Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) is a positive sense single stranded RNA genome containing virus which has infected millions of people all over the world particularly China. The virus has been mutating rapidly enough resulting in the emergence of new variants and sub-variants which have reportedly been spread from Wuhan city of China, the epicenter of the virus, to the rest of China and all over the world. The occurrence of mutations in the viral genome especially in the viral spike protein region has resulted in the evolution of multiple variants and sub-variants which gives the virus benefit of host immune evasion and render the modern-day vaccines and therapeutics ineffective. Therefore, there is a continuous need to study the genetic characteristics and evolutionary dynamics of the SARS-CoV-2 variants. Hence, in this study, a total of 833 complete genomes of SARS-CoV-2 variants (including the wild-type or reference sequence) from Taiyuan and Wuhan cities of China were genetically characterized and studies their phylogenetic and evolutionary dynamics using phylogenetics, genetic similarity, and phylogenetic network analysis. Our results show a lot of useful information regarding the evolutionary dynamics of the viral variants, their genetic similarities/dissimilarities, and their phylogenetic relationships. It has been reported in this study that the SARS-CoV-2 variants have nine recombinant events which suggest very important information regarding the viral evolution. In addition, the phylogenetic network analysis shows the number of mutations resulting in emergence and clustering of the viral variants of both cities which reveal significant information regarding the phylodynamics of the virus. This study, to the best of our knowledge, is the first ever genetic comparative study of Taiyuan and Wuhan cities. This study will help better understand the virus, cope with the emergence, and spread of new variants at local level as well as at international level and inform the public health authorities to make better informed decisions in designing new viral vaccines and therapeutics.

The aim of this paper is to develop a composite coating on St 304 steel employing TIG process. Ti wire cored with graphite powder is used as the means of coating material. The process parameters are varied in order to develop coating with optimum characteristics (i.e., hardness and wear resistance). The microstructure of the coating is analyzed with SEM and XRD. It is found that both of the hardness and wear resistance increase as the current increases while both of these properties decrease as the travelling speed increases. It is found that the coated samples with composite layers have more hardness than the substrate and it could range up to 1100 HV being almost 4.5 times higher than the hardness of St 304. Likewise, the wear resistance of coating is observed to be 4.5 times higher than that of substrate. The high performance of coating, as revealed by microstructural analysis, is due to formation of TiC and Cr₂₃C₆ .The optimum conditions to produce the coating are proposed to be 120 A current and 3.17 mm/s travel speed.

Disadvantaged groups around the world have suffered and endured higher mortality during the current COVID-19 pandemic. This contrast disparity suggests that socioeconomic and health-related factors may drive inequality in disease outcome. To identify these factors correlated with COVID-19 outcome, country aggregate data provided by the Lancet COVID-19 Commission subjected to correlation analysis. Socioeconomic and health-related variables were used to predict mortality in the top 5 most affected countries using ridge regression and extreme gradient boosting (XGBoost) models. Our data reveal that predictors related to demographics and social disadvantage correlate with COVID-19 mortality per million and that XGBoost performed better than ridge regression. Taken together, our findings suggest that the health consequence of the current pandemic is not just confined to indiscriminate impact of a viral infection but that these preventable effects are amplified based on pre-existing health and socioeconomic inequalities.

herein, we have compared the performance of SVM and MLP in emotion recognition using speech and song channels of the RAVDESS dataset. We have undertaken a journey to extract various audio features, identify optimal scaling strategy and hyperparameter for our models. To increase sample size, we have performed audio data augmentation and addressed data imbalance using SMOTE. Our data indicate that optimised SVM outperforms MLP with an accuracy of 82 compared to 75%. Following data augmentation, the performance of both algorithms was identical at ~79%, however, overfitting was evident for the SVM. Our final exploration indicated that the performance of both SVM and MLP were similar in which both resulted in lower accuracy for the speech channel compared to the song channel. Our findings suggest that both SVM and MLP are powerful classifiers for emotion recognition in a vocal-dependent manner.

The aim of this work is evaluation of corrosion behavior of SS 304 coated through TIG process. In this work, Ti tube cored with graphite powder was applied. Experimental processes were shown that the optimum parameters for coating procedure are voltage of 15V, current density of 120A and the speed of 2.1mm/s. Investigations on corrosion resistance of coating are shown that not only this composite coating can improve the tribological behavior of this type of stainless steel, but also there is an improvement in its deteriorative property. Moreover, it is observed that mass reduction in coating is 5 times lower than the base metal.

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) is a single stranded RNA virus which has resulted in the Coronavirus Disease 2019 (COVID-19) pandemic and has infected millions of people all over the world. SARS-CoV-2 has been mutating rapidly resulting in the emergence of multiple variants to escape the host immune system mainly by mutations in its receptor binding domain (RBD) of the spike protein. This rapid evolution of the SARS-CoV-2 posed a great challenge regarding the efficacy and effectiveness of the current SARS-CoV-2 vaccines. The RBD and full-length spike of SARS-CoV-2 is the main target of the neutralizing antibodies. Many SARS-CoV-2 variants are considered to have the potential to escape from the host immune system. In this study, the RBD of Alpha, Beta, Gamma, Kappa and Omicron and the full-length spike of BA.1, BA.2, BA.3, BA.4/5, BQ.1.1 and XBB.1.5 Omicron variants were used as coating antigens in Enzyme Linked Immuno-Sorbent Assay (ELISA) to check the neutralization capability of COVID-19 convalescent sera from patients of first wave of infection occurring in Wuhan. Our results show that the currently circulating Omicron BQ.1.1, XBB.1.5 and previous Omicron BA.1, BA.2 and BA.4/5 do not show significant reduction in neutralization, while Omicron BA.3 and previous variants Alpha, Beta, Gamma, Kappa, and Omicron showed a significantly reduced neutralization when compared to the wild-type Wuhan strain. These results indicate patients recovering from natural infection of early original Wuhan strain may have the potential to resist infection of current circulating variants and the vaccines using the prototype antigen may still working for newly emerged variants.

The COVID-19 pandemic has caused ~ 2 million fatalities. Significant progress has been made in advancing our understanding of the disease process, one of the unanswered questions, however, is the anomaly in the case/mortality ratio with Mexico as a clear example. Herein, this anomaly is explored by spatial analysis and whether mortality varies locally according to local factors. To address this, hexagonal cartogram maps (hexbin) used to spatially map COVID-19 mortality and visualise association with patient-level data on demographics and pre-existing health conditions. This was further interrogated at local Mexico City level by choropleth mapping. Our data show that the use of hexagonal cartograms is a better approach for spatial mapping of COVID-19 data in Mexico as it addresses bias in area size and population. We report sex/age-related spatial relationship with mortality amongst the Mexican states and a trend between health conditions and mortality at the state level. Within Mexico City, there is a clear south, north divide with higher mortality in the northern municipalities. Deceased patients in these northern municipalities have the highest pre-existing health conditions. Taken together, this study provides an improved presentation of COVID-19 mapping in Mexico and demonstrates spatial divergence of the mortality in Mexico.

Pakistan relies mostly on fossil fuel for fulfilling its energy demand which is expensive as 11 well as not friendly for the environment. The need of the hour is to shift from fossil fuels to renew-12 able energy resources like geothermal, wind, solar etc. to cater for global warming issues. Pakistan 13 has a lot of potential geothermal sites because of several fault lines and hot springs. A sound 14 knowledge of ground temperature is essential to use geothermal energy, which is obtained by drill-15 ing boreholes and putting sensors making it an expensive procedure. Thus, to avoid huge cost for 16 drilling bore holes, particularly for ground temperature analysis, a numerical approach has been 17 considered for determining ground temperature. Furthermore, correlation charts between air and 18 ground temperatures have been developed as there were no proper studies on the ground temper-19 ature of Pakistan. Then, with the help of a boreholes drilled in National University of Sciences and 20 Technology Islamabad Pakistan, the actual ground and numerically calculated temperatures have 21 been compared which shows an error margin in the range between 0.27% for higher depths to 7.3% 22 near surface. Thus, it is shown that the proposed method is easy to implement and better than large 23 scale testing methods for the depths at which geothermal energy is extracted.

This comprehensive review delves into the intricate relationship between Sodium-GlucoseCo-Transporter 2 (SGLT2) inhibitors and AF, highlighting the multifaceted actions of thesedrugs and their significant cardiovascular benefits. Mechanisms: SGLT2 inhibitors inducesubstantial hemodynamic changes, impacting blood pressure, adipose tissue function, andinflammation. They enhance glycemic control while potentially reducing oxidative stress andinflammation, and they modulate atrial electrophysiology, collectively contributing to the reductionof AF risk. Clinical Evidence: This review synthesizes a wealth of clinical evidence,drawing from observational studies and randomized controlled trials, consistently demonstratinga correlation between SGLT2 inhibitors, particularly dapagliflozin and empagliflozin, anda decreased risk of AF. This protective effect is most pronounced in patients with type 2 diabetesand heart failure, underscoring their potential as therapeutic agents for high-risk populations.Potential Benefits: SGLT2 inhibitors offer a spectrum of potential benefits, includingsubstantial reductions in AF risk, particularly in patients with type 2 diabetes and heart failure.These drugs have consistently demonstrated benefits in reducing heart failure hospitalizations,cardiovascular mortality, and stroke risk, which indirectly contribute to better AF management.Furthermore, SGLT2 inhibitors exhibit renoprotective properties by slowing theprogression of chronic kidney disease and reducing albuminuria, offering substantial benefitsfor AF patients with comorbid kidney disease. Metabolic improvements involving weight loss,blood pressure reduction, and favorable changes in lipid profiles contribute to enhanced cardiovascular health and reduced AF risk. Originally designed for diabetes management, thesemedications offer a holistic approach to AF risk reduction, particularly in patients with type 2diabetes and heart failure.

In response to the growing demand for high-strength and high-toughness materials in industries like aerospace and automobiles, there is a need for metal matrix composites (MMCs) that can simultaneously enhance strength and toughness. This paper focuses on the design configurations of MMCs, which include both the configurations resulting from reinforcements and the inherent heterogeneity of the matrix itself. The mechanical properties of MMCs are influenced by factors such as reinforcement content, shape, size, and spatial distribution within the composite architecture. Among them, Aluminum matrix composites (AMCs) are particularly significant in aerospace, electronics, and electric vehicles due to their potential for weight reduction and enhanced performance. However, the challenge lies in the inverse relationship between strength and toughness, hindering the widespread utilization and large-scale development of MMCs. The design configuration of composites plays a critical role in achieving concurrent improvements in strength and toughness. This review explores the advantages of toughness, toughening mechanisms, reinforcement distribution characteristics, and structural parameters in the design of composite architectures. Drawing inspiration from biological composites like bone, the development of synthetic composites with homogeneous structural designs provides insights into attaining exceptional strength and toughness in lightweight engineering structures. Additionally, understanding fracture behavior and toughening mechanisms in heterogeneous nanostructures is vital for advancing the field of metal matrix composites. Summarily, the design of composite architectures holds tremendous potential for tailoring AMCs with outstanding strength and toughness, addressing the requirements of lightweight engineering structures in various industries.

Atrial fibrillation (AF) is the most common cardiac arrhythmia encountered in clinical practice, affecting millions of individuals worldwide. The management of AF presents significant challenges due to its complex nature and diverse patient profiles. In recent years, the integration of artificial intelligence (AI) and machine learning technologies has emerged as a promising approach to enhance various aspects of AF management. This comprehensive review aims to explore the evolving role of AI in AF management, with a focus on its potential applications in diagnosis, risk prediction, treatment selection, and patient monitoring. Real academic references were employed to provide evidence-based insights into the impact of AI on AF management.

Atrial fibrillation (AF) is a common cardiac arrhythmia associated with significant morbidity and mortality. The role of epicardial adipose tissue (EAT), particularly in the left atrium, has garnered attention in the pathophysiology of AF. This review aims to provide an overview of the association between the thickness of left atrial epicardial adipose tissue (LA-EAT) and the incidence of AF, while exploring the arrhythmogenic effects of LA-EAT and the impact of epicardial fat tissue changes with age and in pathological conditions. A comprehensive search of PubMed was conducted, and relevant studies were selected based on their inclusion criteria. The findings suggest that increased LA-EAT thickness is associated with an elevated risk of AF, potentially mediated through inflammatory and proarrhythmic effects. Age-related changes in epicardial fat tissue and alterations in pathological conditions, such as obesity, metabolic syndrome, and cardiovascular diseases, further contribute to the development and progression of AF. Further research is needed to deepen our understanding of these mechanisms and their clinical implications.

Distortion of financial statements is recognized as one of the most important issues in the field of accounting and auditing, which is also one of the most common issues today. In this regard, the present research was conducted, in which stock exchange information was used to investigate, predict, and model accounting distortions. For this purpose, financial performance, non-financial metrics, market-based metrics and commitment, or selection items were reviewed over a 6-year period. For collecting data of distorting companies, database of the Society of Certified Public Accountants in Iran was used and the information was analyzed using data mining methods (decision tree, neural networks, and Bayesian method). The results showed that analysis of financial statements҆ information has a high accuracy in determining and identifying the distorted financial statements. Using this information, it is possible to get better acquainted with the methods of document distortion and to take necessary measures in order to control and prevent administrative violations at national and international levels. Given frequent occurrence of these violations, artificial intelligence models can be used to identify these papers.

Electrical contractors have experienced a rise in occupational fatalities in recent years. In 2017, electrical contractors also had the second highest number of non-fatal injuries among specialty trade contractors. Identifying statistically significant dependencies between these catastrophic outcomes and a handful of well-defined contributing factors in construction accidents offers a first step in mitigating the risks of construction accidents in this trade. Therefore, this study used methodologies of descriptive and quantitative statistics to identify the contributing factors most affecting occupational accident outcomes among electrical contracting enterprises, given an accident occurred. Accident reports were collected from the Occupational Safety and Health Administration’s fatality and catastrophe database. To ensure the reliability of the data, the team manually codified more than 600 incidents through a comprehensive content analysis using injury-classification standards. Inclusive of both fatal and non-fatal injuries, the results showed that most accidents happened in nonresidential buildings, new construction, and small projects (i.e., $50,000 or less). The main source of injuries manifested in parts and materials (46%), followed by tools, instruments, and equipment (19%), and structure and surfaces (16%). The most frequent types of injuries were fractures (31%), electrocutions (27%), and electrical burns (14%); the main injured body parts were upper extremities (25%), head (23%), and body system (18%). Among non-fatal cases, falls (37%), exposure to electricity (36%), and contact with objects (19%) caused most injuries; among fatal cases, exposure to electricity was the leading cause of death (50%), followed by falls (28%) and contact with objects (19%). The analysis also investigated the impact of several accident factors on the degree of injuries and found significant effects from such factors such as project type, source of injury, cause of injuries, injured part of body, nature of injury, and event type. In other words, the statistical probability of a fatal accident—given an accident occurrence—changes significantly based on the degree of these factors. Beyond these outcomes, the described content-analysis methodology contributes to the accident-analysis body of knowledge by providing a framework for codifying data from accident reports to facilitate future analysis and modeling attempts (e.g., developing logistic regression models) to subsequently mitigate more injuries in other fields.

β-hydroxy β-methylbutyrate (HMB) is a bioactive metabolite formed from breakdown of the branched-chain amino acid leucine. Given the popularity of HMB supplements among different athletes, specifically, those who engage in regular resistance training, this review was performed to summarize current literature on some aspects of HMB supplementation that have received less attention. Because of the small number of published studies, it has not been possible to conclude the exact effects of HMB on cardiovascular parameters, oxidative stress and inflammatory markers. Thus, the interpretation of outcomes should be taken cautiously. However, the data presented here suggest that acute HMB supplementation may attenuate pro-inflammatory response following an intense resistance exercise in athletes. Also, the available findings collectively indicate that chronic HMB consumption in conjunction with resistance training has no more adaptive advantages associated with decreasing cardiovascular risk factors and oxidative stress markers. Taken together, there is clearly a need for further well-designed, longer duration studies to support these findings and determine whether HMB supplementation affects the adaptations induced by resistance training associated with body’s inflammatory condition, antioxidative defense system, and cardiovascular risk factors in humans.

Atrial fibrillation (AF), a complex cardiac arrhythmia, necessitates a shift in management paradigms, emphasizing an integrative approach that incorporates lifestyle modifications. Investigations into the role of interleukins and inflammatory mediators are underway, exploring their potential as diagnostic markers and predictors of cardiovascular events in AF. Despite the identification of biomarkers related to cardiac remodeling, their predictive ability for post-cardioversion outcomes remains controversial. Computational models are being developed to understand proarrhythmic contributions and identify anti-AF targets. Addressing the rising global incidence of AF, linked to stroke and heart failure, presents a critical health challenge. As risk factors like obesity prevail, AF becomes a pervasive public health concern, expected to double in prevalence over the next 50 years. The impact on mortality and morbidity, especially through hospitalizations, underscores the need for early detection and effective clinical management. Identifying individuals at increased AF risk becomes crucial for screening and prevention strategies. Lifestyle modifications, including weight management and physical activity, emerge as influential factors in AF management, offering the potential to alleviate symptoms and modify disease progression. The paper concludes by emphasizing the need for safer antiarrhythmic therapies based on AF mechanisms, promising improvement in global AF management. The multifaceted nature of AF etiology requires a broader perspective beyond traditional medical interventions, acknowledging the importance of integrated care to address this significant public health challenge.

The aim of this paper is to at first evaluate the influence of three key parameters including weld current, weld time and electrode force on nugget diameter and tensile strength in resistance projection welding. Then, a 2-D axis-symmetric finite element model is developed to simulate the projection welding and predict the nugget diameter. Finally, the FEM results are compared to experimental data to verify the simulation model and simulated results. In the finite element model, the temperature-dependent material properties were taken into account.

The experience of ego dissolution and self-transcendence induced by psychedelic substances offers a profound challenge to our conventional understanding of the self and our relationship with the natural world. This paper delves into the philosophical underpinnings and implications of this phenomenon, exploring how the temporary breakdown of ego boundaries during a psychedelic experience may foster a sense of profound interconnectedness with nature. Drawing from Western and Eastern philosophical traditions, as well as recent insights from neuroscience and consciousness studies, we examine the potential of psychedelic-induced self-transcendence to catalyze a shift in our perspectives on the human-nature relationship. The paper investigates the philosophical implications of this experience, including its resonance with deep ecological philosophies and its potential to inspire more ecocentric ethical frameworks. Furthermore, we explore the possible practical implications of fostering a sense of ecological connectedness through psychedelics, such as promoting environmentally sustainable behaviors and addressing the pressing challenges of climate change and ecological degradation. Ultimately, this exploration highlights the value of engaging with altered states of consciousness as a means to reconsider our place within the intricate web of life on Earth.

Heavy rains have created significant threats to human health and life. Floods and other natural disasters, which have a global impact annually, can be attributed to extended durations of intense precipitation. Accurate rainfall predictions are crucial in nations such as Bangladesh, where agriculture is the predominant occupation. The efficiency of machine learning (ML) methods is enhanced by the non-linearity of rainfall, surpassing the effectiveness of other alternative ways. Machine learning techniques show that individual classifiers exhibit worse accuracy than ensemble learning (EL) methodologies. Ensemble Learning techniques are utilized for rainfall prediction and estimating rainfall quantity and daily average temperature to enhance comprehension of the diverse Machine Learning algorithms. This research implements the machine learning techniques and ensemble-based classifier to predict the rainfall occurrence, along with the machine learning regressor models and ensemble-based regressor for the rainfall amount prediction and daily average temperature prediction, using Bangladesh Weather Dataset. The results of the machine learning and ensemble-based models are compared using the Accuracy and F1 score for rainfall occurrence prediction. In contrast, MAE and RMSE evaluation metrics are used for ensemble regressor and regression algorithms for the rainfall amount and daily average temperature prediction. With an accuracy of 83.41% and a recall of 78.17%, the Ensemble Classifier is the best at predicting when it will rain, but its precision of 51.16% stands in for the lowest. The Ensemble Regression model outperforms Linear Regression, Random Forest, and SVR in rainfall amount prediction, with the lowest MAE of 0.36 and RMSE of 0.90. The Ensemble Regression provides the most precise results for daily average temperature prediction with the lowest MAE 0.42 and RMSE 0.54 highlighting its superiority over the other regression models in forecasting temperature with less error. Ensemble approaches consistently lead task performance metrics.

While microcredits are, by the development practitioners, repeatedly hailed as a useful measure of development policy, this empirical research in Bangladesh arrives at different results: the governing and disciplinary measures are necessary to trigger the best possible repayment success. Based on interviews with the microfinance beneficiaries, loan officers, and microfinance experts in Bangladesh, I chart some of the most frequently used governing and disciplinary techniques including fictitious proximity of loan officers, their fields of visibility and surveillance processes. The “success” of credit delivery is a neoliberal stance that ensures the ceaseless debt repayment process using the intentional and planned conduct, capitalizing social capital and credit honour reputation, surveillance over business enterprises, use of equivocal language and different forms of deception. The development strategy through microfinance in Bangladesh is a development “dispositif” with a changing shift in its declared social missions.

In our exploration of percutaneous mitral valve repair (PMVR) alongside conditions like atrial fibrillation (AF), we uncover a fascinating and complex relationship that significantly impacts patient outcomes. Our journey through various studies, including patient registries, meta-analyses, and cohort investigations, reveals the intricate interplay between AF and PMVR. Despite the advancements in PMVR techniques, AF adds layers of complexity, influencing procedural success rates, long-term clinical outcomes, and post-operative complications. While some studies suggest that AF may not heavily affect short-term results, others highlight its association with increased risks of mortality, heart failure hospitalization, and post-PMVR adverse events. Our exploration also delves into the anatomical and remodeling changes induced by PMVR, shedding light on the importance of understanding how AF interacts with cardiac chamber remodeling. Along the way, we acknowledge the limitations inherent in such research, including data variability, publication bias, and potential confounding factors, urging a cautious approach to interpreting findings. Through this review, we offer valuable insights into the challenges and considerations surrounding AF in the context of PMVR. Our hope is that these insights will guide clinicians and researchers as they navigate this complex landscape, ultimately enhancing patient care and outcomes in the realm of mitral valve repair

With the rapid surge in technological advancements, an equal amount of investment in technology-embedded teaching has become vital to pace up with the ongoing educational needs. Distance education has evolved from the era of postal services to the use of ICT tools in current times. With the aid of globally updated content across the board, technology usage ensures all students receive equal attention without any discrimination. Importantly, web-based teaching allows all kind of students to learn at their own pace, without the fear of being judged, including professionals who can learn remotely without disturbing their job schedules. Having web-based content allows low-cost and robust implementation of the content upgradation. An improved, yet effective, version of the education using such tools is Hybrid Learning (HL). This learning mode aims to provide luxurious reinforcement to its legitimate candidates while maintaining the quality standards of various elements. Incorporated with both traditional and distance learning methods, along with exploiting social media tools for increased comfort level and peer-to-peer collaboration, HL ultimately facilitates the end user and educational setup. The structure of such a hybrid model is realized by delivering the study material via a learning management system (LMS) designed in compliance with quality standards, which is one of the fundamental tackling techniques for controlling quality constraints. In this paper, we present the recently piloted project by COMSATS University Islamabad (previously known as COMSATS Institute of Information Technology) which is driven by technology-embedded teaching model. This model is an amalgam of the traditional class room model with the aid of state-of-the-art online learning technologies. The students are enrolled as full-time students, with all the courses in traditional classroom mode, except one course offered as hybrid course. This globally adapted model helps the students to benefit from both face-to-face learning as well as gaining hands-on experience on technology-enriched education model providing flexibility of timings, learning pace, and boundaries. Our HL model is equipped with two major synchronous and asynchronous blocks. The synchronous block delivers real-time live interaction scenarios using discussion boards, thereby providing a face-to-face environment. Interactions via social network has witnessed equally surging improvement in the output performance. The asynchronous block refers to the lecture videos, slides and handouts, prepared by imminent professors, available 24/7 for students. To ensure quality output, our HL model follows the course learning outcomes (CLOs), and program learning outcomes (PLOs) as per international standards. As a proof of concept, we have deployed a mechanism at the end of each semester to verify the effectiveness of our model. This mechanism fundamentally surveys the satisfaction levels of all the students enrolled in the HL courses. With the surveys already conducted, a significant level of satisfaction has been noted. Extensive results from these surveys are presented in the paper to further validate the efficiency and robustness of our proposed HL model.

We introduce the notion of generalized contraction on dualistic partial metric spaces. A fixed point theorem for mappings satisfying above mentioned contraction is obtained. Some consequences of our result are obtained. We construct an example to demonstrate the effectiveness of our result among the corresponding results in partial metric spaces. Our results provide substantial generalizations and improvements of several well known results existing in the comparable literature. We discuss an application of our fixed point results to show the existence of solution of functional equations.

Early-life adversity can profoundly impact individual development, yet some demonstrate resilience. Longitudinal studies tracking individuals over extended periods have aided our understanding of resilience and growth following adverse childhood experiences. This paper reviews insights from influential studies like the Kauai Longitudinal Study, the Adverse Childhood Experiences (ACE) Study, and the Dunedin Study. It explores key findings on risk and protective factors, adaptation trajectories, and long-term outcomes associated with resilience. Theoretical frameworks and methodological considerations are discussed. The paper examines how this research informs interventions, policies, and support systems for fostering resilience. It highlights the need for diverse samples, integrating biological and environmental factors, and investigating intergenerational impacts. By synthesizing longitudinal research, this paper elucidates complex pathways to resilience and positive development despite early adversity, offering insights for research and supporting vulnerable populations.

Longitudinal studies tracing high-risk samples over decades have illuminated powerful pathways of resilient adaptation among individuals who overcome adversities faced early in life. This research demonstrates resilience is a common phenomenon driven by dynamic interactions between individual traits and environmental resources that enable positive adjustment despite adversity exposure. For clinical social workers supporting clients impacted by childhood trauma, abuse, violence and other adversities, understanding these resilience processes is vital for developing strengths-based, trauma -informed interventions that enhance positive adaptation. This paper synthesizes key findings from landmark longitudinal resilience studies and explores specific practice implications for resilience-oriented clinical assessment, intervention planning, prevention efforts, and social worker training. By integrating resilience research insights, social workers can more effectively identify and cultivate clients' innate capacities for resilience, facilitating a journey towards posttraumatic growth.

The paper's main goal is to introduce a novel type of interpolative convex contraction and build up some fresh findings for it using the interpolative convex contractions' progressive approach. We have established certain conclusions using orbitally S-complete and Suzuki type contractions in F-metric spaces. My research aims to examine the fixed point method's efficacy in solving fractional differential equation with boundary conditions.

Findings of new targeted treatments with adequate safety evaluations is essential for better cancer cures and mortality rates. Immunotherapy holds promise for patients with relapsed disease, with the ability to elicit long-term remissions. Emerging promising clinical results in B-cell malignancy using gene-altered T-lymphocytes uttering chimeric antigen receptors have sparked a lot of interest. This treatment could open the path for a major difference in the way we treat tumors that are resistant or recurring. Genetically altered T cells used to produce tumor-specific chimeric antigen receptors are resurrected field of adoptive cell therapy by demonstrating remarkable success in the treatment of malignant tumors. Because of the molecular complexity of chimeric antigen receptors -T cells, a variety of engineering approaches to improve safety and effectiveness are necessary to realize larger therapeutic uses. In this study, we investigate at new strategies for enhancing chimeric antigen receptors-T cell therapy by altering chimeric antigen receptors proteins, T lymphocytes, and their relations with other solid tumor microenvironment (TME) aspects.

Khabib Nurmagomedov versus Conor McGregor Ultimate Fighting Championship (UFC) 229 battle was among the most controversial mixed martial art fights of the past decade. In this study, the author examined how the various popular media outlets from the Eastern and Western world portrayed Khabib Nurmagomedov after UFC 229. The author used Huntington’s (1993) clash of civilization thesis by grounding it in the Orientalism and Occidentalism paradigms to examine the phenomenon. Fairclough’s (1989, 1995) model for critical discourse analysis was employed to investigate the various Western and Eastern popular press and digital media platforms (i.e., newspapers, blogs, and sporting news websites). The author analyzed 57 (Western n = 38, Eastern n = 19) media reports per the inclusion criteria. The study results unveiled conflicting predispositions present in the Western and Eastern media for Khabib Nurmagomedov. This study contributes to the limited knowledge about how a Muslim man athlete with diverse racial and ethnic backgrounds is seen contrarily in the Western and Eastern media. The author discusses the theoretical implications of the study.

Objectives: One of the home-made herbal remedy being traditionally used against gout is evaluated to identify any new and characteristic marker compound which may be used as characteristic analytical marker to standardize this polyherbal remedy. Materials and method: A distinct and remarkable peak in the HPLC chromatogram of remedy was identified which was further isolated by fraction collector. This fraction was then evaporated and characterized for structure elucidation using set of various techniques such as RP-HPLC,UV, FTIR, TLC, EIMS and NMR. Results: The compound characterized was non polar compound, ester of betulin and palmitoleic acid. Although the remedy has been standardized using set of four markers as discussed in literature but this newly developed marker compound can be used to further standardize this remedy and other herbal preparations comprising of this marker compound.Conclusion: Hence the isolated compound can be used as novel analytical marker in the standardization of this herbal remedy and the other herbal preparations containing such marker compound

Optical modes bearing optical vortices are important light systems in which to encode information. Optical vortices are robust features of optical beams that do not dissipate upon propagation. Thus decoding the modal content of a beam is a vital component of the process. In this work we present a method to decode modal superpositions of light beams that contain optical vortices. We do so using shear interferometry, which presents a simple and effective means of determining the vortex content of a beam, and extract the parameters of the component vortex modes that constitute them. We find that optical modes in a beam are easily determined. Its modal content can be extracted when they are of comparable magnitude. The use of modes of well defined topological charge but not well defined radial-mode content, such as those produced by phase-only encoding, are much easier to diagnose than pure Laguerre-Gauss modes.

Understanding how cannabis affects our heart is like navigating through a maze of conflicting findings. While it's known for its diverse uses, from medicinal to industrial, cannabis also raises red flags regarding its impact on heart health. This review dives into the intricate relationship between cannabis use and cardiovascular well-being, particularly focusing on changes seen in electrocardiogram (ECG) readings, which serve as vital indicators of heart function and rhythm. When someone tokes up, their heart might race temporarily, but chronic cannabis use paints a darker picture, with potential risks of heart attacks and strokes looming on the horizon. The ECG alterations observed in cannabis users, such as irregular heartbeats and unusual wave patterns, hint at disruptions in the heart's normal workings. Furthermore, cannabis has been linked to prolonging the QT interval in the heart's electrical cycle, potentially setting the stage for dangerous arrhythmias. Amidst this mixed bag of effects, some studies even suggest that tiny amounts of THC, the main psychoactive component of cannabis, might lend a helping hand to the heart. However, caution flags are raised, especially for vulnerable groups. While researchers strive to unravel the mysteries of cannabis and the heart, limitations in current studies remind us that we're still far from having all the answers. Moving forward, future investigations should delve deeper into understanding how cannabis tinkers with our heart's inner workings, especially across different demographics. Armed with this knowledge, we can better tailor interventions to safeguard the heart health of cannabis users.

Soft tactile sensors based on piezoresistive materials have various large-area sensing applications. However, their accuracy is often affected by hysteresis, a phenomenon that poses a significant challenge during operation. This paper introduces a novel approach that employs a backpropagation (BP) neural network to address the hysteresis nonlinearity in conductive fibre-based tactile sensors. To assess the effectiveness of the proposed method, four sensor units with different layer configurations (1, 3, 6, and 12) were designed. These sensor units underwent force sequences to collect corresponding output resistances. A backpropagation network was trained using these force sequences, thereby correcting the resistance values. The training process exhibited excellent convergence, effectively adjusting the network's parameters to minimize the error between predicted and actual resistance values. As a result, the trained BP network accurately predicted the output resistances. Several validation experiments were conducted to highlight the primary contribution of this research. The proposed method reduced the maximum hysteresis error from 24.2% of the sensor's full-scale output to 13.5%. This improvement establishes the approach as a promising solution for enhancing the accuracy of soft tactile sensors based on piezoresistive materials. By effectively mitigating hysteresis nonlinearity, the capabilities of soft tactile sensors in various applications can be enhanced. These sensors become more reliable and more efficient tools for force measurement and control, particularly in the fields of soft robotics and wearable technology. Consequently, their widespread applications extend to robotics, medical devices, consumer electronics, and gaming. Notably, the complete elimination of hysteresis in tactile sensors may not be feasible. Nevertheless, the proposed method effectively modifies the hysteresis nonlinearity, leading to improved sensor output accuracy.

Various heterogeneous devices or objects shall be integrated for transplant and seamless communication under the umbrella of internet of things (IoT). It would facilitate the open accession of data for the growth of a glut of digital services. To build a general framework of IoT is very complex task because of heterogeneity in devices, technologies, platforms and services, operating in the same system. In this paper, we mainly focus on the framework for big data analytics in smart city applications , which being a broad category specifies the different domains for each application. IoT is intended to support the vision of Smart City, where advance technologies will be used for communication for the quality life of citizens. A novel approach used in this paper, is for enhancing the energy conservation and to reduce the delay in big data gathering at tiny sensor nodes used in IoT framework. To implement the smart city scenario in terms of big data in IoT, an efficient (optimized in quality of service) WSN is required where communication of nodes is energy effcient. That is why, a new protocol QoS-IoT is proposed on the top layer of the architecture which is validated over the traditional protocols.

Management of chronic inflammation and wounds has always been a key issue in the pharmaceutical and healthcare sector. Curcumin (CCM) is an active ingredient extracted from turmeric rhizomes that has antioxidant, anti-inflammatory, and antibacterial activities, thus showing significant effectiveness toward wound healing. However, its shortcomings such as poor water solubility, poor chemical stability and fast metabolic rate limit its bioavailability and long-term use. In this context, hydrogels appear to be a versatile matrix for carrying and stabilizing drugs due to the biomimetic structure, soft porous microarchitecture, and pleasant biomechanical properties. The drug loading/releasing efficiencies can also be controlled by use of highly crystalline and porous metal organic frameworks (MOFs). Here, a flexible hydrogel composed of sodium alginate (SA) matrix and CCM-loaded MOFs was constructed for long-term drug release and antibacterial activity. The morphology and physicochemical properties of composite hydrogels were analyzed by scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FT-IR), X-ray diffraction (XRD), ultraviolet visible spectroscopy (UV-Vis), Raman spectroscopy and mechanical property tests. The results showed that the composite hydrogel was highly twistable and bendable to mechanically comply with human skin. The as-prepared hydrogel could capture efficient CCM for slow drug release as well as effective killing of bacteria. Therefore, such composite hydrogel is expected to provide a new management system for chronic wound dressings.

Background and Objective: Segmentation of the femur in Dual Energy X-ray (DXA) images poses challenges due to reduced contrast, noise, bone shape variations, and inconsistent X-ray beam penetration. In this study, we investigate the relationship between noise and certain deep learning (DL) techniques for semantic segmentation of the femur to enhance segmentation and bone mineral density (BMD) accuracy by incorporating noise reduction methods into DL models. Methods: Convolutional neural network (CNN) based models were employed to segment femurs in DXA images and evaluate the effects of noise reduction filters on segmentation accuracy and its effect on BMD calculation. Various noise reduction techniques were integrated into DL-based models to enhance image quality before training. We assessed the performance of the fully convolutional neural network (FCNN) in comparison to noise reduction algorithms and manual segmentation methods. Results: Our study demonstrated that FCNN outperformed noise reduction algorithms in enhancing segmentation accuracy and enabling precise calculation of BMD. The FCNN-based segmentation approach achieved a segmentation accuracy of 98.84% and a correlation coefficient of 0.9928 for BMD measurements, indicating its effectiveness in the clinical diagnosis of osteoporosis. Conclusions: In conclusion, integrating noise reduction techniques into DL-based models significantly improves femur segmentation accuracy in DXA images. The FCNN model, in particular, shows promising results in enhancing BMD calculation and clinical diagnosis of osteoporosis. These findings highlight the potential of DL techniques in addressing segmentation challenges and improving diagnostic accuracy in medical imaging.

Identifying ambient noise-based (ANb) signatures of streams can help in the estimation of their erosive potential (EP) that promotes reverie landslides and soil losses in the fluvial valleys. This is particularly imperative on flooding or rainy days, leading to stronger erosion-prone conditions (colluvium and boulders) of the valley beds inferred from georadar attribute analysis. Developing such research direction can benefit the local communities, as is the case with the Cerrado region of Brazil, where these phenomena have high destructive potential with social, economic, and climatic implications. For the present study, a seasonal stream in the Federal District of Brazil was investigated by ANb monitoring supported by Ground Penetration Radar (GPR) for site characterization. The ANb monitoring was conducted (at a safe distance) with a seismometer over several durations of dry and rainy conditions. The power spectral density (PSDs) was computed as a function of several variables, including weather conditions (rainfall, wind speed, and pressure), time-frequency spectrograms, and ambient noise displacement root mean square (RMS). This analysis also considered the single station horizontal-to-vertical spectral ratio (HVSR), where rain, wind, pressure, river flow and anthropogenic signatures were evident (at selective frequency ranges). Multi-peaks that emerged on the HVSR curve were further analyzed to identify amplitude and frequency changes, and the three peaks shift on average to a lower position during the rainy period. The GPR amplitude and waveform variation features were attributed to the stratigraphy (i.e., the boundary between valid and invalid regions and coherence value) of the floodplain and regions susceptible to erosion (erosion-prone lithological spots). This approach provides the basis for non-destructive monitoring tools enabling the detection of 'seismic signatures' and weak spots of the fluvial channels for improving environmental management.

Despite the increasing role of machine learning in various fields, very few works considered artificial intelligence for frequency estimation (FE). This work presents a comprehensive analysis of deep-learning (DL) approach for frequency estimation of single-tones. It is shown that DL network with two layers having a few nodes can estimate frequency more accurately than well-known classical techniques. The study is comprehensive, filling gaps of existing works, where it analyzes error under different signal-to-noise ratios, numbers of nodes, and numbers of input samples; also, under missing SNR information. It is found that DL-based FE is not significantly affected by SNR bias or number of nodes. DL-based approach can work properly using minimal number of input nodes N at which classical methods fail. It is possible for DL to use as little as two layers with two or three nodes each, with complexity of O{N} versus O{Nlog2 (N)} for DFT-based FE, noting that less N is required for DL. Hence, DL can significantly reduce FE complexity, memory, cost, and power consumption, making DL-based FE attractive for resource-limited systems like some IoT sensor applications. Also, reduced complexity opens the door for hardware-efficient implementation using short word-length (SWL) or time-efficient software-defined radio (SDR) communications.

As an on-going pandemic caused by the out-break of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) or simply COVID-19 sweeps through the globe at an unprecedented rate leaving behind trails of high infection and mortality, it is crucial to understand the propagation dynamics of the virus in a host population in order to take urgent and effective remedial and mitigating steps to save life. It is already observed in many countries and communities that accurate and timely testing, tracing, and tracking of the infection lead to better containment and slowing down of the spread. In this exploratory research, the early growth dynamics of infection within a population is pursued based on real data. The study posits that the early growth in a homogenous population follows an exponential pattern motivating further rigorous quantitative treatment based on a number of analytical models such as logistic model, Richard’s model, and Gompertz model– the acceleration pattern of the outbreak is ascertained from the daily inflection data, and regression analysis against population models yields dynamic growth indices which allow very accurate prediction of the successive outbreak size when calibrated continually with updated data. The performance of the various models is evaluated with the real dataset. More, the basic reproduction number of the COVID-19 virus propagation in the community is estimated based on the on-set phase dataset using multi-compartmental epidemiological model. Also, the maximum infection size, infection doubling time and the scope of the herd immunity are also inferred for COVID-19 in a population.

Nowadays industrial dryers are used instead of traditional methods for drying. In designing dryers suitable for controlling the process of drying and reaching a high quality product, it is necessary to predict the instantaneous moisture loss during drying. For this purpose, ten mathematical-experimental models with a neural network model based on the kinetic data of pistachio drying are studied. The data obtained from the cabinet dryer will be evaluated at four temperatures of inlet air and different air velocities. The pistachio seeds will be placed in a thin layer on an aluminum sheet on a drying tray and weighed by a scale attached to the computer at different times. In the neural network, data are divided into three parts: educational (60%), validation (20%) and test (20%). Finally, the best mathematical-experimental model using genetic algorithm and the best neural network structure for predicting instantaneous moisture are selected based on the least squared error and the highest correlation coefficient.

Deep Learning (DL) and Machine Learning (ML) are widely used in many fields, but rarely used in Frequency Estimation (FE) and Slope Estimation (SE) of signals. Frequency and slope estimation for Frequency-Modulated (FM) and single-tone sinusoidal signals are essential in various applications, such as wireless communications, sonar, and radar measurements. In this work, artificial neural network (ANN) and convolutional neural network (CNN) are used in frequency and slope estimation for FM signals under Additive White Gaussian Noise (AWGN) and Additive Symmetric alpha Stable Noise (SαSN). SαS distributions are impulsive noise disturbances found in many communication environments like marine systems; their distribution lacks a closed-form Probability Density Function (PDF), except for specific cases, and infinite second-order statistic, hence Geometric SNR (GSNR) is used in this work to determine the impulsiveness of noise in a mixture of Gaussian and SαS noise processes. ANN is a machine learning classifier, designed with few layers for reducing FE and SE complexity while getting higher accuracy as compared with classical techniques. CNN is a deep learning classifier, designed with many layers for FE and SE, and proved to be more accurate than ANN when dealing with big data and finding optimal features. Simulation results show that SαS noise can be much more harmful for FE and SE of FM signals than Gaussian noise. DL and ML can significantly reduce FE complexity, memory cost, and power consumption, which is important in many systems such as some Internet of Things (IoT) sensor applications. After training DCNN for frequency and slope estimation of LFM signals, the performance of DCNN (in terms of accuracy) can give acceptable results at very low signal-to-noise ratios where TFD fails, giving more than 20dB difference in the GSNR working range.

MicroRNAs (miRNAs) are small and non-coding RNAs displaying aberrant expression in the tissue and plasma of cancer patients in comparison to healthy individuals. In past decades, accumulating data proposed that miRNAs could be diagnostic and prognostic biomarkers in cancer patients. It has been identified that miRNAs can act either as oncogenes through silencing of tumor inhibitors or tumor suppressor via targeting oncoproteins. MiR-144 is located in chromosomal region 17q11.2 that was widely destroyed in many types of cancers. Several studies revealed that miR-144 has different target genes including rapamycin, zonula occludens1, SFRP1, and ANO1. MiR-144 acts as a tumor suppressor or oncogene by targeting specific genes. In this review, we define the role of miR‐144 and its targets in different cancers and provide understanding in tumor proliferation, migration, and apoptosis.

fibrillation (AF) and heart failure with preserved ejection fraction (HFpEF). Join us on a journey that unravels the mysteries of molecular intricacies, delves into advanced imaging techniques, and sheds light on therapeutic pathways. Unveiling the role of cardiac amyloidosis, particularly sub-clinical isolated cardiac amyloidosis (ICA), the narrative unfolds the heightened risk of AF in the elderly. State-of-the-art imaging techniques like cardiac magnetic resonance (CMR) and molecular imaging step into the spotlight, unraveling diagnostic and prognostic nuances of AF and HFpEF. Embarking on the molecular voyage, the article navigates through next-generation sequencing and comprehensive genomic profiling, shedding light on genetic alterations shaping the AF-HFpEF landscape. This molecular compass lays the groundwork for personalized medicine, illuminating pathways to identify therapeutic bullseyes and biomarkers. In the realm of medical strategies, the discussion homes in on the promising notes struck by SGLT2 inhibitors, particularly dapagliflozin, orchestrating a reduction in AF burden. Characters like spironolactone and dronedarone make appearances, each weaving a distinct tale, revealing their potential roles in steering the ship for AF- HFpEF patients. The subplot involving obesity in HFpEF unfolds, with the LEGACY study portraying the benefits of weight reduction. The pivotal intervention of catheter ablation emerges as the hero, boasting positive outcomes in reducing hospitalization rates and overall mortality for AF-HFpEF patients. However, cautionary tales echo about its impact on left atrial function, adding layers of complexity that beckon careful consideration and monitoring. Amidst the narrative, critical discussions unfold about the limitations of current research, echoing the need to define acute heart failure, refine imaging techniques, and tailor management strategies. The narrative passionately advocates for a personalized touch in addressing AF-HFpEF, recognizing the unique blend of clinical and molecular personas. In essence, this review paints a vivid mural of AF-HFpEF, skillfully bridging the realms of molecular intricacies and the artistry of clinical management.

Adult stem cells (ASCs) can be cultured with difficulty from most tissues, often requiring chemical or transgenic modification to achieve adequate quantities. We show here that mouse primary fibroblasts grown in suspension change from the elongated and flattened morphology observed under standard adherent culture conditions generating rounded cells with large nuclei and scant cytoplasm expressing the mesenchymal stem cell (MSC) marker (Sca1; Ly6A) within 24hrs. Based on this initial observation, we describe here a suspension culture method that, irrespective of the lineage used, mouse fibroblast, primary human somatic cells (fibroblasts, hepatocytes and keratinocytes), is capable of generating a high yield of cells in spheroid form which display expression of ASCs surface markers, circumventing the anoikis which often occurs at this stage. Moreover, mouse fibroblasts-derived spheroids can be differentiated into adipogenic and osteogenic lineages. Analysis of single cell RNA sequence data identified 8 distinct cell clusters with one in particular comprising approximately 10% of the cells showing high levels of proliferative capacity expressing high levels of genes related to MSCs and self-renewal as well as extracellular matrix (ECM). We believe the rapid, high-yield generation of proliferative, multi-potent ASC-like cells by the process we term suspension-induced stem cell transition (SIST) could have significant implications for regenerative medicine.

Automated medical diagnosis is one of the important machine learning applications in the domain of healthcare. In this regard, most of the approaches primarily focus on optimizing the accuracy of classification models. In this research, we argue that unlike general-purpose classification problems, medical applications, such as chronic kidney disease (CKD) diagnosis, require special treatment. In the case of CKD, apart from model performance, other factors such as the cost of data acquisition may also be taken into account to enhance the applicability of the automated diagnosis system. In this research, we have proposed two techniques for cost-sensitive feature ranking. An ensemble of decision tree models is employed in both the techniques for computing the worth of a feature in the CKD dataset. An automatic threshold selection heuristic is also introduced which is based on the intersection of features’ worth and their accumulated cost. A set of experiments are conducted to evaluate the efficacy of the proposed techniques on both tree-based and non-tree based classification models. The proposed approaches are also evaluated against several comparative techniques. Furthermore, it is demonstrated that the proposed techniques select around 1/4^th of the original CKD features while reducing the cost by a factor of 7.42 of the original feature set. Based on the extensive experimentation it is concluded that the proposed techniques employing feature-cost interaction heuristic tend to select feature subsets that are both useful and cost-effective.

Developments of new probability models for data analysis are keen interest of importance for all fields. The log-Dagum distribution has a prominent role in the theory and practice of statistics. In this article, a new family of continuous distributions generated from a log Dagum random variable called the log-Dagum Weibull distribution is proposed. The key properties of the proposed distribution are derived. Its density function can be symmetrical, left-skewed, right-skewed and reversed-J shaped and can have increasing, decreasing, bathtub hazard rates shaped. The model parameters are estimated by the method of maximum likelihood and illustrate its importance by means of applications to real data sets.

The objective of this research is to analyze the effect of determinants of Business ethics (HR management ethics, ethics in corporate governance, and ethics in sales & marketing) on the organizational performance of the high-tech industry. The study further examines the influence of multiple serial mediations of human, structural, structural, and relational capital on the connection between Business ethics and organizational performance. Additionally, this research examines how technological change moderates the relationship between Business ethics and organizational performance. Data was collected from the information technology sector in various cities of South Asian countries through structured questionnaires. PLS-SEM modeling was used via Smart-PLS 4.0 software for analysis. The hypothesized & modified measurement and structural model were validated using the HTMT matrix, Fornell-Larcker criterion, R-square values, f-square values, outer loading, construct reliability and validity, path coefficient analysis, Blindfolding, and predictive relevance. The research results suggest that ethics in corporate governance, HR management ethics, and ethics in sales & marketing have a notable positive effect on business ethics, leading to a substantial and affirmative impact on the organizational performance of the high-tech industry. The findings further suggested that dimensions of intellectual capital (human capital, structural capital & relational capital) significantly moderate between exogenous and endogenous constructs. Moreover, technological change significantly & positively moderates the relationship between Business ethics and organizational performance of the high-tech industry. In conclusion, this research provides insights into how these variables interact and what potential mechanisms act upon them. Organizations can make informed decisions about improving their performance and ethical standards by understanding these relationships. This research offers substantial theoretical and managerial implications to research scholars and industry practitioners.

Brain magnetic resonance imaging (MRI) offers a unique lens to study neuroanatomic support of human neurocognition and intelligence. A core mystery is the MRI explanation of individual differences in neurocognition and intelligence. The past four decades have seen great advancement in studying this century-long mystery, but the sample size and population-level studies limit the explanation at the individual level. The recent rise of big data and artificial intelligence offers novel opportunities. Yet, data sources, harmonization, study design, and interpretation need to be carefully considered. This review aims to summarize past work, discuss rising opportunities and challenges, and facilitate further investigations on machine intelligence inferring human intelligence.

This paper investigates the improvement in solar energy collection and conversion using Al₂O₃-Water nanofluids in a solar flat plate collector (SFPC). The efficiency of a solar flat plate collector using water as the fluid medium is analyzed experimentally and theoretically. For theoretical analysis, a mathematical model in MATLAB is used to simulate and is validated by the experimental results. To enhance the solar energy collection and conversion efficiency of the SFPC, Al₂O₃-Water nanofluid was selected as the fluid medium. The nanofluid properties like density, specific heat capacity, thermal conductivity and viscosity are analyzed and compared for several models of Al₂O₃-Water nanofluids and the best model was selected to modify the simulation. Effect of particle diameter in the nanofluid was found to be marginal on the nanofluid properties. The optimum volume concentration of the nanofluid was found to be 4% giving an efficiency increase of 7.78% in the SFPC over the use of water. This reduces the area of the SFPC by 10.5%.

In this project, our foremost intention is to analyze the angle of deflection of photon by NED black holes with many horizons in the context of non-linear electrodynamics. For the accomplishment of our intention at first we manipulated the optical metric of the desired black holes for the calculation of Gaussian curvature. After that, the angle of deflection in leading orders is attained from the desired black holes in weak field limit by using the famous Gauss-Bonnet theorem. Moreover, we evince the effect of plasma medium on the angle of deflection. At last, the graphical impact of the angle of deflection for NED black holes with many horizons is examined with respect to black hole charge and impact parameter in both the plasma and non plasma medium. In addition, we discuss the rigorous bound for grey body factor of the desired black hole and also observer its graphical behavior.

Brain disorders pose a significant global healthcare challenge due to their complexity and profound impact on individuals and society. This review explores recent advancements in brain disease research, deepening our understanding and progress towards treatments. Technological innovations, like high-resolution imaging and optogenetics, play a pivotal role in illuminating cellular structures and dissecting neural circuits. Genetic analysis identifies dis-ease-associated genes and mutations, yet translating findings to clinical applications remains challenging. Epigenomics, transcriptomics, and proteomics provide molecular insights, un-covering disrupted pathways in conditions like Alzheimer's disease. Stem cell models elucidate developmental mechanisms, and immunotherapies, including CAR T cells, emerge as prom-ising strategies for brain cancers like glioblastoma. Genome editing tools, exemplified by CRISPR, enable precise modification of disease genes in models, deepening our understanding of pathogenesis. Multifaceted approaches integrating human studies, animal models, and bi-oinformatics propel the field forward. However, complexities persist, as disease specificity for cell types or brain regions remains an enigma, and genetic factors interact with environmental influences, highlighting disease intricacies. Ultimately, interdisciplinary efforts combining bi-ophysical and psychospiritual perspectives are essential to fully understand brain health and unlock transformative treatments for these debilitating disorders. A comprehensive under-standing of brain disorders is vital to improve the quality of life for millions and alleviate the global burden of neurological afflictions. Continued collaborative research will lead to an era of improved brain health worldwide.

Colorectal cancer (CRC) is considered as one of the leading types of cancer in the world. CD133, as a cancer stem cell marker, has a pivotal role in the development of drug resistance, migration, and stemness properties of CRC cells. This study designed to check the combined effect of CD133siRNA and Oxaliplatin on proliferation, migration, apoptosis, and stemness properties of CRC cells in HT-29 cell line.MTT assay was performed to define the combined effect of CD133siRNA and Oxaliplatin on the viability of HT-29 cells. In order to figure out the effect of this combination therapy on CD133 expression at the gene and protein level, qRT-PCR and western blot were exploited, respectively. The ability of cell migration was tested by wound healing assay as well. Also, colony formation and sphere formation were conducted to assess the stemness properties in the combination group. Flow cytometry was conducted to investigate the apoptosis, cell cycle, and surface expression of CD133 in different groups. Finally, the expression of migration-, and stemness-associated genes were measured by qRT-PCR. We indicated that silencing of CD133 reduces the migration and stemness properties of colorectal cancerous cells. This suppression makes HT-29 cells more sensitive to Oxaliplatin and reduces the effective dose of this chemical drug. Therefore, the suppression of CD133 in combination with Oxaliplatin treatment might be a promising therapeutic approach in the treatment of colorectal cancer.

One of the major limitations of the current 3D-concrete-printing technology is incorporation of reinforcement. Furthermore, there is a need to decrease the ecological footprint of printable concrete. As a possible solution for these challenges, this paper presents a 3D-printable strain-hardening geopolymer composite (3DP-SHGC) that shows pseudo-ductile behaviour under direct tension. The developed 3DP-SHGC is composed of one-part (just-add-water) geopolymer binder made of slag (GGBFS), fly ash (FA) and solid activator. The one-part geopolymer binder eliminates the need for elevated temperature curing and handling of corrosive alkaline liquids. At first, an optimum matrix was identified by studying the effects of FA to GGBFS ratio on the rheological properties and compressive strength. Subsequently the optimum matrix was reinforced by PVA fibres to make the 3DP-SHGC, which printing performance and rheological properties were evaluated. In addition, the influences of curing temperature on the compressive, flexural and tensile performances of the printed specimens were also investigated. The results were compared with those obtained for the mould-cast specimens. The 3DP-SHGC exhibited superior flexural performance, higher tensile strength, and comparable tensile strain capacity to the mould-cast counterpart. Further, the curing temperature had influence on the mechanical properties of both 3D-printed and mould-cast SHGCs. The underlying reasons for the differences are discussed.

It is a theoretical exportation for mass transpiration and thermal transpiration of Casson nanofluid over an extending cylindrical surface. The Stagnation point flow through porous matrix is influenced by magnetic field of form strength. Appropriate similarity functions are availed to yield the transmuted system of leading differential equations. Existence for the solution of momentum equation is proved for various values of Casson parameter β, magnetic parameter M, porosity parameter Kp and Raynolds number Re in two situations of mass transpiration (suction/injuction). Moreover, uniqueness results are discussed and for skin friction factor are established to attain accuracy for large injection values. Thermal and concentration profiles are delineated numerically by applying Runge-Kutta method and shooting technique.

Multi-walled carbon nanotubes (MWCNTs) and graphene oxide (GO) reinforced carbon foam (CF) composite were prepared by direct pyrolysis of MWCNTs, GO and mesophase coal tar pitch. The effect of additive amount of the mixture of MWCNTs and GO on the microstruture and properties of carbon foam was analzyed by transmission electron miscroscopy (TEM), scanning electron microscopy (SEM), X-ray diffraction (XRD), Four-probe resistance meter, universal testing machine, and laser thermal conductivity tester respectively. The result shows that MWCNTs and GO had significant impact on the microstructure of carbon foam. Futhermore, the electrical, mechanical and thermal properties of carbon foam composites were significantly enhanced by increasing the additive amount. Maximum compressive strenght of 19.2 MPa and Young’s modulus of 56.8 MPa of CF composite were observed. Similarly, Highest thermal conductivity of 30.91 W/m.K and electrical conductivity of 27.2 ×103 S/m were observed at 2 wt. % of MWCNTs-GO additive loading.

In this paper, we establish certain Lp bounds for several classes of rough Marcinkiewicz integrals over surfaces of revolution on product spaces. By using these bounds and using an extrapolation argument, we obtain the Lp boundedness of these Marcinkiewicz integrals under very weak conditions on the kernel functions. Several previous results on Marcinkiewicz operators are essentially extended or improved. Our results represent natural extensions and improvements of several known results on Marcinkiewicz integrals over symmetric spaces.

We prove Lp estimates of a class of generalized Marcinkiewicz integral operators with mixed homogeneity on product domains. By using these estimates along with an extrapolation argument we obtain the boundedness of our operators under very weak conditions on the kernel functions. Our results in this paper improve and extend several known results on both generalized Marcinkiewicz integrals and parabolic Marcinkiewicz integrals on product domains.

Optimization using Genetic Algorithms (GA) is a well-known strategy in several scientific disciplines. The crossover is an essential operator of the Genetic Algorithm. It has been an active area of research to develop sustainable forms for this operand. In this work, a new crossover operand is proposed. This operand depends on giving an elicited description for the chromosome with a new structure for alleles of the parents. It is suggested that each allele has two attitudes, one attitude differs contrastingly with the other, and both of them complement the allele. Thus, in case of one attitude is good, the other should be bad. This is suitable for many systems which contain admired parameters and unadmired parameters. The proposed crossover would improve the desired attitudes and damp the undesired attitudes. The proposed crossover can be achieved in two stages: the first stage is a mating method for both attitudes in one parent to improving one attitude at the expense of the other. The second stage comes after the first improvement stage for mating between different parents. Hence, two concurrent steps for improvement would be applied. Simulation experiments for the system shows improvement in the fitness function. The proposed crossover could be helpful in different fields, especially to optimize routing algorithms and network protocols, an application that has been tested as a case study in this work.

Hydraulic conductivity is the key and one of the most uncertain parameters in groundwater modeling. The grid based numerical simulation require spatial distribution of sampled hydraulic conductivity at un-sampled locations in the study area. This spatial interpolation has been routinely performed using variogram based models (two-point geostatistics methods). These traditional techniques fail to capture the complex geological structures, provides smoothing effects and ignore the higher order moments of subsurface heterogeneities. In this work, a multiple-point geostatistics (MPS) method is applied to interpolate hydraulic conductivity data which will be further used in WASH123D numerical groundwater simulation model for regional smart groundwater management. To do this, MPS need ‘training images (TIs) as a key input. TI is a conceptual model of subsurface geological heterogeneity which was developed by using concept of ages, topographic slope as an index criteria and knowledge of geologist. After considerations of full physics of study area, an example shows the advantages of using multiple-point geostatistics compared with the traditional two-point geostatistics methods (such as Kriging) for the interpolation of hydraulic conductivity data in a complex geological formation.

The aim of this work is to analyze and investigate the shape preserving properties of ternary 4-point non-stationary interpolating subdivision schemes constructed by Beccari et al. [1] with a tension parameter !k+1 which can reproducing exponential. Moreover, the conditions on the initial control points are developed that allow user to generate shape preserving limit curves after a nite number of subdivision steps and generalize these results in limiting case. Signicance of derived conditions are illustrated through graphs and the whole discussion is followed by examples.

In this paper a new monolithic Eulerian formulation in the framework of non-classical continuum is presented for the analysis of fluid-strucutre interaction problems. In this respect, Cosserat continuum theory taking into account the micro-rotational degrees of freedom of the particles is considered. Continuum description of the model and variational formulation of the governing flow dynamics for non-classical -fluid-structure interaction nCFSI is presented. The model is analyzed by computing a well known benchmark problem by Hecht and Pironneau [16]. The algorithmic description is presented and implemented using FreeFEM++. Code is validated with the benchmark solution of Turek and Hron [38] in case of flow around a flag attached with cylinder. New microstructral behavior of the solution is studied and numerical simulations and results are shown in the form of figures. Some interesting feature of the flow is observed and microstructural characteristics are discussed.

The continuing public health issue known as COVID-19 (the 2019 Novel Corona virus infection) has a global emphasis. Despite (or perhaps because of) the fact that there are significant gaps in our understanding of COVID-19 epidemiology, transmission dynamics, research methods, and management breakout poses a new kind of global hazard. The good news is that there is currently enough knowledge about the epidemic process to allow for the creation of mathematical forecasting models. We modify a conventional SEIR epidemic model to the unique dynamic compartments and epidemic features of COVID 19 as it spreads in a population with a diverse age structure. Although many US states and other nations around the world followed lockdown and reopening processes, we perform some analysis on using some techniques of the epidemic course. A new perspective of fractional calculus known as piecewise derivatives of fractional order is used to study the proposed model. Sufficient conditions are established to show the existence theory. In addition, a numerical scheme based on Newton’s polynomials is established to simulate the approximate solutions of the proposed model by using various fractional orders. Some real data results are also shown with comparison of the numerical results.

This study measures key indicators of perceived precarity and decent working conditions and their relative effects on life satisfaction of workers in small and medium-sized enterprises. Using a random sample survey, we interviewed 103 workers in four districts of Sylhet division in Bangladesh. Utilizing Smart PLS application for structural equation modeling, we iterated the samples 1000 times which resulted in a new sample size standing at 499. We used a Likert-type five-point scaling technique, with appropriate items related to the latent constructs of the model, for path analysis. We found perceived precariousness has the highest causal relations with a path coefficient of 0.706 with social dialogue (an indicator of decent working conditions) followed by 0.539 with working hour issue, 0.345 with life satisfaction and 0.301 with the unacceptable work issue. The importance-performance map analysis confirms that perceived precarity is the most critical construct for worker’s life satisfaction. Thus, in a ceteris paribus situation, an increase of one point in the performance on the perceived precarity is expected to increase in the performance on the life satisfaction by a total effect level of 0.428. Therefore, we recommend further attention should be paid by the policymakers to the issue of consciousness of the precarity at the workplace for a better life satisfaction of workers.

Breast and Cervical cancer are significant causes of morbidity and mortality among women worldwide. Usually, conventional therapies and early detection has shown success in metastasis and relapse rates and improved overall survival of the patients. The elimination of the efficacy of chemotherapies and target therapies because cancer stem cells create resistance against chemotherapy, metastasis, and relapse. Studies reveal that cancer stem cell markers are important for improving efficiencies of targeted therapies. Our study aims to analyze the stem cell markers and target the most significant stem cell marker by phytochemicals and their derivates. Through the analysis, it was observed that CXCR4 and SOX-2, mir-1, mir-125, and mir-429 may have a crucial role in stemness of breast and cervical cancer respectively, while BPMD & MD simulations revealed Di-valinoyl curcumin as a potential compound for CXCR4 targeting.

This paper presents the systematic development and performance characterization of a non-proprietary 3D-printable ultra-high-performance fiber-reinforced concrete (UHPFRC) for digital construction. Several fresh and hardened properties of the developed 3D-printable UHPFRC matrix (without fiber) and composite (with 2% volume fraction of steel fibers) were evaluated and compared to that of conventionally mold-cast UHPFRC. Additionally, the effects of testing direction on the compressive and flexural strengths of the printed UHPFRC were investigated. The fresh properties of the UHPFRC developed in this study satisfied the criteria for extrudability, buildability, and shape-retention-ability, which are relevant for ensuring printability. The printed UHPFRC exhibited superior flexural performance to the mold-cast UHPFRC due to alignment of the short fibers in the printing direction. The high compressive and flexural strengths, along with the deflection-hardening behavior, of the developed UHPFRC can enable the production of thin 3D-printed components with significant reduction or complete elimination of conventional steel bars.

The Doubly-Fed Induction Generator (DFIG) has significant features in comparison with Fixed Speed Wind Turbine (FSWT), which has popularized its application in power system. Due to partial rated back-to-back converters in the DFIG, Fault Ride-Through (FRT) capability improvement is one of the great subjects regarding new grid code requirements. To enhance the FRT capability of the DFIG, many studies have been carried out. Fault current limiting devices as one of the techniques are utilized to limit the current level and protect switches of the back-to-back converter from over-current damage. In this paper, a review is done based on fault current limiting characteristic of the proposed fault current limiting devices Therefore, Fault Current Limiters (FCLs) and Series Dynamic Braking Resistors (SDBRs) are mainly taken into account. Operation of all configurations including their advantages and disadvantages is explained. Impedance type and the fault current limiting devices’ location are two important factors, which significantly affect the DFIG behaviour in the fault condition. These two factors are basically studied by the simulation and their effects on the key parameters of the DFIG are investigated. Finally, future works in respect to the FCL application in the FRT improvement of the DFIG have also been discussed.

In the era of mounting water scarcity, particularly in arid regions, the agricultural sector is striving to optimize the water resource management practices. Treated wastewater (TWW) has emerged as a viable alternative to freshwater for irrigation, offering a sustainable solution to water conservation. However, ensuring the safety and quality of TWW is paramount to safeguard for both crop health and environmental integrity. Electrical conductivity (EC), a critical factor in assessing TWW suitability for irrigation, can significantly impact crop yields and soil health since it is directly linked to the concentration of dissolved salts and ions. Therefore, this study delves into the development of advanced predictive models for EC assessment by employing supervised machine learning (ML) techniques like the Support Vector Regression (SVR), Adaptive Network-based Fuzzy Inference System (ANFIS), and Multiple Linear Regression (MLR). The TWW samples collected from Al-Qatif area, Saudi Arabia were assessed in the lab through a sensor-based real-time monitoring system integrated with Internet of Things (IoT) network and a multiprobe device. The key water quality parameters assessed involved, temperature (T), pH, Oxidation Reduction Potential (ORP), Resistivity (RES), Total Dissolved Solids (TDS), Salinity and Turbidity which served as the foundation for these models, given their profound influence on and their anticipated impact on EC levels. Following feature selection, two distinct model combinations were identified for the SVR, ANFIS and MLR models. Furthermore, to evaluate prediction performance, four statistical criteria, including coefficient of determination (R²), correlation coefficient (R), mean square error (MSE), and root mean square error (RMSE), were employed. Overall, the comparative performance results proved that ANFIS-M2 indicated the strongest predictive capability with the R² value of 0.99572 and 0.75145 and the RMSE values of 0.00965 and 0.01134, respectively in both calibration and verification stages.

An adaptive deep neural network is used in an inverse system identification setting to approximate the inverse of a nonlinear plant with the aim of constituting the plant controller by copying to the latter the weights and architecture of the converging deep neural network. This deep learning (DL) approach to the adaptive inverse control (AIC) problem is shown to outperform adaptive filtering techniques and algorithms normally used in adaptive control, especially when the plant is nonlinear. The deeper the controller the better the inverse function approximation, provided that the nonlinear plant have an inverse and that this inverse can be approximated. Simulation results prove the feasibility of this DL-based adaptive inverse control scheme. The DL-based AIC system is robust to parameter change of the nonlinear plant in that, under such change, the plant output reassumes the value of the reference signal considerably faster than with the adaptive filter counterpart of the deep neural network. Settling times and rise times of the step response are shown to improve in the DL-based AIC system.

This work combines compressive sensing and short word-length techniques to achieve localization and target tracking in wireless sensor networks with energy-efficient communication between the network anchors and the fusion center. Gradient descent localization is performed using time-of-arrival (TOA) data which are indicative of the distance between anchors and the target thereby achieving range-based localization. The short word-length techniques considered are delta modulation and sigma-delta modulation. The energy efficiency is due to the reduction of the data volume transmitted from anchors to the fusion center by employing any of the two delta modulation variants with compressive sensing techniques. Delta modulation allows the transmission of one bit per TOA sample. The communication energy efficiency is increased by RⱮ, R≥1, where R is the sample reduction ratio of compressive sensing and Ɱ is the number of bits originally present in a TOA-sample word. It is found that the localization system involving sigma-delta modulation has a superior performance to that using delta-modulation or pure compressive sampling alone, in terms of both energy efficiency and localization error in the presence of TOA measurement noise, owing to the noise shaping property of sigma-delta modulation.

Frequency estimation of a single sinusoid in colored noise has received a considerable amount of attention in the research community. Taking into account the recent emergence and advances in compressive covariance sensing (CCS), the aim of this work is to combine the two disciplines by studying the effects of compressed measurements of a single sinusoid in moving-average (MA) colored noise on its frequency estimation accuracy. CCS techniques can recover the second-order statistics of the original uncompressed signal from the compressed measurements, thereby enabling correlation-based frequency estimation of single tones in colored noise using higher-order lags. Acceptable accuracy is achieved for moderate compression ratios and for a sufficiently large number of available compressed signal samples. It is expected that the proposed method would be advantageous in applications involving resource-limited systems such as wireless sensor networks.

Training data for a deep learning (DL) neural network (NN) controller are obtained from the input and output signals of a conventional digital controller that is designed to provide the suitable control signal to a specified plant within a feedback digital control system. It is found that if the DL controller is sufficiently deep (four hidden layers), it can outperform the conventional controller in terms of settling time of the system output transient response to a unit-step reference signal. That is, the DL controller introduces a damping effect. Moreover, it does not need to be retrained to operate with a reference signal of different magnitude, or under system parameter change. Such properties make the DL control more attractive for applications that may undergo parameter variation, like sensor networks.

The COVID-19 pandemic, declared in early 2020, is an unprecedented global health crisis, causing over 7.0 million deaths and ongoing challenges. While the pharmaceutical industry expedited vaccine development, mutant SARS-CoV-2 strains remain a major fear. Moreover, concerns regarding the long-term health repercussions of COVID-19-affected individuals persist since individuals affected by mild and moderate to severe SARS-CoV-2 infection experience long-term cardiovascular complications, liver dysfunction, pulmonary afflictions, kidney impairments, and most importantly neurocognitive deficits. In recent studies, we documented pathophysiological changes in various organs following the post-acute infection of mice with murine hepatitis virus-1 (MHV-1), a coronavirus, at both 7 days and 12 months after infection. One part of the body that can be drastically affected by SARS-CoV-2 is the skin. Studies have shown major changes in the skin post-acute SARS CoV-2 infection in humans. However, long-term dermatologic changes post-COVID have never been explored. For the first time, we show several cutaneous findings both at the acute stages and long-term post-infection of mice with MHV-1 coronavirus (a promising experimental model to study acute and long-COVID). Precisely, we found destruction of the epidermal layer, an increase in the number of hair follicles, extensive collagen deposition in the dermal layer, and hyperplasticity of the sebaceous glands at the acute stages, along with thinning of the panniculus carnosus, as well as the adventitial layer, which corresponds well with studies in humans. In contrast, the cutaneous investigation in the long-COVID phase shows the absence of hair follicles from both the epidermal and dermal layers, the destruction of adipose tissues, and the devastation of the epidermal layer. Further, treatment of these mice with a 15 amino acid synthetic peptide, SPIKENET (SPK), which was effective in preventing Spike glycoprotein-1 binding with host receptors, as well as has a potent anti-inflammatory response to severe inflammatory stimulus) restored the loss of hair follicles and re-architected the epidermal and dermal layers. Additionally, destruction in fatty tissue in the infected mice was successfully restored post-treatment with SPK. These findings suggest that SARS-CoV-2 initiates the changes early post-infection, leading to devastating skin alterations in the long term which can be prevented by our newly identified peptide drug SPK.

Spectrometers have a wide range of applications ranging from optical to non-optical spectroscopy. The need for compact, portable, and user-friendly spectrometers has been the pivot of attention from small laboratories to the industrial scale. Here, the Czerny Turner configuration-based optical spectrometer simulation design is carried out using ZEMAX OpticStudio. A compact and low-cost optical spectrometer in the visible range has been developed by using diffraction grating as a dispersive element and USB-type WebCAM CCD (Charge-coupled device) as a detector instead of an expensive commercial diffraction grating and detector. Using National Instruments LabVIEW, data acquisition, processing, and displaying techniques are made possible. We have employed different virtual images in LabVIEW programs to collect the pixel-to-pixel information and wavelength-intensity information from the image captured using the WebCAM CCD. Finally, we have shown that the OpticStudio-based spectrometer and experimental measurements of the developed spectrometer are in good agreement.

Background: The current coronavirus disease 2019 (COVID-19) is spreading globally at an accelerated rate. There is some previous evidence that weather may influence the incidence of COVID-19 infection. We assessed the role of meteorological factors including temperature (T) and relative humidity (RH) considering the concentrations of two air pollutants, inhalable coarse particles (PM10) and nitrogen dioxide (NO2) in the incidence of COVID-19 infections in Finland, located in arctic-subarctic climatic zone. Methods: We retrieved daily counts of COVID-19 in Finland from Jan 1 to May 31, 2020, nationwide and separately for all 21 hospital districts across the country. The meteorological and air quality data were from the monitoring stations nearest to the central district hospital. A quasi-Poisson generalized additional model (GAM) was fitted to estimate the associations between district-specific meteorological factors and the daily counts of COVID-19 during the study period. Sensitivity analyses were conducted to test the robustness of the results. Results: The incidence rate of COVID-19 gradually increased until a peak around April 6 and then decreased. There were no associations between daily temperature and incidence rate of COVID-19. Daily average RH was negatively associated with daily incidence rate of COVID-19 in two hospital districts located inland. No such association was found nationwide. The sensitivity analyses indicate the results are robust. Conclusions: Weather conditions, such as air temperature and relative humidity, may not be important factors affecting the COVID-19 incidence in the arctic and subarctic winter and spring. More evidence is needed on the associations between weather and COVID-19 during different seasons.

A low-cost medium power carbon dioxide (CO₂) laser system is designed, constructed, and characterized to produce coherent, monochromatic laser radiation in the Infrared region. The laser cavity is simulated and designed by using ZEMAX optic studio. A switch-mode high tension pump source is designed and constructed using a flyback transformer and simulated using NI Multisim to study the voltage behavior at different node points. A prototype cooling system/chiller is designed and built using the Thermo-Electric Coolers (TEC) to remove the excess heat produced during laser action. Various parameters, like pumping mechanism, chiller stability, efficiency, output power, and current at different applied voltages, are studied. The chiller efficiency at different output powers of the laser is analyzed, which clearly shows that the chiller's cooling rate is good enough to compensate for the heat generated by the laser system. The center wavelength of the carbon dioxide laser is 10.6μm with FWHM of 1.2nm simulated in the ZEMAX optic studio. The output beam penetration through salt rock (NaCl), wood, and acrylic sheet at various output powers is analyzed to measure the penetration depth rate of the CO₂ laser.

The extrusion-based 3D concrete printing (3DCP) technology allows the fabrication of permanent formwork with intricate shapes, into which fresh concrete is cast to build structural members with complex geometry. This significantly enhances the geometric freedom of concrete structures without the use of expensive temporary formwork. In addition, with proper material choice for the permanent formwork, the load-bearing capacity and durability of the resulting structure can be improved. This paper investigates 3DCP of permanent formwork for reinforced concrete (RC) beam construction. A 3D-printable engineered geopolymer composite (3DP-EGC, or strain-hardening geopolymer composite, 3DP-SHGC) recently developed by the authors was used for fabrication of the permanent formwork. The 3DP-EGC exhibits strain-hardening behaviour under direct tension. Two different printing patterns were used for the soffit of the permanent formwork to investigate the effect of this parameter on the flexural performance of RC beams. A conventionally mould-cast RC beam was also prepared as the control beam for comparison purposes. The results showed that the RC beams constructed using the 3DP-EGC permanent formwork exhibited superior flexural performance to the control beam. Such beams yielded significantly higher cracking load (up to 43%), deflection at ultimate load (up to 60%), ductility index (50%) and absorbed energy (up to 107%) than those of the control beam. The ultimate load was comparable with or slightly higher than that of the control beam. Furthermore, the printing pattern at the soffit of the permanent formwork was found to have a significant influence on the flexural performance of the RC beams.

With the global increase of photovoltaic (PV) modules deployment in recent years, the need to explore and realize their reported failure mechanisms has become crucial. Despite PV modules being considered reliable devices, failures and extreme degradations often occur. Some degradations and failures can be minor and cause no critical harm if within the expected range. Others, may start mildly and then deteriorate faster to become catastrophic, especially in harsh environments. This paper conducts a state-of-the-art literature review to scan PV failures, types, and their root cause based on PV’s constructed components (from protective glass to junction-box). It outlines the hazardous consequences beyond PV module failures, describing what harm they can bring to the PV system. As we delve into the literature, it becomes clear that every component is vulnerable to certain types of failures; some can deteriorate within themselves, and others infect further PV components resulting in emerging more severe failures. In the end, the review briefly summarises PV failure detection techniques, emphasising electrical characterization techniques, and disclosing the need to engage more electrical parameters. Most importantly, this review can prepare the stage for the PV research community to identify the most prevalent degradation processes. This, in turn, encourages researchers to investigate them throughout modelling and experimental studies to forecast them at the early onset in order to protect the PV systems from hazardous malfunctions.

Breast cancer stands as a predominant health concern for women globally. As mammography is the primary screening tool for breast cancer detection, improving the detection of breast cancer at screening could save more lives. This mammography re-view paper provides a systematic review of computer-aided techniques during a specific time frame for the segmentation and classification of microcalcification, evaluating image processing, machine learning, and deep learning techniques. The systematic review is meticulously carried out, adhering closely to the preferred reporting items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. This article focuses on mammographic breast cancer detection approaches based on automated systems, discussed chronologically from 1970 through 2023. This article encompasses the breadth of artificial intelligence-based methods from the most primitive to the most sophisticated models. Image processing and machine learning-based methods are compre-hensively reviewed. The evaluation of a deep learning architecture based on self-extracted features for classification tasks demonstrated outclass performance. Large-scale datasets required for a broader and in-depth analysis of novel methods for breast cancer detection are also discussed in this article. This research work is aligned with the United Nations' sustainability de-velopment goals.

Developing countries like Pakistan is among those where lack of adoption to science and technology advancement is a major constraint for Satellite Remote Sensing use in crops and land use land cover digital information generation. Exponential rise in country population, increased food demand, limiting natural resources coupled with migration of rural community to urban areas had further led to skewed official statistics. This study is an attempt to demonstrate the possible use of freely available satellite data like Landsat8 under complex cropping system of Okara district of Punjab, Pakistan. An Integrated approach has been developed for the satellite data based crops and land use/cover spatial area estimation. The resultant quality was found above 96% with Kappa statistics of 0.95. Land utilization statistics provided detail information about cropping patterns as well as land use land cover status. Rice was recorded as most dominating crop in term of cultivation area of around 0.165 million ha followed by autumn maize 0.074 million ha, Fallow crop fields 0.067 million ha and Sorghum 0.047 million ha. Other minor crops observed were potato, fodder and cotton being cultivated on less than 0.010 million ha. Population settlements were observed over an area of around 0.081 million ha of land.

Toll plaza optimization is essential for improving traffic flow and easing congestion on road networks. This study aims to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer facilitation. Different resources are used for data collection, like the National Highway Authority of Pakistan. Physical data is collected from the established Toll Plaza of Peshawar, along with the development of each line. The goal of this study is to suggest and assess potential optimization solutions for the Peshawar toll plaza, considering variables like traffic volume, toll-collecting effectiveness, and customer happiness. The analysis of present traffic patterns, the detection of bottlenecks, and the creation of plans to ease congestion and enhance overall traffic flow are all part of the optimization process. The approaches used in this study include data analysis, simulation, and traffic modeling. PTV VISSIM is software for transportation planning, design, and analysis. The simulations consider various conditions, such as variable traffic volumes, toll collection methods, and lane configurations. When comparing the different optimization strategies, performance indicators like trip time, latency, and queue length are utilized to determine which approach is practical. Waiting time Vs. Existing each lane, etc, is explained. In the end, comparing queue lengths and throughput across different lane scenarios shows that maximizing throughput with existing lanes yields up to 45% improvement with E-tag lanes, 40% with existing E-tag lanes, 38% with E-tag plus additional toll and cash lanes, 32% with three cash lanes, and 28% with two E-tag lanes. Failure to regularly monitor and adjust the existing lane pattern scenario based on traffic patterns can lead to queues being 20% longer. Without proactive evaluation and optimization, there is a potential 63% opportunity for queue length improvement

The process of water treatment by nanoparticles is one of the most considerable subjects in the cross-field of hydrodynamics, chemistry and mathematics. This paper is dedicated to the case of the flows that appear when squeezing and stretching a spongy with a mix of water with nanoparticles and contaminants. It is assumed that fluid is homogeneous at the starting moment, the parameters of the nanoparticles and contaminants are known, and there is a constant non-homogeneous magnetic field applied to the system. The flow starts moving when the walls of the channel shift to each other. Exact and numerical solutions of the system of ordinary differential equations are used to receive the results. The article gives an answer to the question about stability of the flow and proposes the technique to evaluate the essential characteristics of the system to achieve the treatment process efficiency. The main result is that the considered system shows excellent treatment properties during some part of squeezing stage. This effect does not appear without magnetic field.

In this paper, we study light rays in a Kazakov-Solodukhin black hole. To this end, we use the optical geometry of the Kazakov-Solodukhin black hole within the Gauss-bonnet theorem. We first show the effect of the deformation parameter $a$ on the Gaussian optical curvature, and then we use the modern method popularized by Gibbons and Werner to calculate the weak deflection angle of light. Our calculations of deflection angle show how gravitational lensing is affected by the deformation parameter $a$. Moreover, we demonstrate the effect of a plasma medium on weak gravitational lensing by the Kazakov-Solodukhin black hole. We discuss that the increasing the deformation parameter $a$, will increase the weak deflection angle of the black hole. Our analysis also uncloak how one may find a observational evidence for a deformation parameter on the deflection angle.

We have conducted a comprehensive year-long sampling campaign for particulate matter (PM) pollutants at a site near a major highway, following standard protocols. Total mass, and elemental and chemical composition of the fine fractions (PM2.5) of traffic-related pollutants are determined utilizing several complementary techniques. These complementary techniques included gravimetric analysis, X-ray fluorescence (XRF), scanning electron microscopy and energy dispersive spectroscopy, X-ray diffraction (XRD) and black carbon multi-wavelength absorption. The results show that the PM2.5 mass concentrations are within or slightly above international standards and include natural dust and anthropogenic pollutants such as black carbon, sulfates and other traffic-related elements. Anthropogenic sources include the secondary pollutants Mascagnite and Koktaite, traffic emissions such as exhaust emissions and tires, brakes and road erosions. It was found that the sulfates are the highest contributors to PM2.5 (~40%), as evident from XRD results and the S content in the XRF analysis.

Peste des Petits Ruminants (PPR) is one of the major transboundary animal diseases that severely impact livestock production and trade worldwide. An investigation was carried into the seroprevalence and risk factors of PPR in small ruminants, camels, and wild ruminants in Bahrain. A total of 1240 serum samples were collected from 119 sheep flocks and 1224 serum samples from 125 goat herds. In addition, 100 sera from camels and 38 sera from wild ruminants, were collected. Serum samples were tested for the presence of PPR specific antibodies using a competitive ELISA. Flock management and health information of the flocks and herds were collected using a semi-structured questionnaire. The individual prevalence of PPR in sheep and goats was 26% and 25.5%, respectively. The flock or herd level prevalence of PPR was 22.7% and 29.6% in sheep and goats, respectively. None of the tested camels or wild ruminants were positive for PPR-specific antibodies. Sheep and goats from the Northern governorate were more likely to be seropositive to PPR than in other governorates. On the individual animal level, the univariate analysis suggested; age (<12 months), sex (male), and health status (weak animals) as risk factors for PPR seropositivity. On the flock/herd level, the multivariable logistic regression analysis identified large flock/herd size and semi-intensive farming as risk factors for PPR seropositivity.

Distributions with bounded support show considerable sparsity over those with unbounded support, despite the fact that there are a number of real-world contexts where observations take values from a bounded range (proportions, percentages, and fractions are typical examples). For proportion modelling, a flexible family of two-parameter distribution functions associated with the exponential distribution is proposed here. Mathematical and statistical properties of the novel distribution are examined, including quantiles, mode, moments, hazard rate function, and its characterization. The parameter estimation procedure using the maximum likelihood method was carried out, and applications to environmental and engineering data were also considered. To this end, various statistical tests are used, along with some other information criterion indicators to determine how well the model fits the data. The proposed model is found to be the most efficient plan in most cases for the datasets considered.

Although advertising literacy leads to critical thinking in the face of advertising, but so far, no action has been taken in Iran regarding a tool to measure this type of literacy. And after the investigations, it was determined that although much research has been done on advertising, but the lack of appropriate measurement tools to measure the level of advertising literacy is clearly evident. Therefore, this research provides a valid tool for measuring advertising literacy from students' perspective. In this study, referring to the dimensions of advertising literacy from the perspective of Malmelin (2010) and the views of related professors, a questionnaire was developed and to determine the validity of the structure, confirmatory factor analysis was used. In this study, the statistical population was high school students, considering the impact of advertising in this age range; finally, a tool with four dimensions of informational literacy, aesthetic literacy, rhetorical literacy and promotional literacy was obtained. According to the confirmation of this tool in the present study, it can be used to examine the status of items, their order and prioritization from the perspective of the mentioned population.

The purpose of this research is to examine the benefits and limitations of implementation of novel digital academic advising systems using automated collection and reporting processes for ABET student outcomes data based on principles of authentic OBE for effective developmental advising. We examine digital developmental advising models of undergraduate engineering programs in two universities that employ customized features of the web-based software EvalTools® - Advising Module based on assessment methodology incorporating the Faculty Course Assessment Report, Performance Indicators and hybrid rubrics classified according to the affective, cognitive and psychomotor domains of Bloom’s learning model. A case study approach over a six-year period is adopted for this research. The two case studies present results of samples of developmental advising activity employing sequential explanatory mixed methods models using a combination of quantitative and qualitative analyses of a) detailed students’ outcomes and Performance Indicators information; and b) self-evaluation of their professional development and lifelong learning skills. The findings of this study show that digital advising systems employing the Faculty Course Assessment Report using Performance Indicators and hybrid rubrics can provide comprehensive and realistic outcomes data to help both developmental advisors and students easily identify the specific cause of performance failures, implement practical recommendations for remedial actions and track improvements. Inherent strong skills can also be identified in academically weak students by observing patterns or trends of relatively better performing outcomes to reinforce natural affinity for learning specialized competencies and pursue related and successful career paths.

Flash flooding, a hazard which is triggered by heavy rainfall is a major concern in many regions of the world often with devastating results in mountainous elevated regions. We adapted remote sensing modelling methods to analyse one flood in July 2015, and believe the process can be applicable to other regions in the world. The isolated thunderstorm rainfall occurred in the Chitral River Basin (CRB), which is fed by melting glaciers and snow from the highly elevated Hindu Kush Mountains (Tirick Mir peak’s elevation is 7708 m). The devastating cascade, or domino effect, resulted in a flash flood which destroyed many houses, roads, and bridges and washed out agricultural land. CRB had experienced devastating flood events in the past, but there was no hydraulic modelling and mapping zones available for the entire CRB region. That is why modelling analyses and predictions are important for disaster mitigation activities. For this flash flood event, we developed an integrated methodology for a regional scale flood model that integrates the Tropical Rainfall Measuring Mission (TRMM) satellite, Geographic Information System (GIS), hydrological (HEC-HMS) and hydraulic (HEC-RAS) modelling tools. We collected and use driver discharge and flood depth observation data for five river sub-stream areas, which were acquired in cooperation with the Aga Khan Rural Support Program (AKRSP) organization. This data was used for the model’s calibration and verification. This modelling methodology is applicable for other regional studies especially for rough mountainous areas which lack local observations and river discharge gauges. The results of flood modelling are useful for the development of a regional early flood warning system and flood mitigation in hazardous flood risk areas. The flood simulations and prepared connected video visualization can be used for local communities. This approach is applicable for flood mitigation strategies in other regions.

Mill scale is the by-product of iron and steel industry. It can be recycled via sinter plant or solid as sinter feed materials. 85 to 90% of the constituent particles are more than 0.008 inches. The iron content is near around 70 %, with a very small amount of alkaline compounds and non-ferrous metals. Mill scale is polluted and contaminated with lubricants, oils and greases from the equipment. In this experiment for recovering of oil from mill scale, it is treated with three different methods. Then all methods were compared according to the ability of oil elimination and iron recovered. After washing and boiling with surfactants, large amount of polluted waste water becomes a problem for environment so this wastewater was utilized for making of Microbial Fuel Cells (MFCs), production of electricity in MFC depends on the biodegradation of organic materials, so different waste were added in wastewater for making MFC. The remaining of MFC was converted in bioethanol, biodiesel and biofertilizer. So this research is economical and environmentally friendly and fulfilled the important aspect of green environment with zero waste. It will be a mile stone in the research of metallurgy, environmental engineering, recycling technology and zero waste management.

Background: Prostate cancer is a complex condition, in which both genetic and environmental factors concomitantly contribute to the tumor initiation and progression. Recently, HOXB13 has been proposed as a susceptibility gene for prostate cancer. Objective: So the current study was conducted to test the existence of potential variations in HOXB13 gene in Iranian men with prostate cancer (PCa) compared to benign prostatic hyperplasia (BPH) cases. Methods: The HOXB13 genetic status was screened in 51 samples, including 21 blood and tissue of PCa cases compared to 30 cases affected by BPH using PCR/sequencing. Then potential association between genomic DNA alterations in blood and tissue PCa specimens was investigated Results: Analysis of BPH tissues showed single nucleotide variations c.366C > T (rs) or c.513T > C (rs9900627) in exon 1, but not in exon 2. Evaluation of PCa tissues revealed two cases with both synonymous c.366C > T and c.513T > C variants and two cases with the synonymous c.366C > T variant in exon 1. The variants c.366C > T and c.513T > C simultaneously or separately were found blood samples of PCa patients. The novel variant c.127A > G in the exon 2 was detected in 1 PCa blood sample. Our analysis indicated a significant reciprocal correlation between HOXB13 mutation in tissue and blood in PCa cases (P=0.02). Conclusion: The variants in exon 2 of HOXB13 may influence the risk of prostate cancer. Also, evaluation of HOXB13 mutation may be considered as a novel marker for screening of PCa. Further investigations are warranted to evaluate the clinical significance of HOXB13 in Iranian population.

Keywords: Innovation ecosystems, Economic sustainability, Hydrocarbon-based economies, Economic diversification, Qualitative approach

Lie algebra plays an important role in the study of singularity theory and other field of sciences. Finding numerous invariants linked with isolated singularities is always a main interest in the classification theory of isolated singularities. Any Lie algebra that characterizes simple singularity produces a natural question. The study of properties such as to find the dimensions of newly defined algebra is a remarkable work. Hussain, Yau and Zuo \cite{HYZ10} have been found a new class of Lie algebra \texorpdfstring{ $ \mathcal{L}_ k (V)$}{LG}, \texorpdfstring{$k\geq 1$}{LG} i.e., Der ($M_{k}(V), M_{k}(V)$) and purposed a conjecture over its dimension $\delta_{k}(V)$ for $k\geq0$. Later they proved it true for $k$ up to $k=1,2,3,4,5$. In this work, the main concern is whether it's true for a higher value of $k$. According to this, we calculate first, the dimension of Lie algebra \texorpdfstring{$\mathcal{L}_ k (V)$}{LG} for $k=6$ and then compute the upper estimate conjecture of fewnomial isolated singularities. Along with, we also justify the inequality conjecture: \texorpdfstring{$\delta_{k+1}(V) < \delta_{k}(V)$}{LG} for $k=6$. Our calculated results are innovative and a new addition to the study of singularity theory.

Background: Eukaryotes whole-genome sequencing is crucial for species identification, gene detection and protein-annotation. Oxford Nanopore sequencing serves as an affordable and rapid platform for sequencing eukaryotes, however the relatively higher error rates require computational and bioinformatic efforts to produce more accurate genome assemblies. Here, we evaluated the effect of read correction tools on eukaryotes genome completeness, gene detection and protein-annotation. Methods: Reads generated by ONT of four eukaryotes, C. albicans, C. gattii, S. cerevisiae, and P. falciparum, were assembled using minimap2 and underwent three rounds of read correction using flye, medaka and racon. The generates consensus FASTA files were compared for total length (bp), genome completeness, gene detection, and protein-annotation by QUAST, BUSCO, BRAKER1 and InterProScan, respectively. Results: genome completeness was dependent on assembly method rather than read correction tool, however, medaka performed better than flye and racon. Racon significantly performed better than flye and medaka in gene detection, while both racon and medaka significantly performed better than flye in protein-annotation. Conclusion: We show that three rounds of read correction significantly affects gene detection and protein-annotation which are dependent on assembly quality in preference to assembly completeness.

The wettability of the oil-brine-rock systems is very challenging mainly for the carbonate reservoirs and it plays a significant role to enhance the oil recovery (EOR). Carbonate rocks are mad-up of complex lithology, hence these requires careful treatments in controlling their wetting behavior for enhanced oil recovery. Therefore, it is extremely important to modify their wettability from an oil-wet rock behavior to a water-wet so that the residual oil can be produced efficiently. Thus, the present study has examined the effectiveness of alumina nanofluid as well as a new chemical methyl blue to alter the wettability. The methyl blue is released on massive scale from various industries i.e. pharma, textile and food industry which is a key environmental concern which contaminates the water table. Hence the study, explores the effects of MB and alumina nanofluid on the wettability. The effect of nanofluids formulated via dispersing the alumina nanoparticles in aqueous solutions at various concentration (0. 0.05, 0.3, 0.50, 0.75 and 1.0 wt. %) were tested for wettability modification under different physio-thermal conditions. Subsequently, the wettability change was examined for the samples treated with different concentrations of MB (10, 15, 30, 50 and 100 mg/L) for 7-days at two different temperatures of (25 and 50 0C). The results showed that hydrophobicity of the SML carbonate rock has significantly reversed while treating with alumina nanofluids and MB. Thus, the wettability modification/reversal via treatment of MB and alumina nanofluid can be an effective mechanism to hydrogen injection and for EOR processes.

This review explores the field of retreatment strategies for the cases filled with calcium silicate-based root canal sealers. Since the introduction of calcium silicate-based materials in dentistry, calcium silicate-based root canal sealers have become popular among dentists because of their biocom- patibility, bioactivity, and sealing ability. Therefore, effective retreatment strategies are indispensable. This article aims to identify the challenges associated with the removal of calcium silicate-based sealers itself and gutta-percha with the sealers during retreatment, evaluate current techniques and materials, and provide future directions for research in this field. Regarding the strategies of removal of root canal sealers, calcium silicate-based sealers are still relatively new materials for clinicians compared with the traditional sealers such as epoxy- or eugenol-based sealers. First, no clinically established solvents have been reported. Second, calcium silicate-based sealers are currently utilized by clinicians in either cold sealer-based technique or warm vertical condensation technique. Third, the setting process of calcium silicate-based sealers generates byproducts primarily calcium hydroxide and secondarily hydroxyapatite, which could interact with dentine. Lastly, there is a lack of clinical studies evaluating the efficacy of retreatment protocols for teeth filled with calcium silicate-based sealers. Therefore, it is important to investigate chemo-mechanical properties of calcium silicate- based sealers itself and their reactions to solvents and/or mechanical instruments, and identify the interfacial properties of calcium silicate-based sealers in respect to dentine and gutta-percha. In addition, researchers in clinical field need to actively gather and report data on retreatments of teeth filled with calcium silicate-based sealers.

It is important to add second- or third-phase materials to improve the physical, mechanical, and biodegradable properties of polypropylene (PP) nanocomposites. A unique hybrid bio-nanocomposite was fabricated using microparticle cellulose powder from wastepaper with a polypropylene (PP) polymeric matrix using a low-shear chaotic mixing method. A small amount (2-5%) of layered micro clay was also added to investigate the hybrid effect of nanocomposite fabrication on the mechanical properties. A low-shear chaotic melt mixing method induces the formation of nano cellulose structures and layered clay nanoparticles by via exfoliation. The nanoparticles were characterized using Fourier transform infrared (FTIR) spectroscopy. Mechanical properties such as the flexural strength (FS) and tensile strength (TS) were investigated using an Izod impact tester (IZ), and a dynamic mechanical testing analyzer (DMTA). Further, scanning electron microscopy (SEM) was used to observe the microstructure of the nanocomposite. In addition, differential scanning calorimetry (DSC) and thermogravimetric analysis (TGA) were conducted to investigate the crystallinity and degradation of the samples, respectively. The addition of wastepaper cellulose powder below 2.5 wt. % did not improve the mechanical properties, however, improvements were observed when more than 5wt.% wastepaper cellulose powder was added. With the addition of a small amount of nano-clay, the mechanical properties were significantly improved, and a synergistic effect could be noticed. The observed results were then compared with PP-clay nanocomposites used in automotive industries to replace cellulose nanocomposites. The application areas can also cover lead production in paper cups and vacuum foaming.

Populaces are prominent indicators of livability in creating ‘street life’ for cities and public spaces. Evaluating people’s experiences is significant in creating a city liveliness and urban heritage trail. The exploration of design strategies that could allow them to integrated and connected with other spaces in the context of city and heritage as well as creating a social life among them. The measuring variables are people’s experience on the spaces; thus, create the pattern of behavior, which cause spaces liveliness, using Kuala Lumpur Heritage Trail as an example of pedestrian priority streets, city environment and heritage value. The aim of this research is to evaluate people’s experiences and analyze their behavior towards the urban street design within cities environment and Malaysia heritage values. The analyzed review comprising on the livability of the street in the context of interrelation between behavioral patter of people and the preferences of urban heritage. Hence, this paper will give an understanding of how people experiences the presence of urban streets and heritage values, thus, bringing liveliness to the streets.

Cervical cancer is known as one of the most important cancers in women worldwide. Chemotherapy is a standard treatment for advanced/recurrent cervical cancer in which the prognosis of the disease is really poor and the 1-year survival chance in these patients is maximally 20%. However, resistance to anticancer drugs is a major problem in treating cancer. Cervical cancer stem cells are considered as a fundamental cause of chemo and radio-resistance and also relapse after primary successful treatment. Signaling pathways include a wide range of molecular mechanisms contribute to drug resistance. Recently, microRNAs (miRNAs) are announced as a group of molecular biomarkers involving in response to chemotherapy in cancer patients. As the miRNAs, there are some long non-coding RNAs (LncRNAs) which their aberrant expression is considered as a biomarker for monitoring chemoresistance. In this review, we summarized current reports about the involvement of signaling pathways during chemoresistance in cervical cancer. Then, genes that have been demonstrated their involvement during drug resistance in cervical cancer were tabulated. Further, miRNAs that have been reported as biomarkers during treatment are listed. By bioinformatic analysis, we predictedmiR-335-5p and miR-16-5p as the most potential biomarkers for monitoring resistance to chemotherapy. Finally, long non-coding RNAs that have been introduced in recent studies as novel biomarkers during the response to chemotherapy were mentioned.

The power system vulnerability leads to faults and the severity of the fault may lead to prolonged load-shedding. The power system needs to be configured in extreme failure scenarios for protecting the network from further contingencies and prolonged load-shedding. Distributed generation resources (DGs) can be useful to form intentional islands after faults to maintain the continuity of power supply to loads based on their weightage during faulty periods and to reduce overall load shedding duration. Power system is bound to collapses and secondary collapse in the formed island is possible. This research represents a novel method of impedance based path finding for intentional islanding, which adapts itself with the changes in the demands, DGs outputs or further severities during restoration period. In this adaptive islanding approach, network adjusts itself with the changes in either the load demands or renewable DGs outputs and rearranges the restoration plan by curtailing or adding some of the loads through controllable switches. Further a secondary collapse in the existing island is studied by injecting multiple faults at various positions of the network to validate the system resilience to cope with severities. A short-term load forecasting approach is used to predict changes in load demands and variations in DG outputs during the islanding scheme. During the restoration period, these variations are tracked and the islands are modified accordingly. In order to minimize the overall generation cost by using less fuel, an economical approach is used in the selection of controllable DGs. The proposed approach is formulated as a multi-objective, that incorporates several operational constraints and simulation is carried out using the modified IEEE 69-bus distribution system to assess the efficacy of the proposed model.

The OPV is the vaccine of choice in polio eradication, especially in developing countries, as it has eliminated the wild poliovirus type 2. However, the immunity induced by IPV is better than that induced by the OPV. The present study compared the mucosal and humoral response to poliovirus vaccines administered to previously OPV-immunized children to assess the immunity gap in children at-risk of high poliovirus transmission. This was a community-based three-arm cluster randomized controlled trial conducted from June 2013 to May 2014 in healthy children under five years of age living in three high-risk districts of Pakistan, i.e., Karachi, Kashmore, and Bajaur. 387 clusters were randomized (131 to arm A, 127 to arm B, and 129 to arm C); however, 360 remained in the trial until the end (116 in arm A, 122 in arm B, and 122 in arm C). These clusters were randomly allocated using a computer algorithm to receive routine polio program (bOPV) activities (control, arm A), additional interventions with community mobilization and provision of short-term preventive maternal and child health services and routine immunization, including bOPV via health camps, (arm B), or all interventions of arm B with an additional provision of IPV (arm C ~ bOPV and IPV). Blood and stool samples were collected from a sub-sample to estimate humoral and intestinal immunity. Study findings showed that the serum titers were highest in Group C (IPV+OPV) at the baseline for P1, where its increase over time was also more prominent. Titers for P2 and P3 were statistically significantly higher amongst those who had received a routine OPV dose versus those who had not; this was true for all study groups and visits. In populations with high Oral Polio Vaccine (OPV) failure rates, administering an Inactivated Polio Vaccine (IPV) booster after a minimum of two OPV doses may effectively bridge immunity gaps. The IPV alone offers limited benefits to humoral immunity and doesn't provide intestinal immunity to prevent the infection and propagation of live poliovirus among unexposed populations.

COVID-19 has created anxiety not only in individuals but also in health organizations, and countries worldwide. Not a single industry is left un-influenced and loss is being estimated in billions of dollars. The widespread of this pandemic disease has challenged researchers all over the world. Some of the researchers are working to invent its cure while, others are applying computing technologies to stop its spread, by analyzing and identifying patterns for prediction and forecasting. This is by no doubt the hottest area of research for the last 100 years. This survey has targeted the research published in computing sub-domains to combat the pandemic. The survey has clustered the scientific efforts into logical groups: surveillance, metrological effects, social media analytics, image processing and business and economy, analysis and modeling. It will serve as a leading source for the followings: researchers who want to identify what has been achieved in different computing sub-domains, those who need fresh authenticated datasets openly accessible for different research contexts and what are future directions in this area of research. The findings of analysis and modeling can be also useful for government agencies who want to set priorities and formulate policies.