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Life-threatening events including terminal illness intensify the search for meaning and incite individuals to get closer to religion. Terminal patients can often find religious practices as helpful as medical therapy for bettering both physical and mental health. The present research aims to explain the interaction between religion, spirituality, and social support in coping with terminal illness among Muslim hepatitis C patients in Pakistan. A semi-structured open-ended interview guide was utilized to collect the data. Participants expressed that the deployment of religious and spiritual beliefs along with socio-emotional support during illness fostered medical therapy. Participants also revealed that belief in God provided them the strength to be steadfast during the terminal stage of the disease. Religious beliefs enabled terminal participants to accept death as an eventual reality and a normal part of their lives. Furthermore, participants put forward their longing for those kinds of religious practices that terminal diseases usually restrained them from receiving. The emotional support stemming from social relationships also improved resilience to cope with the terminal stage of illness. The study concludes that the interplay of religion, spirituality, and social support normalizes the fear of death, lessens pain, and improves resilience among Muslim hepatitis C patients in Pakistan. [ABSTRACT FROM AUTHOR]

Ascites remains a significant challenge in patients with cirrhosis, posing difficulties in management and affecting prognosis. This review examines the current understanding of ascites, including its underlying mechanisms, symptoms, and treatment options, with a specific focus on the innovative Alfa Pump device. The review begins by discussing traditional approaches to managing ascites while also addressing their limitations and potential complications. It then explores the emergence of the Alfa Pump system, a novel implantable device designed to tackle refractory ascites by continuously draining fluid from the abdomen while minimizing circulatory issues. Through a synthesis of current literature and clinical evidence, this narrative review underscores the importance of a multidisciplinary approach in the management of ascites, with a particular emphasis on the evolving role of the Alfa Pump in improving outcomes and quality of life for patients with refractory ascites. [ABSTRACT FROM AUTHOR]

Heart disease (HD) stands as a major global health challenge, being a predominant cause of death and demanding intricate and costly detection methods. The widespread impact of heart failure, contributing to increased rates of morbidity and mortality, underscores the urgency for accurate and timely prediction and diagnosis. This is crucial for effective prevention, early detection, and treatment, thereby reducing the threat to individual health. However, the early and precise prediction of HD remains a significant challenge. The complexity of medical data poses a considerable challenge for healthcare professionals, who are required to interpret and utilize this information swiftly for effective intervention. Addressing this gap, our study introduces a novel Improved Explainable Learning-Based Technique (IELBT) for HD prediction. This technique harnesses a strategic combination of feature selection, Venn diagrams, data normalization methods, optimized parameters, and machine learning algorithms, specifically tailored for predicting HD. We evaluated the performance of our model using the Alizadeh Sani HD dataset, aiming to accurately detect the presence or absence of the condition. Our results demonstrate that the IELBT, employing a support vector machine with a robust scaling approach, optimal parameterization, and a data split ratio of 70:30, achieves an impressive accuracy rate of 96.00%. Beyond achieving high accuracy, the IELBT outperforms similar models in existing literature and provides significant interpretability and explanation, essential elements in the field of medical diagnosis. [ABSTRACT FROM AUTHOR]

This article uses a finite-element approximation approach for solving a three-dimensional flow problem of a nanofluid influenced by heat transfer due to nanoparticles over a non-linearly stretching sheet within an unbounded domain. Utilising similarity transformations, a well-posed coupled system of nonlinear ordinary differential equations is derived from the governing partial differential equations describing the flow and heat transfer processes. The resulting system is then solved by using quadratic Lagrange polynomials as basic functions over a mesh of different finite elements through the Galerkin finite element (GFE) technique. This implementation is based on a regular grid utilising Lagrange polynomials for solving the converted equations. The effects of various parameters of interest are efficiently discussed with the help of graphs and numeric tables. Both numerical and exact solutions are compared favourably, demonstrating a high level of accuracy. It is noteworthy that the GFE method emerges as a much more stable numerical technique than the other existing analytic and semi-analytical methods. Furthermore, the adopted finite-element method reduces the dimensionality of Sobolev's space's finite-dimensional subspace and also improves the solution's convergence rate. Moreover, the velocity is negative, and its magnitude increases as the stretching rates ratio increases due to the downward flow in the vertical direction. The temperature and heat transmission from the sheet are barely impacted by Brownian motion due to the dominance of other forces and length scales involved in the heat transfer process. [ABSTRACT FROM AUTHOR]

Transfer learning (TL) has emerged as a promising area of research in machine learning (ML) due to its ability to enhance learning efficiency and accuracy by leveraging knowledge from related domains. However, traditional TL is limited in its applicability to real-world scenarios where the assumption of identical feature spaces and distributions between source and target domains is untenable. To address this limitation, Heterogeneous Transfer Learning (HeTL) has emerged as an important research direction that enables knowledge transfer between domains with heterogeneous feature spaces and distributions. Motivated by the growing interest and significance of HeTL, this survey paper comprehensively reviews recent HeTL developments, beginning with mathematical TL definitions and a taxonomy of TL categories. It delves into HeTL, explaining its classification and research status, and highlights symmetric and asymmetric HeTL advancements. Next, we explored the applications of HeTL in various disciplines, such as image and text classification, activity recognition, and cross-project defect prediction, emphasizing HeTL's advantages over Traditional TL. Furthermore, we also discuss the challenges in HeTL, such as heterogeneity, transferability, negative learning, interpretability, and explainability. Finally, we conclude with a discussion on HeTL directions for future research. [ABSTRACT FROM AUTHOR]

Heart disease (HD) is a major threat to human health, and the medical field generates vast amounts of data that doctors struggle to effectively interpret and use. Early prediction and classification of HD types are crucial for effective medical treatment. Researchers have found it important to use learning-based techniques from machine and deep learning, such as supervised and deep neural networks, to develop automatic models for HD. These techniques have been used to simulate HD management and extract important features from complex data sets. This survey examines various HD prediction models, classifying the learning-based techniques, datasets, and contexts used, and analyzing the performance metrics of each contribution. It also clarifies which method suits a type of HD. With the growth of data sets, researchers are increasingly utilizing these techniques to create more precise models. However, there is still much work to be done to improve the accuracy of HD predictions. [ABSTRACT FROM AUTHOR]

Background: Malaria remains a formidable worldwide health challenge, with approximately half of the global population at high risk of catching the infection. This research study aimed to address the pressing public health issue of malaria's escalating prevalence in Khyber Pakhtunkhwa (KP) province, Pakistan, and endeavors to estimate the trend for the future growth of the infection. Methods: The data were collected from the IDSRS of KP, covering a period of 5 years from 2018 to 2022. We proposed a hybrid model that integrated Prophet and TBATS methods, allowing us to efficiently capture the complications of the malaria data and improve forecasting accuracy. To ensure an inclusive assessment, we compared the prediction performance of the proposed hybrid model with other widely used time series models, such as ARIMA, ETS, and ANN. The models were developed through R-statistical software (version 4.2.2). Results: For the prediction of malaria incidence, the suggested hybrid model (Prophet and TBATS) surpassed commonly used time series approaches (ARIMA, ETS, and ANN). Hybrid model assessment metrics portrayed higher accuracy and reliability with lower MAE (8913.9), RMSE (3850.2), and MAPE (0.301) values. According to our forecasts, malaria infections were predicted to spread around 99,301 by December 2023. Conclusions: We found the hybrid model (Prophet and TBATS) outperformed common time series approaches for forecasting malaria. By December 2023, KP's malaria incidence is expected to be around 99,301, making future incidence forecasts important. Policymakers will be able to use these findings to curb disease and implement efficient policies for malaria control. [ABSTRACT FROM AUTHOR]

With an aim to eliminate or reduce the spread of hate content across social media platforms, the development of artificial intelligence supported computational predictors is an active area of research. However, diversity of languages hinders development of generic predictors that can precisely identify hate content. Several language-specific hate speech detection predictors have been developed for most common languages including English, Chinese and German. Specifically, for Urdu language a few predictors have been developed and these predictors lack in predictive performance. The paper in hand presents a precise and explainable deep learning predictor which makes use of advanced language modelling strategies for the extraction of semantic and discriminative patterns. Extracted patterns are utilized to train an attention-based novel classifier that is competent in precisely identifying hate content. Over coarse-grained benchmark dataset, the proposed predictor significantly outperforms state-of-the-art predictor by 8.7% in terms of accuracy, precision and F1-score. Similarly, over fine-grained dataset, in comparison with state-of-the-art predictor, it achieves performance gain of 10.6%, 17.6%, 18.6% and 17.6% in terms of accuracy, precision, recall and F1-score. [ABSTRACT FROM AUTHOR]

In this article, we analyze the boundedness for the fractional bilinear Hardy operators on variable exponent weighted Morrey–Herz spaces M K ˙ q , p (⋅) α (⋅) , λ (w) . Similar estimates are obtained for their commutators when the symbol functions belong to BMO space with variable exponents. [ABSTRACT FROM AUTHOR]

Quantifying water-saving potential (WSP) is crucial for sustainable water resource management in canal command areas and river basins. Previous studies have partially or fully ignored the importance of groundwater in WSP assessments, particularly in irrigated areas. This study is aimed at quantifying WSP in the Lower Chenab Canal (LCC) command area of the Indus River Basin, Pakistan, under various scenarios of future climate change and groundwater recharge. These quantifications are conducted using an empirical model based on the Budyko theory. The model was forced using observed, remote sensing, and CMIP6 future climate data for two Shared Socioeconomic Pathways (SSP245 and SSP585) and their ensembles (cold-dry, cold-wet, warm-dry, and warm-wet) for possible futures. The results showed that the average WSP in the LCC command area was 466 ± 48 mm/year during the historical period (2001–2020). The WSP is projected to decrease by – 68 ± 3% under the warm-dry ensemble scenario (SSP245 and SSP585) and – 48 ± 13% under the ensembled cold-wet scenario by 2100. The results also demonstrated that WSP could be increased by up to 70 ± 9% by artificially recharging 20% of the abstracted groundwater per year in the LCC command area by the late twenty-first century. Our findings highlight the importance of adopting artificial groundwater recharge to enhance the WSP and sustainably manage water resources in the LCC command area. Policymakers should consider these findings when deciding on water resource management in the Indus River Basin. [ABSTRACT FROM AUTHOR]

Cost volume is widely used to establish correspondences in optical flow estimation. However, when dealing with low-texture and occluded areas, it is difficult to estimate the cost volume correctly. Therefore, we propose a replacement: feature correlation transformer (FCTR), a transformer with self- and cross-attention alternations for obtaining global receptive fields and positional embedding for establishing correspondences. With global context and positional information, FCTR can produce more accurate correspondences for ambiguous areas. Using position-embedded feature allows the removal of the context network; the positional information can be aggregated within ambiguous motion boundaries, and the number of model parameters can be reduced. To speed up network convergence and strengthen robustness, we introduce a smooth L1 loss with exponential weights in the pre-training step. At the time of submission, our method achieves competitive performance with all published optical flow methods on both the KITTI-2015 and MPI-Sintel benchmarks. Moreover, it outperforms all optical flow and scene flow methods in KITTI-2015 foreground-region prediction. [ABSTRACT FROM AUTHOR]

The global demand for crop production is rapidly growing due to the continued rise in world population. Crop productivity varies generally with soil nutrient profile and climate. The optimal use of fertilizers might help to attain higher crop yield in canola. To circumvent nutrient imbalance issues in soil, two separate field trials were conducted to determine (a) the best source of nitrogen (N) between ammonium sulfate (NH4)2SO4) and ammonium nitrate (NH4NO3), (b) significance of gibberellic acid (GA3) and potassium (K), in an attempt to enhance canola yield and yield attributes. Both experiments were carried out in randomized complete block design (RCBD) with three replicates. The nitrogen source in the form of NH4)2SO4 (0, 10, 20 and 30 kg/ha) and NH4NO3 (0, 50, 75 and 100 kg/ha) was applied in the rhizosphere after 3 and 7 weeks of sowing, referred to as experiment 1 (E1). In another separate experiment (E2), the canola crop was sprayed with four level of GA3 (0, 10, 15, 30 g/ha) and K (0, 2.5, 3.5, 6 g/ha) individually or in combination by using hydraulic spryer, 30 days after sowing (DAS). The data was collected at different growth stages of canola and analyzed statistically. The E1 trail showed that N fortification in the form of NH4NO3 (100 kg/ha) and (NH4)2SO4 (30 kg/ha) had a positive effect on the plant height, number of branches, fruiting zone, seed yield per plant, seed yield per hectare of canola except oil percentage. Moreover, canola plants (E2) also displayed a significant improvement on all studied features with high doses of GA3 (30 g/ha) and K (6 g/ha) individualy and in combined form. The correlation coefficient analysis of (NH4)2SO4 and NH4NO3 was highly significant to plant height, number of branches, fruiting zone, seed yield per plant, seed yield per hectare of canola In a nutshell, compared to both source of N, NH4NO3 was more efficient and readily available source of N. GA3 being a growth elicitor and potassium as a micronutrient serve as potential source to improve yield and to manage nutrient profile of canola. [ABSTRACT FROM AUTHOR]

This article investigates a new autonomous mobile fog computing (MFC) system empowered by multiple unmanned aerial vehicles (UAVs) in order to serve medical Internet of Things devices (MIoTDs) efficiently. The aim of this article is to reduce the energy consumption of the UAVs-empowered MFC system by designing UAVs' trajectories. To construct the trajectories of UAVs, we need to consider not only the order of SPs but also the association among UAVs, SPs, and MIoTDs. The above-mentioned problem is very complicated and is difficult to be handled via applying traditional techniques, as it is NP-hard, nonlinear, non-convex, and mixed-integer. To handle this problem, we propose a novel simulated annealing trajectory optimization algorithm (SATOA), which handles the problem in three phases. First, the deployment (i.e., number and locations) of stop points (SPs) is updated and produced randomly using variable population sizes. Accordingly, MIoTDs are associated with SPs and extra SPs are removed. Finally, a novel simulated annealing algorithm is proposed to optimize UAVs' association with SPs as well as their trajectories. The performance of SATOA is demonstrated by performing various experiments on nine instances with 40 to 200 MIoTDs. The simulation results show that the proposed SATOA outperforms other compared state-of-the-art algorithms in terms of saving energy consumption. [ABSTRACT FROM AUTHOR]

Due to the advancement in technologies and excessive usability of smartphones in various domains (e.g., mobile banking), smartphones became more prone to malicious attacks.Typing on the soft keyboard of a smartphone produces different vibrations, which can be abused to recognize the keys being pressed, hence, facilitating side-channel attacks. In this work, we develop and evaluate AlphaLogger- an Android-based application that infers the alphabet keys being typed on a soft keyboard. AlphaLogger runs in the background and collects data at a frequency of 10Hz/sec from the smartphone hardware sensors (accelerometer, gyroscope and magnetometer) to accurately infer the keystrokes being typed on the soft keyboard of all other applications running in the foreground. We show a performance analysis of the different combinations of sensors. A thorough evaluation demonstrates that keystrokes can be inferred with an accuracy of 90.2% using accelerometer, gyroscope, and magnetometer. [ABSTRACT FROM AUTHOR]

Deep exploration of histone occupancy and covalent post-translational modifications (e.g., acetylation, methylation) is essential to decode gene expression regulation, chromosome packaging, DNA damage, and transcriptional activation. Existing computational approaches are unable to precisely predict histone occupancy and modifications mainly due to the use of sub-optimal statistical representation of histone sequences. For the establishment of an improved histone occupancy and modification landscape for multiple histone markers, the paper in hand presents an end-to-end computational multi-paradigm framework "Histone-Net". To learn local and global residue context aware sequence representation, Histone-Net generates unsupervised higher order residue embeddings (DNA2Vec) and presents a different application of language modelling, where it encapsulates histone occupancy and modification information while generating higher order residue embeddings (SuperDNA2Vec) in a supervised manner. We perform an intrinsic and extrinsic evaluation of both presented distributed representation learning schemes. A comprehensive empirical evaluation of Histone-Net over ten benchmark histone markers data sets for three different histone sequence analysis tasks indicates that SuperDNA2Vec sequence representation and softmax classifier-based approach outperforms state-of-the-art approach by an average accuracy of 7%. To eliminate the overhead of training separate binary classifiers for all ten histone markers, Histone-Net is evaluated in multi-label classification paradigm, where it produces decent performance for simultaneous prediction of histone occupancy, acetylation, and methylation. [ABSTRACT FROM AUTHOR]

Background and objective: Interactions of long non-coding ribonucleic acids (lncRNAs) with micro-ribonucleic acids (miRNAs) play an essential role in gene regulation, cellular metabolic, and pathological processes. Existing purely sequence based computational approaches lack robustness and efficiency mainly due to the high length variability of lncRNA sequences. Hence, the prime focus of the current study is to find optimal length trade-offs between highly flexible length lncRNA sequences. Method: The paper at hand performs in-depth exploration of diverse copy padding, sequence truncation approaches, and presents a novel idea of utilizing only subregions of lncRNA sequences to generate fixed-length lncRNA sequences. Furthermore, it presents a novel bag of tricks-based deep learning approach "Bot-Net" which leverages a single layer long-short-term memory network regularized through DropConnect to capture higher order residue dependencies, pooling to retain most salient features, normalization to prevent exploding and vanishing gradient issues, learning rate decay, and dropout to regularize precise neural network for lncRNA–miRNA interaction prediction. Results: BoT-Net outperforms the state-of-the-art lncRNA–miRNA interaction prediction approach by 2%, 8%, and 4% in terms of accuracy, specificity, and matthews correlation coefficient. Furthermore, a case study analysis indicates that BoT-Net also outperforms state-of-the-art lncRNA–protein interaction predictor on a benchmark dataset by accuracy of 10%, sensitivity of 19%, specificity of 6%, precision of 14%, and matthews correlation coefficient of 26%. Conclusion: In the benchmark lncRNA–miRNA interaction prediction dataset, the length of the lncRNA sequence varies from 213 residues to 22,743 residues and in the benchmark lncRNA–protein interaction prediction dataset, lncRNA sequences vary from 15 residues to 1504 residues. For such highly flexible length sequences, fixed length generation using copy padding introduces a significant level of bias which makes a large number of lncRNA sequences very much identical to each other and eventually derail classifier generalizeability. Empirical evaluation reveals that within 50 residues of only the starting region of long lncRNA sequences, a highly informative distribution for lncRNA–miRNA interaction prediction is contained, a crucial finding exploited by the proposed BoT-Net approach to optimize the lncRNA fixed length generation process. Availability: BoT-Net web server can be accessed at https://sds_genetic_analysis.opendfki.de/lncmiRNA/. [ABSTRACT FROM AUTHOR]

Key message: Sustainable control of fall armyworm (FAW) requires implementation of effective integrated pest management (IPM) strategies, with host plant resistance as a key component. Significant opportunities exist for developing and deploying elite maize cultivars with native genetic resistance and/or transgenic resistance for FAW control in both Africa and Asia. The fall armyworm [Spodoptera frugiperda (J.E. Smith); FAW] has emerged as a serious pest since 2016 in Africa, and since 2018 in Asia, affecting the food security and livelihoods of millions of smallholder farmers, especially those growing maize. Sustainable control of FAW requires implementation of integrated pest management strategies, in which host plant resistance is one of the key components. Significant strides have been made in breeding elite maize lines and hybrids with native genetic resistance to FAW in Africa, based on the strong foundation of insect-resistant tropical germplasm developed at the International Maize and Wheat Improvement Center, Mexico. These efforts are further intensified to develop and deploy elite maize cultivars with native FAW tolerance/resistance and farmer-preferred traits suitable for diverse agro-ecologies in Africa and Asia. Independently, genetically modified Bt maize with resistance to FAW is already commercialized in South Africa, and in a few countries in Asia (Philippines and Vietnam), while efforts are being made to commercialize Bt maize events in additional countries in both Africa and Asia. In countries where Bt maize is commercialized, it is important to implement a robust insect resistance management strategy. Combinations of native genetic resistance and Bt maize also need to be explored as a path to more effective and sustainable host plant resistance options. We also highlight the critical gaps and priorities for host plant resistance research and development in maize, particularly in the context of sustainable FAW management in Africa and Asia. [ABSTRACT FROM AUTHOR]

ZnO-CuO nanocomposites ((CuO)x(ZnO)1-x) have been prepared by the facile co-precipitation method. The prepared nanocomposites have been studied using XRD, SEM, UV–Vis, FTIR, Raman, and dielectric measurements. XRD characterization depicts the formation of hexagonal and monoclinic structures of ZnO and CuO, respectively. SEM studies of the nanocomposites show that ZnO has sheet-like design while CuO has rod like morphology. The FTIR and Raman studies reveal the presence of characteristic starching and bending modes of ZnO and CuO nanostructures with plenty of surface-related defects in the nanocomposites, respectively. Interestingly, the optical band gap of the nanocomposite is red shifted towards the visible range of the solar spectrum with the increased loading of CuO over the surface of the ZnO nanosheets. The enhancement in the dielectric characteristics has also been observed, which may be due to the increased number of interfaces and electron exchange between Zn2+ and Cu+2 ions in the nanocomposites. Furthermore, the photodegradation capability of the prepared nanocomposites has been examined towards methyl orange (MO), an azo dye with a highly stable and toxic nature, mostly found in wastewater from textile industries. The results show that the nanocomposites exhibit appreciable photocatalytic activity (71% dye decomposition in 180 min) under visible light irradiation, which can be ascribed to the enhanced visible light response of the nanocomposites, high specific surface area of the composite, and optimized pH conditions of dye solution. The prepared nanocomposite may find applications in barrier layer capacitors and wastewater treatment technologies. [ABSTRACT FROM AUTHOR]

This paper presents a multi-unmanned aerial vehicle (UAV)-assisted mobile edge computing system, where multiple UAVs are used to serve mobile users. We aim to minimize the overall energy consumption of the system by planning the trajectories of UAVs. To plan the trajectories of UAVs, we need to consider the deployment of hovering points (HPs) of UAVs, their association with UAVs, and their order for each UAV. Therefore, the problem is very complicated, as it is non-convex, nonlinear, NP-hard, and mixed-integer. To solve the problem, this paper proposed an evolutionary trajectory planning algorithm (ETPA), which comprises four phases. In the first phase, a variable-length GA is adopted to update the deployments of HPs for UAVs. Accordingly, redundant HPs are removed by the remove operator. Subsequently, a differential evolution clustering algorithm is adopted to cluster HPs into different clusters without knowing the number of HPs in advance. Finally, a GA is proposed to construct the order of HPs for UAVs. The experimental results on a set of eight instances show that the proposed ETPA outperforms other compared algorithms in terms of the energy consumption of the system. [ABSTRACT FROM AUTHOR]

To solve constrained optimization problems (COPs), teaching learning-based optimization (TLBO) has been used in this study as a baseline algorithm. Different constraint handling techniques (CHTs) are incorporated in the framework of TLBO. The superiority of feasibility (SF) is one of the most commonly used and much effective CHTs with various decisive factors. The most commonly utilized decision-making factors in SF are a number of constraints violated (NCV) and weighted mean (WM) values for comparing solutions. In this paper, SF based on a number of constraints violated (NCVSF) and weighted mean (WMSF) is incorporated in the framework of TLBO and applied on CEC-2006 constrained benchmark functions. The use of a single factor for making the decision of the winner might be not a good idea. The combined use of NCV and WM factors in hybrid superiority of feasibility (HSF) has shown the dominating role of NCV over WM. We have employed NCVSF, WMSF, and HSF in the TLBO framework and suggested three constrained versions, namely NCVSF-TLBO, WMSF-TLBO, and HSF-TLBO. The performance of the proposed algorithms is evaluated upon CEC-2006 constrained benchmark functions. Among them, HSF-TLBO has shown better performance on most of the used constrained optimization problems in terms of proximity and diversity. [ABSTRACT FROM AUTHOR]

With the popularity of autonomous vehicles and the rapid development of intelligent transportation, the application scenarios for detecting pedestrians in everyday life are becoming more and more widespread, with high and high application value. Pedestrian detection is the basis of many human-based tasks, including speed tracking, pedestrian motion detection, automatic pedestrian recognition, and appropriate response measures, or rejecting true false pedestrian detection. Various researchers have done a lot of research in this area, but there are still many errors in the correct identification of rejecting true false pedestrians. This article focuses on the design and implementation of real pedestrian discovery using deep learning technology to identify pedestrian rejections. In this work, our goal is to estimate the achievement of the current 2D detection system with a 3D Convolutional Neural Network on the issues of rejecting true false pedestrians using images obtained from the car's on-board cameras and light detection and ranging (LiDAR) sensors. We evaluate the single-phase (YOLOv3 models) and two-phase (Faster R-CNN) deep learning meta-structure under distinct image resolutions and attribute extractors (MobileNet). To resolve this issue, it is urge to apply a data augmentation approach to improve the execution of the framework. To observe the performance, the implemented methods are applied to recent datasets. The experimental assessment shows that the proposed method/algorithm enhances the accuracy of detection of true and false pedestrians, and still undergoes the real-time demands. [ABSTRACT FROM AUTHOR]

Electrocatalytic water splitting is limited by kinetics-sluggish oxygen evolution, in which the activity of catalysts depends on their electronic structure. However, the influence of electron spin polarization on catalytic activity is ambiguous. Herein, we successfully regulate the spin polarization of Co3O4 catalysts by tuning the concentration of cobalt defects from 0.8 to 14.5%. X-ray absorption spectroscopy spectra and density functional theory calculations confirm that the spin polarization of Co3O4 is positively correlated with the concentration of cobalt defects. Importantly, the enhanced spin polarization can increase hydroxyl group absorption to significantly decrease the Gibbs free energy change value of the OER rate-determining step and regulate the spin polarization of oxygen species through a spin electron-exchange process to easily produce triplet-state O2, which can obviously increase electrocatalytic OER activity. In specific, Co3O4-50 with 14.5% cobalt defects exhibits the highest spin polarization and shows the best normalized OER activity. This work provides an important strategy to increase the water splitting activity of electrocatalysts via the rational regulation of electron spin polarization. [ABSTRACT FROM AUTHOR]

Devices belonging to the realm of edge Internet of Things (IoT) are becoming highly susceptible to intrusion attacks. The large-scale development in edge IoT, ease of availability, and affordability have drastically increased its usage in the real world. The business market revolves around producing better, innovative, and appealing products every day. However, security is often left unchecked to achieve these standards. Therefore, vulnerabilities present in these devices make them susceptible to various intrusion attacks. We devised a model named DF-IDS for detecting intrusions in IoT traffic. DF-IDS consists of two main phases: In the 1 st phase, it comparatively selects the best features from the feature matrix using SpiderMonkey (SM), principle component analysis (PCA), information gain (IG), and correlation attribute evaluation (CAE). In the 2 nd phase, these features along with assigned labels are used to train a deep neural network for intrusion detection. DF-IDS achieves an accuracy of 99.23% with an F1-score of 99.27%. It shows improvement not only in accuracy but also in F1 score as compared to the other comparative models and existing studies. [ABSTRACT FROM AUTHOR]

The present article discusses the boundedness criteria for the fractional Hardy operators on weighted variable exponent Morrey–Herz spaces M K ˙ q , p (⋅) α (⋅) , λ (w) . [ABSTRACT FROM AUTHOR]

Siwalik Group or Siwaliks is famous as a stratigraphic unit containing numerous fossiliferous pockets for vertebrate faunas of Neogene ages. The Middle Siwalik Subgroup (Late Miocene–early Pliocene of Neogene) comprises Nagri and Dhok Pathan formations. Tetraconodon magnus and Sivachoerus prior remains are extremely rare in the Siwaliks, and these tetraconodonts are ascribed in this article from Middle Siwalik deposits of the Late Miocene age. Tetraconodon is considered to have occurred in the Nagri Formation of the Siwalik Group. However, the specimens described here, recovered from the Dhok Pathan Formation (8.2 Ma), are important in relieving the long-held notion of the previous researchers that these are found only from the Nagri Formation (11.5–9 Ma). Similarly, Sivachoerus prior appeared earlier (~ 6.6 Ma) than it was thought previously (5–3 Ma). So, the present studies extended the geological ranges of these Siwalik tetraconodonts. [ABSTRACT FROM AUTHOR]

Wireless sensor network (WSN) is a core component of multiple smart city applications. Utilizing the same WSN for multiple applications helps reduce cost. However, satisfying quality of service requirements of these independent applications is very challenging. For instance, uncoordinated path selection for data dissemination may result in the formation of queues in the WSN violating end-to-end delay requirements of several applications. To this end, we propose a software defined network based approach to ensure satisfaction of individual delay constraints while ensuring minimal increase in the average queue length of the WSN. The approach utilizes a logically centralized controller to generate a comprehensive view of the whole network in a scalable manner. We develop several graph theoretic algorithms to reduce the number of nodes and edges in the communication paths and to identify the most suitable communication paths for each application so that end-to-end delays are minimized. The evaluations demonstrate that our approach performs up to 34% better than existing works and up to 14% worst in comparison to the optimal solution for different topologies, network sizes, and end-to-end delay requirements. Moreover, performance of the proposed graph theoretic algorithms is also measured w.r.t. time. [ABSTRACT FROM AUTHOR]

New remains of Gazella from the Chinji Formation of the Lower Siwalik Subgroup of Pakistan are described here. The studied material was recovered from Dhok Bun Amir Khatoon, Bhilomar and Jand, district Chakwal, Punjab, Pakistan. The material comprises 26 specimens including maxillary and mandibular fragments, as well as upper and lower isolated teeth. The metric and morphological features like narrow styles and external folds, absence of entostyle in the upper molars, the lower molars with goat folds and a small ectostylid support their attribution to Gazella sp. This study also adds comments on the dental morphology of the Middle Miocene Gazella sp. from Pakistan. A busy paleoenvironment is inferred in the Middle Miocene of the Lower Siwalik Subgroup of Pakistan. [ABSTRACT FROM AUTHOR]

Terms that occur too frequently or rarely in various texts are not useful for text classification. Pruning can be used to remove such irrelevant terms reducing the dimensionality of the feature space and, thus making feature selection more efficient and effective. Normally, pruning is achieved by manually setting threshold values. However, incorrect threshold values can result in the loss of many useful terms or retention of irrelevant ones. Existing feature ranking metrics can assign higher ranks to these irrelevant terms, thus degrading the performance of a text classifier. In this paper, we propose a new feature ranking metric, which can select the most useful terms in the presence of these too frequently and rarely occurring terms, thus eliminating the need for pruning these terms. To investigate the usefulness of the proposed metric, we compare it against seven well-known feature selection metrics on five data sets namely Reuters-21578 (re0, re1, r8) and WebACE (k1a, k1b) using multinomial naive Bayes and support vector machines classifiers. Our results based on a paired t-test show that the performance of our metric is statistically significant than that of the other seven metrics. [ABSTRACT FROM AUTHOR]

Malicious Uniform Resource Locators (URLs) embedded in emails or Twitter posts have been used as weapons for luring susceptible Internet users into executing malicious content leading to compromised systems, scams, and a multitude of cyber-attacks. These attacks can potentially might cause damages ranging from fraud to massive data breaches resulting in huge financial losses. This paper proposes a hybrid deep-learning approach named URLdeepDetect for time-of-click URL analysis and classification to detect malicious URLs. URLdeepDetect analyzes semantic and lexical features of a URL by applying various techniques, including semantic vector models and URL encryption to determine a given URL as either malicious or benign. URLdeepDetect uses supervised and unsupervised mechanisms in the form of LSTM (Long Short-Term Memory) and k-means clustering for URL classification. URLdeepDetect achieves accuracy of 98.3% and 99.7% with LSTM and k-means clustering, respectively. [ABSTRACT FROM AUTHOR]

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

Internet of Things (IoT) comprises a plethora of networks of things that could be exemplified with devices like sensors and actuators. Although IoT is expected to play a major role in the development and provisioning of smart applications, things in IoT are still confined into silos making their collaboration in the context of complex composition scenarios quite challenging. This paper addresses the silo concern by allowing things to take part in orchestration- and choreography-based composition scenarios. Orchestration relies on a central component to regulate the progress of things' operations, while choreography relies on peer-to-peer interactions to guide this progress. Things' operations are specified according to a set of transactional properties that dictate when an operation's execution outcome could be canceled despite the success or retried until success. This execution consumes resources that are also specified according to a set of availability properties. Not all resources are constantly available and hence could impact the completion of composition scenarios. To demonstrate the technical feasibility of orchestration- and choreography-based composition of transactional things, 2 scenarios consisting of different use-cases are implemented allowing to benchmark orchestration versus choreography at design-time and run-time. Each use-case mixes transactional properties and availability properties, which ensures good coverage of the composition scenarios in which thing could participate. [ABSTRACT FROM AUTHOR]

The current User Datagram Protocol (UDP) causes unfairness and bufferbloats to delay sensitive applications due to the uncontrolled congestion and monopolization of available bandwidth.This causes call drops and frequent communication/connection loss in delay sensitive applications such as VoIP. We present a Responsive Control Protocol using Bottleneck Bandwidth and Round trip propagation time (RCP-BBR) as an alternate solution to UDP. RCP-BBR achieves low latency, high throughput, and low call drops ratio by efficiently customizing Transmission Control Protocol (TCP) Bottleneck Bandwidth and Round-trip propagation time (TCP-BBR) congestion control. We conducted comprehensive experiments, and the results show that proposed protocol achieves better throughput over UDP in stable networks. Moreover, in unstable and long-distanced networks, RCP-BBR achieved smaller queues in deep buffers and lower delays as compared to UDP, which performed poorly by keeping delays above the call drop threshold. [ABSTRACT FROM AUTHOR]

In the context of an Internet-of-Things (IoT) ecosystem, this paper discusses two necessary stages for managing federations of things. The first stage defines things in terms of duties and non-functional properties that define the quality of these duties. And, the second stage uses these properties to assign appropriate things to future federations. Specialized into ad hoc and planned, federations are expected to satisfy needs and requirements of real-life situations like traffic control that arise at run-time. A set of experiments using a mix of real and simulated datasets, demonstrate the technical doability of thing assignment to federations and are presented in the paper, as well. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) is an emerging paradigm branded by heterogeneous technologies composed of smart ubiquitous objects that are seamlessly connected to the Internet. These objects are often deployed in open environments to provide innovative services in various application domains such as smart cities, smart health, and smart communities. These IoT devices produce a massive amount of confidentiality and security-sensitive data. Thus, security of these devices is very important in order to ensure the safety and effectiveness of the system. In this paper, a decentralized authentication and access control mechanism is proposed for lightweight IoT devices and is applicable to a large number of scenarios. The mechanism is based on the technology of the fog computing and the concept of the public blockchain. The results gained from the experiments demonstrate a superior performance of the proposed mechanism when compared to a state-of-the-art blockchain-based authentication technique. [ABSTRACT FROM AUTHOR]

The natural red pigment compound of tomato is lycopene which is responsible for red pigment in some fruits and vegetables. We have determined the lycopene concentration from the measured absorbance in fresh tomatoes with and without peel with different concentrations using spectrophotometric method. The measured lycopene contents vary from 3 to 33.5 mg/kg for spectral range of 444 to 560 nm. For qualitative analyses, optical coherence tomography (OCT) was utilized for vital visualization of plant tissues in tomato (Lycopersicon esculentum Mill). The obtained OCT slices permitted the morphological and practical condition of plant tissues to be surveyed in vitro. The OCT technique permitted plant morphological structures to be related to more prominent confidence. The morphological and practical condition of tissues can be checked with a time resolution of 1–4 s in intact plants, without expelling them from the habitat. [ABSTRACT FROM AUTHOR]

This paper presents an approach for allowing the transparent co-existence of citizens and IoT-compliant things in smart cities. Considering the particularities of each, the approach embraces two concepts known as social machines and data artifacts. On the one hand, social machines act as wrappers over applications (e.g., social media) that allow citizens and things to have an active role in their cities by reporting events of common interest to the population, for example. On the other hand, data artifacts abstract citizens' and things' contributions in terms of who has done what, when, where, and why. For successful smart cities, the approach relies on the willingness and engagement of both citizens and things. Smart cities' initiatives are embraced and not imposed. A case study along with a testbed that uses a real dataset about car-traffic accident in a state in Brazil demonstrate the technical doability and scalability of the approach. The evaluation consists of assessing the time to drill into the different generated data artifacts prior to generating useful details for decision makers. [ABSTRACT FROM AUTHOR]

Multishot Magnetic Resonance Imaging (MRI) is a promising data acquisition technique that can produce a high-resolution image with relatively less data acquisition time than the standard spin echo. The downside of multishot MRI is that it is very sensitive to subject motion and even small levels of motion during the scan can produce artifacts in the final magnetic resonance (MR) image, which may result in a misdiagnosis. Numerous efforts have focused on addressing this issue; however, all of these proposals are limited in terms of how much motion they can correct and require excessive computational time. In this paper, we propose a novel generative adversarial network (GAN)-based conjugate gradient SENSE (CG-SENSE) reconstruction framework for motion correction in multishot MRI. First CG-SENSE reconstruction is employed to reconstruct an image from the motion-corrupted k-space data and then the GAN-based proposed framework is applied to correct the motion artifacts. The proposed method has been rigorously evaluated on synthetically corrupted data on varying degrees of motion, numbers of shots, and encoding trajectories. Our analyses (both quantitative as well as qualitative/visual analysis) establish that the proposed method is robust and reduces several-fold the computational time reported by the current state-of-the-art technique. [ABSTRACT FROM AUTHOR]

Fear memories are critical for survival. Nevertheless, over-generalization of these memories, depicted by a failure to distinguish threats from safe stimuli, is typical in stress-related disorders. Previous studies have supported a protective role of ketamine against stress-induced depressive behavior. However, the effect of ketamine on fear generalization remains unclear. In this study, we investigated the effects of ketamine on fear generalization in a fear-generalized mouse model. The mice were given a single sub-anesthetic dose of ketamine (30 mg/kg, i.p.) 1 h before, 1 week before, immediately after, or 22 h after fear conditioning. The behavioral measure of fear (indicated by freezing level) and synaptic protein expression in the basolateral amygdala (BLA) and inferior-limbic pre-frontal cortex (IL-PFC) of mice were examined. We found that only ketamine administered 22 h after fear conditioning significantly decreased the fear generalization, and the effect was dose-dependent and lasted for at least 2 weeks. The fear-generalized mice showed a lower level of brain-derived neurotrophic factor (BDNF) and a higher level of GluN2B protein in the BLA and IL-PFC, and this was reversed by a single administration of ketamine. Moreover, the GluN2B antagonist ifenprodil decreased the fear generalization when infused into the IL-PFC, but had no effect when infused into the BLA. Infusion of ANA-12 (an antagonist of the BDNF receptor TrkB) into the BLA or IL-PFC blocked the effect of ketamine on fear generalization. These findings support the conclusion that a single dose of ketamine administered 22 h after fear conditioning alleviates the fear memory generalization in mice and the GluN2B-related BDNF signaling pathway plays an important role in the alleviation of fear generalization. [ABSTRACT FROM AUTHOR]

This paper presents the technical, environmental, and economic evaluation of integrating various combinations of renewable energy sources-based systems in the expansion of a district heating and cooling network of a Technology Park near Barcelona in Spain. At present, a combined heat and power plant running on fossil fuels serves the heating, cooling, and electricity demand of the Park. However, this energy demand is expected to increase substantially in the coming years. EnergyPRO software was used to model the energy demand growth till 2030. Validation of the software application was done by making a base model using real plant data from the year 2014. The software was then used to project the energy supply based on three 15-year scenarios, having different combinations of renewable energy technologies, from 2016 until 2030. Primary energy consumption, CO2 emissions, and the net present value obtained in each scenario were used to decide the best combinations of renewable energy sources. The results of the study showed that presently, biomass boilers combined with absorption chillers and supported with solar thermal cooling are the most competitive technologies in comparison to ground source heat pumps for large DHC networks. This is mainly because of the lower primary energy consumption (624,380 MWh/year in 2030 vs. 665,367 MWh/year), higher net present value (NPV) (222 million € vs. 178 million €), and lower CO2 emissions (107,753 tons/year in 2030 vs. 111,166 tons/year) obtained as a result of the simulations. [ABSTRACT FROM AUTHOR]

Giraffokeryx punjabiensis is reported from Dhok Bun Amir Khatoon (DBAK), district Chakwal, Punjab, Pakistan. The newly recovered material includes two maxillary fragments, three mandibular rami, and seven isolated teeth. The well-preserved specimens enhance our knowledge about G. punjabiensis and can be used as a reference material in the future. The existence of G. punjabiensis along with Dorcatherium and suids suggests the presence of wet meadows, floodplain, and open woodland at the time of the early Middle Miocene deposition in Dhok Bun Amir Khatoon, Lower Siwalik Subgroup. A few studied specimens clearly showed signs of enamel hypoplasia as a nutritional and ecological stress indicator for the late Middle Miocene giraffids of Dhok Bun Amir Khatoon. [ABSTRACT FROM AUTHOR]

Term mismatch is a serious problem effecting the performance of information retrieval systems. The problem is more severe in biomedical domain where lot of term variations, abbreviations and synonyms exist. We present query paraphrasing and various term selection combination techniques to overcome this problem. To perform paraphrasing, we use noun words to generate synonyms from Metathesaurus. The new synthesized paraphrases are ranked using statistical information derived from the corpus and relevant documents are retrieved based on top n selected paraphrases. We compare the results with state-of-the-art pseudo relevance feedback based retrieval techniques. In quest of enhancing the results of pseudo relevance feedback approach, we introduce two term selection combination techniques namely Borda Count and Intersection. Surprisingly, combinational techniques performed worse than single term selection techniques. In pseudo relevance feedback approach best algorithms are IG, Rochio and KLD which are performing 33%, 30% and 20% better than other techniques respectively. However, the performance of paraphrasing technique is 20% better than pseudo relevance feedback approach. [ABSTRACT FROM AUTHOR]

Due to a rapidly increasing aging population and its associated challenges in health and social care, Ambient Assistive Living has become the focal point for both researchers and industry alike. The need to manage or even reduce healthcare costs while improving the quality of service is high government agendas. Although, technology has a major role to play in achieving these aspirations, any solution must be designed, implemented and validated using appropriate domain knowledge. In order to overcome these challenges, the remote real-time monitoring of a person's health can be used to identify relapses in conditions, therefore, enabling early intervention. Thus, the development of a smart healthcare monitoring system, which is capable of observing elderly people remotely, is the focus of the research presented in this paper. The technology outlined in this paper focuses on the ability to track a person's physiological data to detect specific disorders which can aid in Early Intervention Practices. This is achieved by accurately processing and analysing the acquired sensory data while transmitting the detection of a disorder to an appropriate career. The finding reveals that the proposed system can improve clinical decision supports while facilitating Early Intervention Practices. Our extensive simulation results indicate a superior performance of the proposed system: low latency (96% of the packets are received with less than 1 millisecond) and low packets-lost (only 2.2% of total packets are dropped). Thus, the system runs efficiently and is cost-effective in terms of data acquisition and manipulation. [ABSTRACT FROM AUTHOR]

Collaborative filtering (CF) is one of the renowned recommendation techniques that can be used for predicting unavailable Quality-of-Service (QoS) values of Web services. Although several CF-based approaches have been proposed in recent years, the accuracy of the QoS values, that these approaches provide, raises some concerns and hence, could undermine the real "quality" of Web services. To address these concerns, context information such as communication-network configuration and user location could be integrated into the process of developing recommendations. Building upon such context information, this paper proposes a CF-based Web services recommendation approach, which incorporates the effect of locations of users, communication-network configurations of users, and Web services run-time environments on the recommendations. To evaluate the accuracy of the recommended Web services based on the defined QoS values, a set of comprehensive experiments are conducted using a real dataset of Web services. The experiments are in line with the importance of integrating context into recommendations. [ABSTRACT FROM AUTHOR]

Leather tanneries which produce significant amounts of solid waste, effluents, and emissions are a major contributor to industrial waste. A cleaner production program was launched by the government of Pakistan to implement the cleaner production measures for tanneries of Sialkot from 1999 to 2005. The main objective of this study is to identify the impact of a cleaner production program, along with other determinants of a cleaner production in the leather industry. The study analyses firm-level primary data collected from leather tanneries in Sialkot. The primary data were collected from tanneries in Sialkot. The econometric analysis is conducted using the Poisson regression analysis. Overall results show that there is no significant impact of cleaner production in 2015, while the panel data results indicate that the effect of cleaner production support by CPC on cleaner production practices diminished once the support came to an end. The other main factor is firm size, which indicates the financial position of the firm; international and regulator pressures are major determinants of the adoption of cleaner production measures. The analysis also indicates that there is higher probability of large firms adopting a cleaner production in comparison with small ones. Export orientation of firms is another important determinant of cleaner production. The enforcement of the environment compliance laws also has positive effect. The compliance with cleaner production measures is quite low, at 6.4 out of 19 cleaner production practice measures. There is a need to adopt measures that are environmentally friendly and are favorable towards both labor health and product quality, which are important for the sustainable growth of the tanning industry. [ABSTRACT FROM AUTHOR]

Organic matter incorporation into soil act as nutrients reservoir which frequently supply minerals and nutrients to plant and optimizing yield. Humic acid act as a bio-stimulants which improve biological and chemo-physical properties of the soil. In this context an experiment was conducted to determine the effect of different levels of nitrogen and humic acid application on the yield and yield component of mungbean at Palato Farm of the University of Agriculture Peshawar, Amir Muhammad Khan Campus Mardan during summer 2012. The experiment was conducted in randomized complete block design (RCBD) with three replications and plot size was kept 3 × 4 m. The experiment was consisted on two levels of nitrogen (20 kg and 40 kg ha–1) and four levels of humic acid (3, 6, 9, and 12 kg ha–1). Mungbean variety "NCM 213" was sown on a well prepared clay loam soil. Effect of nitrogen and humic acid was found significant for number of plants m–2, plant height (cm), number of leaves plant–1, pods length (cm), numbers of seeds pod–1, number of pods plants–1, thousand seed weight, seed yield and for harvest index. Interaction between N and HA was found significant for some of the parameters and non-significant for the rest. Control plots (un fertilized) resulted lower in all measured parameters as compared to the rest plots (fertilized plots). [ABSTRACT FROM AUTHOR]

This work describes the contribution of researchers in the field of the energy from Pakistan in the period 1990-2016. A scientometric approach was applied to analyze the scientific publications in the field using the Scopus Elsevier database. Different aspects of the publications were analyzed, such as publication type, major research areas, journals, citations, authorship pattern, affiliations as well as the keyword occurrence frequency. The present research trends are analyzed and future research directions are outlined. The impact factor, h-index and number of citations were used to investigate the strength of active institutes, authors, and journals in the field of the energy in Pakistan. From 1990 to 2016, 991 articles have been published by 2139 authors from 213 research institutes. The total number of citations and impact factor are 10,287 and 2301 respectively, corresponding to 10 citations per paper and an impact factor of 2.32 per publication. The research articles originate primarily from COMSATS, NUST, PIEAS, and PINSTECH. Pakistan has published 60% of publication with the collaboration of the foreign institutes, mainly from the United States, the United Kingdom, China and Malaysia. The core research activities in the field are mainly focused on resource assessment, energy policy, energy efficiency, feasibility study, energy economics, and performance assessment. The most productive journal, author, institution, are renewable & sustainable energy review, Shahbaz M., and COMSATS, respectively. [ABSTRACT FROM AUTHOR]