This work introduces Aviation-BERT-NER, a Named Entity Recognition (NER) system tailored for aviation safety reports, building on the Aviation-BERT base model developed at the Georgia Institute of Technology's Aerospace Systems Design Laboratory. Unlike general NER models, this system integrates aviation domain-specific data, including aircraft types, manufacturers, quantities, and aviation terminology, to enhance its precision in identifying and classifying named entities critical for aviation safety analysis. A key innovation of Aviation-BERT-NER is its template-based approach to fine-tuning, which utilizes structured datasets to generate synthetic training data that mirrors the complexity of real-world aviation safety reports. This method significantly improves the model's generalizability and adaptability, enabling rapid updates and customization to meet evolving domain-specific requirements. The development process involved careful data preparation, including the synthesis of entity types and the generation of labeled datasets through template filling. Fine-tuning of the Aviation-BERT model demonstrated strong performance in precision, recall, and F1 scores, showing its efficacy in accurately identifying a wide range of entity types within aviation safety reports. Testing on real-world narratives from the National Transportation Safety Board (NTSB) database highlighted the model's robustness, with performance metrics indicating its potential to significantly enhance the automation of aviation safety report analysis. This work addresses a critical gap in English language NER models for aviation safety and establishes a new benchmark for domain-specific NER systems, promising substantial improvements in the analysis and understanding of aviation safety reports.

For the first time, we describe phylogenomic signatures of an epidemic lineage of vesicular stomatitis Indiana virus (VSIV). We applied multiple evolutionary analyses to a dataset of full-length genome sequences representing the circulation of an epidemic VSIV lineage in the US between 2019 and 2020. Based on phylogenetic analyses, we predicted the ancestral relationship of this lineage with a specific group of isolates circulating in the endemic zone of Chiapas, Mexico. Subsequently, our findings indicate that the lineage diversified into at least four different subpopulations during its circulation in the US. We identified single nucleotide polymorphisms (SNPs) that differentiate viral subpopulations and assessed their potential relevance using comparative phylogenetic methods, highlighting the preponderance of synonymous mutations during the differentiation of these populations. Purifying selection was the main evolutionary force favoring the conservation of this epidemic phenotype, with P and G genes as the main drivers of the evolution of this lineage. Our analyses identified multiple codon sites under positive selection and the association of these sites with specific functional domains at P, M, G, and L proteins. Based on ancestral reconstruction analyses, we showed the potential relevance of some of the sites identified under positive selection to the adaptation of the epidemic lineage at the population level. Finally, using a representative group of viruses from Colorado, we established a positive correlation between genetic and geographical distances, suggesting that positive selection on specific codon positions might have favored the adaptation of different subpopulations to circulation in specific geographical settings. Collectively, our study reveals the complex dynamics that accompany the evolution of an epidemic lineage of VSIV in nature. Our analytical framework provides a model for conducting future evolutionary analyses. The ultimate goal is to support the implementation of an early warning system for vesicular stomatitis virus in the US, enabling early detection of epidemic precursors from Mexico.

The permeability, heat conductivity and reaction rate will be varied with the change of natural gas hydrate saturation, when thermal fluid injected into the natural gas hydrate reservoirs. In order to characterize the variation of physical field parameters with hydrate saturation, DDF-LBM was applied to simulate the hydrate dissolving process by thermal fluid injection under pore scale modelling. Based on the forced conjugate heat transfer case, the relaxation frequency of the thermal lattice in the pores is corrected. Based on the P-T phase equilibrium relationship of hydrates and considering the heat absorbed by the hydrate reaction, the solid-liquid state of the hydrate lattice is judged in real time, and the dynamic simulation of the heat flow solidification multi-physics field is realized. The simulation results show that the dissolution rate of hydrates by thermal fluid injection was higher than that by heating the hydrate surface alone, and was positively correlated with the hydrate saturation. On the basis of the above results, this paper provided exponential fitting equations between different hydrate saturations and average permeability, effective thermal conductivity, and inherent reaction rate. The fitting results show that saturation has a negative correlation with relative permeability and effective thermal conductivity, and a positive correlation with the inherent reaction rate. The above results can provide a reference basis for accurately describing the heat and mass transfer of natural gas hydrate under macro-scale.

Text recognition is an important area of the pattern recognition field. Natural language processing (NLP) and pattern recognition have been utilized efficiently in script recognition. Much research has been done on handwritten script recognition. However, the research on the Arabic language for handwritten text recognition received little attention compared with other languages. Therefore, it is crucial to develop a new model that can recognize Arabic handwritten text. Most of the existing models used to acknowledge Arabic text are based on traditional machine learning techniques. Therefore, we implemented a new model using deep machine learning techniques by integrating two deep neural networks. The architecture of the Residual Network (ResNet) model is used to extract features from raw images. Then, the Bidirectional Long Short-Term Memory (BiLSTM) and connectionist temporal classification (CTC) are used for sequence modeling. Our system improved the recognition rate of Arabic handwritten text compared to other models of similar type with a character error rate of 13.2% and character error rate of 27.31%. In conclusion, the domain of Arabic handwritten recognition is advancing swiftly with the use of sophisticated deep learning methods.

In modern agriculture, decision-making is deeply intertwined with data, yet essential agricultural knowledge remains encased within dense scientific texts such as journals, manuals, and free-text reports. This necessitates specialized search systems capable of distilling and delivering precise information in response to specific queries from agricultural users. This study introduces AgriQuery, a sophisticated agent designed to field natural language questions from the agricultural sector by exploring vast troves of scientific documents. A comprehensive review and analysis of farmers' information needs have been conducted, leading to the creation of an information retrieval test collection that includes actual queries, a vast array of scientific documents segmented into passages, and a set of ground truth relevance assessments that map these passages to corresponding queries. We explore and benchmark several information retrieval models, highlighting the efficacy of advanced neural ranking models in this context. AgriQuery's proposed deployment architecture features a client interface on the Telegram platform and a backend model on standard hardware. This test collection aims to catalyze further research into matching natural language queries with scientific text answers, with implications extending beyond agriculture.

The proliferation of fake news on social media platforms poses significant challenges to information authenticity and public trust. In this study, the impact of various word embedding techniques on the performance of both machine learning and deep learning models for fake news detection is systematically evaluated. The comprehensive TruthSeeker dataset, which spans over a decade of labeled news articles and social media posts, is utilized. Three popular word embedding techniques are analyzed: TF-IDF, Word2Vec, and FastText. These embeddings were tested with multiple machine learning classifiers, including logistic regression, naive Bayes, K-nearest neighbors, support vector machines, multilayer perceptrons, decision trees, and random forests, as well as deep learning models, specifically convolutional neural networks (CNNs). Among machine learning models, the Support Vector Machine (SVM) with TF-IDF achieved the highest accuracy of 99.03%, precision of 98.92%, recall of 99.15%, and F1 score of 99.03%. The Multilayer Perceptron (MLP) also demonstrated strong performance with Word2Vec, reaching an accuracy of 95.24%, precision of 94.95%, recall of 95.50%, and F1 score of 95.07%. For deep learning models, CNNs combined with TF-IDF embeddings showed outstanding results, achieving an accuracy of 98.99%, precision of 98.56%, recall of 99.48%, and F1 score of 99.02%. These results highlight the critical role of word embeddings in enhancing model accuracy and reliability in misinformation detection.

Agro-waste fibres for polymer composite reinforcement have gained increased interest in industry and academia as a more sustainable alternative to synthetic fibres. However, natural fibre composites (NFC) hygroscopicity is still an issue that needs to be solved. This work investigates how prolonged exposure to water affects the properties of the polypropylene (PP)--based injection moulded composites reinforced with different contents of rice husk (rh) and olive pit (op) fibres. Both rh and op composites became more hydrophilic with increased fibre charge due to the affinity of cellulose and hemicellulose OH groups. Meanwhile, lignin contributes to the protection of the composites from thermo-oxidative degradation caused by water immersion. The PPrh composites had a higher saturation water content of 1.47% (20 wt.% rh) and 2.38 (30 wt.% rh) in comparison to PPop composites with absorption of 1.13% (20 wt.% op) and 1.59% (30 wt.% op). Tensile elastic modulus has slightly increased, at the cost of increased saturated composites' rigidity, in composites with 30% rh and op fibre content (up to 13%) while marginally decreasing (down to 8%) in PP30%op compared to unsaturated counterparts. A similar trend was observed for the flexural modulus, enhanced up to 18%. However, rh and op composites with 30% fibre content ruptured in bending, highlighting their fragility after hydrolytic ageing.

The growing deforestation of tropical forests requires the implementation of restoration actions capable of assisting the recovery of biodiversity and functioning of these ecosystems. This research aimed to identify the environmental factors that influence woody plant recruitment abundance and diversity in an Andean forest restoration project in Medellin (Colombia). Data from all individuals taller than 80 cm was collected in 22 plots of 200 m2. The environmental factors selected were edaphic variables, plantation structure, slope, elevation, prior land use and landscape forest cover. A generalized linear model was adjusted for recruitment density, and linear mixed models were built for recruited species richness, diversity and dominance. Woody plant recruitment attributes in our study area were similar to a typical secondary succession in an Andean forest, but planted trees contributed little to these attributes. While recruitment density was affected by slope, canopy closure and landscape forest cover, recruitment diversity was influenced by physical (bulk density) and chemical (pH, aluminum, CEC) edaphic factors, planted trees diversity (species richness and composition), canopy closure and mortality rate of planted trees. We conclude that a good development of planted trees enhances both recruitment attributes, indicating a synergy between active restoration and passive regeneration processes.

Plastics, once regarded as a revolutionary material shaping modern society, now poses an unprecedented threat to our environment. Household solid waste sorting stations produce several fractions, one of which contains a high concentration of Low-Density Polyethylene (LDPE) film waste (packaging, sunscreen film, etc.). This fraction is difficult to recycle because it contains quite a lot of impurities. Usually, it is sent to the cement factories that burn it together with other fuels. However, with some processing techniques such catalytic pyrolysis this fraction could be valorized. In this paper the experiments which were carried out at a laboratory scale installation, in batches with a processing capacity of 1-3 kg of waste. The pyrolysis reactor is connected to a distillation column giving the possibility to separate the fractions. The gaseous and liquid fractions were characterized by GC-FID-TCD (gases) and GC-MS (liquids) analysis. Natural catalysts such as bentonite or clinoptilolite were studied and used in the melting of plastic mass to simplify the process as much as possible. To test the activity of the catalysts, the pyrolysis of LDPE granules was initially studied. It was found that natural zeolites are much more active than bentonite and that a minimum concentration of 5-10% is needed to have a positive effect on the composition of the fractions (increasing the weight of the light fractions C1-C6, C6-C10 and C11-C13) in relation to the heavy fractions C13-C20 and C20+. Catalytic pyrolysis gives a completely different distribution of light hydrocarbons. The best catalyst selected from LDPE lab experiments, was then tested upon the pyrolysis of plastic film waste obtained by a waste treatment plant. The research objective reported in this paper was to obtain a fraction of combustible gases in the largest possible proportion, which can be much easier exploited by burning in an engine that drives an electric generator.

Multimodal emotion classification (MEC) involves analyzing and identifying human emotions by integrating data from multiple sources, such as audio, video, and text. This approach leverages the complementary strengths of each modality to enhance the accuracy and robustness of emotion recognition systems. However, one significant challenge is effectively integrating these diverse data sources, each with unique characteristics and levels of noise. Additionally, the scarcity of large, annotated multimodal datasets in Bangla limits the training and evaluation of models. In this work, we unveiled a pioneering multimodal Bangla dataset, MAViT-Bangla (Multimodal Audio Video Text Bangla dataset). This dataset, comprising 1002 samples across audio, video, and text modalities, is a unique resource for emotion recognition studies in the Bangla language. It features emotional categories such as anger, fear, joy, and sadness, providing a comprehensive platform for research. Additionally, we developed a framework for emotion recognition that employs a cross-modal attention mechanism among unimodal features. This mechanism fosters the interaction and fusion of features from different modalities, enhancing the model’s ability to capture nuanced emotional cues. The effectiveness of this approach was demonstrated by achieving an F1-score of 0.64, a significant improvement over unimodal methods.

As the understanding of natural gas hydrates as a vast potential resource deepens, their importance as a future clean energy source becomes increasingly evident. However, natural gas hydrates tend to secondary generation during extraction and transportation, leading to safety issues such as pipeline blockages. Consequently, developing new and efficient natural gas hydrate inhibitors has become a focal point in hydrate research. Kinetic Hydrate Inhibitors (KHIs) offer an effective solution by disrupting the nucleation and growth processes of hydrates without altering their thermodynamic equilibrium conditions. This paper systematically reviews the latest research progress and development trends of KHIs for natural gas hydrates, covering their development history, classification, and inhibition mechanisms. It particularly focuses on the chemical properties, inhibition effects, and mechanisms of polymer inhibitors such as polyvinylpyrrolidone (PVP) and polyvinylcaprolactam (PVCap). Studies indicate that these polymer inhibitors provide an economical and efficient solution due to their low dosage and environmental friendliness. Additionally, this paper explores the environmental impact and biodegradability of these inhibitors, offering guidance for future research, including the development, optimization, and environmental assessment of new inhibitors. Through a comprehensive analysis of existing research, this work aims to provide a theoretical foundation and technical reference for the commercial development of natural gas hydrates, promoting their safe and efficient use as a clean energy resource.

Considering that emotional events diversify as one ages, this study formulates the Salient Episodic Memory Aggregation Hypothesis, which claims that aging induces homogenization of episodic memory and consequently increases the salience of emotional episodes. To test this hypothesis, the study focused on sadness episodic memory and conducted a web-based survey and a combined analysis consisting of natural language processing, machine learning, and network graph analysis. A sample of 2,954 adults aged between their 20s and over 70s participated in this study. They were asked to describe a sad event they had experienced and assess the psychological properties of the recalled episodes. After isolating and lemmatizing episodic words using natural language processing, vocabulary frequency was counted for each generation, and network graph analysis based on machine learning was performed to compare the structures of sadness episodes between generations. As a result, there were mainly two types of episodic words for which frequencies either declined or increased with age. The network structures of episodic words for the older generations possessed higher proximity (shortest path length) to higher concentrations of episodic words. These findings support the current hypothesis, indicating that the nature of sadness episodes differ between generations, and that older people have more aggregated episodic memory structures.

Pygeum africanum bark has been shown to inhibit the production of pro-inflammatory prostaglandins in the prostate and reduce the production of leukotrienes and other 5-lipoxygenase (5-LO) metabolites. It has been suggested that inflammation plays an important role in the pathophysiology of benign prostatic hyperplasia (BPH). In clinical trials, P. africanum improved the symptoms and objective measures of BPH. This in vitro study aimed to assess the anti-inflammatory potential of a proprietary Pygeum bark standardized extract (Prunera®) on cytokine release by lipopolysaccharide-simulated human peripheral blood mononuclear cells (PBMCs). PBMCs were obtained from four donors and a bead-based assay (ProcartaPlex™ panel) was used for the detection and quantitation of cytokines. Pygeum africanum bark standardized extract (PABE) showed the following effects: IL-2 was lowered in all donors in absence of a clear dose–response relationship; IL-4, IL-5, IL-6, IL-9, and IL-13 levels were decreased in most donors; IL-22 levels seemed to be suppressed only for donor 4 at lower and medium concentrations; and IL-27 and levels of TNF-α decreased at all PABE concentrations in all donors. The anti-inflammatory effect of PABE supports the potential use of this natural compound in the management of BPH and other conditions in which pro-inflammatory cytokines are involved in their underlying pathophysiological mechanisms.

Securing the structural safety of blades has become crucial, owing to the increasing size and weight of blades resulting from the recent development of large wind turbines. Composites are primarily used for blade manufacturing because of their high specific strength and specific stiff-ness. However, in composite blades, joints may experience fractures from the loads generated during wind turbine operation, leading to deformation caused by changes in the structural stiff-ness. In this study, 7,132 debonding damage data, classified by damage type, position, and size, are selected to predict debonding damage based on natural frequency. The change in the natural frequency caused by debonding damage is acquired through finite element (FE) modeling and modal analysis. Synchronization between the FE analysis model and manufactured blades is achieved through modal testing and data analysis. Finally, the relationship between the debond-ing damage and the change in natural frequency is examined using artificial neural network techniques

Candida auris is considered an emerging Candida species that has rapidly spread all over the world. The evidence of its origin and emerging resistance is still unclear. The severe infection by this species causes significant mortality and morbidity among the elderly and immune compressed individuals. Development of drug resistance is the major factor associated with therapeutic failure of existing antifungal agents. Previous studies addressed the antifungal resistance profile and drug discovery for C. auris. However, comprehensive coverage of these information in a single investigation yet to be available. In this review, we have mainly focused on recent development in therapeutic strategy against C. auris. Based on the available information, several differential approaches were discussed, including existing antifungal drugs, chemical compounds, essential oils, natural products, antifungal peptides, immunotherapy, antimicrobial photodynamic therapy, drug repurposing, and drug delivery systems. Among them, chemical medications, natural products, and antifungal peptides are the prime contributors. However, a limited number of resources are available to prove the efficiency of these potential therapies in clinical usage. Hence, we hope that the data gathered in this review can encourage in vivo studies and clinical trials.

Ultramafic rocks become promising candidates for carbon sequestration by enhanced carbon dioxide (CO2) mineralization strategies due to their highly CO2-reactive mineral composition and its abundant availability. This study reports a mineralogy and microtextures of a representative ultramafic rock from the Ma-Hin Creek in northern Thailand and observes evidence of CO2 mineralization occurring through the interaction between CO2 and the rock with the existence of water under ambient conditions. After sample collection, rock description was determined by optical petrographic analysis. The rock petrography reveals a cumulated wehrlite comprising over 50% olivine and minor amounts of clinopyroxene, plagioclase, and chromian spinel. Approximately 25% of the wehrlite has altered to serpentine and chlorite. A series of CO2 batch experiments were conducted on six different rock sizes at a temperature of 40°C and pressure of 1 atm over five consecutive days. The post-experimental products were dried, weighed, and geochemically analyzed to detect changes in mineral species. Experimental results showed that product weight and the presence of calcite increased with reducing grain size. Additionally, the modal mineralogy of the wehrlite theoretically suggests a potential CO2 uptake of up to 53%, which is higher than the average uptake values of mafic rocks. These findings support the suitable rock investigation approach and the preliminary assessment of carbon mineralization potential, contributing to enhanced rock weathering techniques for CO2 removal that could be adopted by mining and rock supplier industries.

This paper investigates women’s empowerment and their active involvement in governance aimed at promoting sustainable development. The study focuses on examining the roles and processes of co-creation of nature-based solutions (NBS) for water management in Milan. Three research directions were explored: 1) to determine gender-based roles within local governance; 2) to detect semantic groups of NBS based on stakeholders' gender; c) to analyze gender-based linguistic and conceptual patterns of NBS. To achieve our objectives, we converted a sample of audio-video interviews of local stakeholders into textual data along with metadata indicating speakers' roles and gender. Descriptive statistics were applied to explore the governance's roles. We employed a combination of textual analysis and network analysis to identify thematic groups of NBS, and applied a word embedding approach to uncover linguistic patterns indicative of semantic sub-structures. The findings revealed significant demographic diversity. Male stakeholders perceive sustainable development as a process driven by the circular economy and the coordination of territorial authorities, while female stakeholders envision a transformative approach rooted in science, culture, and collaboration. The semantic sub-structure analysis highlighted a tendency among male governance to emphasize specialized skills, whereas female governance placed greater emphasis on problem definition rather than resolution.

Organic solar cells are contributing extensively to the production of future large area and cost-effective sources of renewable energy with a compact environmental impact. Moreover, due to tunable optical properties of the active medium, the semi-transparent organic solar cell can be fabricated and become a promising nominee for building Integrated Photovoltaics (BIPV), vehicles, and mobile electronic devices. Therefore, three newly synthesized polymers used as were host materials for the active medium and blended with beetroot dye. Hence, based on BHJ structure two ternary systems have been constructed and utilized to fabricate semitransparent solar cells and characterized. The results show that the natural dye has a crucial impact on solar cell characteristic parameters. Consequently, large value of V_OC close to 1V were obtained for ternary systems and acceptor material named PINDOLE exhibits potentials for electron transport layer (ETL) and shows an improvement in both fill factor and device efficiency.

Based on screening in computational biology and biological in vitro assays, five natural products isolated from extracts of the herbal medicine toad skin, such as cinobufagin (CBFi), bufalin (BFi), arenobufagin (ABFi), telocinobufagin (TBFi), bufotalin (BFTi), were subjected to molecular docking calculations with the use of SARS-CoV-2 main protease (PDB 6LU7 and 7BTF) and top-scoring ligand-receptor complexes were obtained. The results showed that the binding energy of ABFi to the 3CL protein was -17.044kcal/mol, which was higher than CBFi and TBFi. However, the binding energy of ABFi to the RdRp protease was -23.250 kcal/mol, which was much lower than that of CBFi and TBFi, EVEN lower than that of ABFi to the 3CL protein. ABFi also has polar interactions with amino acids such as Glu811, Ser814, Ser681 and Thr680 of RdRp enzyme. The results revealed that ABFi had a moderate inhibitory effect on the cell proliferation of SARS-CoV-2 in vitro, with an inhibition rate of 61.12%, even weaker than Remdesivir. This new discovery provides us with new ideas for in-depth studies on the development of natural products with this class of structural generalizations as inhibitors of SARS-CoV-2, and provides an experimental basis for the next step of mechanistic studies.

Sentiment analysis plays a crucial role in understanding customer feedback, guiding product development, and informing business decisions. This paper introduces term sentiment entropy (TSE), a novel weighting method that leverages the distribution of sentiment labels associated with words to enhance text classification accuracy. TSE complements traditional TF-IDF techniques by capturing the nuances of sentiment variation across different contexts. Experiments across diverse public datasets demonstrate TSE's potential to improve sentiment analysis performance, especially in capturing subtle sentiment shifts and adapting to specific domains. While computational cost and label quality pose challenges, TSE offers a promising avenue for refining sentiment analysis and opening new research frontiers.

Keywords: conservation; cultural pluralism; environmental regulation; community-based natural resource management; Indigenous law

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.

Aim: Utilizing the subjective experience of nurse executives who have supervised nurses during a major disaster in a hospital setting, this study aims to describe the subjective experience of nurse executives (NE) who have supervised nurses’ response to major disaster. This paper will focus on strategies to support nursing response to disaster, specifically to strengthen resiliency and the ability to maintain function despite the shock of disaster, including those caused by climate changes. Methods: Semi-structured interviews were conducted with 11 hospital-based nurse executives who supervised nurses during some of the worst natural and human-made disasters in different regions of the United States. A phenomenological approach was used to analyze and describe emerging themes from qualitative data. Results: Three Nurse Executives demonstrated theme saturation for mitigation steps to augment traditional disaster readiness activities: 1.) Assessment of Human Infrastructure: Daily Skills Needed During Disaster Response, 2.) Identification and Study of Failure Points & Metrics, 3.) Strengthening Human Infrastructure: Rectifying Deficiencies), and 4.) Monitoring Metrics & Making Corrections During Conventional Periods. Conclusion: Mitigation steps may improve outcomes in hospital function during conventional times; therefore, may improve resiliency and the ability to maintain functions during major disasters, including climate change.

Deep eutectic solvents are a mixture of two or more components, that at a particular composition become liquids at room temperature. When the compounds that constitute the DES are primary metabolites, namely, amino acids, organic acids, sugars, or choline derivatives, the DES are so called natural deep eutectic solvents (NADES). NADES fully represent green chemistry principles. These solvents are highly welcome, as they are obtained from renewable resources, and gratifyingly biodegradable and biocompatible. They are an alternative to room temperature ionic liquids (RTILs). From the pharmaceutical industry point of view, they are highly desirable, but unfortunately have been rarely used despite their enormous potential. In this review, we will look at their impact on the asymmetric catalytic synthesis of key target molecules, via metal-based catalysis, biocatalysis and organocatalysis. In many cases the NADES that have been used are chiral and can even promote enantioselective reactions, this crucial and very exciting aspect will also be discussed and analysed.

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

Certain configurations of liquefied natural gas refueling stations exhibit a deficiency in managing boil-off gas. Furthermore, the ill-conceived linkage between the submersible pump and the gas storage tank pipeline leads to impeded natural gas transmission. This study employs Computational Fluid Dynamics (CFD) methodology to scrutinize the hydrodynamic attributes of T-type tee and dovetail tee configurations implemented in the pipeline design connecting the submersible pump and storage tank in an LNG filling station across diverse operational scenarios. The T-type tee induces detachment of the primary flow from the inner wall due to inertial forces, resulting in vortex formation and heightened resistance, accompanied by increased energy dissipation. Conversely, the dovetail tee, with its circular wall transition, mitigates vortex generation, reducing the separation zone and consequently minimizing resistance and energy loss. The maximum static differential pressure between the inlet and outlet of the dovetail tee is diminished by 52.52% when compared to that of the T-type tee. In the most noteworthy scenarios, the local resistance coefficient of the dovetail tee is even less than 1/10 of that of the T-type tee. In practical engineering applications, the incorporation of dovetail tees leads to a 17.58% reduction in the required elevation difference. This results in a more uniform flow field and enhanced stability in both flow rate and pressure. Such improvements contribute to heightened engineering efficiency and environmental sustainability, particularly evident in the design of LNG filling stations.

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

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

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.

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.

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.

Background: Real-world data (RWD) related to the health status and care of cancer patients reflect the ongoing medical practice, and their analysis yields essential real-world evidence. Advanced information technologies are vital for their collection, qualification, and reuse in research projects. Methods: UNICANCER, the French federation of comprehensive cancer centres, has innovated a unique research network : Consore. This potent federated tool enables the analysis of data from millions of cancer patients across eleven French hospitals. Results: Currently operational within eleven French cancer centres, Consore employs natural language processing to structure the therapeutic management data of approximately 1.3 million cancer patients. This data originates from their electronic medical records, encompassing about 65 millions of medical records. Thanks to the structured data, which is harmonized within a common data model, and its federated search tool, Consore can create patient cohorts based on patient or tumor characteristics, and treatment modalities. This ability to derive larger cohorts is particularly attractive when studying rare cancers. Conclusions: Consore serves as a tremendous data mining instrument that propels French cancer centres into the big data era. With its federated technical architecture and unique shared data model, Consore facilitates compliance to regulations and acceleration of cancer research projects.

The modification of cellulose woven fabrics and viscose nonwovens was carried out with the aim of preparing sustainable coatings from biodegradable natural polymers. The modification of fabrics with biodegradable natural polymers represents an ecological alternative to other textile modifications such as the sol-gel process. Coatings were prepared from erythritol, gelatin and collagen in various formulations with the addition of propolis and alginate fibers and a natural plasticizer (glycerin). The morphology of the materials was determined before and after modification with Dino-lite, the pH value, the drop test method, the angle recovery angle, the thickness and the mass per unit area. The modifications have no significant effect on the thickness and mass per unit area, in a larger proportion they show hydrophilic properties, which favours the application for medical purposes - for example the absorption of exudates, wound dressings etc. Due to the neutral and slightly alkaline pH value of the modified samples, they are suitable for external application on the skin. The recovery angle of the modified samples proves that the samples do not tend to crease and that they retain their elasticity after modification and have a pleasant textile feel (fabric hand).

The case of naturalization of the zebra mussel, Dreissena polymorpha (Pallas), in coun-tries lying beyond its native Ponto-Caspian range is remarkable as one of the first instances, when the scientific community as early as the mid-19th century was fully aware of the non-indigenous status of a particular species as well as of the need for the study and monitoring of this process. Based on a study of contemporary sources, I reconstruct the early response of the European naturalists (including those that today would be called ‘citizen scientist’) to the invasion of Dreissena and describe their attitudes to the problem, including the divergence of opinion about the origin and the means of dispersal of this bivalve species. An analysis of pa-pers of the English, French, German, and Russian authors published between 1774 and 1920 has shown that the invasion of D. polymorpha was by no means ‘silent’; quite opposite, it provoked an immediate reaction of naturalists, and the scientific agenda for the study of the new invader was proposed in England as early as 1838

The new Common Agricultural Policy of the European Union (EU) (2023-2027) envisages a stronger Agricultural Knowledge and Innovation System (AKIS) that will unite all relevant ac-tors in a co-creation process to ensure knowledge flows within and across Member States. A sig-nificant and increasing body of literature focuses on the strategy and processes of strengthening AKIS. However, there is a relative gap in research related to the role of AKIS in achieving sus-tainability in the farming sector. The purpose of this paper is to provide an overview of the advancements so far of AKIS across the EU when it comes to facilitating the sustainable management of natural resources (SMNR). The paper builds upon a comprehensive review of rel-evant literature during the last 15 years covering all 28 EU countries (including the United King-dom until 2019). We conclude that AKIS architecture in most EU countries lacks a comprehensive structure promoting SMNR. Based on the results we identify countries with relatively high inten-sity of research and development projects linking AKIS to SMNR and where SMNR is better in-tegrated to AKIS. Our results can be of interest to the design of stronger AKIS during the new programming period.

The wound management sector has attracted considerable attention due to the increasing challenge posed by antimicrobial resistance (AMR) and the specialized care required for chronic wounds. Effective wound care involves selecting appropriate dressings tailored with antimicrobial agents to prevent bacterial infection. Among various types of wound dressings, fibers and electrospun fibers, with their unique characteristics, have emerged as innovative new materials. However, the rise of AMR necessitates the exploration of new antimicrobial agents for wound dressings, particularly for addressing bacterial pathogens like methicillin-resistant Staphylococcus aureus (MRSA). Endophytic fungi, known for producing diverse bioactive compounds, including novel antibacterial agents, represent a promising source of such new agents. This study tested thirty-two endophytic fungi from thirteen distinct Australian native plants for their antibacterial activity against S. aureus, the most common wound-associated bacteria. Ethyl acetate (EtOAc) extracts from fungal culture filtrates exhibited inhibitory effects against both methicillin-sensitive S. aureus ATCC 25923 and MRSA strains M173525 and M180920. DNA sequence analysis was employed for fungal identification, with EtOAc extracts from the most active sample, EL 19 (Chaetomium globosum), selected for further bactericidal and antibiofilm testing against the chosen bacteria. Biofilm of S. aureus ATCC 25923 was reduced by 55% by EtOAc extracts of EL 19 at 1/2 × MIC. To demonstrate the potential application in wound dressing materials, three different concentrations of the extract were incorporated into Polycaprolactone fiber mats through electrospinning, with resultant inhibition of S. aureus ATCC 25923 being observed. This research underscores the potential of endophytic fungi from Australian plants as sources of substances effective against common wound pathogens. Further exploration of the responsible compounds and their mechanisms could facilitate the development of wound dressings effective against MRSA and innovative biofilm-resistant electrospun fibers, contributing to the global efforts to combat AMR.

Natural deep eutectic solvents (NADES) have been shown as selective and environmentally friendly solvents for the extraction of bioactive compounds. However, studies on the solubility of low molecular weight carbohydrates (LMWC) in NADES are scarce. In this work, new solubility data of LMWC in NADES are provided and a new approach based on the use of these solvents for the efficient fractionation of bioactive carbohydrates was explored for the first time. Several mono- and disaccharides and three NADES based on choline chloride (ChCl) and different donors [2-ethylene glycol (EtG), glycerol (Gly) and ethanedioic acid dihydrate (Eth)] were considered. While degradation of carbohydrates, mainly ketoses, was detected with ChCl:Eth due to its acidic nature, ChCl:EtG and ChCl:Gly were found to be useful alternatives for selectively separating bioactive ketoses and their corresponding aldoses (e.g. lactulose/lactose and tagatose/galactose) present in equimolar binary mixtures. In addition, the usefulness of ChCl:EtG for the selective enrichment of lactulose to be used as food ingredient or nutraceutical was proven (from a 25% in the reaction mixture to a 56% in the purified sample). NADES could be used for the selective fractionation of value-added carbohydrates from interfering sugars for several applications including food science, engineering or pharmaceuticals.

Biosynthetic gene clusters are specific genomic regions in microorganisms, like bacteria and fungi, responsible for producing bioactive compounds. Identifying these clusters is complex due to their diverse nature. This research presents a comprehensive approach to effective BGC identification. The study focuses on five classes of Natural Products: PKS , NRPS, RiPP, Terpenes, and Hybrid PKS-NRPS. Data was gathered from the MiBIG database in GBK format. Protein sequences from each file were extracted, and sequences under the same BGC ID were combined. Physicochemical properties were calculated, and sequence embeddings were generated using NLP techniques like CountVec, TFIDF, and Word2Vec specific to each NP class. An integrated feature matrix was created by merging physicochemical properties and generated embeddings. This matrix was used for training and testing of nine ML models such SVM, RF and many more. The study explored data balancing techniques with and without SMOTE and employed Grid Search for parameter optimization. This led to six datasets and 54 models. The LR model, using TFIDF with SMOTE, emerged as the most effective, achieving an accuracy of 0.96, AUC of 0.9912, and other strong metrics. This method enhances BGC identification for drug development, offering a broader understanding of their applications in medicine and biotechnology.

Chiral hydroxamic acid (HA) and bis-hydroxamic acid (BHA) ligands have made significant contributions to the field of asymmetric synthesis, particularly in the synthesis of natural products. These ligands possess unique molecular structures that allow for exceptional stereochemical control, leading to their widespread use in catalytic systems. This review highlights the advancements made in asymmetric synthesis using chiral hydroxamic acid and bis-hydroxamic acid ligands and their impact on the synthesis of complex natural products. The discussion encompasses their role in enantioselective C-C bond formation, the functionalization of C-H bonds, asymmetric transformations involving heteroatoms, and their application in the total synthesis of natural products. The versatility and efficiency of chiral hydroxamic acid ligands and bis-hydroxamic acid ligands make them invaluable tools for synthetic chemists working towards the efficient and selective synthesis of natural products. This review provides a comprehensive overview of their contributions, showcasing their potential to expand the boundaries of chemical synthesis and access the diverse array of natural product scaffolds.

In recent years, the use of long-term seismic monitoring systems for the protection of structures is becoming increasingly widespread. This is because more and more awareness is gradually developing regarding the importance of keeping structures under observation so as to prevent any damage scenarios. Within the field of Structural Health Monitoring, data driven techniques allow to process and analyze data collected on site. This paper presents a Mode Tracking procedure to obtain the time-histories of the natural frequencies of a monumental structure. The procedure is applied on the Sanctuary of Vicoforte, an important monumental structure site in Piedmont known for its imposing oval dome and characterized by a permanent structural monitoring system. Through the procedure presented in the paper it has been possible to obtain and observe the time series of the first natural frequencies of the structure considering various aspects, including the temperature variation.

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.

The natural units of measure lauded by Max Planck more than 100 years ago are rarely used today. But many physical constants, including Planck's constant, the gravitational constant, the speed of light, vacuum permittivity, and vacuum permeability, consist of natural units in their unit dimensions. The natural units are present in all formulas containing these constants. The defining characteristic of the natural units is an alignment of unit values at the Planck scale. This alignment gives a computational basis of proportionality from which the correlated properties and dynamics of elementary particles, including wavelength, period, mass, momentum, and energy, manifest in equal or inversely proportional ratios of the Planck scale. These correlations explain many of the defining equations of quantum mechanics, classical gravity, and electromagnetism.

In recent times, there has been a revolutionary surge in antioxidant research, with a focus on harnessing microalgae to enhance wellness and extend human longevity. Microalgae, a diverse group of unicellular photosynthetic organisms, have emerged as promising sources of natural antioxidants due to their ability to synthesize various bioactive compounds, including carotenoids, polyphenols, and tocopherols. These antioxidants play a pivotal role in scavenging free radicals and reducing oxidative stress, known contributors to aging and chronic diseases. This review provides an overview of recent advancements in understanding microalgae's antioxidant potential, covering their biochemical composition, extraction techniques, and purification methods. Moreover, it delves into compelling in vitro and in vivo studies showcasing microalgae-derived antioxidants' protective effects against oxidative damage, inflammation, cardiovascular diseases, and neurodegenerative disorders. The sustainable cultivation of microalgae in controlled environments further supports the potential for large-scale production and commercialization of their antioxidant compounds. As microalgae continue to revolutionize antioxidant research, they hold immense promise in developing novel preventive and therapeutic strategies to promote human health and wellbeing.

Powder metallurgy is a preferred manufacturing method in various industries because it offers design flexibility, material efficiency and a cost-effective production. In this work, we study the effects of different compaction directions on the strength of parts produced by powder metallurgy. Al–4wt.%Cu alloys are used due to their recyclability, and three distinctive compaction pressures are applied, resulting in two sample groups. One group is air-cooled after sintering, while the other is water-quenched and naturally aged (T4 temper). Both the compressive and tensile strengths are measured and analyzed. The study reveals that both heat treatments and compaction directions significantly influence anisotropic strengths. The novelty of this research lies in the use of powders that can be reused from machining, turning, or foundry rejection. By omitting or reducing the melting stage and employing simple powder metallurgy, the cost-effective and environmentally friendly processes are achieved. The planning of compaction load, compaction direction, and heat treatments is found to play a crucial role in determining the final mechanical performance. Notably, this approach aligns with the environmental, social and governance (ESG) practices that have been increasingly adopted by industry.

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.

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.

Petrosamine (1) – a colored pyridoacridine alkaloid from the Belizean sponge, Petrosia sp. that is also a potent inhibitor of acetylcholine esterase (AChE) – was investigated by spectroscopic and computational methods. Analysis of the petrosamine free energy landscapes, pKa and tautomerism revealed an accurate electronic depiction of the molecular structure of 1 as the di-keto form, with net charge of q = +1, rather than a dication (q = +2) under ambient conditions of isolation-purification. The pronounced solvatochromism (UV-vis) reported for 1, and related analogs, was investigated in detail and is best explained by charge delocalization and stabilization of the ground state (HOMO) of 1 rather than an equilibrium of competing tautomers. Refinement of the molecular structure of 1 by QM methods complements published computational docking studies to define the contact points in the enzyme active site that may improve design of new AChE inhibitors based on the pyridoacridine alkaloid molecular skeleton.

The purpose of this article is to conduct a bibliometric review of literary production in terms of market interdependence. We investigate the effect of contagion on agricultural commodities and identify the commodities and methods used in the most cited publications over the decades. For this, we used the SCOPUS database, sorting with Rayyan, Excel, and finally, the Bibliometrix/R-project. The results showed that corn, wheat, soybeans, cotton, and sugar are the most studied commodities. There is no uniqueness concerning the methods used to identify interdependence and the contagion effect. Also, it is possible to perceive the growing scientific interest concerning the subject, with a diversity of authors and magazines. However, there is a direction of the and magazines most cited articles for analyzing agricultural commodities and energy, demonstrated a worldwide trend of studies relating natural resources, biofuels, clean energy, and efficiency. In addition, countries like China and the United States stand out in this scientific scenario as the countries that produce the most articles.

Depressive disorder is a disease that causes a decrease in daily function, and the prevalence of depressive disorder in Korea is the highest among OECD countries. However, due to lack of manpower and negative social awareness, timely treatment is not being performed properly. As a solution to this, AI counseling chatbots are emerging, and it is essential to use labeled datasets to create chatbots. Currently, many countries around the world, including Korea, use the Diagnostic and Statistical Manual of Mental Disorders Fifth Edition, DSM-5, but there are no nine labeled datasets according to the DSM-5 depression disorder diagnosis criteria. This study collects sympathetic datasets, analyzes morphemes using Kind Korean Morpheme Analyzer and augments and builds a word dictionary using Word2Vec. As a result, we have labeled the dataset with nine criteria for diagnosing DSM-5 depressive disorder, which will enhance the performance of the counseling chatbot.

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.

Numerous goods and services are now offered through online platforms due to the recent growth of online transactions like e-commerce. Users have trouble locating the product that best suits them from the numerous products available in online shopping. Many studies in deep learning-based recommender systems (RSs) have focused on the intricate relationships between the attributes of users and items. Deep learning techniques have used consumer or item-related traits to improve the quality of personalized recommender systems in many areas, such as tourism, news, and e-commerce. Various companies, primarily e-commerce, utilize sentiment analysis to enhance product quality and effectively navigate today's business environment. Customer feedback regarding a product is gathered through sentiment analysis, which uses contextual data to split it into separate polarities. The explosive rise of the e-commerce industry has resulted in a large body of literature on e-commerce from different perspectives. Researchers have made an effort to categorize the recommended future possibilities for e-commerce study as the field has grown. There are several challenges in e-commerce, such as fake reviews, frequency of user reviews, advertisement click fraud, and code-mixing. In this review, we introduce an overview of the preliminary design for e-commerce. Second, the concept of deep learning, e-commerce, and sentiment analysis are discussed. Third, we represent different versions of the commercial dataset. Finally, we explain various difficulties facing RS and future research directions.

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

Large Language Models (LLMs) have revolutionized the field of Artificial Intelligence with their ability to understand and generate natural language discourse. This has led to the use of LLMs in various creative fields such as music, art, and storytelling, which has sparked a debate on the impact of LLMs on human creativity. This article explores the cyclic effect of LLMs on human creativity, where LLMs can generate an ever-evolving cycle of creative output from both humans and machines. The article also highlights the importance of responsible and ethical use of LLMs, including privacy and copyright issues with the training data used by LLMs. The article emphasizes the need for transparency, data security measures, and AI governance policies to protect user data and ensure the safe use of LLMs. It also calls for the development of educational materials and resources to increase public understanding of LLMs and their ethical implications. The article concludes by highlighting the potential of LLMs as powerful tools for communication and education, but also emphasizes the need for ethical considerations to ensure the responsible and beneficial use of LLMs in society.

The abundance of information in academic articles, reports, and studies can make it challenging for researchers to gain insights from the existing literature. To address this issue, there is a growing demand for tools that can help researchers effectively parse and analyze large volumes of data. One such tool is DataDiscoveryLab, a software system that utilizes computer vision algorithms and NLP techniques to parse academic articles into text and figures, creating three separate databases. These databases allow researchers to quickly identify articles that may be relevant to their research questions, gain a deeper understanding of the research presented, and analyze visual data. The integration of article mining and computer vision in the DataDiscoveryLab software system provides researchers with a powerful tool for navigating the vast amount of scientific literature available today. Yet, as we will discuss in the latter papers these databases’ purpose is to create a bridge between researchers’ data and practically unlimited scientific publications. Yet, in this article, we will discuss how we plan to do that, and our efforts on integrating deep learning modes. After all, unlike already existing AI models, DataDiscoveryLab can be their combination and the first Generative AI in academia that can encompass every part of the natural sciences.

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

Land Use /Land Cover (LULC) change is a major global concern and a topic of scientific debate. In West Africa, the key among the past changes is the loss of natural vegetation related to changes in different Land Use/Land Cover (LULC) categories, e.g., water bodies, wetland and bare soil. However, not all detected changes in these LULC categories are relevant for LULC change management intervention in a resource-constrained continent, as a massive change in the dominant LULC types may be due to errors in the LULC maps. Previous LULC change analysis detected large discrepancies in the existing LULC maps in the Africa continent. Here we applied an open and synergistic framework to update and improve the existing LULC maps at every five years intervals from 1990 to 2020 in West Africa at a finer spatial resolution of 30m. Next, we detected spatial temporal pattern in past and present LULC changes with the intensity analysis framework in the periods of 1990–2000, 2000–2010, and 2010–2020. A faster annual rate of overall transition was detected in 1990–2000 (first period) and 2010–2020 (recent period) than in 2000–2010 (middle). We observed consistent gains in rangeland (shrubland and grassland) in all the periods, which confirms the observed re-greening of rangeland in the West Africa continent. By contrast, forestland areas experienced consistent losses for the entire periods, which indicate deforestation and degradation. As regards cropland, a net loss was observed in the drought period and net gains in the subsequent periods. The settlement category also gained actively in all periods. Net losses of wetland and bare land categories were also observed in all the periods. We observed net gains in water bodies in 1990–2000 and net losses in the last period. We highlighted the active forestland losses as systematic and hence a clarion call for an intervention. The simultaneous active gross loss and gain intensity of cropland raises food security concern and must send an early warning signal to policy makers that the food security of marginal geographic locations is under threat despite the massive expansion of cropland observed in this study area. The intensity analysis framework was vital to identify the settlement category as relevant for LULC change management intervention across time and space in West Africa instead of focusing on dynamics of all the LULC categories that may be irrelevant and hence a cost effective LULC change management approach. Policies to control the expansion of settlements will indirectly regulate the expansion of cropland field because we observed that as settlement encroached on cropland, simultaneously cropland encroached on natural vegetation to meet the food security requirements of the inhabitants.

Glioblastoma multiforme (GBM) is one of the deadliest cancers. Temozolomide (TMZ) is the most common chemotherapy used for GBM patients. Recently, combination chemotherapy strategies have more effective antitumor effects and focus on slowing down the development of chemotherapy resistance. A combination of TMZ and cholesterol lowering medications (statins) is currently under investigation in in vivo and clinical trials. In our current investigation, we have used a triple combination therapy of TMZ, Simvastatin (Simva), and Acetylshikonin (ASH) and investigated its apoptotic mechanism in GBM cell lines (U87 and U251). We used viability, apoptosis, reactive oxygen species (ROS), mitochondrial membrane potential (MMP), caspase-3/-7, acridine orange (AO) and immunoblotting autophagy assays. Our results showed that TMZ/Simva/ASH combination therapy significantly induced more apoptosis compared to TMZ, Simva, ASH, and TMZ/Simva treatments in GBM cells. Apoptosis via TMZ/Simva/ASH treatment induced mitochondrial damage (increase of ROS, decrease of MMP) and induced caspase-3/7 activation in both GBM cell lines. Compared to all single treatments and the TMZ/Simva treatment, TMZ/Simva/ASH significantly increased positive acidic vacuole organelles. We further confirmed that the increase of AVOs during the TMZ/Simva/ASH treatment was due to partial inhibition of autophagy flux (accumulation of LC3β-II and decrease in p62 degradation) in GBM cells. Our investigation also showed that TMZ/Simva/ASH-induced cell death was depended on autophagy flux as further inhibition of autophagy flux increased TMZ/Simva/ASH-induced cell death in GBM cells. Finally, our results showed that TMZ/Simva/ASH treatment potentially depends on an increase of Bax expression in GBM cells. Our current investigation might open new avenues for more effective treatment of GBM but further investigations are required for better identification of the mechanisms.

In French Polynesia, Wuhan, Delta and Omicron SARS-CoV-2 variants-of-concern (VOCs) caused epidemics with variable severities. We assessed the prevalence and titers of anti-SARS-CoV-2 antibodies related to natural infection and/or vaccination, from a representative sample (N=673) of the adult population of Tahiti recruited during November-December 2021 (after the Delta outbreak and just before the Omicron epidemic). Of the 673 participants tested, 644 (95.7%) had detectable antibodies against SARS-CoV-2-S and/or -N proteins resulting from natural infection and/or vaccination, and 388 (57.7%) were positive only for the detection of anti-N antibodies indicating natural infection. SARS-CoV-2 seroprevalence extrapolated to the adult population of Tahiti was estimated at 95.9%. Concentrations of anti-SARS-CoV-2-S antibodies significantly increased with age, number of self-reported SARS-CoV-2 infections (0 or ≥1), and number of COVID-19 vaccine doses (0, 1, 2, or 3) received by the participants. Elderly people, who are at higher risk of severe outcomes, had received more vaccine doses than younger individuals both in our sample and in the general population. The high level of antibody responses related to past infections and vaccination, especially booster doses, has likely contributed to reducing the severity of the Omicron outbreak in French Polynesia.

This research work focuses on the potential application of an organic compound, santalol obtained from santalum album in the inhibition of the enzyme tyrosinase which is actively involved in the biosynthesis of the melanin pigment. Over-production of melanin causes undesirable pigmentation in humans as well as other organisms that significantly downgrade their aesthetic value. The study is designed to explain the purification of tyrosinase from the mushroom Agaricus bisporus, followed by activity assay and enzyme kinetic to give insight into the santalol modulated tyrosinase inhibition in a dose dependent manner. The multi-spectroscopic techniques like UV-vis, fluorescence, and isothermal calorimetry are employed to deduce the efficiency of santalol as potential candidate against the tyrosinase enzyme activity. Experimental results are further verified by molecular docking. Santalol derived from the essential oils of santalum album, is widely used as remedy for skin disorders and potion for fair complexion since ancient times. Based on enzyme kinetics and biophysical characterization, this is the first scientific evidence where santalol inhibits tyrosinase, which may be employed in agriculture, food, and cosmetic industries by prevent excess melanin formation or browning.

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

Myxobacteria feature unique biological characteristics including their capability to glide on surface, undergo different multicellular developmental stages and produce structurally unique natural products such as the catecholate-type siderophores myxochelin A and B. Herein, we report the isolation, structure elucidation and biosynthesis of the new congener myxochelin B-succinate from the terrestrial myxobacterial strain MSr12020, featuring a succinyl decoration at its primary amine group. Myxochelin-B-succinate exhibited antifungal growth inhibition and moderate cytotoxic activity against selected human cancer cell lines. This unique chemical modification of myxochelin B might provide interesting insights for future microbiological studies to understand the biological function and biosynthesis of secondary metabolite succinylation.

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.

Natural products (NPs) are a rich source of structurally novel molecules, and the chemical space they encompass is far from being fully explored. Over history, NPs have represented a significant source of bioactive molecules and have served as a source of inspiration for developing many drugs on the market. On the other hand, computer-aided drug design (CADD) has contributed to drug discovery research, mitigating costs and time. In this sense, compound databases represent a fundamental element for the CADD. This work reviews the progress toward developing compound databases of natural origin, particularly databases developed in Latin America, and their practical applications in the drug discovery area. We also survey the computational methods, emphasizing chemoinformatic approaches to profile natural product databases.

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 construction field uses up over one-third of the global energy consumption and contribute to 40% of CO2 emissions according to the International Energy Agency (IEA) and the 2020 annual reporter of United Nation, Goal 11 (Make cities inclusive, safe, resilient and sustainable) which discusses sustainable, safe and efficient buildings. Therefore, Morocco has a commitment to this program by publishing the law 47-09 of energy efficiency. This work aims to study the energy efficiency of two types of building, a conventional and a natural building. Conventional building is constructed using concrete, while the natural one uses sand clay and straws. As for the technique of making the natural building, it perpetually follows the same approach accustomed in rural zones of Atlas Mountains in Morocco. In this research we also simulate, temperature and humidity variation inside these buildings using TRNSYS software. Sketch Up software was employed to design these houses. The weather database is used for a typical meteorological year (TMY). In the case of natural building, many building configurations were simulated: roof insulation, floor insulation, different types of glazing and sun protection. What's more, the thermal comfort is revealed to be more conspicuous in the case of natural building.

The classification of goods involved in international trade in Brazil is based on the Mercosur Common Nomenclature (NCM). The classification of these goods represents a real challenge due to the complexity involved in assigning the correct category codes especially considering the legal and fiscal implications of misclassification. This work focuses on the training of a classifier based on Bidirectional En-coder Representations from Transformers (BERT) for the tax classification of goods with NCM codes. In particular, this article presents results from using a specific Portuguese Language tuned BERT model as well results from using a Multilingual BERT. Experimental results justify the use of these models in the classification process and also that the language specific model has a slightly better performance.

Thanks to rapid development of artificial intelligence technology in recent years, the current artificial intelligence technology is contributing to many part of society. Education, environment, medical care, military, tourism, economy, politics, etc. are having a very large impact on society as a whole. For example, in the field of education, there is an artificial intelligence tutoring system that automatically assigns tutors based on student's level. In the field of economics, there are quantitative investment methods that automatically analyze large amounts of data to find investment laws to create investment models or predict changes in financial markets. As such, artificial intelligence technology is being used in various fields. So, it is very important to know exactly what factors have an important influence on each field of artificial intelligence technology and how the relationship between each field is connected. Therefore, it is necessary to analyze artificial intelligence technology in each field. In this paper, we analyze patent documents related to artificial intelligence technology. We propose a method for keyword analysis within factors using artificial intelligence patent data sets for artificial intelligence technology analysis. This is a model that relies on feature engineering based on deep learning model named KeyBERT, and using vector space model. A case study of collecting and analyzing artificial intelligence patent data was conducted to show how the proposed model can be applied to real-world problems.

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

Paraphrase Generation is one of the most important and challenging tasks in the field of Natural Language Generation. The paraphrasing techniques help to identify or to extract/generate phrases/sentences conveying the similar meaning. The paraphrasing task can be bifurcated into two sub-tasks namely, Paraphrase Identification (PI) and Paraphrase Generation (PG). Most of the existing proposed state-of-the-art systems have the potential to solve only one problem at a time. This paper proposes a light-weight unified model that can simultaneously classify whether given pair of sentences are paraphrases of each other and the model can also generate multiple paraphrases given an input sentence. Paraphrase Generation module aims to generate fluent and semantically similar paraphrases and the Paraphrase Identification systemaims to classify whether sentences pair are paraphrases of each other or not. The proposed approach uses an amalgamation of data sampling or data variety with a granular fine-tuned Text-To-Text Transfer Transformer (T5) model. This paper proposes a unified approach which aims to solve the problems of Paraphrase Identification and generation by using carefully selected data-points and a fine-tuned T5 model. The highlight of this study is that the same light-weight model trained by keeping the objective of Paraphrase Generation can also be used for solving the Paraphrase Identification task. Hence, the proposed system is light-weight in terms of the model’s size along with the data used to train the model which facilitates the quick learning of the model without having to compromise with the results. The proposed system is then evaluated against the popular evaluation metrics like BLEU (BiLingual Evaluation Understudy):, ROUGE (Recall-Oriented Understudy for Gisting Evaluation), METEOR, WER (Word Error Rate), and GLEU (Google-BLEU) for Paraphrase Generation and classification metrics like accuracy, precision, recall and F1-score for Paraphrase Identification system. The proposed model achieves state-of-the-art results on both the tasks of Paraphrase Identification and paraphrase Generation.

Sustainable urban design or planning is not a LEGO-like assembly of prefabricated elements, but an embryo-like growth with persistent differentiation and adaptation towards a coherent whole. The coherent whole has a striking character – called living structure – that consists of far more small substructures than large ones. To detect the living structure, natural streets or axial lines have been previously adopted to be topologically represent an urban environment as a coherent whole. This paper develops a new approach to detecting the underlying living structure of urban environments. The approach takes an urban environment as a whole and recursively decomposes it into meaningful subwholes at different levels of hierarchy or scale ranging from the largest to the smallest. We compared the new approach to natural street and axial line approaches and demonstrated, through four case studies, that the new approach is better and more powerful. Based on the study, we further discuss how the new approach can be used not only for understanding, but also for effectively designing or planning the living structure of an urban environment to be more living or more livable.

Early Life Adversity (ELA) is closely associated with the risk for developing diseases later in life, such as autoimmune diseases, type-2 diabetes and cardiovascular diseases. In humans, early parental separation, physical and sexual abuse or low social-economic status during childhood are known to have great impact on brain development, in the hormonal system and immune responses. Maternal deprivation (MD), the closest animal model available to the human situation, is known to similarly induce long lasting behavioural effects, to cause changes in the HPA axis and to have an impact in the immune system. Even though the immune responses to potential pathogens after early stress have been somehow documented, the mechanisms by which they occur are still not fully understood. Here, we have demonstrated that maternal separation, in both humans and rats, significantly affects the sensitivity of the immune system in adulthood. Particularly, NK cells’ profile and response to target cell lines are significantly changed after childhood adversity. These immune cells in rats are not only less cytotoxic towards YAC-1 cells, but also show a clear increase in the expression of maturation markers after 3h of maternal separation. Similarly, individuals who suffered from ELA display significant changes in the cytotoxic profile of NK cells together with decreased degranulation capacity. Altogether, these results lead us to conclude that one of the key mechanisms by which the immune system becomes impaired after ELA might be due to a shift on the senescent state of the cells, specifically NK cells. Elucidation of such a mechanism highlights the importance of ELA prevention and how NK targeted immunotherapy might help attenuating ELA consequences.

Electronic health records (EHRs) are becoming a vital source of data for healthcare quality improvement, research, and operations. However, much of the most valuable information contained in EHRs remains buried in unstructured text. The field of clinical text mining has advanced rapidly in recent years, transitioning from rule-based approaches to machine learning and, more recently, deep learning. With new methods come new challenges, however, especially for those new to the field. This review provides an overview of clinical text mining for those who are encountering it for the first time (e.g. physician researchers, operational analytics teams, machine learning scientists from other domains). While not a comprehensive survey, it describes the state of the art, with a particular focus on new tasks and methods developed over the past few years. It also identifies key barriers between these remarkable technical advances and the practical realities of implementation at health systems and in industry.

Natural circulation loop (NCL) systems are buoyancy-driven heat exchangers that are used in various industrial applications. The concept of passive heat exchange in NCL systems is attractive, because there is no need for an externally driven equipment (e.g., a pump) to maintain the fluid circulation. However, relying on buoyancy as the sole driving force may lead to several potential difficulties, one of which is generation of (possibly) time-varying nonlinearities in the dynamical system, where a difference in the time scales of heat transfer and fluid flow causes the flow to change from a steady-state regime to either an oscillatory regime or a flow-reversal regime, both of which are undesirable. In this paper, tools of symbolic time series analysis (e.g., probabilistic finite state automata (PFSA)) are proposed to identify selected regimes of operation in NCL systems, where the underlying principle is built upon the concept of pattern classification from measurements of fluid-flow dynamics. The proposed method is shown to be capable of identifying the current regime of operation from the initial time response under a given set of operational parameters. The efficacy of regime classification is demonstrated by testing on two data sets, generated from numerical simulation of a MATLAB SimuLink model that has previously been validated with experimental data. The results of the proposed PFSA-based classification are compared with those of a hidden Markov model (HMM) that serves as the baseline.

In December 2019, the novel severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) emerged in the province of Wuhan, China. Since then, it has spread worldwide with new mutations being reported. We performed genomic analysis to identify the changes in genetic diversity of SARS-CoV-2 between December 2019 and November 2020, and through molecular surveillance, we monitored the mutations that could be involved in viral fitness. We analyzed 2,213 complete genomes from 6 geographical regions worldwide, which were downloaded from GenBank and GISAID databases. Although SARS-CoV-2 presented low genetic diversity, there has been an increase over time, with the presence of several hotspot mutations throughout its genome. We identified 7 frequent mutations that resulted in non-synonymous substitutions (dN). Two of them, C14408T>P323L and A23403G>D614G, located in the nsp12 and Spike protein, respectively, emerged early in the pandemic and showed a considerable increase in frequency over time. Two other mutations, A1163T>I120F in nsp2 and G22992A>S477N in the Spike protein emerged recently and have spread in Oceania and Europe. Continuous molecular surveillance of SARS-CoV-2 will be necessary to detect and describe the transmission dynamics of new variants of the virus with clinical relevance. This information is important to improve programs to control the virus.

Incidence of autoimmune diseases increases. Antinuclear antibodies (ANA) testing is a critical tool for their diagnosis. However, ANA prevalence in health increased over last decades, especially among young people. ANA in health occur in low concentrations, with prevalence up to 50% in some populations, which demands a cutoff revision. The review deals with origin and probable physiological or compensatory function of ANA in health, according to the concept of immunological clearance, theory of autoimmune regulation of cell functions and the concept of functional autoantibodies. Considering ANA titers ≤1:320 as a serological marker of autoimmune diseases seems inappropriate. The role of anti-DFS70/LEDGFp75 autoantibodies is highlighted as possible anti-risk biomarker for autoimmune rheumatic disorders. ANA prevalence in health is different in various regions due to several underlying causes discussed in the review, all influencing in additive combinations according to the concept of the mosaic of autoimmunity. Not only titer, but the HEp-2 IFA staining patterns, like AC-2, is also important. Accepting autoantibodies as a kind of bioregulators, not only upper, but also lower borders of their normal range should be determined. Not only their excess, but also lack of them or “autoimmunodeficiency” could be a reason of disorders.

This paper investigated the effectiveness of natural wetlands (Phragmites australis) along Wadi Zomer in reducing the organic and inorganic pollution loads from diverse industrial discharges including occasional emergency discharges from Nablus West Sewage Treatment Plant (NWSTP), Palestine. We monitored physical and chemical parameters at four selective sampling stations (S1-S4) along Wadi Zomer with a length of 5 km downstream of NWSTP to assess the purification capacity of Wadi Zomer treatment wetlands (water, sediment, and vegetation) with Phragmites australis in pollution loads reduction. The results showed that S2 (0+0.5 km) and S3 (0+3.0 km) reflected an increase in pollution loads due to illicit industrial discharge and sewer overflow discharge from NWSTP during emergency conditions. BOD values varied significantly along the sampling sites from 6.64 mg/l (S1) to 437.10 mg/l (S3). The BOD at S1 and S2 in water samples were below the Palestinian Water Standard (PWS) compared to S3 and S4 with 437.1 and 333.9 mg/l, respectively. Water samples from all sites (S1-S4) showed a decreasing tendency in heavy metals concentrations (Fe>Cu>Zn>Cr >Ni) and were below the PWS limits, sediment samples followed the same decrease pattern for Zn, Cr, and Ni content with Wadi Zomer flow course. The concentration of Fe (6687 mg/kg) and Cu (1384.7 mg/kg) were highest in the sediment samples (S1-S4); this might be due to non-point sources of pollution. The research demonstrated that phytoremediation is a sustainable nature-based technology for the restoration of heavily polluted surface water bodies in Palestine.

Social media gives researchers an invaluable opportunity to gain insight into different facets of human life.Researchers put a great emphasis on categorizing the socioeconomic status (SES) of individuals to help predict various findings of interest. Forum uses, hashtags and so on are common tools of conversations grouping. On the other hand, crowdsourcing is a concept that involves gathering intelligence to group online user community based on common interest. This paper provides a mechanism to look at writings on social media and group them based on their academic background. We build upon earlier work where we analyzed online forum posts from various geographical regions in the USA and Canada and characterized the readability scores of such users. Specifically, we collected 1000 tweets from the members of the US Senate and computed the Flesch-Kincaid readability score for the Senators. Comparing the Senators’ tweets to the ones from average citizens, we note the following. 1) US Senators’ readability based on their tweets rate is much higher affirming the gap between the academic performance of US Senators and their average citizen, and 2) the immense difference among average citizen’s score compared to those of US Senators is attributed to the wide spectrum of academic attainment.

Calandrinia compressa Schrad. ex DC (Prodr. 3: 359. 1828) is the name currently widely applied to a polymorphic annual species of Calandrinia sect. Calandrinia endemic to the Chilean Floristic Region. A total of ten validly published heterotypic names plus six horticultural designations (and orthographic variants thereof) have been considered at some point as taxonomically the same as C. compressa. Two additional heterotypic taxa and their synonyms and two other designations possibly refer to this species. Two other taxa sometimes referred to this species are excluded. I treat here a total of 77 distinct nomenclaturally valid names and invalid designations and variants thereof. The type of one of the valid and legitimate names, Calandrinia pilosiuscula DC (Rev. Portulac. 9. 1827), is judged here to be taxonomically the same as the type of C. compressa. Because of its chronological priority, Calandrinia pilosiuscula DC must be accepted as the correct name for Calandrinia compressa Schrad. ex DC when the two are considered taxonomic synonyms. Here, the taxonomic history of this taxon is articulated and, where possible, types are identified and/or designated. The natural and anthropogenic history of the species is summarized critically in a theoretical context.

Despite the implementation of control strategies, the invasive parasitic mite Varroa destructor remains one of the principal causes of honey bee (Apis mellifera) colony losses in numerous countries. For this reason, the parasite represents a serious threat to beekeeping and to agro-ecosystems that benefit from the pollination services provided by honey bees. Numerous selection programmes have been initiated over the last three decades with the aim of promoting the establishment of balance in the host–parasite relationship and, thus, helping European honey bees to survive in the presence of the parasite without the need for acaricide treatments. Such programmes have focused on either selective breeding for putative resistance traits or natural selection. To date, no clear overview of these attempts has been available, which has prevented building on past successes or failures and, therefore, hindered the development of a sustainable strategy for solving the V. destructor problem. In the present study, we review past and current selection strategies, report on their outcomes and discuss their limitations. Based on this state-of-the-art knowledge, we propose a strategy for increasing response to selection and colony survival against V. destructor infestations. Developing in-depth knowledge regarding the selected traits, optimising selection programmes and communicating their outcomes are all crucial to our efforts to establish a balanced relationship between the invasive parasite and its new host.

A novel method for finding linear mappings among word embeddings for several languages, taking as pivot a shared, universal embedding space, is proposed in this paper. Previous approaches learn translation matrices between two specific languages, but this method learn translation matrices between a given language and a shared, universal space. The system was first trained on bilingual, and later on multilingual corpora as well. In the first case two different training data were applied; Dinu’s English-Italian benchmark data, and English-Italian translation pairs extracted from the PanLex database. In the second case only the PanLex database was used. The system performs on English-Italian languages with the best setting significantly better than the baseline system of Mikolov et al. [1], and it provides a comparable performance with the more sophisticated systems of Faruqui and Dyer [2] and Dinu et al. [3]. Exploiting the richness of the PanLex database, the proposed method makes it possible to learn linear mappings among an arbitrary number of languages.

The Abnormal increase in the mass of adipose tissue during adipogenesis results in obesity. Obesity is a predominant disorder and its widespread has become a critical concern worldwide. This is due to the burden of its associated co-morbidities like cancer, cardiovascular diseases and diabetes. Over-nutrition and the sedentary lifestyle are considered as the most significant causes of abnormal adipose tissue development. Appropriate lifestyle and behavioural interventions are the corner stones of successful weight loss, but to maintain such a lifestyle is highly challenging. Accordingly, natural products from plants either as crude extracts or purified phytochemicals have shown to have anti-obesogenic properties and are generally non-toxic and cost-effective. In this review, we discuss the comparative analysis of molecular mechanisms involved in adipogenesis and the recently reported anti-obesogenic effects of various plant products. We will provide a common platform for understanding obesity and adipogenesis, and a possible mechanism of action for anti-obesogenic plant products through AMP-activated protein kinase (AMPK), which will support the scientific development of traditional herbal medicine.

As a holistic medical school, Iranian traditional medicine (ITM) considers the human body as a dynamic and intricate network of interconnecting processes. Currently, systems biology and more precisely systems medicine and pharmacology can be an aid in providing rationalizations for many traditional medications and treatments and elucidating a great deal of knowledge they can offer to guide future research in medicine. Therefore, re-organization and standardization of traditional medicine data are requested more than ever before. To address this issue, we have constructed UNaProd, a Universal Natural Product database for materia medica of ITM. Primarily based on Makhzan al-Advieh, which is the most recent encyclopedia of materia medica in ITM with the largest number of monographs, this database was created using both text mining methods and manual editing. UNaProd is currently hosting to 2696 monographs from herbal to animal to mineral compounds in 16 diverse attributes such as origin and scientific name. In the current version, UNaProd is hyperlinked to IrGO and CMAUP databases for Mizaj and molecular features respectively and it is freely available at http://jafarilab.com/unaprod/.

Fire behavior of natural rubber latex foam under different thickness conditions(d=1,2and 5cm)were explored though a little of experiments by using the self-built small scale experimental platform. It can be shown that the flame spread law of thermally thin and thermally thickness are different. Natural rubber latex foam with thickness of 2cm show higher fire risk, which value of flame spread rate, maximum flame height, maximum mass loss rate and maximum temperature is 0.00293m/s, 851.875mm, 1.83g/s, 948K,repectively.That may because the thickness of residue formed of thermally thick materials is larger than the thin one, obstructing the contact of the natural rubber latex foam with fresh air .In addition, a special phenomenon is noticed that during the second stage, the bottom unburned zone located in the four edges(thermally thin material) and middle player(thermally thick material).

Increased gut permeability (leaky gut) with increased translocation of Gram-negative bacteria plays a role in the gut-brain axis through effects on systemic immune-inflammatory processes. Deficit schizophrenia is characterized by an immune-inflammatory response combined with a deficit in natural IgM antibodies to oxidative specific epitopes (OSEs), which are a first line defense against bacterial infections. This study measured plasma IgA/IgM responses to 5 Gram-negative bacteria in association with IgM responses to malondialdehyde (MDA) and azelaic acid in 80 schizophrenia patients (40 with the deficit syndrome and 40 without) and in 38 healthy controls.Deficit schizophrenia was characterized by significantly increased IgA responses to Hafnei alvei, Pseudomonas aeruginosa, Morganella morganii and Klebsiella pneumoniae as compared with non-deficit schizophrenia. The presence of deficit schizophrenia was highly predicted by increased IgA responses to Pseudomonas putida and IgM responses to all 5 Gram-negative bacteria and lowered natural IgM to MDA and azelaic acid with a bootstrap area under the ROC curve of 0.960 (2000 random curves). A large proportion of the variance (41.5%) in the PANSS negative score was explained by the regression on IgA responses to K. pneumoniae and IgM responses to the 5 enterobacteria coupled with lowered IgM antibodies to azelaic acid. There were significant associations between IgA levels to Gram-negative bacteria and Mini Mental State Examination, Boston naming test, Verbal Fluency and Word List Memory test scores.These findings provide further evidence that deficit schizophrenia is a distinct phenotype of schizophrenia, which is characterized by an increased impact of Gram-negative commensal bacteria coupled with a deficit in natural IgM, pointing to aberrations in B1 cells. It is concluded that increased bacterial translocation and deficits in the compensatory immune-regulatory system (CIRS) may drive negative symptoms and neurocognitive impairments, which are hallmarks of deficit schizophrenia.

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.

The enantioselective alpha-amination of 1,3-dicarbonyl compounds has been performed using a catalytic system based on deep eutectic solvents (DES) and chiral 2-amino benzimidazole-derived organocatalysts. This procedure avoids the use of toxic volatile organic compounds (VOCs) as a reaction medium, providing access to highly functionalized chiral molecules, which are important intermediates for the natural product synthesis, in a selective and efficient manner. Furthermore, the reaction can be performed on a large scale and recycling the catalytic system is possible for at least five times, leading to a clean, cheap, simple, and scalable procedure that meets most of the criteria required to be a green and sustainable process.

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.

A multi-tool analytical practice was used for characterization of 16^th century carpet manufactured in Cairo. Mild extraction method with hydrofluoric acid enabled isolation of intact flavonoids and their glycosides, anthraquinones, tannins and indigoids from fibre samples. High-performance liquid chromatography coupled to spectroscopic and mass spectrometric detectors was used for identification of natural dyes present in the historical samples. Weld, young fustic and brazilwood were identified as the dye sources in yellow thread samples. Red fibres have been colored with lac dye, whereas green fibre shades were obtained with indigo and weld. Tannin-containing plant material in combination with indigo and weld were used to obtain brown hue of thread. Four uncommon and thus-far unknown dye components were also found in the historical samples. These compounds probably represent unique fingerprint of dyed threads from this region. Scanning electron microscopy with energy-dispersive X-ray detector (SEM-EDS) and Fourier transformation infrared spectroscopy (FT-IR) were used for identification and characterization of substrates and mordants present in the historical carpet. Carbon and oxygen were detected in large quantities as a part of the wool protein. The presence of aluminum, iron and calcium indicated their usage as mordants. FT-IR analysis showed bands characteristic to woolen fibres and SEM micrographs definite structure of wool.

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

In the second half of the 19th century new methods for quarrying and processing natural stone are developed. In the Nordic countries Sweden, Norway and Finland this technological pro­gress goes hand in hand with a systematic geological mapping and large-scale exploitation of natural stone deposits. As a result, new constructions are developed—changing the building practice in these countries. With the end of historicism a new architecture arises that particularly in Norway and Finland acquires a national-romantic character. This paper examines the interaction between geological exploration, commercial development, technical inventions and the development of a national-romantic architecture.

Community resilience represents an important concept in the comprehensive approach to disaster management. It is assumed that optimal community resilience can mitigate negative impacts and enable adaptation, thereby reducing the negative consequences of future disasters. The measurement of community resilience to disasters has advanced over the past two decades, primarily through the use of indicators that measure community resilience indices. The Baseline Resilience Indicators for Communities (BRIC) represent an increasingly popular and frequently used quantitative method for measuring community resilience indices to disasters. This literature review was conducted to examine and assess the use and adaptation of the BRIC method in measuring community resilience. The review comprehensively analyzes relevant scientific papers, extracting and synthesizing the most important findings from the studies, which provided the necessary data to meet the objectives of the literature review. A total of 37 relevant studies were identified for analysis in the review. The indicators used were examined, including their selection, grouping, and evaluation procedures, as well as the calculation of the appropriate index. The review also covered the method of data collection needed to measure the impact of indicators on resilience, as well as the shortcomings of this process. The measurement of community resilience to disasters is a contemporary issue, with several detected shortcomings in the process itself. The need to adapt the BRIC method to the local context has been established. Improving the process of determining indicators and measuring indices would lead to broader use of the method in efforts to reduce disaster risk and enhance resilience to disasters.

This study investigates the innovative use of natural porous clays from the Bejaad region in Morocco as a support matrix for the encapsulation and controlled release of lemon essential oil (Citrus limonum, EOCL), a natural compound with well-documented insecticidal properties. The research aims to address the inherent challenges of essential oils, particularly their high volatility and rapid degradation, by improving their stability and insecticidal efficiency against the grain pest Sitophilus granarius. By anchoring EOCL onto clay matrices, the study seeks to achieve a sustained and controlled release of the active components, thereby enhancing their practical application as biopesticides. The clays were comprehensively characterized using advanced analytical techniques, including X-ray diffraction (XRD), X-ray fluorescence (XRF), Fourier-transform infrared spectroscopy (FT-IR), scanning electron microscopy with energy-dispersive X-ray analysis (SEM-EDX), and thermogravimetric analysis (TGA). These techniques revealed the mineralogical composition, thermal properties, and morphology of the clays, demonstrating their suitability for effectively adsorbing and retaining EOCL. The insecticidal performance of the clay/EOCL composites was rigorously tested under controlled conditions, revealing a marked improvement in efficacy, with significantly lower lethal doses required to achieve high mortality rates in Sitophilus granarius. The diffusion of EOCL through the clay matrix was modeled using Fick law of diffusion, and the results were further refined through statistical optimization to identify key parameters that influence the release and effectiveness of the active compounds. Complementing the experimental approach, a bioinformatics analysis was conducted to explore the molecular interactions between limonene, the primary active component of EOCL, and target proteins in insects. This theoretical investigation provided insights into the potential mechanisms of action, reinforcing the empirical findings. The study concludes that encapsulating EOCL within porous clay matrices not only enhances the stability and controlled release of the oil but also significantly boosts its insecticidal effectiveness. This approach presents a promising, environmentally sustainable strategy for crop protection, integrating material science, theoretical modeling, and bioinformatics to develop more efficient and durable biopesticides.

In this investigation, four distinct by-products of natural materials were used to compose a mix for rammed earth construction. The mix of these materials, which have various granulometry, affords many potential combinations, making the analysis of different combinations highly complex. The Taguchi method, a statistical approach renowned for significantly reducing the number of ex-periments in complex systems, was employed to address this challenge. Each of the four ma-terials was assessed across four different levels, with moisture content also factored in as a pa-rameter within the statistical analysis. Two separate studies were conducted to obtain two optimal com-binations of the materials to enhance the dry density and the Unconfined Compressive Strength (UCS). Taguchi method demonstrated its efficacy in predicting the optimal combination of mix-tures for enhancing both objective parameters. A natural by-product mix demonstrated its po-tential for use as a building material. The enhancement of the dry density due to a modification of the mix’s Particle Size Distribution (PSD) did not produce a direct improvement in the mix UCS. The factor to be prioritized should be the mechanical strength, rather than the dry density of the sample.

Natural wood is generally perceived as expensive, while wood-based boards are cheap, but re-search has yet to explain definitively why these two materials receive such disparate evaluations. This study aimed to uncover the reasons behind this phenomenon by proposing an emo-tion-driven approach to material research. We collected 10 types of natural wood and artificial boards as experimental samples and tested 20 subjects' subjective evaluation of the samples and physiological indicators (electroencephalography and electrodermal activity). Through data analysis, we identified the relationships between the subjective and physiological data and the impact of material composition on these data. Specifically, we observed significant correlations between subjective and physiological data; however, no correlation was found between subjective and physiological valence. We also discovered that the components of materials significantly influence participants' subjective and physiological emotions. For example, materials with a rough texture elicited higher positive affectivity than those with a smooth texture, materials with coarse and mixed textures elicited higher positive affectivity than those with fine textures, and the brighter materials evoke more positive emotions in participants. This study revealed how material components influence participants' emotions and, more importantly, analyzed the processing techniques behind these components, providing a theoretical basis for the future development of high-value-added products made from wood-based boards.

Water quality control employs techniques mostly targeting individual analytes; group detection is also practiced, but the choice of group methods is limited, which supports interest in developing such methods. We have examined an interaction of hypochlorite with a chlorine-containing heptamethine carbocyanine dye in the presence of 30 organic and inorganic model analytes that were found to induce diverse color changes in the system. The main supposed mechanisms are retardation of the dye oxidation with hypochlorite (presumably by scavenging chlorine radicals) and substitution of chlorine atom in the dye by the most nucleophilic analytes (amines, amino acids, proteins, DNA, phenol). The grass-green substitution product is more contrastingly visible against the dark-purple hypochlorite oxidation product of the dye than against the original emerald-green dye. The indicator reaction is monitored photographically for 10–40 min and the images are processed using principal component analysis (PCA) or linear discriminant analysis (LDA), allowing for data convolution for the complex color transitions. Nitrogen compounds are discriminated from the others, and more reactive analytes (tryptophan, cysteine, bovine serum albumin, and DNA) are detected in the presence of less reactive ones in natural water. The system is promising for the development of group assays for dissolved organic matter and discrimination of water samples.

This study explored the effectiveness of natural additives in prolonging the life of cut roses. A Completely Randomized Design (CRD) was used, with four different treatments: distilled water (control), a sucrose solution, moringa leaf extracts, and a combination of sucrose and moringa. The roses were observed over eight days to assess various factors such as leaf drop, stem rotting, bent necks, petal drop, flower condition, and petal color. The results showed that the combined treatment of sucrose and moringa led to the highest number of leaf drops, with 15 leaves dropping by day five. Distilled water resulted in 12 dropped leaves, moringa extract caused four leaves to drop, and sucrose had the least impact with only two leaves dropping. The combination treatment also caused the most significant stem rotting (32.75 mm) and the most bent necks (14 roses). In contrast, roses treated with sucrose or moringa separately had better outcomes, with the least bent necks and minimal leaf drops. For petal drop, the combined treatment again had the worst results, with eight petals dropping, followed by distilled water with six petals. The overall flower condition was poorest with the combined treatment, scoring 1 (completely open/damaged), while roses treated with sucrose or moringa separately had better scores, indicating they remained fresher. Petal color was also most affected by the combined treatment, which resulted in a light brown color, whereas the separate treatments maintained a darker red hue. Hence, while sucrose and moringa are beneficial when used individually, their combination negatively impacts the longevity and appearance of cut roses.

Oxidative stress plays an essential role in the pathological processes that trigger various chronic liver diseases and contributes to the development of hepatocarcinogenesis. Antioxidants from natural sources trap free radicals by converting them into non-harmful products and minimize oxidative stress, playing a crucial function in the treatment of free radical-induced diseases. However, the activity of these antioxidants is limited due their poor bioavailability, which is why various nano-drug delivery systems have been developed to improve their stability, promote smooth release, and targeted delivery for liver cancer treatment. Specifically, this review aims to report recent scientific findings of novel nano-formulations containing natural antioxidant molecules targeted for the treatment of liver cancer.

Countless disasters have resulted from climate change, causing severe damage to infrastructure and the economy. These disasters have significant societal impacts, necessitating mental health services for the millions affected. To prepare for and respond effectively to such events, it is crucial to understand people's emotions and the life incidents they experience both before and after a disaster strikes. In this case study, we mined the emotions and life incidents expressed on Twitter during the 2017 Hurricane Harvey. Between August 20 and August 30, we collected a dataset of approximately 400,000 public tweets related to the storm. Using a BERT-based model, we predicted the emotions associated with each tweet. Following emotion prediction, we applied various natural language processing techniques to analyze the content of the tweets, aiming to uncover the 'topics' discussed alongside specific 'emotions.' To efficiently identify these topics, we utilized the Latent Dirichlet Allocation (LDA) technique for topic modeling, which allowed us to bypass manual content analysis and extract meaningful patterns from the data. However, rather than stopping at topic identification, we further refined our analysis by integrating Graph Neural Networks (GNN) and Large Language Models (LLM). The GNN was employed to generate embeddings and construct a similarity graph of the tweets, which was then used to optimize clustering. This step ensured that related tweets were accurately grouped together based on their content and context. Subsequently, we used an LLM to automatically generate descriptive names for each event cluster. This advanced approach allowed us to identify and label life incidents automatically, providing a nuanced understanding of the key issues faced by the community during the hurricane. The results revealed significant emotional shifts as the hurricane reached its most devastating phase. These shifts coincided with pressing concerns and demands from the population, such as evacuation plans, animal safety, safety updates, public policy, help requests, flood impacts, and the spread of fake news. By combining GNN and LLM for automatic event name prediction, our study not only mapped out the emotions but also accurately categorized the specific life incidents that were most relevant during the disaster, offering critical insights for disaster preparedness and response strategies.

Bark beetles are one of the major causes of tree mortality in coniferous forests worldwide. The large-scale disturbances by beetle outbreaks have strongly increased in Honduras and other countries, leading to significant ecological, economic, and social consequences. However, the taxonomy, local distribution, and environmental aspects of most bark beetles and their biological predators remain poorly studied. Here, we applied a morphological approach to identify Scolytinae individuals across different forest areas in Honduras. Moreover, the barcoding approach was used by amplifying and sequencing the mitochondrial COI gene. The intraspecific genetic diversity of Ips apache was also analyzed. 1,131 individuals were examined, and 27 genera were identified. Most of the specimens were identified as belonging to the genus Ips, accounting for 53.2% of the total. Xyleborus accounted for 16.5% and Temnoscheila accounted for 10%. Less than four individuals were found for fifteen genera. Ips, Temnoscheila, Xyleborus, Hypothenemus and Pityophthorus exhibited the most extensive geographic distribution among the sampled sites. At the genus level, Olancho, El Paraíso, and Copán displayed the highest diversity. This study is the first report of two genera (Xylomeira and Stephanopachys) in Honduran pine forests. Among individuals of I. apache, there was evidence of intraspecific genetic diversity, but no population structure was detected. While our findings update the list of bark beetles and biological predators associated with Honduran coniferous forests, more taxonomic inventories and ecological studies are required to understand the spread of bark beetles in pine forests.

This study explores advanced feature engineering and semantic enrichment methods to enhance text classification, focusing on detecting figurative language in tweets. The novel features introduced, Syno\_Lower\_Mean and Syn\_Mean, measure the use of uncommon synonyms and the mean frequency of synonyms, capturing semantic richness crucial for detecting figurative expressions. Using resources like SenticNet and Framester, we enrich our feature set with sentiment and frame semantic information. Our approach includes extensive data preprocessing, sophisticated feature selection, and implementing various classification models, such as SVM, KNN, Logistic Regression, Decision Trees, Random Forest, BERT, and LSTM networks. We rigorously evaluate each model's performance to assess the effectiveness of our features and enrichment methods. Putting emphasis on model explainability, we use decision tree analysis, feature importance analysis, and the TREPAN algorithm to approximate SVM decisions. Although we focus on figurative language detection, our methods have broader implications for various NLP text classification tasks. Our findings demonstrate significant improvements in classification accuracy and interpretability through innovative feature design and dataset enrichment.