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الملخص: أهداف البحث: التحمل القلبي الرئوي مهم للمشاركة بشكل مريح في أنشطة الحياة اليومية. تستخدم اختبارات التمرين، مثل اختبار المشي لمدة ست دقائق، بشكل شائع لتقييم القدرة على التحمل القلبي الرئوي. نحن نهدف إلى دراسة تأثيرات اختبار المشي لمدة ست دقائق القائم على الممر والمعتمد على مختبر تحليل المشية في الوقت الحقيقي التفاعلي على الأداء الوظيفي. طريقة البحث: تم تقسيم ثلاثين رجلا أصحاء بشكل عشوائي إلى مجموعتين. أجرى المشاركون في المجموعة (أ) اختبار المشي لمدة ست دقائق على الممر، تليها فترة الراحة (ساعة واحدة)، وبعد ذلك، قاموا بإجراء اختبار المشي في الوقت الحقيقي للتحليل التفاعلي القائم على المشي لمدة ست دقائق. في المجموعة (ب)، أجرى المشاركون الاختبارات بتسلسل عكسي لتلك التي أجراها المشاركون في المجموعة (أ). النتائج: أدى اختبار المشي لمدة ست دقائق القائم على الممر إلى زيادة كبيرة في مسافة ست دقائق سيرا على الأقدام (6 ميجاوات) وسرعة المشي لمدة ست دقائق مقارنة باختبار المشي لمدة ست دقائق القائم على مختبر التحليل التفاعلي في الوقت الحقيقي. لم يلاحظ أي فروق ذات دلالة إحصائية بين المجموعتين في أي من النتائج الثانوية التالية: ضغط الدم الانقباضي، وضغط الدم الانبساطي، وتشبع الأكسجين، ومعدل ضربات القلب، وضيق التنفس، والتعب العام. ولوحظ وجود علاقة إيجابية قوية بين مسافة اختبار المشي البالغة ست دقائق وسرعة اختبار المشي لمدة ست دقائق. الاستنتاجات: لا ينبغي استخدام اختبار المشي لمدة ست دقائق القائم على الممر واختبار المشي في الوقت الحقيقي للتحليل التفاعلي القائم على المختبر لمدة ست دقائق بشكل تبادلي. Abstract: Objective: Cardiopulmonary endurance is important for comfortably participating in activities of daily living. Exercise tests, such as the 6-minute walk test (6MWT), are commonly used to evaluate cardiopulmonary endurance. We investigated the effects of the Gait Real-Time Analysis Interactive Lab (GRAIL)- and corridor-based 6MWTs on functional performance. Methods: Thirty healthy men were randomly divided into two groups. Group A participants performed a corridor-based 6MWT, followed by a washout period (1 h). Subsequently, they performed the GRAIL-based 6MWT. Group B participants performed the tests in the reverse order of that performed by Group A participants. Results: The corridor-based 6MWT resulted in significantly higher 6MW distance and 6MW speed than the GRAIL-based 6MWT. No significant differences were observed between the two groups in any of the following secondary outcomes: systolic blood pressure, diastolic blood pressure, oxygen saturation, heart rate, dyspnea, and overall fatigue. A strong positive correlation was observed between the 6MW distance and 6MW speed. Conclusion: The corridor- and GRAIL-based 6MWT should not be used interchangeably.

This work tackles the scheduling challenge of microgrids for smart homes, aiming to optimize energy management with both renewable and non-renewable sources. A power control center orchestrates the microgrid, coordinating distributed energy resources (DERs) for peak demand fulfillment and excess energy utilization. We propose a proportional-integral control system for efficient demand response, achieving reduced post-scheduling costs and a peak-to-average ratio. Comparative analysis reveals Ant Colony Optimization outperforms Binary Particle Swarm Optimization in cost and peak-to-average ratio reduction. Simulations explore two scenarios: Case 1 integrates with the main grid for reliability, while Case 2 utilizes solely renewable energy sources. Although Case 2 exhibits superior performance, Case 1’s dependence on the main grid offers greater real-world feasibility. Therefore, Case 1 with optimized DER scheduling emerges as the recommended solution for enhancing microgrid efficiency and ensuring reliable power supply in smart homes.

Graph Neural Networks (GNNs) have emerged as a powerful tool for node representation learning within graph structures. However, designing a robust GNN architecture for node classification remains a challenge. This study introduces an efficient and straightforward Residual Attention Augmentation GNN (RAA-GNN) model, which incorporates an attention mechanism with skip connections to discerningly weigh node features and overcome the over-smoothing problem of GNNs. Additionally, a novel MixUp data augmentation method was developed to improve model training. The proposed approach was rigorously evaluated on various node classification benchmarks, encompassing both social and citation networks. The proposed method outperformed state-of-the-art techniques by achieving up to 1% accuracy improvement. Furthermore, when applied to the novel Twitch social network dataset, the proposed model yielded remarkably promising results. These findings provide valuable insights for researchers and practitioners working with graph-structured data.

Abstract Unmanned Aerial Vehicle (UAV) integrated Heterogeneous Network (HetNet) is currently considered as one of the promising technologies. This technology helps in fulfilling high demands of bandwidth, volume, and range as per user needs. In this paper, the work is carried out by authors to assure high security for the users in such a network by incorporating an authentication‐based security known as Identity based Scheme (IBA). The IBA based implemented networks improve the confidentiality of data and the authenticity of the users. Privacy and reliability come through the entity's capacity to authenticate, sign and transact authenticated data without central directory interference. Therefore,the primary scope of this research project is to create an identity‐based authentication mechanism in HetNets that are assisted by UAVs and to secure communication against potential intruder threats. In this way, the security assurance is increased and dreadful intruder attacks are minimized. Moreover, in this research work, the implementing zones would be areas of defence for observation of a region to locate an enemy or as certain strategic features. The security rules are formulated in the coded language known as High Level Protocol Specification Language (HLPSL) and outcomes are verified through Automated Validation Internet Security Protocols and Applications (AVISPA).

The paper was devoted to the results of the study of methods to obtain superhydrophobic film based on the plasma polymerisation of hexamethyldisiloxane (HMDSO) inside the plasma jet at atmospheric pressure. The 3D printing technology was intended for film deposition, which has the advantage of producing superhydrophobic surfaces over a wide range of scales. The effect of synthesis parameters on the hydrophobic properties of the film has been studied. The obtained superhydrophobic films demonstrated stability and resistance in chemical solutions, at high temperatures, under the influence of UV-irradiation and in various weather conditions. The results can be used in various fields, including automotive, construction, electronics, medicine and others, where surface protection against moisture, contamination and corrosion is required.

Abstract Cloud computing is the most widely adapted computing model to process scientific workloads in remote servers accessed through the internet. In the IaaS cloud, the virtual machine (VM) is the execution unit that processes the user workloads. Virtualization enables the execution of multiple virtual machines (VMs) on a single physical machine (PM). Virtual machine placement (VMP) strategically assigns VMs to suitable physical devices within a data center. From the cloud provider's perspective, the virtual machine must be placed optimally to reduce resource wastage to aid economic revenue and develop green data centres. Cloud providers need an efficient methodology to minimize resource wastage, power consumption, and network transmission delay. This paper uses NSGA-III, a multi-objective evolutionary algorithm, to simultaneously reduce the mentioned objectives to obtain a non-dominated solution. The performance metrics (Overall Nondominated Vector Generation and Spacing) of the proposed NSGA-III algorithm is compared with other multi-objective algorithms, namely VEGA, MOGA, SPEA, and NSGA-II. It is observed that the proposed algorithm performs 7% better that the existing algorithm in terms of ONVG and 12% better results in terms of spacing. ANOVA and DMRT statistical tests are used to cross-validate the results.

Tadesse Gure Eticha,1 Roba Ararso,1 Semir Sultan,1 Kiros Terefe,2 Solomon Wubneh Berhe,2 Temesgen Tsega,3 Yeshiwas Abebe,2 Chernet Baye2 1Department of Obstetrics and Gynecology, Haramaya University, Harar, Ethiopia; 2Department of Obstetrics and Gynecology, University of Gondar, Gondar, Ethiopia; 3Department of Pediatrics and Child Health, Saint Paul Millennium Medical College, Addis Ababa, EthiopiaCorrespondence: Tadesse Gure Eticha, Tel: +251913868714, Email tadebuna@yahoo.comIntroduction: Fetal cardiac rhabdomyoma is one of the rare benign cardiac masses which is commonly associated with the tuberous sclerosis complex (TSC). Though mostly fetal cardiac rhabdomyoma is asymptomatic it may lead to life-threatening conditions like outflow obstruction, arrhythmias, hydrops fetalis, or sudden fetal death.Case Report: We are reporting an isolated, asymptomatic fetal intra-cardiac mass (rhabdomyoma) that was discovered at 32 weeks of gestation and was followed as an outpatient until 39 weeks plus one day, at which point a cesarean section was performed. After delivery, the child underwent evaluations at the 1st day, 7th day, 30th day, 7th month, and 12th month of age. Following a checkup, the child’s anthropometric and neurobehavioral growth were both healthy. Except for the tumor, which was neither growing nor shrinking in size, none of the clinical diagnostic criteria for tuberous sclerosis complex were met for this child up to the age of one year.Conclusion: The most common primary benign fetal cardiac tumor is cardiac rhabdomyoma, which is usually associated with tuberous sclerosis. In developing nations where it is challenging to obtain MRIs and genetic studies, and in a similar patient like ours with no other features of tuberous sclerosis, the child needs to be followed in the future, bearing in mind that tuberous sclerosis manifestations will continue to develop over a patient’s lifetime.Keywords: cardiac, rhabdomyoma, tumor, fetal, tuberous sclerosis, case report, Harar, Ethiopia

Background: The widespread utilization of telehealth recently in Saudi Arabia is unprepared and may have a remarkable adverse effect on healthcare delivery. The study aimed to investigate attitude, satisfaction, and barriers of telehealth application among patients. Materials and Methods: A cross-sectional study was conducted at primary healthcare centers during the period of December 2021–January 2022. All Saudi patients (≥18 years) who were treated through telehealth programs in specialized polyclinic were eligible for study inclusion. A self-administered questionnaire was utilized and consists of four main parts: personal information, history of using telehealth, assessment of satisfaction with telehealth program, and attitude toward telemedicine. Results: A total of 641 participants were included. Females represented 57.3% of them. A majority (82.7%) of patients were satisfied with telemedicine services. Multivariate logistic regression analysis revealed that Bachelor/postgraduates were more likely to be satisfied with telemedicine services (P < 0.001), whereas patients reported no appointment slot in the clinic were less likely to be satisfied with telemedicine services (P = 0.042). A majority of patients (84.1%) expressed positive attitude toward telemedicine services. Multivariate logistic regression analysis revealed that older patients (P = 0.002) and widowed (P = 0.001) were less likely to have positive attitude toward utilization of telemedicine services, whereas Bachelor/postgraduates (P = 0.038) and patients with more than this time were more likely to have positive attitude toward telemedicine services (P = 0.001). Conclusion: Patients were overall satisfied and expressed positive attitude concerning the service of telemedicine consultation. However, more in-depth investigation is recommended to better understand the predictors of satisfaction with telemedicine and barriers for its proper application.

This research article aims to study a multi-objective linear fractional programming (FMOLFP) problem having fuzzy random coefficients as well as fuzzy pseudorandom decision variables. Initially, the FMOLFP model is converted to a single objective fuzzy linear programming (FLP) model. Secondly, we show that a fuzzy random optimal solution of an FLP problem is resolved into a class of random optimal solution of relative pseudorandom linear programming (LP) model. As a result, some of theorems show that a fuzzy random optimal solution of a fuzzy pseudorandom LP problem is combined with a series of random optimal solutions of relative pseudorandom LP problems. As an application, the developed approach is implemented to an inventory management problem by taking the parameters as trapezoidal fuzzy numbers, ultimately resulting in a new initiative for modelling real-world problems for optimization. In the last, some numerical examples are introduced to clarify the obtained results and their applicability.

Industrial development with the growth, strengthening, stability, technical advancement, reliability, selection, and dynamic response of the power system is essential. Governments and companies invest billions of dollars in technologies to convert, harvest, rising demand, changing demand and supply patterns, efficiency, lack of analytics required for optimal energy planning, and store energy. In this scenario, artificial intelligence (AI) is starting to play a major role in the energy market. Recognizing the importance of AI, this study was conducted on seven different energetics systems and their variety of applications, including: i) electricity production; ii) power delivery; iii) electric distribution networks; iv) energy storage; v) energy saving, new energy materials, and devices; vi) energy efficiency and nanotechnology; and vii) energy policy, and economics. The main drivers are the four key techniques used in current AI technologies, including: i) fuzzy logic systems; ii) artificial neural networks; iii) genetic algorithms; and iv) expert systems. In developed countries, the power industry has started using AI to connect with smart meters, smart grids, and the Internet of Things devices. These AI technologies will lead to the improvement of efficiency, energy management, transparency, and the usage of renewable energies. In recent decades/years, new AI technology has brought significant improvements to how power system devices monitor data, communicate with the system, analyze input–output, and display data in unprecedented ways. New applications in the energy system become feasible when these new AI developments are incorporated into the energy industry. But on the contrary, much more investment is needed in global research into AI and data-driven models. In terms of power supply, AI can help utilities provide customers with renewable and affordable electricity from complex sources in a secure manner, while at the same time providing these customers with the opportunity to use their own energy more efficiently. Moreover, policy recommendations, research opportunities, and how industry 4.0 will improve sustainability have been briefly described.

One of the most energy-saving design aspects is the reliability of the envelopes, which helps to monitor the temperature of the building and thus reduces dependence on mechanical heating, ventilation, and air conditioning systems for heating or cooling the building. This study designs home retrofits and new construction of single-family buildings by assessing parametric sweeps, building design, and cost-based energy savings building analysis. The goal of this research is to determine the most cost-effective paths for a single-family building. For building design, we chose nine capacity points with the lowest cost. BEopt offers comprehensive building-based assessments, including scale, occupancy, design, location, vintage, and utility prices. Equipment and discrete envelope options that reflect realistic building practices and materials are evaluated. The impact of carbon emission reductions is calculated on an annual basis. The proposed building's energy savings are calculated concerning a B10 benchmark. The parametric analysis results were compared to the B10 Benchmark, and the proposed building energy consumption of Point 4 (12.4%), Point 6 (13%), Point 8 (7.4%), and Point 9 (8.2%) was lower than the B10 Benchmark. Point 6 is our proposed design's most cost-effective capacity point. Our proposed method allows us to analyze residential building structures and define cost-optimal performance packages at different energy consumption rates in all single-family buildings. Users can specify options to be considered in the optimization by selecting predefined options from various categories.

Coronavirus disease 2019 (COVID-19) is caused by the severe acute respiratory syndrome coronavirus-2 (SARS-CoV-2). To assess the effect of COVID-19 disease on hematology, coagulation profiles, renal and liver function over the course of the disease, the following laboratory tests were performed: WBCs per mm3, lymphocytes count, Platelet, D-dimmer, AST, Albumin, LDH, Ferritin, CRP, blood culture and viral loads. Patients were grouped according to their initial viral load (Group1: low viral load (L), Group 2: moderate viral load (I), and Group 3, high viral load (H)). The study population median age of the patients was 58 years, and 69% were male. Generally, all patients were admitted to the intensive care unit. Most of the patients (79.5%) had an intermediate viral load, 14.5% had a high viral load, and 5.7% had a low viral load. The Kusakal-Walli’s test revealed a significant difference in the levels of white blood cells, lymphocytes, platelet, D-dimer, AST, CRP, and ferritin (p

In Saudi Arabia, community pharmacies offer healthcare services for different conditions. However, clarity of the competence of pharmacists in managing migraines is lacking. This study aimed to explore the current knowledge, attitude, and practice patterns of community pharmacists concerning migraine management in the northwestern part of Saudi Arabia. A cross-sectional study was carried out between June and September 2022 among 215 Saudi community pharmacists. Data analysis was performed by descriptive and inferential statistics using SPSS version 27. Most community pharmacists (87.9%) feel that migraine management is essential to their practice, and 83.3% suggest between one and five over-the-counter (OTC) migraine products daily. Among the study pharmacists, 83.7% feel migraine patients should try OTC before prescription medications. Only 9.3% of the community pharmacists do not believe that migraine is a neurological disorder. The medications most prescribed for migraine were triptans, representing 52.1% of prescriptions. There were significant differences between the gender of the pharmacists and their knowledge, attitude, and practice overall score (p-value = 0.04). Male pharmacists exhibited higher knowledge, attitude, and practice scores than female pharmacists. Although many community pharmacists acknowledge their expertise and involvement in managing migraines, there is a requirement for further education and training to enhance their capacity to offer complete care to migraine patients. Pharmacists should also consider non-pharmacological interventions and complementary therapies when treating migraine symptoms.

Objective: To determine the nature of adrenal pathology in patients undergoing adrenalectomy in Saudi Arabia over the last decade and compare it with the literature. We compared perioperative outcomes between minimally invasive adrenalectomy (MIA) and open adrenalectomy (OA). Methods: This retrospective study included patients who underwent adrenalectomy at five tertiary care centers in Saudi Arabia from 2010 to 2020. We collected patients’ baseline and perioperative characteristics and detailed hormonal evaluation of adrenal masses. Results: Among 160 patients (mean age 44 ± 14.5 years; mean BMI 29.17 ± 5.96 kg/m2), 84 (51.5 %) were men and 51.5 % had left-sided adrenal masses. The mean tumor size was 6.1 ± 4.2 (1.0–19.5) cm, including 60 (37.5 %) incidentalomas and 65 (40.6 %) functioning masses. Histopathology revealed 74 (46.2 %) adenomas and 24 (15 %) cancers or metastases from other primary organs; 20 %, 8.8 %, and 2.5 % of patients had pheochromocytoma, myelolipoma, and 2.5 % ganglioneuroblastoma, respectively. MIA and OA were performed in 135 (84.4 %) and 21 (15.6 %) patients, respectively. Adrenalectomy was increasingly performed over three equal periods in the last decade (17.5 % vs 34.4 % vs 48.1 %), with increasing numbers of MIAs to replace OAs. OA patients had larger tumors and needed blood transfusion more frequently (47.6 % vs 10.8 %, p

Abstract Lung cancer is one of the most profound causes of cancer‐related deaths in the world. Early detection is known to significantly improve the chances of survival. Several detection and diagnostic methods are used for this purpose. CT is one of the most widely used non‐invasive medical imaging modalities in this domain. The biggest challenge faced by radiologists in this case is detection and diagnosis of cancerous lung nodules. The growth of ground glass opacity (GGO) lesions is an indication of malignancy. However, GGO is difficult to capture for physicians as it manifests in the form of tiny, faint shadows. This research paper proposes an approach for aiding GGO identification in CT lung images for improved lung cancer prognosis. In the proposed approach, morphological reconstruction is used for segmentation. Once the region of interest (ROI) is extracted, statistical analysis using mean, standard deviation, variance, entropy, skewness, kurtosis, minimum grey scale value, maximum grey scale value and range is performed. The same statistical measures are determined for normal lung and distribution plot is drawn for comparison. It is observed that maximum grey‐scale value demonstrates minimum overlap of approximately 7.4%. To reduce this, a joint feature by summing values of feature mean, skewness, and maximum grey‐scale value was used. This approach reduced the overlap to approximately 1.32%. Lastly, ANN was used for classification of GGO and non‐GGO lung tissue with an achieved accuracy of 99.5%.

Proteases and RNA-Dependent RNA polymerase, major enzymes which are essential targets involved in the life and replication of SARS-CoV-2. This study aims at in silico examination of the potential ability of coumarins and their derivatives to inhibit the replication of SARS-Cov-2 through multiple targets, including the main protease, papain-like protease and RNA-Dependent RNA polymerase. Several coumarins as biologically active compounds were studied, including coumarin antibiotics and some naturally reported antiviral coumarins. Aminocoumarin antibiotics, especially coumermycin, showed a high potential to bind to the enzymes’ active site, causing possible inhibition and termination of viral life. They demonstrate the ability to bind to residues essential for triggering the crucial cascades within the viral cell. Molecular dynamics simulations for 50 ns supported these data pointing out the formation of rigid, stable Coumermycin/enzyme complexes. These findings strongly suggest the possible use of Coumermycin, Clorobiocin or Novobiocin in the fight against COVID-19, but biological evidence is still required to support such suggestions.

Background: Helicobacter pylori (H. pylori) is significantly linked to various diseases that seriously impact human health, such as gastric ulcers, chronic gastritis and gastric adenocarcinoma. Methods: The compositional shifts in bacterial communities of the orointestinal axis were surveyed pre/post-eradication of H. pylori. In total, 60 samples, including stool and salivary specimens, were collected from 15 H. pylori-positive individuals (HPP) before beginning and 2 months after receiving the eradication therapy. The V3-V4 regions of the 16S rRNA gene were sequenced using MiSeq. Results: Overall, oral microbiomes were collectively more diverse than the gut microbiomes (Kruskal–Wallis; p = 3.69 × 10−5). Notably, the eradication of H. pylori was associated with a significant reduction in the bacterial diversity along the orointestinal axis (Wilcoxon rank sum test; p = 6.38 × 10−3). Interestingly, the oral microbiome of HPP showed a positive correlation between Proteobacteria and Fusobacteria, in addition to a significant predominance of Streptococcus, in addition to Eubacterium_eligens, Haemophilus, Ruminococcaceae, Actinomyces and Staphylococcus. On the other hand, Fusobacterium, Veillonella, Catenibacterium, Neisseria and Prevotella were significantly enriched upon eradication of H. pylori. Generally, Bacteroidetes and Fusobacteria positively coexisted during H. pylori infection along the orointestinal axis (r = 0.67; p = 0.0006). The eradication of H. pylori was positively linked to two distinctive orotypes (O3 and O4). Orotype O4 was characterized by a robust abundance of Veillonella and Fusobacteria. The gut microbiomes during H. pylori infection showed a remarkable predominance of Clostridium_sensu_stricto_1 and Escherichia_Shigella. Likewise, Bifidobacterium and Faecalibacterium were significantly enriched upon eradication of H. pylori. Conclusions: Finally, the impact of eradication therapy clearly existed on the representation of certain genera, especially in the oral microbiome, which requires particular concern in order to counteract and limit their subsequent threats.

Besides its impacts on governance, economics, human culture, geostrategic partnership and environment, globalization greatly exerted control over science and security policies. Biosecurity is the critical job of efforts, policy and preparation to protect health of human, animal and environmental against any biological threats. With the transition into a global village, the possibility of biosecurity breaches has significantly increased. The COVID-19 pandemic is an example of an infringement on biosecurity that has posed a serious threat to the world. Since the first report on the recognition of COVID-19, a number of governments have taken preventive measures, like; lockdown, screening and early detection of suspected and implementing the required response to protect the loss of life and economy. Unfortunately, some of these measures have only recently been taken in some countries, which have contributed significantly to an increased morbidity and loss of life on a daily basis. In this article, the biological risks affecting human, animal and environmental conditions, biosafety violations and preventive measures have been discussed in order to reduce the outbreak and impacts of a pandemic like COVID-19.

Tadesse Gure,1 Semir Sultan,1 Rehama Alishum,1 Abdek Ali,2 Bayissa Dibaba,1 Ibsa Usmael,1 Setegn Tsegaye1 1Department of Gynecology and Obstetrics, Haramaya University, Harar, Ethiopia; 2Department of Gynecology and Obstetrics, Jigjiga University, Jigjiga, EthiopiaCorrespondence: Tadesse Gure Tel +251 913868714Email tadebuna@yahoo.comPurpose: To present a rare case of term abdominal pregnancy with live baby.Observation: A 30-year-old gravida VI para V, all alive, woman who did not remember her last menstrual period but was stated amenorrheic for 9 months visited Hiwot Fana Specialized University Hospital (HFSUH) with abdominal pain of two weeks duration. The woman was diagnosed with a term abdominal pregnancy and underwent a laparotomy. The outcome of treatment was a 3200 gm healthy male baby and a total abdominal hysterectomy was done.Conclusion and Importance: Term abdominal pregnancy is an extremely rare type of ectopic pregnancy that can cause diagnostic challenges and leads to life threatening conditions both before and after diagnosis because of the rupture of major blood vessels.Keywords: abdominal pregnancy, ectopic, term, live baby, Ethiopia, Harar

This article discusses the importance of cross-platform UX/UI designs and frameworks and their effectiveness in building web applications and websites. Third-party libraries (TPL) and plug-ins are also emphasized, as they can help developers quickly build and compose applications. However, using these libraries can also pose security risks, as a vulnerability in any library can compromise an entire server and customer data. The paper proposes using multi-authentication with specific parameters to analyze third-party applications and libraries used in cross-platform development. Based on multi-authentication, the proposed model will make setting up web desensitization methods and access control parameters easier. The study also uses various end-user and client-based decision-making indicators, supporting factors, and data metrics to help make accurate decisions about avoiding and blocking unwanted libraries and plug-ins. The research is based on experimentation with five web environments using specific parameters, affecting factors, and supporting data matrices.

Conventional scolicidal agents are still unsatisfactory in combating hydatid disease due to their low efficacy and increased drug side effects. Therefore, novel scolicides are required. This study aimed to evaluate the antihydatic and immunomodulatory effects of eugenol essential oil (Eug) and its nanoemulsion (Eug-NE) in cystic echinococcosis (CE). Eug and Eug-NE were administered orally to CE-infected rats and compared to albendazole (ABZ). Hydatid cyst development was assessed based on organ weight and hypertrophy indicators of the infected organs, along with a histopathological and histochemical evaluation of collagen content. The immunomodulatory effects of treatment on CE were evaluated by serum cytokine levels measurement of interferon-γ (IFN-γ) and interleukin (IL)-4 and immunohistochemical (IHC) analysis of signal transducer and activator of transcription 4 (STAT4) and GATA-binding protein 3 (GATA3) markers. Eug-NE was the most effective in reducing the cyst weights, organ weights, and hypertrophy indicators and improving histopathological lesions with reduced collagen content. Eug and Eug-NE significantly increased the IFN-γ levels and decreased the IL-4 levels, while IHC analysis demonstrated a significant reduction in STAT4 and GATA3 expression in all treated groups. Eug and Eug-NE demonstrated antihydatic and preventative effects, with a substantial decrease in liver fibrosis compared to that of ABZ. Besides their promising immunomodulatory effects, their good treatment response suggests their use as alternatives or complementary scolicidal agents in hydatid cyst treatment.

Knee osteoarthritis is a challenging problem affecting many adults around the world. There are currently no medications that cure knee osteoarthritis. The only way to control the progression of knee osteoarthritis is early detection. Currently, X-ray imaging is a central technique used for the prediction of osteoarthritis. However, the manual X-ray technique is prone to errors due to the lack of expertise of radiologists. Recent studies have described the use of automated systems based on machine learning for the effective prediction of osteoarthritis from X-ray images. However, most of these techniques still need to achieve higher predictive accuracy to detect osteoarthritis at an early stage. This paper suggests a method with higher predictive accuracy that can be employed in the real world for the early detection of knee osteoarthritis. In this paper, we suggest the use of transfer learning models based on sequential convolutional neural networks (CNNs), Visual Geometry Group 16 (VGG-16), and Residual Neural Network 50 (ResNet-50) for the early detection of osteoarthritis from knee X-ray images. In our analysis, we found that all the suggested models achieved a higher level of predictive accuracy, greater than 90%, in detecting osteoarthritis. However, the best-performing model was the pretrained VGG-16 model, which achieved a training accuracy of 99% and a testing accuracy of 92%.

COVID-19 is caused by coronavirus (SARS-CoV-2) is a worldwide health crisis. This severe acute respiratory syndrome was declared an outbreak after the first case was reported in Wuhan, the capital city of Hubei Province in China. On March 11th, 2020, the World Health Organization (WHO) declared it as a pandemic. The pharmaceutical treatment of COVID-19 has garnered significant critical attention due to the unavailability of medications to treat COVID-19. Recently, researchers have shown an increased interest in the monoclonal antibody drugs to treat COVID-19 especially REGEN-COV (casirivimab and imdevimab). This review aims to highlight the relation between the drug and COVID-19 and the recently updated information on the monoclonal antibody REGEN-COV from the U.S. Food and Drug Administration and other authorities. It is also designed to focus on the bibliometric data of REGEN-COV for the last three years (2020, 2021, and 2021) in PubMed and Google Scholar.

Vitamin D is among the increasingly consumed dietary supplements during the COVID-19 pandemic. It plays a regulatory role in the immune system and moderates the renin–angiotensin system, which is implicated in infection pathogenesis. However, the investigation of serum levels of vitamin D3 forms and their relative ratios in COVID-19 patients is worth investigation to understand the impacts of disease severity. Hence, we investigated the serum levels of vitamin D3 (cholecalciferol) and its metabolites (calcifediol and calcitriol), in addition to their relative ratios and correlations with angiotensin-converting enzyme 2 (ACE2), interleukin-6 (Il-6), and neutrophil–lymphocyte ratio (NLR) in COVID-19 patients compared with healthy controls. Oropharyngeal specimens were collected from the study subjects for polymerase chain reaction testing for COVID-19. Whole blood samples were obtained for blood count and NLR testing, and sera were used for the analysis of the levels of the vitamin and its metabolites, ACE2, and IL-6. We enrolled 103 patients and 50 controls. ACE2, Il-6, and NLR were significantly higher in the patients group (72.37 ± 18.67 vs. 32.36 ± 11.27 U/L, 95.84 ± 25.23 vs. 2.76 ± 0.62 pg/mL, and 1.61 ± 0.30 vs. 1.07 ± 0.16, respectively). Cholecalciferol, calcifediol, and calcitriol were significantly lower in patients (18.50 ± 5.36 vs. 29.13 ± 4.94 ng/mL, 14.60 ± 3.30 vs. 23.10 ± 3.02 ng/mL, and 42.90 ± 8.44 vs. 65.15 ± 7.11 pg/mL, respectively). However, their relative ratios were normal in both groups. Levels of the vitamin and metabolites were strongly positively, strongly negatively, and moderately negatively correlated with ACE2, Il-6, and NLR, respectively. COVID-19 infection severity is associated with a significant decrease in vitamin D3 and its metabolites in a parallel pattern, and with a significant increase in ACE2, Il-6, and NLR. Higher levels of vitamin D and its metabolites are potentially protective against severe infection.

Sex cord-stromal tumors are the second most common testicular tumors after germ cell tumors. They account for about 2%–5% of adult testicular tumors. Most of these tumors are benign. The most common sex cord-stromal tumor is Leydig cell tumor. In contrast, testicular fibroma is a very rare type of sex cord-stromal tumors. Histologically, testicular fibromas resemble their ovarian counterparts; however, they are much less common than ovarian fibromas. To the best of our knowledge,

Purpose: This study evaluates the satisfaction of urology residents with the Saudi Board of Urology (SBU) Training Program and identifies areas of weakness and strength to improve the educational environment, surgical competency, and overall satisfaction of urology residents with the program. Methods: We administered an electronic self-made questionnaire that included two sections. One comprised demographic data (age, gender, weight, height, marital status, level of training, city of training, and center of training), while the other concerned SBU evaluation (satisfaction with different aspects of training, such as ways of assessment, mentors' feedback, surgical competency, research, and strengths and weaknesses of SBU). Results: The overall satisfaction of urology residency program was 28.8% while 44.2% of residents had a neutral response. The highest level of satisfaction with clinical and surgical practice was among graduates (56.9%) and Riyadh residents (45.1%). Furthermore, good work/life balance received the lowest level of satisfaction (5.2%) among senior residents, while good clinical experience received the highest level (62.7%) among the graduates. Residents reported a high exposure in endourology and pediatric urology, while transplant, reconstructive, and neurourology had the lowest exposure. Forty-two percent of respondents undertook research during their residency training, but most respondents (54%) did not publish any research papers during their training. Sixty-two percent of graduates felt that their training program did not prepare them adequately to perform well on the board examinations. Conclusion: Our results confirmed that satisfaction of residents with the urology program process is variable according to the city of training. Having high satisfaction level in some cities reflects the improvement of urology training program after restructuring. We identified new areas in need of improvement, namely lack of mentorship, clear and formal assessment process, and variation of training process between central and peripheral programs.

There is a need to synthesize eco-friendly nanoparticles with more effective and potent antibacterial activities. A green and cost-effective method for the synthesis of silver nanoparticles (AgNPs) using Thymus vulgaris, Mentha piperita, and Zingiber officinale extracts was developed. The analytical instrumentation, namely, UV/Vis, absorption spectroscopy, FTIR, and scanning electron microscopy (SEM), was used to determine the developed AgNPs, confirming the functional groups involved in their reduction. Acidic molybdate, DPPH, and FRAP regents were reacted with AgNPs extract to evaluate their antioxidant, scavenging, and oxidative activities. The agar well diffusion method was used to determine the antibacterial potential of AgNPs extracts using clinical isolates. The developed AgNPs showed peaks at 25 cum\Diff, 50 cum\Diff, and 75 cum\Diff, respectively, of 16.59 ± 0.78, 45.94 ± 1.07, and 81.04 ± 0.98 nm, for Thymus vulgaris, Mentha piperita, and Zingiber officinale. SEM revealed uniform prepared and encapsulated AgNPs by plant extracts matrix. The FTIR shows the involvement of amide (-CO-NH2), carbonyl (-CO), and hydroxyl (-OH), which resulted in the reduction of AgNPs. The AgNPs extract showed significantly higher TAA, DPPH, and FRAP values than free AgNPs and plant extract (p

Fournier gangrene is a life-threatening surgical emergency caused by multiple infectious organism usually affecting the subcutaneous perineal, genital or perianal region with high incidence in immunocompromised patients.In very rare situations such our case when it involves the Deep perineal fascia and invading into the corporal body, the necrotizing tissue spread and control becomes more challenging and lead into devastating events with high mortality rate.Staged timely debridement with highly daily examination to rule out necrosis spread is highly recommended.

The swarm-based Artificial Bee Colony (ABC) algorithm has a significant range of applications and is competent, compared to other algorithms, regarding many optimization problems. However, the ABC’s performance in higher-dimension situations towards global optima is not on par with other models due to its deficiency in balancing intensification and diversification. In this research, two different strategies are applied for the improvement of the search capability of the ABC in a multimodal search space. In the ABC, the first strategy, Patron–Prophet, is assessed in the scout bee phase to incorporate a cooperative nature. This strategy works based on the donor–acceptor concept. In addition, a self-adaptability approach is included to balance intensification and diversification. This balancing helps the ABC to search for optimal solutions without premature convergence. The first strategy explores unexplored regions with better insight, and more profound intensification occurs in the discovered areas. The second strategy controls the trap of being in local optima and diversification without the pulse of intensification. The proposed model, named the PP-ABC, was evaluated with mathematical benchmark functions to prove its efficiency in comparison with other existing models. Additionally, the standard and statistical analyses show a better outcome of the proposed algorithm over the compared techniques. The proposed model was applied to a three-bar truss engineering design problem to validate the model’s efficacy, and the results were recorded.

The present study was undertaken to assess the current supply chain system of pharmaceuticals and vaccines in Pakistan in terms of structure, process, and outcomes, as well as related barriers and solutions for an effective supply chain system. A qualitative study was designed to explore stakeholders’ perceptions selected using the snowball sampling technique. A semi-structured interview guide was used to interview these respondents at a convenient time and place. After data collection, recorded interviews were transcribed verbatim and subjected to thematic analysis. The results highlighted that the standard operating procedures (SOPs), checklists, and government guidelines were available at different levels, except for community pharmacies. Timely delivery of quality products and services along with market reputation, experience, and authorization were the key criteria used for supplier selection and evaluation. Good inventory management, financial models, effective coordination, training, and skill development programs were identified as key factors responsible for an efficient supply chain process. Availability of vaccines, their appropriate temperature monitoring, and transportation are also highly compromised in Pakistan. The results of the present study concluded that the current supply chain system in Pakistan is not up to the mark; major factors include poor forecasting and inventory control, delayed order placement, lack of training, inadequate involvement of professionally qualified staff, inadequate financing and procurement processes, and poor coordination and integration among all stakeholders.

Economic Load Dispatch (ELD) belongs to a non-convex optimization problem that aims to reduce total power generation cost by satisfying demand constraints. However, solving the ELD problem is a challenging task, because of its parity and disparity constraints. The Pigeon-Inspired Optimizer (PIO) is a recently proposed optimization algorithm, which belongs to the family of swarm intelligence algorithms. The PIO algorithm has the benefit of conceptual simplicity, and provides better outcomes for various real-world problems. However, this algorithm has the drawback of premature convergence and local stagnation. Therefore, we propose an Oppositional Pigeon-Inspired Optimizer (OPIO) algorithm—to overcome these deficiencies. The proposed algorithm employs Oppositional-Based Learning (OBL) to enhance the quality of the individual, by exploring the global search space. The proposed algorithm would be used to determine the load demand of a power system, by sustaining the various equality and inequality constraints, to diminish the overall generation cost. In this work, the OPIO algorithm was applied to solve the ELD problem of small- (13-unit, 40-unit), medium- (140-unit, 160-unit) and large-scale (320-unit, 640-unit) test systems. The experimental results of the proposed OPIO algorithm demonstrate its efficiency over the conventional PIO algorithm, and other state-of-the-art approaches in the literature. The comparative results demonstrate that the proposed algorithm provides better results—in terms of improved accuracy, higher convergence rate, less computation time, and reduced fuel cost—than the other approaches.

Recent innovations in digital image capturing techniques facilitate the capture of stationary and moving objects. The images can be easily captured via high-end digital cameras, mobile phones and other handheld devices. Most of the time, captured images vary compared to actual objects. The captured images may be contaminated by dark, grey shades and undesirable black spots. There are various reasons for contamination, such as atmospheric conditions, limitations of capturing device and human errors. There are various mechanisms to process the image, which can clean up contaminated image to match with the original one. The image processing applications primarily require detection of accurate noise type which is used as input for image restoration. There are filtering techniques, fractional differential gradient and machine learning techniques to detect and identify the type of noise. These methods primarily rely on image content and spatial domain information of a given image. With the advancements in the technologies, deep learning (DL) is a technology that can be trained to mimic human intelligence to recognize various image patterns, audio files and text for accuracy. A deep learning framework empowers correct processing of multiple images for object identification and quick decision abilities without human interventions. Here Convolution Neural Network (CNN) model has been implemented to detect and identify types of noise in the given image. Over the multiple internal iterations to optimize the results, the identified noise is classified with 99.25% accuracy using the Proposed System Architecture (PSA) compared with AlexNet, Yolo V5, Yolo V3, RCNN and CNN. The proposed model in this study proved to be suitable for the classification of mural images on the basis of every performance parameter. The precision, accuracy, f1-score and recall of the PSA are 98.50%, 99.25%, 98.50% and 98.50%, respectively. This study contributes to the development of mural art recovery.

Abstract Flexible cystoscopy under local anaesthesia is standard for the surveillance of bladder cancer. Frequently, several reusable cystoscopes fail to reprocess. With the new grasper incorporated single-use cystoscope for retrieval of ureteric stents, we explored the feasibility of using it off-label for diagnosis and the detection of bladder cancer. Consecutive diagnostic flexible cystoscopies between Mar 2016 and Nov 2018 were reviewed comparing the reusable versus the disposable cystoscopes. A total of 390 patients underwent 1211 cystoscopies. Median age was 61.5 years (SD 14.2, 18.8–91.4), males 331 (84.9%) and females 59 (15.1%). Indication for cystoscopy was prior malignancy in 1183 procedures (97.7%), haematuria 19 (1.6%) or bladder mass 7 (0.6%). There were 608 reusable and 603 disposable cystoscopies. There was no significant difference between groups at baseline in age, sex, BMI, smoking status, or prior tumor risk category. There was no significant difference in positive findings (123/608, 20.2% vs 111/603, 18.4%, p = 0.425) or cancer detection rates (95/608, 15.6% vs 88/603, 14.4%, p 0.574) among the two groups, respectively. We conclude that the disposable grasper integrated cystoscope is comparable to reusable cystoscope in the detection of bladder cancer.

Novel explicit wave solutions are constructed for the fractional nonlinear model of the low–pass electrical transmission lines. A new fractional definition (Atangana–Baleanu derivative operator) is employed through the modified Khater method to get new wave solutions in distinct types of this model. The stability property of the obtained solutions is tested to show the ability of our obtained solutions in using through the physical experiments. Moreover, the obtained analytical solutions are used to evaluate the initial and boundary conditions that allows applying the cubic & septic B–spline schemes to investigate the numerical solutions of this model. The novelty and advantage of the proposed method are illustrated by applying to this model. Some sketches are plotted to show more about the dynamical behavior of this model.

In a graph G, the first and second degrees of a vertex v are equal to the number of their first and second neighbors and are denoted by d(v/G) and d2(v/G), respectively. The first, second and third leap Zagreb indices are the sum of squares of second degrees of vertices of G, the sum of products of second degrees of pairs of adjacent vertices in G and the sum of products of first and second degrees of vertices of G, respectively. In this paper, we initiate in studying a new class of graphs depending on the relationship between first and second degrees of vertices and is so-called a leap graph. Some properties of the leap graphs are presented. All leap trees and {C3, C4}- free leap graphs are characterized.

Urethral clear cell carcinoma is very rare disease affecting both sexes, however it is mostly described in female urethra. The origin of this cancer is yet to be discovered.We report a 57 years old lady who presented to our clinic with obstructive lower urinary tract symptoms and found to have a urethral diverticulum containing a soft tissue lesion found to be a clear cell carcinoma after excision.Having high suspicion and early detection of these cases leads to a better outcome.

Protecting patient privacy and medical records is a legal requirement. Traditional encryption methods fall short of handling the large volume of medical image data and their peculiar statistical properties. In this paper, we propose a generic medical image encryption framework based on a novel arrangement of two very efficient constructs, dynamic substitution boxes (S-boxes) and chaotic maps. The arrangement of S-box substitution before and after chaotic substitution is shown to successfully resist chosen plaintext and chosen ciphertext attacks. Special precautions are taken to fend off the reset attack against pseudorandom number generators. We show how to implement the generic framework using any key-dependent dynamic S-box construction method and any chaotic map. Experimental results show that the proposed framework successfully passes all security tests regardless of the chaotic map used for implementation. Based on speed analysis, we recommend the use of the classical Baker map or Henon map to achieve encryption throughput approaching 90 MB/s on a modern PC without hardware acceleration.

COVID-19 is a global epidemic. Till now, there is no remedy for this epidemic. However, isolation and social distancing are seemed to be effective preventive measures to control this pandemic. Therefore, in this article, an optimization problem is formulated that accommodates both isolation and social distancing features of the individuals. To promote social distancing, we solve the formulated problem by applying a noncooperative game that can provide an incentive for maintaining social distancing to prevent the spread of COVID-19. Furthermore, the sustainability of the lockdown policy is interpreted with the help of our proposed game-theoretic incentive model for maintaining social distancing where there exists a Nash equilibrium. Finally, we perform an extensive numerical analysis that shows the effectiveness of the proposed approach in terms of achieving the desired social-distancing to prevent the outbreak of the COVID-19 in a noncooperative environment. Numerical results show that the individual incentive increases more than 85% with an increasing percentage of home isolation from 25% to 100% for all considered scenarios. The numerical results also demonstrate that in a particular percentage of home isolation, the individual incentive decreases with an increasing number of individuals.

Background: Previous studies have reported the association between gastroesophageal reflux disease (GERD) and obstructive sleep apnea (OSA). Objectives: The aim of the present study was to assess the prevalence of GERD among Saudi population, and to determine the risk of OSA among those diagnosed with GERD in Taif city of Saudi Arabia. Materials and Methods: A cross-sectional online survey was done among 843 Saudi participants using a questionnaire by collecting demographic data, weight, height, blood group, and having a previous nose or throat surgery. The GERD questionnaire and the sleep apnea symptom index were used to determine GERD and OSA prevalence. Results: The prevalence of GERD and OSA was 17.6% and 2.4%, respectively. A significantly higher prevalence of GERD was found among males, those with age> 50 years, employees and obese participants, and those having OSA. Participants with an age> 50 years and males, had a significantly higher prevalence of OSA. Being a male and older age were predictors for GERD, and the presence of GERD was an independent predictors for OSA. Conclusion: Future population-based studies including a representative sample of the population should be done to confirm the revealed association between GERD and OSA. It is necessary to assess GERD in patients with OSA in clinical practice.

Objective: The objective of this study is to investigate medical students' perception, choices of future career, and competency in urology. Methods: A cross-sectional survey was distributed among 5th, 6th, and 7th (interns) year medical students at King Saud bin Abdulaziz University for Health Sciences using both hard copies and soft copies. Major outcomes were medical students' perception, future career decision, and core skills in urology. Results: The overall response rate was 51.3%. A total number of 163 responses (122 were males and 41 females) were collected. Only 8% indicated that they would pursue a surgical career in urology and 42% thought that they had received a good clinical exposure to urology. Of the participants, 67.5% viewed urology as a male-dominated field. Only 17% of the respondents either agreed or strongly agreed that they were considering a future career in urology. Female students were less likely to consider a urological career (P < 0.01). About 32.5% were confident at urethral catheterization. About 66.9% felt that a workshop day to enhance urological skills and knowledge will be beneficial. Females were more confident at assessing a urological case in an acute setting (P < 0.05). Conclusion: Most of the students agreed that their urology exposure was inadequate and their confidence at urethral catheterization was low. As in many different global studies, urology is still regarded as a specialty with a male dominance. This report is consistent with the global decline in formal urological education.

Admission-based circulating biomarkers for the prediction of outcomes in trauma patients could be useful for clinical decision support. It is unknown which molecular classes of biomolecules can contribute biomarkers to predictive modeling. Here, we analyzed a large multi-omic database of over 8500 markers (proteomics, metabolomics, and lipidomics) to identify prognostic biomarkers in the circulating compartment for adverse outcomes, including mortality and slow recovery, in severely injured trauma patients. Admission plasma samples from patients (n = 129) enrolled in the Prehospital Air Medical Plasma (PAMPer) trial were analyzed using mass spectrometry (metabolomics and lipidomics) and aptamer-based (proteomics) assays. Biomarkers were selected via Least Absolute Shrinkage and Selection Operator (LASSO) regression modeling and machine learning analysis. A combination of five proteins from the proteomic layer was best at discriminating resolvers from non-resolvers from critical illness with an Area Under the Receiver Operating Characteristic curve (AUC) of 0.74, while 26 multi-omic features predicted 30-day survival with an AUC of 0.77. Patients with traumatic brain injury as part of their injury complex had a unique subset of features that predicted 30-day survival. Our findings indicate that multi-omic analyses can identify novel admission-based prognostic biomarkers for outcomes in trauma patients. Unique biomarker discovery also has the potential to provide biologic insights.

A Wireless Sensor Network (WSN) is a group of autonomous sensors that are distributed geographically. However, sensor nodes in WSNs are battery-powered, and the energy drainage is a significant issue. The clustering approach holds an imperative part in boosting the lifespan of WSNs. This approach gathers the sensors into clusters and selects the cluster heads (CHs). CHs accumulate the information from the cluster members and transfer the data to the base station (BS). Yet, the most challenging task is to select the optimal CHs and thereby to enhance the network lifetime. This article introduces an optimal cluster head selection framework using hybrid opposition-based learning with the gray wolf optimization algorithm. The hybrid technique dynamically trades off between the exploitation and exploration search strategies in selecting the best CHs. In addition, the four different metrics such as energy consumption, minimal distance, node centrality and node degree are utilized. This proposed selection mechanism enhances the network efficiency by selecting the optimal CHs. In addition, the proposed algorithm is experimented on MATLAB (2018a) and validated by different performance metrics such as energy, alive nodes, BS position, and packet delivery ratio. The obtained results of the proposed algorithm exhibit better outcome in terms of more alive nodes per round, maximum number of packets delivery to the BS, improved residual energy and enhanced lifetime. At last, the proposed algorithm has achieved a better lifetime of ≈20%, ≈30% and ≈45% compared to grey wolf optimization (GWO), Artificial bee colony (ABC) and Low-energy adaptive clustering hierarchy (LEACH) techniques.

Sustainable Cloud Computing is the modern era’s most popular technology. It is improving daily, offering billions of people sustainable services. Currently, three deployment models are available: (1) public, (2) private, and (3) hybrid cloud. Recently, each deployment model has undergone extensive research. However, relatively little work has been carried out regarding clients’ adoption of sustainable public cloud computing (PCC). We are particularly interested in this area because PCC is widely used worldwide. As evident from the literature, there is no up-to-date systematic literature review (SLR) on the challenges clients confront in PCC. There is a gap that needs urgent attention in this area. We produced an SLR by examining the existing cloud computing models in this research. We concentrated on the challenges encountered by clients during user adoption of a sustainable PCC. We uncovered a total of 29 obstacles that clients confront when adopting sustainable PCC. In 2020, 18 of the 29 challenges were reported. This demonstrates the tremendous threat that PCC still faces. Nineteen of these are considered critical challenges to us. We consider a challenge a critical challenge if its occurrence in the final selected sample of the paper is greater than 20%. These challenges will negatively affect client adoption in PCC. Furthermore, we performed three different analyses on the critical challenges. Our analysis may indicate that these challenges are significant for all the continents. These challenges vary with the passage of time and with the venue of publication. Our results will assist the client’s organization in understanding the issue. Furthermore, it will also help the vendor’s organization determine the potential solutions to the highlighted challenges.

According to United Nations (UN) 2030 agenda, the pollution detection system needs to be improved for the establishment of fresh air to obtain healthy life of living things. There are many reasons for the pollution and one of the reasons for pollution is from the emissions of the vehicles. Currently digital technologies such as the Internet of Things and Long-Range are showing significant impact on establishment of smart infrastructure for achieving the sustainability. Based on this motivation, this study implemented a sensor node and gateway-based Internet of Things architecture to monitor the air quality index value from any location through Long-Range communication, and Internet connectivity. To realize the proposed system, a customization of hardware is carried out and implemented the customized hardware i.e., sensor node and gateway in real-time. The sensor node is powered with node mapping to minimize the data redundancy. In this study, the evaluation metrics such as bit rate, receiver sensitivity, and time on air are evaluated by spreading factor (SF), code rate (CR), bandwidth, number of packets, payload size, preamble, and noise figure. The real-time sensor values are logged on the cloud server through sensor node and gateway. The sensor values recorded in the cloud server is compared with optimal values and concluded that the PM10, PM2.5 are high in the air and remaining values of NO2, O3, CO are optimal in the air. Along with this an architecture is proposed for interfacing the hardware with blockchain network through cloud server and API for node authentication.

The nurse scheduling problem (NSP) is an NP-Hard combinatorial optimization scheduling problem that allocates a set of shifts to the group of nurses concerning the schedule period subject to the constraints. The objective of the NSP is to create a schedule that satisfies both hard and soft constraints suggested by the healthcare management. This work explores the meta-heuristic approach to an artificial bee colony algorithm with the Nelder–Mead method (NM-ABC) to perform efficient nurse scheduling. Nelder–Mead (NM) method is used as a local search in the onlooker bee phase of ABC to enhance the intensification process of ABC. Thus, the author proposed an improvised solution strategy at the onlooker bee phase with the benefits of the NM method. The proposed algorithm NM-ABC is evaluated using the standard dataset NSPLib, and the experiments are performed on various-sized NSP instances. The performance of the NM-ABC is measured using eight performance metrics: best time, standard deviation, least error rate, success percentage, cost reduction, gap, and feasibility analysis. The results of our experiment reveal that the proposed NM-ABC algorithm attains highly significant achievements compared to other existing algorithms. The cost of our algorithm is reduced by 0.66%, and the gap percentage to move towards the optimum value is 94.30%. Instances have been successfully solved to obtain the best deal with the known optimal value recorded in NSPLib.

Vehicle identification and classification are some of the major tasks in the areas of toll management and traffic management, where these smart transportation systems are implemented by integrating various information communication technologies and multiple types of hardware. The currently shifting era toward artificial intelligence has also motivated the implementation of vehicle identification and classification using AI-based techniques, such as machine learning, artificial neural network and deep learning. In this research, we used the deep learning YOLOv3 algorithm and trained it on a custom dataset of vehicles that included different vehicle classes as per the Indian Government’s recommendation to implement the automatic vehicle identification and classification for use in the toll management system deployed at toll plazas. For faster processing of the test videos, the frames were saved at a certain interval and then the saved frames were passed through the algorithm. Apart from toll plazas, we also tested the algorithm for vehicle identification and classification on highways and urban areas. Implementing automatic vehicle identification and classification using traditional techniques is a highly proprietary endeavor. Since YOLOv3 is an open-standard-based algorithm, it paves the way to developing sustainable solutions in the area of smart transportation.

Software-defined networking (SDN) is an innovative network architecture that splits the control and management planes from the data plane. It helps in simplifying network manageability and programmability, along with several other benefits. Due to the programmability features, SDN is gaining popularity in both academia and industry. However, this emerging paradigm has been facing diverse kinds of challenges during the SDN implementation process and with respect to adoption of existing technologies. This paper evaluates several existing approaches in SDN and compares and analyzes the findings. The paper is organized into seven categories, namely network testing and verification, flow rule installation mechanisms, network security and management issues related to SDN implementation, memory management studies, SDN simulators and emulators, SDN programming languages, and SDN controller platforms. Each category has significance in the implementation of SDN networks. During the implementation process, network testing and verification is very important to avoid packet violations and network inefficiencies. Similarly, consistent flow rule installation, especially in the case of policy change at the controller, needs to be carefully implemented. Effective network security and memory management, at both the network control and data planes, play a vital role in SDN. Furthermore, SDN simulation tools, controller platforms, and programming languages help academia and industry to implement and test their developed network applications. We also compare the existing SDN studies in detail in terms of classification and discuss their benefits and limitations. Finally, future research guidelines are provided, and the paper is concluded.

The upcoming models of vehicles will be able to communicate with each other and will thus be able to share and/or transfer information. A vehicular ad hoc network (VANET) is an application of this vehicular communication that leads to an intelligent transportation system (ITS). Vehicle-to-vehicle (V2V) and vehicle-to-infrastructure (V2I) are the two distinct types of vehicular ad hoc networks (VANET). V2V and V2I technologies are together known as V2X and are recently being tested. Continuous research to enhance routing considers different characteristics and exciting aspects of VANETs. The proposed schemes are classified based on the operational scenario. A survey of proposed routing schemes in the last eight years is presented to determine the design considerations and the approach used in every proposed system, along with their shortcomings. This survey will assist new scholars in this field to analyze existing state-of-the-art systems. The table at the end of each routing scheme shows the proposed routing scheme’s simulation, routing, and scenario parameters. This paper also reviews VANET technology, its role in the intelligent transportation system, recent development in the field, and the timeline for implementation of the system.

Moisturizers are one of the most widely used preparations in cosmetics and have been extensively used to soften the skin for consumers. Moisturizers work effectively in combating dry skin which may cause pain, tightness, itch, stinging, and/or tingling. The aim of this review is to evaluate published studies on the history, ingredients, preparation processes, characteristics, uses, and applications of moisturizers. Moisturizers bridge the gap between medicine and consumer goods by being used to make the skin more beautiful and healthy. In the future, in moisturizer therapy, the capacity to adapt specific agents to specific dermatological demands will be crucial. Cosmetically, moisturizers make the skin smooth by the mechanism of increasing the water content in the stratum corneum, hence exerting its most vital action, which is moisturizing action and maintaining a normal skin pH.

Deep learning-based machine learning models have shown significant results in speech recognition and numerous vision-related tasks. The performance of the present speech-to-text model relies upon the hyperparameters used in this research work. In this research work, it is shown that convolutional neural networks (CNNs) can model raw and tonal speech signals. Their performance is on par with existing recognition systems. This study extends the role of the CNN-based approach to robust and uncommon speech signals (tonal) using its own designed database for target research. The main objective of this research work was to develop a speech-to-text recognition system to recognize the tonal speech signals of Gurbani hymns using a CNN. Further, the CNN model, with six layers of 2DConv, 2DMax Pooling, and 256 dense layer units (Google’s TensorFlow service) was also used in this work, as well as Praat for speech segmentation. Feature extraction was enforced using the MFCC feature extraction technique, which extracts standard speech features and features of background music as well. Our study reveals that the CNN-based method for identifying tonal speech sentences and adding instrumental knowledge performs better than the existing and conventional approaches. The experimental results demonstrate the significant performance of the present CNN architecture by providing an 89.15% accuracy rate and a 10.56% WER for continuous and extensive vocabulary sentences of speech signals with different tones.