Pair production of heavy charged gauge bosons in pppp collisions at LHC

Two opposite charged new heavy gauge boson pair production at the Large Hadron Collider (LHC) is presented in this paper. These bosons are known as $W^{'}$ boson due to the reason that it is the heavy version of Standard Model's weak force carrier, the $W$ boson. The production cross section and decay width in proton-proton ($pp$) collision at \sqrts~= 8 TeV are calculated for different masses and coupling strengths of $W^{'}$. Efficiencies for different signal regions and branching ratios for different decay channels are computed. In this study, the pair production ($W^{'^{+}}W^{'^{-}}$) is considered in emerging new physics as a result of $pp$ collision at \sqrts~= 8 TeV at the LHC with final state containing two tau ($\tau$) leptons and two neutrinos (each $W^{'}$ decay to $\tau$ and its neutrino). The event selection efficiency similar to the CMS experiment is used for the mass of $W^{'}$ to set lower limits for different coupling strengths of $W^{'}$ and results are presented in this work. For heavy gauge bosons, when coupling strength is similar to that of Standard Model's $W$ boson, the mass of $W^{'}$ below 305 GeV are excluded at confidence level of $95\%$.Comment: 21 pages, 16 figure

Enteric Fever as an Antecedent to Development of Miller-Fisher Syndrome and Possible Role of COVID-19 Vaccination

Summary: Guillain-Barre Syndrome is an immune-mediated demyelinating disorder. Miller-Fisher Syndrome is an uncommon subtype of GBS. It is characterized by findings of ophthalmoplegia, ataxia, and areflexia. Here we present the case of Miller-Fisher Syndrome following an episode of typhoidal diarrhea. The presentation was of rapidly progressing weakness beginning in the lower extremity with diplopia. Examination revealed diminished reflexes. CSF testing revealed albuminocytologic dissociation which was later supported by neurophysiological testing. The patient was treated with intravenous immunoglobulins (IVIG). We conclude that Miller-Fisher syndrome should be considered in the diagnostic workup of patients presenting with new sensorimotor deficits following diarrheal illnesses and/or COVID-19 mRNA vaccination. Early recognition is essential given the propensity of GBS to cause life-threatening respiratory failure and prompt IVIG administration is associated with a better prognosis. Keywords: Enteric Fever, Miller-Fisher Syndrome, COVID-19, Vaccinatio

Tissue characterization of benign cardiac tumors by cardiac magnetic resonance imaging, a review of core imaging protocol and benign cardiac tumors

Generally, cardiac masses are initially suspected on routine echocardiography. Cardiac magnetic resonance (CMR) imaging is further performed to differentiate tumors from pseudo-tumors and to characterize the cardiac masses based on their appearance on T1/T2-weighted images, detection of perfusion and demonstration of gadolinium-based contrast agent uptake on early and late gadolinium enhancement images. Further evaluation of cardiac masses by CMR is critical because unnecessary surgery can be avoided by better tissue characterization. Different cardiac tissues have different T1 and T2 relaxation times, principally owing to different internal biochemical environments surrounding the protons. In CMR, the signal intensity from a particular tissue depends on its T1 and T2 relaxation times and its proton density. CMR uses this principle to differentiate between various tissue types by weighting images based on their T1 or T2 relaxation times. Generally, tumor cells are larger, edematous, and have associated inflammatory reactions. Higher free water content of the neoplastic cells and other changes in tissue composition lead to prolonged T1/T2 relaxation times and thus an inherent contrast between tumors and normal tissue exists. Overall, these biochemical changes create an environment where different cardiac masses produce different signal intensity on their T1- weighted and T2- weighted images that help to discriminate between them. In this review article, we have provided a detailed description of the core CMR imaging protocol for evaluation of cardiac masses. We have also discussed the basic features of benign cardiac tumors as well as the role of CMR in evaluation and further tissue characterization of these tumors

Constituents and Consequences of Online-Shopping in Sustainable E-Business: An Experimental Study of Online-Shopping Malls

With the growth of the internet, electronic (online) business has become an important trend in the economy. This study investigates how retailers could enhance their shopping processes and hence help sustain their e-business development. Therefore, we propose a unified information system-consumer behavior (IS-CB) model for online shopping to analyze factors that impact online shopping. We used an online survey to gather data from 633 online customers to test the theoretical model, matching differences using structural equation modeling. Highly influencing factors for the IS-CB online shopping model included perceived value (PV), perceived risk (PR), social factors (SF), perceived ease of use (PEOU), perceived usefulness (PU), online shopping intention, trust, online shopping experience, actual online shopping purchases, entertainment gratification (EG), website irritation (WI), information design (ID), visual design (VD), and navigation design (ND). This study provides important theoretical and practical implications. PV and trust in online shopping can nurture positive attitudes and shopping intentions among online customers. Well-designed websites produce higher levels of trust and reduced WI. Similarly, online shopping sites with better ID, ND, and VD also reduce WI and increase trust. This study fills gaps in previous studies relating to IS and CB and provides explanations for IS and CB constituent impacts on acceptance and use of online shopping. The proposed unified IS-CB explains consumer online shopping patterns for a sustainable e-business

Data-Driven Cervical Cancer Prediction Model with Outlier Detection and Over-Sampling Methods

Globally, cervical cancer remains as the foremost prevailing cancer in females. Hence, it is necessary to distinguish the importance of risk factors of cervical cancer to classify potential patients. The present work proposes a cervical cancer prediction model (CCPM) that offers early prediction of cervical cancer using risk factors as inputs. The CCPM first removes outliers by using outlier detection methods such as density-based spatial clustering of applications with noise (DBSCAN) and isolation forest (iForest) and by increasing the number of cases in the dataset in a balanced way, for example, through synthetic minority over-sampling technique (SMOTE) and SMOTE with Tomek link (SMOTETomek). Finally, it employs random forest (RF) as a classifier. Thus, CCPM lies on four scenarios: (1) DBSCAN + SMOTETomek + RF, (2) DBSCAN + SMOTE+ RF, (3) iForest + SMOTETomek + RF, and (4) iForest + SMOTE + RF. A dataset of 858 potential patients was used to validate the performance of the proposed method. We found that combinations of iForest with SMOTE and iForest with SMOTETomek provided better performances than those of DBSCAN with SMOTE and DBSCAN with SMOTETomek. We also observed that RF performed the best among several popular machine learning classifiers. Furthermore, the proposed CCPM showed better accuracy than previously proposed methods for forecasting cervical cancer. In addition, a mobile application that can collect cervical cancer risk factors data and provides results from CCPM is developed for instant and proper action at the initial stage of cervical cancer

Constituents and Consequences of Online-Shopping in Sustainable E-Business: An Experimental Study of Online-Shopping Malls

With the growth of the internet, electronic (online) business has become an important trend in the economy. This study investigates how retailers could enhance their shopping processes and hence help sustain their e-business development. Therefore, we propose a unified information system-consumer behavior (IS-CB) model for online shopping to analyze factors that impact online shopping. We used an online survey to gather data from 633 online customers to test the theoretical model, matching differences using structural equation modeling. Highly influencing factors for the IS-CB online shopping model included perceived value (PV), perceived risk (PR), social factors (SF), perceived ease of use (PEOU), perceived usefulness (PU), online shopping intention, trust, online shopping experience, actual online shopping purchases, entertainment gratification (EG), website irritation (WI), information design (ID), visual design (VD), and navigation design (ND). This study provides important theoretical and practical implications. PV and trust in online shopping can nurture positive attitudes and shopping intentions among online customers. Well-designed websites produce higher levels of trust and reduced WI. Similarly, online shopping sites with better ID, ND, and VD also reduce WI and increase trust. This study fills gaps in previous studies relating to IS and CB and provides explanations for IS and CB constituent impacts on acceptance and use of online shopping. The proposed unified IS-CB explains consumer online shopping patterns for a sustainable e-business

Editorial: Recent Advances in Deep Learning and Medical Imaging for Cancer Treatment

In the evolving landscape of medical imaging, the escalating need for deep-learningmethods takes center stage, offering the capability to autonomously acquire abstract datarepresentations crucial for early detection and classification for cancer treatment. Thecomplexities in handling diverse inputs, high-dimensional features, and subtle patternswithin imaging data are acknowledged as significant challenges in this technologicalpursuit. This Special Issue, “Recent Advances in Deep Learning and Medical Imagingfor Cancer Treatment”, has attracted 19 high-quality articles that cover state-of-the-artapplications and technical developments of deep learning, medical imaging, automaticdetection, and classification, explainable artificial intelligence-enabled diagnosis for cancertreatment. In the ever-evolving landscape of cancer treatment, five pivotal themes haveemerged as beacons of transformative change. This editorial delves into the realms ofinnovation that are shaping the future of cancer treatment, focusing on five interconnectedthemes: use of artificial intelligence in medical imaging, applications of AI in cancerdiagnosis and treatment, addressing challenges in medical image analysis, advancementsin cancer detection techniques, and innovations in skin cancer classification

Hybrid Prediction Model for Type 2 Diabetes and Hypertension Using DBSCAN-Based Outlier Detection, Synthetic Minority Over Sampling Technique (SMOTE), and Random Forest

As the risk of diseases diabetes and hypertension increases, machine learning algorithms are being utilized to improve early stage diagnosis. This study proposes a Hybrid Prediction Model (HPM), which can provide early prediction of type 2 diabetes (T2D) and hypertension based on input risk-factors from individuals. The proposed HPM consists of Density-based Spatial Clustering of Applications with Noise (DBSCAN)-based outlier detection to remove the outlier data, Synthetic Minority Over-Sampling Technique (SMOTE) to balance the distribution of class, and Random Forest (RF) to classify the diseases. Three benchmark datasets were utilized to predict the risk of diabetes and hypertension at the initial stage. The result showed that by integrating DBSCAN-based outlier detection, SMOTE, and RF, diabetes and hypertension could be successfully predicted. The proposed HPM provided the best performance result as compared to other models for predicting diabetes as well as hypertension. Furthermore, our study has demonstrated that the proposed HPM can be applied in real cases in the IoT-based Health-care Monitoring System, so that the input risk-factors from end-user android application can be stored and analyzed in a secure remote server. The prediction result from the proposed HPM can be accessed by users through an Android application; thus, it is expected to provide an effective way to find the risk of diabetes and hypertension at the initial stage

Cancerous Tumor Controlled Treatment Using Search Heuristic (GA)-Based Sliding Mode and Synergetic Controller

Cancerous tumor cells divide uncontrollably, which results in either tumor or harm to the immune system of the body. Due to the destructive effects of chemotherapy, optimal medications are needed. Therefore, possible treatment methods should be controlled to maintain the constant/continuous dose for affecting the spreading of cancerous tumor cells. Rapid growth of cells is classified into primary and secondary types. In giving a proper response, the immune system plays an important role. This is considered a natural process while fighting against tumors. In recent days, achieving a better method to treat tumors is the prime focus of researchers. Mathematical modeling of tumors uses combined immune, vaccine, and chemotherapies to check performance stability. In this research paper, mathematical modeling is utilized with reference to cancerous tumor growth, the immune system, and normal cells, which are directly affected by the process of chemotherapy. This paper presents novel techniques, which include Bernstein polynomial (BSP) with genetic algorithm (GA), sliding mode controller (SMC), and synergetic control (SC), for giving a possible solution to the cancerous tumor cells (CCs) model. Through GA, random population is generated to evaluate fitness. SMC is used for the continuous exponential dose of chemotherapy to reduce CCs in about forty-five days. In addition, error function consists of five cases that include normal cells (NCs), immune cells (ICs), CCs, and chemotherapy. Furthermore, the drug control process is explained in all the cases. In simulation results, utilizing SC has completely eliminated CCs in nearly five days. The proposed approach reduces CCs as early as possible

Social Media Bullying in the Workplace and Its Impact on Work Engagement: A Case of Psychological Well-Being

The hotel industry has transformed the social and official interaction and communication landscape due to information technology. This has created a new venue for bullying, known as cyberbullying. This study aims to examine the impact of workplace cyberbullying on the work engagement of hotel employees while examining the mediating role of psychological well-being and work meaningfulness using the job demand resource model and conservation of resource theory. The data (n = 470) were collected from 4-star and 5-star hotel employees in Pakistan. The results reported that psychological well-being mediates the relationship between workplace cyberbullying and work engagement. Moreover, work meaningfulness also mediates the relationship between psychological well-being and work engagement. Findings suggest that the hotel industry of Pakistan should acknowledge the presence of cyberbullying and design policies and procedures to maintain a healthy work environment for employees’ psychological well-being and ensure that hotel employees find their work meaningful