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Neuropsychological disorders (e.g., dementia, epilepsy, brain cancer, autism, stroke, and multiple sclerosis) adversely affect the quality of life of patients and their families; moreover, in some instances, they may lead to loss of life. The primary aim was to evaluate and compare the use of machine learning in neuropsychological research in contrast to traditional approaches such as through case studies. This was achieved by referring to earlier studies on this subject. This article presented the use of support vector machines (SVMs) and convolutional neural networks (CNN) for detecting and predicting neuropsychological diseases, such as dementia and Alzheimer's disease. Challenges in using these models include data availability, quality, variability, model interpretability, and validation. Experimental findings have demonstrated the potential of these models in this field. It has been shown that SVM models are robust and efficient in processing and classifying data, particularly in neuroimaging applications, such as magnetic resonance imaging (MRI). CNNs have excelled in handling visual input; thus, they have been used in neuroimaging segregation, recognition, and classification, with applications in brain tumor segmentation, radiation therapy, robotic neurosurgery, and disease prediction. Future research will explore asymmetric differences among left- and right-handed patients, incorporate longitudinal studies, and utilize larger sample sizes. The use of machine learning models has the potential to revolutionize the diagnosis and treatment of neuropsychological diseases, allowing for early detection and intervention. This approach could offer significant advantages to healthcare, such as cost-effective diagnosis and treatment, to help save lives and preserve the quality of life of patients.

The topic of electric vehicles is currently generating significant interest worldwide. They are beneficial for the environment as they save up fuel, reduce pollution and carbon footprint too, but their market consumption is at the nascent level. The demand for these electric vehicles in developing countries is currently very slow and not that steady. The importance of Electric Vehicles (EVs) in the upcoming future is high and so it is crucial to figure out why these EVs are not being sold as much as we expect them in the market today. This paper tries to find out the multiple reasons why these vehicles are not in high demand, and why their counterparts, gasoline-fuelled vehicles are more in demand in the market. This paper discusses, after reviewing multiple other papers, why the consumption of EVs is low. Why their market demand is not where it should be and also multiple other facets. Finally, the paper finds that reasons like higher costs and low government incentives among multiple others are some factors leading to a low market demand for EVs. Also, the paper discusses the future scope of these EVs and how we can increase their consumption in the market.

Agriculture is an extremely important sector in the economic growth of countries. In India, agriculture serves over 70 percent of the country's population and contributes approximately one-third of the country's GDP. Agriculture-related problems have continuously acted as a roadblock to the nation's progress toward its full potential. The only viable solution to this issue is to practice sustainable agriculture, which involves bringing traditional agricultural practices up to date. As a result, the initiative intends to utilise automation and Internet of Things technologies in order to make farming smarter. The main objective of this study is to present outcomes for decision-making for improved productivity, agricultural growth, and sustainability of farms for quality food production and food security. For the purposes of this study, the reference period of the data that will be covered spans from January 2015 to December 2019. The R programming language and the R Studio integrated development environment (IDE) are the tools utilized in the processing and analysis approach. This paper analyzed the underlying factors for the agriculture sector to integrate with emerging technologies. This research study identifies major factors for farmers to adopt new emerging technologies to enhance productivity through improved farming processes. Additionally, it recommends improving infrastructure, expanding internet access to rural areas, and encouraging collaborative platforms to boost agricultural technology innovation and knowledge exchange.

The rapid increase in the number of greenhouse gases that may be attributed to anthropogenic activity has led to a significant rise in the average surface temperature of the globe, which in turn has caused global warming. These impacts are assessed using an indication such as global warming potential, represented in units of carbon dioxide equivalent and used to show a region's overall carbon footprint. Human activities' influence on the environment may be measured using a “carbon footprint,” which refers to the total amount of greenhouse gases those activities generate. Carbon Capture Utilization and Storage (CCUS) will play a significant and essential part in India's efforts to cut its CO2 emissions in half by the year 2050 and reach net-zero emissions by 2070. This study identified prospective sources of CO2 emissions from various sectors in India. Additionally, this study investigates the technology for CO2 capture, utilization, and potential storage in India.

Alzheimer's Disease (AD) is a disorder that worsens over time causing loss of memory and decline of cognitive functions. Current methods for diagnosis consist of neuroimaging scans, magnetic resonance imaging (MRI scans), positron emission tomography (PET scans), and identifying biomarkers in cerebrospinal fluid (CSF). New forms of advanced technology such as machine learning are rising to quickly diagnose AD. This work is a comprehensive review of the research that uses machine learning methods to classify AD cases early. It is a study to provide details for MRI scans and biomarkers used for the recognition of AD and evaluates the execution of both applications while using different classifiers. This paper will discuss and compare various machine learning methods that can be implemented for the classification of Alzheimer's disease. The applications of these algorithms (MRI and biomarkers) are also discussed ultimately proposing the best algorithm and application for classification.

A stroke is a medical emergency characterized by the sudden interruption of blood flow to the brain, leading to cell death from oxygen deprivation. As a leading cause of mortality and long-term disability worldwide, strokes present a growing socioeconomic challenge, exacerbated by demographic shifts and an increasing incidence in younger populations. Analyzing post-stroke conditions is essential for predicting recovery trajectories and guiding personalized treatments. The process is crucial for mitigating long-term cognitive deficits and improving the quality of life for stroke survivors and their families. Epigenetic mechanisms are emerging as key influencers, with four significant modulators—miRNAs, lncRNAs, DNA methylation, and histone modifications—being identified as central to modulating neuronal repair and regeneration. However, this is a lack of knowledge as to how major epigenetic signatures affect post-stroke recovery. The paper systematically reviews the capacity of miRNAs to fine-tune neuroprotective responses and the broader regulatory functions of lncRNAs. Additionally, we examine how shifts in DNA methylation patterns and histone modifications correlate with stroke outcomes, presenting a case for precise epigenetic therapies. In this review, we highlight that these modulators offer promising therapeutic targets for enhancing recovery and provide novel insights into stroke pathology and treatment. We also offer a comparative analysis of the four modulators and present the challenges and future perspectives for each one. The review sets forth a trajectory for future research, underscoring the development of novel epigenetic-based therapeutics as a groundbreaking approach to enhance post-stroke neurorehabilitation and improve patient prognoses.

Skin cancer is a significant health risk that requires early detection for effective treatment. This paper discusses two automated techniques, Artificial Neural Network (ANN) and Convolutional Neural Network (CNN), which make use of deep learning techniques for skin cancer detection. Through evaluation of research on skin cancer detection using ANN and CNN, the effectiveness and performance of these techniques in early and efficient diagnosis of skin cancer were established. The study found that ANN and CNN were successful in early detection of skin cancer using different data sets and hybrid models, demonstrating the potential for these technologies to improve accuracy in skin cancer detection. The paper highlights the novelty of using deep learning techniques for skin cancer detection and emphasises the critical need for an automated system for skin lesion recognition to reduce effort and time in the diagnosis process. The possible applications of this study include the development of more efficient and accurate skin cancer detection systems that can lead to earlier diagnosis and improved treatment outcomes. Overall, this research underscores the importance of using advanced technologies, such as ANN and CNN, in the fight against skin cancer and highlights the potential impact of these techniques in improving patient outcomes.

Skin cancer is among the most common and lethal cancer types, with the number of cases increasing dramatically worldwide. If not diagnosed in the nascent stages, it can lead to metastases, resulting in high mortality rates. Skin cancer can be cured if detected early. Consequently, timely and accurate diagnosis of such cancers is currently a key research objective. Various machine learning technologies have been employed in computer-aided diagnosis of skin cancer detection and malignancy classification. Machine learning is a subfield of artificial intelligence (AI) involving models and algorithms which can learn from data and generate predictions on previously unseen data. The traditional biopsy method is applied to diagnose skin cancer, which is a tedious and expensive procedure. Alternatively, machine learning algorithms for cancer diagnosis can aid in its early detection, lowering the workload of specialists while simultaneously enhancing skin lesion diagnostics. This article presented a critical review of select state-of-the-art machine learning techniques used to detect skin cancer. Several studies had been collected, and an analysis of the performance of k-nearest neighbors, support vector machine, and convolutional neural networks algorithms on benchmark datasets was conducted. The shortcomings and disadvantages of each algorithm were briefly discussed. Challenges in detecting skin cancer were highlighted and the scope for future research was proposed.

The aim of this study is to conduct and examine the results of a Magnetotelluric (MT) survey in Unai, Gujarat, India for geothermal prospect identification. At Unai, the data was acquired, processed, and interpreted in the form of 2D and 3D MT surveys. Data were acquired at 56 stations in uniformly spaced profiles along the ENE-WSE direction (geological strike direction) with a station spacing of 2 km. Qualitative and quantitative data understanding has been done for geothermal prospect identification. Deep and shallow 1D and 2D models of electrical resistivity were made, which suggests the presence of a geothermal aquifer. This is corroborated by polar and skew tipper diagrams. The one-dimensional Occam models and two-dimensional conjugate gradient models confirm the presence of a low resistivity anomaly near the surface ranging from the depth of 100–700 m. This suggests the presence of a shallow geothermal zone. Apart from the shallow geothermal prospect, this study also delineates a deep conductive body which may be attributed to a matured geothermal reservoir. For a better understanding of the prospect geoelectrical cross sections are analysed from a depth of 10 m to 15 km. The MT cross-sections show the presence of geological features such as a series of faults. These faults form horst and graben in nature and may act as a good trap for the shallow reservoirs.

Hemoglobin switching is a complex biological process not yet fully elucidated. The mechanism regulating the suppression of fetal hemoglobin (HbF) expression is of particular interest because of the positive impact of HbF on the course of diseases such as β-thalassemia and sickle cell disease, hereditary hemoglobin disorders that affect the health of countless individuals worldwide. Several transcription factors have been implicated in the control of HbF, of which BCL11A has emerged as a major player in HbF silencing. SOX6 has also been implicated in silencing HbF and is critical to the silencing of the mouse embryonic hemoglobins. BCL11A and SOX6 are co-expressed and physically interact in the erythroid compartment during differentiation. In this study, we observe that BCL11A knockout leads to post-transcriptional downregulation of SOX6 through activation of microRNA (miR)-365-3p. Downregulating SOX6 by transient ectopic expression of miR-365-3p or gene editing activates embryonic and fetal β-like globin gene expression in erythroid cells. The synchronized expression of BCL11A and SOX6 is crucial for hemoglobin switching. In this study, we identified a BCL11A/miR-365-3p/SOX6 evolutionarily conserved pathway, providing insights into the regulation of the embryonic and fetal globin genes suggesting new targets for treating β-hemoglobinopathies.

Significant CO2 concentration increases are predicted to contribute greatly to long-term global warming and related climatic instability, resulting in unforeseeable natural disasters, glacier melting, and extreme weather patterns. These difficulties may be solved by successfully collecting CO2 in order to achieve a cost-effective and environmentally sustainable balance between CO2 emissions and utilization. Due to the absence of a current evaluation of state-of-the-art advances in projected carbon utilization methods, it is vital to present an accurate and timely explanation that demonstrates a thorough comprehension of such sophisticated techniques. Given the critical role of carbon consumption in carbon management, it is necessary to analyse the overall picture. To overcome this gap, our study will investigate the benefits of CO2 recycling in a number of industrial processes, including enhanced oil recovery (EOR), fuel and chemical conversion, mineralization, and biological conversion. The objective of this study is to integrate current research in carbon management through analysis of its application in areas and methodologies such as increased petroleum recovery, CO2 conversion to value-added fuels and chemicals, mineralization and biological conversion to value-added products.

Over here in this paper, we have tried to present detailed information to the best of our knowledge regarding carbon capture storage and its utilization. CCU as well as CCS may be imagined with corresponding parts inside a coordinated framework that could move towards zero discharges that may be required for environment adjustment in the near times. Carbon Capture Storage may be considered as the wide-range extraction of CO2 from various important resources, it is then allowed and made ready for long-term isolation from the atmosphere and, then followed by its appropriate use. Our objective for this paper was to incorporate many technologies in these fields from the research and developmental activities which were a part of many articles and papers till date to the commercial uses and the challenges which are faced in this field and future scopes in this area. The sole purpose of the paper is to present the information in this domain available via different sources in one single paper which would help researchers a lot during their future references or research. Still, there are many kinds of research going on in this field that could be enhanced and paced up by such informative papers where all the required information is stored in a single paper.For the past twenty years, the oil industry and several scientific institutions have given importance to the concept of carbon capture and storage (CCS). A feasible method for storing carbon must be economical, environmentally friendly, and sustainable over the long term. As a result, carbon capture, utilization, and storage (CCUS) has emerged from CCS. The development of CCUS technology goes beyond the narrow focus on storage as it expanded to use carbon dioxide in oil extraction, treat alkaline industrial waste, and conversion of CO2 into useful chemicals to make this greenhouse gas economically viable. Fossil fuels will continue to be a significant source of energy in the following decades despite worldwide commitments to limit CO2 emissions. By converting high-emission industries to low-emission ones, CCUS contributes to the development of the low-emission economy of the future. Therefore, to improve our understanding of the long-term implications of developing alternative options such as large-scale CO2 injection into geological formations, carbon mineralization, and conversion of CO2 to synthesis, state-of-the-art research on carbon storage and utilization is required, and further analysis to understand the risks of CCUS from a technical, legislative, and political perspective. For individuals up for the challenge, CCUS research offers a chance to tackle technological issues that result in innovations with substantial economic and political implications for the oil and petrol sector.

Pneumonia is a respiratory infection caused by microbes and other environmental factors. It infects the lungs causing a buildup of fluid and difficulty in breathing and is the leading cause for death in children under the age of 5 years. Timely detection proves essential in preventing adverse consequences including death. However, most areas in underdeveloped and developing nations do not have access to conventional diagnostic measures, preventive measures and adequate expert treatment. Computer-aided systems based on machine learning techniques can aid this task. However, most smart diagnostic systems may have the drawback of requiring extensive hardware and heavy computation power. The objective of this experiment is to develop a lightweight, deployable and accurate model to aid in the detection of Pneumonia. A Convolutional Neural Network architecture utilizing three different models of varying kernel sizes was developed. The outputs of these models were combined using a novel weighted ensemble approach which proposes an adjustable threshold value to change the model’s diagnostic capabilities as required. The flexible threshold value provides a means to adjust the weightage given to each model’s output and hence change the classification result depending on the actual case on hand. The model was evaluated on metrics including accuracy, recall, precision and f1-score and was able to achieve a high recall value of 99.23% with an f1-score of 88.56% which are critically high values for the given domain resulting in almost no chances of a Pneumonia positive case being misclassified. The absence of transfer learning or deep neural networks makes the model lightweight and hence, a plausibly deployable diagnostic-aid solution. Further studies were carried out to find methods such as – larger dataset, better preprocessing and more – to improve the model performance.

Many regions of the world have unpredictable rainfall, making agriculture impossible to sustain without diverse irrigation technologies. Irrigation has a huge impact on crop productivity, but traditional irrigation systems can be replaced with Advanced and Smart irrigation techniques, which can enhance production yield even more. In arid regions where water is scarce, drip irrigation is a good option because water is supplied at a low rate and it also saves a lot of water, whereas, in horticulture, sprinkler systems are widely used because they do not require large pipelines all over large lands and instead spray water droplets all over the area like rain. The purpose of this article is to analyze the various types of smart drip and sprinkler irrigation systems with the help of IoT and also find solution for the various challenges found in the various methods. Various irrigation technologies which include IoT are reviewed, with a focus on smart drip and sprinkler systems. Various comparisons have been conducted between various methods to find the most efficient one in both irrigation techniques. A table summarizing the similarities between the various methods is also presented. Finally, all of this leads to saving water and making farmers' work easier.

Autologous chimeric antigen receptor-modified T-cell (CAR T) products have demonstrated un-precedent efficacy in treating many relapsed/refractory B-cell and plasma cell malignancies, leading to multiple commercial products now in routine clinical use. These positive responses to CAR T therapy have spurred biotech and big pharma companies to evaluate innovative production methods to increase patient access while maintaining adequate quality control and profitability. Autologous cellular therapies are, by definition, manufactured as single patient batches, and demand has soared for manufacturing facilities compliant with current Good Manufacturing Practice (cGMP) regulations. The use of a centralized production model is straining finite resources even in developed countries in North America and the European Union, and patient access is not feasible for most of the developing world. The idea of having a more uniform availability of these cell therapy products promoted the concept of point-of-care (POC) manufacturing or decentralized in-house production. While this strategy can potentially decrease the cost of manufacturing, the challenge comes in maintaining the same quality as currently available centrally manufactured products due to the lack of standardized manufacturing techniques amongst institutions. However, academic medical institutions and biotech companies alike have forged ahead innovating and adopting new technologies to launch clinical trials of CAR T products produced exclusively in-house. Here we discuss POC production of CAR T products.

Abstract Around 450 million people are affected by pneumonia every year, which results in 2.5 million deaths. Coronavirus disease 2019 (Covid‐19) has also affected 181 million people, which led to 3.92 million casualties. The chances of death in both of these diseases can be significantly reduced if they are diagnosed early. However, the current methods of diagnosing pneumonia (complaints + chest X‐ray) and Covid‐19 (real‐time polymerase chain reaction) require the presence of expert radiologists and time, respectively. With the help of deep learning models, pneumonia and Covid‐19 can be detected instantly from chest X‐rays or computerized tomography (CT) scans. The process of diagnosing pneumonia/Covid‐19 can become faster and more widespread. In this paper, we aimed to elicit, explain, and evaluate qualitatively and quantitatively all advancements in deep learning methods aimed at detecting community‐acquired pneumonia, viral pneumonia, and Covid‐19 from images of chest X‐rays and CT scans. Being a systematic review, the focus of this paper lies in explaining various deep learning model architectures, which have either been modified or created from scratch for the task at hand. For each model, this paper answers the question of why the model is designed the way it is, the challenges that a particular model overcomes, and the tradeoffs that come with modifying a model to the required specifications. A grouped quantitative analysis of all models described in the paper is also provided to quantify the effectiveness of different models with a similar goal. Some tradeoffs cannot be quantified and, hence, they are mentioned explicitly in the qualitative analysis, which is done throughout the paper. By compiling and analyzing a large quantum of research details in one place with all the data sets, model architectures, and results, we aimed to provide a one‐stop solution to beginners and current researchers interested in this field.

The current rise of artificial intelligence and machine learning has been significant. It has reduced the human workload improved quality of life significantly. This article describes the use of artificial intelligence and machine learning to augment drug discovery and development to make them more efficient and accurate. In this study, a systematic evaluation of studies was carried out; these were selected based on prior knowledge of the authors and a keyword search in publicly available databases which were filtered based on related context, abstract, methodology, and full text. This body of work supported the roles of machine learning and artificial intelligence in facilitating drug development and discovery processes, making them more cost-effective or altogether eliminating the need for clinical trials, owing to the ability to conduct simulations using these technologies. They also enabled researchers to study different molecules more extensively, without any trials. The results of this paper demonstrate the prevalent application of machine learning and artificial intelligence methods in drug discovery, and indicate a promising future for these technologies; these results should enable researchers, students, and pharmaceutical industry to dive deeper into machine learning and artificial intelligence in a drug discovery and development context.

The Corona Virus Disease 2019 (COVID-19) pandemic has taught us many valuable lessons regarding the importance of our physical and mental health. Even with so many technological advancements, we still lag in developing a system that can fully digitalize the medical data of each individual and make it readily accessible for both the patient and health worker at any point in time. Moreover, there are also no ways for the government to identify the legitimacy of a particular clinic. This study merges modern technology with traditional approaches, thereby highlighting a scenario where artificial intelligence (AI) merges with traditional Chinese medicine (TCM), proposing a way to advance the conventional approaches. The main objective of our research is to provide a one-stop platform for the government, doctors, nurses, and patients to access their data effortlessly. The proposed portal will also check the doctors’ authenticity. Data is one of the most critical assets of an organization, so a breach of data can risk users' lives. Data security is of primary importance and must be prioritized. The proposed methodology is based on cloud computing technology which assures the security of the data and avoids any kind of breach. The study also accounts for the difficulties encountered in creating such an infrastructure in the cloud and overcomes the hurdles faced during the project, keeping enough room for possible future innovations. To summarize, this study focuses on the digitalization of medical data and suggests some possible ways to achieve it. Moreover, it also focuses on some related aspects like security and potential digitalization difficulties.

Air pollution caused by cars is a major contributor to the greenhouse effect, making it an issue of widespread concern. End-of-combustion of an engine produces pollutants such carbon monoxide (CO), oxides of nitrogen (NOx), oxides of sulphur (SOx), particulate particles and unburned hydrocarbons (HC). The environment, air quality, and human health are all negatively affected by these contaminants. A catalytic converter is a pollution control device for every fuel-operated automobile exhaust system. Increasingly low emission standards and automobile space constraints necessitate optimizing the device geometry. The uniformity index parameter mainly judges a catalytic converter’s smooth working and operational life. In the present study, 16 catalytic converter geometry designs are investigated. Their effects on exhaust flow distribution, i.e., uniformity index, pressure drop, and peak pressure at monolith substrate entry, are identified and visualized through contour plots. A finite volume approach is used to study hydraulic diffusion inside the device with the help of the ANSYS Fluent CFD package. Two-dimensional axisymmetric CFD simulations identify optimum design parameters among the considered geometries. The design of diffuser geometry is crucial in maintaining a uniform flow across monolith honeycomb structures.

ABSTRACT: Due to global warming and the salinity of drinkable natural resources, water scarcity has become a significant impediment to the development of many regions of the world, prompting the development of novel desalination techniques for brackish and seawater. The only way to satisfy society's freshwater demands is to transform abundant seawater into potable water by desalination. Numerous research efforts have been effective in establishing large desalination plants. However, significantly fewer attempts are made for dry regions where low-cost, maintenance-free, and low-operational-cost approaches are required. Both rapidly developing and underdeveloped nations struggle to provide their populations with pure drinking water. In the desalination sector, CO2 emissions and significant ecological problems have increased. The desalination sector may be sustainable by integrating renewable energy and using proper brine disposal techniques. In this review, various desalination systems that incorporate renewable energy sources, with an emphasis on solar energy, are examined. The main objective of this paper is to explain what solar desalination is, why it is performed, and what techniques can be used to make desalination more structured and cost-effective. In addition, this study provides a comprehensive review of all the solar desalination systems, indirect and direct, along with plant-specific technical data. In addition, the efforts that have been made to evaluate the economic viability of each desalination technology and the elements that determine its cost are discussed.

Abstract Microbial communities in freshwater streams play an essential role in ecosystem functioning via biogeochemical cycling. Yet, the impacts of treated wastewater influx into stream ecosystems on microbial strain diversity remain mostly unexplored. Here, we coupled full‐length 16S ribosomal RNA gene Nanopore sequencing and strain‐resolved metagenomics to investigate the impact of treated wastewater on a mesocosm system (AquaFlow) run with restored river water. Over 10 days, community Bray–Curtis dissimilarities between treated and control mesocosm decreased (0.57 ± 0.058 to 0.26 ± 0.046) based on ribosomal protein S3 gene clustering, finally converging to nearly identical communities. Similarly, strain‐resolved metagenomics revealed a high diversity of bacteria and viruses after the introduction of treated wastewater; these microbes also decreased over time resulting in the same strain clusters in control and treatment at the end of the experiment. Specifically, 39.2% of viral strains detected in all samples were present after the introduction of treated wastewater only. Although bacteria present at low abundance in the treated wastewater introduced additional antibiotic resistance genes, signals of naturally occurring ARG‐encoding organisms resembled the resistome at the endpoint. Our results suggest that the previously stressed freshwater stream and its microbial community are resilient to a substantial introduction of treated wastewater.

Background Over-ordering of daily laboratory tests adversely affects patient care through hospital-acquired anaemia, patient discomfort, burden on front-line staff and unnecessary downstream testing. This remains a prevalent issue despite the 2013 Choosing Wisely recommendation to minimise unnecessary daily labs. We conducted a systematic review of the literature to identify interventions targeting unnecessary laboratory testing.Methods We systematically searched MEDLINE, EMBASE, Cochrane Central and SCOPUS databases to identify interventions focused on reducing daily complete blood count, complete metabolic panel and basic metabolic panel labs. We defined interventions as ‘effective’ if a statistically significant reduction was attained and ‘highly effective’ if a reduction of ≥25% was attained.Results The search yielded 5646 studies with 41 articles that met inclusion criteria. We grouped interventions into one or more categories: audit and feedback, cost display, education, electronic medical record (EMR) change, and policy change. Most interventions lasted less than a year and used a multipronged approach. All five strategies were effective in most studies with EMR change being the most commonly used independent strategy. EMR change and policy change were the strategies most frequently reported as effective. EMR change was the strategy most frequently reported as highly effective.Conclusion Our analysis identified five categories of interventions targeting daily laboratory testing. All categories were effective in most studies, with EMR change being most frequently highly effective.PROSPERO registration number CRD42021254076.

Carbon dioxide emission has emerged as a major concern in the 21st century. The rising global average temperature and its impact on climate change has a major impact on the socioeconomic affairs of the world. This rise, directly causes the melting of the polar ice caps which in turn, brings about many other issues including extinction of polar animals, flooding of coastal regions, exposure to ancient microbial life and bacteria frozen in the snow which pose a risk of many more global pandemics and unseen diseases. An urgent need to control this carbon emission is required. The initial step in this process is to accurately identify the threat levels and milestones. Certain thresholds need to be mapped that express the most critical levels of CO2 such as – the risk point, point of no return, etc. This is the main issue that this paper aims to address. The paper also aims to suggest some methodologies to deal with the same issue. The flow of the experiment and paper is described in the following lines. Historical data was used to make a prediction for the year in which the earth will hit a particular threshold for carbon dioxide concentration in the atmosphere. This level must not be breached and is essential in the fight against climate change. Next, analysis and data are used to calculate the reduction needed in the emission levels in order to bring back the CO2 concentrations into a safer range. The study concludes that the critical level of CO2 - 500 ppm, will be achieved by the year 2047. This level is considered a point of no return. A reduction rate of 6.37% and reversal rate of 23.38% is required to bring the emissions back to safe levels. The study also concluded that various socioeconomic factors such as population, greenhouse gasses, combustion industries contribute the most to these emissions. The authors recommend that further research be carried out on this problem to ascertain further predictions on the point of no return so that an action plan can be developed accordingly. The authors also recommend that a shift to renewable energy sources be undertaken speedily and that carbon neutrality be a crucial goal of every organization.

Breast cancer is a widely occurring cancer in women worldwide and is related to high mortality. The objective of this review was to present several approaches to investigate the application of multiple algorithms based on machine learning (ML) approach and biosensors for early breast cancer detection. Automation is needed because biosensors and ML are needed to identify cancers based on microscopic images. ML aims to facilitate self-learning in computers. Rather than relying on explicit pre-programmed rules and models, it is based on identifying patterns in observed data and building models to predict outcomes. We have compared and analysed various types of algorithms such as fuzzy extreme learning machine – radial basis function (ELM-RBF), support vector machine (SVM), support vector regression (SVR), relevance vector machine (RVM), naive bayes, k-nearest neighbours algorithm (K-NN), decision tree (DT), artificial neural network (ANN), back-propagation neural network (BPNN), and random forest across different databases including images digitized from fine needle aspirations of breast masses, scanned film mammography, breast infrared images, MR images, data collected by using blood analyses, and histopathology image samples. The results were compared on performance metric elements like accuracy, precision, and recall. Further, we used biosensors to determine the presence of a specific biological analyte by transforming the cellular constituents of proteins, DNA, or RNA into electrical signals that can be detected and analysed. Here, we have compared the detection of different types of analytes such as HER2, miRNA 21, miRNA 155, MCF-7 cells, DNA, BRCA1, BRCA2, human tears, and saliva by using different types of biosensors including FET, electrochemical, and sandwich electrochemical, among others. Several biosensors use a different type of specification which is also discussed. The result of which is analysed on the basis of detection limit, linear ranges, and response time. Different studies and related articles were reviewed and analysed systematically, and those published from 2010 to 2021 were considered. Biosensors and ML both have the potential to detect breast cancer quickly and effectively.

Abstract Two-dimensional (2D) heterostructures (HS) formed by transition-metal dichalcogenide (TMDC) monolayers offer a unique platform for the study of intralayer and interlayer excitons as well as moiré-pattern-induced features. Particularly, the dipolar charge-transfer exciton comprising an electron and a hole, which are confined to separate layers of 2D semiconductors and Coulomb-bound across the heterojunction interface, has drawn considerable attention in the research community. On the one hand, it bears significance for optoelectronic devices, e.g. in terms of charge carrier extraction from photovoltaic devices. On the other hand, its spatially indirect nature and correspondingly high longevity among excitons as well as its out-of-plane dipole orientation render it attractive for excitonic Bose–Einstein condensation studies, which address collective coherence effects, and for photonic integration schemes with TMDCs. Here, we demonstrate the interlayer excitons’ out-of-plane dipole orientation through angle-resolved spectroscopy of the HS photoluminescence at cryogenic temperatures, employing a tungsten-based TMDC HS. Within the measurable light cone, the directly-obtained radiation profile of this species clearly resembles that of an in-plane emitter which deviates from that of the intralayer bright excitons as well as the other excitonic HS features recently attributed to artificial superlattices formed by moiré patterns.

Abstract In the past decades, there have been numerous advancements in the field of technology. This has led to many scientific breakthroughs in the field of medical sciences. In this, rapidly transforming world we are having a difficult time and the problem of fatigue is becoming prevalent. So, this study aimed to understand what is fatigue, its repercussions, and techniques to detect it using machine learning (ML) approaches. This paper introduces, discusses methods and recent advancements in the field of fatigue detection. Further, we categorized the methods that can be used to detect fatigue into four diverse groups, that is, mathematical models, rule‐based implementation, ML, and deep learning. This study presents, compares, and contrasts various algorithms to find the most promising approach that can be used for the detection of fatigue. Finally, the paper discusses the possible areas for improvement.

The challenges found in geothermal well drilling have forced the industry to develop new significant cost effective, time saver technologies to go for deeper geothermal resources beyond their traditional limits. To overcome these challenges, the new Dual String Drilling (DSD) technology which is based on penetration pathway of drilling fluid and rock cuttings during drilling a well. The principle of DSD employs drilling fluid flows through the annulus of inner and outer string pipe while cutting from the bottom of the well through inner pipe. The paper describes the novel Dual String Drilling (DSD) technology, predicting their occurrence and advantages on drilling of geothermal well. The outcome shows that with DSD approach a lot of time will be saved to circulate the kick out of the well. Additionally, the characteristic response of DSD enables to improve the hole cleaning capacity, to prevent pipe stuck, better well stability, reduction in torque and drag, to remove the dynamic equivalent circulating density gradient in geothermal wells. Moreover, the paper also depicts the system and method for dual coil tubing drilling of horizontal well which follows the same principle as DSD. The comparison of DSD with conventional drilling assesses that the DSD technology is affordable and appropriate for geothermal well drilling.

In the Oil and Gas industry, the implementation of artificial intelligence techniques gives advantages of better use of existing infrastructure. It provides better future outcomes, which makes it an essential technology in the operations of the industry. In this paper, the implementation of artificial intelligence techniques for good planning, determination of reservoir rock properties, drilling optimization and production facilities are reviewed. With precise knowledge of permeability and porosity, reservoir engineers can develop robust reservoir development plan and manage hydrocarbon recovery effectively. For porosity prediction, wire-line log data and seismic attributes are used in artificial intelligence-based models. Permeability prediction has been discussed by considering the different types of well-log data in the modeling of artificial intelligence techniques to predict single-phase fluid. Solution of specific developed problems during the determination of permeability in the actual experiment is also mentioned. Artificial intelligence techniques are used in the design and accurate analysis of the drilling fluid. It assists in the selection of drill bit and drilling-related problems. If a reservoir cannot produce naturally, we can select a suitable artificial lift and design the production system using Artificial intelligence techniques. For EOR methods, SADA and NAVA knowledge base systems are discussed. Artificial intelligence can help in optimizing production, improve production efficiency and recovery of hydrocarbons.

Abstract Diabetes mellitus has been an increasing concern owing to its high morbidity, and the average age of individual affected by of individual affected by this disease has now decreased to mid-twenties. Given the high prevalence, it is necessary to address with this problem effectively. Many researchers and doctors have now developed detection techniques based on artificial intelligence to better approach problems that are missed due to human errors. Data mining techniques with algorithms such as - density-based spatial clustering of applications with noise and ordering points to identify the cluster structure, the use of machine vision systems to learn data on facial images, gain better features for model training, and diagnosis via presentation of iridocyclitis for detection of the disease through iris patterns have been deployed by various practitioners. Machine learning classifiers such as support vector machines, logistic regression, and decision trees, have been comparative discussed various authors. Deep learning models such as artificial neural networks and recurrent neural networks have been considered, with primary focus on long short-term memory and convolutional neural network architectures in comparison with other machine learning models. Various parameters such as the root-mean-square error, mean absolute errors, area under curves, and graphs with varying criteria are commonly used. In this study, challenges pertaining to data inadequacy and model deployment are discussed. The future scope of such methods has also been discussed, and new methods are expected to enhance the performance of existing models, allowing them to attain greater insight into the conditions on which the prevalence of the disease depends.

Quality water is used for various daily chores like drainage, drinking, sanitation, agricultural, and other industrial applications, thus being the need of the hour. Water is a dominant raw material in manufacturing pharmaceuticals and chemicals; reliable and superior water sources are needed for many processes, including cooling, refining, and material extraction. The purpose of urban and industrial wastewater treatment is to eliminate contaminants, destroy toxicants, neutralise coarse particles, and destroy bacteria to increase the consistency of the discharged water to maintain the allowable amount of water to be discharged into or for agricultural property. So, the goal of water treatment is to lower BOD, COD, eutrophication, etc., in receiving water sources and stop radioactive compounds from spreading through the food chain. Pharmaceutical wastewater has a wide range of characteristics, including a high amount of organic matter, microbial contamination, a high salt content, and the inability to biodegrade. Following secondary application, residual quantities of suspended solids and dissolved organic matter exist. Therefore, advanced treatment is necessary to increase the efficiency of pharmaceutical wastewater effluent. In the methods described in this study, Advanced Oxidation and Bioremediation—the latter emerges as the most environmentally and commercially viable option. This paper discusses the many types of bioremediations, their applications, and their limits in the treatment of industrial wastewater with the goal of reducing the ecotoxicological impacts of pharmaceutical wastewater.

Abstract The mass spectra for the heavy-light $$(c{\bar{q}}) ;$$ ( c q ¯ ) ; $$q = u$$ q = u or d charmed mesons are computed based on a relativistic framework. The low-lying 1P states are found to be in an excellent agreement with the PDG reported values. Using the computed mass spectra and following effective Lagrangian approach based on heavy quark and chiral symmetry, the OZI allowed two body strong decays are computed. The computed decay rates, ratios and branching fractions allow us to identify the proper spin-parity assignments of the newly observed charm states. Accordingly, we could identify $$D_J(2560)$$ D J ( 2560 ) as $$2^1S_0$$ 2 1 S 0 , $$D_J^*(2680)$$ D J ∗ ( 2680 ) as $$2^3S_1$$ 2 3 S 1 , $$D_J(2740)$$ D J ( 2740 ) as $$1^3D_2$$ 1 3 D 2 , $$D_J^*(2760)$$ D J ∗ ( 2760 ) as $$1^3D_3$$ 1 3 D 3 , $$D^*_J(3000)$$ D J ∗ ( 3000 ) as $$2^3P_0$$ 2 3 P 0 , $$D_J(3000)$$ D J ( 3000 ) as $$2^1P_1$$ 2 1 P 1 and $$D^*_2(3000)$$ D 2 ∗ ( 3000 ) as $$1^3F_2$$ 1 3 F 2 open charm states. The effective coupling constants, $$g_T$$ g T , $$\tilde{g_H}$$ g H ~ , $$g_Y$$ g Y , $$\tilde{g_S}$$ g S ~ and $$g_Z$$ g Z extracted from the present study are found to be in accordance with the reported values. These coupling constants would be useful in further investigations. We found $$D^{*+} \pi ^-$$ D ∗ + π - as a favorable channel for the experimental search of the missing $$1^1F_3$$ 1 1 F 3 state.

While performing various steps for exploration and exploitation of geothermal resources, the applications of geochemical methods are pivotal for reservoir characteristics evaluation. It is used concurrently with geological and hydro-geological appraisals to supplement available information regarding the prospectively of the region of investigation at the relatively low cost compared to geophysical methods or drilling. Temperature range of 40 °C to 47 °C is demonstrated by the study area comprising of several thermal water manifestations. Studies from existing wells and surveys have suggested the presence of a sizable low enthalpy geothermal resource in the field. One of the most prominent uses of geochemical sampling is the determination of subsurface temperatures using Geothermometry. In this study, commonly accepted concepts of cationic and Silica Geothermometry have been applied to understand conditions in the reservoir. Interpretation of the data has given us some input on the reservoir characteristics like reservoir temperature, mixing process and multiple fluid origins. In Dholera geothermal field for determination of variation in hydrochemical facies and understanding of the advancement of the hydrochemical forms, the current research additionally imagines the significance of graphical portrayals like Piper chart, Scholler and so forth individually. The seawater intrudes the composition of water shows the reservoir. The physio-chemical properties of the water also influence exploitation strategies.

Primary objective of the paper is to represent the functionality of nanotechnology in Enhanced Oil Recovery (EOR). Nanoparticles can resist high temperature and pressure in subsurface oil reservoir system and exhibit different properties compared to same fine or bulk molecules. Due to small in size, it increases surface area and it creates massive diffusion driving force at higher pressure and temperature. In small surface area, it contains much higher concentration of atoms. Nanoparticles are used to modify optical, specific, thermal, interfacial properties of tight oil reservoir with 5–50 μm size pore diameter which consists trapped oil in place. Study reveals that by controlling nano mineral complexes can increase recovery of oil in oil field due to capillary hysteresis value change and specific behavior of clay mineral. Nanoparticles boost the oil recovery by mechanism of reduction in mobility ratio which reduces viscosity of heavy oil and interfacial tension and increase in fault lines permeability. Nanoparticles like silane treated silicon oxides, aluminium oxides in brine can be used. They have tendency of displacement of water and magnifies flow of oil in well. Some nanoparticles performance depends upon its operational condition for EOR process. In modern oil and gas domain by using EOR technique have oil recovery factor magnitude of 0.3–0.5; but by using nanotechnology there will be growth in oil & gas recovery efficiently and guarantying oil recovery factor range up to 0.5 to 0.6. It is not possible to know all application of nanotechnologies; but some experimental results and advanced technologies deduce that there is reduction in cost of hydrocarbon production for its market use by application of nanotechnology in EOR. Further advancement in nanotechnologies can increase more recovery of oil & gas.

Numerous recent studies have attempted to create efficient mechanical trading systems through the use of machine learning approaches for stock price estimation and portfolio management. Using the ability to foresee the future trends of the stock performance, the return of investment can be maximized for short-term trading. This paper will review various Artificial Intelligence (AI) and Machine Learning (ML) strategies for stock price forecasting. The aim of this review is to discuss various techniques for stock price prediction that incorporate ARIMA, LSTM, Hybrid LSTM, CNN, and Hybrid CNN. Additionally, it will also discuss the limitations and accuracy of the various models, including the ARIMA model, the LSTM model, the MI-LSTM model, the Bi-LSTM model, the LSTM-DRNN model, the CNN model, the GC-CNN model, the CNN-LSTM model, the CNN-TLSTM model, and the CNN-BiLSTM model, in terms of percentage of accuracy or error calculation in terms of standard accuracy measures like RMSE, MAPE, MAE. The models can be used to forecast either the accurate stock rate, induced by the low MSE, RMSE and MAE of LSTM models, or the general trend and deflection range of the stock the following day, induced by the ability to dynamically capture swift changes in the system of CNN models. These characteristics consequently illustrate the advantages of the hybrid model at efficiently and accurately forecasting stock attributes.

Abstract Although the education sector is improving more quickly than ever with the help of advancing technologies, there are still many areas yet to be discovered, and there will always be room for further enhancements. Two of the most disruptive technologies, machine learning (ML) and blockchain, have helped replace conventional approaches used in the education sector with highly technical and effective methods. In this study, a system is proposed that combines these two radiant technologies and helps resolve problems such as forgeries of educational records and fake degrees. The idea here is that if these technologies can be merged and a system can be developed that uses blockchain to store student data and ML to accurately predict the future job roles for students after graduation, the problems of further counterfeiting and insecurity in the student achievements can be avoided. Further, ML models will be used to train and predict valid data. This system will provide the university with an official decentralized database of student records who have graduated from there. In addition, this system provides employers with a platform where the educational records of the employees can be verified. Students can share their educational information in their e-portfolios on platforms such as LinkedIn, which is a platform for managing professional profiles. This allows students, companies, and other industries to find approval for student data more easily.

Food-borne microorganisms are one of the biggest concern in food industry. Food-borne microorganisms such as Listeria monocytogenes, Escherichia coli, Salmonella spp., Vibrio spp., Campylobacter jejuni, Hepatitis A are commonly found in food products and can cause severe ailments in human beings. Hence, disinfection of food is performed before packaging is performed to sterilize food. Traditional methods for disinfection of microorganisms are based on chemical, thermal, radiological and physical principles. They are highly successful, but they are complex and require more time and energy to accomplish the procedure. Cold plasma is a new technique in the field of food processing. CP treatments has no or very low effect on physical, chemical and nutritional properties of food products. This paper reviews the effect of plasma processing on food products such as change in colour, texture, pH level, protein, carbohydrate, and vitamins. Cold plasma by being a versatile, effective, economical and environmentally friendly method provides unique advantages over commercial food processing technologies for disinfection of food.

Abstract A crime is a deliberate act that can cause physical or psychological harm, as well as property damage or loss, and can lead to punishment by a state or other authority according to the severity of the crime. The number and forms of criminal activities are increasing at an alarming rate, forcing agencies to develop efficient methods to take preventive measures. In the current scenario of rapidly increasing crime, traditional crime-solving techniques are unable to deliver results, being slow paced and less efficient. Thus, if we can come up with ways to predict crime, in detail, before it occurs, or come up with a “machine” that can assist police officers, it would lift the burden of police and help in preventing crimes. To achieve this, we suggest including machine learning (ML) and computer vision algorithms and techniques. In this paper, we describe the results of certain cases where such approaches were used, and which motivated us to pursue further research in this field. The main reason for the change in crime detection and prevention lies in the before and after statistical observations of the authorities using such techniques. The sole purpose of this study is to determine how a combination of ML and computer vision can be used by law agencies or authorities to detect, prevent, and solve crimes at a much more accurate and faster rate. In summary, ML and computer vision techniques can bring about an evolution in law agencies.

Abstract Mass spectra of bottomonium states are computed using the Instanton Induced potential obtained from Instanton Liquid Model for QCD vacuum and incorporating a stronger confinement term. Spin dependent interactions through confined one gluon exchange potential are incorporated to remove the mass degeneracy. The mass spectra of the $$b\bar{b}$$ b b ¯ states up to 4S states are found to be in good agreement with the values reported by PDG(2020). Mixing of nearby isoparity states are also studied. We found the state $$\varUpsilon (10{,}860)$$ Υ ( 10 , 860 ) as an admixture of $$5^3S_1$$ 5 3 S 1 and $$6^3D_1$$ 6 3 D 1 Upsilon states with mixing angle $$\theta = 39.98^{\circ }$$ θ = 39 . 98 ∘ and the mixed state di-leptonic decay width is found to be 0.25 keV as against the width of $$0.31 \pm 0.07$$ 0.31 ± 0.07 keV reported by PDG. Further the state $$\varUpsilon (11{,}020)$$ Υ ( 11 , 020 ) is also found to be the admixture of $$6^3S_1$$ 6 3 S 1 and $$5^3D_1$$ 5 3 D 1 Upsilon states with the mixing angle $$\theta = 51.69^{\circ }$$ θ = 51 . 69 ∘ and the di-leptonic decay width of the mixed state is obtained as 0.14 keV which is very close to the width of $$0.13 \pm 0.03$$ 0.13 ± 0.03 keV reported by PDG. Present results indicates that addition of confinement to the instanton potential is crucial for the determination of the mass spectroscopy of heavy hadrons.

Abstract There is a significant interest in designing therapeutic agents that can enhance ADCC and thereby improve clinical responses with approved antibodies. We recently reported the combination of an imidazoquinoline-based TLR7/8 agonist (522) with a monoclonal antibody improved ADCC in vitro and in vivo. In the present study, we tested several new small molecule TLR7/8 agonists that induce significantly higher cytokines compared to both the FDA-approved TLR7 agonist, imiquimod, and 522. We evaluated these agonists in combination with monoclonal antibody therapy, with the main goal of enhancing ADCC. Our studies show these TLR7/8 agonists induce robust pro-inflammatory cytokine secretion and activate NK cells. Specifically, we found the agonists 574 and 558 significantly enhanced NK cell-mediated ADCC in vitro as well as enhanced the anti-cancer efficacy of monoclonal antibodies in two different in vivo mouse models. Additionally, we found the agonists were able to stimulate CD8 T cells, likely indicative of an early adaptive immune response.

Diabetes is an ailment in which glucose level increase in at high rates in blood due to body’s inability to metabolize it. This happens when body does not produce sufficient amount of insulin or it does not respond to it properly. Critical and long-term health issues arise if diabetes is not handled or properly treated which includes: heart problems, disorders of the lungs, skin and liver complications, nerve damage, etc. With increasing number of diabetic patients, its early detection becomes essential. In this paper, our major focus areas are data mining and fuzzy logic techniques used in diabetes diagnosis. Data mining is used for locating patterns in huge datasets using a composition of different methods of machine learning, database manipulations and statistics. Data mining offers a lot of methods to inspect large data considering the expected outcome to find the hidden knowledge. Fuzzy logic is similar to human reasoning system and hence it can handle the uncertainties found in the data of medical diagnosis. These systems are called expert systems. The fuzzy expert systems (FES) analyze the knowledge from the available data which might be vague and suggests linguistic concept with huge approximation as its core to medical texts. In this paper, the methodology section delivers the pipeline of various tasks such as selecting the dataset, preprocessing the data by applying numerous methods such as standardization, normalization etc. After that, feature extraction technique is implemented on the dataset for improving the accuracy and finally dataset worked on data mining and fuzzy logic various classification algorithms. While analyzing different data mining methods, the accuracy computed through random forest classifiers as high as 99.7% and in case of numerous fuzzy logic approaches, high precision and low complexity was found to contribute a fairly high accuracy of 96%.

This paper aims to review Artificial neural networks, Multi-Layer Perceptron Neural network (MLP) and Convolutional Neural network (CNN) employed to detect breast malignancies for early diagnosis of breast cancer based on their accuracy in order to identify which method is better for the diagnosis of breast cell malignancies. Deep comparison of functioning of each network and its designing is performed and then analysis is done based on the accuracy of diagnosis and classification of breast malignancy by the network to decide which network outperforms the other. CNN is found to give slightly higher accuracy than MLP for diagnosis and detection of breast cancer. There still is the need to carefully analyse and perform a thorough research that uses both these methods on the same data set under same conditions in order identify the architecture that gives better accuracy.

“Society Sync” is society administration software in the form of a website/app that makes managing residential societies easier. It's a step toward digitalization and increased transparency in society's work. It will also assist the authorities in keeping track of their records. The watchman module will ensure that society's security functions and is maintained digitally, while also reducing the watchman's workload and increasing efficiency. The secretary module will include all administrative functions such as watchman attendance, resident payment dues, and so on, making his or her job easier and more efficient. The resident module will have services that will benefit them, such as polling, filing complaints, receiving daily notifications, knowing about their maintenance expenses, car activities, and so on. Visitor management, parking, maintenance, polling, amenities management, and attendance management are just a few of the services available. As a result, the society management software handles and manages the society digitally, which provides improved security, efficiency, involvement, hassle-free management, easy administration, and record maintenance, among other benefits. For vehicle management and attendance, we employed AI technologies such as OCR and face recognition to construct different characteristics in different modules of our system. We've employed AI/machine learning algorithms to keep track of features like complaint logs and vehicle logs so that we can deliver the best outcomes for user. The utilization of Big Data in our system is to improve the psychological health of our system. With the use of big data analysis, we have tracked how consumers react to various elements and designs to get the best desired results for user experience and user interface design.

Spontaneous Intracerebral hemorrhage (ICH) is a devastating injury that accounts for 10–15% of all strokes. The rupture of cerebral blood vessels damaged by hypertension or cerebral amyloid angiopathy creates a space-occupying hematoma that contributes toward neurological deterioration and high patient morbidity and mortality. Numerous protocols have explored a role for surgical decompression of ICH via craniotomy, stereotactic guided endoscopy, and minimally invasive catheter/tube evacuation. Studies including, but not limited to, STICH, STICH-II, MISTIE, MISTIE-II, MISTIE-III, ENRICH, and ICES have all shown that, in certain limited patient populations, evacuation can be done safely and mortality can be decreased, but functional outcomes remain statistically no different compared to medical management alone. Only 10–15% of patients with ICH are surgical candidates based on clot location, medical comorbidities, and limitations regarding early surgical intervention. To date, no clearly effective treatment options are available to improve ICH outcomes, leaving medical and supportive management as the standard of care. We recently identified that remote ischemic conditioning (RIC), the non-invasive, repetitive inflation-deflation of a blood pressure cuff on a limb, non-invasively enhanced hematoma resolution and improved neurological outcomes via anti-inflammatory macrophage polarization in pre-clinical ICH models. Herein, we propose a pilot, placebo-controlled, open-label, randomized trial to test the hypothesis that RIC accelerates hematoma resorption and improves outcomes in ICH patients. Twenty ICH patients will be randomized to receive either mock conditioning or unilateral arm RIC (4 cycles × 5 min inflation/5 min deflation per cycle) beginning within 48 h of stroke onset and continuing twice daily for one week. All patients will receive standard medical care according to latest guidelines. The primary outcome will be the safety evaluation of unilateral RIC in ICH patients. Secondary outcomes will include hematoma volume/clot resorption rate and functional outcomes, as assessed by the modified Rankin Scale (mRS) at 1- and 3-months post-ICH. Additionally, blood will be collected for exploratory genomic analysis. This study will establish the feasibility and safety of RIC in acute ICH patients, providing a foundation for a larger, multi-center clinical trial.

Abstract Text-mining technologies have substantially affected financial industries. As the data in every sector of finance have grown immensely, text mining has emerged as an important field of research in the domain of finance. Therefore, reviewing the recent literature on text-mining applications in finance can be useful for identifying areas for further research. This paper focuses on the text-mining literature related to financial forecasting, banking, and corporate finance. It also analyses the existing literature on text mining in financial applications and provides a summary of some recent studies. Finally, the paper briefly discusses various text-mining methods being applied in the financial domain, the challenges faced in these applications, and the future scope of text mining in finance.

Abstract Alzheimer’s disease (AD) is the most common type of dementia. The exact cause and treatment of the disease are still unknown. Different neuroimaging modalities, such as magnetic resonance imaging (MRI), positron emission tomography, and single-photon emission computed tomography, have played a significant role in the study of AD. However, the effective diagnosis of AD, as well as mild cognitive impairment (MCI), has recently drawn large attention. Various technological advancements, such as robots, global positioning system technology, sensors, and machine learning (ML) algorithms, have helped improve the diagnostic process of AD. This study aimed to determine the influence of implementing different ML classifiers in MRI and analyze the use of support vector machines with various multimodal scans for classifying patients with AD/MCI and healthy controls. Conclusions have been drawn in terms of employing different classifier techniques and presenting the optimal multimodal paradigm for the classification of AD.

Abstract This paper presents a detailed review of the applications of augmented reality (AR) in three important fields where AR use is currently increasing. The objective of this study is to highlight how AR improves and enhances the user experience in entertainment, medicine, and retail. The authors briefly introduce the topic of AR and discuss its differences from virtual reality. They also explain the software and hardware technologies required for implementing an AR system and the different types of displays required for enhancing the user experience. The growth of AR in markets is also briefly discussed. In the three sections of the paper, the applications of AR are discussed. The use of AR in multiplayer gaming, computer games, broadcasting, and multimedia videos, as an aspect of entertainment and gaming is highlighted. AR in medicine involves the use of AR in medical healing, medical training, medical teaching, surgery, and post-medical treatment. AR in retail was discussed in terms of its uses in advertisement, marketing, fashion retail, and online shopping. The authors concluded the paper by detailing the future use of AR and its advantages and disadvantages in the current scenario.

Oil and Gas Industry is a Multi-Billion Dollar Industry. Drilling a well is costly, right from exploration to drilling and production to Enhanced Oil Recovery (EOR). Nanotechnology has the potential to introduce revolutionary changes in several areas of the oil and gas industry, such as exploration, drilling, cementation, production, EOR, etc. Use of Nanotechnology in the cement slurry can also achieve solutions to some of the problems pertaining to oil well cementation. Nano-silica is a better alternative compared to conventional additives like calcium chloride and silica, because as compared to calcium chloride and silica, the amount of nano-silica to be added is very small. Nano-silica acts as a multi-functional additive. Upon addition of nano-silica to cement slurry, there is a decrease in the thickening time, an increase in the compressive strength, decrease in porosity and permeability within the cement and also a decrease in the fluid loss. Incorporation of nano-silica ensures proper cementation and greater integrity of the well. Nano-silica helps in decreasing the wait on cement (WOC) time and therefore reduces the overall capital cost. Nano-silica is highly recommended for deep offshore wells where high temperature and high pressure are often encountered. This paper discusses the behavior of Nano-silica at high temperatures and also reviews effects of Nano-silica on various properties of cement.

Abstract With the increasing scarcity of water and unavailability of portable drinking water at many places, in this paper, we present a feasible solution to this problem. The only solution in sight right now is to carry out the desalination of the huge saline water bodies. The conventional methods to do so have resulted in a failure as with the consumption of fossil fuel to run them, which in turn results in global warming. This draws us more towards the usage of renewable energy and its importance in order to obtain portable water from the saline and brackish water. In this study, we present to you a novel hybrid method that uses the geothermal energy and sun energy to produce fresh drinkable desalinated water from the seawater. This proposed system does not require fossil fuel to run, and hence, along with keeping the environment pure, it is a solution to the burning problem of water crisis throughout the world. With the help of engineering and technological skills, we can draft a better future for humanity.

Abstract Due to soaring demand for universal energy, industry forced to look forward in either expand the limit of conventional energy resources or to look at other possibilities such as renewable energy resources and unconventional hydrocarbon resources. The challenges might be figured out by revolutionary technological developments in the energy sector by science and technology. The industry needs splendid technological breakthroughs in the energy sector to push the final frontier of conventional energy resources. Owing to its superior particle size and properties, nanotechnology can likely of moving far that current energy supply by introducing new technologies. The exact exploitation and manipulation of matter at measurements of (1–100) nanometres have revolutionized many sectors, including the petroleum sector. The upgrade in nanoscale organized materials represents one of the fascinating, inventive viewpoints bringing innovative advances in numerous industries. The charge of oil extraction is under heavyweight, and it becomes increasingly difficult to legitimize it when the gross price of oil is powerless and depressing. There is a universal belief that nanotechnologies can be to produce new, more valuable nanomaterials to oppose these technological limitations. Many research endeavours are being coordinated towards the opening of immense and diverse advantages of nanotechnology in the oil and gas industry. The research experts have experienced the utilization of different nanoparticle types and sizes. Nanoparticles show exceptional properties because of their large surface area and highly activated particle surface. The nanotechnology can be performed at a different scale in petroleum engineering from exploration, drilling, cementing, reservoir, completion, production, and processing and refinery in each stage. This paper intends to give a concise thought of the significant uses of nanoparticles, their potential advantages, associated economic and technical challenges, and solutions.

Abstract Tumor lysis syndrome is uncommon in solid tumors but with the use of immunotherapy (checkpoint inhibitors) their incidence is increasing. Physicians need to take adequate precautions while treating patients with immunotherapy. The findings of our case report will help improve our current understanding of tumor lysis syndrome specially in solid tumors and will help in developing multidisciplinary treatment and prophylaxis strategies for this uncommon, but potentially fatal complication.