variable speed, viscosity, load, pressure, and bearing clearance. The numerical result shows that the proposed active hydrostatic journal due to active lubrication has good performance including stability and controllability, high stiffness, faster response, and strong resistance. Furthermore, two strategies such as proportional–integral–derivative and integrator backstepping control are also presented to improve the performance and efficiency of active hydrostatic journal bearing with servo control. The result shows that integrator backstepping control is more effective than proportional–integral–derivative control in terms of faster response and greater amplitude reduction, quickly regaining equilibrium position, and good stiffness against load. Hence, it is observed that active lubrication in hydrostatic journal bearing provides us better dynamic characteristics which will have potential applications in heavy and high-speed machinery.

Background Each year more than 110,000 people die because of tobacco use in Pakistan. There are approximately 23.6 million (age 15-45 years) tobacco users in Pakistan. GYTS-2014 revealed that 13.3% of boys and 6.6% of girls (age 13-15 years) used tobacco products. Pakistan is the signatory of FCTC since 2004. Although efforts are being made but TI is continuously using its tactics targeting youth.The aim of this study is to examine the claims by tobacco industry as well as the counter-arguments by tobacco control advocates in the year 2016-17 in Pakistan to show patterns of arguments. Methods The present study took a qualitative approach to examine the articles about tobacco (cigarettes) in English Pakistan newspapers from October, 2016, to October, 2017. All tobacco related articles were chosen. The selected seventy articles were likely to represent most of the tobacco related news coverage in top five national newspapers during this period. Content analysis was done to understand the main messages conveyed by the articles and different themes were generated. Results Three major players were identified which included the government, civil society and the tobacco industry. Main stories generated were either focused on Tobacco Taxation, Graphical Health Warnings or Tobacco Control in general. The analysis showed that to reduce levies on cigarettes, TI worked in two phases. Firstly it created a smokescreen that counterfeit cigarettes have taken over the legitimate cigarette market. Secondly, TI related it to the loss of government revenues. Conclusions The findings indicate that tobacco Industry revolves around the similar patterns of tactics throughout the world taking a step by step approach. Tobacco advocates should study cases of other countries as well to better understand TI tactics and counter ways.

In order to enhance salt rejection level and high pressure mechanical integrity, functionalized nanokaolin decorated multiwall carbon nanotubes (FNKM, 0–5 wt % loading) were incorporated into a cellulose acetate (CA) matrix using high temperature solution mixing methodology. Scanning electron microscopy (SEM), X-ray diffraction technique (XRD), thermo-gravimetric analyzer (TGA) and Fourier transform infrared spectrometer (FTIR) were used to characterize the prepared membranes. The obtained results revealed that with increasing FNKM concentration in the host polymeric matrix, composite membrane’s structural, functional, thermal, water permeation/flux and salt rejection characteristics were also modified accordingly. Percent enhancement in salt rejection was increased around threefold by adding 5 wt % FNKM in CA.