Damage detection based on modal parameter changes becomes popular in the last decades. Nowadays are available robust and reliable mathematical relations to predict the natural frequency changes if damage parameters are known. Using these relations, it is possible to create databases containing a large variety of damage scenarios. Damage can be thus assessed by applying an inverse method. The problem is the complexity of the database, especially for structures with more cracks. In this paper, we propose two machine learning methods, namely the random forest (RF) and the artificial neural network (ANN) as search tools. The databases we developed contain damage scenarios for a prismatic cantilever beam with one crack and ideal and non-ideal boundary conditions. The crack assessment is made in two steps. First, a coarse damage location is found from the networks trained for scenarios comprising the whole beam. Afterward, the assessment is made involving a particular network trained for the segment of the beam on which the crack is previously found. Using the two machine learning methods, we succeed to estimate the crack location and severity with high accuracy for both simulation and laboratory experiments. Regarding the location of the crack, which is the main goal of the practitioners, the errors are less than 0.6%. Based on these achievements, we concluded that the damage assessment we propose, in conjunction with the machine learning methods, is robust and reliable.

The experimental verification for the computational method sometimes varies due to numerous factors such as the manufacturing process and the materials' property change due to environmental aspects. In this work, we performed verification of experimental and computational evaluation of a hybrid composite moderate thick plate. The experiment was performed with simplistic approaches and without the advanced tools of preparing composite materials. This is due to the fact that most of the students in many developing countries around the world cannot have access to such equipment. As such, in this research, we are presenting cheap and easy preparation methods, with some details, for even equipment calibration and some tricks to attain a reliable composite structure for educational purposes. Moreover, the software and solvers used in this study are freely provided by the supplier for educational purposes. This study examined two methods for producing carbon and glass/polyester composite plates and discussed which one was best based on mechanical properties for different volume fractions, random stacking sequences, and ply angles (using OCTAVE's random estimation program). It also determined the three natural frequencies experimentally and with the aid of ANSYS. Less than 6% separated the experimentally determined natural frequencies from the calculated results.

Tree pruning is a management tool in agroforestry systems for reducing shade, enhancing nutrient cycling or providing fodder. However, little information is available on the effect of pruning management on plant growth, non-structural carbohydrate (NSC) levels in the roots and N2 fixation of Sesbania sesban. A glasshouse experiment was conducted to assess the effect of pruning frequency on biomass production, NSC levels and N2 fixation of Sesbania sesban. Treatments included pruning at (i) 3 months only, (ii) 3 and 6 months, and (iii) 3, 6 and 9 months, with each pruning removing shoot biomass above 50% of the initial height. Increased pruning frequency decreased above- and belowground dry matter (DM), and root NSC levels. The decrease in aboveground DM correlated with reduced levels of starch, sugar and total non-structural carbohydrates. Pruning more frequently significantly decreased nodulation, the percentage N derived from the atmosphere and N2 fixed. It could be concluded that more frequent prunings decreases biomass and NSC levels which could reduce the regeneration capacity of trees since they rely on NSCs to regrow. The suppressive effects of increased pruning frequency on N2 fixation could decrease the desirable benefits of legume trees on soil N fertility improvement.

Micro satellites must survive severe mechanical conditions during their launch phase. Therefore, structural design requirements are imposed on the micro satellites. These requirements are the strength requirement and the stiffness requirement. Usually, the structural design of a micro satellite is performed using the internal stress analysis and the natural frequency analysis, which are based on a Finite Element Method (FEM). The validity of this structural design is evaluated through vibration tests. In an early stage of development, which has a FEM model of a satellite in the process of creation, presumption of the natural frequency of a satellite may be difficult. In this study, a simple method for determining the longitudinal and lateral minimum natural frequencies of the micro satellites during the ascent phase was clarified. The structure of the micro satellites used in this research is made of aluminum alloy, and they have a monocoque structure. The Young's modulus and moment of inertia of area used to calculate the minimum natural frequencies were determined using the area ratio of the monocoque structure to the entire satellite. When this method is used, the estimated minimum natural frequencies of the longitudinal direction and the lateral direction are in agreement with the measured values acquired by the vibration tests. In order to shorten the process of micro satellites development, this paper describes a practical estimation method of the minimum natural frequency for micro satellites.

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}

Concern over microplastics (MPs) in the environment is rising. Microplastics are generally known to exist in aquatic settings, but less is known about their occurrence in soil ecosystems. When plastic waste builds up in agricultural areas, it can have a negative impact on the environment and food sources, as well as have an indirect effect on all trophic levels of the food chain. This paper addresses the relationship between microplastics and the management of plastic waste, which contributes to their accumulation, and it describes the sources and primarily the movement processes of microplastics in agricultural soils as a result of natural events. Evaluating the impact of weather on coastal microplastic contamination is critical, as extreme weather events have become more frequent in recent years. This study sheds light on how weather patterns affect the dispersion of plastic waste in terrestrial habitats, including the impacts of seasonality and extreme weather. According to our data, typhoons, monsoons, rainfall, and floods all contribute significantly more microplastics to the surface sediment through surface runoff and wind transport, particle redistribution caused by agitated waves, and fragmentation under intense abrasion forces. Severe weather conditions have the potential to disperse larger and more varied kinds of microplastics.

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.

The advent of Large Language Models (LLMs) has significantly advanced natural language processing (NLP), yet their application to historical texts remains challenging due to archaic language, distinct terminologies, and varied contextual backgrounds. This study introduces Historical Domain Large Language Models, designed to bridge this gap by adapting LLMs for better comprehension and processing of historical data. Our approach leverages context-aware and task-specific prompts to enhance model performance in tasks such as named entity recognition (NER), sentiment analysis, and information extraction within historical contexts. We propose an iterative refinement process to improve prompt quality and model outputs continuously. Instruction tuning on newly collected evaluation data ensures our methods' efficacy, avoiding biases from previously used datasets. Evaluations using GPT-4 demonstrate significant improvements in handling historical texts, underscoring the potential of our approach to unlock profound insights from historical data. This work highlights the importance of tailored LLM adaptations for specialized domains, offering a robust framework for future research in historical NLP.

RNA sequencing techniques, like bulk RNA-seq and Single Cell (sc) RNA-seq, are critical tools for the biologist looking to analyze the genetic activity/transcriptome of a tissue or cell during an experimental procedure. Platforms like Illumina's next-generation sequencing (NGS) are used to produce the raw data for this experimental procedure. This raw FASTQ data must then be prepared via a complex series of data manipulations by bioinformaticians. This process currently takes place on an unwieldy textual user interface like a terminal/command line that requires the user to install and import multiple program packages, preventing the untrained biologist from initiating data analysis. Open-source platforms like Galaxy have produced a more user-friendly pipeline, yet the visual interface remains cluttered and highly technical, remaining uninviting for the natural scientist. To address this, SeqMate is a user-friendly tool that allows for one-click analytics by utilizing the power of a large language model (LLM) to automate both data preparation and analysis (differential expression, trajectory analysis, etc). Furthermore, by utilizing the power of generative AI, SeqMate is also capable of analyzing such findings and producing written reports of upregulated/downregulated/user-prompted genes with sources cited from known repositories like PubMed, PDB, and Uniprot.

Phytochemicals have a long and successful history in drug discovery. With recent advancements in analytical techniques and methodologies, discovering bioactive leads from natural compounds has become easier. Computational techniques like molecular docking, QSAR modelling and machine learning, and network pharmacology are among the most promising new tools that allow researchers to make predictions concerning natural products' potential targets, thereby guiding experimental validation efforts. Additionally, approaches like LC-MS or LC-NMR speed up compound identification by streamlining analytical processes. Integrating structural and computational biology aids in lead identification, thus providing invaluable information to understand how phytochemicals interact with potential targets in the body. Emerging computational approach is machine learning involving QSAR modelling and deep neural networks that interrelate phytochemical properties with diverse physiological activities such as antimicrobial or anticancer effects.

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.

The escalating global population and subsequent demand for agricultural products have led to a surge in agricultural waste generation, posing significant disposal challenges. Conventional disposal methods such as burning and dumping not only harm the environment but also jeopardize human health and safety. Recognizing the urgent need for sustainable waste management, researchers have increasingly focused on repurposing agricultural plant waste as a valuable resource. This paper presents a comprehensive review of the potential of agricultural plant waste in wealth creation and sustainable development. It highlights the detrimental impacts of current disposal methods and emphasizes the necessity for alternative approaches. By analyzing the physical, mechanical, and chemical properties of plant fibers, particularly cellulose, hemicellulose, and lignin, this review underscores their suitability for diverse applications. Moreover, it explores the emerging trend of utilizing pineapple leaf fiber, a sustainable and lightweight material, in structural applications such as UAV construction. With its exceptional mechanical properties and biodegradability, pineapple leaf fiber holds promise as a viable alternative to traditional materials, contributing to a more sustainable future. In conclusion, this review advocates for a paradigm shift towards embracing agricultural plant waste as a valuable asset for economic prosperity and environmental sustainability. It underscores the importance of continued research and technological advancements to unlock the full potential of agricultural waste in fostering a circular economy and driving sustainable development globally.

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.

Postoperative ileus (POI) is a delay in gastrointestinal transit following surgery that leads to various complications. There is limited understanding of its effective treatment options. CKD-495 and eupacidin are natural products licensed for treating mucosal lesions in acute and chronic gastritis; however, little is known about their effects on intestinal permeability. This study evaluated the effects of CKD-495, eupacidin, and its components (eupatilin and cinnamic acid) on intestinal per-meability in an animal model of POI. Guinea pigs underwent surgical procedures and were ran-domly assigned to different treatment groups. Drugs were administered orally prior to surgery. Intestinal permeability, leukocyte count, and the expression of calprotectin and tight junction proteins were measured in the harvested ileum tissue. The intestinal permeability and leukocyte count were higher in the POI group than in the control group. Pre-administration of CKD-495, cinnamic acid, eupacidin, and eupatilin effectively prevented these changes in the POI model. No significant differences were observed in the expression of tight junction proteins. CKD-495, cinnamic acid, eupacidin, and eupatilin exerted protective effects against increased intestinal permeability and inflammation in an animal model of POI. These natural products have potential as therapeutic options for the treatment of POI.

Disaster issue consultation is crucial for effective emergency management, requiring timely and accurate information to support decision-making processes. This paper explores the integration of large language models (LLMs) with advanced prompt engineering to enhance the performance of AI systems in disaster management. We propose a novel multi-round prompt engineering approach that guides LLMs to iteratively refine their responses based on evolving disaster scenarios. Our method leverages diverse disaster-related datasets and employs a combination of accuracy metrics and a customized GPT-4 scoring system for evaluation. We conduct comprehensive experiments across multiple LLM platforms, including Qwen2, ChatGPT, Claude, and GPT-4, comparing our approach with baseline models and the Tree of Thoughts (ToT) method. The results demonstrate significant improvements in both accuracy and response quality, highlighting the effectiveness of our method in providing contextually relevant and actionable insights. Additional analysis confirms the robustness and generalizability of our approach across various disaster scenarios, establishing it as a valuable tool for enhancing disaster management practices.

The interest in obtaining new composite materials with diverse properties has created a growing interest in natural fi-bers as reinforcement in polymers, which has generated an impact in sectors such as automotive and construction, dis-placing synthetic fibers. Properties such as low density, low cost, and rene.wable make these fibers an attractive alterna-tive to reduce environmental impact. Their application in polymers, such as polypropylene, polyethylene, and epoxy resins, has allowed the modification of polymeric composite materials' mechanical and physical properties (NFRP). This review focuses on investigating the status of natural fiber-reinforced polymers. It analyzes their worldwide demand, chemical characteristics, physical and mechanical properties, and various applications in different sectors and indus-tries. In addition, the advantages and disadvantages of NFRPs are highlighted, highlighting their potential for new ap-plications.

Human hair is characterized by significant diversity, which is dictated by the individual genetic makeup. It is estimated that up to 70% of the world’s population has textured hair, with a signif-icant proportion also living in areas with high exposure to ultraviolet radiation (UVR). This highlights the unique requirements for textured hair care products that can protect hair shafts from the harmful effects of the environment whilst enhancing the natural features of the fibre. In this study, we investigated the UV-protective effects of hair conditioner formulated from active ingredients, mangiferin, ferulic acid and naringin. The analysis of UV-irradiated straight and textured hair was performed by measurements of hair thickness and darkness, the uptake of the fluorescent dye, spectral absorbance of keratin extracts, ATR-FTIR and SEM. We found that tex-tured hair was more sensitive to UVR-induced changes than straight hair, with the primary mo-lecular targets represented by chemical groups in keratins. Pre-treatment of the hair with condi-tioner had protective effects against structural damage and oxidative stress caused by UVR. These data indicate the importance of the personalized approach to hair research and the formu-lation of hair care products tailored to the individual demands of textured hair types.

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.

Sentiment analysis has emerged as a vital application of Natural Language Processing (NLP), enabling the extraction of subjective information from textual data. This study conducts a comparative analysis of various machine learning algorithms employed in sentiment analysis, including traditional models such as Naïve Bayes, Support Vector Machines (SVM), and Decision Trees, as well as contemporary techniques such as Random Forest, Gradient Boosting, and deep learning approaches like Recurrent Neural Networks (RNN) and Long Short-Term Memory (LSTM) networks. Using a comprehensive dataset sourced from social media platforms and product reviews, we evaluate the performance of these algorithms based on accuracy, precision, recall, and F1-score. Our findings highlight the strengths and weaknesses of each algorithm in handling sentiment classification tasks, emphasizing the influence of feature extraction techniques, such as Bag of Words and Word Embeddings, on model performance. The results indicate that while deep learning models generally outperform traditional algorithms, the choice of algorithm should be tailored to the specific context and requirements of the analysis. This study contributes to the ongoing discourse on the efficacy of machine learning methods in NLP, offering insights that can guide researchers and practitioners in selecting appropriate algorithms for sentiment analysis tasks.

The interest in natural remedies in modern medicine has revealed potent disease-fighting agents, such asCUR(CUR) and nicotinamide (NIC), known for their antioxidant, anti-inflammatory, and immune-boosting properties. This study explores the antibacterial and antimelanoma potential of a CUR-NIC combination and its liposomal formulation. The antibacterial efficacy was assessed against skin infection-causing bacteria, Staphylococcus aureus, while cytotoxicity and migration assays were conducted on the melanoma B16 cancer cell line. The CUR-NIC combination (1:1) demonstrated an inhibition zone of 18±0.8 mm against S. aureus and a MIC of 31.25 µg/ml, outperforming CUR alone. Whereas Lip-CUR-NIC showed an inhibition zone of 12±0.8 mm against S. aureus and a MIC of 37.5 µg/ml. Notably, the cytotoxicity assay revealed that the CUR-NIC duo synergistically inhibited melanoma cell proliferation (CI<1). Liposomal preparations further enhanced this effect, with Lip-CUR-NIC showing a remarkably low IC50 of 8.5 ± 0.3 µM compared to CUR (IC50= 9.8 ± 2.2 µM) and NIC (IC50=135.95 ± 10.2 µM). These findings highlight the synergistic potential of CUR and NIC, especially in their liposomal form, offering a promising strategy for more effective cancer treatment.

Sour rot, caused by Geotrichum citri-aurantii, is a significant post-harvest disease in citrus, resulting in economic losses due to the lack of effective fungicides. This study investigates the antifungal activity of essential oils from Late IAC 585 willowleaf and Pera IAC sweet orange in controlling sour rot in Tahiti acid lime fruits. Essential oils were extracted via hydrodistillation and tested in vitro and in vivo. In vitro, assays evaluated mycelial growth inhibition at concentrations ranging from 2 to 32 µl ml⁻¹. In vivo trials involved preventive and curative treatments on artificially inoculated fruits stored at 25°C ± 2. Results showed that Pera IAC sweet orange oil, at 32 µl ml⁻¹, reduced disease severity by 96% in curative treatments. In contrast, Late mandarin oil demonstrated moderate inhibition (44%) at the highest concentration in vitro. The oils did not affect key fruit quality parameters such as juice yield and total soluble solids. These findings suggest that citrus essential oils could be natural alternatives to synthetic fungicides for post-harvest sour rot management, combining effectiveness with maintaining fruit quality. Further research is needed to explore the mechanisms of action and commercial application potential.

In the digital age, social media platforms have become a significant source of both information and misinformation, presenting challenges and opportunities for threat intelligence. Natural Language Processing (NLP) has emerged as a powerful tool for extracting actionable insights from the vast amounts of unstructured text generated on these platforms. This paper explores the application of NLP techniques to enhance social media threat intelligence, focusing on methodologies for detecting and analyzing threats such as disinformation, cyberbullying, and extremist content. We examine various NLP approaches, including sentiment analysis, topic modeling, and entity recognition, and their effectiveness in identifying and mitigating potential risks. The paper also addresses the challenges associated with processing social media data, such as dealing with slang, context, and multilingual content. By leveraging NLP, organizations can improve their ability to monitor and respond to emerging threats in real-time, ultimately enhancing their overall security posture. The findings suggest that while NLP offers significant benefits, it must be complemented by human expertise and ethical considerations to ensure accurate and responsible threat assessment.

Limiting training data in low-resource languages is a barrier to Natural Language Processing (NLP). Regardless, languages with thousands of users worldwide require improved NLP capabilities. The Table-to-Text task—typically develops natural language descriptions using data tables, tests model reasoning abilities—although it is especially tough in multi-language settings. System output frequently lack credit to their underlying data. Intermediate planning strategies—which include Question-Answer (QA) blueprints—improving summarization tasks by presenting related QA pair prior to verbalization. Therefore, this study analyses whether QA blueprints improve multilingual Table-to-Text output attribution. An enlarged multilingual dataset, encompassing African languages, contains QA blueprints that were produced and filtered heuristically. This dataset is used to fine-tune sequence-to-sequence model (transformers)—both with and without blueprints. Two configurations are evaluated—English reference blueprints with language target verbalization and modified blueprints. These results show that English-only models benefit from blueprints, whereas multi language models cannot. Errors in machine-translating blueprints provide challenges, as do models that rely on their generated blueprints.

Three filamentous freshwater cyanobacterial strains were grown at high light intensity to produce lipidic dyes composed of xanthophylls, carotenes and chlorophyll a. The properties of the pigments were evaluated as suitable natural compounds to be applied in dye-sensitized solar cells (DSSC). The assembled DSSC were characterized using the density current vs. potential profiles and electrochemical impedance spectroscopy. With an efficiency of 0.127 %, our results are higher than those previously reported using similar structured compounds from natural sources as algae and cyanobacteria, among others. The best efficiencies were probably related to the strains with high content in pigments probably related to myxoxanthophyll-like derivates and aphanizophyll, carotenoids with many hydroxyl groups able to interact with the semiconductor surface. The stability of the bonding between the dyes and the titanium oxide of the photoelectrode is crucial to ensuring acceptable performance of the DSSC, which was successfully achieved in our experiments with carotenoids with many hydroxyl groups. Our results point to cyanobacterial pigments as a promising source of natural dyes for use in solar cells.

The objective of the described activity is to develop technologies or proposals that can be implemented within the cycle to enhance the relationship between climate change, water, energy, and food. The focus is on analyzing natural treatment systems for wastewater (NTSW) within the context of Macaronesia, considering factors such as life cycle assessment (LCA), carbon footprint, impacts, and mitigation capacity. The analysis of real case data from the Canary Islands and Cape Verde will inform the development of appropriate technologies tailored to different areas and scales within Macaronesia. This work includes a comprehensive life cycle analysis of the Santa Catarina (Cape Verde) NTSW. This analysis encompasses: a) Inventory analysis of the construction phase: This involves the assessment of inputs and outputs associated with the construction of the NTSW, including materials, energy consumption, transportation, and waste generation. The maintenance and operation phase is then evaluated, with a focus on the ongoing maintenance and operation activities required for the NTSW, including energy consumption, water usage, chemical inputs (if any), labour, and equipment maintenance. Finally, the impacts of the NTSW are evaluated. The environmental, social, and economic impacts generated by the NTSW are assessed. This includes an analysis of factors such as carbon emissions, water usage, land use, ecosystem impacts, human health effects, and economic costs. By conducting a comprehensive analysis of the Santa Catarina NTSW, the document aims to provide insights into the environmental performance and sustainability of the system. This information can then be used to identify areas for improvement, develop mitigation strategies, and inform decision-making processes regarding wastewater treatment technologies in Macaronesia. Furthermore, lessons learned from real case studies in the Canary Islands and Cape Verde can be applied to similar regions within the Macaronesia archipelago.

Word Sense Disambiguation (WSD) stands as a persistent challenge within the Natural Language Processing (NLP) community. While various NLP packages exist, the Lesk algorithm in the NLTK library, a widely recognized tool, demonstrates suboptimal accuracy. Conversely, the application of deep neural networks offers heightened classification precision, yet their practical utility is constrained by demanding memory requirements. This research paper introduces an innovative method addressing WSD challenges by optimizing memory usage without compromising state-of-the-art accuracy. The presented methodology facilitates the development of WSD system that seamlessly integrates into NLP tasks, resembling the functionality offered by the NLTK library. Furthermore, this paper advocates treating the BERT language model as a gold standard, proposing modifications to manually annotated datasets and semantic dictionaries such as WordNet to enhance WSD accuracy. The empirical validation through a series of experiments establishes the effectiveness of the proposed method, achieving state-of-the-art performance across multiple WSD datasets. This contribution represents advancement in mitigating the challenges associated with WSD, offering a practical solution for integration into NLP applications.

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.

Soil erosion is one of the biggest problems in the agricultural sector that can affect ecosystems and human societies. A field of 50 slope was selected to study the runoff, soil and nutrients’ loss as well as crop productivity in different treatments (conventional tillage (CT) vs. no-tillage (NT), plant vs. no plant cover, contour cultivation (CC) vs. perpendicular to the contour cultivation, (PC) under natural rainfall. The experiment was conducted in central Greece in two cultivation periods. In autumn, the field was cultivated with intercropping Triticosecale and Pisum sativum and in spring with Sunflower. The total rainfall was 141.4 mm in the 1st year and 311 mm in the 2nd. We found that runoff in the treatment of no tillage with contour cultivation was 85% lower in both years compared to the no tillage-no plant control. Therefore, the contour cultivation-no tillage treatment had a positively effect in decreasing phosphorus and potassium concentrations lost from soil: indeed, there was a decrease by 55% and 62% in P and K, respectively, in the NT compared to the CC treatments. We conclude that the NT-CC treatment with plant cover was the most effective in reducing water runoff, soil nutrients’ loss and increasing yield.

Retweet prediction is an important task related to different problems such as information spreading analysis, the automatic detection of fake news, social media monitoring, etc. In this study we explore the possibilities of retweet prediction based on heterogeneous data sources. In order to classify the tweet according to the amount of retweets, we combine features extracted from the multilayer network and the text. More specifically, we introduce a multilayer framework that proposes the multilayer network representation of Twitter. This formalism captures different users' actions and complex relationships as well as other key properties of communication on Twitter. We select a set of local network measures from each layer and construct a set of multilayer network features. In addition, we adopt a BERT-based language model, namely Cro-CoV-cseBERT to capture high-level semantics and structure of tweets as a set of text features. Then, we train six machine learning (ML) algorithms: random forest, multilayer perceptron, light gradient boosting machine, category embedding model, neural oblivious decision ensembles and attentive interpretable tabular learning model in the task of retweet prediction. We compare the performance of all six algorithms in three different setups (i) using only text features, (ii) using only multilayer network features and (iii) using both sets of features. We evaluate all setups in terms of standard evaluation measures i.e. precision, recall, F1-score and accuracy. For this task, we first prepare and use an empirical dataset of 199,431 tweets in the Croatian language posted during the period between January 1, 2020 and May 31, 2021. Our results indicate that by integrating multilayer network features with text features the prediction model would perform better than using just one set of features.

Myxobacteria generate natural products with unique chemical structures, which not only feature remarkable biological functions but also demonstrate unprecedented biosynthetic assembly strategies. The stigmatellins have been previously described as potent inhibitors of the mitochondrial and photosynthetic respiratory chain and originate from an unusual polyketide synthase assembly line. While previous biosynthetic investigations were focused on the formation of the 5,7-dimethoxy-8-hydroxychromone ring, side chain decoration of the hydrophobic alkenyl chain in position 2 was investigated less thoroughly. We report here the full structure elucidation as well as cytotoxic and antimicrobial activities of three new stigmatellins isolated from the myxobacterium Vitiosangium cumulatum MCy10943T with side chain decorations distinct from previously characterized members of this compound family. These findings provide further implications considering the side chain decoration of these aromatic myxobacterial polyketides and their underlying biosynthesis.

Emerging evidence suggests that sustained diabetes-associated factors such as inflammation, hyperinsulinemia, and hyperglycemia are major contributors to aberrant cell proliferation and subsequent neoplastic transformation. Epidemiological studies have also highlighted that diabetes promoting a sedentary lifestyle, with or without the direct involvement of insulin, is frequently linked to cancer. However, our knowledge regarding the molecular mechanisms that correlate hyperglycemia to oncogenic transformations remains limited. In this regard, a recent study has proved that hyperglycemia inactivates AMPK, which results in the destabilization of the TET2 and its tumour-suppressive role ultimately predisposing diabetes mellitus patients to cancer. To the management of hyperglycemia associated with oncogenesis, we need to explore a reverse pharmacology-based ethnopharmacological approach. Botanical-derived natural products are structurally and functionally more diverse with fewer or no side effects on humans. The present review discusses the molecular link between hyperglycemia and cancer progression with the effect of natural products as therapeutic agents on the hyperglycemia-cancer associated signalling pathway.

The purpose of this study is to investigate the perceived benefits of community gardening. A garden colony is a collection of adjacent plots of land primarily for the purpose of gardening. The methodology of this research consists of observations, interviews, questionnaires, and focus group. As a result of this research we found that gardening is a natural and safe way to meet the lifelong demands for a healthy life. We found specific mental and physical benefits as a result of this community gardening. We encourage communities to allow people to buy plots of land away from their homes to promote this healthy activity. The garden colony provides a place for individual renewal and restoration; this urban oasis offers a way to maintain and promote lifelong healthy living, and they are an important contribution to one’s life by adding a sense of meaning and purpose.

This research was a study of the effect of addition linear alkylbenzene sulfonates (LAS), NaHCO3, and NaCl and pasteurization on the preservation of natural rubber (NR). The samples were collected from rubber plantations of Chiang Rai province which were added with three surfactants in samples already. Physical and chemical properties were evaluated using pH, deterioration, viscosity, color, and odor. Then, the samples were stored at 28-30°C periods times of 0, 15, 30, 45, and 60 days. The experiment found that the color, viscosity, odor, and texture of NR samples were not spoiled after being preserved for 30 days but after 45 and 60 days found some coagulation of NR. In the case of non-preserved NR was found that spoiled NR in every period time range of 15-60 days. The pH testing found that increasing period times affect decreased pH value and increased viscosity due to salt of sulfate, carbonate, chloride, and thermal treatment of pasteurization which kill microorganisms and evaporated water. It concluded that the reagents were the process of cosurfactants with heat and frozen for increased effectiveness of anti-acid-producing bacteria and can use as short and long-term preservation of NR under the planting area condition of Thailand.

Documenting cultural heritage by using artificial intelligence (AI) is crucial for preserving the memory of the past and a key point for future knowledge. However, modern AI technologies make use of statistical learners that lead to self-empiricist logic, which, unlike human minds, use learned non-symbolic representations. Nevertheless, it seems that it is not the right way to progress in AI. If we want to rely on AI for these tasks, it is essential to understand what lies behind these models. Among the ways to discover AI there are the senses and the intellect. We could consider AI as an intelligence. Intelligence has an essence, but we do not know whether it can be considered “something” or “someone”. Important issues in the analysis of AI concern the structure of symbols -operations with which the intellectual solution is carried out- and the search for strategic reference points, aspiring to create models with human-like intelligence. For many years, humans, seeing language as innate, have carried out symbolic theories. Everything seems to have skipped with the advent of Machine Learning. In this paper, after a long analysis of history, the rule-based and the learning-based vision, we propose KERMIT as a unit of investigation for a possible meeting point between the different learning theories. Finally, we propose a new vision of knowledge in AI models based on a combination of rules, learning and human knowledge.

Natural products endowed of biological activity represent a primary source of commodities ranging from nutrition to therapeutic agents, as well as cosmetic tools, and recreational principles. These natural means have been used by mankind since centuries if not millennia. They are commonly used all over the world and socio-economical contexts but are particularly attractive in disadvantaged area or economically emerging situations all over the world. This is very likely due to the relatively easy recovery of these bioactive principles from the environment, to the low if any cost as well as ease of administration and to the general popular compliance concerning their consumption/ingestion. In this concise review, we focus on some popular bioactive principles of botanical origin which find a wide use in the Madagascan populations. But, due to space limitations only some most common and largely diffused principles in this country are considered. Finally, a possible nanotechnological administration is discussed in the case where a potential therapeutic usage is envisaged.

Many inflammatory mechanisms are involved in the pathophysiology of COVID-19 infection. COVID-19 inhibits IFN antiviral responses, so we should expect an out-of-control viral replication. “Cytokine storms” occur due to the over-production of pro-inflammatory cytokines after an influx of neutrophils and monocytes/macrophages and may be responsible for the immunopathology of the lung involvement. Several cascades have been reported in the activation process of NF-κB. In this paper, to find new therapeutic options for COVID-19 infection, we reviewed some natural products that could potentially inhibit the NF-κB pathway. We found that sevoflurane, quercetin, resveratrol, curcumin, KIOM-C, bergenin, garcinia kola, shenfu, piperlongumine, wogonin, oroxylin, plantamajoside, naringin, ginseng, kaempferol, allium sativum L, illicium henryi, isoliquiritigenin, lianhua qingwen, magnoflorine, and ma Huang Tang might be effective in inhibiting the NF-KB pathway. These natural products could be helpful in the control of COVID-19 infections. However, larger clinical trials are needed to ascertain the efficacy of these products fully.

The use of natural carotenoids as food colorants is an important trend of innovation in the industry due to their low toxicity, their potential as bio-functional ingredients, and the increasing demand for natural and organic foods. Despite these benefits, their inclusion in food matrices presents multiple challenges related to their low stability and low water solubility. The present review covers the main concepts and background of carotenoid inclusion complex formation in cyclodextrins as a strategy for their stabilization, and subsequent inclusion in food products as color additives. The review includes the key aspects of the molecular and physicochemical properties of cyclodextrins as complexing agents, and a detailed review of the published evidence on complex formation with natural carotenoids from different sources in cyclodextrins, comparing complex formation methodologies, recovery, inclusion efficiency, and instrumental characterization techniques. Moreover, process flow diagrams (PFD), based on the most promising carotenoid-cyclodextrin complex formation methodologies, are proposed, and discussed as a potential tool for their future scale-up. This review shows that the inclusion of carotenoids in complexes with cyclodextrins constitutes a promising technology for the stabilization of these pigments, with possible advantages in terms of their stability in food matrices.

Deep neural networks are hegemonic approaches to many machine learning areas, including natural language processing (NLP). Thanks to the availability of large corpora collections and the capability of deep architectures to shape internal language mechanisms in self-supervised learning processes (also known as "pre-training"), versatile and performing models are released continuously for every new network design. But these networks, somehow, learn a probability distribution of words and relations across the training collection used, inheriting the potential flaws, inconsistencies and biases contained in such a collection. As pre-trained models have found to be very useful approaches to transfer learning, dealing with bias has become a relevant issue in this new scenario. We introduce bias in a formal way and explore how it has been treated in several networks, in terms of detection and correction. Also, available resources are identified and a strategy to deal with bias in deep NLP is proposed.

Oxidative stress is an elevated intracellular level of free oxygen radicals that cause lipid peroxidation, protein denaturation, DNA hydroxylation, and apoptosis, ultimately negotiating cells viability. Antioxidants can scavenge such free radicals, thus reducing the oxidative stress and eventually prevent cellular damage. Medicinal plants, fruits, and spices remain the prioritized sources of antioxidants and antimicrobial properties since the time immemorial, but in contrast to plants, microorganisms can be grown at a faster rate under controlled conditions. They are non-toxic, non-carcinogenic, and biodegradable as compared to synthetic antioxidants. Microorganisms including actinomycetes, archaea, bacteria, protozoa, yeast, and fungi are auspicious source of vital bioactive compounds. The list comprises ample of bioactive components from microorganisms. One of them is bacteriocins, which are ribosomally synthesized antimicrobial peptides product of Eurotium sp., Streptomyces parvulus, S. thermophiles, Lactococcus lactis, etc. It has a great potential as next-generation antibiotics targeting the multiple-drug resistant pathogens. Pneumocandins are antifungal lipohexapeptides derived from the fungus Glarea lozoyensis, and inhibit 1,3-β-glucan synthase of the fungal cell wall and act as a precursor for the synthesis of caspofungin. It is widely used against invasive fungal infections and has been recently approved by the FDA. Taxol (paclitaxel), a chemotherapeutic drug derived from the bark of Taxus brevifolia can also be produced by endophytic fungi Taxomyces andreanae and Nodulisporium sylviforme. It is known to inhibit several fungi such as Pythium, Aphanomyces and Phytophthora. Hispidin and its derivate isolated from P. hispidus, reduce inducible nitric oxide synthase (iNOS) expression, obstruct the transcriptional activity of NF-κB, and also decrease the production of reactive oxygen species (ROS) in macrophages. Astaxanthin, known as an “aquatic” carotenoid produced by H. pluvialis, also has excellent ROS quenching activity. This study mainly focuses on fascinating antioxidant and antimicrobial compounds that have been scarcely investigated in microorganisms and discuss the promise and challenges of microorganisms as providers of health benefits.

There are currently worldwide efforts to reduce sugar intake due to the various adverse health effects linked with the overconsumption of sugars. Artificial sweeteners have been used as an alternative to nutritive sugars in numerous applications; however, their long-term effects on human health remain controversial. This led to a shift in consumer preference towards non-caloric sweeteners from natural sources. Thaumatins are a class of intensely sweet proteins found in arils of the fruits of the West-African plant Thaumatococcus danielli. Thaumatins’ current production method through aqueous extraction from this plant and uncertainty of the harvest from tropical rainforests limits its supply while the demand is increasing. Despite successful recombinant expression of the protein in several organisms, no large-scale bioproduction facilities exist. We present preliminary process design, process simulation, and economic analysis for a large-scale (50 metric tons/year) production of thaumatin II variant by several different molecular farming platforms.

Anthelmintic drugs are the major line of defense against parasitic nematode infections, but the arsenal is limited and resistance threatens sustained efficacy of the available drugs. Discoveries of the modes of action of these drugs and mechanisms of resistance have predominantly come from studies of a related non-parasitic nematode species, Caenorhabditis elegans, and the parasitic nematode Haemonchus contortus. Here, we discuss how our understanding of anthelmintic resistance and modes of action came from the interplay of results from each of these species. We argue that this “cycle of discovery”, where results from one species inform the design of experiments in the other, can use the complementary strengths of both to understand anthelmintic modes of action and mechanisms of resistance.

Molecular engineering research is the fundamental way and the only way for the development of biomaterials. Based on molecular engineering, the biocompatibility of natural conformation and polymer biomaterials was studied. In this paper, we discuss that natural conformation is the basis of protein biological function, and that the synergistic action of peptide chain and side group is the motive force for protein to construct natural conformation and complete biological function. On the basis of the influence of the adsorption of polymer biomaterials on the natural conformation of proteins, the relationship between biocompatibility of biomaterials and protein conformation is further explained. Studies have shown that bismuth molecular materials can only be applied in the market and have their functionality if they have good biocompatibility. Therefore, the biocompatibility evaluation of new materials has important practical significance.

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.

Amyloids result from the aggregation of several unrelated proteins, due to either specific mutations or promoting intra- or extra-cellular conditions. Structurally, they are rich in intermolecular β-sheets and are the causative agents of several diseases, both neurodegenerative and systemic. It is believed that the most toxic species are small aggregates, referred to as oligomers, rather than the final fibrillar assemblies. Their mechanisms of toxicity are mostly mediated by aberrant interactions with the cell membranes, with resulting derangement of membrane-related functions. Much effort is being put in the search for natural antiamyloid agents, and/or in the development of synthetic molecules. Actually, it is well documented that the prevention of amyloid aggregation results in several cytoprotective effects. Here, we portray the state of the art in the field. Several natural compounds are effective antiamyloid agents, notably tetracyclines and polyphenols. They are generally non-specific, as documented by their partially overlapping mechanisms, and the capability to interfere with the aggregation of several unrelated proteins. Among rationally designed molecules, we mention the prominent examples of β-breakers peptides, whole antibodies and fragments thereof, and the special case of drugs contrasting transthyretin aggregation. In this framework, we stress the pivotal role of the computational approaches. When combined with biophysical methods, in several cases they have helped clarify in detail the protein/drug modes of interaction, which make it plausible that more effective drugs will be developed in the future.

This paper proposes a new approach to investigation into the aesthetics. Specifically, it argues that it is possible to explain the aesthetic and its underlying dynamic relations with axiomatic structure (the octahedral axiom derived category) based on contemporary mathematics – namely, category theory – and through this argument suggests the possibility for discussion about the mathematical structure of the aesthetic. If there was a way to describe the structure of aesthetics with the language of mathematical structures and mathematical axioms – a language completely devoid of arbitrariness – then we would make possible a synthetical argument about the essential human activity of “the aesthetics”, and we would also gain a new method and viewpoint on the philosophy and meaning of the act of creating a work of art and artistic activities. This paper presents one hypothesis as a first step in constructing the science of dynamic generative aesthetics based on axiomatic functionalism, which is in turn based on a new interdisciplinary investigation into the functional structure of aesthetics.

Dengue virus (DENV) and chikungunya virus (CHIKV) are reemergent arboviruses that are transmitted by mosquitoes of the Aedes genus. During the last several decades, these viruses have been responsible for millions of cases of infection and thousands of deaths worldwide. Therefore, several investigations were conducted over the past few years to find antiviral compounds for the treatment of DENV and CHIKV infections. One attractive strategy is the screening of compounds that target enzymes involved in the replication of both DENV and CHIKV. In this review, we describe advances in the evaluation of natural products targeting the enzymes involved in the replication of these viruses.

This paper aims to build up a preference function to evaluate the public benefits of the type of agricultural farming, biodiversity, water provisions, land use type, ecotourism modes, and a monetary attribute (environmental trust fund and willingness to contribute) associated with an ecosystem service and land use program in a forest park. This study used the choice experiments to build a random utility model, analyze the average preference for the above land use attributes based on the conditional logit and used a latent class model to test the resident’s heterogeneous preferences for land use planning in the forest park. We also estimated the welfare derived from various land use programs. The empirical result had shown that: (1) increasing organic farming area, maintaining the status quo of species biodiversity, increasing the surface water provision, increasing the area of custom flora, increasing the wetland area, and setting up an integrated framework for ecotourism increase the public’s preference for the land use program; (2) we found that farmer and non-farmer haven’t the same land use preferences; (3) the ecotourism development program incorporating biodiversity, organic farming, ethnobotany, and wetland area with integrated ecotourism are more preferred than other land use program scenarios.

Current language models have achieved remarkable success in NLP tasks. Nonetheless, individual decoding methods face difficulties in realizing the immense potential of these models. The challenge is primarily due to the lack of a decoding framework that can integrate language models and decoding methods. We introduce the DecoStrat which bridges the gap between language modeling and the decoding process in D2T generation. By leveraging language models, DecoStrat facilitates the exploration of alternative decoding methods tailored to specific tasks. We fine-tune the model on the MultiWOZ dataset to meet task-specific requirements and employ it to generate output(s) through multiple interactive modules of the framework. The Director module orchestrates the decoding processes, engaging the Generator to produce output(s) text based on the selected decoding method and input data. The Manager module enforces a selection strategy, integrating Ranker and Selector to identify the optimal result. Evaluations on the stated dataset show that DecoStrat effectively produces diverse and accurate output, with MBR variants consistently outperforming other methods. DecoStrat with the T5-small model surpasses some baseline. Generally, the findings highlight DecoStrat's potential for optimizing decoding methods in diverse real-world applications.

There is an increasing interest in revisiting plants for drug discovery proving scientifically their role as remedies. Pistacia lentiscus (PL) is a wild-growing shrub rich in terpenoids, which are pharmacological appealing. The more recurrent components in the oil are represented by α-pinene, terpinene, caryophyllene, limonene, and myrcene. High concentration of polyphenols enriches the extracts. PL-extracts showed in vitro and in animal model strong anti-inflammatory and anti-oxidative activities. The anti-inflammatory activity mainly occurs due to inhibition of NF-kB pathway or directly toward the proinflammatory cytokines, or arachidonic acid cascade against COX-2 and LOX. The antimicrobial activity of PL essential oil and extracts includes among others Staphylococcus aureus, Escherichia coli, periodontal bacteria and Candida sp.. In conclusion, the biological properties, and particularly the anti-inflammatory and anti-microbial capacity, propose PL as a new safe pharmaceutical agent.

The difficulties faced by the society in its goal of reducing mortality and improving the health status during the period of the COVID-19 pandemic showed that it is necessary to develop new approaches in the therapy of infectious diseases, leading to higher treatment efficiency and few side effects. The subject of the present study is the formation and characterization of the proper-ties of stable liposomes loaded with extracts from Bulgarian medicinal plants and monitoring their antiviral action against the human strain of coronavirus OC43 (HCoV-OC43) in vitro. The hydrodynamic diameter and -potential of the liposomes containing each type of payload were determined with DLS and electrokinetics. The encapsulated amount of extracts was evaluated against quercetin and gallic acid by using colourimetric methods. The BALB 3T3 Neutral Red Uptake (NRU) phototoxicity/cytotoxicity assay was used to estimate the safety of the compounds. For assessment of the antiviral activity against HCoV-OC43 of liposomes (Lip) containing medicinal plant extracts, the cytopathic effect inhibition test was used. Measurements of the virucidal activity and the effect on the virus adsorption stage was carried out using the endpoint dilution method, and ∆lgs were calculated by comparison with untreated controls. The estimated photo irritation factors (PIF) for the Lip containing extracts were < 2 which assigns them to the category of non-phototoxic substances. When the extracts were introduced into the liposomes as their carriers, the antiviral activity of the vesicles against the replication of HCoV-OC43 in vitro in-creased by several times compared to their individual activity described in our previous study. The determination of the effect of structures loaded with extracts on the extracellular virions and the stage of their adsorption to sensitive HCT-8 cells proved that the liposomes do not affect directly the viral surface or cell membrane, but only perform a function of carriers of the active substances and the observed result is due solely to the intracellular action of the extracts.

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.

In this study, the asymmetric effect of oil and natural gas price shocks on the trade balance of 11 CESEE countries was estimated. These countries are net importers of oil and natural gas, and price shocks are often the cause of imbalances in trade. Quarterly data for the period 1994Q1 to 2023Q4 and nonlinear ARDL model were used. In the long run, a significant asymmetric effect due to the oil price shock on the trade balance of Bulgaria, Czechia, Latvia and Slovenia was found, as well as a significant asymmetric effect due to the natural gas price shock on the trade balance of Croatia. Also in the long run, a significant asymmetric effect due to wholesale prices on the trade balance of Romania, Bulgaria and Croatia was found as well as a significant asymmetric effect due to industrial production on the trade balance of Croatia, Czechia and Latvia was found, in addition to a significant asymmetric effect due to the exchange rate on the trade balance of Czechia, Poland and Romania.

This study presents an innovative method for producing thermosensitive bio-ink from chitosan hydrogels saturated with carbon dioxide and agarose. It focuses on a detailed characterization of their physicochemical properties and potential applications in biomedicine and tissue engineering. The ORO test approved rapid regeneration of the three-dimensional structure of chitosan-agarose composites in the unidirectional bench press simulation test. The diffusion of dyes through chitosan-agarose hydrogel membranes strongly depended on the share of both polymers in the composite and the molecular weight of the dyes. Glucose, as a nutrient marker, also diffused through all membranes regardless of composition. Biocompatibility assessment using MTT tests on 46BR.1N fibroblasts and HaCaT keratinocytes confirmed the safety of the bioink. The regenerative potential of the bioink was confirmed by efficient cell migration, especially HaCaT. Long-term viability studies showed that chitosan-agarose scaffolds, unlike the agarose ones, support cell proliferation and survival, especially 14 days after bioink extrusion. Experiments in a skin wound model in mice confirmed the biocompatibility of tested dressing and the beneficial action of chitosan on healing. Studies on vessel formation in chicken embryos highlight the potential of the chitosan-agarose composition for enhancing proangiogenic effects. This composition meets all entry criteria and possesses excellent biological properties.

Based upon the latest molecular phylogenies of Gonyosoma sensu lato, which recovered five clades with robust support, we utilize morphological characters to demonstrate the distinctiveness of each clade, resulting in the resurrection of three genera (Gonyophis Boulenger, 1891 [1], Rhynchophis Mocquard, 1897 [2], and Rhadinophis Vogt, 1922 [3]) and proposal of a new genus and a new tribe. A synopsis of the group, with descriptions and diagnoses, is provided for the five genera and eight species in addition to distribution maps and illustrations of the head of each taxon. An artificial key to the species and genera in the new tribe is presented in addition to estimated origin times for each clade.