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Coal is the cheapest source of energy. It is much cheaper than nuclear and petroleum. However, spontaneous combustion is inherent in coal seams that lead to fires at their place of storage. Therefore, early detection is required to ensure safety. This article aims to develop an intelligent system for the classification of coal seams based on their spontaneous heating on the Internet of Things (IoT) paradigm. It utilizes an artificial intelligence (AI) approach by utilizing a two-stage neural network architecture to render an accurate and robust categorization of coal samples. Three commonly available proximate analysis parameters of the coal samples have been used as input to this system. These inherent parameters are found to be sufficient to classify coal samples into different categories according to their combustion susceptibility. Three publicly available datasets are involved in the assessment purpose. Our proposed method has outperformed previous approaches. The coal seams classification results agree with the real field experiences. [ABSTRACT FROM AUTHOR]

Summary: In this paper, we study a UAV‐based fog or edge computing network in which UAVs and fog/edge nodes work together intelligently to provide numerous benefits in reduced latency, data offloading, storage, coverage, high throughput, fast computation, and rapid responses. In an existing UAV‐based computing network, the users send continuous requests to offload their data from the ground users to UAV–fog nodes and vice versa, which causes high congestion in the whole network. However, the UAV‐based networks for real‐time applications require low‐latency networks during the offloading of large volumes of data. Thus, the QoS is compromised in such networks when communicating in real‐time emergencies. To handle this problem, we aim to minimize the latency during offloading large amounts of data, take less computing time, and provide better throughput. First, this paper proposed the four‐tier architecture of the UAVs–fog collaborative network in which local UAVs and UAV–fog nodes do smart task offloading with low latency. In this network, the UAVs act as a fog server to compute data with the collaboration of local UAVs and offload their data efficiently to the ground devices. Next, we considered the Q‐learning Markov decision process (QLMDP) based on the optimal path to handle the massive data requests from ground devices and optimize the overall delay in the UAV‐based fog computing network. The simulation results show that this proposed collaborative network achieves high throughput, reduces average latency up to 0.2, and takes less computing time compared with UAV‐based networks and UAV‐based MEC networks; thus, it can achieve high QoS. [ABSTRACT FROM AUTHOR]

UAVs or Drones can be used to support wireless communication by acting as flying or mobile Base Stations for the accumulation of the different types of data to train the models. However, in traditional or DL-based UAVs, the raw data is sent from the devices to the centralized server, which causes problems in terms of the privacy of the devices and the UAVs' communication resources or limited processing. Therefore, the issue with DL-based UAVs is that sending the original data to the centralized body raises questions about security and privacy. The transmission of distributed, unprocessed data from the drones to the cloud, including interactive media information data types, requires a significant amount of network bandwidth and more energy, which has an enormous effect on several trade-offs, including communication rates and computation latencies. Data packet loss caused by asynchronous transmission, which doesn't prevent peer-to-peer communication, is a concern with AFL-based UAVs. Therefore, in order to address the aforementioned issues, we have introduced SFL-based UAVs that focus on creating algorithms in which the models simultaneously update the server as they wait for all of the chosen devices to communicate. The proposed framework enables a variety of devices, including mobile and UAV devices, to train or learn their algorithms for machine learning before updating the models and parameters simultaneously to servers or manned aerial data centers for model buildup without transferring their original private information. This decreases packet loss and privacy threats while also enhancing round effectiveness as well as model accuracy. The comparative analysis of AFL and SFL techniques in terms of accuracy, global rounds, and communication rounds are offered. Simulation findings suggest that the proposed methodology improves in terms of global rounds and accuracy. [ABSTRACT FROM AUTHOR]

This paper aims to overcome the problems and limitations of the communications of Unmanned Aerial Vehicles (UAV) by incorporating Intelligent Reflecting Surface (IRS) into UAV for channel tracking. Since IRS may change the propagation environment, is a desirable option for combining with UAV to improve wireless network security. Due to its capacity to proactively configure the wireless environment, IRS technology is a potential one for future communication systems. IRS is able to provide steady communications and serve a greater coverage area by reflecting signals to create virtual LoS routes. Moreover, we develop a federated learning-based channel tracking technique in which federated learning is used to determine the security and pre-estimation constituent. In addition, for channel tracking, Long Short-Term Memory (LSTM) is developed. Due to their ability to understand long-term connections between data time steps, LSTMs are frequently used to learn, analyze, and classify sequential data. [ABSTRACT FROM AUTHOR]

Summary: UAVs are capable of providing significant potential to IoT devices through sensors, cameras, GPS systems, and so forth. Therefore, the smart UAV‐IoT collaborative system has become a current hot research topic. However, other concerns require in‐depth investigation and study, such as resource allocation, security, privacy preservation, trajectory optimization, intelligent decision‐making, energy harvesting, and so forth. Here, we suggest a task‐scheduling method that splits IoT devices into distinct clusters based on physical proximity and saves time and energy. Cluster heads can apply an auto regressive moving average (ARMA) model to predict intelligently the timestamp of the arrival of the next task and associated estimated payments. Based on the overall expected payment, a cluster head can smartly advise the UAV about its time of next arrival. According to the findings of the simulation, the proposed ETTS algorithm significantly outperforms Task TSIE and TDMA‐WS in terms of energy use (67%) and delays (36%). [ABSTRACT FROM AUTHOR]

Summary: A drone or unmanned aerial vehicles (UAVs) is becoming a trending area for researchers worldwide. UAV's contribution is increasing in day‐to‐day life, whether it is in a military zone, disaster management, healthcare sector, smart cities, Internet of Things (IoT), urban air mobility, and many more. In contrast, UAV's limited computational capability and low‐energy sources pose significant challenges for real‐time data processing, storage, networking, and security that are critical in emergencies such as floods, earthquakes, and cyclones. UAVs are rapidly used to satisfy user requirements as well as services. As the demand for UAVs aided heterogeneous wireless networks increases in critical emergencies, fog computing serves several benefits to fulfill users' demands in terms of low latency, support, data storage, mobility, availability, scalability, and so on. This study aims to present a comprehensive study with their technical aspects for understanding fog computing, security issues, privacy concerns, and risks, along with its solutions. This paper suggests the collaboration of UAV‐Fog architecture based on the four‐tier network consisting of smart things, local UAVs, UAV‐Fog, and cloud server, to control UAV's data and also described some of the security issues faced by this cloud infrastructure. Further, this research article also sheds new light on some scenarios of UAV‐Fog for such deployments, applications, opportunities, challenges, and their major security threats and their countermeasures. Afterward, we design taxonomy of the collaboration of UAV‐Fog with their respective approaches. [ABSTRACT FROM AUTHOR]

One common genetic alteration in cancer is gene fusion resulting from chromosomal translocations. The mechanisms that create such oncogenic fusion genes are not well understood. Previously, we provided the direct evidence that expression of a designed chimeric RNA can drive the formation of TMPRSS2-ERG gene fusion. Central to this RNA-mediated gene fusion mechanism is a proposed three-way junction formed by RNA/DNA hybrid and the intergenic DNA stem formed by target genes. In this study, we determined the important parameters for chimeric RNA-mediated gene fusion using TMPRSS2-ERG fusion gene as the model. Our results indicate that both the chimeric RNA lengths and the sizes of unpaired bulges play important roles in inducing TMPRSS2-ERG gene fusion. The optimal length of unpaired bulges was about 35 nt, while the optimal chimeric RNA length was about 50 nt for targeting. These observations were consistent regardless of the target locations within TMPRSS2 and ERG genes. These empirically determined parameters provide important insight for searching cellular RNAs that may initiate oncogenic fusion genes. The knowledge could also facilitate the development of useful genomic technology for manipulating mammalian genomes. [ABSTRACT FROM AUTHOR]

Oncogenic fusion genes as the result of chromosomal rearrangements are important for understanding genome instability in cancer cells and developing useful cancer therapies. To date, the mechanisms that create such oncogenic fusion genes are poorly understood. Previously we reported an unappreciated RNA-driven mechanism in human prostate cells in which the expression of chimeric RNA induces specified gene fusions in a sequence-dependent manner. One fundamental question yet to be addressed is whether such RNA-driven gene fusion mechanism is generalizable, or rather, a special case restricted to prostate cells. In this report, we demonstrated that the expression of designed chimeric RNAs in human endometrial stromal cells leads to the formation of JAZF1-SUZ12, a cancer fusion gene commonly found in low-grade endometrial stromal sarcomas. The process is specified by the sequence of chimeric RNA involved and inhibited by estrogen or progesterone. Furthermore, it is the antisense rather than sense chimeric RNAs that effectively drive JAZF1-SUZ12 gene fusion. The induced fusion gene is validated both at the RNA and the genomic DNA level. The ability of designed chimeric RNAs to drive and recapitulate the formation of JAZF1-SUZ12 gene fusion in endometrial cells represents another independent case of RNA-driven gene fusion, suggesting that RNA-driven genomic recombination is a permissible mechanism in mammalian cells. The results could have fundamental implications in the role of RNA in genome stability, and provide important insight in early disease mechanisms related to the formation of cancer fusion genes. Author summary: Fusion genes resulting from chromosomal translocations are important for understanding cancer mechanisms and developing anti-cancer therapies. Fusion gene are presumed to occur prior to fusion RNA expression. However, studies have reported the presence of fusion RNAs in individuals who were negative for chromosomal translocations. The observation, that fusion RNA could be present prior to fusion gene formation, raises the provocative hypothesis that fusion RNA, or any cellular RNA with sequence compositions resembling that of fusion RNA, could act as a template to mediate genomic rearrangement which leads to the final gene fusion. In this report, we demonstrated that the expression of designed chimeric RNAs in human endometrial stromal cells leads to the formation of JAZF1-SUZ12, a cancer fusion gene found in endometrial stromal sarcomas. The process is specified by the sequence of chimeric RNA involved and inhibited by estrogen or progesterone. Furthermore, it is the antisense rather than sense chimeric RNAs that effectively drive JAZF1-SUZ12 gene fusion. The results could have fundamental implications in the role of RNA in mammalian genome stability, provide important insight in early disease mechanism, as well as developing gene editing technology via mechanisms native to mammalian cells. [ABSTRACT FROM AUTHOR]

Mobile ad hoc network (MANET) is an infrastructure-less, self-motivated, arbitrary, self-configuring, rapidly changing, multi-hop network that is self-possessing wireless bandwidth-conscious links without centrally managed router support. In such a network, wireless media is easy to snoop. It is firm to the surety to access any node, easier to insertion of bad elements or attackers for malicious activities in the network. Therefore, security issues become one of the significant considerations for such kind of networks. The deployment of an effective intrusion detection system is important in order to provide protection against various attacks. In this paper, a Digitally Signed Secure Acknowledgement Method (DSSAM) with the use of the RSA digital signature has been proposed and simulated. Three different parameters are considered, namely secure acknowledgment, node authentication, and packet authentication for study. This article observes the DSSAM performance and compares it with two existing standard methods, namely Watchdog and 2-ACK under standard Dynamic Source Routing (DSR) routing environment. In the end, it is noticed that the rate of detection of malicious behaviour is better in the case of the proposed method. However, associated overheads are high. A trade-off between performance and overhead has been considered. [ABSTRACT FROM AUTHOR]

Biogas, which is a renewable alternative fuel, has high antiknocking properties with the potential to substitute fossil fuels in internal combustion engines. In this study, performance characteristics of a spark-ignition (SI) engine operated under methane (baseline case) and biogas are compared at the compression ratio (CR) of 8.5:1. Subsequently, the effect of CR on operational limits, performance, combustion, and emission characteristics of the engine fueled with biogas is evaluated. A variable compression ratio, spark-ignition engine was operated at various CRs of 8.5:1, 10:1, 11:1, 13:1, and 15:1 over a wide range of operating loads at 1500rpm. Results showed that the operating range of the engine at 8.5:1 CR reduced when biogas was utilized in the engine instead of methane. However, the operating range of the engine for biogas extended with an increase in CR--an increase from 9.6N-m-16.5 N-m to 2.8N-m-15.1 N-m was observed when CR was increased from 8.5:1 to 15:1. The brake thermal efficiency improved from 13.7% to 16.3%, and the coefficient of variation (COV) of indicated mean effective pressure (IMEP) reduced from 12.7% to 1.52% when CR was increased from 8.5:1 to 15:1 at 8N-m load. The emission level of carbon dioxide was decreased with an increase in CR due to an improvement in the thermal efficiency and the combustion process. [ABSTRACT FROM AUTHOR]

Biogas is a promising alternative fuel to reduce the consumption of petroleum-based fuels in internal combustion (IC) engines. In this work, the effect of various biogas compositions on the performance, combustion, and emission characteristics of a spark-ignition (SI) engine is investigated. Additionally, the effect of Wobbe index (WI) of various fuel compositions was also evaluated on the operational limits of the engine. While considering a wide range of biogas compositions (including bio-methane), the percentage of carbon dioxide (CO2) (in a blend of methane and CO2) was increased from 0 to 50% (by volume). A single-cylinder, water-cooled, SI engine was operated at 1500 rpm over a wide range of operating loads with compression ratio of 8.5:1. With the increase in WI of the fuel, both low (limited by coefficient of variation (COV) of indicated mean effective pressure (IMEP)) and high (limited by pre-ignition) operating loads were decreased; however, it was found that the overall operating range was increased. Results also showed that for a given operating load, with the increase of CO2 percentage in the fuel, the brake thermal efficiency was decreased, and the flame initiation and combustion durations were increased. The brake thermal efficiency was decreased from 16.8% to 13.7%, when CO2 was increased from 0% to 40% in methane-CO2 mixture at 8 N·m load. Concerning to emissions, a considerable decrease was noted in nitric oxide, whereas hydrocarbon, carbon monoxide and carbon dioxide emissions were increased, with the increase in CO2 percentage. [ABSTRACT FROM AUTHOR]

Fuel injection timing for early injection mode at higher loads in a gasoline direct-injection engine is critical for mixture formation, combustion process and emissions. In the present work, the effect of fuel injection timing on cycle-to-cycle spray variations and macroscopic spray characteristics is quantitatively analyzed at wide-open throttle condition with stoichiometric air–fuel ratio. A Mie scattering technique was employed to visualize the liquid phase of the fuel dispersion with early injection timings at 180° before top dead center (BTDC), 210° BTDC and 240° BTDC of compression with fuel pressure of 5 MPa. A quantitative analysis using proper orthogonal decomposition revealed that cycle-to-cycle spray variations were not significant. Comparing different injection timings, results, however, showed that spray variations were slightly higher at injection timing of 240° BTDC compared to injection timings at 210° BTDC and 180° BTDC. Concerning macroscopic characteristics, it was found that the spray tip penetration length was longer at 210° BTDC compared to injection timings at 240° BTDC and 180° BTDC. It was also observed that the spray areas were comparable for different injection timings until about 0.9 ms after the start of injection, but it was enhanced at later time stamps for injection at 240° BTDC compared to injections at 210° BTDC and 180° BTDC. [ABSTRACT FROM AUTHOR]

One of the hallmarks of cancer is the formation of oncogenic fusion genes as a result of chromosomal translocations. Fusion genes are presumed to form before fusion RNA expression. However, studies have reported the presence of fusion RNAs in individualswho were negative for chromosomal translocations. These observations give rise to "the cart before the horse" hypothesis, inwhich the genesis of a fusion RNA precedes the fusion gene. The fusion RNA then guides the genomic rearrangements that ultimately result in a gene fusion. However, RNA-mediated genomic rearrangements in mammalian cells have never been demonstrated. Here we provide evidence that expression of a chimeric RNA drives formation of a specified gene fusion via genomic rearrangement in mammalian cells. The process is: (i) specified by the sequence of chimeric RNA involved, (ii) facilitated by physiological hormone levels, (iii) permissible regardless of intrachromosomal (TMPRSS2-ERG) or interchromosomal (TMPRSS2-ETV1) fusion, and (iv) can occur in normal cells before malignant transformation. We demonstrate that, contrary to "the cart before the horse" model, it is the antisense rather than sense chimeric RNAs that effectively drive gene fusion, and that this disparity can be explained by transcriptional conflict. Furthermore, we identified an endogenous RNA AZI1 that functions as the "initiator" RNA to induce TMPRSS2-ERG fusion. RNA-driven gene fusion demonstrated in this report provides important insight in early disease mechanisms, and could have fundamental implications in the biology of mammalian genome stability, as well as gene-editing technology via mechanisms native to mammalian cells. [ABSTRACT FROM AUTHOR]

The certification of wine quality is essential to the wine industry. The main goal of this work is to develop a machine learning model to forecast wine quality using the dataset. We utilised samples from the red wine dataset (RWD) with eleven distinct physiochemical properties. With the initial RWD, five machine learning (ML) models were trained and put to the test. The most accurate algorithms are Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Using these two ML approaches, the top three features from a total of eleven features are chosen, and ML analysis is performed on the remaining features. Several graphs are employed to demonstrate the feature importance based on the XGBoost model and RF. Wine quality was predicted using relevant characteristics, often referred to as fundamental elements, that were shown to be essential during the feature selection procedure. When trained and tested without feature selection, with feature selection (RF), and with key attributes, the XGBoost classifier displayed 100% accuracy. In the presence of essential variables, the RF classifier performed better. Finally, to assess the precision of their predictions, the authors trained an RF classifier, validated it, and changed its hyperparameters. To address collinearity and decrease the quantity of predictors without sacrificing model accuracy, we have also used cluster analysis. [ABSTRACT FROM AUTHOR]

The parasite Trypanosoma brucei is the causative agent of African sleeping sickness and is known for its unique RNA processing mechanisms that are common to all the kinetoplastidea including Leishmania and Trypanosoma cruzi. Trypanosomes possess two canonical RNA poly (A) polymerases (PAPs) termed PAP1 and PAP2. PAP1 is encoded by one of the only two genes harboring cis -spliced introns in this organism, and its function is currently unknown. In trypanosomes, all mRNAs, and non-coding RNAs such as small nucleolar RNAs (snoRNAs) and long non-coding RNAs (lncRNAs), undergo trans- splicing and polyadenylation. Here, we show that the function of PAP1, which is located in the nucleus, is to polyadenylate non-coding RNAs, which undergo trans- splicing and polyadenylation. Major substrates of PAP1 are the snoRNAs and lncRNAs. Under the silencing of either PAP1 or PAP2, the level of snoRNAs is reduced. The dual polyadenylation of snoRNA intermediates is carried out by both PAP2 and PAP1 and requires the factors essential for the polyadenylation of mRNAs. The dual polyadenylation of the precursor snoRNAs by PAPs may function to recruit the machinery essential for snoRNA processing. [ABSTRACT FROM AUTHOR]

The certification of wine quality is essential to the wine industry. The main goal of this work is to develop a machine learning model to forecast wine quality using the dataset. We utilised samples from the red wine dataset (RWD) with eleven distinct physiochemical properties. With the initial RWD, five machine learning (ML) models were trained and put to the test. The most accurate algorithms are Random Forest (RF) and Extreme Gradient Boosting (XGBoost). Using these two ML approaches, the top three features from a total of eleven features are chosen, and ML analysis is performed on the remaining features. Several graphs are employed to demonstrate the feature importance based on the XGBoost model and RF. Wine quality was predicted using relevant characteristics, often referred to as fundamental elements, that were shown to be essential during the feature selection procedure. When trained and tested without feature selection, with feature selection (RF), and with key attributes, the XGBoost classifier displayed 100% accuracy. In the presence of essential variables, the RF classifier performed better. Finally, to assess the precision of their predictions, the authors trained an RF classifier, validated it, and changed its hyperparameters. To address collinearity and decrease the quantity of predictors without sacrificing model accuracy, we have also used cluster analysis. [ABSTRACT FROM AUTHOR]

Over the past few years, the awful COVID-19 pandemic effect has become a lethal sickness. The processing of the gathered samples requires extra time due to the use of medical diagnostic equipment, methodologies, and clinical testing procedures for the early diagnosis of infected individuals. An innovative multimodal paradigm for the early diagnosis and precise categorization of COVID-19 is put up as a solution to this issue. To extract distinguishing features from the prepared chest X-ray picture and cough (audio) database, chest X-ray-based and cough-based model are used here. Other public chest X-ray image datasets, and the Coswara cough (audio) dataset containing 92 COVID-19 positive, and 1079 healthy subjects (people) using the deep Uniform-Net, and Convolutional Neural Network (CNN). The weighted sum-rule fusion method and ensemble deep learning algorithms are utilized to further combine the extracted features. For the early diagnosis of patients, the framework offers an accuracy of 98.67%. [Display omitted] • A novel multimodal framework is proposed for the early diagnosis and accurate classification of COVID-19. • The extracted features are fused using the weighted sum-rule fusion technique for early diagnosis and accurate prediction. • The framework provided an accuracy of 98.67% (X-ray based) and 86.53% (cough based diagnosis) for early diagnosis of patients. [ABSTRACT FROM AUTHOR]

Highlights: [•] The Trypanosoma brucei telomerase RNA is a C/D snoRNA that is localized near the nucleolus. [•] TER silencing causes shortening of the chromosomes but no growth arrest. [•] pre-TER is trans-spliced linking trans-splicing to the processing of non-coding RNAs. [Copyright &y& Elsevier]

In trypanosomatids, all mRNAs are processed via trans-splicing, although cis-splicing also occurs. In trans-splicing, a common small exon, the spliced leader (SL), which is derived from a small SL RNA species, is added to all mRNAs. Sm and Lsm proteins are core proteins that bind to U snRNAs and are essential for both these splicing processes. In this study, SmD3- and Lsm3-associated complexes were purified to homogeneity from Leishmania tarentolae. The purified complexes were analyzed by mass spectrometry, and 54 and 39 proteins were purified from SmD3 and Lsm complexes, respectively. Interestingly, among the proteins purified from Lsm3, no mRNA degradation factors were detected, as in Lsm complexes from other eukaryotes. The U 1A complex was purified and mass spectrometry analysis identified, in addition to U1 small nuclear ribonucleoprotein (snRNP) proteins, additional co-purified proteins, including the polyadenylation factor CPSF73. Defects observed in cells silenced for U1 snRNP proteins suggest that the U1 snRNP functions exclusively in cis-splicing, although U1A also participates in polyadenylation and affects trans-splicing. The study characterized several trypanosome-specific nuclear factors involved in snRNP biogenesis, whose function was elucidated in Trypanosoma brucei. Conserved factors, such as PRP19, which functions at the heart of every cis-spliceosome, also affect SL RNA modification; GEMIN2, a protein associated with SMN (survival of motor neurons) and implicated in selective association of U snRNA with core Sm proteins in trypanosomes, is a master regulator of snRNP assembly. This study demonstrates the existence of trypanosomatid-specific splicing factors but also that conserved snRNP proteins possess trypanosome- specific functions. [ABSTRACT FROM AUTHOR]

Plant leaf diseases are challenging to categorize due to the complexity of the pattern variations and the high degrees of inter‐class similarity. Plant ailments harm food quality and production. To ensure the quality and quantity of harvests, it is essential to protect plants from disease. Detection of diseases at an early stage is the main and the most complex task for farmers due to common morphological properties like colour, shape, texture, and edges. In this study, a Hybrid Deep Learning model named Hybrid‐Convolutional Support Machine (H‐CSM) based on 'Support Vector Machine (SVM)', 'Convolutional Neural Network (CNN)' and 'Convolutional Block Attention Module (CBAM)' is proposed for the early diagnosis and classification of leaf diseases in plants leaf. The suggested model can initially identify different plant leaf illnesses, although it is not constrained to these. A database of pictures of plant leaves is used to test the suggested method based on different evaluation parameters. The results were highly promising, with an accuracy of up to 98.72% which has been increased by applying better learning methods. Farmers can quickly identify 36 common diseases with a little instruction for 14 plant categories, enabling them to take prompt preventive measures using the proposed method. [ABSTRACT FROM AUTHOR]

The challenge of developing an Android malware detection framework that can identify malware in real-world apps is difficult for academicians and researchers. The vulnerability lies in the permission model of Android. Therefore, it has attracted the attention of various researchers to develop an Android malware detection model using permission or a set of permissions. Academicians and researchers have used all extracted features in previous studies, resulting in overburdening while creating malware detection models. But, the effectiveness of the machine learning model depends on the relevant features, which help in reducing the value of misclassification errors and have excellent discriminative power. A feature selection framework is proposed in this research paper that helps in selecting the relevant features. In the first stage of the proposed framework, t-test, and univariate logistic regression are implemented on our collected feature data set to classify their capacity for detecting malware. Multivariate linear regression stepwise forward selection and correlation analysis are implemented in the second stage to evaluate the correctness of the features selected in the first stage. Furthermore, the resulting features are used as input in the development of malware detection models using three ensemble methods and a neural network with six different machine-learning algorithms. The developed models' performance is compared using two performance parameters: F-measure and Accuracy. The experiment is performed by using half a million different Android apps. The empirical findings reveal that malware detection model developed using features selected by implementing proposed feature selection framework achieved higher detection rate as compared to the model developed using all extracted features data set. Further, when compared to previously developed frameworks or methodologies, the experimental results indicates that model developed in this study achieved an accuracy of 98.8%. [ABSTRACT FROM AUTHOR]

This article presents a practical method for the 3D placement of a group of HetNet of unmanned aerial vehicles (UAV)-mounted base station (mBS) to offer maximum wireless connectivity and coverage for terrestrial users in a particular region. There are two ways to solve this issue. First, the ideal flying height for each UAV is determined based on the transmit power of the UAV, which offers the maximum ground coverage radius. Depending on their ideal flying altitudes and transmitting power, UAVs are divided into several categories. Given a collection of UAVs, the proposed approach identifies an optimum subset of the available UAVs and places them optimally in 3D to offer maximum coverage of the network for a particular area on the terrestrial ground while using the least amount of power. The results of the simulations show that the suggested approach is effective, and they also give important visions about the behavior of the HetNet supported UAVmBS cell subnetworks. [ABSTRACT FROM AUTHOR]

Major obstacles in deploying online voting system (OVS) are its susceptibility and vulnerability to attack and incompatibility to voting fundamental. Various security risks like attacks on communication network, viruses and similar attacks on voting terminals, chances of coercion etc. are associated with OVS. Trapping the electronic ballots from communication network during transmission is also an identified security problem in OVS. Attackers may try to change votes in favor of particular candidates or may try random modifications to disrupt the whole system. Errors may automatically get introduced in the communication channel too. This paper proposes a solution using "dummy voting to detect error" in electronic ballot during transmission in communication network or modification by attackers. This error and modification detection technique will assist legitimate election authorities in making decisions such as cancelling the whole or part of votes cast or approving them to be legitimate. [ABSTRACT FROM AUTHOR]

Postpartum haemorrhage (PPH) is the loss of blood above 500 ml during vaginal or caesarean deliveries. It is difficult to find a PPH in an earlier stage, so pregnant women are exposed to excess blood loss that makes them suffer and die. Antenatal practices help in identifying risk factors, and modern technology is used to overcome the risk. Still, the morbidity rate and the mortality arise due to the unpredicted and unexpected cause. PPH is still a significant cause of maternal morbidity and mortality worldwide. The novelty of this research work is to alert medical practitioner about excessive bleeding of pregnant women during childbirth. We are proposing an automation system using wearable devices to prevent pregnant women from the PPH. These devices measure parameters like temperature, pulse rate, blood pressure, and sweat rate of pregnant women. Fuzzy neural technique-based rules are used for each parameter to predict the risk in developing PPH and to evaluate the performance of proposed system for reducing mortality and morbidity rates. Our findings of experiment are carried on metrics of HPPH (high-level postpartum haemorrhage), NPPH (normal-level postpartum haemorrhage), and MPPH (medium-level postpartum haemorrhage) for 15 patients. Fuzzy output value of 1 indicates patient state with NPPH, 0 indicates patient state with HPPH, and values in between 0 and 1 indicate MPPH. Based on the sensitivity of the predicted values, medical attention is taken from doctors or nurses in nearby locations using Internet of Things infrastructure. [ABSTRACT FROM AUTHOR]

Summary: The traditional centralized cloud computing (CC) model faces a range of problems with the exponential growth of the Internet of Things (IoT) applications, like high latency, reduced bandwidth, and network instability. Fog computing (FC) takes the cloud closer to IoT computers to overcome these problems. Rather than moving them to the cloud, the FC provides local IoT data processing and storage on IoT computers. This paper focuses on the Routing Protocol for Low‐Power and Lossy Network (RPL), a universally routing protocol for a static environment that is used for reducing energy consumption, delay, and packet loss with data aggregation at the border router using a fog simulation model in Contiki Cooja. The objective function (OF) choice has an impact on network topology, as each node selects a set of potential parents to send to the destination. However, no systematic analysis of the effects of OF behavior in the RPL environment has been undertaken. Here, three different OFs, Objective Function Zero (OF0), Advanced Objective Function Zero (AOF0), and Minimum Rank with Hysteresis Objective Function (MRHOF) for RPL in the static environment for different node numbers, have been compared. The findings demonstrate that altering all three OFs has a significant impact on RPL. The energy consumption is reduced in the case of the AOF0 in the fog node by 50.86%, which is less than the case of the OF0 and MRHOF function. Extensive simulations show that AOF0 outperforms the existing OFs. [ABSTRACT FROM AUTHOR]

One of the major applications of the Heterogeneous Network (HetNet) is in the healthcare system. Deploying HetNet in healthcare systems enables patients, physicians, and other stakeholders to communicate easily with each other. Due to the large growth in the network's subscribers, the security of the stored health data became one of the major concerns because unauthorized access to this data may lead to very serious complications, and unreliable transmission of data may lead to fatal risks to the patient's life. Therefore, taking data integrity into consideration, user authentication has become one of the main factors. However, significant research work has been performed at HetNet's physical layer to secure communication, but the result of this leads to an increase in hardware components. The increasing hardware components not only costs money but also power consumption. Therefore, this paper presents an alternate way of securing communication in HetNet at the network layer. However, resolving security problems at the network layer increases computational complexity. Nevertheless, earlier, some encryption techniques like identity-based encryption (IBE), symmetric key encryption (SKE), and public-key encryption (PKE) have been utilized for securing data. Due to their own disadvantages, this paper utilizes an attribute-based encryption (ABE) authentication scheme for securing health data in medical applications. With the help of this method, access to the intruders is denied which results in reduced communication overhead. This authentication scheme helps protect the essential information against attacks by the intruders. It includes a third party server that helps to authenticate and store patient's information. The whole security technique has been written in the form of HLPSL (high-level protocol specification language) codes, and the results are then validated with the help of AVISPA (automated validation of Internet security protocols and applications) tool. [ABSTRACT FROM AUTHOR]

Unmanned Aerial Vehicles (UAVs) are capable of providing significant potential to the Internet of Things (IoT) devices through sensors, cameras, GPS systems, etc. Therefore, the smart UAV-IoT collaborative system has become a current hot research topic. However, there are issues like resource allocation, security, and privacy preservation, trajectory optimization, intelligent decision, energy harvesting, etc. that need extensive research and analysis. In this article, we propose an energy-efficient and time-saving task scheduling algorithm that divides the IoT devices into certain clusters based on physical proximity. By utilizing the algorithm, cluster heads can apply an Auto Regressive Moving Average (ARMA) model to predict intelligently the timestamp of the arrival of the next task and associated estimated payments. Based on the overall expected payment, a cluster head can smartly advise the UAV about its time of next arrival. Simulation results demonstrate that our proposed Energy and Time-saving Task Scheduling (ETTS) algorithm show significant improvement in terms of energy (around 67%) as well as a delay (around 36%) over the Task scheduling for Indoor Environment (TSIE) and Time Division Multiple Access-Workflow Scheduler (TDMA-WS). The improvement in delay arises from the saved time of retransmissions. ARMA model basically tries to ensure that processing capacity of an UAV doesn't remain unutilized or under-utilized. The energy that UAV invests to arrive at one particular clusterhead, should be reciprocated by a full or close-to-full task queue of a clusterhead. [ABSTRACT FROM AUTHOR]

• A novel multimodal framework is proposed for COVID-19 patients early diagnosis and accurate prediction using DL techniques. • Chest X-ray images and cough-based models with extraction of discriminatory features and early prediction of COVID-19 patients. • Use of U Net DL technique, CNN processing techniques to perform segmentation and to extract features from chest X-ray images. • Results compared with existing methods for accurate classification based on different settings and protocols. COVID-19 outbreak has become one of the most challenging problems for human being. It is a communicable disease caused by a new coronavirus strain, which infected over 375 million people already and caused almost 6 million deaths. This paper aims to develop and design a framework for early diagnosis and fast classification of COVID-19 symptoms using multimodal Deep Learning techniques. Methods: we collected chest X-ray and cough sample data from open source datasets, Cohen and datasets and local hospitals. The features are extracted from the chest X-ray images are extracted from chest X-ray datasets. We also used cough audio datasets from Coswara project and local hospitals. The publicly available Coughvid DetectNow and Virufy datasets are used to evaluate COVID-19 detection based on speech sounds, respiratory, and cough. The collected audio data comprises slow and fast breathing, shallow and deep coughing, spoken digits, and phonation of sustained vowels. Gender, geographical location, age, preexisting medical conditions, and current health status (COVID-19 and Non-COVID-19) are recorded. The proposed framework uses the selection algorithm of the pre-trained network to determine the best fusion model characterized by the pre-trained chest X-ray and cough models. Third, deep chest X-ray fusion by discriminant correlation analysis is used to fuse discriminatory features from the two models. The proposed framework achieved recognition accuracy, specificity, and sensitivity of 98.91%, 96.25%, and 97.69%, respectively. With the fusion method we obtained 94.99% accuracy. This paper examines the effectiveness of well-known ML architectures on a joint collection of chest-X-rays and cough samples for early classification of COVID-19. It shows that existing methods can effectively used for diagnosis and suggesting that the fusion learning paradigm could be a crucial asset in diagnosing future unknown illnesses. The proposed framework supports health informatics basis on early diagnosis, clinical decision support, and accurate prediction. [ABSTRACT FROM AUTHOR]

All witnessed the terrible effects of the COVID-19 pandemic on the health and work lives of the population across the world. It is hard to diagnose all infected people in real time since the conventional medical diagnosis of COVID-19 patients takes a couple of days for accurate diagnosis results. In this paper, a novel learning framework is proposed for the early diagnosis of COVID-19 patients using hybrid deep fusion learning models. The proposed framework performs early classification of patients based on collected samples of chest X-ray images and Coswara cough (sound) samples of possibly infected people. The captured cough samples are pre-processed using speech signal processing techniques and Mel frequency cepstral coefficient features are extracted using deep convolutional neural networks. Finally, the proposed system fuses extracted features to provide 98.70% and 82.7% based on Chest-X ray images and cough (audio) samples for early diagnosis using the weighted sum-rule fusion method. [ABSTRACT FROM AUTHOR]

• Systematic framework is established that formalizes the scope of the smart grids. • A deep learning-based approach is proposed for predicting load in smart grids quite accurately with use of features of various neural networks for efficient load balancing in smart grids. • Use of the proposed approach to efficiently determine the load forecasting characteristics in macro grids and microgrids. • Comparative analysis of proposed work with state-of-the-art for load forecasting and feature extraction in smart grids. Load forecasting plays an essential role in effective energy planning and distribution in a smart grid. However, due to the unpredictable and non-linear structure of smart grids and large datasets' complex nature, accurate load forecasting is still challenging. Statistical techniques are being used for a long time for load forecasting, but it is inefficient. This paper tries to resolve challenges imposed by conventional methods like mean and mode by suggesting an ANN model for accurate load forecasting. Specifically, the LSTM and random forest approach has been used here. We compared our model to other models that use similar parameters and found that ours is more reliable and can be used for long-term forecasting. Our model has achieved an average overall accuracy of 96% and an average MSE of 4.486 with average CPU time consumption of 904.47 s, 872.43 s, and 908.32 s, respectively. Hence, the present model outperforms other existing methods. [ABSTRACT FROM AUTHOR]

Abstract: Early in the assembly of the spliceosome of eukaryotes the branch-point binding protein (BBP, also called SF1) recognizes the branch point sequence, whereas the heterodimer U2AF consisting of a 65 and a 35kDa subunit, contacts the polypyrimidine tract and the AG splice site, respectively. Herein, we identified, cloned and expressed the Trypanosoma cruzi and Trypanosoma brucei U2AF35, U2AF65 and SF1. Trypanosomatid U2AF65 strongly diverged from yeast and human homologues. On the contrary, trypanosomatid SF1 was conserved but lacked the C-terminal sequence present in the mammalian protein. Yeast two hybrid approaches were used to assess their interactions. The interaction between U2AF35 and U2AF65 was very weak or not detectable. However, as in other eukaryotes, the interaction between U2AF65 and SF1 was strong. At the cellular level, these results were confirmed by fractionation and affinity-selection experiments in which SF1 and U2AF65 co-fractionated in a complex of approximately 400kDa and U2AF65 was affinity-selected with TAP tagged SF1, but not with TAP tagged U2AF35. Silencing of the three factors affected growth and trans-splicing in the first step of this reaction. Trypanosomes are the first described example of eukaryotic cells in which the interaction of two expressed U2AF factors seemed to be very weak, or even undetectable. [Copyright &y& Elsevier]

Background: Epithelial-to-mesenchymal transition (EMT) has, in recent years, emerged as an important tumor cell behavior associated with high metastatic potential and drug resistance. Interestingly, protein SUMOylation and hepatocyte growth factor could respectively reduce the effect of small molecule inhibitors on tyrosine kinase activity of mutated epidermal growth factor receptor of lung adenocarcinomas (LADC). The actual mechanism is yet to be resolved. Methods: Immunohistochemistry was used to stain proteins in LADC specimens. Protein expression was confirmed by Western blotting. In vitro, expression of proteins was determined by Western blotting and immunocytochemistry. Levels of circular RNA were determined by reverse transcription-polymerase chain reaction. Results: SAE2 and cirRNA CCDC66 were highly expressed in LADC. Expression of SAE2 was mainly regulated by EGFR; however, expression of cirRNA CCDC66 was positively regulated by FAK and c-Met but negatively modulated by nAchR7α. EGFR-resistant H1975 also highly expressed cirRNA CCDC66. Immediate response of hypoxia increased phosphorylated c-Met, SAE2, and epithelial-to-mesenchymal transition. Either activation of FAK or silencing of nAchR7α increased cirRNA CCDC66. Conclusions: HGF/c-Met regulates expression of SAE2 and cirRNA CCDC66 to increase EMT and drug resistance of LADC cells. Multimodality drugs concurrently aiming at these targets would probably provide more benefits for cancer patients. [ABSTRACT FROM AUTHOR]