Drug resistance has become an extremely serious problem worldwide. Antibiotic resistance genes (ARGs) entering the environment with wastewaters promote replenishment of the resistome of natural microbioms. Distribution of several clinically significant ARGs in wastewaters of Rostov-on-Don (Southern Russia), lower reaches of the Don River and natural waters of the neighboring region was investigated. Metagenomic DNA samples isolated from 250 ml of wastewaters or natural waters and 200 mg of surface sediments were used for the study. Identification of the ARGs was carried out with end-point detection PCR. Presence of NDM, OXA-48, CTX-M, VanA, VanB, ErmB, and TetM/TetO genes was detected in urban wastewaters. Samples of wastewater treatment plant (WWTP) sewage were enriched with ARGs in contrast to non-treated wastewaters from the sewage collector. NDM, VanA, ErmB, TetM/TetO genes were found only in wastewaters and were absent in samples of natural waters and surface sediments. Only OXA-48, VanB and CTXM genes were found in natural waters and surface sediments. The described ARGs are quite typical for urban and hospital wastewaters. The target ARGs were detected in the samples connected to the anthropogenous sources of pollution such as Rostov municipal WWTP or livestock enterprise effluents.

Cancer treatment is a significant focus in medicine owing to the increasing global incidence. Patients with advanced cancers that do not respond to conventional therapies have limited options and an unfavorable prognosis. Consequently, researchers are investigating complementary approaches to conventional treatments. One such approach is alkalization therapy, which aims to neutralize the acidic tumor microenvironment (TME) by increasing its pH level. The acidic TME promotes inflammation, tumor progression, and drug resistance. Alkalization therapy has been demonstrated to be effective for various cancers. In addition, natural products, such as triterpenoids, parthenolides, fulvic acid, Taxus yunnanensis, and apple pectin have the potential to alleviate symptoms, maintain physical fitness, and improve treatment outcomes of cancer patients through their anti-inflammatory, antioxidant, and anticancer properties. In this review, we focus on the effects of alkalization therapy and natural products on cancer. Furthermore, we present a case series of advanced cancer patients who received alkalization therapy and natural products alongside standard treatments, resulting in long-term survival. We posit that alkalization therapy together with the supplementation of natural products may confer benefits to cancer patients by mitigating the side effects of chemotherapy and complementing standard treatments. However, further research is warranted to validate these clinical findings.

This study explores the Experience Economy dimensions within fishing tourism, adopting a data science perspective by employing web crawling for extensive data gathering on reviewer profiles from TripAdvisor. Utilizing Natural Language Processing (NLP) techniques, it scrutinizes the relationship between Experience Economy dimensions and user profiling aspects. Findings reveal "Entertainment" as the predominant dimension in tourist reviews, followed by "Aesthetic," "Educational," and "Escapist." Particularly notable are the frequent co-occurrences of "Entertainment"-"Aesthetic" and "Educational"-"Entertainment" pairs. The practical implications underscore the potential socioeconomic benefits of fishing tourism for local communities and fishermen, emphasizing the necessity for governmental support in terms of infrastructure, leadership, legislation, and financial backing to foster sustainable development in this sector. Notably, this research stands as a pioneering effort in its focus on Experience Economy dimensions and user profiling within the context of fishing tourism, drawing data from business pages and user profiles.

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.

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.

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.

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.

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.

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.

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

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 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.

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

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.

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.

Cyanobacteria are photosynthetic microorganisms that colonize diverse environments worldwide, ranging from ocean to freshwaters, soils, and extreme environments. Their adaptation capacities and the diversity of natural products (molecules, metabolites, or compounds) that they synthesize support the cyanobacterial success for the colonization of their respective ecological niches. Although cyanobacteria are well-known for their toxin production and their relative deleterious consequences, they also produce a large variety of molecules that exhibit beneficial properties with high potential for various fields of application (e.g., synthetic analog of the dolastatin 10 used against Hodgkin lymphoma). The present review specially focuses on the beneficial activities of cyanobacterial molecules described so far. Based on an analysis of 670 papers, it appears that more than 90 genera of cyanobacteria have been found to produce compounds with potential beneficial activities, most of them belonging to the orders Oscillatoriales, Nostocales Chroococcales, and Synechococcales. The rest of the cyanobacterial orders (i.e., Pleurocapsales, Chroococcidiopsales, and Gloeobacterales) remain poorly explored in terms of their molecular diversity and relative bioactivity. The diverse cyanobacterial molecules presenting beneficial bioactivities belong to 10 different chemical classes (alkaloids, depsipeptides, lipopeptides, macrolides/lactones, peptides, terpenes, polysaccharides, lipids, polyketides, and others) that exhibit 14 major kinds of bioactivity. However, no direct relation between the chemical class and the bioactivity of these molecules has been demonstrated. We further selected and specifically described 50 molecule families according to their specific bioactivities and their potential uses in pharmacology, cosmetology, agriculture, or other specific fields of interest. This up-to-date review takes advantage of the recent progresses in genome sequencing and biosynthetic pathway elucidation, and presents new perspectives for the rational discovery of new cyanobacterial metabolites with beneficial bioactivity.

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.

Kandelia obovata (Ko) and Aegiceras corniculatum (Ac) are common and dominant plant species in mangrove wetlands in South China, and distribute in the similar tidal zones along the coastline. The present study aimed to determine the allelopathic effects of leaf litter leachates (LLLs) from Ko and their purified condensed tannins (PCTs) on the germination and growth of Ac by mangrove microcosms. Replicate pots containing five different levels of LLLs and PCTs were separately prepared and propagules of Ac were placed in each treatment. Both LLLs and PCTs significantly inhibited the germination and growth of Ac, especially in high levels. The final germination rates of roots, stems, and the number of fine roots declined continuously while other growth indicators, including the lengths of fine roots, nutritive roots, the biomasses of roots, stems, leaves, increased firstly and then decreased with increasing levels. These results indicated that LLLs from the leaf litter of Ko, in particular, their PCTs exerted an inhibition effect on propagule germination and seedling growth of Ac, and the inhibitory effects were concentration dependent. This study suggested that condensed tannins from leaf litter, acting as allelochemicals, could regulate the natural regeneration of a mangrove forest.

The Ebola virus disease (EVD) is an extremely contagious and fatal illness caused by the Ebola virus. Recently, Uganda witnessed an outbreak of EVD, which generated a lot of attention on various social media platforms. To ensure effective communication and implementation of targeted health interventions, it is crucial for stakeholders to comprehend the sentiments expressed in the posts and discussions on these online platforms. In this study, we used deep learning techniques to analyze the sentiments expressed in Ebola-related tweets during the outbreak. We explored the application of three deep learning techniques to classify the sentiments in 8,395 tweets as positive, neutral, or negative. The techniques examined included a 6-layer convolutional neural network (CNN), a 6-layer long-short-term memory model (LSTM), and an 8-layer Bidirectional Encoder Representations from Transformers (BERT) model. The study found that the BERT model outperformed both the CNN and LSTM-based models across all the evaluation metrics, achieving a remarkable classification accuracy of 95\%. These findings confirm the reported effectiveness of transformer-based architectures in tasks related to natural language processing, such as sentiment analysis.

Fusarium graminearum is a dangerous pathogen of the cereals producing mycotoxins (trichothecene and zearalenone) harmful for human and animal health. There were evaluated sixteen winter wheat varieties for their response in conditions of natural infection with F. graminearum in the epidemic year 2019, being well known that accumulation of mycotoxins (DON, ZON and T-2) is induced by different biotic and abiotic factors. Field plot was organized in Latin rectangle randomized with three replicates. For all evaluated wheat varieties were collected field data (incidence, severity and infection degree of the fungus F. graminearum) and laboratory data (mycotoxins concentration in grains) that have been processed using the software JASP (Version 0.14) for descriptive statistics, and exploratory factor analysis (EFA). Microsoft Excel 2019 was used to calculate Pearson’s correlation coefficients. The results showed negative corelation between plants’ density and F. graminearum attack frequency. Positive correlations were found between DON and T-2 and between DON and fungus attack intensity. This work highlights that during a F. graminearum epidemic year some of the most influential factors in the contamination with harmful mycotoxins (DON, ZON and T-2) are: plants density, frequency of the attack on ear, diseased ears and attack intensity on ears.

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

Over the years, there has been a significant rise in the world's scientific knowledge. However, most of it lacks structure and is often termed as Dark Data. Both humans and expert systems have continually faced difficulty in analyzing and comprehending such overwhelming amounts of information which is crucial in solving several real-world problems. Information and data visualization techniques proffer a promising solution to explore such data by allowing quick comprehension of information, the discovery of emerging trends, identification of relationships and patterns, etc. In this tutorial, we utilize the rich corpus of PubMed comprising of more than 30 million citations from biomedical literature to visually explore and understand the underlying key-insights using various information visualization techniques. With this study, we aim to diminish the limitation of human cognition and perception in handling and examining such large volumes of data by speeding up the process of decision making and pattern recognition and enabling decision-makers to fully understand data insights and make informed decisions.

Social media, traditionally reserved for social exchanges on the net, has been increasingly used by researchers to gain insight into different facets of human life. Unemployment is an area that has gained attention by researchers in various fields. Medical practitioners especially in the area of mental health have traditionally monitored the effects of involuntary unemployment with great interest. In this work, we compare the feedback gathered from social media using crowdsourcing techniques to results obtained prior to the advent of Big Data. We find that the results are consistent in terms of 1) financial strain is the biggest stressor and concern, 2) onslaught of depression is typical and 3) possible interventions including reemployment and support from friends and family is crucial in minimizing the effects of involuntary unemployment. Lastly, we could not find enough evidence to study effects on physical health and somatization in this work.

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.

The results of the investigation showed that second-order kinetics govern the adsorption process, and the corresponding rate constants were found. To evaluate the parameters related to the adsorption process, the adsorption equilibrium was examined using a variety of mathematical models, such as the Langmuir, Freundlich, Temkin, and Dubinin-Radushkevich isotherm models. The Langmuir isotherm was found to be the best appropriate among all models for describing the adsorption of Cu2+ and Ni2+ ions using bio-nanocomposite beads. The positive values of ΔH° indicate that the adsorption is physical and endothermic, in agreement with experimental results. The negative value of ∆G° shows that the adsorption process is spontaneous. Positive ΔS° values indicate increased randomness at the solid/liquid interface, during adsorption of Cu2+ and Ni2+ cations onto the engineered bio-nanocomposite. The maximum adsorbed amounts of metal ions by the bio-nanocomposite used were 370.37 mg/g for Ni2+ and 454.54 mg/g for Cu2+ from single system. For the binary system, according to the Langmuir isotherm, the maximum adsorbed amounts of Ni2+ and Cu2+ were 357.14 mg/g and 370,37 mg/g, respectively. There is proof that Alginate-Moroccan clay bio-nanocomposites can serve as a different, less expensive source of sorbents for the removal of metal ions from single and binary systems.

This study is to explore the feasibility using neural network (NN) and deep learning to diagnose three common respiratory diseases with only few symptom words. These three diseases are nasopharyngitis, upper respiratory infection, and bronchitis/bronchiolitis. Through natural language processing, the symptom word vectors are encoded by GPT-2 and classified by the last linear layer of the NN. The experimental results are promising, showing that this model achieves a high performance in predicting all these three diseases. They reach 90% in accuracy, which suggests the implications of the developed model, highlighting its potential use in assisting patients understanding their conditions via a remote diagnosis. Unlike previous studies that focus on extracting various categories of information from medical records, this study directly extracts sequential features from unstructured text data, reducing the effort required for data pre-process.

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

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.

During the production of furniture, large amounts of waste of these materials are generated, which are most often stored in warehouses without a specific purpose for their subsequent use. In highly developed countries, as many as 25 million tons of textile waste are produced annually, of which approximately 40% is non-clothing waste, such as carpets, furniture and car upholstery. The aim of the research was to produce and evaluate the dry-formed high-density fiberboards (HDF) with various shares of recycled particles of natural leather used in upholstery furniture production at different contents (1, 5 and 10% by weight). Mechanical properties (modulus of rupture, modulus of elasticity and screw withdrawal resistance) and physical properties (density profile, thickness swelling after water immersion, water absorption, surface absorption) were tested. The density profile and contact angle of natural leather have been also characterized. The results show that increasing the content of leather particles in HDF mostly has a positive effect on mechanical properties, especially screw withdrawal resistance and water absorption. It can be concluded, that, depending on the further use of HDF, it is possible to use recovered upholstery leather particles as a reasonable addition to wood fibers in HDF technology.

This study investigates the impact of climate change on the efficiency of organic honey production in Türkiye using a machine learning approach. The eXtreme Gradient Boosting (XGBoost) algorithm is employed for the Machine Learning (ML) modelling. The analysis includes data on organic honey production performance from 2004 to 2023, spanning a period of 20 years, and 8 different climate variables (16 variables depending on daytime and nighttime values). The dataset created to investigate the impact of climate change on organic honey yield comprises 28 columns and 120,960 rows. The study aimed to determine the significance of different climatic variables on the efficiency of organic honey production. The study found that, in addition to daytime solar radiation and temperature, wind speed and direction were also important factors. The sensitivity analysis has shown that minor alterations in the variables have a significant impact on the model's predictions. Additionally, non-linear relationships between the variables and honey production were identified. These findings suggest that it may be beneficial to consider implementing climate change adaptation and mitigation strategies in agricultural policies and beekeeping practices. The study suggests that machine learning has the potential to provide valuable insights into the complex relationship between climate change and agricultural production. This could aid in the sustainability and economic optimization of the sector.

In this experiment, an efficient and accurate network of detecting automatically disseminated (bot) content on social platforms is devised. Through the utilisation of parallel convolutional neural network (CNN) which processes variable n-grams of text 15, 20, and 25 tokens in length encoded by Byte Pair Encoding (BPE), the complexities of linguistic content on social platforms are effectively captured and analysed. With validation on two sets of previously unexposed data, the model was able to achieve an accuracy of around 96.6% and 97.4% respectively — meeting or exceeding the performance of other comparable supervised ML solutions to this problem. Through testing, it is concluded that this method of text processing and analysis proves to be an effective way of classifying potentially artificially synthesized user data — aiding the security and integrity of social platforms.

The herd effect is a common phenomenon in social society. The detection of this phenomenon is of great significance in many tasks based on social network analysis such as recommendation. However, the research on social network and natural language processing seldom focuses on this issue. In this paper, we propose an unsupervised data mining method to detect herding in social networks. Taking shopping review as an example, our algorithm can identify other reviews which are affected by some previous reviews and detect a herd effect chain. From the overall perspective, the cross effects of all views form the herd effect graph. This algorithm can be widely used in various social network analysis methods through graph structure, which provides new useful features for many tasks.

Different alcoholic beverages can have different effects on blood alcohol concentrations (BAC) and neurotoxicity even if equalized for alcohol content by volume. Anecdotal evidence suggested that natural wine is metabolized differently from conventional wines. This triple-blind study compared the BAC of 55 healthy male subjects after consuming the equivalent of 2 units of alcohol of a natural or conventional wine over 3 mins in two separate sessions one week apart. BAC was measured using a professional breathalyzer every 20 mins after consumption for 2 hrs. The BAC curves in response to the two wines diverged significantly at twenty minutes, at forty minutes and also at their maximum concentrations (peaks), with the natural wine inducing a lower BAC than the conventional wine (T20 0.40 vs. 0.46 [p<0.0002], T40 0.49 vs. 0.53 [p<0.0015], peak 0.52 vs. 0.56 [p<0.0002]). These differences are likely related to the development of different amino acids and antioxidants in the two wines during their production. This in turn may affect the kinetics of alcohol absorption and metabolism. Other contributing factors may also include pesticide residues, differences in dry extract content and the use of indigenous or selected yeasts. Further studies are needed to fully understand why natural wines are metabolized differently from conventional wines.

In this review paper, we discuss the use of BERT, one of the most well-liked deep learning-based language models. The model's operation mechanism, key areas of applicability to text analytics tasks, comparisons with related models for each activity, and a description of certain proprietary models are all covered in this work. The data from several dozen original scientific studies that have been published in the last few years and have garnered the greatest interest from the scientific community were systematized in order to prepare this review. All researchers and students who wish to learn about the most recent developments in the field of natural language text analysis may find this survey helpful. Bidirectional Encoder Representations from Transformers, or BERT for short, is the subject of a thorough investigation that we present. The area of natural language processing holds significant importance in the creation of intelligent systems. To do a variety of activities, one must comprehend the sentence's correct meaning in order to produce the desired result. Computers have a hard time understanding languages because of how context constantly changes. The main challenge in natural language processing tasks is getting computers to understand text context, and BERT is seen as a revolution in this regard. It picks up the language and its meaning in a manner that is quite similar to how the human brain processes meaning from sentences. Its capacity to identify a word from both the left and right contexts of a sentence makes it special. A new era in the perception and comprehension of natural languages has been ushered in with the development of BERT, which could help computers understand natural languages more fully. This study aims to provide readers with a deeper knowledge of the BERT language model and how it is applied to different NLP tasks.

We have particularly investigated the correlation law of the effect of different carbon black fillings on the hyper-elastic mechanical behavior of natural rubber by conducting uniaxial tensile tests over a wide range of deformations with different volume fractions of carbon black fillings (0%, 4.7%, 8.9%, 12.8%, 16.4%, 19.7%, 22.7%, 25.2%). The results show that the stress-strain curve for carbon black filled rubber increases with the amount of filling, meaning that the rubber gradually becomes "harder". We explore the correlation between the carbon black filling volume and the parameters of the Yeoh constitutive model by examining the Yeoh constitutive model to characterize the hyper-elastic mechanical behavior of rubber with different carbon black fillings. A quantitative relationship between the material parameters and the carbon black filling volume in the Yeoh constitutive model is presented. A method for calculating the material parameters of the Yeoh constitutive model is developed and it predicts the correlation between the hyper-elastic properties of rubber and the volume fraction of carbon black filling.

At the end of 2018, a high school student asked a question in Zhihu community, claiming that he had proved Goldbach's conjecture. The problem caused an explosive reaction and a large number of users participated in the discussion. And has caused the widespread influence. On January 1, 2019, the questioner issued his "proof". His proof was soon proved wrong. The heated discussion caused by the incident contains a lot of information of social science analysis value. Therefore, we follow up the event in the first time and build a time series dataset for the event. Taking the questioner's "proof" as the dividing line, all the answers, comments, sub comments and user information of writing these texts before and after two days were recorded. This series of temporal information can reflect the dynamic features of the interaction between user opinions, and the impact of exogenous shocks (proof release) on community opinions. The dataset can be used not only for the demonstration of various social network analysis algorithms, but also for a series of natural language processing tasks such as fine-grained sentiment analysis for long texts, as well as multimodal tasks combining natural language processing and social network analysis. This paper introduces the characteristics and structure of the dataset, shows the visualization effect of social network, and uses the dataset train the benchmark model of emotion analysis.

Objective: The purpose of this study was to determine if Porphyra tenera extract (PTE) has immune-enhancing effects and is safe in healthy adults. Methods: Subjects (3x10³ ≤ peripheral blood leukocyte levels < 8x10³ cells/μl) who met the inclusion criteria were recruited for this study. Enrolled subjects (n=120) were randomly assigned to either the PTE group (n=60) who were given 2.5 g/day of PTE (as Porphyra tenera extract) in capsule form or the placebo group (n=60) who were given crystal cellulose capsules with the identical appearance, weight, and flavor as the PTE capsules for 8 weeks. Outcomes were assessed by measuring natural killer cell (NK-cell) activity, cytokines, and upper respiratory infection (URI), and safety parameters were assessed at baseline and 8 weeks. Results: Compared to baseline, NK cell activity (%) increased for all effector cell to target cell ratios in the PTE group after 8 weeks, but there were no changes in the placebo group (p<0.1). Subgroup analysis of 101 subjects without an URI revealed that NK-cell activity in the PTE group tended to be increased for all E:T ratios (E:T=12.5:1 p=0.068; E:T=25:1 p=0.036; E:T=50:1 p=0.081) compared to the placebo group. There was a significant difference between these two groups for the E:T=25:1 ratio, which increased from 20.3±12.0% at baseline to 23.2±12.4% after 8 weeks in the PTE group (p=0.036). There was no significant difference in levels of cytokines between these two groups. Conclusions: PTE supplementation appears to enhance immune function by improving NK-cell activity without adverse effects in healthy adults.

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.

Integrating voice assistant technology can potentially enhance accessibility and user interaction in web-based platforms for geospatial data visualisation and processing. This paper presents an open-source development incorporating a voice virtual assistant into the BStreams web application platform, designed specifically for visualising geospatial data. The study aimed to mitigate the observed limitations of virtual assistants in understanding the semantics and context of user commands in the geospatial domain. A discourse framework specialised for geospatial data was developed using a structured prototype methodology. In addition, a survey assisted in identifying commonly used English terminologies for geospatial data interaction. The questionnaire results were subsequently assessed using the ChatGPT natural language processing model. The application used open-source frameworks, including Web Speech API, Leaflet, Mapbox geocoding, and JavaScript. This investigation indicates that voice virtual assistants can potentially enhance human-computer interaction for geospatial data visualisation and processing. However, challenges related to natural language understanding, accents, and domain knowledge were raised. The paper advocates for further research to refine the integration of voice technology in this domain.

In the past decade, considerable efforts have been devoted to the development of semitransparent organic solar cells. Numerous materials and architectures have been proposed for the future commercialization of these devices. Among these, the use of ternary active layers demonstrated a great promise for the development of efficient semi-transparent organic solar cells with the potential for future applications including self-powered greenhouses and powered windows. Researchers seek alternative solutions to the trade-off between device power conversion efficiency (PCE) and average visible transmittance (AVT), with photoactive materials being the key parameters that govern both (PCE) and (AVT) as well as device stability. Several new organic materials, including polymers and small molecules, were synthesized, and used in conjunction with a variety of techniques to achieve semitransparent conditions. In this review paper, we will look at the working principle and key parameters of semi-transparent organic solar cells, as well as the methods that have been used to improve the performance of ternary-based semi-transparent organic solar cells. The main approaches were concluded to be spectral enhancement and transparency increment of the active layer through bandgap tuning, utilizing novel organic semiconductors, and design architecture of the active layers.

Vagueira Beach on the Central Portuguese coast, is known as one of the places in Europe most affected by coastal erosion. The area has suffered more than 156 meters of coastline retreat over the period 1958 to 2001. With the aim to evaluate the influence of local factors on coastal erosion, this paper assesses the anthropogenic and natural factors that are related with the retreat of the coastline, by adopting statistical correlation and regression analyses. Through the Pearson's correlation coefficient (r) it was observed that local factors such as annual dredging at the Aveiro Port entrance (r = 0.93), the total length of groins in the Espinho-Vagueira section (r = 0.89), and storm events (r = 0.52) are directly related to coastline retreat in the area. A multiple linear regression model was developed in which coastline retreat is explained by these same factors over the period 1980 to 2006. With a coefficient of determination of R² = 0.91, it was observed that length of groins (significant at the 1% level), dredging of the port entrance (significant at the 5% level) and precipitation (as a proxy for storm events; significant at the 10% level) are significantly correlated with coastline retreat. Hence, it is shown that anthropogenic factors are the main drivers of coastline retreat in Vagueira Beach. This study provides an innovative approach for the assessment of coastal erosion, resulting in important information that can be used for decision-making related to coastal zone management as it allows to understand in greater detail the main drivers of coastal erosion.

In this research, the antioxidant and antibacterial activities of active films based on potato starch containing Viola odorata extract (VOE) were investigated both in vitro and in chicken fillets. The VOE was added to the starch film formulation at 0, 1, 2, and 3% (w/v). The results showed that by increasing the extract level, the total phenol content and antioxidant and antibacterial activity of the films against Escherichia coli, Staphylococcus aureus, and Salmonella typhimurium improved remarkability. The results of meat tests indicated significant antioxidant and antimicrobial activity of active films containing different levels of VOE in chicken fillets, and a direct relationship was observed between the concentration of the extract and the functional activity of the films, so with the increase in the concentration of the extract in the films, the rate of lipid oxidation and growth of microorganisms in chicken fillets decreased significantly during the storage period, and less volatile nitrogen bases, metmyoglobin, and oxidation products were produced in the fillets. In general, the results of this research demonstrated that active film based on potato starch containing VOE (especially 2 and 3% levels) has the ability to extend the oxidative and microbiological shelf life of chicken fillets during cold storage for at least eight days.

As the problem of light pollution becomes more serious, more and more scholars pay attention to this issue and carry out related research. In the perspective of cities, the measurements of light pollution mainly focus on the brightness of the sky or artificial lighting on the ground. However, there is lack of research on the whole urban space. With the two-dimensional brightness analyses, this paper processes the changes of the light environment of the whole urban space into image quantization. It gets the 3D and 2D light environment changes of luminance distribution, color temperature distribution and chromaticity in the three space layers, the ground layer, the urban canopy layer and the sky layer, from dusk with natural light to night with artificial lighting completely. It is found that the brightness difference between the light environments among the three city levels gradually reduces with the measuring time, and the final values maintain at 0.11~0.25 cd/m². In the ground layer, the light environment is mainly affected by the lighting facilities, and vegetation can prevent the light from scattering up. The light environment of the urban canopy layer is the brightest in the whole city space and has the largest influence on the sky layer. The color concentrates in the range of yellow and red. The color temperature near the ground distributes in 3000K~15000K, and near the sky distributes in 2300K~2700K which is warmer than the natural night sky. The sky brightness of Dalian city is about 951 times than the natural night sky.

The main restriction of the Semantic Web is the difficult of the SPARQL language, that is necessary to extract information from the Knowledge Representation also known as ontology. Making the Semantic Web accessible for people who do not know SPARQL, is essential the use of friendlier interfaces and a good alternative is Natural Language. This paper shows the implementation of a friendly prototype interface to query and retrieve, by voice, information from website building with the Semantic Web tools. In that way, the end users avoid the complicated SPARQL language. To achieve this, the interface recognizes a speech query and converts it into text, it processes the text through a java program and identifies keywords, generates a SPARQL query, extracts the information from the website and read it in voice, for the user. In our work Google Cloud Speech API makes Speech-to-Text conversions and Text-to Speech conversions are made with SVOX Pico. As results, we have measured three variables: The success rate in queries, the response time of query and a usability survey. The values of the variables allows the evaluation of our prototype. Finally the interface proposed provides us a new approach in the problem, using the Cloud like a Service, reducing barriers of access to the Semantic Web for people without technical knowledge of Semantic Web technologies.

Breast cancer is one of the most common types of malignant tumors worldwide and the leading cause of cancer-related deaths in women. According to the latest report from the World Health Organization, breast cancer has surpassed lung cancer, becoming the most diagnosed cancer globally. Herein, a systematic, quantitative, and visual analysis of the scientific achievements in natural products research and development for breast cancer over the past 30 years is presented. The study utilizes bibliometric and knowledge graph visualization techniques to analyze the trends, co-occurrence of keywords, cooperative networks, cited literature, and research hotspots in the field. The analysis shows that breast cancer natural drug development involves mutual cooperation between multiple countries, with China, the United States, and India being the most active players. Pharmacology is the discipline with the largest number of articles published in nearly 30 years. The study also reveals that the extraction of drugs from organisms and their metabolites is an important direction in the current development of anticancer drugs. The development of natural medicines for breast cancer together with the mining of genetic knowledge play an important role in treating human breast cancer. The research presented here provides insights into the current state of natural product research and development for breast cancer, and highlights the need for strengthening mutual communication and cooperation between countries and scientific research institutions to enhance the development of breast cancer treatment technologies and natural medicines.

Planck’s constant and the gravitational constant are comprised of more fundamental quantities of length, mass, and time. Reformulating traditional equations in terms of these fundamental units offers a more granular view of the physical transformations encoded in the equations of physics. The composite structure of h and G conceals a simple model in which maximum unit potentials are reduced by dimensionless proportionality operators. Natural symmetries correlate the three unit dimensions, yielding predictable quantities of physical dynamics. Insights are organized into a New Foundations Model of physics that reformulates traditional constants and equations in elementary form. The New Foundations Model offers a common language for describing quantum mechanical, gravitational, and electromagnetic phenomena.

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

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.

Objective: Food security during public health emergencies relies on situational awareness of needs and resources. Artificial intelligence (AI) has revolutionized situational awareness during crises, allowing the allocation of resources to needs through machine learning algorithms. Limited research exists monitoring Twitter for changes in the food security-related public discourse during the COVID-19 pandemic. We aim to address that gap with AI by classifying food security topics on Twitter and showing topic frequency per day. Methods: Tweets were scraped from Twitter from January 2020 through December 2021 using food security keywords. Latent Dirichlet Allocation (LDA) topic modeling was performed, followed by time-series analyses on topic frequency per day.Results: 237,107 tweets were scraped and classified into topics, including food needs and resources, emergency preparedness and response, and mental/physical health. After the WHO’s pandemic declaration, there were relative increases in topic density per day regarding food pantries, food banks, economic and food security crises, essential services, and emergency preparedness advice. Threats to food security in Tigray emerged in 2021.Conclusions: AI is a powerful yet underused tool to monitor food insecurity on social media. Machine learning tools to improve emergency response should be prioritized, along with measurement of impact. Further food insecurity word patterns testing, as generated by this research, with supervised machine learning models can accelerate the uptake of these tools by policymakers and aid organizations.

The study utilizes the energy-flux-vector method to analyze the heat transfer characteristics of natural convection in a wavy-wall porous square cavity with a partially-heated bottom surface. The effects of the modified Darcy number and modified Rayleigh number on the energy-flux-vector distribution and mean Nusselt number are examined. The results show that when a low modified Darcy number with any value of modified Rayleigh number is given, the recirculation regions are not formed in the energy-flux-vector distribution within the porous cavity. Therefore, a low mean Nusselt number is obtained. The recirculation regions do still not form and thus the mean Nusselt number has a low value when a low modified Darcy number with a high modified Rayleigh number is given. However, when the values of the modified Darcy number and modified Rayleigh number are high, the energy flux vectors generate recirculation regions and thus a high mean Nusselt number is obtained.

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.

The changes in consumer lifestyles have raised awareness of the variety of food options and packaging technologies. Active and smart packaging represents an innovative technology that serves to enhance the safety and quality of products. Smart packaging, as a subset of this technology, entails the integration of additives into packaging materials, thereby facilitating the preservation or extension of product quality and shelf life. This technological approach stimulates a heightened demand for safer food products with prolonged shelf life. Active packaging predominantly relies on the utilization of natural active substances. Therefore, the combination of active substances has a significant impact on the characteristics of active packaging particularly on polymeric blend like Polylactic acid (PLA) as a matrix. So, This review will summarize and explains how the addition of natural active agents influenced the properties related to the performance of active packaging through systematic analysis, providing new insights about the relationship between the types of active agents on physical-mechanical properties, bacterial colony reduction, and applications in foods packaging such as meat, fish, fruit, and vegetables. Through their integration, the market for active and smart packaging systems is expected to have a bright future.

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

Pancreatic ductal adenocarcinoma (PDAC) a highly malignant neoplasm, is classified as one of the most severe and devastating types of cancer. PDAC is a notable malignancy that exhibits a discouraging prognosis and a rising occurrence. The interplay between diabetes and pancreatic cancer exhibits a reciprocal causation. The identified metabolic disorder has been observed to possess noteworthy consequences on health outcomes, resulting in elevated rates of morbidity. The principal mechanisms involve the suppression of the immune system, the activation of pancreatic stellate cells (PSCs), and the onset of systemic metabolic disease caused by dysfunction of the islets. From this point forward, it is important to recognize that pancreatic cancer-related diabetes (PCRD) has the ability to increase the likelihood of developing pancreatic cancer. This highlights the complex relationship that exists between these two physiological states. Therefore, we investigate into the complex domain of PSCs, elucidating their intricate signaling pathways and the profound influence of chemokines on their behavior and final outcome. In order to surmount the obstacle of drug resistance and eliminate PDAC, researchers have undertaken extensive efforts to explore and cultivate novel natural compounds of the next generation. Additional investigation is necessary in order to comprehensively comprehend the effect of PCRD-mediated apoptosis on the progression and onset of PDAC through the utilization of natural compound. This study aims to examine the potential anticancer properties of natural compound in individuals with diabetes who are undergoing chemotherapy, targeted therapy, or immunotherapy. It is anticipated that these compounds will exhibit increased potency and possess enhanced pharmacological benefits. According to our research findings, it is indicated that naturally derived chemical compounds hold potential in the development of PDAC therapies that are both safe and efficacious.

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

Through the half-unit circle area computation using the integration of the corresponding curve power series representation, we deduce a slow converging positive infinite series to $\pi$. However, by studying the remainder of that series we establish sufficiently close inequalities with equivalent lower and upper bound terms allowing us to estimate, more precisely, how the series approaches $\pi$. We use the obtained inequalities to compute up to four-digit denominator, what are in this sense, the best rational numbers that can replace $\pi$. It turns out that the well-known convergents of the continued fraction of $\pi$, $22/7$ and $355/113$ called, respectively, Yuel\"{u} and Mil\"{u} in China are the only ones found. Thus we apply a similar process to find rational estimations to $\pi+e$ where $e$ is taken as the power series of the exponential function evaluated at $1$. For rational numbers with denominators less than $2000$, the convergent $920/157$ of the continued fraction of $\pi+e$ turns out to be the only rational number of this type.

Windthrows in the forestry fund, which have become more frequent due to the increase in extreme weather events, have had and continue to have negative economic and ecological effects, making them a pressing issue in forestry research. Their urgency has been amplified in light of the need to develop sustainable forest management systems. The main objectives of this study are to evaluate the effects of windthrows on some microbiological properties of forest soils and to monitor the evolution of the degraded tree regeneration, 4 years after the event, for three tree species: Norway spruce (Picea abies L.), sessile oak (Quercus petraea), and European beech (Fagus sylvatica L.). The experimental plot used is arranged in dispersed blocks and subdivided plots, with three repetitions, bifactorial, factor A representing the tree species, and factor B the windthrows: in two situations: affected (AW) and not affected by felling (WW). Three representative soil profiles were studied for each tree species, including dehydrogenase activity (DA) and the number of fungi (NF) in the organic horizon at the soil surface. Dehydrogenase activity was determined by the extraction of triphenylformazan, and NF was evaluated using the Plate Count Method (Petri plates) and the Sabouraud Agar culture medium. The values of Current Dehydrogenase Activity (CDA) and Potential Dehydrogenase Activity (PDA) did not show significant statistical differences in relation to the windthrow factor, but were distinctly significant (p &lt; 0.01) for the tree species factor. On the other hand, NF showed statistical significance for both tree species and windthrows factors, at a level of p &lt; 0.01. Correlations were highlighted between the differences in AW and WW of CDA, PDA, and NF with the number of naturally regenerated seedlings (NRS), and the type of soil. The regressions established between NRS and the differences in CDA and PDA, the dependence of the differences in PDA on the differences in NF, and the regression between the differences in NF and the type of soil demonstrate the importance of soil characteristics in the natural regeneration process of the main tree species - Norway spruce, sessile oak, and European beech. Stimulating NRS in AW forests and increasing the volume of terrestrial organic carbon (TOC) biomass is directly dependent on soil fertility, primarily determined by soil organic carbon (SOC), which accumulates in the soil as a result of organic matter, deposited on the surface. Sustainable forest management of AW plots should stimulate the accumulation of SOC, including the partial or total preservation of dead trees, provided that the attack of specific diseases and pests is avoided.

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

Nowadays, an analysis of virtual media to predict society’s reaction to any events or processes is a task of great relevance. Especially it concerns meaningful information on healthcare problems. Internet sources contain a large amount of pharmacologically meaningful information useful for pharmacovigilance purposes and repurposing drug use. An analysis of such a scale of information demands developing the methods that require the creation of a corpus with labeled relations among entities. Before, there have been no such Russian language datasets. This paper considers the first Russian language dataset where labeled entity pairs are divided into multiple contexts within a single text (by used drugs, by different users, by the cases of use, etc.), and a method based on the XLM-RoBERTa language model, previously trained on medical texts to evaluate the state-of-the-art accuracy for the task of indication of the four types of relationships among entities: ADR–Drugname, Drugname–Diseasename, Drugname–SourceInfoDrug, Diseasename–Indication. As shown based on the presented dataset from the Russian Drug Review Corpus, the developed method achieves the F1-score of 81.2% (obtained using cross-validation and averaged for the four types of relationships), which is 7.8% higher than the basic classifiers.

Social media platforms are increasingly enabling the propagation of content from groups related to drug use, thus posing risks for the wider population and, in particular, individuals who are amenable to drug use and drug addiction. The detection of drug use content on social media platforms is a priority for governments, technology companies, and drug law enforcement organizations. To counter this issue, various techniques have been developed to identify and promptly remove drug use content, while also blocking its creators from network access. In this paper, we introduce a manually annotated Twitter dataset, comprising 156,521 tweets published between 2008 and 2022, specifically compiled for the purpose of drug use detection. The dataset underwent annotation by several group of expert annotators who classified the tweets as either drug use or non-drug use. Exploratory data analysis was conducted to comprehend the dataset's characteristics. Various classification algorithms, including SVM, XGBoost, RF, NB, LSTM, and BERT were employed using the dataset. Among the traditional machine learning models, SVM utilizing term frequency-inverse document frequency features achieved the highest F1-Score (0.9017). However, BERT with textual features concatenated with numerical and categorical features in ensemble method surpassed the performance of traditional models, attaining F1-Score of 0.9112. To facilitate future research and enhance English online drug use classification accuracy, the dataset will be made publicly available.

The shear factor of ore drawing is one of the important factors affecting the formation of underground debris flow, and it has an important contribution to the formation of underground debris flow. The purpose of this paper is to study the effect of mining shear factor on underground debris flow in natural caving method. Based on the research background of the underground debris flow in Plan copper mine, this paper analyzes the characteristics of the slurry material structure of the underground debris flow, the influence of drawing shear factor on formation mechanism of underground debris flow is analyzed. The results show that the slurry of the underground debris flow in Plan mine is not only a pseudoplastic fluid but also a thixotropic fluid. It is indicated that once there is shearing force in drawing, it will deform, and its viscosity will decrease with the increase of shear rate and time. It is considered that the shear force produced by the flow of ore particles first produces shear action on the paste in the shear boundary region of ore drawing, it is reduced in viscosity and increased in fluidity, so that its “Activation” and then become a flowable paste, along with the bulk ore flow through the mouth. The continuous ore drawing process will continuously shear the new moraine slurry in the ore drawing channel and continuously “Activate” the moraine slurry in the ore drawing channel, finally, a certain scale and destructive down-hole debris flow accident. This paper is the first to study the effect of ore drawing shear factor on the formation mechanism of underground debris flow. It not only broadens the research field of debris flow, but also fills up the deficiency of systematic research on underground debris flow, and provides theoretical guidance for the prevention and control of underground debris flow.

Due to environmental concerns, natural refrigerants and their use in refrigeration and air conditioning systems are receiving more attention from manufacturers, end users and the scientific community. The study of heat transfer and pressure drop is essential for accurate design and more energy efficient cycles using natural refrigerants. The aim of this work is to provide an overview of the latest outcomes related to heat transfer and pressure drop correlations for ammonia, propane, isobutane and propylene and to investigate the current state of the art in terms of operating conditions. Available data on the existing correlations of heat transfer coefficient and pressure drop for natural refrigerants have been collected through a systematic search. Whenever possible, validity intervals are given for each correlation and the error is quantified. It is the intention of the authors that this could be a valuable support for researchers and an aid to design, with particular reference to heat pumps. A procedure based on the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) statement was adopted, and the Scopus database was used to query the relevant literature. A total of 135 publications qualified for inclusion in the survey; 34 articles report experimental investigations for not usual geometric conditions. Of the 101 selected papers related to usual geometric conditions, N = 50 deal only with HTC, N = 16 deal only with pressure drop and the remainder (N = 35) analyse both HTC and pressure drop. Among the 85 HTC papers (N = 53) deal with the evaporating condition, N = 30 with condensation and only N = 2 with the heat transfer correlations under both conditions. Most of the 101 articles concern propane and isobutane. The high temperatures are less widely investigated.

This comprehensive review paper provides a detailed analysis of the extraction methods employed in the dimension stone industry. Natural rock materials used in construction, have been historically extracted using primitive techniques such as thermal shock, hammer and chisel, and plug and feather. However, significant technological advancements have revolutionized the industry, leading to the widespread adoption of mechanized methods. One highly efficient technique is diamond wire cutting, which involves the utilization of diamond-engraved bits affixed to a tensioned steel wire. This method has demonstrated remarkable capabilities in minimizing waste generation and ensuring exceptional machining quality. Nonetheless, challenges such as high operating costs and wire breakage persist and require further investigation. In addition to diamond wire cutting, this review explores the utilization of circular diamond saws and frame sawing as alternative extraction methods. The geotechnical characteristics of the stone play a critical role in the selection of appropriate machinery, and researchers worldwide have conducted numerous studies to investigate the impact of each parameter on the extraction efficiency of these mechanized techniques. Moreover, controlled blasting with explosives emerges as a promising alternative for dimension stone extraction, warranting further exploration and optimization.

Wounds are breaks in the continuity of the skin and the underlying tissues, due to external causes, such as cuts, blows and impacts, or even, during surgical interventions. Millions of people suffer minor or serious injuries, sometimes ending in death. Today, there are many commercially available products that promote healing from wounds. The high demand from the healthcare industry has created a huge market that continues to grow, fueling research and development of new wound healing products. In the present article, we review recent advances in wound healing products based on natural biopolymers, stem cells, and microRNAs. We analyze the advances of both, the commercial products and the products that are still in research phase, and we also include an overview of the opportunities, barriers, and regulatory issues for the commercialization of these products in the European, American, and Asian markets.

Abstract: Understanding social media networks and group interactions are crucial to the ad-vancement of linguistic and cultural behaviour. This includes the manner in which people ac-cessed advice on health, especially during the global lockdown periods. Some people turned to social media to access information on health where most activities were curtailed with isolation rules, especially for older generations. Facebook public pages, groups and verified profiles, using "senior citizen health", "older generations", and "healthy living" keywords were analysed over a 12-month period to analyse the engagement promoting good mental health. CrowdTangle was used to source English language status updates, photo and video sharing information which resulted in an initial 116,321 posts and 6,462,065 interactions Data analysis and visualisation were used to explore large datasets, including natural language processing for “Message” content discovery, word frequency and correlational analysis and co-word clustering. Preliminary results indicate strong links to healthy aging information shared on social media which showed correla-tions to global daily confirmed case and daily death totals. The results can be used to identify public concerns early on and address mental health issues in the senior generation on Facebook.

In this work, we explore the extreme searching of multidimensional functions by natural gradient descent based on Dirichlet and generalized Dirichlet distributions. The natural gradient is based on describing multidimensional surface with probability distributions, which allows us to reduce changing the accuracy of gradient and step-size. In this article, we propose an algorithm of natural gradient descent based on Dirichlet and generalized Dirichlet distributions. We demonstrate that the natural gradient descent with step-size adaptation with Dirichlet and generalized Dirichlet distributions has higher accuracy and does not take a large number of iterations for minimizing test functions than gradient descent and Adam.

Cancer immunotherapies are gaining a large popularity and many of them have been approved as standard second-line or in some cases even as first-line treatment for a wide range of cancers. However, immunotherapy has not shown a clinically relevant success in glioblastoma (GBM), principally due to the brain’s “immune-privileged” status and the peculiar tumor microenvironment (TME) of GBM featured by lack of presence of tumor-infiltrating lymphocytes and the establishment of immunosuppressive mechanisms. Emerging evidence has highlighted the key role played by innate immune cells in immunosurveillance and in initiating and driving immune responses against GBM. Immunogenic cell death (ICD) is a promising approach to elicit direct activation of the innate immune system by inducing in target cancer cells the expression of molecular signatures recognized through a repertoire of innate immune cell pattern recognition receptors (PRRs) by effector innate immune cells. Herein, we explored local mild thermal treatment, generated by using ultrasmall (size ~ 17 nm) cubic-shaped iron oxide nanoparticles exposed to an external alternating magnetic field (AMF), to induce ICD in U87 glioblastoma cells. In accordance with what has been previously observed with other types of tumors, we found that mild hyperthermia modulates the immunological profile of U87 glioblastoma cells by inducing stress-associated signals leading to enhanced phagocytosis and killing of U87 cells by macrophages. Finally, we demonstrated that mild magnetic hyperthermia has a modulatory effect on the expression of inhibitory and activating NK cell ligands on target cells. Interestingly, alteration in the expression of NK ligands, caused by mild hyperthermia treatment, in U87 glioblastoma cells, increased their susceptibility to NK cell killing and NK cell functionality. The overall findings demonstrate that mild magnetic hyperthermia stimulates ICD and sensitizes GBM cells to NK-mediated killing by inducing the upregulation of specific stress ligands, providing a novel immunotherapeutic approach for GBM treatment, with potential to synergize with existing NK cell-based therapies thus improving their therapeutic outcomes.

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.

The present state of the natural environment is significantly influenced by the construction sector. Due to the large amounts of consumed natural resources and materials as well as the high volume of emitted pollutants, the building industry is considered to have a key influence in achieving a global sustainable development. A considerable part of the ecological impact of the built environment is influenced by the energy consumed for creating appropriate indoor conditions as well as by the greenhouse gases that are emitted during the operation stage of a building’s life cycle. Therefore, the energy performances of existing buildings must be significantly improved in the near future. This paper analyzes the influence of the thermal insulation thickness of walls on the linear thermal transmittances and the heating demand of an existing building. The resulted values show that while increasing the insulation of the walls leads to a serious heat demand reduction, a deep renovation of the building cannot be achieved solely by using this solution.

Aiming to improve the flame retardancy performance of natural rubber (NR), we developed a novel flame retardant synergistic agent through grafting of MCM-41 to graphene oxide (GO)，named as GO-NH-MCM-41，as an assistant of intumescent flame retardants (IFR). The structure of GO-NH-MCM-41 was characterized by FTIR, TEM and SEM tests, which confirmed that a fine grafting had been applied between GO and MCM-41. The flame retardancy of NR/IFR/GO-NH-MCM-41 composites was evaluated by limited oxygen index (LOI), UL-94 and cone calorimeter test. The LOI value of NR/IFR/GO-NH-MCM-41 reached to 26.3%; the UL-94 ratings improved to V-0 rating. Moreover, the addition of GO-NH-MCM-41 obviously decreased the peak heat release rate (PHRR) and the total heat release (THR) of the natural rubber composites. And the addition of GO-NH-MCM-41 increased the thickness of char residue. The images of SEM indicated the char residue were more compact and continuous. The degradation of GO-NH-MCM-41 based NR composites completed with a mass loss of 35.57% at 600 ℃. The tensile strength and the elongation at break of NR/IFR/GO-NH-MCM-41 composites were 13.9 MPa and 496.7%, respectively. The results of rubber process analyzer (RPA) reached the maximum value, probably due to a better network of the fillers in the matrix.

In the present paper, the comparison is conducted between three classical shell theories as applied to the linear vibrations of single-walled carbon nanotubes (SWCNTs); specifically, the evaluation of the natural frequencies is conducted via Donnell, Sanders and Flügge shell theories. The actual discrete SWCNT is modelled by means of a continuous homogeneous cylindrical shell considering equivalent thickness and surface density. In order to take into account the intrinsic chirality of carbon nanotubes (CNTs), a molecular based anisotropic elastic shell model is considered. Simply supported boundary conditions are imposed and complex method is applied to solve the equations of motion and to obtain the natural frequencies. Comparisons with the results of molecular dynamics simulations available in literature are performed to check the accuracy of the three different shell theories, where Flügge shell theory is found to be the most accurate. Then, a parametric analysis evaluating the effect of diameter, aspect ratio and number of waves along the longitudinal and circumferential directions on the natural frequencies of SWCNTs is performed in the framework of the three different shell theories. Assuming the results of Flügge shell theory as references, it is obtained that Donnell shell theory is not accurate for relatively low longitudinal and circumferential wavenumbers, for relatively low diameters and for relatively high aspect ratios. On the other hand, it is obtained that Sanders shell theory is very accurate for all the considered geometries and wavenumbers, and therefore it can be correctly adopted instead of the more complex Flügge shell theory for the vibration modelling of SWCNTs.

The objective is to study the local perceptions on the conservation of wild chimpanzees (Pan troglodytes verus) in the Réserve Naturelle Communautaire de Dindéfélo (RNCD), southeast of Senegal, to design specific actions to improve conservation management. We conducted 338 semi-structured interviews in three main villages of RNCD. Three-fourths of the population were farmers. Of those interviewed, 29% received elementary education. Two of the three villages participated in a project to plant trees as fences. On average, 66% of the respondents were animists. Of the respondents who were afraid to see a chimpanzee, 68% answered because they attack. Seventy-seven percent washed their clothes in the forest river because there was more water than in the village wells. Of the interviewees who threw the old clothes into the forest, 50% did it due to tradition. Ninety-six percent of respondents stated that chimpanzees do not feed from their crops. The main problems of the locals with the Reserve were lack of water and basic resources and not been allow to cut trees in protected areas. There were significant relationships between education (1 relationship), environmental project (4 relationships) and animism (11 relationships) with local perceptions. The 93% of the respondents who had the traditional belief that “if the old clothes were burnt, children would become sick” feel fear of chimpanzees, while those who did not have this animistic belief the 6% are afraid (χ² = 1.57, P < 0.02). These local perceptions allow us to design specific course of action to improve chimpanzee conservation and sustainable coexistence in this complex period of the Anthropocene.

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}

Software engineers from all over the world solve independently a lot of similar problems. In this condition the problem of code or even better architecture reusing becomes an issue of the day. In this paper two phase approach to determining the functional and structural likenesses of software projects is proposed. This approach combines two methods of artificial intelligence: natural language processing techniques with a novel method for comparing software projects based on ontological representation of their architecture automatically obtained from the projects source code. Additionally several similarity metrics are proposed to estimate similarity between projects.

Natural dyes offer a bio-based opportunity to support attractive coloration to textile fabrics made from natural fibers like cotton, wool, hemp and many other materials. They can be part of a strategy to realize fully bio-based textile materials and clothing. Following this statement, the actual study investigates the use of wood extracts for dyeing of cotton fabrics. Actually extract powders of Logwood (Haematoxylon campechianum L.), Brazilwood (Caesalpinia spp.), and Quebracho wood (Schinopsis lorentzii) are used. The dyeing process is modified using different mordants based on iron and aluminum salts. These mordants are applied in pre-, meta- or post-mordant procedures. Color and fastness properties of prepared textile samples are determined by spectroscopic measurements, color measurements, washing procedures and Xenotest for measuring the light fastness. Finally, a broad range of different colorations can be realized by different combinations of wood extracts and mordanting procedures. A good wash fastness is reached for some color shades. For reaching good wash fastness, pre- and meta-mordanting procedures are more advantageous compared to post-mordanting. However, the light fastness of realized samples is only moderate to low. In conclusion, it can be stated that dyes from wood extracts are excellent materials to dye natural fibers if they are combined with the right mordanting agent in pre- or meta-mordanting procedures. The actual study is therefore a good proof-of-concept for the realization of fully bio-based colored textile materials.

Weeds are a permanent component of anthropogenic ecosystems, which requires strict control to avoid the accumulation of their long–lasting seeds in the soil. With high crop infestation, many elements of crop production technologies (fertilization, productive varieties, growth stimulators, etc.) turn out to be practically meaningless due to high yield losses. Intensive use of chemical herbicides (CH) has led to undesirable consequences: contamination of soil and wastewater, accumulation of their residues in the crop, the emergence of CH-resistant populations of weeds. In this regard, the development of environmentally friendly CH with new mechanisms of action is relevant. Natural phytotoxins of plant or microbial origin may be directly explored in herbicidal formulations (biorational CH) or indirectly as scaffolds for nature-derived CH. This review considers: 1) the main current trends in the development of CH that may be important for enhancement of biorational herbicides; 2) advances in the development and practical application of natural compounds for weed control; 3) the use of phytotoxins as prototypes of synthetic herbicides. Some modern approaches such as computational methods of virtual screening and design herbicidal molecules, development of modern formulations, determination of molecular targets are stressed as important to make exploration of natural compounds more effective.

At the Rio Conference in 1992, the sustainable development agenda promised a new era for natural resource management, where the well-being of human society would be enhanced through the sustainable use of natural capital. Several decades on, economic growth continues unabated at the expense of natural capital, as evidenced by biodiversity loss, climate change and further environmental issues. Why is this happening and what can be done about it? In this research, we present three Agent-Based Models that explore the social, economic and governance factors driving (un)sustainability in complex social-ecological systems. Our modelling results reinforce the idea that the current economic system does not protect the natural capital on which it depends. This is due to a disjunction between the economic and environmental elements upon which the sustainable development paradigm is founded. Additionally, various factors appear to enhance social-ecological system unsustainability: the role of financial entities and monetary debt; economic speculation; technological development and efficiency; lack of long-term views and late government interventions; inefficient tipping point management; and the absence of strong top-down and bottom-up conservation forces. Interestingly, alternative scenarios showed that these same factors could be redirected to enhance sustainable development. The current economic system may, therefore, not be inherently unsustainable, but rather specific economic mechanisms, agents’ decision-making, and the kinds of links between economic and natural systems could be at the root of the problem. We argue that short- and medium-term sustainability can be enhanced by implementing mechanisms that shift capitalist forces to support environmental conservation. Long-term sustainability, however, requires further paradigm change: where the economy integrates, and fully accounts for, externalities and recognises the actual value of natural capital.

Understanding how the public perceives various risks and hazards associated with our well-being and health is crucial for government and policymakers. The present research aimed to assess the public's perception of various risks and hazards associated with their well-being and health. The study combined two well-known approaches to risk assessment, namely, the analytical hierarchical process (AHP) and the psychometric paradigm. Seven risk attributes were chosen from the risk perception literature to evaluate 27 risks and hazard activities using a survey questionnaire developed based on the psychometric paradigm literature. The collected data was then analyzed using AHP to determine the priority weight for each risk attribute. The results showed that the most important risk attribute was voluntariness of risk, followed by chronic-catastrophic and newness of risk. Furthermore, the study found that natural hazards were ranked the highest, followed by refugee influx and fire hazards. In contrast, the mobile phone was perceived as the lowest type of risk. The findings can be used by policymakers to develop effective and sustainable risk communication strategies. Policymakers can use the research findings to create effective and sustainable risk communication strategies that help the government to inform and educate the public about potential risks, improve coordination among agencies and stakeholders, and enhance public trust in government decision-making.

Several therapeutic monoclonal antibodies are approved by FDA against the PD-1/PD-L1 (programmed death-1/programmed death ligand-1) immune checkpoint, which has been a great success in cancer treatment. However, existing therapeutics, including small molecules inhibitors against PD-L1 checkpoint have certain drawbacks such as high cost and drug resistance that challenge the current available anti-PD-L1 therapy. Thereof, this study presents the screening of 32552 compounds from Natural Product Atlas database against PD-L1, including three steps of structure-based virtual screening and binding free energy refinement for the selection of potent PD-L1 inhibitors. Subsequently, five natural compounds, i.e., Neoenactin B1, Actinofuranone I, Cosmosporin, Ganocapenoid A, and 3-[3-hydroxy-4-(3-methylbut-2-enyl)phenyl]-5-(4-hydroxybenzyl)-4-methyldihydrofuran-2(3H)-one, were collected based on the ADMET (absorption, distribution, metabolism, excretion, and toxicity) profiling and binding free energy (>-70 kcal/mol) for further computational investigation by comparison to co-crystallised ligand, i.e. JQT inhibitor. Based on interaction mapping, explicit 100 ns molecular dynamics simulation, and post binding free energy calculations, all the compounds exhibited intermolecular interactions (hydrogen and hydrophobic) with essential residues and substantial complex stability by comparison to the JQT inhibitor. Collectively, the calculated results advocate the selected natural compounds as the putative potent inhibitors of PD-L1 and, therefore, can be considered for further development of PD-L1 immune checkpoint inhibitor in cancer immunotherapy.

The UK Government is committed to reducing greenhouse gas (GHG) emissions by 80% by 2050. Buildings are responsible for 37% of the total GHG emissions in the UK and the need to reduce their emissions has resulted in more stringent building regulations in the recent past. The regulations, energy rating systems and voluntary guidelines — all are primarily aimed at reducing the need for heating and associated energy use by increasing insulation and air-tightness. However, future climates are projected to be warmer than the present day. Internal gains dominated non-domestic buildings will likely overheat, the adaptation to which will require energy-intensive cooling solutions, thus defeating the purpose of heating-focused regulations. This research investigated the effects of warming climate on overheating, and energy use and resulting emissions in representative urban office spaces in London in the present-day and future climates using hourly dynamic thermal simulations. Findings suggest that more airtight and highly—insulated office buildings designed for heating—dominated temperate UK climate will overheat in the 2050s. Heating demand reduces but electricity consumption increases by 121% when hybrid cooling is adopted to ameliorate overheating. Despite the rise, adopting a mixed-mode ventilation strategy was one of the ways of achieving overall energy efficiency while meeting benchmark overheating and carbon emissions target in present and future climatic contradictions. Current heating-focused legislations need to be urgently re-evaluated to account for the effects of climatic variability and overheating risks.

Polycystic ovary syndrome (PCOS) is a common endocrine disorder in women, characterized by fluid-filled sacs in the ovaries and various symptoms including high androgen levels, endometrial irregularities, and cysts. While the cause of PCOS is unknown, it has been linked to genetic, endocrine, and metabolic factors, and there are several treatment options, including lifestyle modifications, medications, and surgery. Natural products, such as medicinal plants and fruits, are being explored as potential treatments for PCOS due to their bioactive compounds with pharmacological effects related to antioxidant, antimicrobial, anticancer, and antidiabetic properties. Some of these compounds improve insulin sensitivity, reduce inflammation, and enhance glucose metabolism, which is beneficial for women with PCOS. This review examined the effects of natural products on PCOS, including their effects on ovarian histological changes, blood glucose, sex hormones, and lipid profile, based on animal and human studies. This study suggests that the use of natural products as complementary medicine can be a promising resource for the development of effective therapeutics for PCOS, but that further research is needed to fully understand their benefits

Argulus is an ectoparasite that frequently infects goldfish, with severe effects and a high death rate. The use of plant extracts has been widely reported to have antiparasitic activities, including one of the solasodine compounds in sour eggplant extracts. This research evaluated the antiparasitic activities of Compositions A (Solanum ferox and Zingiber zerumbet extract at 400 and 200 ppm, respectively), B (SFE and Boesenbergia pandurata at 900 ppm), and C (single Solanum ferox at 400 ppm). The research stages comprised 1) isolation and analysis of solasodine content in sour eggplant extracts and its extract composition, 2) in vitro antiparasitic activities on Argulus sp. at doses of 50 and 100 ppm with a testing time of 60–240 minutes, and 3) in vivo antiparasitic activities on Argulus sp. on goldfish at a dose of 100 ppm and observation for 2–12 hours of medication. The results showed that sour eggplant extracts had a solasodine concentration of 7.151 mg/L, whereas the derivative compositions A, B, and C were 656, 485, and 295 ppm, respectively. The in vitro testing demonstrated that composition A was effective for killing approximately 80–100% of Argulus, whereas compositions B and C killed 80–90% and 60–70%, respectively. Viewed from the effectiveness for killing parasites, the result was excellent (above 50%). The in vivo medication test was continued using three extract compositions at a dose of 100 ppm. Compositions A, B, and C were found to be capable of releasing 81.33, 75.67, and 71.00 arguli, respectively, per fish. We concluded that the single SFE extract and a concoction with BPE and ZZE had reasonable antiparasitic activity, whereas the concoction of SFE and ZZE killed more Argulus parasites at a higher rate.

The article presents a literature review of three Chinese manuscripts containing natural dyes recipes and three containing synthetic dyes from the Ming Dynasty to the Republican Period. The manuscripts mentioned were exclusively used as references and sources. Four databases were used to access the original documents, then, a translation from old mandarin to Spanish was made. For natural dyes, the results show four dyestuffs were commonly used to produce colors, sappanwood (Caesalpinia sappan L.), followed by safflower (Carthamus tinctorius L.), indigo (Indigofera tinctoria L.), and sophora (S. japonicum L.). While the synthetic dyes exported to or produced in China were mainly bluish, greenish, and violet probably because they were difficult to obtain from natural ones. The literature review of the dyes and their recipes can be a key factor in any art history or conservation study, improving the understanding of how the colors were achieved and as complementary information for analytical analyses. It provides useful details that contribute to the preservation of Chinese dye history on silk heritage.

(1) Background: Most hemodialysis patients may experience physiological and psychological stress. Exposure to nature has been previously reported to reduce the measures of psychological and physiological stress, and immune function. This study aimed to investigate psychological and physiological effects of integrated indirect forest therapy on chronic renal failure patients undergoing hemodialysis. (2) Methods: As a quasi-experiment, this study employed a nonequivalent control group, repeated measurements, and a non-synchronized design. A total of 54 participants were included: 26 and 28 in the experimental and control groups, respectively. During hemodialysis, five types of forest therapy stimuli (visual, auditory, olfactory, tactile, and motor) were applied 3 times per week for 4 weeks during 15-minute sessions. (3) Results: Positive but not negative emotion measures differed between the groups after the intervention. Fatigue and physiological stress levels were significantly reduced in the experimental group, whereas no significant difference was found between the groups on the measures of psychological stress. Activation of both the parasympathetic and sympathetic nervous systems was similar in both groups, as was the number of natural killer cells. (4) Conclusion: Integrated indirect forest therapy may help increase positive emotions and reduce fatigue and stress levels during hemodialysis in patients with chronic renal failure.

By using the Black-Derman-Toy (BDT) model, we predict the future trend of the riskless rate, and then we build an equation that relates the market price of zero-coupon bonds and the theoretical price of zero-coupon bonds calculated using a binomial option pricing model. Based on this, we can find the implied daily return μ, implied natural upturn probability p, and implied daily volatility σ with respect to different time-to-maturity values of zero-coupon bonds. With these results, we can give some suggestions to bond traders.

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.

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

Landscape configuration and forest structure assume an increasing importance as determinants of animal communities. This paper focused on nocturnal lepidoptera inhabiting alder patches in the Sila National Park, Italy. According to their shape, patches were divided into linear and compact ones to disentangle the role of forest structure and landscape configuration in determining the composition of nocturnal lepidopteran communities at different observation scales. We used the Mann-Whitney test for medians to Shannon diversity, Equitability, Fisher’s alpha, and nestedness to test differences among moth communities. We found that compact patches inhabit richer and more abundant communities. The abundance-based Correspondence Analysis showed moth communities clustered according to woodlot shape, except a compact woodlot having a linear-like moth community because entirely surrounded by grasslands. Percentage of forested area and abundance and composition of communities are positively correlated at 50 and 200 m buffers, while correlations are absent at smaller and larger buffers. Our results demonstrated that a width of 50 m may not be sufficient to give proper functionality to the wooded area, at least for moths. As consequence, planning of forest restorations should consider the importance to increase structural habitat continuity at larger scales.

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

In today's business world, many companies have realized that consumer feedback evaluations play a crucial part in shaping the company's future activities. The hotel and tourism industries are well-known examples where the user feedback has become crucial. Even though most accommodations want their clients to express their opinions about their services, these reviews are nevertheless carefully reviewed by them. This manual approach is time demanding and error prone, the level of expertise of the individual analyzing the evaluations determines the quality of the manual analysis. This research focuses on the development of a tool-based support system that analyzes text-based customer evaluations and then highlights the maintenance issues identified in the reviews, removing the need to manually evaluate and analyze the customer reviews. The process includes data preparation, sentiment analysis, and a fully trained deep learning model to extract critical insights. Using sentiment analysis techniques after data preprocessing and trained deep learning model, an accuracy of 96\% was achieved, and this paper discusses how this technology helps to meet the needs of accommodation management by completing activities efficiently. This might save management time and helps to save money without losing clients, allowing them to enhance their additional sales.

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

One of the principal hallmarks of Alzheimer’s disease (AD) is related to the aggregation of amyloid-β fibrils in an insoluble form in the brain, also known as amyloidosis. Therefore, a prominent therapeutic strategy against AD consists either in blocking the amyloid aggregation and/or destroying the already formed aggregates. Natural products have shown significant therapeutic potential as amyloid inhibitors from in vitro studies as well as in vivo animal tests. In this study, the interaction of five natural biophenols (curcumin, dopamine, (-)-Epigallocatechin-3-gallate, Quercetin, and Rosmarinic acid) with the amyloid-β(1-40) fibrils has been studied through computational simulations. The results allowed the identification and characterization of the different binding modalities of each compounds and their consequences on fibril dynamics and aggregation. It emerges that the lateral aggregation of the fibrils is strongly influenced by the intercalation of the ligands, which modulate the double-layered structure stability.

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.

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.