The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has forced the development of direct-acting antiviral drugs due to the coronavirus disease 2019 (COVID-19) pandemic. The main protease of SARS-CoV-2 is a crucial enzyme that breaks down polyproteins synthesized from the viral RNA, making it a validated target for the development of SARS-CoV-2 therapeutics. New chemical phenotypes are frequently discovered in natural goods. In the current study, we used a fluorogenic assay to test a variety of natural products for their ability to inhibit SARS-CoV-2 Mpro. Several compounds were discovered to inhibit the Mpro at low micromolar concentrations. It was possible to crystallize robinetin together with SARS-CoV-2 Mpro, and the X-ray structure revealed covalent interaction with the protease's catalytic Cys145 site. Selected potent molecules also exhibited antiviral properties without cytotoxicity. Some of these powerful inhibitors might be utilized as lead compounds for COVID-19 research.

Hydrogen gas is seeping from the sedimentary basin of São Franciso, Brazil. The seepages of H₂ are accompanied by helium whose isotopes reveal a strong crustal signature. Geophysical data indicates that this intra-cratonic basin is characterized by i) a relatively high geothermal gradient, ii) deep faults delineating a horst and graben structure and affecting the entire sedimentary sequence, iii) an archean to paleoproterozoïc basements enriched in radiogenic elements and displaying mafic and ultramafic units, and iv) a possible karstic reservoir located 400 m below the surface. The high geothermal gradient could be due to a thin lithosphere enriched in radiogenic elements, which can also contribute to a massive radiolysis process of water at depth, releasing an important amount of H₂. Alternatively, ultramafic rocks that may have generated H₂ during their serpentinization are also documented in the basement. The seismic profiles show that the faults seen at the surface are deeply rooted in the basement, and can drain deep fluids to shallow depths in a short time scale. The carbonate reservoirs within the Bambuí group which forms the main part of the sedimentary layers are crossed by the fault system and represent good candidates for temporary H₂ accumulation zones. The formation by chemical dissolution of sinkholes located at 400 m depth might explain the presence of sub-circular depressions seen at the surface. These sinkholes might control the migration of gas from temporary storage reservoirs in the upper layer of the Bambuí formation to the surface. The very high fluxes of H₂ escaping out of these structures which have been recently documented are, however, in disagreement with the newly developed H₂ production model in the Precambrian continental crust. They either question the validity of these models or the measurement methodology.

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

Poly(vinyl acetate) (PVAC) is a ubiquitous polymer in various industrial applications, including paints and adhesives. While extensive research has focused on understanding the degradation mechanisms of PVAC-based paint, under different environmental conditions, limited attention has been paid to the long-term stability of PVAC-based white glues, especially when used in artworks. This study investigates the accelerated degradation of a commercial PVAC-based white glue considered as representive of this class of materials as used in contemporary artworks, to predict its durability and assess its behavior in art objects. Through accelerated aging experiments and comparison with natural aging observed in artworks, the study reveals the formation of chromophores and the release of plasticizer as key degradation processes. Despite minimal structural modifications in PVAC, the loss of plasticizer leads to an increase in glass transition temperature, affecting the adhesive's cohesive strength and contributing to detachment of materials in artworks. The findings underscore the importance of preventive conservation measures to mitigate degradation issues in PVAC-based artworks.

The high demand for liquefied natural gas (LNG) requires more LNG carriers (LNGCs) to be in operation. During transportation, there is a high risk due to the required extremely low temperatures and the explosive nature of LNG cargo. Moreover, when there is a lack of experience in operating old LNGCs, there is a serious concern regarding operational accidents. A systematic maintenance strategy, especially for LNG cargo containment systems, is crucial for maintaining safe LNG transportation at sea. The purpose of this study is to develop preventive LNG cargo containment system maintenance models by using LNGC dock specifications from LNGCs of various ages. The dock specifications from a conventional LNGC repairing dock were analyzed using natural language processing techniques in order to develop preventive maintenance models of the LNG cargo containment system. From these results, and by considering the ship’s age, it was found that for young LNGCs the priority for repair should focus on checking routine consumable spare parts by tank inspections, whereas for older LNGCs the focus should be on tank condition maintenance rather than on other facilities. These results are expected to be useful in the development of a maintenance strategy of preventive LNG cargo containment systems in maritime LNG transportation.

Self-medication with natural products during disease outbreaks in developing countries poses significant public health challenges yet remains relatively unexplored in the published literature. This study aims to assess the implications of such practices, address the state and quality of evidence, identify gaps in the literature, explore determinants and contributing factors, elucidating the implications, and propose possible solutions. Using a modified systematic literature review methodology, 20 articles were selected from 1,683 reports after applying rigorous selection criteria and quality assessment using the AXIS critical evaluation tool. The findings revealed a dearth of literature concerning the implications of self-medication with natural products during disease outbreaks, highlighting the need for further investigation in this domain. Key determinants included gender, acquisition, and ideological factors, while common side effects comprised diarrhoea, stomach pain, sweating, headache, and nausea/vomiting. Notably, self-medication with natural products was associated with reduced communication with healthcare providers, leading to underreported side effects. It also creates a false sense of safety which may potentially exacerbate the spread of the prevailing disease. In conclusion, the rampant use of natural products for self-medication necessitates increased awareness among the population, encouraging individuals to seek professional medical care and support when faced with disease outbreaks.

Breast cancer (BC) is the most common malignancy among women worldwide. In recent years, significant progress has been made in BC therapy. However, serious side effects resulting from the use of standard chemotherapeutic drugs, as well as the phenomenon of multidrug resistance (MDR), limit the effectiveness of approved therapies. Advanced research in the BC area is necessary to create more effective and safer forms of therapy to improve the outlook for individuals diagnosed with this aggressive neoplasm. For decades, plants and natural products with anti-cancer properties have been successfully utilized in treating various medical conditions. Anthracene derivatives are tricyclic secondary metabolites of natural origin that have been identified in plants, lichens, and fungi. They represent a few botanical families, e.g., Rhamnaceae, Rubia-ceae, Fabaceae, Polygonaceae, and others. The review comprehensively covers and analyzes the most recent advances in the anticancer activity of anthracene derivatives (emodin, aloe-emodin, hypericin, chrysophanol, rhein, and physcion) applied both individually, or in combination with other chemotherapeutic agents in in vitro and in vivo BC models.

Although natural products are an important source of drugs and drug leads, identification and validation of their target proteins have proven difficult. Here, we report the development of a systematic strategy for target identification and validation employing drug affinity responsive target stability (DARTS) and mass spectrometry imaging (MSI) without modifying or labeling natural compounds. Through a validation step using curcumin, which targets aminopeptidase N (APN), we successfully standardized the systematic strategy. Using label-free voacangine, an antiangiogenic alkaloid molecule as the model natural compound, DARTS analysis revealed vascular endothelial growth factor receptor 2 (VEGFR2) as a target protein. Voacangine inhibits VEGFR2 kinase activity and its downstream signaling by binding to the kinase domain of VEGFR2, as was revealed by docking simulation. Through cell culture assays, voacangine was found to inhibit the growth of glioblastoma cells expressing high levels of VEGFR2. Specific localization of voacangine to tumor compartments in a glioblastoma xenograft mouse was revealed by MSI analysis. The overlap of histological images with the MSI signals for voacangine was intense in the tumor regions and showed colocalization of voacangine and VEGFR2 in the tumor tissues by immunofluorescence analysis of VEGFR2. The strategy employing DARTS and MSI to identify and validate the targets of a natural compound as demonstrated for voacangine in this study is expected to streamline the general approach of drug discovery and validation using other biomolecules including natural products.

The digital world is increasingly invading our reality, which leads to the formation of a significant reflection of the processes and activities taking place in the smart city. Such activities include well-known urban events, celebrations, and those with a very local character. Due to the mass occurrence, events have a comparable influence on the formation of the spirit and the urban atmosphere. This work presents an enhanced semantic version of the ConvTree algorithm - SemConvTree. It allows considering the semantic component of the data obtained by using semi-supervised learning of topic modeling ensemble (consisting of improved models BERTopic, TSB-ARTM, SBert-Zero-Shot). We also present an improved event search algorithm based on both statistical evaluations and semantic analysis of posts. This algorithm allows fine-tuning the mechanism of discovering the required entities with the specified particularity (such as a particular topic). Experimental studies were conducted within the area of New York City. They showed an improvement in the detection of posts devoted to events (about 40% higher f1-score) due to the accurate handling of events of different scales. These results lead in the long term to talk about the potential perspective in creating a semantic platform for the analysis and monitoring of urban events in the future.

The predominance of English and Latin-based large language models (LLMs) has led to a notable deficit in native Arabic LLMs. This discrepancy is accentuated by the prevalent inclusion of English tokens in existing Arabic models, detracting from their efficacy in processing native Arabic's intricate morphology and syntax. Consequently, there is a theoretical and practical imperative for developing LLMs predominantly focused on Arabic linguistic elements. To address this gap, this paper proposes ArabianGPT, a series of transformer-based models within the ArabianLLM suite designed explicitly for Arabic. These models, including ArabianGPT-0.1B and ArabianGPT-0.3B, vary in size and complexity, aligning with the nuanced linguistic characteristics of Arabic. The AraNizer tokenizer, integral to these models, addresses the unique morphological aspects of Arabic script, ensuring more accurate text processing. Empirical results from fine-tuning the models on tasks like sentiment analysis and summarization demonstrate significant improvements. For sentiment analysis, the fine-tuned ArabianGPT-0.1B model achieved a remarkable accuracy of 95%, a substantial increase from the base model's 56%. Similarly, in summarization tasks, fine-tuned models showed enhanced F1 scores, indicating improved precision and recall in generating concise summaries. Comparative analysis of fine-tuned ArabianGPT models against their base versions across various benchmarks reveals nuanced differences in performance, with fine-tuning positively impacting specific tasks like question answering and summarization. These findings underscore the efficacy of fine-tuning in aligning ArabianGPT models more closely with specific NLP tasks, highlighting the potential of tailored transformer architectures in advancing Arabic NLP.

Transforming Small-Scale LNG Business, Enhancing Efficiency and Resilience in an Evolving Supply Chain In various industries, innovative systems have proven their value due to their efficiency and adaptability. A prime example of this lies in the rapid growth of the liquefied natural gas (LNG) market. LNG, a fuel derived from natural gas, has gained significant traction in recent years. In light of this, we aim to explore optimal strategies for small-scale LNG businesses, with a focus on revolutionizing their supply chain to ensure adaptability and sustainability. Our objective is to enhance efficiency and sturdiness in this evolving sector. By devising innovative methods and implementing cutting-edge technologies, we aim to revolutionize the small-scale LNG market, paving the way for a more optimized and resilient supply chain. Through this transformation, we strive to create a sustainable and adaptable framework that will drive the growth and success of a small-scale LNG businesses.LNG' ability to be transported and stored more cost-effectively in its reduced volume compared to natural gas in its original state is a significant advantage. The distribution network for LNG starts at numerous supply terminals and extends to a diverse range of consumption centers, each with varying tank capacities. In the broader economic context, the establishment of efficient routes for LNG Truck tank plays a critical role.To tackle the challenge of selecting a pickup location and determining optimal destinations with specific unloading volumes for liquefied natural gas (LNG), this research presents an innovative solution utilizing state-of-the-art machine learning techniques. The proposed approach combines the power of reinforcement learning, recurrent neural networks, online learning, and graph theory. By leveraging these cutting-edge techniques, we aim to develop an intelligent system that can effectively determine the most efficient pickup location and sequence of destinations for LNG delivery. This system will take into account various factors such as transportation costs tank capacity, and demand fluctuations. Through the integration of reinforcement learning, the system will continuously learn and improve its decision-making process in real-time. The recurrent neural networks will allow for the analysis of historical aim to model and optimize the complex network of pickup and delivery routes, considering factors such as distance, capacity constraints, and operational efficiencies. With this innovative approach, we strive to revolutionize the selection and planning process for LNG pickup and delivery, ultimately enhancing efficiency and reducing cost within the LNG supply chain.Improving the distribution network offers several financial benefits, including cost reduction, faster delivery times, more affordable distribution, and enhanced efficiency in the utilization of tanker trucks for LNG transportation. By optimizing the distribution network, transit costs associated with LNG transportation can be significantly lowered. This can be achieved through intelligent route planning, taking into account factors such as distance, traffic conditions, and fuel consumption. Minimizing transit costs contributes to overall savings for businesses operating in the LNG market. Faster delivery times are another advantage of an enhanced distribution network. By designing efficient routes and eliminating unnecessary detours or delays, the time taken for LNG to reach its intended destinations can be reduced. This not only improves customer satisfaction but also allows for more frequent deliveries and better coordination of supply and demand. Cheaper distribution is a direct outcome of an optimized distribution network. By strategically planning pickup locations, destinations, and intermediate stops, the costs associated with transferring LNG between terminals and consumption centres can be minimized. This leads to savings in transportation expenses, ultimately benefiting both businesses and end-users. Efficient utilization of tanker trucks is crucial for maximizing profitability in the LNG industry. An improved distribution network enables better allocation of resources, ensuring that tanker trucks are utilized optimally. This includes reducing empty or underutilized trips, maximizing load capacities, and minimizing idle time. Such efficiency improvements translate to cost savings and increased revenue for businesses involved in LNG transportation. Overall, the financial gains from improving the distribution network are substantial. Lower transit costs, faster delivery times, cheaper distribution, and more efficient use of tanker trucks contribute to increased profitability, competitiveness, and sustainability in the LNG market.

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

Urban waterways are significant natural elements in urban development activities, which might cause long-term changes in the historic built environment. As it runs through the historic city center of Bursa, a World Heritage Site in Türkiye, Cilimboz (Phillippoz) Creek has witnessed urban development activities along its surrounding landscape. Its valley, the study area, is composed of natural, residential, and industrial heritage as well as archaeological remains dating to ancient times while having undergone permanent changes as a result of the rapid increase in population and squatting, since the 1960s. This study aims to assess the challenges of sustainability in a multi-layered historic urban landscape while discussing the outcomes of various land use implementations along Cilimboz Creek. The cultural assets in the study area are presented by using a combination of interpretive-historical and case-study research methods to understand historic urban texture. Afterward, the changes in the composition of natural and architectural heritage are described to assess the impacts of urban development activities on the sustainability of its multi-layered historic urban identity. Consequently, this evaluation reveals the need for a holistic approach by the participation of all relevant stakeholders to co-develop solutions for architectural, social, and environmental conservation issues in this cultural landscape.

The COVID-19 pandemic has posed a significant threat to society in recent times, endangering human health, life, and economic well-being. The disease spreads quickly due to the highly infectious SARS-CoV-2 virus, which has undergone numerous mutations. Despite intense research efforts by the scientific community since its emergence in 2019, no effective therapeutics have been discovered yet. While some repurposed drugs have been used to control the global outbreak and save lives, none have proven universally effective, particularly for severely infected patients. Although the spread of the disease is generally under control, anti-SARS-CoV-2 agents are still needed to combat current and future infections. This study reviews some of the most promising repurposed drugs containing indolyl heterocycle, which is an essential scaffold of many alkaloids with diverse bio-properties in various biological fields. The study also discusses natural and synthetic indole-containing compounds with anti-SARS-CoV-2 properties, as well as computer-aided drug design (in-silico studies) for optimizing anti-SARS-CoV-2 hits/leads.

Forest vegetation dynamics (succession and dominance) is an ecological phenomenon that is still difficult to characterize and integrate into management practices. In this study, the understanding of forest dynamics is explored based on Grime’s theoretical triangular model of plant adaptive strategies using the example of Lithuania’s forest ecosystems. The idea behind this is the hypothesis that forest dynamics is linked to natural selection as an evolutionary process that exhibits differential species responses to competition, stress, and disturbance. The aim of this study is to explore the adaptive relationships in hemi-boreal forests. Grime’s and Pierce’s secondary CSR strategies, which describe various equilibria between competitiveness (C), stress tolerance (S), and ruderalism (R), were considered to reflect four establishment and development adaptive specialization characteristics of forest tree species. As a result of the study, four types of tree functional groups were identified: stress-resistant ruderals, competitive stress-sensitive ruderals, ruderal stress-sensitive competitors, and stress-resistant competitors. Based on this, we propose that reforestation move away from single species regeneration by implementing the maintenance of these four types of functional groups. In conclusion, forest management must consider the existence of the established equilibria between plant competitiveness, stress tolerance, and ruderalism. The formal concepts presented in this article can serve as a guide for future relevant research and development of appropriate methods for studying real forests. This study is unique in that no previous work has linked forest dynamics and natural selection in the context of Lithuania’s forest ecosystems.

Archaeology is often argued to provide a unique long-term perspective on humans that can be utilised for effective policy-making, for example in discussions of resilience and sustainability. However, the specific archaeological evidence for resilient/sustainable systems is rarely explored, with these terms used simply to describe a community that survived a particular shock. In this study, a set of 74 case studies of papers discussing archaeological evidence for resilience/sustainability are identified and analysed using bibliometric methods. Variables from the papers are also quantified to assess patterns and provide a review of current knowledge. A great variety of scales of analysis, case study locations, stressors, resilient/sustainable characteristics, and archaeological evidence types are present. Climate change was the most cited stressor (n=40) and strategies relating to natural resources were common across case studies, especially subsistence adaptations (n=35), other solutions to subsistence deficiencies (n=23), and water management (n=23). Resilient/sustainable characteristics were often in direct contrast to one-another, suggesting the combination of factors is more important than each factor taken individually. Further quantification of well-defined variables within a formally-produced framework is required to extract greater value from archaeological case studies of resilience/sustainability.

The concept of green energy is now at the forefront of development discourse, with the United Nations Sustainable Development Goals (SDGs) 7, 11, and 12 all aimed at promoting green energy consumption to combat the three planetary crises: climate change, biodiversity loss, and pollution. Similarly, issues regarding Africa’s natural resource curse have caused a stir in the growth and development literature for some time now and there is no sign that it will die out. This study, the first of its kind, simultaneously assesses the impact of green energy consumption and Africa’s natural resources rents on economic growth by applying the Feasible Generalized Least Square (FGLS) estimator and the dynamic panel models of the Pooled Mean Group (PMG) and Mean Group (MG) estimators on data from 1990 to 2020 for 24 selected African countries. The results show that green energy consumption has a short-run growth-limiting effect and a long-run growth-enhancing effect in Africa. The study also found evidence of the natural resource curse phenomenon in Africa. The study, therefore, calls for the advancement and usage of green energy for both domestic and industrial production in Africa. The study further calls for a revamp in the global tax policy to curb illicit financial activities and strengthening institutional quality for transparency and accountability in the entire value chain of natural resource management in Africa.

Several biomedical products, like scaffolds, implants, prostheses, and orthoses, require materials having superior physicochemical and biological properties. Polyethylene terephthalate glycol (PETG) is being increasingly used for various biomedical applications. There are a few studies on PETG-based composites, however, natural fibers like silk short fibers reinfored PETG composites have not been attempted. Being a cost-effective widely available material, PETG-Silk combination can be potential biocomposite for several biomedical applications. Here, we report a novel short silk fiber reinforced PETG composite prepared by a wet-mixing route, ensuring homogenous dispersion of the filler. Different ratios (2-10%) of short silk fibers were used to prepare composites with varied compositions. The mechanical, physicochemical, and biological properties of the prepared composites were analyzed. Thermogravimetric analysis showed that such composites are thermally stable up to 390 °C and can be used for thermal extrusion-based manufacturing. The tensile modulus of the samples increased with fiber content; however, the failure strain reduced with fiber content. Furthermore, upon annealing, the tensile modulus increased but, the failure strain of the composites decreased, XRD study revealed that heat treatment has altered the crystalline nature of the composites. Finally, we evaluated the cytocompatibility of the prepared composites to assess their suitability for various biomedical applications.

Convolutional neural network (CNN), a class of artificial neural network (ANN) is attracting interests of researchers in all research domain. CNN was invented for computer vision. They have also shown to be useful for semantic parsing, sentence modeling and other natural language processing related tasks. Here in this paper we discuss the basics of CNN models and their scope to provide a reference/baseline to the researchers interested in using CNN models in their research.

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

Distributional word vector representation orword embedding has become an essential ingredient in many natural language processing (NLP) tasks such as machine translation, document classification, information retrieval andquestion answering. Investigation of embedding model helps to reduce the feature space and improves textual semantic as well as syntactic relations.This paper presents three embedding techniques (such as Word2Vec, GloVe, and FastText) with different hyperparameters implemented on a Bengali corpusconsists of180 million words. The performance of the embedding techniques is evaluated with extrinsic and intrinsic ways. Extrinsic performance evaluated by text classification, which achieved a maximum of 96.48% accuracy. Intrinsic performance evaluatedby word similarity (e.g., semantic, syntactic and relatedness) and analogy tasks. The maximum Pearson (ˆr) correlation accuracy of 60.66% (Ssˆr) achieved for semantic similarities and 71.64% (Syˆr) for syntactic similarities whereas the relatedness obtained 79.80% (Rsˆr). The semantic word analogy tasks achieved 44.00% of accuracy while syntactic word analogy tasks obtained 36.00%

There is a marked momentum towards the use of clean hydrogen energy as an alternative for fossil fuels. Renewable energies such as solar and wind are being used to generate hydrogen from the water hydrolysis process. Indeed, this approach stores renewable energies in the form of combustible hydrogen for other energy uses. The other alternative that could be economically more cost-effective at the current technology stage is to explore the natural “Hydrogen System” where the natural hydrogen is generated and accumulated within the earth system, the same that stands for a “Petroleum System”. The Discovery of a large accumulation of relatively pure natural hydrogen (H2) in Mali has triggered the opportunity of searching for natural hydrogen accumulations in other countries. The generation of hydrogen from a circular depression in Mali and some other countries is linked to the presence of geologically very old iron-rich basement rocks. Solid-liquid redox reactions between iron-rich minerals and groundwater that split water are a possible source of H2 in deep basement rocks. It is believed that the hydrogen degassing may be detected by surface topographic circular to sub-circular shallow depressions. Chemical processes such as dissolution by hydrogen are considered to play the main role in the formation of the circular depressions through preferential vertical hydrogen migration channel. Archean iron-rich Yilgarn Craton that covers a vast area of Western Australia (WA) contains abundant iron-rich mafic-ultramafic rocks. The craton reveals many surficial circular depressions visible through satellite images. The area has abundant fault systems and is blanketed with Eocene sedimentary rocks containing high-quality reservoir rocks. All these characteristics seem to provide most of the required elements, such as hydrogen source, migration pathway, and reservoir rock for a “Hydrogen System” in this area.

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

In the case of a severe accident, natural convection plays an important role in the atmosphere mixing of nuclear reactor containments. In the previous study, to simulate the natural convection in the accident scenario within a nuclear reactor containment, the steady thermal boundary conditions (BCs) were prescribed on either cooled or heated wall. The present study, therefore, aims at the transient 3D numerical simulations of natural convection of air around a cylindrical containment with unsteady thermal BCs at the vessel wall. For that purpose, the experiment series was done in the CIGMA facility at Japan Atomic Energy Agency (JAEA). The upper vessel or both the upper vessel and the middle jacket was cooled by subcooled water, while the lower vessel was thermally insulated. A 3D model was simulated with OpenFOAM®, applying the Unsteady Reynolds-averaged Navier–Stokes equations (URANS) model. Different turbulence models were studied, such as the standard k-ε, standard k-ω, k-ω Shear Stress Transport (SST) and, low-Reynolds-k-ε Launder-Sharma. The results of the four turbulence models were compared versus the results of experimental data. The k-ω SST showed a better prediction compare to other turbulence models. Also, the accuracy of the predicted temperature and pressure were improved when the heat conduction on the internal structure, i.e., flat bar, was considered in the simulation. Otherwise, the predictions on both temperature and pressure were underestimated compared with the experimental results. Hence, the conjugate heat transfer in the internal structure inside the containment vessel must be modeled accurately.

Around the World, the number of compound databases of natural products in the public domain is rising. This is in line with the increasing synergistic combination of natural product research and chemoinformatics. Towards this global endeavor, countries in Latin America are assembling, curating, and analyzing the contents and diversity of natural products available in their geographical regions. In this manuscript we collect and analyze the efforts that countries in Latin America have made so far to build natural product databases. We further encourage the scientific community in particular in Latin America, to continue their efforts to building quality natural product databases and, whenever possible, to make them publicly accessible. It is proposed that all compound collections could be assembled into a unified resource called LANaPD: Latin America Natural Products Database. Opportunities and challenges to build, distribute, and maintain LANaPD are also discussed

In this review, we summarize the current state-of-the-art in the utilization of close-range sensing in forest monitoring. We include technologies, such as terrestrial and mobile laser scanning as well as unmanned aerial vehicles, which are mainly used for collecting detailed information from single trees, forest patches or small forested landscapes. Based on the current published scientific literature, the capacity to characterize changes in forest ecosystems using close-range sensing has clearly been recognized. Forest growth has been the most investigated cause for changes and terrestrial laser scanner the most applied sensor for capturing forest structural changes. Unmanned aerial vehicles, on the other hand, have been used to acquire aerial imagery for detecting tree height growth and monitoring forest health. Mobile laser scanning has not yet been used in forest change monitoring except for a few early investigations. Considering the length of the forest growth process, investigated time spans have been rather short, less than 10 years. In addition, data from only two time points have been used in many of the studies, which has further been limiting the capability of understanding dynamics related to forest growth. In general, method development and quantification of changes have been the main interests so far regardless of the driver of change. This shows that the close-range remote sensing community has just started to explore the time dimension and its possibilities for forest characterization.

The current energy policy recommends the idea of energy efficiency over fossil energy as a primary matter for the coming years. The kingdom of Morocco requires restructuring of its power equipment by increasing the percentage of renewable energy supplies, optimizing their systems and power storage. Therefore, increasing energy efficiency is an as important obligation as reducing the overall energy consumption. The purpose of this research is to present the energy transition in Morocco towards renewable energies and to assess the diversity of available marine natural resources. Recent research in conversion of ocean thermal energy, wave energy, tidal energy, offshore wind energy, and osmotic energy into power supply has started to encourage different technologies. This research has led to commercial deployment in some cases such as our 550 km long Mediterranean coast and 3000 km long Atlantic. This does not only result in fossil energies independency but also provides advantages like less cost and no pollution.

Flavonoids are widely recognized as natural polydrugs, given their anti-inflammatory, antioxidant, sedative and antineoplastic activities. Recently, different studies have shown that flavonoid have the potential to inhibit BET bromodomains. Previous reports suggest that flavonoids are putative inhibitors of the ZA channel due to their orientation and interactions with P86, V87, L92, L94 and N140. Herein, a comprehensive characterization of the binding mode of the biflavonoid amentoflavone and fisetin is discussed. To this end, both compounds were docked with BRD4 using four docking programs. Results were post-processed with protein-ligand interaction fingerprints. To gain further insights into the binding mode of the two natural products, docking results were further analyzed with molecular dynamics. Results showed that amentoflavone makes numerous contacts in the ZA channel, as previously described for flavonoids and kinase inhibitors. It was also found that amentoflavone can potentially make contacts with non-canonical residues for BET inhibition. Most of these contacts were not observed with fisetin. Based on these results, amentoflavone was tested for BRD4 inhibition, showing activity in the micromolar range. This work may serve as basis for scaffold optimization and further characterization of flavonoids as BET inhibitors.

With the increment of the aging population in recent years, neurodegenerative diseases exert a major global disease burden, essentially as a result of the lack of treatments that can stop the disease progression. Alzheimer’s Disease (AD) is an example of a neurodegenerative disease, that affects millions of people globally, with no effective treatment. Natural compounds have emerged as a viable therapy to fill a huge gap in AD management, and in recent years, mostly fuelled by the Covid-19 pandemic, RNA-based therapeutics have become a hot topic in the treatment of several diseases. Treatments of neurodegenerative diseases face significant limitations due to the complex and interconnected pathways that lead to their hallmarks and, also due to the necessity to cross the blood–brain barrier. Nanotechnology has contributed to surpass this bottleneck in the treatment of AD, by promoting safe and enhanced drug delivery to the brain. In particular, exosome-like nanoparticles, a hybrid delivery system combining exosomes and liposomes' advantageous features, are demonstrating great potential in the treatment of central nervous system diseases.

In this paper, a resonant system that produces a movement of low amplitude and ultrasonic frequency is used to achieve the vibration assistance in a ball-burnishing process. A full vibration characterization of this process performed in a lathe was done. It is carried out by a new tool designed in the research group of the authors. Its purpose is to demonstrate that the machine and the tool do not have any resonance problem during the process and to prevent possible failures. The analysis of this dynamic behaviour permits to validate the suitability of the tool when it is anchored to a numerical control lathe. This is very important for its future industrial implementation. It is also intended to confirm that the system adequately transmits vibrations through the material. To do this, a methodology to validate the dynamic tool behaviour was developed. Several techniques that combine the usual and ultrasonic vibration ranges through static and dynamic measurements were merged: vibration and acoustic emission measurements. An operational deflection shape (ODS) exercise has been also performed. Results show the suitability of the tool used to transmit the assistance vibrations, and that no damage is produced in the material in any case.

To improve the interfacial bonding of sisal fiber reinforced polylactide biocomposites, polylactide (PLA) and sisal fibers (SF) were melt-blended to fabricate bio-based composites via in situ reactive interfacial compatibilization with the addition of an epoxy-functionalized oligomer (ADR). The FTIR analysis and SEM characterization demonstrated that PLA molecular chain was bonded to the fiber surface and epoxy-functionalized oligomer played a hinge-like role between sisal fibers and PLA matrix, which resulted in improved interfacial adhesion between fibers and PLA matrix. The interfacial reaction and microstructures of composites were further investigated by thermal and rheological analyses, which indicated that the mobility of the PLA molecular chain in composites was restricted because of the introduction of ADR oligomer, which in turn reflected the improved interfacial interaction between SF and PLA matrix. These conclusions were further investigated by the calculated activation energies of glass transition relaxation (△Ea) of composites via dynamic mechanical analysis. The mechanical properties of PLA/SF composites were simultaneously reinforced and toughened via addition of ADR oligomer. The interfacial interaction and structure-properties relationship of composites are key points of this study.

This article discusses the use of a hand held electronic nose to obtain information on the presence of some aromatic defects in natural cork stoppers, such as haloanisoles, alkylmethoxypyrazines and ketones. The proposed prototype has been developed as an instrumentation system with up to eight commercial gas sensors. Machine learning algorithms such as k-nearest neighbors and artificial neural networks has been used. The use of this system tries to improve the current aromatic defect detection process in the cork stopper industry, which is done by gas chromatography or human test panels.

Social media platforms have been entirely an undeniable part of the lifestyle for the past decade. Analyzing the information being shared is a crucial step to understanding human behavior. Social media analysis aims to guarantee a better experience for the user and risen user satisfaction. For deriving any further conclusion, first, it is necessary to know how to compare users. In this paper, a hybrid model has been proposed to measure Twitter profiles’ similarity and quantifies the likeness degree of profiles by calculating features considering users’ behavioral habits. For this, first, the timeline of each profile has been extracted using the official TwitterAPI. Then, in parallel, three aspects of a profile are deliberated. Behavioral ratios are time-series-related information showing the consistency and habits of the user. Dynamic time warping has been utilized to compare the behavioral ratios of two profiles. Next, the audience network is extracted for each user, and for estimating the similarity of two sets, Jaccard similarity is used. Finally, for the Content similarity measurement, the tweets are preprocessed respecting the feature extraction method; TF-IDF and DistilBERT for feature extraction are employed and then compared using the cosine similarity method. Results have shown that TF-IDF has slightly better performance; therefore, the more straightforward solution is selected for the model. Similarity level of different profiles. As in the case study, a Random Forest classification model was trained on almost 20000 users revealed a 97.24% accuracy. This comparison enables us to find duplicate profiles with nearly the same behavior and content.

The prevalence and incidence of obesity and the comorbidities linked to it are increasing in the world population. Current therapies for obesity and associated pathologies have proven to cause a broad number of adverse effects and often, they are overpriced or not affordable for all patients. Among the alternatives currently available, natural bioactive compounds stand out. These are frequently contained in pharmaceutical presentations, nutraceutical products, supplements, or functional foods. The clinical evidence for these molecules is increasingly solid, among which epigallocatechin-3-gallate, ellagic acid, resveratrol, berberine, anthocyanins, probiotics, carotenoids, curcumin, silymarin, hydroxy citric acid, and α-lipoic acid stand out. The molecular mechanisms and signaling pathways of these molecules have been shown to interact with the endocrine, nervous, and gastroenteric systems, as well as regulate the expression of multiple genes and, therefore, proteins involved in starvation-satiety processes, activation of brown adipose tissue, increased lipolysis, decreased lipogenesis, and inflammation, beneficial changes in metabolism and improved insulin sensitivity. This review provides a comprehensive view of nature-based therapeutic options to address the increasing prevalence of obesity. It offers a valuable perspective for future research and subsequent clinical practice, addressing everything from the molecular, genetic, and physiological bases to the clinical study of the bioactive compound.

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

This review seeks to explain four features of viral respiratory illnesses that have perplexed generations of virologists: (1) the seasonal timing of respiratory illness; (2) the common viruses causing respiratory illness worldwide, including year-round disease in the Tropics; (3) the rapid response of outbreaks to weather, specifically temperature; (4) the rapid arrival and termination of epidemics caused by influenza and other viruses. The inadequacy of the popular explanations of seasonality is discussed, and a simple hypothesis is proposed, called Temperature Dependent Viral Tropism (TDVT), that is compatible with the above features of respiratory illness. TDVT notes that viruses can transmit themselves more effectively if they moderate their pathogenicity (thereby maintaining host mobility) and suggests that endemic respiratory viruses accomplish this by developing thermal sensitivity within a range that supports organ-specific viral tropism within the human body, whereby they replicate most rapidly at temperatures below body temperature. This allows them to confine themselves to the upper respiratory tract and to avoid infecting the lungs, heart, gut etc. Biochemical and tissue-culture studies show that “wild” respiratory viruses show such natural thermal sensitivity. The typical early autumn surge of colds and the existence of respiratory illness in the Tropics year-round at intermediate levels are explained by the tendency for strains to adapt their thermal sensitivity to their local climate and season. The TDVT hypothesis has important practical implications for preventing and treating respiratory illness including Covid-19. TVDT is testable with many options for experiments to increase our understanding of viral seasonality and pathogenicity.

With the COVID-19 pandemic behind us, the U.S. Food and Drug Administration (FDA) has approved 55 new drugs in 2023, a figure consistent with the number authorized in the last five years (53 per year on average). Thus, 2023 is the second-best FDA harvest after 2018 (59 approvals) in all the series. Monoclonal antibodies (mAbs) continue to be the class of drugs with most approvals, with an exceptional 12, a number that makes it the most outstanding year for this class. As in 2022, five proteins/enzymes have been approved in 2023. However, no antibody–drug conjugate (ADC) has been released onto the market. With respect to TIDES (peptides and oligonucleotides), 2023 has proved a spectacular year, with a total of nine approvals, corresponding to five peptides and four oligonucleotides. Natural products continue to be the best source of inspiration for drug development, with 10 new products on the market. Three drugs in this year’s harvest are pegylated, which may indicate the return of pegylation as a method to increase the half-lives of drugs, after the withdrawal of peginesatide from the market in 2013. Following the trends of recent years, two bispecific drugs have been authorized in 2023. As in preceding years, fluorine and/or N-aromatic heterocycles are present in most of the drugs. Herein, the 55 new drugs approved by the FDA in 2023 are analyzed exclusively on the basis of their chemical structure. They are classified as the following: biologics (antibodies, proteins/enzymes); TIDES (peptide and oligonucleotides); combined drugs; pegylated drugs; natural products; nitrogen aromatic heterocycles; fluorine-containing molecules; and other small molecules.

Porifera, commonly referred to as marine sponges, have stood out as major producers of marine natural products (MNPs). Sponges of the genus Phorbas have attracted much attention along years. They are widespread in all continents, and several structurally unique compounds have been identified from species of this genus. Terpenes, mainly sesterterpenoids, represent the great majority of secondary metabolites isolated from Phorbas species, even though several alkaloids and steroids have also been reported. Many of these compounds have shown a variety of biological activities. Particularly, Phorbas sponges have been demonstrated to be a source of cytotoxic metabolites. In addition, MNPs exhibiting cytostatic, antimicrobial and anti-inflammatory activities, have been isolated and structurally characterized. This work brings an overview of Phorbas secondary metabolites reported since the first study published in 1993 until 2020, and their biological activities.

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

Repeated air pollution exposure is one of the major threats to skin health. Air pollution causes skin damage and accelerates skin ageing mainly through oxidative stress mechanisms. Since it is difficult to minimize skin exposure from air pollutants, especially in urban areas, strategies to protect the skin are needed. On the other hand, plant phenolic compounds have been found to be effective in attenuating cellular oxidative stress and inflammation induced by different air pollutants. Therefore, we conducted a 12-week randomized, placebo-controlled, double blind study to assess the efficacy of a polyphenol-enriched dietary supplement (comprised of olive leaf, lemon verbena, rosemary and Sophora japonica extracts) in reducing pollution-induced oxidative stress and in improving different skin parameters related to skin ageing of Caucasian and Asian volunteers living in a polluted urban European area (Milan). One hundred healthy women were recruited and randomized in the placebo or in the test dietary supplement treatment arms. To assess the efficacy of the dietary supplement, the total antioxidant capacity on saliva (FRAP) was determined, as well as the oxidative damage on skin (lipoperoxides content), the skin moisture (corneometry), the trans-epidermal water loss (Tewameter), the skin radiance and the skin colour, (spectrophotometry), the skin elasticity (cutometry), the skin sebum content (sebumeter), and the skin roughness (image analysis). As a result, both inter-group and intra-group analysis proved that the dietary supplement improved all clinical and biochemical monitored parameters, in both Caucasian and Asian individuals. In conclusion, the results of the study indicate a reduced oxidative stress-induced skin damage in both Asian and Caucasian women living in a polluted urban area.

The structures of recently discovered primarolides A and B suggest their non-enzymatic formation from a common 2-formylbenzophenone precursor. This hypothesis is based on the experimentally proven facile conversion of pestalone (also a 2-formyl-benzophenone) either into the isomeric lactone pestalalactone or the structurally related isoindolinone pestalachloride A. In a related fashion, the racemic isoindolinone natural product mariline A is supposed to biosynthetically originate from the corresponding keto-aldehyde and an aniline, as experimentally supported by model studies. Due to the close structural relationship with known systems, it appears highly probable that primarolides A and B were generated under the fermentation conditions from a massarinin-related 2-formylbenzophenone (proprimarolide) by reaction either with aniline or a nucleophilic catalyst, respectively. Suberoylanilide hydroxamic acid (SAHA), used as an additive during the fermentation, is supposed to act both as a source of aniline and as a nucleophilic catalyst.

What proportions should a thermally massive building have? How should the thermal mass be distributed? Should the "massing" change with the choice of material? This paper shows how to optimize the physical proportions of a building so that it synchronizes ambient heat exchanges in a natural feedback cycle. The internal mass is thermally coupled with buoyancy ventilation; the cycle is driven by the daily swing of outdoor temperature. Tripling up functions in this way—so that structural materials can reliably cool and power the ventilation for buildings—could help decarbonize the construction industry and provide an effective strategy for adapting to life-threatening heatwaves. Based on harmonic analysis, the method allows designers to thermally tune the form and mass of a building to meet chosen targets for temperature and ventilation in free-running mode. Once the optimal balance of exchange rates is known, design teams can proportionally vary the building height and ventilation openings against the surface area and thickness of an internal thermal mass. The possible permutations are infinite but parametrically constrained, allowing teams to fairly compare the functional and environmental credentials of different construction materials while they produce and evaluate preliminary options for organizing the exterior form and interior spaces of a building. An example study suggests that thin-shell structures of minimum weight, and even timber buildings, may be optimally tuned to produce ample ventilation and temperature attenuation.

The establishment of a well characterized non-human primate model of Zika virus (ZIKV) infection is critical for the development of medical interventions. In this study, challenging Indian rhesus macaques (IRMs) with ZIKV strains of the Asian lineage resulted in dose dependent peak viral loads between days 2 and 5 post infection; and a robust immune response which protected the animals from homologous and heterologous re-challenge. In contrast, viremia in IRMs challenged with an African lineage strain was below the assays lower limit of quantitation and the immune response was insufficient to protect from re-challenge. These results corroborate previous observations but are contrary to reports using other African strains obviating the need for additional studies to elucidate the variables contributing to the disparities. Nonetheless, the utility of an Asian lineage ZIKV IRM model for countermeasures development was verified by vaccinating animals with a formalin inactivated reference vaccine and demonstrating sterilizing immunity against a subsequent subcutaneous challenge.

Alzheimer's disease is a multifactorial neurodegenerative disorder, with Acetylcholinesterase, Butyrylcholinesterase, Beta secretase, Glycogen Synthase Kinase beta and Monoamine oxidase B playing central role in it's pathogenesis. Therefore, this research aims to discover potential curative multitarget drugs derived from African natural products capable of addressing the multifactorial characteristics of the disease. In silico approaches were used to filter a 880 african natural compounds library based on selected pharmacokinetic properties. Molecular docking against the five aforementioned proteins, followed by Molecular Mechanics with Generalised Born and Surface Area Solvation scoring for the top compounds were used to assess binding. Density Functional Theory studies were used to assess electronic transitions. Pharmacokinetic filters resulted in 200 compounds, of which only five were selected after molecular docking. Density Functional Theory and Molecular Mechanics with Generalised Born and Surface Area Solvation scoring resulted in 3 potential multitarget compounds. Compound 157 (ZINC000095485950) showed triple-target inhibitory activity against Butyrylcholinesterase, Glycogen Synthase Kinase beta and Beta-secretase, while compounds 159 (ZINC000095485952) and 696 (ZINC000039144622) showed dual-target inhibitory activity against Butyrylcholinesterase and Glycogen Synthase Kinase beta. Molecular dynamics simulation and further experimental assessments are suggested.

Windthrow (i.e., trees broken and uprooted by wind) is a major natural disturbance in the Amazon. Images from medium-resolution optical satellites (mostly Landsat) combined with extensive field data have allowed researchers to assess patterns of tree mortality and monitor forest recovery over decades of subsequent succession in different regions. Although satellites with high spatial-resolution have become available for the Amazon in the last decade, they have not yet been employed for the mapping and quantification of windthrow tree-mortality. Here, we address how increasing the spatial resolution of satellites affects plot-to-landscape estimates of windthrow tree-mortality. We combined forest inventory data with Landsat 8 (30 m pixel), Sentinel 2 (10 m), and WorldView 2 (2 m) imagery over an old-growth forest in the Central Amazon that was disturbed by a single windthrow event in November/2015. Remote sensing estimates of tree mortality were produced with Spectral Mixture Analysis and analyzed together with forest inventory data using Generalized Linear Models. Windthrow tree-mortality measured in 3 transects (30 subplots) crossing the entire disturbance gradient was 26.9 ± 11.1% (mean ± 95% CI). Based on this ground truth, the three satellites produced reliable and statistically similar estimates (from 26.5% to 30.3% windthrow tree-mortality, p<0.001). The mean-associated uncertainties decreased systematically with increasing spatial resolution (i.e., from Landsat 8 to Sentinel 2 and WorldView 2). However, the overall quality of fit of models showed the opposite pattern, which may reflect the influence of crown damage not accounted for in our field study, and fast-growing regeneration of leaf area. Among the satellites studied, Landsat 8 most accurately captured field observations of variations in tree mortality across the disturbance gradient (i.e., lower under- and/or overestimation from undisturbed to extremely damaged forest). Although satellites with high spatial-resolution can refine estimates of windthrow severity by allowing the quantification of individual tree damage and mortality, our results validate the reliability of Landsat imagery for assessing patterns of windthrow tree-mortality in dense and heterogeneous tropical forests. Although high-resolution imagery may improve estimates of tree damage and mortality, these should be validated using field data at compatible scales.

The practice of nurse health coaching (NHC) draws from the art and science of nursing, behavioral sciences, and evidence-based health coaching methods. This secondary analysis of the audio-recorded natural language of participants during NHC sessions of our recent 8-week RCT evaluates improvement over time in cognitive-behavioral outcomes: Change Talk, Resiliency, Self-Efficacy/Independent Agency, Insight & Pattern Recognition, and Building Towards Sustainability. We developed a measurement tool for coding, Indicators of Health Behavior Change (IHBC), that was designed to allow trained health coach experts to assess the presence and frequency of the indicators in the natural language content of participants. We used a two-step method for randomly selecting the 20-minute audio-recorded session that was analyzed at each time point. Fifty-six participants had high-quality audio recordings of the NHC sessions. Twelve participants were placed in the social determinants of health (SDH) group based on the following: low income (<$20,000/year), early-onset hypertension, and social disadvantages. Our analyses significantly improved Change Talk and the other four factors over time. Our factor analyses indicated two distinct factors at each measurement point of the study, demonstrating the stability of the outcome measures over time. Our newly developed measurement tool, IHBC, proved stable in structure over time and sensitive to change. This NHC program shows promise in improving cognitive-behavioral indicators associated with health behavior change in both non-SDH and SDH individuals.

The risk related to embankment dam breaches needs to be evaluated in order to prepare emergency action plans. The physical and hydrodynamic parameters of the flood wave generated from dam-failure event correspond to various breach parameters such as width, slope and formation time. This study aimed to simulate dam-breach failure scenario of Yabous dam (NE Algeria) and analyze its influence on areas (urban and natural environments) downstream the dam. The simulation was completed using the sensitivity analysis method in order to assess the impact of breach parameters on the dam-break scenario. The propagation of flood wave associated to dam-break was simulated using the one-dimensional HEC-RAS hydraulic model. This study ap-plied a sensitivity analysis of three breach parameters (slope, width, and formation time) in five sites selected downstream the embankment dam. The simulation showed that the maximum flow of the flood wave recorded at the level of the breach was 8768 m3/s, which gradually attenuated along the river course to reach 1579.2m3/s at about 8.5km downstream the dam. This study estab-lished the map of flood-prone areas that illustrated zones threatened with the flooding wave trig-gered by the dam failure due to extreme rainfall events. The sensitivity analysis showed that flood wave flow, height and width revealed positive and similar changes for the increase in adjustments (±25% and ±50%) of breach width and slope in the 5 sites. However, flood wave parameters of breach formation time showed significant trends that changed in the opposite direction compared to breach slope and width.

The acquired immunodeficiency syndrome (AIDS) caused by the human immunodeficiency virus (HIV) continues to be a public health problem. In 2020, 680,000 people died from HIV-related causes, and 1.5 million people were infected. Antiretrovirals are only a way to control HIV infection but not to cure AIDS. As such, effective treatment must be developed to control AIDS. Developing a drug is not an easy task, and there is an enormous amount of work and economic resources invested. For this reason, it is highly convenient to employ computer-aided drug design methods, which can help generate and identify novel molecules. Using the de novo design, new novel molecules can be developed using fragments as building blocks. In this work, we develop a virtual-focused compound library of HIV-1 viral protease inhibitors from natural product fragments. Natural products are characterized by a large diversity of functional groups, many sp3 atoms, and chiral centers. Pseudo-natural products are a combination of natural products fragments that keep the desired structural characteristics from different natural products. An interactive version of chemical space visualization of virtual compounds focused on HIV-1 viral protease inhibitors from natural product fragments is freely available at https://figshare.com/s/ceb58d58e8f5585ce67e.

Due to the high impact of the building sector on the environment, a growing interest focuses on insulating materials able to ensure good thermo-acoustic performance for the building envelope, in a sustainable and circular economy perspective. In this context, Moroccan natural gypsum was mixed with natural waste local materials. Thermal and acoustic properties of the samples were measured; they were compared to those of synthetic- and mineral-based gypsum plasters, manufactured with the same technique. A Small Hot Box apparatus was used for thermal characterization, whereas acoustic performance was investigated by means of a Kundt’s Tube. Natural and synthetic additives involved a reduction in density and an improvement in thermal performance. Conductivity values in the 0.181 – 0.238 W/mK range were obtained, depending on the type of natural additive, with respect to 0.275 – 0.323 W/mK of mineral-based gypsum plasters. The acoustic measurements showed that all the composites have similar performance in terms of acoustic absorption, whereas high Transmission Loss values were obtained for the natural additives (TL = 35 – 59 dB). Petiol of Palm and Stipa Tenacissima were found as the materials able to improve both thermal and acoustic properties.

Whiteflies of Aleurocanthus spiniferus and A. woglumi (Hemiptera: Aleyrodidae) are serious pests of citrus and other important fruit crops. The problem in citrus has initiated the successful introductions of several natural enemies for biocontrol programs in Hawaii and many other countries. Here we summarized the history of infestation and biocontrol efforts of the two whiteflies in Hawaii for possible importation into Greece. Two Platygasteridae (Amitus hesperidum Silvestri, A. spiniferus (Brethes) and three Aphelinidae (Encarsia clypealis (Silvestri), E. smithi (Silvestri), E. perplexa Huang and Polaszek) were released in Hawaii for biocontrol of the citrus whiteflies during the period 1974-1999. The aphelinid, Cales noacki Howard, purposely released for Aleurothrixus flococcus (Maskell) in 1982 was also reported to attack other whiteflies including Aleurocanthus species on citrus. An additional aphelinid parasitoid, Encarsia nipponica Silvestri, native to Japan and China, was accidently introduced and found to attack both citrus whiteflies on the islands. Since the colonization of introduced parasitoids in infested fields on four Hawaiian Islands, no survey was done to evaluate their potential impact. We made two short surveys during September-November 2022 on Kauai, Hawaii, and Oahu Islands to introduce the dominant parasitoids to Greece for the biocontrol of A. spiniferus. Results showed that the infestation level was very low on Kauai, Hawaii, and Oahu Islands with mean infestation level ranged 1.4 - 3.1 on Hawaii and Oahu Islands mostly on pummelo and sweet orange, with no detection on Kauai Island. Only was E. perplexa the dominant parasitoid with parasitism rates ranged 0 -28 % on Hawaii Island and 11-65% on Oahu Island. Starter colony of the parasitoids has been colonized in Greece for evaluation and approval for release. This was the first field survey on Hawaii since the introduction and release of citrus whitefly natural enemies.

Dysregulation of the arsenal of cells, growth factors, cytokines, proteases, and proteins involved in wound healing (WH), could delay the healing process. Plants and plant-derived molecules (PDMs) have been used as medicines for skin wounds. This study aimed to select and evaluate the pro-healing capacity of PMDs. Thirty-three PDMs and 10 therapeutic targets (TTs) were chosen for molecular docking and PDMs: aristolochic acid (AA), α-copaene, selinenes, β-caryophyllene (BC), and BCoxide, and TTs: metalloproteinase (MMP) 3, MMP13, and tumor necrosis factor (TNF α) for molecular dynamics. The bests inhibitor was AA, but because of its toxicity, BC and BCoxide were chosen for evaluation in HaCat cells and BALB/c mice excisional model. Both compounds were not toxic, and higher percentages of lesion area closure (LAC) were induced by BC treatment (p<0.05). All mice healed; however, an initial delay of LAC was induced by 0.05, 0.1% BCoxide, 0.1% BC, and allantoin. At the end of treatment, lower numbers of mast cells, level of cellular infiltrates (except 1% BCoxide and 0.05, 0.5%BC), epidermal thickness tendency (except 0.1, 0.5% BCoxide), and higher values of SC-thickness, and collagen fibers only by 0.5% BC treatment compared with untreated control (p<0.05) were observed. Our results show that BC is a promising multitarget candidate for wound healing treatment and optimization campaigns.

(1) Background: Information overload challenges decision-making in the Industry 4.0 era. While Natural Language Processing (NLP), especially Automatic Text Summarization (ATS), offers solutions, issues with factual accuracy persist. This research bridges cognitive neuroscience and NLP, aiming to improve model interpretability. (2) Methods: This research examined four fact extraction techniques: dependency relation, named entity recognition, part-of-speech tagging, and TF-IDF, in order to explore their correlation with human EEG signals. Representational Similarity Analysis (RSA) was applied to gauge the relationship between language models and brain activity. (3) Results: Named entity recognition showed the highest sensitivity to EEG signals, marking the most significant differentiation between factual and non-factual words with a score of -0.99. The dependency relation followed with -0.90, while part-of-speech tagging and TF-IDF resulted in 0.07 and -0.52, respectively. Deep language models such as GloVe, BERT, and GPT-2 exhibited noticeable influences on RSA scores, highlighting the nuanced interplay between brain activity and these models. (4) Conclusions: Our findings emphasize the crucial role of named entity recognition and dependency relations in fact extraction and demonstrate the independent effects of different models and TOIs on RSA scores. These insights aim to refine algorithms to reflect human text processing better, thereby enhancing ATS models’ factual integrity.

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

Solar reflective index (SRI) is one of the important parameters in the analysis of a building’s energetic and thermal performance, especially for “cool” reflective paints or coatings. However, there exists less information on the typical performance of the cool materials exposed for long term in the Middle East and more specifically in the United Arab Emirates (UAE). In this study, we determined values of reflectance, emittance, solar reflective index (SRI), and color difference, for three different high SRI white paints exposed naturally on low and high sloped racks for three years. After 3 years, reflectance values of exposed paint panels significantly decreased with an increased color difference in comparison with original unexposed paint surfaces. Emissivity of the paint changed very little for all of the exposed samples, but SRI, determined from both the reflectance and emittance, was reduced from between 95 and 110 to between 60 and 90 after three years. This appeared to be related to exposure to high temperatures, UV radiation, and adhesion of airborne contaminants in conjunction with low precipitation. Macroscopically, panels exposed on a low slope were the most discolored with the greatest presence of dust infusion and reduction in increase in surface temperatures which was demonstrated through thermal imaging. Such natural weathering research study is necessary for the development of standard exposure tests and determination of various control elements to increase the durability of cool materials in hot and arid climatic conditions of UAE.

The cosmological relevance of emptiness—that is, space without bodies—is not yet sufficiently appreciated in natural philosophy. This paper addresses two aspects of cosmic emptiness from the perspective of natural philosophy: the distances to the stars in the closer cosmic environment and the expansion of space as a result of the accelerated expansion of the universe. Both aspects will be discussed from both a historical and a systematic perspective. Emptiness can be interpreted as “coming” in a two-fold sense: Whereas in the past knowledge of emptiness as it were came to human beings, in the future it is coming insofar as its relevance in the cosmos will increase.The longer and more closely emptiness was studied since the beginning of modernity, the larger became the spaces over which it was found to extend. From a systematic perspective, I will show with regard to the closer cosmic environment that the earth may be separated from the perhaps habitable planets of other stars by an emptiness that is inimical to life and cannot be traversed by humans. This assumption is a result of the discussion of the constraints and possibilities of interstellar space travel as defined by the known natural laws and technical means. With the accelerated expansion of the universe, the distances to other galaxies (outside of the so-called local group) are increasing. According to the current standard model of cosmology and assuming that the acceleration will remain constant, in the distant future this expansion will lead first to a substantial change in the epistemic conditions of cosmological knowledge and finally to the completion of the cosmic emptiness and of its relevance, respectively. Imagining the postulated completely empty last state leads human thought to the very limits of what is conceivable.

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.

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

The 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.

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 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.

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.

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.

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.

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&lt;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.

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.

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.

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.

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.

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.

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.

Plants of the genus Monochoria have long been utilized in food, cosmetics, and traditional herbal treatment. Thailand has the highest species diversity of this genus and a new member, Monochoria angustifolia (G. X. Wang) Boonkerd & Tungmunnithum has been recently described. This plant is called “Siam Violet Pearl” as a common name or “ไข่มุกสีม่วงแห่งสยาม” as its vernacular name in the same meaning in Thai language. Despite their importance, few researches on Monochoria species have been conducted. This study, thus, provided the results to fill in this gap by: i) determining flavonoids phytochemical profiles of 25 natural populations of M. angustifolia covering the whole floristic regions in Thailand, and ii) determining antioxidant activity using various antioxidant assays to investigate the probable mechanism. The results revealed that M. angustifolia presented a higher flavonoid content than the outgroup, M. hastata. Our results also revealed that flavonoids might be used to investigate Monochoria evolutionary connections and for botanical authentication. The various antioxidant assays revealed that M. angustifolia extracts preferentially act through a hydrogen atom transfer antioxidant mechanism. Pearson correlation analysis indicated significant correlations emphasizing that the antioxidant capacity is most probably the result of a complex phytochemical combinations rather than of a single molecule. Altogether, these results showed that this new species provide an attractive alternative starting material with phytochemical variety and antioxidant potential for the phytopharmaceutical industry.

The new climate law introduces a policy of sustainable construction, the assumption of which is the reduction of CO2 by the construction industry and the use of environmentally friendly materials, such as agricultural, mineral, and recycled waste, while limiting the consumption of natural resources. The article is a literature review that analyzes selected waste materials from various sectors of the economy that can be used as additives or partial substitutes for natural resources in the production of cement and in and cement building materials, the production of which reduces CO2 emissions, producing materials with high mechanical strength and environmentally friendly.

The interaction between spinors and gravity is the most complicated and subtle interaction in the universe, which involves the basic problem to unified quantum theory and general relativity. By means of Clifford Algebra, a unified language and tool to describe the rules of nature, this paper systematically discusses the dynamics and properties of spinor fields in curved space-time, such as the decomposition of the spinor connection, the classical approximation of Dirac equation, the energy momentum tensor of spinors and so on. To split spinor connection into Keller connection $\Upsilon_\mu\in\Lambda^1$ and pseudo-vector potential $\Omega_\mu\in\Lambda^3$ by Clifford algebra not only makes the calculation simpler, but also highlights their different physical meanings. The representation of the new spinor connection is dependent only on the metric, but not on the Dirac matrix. Keller connection only corresponds to geometric calculations, but the potential $\Omega_\mu$ has dynamical effects, which couples with the spin of a spinor and may be the origin of the celestial magnetic field. Only in the new form of connection can we clearly define the classical concepts for the spinor field and then derive its complete classical dynamics, that is, Newton's second law of particles. To study the interaction between space-time and fermion, we need an explicit form of the energy-momentum tensor of spinor fields. However, the energy-momentum tensor is closely related to the tetrad, and the tetrad cannot be uniquely determined by the metric. This uncertainty increases the difficulty of deriving rigorous expression. In this paper, through a specific representation of tetrad, we derive the concrete energy-momentum tensor and its classical approximation. In the derivation of energy-momentum tensor, we obtain a spinor coefficient table $S^{\mu\nu}_{ab}$, which plays an important role in the interaction between spinor and gravity. From this paper we find that, Clifford algebra has irreplaceable advantages in the study of geometry and physics.

During the MoDeCo2000 scientific and research project on mortars used in the territory of the Roman Danube Limes in Serbia, the biggest challenge was the quest for the provenance of used raw materials. The area where the largest city in the province of Moesia Superior developed, with millennial continuity of land use and settlement, was selected as a case study for deeper research. The material and immaterial values of Roman Viminacium have survived in the later life of the landscape, through the preserved building remains and artifacts, secondary use of building materials, but also toponyms, customs, and stories. Recycling of materials is commonly recognised in the modern age as the industrial processing of existing products in order to obtain raw materials and later prepare new products, representing one of the basic elements of sustainability. However, people throughout history have always used what they had at hand and the building remains were reused, but also recycled for new constructions. In this study we follow the presence of the specific material we call natural brick in the historical Viminacium landscape, focusing on Roman construction and specifically its potential use in lime mortars, connecting humanistic and natural sciences.

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

This data descriptor summarizes the process applied and data gathered from 50 publications/papers reporting on the use of photography generated by tourists, tour operators and members of the public, with a particular focus on the crowdsourcing of photographs through online platforms and social networking sites (SNSs) as a method of research for wildlife conservation and ecotourism. The papers were collected in a systematic literature review to inform a pilot study of the feasibility of using SNSs to crowdsource georeferenced photographs of endangered Bornean Pygmy Elephants (Elephas maximus borneensis) taken by ecotourists along the Lower Kinabatangan River region of Sabah, Malaysia. Papers were sourced using the Murdoch University Findit online-search tool to search over 100 databases, including Proquest, Scopus and Web of Science. The criteria for a paper to be included in the review (and shared via the dataset attached to this this data descriptor) were that it was peer-reviewed, published in English, between 1997 and the 31 December 2017, had the full text accessible online and reported on a study or studies that utilized photographs that tourists, tour operators and/or members of the public generated and shared via SNSs or online platforms.

This study presents a dynamic model of a passenger vehicle and analyzes its dynamic characteristics and responses. A dynamic vehicle model with seven degrees of freedom was established to analyze the behavior of a driving vehicle. The vehicle model had three degrees of freedom for the motion of the sprung mass and four degrees of freedom for the unsprung masses. For this model, the equations of motion were derived using Lagrange’s equation. To verify the model, the suspension deformations computed using the model were compared with the deformations measured through three actual vehicle driving tests which are the slalom, double lane change, and step steer tests. Furthermore, we investigated the effects of suspension stiffness, suspension damping, and anti-roll bar torsional stiffness on the dynamic characteristics and responses of the vehicle model. The developed dynamic vehicle model may help vehicle designers predict the dynamic responses of a vehicle through simulation without performing a driving test.

The pharmaceutical industry is one of the largest pollutants and generator of toxic solvent resi-dues for the environment and human beings. In the last decade, there has been a huge evolution in the design and development of new environmentally friendly products and processes, in re-sponse to the global environmental concern and the growing demand for products with quality ingredients and manufacturing processes aimed at achieving sustainability. Consumers are looking for products with the benefits of natural products, with sustainable and environmentally friendly formulations with few synthetic molecules as possible. Thus, based on the origins of the pharmaceutical industry, it is important to return to Nature and enjoy the benefits that natural products bring to us. Deep eutectic solvents appear as an alternative to toxic solvents commonly used to extract bioactive compounds. These solvents are considered green solvents because they are made up of natural compounds such as sugars, amino acids, and carboxylic acids. In addition, they are simple to prepare, with an atomic economy of 100%, with attractive physicochemical properties. This paper is a comprehensive review in which is described the main properties, characteristics, and production methods of deep eutectic solvents as well as its application to extract from natural sources bioactive compounds with pharmaceutical interest.

Background: Both natural immunity and vaccine-induced immunity to COVID-19 may be useful to reduce the mortality/morbidity of this disease, but still a lot of controversy exists. Aims: This narrative review analyzes the literature about: a) the duration of natural immunity; b) cellular immunity; c) cross-reactivity; d) the duration of post-vaccination immune protection; e) the probability of reinfection and its clinical manifestations in the recovered patients; f) comparisons between vaccinated and unvaccinated in the possible reinfections; g) the role of hybrid immunity; h) the effectiveness of natural and vaccine-induced immunity against Omicron variant; i) comparative incidence of adverse effects after vaccination in recovered individuals vs. COVID-19-naïve subjects. Material and Methods: through multiple search engines we investigated COVID-19 literature related to the aims of the review, published since April 2020 through July 2022, including also the previous articles pertinent to the investigated topics. Results: nearly 900 studies were collected and 238 pertinent articles were included. It was highlighted that the vast majority of individuals after COVID-19 develop a natural immunity both of cell-mediated and humoral type, which is effective over time and provides protection against both reinfection and serious illness. Vaccine-induced immunity was shown to decay faster than natural immunity. In general, the severity of the symptoms of reinfection is significantly lower than in the primary infection, with a lower degree of hospitalizations (0.06%) and an extremely low mortality. Conclusions: this narrative review regarding a vast number of articles highlighted the valuable protection induced by the natural immunity after COVID-19, which seems comparable or superior to the one induced by anti-SARS-CoV-2 vaccination. Vaccination of the unvaccinated COVID-19-recovered subjects may not be indicated. Further research is needed in order to: a) measure the durability of immunity over time; b) evaluate both the impacts of Omicron-5 on vaccinated and healed subjects and of hybrid immunity.

In the recent years, tissue engineering researchers have exploited a variety of biomaterials that can potentially mimic extracellular matrix (ECM) for tissue regeneration. Natural cellulose, mainly obtained from bacterial (BC) and plant-based (PC) sources, can serve as a high potential scaffold material for different regenerative purposes. Natural cellulose has drawn the attention of researchers due to its advantage over synthetic cellulose in terms of availability, cost-effectiveness, perfusablility, biocompatibility, negligible toxicity, mild immune response and due to imitating native tissues. In this article, we will review the recent in vivo and in vitro studies aimed to assess the potentials of natural cellulose for the purpose of soft (skin, heart, veins, nerve, among others) and hard (bone and tooth) tissue engineering.

Large language models (LLMs) excel in providing natural language responses that sound authoritative, reflect knowledge of the context area, and can present from a range of varied perspectives. Agent Based Models and Simulation consist of simulated agents that interact within a simulated environment to explore societal, social, and ethical, among other, problems. Agents generate large volumes of data over time and discerning useful and relevant content is an onerous task. LLMs can help in communicating agents’ perspectives on key events by providing natural language narratives. However, these narratives need to be factual, transparent, and reproducible. To this end, we present a structured narrative prompt for sending queries to LLMs. Chi-square tests and Fisher’s Exact tests are applied to assess statistically significant difference in sentiment scores of the narrative messages between simulation generated narratives, ChatGPT-generated narratives, and real tweets. The narrative prompt structure effectively yields narratives with the desired components from ChatGPT. This structure is expected to be extensible across LLMs. In 14 out of 44 categories, ChatGPT generated narratives which has sentiment scores that were not discernibly different, in terms of statistical significance (alpha level of 0.05), from the sentiment expressed in real tweets. Three outcomes are provided: (1) a list of benefits and challenges for LLMs in narrative generation; (2) a structured prompt for requesting narratives of a LLM based on simulated agents’ information; and (3) an assessment of statistical significance in the sentiment prevalence of the generated narratives compared to real tweets. This indicates significant promise in the utilization of LLMs for helping to connect simulated agent’s experiences with real people.

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

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.

In recent years, humanity has faced a growing challenge posed by the rise of antibiotic-resistant bacteria, resulting in an alarming surge in fatalities and the emergence of increasingly hard-to-manage diseases. Among these resilient pathogens is Acinetobacter baumannii, due to its escalating prevalence in hospitals, elevated mortality rates, and resistance to treatment. Despite its clinical significance, our understanding of this pathogen in non-hospital settings and its virulence factors remains limited. Therefore, this review aims to shed light on the latest research regarding its ecological niche, microbiological traits, and antibiotic resistance profiles. Recent investigations have expanded its detection to various environmental niches, including rivers, treatment plants, and soils. Moreover, this bacterium has been discovered in diverse food sources such as meat and vegetables, as well as in farm animals and household pets like dogs and cats. This broader presence of Acinetobacter baumannii outside of hospital environments underscores a significant risk of environmental contamination, necessitating heightened awareness and preventive measures to address potential public health implications.

: Molecular targeting strategies have been used for years to control cancer progression and are often based on targeting various enzymes involved in metabolic pathways. Keeping that in mind, it is essential to know the role of each enzyme in a particular metabolic pathway. In this review, we are providing in-depth knowledge of various enzymes such as ceramidase, sphingosine kinase, sphingomyelin synthase, dihydroceramide desaturase, and ceramide synthase, which are associated with multiple types of cancer that depend on ceramide metabolism. Focus has also been given to discussing physicochemical properties of well-studied inhibitors of natural products origin and their related structures for these enzymes. Targeting ceramide metabolism exhibited promise in mono and combination therapies at preclinical stages to prevent cancer progression and paved the way for the significance of sphingolipid metabolism in cancer treatments. Targeting ceramide metabolizing enzymes will help the medicinal chemist to design potent and selective small molecules for treating cancer progression at various levels.

Food saving and further valorization is of major interest in our days. Worldwide, scientists intensify their efforts to increase shelf life of products due to the global danger of coming food crisis. Even though many chemicals could achieve such target, the circular economy spirit and the global trend against the greenhouse effects impose the turn of research activities using environmentally friendly biomass materials, biowastes, and biodegradable materials. In this study, the promising biopolymer chitosan incorporated with the promising biodegradable polymer poly-vinyl-alcohol to produce a biopolymeric matrix. This biodegradable biopolymer was further mixed homogeneously with 15% thymol/nano-zeolite nanohybrid material and the final developed film was extremely improved compared to the relevant of chitosan/poly-vinyl-alcohol film. Mechanical properties were increased significantly i.e., 34% for Young Modulus and 46,5% for ultimate tensile strength, while the antioxidant activity increased by 53,4%. The antibacterial activity increased by 134% for Ecoli, 87,5% for Saureus, 32% for Lmonocytogenes, and 9% for Senterica. The water-vapor diffusion coefficient and the oxygen permeability coefficient decreased down to -51% and -74% respectively, and thus the water-vapor and oxygen barrier increased. In-vivo experiments on strawberries show an extension of 233% for shelf-life duration which resulted from the modified atmosphere by the control-released thymol.

Evolutionary ecology may be described as explaining ecology through evolution and vice versa, but one may also view it as an integration of the two fields, where one takes the view that ecology and evolution are inseparable, and one can only begin to understand the biology of organisms by synthesizing the two fields. An example of such a synthesis is the biology of high fecundity and the associated concept of sweepstakes reproduction, or skewed individual recruitment success. As an illustration we consider selection at linked sites under various dominance and epistasis mechanisms in a diploid population evolving according to random sweepstakes and experiencing recurrent bottlenecks. Using simulations we give a few examples of the impact of the stated elements on selection. We show that depending on the dominance mechanisms random sweepstakes can shorten the time to fixation (conditional on fixation) of the fit type at all sites. Bottlenecks tend to increase the fixation time, with random sweepstakes counteracting the effects of bottlenecks on the fixation time. Understanding the effect of random sweepstakes, recurrent bottlenecks, dominance mechanisms and epistasis on the fate of selectively advantageous mutations may help with explaining genetic diversity in natural highly fecund populations possibly evolving under sweepstakes reproduction.

heterocycles are particularly common moieties within marine natural products. Specifically, tetrahydrofuranyl rings are present in a variety of compounds which present complex structures and interesting biological activities. Focusing on terpenoids, a high number of tetrahydrofuran-containing metabolites have been isolated during the last decades. They show promising biological activities, making them potential leads for novel antibiotics, antikinetoplastid drugs, amoebicidal substances or anticancer drugs. Thus, they have attracted the attention of the synthetic community, and numerous approaches to their total syntheses have appeared. Here, we offer the reader an overview of marine-derived terpenoids and related compounds, with a special focus on their isolation, structure determination, biological profiles and total syntheses.

Measured values of the electron mass and Compton wavelength produce a value of Planck's constant with a relative standard uncertainty of 3 x 10^-10. This is only slightly larger than the 1.3 x 10^-10 relative standard uncertainty in measurements performed using the Kibble balance. Compton scattering represents an alternative pathway to improving the value of Planck's constant in the future. Natural units of length, mass, and time offer new pathways to improving the values of physical constants. While extensive values of the Planck units lie beyond the reach of present-day instrumentation, certain product and quotient pairs of Planck units such as the speed of light can be measured with relatively high precision. Better measurements of certain unit pairs will improve the value of the gravitational constant.

The computer network world is changing and the NetDevOps approach has brought the dynamics of applications and systems into the field of communication infrastructure. Businesses are changing and businesses are faced with difficulties related to the diversity of hardware and software that make up those infrastructures. The "Intent-Based Networking - Concepts and Definitions" document describes the different parts of the ecosystem that could be involved in NetDevOps. The recognize, generate intent, translate and refine features need a new way to implement algorithms. This is where artificial intelligence comes in.

Natural cycles underpin the very stuff of life. In this commentary we consider unnatural cycles: that is, anthropogenic activities which have a circularity, but whose nature is to have a detrimental effect on human health, exacerbating existing problems. Natural cycles have feedback loops, some of which have recently come to light, with an understanding that everything is connected in some way. In health, feedback loops are imperative in homeostatic mechanisms. However, in the unnatural cycle the feedback loops serve to reinforce (and in some cases amplify) negative problems. We offer a commentary on an unnatural cycle moving from air quality to lung function and back to air quality; we call this the lung disease unnatural cycle. We suggest where links occur, and where wider consideration of interactions between various disciplines can lead to breaking this unnatural (or vicious) cycle, changing it to a healthy cycle where individual health can be improved, along with better global scale outcomes. We suggest that many activities within this unnatural cycle occur within silos. However, the improved cycle incorporates joint activities at geological, health, and financial levels, to the mutual benefit of all, breaking the unnatural cycle, and improving health, life and financial costs.

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