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Background: Parental stress is the major factor that increases a risk of unwillingly placing children with disabilities. The aim of our study is to assess the frequency of depression amongst parents of children with intellectual disability. Methods: This was a cross sectional study conducted for 10 months in various hospitals and rehabilitation centers in Karachi, Pakistan. Participants were inducted using purposive sampling technique. Parents of intellectually challenged children up to 18 years of age were included and a self-administered questionnaire was utilized for data collection both in English and Urdu. PHQ 8 was used to assess the severity of depression amongst parents. For all purposes, p-value of

Abstract Globally, COVID-19 affected radiopharmaceutical laboratories. This study sought to determine the economic, service, and research impacts of COVID-19 on radiopharmacy. This online survey was conducted with the participation of employees from nuclear medicine and radiopharmaceutical companies. The socioeconomic status of the individuals was collected. The study was participated by 145 medical professionals from 25 different countries. From this work, it is evident that 2-deoxy-2-[18F]fluoro-D-glucose (2-[18F]FDG), and 99m Tc-labeled macro aggregated albumin 99mTc-MAA were necessary radiopharmaceuticals used by 57% (83/145and 34% (49/145;) respondents, respectively for determining how COVID infections affect a patient’s body. The normal scheduling procedure for the radiopharmacy laboratory was reduced by more than half (65%; 94/145). In COVID-19, 70% (102/145) of respondents followed the regulations established by the local departments. Throughout the pandemic, there was a 97% (141/145) decrease in all staffing recruitment efforts. The field of nuclear medicine research, as well as the radiopharmaceutical industry, were both adversely affected by COVID-19.

Healthcare sector is one of the most important sectors of any country as a big part of the country’s economy is associated with it. The research is about to contribute to the health sector by minimizing the expenses of a lung cancer diagnosis. The study tries to devise an efficient method for initial screening of the patients with symptoms through their demographic and clinical data. The study seeks appropriate feature transformation techniques from dimensionality reduction techniques in combination with an apposite regression model that can perform this task robustly using the lung cancer dataset for early carcinoma diagnosis. To equip the health sector with state-of-the-art technology and for the betterment of humankind, the most beneficial tool of today is machine learning. Lungs play a vivacious role in the human body, oxygen is circulated in the body, and the air is taken from the atmosphere and sends it into the bloodstream. Several people decease every year because of lung carcinoma as lung cancer is normally identified at the latter phase due to lack of awareness. It stays unidentified because people have pneumonia often, which converts into lung cancer later. The projected research seeks to enable health professionals to the rationalization of primary diagnosis and treatment of lung carcinoma in developing countries. The proposed methodology has selected the optimized combination of regression-based machine learning technique and feature transformation technique for the available patterns based on demographic and clinical features of lung cancer patients. Based on the gathered results during training (RMSE = 0.1324, R2-Score = 0.7428) and testing (RMSE = 0.1273, R2-Score = 0.7405) it can be concluded that the fast independent component analysis and elastic net regression technique provided optimized results and outperform the other aspirant techniques.

Rapid spectrum usage in wireless networks may reduce energy efficiency, requiring cognitive radio networks to be more efficient than conventional ones. Due to increased data transmission demand, cognitive radio networks arose from a lack of spectrum bandwidth. Spectrum sensing and handoff decision are two cognitive radio network strategies that help avoid interference, channel access, and cohabitation between primary and secondary users. Current research focuses on handoff decision and cooperative spectrum sensing to improve sensing efficiency and system throughput while neglecting energy efficiency and handoff latency. Spectrum mobility and sensing are essential for energy-efficient cognitive radio networks. This study provides a second priority user transmission system using cooperative spectrum sensing to sense available channels. An energy detection technique optimizes the sensing process's energy usage, leading to energy efficiency. A primary user traffic pattern-based threshold approach is presented for spectrum mobility management. A threshold approach is utilized to calculate probabilistic stay-and-wait and QoS handoff values. The transmission channel is selected using a hybrid handoff strategy based on dynamic spectrum aggregation. Moreover, a cooperative spectrum sensing algorithm is described and simulated to identify the optimum channel with the greatest throughput and minimum energy consumption. The proposed approach increases energy efficiency and throughput while maintaining handoff delay and avoids miss-detection and false alarm. This technique improves energy efficiency, sensing performance, throughput, and handoff time. miss-detection.

Attention and Trust remain the crux of robotic sensory perception when associated with cloud robotics. It involves tasks that compete for attention from several stimuli taken by the robot itself upon its selective attention mode, focuses its attention on the task with higher precedence, and filters out the rest to be performed later. The robot could unload storage-extensive and computation extensive jobs towards the cloud while keeping trust establishment in control. Factors leading to these robots' availability, confidentiality, data protection, and isolation security trigger attention. Trust involving suppliers and users is intended to attain safety measures that endorse various cloud suppliers' status and accessible services. It takes several input stimuli from the robot, i.e., confidence, experience, and emotion, and gives output as trust level to pay attention during social interactions among robots. Input parameters are mapped into fuzzy sets, taking a range of input and output membership functions. The fuzzifier and defuzzifier are designed according to the proposed scheme. The developed system, named Trust Annotator, is tested and analyzed using MATLAB R2021a. Mamdani model is conferred, which yielded some unusual yet promising outcomes. These outcomes show conformity between the designed and simulated systems.

Clinical image processing plays a significant role in healthcare systems and is a widely used methodology of the current era. The Intracranial tumor affects children and adults as it is the 10th most common form of tumor. Accurate tumor segmentation in the brain is complicated even though Intracranial tumor images are acquired properly. The tumor can be curable if its stage and form can be identified on time, and for this purpose, researchers have been developing sophisticated techniques and methods. An automatic detection, segmentation, colorization, and classification of tumor region are done to identify abnormalities in a medical image using T1-CE, an MRI dataset that assists in diagnosis. To assist the medical practitioners in visualizing tumor shape, size, and orientation; only the tumor region is colored in a greyscale image. Pix2Pix Conditional Generative Adversarial Neural Networks (Pix2Pix-cGANs) have generated MRI images with color tumor regions. In qualitative measures, we achieved the Structure Similarity Index (SSIM) average score of 0.92% and an average 28% Peak Signal to Noise Ratio (PSNR) value on generated images that outperform the existing techniques. Moreover, we achieved Classification Accuracy (CA) 88.5% and 92.4% quantitatively in pre and post colorization phases, respectively, with other measures using NASNet-Large.

Nuclear Command and Control Center (NCCC) security must be considered a high priority in the nation, country, and ultimately humanity’s best interest. Presently, with the expanding nuclear security risks, it is pivotal to guarantee that malevolent agent never acquires nuclear access. The purpose of this manuscript is to develop Cognitively Managed Multi-Level Authentication (CMMLA) through a collaboration among Cognitive Computing (CC), Quantum Computing (QC) and Cyber Security (CS). Thus, the proposed scheme will provide NCCC with the capability of self-learning, management, inherent security, rigorous, and reliable authentication protocols. Fuzzy Logic-based Quantum Key Distribution (FLQKD) is used to implement CMMLA and perform the experimentation for tangible results over the Simulink from MATLAB R2021a. For the reliability analysis of irregular Charles H. Bennett and Gilles Brassard 1984, (BB84) algorithm that we used and is a variant of Quantum Key Distribution (QKD), experimentation is done through an online QKD Simulator. Consequently, the results of FLQKD implementation show high throughput for the security of NCCC by combining the advantages of well-known techniques of their kind, where the authorized and unauthorized persons are successfully identified, and access granted only to the authenticated persons while considering the different scenarios of access allowance

Aims and Objectives: The aim of the current cross-sectional study was to conduct a survey among the oral and maxillofacial surgeons of South India regarding their experiences of incidence of inferior alveolar nerve (IAN) neurosensory deficit after bilateral sagittal split osteotomy (BSSO) for correction of mandibular retrognathism and to assess the intra-operative nerve encounters and its effect on the inferior alveolar neurosensory deficit (NSD), 6 months post-operatively. Materials and Methods: A self-administered questionnaire (SAQ) was prepared using Google Forms (Google Inc.) and sent to the prospective participants through various social media outlets such as Facebook, WhatsApp groups etc., of the maxillofacial surgery specialty for a period of 3 months. SAQ from surgeons with more than 5 years of experience in orthognathic surgery were included. Results: The incidence of NSD post-BSSO advancement surgery from 859 cases after 6 months was 15.1% (130). After splitting the mandible, the IAN was seen in the proximal fragment in 472 sites and needed dissection. The nerve was transected and neurorrhaphy was carried out in 26 sites. A Chi-square test was used to analyse the qualitative variables. The IAN was not visible post-osteotomy in 140 sites and in the distal fragment in 1080 sites. These groups had decreased incidence of NSD. The NSD was significantly higher in cases where the nerve was transected and sutured, P value

Not only weighty energy usage pose issues for the environment, but it also raises server maintenance costs in data centers. The massive task with the various power control functions in computer components was made to minimize energy consumption. Increasing consumption of energy in data server environments means that data centers will have high maintenance costs. Various geo-distributed data centers are starting to grow in an age of data proliferation and information growth. Energy management for servers is now demanded for technological, environmental, and economic reasons. In this environment, the main memory is a major energy consumer, not less than the processor. At the same time, an energy-efficient task scheduling strategy is a viable way to meet these goals. Unfortunately, mapping Virtual Machine (VM) resources to the Main Memory (MM) demands to achieve good performance by minimizing the energy consumption within a certain limit is a huge challenge. This paper simulates energy-efficient task scheduling algorithms in a heterogeneous virtualized environment using real-time virtual machine scheduling to resolve the issue of energy consumption. Using a simulator Real-Time system SIMulator (RTSIM), several hardware-based scheduling algorithms are implemented to observe VM memory scheduling efficiency to save memory energy. The simulation results show that, compared to current energy-efficient scheduling methods Rate Monotonic (RM), Earliest-Deadline-First (EDF), and Least-Laxity-First (LLF), helps to reduce energy consumption and improve performance. It is also observed that memory-aware energy management architecture reduces energy and memory consumption efficiently by using EDF scheduling algorithms. In particular, EDF saves approximately 58.3 percent of memory energy than conventional systems that cannot benefit from memory-aware energy management algorithms. The energy efficiency of the algorithms continues to improve as the level of server consolidation rises. We also implemented the EDF scheduling algorithm in Xen’s Credit scheduler to see if the simulation outcomes can be simulated on physical systems. Results of simulation and deployment are equated, and comparable outcomes are achieved. We also identified that shared memory between virtual machines deliberately affects memory’s energy consumption based on the implementation.

Interest in serious games for education has grown during the last years. The credit goes to the potential for engaging students with new ways to capture and maintain attention. The goal of this paper is to introduce a new multidisciplinary approach that incorporates psychological analysis theory with fuzzy inference and neural networks in the Foreign Language Learning prospects identification through serious games. Our research first used a Delphi method to accumulate Information Systems students’ opinions resulting from a SWOT analysis of the use of serious games to learn English language. Then, we designed a Fuzzy Logic based Foreign Language Learning Prospects Identification System through serious games that takes four standard input parameters (Strengths, Weaknesses, Opportunities, Threats) already identified during our SWOT analysis and predicts the output (Learning Prospects of Foreign Language) by considering impact of certain variations in input parameters. Implementation results have been obtained through (MATLAB R2020a) and have shown reliable incites and findings.

This study presented a novel shape photonic crystal fiber based gas sensor for the first time. Perfectly circular shape air hole are arranged to form core and cladding region. Based on full vector finite element method (FV-FEM) and circular shape perfectly matched layer (PML) the designed sensor has been investigated. The model field, effective mode index, sensitivity, confinement loss, effective mode area, nonlinearity, V parameter of the sensor fiber fundamental mode are investigated through FEM based commercial software package COMSOL Multiphysics. Numerical simulations evidences that the sensor shows the sensitivity responses of 64.69% and confinement loss of 4.38 × 10-06 dB/cm at the transmission wavelength λ = 1.55 µm. Besides, the sensor achieves single mode operation over the whole operating wavelength. Based on these excellent optical behaviors of the designed sensor it can be undoubtedly expect that, this sensor will play an influential role detecting gas molecules as well as PCF based optical sensing areas.

In this article, we propose, investigate, and analyze a novel designed two guided modes regions ring-core photonic crystal fiber (PCF) with relatively greater effective refractive index separation and also high isolation value which can be negligible the crosstalk among the guided channels. The designed PCF can be well supported up to 76 orbital angular momentum (OAM) modes and 6 linear polarization (LP) modes over 800 nm bandwidth. The background and ring-core materials of the proposed PCF are silicon and Shcott sulfur difluoride (SF2) glass, respectively. All characteristic features of the proposed PCF are successively analyzed using the finite element method (FEM) and the perfectly matched layer (PML) as a boundary condition within the COMSOL Multiphysics simulator. The designed PCF possess the good propagation features because the results show that the better mode quality (96%), the isolation value is kept above 72 dB that produces the very low crosstalk between the guided channels, high numerical aperture, relatively flat and small dispersion variations, and low confinement loss, etc. Therefore, these outstanding properties of the designed PCF prove that it has potential applications to enable the large-capacity transmission along with the high-speed optics communications.

Objective: To assess the frequency of mental health problems among healthcare workers related to COVID-19 pandemic among health care workers in a tertiary cardiac care center. Study Design: Descriptive cross sectional study. Place and Duration of Study: This study was conducted at Armed Forces Institute of Cardiology/ National Institute of Heart Diseases (AFIC/NIHD) Rawalpindi (Pakistan) from Apr 2020 to Jun 2020. Methodology: This study was a hospital-based cross-sectional study consisted of 100 healthcare workers including 50 nurses and 50 doctors who volunteered to enroll in our study were included. Approval from the clinical research ethics committee of cardiac care hospital was received before the initiation of this study. Verbal informed consent was provided by all study participants prior to their enrollment. Exclusion criteria comprised of the healthcare workers who refused to participate in it. This study followed the COVID-19 Pandemic Mental Health Questionnaire (CoPaQ) 14. Descriptive statistics were used to describe the percentages and frequencies. Descriptive statistics were used to describe the percentages and frequencies were calculated and analyzed using the Statistical Package for the Social Sciences (SPSS), version 23.0. Results: A total of 100 healthcare workers completed the survey. Mean age of the healthcare workers was 36.5 ± 27.0 years (median=36.00), and 75 (75%) were women and men comprised 25 (25%). Of all participants, 50 (50.0%) were nurses, and 50 (50%) were doctors. A considerable proportion of participants reported symptoms of COVID19, 6 (6.0%), tested positive 14 (14%), currently quarantine 24 (24.0%). While 12 healthcare workers experienced 14 day quarantine time period. 16 healthcare workers reported COVID test positive of their closed one. The fear of infecting others with COVID-19 was found to be 30 (30%). The mental stress that that COVID-19 will personally affect us was found in 28 (28%) of healthcare workers. The fear that consequences of the COVID-19 will be severe health disasters was found in 40 (40%) of the individuals. Conclusion: In this study of physicians and nurses in hospitals with wards for patients with COVID-19, health care workers responding to the spread of COVID-19 reported high rates of symptoms of anxiety, fear, and distress.

Power communication network is an indispensable unit to maintain power network operation. The application of optical fiber nanotechnology in power communication transmission is studied in this paper. The optical fiber nanotechnology is applied to the power communication network (optical transmission network technology is the main transmission technology), and the functional model of the optical transmission network device node is established. The form of the terminal multiplexing device function module, the electrical cross device function module, and the photoelectric hybrid cross device function module in the optical transmission network is analyzed. The optical fiber nanotechnology is applied to the optical multiplex section and the optical transmission section using optical transmission network technology. The data in the power communication network is transmitted by strong third-order optical nonlinearity of optical fiber nanotechnology and optical soliton communication. The experimental results show that when optical fiber nanotechnology is applied to power communication transmission, the loss of communication cable is within the standard range, and the rate of digital communication interface is higher than 7,000 bit/s in different service quantities. This effectively validates the reliability of optical fiber nanotechnology for power communication transmission.

Objective: To determine in hospital early complications in diabetics’ vs non diabetics with acute ST elevation myocardial infarction. Study Design: Comparative cross-sectional study. Place and Duration of Study: The study was conducted in emergency departments and adult cardiology wards of Armed Forces Institute of Cardiology/National Institute of Heart Diseases, from Aug to Nov 2019. Methodology: A comparative cross sectional study was conducted on 380 patients (190 patients with diabetes and 190 patients without diabetes) who presented with acute ST-Elevation MI in age group 30 to 70 years to emergency department of Armed Forces Institute of Cardiology/National Institute of Heart Disease during specified period. Patients with rescue PCI and new onset of LBBB were also included. Patients with age group lesser than 30 years or greater than 70 years with STEMI were excluded. Sampled with consecutive non probability technique in patients was assessed with ECG, cardiac enzymes, transthoracic echocardiography, renal Doppler and RFTs. All patients were admitted in wards and were followed up during hospital stay. Patients were evaluated and their record of Primary PCI and thrombolysis was also noted. Data was entered and analyzed with SPSS-23. Results: A total of 380 patients with STEMI were included in this study consisting of 292 (76.8%) male and 88 (23.1%) female from 30 to 70 years. The patients were divided in two groups i.e. diabetic and non-diabetic, 190 patients were included in each group. It was found that frequency of ST-Elevation MI was higher in diabetic group as compared to non-diabetic group. All the patients were analyzed for complications occurred after STElevation MI. LV failure, Brady arrhythmias, atrial fibrillation, post MI angina and cardiogenic shock were the main complications noted. It was found that all these complications are more in diabetic group post MI angina which occurred most frequently in non-diabetic group. Conclusion: In our study we observed that left ventricular failure and arrhythmias were the major complications. Mortality was higher in diabetic patients than non-diabetic patients. In both groups PPCI and SK reduced mortality. Post MI angina were found more frequent in non-diabetic group.

Objective: To compare the hemodynamic effects of Atracurium versus Cisatracurium in Cardiac Patients Undergoing coronary artery bypass graft Surgery. Study Design: Randomized controlled trial. Place and Duration of Study: The study was conducted at Armed Forces Institute of Rawalpindi, a tertiary care hospital, after seeking hospital ethics committee permission, from Apr 2019 to Oct 2019. Methodology: A total of 200 consecutive patients were randomized into two equal groups, group-A and group-C (100 in each group). Group-A patients were induced with 0.5mg/kg of Atracurium, followed by infusion @ 10μg/kg/min whereas group C patients received Cisatracurium @ 0.2mg/kg at induction, followed by infusion @ 2μg/kg/min during the maintainence phase. Systolic blood pressure, diastolic blood pressure, Mean arterial pressure and heart rate were noted at different intervals of both groups. Decrease in systolic blood pressure of >20% from baseline or a value 0.05). Heart rate remained more steady with cisatracurium (p0.05). Conclusion: Cisatracurium found to be more suitable drug in maintaining the stable hemodynamics and preventing fluctuations in pressure in cardiac patients undergoing on-pump bypass surgery.

Memory recollection involves reactivation of neural activity that occurred during the recalled experience. Here, the authors show that neural reactivation can be decomposed into visual-semantic features, is widely synchronized throughout the brain, and predicts memory vividness and accuracy.

The liver is the human body’s mandatory organ, but detecting liver disease at an early stage is very difficult due to the hiddenness of symptoms. Liver diseases may cause loss of energy or weakness when some irregularities in the working of the liver get visible. Cancer is one of the most common diseases of the liver and also the most fatal of all. Uncontrolled growth of harmful cells is developed inside the liver. If diagnosed late, it may cause death. Treatment of liver diseases at an early stage is, therefore, an important issue as is designing a model to diagnose early disease. Firstly, an appropriate feature should be identified which plays a more significant part in the detection of liver cancer at an early stage. Therefore, it is essential to extract some essential features from thousands of unwanted features. So, these features will be mined using data mining and soft computing techniques. These techniques give optimized results that will be helpful in disease diagnosis at an early stage. In these techniques, we use feature selection methods to reduce the dataset’s feature, which include Filter, Wrapper, and Embedded methods. Different Regression algorithms are then applied to these methods individually to evaluate the result. Regression algorithms include Linear Regression, Ridge Regression, LASSO Regression, Support Vector Regression, Decision Tree Regression, Multilayer Perceptron Regression, and Random Forest Regression. Based on the accuracy and error rates generated by these Regression algorithms, we have evaluated our results. The result shows that Random Forest Regression with the Wrapper Method from all the deployed Regression techniques is the best and gives the highest R2-Score of 0.8923 and lowest MSE of 0.0618.

The research area falls under the umbrella of affective computing and seeks to introduce intelligent agents by simulating emotions artificially and encouraging empathetic behavior in them, to foster emotional empathy in intelligent agents with the overarching objective of improving their autonomy. Raising the emotional empathy of intelligent agents to boost their autonomic behavior can increase their independence and adaptability in a socially dynamic context. As emotional intelligence is a subset of social intelligence, it is essential for successful social interaction and relationships. The purpose of this research is to develop an embedded method for analyzing empathic behavior in a socially dynamic situation. A model is proposed for inducing emotional intelligence through a deep learning technique, employing multimodal emotional cues, and triggering appropriate empathetic responses as output. There are 18 categories of emotional behavior, and each one is strongly influenced by multimodal cues such as voice, facial, and other sensory inputs. Due to the changing social context, it is difficult to classify emotional behavior and make predictions based on modest changes in multimodal cues. Robust approaches must be used to be sensitive to these minor changes. Because a one-dimensional convolutional neural network takes advantage of feature localization to minimize the parameters, it is more efficient in this exploration. The study’s findings indicate that the proposed method outperforms other popular ML approaches with a maximum accuracy level of 98.98 percent when compared to currently used methods.

Supply chains are evolving into automated and highly complex networks and are becoming an important source of potential benefits in the modern world. At the same time, consumers are now more interested in food product quality. However, it is challenging to track the provenance of data and maintain its traceability throughout the supply chain network. The traditional supply chains are centralized and they depend on a third party for trading. These centralized systems lack transparency, accountability and auditability. In our proposed solution, we have presented a complete solution for blockchain-based Agriculture and Food (Agri-Food) supply chain. It leverages the key features of blockchain and smart contracts, deployed over ethereum blockchain network. Although blockchain provides immutability of data and records in the network, it still fails to solve some major problems in supply chain management like credibility of the involved entities, accountability of the trading process and traceability of the products. Therefore, there is a need of a reliable system that ensures traceability, trust and delivery mechanism in Agri-Food supply chain. In the proposed system, all transactions are written to blockchain which ultimately uploads the data to Interplanetary File Storage System (IPFS). The storage system returns a hash of the data which is stored on blockchain and ensures efficient, secure and reliable solution. Our system provides smart contracts along with their algorithms to show interaction of entities in the system. Furthermore, simulations and evaluation of smart contracts along with the security and vulnerability analyses are also presented in this work.

In modern times, many individuals, businesses and the Internet of Things (IoT) integrated industries collect huge amounts of meaningful data daily, which may be beneficial for other individuals and businesses as well. By utilizing this data, future trends to make the right decisions on the bases of facts and figures are analyzed efficiently. In addition to that, many new ways are paved for researchers to utilize this data in their upcoming research. However, due to some major issues like security, privacy and access control of data, data owners avoid sharing data among themselves. Another main problem is the selfish behavior of data owners. Businesses also act selfishly and invest huge amounts of money to collect and maintain the data for their benefits. Therefore, data owners are hesitant to share their data with others without the availability of a fair profit and secure data-sharing platform. Moreover, consumers are not much motivated to buy data from Data Providers (DPs) due to its bad quality and inconsistency. The data provided by data owners is mostly incomplete, outdated, heterogeneous and costly. In this paper, a subscription-based data-sharing model is proposed by leveraging the blockchain technology and Data as a Service (DaaS) concept. In this model, users subscribe to a DP for a specific period to get access to the data and pay according to the subscription plan. The DP keeps receiving revenue recurrently for a long-time, which has a huge profit margin in comparison with selling data at once. Furthermore, two major pricing models, Flat Rate Pricing (FRP) and Usage-Based Pricing (UBP), are discussed to set standards for data owners to monetize their data, and a new hybrid pricing model is also proposed. Blockchain technology is utilized in the proposed model to make it secure, transparent and immutable. To investigate the performance of the proposed model, a private blockchain network is deployed using a web interface provided by MultiChain blockchain. The simulation results demonstrate that the proposed model is feasible and efficient. The theoretical discussion proves that the proposed model is beneficial for both data owners and data consumers and has a good scope in the future for data management and trading processes.

The contribution of this paper is two fold. It first analyses the flooding strategies for the Wireless Multihop Networks (WMNs) then it enhances the reactive and proactive routing protocols. For analysis purpose, we select four widely used flooding techniques for routing: i. traditional flooding, ii. Time-To-Live based Expanding Ring Search (TTL-based ERS) flooding scheme, iii. TTL-based Scope Routing (SR) flooding and iv. Multi-Point Relays (MPR) flooding. These techniques play a vital role and act as a backbone for routing protocols. Therefore, we compare efficiency of these techniques for six widely used routing protocols: Ad-hoc On-demand Distance Vector (AODV), Destination Sequenced Distance Vector (DSDV), Dynamic Source Routing (DSR), DYnamic MANET On-demand (DYMO), Fish-eye Scope Routing (FSR) and Optimized Link State Routing (OLSR). DSDV uses traditional flooding, AODV, DSR and DYMO use TTL-based ERS flooding, FSR uses TTL-based SR flooding and OLSR uses MPR flooding. This paper also presents mathematical models for flooding techniques and studies the affects of these techniques on their respective protocols in terms of energy and time consumption. This is done to measure the cost incurred by the routing protocols in the form of routing overhead and latencies. A novel contribution of this work is the enhancement in search set values and intervals of routing algorithms to improve the efficiency of selected existing protocols. A detailed comparison analysis of selected protocols with their default and enhanced routing algorithms in NS-2 is also a part of this work.

In this effort, solid hybrid nanofluid flowing and thermal transport characteristics over a slippery, nonlinear, uniform stretching surface are proposed. The influence of nanosolid particle shapes, permeability material, viscous dissipative flow, Cattaneo-Christov heat flux and radiate flux are studied. The predominant flow equations are systemized in form of partial-differential equations (PDEs). Keller-box's computational method is the employed method to identify the self-similar resolution for transformed principles into the ordinary-differential equations (ODEs) by appropriate transmutations. Williamson hybrid nanofluidcontaining of dual varied types of nanoparticles, named Copper (Cu) and Zirconium dioxide (ZrO2) in the rich viscid; based fluid of kind EO- Engine Oil is utilized in this research. The remarkable consequence of this analysis is reached by comparison of thermal transmission level of such type of fluid (ZrO2−Cu/EO), which has increasingly more gains to traditional nanofluids (Cu−EO). The lamina-figured elements effect the utmost major thermal conductivity in the boundary-layer, whilst the lowermost thermal conductivity is detected in sphere geometric nanoparticle.

Cloud Computing (CC) provides a combination of technologies that allows the user to use the most resources in the least amount of time and with the least amount of money. CC semantics play a critical role in ranking heterogeneous data by using the properties of different cloud services and then achieving the optimal cloud service. Regardless of the efforts made to enable simple access to this CC innovation, in the presence of various organizations delivering comparative services at varying cost and execution levels, it is far more difficult to identify the ideal cloud service based on the user’s requirements. In this research, we propose a Cloud-Services-Ranking Agent (CSRA) for analyzing cloud services using end-users’ feedback, including Platform as a Service (PaaS), Infrastructure as a Service (IaaS), and Software as a Service (SaaS), based on ontology mapping and selecting the optimal service. The proposed CSRA possesses Machine-Learning (ML) techniques for ranking cloud services using parameters such as availability, security, reliability, and cost. Here, the Quality of Web Service (QWS) dataset is used, which has seven major cloud services categories, ranked from 0–6, to extract the required persuasive features through Sequential Minimal Optimization Regression (SMOreg). The classification outcomes through SMOreg are capable and demonstrate a general accuracy of around 98.71% in identifying optimum cloud services through the identified parameters. The main advantage of SMOreg is that the amount of memory required for SMO is linear. The findings show that our improved model in terms of precision outperforms prevailing techniques such as Multilayer Perceptron (MLP) and Linear Regression (LR).

The fast-growing use of online social networks (OSNs) has prompted stakeholders to change their market strategies and hencehave raised several questions on the users' decision making (DM). OSNs, as being regularly used, have resulted in playing asignificant role in supporting consumer’s rational DM. We use a hybrid approach i.e. an online survey and fuzzy modeldevelopment to indicate how OSNs have resulted in giving an impact on the decision making. The research depicts that OSNssupport and empower users in the DM process specifically in Rationality, Design, & Choice and the model predicts the DMstrategy of each user according to their present decisions using fuzzy logic. Our results also reveal that different types of users(observers, seekers, and advisers) have significantly different participation styles, which in turn have an impact on the efficacyof the DM process. We discussed the implications for OSN designers and developers based on the findings from the research.

The Android-based devices are gaining popularity now a day. With the widespread use of smartphones both in private andwork-related areas, securing these devices has become of paramount importance. These devices are prone to varioussecurity issues of malicious attacks and performance problems. Owners use their smartphones to perform tasks rangingfrom everyday communication with friends and family to the management of banking accounts and accessing sensitivework-related data. These factors, combined with limitations in administrative device control through owners and securitycritical applications, make Android-based smartphones a very attractive target for attackers and malware authors of anykind and motivation. Applications keep and manage diverse intrinsic data as well as sensitive private information such asaddress books. Smartphones enable swift and easy data exchange via 3G, 4G, and Wi-Fi. Thus, personal informationstored on smartphones is prone to leakage. Up until recently, the Android Operating System’s security model hassucceeded in preventing any significant attacks by malware. This can be attributed to a lack of attack vectors which couldbe used for self-spreading infections and low sophistication of malicious applications. The research provides a distinctivesolution to the security threats being found in the Android operating system. This paper presents a data security and qualityenhancement method based on amalgamating quantum attributes into the Android operating system that could effectivelysolve the issue raised. The paper provides a proposed architecture of Quantum Key distribution being embedded within theAndroid OS to improve efficiency. However, QKD is a new technology. The research unleashes the possible ways inwhich quantum could be effectively embedded in smartphones to resolve certain data security problems. Quantum keydistribution implements the Android to guard and use in the case of a run-time kernel compromise. That is, even with afully compromised kernel, an attacker cannot read key material stored in Quantum key.

Water plays a vital role for the survival of life. We get water in different forms of precipitation and rain is the most beneficial of all types. The demand for water is very high throughout the Pakistan, especially where we have limited natural water resources. Summer monsoon season has profound impact in reducing water scarcity in Pakistan. Heavy rainfall results into floods whereas less rainfall creates drought which not only affects the economy but also puts humanlife in risk. The aim of this research is to manage the movement of clouds from heavy rainfall areas to those areas where there is paucity of water, through satellite using electromagnetic waves. To accommodate such a sensitive satellite data which is paramount to a country, it is required to have some special systems based on quantum mechanics that are more efficient and pregnable than conventional computers. The consequences will be very high of not securing weathermanagement system, implies to country level disasters. In this paper we first discuss the technology behind quantum computer then proposed a secure architecture by employing quantum computers to ensure the security of data transmission for weather management system.

The theory of modern organizations considers emotional intelligence to be the metric for tools that enable organizations to create a competitive vision. It also helps corporate leaders enthusiastically adhere to the vision and energize organizational stakeholders to accomplish the vision. In this study, the one-dimensional convolutional neural network classification model is initially employed to interpret and evaluate shifts in emotion over a period by categorizing emotional states that occur at particular moments during mutual interaction using physiological signals. The self-organizing map technique is implemented to cluster overall organizational emotions to represent organizational competitiveness. The analysis of variance test results indicates no significant difference in age and body mass index for participants exhibiting different emotions. However, a significant mean difference was observed for the blood volume pulse, galvanic skin response, skin temperature, valence, and arousal values, indicating the effectiveness of the chosen physiological sensors and their measures to analyze emotions for organizational competitiveness. We achieved 99.8% classification accuracy for emotions using the proposed technique. The study precisely identifies the emotions and locates a connection between emotional intelligence and organizational competitiveness (i.e., a positive relationship with employees augments organizational competitiveness).

Myoepithelial carcinoma is a rare neoplasm of salivary glands that account for < 1% of all salivary gland tumors. The most common sites of involvement are major salivary glands mainly parotid gland. Intraorally, it can arise from minor salivary glands; palate is the most common site of occurrence. It also occurs in nasopharynx, paranasal sinuses, nasal cavity and larynx in head and neck region. Myoepithelial tumors were first described in 1943. Their malignant variant, myoepithelial carcinoma, was first reported by Stromeyer et al., in 1975, characterized by distinct morphologic heterogeneity and an infiltrative growth pattern into adjacent tissues. Here, we report a rare case of a 55-year-old female with myoepithelial carcinoma of buccal mucosa. It was also rare because of unusual location of tumor. Our patient was treated with wide local resection and remained free of disease for 15 months.

Sensor nodes in underwater sensor networks may acquire data at a higher rate than their ability to communicate over underwater acoustic channels. Autonomous underwater vehicles may mitigate this mismatch by offloading high volumes of data from the sensor nodes and ferrying them to the sink. Such a mode of data transfer results in high latency. Occasionally, these networks need to report high priority events such as catastrophes or intrusions. In such a scenario the expectation is to have a minimal end-to-end delay for event reporting. Considering this, underwater vehicles should schedule their visits to the sensor nodes in a manner that aids efficient reporting of high-priority events. We propose the use of the Value of Information metric in order to improve the reporting of events in an underwater sensor network. The proposed approach classifies the recorded data in terms of its value and priority. The classified data is transmitted using a combination of acoustic and optical channels. We perform experiments with a binary event model, i.e., we classify the events into high-priority and low-priority events. We explore a couple of different path planning strategies for the autonomous underwater vehicle. Our results show that scheduling visits to sensor nodes, based on algorithms that address the value of information, improves the timely reporting of high priority data and enables the accumulation of larger value of information.

Pesticides have an important role in rising the overall productivity and yield of agricultural foods by eliminating and controlling insects, pests, fungi, and various plant-related illnesses. However, the overuse of pesticides has caused pesticide pollution of water bodies and food products, along with disruption of environmental and ecological systems. In this regard, developing low-cost, simple, and rapid-detecting approaches for the accurate, rapid, efficient, and on-site screening of pesticide residues is an ongoing challenge. Electrochemiluminescence (ECL) possesses the benefits of great sensitivity, the capability to resolve several analytes using different emission wavelengths or redox potentials, and excellent control over the light radiation in time and space, making it a powerful strategy for sensing various pesticides. Cost-effective and simple ECL systems allow sensitive, selective, and accurate quantification of pesticides in agricultural fields. Particularly, the development and progress of nanomaterials, aptamer/antibody recognition, electric/photo-sensing, and their integration with electrochemiluminescence sensing technology has presented the hopeful potential in reporting the residual amounts of pesticides. Current trends in the application of nanoparticles are debated, with an emphasis on sensor substrates using aptamer, antibodies, enzymes, and molecularly imprinted polymers (MIPs). Different strategies are enclosed in labeled and label-free sensing along with luminescence determination approaches (signal-off, signal-on, and signal-switch modes). Finally, the recent challenges and upcoming prospects in this ground are also put forward. [ABSTRACT FROM AUTHOR]