Natural gas is an important energy source among various fossil fuels. However, natural gas typically contains a significant amount of water vapor, which can pose challenges in its usage. Therefore, the process of removing water vapor from natural gas, known as gas dehydration, is crucial in the gas industry. The presence of water vapor in the gas supply can lead to the formation of hydrates, which can cause issues such as blockages in pipelines. To tackle this problem effectively, gas dehydration makes use of Tri ethylene glycol (TEG) as a drying agent. TEG works by selectively absorbing water vapor from the natural gas flow, thereby significantly reducing its moisture content.In the gas dehydration process, wet gas is treated by using lean glycol in an absorber, where the water vapor is removed. The resulting rich glycol is then recovered and recycled for further use. In this study, we explore the possibility of replacing nitrogen with dry natural gas in the re-generator of the glycol dehydration system. The aim is to evaluate the feasibility and effectiveness of this alternative approach. To assess the performance of both techniques, we employed HYSYS modeling and simulation. Through this analysis, we compared the capital and utility expenses associated with each method while ensuring that the glycol purity requirements remained unchanged. By examining the results obtained from the simulations, we can gain insights into the economic viability and efficiency of utilizing dry natural gas in the re-generator stage of the glycol dehydration process.In addition to the primary purpose of removing water vapor, the wet gas obtained from the stripping mechanisms in the glycol dehydration process can serve additional functions. It can be utilized to power steam pumps and compressors, thereby maximizing energy efficiency within the system. Alternatively, the wet gas can be recycled back into the process for further treatment. To develop a comprehensive understanding of the entire mechanism, we constructed a model based on the actual flow diagram. This model takes into account the various components and processes involved in the glycol dehydration system, including the stripping mechanisms and their connection to other equipment. By analyzing the data and outcomes generated by this model, we can derive valuable insights. These findings can be utilized to optimize the heat and material balance within the plant, ensuring efficient operation and potentially leading to the design of an improved system in terms of energy consumption and overall performance

India, the world’s most populous country, is the world's third-largest emitter of greenhouse gases (GHGs). Despite employing several energy sources, it still relies heavily on coal, its primary energy source. Given India’s swiftly rising energy demand, this challenges meeting emission reduction targets. India has significantly increased investments in renewables like solar and hydrogen in recent years. While commendable, these initiatives alone cannot meet the country's expanding energy demands. In the short term, India must rely on both domestic and imported fossil fuels, with natural gas being the most environmentally friendly option. In this context, this paper attempts to forecast energy consumption, natural gas production, and consumption in India till 2050 using both univariate and multivariate forecasting methods. For multivariate forecasting, we have assumed two alternative possibilities for GDP growth: the business-as-usual and the high-growth scenarios. Each of our forecasts indicates a notable shortfall in the projected production of natural gas compared to the expected demand, implying our results are robust. Our model predicts that nearly 30-50 percent of India’s natural gas consumption will be met by imports, mainly in the form of LNG. Based on these findings, this paper recommends that Indian government policies emphasize increasing domestic natural gas production, importing LNG, and expanding renewable energy resources.

Among other fossil fuels, natural gas is a major energy source. It is generally created saturated with water vapor. Dehydration of natural gas is vital in the gas business to remove water vapor from the gas supply, which may cause hydrate development in pipes. Gas dehydration employs Tri ethylene glycol (TEG) to remove water vapor from natural gas flow. The wet gas is dehydrated with lean glycol in an absorber, and the rich glycol is recovered and reused. This work investigates the use of dry natural gas instead of nitrogen in the glycol dehydration re-generator, and compares the results using HYSYS modeling and simulation. The two techniques were compared for capital and utility expenses, while maintaining the identical glycol purity criteria. The wet gas from the stripping mechanisms may also be utilized to run steam pumps and compressors or recycled. The model is based on the real mechanism flow diagram. Finally, the findings of this model might be used to design a new heat and material balance for the plant.

The present study focuses on the development and optimization of a highly efficient unique hybrid technique — membrane-assisted gas absorption (MAGA) in designing the separation unit, which provides the improvement in mass-transfer of a target component during the natural gas processing in terms of sweetening stage. A novel design of membrane-assisted gas absorption cell is used to enhance the ratio between absorbent amount and membrane surface available for permeation. The performance tests were implemented on example of two gas mixtures separation: one is model ternary gas mixture and the second is quasi real natural gas, which includes number of hydrocarbons, nitrogen, acid impurities and xenon. For both cases, the MAGA unit demonstrated high acid gases removal efficiency, and high recovery of hydrocarbons. The ultimate efficiency of studied system was up to 96 and 61 % of removed acid gases with hydrocarbons losses up to 1 % at highest productivity. In this way, the residual sum of acid gases content may be lowered to 1 vol.% using the heat-free hybrid membrane-assisted gas absorption unit maintaining suitable recovery rate of methane, ethane, propane and n-butane. Nevertheless, the overall efficiency of the process may be enhanced using the specific absorption agents with aqueous amino alcohol solutions.

There is a strong need and demand from the United Nations, public institutions, and private sector for classifying government publications, policy briefs, academic literature, and corporate social responsibility reports according to their relevance to the Sustainable Development Goals (SDGs). It is well understood that the SDGs play a major role in the strategic objectives of various entities. However, linking projects and activities to the SDGs has not always been straightforward or possible with existing methodologies. Natural language processing (NLP) techniques offer a new avenue to identify linkages for SDGs from text data. This research examines various machine learning approaches optimized for NLP-based text classification tasks for their success in classifying reports according to their relevance to the SDGs. Extensive experiments have been performed with the recently released Open Source SDG (OSDG) Community Dataset, which contains texts with their related SDG label as validated by the community volunteers. Results demonstrate that especially RoBERTa achieves very high performance in the attempted task, which is promising for automated processing of large collections of sustainability reports for detection of relevance to SDGs.

Natural gas hydrate, also known as combustible ice and mainly composed of methane, it is identified as the potential clean energy in the 21th century. Due to its large reserves, gas hydrate can ease problems caused by energy resource shortage and has gained attention around the world. In this paper, we focus on the exploration and development of gas hydrate as well as discussing its status and future development trend in China and abroad, then we analyze its opportunities and challenges in China from four aspects: resource, technology, economy and police with five forces model and PEST method. The results show, China has abundance gas hydrate resource; however the backward technologies and inadequate investment has seriously hindered the future development of gas hydrate, so China should establish relevant cooperation framework and intuitional arrangement to attract more investment as well as breaking through technical difficulties to make gas hydrate commercialization as soon as possible.

Accent is on determination of appropriate friction factor of the pipes and on selection of the representative equation for water or natural gas flow which is valuable for existing conditions in the looped network of pipelines. Note that in a municipal gas pipeline, natural gas can be treated as incompressible fluid (liquid) i.e. as water or oil. Even under this circumstance, calculation of water pipelines cannot be literary copied and applied for calculation of gas pipelines. Inappropriate friction factor, equally as e.g. inappropriate usage of water flow equations for calculation of gas networks can lead to inaccurate final results. Few iterative methods for determining the optimal hydraulic solution of water- and gas- looped pipeline networks, such as, Hardy Cross, modified Hardy Cross, node-loop method, node and M.M. Andrijashev method, will be shown. Speed of convergence will be compared and discussed using a simple network with three loops.

Recent maritime legislations demand the transformation of the sector to greener and more energy efficient transportation. Liquified Natural Gas (LNG) seems a promising alternative fuel solution that could replace the conventional fuel sources. Various studies have been focused on the prediction of LNG price, however, no previous work has been made on the forecast of spot charter rate of LNG carrier ships. An important knowledge for the maritime industries and companies when it comes to decision-making. Therefore, this study is focused on the development of a machine learning pipeline to address the aforementioned problem by: (i) forming a dataset with variables relevant to LNG; (ii) identifying the variables that impact on the freight price of LNG carrier; (iii) developing and evaluating regression models for short and mid-term forecast. The results showed that the General Regression Neural Network presented a stable overall performance for 2, 4 and 6 months forecast.

It is essential to deal with data scattered among heterogeneous information sources, which can be structured, semi-structured, or unstructured, to give consumers a cohesive perspective of the data. Information gathering is challenging as a result, and one of the major causes of this is that data sources are developed to support specific applications. A method to simplify this procedure is to use ontology representation as an intermediate step. An ontology represents a knowledge structure that reasonably reflects the real world's complexity and is ideally used in many industries today. This research aims to enhance the functionality of a global ontology system by implementing and integrating a natural language query system. Leveraging NLP-based approaches, we constructed SPARQL queries for our ontologies to facilitate natural language translation into a well-structured query format. A dedicated query component was developed to transform natural language questions into SPARQL queries, with the selection of the optimal query graph to generate the final queries. In the subsequent phase, we explored the integration of distinct sentence encoders to improve latent sentence representations during query construction. The generated SPARQL queries were executed on the global ontology output and then translated into source-specific questions. This innovative approach enables unified access to heterogeneous data sources through a user-friendly natural language querying interface.

Today, two very efficient methods for calculation of flow distribution per branches of a looped gas pipeline are available. Most common is improved Hardy Cross method, while the second one is so-called unified node-loop method. For gas pipeline, gas flow rate through a pipe can be determined using Colebrook equation modified by AGA (American Gas Association) for calculation of friction factor accompanied with Darcy-Weisbach equation for pressure drop and second approach is using Renouard equation adopted for gas pipeline calculation. For the development of Renouard equation for gas pipelines some additional thermodynamic properties are involved in comparisons with Colebrook and Darcy-Weisbach model. These differences will be explained. Both equations, the Colebrook’s (accompanied with Darcy-Weisbach scheme) and Renouard’s will be used for calculation of flow through the pipes of one gas pipeline with eight closed loops which are formed by pipes. Consequently four different cases will be examined because the network is calculated using improved Hardy Cross method and unified node-loop method. Some remarks on optimization in this area of engineering also will be mentioned.

Here is shown method for the hydraulic solution of a looped gas-pipeline networks. Calculation of presented network is done according to principles of Hardy Cross method. The optimization was carried out by iteration of the pipes diameters, node consumptions are known and flow velocities through pipes have to stand below certain values. Accent is on determination of appropriate friction factor, and on selection of representative equation for natural gas flow under presented conditions in the network. Inappropriate usage of friction factor, equally as inappropriate usage of gas flow equation can lead to inaccurate final results. Here is shown new facts in comparison to previous calculation of gas distribution network in Kragujevac, Serbia which is done in 1994. After the implementation, measurements in situ have performed, and real measured values deviate from calculated. Causes for these errors are investigated, and improved and more accurate procedure is shown.

With the increasing use of cloud-based services for training and deploying machine learning models, data privacy has become a major concern. This is particularly important for natural language processing (NLP) models, which often process sensitive information such as personal communications and confidential documents. In this study, we propose a method for training NLP models on encrypted text data to mitigate data privacy concerns while maintaining similar performance to models trained on non-encrypted data. We demonstrate our method using two different architectures, namely Doc2Vec+XGBoost and Doc2Vec+LSTM, and evaluate the models on the 20 Newsgroups dataset. Our results indicate that both encrypted and non-encrypted models achieve comparable performance, suggesting that our encryption method is effective in preserving data privacy without sacrificing model accuracy. In order to replicate our experiments, we have provided a Colab notebook at the following address: https://t.ly/lR-TP

Version Control and Source Code Management Systems, such as GitHub, contain large amount ofunstructured historical information of software projects. Recent studies have introduced Natural Language Processing (NLP) to help software engineers retrieve information from very large collection of unstructured data. In this study, we have extended our previous study by increasing our datasets and ML and clustering techniques. Method: We have followed a complex methodology made up of various steps. Starting from the raw commit messages we have employed NLP techniques to build a structured database. We have extracted their main features and used as input of different clustering algorithms. Once labelled each entry, we have applied supervised machine learning techniques to build a prediction and classification model. Results: We have developed a machine learning-based model to automatically classify commit messages of a software project. Our model exploits a ground-truth dataset which includes commit messages obtained from various GitHub projects belonging to the HEP context. Conclusions: The contribution of this paper is two-fold: it proposes a ground-truth database; it provides a machine learning prediction model. They automatically identify the more change-proneness areas of code. Our model has obtained a very high average precision, recall and F1-score.

This article should examine ideas of how a chatbot can support decision making processes by col-lecting general opinion about the certain topics. The ChatGPT is tested in this article in respect of natural gas security of supply of the Republic of Serbia in the context of the current political situation in the world. The author first describes its own stance on the topic and then the ChatGPT is asked for its opinion (which most probably collects and compiles phrases from English speaking sources to build a text in a smart way). The topic is ort only about security of gas supply of Serbia but also political and highly sensitive, so it is examined if the ChatGPT can be useful tool to reach a balance between less on more favorable options in the process of policy making.

Natural gas plays a significant role in Tunisia's energy mix. Tunisia imports 66% of natural gas (NG) from Algeria (ANG), and the rest is provided by local gas fields mainly Miskar gas (MNG). To counteract this deficit, the Power-to-Gas approach emerges as a promising solution, given Tunisia's abundant renewable energy resources. By blending green hydrogen into the NG network, it is possible to (i) address the current energy crisis, (ii) lower the carbon intensity of the gas grid, and (iii) encourage sector coupling through the use of various renewable energy sources. This study presents an overview of various interchangeability indicators and investigates accordingly the allowable ratios for hydrogen blending with two types of natural gas distributed in Tunisia (ANG and MNG). It also explores the impact of hydrogen injection on the energy content variation and various combustion parameters. The results confirm that both ANG and MNG can withstand a maximum hydrogen blend of up to 20%. The article concludes with recommendations for further experimental testing of the findings and emphasizes the need to look into infrastructure and safety requirements with regards to the Natural gas network in Tunisia. This research serves as a critical step towards harnessing the potential of green hydrogen in Tunisia.

Transient analysis of gas flow in pipeline was studied. Finite Element Method based on ANSYS FLOTRAN was used to account for changes in pressure, temperature and flow rate. Compressibility factor function of temperature and pressure was considered. For non-isothermal transient results, the pressure and the flowrate gave wave propagation as a result of slow transients created by demand condition at the outlet end of custody transfer. Results obtained were in agreement with the demand restrictions at the outlet end of custody transfer indicating that the predictions are accurate and reliable. The results demonstrated that the Finite Element Method gave accurate prediction of pressure, temperature and flowrate in transient gas studies. For steady state non-isothermal model, results showed that the magnitude of the average pressure drop was higher when pressure was predicted with constant compressibility factor, but the same average pressure drop was reduced when the pressure was predicted with variable compressibility factor, z. Since compressibility factor is a function of temperature and pressure, the above findings signifies that in the case when gas temperature does not stabilize, the prediction of pressure with isothermal model and constant compressibility factor will lead to significant errors.

Some of Iran's gas resources are known as sour gas because one of the major by-products of this gas is water. In fact, natural gas is combined with water vapor in underground reservoirs. In some areas, the amount of water vapor in natural gas is so high that it can be used as a source of drinking water. However, it should be noted that water vapor in natural gas may exacerbate corrosion in pipes and industrial equipment, and the combustion of more natural gas produces high temperatures that can lead to the production of nitrogen oxides (NOx), which They are the cause of air pollution. This is while the processed gas has a standard percentage composition in which the amount of water has the lowest position. The removal of moisture from natural gas is due to the need to prevent the reduction of the calorific value of the gas and also to purify the gas in order to achieve the necessary standards for delivering gas to petrochemicals, industries and domestic uses. The main goal of this research is to investigate the influencing parameters on zeolite TiO2 nanocomposite membranes in order to absorb moisture from gas. For this reason, with the construction of a TiZ-V membrane as a standard membrane, the result of the initial evaluation of a suitable membrane for gas dehumidification was made, and this membrane was placed as a standard for measuring the effect of the manufacturing parameters in a way that in each stage of membrane construction It was found that it was different from standard TiZ-V membrane only in terms of one parameter. This type of investigation made it possible to determine the effect of various effective parameters in membrane construction on efficiency. The findings showed that increasing the concentration of SiO2 has the greatest effect on increasing the water flux of the membrane. According to the other findings of this study, the effect of increasing the reaction time of the vapor phase carrier has been to reduce the process of paternity selectivity loss at higher pressures. Although due to the safety limitation of the equipment to measure the efficiency of the membrane at pressures higher than 7 bar, this measurement was not carried out, but charts were presented regarding the state of the water in the water, which were a measure of the superiority of the water in the water in comparison to the gas in the water, which is the higher water in the water It is an indicator of the appropriateness of the membrane's efficiency at its corresponding pressure, so that the examination of the difference between these two graphs shows the possibility of using the membrane at higher pressures

This paper describes a novel concept of producing energy efficient ”Maroon enriched natural gas “ and then Pink hydrogen” from any hydrocarbon base. The key idea is the extraction of hydrogen from water in addition to that from the hydrocarbon in an optimal fashion. This has the benefit of higher water vapor to CO2 exhaust ratio than conventional carbonaceous fuels when generating energy via combustion, a prudent step in achieving Netzero goals in a shorter time, and creating energy independence in most places.. The process of production makes concentrated CO₂ available for use and or sequestration. The process also maximizes use of renewable electricity in hydrogen generation, and maximizes use of existing infrastructure, with a minimum capital cost by energy recycle in the process. The process design applies sound thermodynamic principles which evolved during the nineteenth century, and mimics the geochemical processes going on in some of the natural 'colorless hydrogen'.

Introduction: Around a million people are reported to die by suicide every year, and due to the stigma associated with the nature of the death, this figure is usually assumed to be an underestimate. Suicide may be prevented if prompt intervention is taken to mitigate risk. Machine learning and artificial intelligence-based modelling, such as natural language processing (NLP) and other text analytics approaches, has the potential to become a major technique for the detection, diagnosis, and treatment of people who are suffering from mental health issues. The primary aims of this research are to determine whether NLP techniques have been utilised in the field of suicide prevention, and if so, were they effective? What were their limitations? Methods: PubMed, EMBASE, MEDLINE, PsycInfo, and Global Health databases were searched for studies that reported use of NLP for suicide ideation or self-harm. Thematic analysis was used to synthesise and analyse the included studies. Findings were reported using the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) statement, and the Mixed Methods Appraisal Tool (MMAT) was used in assessing paper quality. Result: The preliminary search of five databases generated 387 results. Removal of duplicates resulted in 158 potentially suitable studies. Twenty papers were finally included in this review. Discussion: Studies show that combining structured and unstructured data in NLP data modelling yielded more accurate results than utilizing either alone. Also, to reduce suicides, people with mental problems must be continuously and passively monitored. Further, NLP and other machine learning/artificial intelligence technologies can be used to address health inequities and electronic health records provide valuable data for creating suicide risk tools. Finally, Online, social media, and smartphone applications can be leverage in detecting people with suicide ideation. Conclusion: The use of artificial intelligence and machine learning opens new avenues for considerably guiding risk prediction and advancing suicide prevention frameworks. The review's analysis of the included research revealed that the use of NLP may result in low-cost and effective alternatives to existing resource-intensive methods of suicide prevention. To summarise, there is substantial evidence that NLP is useful in identifying people who have suicide ideation.

The ability to identify and rank risk is essential for efficient and effective supervision of financial service firms, such as banks and insurers. Risk ranking ensures limited resources are allocated where they are most needed. Today, automatic risk identification within insurance supervision primarily relies on quantitative metrics based on numerical data (e.g. returns). The purpose of this work is to assess whether Natural Language Processing (NLP) and cognitive networks can achieve similar automated risk ranking and identification by analysing textual data, i.e. NIDT=829 investor transcripts from Bloomberg. To this aim, this work explores and tunes 3 NLP techniques: (1) keyword extraction enhanced by cognitive network analysis; (2) valence/sentiment analysis; and (3) topic modelling. Results highlight that keyword analysis, enriched by term frequency-inverse document frequency scores and semantic framing through cognitive networks, could detect events of relevance for the insurance system like cyber-attacks or the COVID-19 pandemic. Cognitive networks were found to highlight events that related to specific financial transitions: The semantic frame of "climate" grew in size by +538% between 2018 and 2020 and outlined an increased awareness that agents and insurers expressed towards climate change. A lexicon-based sentiment analysis achieved a Pearson’s correlation of ρ=0.16 (p<0.001,N=829) between sentiment levels and daily share prices. Although relatively weak, this finding indicates that insurance jargon is insightful to support risk supervision. Topic modelling is considered less amenable to support supervision, because of a lack of results’ stability and an intrinsic difficulty to interpret risk patterns. We discuss how these automatic methods could complement existing supervisory tools in automated risk ranking.

CONTEXT– Energy is widely involved in human activity and corresponding emissions of SOX, NOX and CO2 from energy generation processes affect global climate change. Clean fuels are desired by society because of their reduced greenhouse gas emissions. Hydrogen is once such candidate fuel. Much hydrogen is produced from fossil fuel, with biomass being an alternative process. OBJECTIVE– The project compared the environmental impact of hydrogen production by natural gas steam reforming vs. biomass gasification. METHOD–Environmental impact was calculated from the input and output data from life cycle inventory analysis. The impact assessment was focused on greenhouse gas emission, acidification, and eutrophication. Models of the two processes were developed and analysed in OpenLCA. The agribalyse database was used to connect inventory flow data to environmental impacts. FINDINGS– For all three metrics, biomass gasification had lower impacts than natural gas steam reforming, sometimes by large margins. For biomass gasification the silica sand production contributes most to all three impact categories, whereas for natural gas steam reforming it is the LPG extraction.

Coronavirus, also known as COVID-19, has been declared a pandemic by the World Health Organization (WHO). At the time of conducting this study, it had recorded over 1.6 million cases while more than 105,000 have died due to it, with these figures rising on a daily basis across the globe. The burden of this highly contagious respiratory disease is that it presents itself in both symptomatic and asymptomatic patterns in those already infected, thereby leading to an exponential rise in the number of contractions of the disease and fatalities. It is therefore crucial to expedite the process of early detection and diagnosis of the disease across the world. The case-based reasoning (CBR) model is an effective paradigm that allows for the utilization of cases’ specific knowledge previously experienced, concrete problem situations or specific patient cases for solving new cases. This study therefore aims to leverage the very rich database of cases of COVID-19 to solve new cases. The approach adopted in this study employs the use of an improved CBR model for state-of-the-art reasoning task in classification of suspected cases of Covid19. The CBR model leverages on a novel feature selection and semantic-based mathematical model proposed in this study for case similarity computation. An initial population of the archive was achieved with 68 cases obtained from the Italian Society of Medical and Interventional Radiology (SIRM) repository. Results obtained revealed that the proposed approach in this study successfully classified suspected cases into their categories at an accuracy of 97.10%. The study found that the proposed model can support physicians to easily diagnose suspected cases of Covid19 base on their medical records without subjecting the specimen to laboratory test. As a result, there will be a global minimization of contagion rate occasioned by slow testing and as well reduce false positive rates of diagnosed cases as observed in some parts of the globe.

This paper investigates if Corporate Social Responsibility (CSR) reports published by a selected group of Nordic companies are aligned with the Global Reporting Initiative (GRI) standards. To achieve this goal, several natural language processing, and text mining techniques were implemented and tested. We extracted strings, corpus, and hybrid semantic similarities from the reports and evaluated the models through the intrinsic assessment methodology. A quantitative ranking score based on index matching was developed to complement the semantic valuation. The final results show that Latent Semantic Analysis (LSA) and Global Vectors for Word Representation (GloVE) are the best methods for our study. Our findings will open the door to the automatic evaluation of sustainability reports which could have a strong impact on the environment.

In recent years, e-commerce platforms have replaced conventional marketplaces. People are rapidly adopting internet shopping due to the convenience of making purchases from the comfort of their homes. Online review sites allow customers to share their thoughts on products and services. Customers and businesses are increasingly relying on online reviews to assess and improve the quality of products. Existing literature uses Natural language processing (NLP) to analyze customer reviews for different applications. Due to the growing importance of NLP for online customer reviews, this study attempts to provide a taxonomy of NLP applications based on existing literature. This study also examined emerging methods, data sources, and research challenges by reviewing 154 publications from 2013 to 2023 that explore state-of-the-art approaches for diverse applications. Based on existing research, the taxonomy of applications divides literature into five categories: sentiment analysis, review analysis, customer feedback and satisfaction, user profiling, and marketing and reputation management. It is interesting to note that the majority of existing research relies on Amazon user reviews. Additionally, recent research has encouraged the use of advanced techniques like Bidirectional Encoder Representations from Transformers (BERT), Long Short-Term Memory (LSTM), and ensemble classifiers. The rising number of articles published each year indicates increasing interest of researchers and continued growth. This survey additionally addresses open issues, providing future directions in online customer review analysis.

The scope of this work is to empirically check and prove the practical applicability of the Primordially Hydridic Earth (hereinafter PHE) concept for early exploration of the resources of naturally occurring hydrogen. With the PHE concept postulates interpreted within the local geological, tectonic, petrological and geophysical context, the reconnaissance plan, as well as the field exploration and data acquisition programs, were put together and implemented in the field. The results obtained from the surface (

The pre-chamber technologies can improve the ignition performance of IC engines by more than two orders in magnitude and thereby substantial economic benefits. Compared with the common pre-chamber, a plug-and-play passive scheme is suitable for quick retrofit, which is getting more attention from the automobile industry. Good scavenging is the precondition for improving turbulent jet ignition performance for a passive pre-chamber. Therefore, detailed evaluations of the scavenging process and turbulent jet ignition deserve investigations for new pre-chamber schemes. In this paper, the effects of design parameters on ignition processes of plug-and-play passive pre-chamber were numerically studied, allowing for the lateral angle, orifice diameter, and vertical angle design. Seven pre-chamber schemes were evaluated, and four optimal ones were selected for bench tests. The characteristics of the scavenging process, turbulent jet ignition, and main-chamber combustion were investigated and analyzed. The results show that allowing for the trade-off between ignition energy and scavenging efficiency, the volume ratio of the pre-chamber to clearance is recommended to be 0.2~0.7%, and the corresponding area-volume ratio is 0.003~0.006 mm-1. Compared with the original natural gas engine, the pre-chamber retrofit can save up to 13.2% fuel consumption, which presents a significant improvement in fuel economy.

This paper proposes a method based on genetic algorithm (GA) for the security-constrained optimal dispatch of integrated natural gas and electricity networks, considering operating scenarios in both energy systems. The mathematical formulation of the optimization problem consists of a multi-objective function which aims to minimize both cost of thermal generation (diesel and natural gas) as well as the production and transportation of natural gas. The joint gas-electricity system is modeled by two separate groups of nonlinear equation, which are solved by the combination of Newton's method with the GA. The applicability of the proposed method is tested in the Belgian gas network integrated with the IEEE 14-bus test system and a 15-node natural gas network integrated with the IEEE 118-bus test system. The results demonstrate that the proposed method provides efficient and secure solutions for different operating scenarios in both energy systems.

The Life Cycle Analysis (LCA) was used to assess the impact of biomethane plant of the “La Católica” in Pedregal-Majes-Arequipa farm, fed with cow manure and holding a production of 60 Nm3/day of purified biogas. Life cycle inventory, impact assessment and interpretation were performed. The functional unit established was 1 MJ of energy produced; the study was modeled with SimaPro software, Ecoinvent Database and ReCiPe Midpoint (H) impact assessment methodology, according to the impact categories of climate change and fossil resource depletion. The impact analysis was limited to the Well to Tank (WTT) approach, which involves feedstock transport, substrate mixed, anaerobic digestion, biogas purification, storage and injection of the fuel into transport vehicles. The digestion process generated the highest amount of CO2 emissions (1.79E-02 kg CO2 eq/MJ-biomethane) and the highest depletion of fossil resources (6.58E-03 kg oil eq/MJ-biomethane), compared to the other fuel production, due to energy consumption and transport infrastructure. Biomethane was then compared to fossil fuels, resulting in natural gas generating the least amount of CO2 emissions, followed by diesel and finally biomethane. For the fossil resource depletion category, biomethane presented the lowest amount of fossil fuel consumption (1.37E-02 kg oil eq/MJ-biomethane), followed by natural gas and diesel.

In the context of carbon neutrality and carbon peaking, in order to achieve low carbon emissions and promote the efficient utilization of wind energy, hydrogen energy as an important energy carrier is proposed to mix hydrogen and natural gas to form hydrogen enriched compressed natu-ral gas (HCNG). It is also injected into the natural gas pipeline network to realize the transmission and utilization of hydrogen energy. At the same time, the participation of demand response is considered, the load peak and trough period is adjusted, and large-scale consumption of renewa-ble energy and the reduction of carbon emissions are realized. First of all, a fine model of hydro-gen production and hydrogen use equipment is established to analyze the impact of adding hy-drogen mixing on the economy and low-carbon of the system. With green certificates and demand response, the utilization rate of hydrogen energy is improved to further explore the energy utiliza-tion rate and emission reduction capacity of the system. Secondly, on the basis of modeling, the optimal scheduling strategy is proposed with the sum of energy purchase cost, equipment opera-tion cost, carbon emission cost, wind curtailment cost and green certificate income as the lowest objective function. Considering the constraints such as hydrogen blending ratio and flexible load ratio of the pipeline network, a low-carbon economic scheduling model of hydrogen mixed natu-ral gas was established. The model was linearized and solved by using MATLAB and CPLEX solver. By comparing different scenarios, the superiority of the model and the effectiveness of the strategy are verified.

Colonoscopy is used for colorectal cancer (CRC) screening. Extracting details of the colonoscopy findings from free text in electronic health records (EHRs) can be used to determine patient risk for CRC and colorectal screening strategies. In this study, we developed and evaluated the accuracy of a deep learning model framework to extract information for the clinical decision support system to analyze relevant free-text colonoscopy reports, including indications, pathology, and findings notes. The Bio-Bi-LSTM-CRF framework was developed using Bidirectional Long Short-term Memory (Bi-LSTM) and Conditional Random Fields (CRF) to extract several clinical features from these free-text reports, including indications for the colonoscopy, findings during the colonoscopy, and pathology of the resected material. We then trained the Bio-Bi-LSTM-CRF and existing Bi-LSTM-CRF models on 80% of 4,000 manually annotated notes obtained from the colonoscopy reports of 3,867 patients. The clinical notes were from a group of patients aged over 40 years old enrolled in four Veterans Affairs Medical Centers. A total of 10% of the remaining annotated notes were used to train hyperparameter, while the remaining 10% were used to evaluate the accuracy of our model (Bio-Bi-LSTM-CRF) and to compare the results with the outcomes obtained using Bi-LSTM-CRF. The results of our experiments showed that the bidirectional encoder representations by integrating dictionary function vector from Bio-Bi-LSTM-CRF and strategies character sequence embedding approach is an effective way to identify colonoscopy features from EHR-extracted clinical notes. Therefore, the Bio-Bi-LSTM-CRF model is concluded to be capable of creating new opportunities to identify patients at risk for colon cancer and to study their health outcomes.

Based on the geologic conditions of natural gas hydrate (NGH) accumulations in the Shenhu area, northern slope of the South China Sea, this paper used a method of combining reservoir stimulation technique (RST) with depressurization to investigate the conversion efficiency of marine NGH reservoirs in different intrinsic permeability and initial NGH saturation conditions, and analyze the influence of variably-stimulation effect on marine NGH conversion efficiency in different accumulation conditions, provided a reference scheme for improving the NGH conversion efficiency in the Shenhu area. In this work, we performed calculations for the variations in CH4 production rate and cumulative volume of CH4 in different initial NGH saturation, intrinsic permeability and stimulation effect conditions, variance analysis and range analysis methods were used to analyze the significance of these key factors and their interaction, and investigated the sensitivity of stimulation effect on NGH conversion efficiency, respectively. The simulation results showed that stimulation effect have a significant influence on NGH conversion efficiency, and the influence of interaction between these three factors were not obvious. Possibly most important, we clarified an optimum stimulation effect for higher NGH conversion efficiency under different accumulation conditions, especially in low-permeability and high-saturation, which corresponding stimulation effect were different.

Ongoing COVID-19 Pandemic has resulted into massive damage to various platforms of global economy which has caused disruption to human livelihood. Natural Language Processing has been extensively used in different organizations to categorize sentiments, perform recommendation, summarizing information and topic modelling. This research aims to understand the non-medical impact of COVID-19 on global economy by leveraging the natural language processing methodology. This methodology comprises of text classification which includes topic modelling on unstructured COVID-19 media articles dataset provided by Anacode. Like other Natural Language Processing algorithms, Latent Dirichlet allocation (LDA) and Non-negative matrix factorization (NMF) has been proposed to classify the media articles dataset in order to analyze COVID-19 pandemic impacts in the different sectors of global economy. Model Accuracy was examined based on the coherence and perplexity score which came out to be 0.51 and -10.90 using LDA algorithm. Both the LDA and NMF algorithm identified similar prevalent topics that was impacted by COVID-19 pandemic in multiple sectors of economy. Through intertopic distance map visualization produced by LDA algorithm, it can be reciprocated that general industries which includes children schooling, parental care, and family gatherings had the major impact followed by business sector and the financial industry.

A Natural gas is increasingly being sought after as a vital source of energy, given that its production is very cheap and does not cause the same environmental harms that other resources, such as coal combustion, do. Understanding and characterizing the behavior of natural gas is essential in hydrocarbon reservoir engineering, natural gas transport, and process. Natural gas compressibility factor, as a critical parameter, defines the compression and expansion characteristics of natural gas under different conditions. In this study, a simple second-order polynomial model based on the group method of data handling (GMDH) is presented to determine the compressibility factor of different natural gases at different conditions, using corresponding state principles. The accuracy of the model evaluated through graphical and statistical analyses. The results show that the model is capable of predicting natural gas compressibility with an average absolute error of only 2.88%, a root means square of 0.03, and a regression coefficient of 0.92. The performance of the developed model compared to widely known, previously published equations of state (EOSs) and correlations, and the precision of the results demonstrates its superiority over all other correlations and EOSs.

Nowadays, the imidazolium-based ionic liquids containing acetate counter-ions are attracting much attention as both highly selective absorbents of the acidic gases and CO₂ carriers in the supported ionic liquid membranes. In this regard, the investigation of the gas transport properties of such membranes may be appropriate for better understanding of various factors affecting the separation performance and the selection of the optimal operating conditions. In this work, we have tested CH₄, CO₂ and H₂S permeability across the SILM impregnated by 1-butyl-3-methylimidazolium acetate (bmim[ace]) with the following determination of the ideal selectivity in order to compare the facilitated transport membrane performance with the SILM that dissolves acidic gases physically, namely, containing 1-butyl-3-methylimidazolium tetrafluoroborate (bmim[BF₄]). Both SILMs have showed modest individual gases permeability and ideal selectivity of CO₂/CH₄ and H₂S/CH₄ separation that achieves values up to 15 and 32, respectively. The effect of the feed gas mixture composition on the permeability of acidic gases and permeselectivity of the gas pair was investigated. It turned out that the permeation behavior for the bmim[ace]-based SILM toward the binary CO₂/CH₄, H₂S/CH₄ and ternary CO₂/H₂S/CH₄ mixtures was featured with high acidic gases selectivity due to the relatively low methane penetration through the liquid phase saturated by acidic gases.

One way to increase overall natural gas engine efficiency is to transform exhaust waste heat into useful energy by means of a bottoming cycle. Organic Rankine cycle (ORC) is a promising technology to convert medium and low grade waste heat into mechanical power and electricity. This paper presents an energy and exergy analysis of three ORC-Waste heat recovery configurations by using an intermediate thermal oil circuit: Simple ORC (SORC), ORC with Recuperator (RORC) and ORC with Double Pressure (DORC), and Cyclohexane, Toluene and Acetone have been proposed as working fluids. An energy and exergy thermodynamic model is proposed to evaluate each configuration performance, while available exhaust thermal energy variation under different engine loads was determined through an experimentally validated mathematical model. Additionally, the effect of evaportating pressure on net power output , absolute thermal efficiency increase, absolute specific fuel consumption decrease, overall energy conversion efficiency, and component exergy destruction is also investigated. Results evidence an improvement in operational performance for heat recovery through RORC with Toluene at an evaporation pressure of 3.4 MPa, achieving 146.25 kW of net power output, 11.58% of overall conversion efficiency, 28.4% of ORC thermal efficiency, and an specific fuel consumption reduction of 7.67% at a 1482 rpm engine speed, a 120.2 L/min natural gas Flow, 1.784 lambda, and 1758.77 kW mechanical engine power.

During the last two years the COVID-19 pandemic has affected the world population in several ways. An important increase in mental health problems is a consequence of this pandemic that is ubiquitous worldwide. In this work we study the effect of the pandemic on the mental health of a population of teenagers and youth based on the analysis of natural language processing, machine learning algorithms and expert knowledge. The data analysed was obtained from a chat helpline called Safe time from theIt Get’s Better Foundation in Chile. The data consists of 10,986 conversations gathered from 2018 until 2020 between volunteers from the foundation and users of the platform. We compared the conversationsbefore and during the pandemic in terms of their thematic content. Our analysis found: a significantdecrease in self-image appreciation during the pandemic; a significant decrease in the quality of personalrelationships during the pandemic, and a significant increase of performance appreciation.

Recently natural gas (NG) global market attracted much attention in case it is cleaner than oil, and simultaneously in most regions is cheaper than renewable energy sources. However, price fluctuations, environmental concerns, technological development, emerging unconventional resources, energy security challenges, and shipment are some of the forces that made the NG market more dynamic and complex. From a policy-making perspective, it is vital to uncover demand-side future trends. This paper proposed an intelligent forecasting model to forecast NG global demand, however investigating a multi-dimensional purified input vector. The model starts with a data mining (DM) step to purify input features, identify the best time lags, and to pre-process selected input vector. Then a hybrid artificial neural network (ANN) which equipped with genetic optimizer is applied to set up ANN’s characteristics. Among 13 available input features, six features (e.g. Alternative and Nuclear Energy, CO2 Emissions, GDP per Capita, Urban Population, Natural Gas Production, Oil Consumption) selected as the most critical feature via the DM step. Then, the hybrid prediction model is designed to extrapolate the consumption of future trends. The proposed model overcomes competitive models refer to different error based evaluation statistics. Besides, as the model proposed the best input feature set, results compared to the model which used the raw input set, with no DM purification process.

Text classification is one of the most important task in natural language processing, In this research, we carried out several experimental research on three (3) of the most popular Text classification NLP classifier in Convolutional Neural Network (CNN), Multinomial Naive Bayes (MNB), and Support Vector Machine (SVN). In the presence of enough training data, Deep Learning CNN works best in all parameters for evaluation with 77% accuracy, followed by SVM with accuracy of 76%, and multinomial Bayes with least performance of 69% accuracy. CNN has the best performance in the presence of large enough training dataset because of the presence of filter/ kernels which help to indentify patterns in text data regardless of their position in the sentence. We repeated the training again with just one-third of our data, at this point SVM comes with the best performance, the performance of CNN noticeably drops but still better than Multinomial Naive Bayes, the reason why SVM works best when we reduce the training data was because of its ability to look for a hyper-plane that creates a boundary between different classes of data so as to properly classify them, so we believed that getting the hyper-plane was more efficient when we reduce the dataset, hence reason for the good performance. Multinomial Naive Bayes have the least performance which we attributed to its assumption of independency between the features which sometimes does not hold true. We concluded that availability of data should be an important factor when choosing classifier for Natural Language Processing Text Classification task. CNN should be use in the presence of enough dataset, and SVM should be use when data is not enough. Multinomial Naive Bayes must not be trusted with state of the art NLP task due to its assumption of independency between the features.

This study aimed to overcome the difficulty of conducting the horizontal-well cavity leaching test in the field and to investigate the long-term stability of the horizontal-well salt-cavity natural gas storage. The simulation test design is combined with the similarity theory to study the cavity expansion characteristics and the influence law of cavity leaching parameters. Through the design of a rubber hose connection, an integrated closed test system for multi-stage horizontal-well cavity leaching and brine drainage was built. The test system also realises the repeatable backward movement of the injection well during the test. A similarity simulation of the test design was carried out, and the test platform was constructed to carry out multi-stage horizontal-well leaching tests with a nitrogen cushion. In the horizontal-well leaching tests with a nitrogen cushion, the influence laws of the well spacing, flow rate and liquid level position on cavity expansion were investigated. Based on the morphological characteristics of the horizontal-well cavity, a numerical model of the horizontal-well salt cavity was developed, which reflects the real cavity morphology leached in the test. The long-term stability of the horizontal-well salt-cavity natural gas storage under different internal pressures was investigated through numerical simulation.

Background: Although electronic health records (EHR) provide useful insights into disease patterns and patient treatment optimisation, their reliance on unstructured data presents a difficulty. Because of their narrative structure, echocardiography reports, which provide extensive pathology information for cardiovascular patients, are particularly challenging to extract and analyse. Although natural language processing (NLP) has been utilised successfully in a variety of medical fields, it is not commonly used in echocardiography analysis. Objectives: To develop an NLP-based approach for extracting and categorizing data from echocardiography reports by accurately converting continuous (e.g. LVOT VTI, AV VTI, and TR Vmax) and discrete (e.g. Regurgitation severity) outcomes in semi-structured narrative format into structured and categorized format, allowing for future research or clinical use. Methods: 135,062 Trans-Thoracic Echocardiogram (TTE) reports were derived from 146967 baseline Echocardiogram reports and split into three cohorts: Training and Validation (n = 1075), Test Dataset (n = 98) and Application Dataset (n = 133,889). The NLP system was developed and iteratively refined using medical expert knowledge. The system was used to curate a moderate-fidelity database from extractions of 133,889 reports. A hold-out validation set of 98 reports was blindly annotated and extracted by two clinicians for comparison with the NLP extraction. Agreement, discrimination, accuracy and calibration of outcome measure extractions were evaluated. Results: Continuous outcomes including LVOT VTI, AV VTI, and TR Vmax exhibited perfect inter-rater reliability using intra-class correlation scores (ICC=1.00, P< 0.05) alongside high R2 values, demonstrating an ideal alignment between the NLP system and clinicians. Good level (ICC =0.75-0.9, P<0.05) of inter-rater reliability were observed for outcomes such as LVOT Diam, Lateral MAPSE, Peak E Velocity, Lateral E' Velocity, PV Vmax, Sinuses of Valsalva, and Ascending Aorta diameters. Furthermore, the accuracy rate for discrete outcome measures was 91.38% in the confusion matrix analysis, indicating effective performance. Conclusions: The NLP-based technique yielded good results when it came to extracting and categorising data from echocardiography reports. The system demonstrated a high degree of agreement and concordance with clinician extractions. This study contributes to the effective use of semi-structured data by providing a useful tool for converting semi-structured text to structured echo report that can be used for data management. Additional validation and implementation in healthcare settings can improve data availability and support research and clinical decision-making.

Highly-skilled migrants and refugees finding employment in low-skill vocations, despite professional qualifications and educational background, has become a global tendency, mainly due to the language barrier. Employment prospects for displaced communities are mostly decided by their knowledge of the sublanguage of the vocational domain they are interested in working. Common vocational domains include agriculture, cooking, crafting, construction, and hospitality. The increasing amount of user-generated content in wikis and social networks provides a valuable source of data for data mining, Natural Language Processing and machine learning applications. This paper extends the contribution of the authors’ previous research on automatic vocational domain identification by further analyzing the results of the machine learning experiments with the domain-specific textual data set, considering 2 research directions: a. predictions analysis and b. data balancing. Wrong predictions analysis and the features that contributed to misclassification, along with correct predictions analysis and the features that were the most dominant, contributed to the identification of a primary set of terms for the vocational domains. Data balancing techniques were applied on the data set to observe their impact on the performance of the classification model. A novel 4-step methodology is proposed in this paper for the first time, consisting of successive applications of SMOTE oversampling on imbalanced data. Data oversampling obtains better results than data undersampling in imbalanced data sets, while hybrid approaches perform reasonably well.

In recent years, the genesis of natural gas and the definition of major gas source rocks in the Baiyun Sag of the Pearl River Mouth Basin have attracted extensive attention from scholars. There is still considerable controversy over the genesis and the main gas source rocks of natural gas. In this study, the chemical kinetics method of natural gas generation and carbon isotope fractionation was used to study the generation history and isotope evolution history of Paleogene natural gas in the Y5 well area located on the north slope of Baiyun Sag. And other geochemical discriminant methods were applied simultaneously to determine the type and source rocks of natural gas. The results show that the natural gas in the Y5 well area on the northern slope of Baiyun Sag is mainly kerogen degradation gas, mixed with a small amount of oil cracking gas. The mixture of kerogen degradation gas and a small amount of crude oil cracking gas caused the inversion of the carbon isotope sequence of ethane and propane. The natural gas in the Y5 well area mainly comes from delta–shallow lacustrine facies source rock. The high–quality reservoirs in the shallower strata accumulate natural gas generated in the early stage, and the tight reservoirs in the deeper strata mainly accumulate natural gas generated in the late stage.

Since the exploitation of mineral resources results in the release of radionuclides, and consuming radionuclides affects public health in the short and long term. A case study of the environmental radiation impact from coal mining and germanium processing was carried out in southwest China. The coal mines contain germanium and uranium and have been exploited for more than 40 years. The farmlands around the site of coal mining and germanium processing have been contaminated by the solid waste and mine water in some extend since then. Samples of crops have been collected from contaminated farmlands in research area. The research area covers a radius of 5 km, in which there are 2 coal mines located. 210Pb and 210Po have been analyzed as the key radionuclides during monitoring program. The average activity concentrations of 210Pb and 210Po in the crops were 1.38 and 1.32 Bq/kg in cereals, 4.07 and 2.19 Bq/kg in leafy vegetables and 1.63 and 1.32 Bq/kg in root vegetables. The annual effective doses due to the ingestion of 210Pb and 210Po in consumed crops have been estimated for adult residents living in research area. The average annual effective dose was 0.336mSv/a, while the minimum was 0.171 mSv/a and the maximum was 0.948 mSv/a. The results show that crops grown on contaminated farmland contained an enhanced level of radioactivity concentration. Ingestion doses of local residents in research area were significantly higher than the China average level of 0.112 mSv/a, and the world average level of 0.042 mSv/a through 210Pb and 210Po in crops intake respectively.

Natural ventilation in a building is an effective way to achieve acceptable indoor air quality. Ventilation dilutes contaminants such as bio-effluents generated by occupants, substances emitted from building materials, and water vapor generated by occupants' activities. In a building that requires heating and cooling, adequate ventilation is crucial to minimize energy consumption while maintaining healthy indoor air quality. However, measuring the actual magnitude of the ventilation rate, including infiltration through the building envelope and airflow through building openings, is not always feasible. In this study, the occupant-generated carbon dioxide (CO2) tracer gas decay method was applied to estimate the ventilation rates in an office room in Seoul, South Korea, from summer to winter. Using the method, real-time ventilation rates can be calculated by monitoring indoor and outdoor CO2 concentrations without injecting a tracer gas. 145mm-diameter circular openings on the fixed glass were used for natural ventilation in the test room. As a result, firstly, the indoor CO2 concentrations were used as an indicator to evaluate how much the indoor air quality deteriorated when all the windows were closed in an occupied office room compared to international standards for indoor air quality. Moreover, we found out the estimated ventilation rates varied depending on various environmental conditions, even with the same openings for natural ventilation. Considering the indoor and outdoor temperature differences and outdoor wind speeds as the main factors influencing the ventilation rates, we analyzed how they affected the ventilation rates in different seasons of South Korea. When the wind speeds were calm, the temperature difference played as a factor that influences the estimated ventilation rates. On the other hands, when the temperature differences were low, the wind speed was the primary factor. This study raises awareness about the risk of poor indoor air quality in office rooms that could lead to health problems or unpleasant working environments. This study presents an example of estimating the ventilation rates in an existing building. In the same way, the ventilation rate in an existing building can be simply estimated while using the building as usual, and appropriate ventilation strategies for the building can be determined to maintain desired indoor air quality.

The effect of H2S with high concentrations on catalytic dry reforming of methane (DRM) process has seldom been focused on previously. Herein, a thermodynamic analysis of the DRM reactions in the presence of H2S with the concentration varying in 0-20 vol% was conducted firstly. Several typical types of catalysts including MgO, NiO/MgO and LaNiO3 in different states were prepared for executing DRM at 800 C, 0.1 MPa in the feed of 20 vol% CO2 and 20 vol% CH4 balanced with N2. The catalytic performance of each catalyst for DRM process under conditions of absence and presence of H2S was compared. A promotion effect of increasing H2S concentration on both the conversions of CO2 and CH4 and the molar yields of CO and H2 was observed on all the catalysts especially the MgO and the pristine NiO/MgO. While a significant decline in catalytic activity of either the reduced NiO/MgO or the reduced LaNiO3 catalyst after adding H2S, moderate reactant conversions still sustained. The results of process analysis and catalyst structure characterization suggest that H2S participation can contribute to the increment in CO2 and CH4 conversion, and active S-adsorbed species may play the key role of catalysis in the reactions involving H2S.

“You should know the words by the company they keep!” has been one of the most famous slogans attributed to John Rubert Firth, 1957. This has ignited a whole school in linguistic research known as the British empiricist contextualism. Sixty years later, many un- or semi-supervised machine learning algorithms have been successfully designed and implemented aiming at extracting word meaning from within the context of a text corpus. These algorithms treat words, more or less, as vectors of real numbers representing frequencies of word occurrences within context and word meaning as positions of words in a high-dimensional vector space model. Word associations, in turn, are treated as calculated distances among them. With the rise of Deep Learning (DL) and other artificial neural networks based architectures, learning the positioning of words and extracting word associations as measured by their distances has further improved. In this paper, however, we revisited the main stream of algorithmic approaches and set the stage for a partly cross-disciplinary evaluation framework to judge about the nature of the extracted word associations by state-of-the-art machine learning algorithms. Our preliminary results are based on word associations extracted from the application of DL framework on a Google News text corpus, as well as on comparisons with human created word association lists such as word collocation dictionaries and psycholinguistic experiments. The results and conclusions provide some insights into the inherited limitations in interpreting the type of word associations and underpinning relations between words with inevitable consequences in other areas, such as extraction of knowledge graphs or image understanding.

The review of previous works shows this study is the first attempt to analyse the lockdown effect using Natural Language Processing Techniques, particularly sentiment analysis methods applied at large scale. On the other hand, it is also the first of its kind to analyse the impact of COVID 19 on the university community jointly on staff and students and with a multi-country perspective. The main overall findings of this work show that the most often related words were family, anxiety, house and life. On another front, it has also been shown that staff have a slightly less negative perception of the consequences of COVID in their daily life. We have used artificial intelligence models like swivel embedding and the Multilayer Perceptron, as classification algorithms. The performance reached in terms of accuracy metric are 88.8% and 88.5%, for student and staff respectively. The main conclusion of our study is that higher education institutions and policymakers around the world may benefit from these findings while formulating policy recommendations and strategies to support students during this and any future pandemics.

Natural Gas (NG) processing is a prominent industrial separation process. Among the available techniques, the innovative membrane process shows potential for efficient removal of impurities, including carbon dioxide (CO₂). This study focuses on utilizing breakthroughs in membrane separation to drive sustainable CO₂ extraction from natural gas. Through comprehensive research and analysis, we explore the effectiveness and feasibility of membrane-based systems in removing CO₂ impurities from NG, thus promoting greener and more sustainable industrial practices. Our findings underscore the transformative nature of membrane separation technology, presenting new possibilities for a more environmentally-friendly and sustainable approach to CO₂ extraction from natural gas.Natural Gas (NG) processing utilizes various techniques for impurity removal, with the membrane process emerging as a promising option for efficient carbon dioxide (CO₂) extraction. This research proposes the integration of a simple mathematical model into ASPEN HYSYS to design a membrane system for CO₂/CH₄ separation. The study also investigates parameter sensitivities by altering operating conditions, such as feed composition and pressure, as well as membrane properties, including selectivity. By analyzing these variables, we aim to optimize the performance and efficiency of the membrane system, facilitating the sustainable extraction of CO₂ from NG. The findings contribute to advancing the design and operation of membrane-based processes for CO₂ separation, paving the way for greener and more sustainable industrial practices. In addition, this study explores various configurations for optimizing the design of the membrane system, including single-stage with and without recycling as well as double-stage configurations. The investigation demonstrates that methane recovery can be enhanced through the recycling of the permeate stream and by implementing a double stage membrane system. These findings highlight the potential for improving the efficiency and performance of the membrane system, enabling higher methane recovery rates. By considering different configurations, this research contributes to the development of more effective and sustainable CO₂ extraction processes from natural gas.

The incidence of gastrointestinal diseases (cancer in particular) has increased progressively with considerable morbidity, mortality, and a high economic impact on the healthcare system. Dietary intake of natural bioactive phytochemicals showed to have cancer-preventing and therapeutic effects. This includes the cruciferous vegetable derivative phenethyl isothiocyanate (PEITC), a bioactive compound from watercress. PEITC antioxidant, anti-inflammatory and anti-cancer properties are of particular importance. This review summarizes the current knowledge on the role of PEITC as a potential natural nutraceutical or an adjuvant against oxidative/inflammatory-related disorders in the gastrointestinal tract. We also discuss the safe and recommended dose of PEITC. Besides, we establish a framework to guide the research and development of sustainable methodologies for obtaining and stabilizing this natural nutraceutical for industrial use. This is a topic that still needs more scientific development, but with the potential to lead to a viable strategy in the prevention of cancer and other associated diseases of the gastrointestinal tract.

The annual budget for the United States National Park Service was roughly three billion dollars in 2016. This is distributed amongst 405 National Parks, 23 national scenic and historic trails, and 60 wild and scenic rivers. Entrance fees and concessions generate millions of dollars in income for the National Park Service; however, this metric fails to account for the total value of the National Parks. In failing to consider the value of the ecosystem services provided by the National Parks we fail to quantify and appreciate the contributions our parks make to society. This oversight allows us to continue to underfund a valuable part of our natural capital and consequently damage our supporting environment, national heritage, monetary economy, and many of our diverse cultures. We explore a simple benefits transfer valuation of the United States national parks using National Land Cover Data from 2011 and ecosystem service values determined by Costanza (et al). This produces an estimate suggesting the parks provide $84 billion / year in ecosystem service value. If the natural infrastructure 'asset' that is our national park system had a budget comparable to a piece of commercial real estate of this value, the annual budget of the National Park Service would be roughly an order of magnitude larger at something closer to $30 billion rather than $3 billion.

New class of biologically active and non-active compounds can be synthesized via transition metal mediated Suzuki cross coupling reaction that has a great impact on the advancement of organic chemistry. These resulted products can lend a helping hand in pharmaceutical and polymer chemistry for the betterment of mankind. Suzuki-Miyaura cross coupling reaction is one of the best tools through which many natural and non-natural compounds can be synthesized.

Natural products (NPs) have been the centre of attention of the scientific community in the last decencies and the interest around them continues to grow incessantly. As a consequence, in the last 20 years, there was a rapid multiplication of various databases and collections as generalistic or thematic resources for NP information. In this review, we establish a complete overview of these resources, and the numbers are overwhelming: over 120 different NP databases and collections were published and re-used since 2000. 98 of them are still somehow accessible and only 50 are open access. The latter include not only databases but also big collections of NPs published as supplementary material in scientific publications and collections that were backed up in the ZINC database for commercially-available compounds. Some databases, even published relatively recently are already not accessible anymore, which leads to a dramatic loss of data on NPs. The data sources are presented in this manuscript, together with the comparison of the content of open ones. With this review, we also compiled the open-access natural compounds in one single dataset a COlleCtion of Open NatUral producTs (COCONUT), which is available on Zenodo and contains structures and sparse annotations for over 400000 non-redundant NPs, which makes it the biggest open collection of NPs available to this date.

A review of the concept of "fitness" as it is used in evolutionary theory.

The Loess Plateau is an important region for vegetation restoration in China, however, changes in soil organic carbon (SOC), soil nutrients, and stoichiometry after restoration in this vulnerable ecoregion are not well understood. Typical restoration types, including orchardland (OL), grassland (GL), shrubland (SL), and forestland (FL) were chosen to examine changes in the stocks and stoichiometry of SOC, soil total nitrogen (TN), and soil total phosphorus (TP) at different soil depths and recovery times. Results showed that SOC stocks first increased and then stabilized in OL, GL, and SL at 0–30 cm depth, while in FL, stocks gradually increased. Soil TN stocks first increased and then decreased in OL, SL, and FL with vegetation age at 0–30 cm depth, while soil TP stocks showed little variation between restoration types. In the later stages of restoration, the stocks of SOC and soil TN at 0–30 cm soil depth were still lower than those in natural grassland (NG) and natural forest (NF). The overall C:N, C:P, and N:P ratios increased with vegetation age. Additionally, the SOC, soil TN and soil TP stocks, and C:N, C:P, and N:P ratios decreased with soil depth. The FL had the highest rate of change in SOC and soil TN stocks, at 0-10 cm soil depth. These results indicate a complex response of SOC, soil TN, and soil TP stocks and stoichiometry to vegetation restoration, which could have important implications for understanding C, N, and P changes and nutrient limitations after vegetation restoration.

Forest managers are often required to restore forest stands following natural disturbances, a situation that may become more common and more challenging under global change. In parts of Central Europe, particularly in mountain regions dominated by mixed temperate forests, the use of relatively low intensity, uneven-aged silviculture is a common management approach. Because this type of management is based on mimicking less intense disturbances, the restoration of more severe disturbance patches within forested landscapes has received little attention within the context of uneven-aged silviculture in the region. The goal of this paper is to synthesize research on the restoration of forests damaged by disturbances in temperate forests of Slovenia and neighbouring regions of Central Europe, where uneven-aged silviculture is practiced. We place particular emphasis on the most important biotic and abiotic drivers of post-disturbance regeneration, and use this information to inform silvicultural decisions about applying natural or artificial regeneration in disturbed areas. We conclude with guidelines for restoration silviculture in uneven-aged forest landscapes.

Geotechnology techniques were used to specialize the occurrences of the species, based on forest inventories and information available in open-access databases, as well as data from herbaria with catalogs available online, and bibliographical references indicating places of occurrence in the Amazon. GIS-integrated Weighted Mean Ordination (WMO) was chosen as the technique for evaluating the topoclimatic index. Thirteen topoclimatic variables were used, applying principal component analysis techniques such as multidimensional preference to compare the participation of each variable in the two components; Biplot to identify the relationships between the variables and cluster to identify the average distance between proximal classes. The interpolated surfaces of the specialized climatic variables were separated into frequency bands that helped calculate the probability of occurrence, from which a scale of 0 to 10 was established, indicating lower and higher probabilities. Three potential zones were identified, classified as high, medium and low, where areas with high potential occupied 32% of the territory of Pará, medium areas 36% and 32% with low potential for cultivation and presence. The intermediate class had the highest percentage, totaling 36% of the state, explained by the average ratio between the weights adopted.

Natural language processing (NLP) is being widely used globally for a variety of value-creation tasks ranging from chat-bots and machine translations to sentiment and topic analysis and multilingual large language models (LLMs). However, most of the advances are initially implemented within the English language framework, and it takes time and resources to develop comparable resources in other languages. The advances in machine translations have enabled the rapid and effective conversion of content in global languages into English and vice-versa. This creates potential opportunities to apply English language NLP methods and tools to other languages via machine translations. However, although this idea is powerful, it needs to be validated and processes and best practices need to be developed and kept updated. The present research is an effort to contribute to the development of best practices and an evaluation framework. We present a systematic and repeatable state-of-the-art process to evaluate the viability of applying English language sentiment analysis tools to Italian text by using multiple English language machine translation mechanisms such that it can be easily extended to other languages. hor[RU,RA]{\large Richard Anderson} \address[RU]{\large Rutgers University, USA} \address[RA]{\large rick.anderson@rutgers.edu} \author[RU]{\large Carmela Scala} \author[RU]{\large Jim Samuel} \author[UC]{\large Vivek Kumar} \address[UC]{\large University of Cagliari, Italy} \author[RU]{\large Parth Jain}

Rice bran arabinoxylan compound (RBAC) is derived from rice bran enzymatically treated with Lentinus Edodes mycelium. This review explores biologically active compounds and mechanisms of action that support RBAC as an immunomodulating nutraceutical in generally healthy and/or aging individuals. Thirty-seven (n=37) primary research articles fulfiled the selection criteria for review. Most research is based on Biobran MGN-3, which consists of complex heteropolysaccharides with arabinoxylan as its primary structure while also containing galactan and glucan. RBAC was found to invoke immunological activities through direct absorption via the digestive tract and interaction with immune cells at the Peyer’s patches. RBAC was shown to promote the innate defence by upregulating the macrophage phagocytosis and enhancing natural killer cell activity while lowering oxidative stress. Through induction of dendritic cell maturation, RBAC also augments adaptive immunity by promoting T and B lymphocyte proliferation. RBAC acts as an immunomodulator by inhibiting mast cell degranulation during allergic reactions, attenuating inflammation, and downregulating angiogenesis by modulating cytokines and growth factors. RBAC is shown to be a safe and effective neutraceutical for improving immune health, notably in aging individuals with reduced immune function. Human clinical trials with geriatric participants have demonstrated RBAC to have prophylactic benefits against viral infection and may improve their quality of life. Further research should explore RBAC’s bioavailability, pharmacodynamics, and pharmacokinetics of the complex heteropolysaccharides within. Translational research to assess RBAC as an nuetraceutical for the aging population is still required, particularly in human studies with larger sample sizes and cohort studies with long follow-up periods.

Natural capital is the wealth of nations that give them the economic status they represent. Worldwide, vulnerable people depend on natural capital for employment, salaries, wealth, and livelihoods and, in turn, determine the developmental index of the nation to which they belong. The availability of ecological services is crucial for clean water and air, food and fodder, and agricultural development. In this short commentary, we have tried to sum up the ideas and discussions over natural capital's role in ascribing economic status to countries. We have discussed how the prosperity of humans is intertwined with the services ecosystems provide and how poor natural resource management (NRM) has adversely cost human well-being. The paper concludes that to ensure the current and future human well-being, an in-depth understanding of the services ecosystems provide, is essential.

Natural tourist destinations stand out as the most preferred choice in Rembang Regency compared to cultural and artificial tourist destinations. However, until now, there have been no standard indicators established to assess the potential for natural tourism in Rembang Regency, which could assist the Culture and Tourism Office in determining potential tourist locations. In this research, the grouping of natural tourism potential indicators will be carried out based on six main aspects, namely Attractions, Amenities, Accommodation, Activities, Accessibility and Ancillary Services, together with environmental aspects and Human Resources. The procedure for determining natural tourism potential will be developed from the results of this grouping using a taxonomic model. Indicators and the weight of each indicator will be determined using the hierarchical analysis method. The results of this research will produce a taxonomy model that can overcome challenges in managing and utilizing information and knowledge, which in turn will facilitate the process of sharing and utilizing information. Furthermore, the procedures developed will enable accurate and efficient determination of natural tourism potential in accordance with established procedures. This research method will also enable the development of a comprehensive knowledge management system at the Rembang Regency Culture and Tourism Service, through the next stages in the designed taxonomy model. This research is expected to contribute to the sustainable management of natural tourism, which can contribute to the preservation of nature while improving the tourist experience.

In view of the diminishing contribution of natural products from plants to the discovery of new chemical structures, the aim of this study was to compile the articles from 2021 to 2022, assessing: (1) which classes of compounds these new structures belong to; (2) which plants and families these compounds belong to and (3) assessing the degree of novelty of the compound with an already known structure. The review was elaborate following the Preferred Reporting Items for System-atics Reviews and Meta-analyses (PRISMA) statement checklist for guide reporting of systematic reviews. A total of 464 articles were selected for the new compounds of natural origin survey. We included 117 complete articles in this review and reported approximately 109 new structures elucidated during the years 2021 and 2022. Many of the compounds showed small structural var-iations in relation to already known molecules. For some, however, this small modification was decisive for the biological activity reported, demonstrating the importance of descriptive phytochemical studies.

English based datasets are commonly available from Kaggle, GitHub, or recently published papers. Although benchmark tests with English datasets are sufficient to show off the performances of new models and methods, still a researcher need to train and validate the models on Korean based datasets to produce a technology or product, suitable for Korean processing. This paper introduces 15 popular Korean based NLP datasets with summarized details such as volume, license, repositories, and other research results inspired by the datasets. Also, I provide high-resolution instructions with sample or statistics of datasets. The main characteristics of datasets are presented on a single table to provide a rapid summarization of datasets for researchers.

The sentiment of a word varies based on its context of usage: the words used around it and the part-of-speech it is used as. This paper proposes a technique to suggest the sentiment of a word by combining its part-of-speech and the semantic similarities of its co-occurrences with both context-specific and pre-trained embeddings to achieve powerful and fast results. A study was conducted across domains and sub-domains to measure variance of sentiment by switching domains and switching context within the same domain. Re-scoring a commonly used polarity lexicon showed that 10% of words changed scores while switching domains and 8% changed scores within domains while switching context. Part of Speech analysis on 65,353 commonly used sentiment lexicons showed that 81% of sentiment bearing (non-neutral) lexicons were of the tags NN (Common Noun), JJ (Adjective) or NNS (Proper Noun).

Hepatocellular carcinoma (HCC) is currently the third leading cause of malignancy-related mortalities worldwide. Natural killer (NK) cells are involved in the critical role of first line immunological defense against cancer development. Defects in NK cell functions are recognized as important mechanisms for immune evasion of tumor cells. NK cell function appears to be attenuated in HCC, and many previous reports suggested that NK cells play a critical role in controlling HCC, suggesting that boosting the activity of dysfunctional NK cells can enhance tumor cell killing. However, the detailed mechanisms of NK cell dysfunction in tumor microenvironment of HCC remain largely unknown. A better understanding of the mechanisms of NK cell dysfunction in HCC will help in the NK cell-mediated eradication of cancer cells and prolong patient survival. In this review, we describe the various mechanisms underlying NK cell dysfunction in HCC. Further, we summarize current advances in the approaches to enhance endogenous NK cell function and in adoptive NK cell therapies, to cure this difficult-to-treat cancer.

P2Y2 and P2Y4 receptors are physiologically activated by UTP and are widely expressed in many cell types in humans. They promote an increase in intracellular calcium via PLCβ/ IP3 and act on ion flux and water secretion. P2Y2 plays an important role in inflammation and proliferation of tumor cells, which could be attenuated with the use of antagonists. However, little is known about the physiological functions related to P2Y4 due to the lack of selective ligands for these receptors, which can be solved through the search of novel compounds with antagonistic activity. In the present study, we have applied a methodology of calcium measurement to identify new antagonist candidates for these receptors. Firstly, we established optimal conditions for calcium assay using J774.G8, a murine macrophage cell line, which expresses functional P2Y2 and P2Y4 receptors. J774.G8 cells were loaded with 2 μM of Fluo-4 to test its sensitivity in responding calcium stimuli. ATP and ionomycin, known as inductors of intracellular calcium rise, were used to stimulate cells. The EC50 obtained were 11 μM and 103 nM, respectively. Subsequently, investigation of P2Y2 and P2Y4 expression was performed. These cells responded with EC50 of 1.021 μM to the UTP stimulation. Screening assays were performed and a total of 100 extracts from Brazilian natural products were tested. JA2, RA3, and RB3 extracts stood out for their ability to inhibit UTP-induced responses without causing cytotoxicity and presented IC50 of 32.32 μg/mL, 14.99 μg/mL, and 12.98 μg/mL, respectively. Collectively, our results point to the discovery of potential antagonists candidates from natural products for UTP-activated receptors.

The question of the relevance of human and natural capital, as well as the potential adverse effect of natural capital on economic growth, has gained increased attention in development economics. The aim of this paper is to theoretically and empirically assess the relevance of several forms of capital on economic growth in small economies that are dependent upon tourism or natural resources. The empirical framework is based on Impulse Response Functions obtained from Vector Autoregressive models in which we focus on the model where economic growth is the dependent variable for ten small economies that are dependent upon either tourism or natural resources. We find that there is evidence of the ‘’natural resource curse’’, especially in the economies that have a strong dependence on resources that are easily substitutable and whose prices constantly fluctuate. We further find that in the majority of observed cases the type of capital these small economies are most dependent on for their economic growth causes negative impulses in the majority of the observed periods. The main policy recommendation should be to assure that even these small economies should strive towards further diversification and avoid dependence on only one segment of their economy.

In the past few decades, the use of agricultural waste has been increasing due to the increasing demand for sustainable materials. This paper aims to review the significance of agricultural waste as a type of wealth and its potential as an alternative material in the composite field. It was found that many agricultural wastes had been utilized in various production of ecological products. The review discusses the physical, mechanical, and chemical properties of the natural fibers made from agricultural waste and the use of Pineapple Leaf Fiber Reinforced Composite to manufacture UAVs (Unmanned Aerial Vehicles). The extracted waste fibers’ physical, mechanical, and chemical properties show that these agricultural wastes can produce better advanced composite materials suitable for structural applications. The use of pineapple leaf fiber in Unmanned Aerial Vehicles manufacturing has the potential to revolutionize this industry and contribute to a more sustainable future.

In the dynamic landscape of E-commerce, the proliferation of user-generated queries regarding products highlights the critical need for advanced automatic question answering (AQA) systems. These systems are instrumental in harnessing the vast array of information available online to deliver immediate and informative responses to potential buyers. Recognizing the complexity of such queries, which often require synthesis of reviews, product specifications, and responses to similar questions, we introduce a sophisticated model, the Comprehensive E-commerce Answer Generation System (CEAGS). This model adeptly navigates the challenges posed by irrelevant data and sentiment ambiguity in user-generated content. Our approach leverages a dual-phase process of relevance determination and sentiment clarity, setting the stage for a transformative response generation mechanism. Empirical analyses reveal the superiority of CEAGS, with our relevance assessment framework outstripping existing models by a notable margin in precision metrics, and our answer generation module achieving unprecedented gains in content preservation and coherence. Notably, CEAGS marks a pioneering contribution to the E-commerce domain by integrating disparate information sources to formulate responses that are not only accurate but also contextually rich and user-centric.

Pancreatic cancer (PC) is considered one of the most challenging and formidable malig-nancies to treat because it is usually diagnosed at terminal stage, thus it is characterized with a poor prognosis with 5% of patients living over 5 years. The late diagnosis leads to a lower suc-cess rate of resectability, hence chemotherapy or chemoradiotherapy are the only hopes for most PC patients. However, the available drug combinations for PC treatment did not provide satisfactory clinical outcomes. There is a pressing and urgent need to discover new chemothera-peutics which fight PC with tolerable side effects and less liability for resistance. Pentacyclic triterpenes (PTs) are privileged structures with a multitude of biological activities mitigating several ailments, particularly in neoplasm field. They are characterized by a high therapeutic window and their activity can be highly enhanced through structural modifications. Indeed, several parent PTs such as oleanolic acid, ursolic acid, lupeol, betulinic acid, and celastrol proved effective against different PC phenotypes inhibiting their proliferation, invasion, and metastasis mainly via KRAS modulation. Some PTs worked as adjuvant chemosensitizers in gemcitabine-resistant or TRAIL-resistant cells. Owing to their potential in PC treatment, we pre-sent this review article summarizing PC pathogenesis and the promising role of PTs in the man-agement of PC when used either solely or in combination with other chemotherapies such as gemcitabine and 5-Fluorouracil.

The importance of oxygen role during aging process of wine has been long known. Many articles evaluating oxygen inlet for different wine stoppers have been published. However, none of these have studied the impact of the natural cork stoppers production process over oxygen inlet. At the moment, a standardized methodology widely accepted for measuring the oxygen transfer rate (OTR) and/or oxygen permeability of the stopper does not exist. Thus, this article is focused on the aging process (the final phase of the winemaking) and aims to test how some production process of natural cork stoppers impact on the stoppers permeability using chemiluminescence methodology and how also this affects wine ageing. In this study it has been seen that some of its processes such as the application of a surface treatment modify the permeability of the stopper. Surface plays an important role in the corks stoppers production and allows an easier insertion and extraction of the stopper into the bottle, avoiding leaks and homogenizing the stoppers. It was shown that the cork stoppers sample with surface treatment 1 had three times higher oxygen transfer rate than the cork stoppers sample with surface treatment 2 after one year of evolution. The two groups of stoppers had the same in terms of dimensions, density, visual class, batch and manufacture, the only difference between them was the surface treatment used. A red wine was corked with these two types of cork stoppers and its evolution was monitored up to 30 months of ageing. Differences were observed in the evolution of the wines both in certain oenological parameters such as the free SO2, the colour and the evolution of the polyphenols as well as at the sensory level. The wine corked with the stoppers of treatment 1 presented a faster evolution and a higher valuation in the sensory tastes of months 12th, while from the 18th month it presented an over evolution, and the score was below that of the wine with the corks of treatment 2.The data collected in this study shows that the differences observed with the chemiluminescence methodology of cork permeability test at a laboratory level are transferred to wine at the cellar. Therefore, it paves the way to standardize chemiluminescence as a methodology for permeability testing of cork stoppers. This opens a new field for the regulation of the permeability of natural cork stoppers by type of surface treatment applied.

Bioactive peptides are promising cosmetic active ingredients that can improve skin health and appearance. They exhibit a broad spectrum of activity, including anti–ageing, antioxidant, antimicrobial and anti–inflammatory. The aim of this study was to develop a safe, stable and efficacious environmentaly friendly ("green") emulsion using milk protein hydrolysate as a model active ingredient. Potential emulsions were formulated with biodegradable emollients, stabilised with a naturally-derived mixed emulsifier and prepared by cold process. They were evaluated for rheological behavior (continuous rotation and oscillation tests), physical stability (dynamic mechanical thermal analysis - DMTA test) and texture profiles, as well as for cytotoxic, antioxidant and antimicrobial effects. Rheological characterization revealed shear-thinning flow behaviour with yield point from continuous rotation tests and predominantly elastic character from oscillation (amplitude and frequency sweep) tests, with small structural change detected in the DMTA test. These results implied satisfactory rheological properties and good stability. Texture analysis revealed acceptable spreadability and substantivity of the emulsions. The protein hydrolysate showed antioxidant activity. The developed emulsions showed low antibacterial activity against selected microorganisms, but this was due to the action of preservatives, not peptides. All potential emulsions showed desirable safety profile. The results obtained provide the basis for the next stage of formulation development, i.e. in vivo efficacy tests.

Viral infection activates the transcription factors IRF3 and NF-κB, which synergistically induce type I interferons (IFNs). Here, we identify the E3 ubiquitin ligase RNF138 as an important negative regulator of virus-triggered IRF3 activation and IFN-β induction. Overexpression of RNF138 inhibited virus-induced activation of IRF3 and transcription of the IFNB1 gene, whereas knockout of RNF138 promoted virus-induced activation of IRF3, and transcription of the IFNB1 gene. We further found that RNF138 promotes ubiquitination of PTEN and subsequently inhibits PTEN interactions with IRF3, which is essential for PTEN-mediated nuclear translocation of IRF3, thereby inhibiting IRF3 import into the nucleus. Our findings suggest that RNF138 negatively regulates virus-triggered signaling by inhibiting the interaction of PTEN with IRF3, and these data provide new insights into the molecular mechanisms of cellular antiviral responses.

Skin cancer is a disease that reflects most malignant neoplasms worldwide, whose rate of involvement has increased significantly in recent decades. Treatments that offer efficient results with attenuation of adverse reactions and greater patient compliance have been the subject of many systematic investigations. This review aims to qualitatively present published works that showed new strategies to increase the penetration power of natural substances through the skin in the topical treatment against skin cancer. In this sense, nanoengineering mechanisms, the iontophoresis technique, and photodynamic therapy using natural products (NP) contribute to new knowledge about substance carrier strategies in topical treatments against skin cancer.

In the present study a numerical bifurcation analysis of a thermistor problem is carried out, considering a realistic heat dissipation mechanism due to conduction, nonlinear temperature dependent natural convection and radiation. The electric conductivity is modeled as a strongly nonlinear and smooth function of the temperature between two limiting values, based on measurements. The problem formulated in this way admits multiple steady state solutions that do not depend on the external circuit. The analysis reveals that the conduction-convection parameter and the type of the boundary conditions have a profound effect on the solution structure and the temperature profiles. Depending on the boundary conditions the complex multiplicity pattern appears either as a series of nested cusp points or as enclosed branches emanating from pitchfork bifurcation points.

Kratom (Mitragyna speciosa) is a Southeast Asian plant containing various alkaloids which induce pharmacological effects in humans. In Western countries, online vendors sell a variety of different kratom strains which are marketed to have distinct effect profiles. However, as of yet such marketing claims are unsubstantiated, and therefore the current study investigated whether differently colored kratom products can induce unique effects, as self-reported by users. 644 current kratom users were anonymously surveyed to compare the self-reported effects and motivations for using kratom products sold as red, green, and white strains. Most of the survey respondents were customers of the same kratom vendor, the products of which had been assessed for their alkaloid content by an independent laboratory. The survey respondents reported distinct subjective experiences for different kratom strains, in a manner congruent with common marketing descriptions. However, the product analyses revealed no significant cross-strain differences in alkaloid content, suggesting that the reported effect differences might be disproportionally influenced by marketing narratives and anecdotal reports. Future studies should engage a more diverse population and include kratom strains from various vendors. Controlled, blinded experiments could assess whether the reported effect differences stem from a placebo effect or from alternative factors, e.g. minor alkaloids and terpenes.

The daVinci Surgical Robot has revolutionized minimally invasive surgery by enabling greater accuracy and less invasive procedures. However, the system lacks advanced features and autonomy necessary for it to function as a true partner. To enhance its usability, we introduce the implementation of a ChatGPT-based natural language robot interface. Overall, our integration of a ChatGPT-enabled daVinci Surgical Robot has potential to expand the utility of the surgical platform by supplying a more accessible interface. Our system can listen to the operator speak and, through the ChatGPT-enabled interface, translate the sentence and context to execute specific commands to alter the robot’s behavior or to activate certain features. For instance, the surgeon could say (even in Spanish) “please track my left tool” and the system will translate the sentence into a specific track command. This specific error-checked command will then be sent to the hardware which will respond by controlling the camera of the system to continuously adjust to center the left tool in the field of view. We have implemented many commands including “Find my tools” (tools that are not in the field of view) or start/stop recording that can be triggered based on a natural conversational context. Here, we present details of our prototype system, give some accuracy results, and explore its potential implications and limitations. We also discuss how artificial intelligence tools (such as ChatGPT) of the future could be leveraged by robotic surgeons to reduce errors and enhance the efficiency and safety of surgical procedures and even ask for help.

Insulin resistance, as a common pathological process of many metabolic diseases, including diabetes and obesity, has attracted much attention due to its relevant influencing factors. To date, studies have mainly focused on the mechanisms between mitochondrial stress and insulin resistance, and they are now pursued as a very attractive therapeutic target due to their extensive involvement in many human clinical settings. In view of the complex pathogenesis of diabetes, natural drugs have become new players in diabetes prevention and treatment because of their wide targets and few side effects. In particular, plant phenolics have been paid more attention because of their close relationship with oxidative stress. In this review, we briefly review the mechanisms by which mitochondrial stress leads to insulin resistance. Moreover, we list some cytokines and genes that have recently been found to play roles in mitochondrial stress and insulin resistance. Furthermore, we describe several natural drugs that are currently widely used and give a brief overview of their therapeutic mechanisms. Finally, we suggest possible ideas for future research related to the unique role that natural drugs play in the treatment of insulin resistance through the above targets.

Unusual outbreak of Covid 19 has put the whole new challenges around the globe. In a short span of time, the world has been changed, lifestyles, social events, cultural, political events everything has been put on hold as number of countries has enforced partial or complete lockdown that resulted positive in context of controlling spread and contamination of COVID-19. Such activities have also put some effects on environment in both positive and negative way. In this study only positive impacts of COVID-19 on Environment have been highlighted that played role in regeneration of natural systems i.e., reduced air emissions, cleaner air, beaches &water bodies, reduced noise levels, biodiversity rehabilitation etc.

The cosmetics industry is currently looking for innovative ingredients with higher bioactivity and bioavailability for the masses of natural and organic cosmetics. Bioferments are innovative ingredients extracted from natural raw materials by carrying out a fermentation process with appropriate strains of microorganisms. The review was conducted using the SciFinder database with the keywords fermented plant, cosmetics, fermentation. Mainly bioferments are made from plant-based raw materials. The review covers a wide range of fermented raw materials from waste materials (whey with beet pulp) to plant oils (F-Shiunko, F-Artemisia, F-Glycyrrhiza). The spectrum of applications for bioferments is broad and includes properties such as skin whitening, antioxidant properties (blackberry, soybean, goji berry), anti-ageing, anti-aging (red ginseng, black ginseng, Citrus unshiu peel), hydrating and anti-allergic (aloe vera, skimmed milk). Fermentation increases the biochemical and physiological activity of the substrate by converting high-molecular compounds into low-molecular structures, this makes fermented raw materials more compatible compared to unfermented raw materials.

Natural compounds have diverse structures and are present in different forms of life. Metabolites such as tannins, anthocyanins, and alkaloids, among others, serve as a defense mechanism in live organisms and are undoubtedly compounds of interest for the food, cosmetic and pharmaceutical industries. Plants, bacteria, and insects represent a source of biomolecules with diverse activities, poorly studied in many cases. To use these molecules for different applications, it is essential to know their structure, concentrations, and biological activity potential. In vitro techniques that evaluate the biological activity of the molecules of interest have been developed since the 1950s. Currently, different methodologies have emerged to overcome some of the limitations of these traditional techniques, mainly the reduction of time and costs. However, emerging technologies continue to appear due to the urgent need to expand the analysis capacity of a growing number of reported biomolecules and the lack of therapeutic options to treat various diseases. This review presents an updated summary of the conventional and current methods to evaluate natural compounds' biological activity, including a diagram that summarizes the minimum techniques essential for correctly assessing molecules with biological potential.

The recent revival of the study of organic natural products as renewable sources of medicinal drugs, cosmetics, dyes, and materials motivated the creation of general-purpose structural databases. Dereplication, the efficient identification of already reported compounds, relies on the grouping of structural, taxonomic and spectroscopic databases that focus on a particular taxon (species, genus, family, order…). A set of freely available python scripts, CNMRPredict, is proposed for the quick supplementation of taxon-oriented search results from the LOTUS database (lotus.naturalproducts.net) with predicted carbon-13 NMR data from the ACD/Labs (acdlabs.com) CNMR predictor and DB software to provide easily searchable databases. The database construction process is illustrated using Brassica rapa as taxon example.

Acquired Immunodeficiency Syndrome (AIDS) which is chiefly originated by a retrovirus named Human Immunodeficiency Virus (HIV), has influenced about 70 million populations worldwide. Even though several advancements have been invented in the field of antiretroviral combination therapy, still HIV has become the dominant reason for death in South Africa, for example. The current antiretroviral therapies have achieved success in providing instant HIV suppression but with countless undesirable adverse effects. In the present day, the biodiversity of the plant kingdom is being explored by several researchers for the discovery of potent anti-HIV drugs with different mechanisms of action. The primary challenge is to afford a treatment that is free from any sort of risk of drug resistance and serious side effects. Hence, there is a strong demand to evaluate the drugs obtained from natural plants as well as the synthetic derivatives that have been derived from the natural compounds by various chemical reactions. Several plants such as Andrographis paniculata, Dioscorea bulbifera, Aegle marmelos, Wistaria floribunda, Lindera chunii, Xanthoceras sorbifolia and others have displayed significant anti-HIV activity showing more potent anti-HIV activity along with their structures, SARs & important key findings.

In this review, we discuss novel natural products discovered within the last decade that are reported to have antifungal activity against pathogenic species. Nearly a hundred natural products were identified that originate from bacteria, alga, fungi, sponges and plants. Fungi were the most prolific source of antifungal compounds discovered during the period of review. The structural diversity of these antifungal leads encompasses all the major classes of natural products including polyketides, shikimate metabolites, terpenoids, alkaloids and peptides.

A twin prime numbers are two prime numbers which have the difference of 2 exactly. In other words, twin primes is a pair of prime that has a prime gap of two. Sometimes the term twin prime is used for a pair of twin primes; an alternative name for this is prime twin or prime pair. Up to date there is no any valid proof/disproof for twin prime conjecture. Through this research paper, my attempt is to provide a valid disproof for twin prime conjecture.

We examine the extensions of the basic arithmetical operations of addition and multiplication on the natural numbers into higher-rank hyper-operations also on the natural numbers. We go on to define the concepts of prime and composite numbers under these hyper-operations and derive some results about factorisation, resulting in fundamental theorems analogous to the Fundamental Theorem of Arithmetic.

Natural hazards such as earthquakes take place around the world and when combined with humans create natural disasters. Earthquakes, a form of natural hazard, have, in recent years, caused damage and destruction in many rural areas due to the lack of sustainability in political, economic, social, physical and operational criteria. Thus, to overcome the damage caused by earthquakes in rural areas, an assessment of sustainability status seems necessary to plan and strengthen in relation to the status of sustainability indicators. Data collection was performed through field methods and questionnaires. To test the hypothesis, T statistical methods, correlation method and F-test were performed using SPSS software (V22.0, IBM Corporation, Armonk, NY, USA). The results of the study showed that villages were at a low and undesirable level for all aspects, except social index in terms of sustainability. Comparisons showed that there was a significant mean difference among villages in terms of sustainability. The multi-criteria decision-making analysis has been considered and applied to a ranking of villages in terms of sustainability against the hazard of earthquakes. Finally, in order to improve the sustainability indicators of villages, some strategies have been presented.

The exploration and understanding of sentiments and emotions through textual analysis are foundational to advancements in natural language processing (NLP). While sentiment analysis, which involves discerning the polarity of text, has been extensively explored, the nuanced detection of complex emotions within textual content is still an emerging area of study. In this paper, we introduce EmoLeverage, a model leveraging Transformer architecture and enhanced with a novel Fusion Adapter module. This model aims to deepen emotion detection capabilities by drawing insights from fundamental sentiment analysis tasks, focusing exclusively on textual data. EmoLeverage sets a new standard in the field by demonstrating that deep, nuanced understanding of emotions is achievable through text alone, challenging and surpassing existing methodologies in emotion identification on key datasets. This is a significant step forward, considering the complexity of human emotions and the subtleties involved in their expression through language. By concentrating on textual input, EmoLeverage offers a promising avenue for applications where audio or visual data is not available, highlighting the versatility and potential of text-based emotional analysis. Our approach illustrates the untapped potential of leveraging sophisticated NLP techniques to enhance the detection and comprehension of a wide array of emotions from text. The success of EmoLeverage in outperforming conventional models in emotion recognition tasks underscores the model's effectiveness in parsing and understanding the intricate expressions of human emotions through linguistic cues alone. This achievement paves the way for more accurate, efficient, and nuanced emotion detection tools, potentially transforming various sectors by providing deeper insights into human emotional states through text.

Natural springs, recognized as biodiversity hotspots and keystone ecosystems, exert positive ecological influences beyond their immediate extent, particularly in dryland environments. The water feeding these springs, largely governed by natural climatic conditions, is susceptible to anthropogenic impacts. The objective of this study was to determine the factors that cause fluctuations in water availability to springs of the hyper-arid Arava Valley (Israel/Jordan). Using the Standard Precipitation Index, we statistically classified the historical record of yearly rainfall for the past four decades into clusters of dry and wet sub-periods. We assessed changes in vegetation cover around the springs using the Landsat-derived Normalized Difference Vegetation Index (NDVI) for each sub-period. To assess the anthropogenic effects, we examined the correlations between vegetation cover, water extraction from the aquifer, and the status of adjacent agricultural plots that share a hydrological connection with the springs. Our findings reveal fluctuations between wet and dry sub-periods over the last four decades. We observed high responsiveness of vegetation cover around the springs to these fluctuating sub-periods. Of the 25 studied springs, 12 were directly influenced by anthropogenic factors — seven experienced a decline in vegetation which we attributed to water extraction from the aquifers, while vegetation increase in five springs was attributed to water seepage from agricultural areas upstream. In conclusion, addressing vital habitats such as natural springs in arid drylands requires a holistic approach that integrates long-term climatic, ecological, and anthropogenic observations.

Biological polymers such as tissue-engineered scaffolds and skin grafts with improved properties are very required in the pharmaceutical field. The wound-dressing gelatin/chitosan/moringa leaf extract (G-CH-M biopolymer) was synthesised and characterised. The blood compatibility, anti-inflammatory, antioxidant, and antibacterial activities of the biopolymer were investigated. Our results showed that S. aureus swarming motility was drastically affected. However, the biopolymer had no significant effect on the swarming motility of E. coli compared to the control. In addition, biopolymers showed high antibacterial capacities, especially against S. aureus. The biopolymer was also found to be effective in protecting plasmid DNA against oxidative stress. Furthermore, the biopolymer exhibits greatly suppressed hemolysis (lower than 2%), even at a high concentration of 50 mg/mL. These results indicated that this novel biopolymer formulation could be further developed for wound care and contamination prevention.

Flavonoids, a diverse group of polyphenolic compounds found abundantly in fruits, vegetables, and beverages like tea and wine, offer a plethora of health benefits. However, their potential interaction with drug metabolism, particularly through inhibition of the cytochrome P450 3A4 enzyme, the most versatile and abundant enzyme in the liver. CYP3A4 is responsible for metabolizing approximately 50% of clinically prescribed drugs across diverse therapeutic classes, so these interactions have raised concerns about potential adverse effects. This review delves into the scientific evidence surrounding flavonoid-mediated CYP3A4 inhibition, exploring the inhibitory potential of investigated flavonoids and future implications. Kusehnol I, chrysin, leachianone A and sophoraflavone G showed the largest inhibitory potentials and lowest IC50 values. While the clinical significance of flavonoid-mediated CYP3A4 inhibition in dietary contexts is generally considered low due to moderate intake and complex interactions, it poses a potential concern for individuals consuming high doses of flavonoid supplements or concurrently taking medications metabolized by CYP3A4. This can lead to increased drug exposure, potentially triggering adverse reactions or reduced efficacy.

Nudibranchs are marine gastropod mollusks that have been extensively studied for their poten-tial to yield new bioactive compounds. This review aims to share data about the chemical and biological activity of extracts and isolated compounds from nudibranchs. The systematic review followed the PRISMA (a systematic review protocol) and covers the studies from the literature published from January 1950 to September 2023, and various scientific databases, including Elsevier, web of science, ACS publications, Taylor and Francis, Wiley Online Library, MDPI, Springer and the Marine Pharmacology website. The results presented are based on the complete set of studies that met our inclusion criteria. Among the selected studies, chemical characteriza-tion was the focus in 79.6% of cases, while biological activity assessment accounted for 20.4%. Ten different types of biological activity assays were performed, with the most frequently studied activities being cytotoxicity (43.6%), antimycobacterial (43.6%), and ichthyotoxic (9.8%) effects. Most positive results obtained from these assays were related to antimycobacterial, cytotoxicity, and ichthyotoxic activities. Eight different types of metabolites were identified, with terpenes be-ing the most prevalent (78.5% of articles), followed by alkaloids (8.1%) and aromatic polyketides (4.7%). Gathering the existing evidence can aid in identifying research gaps and planning future investigations. Our findings indicate that certain nudibranch families, such as Cadlinidae, have received limited attention in terms of studies conducted, yet show promising results in terms of biological activities.

Pollen, a remarkably versatile natural compound collected by bees for its abundant source of proteins and nutrients, represents a rich reservoir of diverse bioactive compounds with noteworthy chemical and therapeutic potential. Its large range of biological effects has been known and exploited since ancient times. Today, there is an increased interest in finding natural compounds agaisnt oxidative stress, a factor that contributes to various diseases. Recent research has unraveled a multitude of biological activities associated with bee pollen, ranging from antioxidant, anti-inflammatory, antimicrobial, and antifungal properties to potential antiviral and anticancer applications. Comprehending the extensive repertoire of biological properties across various pollen sources remains a challenge. By investigating a spectrum of pollen types and their chemical composition, this review produces an updated analysis of the bioactive constituents and the therapeutic prospects they offer. The findings of this review emphasize the necessity for further exploration and standardization of diverse pollen sources and bioactive compounds that could contribute to the development of innovative therapies.

Our natural language approach concerns syntactic analysis using a dedicated Javascript library - wink-nlp - and semantic analysis based on Prolog programming language, facilitated by another Javascript library - tau-prolog - that allows defining logical programs, declaring rules and checking for goals inside Javascript language. Firstly, our program splits the original text into sentences, than into tokens and identifies each part of the sentence, dynamically maps entities into Prolog rules, then check the spelling accordingly to the Definite Clause Grammar (DCG) by querying the pre-defined program for initial goals (the sentence itself). Basically, we let the parser infer its own rules from the syntactic point of view, then check the grammar from a semantic perspective against the DCG inside the same work flow or pipeline of steps.The provided article combine the usage of wink-nlp and tau-prolog packages for natural language processing (NLP) and understanding (NLU).

Sri Lanka is a top producer of premium quality concentrated latex (CL), which becomes a base material for dipped rubber products such as gloves and condoms. The processing of CL is resource-intensive, requiring significant amounts of energy, fuel, water, and chemicals. This process leads to various environmental issues such as wastewater pollution, malodor, and greenhouse gas emissions. Several environmental life cycle assessments (ELCA) have been conducted at international and local levels to address the aforesaid issues. However, ELCAs encapsulating different environmental impact areas on CL processing in Sri Lanka are absent. The study revealed that electricity usage was the main hotspot of the environmental burden, significantly impacting Abiotic depletion (fossil fuels), Global warming potential, Ozone layer depletion, Photochemical oxidation, and Acidification. Heavy reliance on coal in the Sri Lankan power grid was identified as the root of this trend. The study suggested two viable options to mitigate the environmental impact: installing inverters to centrifuge separators and solar systems in the factories. The second option was deemed more effective, reducing Acidification, Photochemical oxidation, and Global Warming Potential by approximately 37%, 36%, and 28%, respectively. Relevant officials may immediately consider these improvement options and collaborate to pave the way to a sustainable natural rubber industry.

Background: The mental state is important in the health/illness process. Including stress, depression and anxiety. Nature immersion therapies have been implemented as a valid method for reducing these disorders. The aim will evaluate the effect of forest intervention therapies on levels of stress, depression, and anxiety. Methods: We searched for randomized clinical trials related to stress, anxiety and depression levels. Risk of bias was assessed according to the guidelines of the Cochrane Handbook for International Systematic Reviews. Results: We identified and synthesized eight publications. Two studies for cortisol showed significant differences between the pre-test and post-test levels in the intervention groups. One study including Natural killer cells, that reported a significant decrease in the intervention group compared to the control group. Two studies used the Stress Response Inventory, indicated a significant reduction in stress levels in the intervention group compared to the control group. Regarding Restorative Out-come Scale, no significant differences were found. Three studies were selected for the evaluation of anxiety and depression levels according to the Positive and Negative Affect Schedule. Four studies used The Profile of Mood States scale; no significant difference was observed. Conclusions: There is limited evidence to recommend forest bathing for reducing these disorders.

This paper aims to analyze the role of natural heritage cataloging when criminal events challenge natural history museums. This study investigates the rhino horn thefts in Italian natural history museums from 2011 to 2015 to provide a comprehensive framework for these robberies while highlighting the weaknesses and strengths to prevent thefts and safeguard both rhino horns and natural history collections from future targeting

In recent years, there has been a growing interest in utilizing natural antioxidants as alternatives to synthetic additives in food products. Apples and apple by-products have gained attention as a potential source of natural antioxidants due to their rich phenolic content. This review aims to provide a comprehensive overview of the antioxidant properties of apples and apple by-products, their extraction techniques as well as potential applications in food products because of their an-tioxidant or nutritional properties. The findings here reported highlights the proper utilization of apples and their by-products in food to reduce the detrimental effect on the environment and provide a positive impact on the economy.

Global pollution from fossil plastics is one of the top environmental threats of our times. The end-of-life scenarios of fossil plastic, including recycling, incineration and disposal result in mi-croplastic formation, elevated atmospheric CO2 levels, and littering of terrestrial and aquatic environments by plastic waste. Currently thought-out regulations centered around banning plastics production and use and only recycling on a regional, national and global level are impeding efforts to rapidly replace fossil plastics through the use of natural alternatives. In particular, this review demonstrates how mi-crobial polyhydroxyalkanoates (PHA), a class of intrinsically natural polymers, can easily remedy for the fossil and persistent plastic dilemma. PHA are bio-based, bio-synthesized, bio-compatible, bio-degradable, and home- and industrially compostable. Therefore, they are one of the perfect replacements for our fossil plastics pollution dilemma, providing us with the benefits of fossil plastics and meeting all requirements of a truly circular economy. PHA biopolyesters are natural and green materials in all aspects of their life cycle. The review elaborates in detail how PHA’s production, consumption, and end-of-life profile are perfectly embedded in the current topical 12 Principles of Green Chemistry, which constitute the basis for sustainable product manufacturing. It is shown that it is time for a paradigm shift in plastics manufacturing, use and disposal. Hu-mankind needs alternatives to fossil plastics, which, as recalcitrant xenobiotics, contribute to the increasing deterioration of our planet. Natural PHA biopolyesters represent that paradigm shift!

This paper explores how Artificial Intelligence (AI) can help reduce the communication gap and streamline the distribution of resources. It uses an exploratory case study to examine the current state of the communication gap and the potential opportunities AI can bring to close it. The paper will discuss the current state of the communication gap, the current methods of resource distribution, and the potential opportunities AI can bring to reduce the communication gap. It will also explore the potential implications of AI on resource distribution and the communication gap. The paper will review the existing literature on the communication gap, resource distribution, and AI. It will then focus on the opportunities AI can bring to the communication gap. It will analyze the use of machine learning, natural language processing, and other AI-based technologies to reduce the communication gap. It will also examine the potential implications of AI on resource distribution, such as developing more efficient distribution channels, increasing transparency, and reducing costs. Finally, the paper will analyze the impact of AI on the communication gap and resource distribution. It will discuss the potential challenges and benefits of using AI, such as the need for data security, the potential for bias, and the limitations of AI technology. The paper will also explore the potential for AI to improve the distribution of resources and reduce the communication gap. Overall, this paper provides an exploratory case study on the opportunities of how AI can contribute to reducing the communication gap and streamlining the distribution of resources. It discusses the potential implications of AI on the communication gap and resource distribution, and it reviews the potential challenges and benefits of using AI. The paper provides an in-depth analysis of the potential opportunities of using AI to reduce the communication gap and streamline the distribution of resources.

Wound healing is a process through which skin maintains itself. Once a wound occurs, the inflammatory and proliferative stages are instigated in reaction to injury. It is established that wound restorative comprises four stages including haemostasis, inflammation, proliferation, and remodeling. The amelioration of wound healing is very challenging as tumors can develop at the site of chronic injury. There are numerous plants, plant extracts and plant based natural products were widely used by tribal communities from ancient times for the treatment of cuts, burns, scars, burns and wounds. The therapeutic potential of these plants is recognized due to the presence of phytomolecules such as phenolic compounds, flavonoids, triterpenoids, saponins, tannins, alkaloids and glycosides. The plant used for the treatments of wound healing includes Achillea millefolium, Andrographis paniculata, Boswellia sacra, Calendula officinalis, Crocus sativus, Curcuma longa, Ehretia laevis, Ehretia microphylla, Glycyrrhiza glabra, Malva sylvestris, Rosmarinus officinalis and Salvia officinalis. This assemblage comprises the structures of phytomolecules isolated from the different extracts of these plants, mechanistic insights and important key findings responsible for wound healing. The mechanistic insights involved in wound healing are similar to cytotoxic, anti-inflammatory and antioxidant agents such as ROS generation, DNA fragmentation and western blotting. This review article is an effort to bridge the gaps in the prevailing literature and thus offers gigantic scope for researchers and academicians betrothed in validation of the customary claims and development of safer and efficient and worldwide recognized natural potential candidates as drugs for healing of wounds, burns and cuts.