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.

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.

This paper presents our approaches for the SMM4H’24 Shared Task 5 on the binary classification of English tweets reporting children’s medical disorders. Our first approach involves fine-tuning a single RoBERTa-large model, while the second approach entails ensembling the results of three fine-tuned BERTweet-large models. We demonstrate that although both approaches exhibit identical performance on validation data, the BERTweet-large ensemble excels on test data. Our best-performing system achieves an F1-score of 0.938 on test data, outperforming the benchmark classifier by 1.18%.

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.

In recent years, there has been a growing interest in the use of medicinal plants and phytochemicals as potential treatments for acne vulgaris. This condition, characterized by chronic inflammation, predominantly affects adolescents and young adults. Conventional treatment typically targets the key factors contributing to its development: the proliferation of Cutibacterium acnes and the associated inflammation. However, these treatments often involve the use of potent drugs. As a result, the exploration of herbal medicine as a complementary approach has emerged as a promising strategy. By harnessing the therapeutic properties of medicinal plants and phytochemicals, it may be possible to address acne vulgaris while minimizing the reliance on strong drugs. This approach not only offers potential benefits for individuals seeking alternative treatments but also underscores the importance of natural remedies in dermatological care. The primary aim of this study was to assess the antimicrobial, antioxidant, and anti-inflammatory properties of plants and their phytochemical constituents in the management of mild acne vulgaris. A comprehensive search of scientific databases was conducted from 2018 to September 2023. The findings of this review suggest that medicinal plants and their phytochemical components hold promise as treatments for mild acne vulgaris. However, it is crucial to note that further research employing high-quality evidence and standardized methodologies is essential to substantiate their efficacy and safety profiles.

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.

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.

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.

Rising organic charge in northern freshwaters is attributed to increasing levels of Dissolved Natural Organic Matter (DNOM) and shifts in water chemistry. Organic charge concentration may be determined by charge balance calculations (Org.-) or modelled (OAN-) using the conceptual Oliver and Hruška models based on the density of weak acid functional sites (SD) present in DNOM. The charge density (CD) is governed by the SD and protonation and complexation reactions on the functional groups. The models use this SD as a key parameter in empirically fitting the model to Org.-. Utilizing extensive datasets of water chemistry, this study shows that spatial and temporal differences in SD and CD are influenced by variations in the humic-to-fulvic ratio of DNOM and the organic aluminum (Al) complexation, as well as the mole fraction of CD to SD governed by the acidity. Site median SD obtained for 44 long-term monitored acid-sensitive lakes was 11.1 and 13.9 µEq/mg C for the Oliver and the Hruška model, respectively. During the 34 years of monitoring the CD increased by 70%, likely due to rising pH and declining Al complexation with DNOM. Present-day median SD values for the Oliver and Hruška models in 16 low-order streams are 13.8 and 15.8 µEq/mg C, respectively, and 10.8 and 12.5 µEq/mg C, respectively, in 10 high order rivers.

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.

: This study aimed to validate the effects of self-compassion-based forest healing therapy as an intervention for reducing stress and promoting coping resources. Therefore, we investigated the effects of self-compassion-based forest healing therapy on stress, self-compassion, and connectedness to nature. A total of 30 participants were divided into an experimental and control group. Two sessions of forest healing meditation activities were held at the Forest of Healing every two weeks between October and November 2023. As a repeated measures experiment, data were collected before the intervention, after the first intervention, and after the second intervention. Perceived stress, positive and negative affect, and salivary cortisol were measured to assess physiological and psychological stress in participants. Connectedness to nature and self-compassion were measured to evaluate stress-coping resources. The results showed that the forest healing program had significant positive effects on stress and emotional states in the experimental group after the intervention. Further, there was a significant incremental effect on connectedness to nature and self-compassion in the experimental group. Therefore, self-compassion meditation-based forest healing therapy is a feasible and efficient approach for facilitating stress management and coping resource building.

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.

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

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.

Fiber-reinforced mycelium (FRM) composites offer an innovative and sustainable approach to construction materials for architectural structures. Mycelium, the root structure of fungi, can be combined with various natural fibers (NF) to create a strong and lightweight material with environmental benefits. Incorporating NF like hemp, jute, or bamboo into the mycelium matrix enhances mechanical properties. This combination results in a composite that boasts enhanced strength, flexibility, and durability. Natural FRM composites offer sustainability through the utilization of agricultural waste, reducing the carbon footprint compared to conventional construction materials. Additionally, the lightweight yet strong nature of the resulting material makes it versatile for various construction applications, while its inherent insulation properties contribute to improved energy efficiency in buildings. Developing and adopting natural FRM composites showcase a promising step towards sustainable and eco-friendly construction materials. Ongoing research and collaboration between scientists, engineers, and the construction industry will likely lead to further improvements and expanded applications. This article provides a comprehensive analysis of the current research and applications of natural FRM composite for innovative and sustainable construction materials. Additionally, the paper reviews the mechanical properties and potential impacts of these natural FRM composites in the context of sustainable architectural construction practices. Recently, the relevance of mycelium-based materials has extended beyond their original fields of microbiology and mycology to architecture.

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.

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

This article presents a dataset of tweets in the Urdu language. There are 1,140,824 tweets in the dataset, collected from Twitter for September and October 2020. This large-scale corpus of tweets is generated by performing pre-processing which includes removing columns containing user information, retweet’s count, followers information, duplicate tweets, removing unnecessary punctuation, links, symbols, and spaces, and finally extracting emojis if present in the tweet text. In the final dataset each tweet record contains columns for tweet id, text, and emoji extracted from the text with a sentiment score. Emojis are extracted to validate Machine Learning models used for the multilingual sentiment and behavior analysis. These are extracted using a Python script that searches for an emoji from the list of 751 most frequently used emojis. If an emoji is present in the text, a column with the emoji description and sentiment score is added.

Motivated by the #Metoo movement, we explore in this paper people’s perception of female bullying at workplace. We looked at #workplacebullying and found that 1) people were split between identifying the prevalence of workplace bullying against female and the view that such bullying simply does not exist and is a nuisance, 2) The tweets also showed the existence of psychological effects of cyberbullying, and 3) the tweets showed many intervention techniques that can minimize the effects of such bullying. We further explored the top three recurring hashtags mentioned under the #workplacebullying and found that the three top hashtags were #sexism, #feminism and #equality. Our results showed that the above hashtags represent the positive and negative approach to workplace bullying i.e. #feminism hashtag was mostly used by people who denied that workplace bullying against females exist while # sexism was mentioned as the prime cause by people who agree that such bullying exist. #equality overwhelmingly comprises of techniques to minimize workplace bullying against females.

The violation of privacy, others people or personal, is a very current problem, which concerns not only on the web but also in private life. In the years 1990 it was expected that nowadays, that any routine operation was carried out "manually", and it would be performed through mobile phones or personal computers. The problem pertains the distribution network that allows to share and bring together information and as result the network becomes unsafe, if subjected to attacks. Nowaday we put personal information on web because otherwise we are seen as “weak”. This work aims to measure and analyze how much information are shared by users of a pre-established social network and it is carried out through a set of algorithms techniques of machine learning.

The management and proper use of the Urban Public Transport Systems (UPTS) constitutes a critical field that has not been investigated in accordance to its relevance and urgent idiosyncrasy within smart cities realm. Swarm Intelligence is a very promising paradigm to deal with such complex and dynamic systems. It presents robust, scalable and self-organized behaviour to deal with dynamic and fast changing systems. The intelligence of cities can be modelled as a swarm of digital telecommunication networks (the nerves), ubiquitously embedded intelligence, sensors and tags, and software. In this paper, a new approach based on the use of the Natural Computing paradigm and Collective Computation is shown, more concretely taking advantage of an Ant Colony Optimization algorithm variation and Fireworks algorithms in order to build a system that makes the complete control of the UPTS a tangible reality.

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

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.

The theories of Evolution and Big Bang have brought a major impact upon the scientific and societal perception upon the origins, abilities and roles of mankind on Earth. It is conclusively evident that Mother Nature and the physical realm is autonomous and all phenomena in the world occur without an imposition against the “free will” of such events and patterns. Nevertheless, this extent of freedom does not imply that life on Earth represents an accident and has no purpose. By careful analysis of ancient religious texts, it can be suggested even otherwise, that it is especially free will that is offered as a gift from outside of the physical dimensions of existence, with the purpose of a continuous edification and improvement of the overall life conditions and of the human perception. It may be that the multi-generational focus on improvement and progress have been misattributed as a long-term evolution of species, that humans came from animal ancestors and that there is a blurred line associating humans with animals. Furthermore, it may be that scientists based their suggestions that no outside Creator would have brought the Earth into existence on physical perspectives, when the extra-dimensional process of Creation implies that all elements of the physical existence undergo a process of Creation from the outside, where time, space and physical matter do not exist, in spite of such elements possibly reflecting unseen elements of the meta-physical world. Finally, there is the following example that deserves attention within the scientific community; just because all living cells - human, animal and plant - contain DNA chromosomes, ribosomes, mitochondria and a plasma membrane, it does not indicate that humans are animals, just as this does not indicate that plants are animals. In Science, there is a golden statement, that correlation does not imply causation. This example is in complete agreement with the verses of the Book of Genesis in the Holy Scriptures regarding the origins of life; namely, that animals could have been created by an Author of all life on Earth so that man would experience companionship. Another major accordance with the teachings of the Holy Scriptures is the fact that life emerged from the water, and not from the soil, although the soil constituted the second principal means of the commencement and extension of life. Namely, after life first emerged in the seas and the oceans from bacteria, a few bacteria were passed from the marine water into the nearby soil on islands and surrounding land through the circulatory air above the sea level. Afterward, the first life forms emerged from the soil, via the transferred bacteria. Eastern Orthodox Christianity regards water as the principal environment of both physical and spiritual birth. Overall, an improved scientific perception of the timeline of life on Earth may be reached via an approach that combines molecular biology with quantum physics, given that the three layers of Science - Biology, Chemistry and Physics - together with their foundational layer of Mathematics, are in fact situated in a relationship of complete harmony.

The aim of this study was to build a tool to analyze, using artificial intelligence, the sentiment perception of users who answered two questions from the CSQ – 8 questionnaires with raw Spanish free-text. Their responses are related to mindfulness, which is a novel technique used to control stress and anxiety caused by different factors in daily life. As such, we proposed an online course where this method was applied in order to improve the quality of life of health care professionals in COVID 19 pandemic times. We also carried out an evaluation of the satis-faction level of the participants involved, with a view to establishing strategies to improve fu-ture experiences. To automatically perform this task, we used Natural Language Processing (NLP) models such as swivel embedding, neural networks and transfer learning, so as to classify the inputs into the following 3 categories: negative, neutral and positive. Due to the lim-ited amount of data available - 86 registers for the first and 68 for the second - transfer learning techniques were required. The length of the text had no limit from the user’s standpoint, and our approach attained a maximum accuracy of 93.02 % and 90.53 % respectively based on ground truth labeled by 3 experts. Finally, we proposed a complementary analysis, using com-puter graphic text representation based on word frequency, to help researchers identify relevant information about the opinions with an objective approach to sentiment. The main conclusion drawn from this work is that the application of NLP techniques in small amounts of data using transfer learning is able to obtain enough accuracy in sentiment analysis and text classification stages

Remotely sensed data play a crucial role in monitoring the El-Niño/La-Niña Southern Oscillation (ENSO), which is an oceanic-atmospheric phenomenon occurring quasi-periodically with several impacts worldwide, such as specific biological and global climate responses. Since 1980, Earth has witnessed three strong ENSO events (1982–1983, This study aims, 2015-2016). In September 2022, La-Niña entered its third year and was unlikely to continue through 2023. Instead, since 2022 forecasts pointed to a transition from La-Nina to a Neutral phase in summer or late 2023. The onset of the El-Niño occurred around April 2023, and it is anticipated by sophisticated models to be a strong event through the Northern Hemisphere winter (December 2023 - February 2024). The aim of this study is to demonstrate the ability of the combination of two new methods to improve the accuracy of the above claim because El-Niño apart from climate anomalies, significantly impacts Earth’s ecosystems and human societies, regulating the spread of diseases by insects (e.g., malaria and dengue fever), and influencing nutrients, phytoplankton biomass, and primary productivity. This is done by exploring first the previous major El-Niño events in the period January 1876-July 2023. Our calculations show that the ongoing 2023-2024 El-Niño will not be the strongest.

Flavin-dependent monooxygenases (FMOs) have made substantial influence as catalysts for monooxygenation reactions, impacting diverse field such as drug metabolism, environmental studies, and natural product synthesis. Their application in biocatalysis boasts several advantages over conventional chemical catalysis, such as heightened selectivity, safety, sustainability, and eco-friendliness. In the realm of biomedicine, FMOs are pivotal in antibiotic research, modulating natural products, antimicrobial agents, and pathways involved in drug synthesis. They're also underscored as potential pharmaceutical targets, pivotal in disease progression and viable for therapeutic intervention. Additionally, FMOs play a substantial role in environmental science, especially in pesticide processing and plant vitality. Their involvement in biosynthesizing compounds like polyethers, tropolones, and ω-hydroxy fatty acids, with remarkable regio- and stereoselectivity, renders them indispensable in drug discovery and development. As our comprehension of FMOs' catalytic mechanisms and structures advances, coupled with cutting-edge biotechnologies like computational design and directed evolution, FMOs are poised to occupy an increasingly significant role in both scientific exploration and industrial applications.

The work of this paper presents multiple novel findings from a comprehensive analysis of about 150,000 tweets about exoskeletons posted between May 2017 and May 2023. First, findings from content analysis and temporal analysis of these tweets reveal the specific months per year when a significantly higher volume of Tweets was posted and the time windows when the highest number of tweets, the lowest number of tweets, tweets with the highest number of hashtags, and tweets with the highest number of user mentions were posted. Second, the paper shows that there are statistically significant correlations between the number of tweets posted per hour and different characteristics of these tweets. Third, the paper presents a multiple linear regression model to predict the number of tweets posted per hour in terms of these characteristics of tweets. The R2 score of this model was observed to be 0.9540. Fourth, the paper reports that the 10 most popular hashtags were #exoskeleton, #robotics, #iot, #technology, #tech #innovation, #ai, #sci, #construction and #news. Fifth, sentiment analysis of these tweets was performed using VADER and the DistilRoBERTa-base library. The results show that the percentage of positive, neutral, and negative tweets were 46.8%, 33.1%, and 20.1%, respectively. The results also show that in the tweets that did not express a neutral sentiment, the sentiment of surprise was the most common sentiment. It was followed by the sentiments of joy, disgust, sadness, fear, and anger. Furthermore, analysis of hashtag-specific sentiments revealed several novel insights, for instance, for almost all the months in 2022, the usage of #ai in tweets about exoskeletons was mainly associated with a positive sentiment. Sixth, text processing-based approaches were used to detect possibly sarcastic tweets and tweets that contained news. Finally, a comparison of positive tweets, negative tweets, neutral tweets, possibly sarcastic tweets, and tweets that contained news, in terms of different characteristic properties of these tweets are presented. The findings reveal multiple novel insights, for instance, the average number of hashtags used in tweets that contained news has considerably increased since January 2022.

The provision requirement of 10% openings of the total floor area stated in the Uniform Building by Law 1984 Malaysia has been practiced by designers for building plan submission approval. However, the effectiveness of thermal performance in landed residential buildings, despite the imposition by the by-law, has never been empirically measured and proven. Although terraced houses in Malaysia have dominated 40.9% of the total property transaction in 2019, such mass production with typical designs hardly provides its occupants with thermal comfort due to the static outdoor air condition and lack of external windows, where the conventional ventilation technique does not work well, even for houses with an air well system. Consequently, the occupants need to rely on mechanical cooling, which is a high energy-consuming component contributing to outdoor heat dissipation and therefore urban heat island effect. Thus, encouraging more effective natural ventilation to eliminate excessive heat from the indoor environment is critical. Since most of the research focuses on simulation modelling lacking sufficient empirical validation, this paper drawing on field measurement investigates natural ventilation performance in terraced housing with an air well system. More importantly, the key concern as to what extent the current air well system serving as a ventilator is effective to provide better thermal performance in the single storey terraced house is to be addressed. By adopting an existing single storey air welled terrace house, the existing indoor environmental conditions and thermal performance were monitored and measured using scientific equipment, namely HOBO U12 air temperature and air humidity, the HOBO U12 anemometer and the Delta Ohm HD32.3 Wet Bulb Globe Temperature meter for a six-month duration. The findings show that the air temperature of the air well ranged from 27.48°C to 30.92°C, while the mean relative humidity were from 72.67% to 79.25%. The mean air temperature for a test room (single sided ventilation room) ranged from 28.04°C to 30.92°C with a relative humidity of 70.16% to 76%. These empirical findings are of importance, offering novel policy insights and suggestions to potentially revising the existing building code standard and by laws; since the minimum provision of 10% openings has been revealed to be less effective to provide a desired thermal performance and comfort, mandatory compliance with, and the necessity for, the bylaw requirement should be revisited and further studied.

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

Caribbean sponges of the genus Smenospongia are a prolific source of chlorinated secondary metabolites. The use of molecular networking as a powerful dereplication tool revealed the presence in the metabolome of S. aurea of two new members of the smenamide family, namely smenamide F (1) and G (2). The structure of smenamide F (1) and G (2) was determined by spectroscopic analysis (NMR, MS, ECD). The relative and the absolute configuration at C-13, C-15, and C-16 was determined on the basis of the conformational rigidity of a 1,3-disubstituted alkyl chain system (i.e. the C-12/C-18 segment of compound 1). Smenamide F (1) and G (2) were shown to exert a selective moderate antiproliferative activity against cancer cell lines MCF-7 and MDA-MB-231, while being inactive against MG-63.

Textile industry is one of the major industries responsible for environmental pollution globally. This is mainly due to the use of synthetic dyes, toxic ingredients and solvents for dy-ing/treatments/protocols of textiles. Replacement of those non ecofriendly ingredients would be a very challenging target to develop novel sustainable textile dying strategies. Using biobased and bioactive pigments, antibacterial ingredients and especially natural solvents are recently pointed out as substances to included in the related formulations. In this study the dyeing ability of the crude gel prodigiosin produced by non-pathogenic bacteria Serratia plymuthica as a natural pigment was investigated on various multifiber fabrics at different condition (temperature and pH) and by using salts (NaOH and Na2S2O4) and alternative mordants (a conventional: FeSO4 and a new bio-mordant: L-Cysteine (L-Cys)) in the recipes with different compositions and especially replacing of organic solvent with a new type of gel-based Choline chloride (ChCl) : Lactic acid (LA) (1 : 2) deep eutectic solvents (DES) as a dyeing medium. For determination of the dying process performances, the color strength (K/S value) of dyed fabric and the dyeing equalization (dE) parameters were evaluated. The fastness properties of the dyed fabrics which displayed as a higher K/S value and good dE were evaluated. The nylon fabrics dyed with 3.0% owf L-Cys at pH = 8.3 improved the color fastness to washing, while the gel-based ChCl : LA (1 : 2) DES dyebath provided a better color fastness to light. Moreover, the nylon fabrics under these conditions exhibited remarkable anti-microbial activity against Staphylococcus aureus (S. aureus) and Pseudomonas aeruginosa (P. aeruginosa). As conclusion, the use of the crude gel prodigiosin pigment revealed an advantageous ability to dye the textile materials, responding to consumers' demand for more ecological and sustainable products. Furthermore, the natural reducing agent L-Cys, never tested before as a bio-mordant, and the use of gel-based DES as a dyeing medium demonstrated enhanced colorimetric and antibacterial properties on dyed nylon with crude gel prodigiosin pigment.

Agricultural landscapes are more and more characterized by intensification and habitat losses. Landscape composition and configuration are known to mediate insect abundance and richness. In the context of global insect decline, and despite 75% of crops being under insect’s dependence, there is still a gap of knowledge about the link between pollinators and aromatic crops. Fennel (Foeniculum vulgare) is an aromatic plant cultivated in South of France, for its essential oil which is of great economic interest. Using pan-traps, we investigated the influence of the surrounding habitats at landscape scale (semi-natural habitat proportion and vicinity, landscape configuration) and local scale agricultural practices (insecticides and patch size) on fennel-flower-visitor abundance and richness and their subsequent impact on fennel essential oil yield. We found that fennel may to be a generalist plant species. We did not find any effect of intense local management practices on insect abundance and richness. Landscape configuration and the proximity to semi-natural habitat were the main drivers of flying insect’s family richness. This richness positively influenced fennel essential oil yield. Maintaining a complex configuration of patches at the landscape scale are important to sustain insect diversity and crop yield.

We report on the occurrence of natural Magnesium Alumina Silicate Hydrate (M-(A)-S-H) cement at Feragen and Leka in eastern and western Trøndelag Norway, respectively. Both occurrences are located in the sub-arctic climate zone and form in glacial till and moraine material deposited on ultramafic rock during the Weichselian glaciation. Weathering of serpentinized peridotite dissolve brucite and result in a relative high pH alkaline fluid that subsequently reacts with the felsic minerals of the till (quartz, plagioclase, K-feldspar) to form a cement consisting of an amorphous material or a mixture of nanocrystalline Mg-rich phyllosilicates, including illite. The presence of plagioclase in the till results in the enrichment of alumina in the cement, i.e., forms M-A-S-H instead of the M-S-H cement. Dissolution of quartz results in numerous etch-pits and negative quartz crystals filled with M-A-S-H cement. Where the quartz dissolution is faster that the cement precipitation, a honeycomb-like texture is formed. Compositionally the cemented till (tillite) contains more MgO and has a higher loss of ignition than the till, suggesting that the cement is formed by a MgO fluid that previously reacted with the peridotite. The M-(A)-S-H cemented till represent a new type of duricrust, coined magsilcrete. The study of natural Mg-cement provides information on peridotites as a Mg-source for Mg-cement and as a feedstock for CO2 sequestration.

Since the outbreak of COVID-19, social media platforms, such as Twitter, have experienced a tremendous increase in conversations related to Long COVID. The term “Long COVID” describes the persistence of symptoms of COVID-19 for several weeks or even months following the initial infection. Recent works in this field have focused on sentiment analysis of Tweets related to COVID-19 to unveil the multifaceted spectrum of emotions, viewpoints, and perspectives held by the Twitter community. However, most of these works did not focus on Long COVID, and the few works that focused on Long COVID have limitations. Furthermore, no prior work in this field has investigated Tweets where individuals self-reported experiencing Long COVID on Twitter. The work presented in this paper aims to address these research challenges by presenting multiple novel findings from a comprehensive analysis of a dataset comprising 1,244,051 Tweets about Long COVID, posted on Twitter between May 25, 2020, and January 31, 2023. First, the analysis shows that the average number of Tweets per month where individuals self-reported Long COVID on Twitter, has been considerably high in 2022 as compared to the average number of Tweets per month in 2021. Second, findings of sentiment analysis using VADER show that the percentage of Tweets with positive, negative, and neutral sentiment were 43.12%, 42.65%, and 14.22%, respectively. Third, the analysis of sentiments associated with these Tweets also shows that the emotion of sadness was expressed in most of these Tweets. It was followed by the emotions of fear, neutral, surprise, anger, joy, and disgust, respectively.

The exoskeleton technology has been rapidly advancing in the recent past due to its multitude of applications and diverse use-cases in assisted living, military, healthcare, firefighting, and industry 4.0. The exoskeleton market is projected to increase by multiple times of its current value within the next two years. Therefore, it is crucial to study the degree and trends of user interest, views, opinions, perspectives, attitudes, acceptance, feedback, engagement, buying behavior, and satisfaction, towards exoskeletons, for which the availability of Big Data of conversations about exoskeletons is necessary. The Internet of Everything style of today's living, characterized by people spending more time on the internet than ever before, with a specific focus on social media platforms, holds the potential for the development of such a dataset, by the mining of relevant social media conversations. Twitter, one such social media platform, is highly popular amongst all age groups, where the topics found in the conversation paradigms include emerging technologies such as exoskeletons. To address this research challenge, this work makes two scientific contributions to this field. First, it presents an open-access dataset of about 140,000 tweets about exoskeletons that were posted in a 5-year period from May 21, 2017, to May 21, 2022. Second, based on a comprehensive review of the recent works in the fields of Big Data, Natural Language Processing, Information Retrieval, Data Mining, Pattern Recognition, and Artificial Intelligence that may be applied to relevant Twitter data for advancing research, innovation, and discovery in the field of exoskeleton research, a total of 100 Research Questions are presented for researchers to study, analyze, evaluate, ideate, and investigate based on this dataset.

Whey is the main by-product of the dairy industry and contains sugars (lactose) and proteins (especially serum proteins and, at lesser extent, residual caseins), which can be valorized by the fermentative action of yeasts. In the present study, we characterized the spoilage yeast fraction inhabiting natural whey starter (NWS), the undefined starter culture of thermophilic lactic acid bacteria used in Parmigiano Reggiano (PR) cheesemaking, and evaluated thermotolerance, mating type, and the aptitude to produce ethanol and bioactive peptides from whey lactose and proteins, respectively, in a selected pool of strains. We found that PR NWS yeast population consists of other species (Saccharomyces cerevisiae, Wickerhamiella pararugosa, and Torulaspora delbrueckii) in addition to the well-documented Kluyveromyces marxianus, with multiple biotypes scored within each species. Haploid and diploid K. marxianus strains were identified through MAT genotyping, while thermotolerance assay allowed the selection of strains suitable to grow up to 48 °C. In whey fermentation assay, one thermotolerant strain was suitable to release ethanol with yield of 86.5%, while another candidate was able to produce the highest amounts of both ethanol and bioactive peptides with potentially anti-hypertensive function. The present work demonstrated that PR NWS is a reservoir of ethanol and bioactive peptides producer yeasts, which can be exploited to valorize whey, in agreement with the principles of circularity and sustainability.

Integrated landscape approaches (ILA) aim to reconcile multiple, often competing, interests across agriculture, nature conservation, and other land uses. Recognized ILA design principles provide guidance for their implementation, yet application remains challenging, and a strong performance evidence-base is yet to be formed. A comprehensive literature review and focus group discussions with practitioners identified considerable diversity of ILA in actors, temporal, and spatial scales, inter alia. This diversity hampers learning from and steering these integrated planning approaches because of its intractable nature. Therefore, we developed a tool—an ‘ILA mixing board’—to structure the complexity of ILA into selectable and scalable attributes in a replicable way to allow planning, diagnostics, and comparative assessment of ILA. The ILA mixing board tool presents seven qualifiers, each representing a key attribute of ILA design and performance such as project flexibility, inclusiveness of the dialogue, and the centrality of the power distribution. Each qualifier has five (non-normative) outcome indicators that can be registered as present or absent. This process in turn guides planners, evaluators and other participating stakeholders involved in landscape management to diagnose the ILA type, and or its performance. We apply the ILA mixing board as a diagnostic tool to three ILA cases in Nicaragua, Madagascar, and the Congo Basin to show some of the many possible configurations of qualifiers on the mixing board. Overall, the tool allows comparative analyses of the complexity of ILA in a structured and manageable way.

This study analyzed the impact of climate-related natural disasters (droughts, floods, storms/rainstorms) on economic and social variables. As the Africa-specific empirical literature is limited, this study used panel data from 1961–2011 on Africa. The study used a panel data regression model analysis. The results showed that climate change-related natural disasters affected Africa’s economic growth, agriculture, and poverty and caused armed conflicts. Among the disasters, droughts are the main cause of negative impact, severely affecting crops such as maize and coffee and resulting in increased urban poverty and armed conflicts. In contrast, international aid has a positive effect but the impact is insignificant compared to the negative consequences of climate-related natural disasters. Cereal food assistance has a negative crowding-out effect on cereal production. International donors should review their interventions to support Africa’s adaptative capacity to disasters. Government efficiency has reduced the number of deaths, and this is an area that supports Africa’s adaptative efforts.

The article justifies the need to involve the technogenic deposits (off-balance ore and wastes) into the economic circulation of mining enterprises when there is natural resources depletion. It could be considered as one of the tools of the circular economy. The authors analyze global trends in the copper deposits development, global demand for copper, and design recommendations for possible alternative options for the copper production. The authors use a case of Erdenet Mining Corporation SOE based in Mongolia to develop the approach for economic, social and environmental problem-solving. The millions of mining dumps are proposed to develop as technogenic resources for recycled materials, prolonging profitable activities of the mine. The Hierarchy analysis method is used to obtain optimum order of the mining dumps development to get economic, social and environmental effect.

Man-made shallow fishponds in the Czech Republic have been facing a high eutrophication since 1950s. Anthropogenic eutrophication and feeding of fish have strongly affected the physico-chemical properties of water and its aquatic community composition leading to harmful algal bloom formation. In our current study, we have characterised the phytoplankton community across three hypertrophic ponds to assess the phytoplankton dynamics during the vegetation season. We microscopically identified and quantified 29 cyanobacterial taxa comprised of non-toxigenic and toxigenic species. Further, a detailed cyanopeptides (CNPs) profiling was performed using molecular networking analysis of liquid chromatography tandem mass spectrometry (LC–MS/MS) data coupled with dereplication strategy. This MS networking approach coupled with dereplication on online global natural product social networking (GNPS) web platform led us to putatively identify forty CNPs: fourteen anabaenopeptins, ten microcystins, five cyanopeptolins, six microginins, two cyanobactins, a dipeptide radiosumin, a cyclooctapeptide planktocyclin and epidolastatin12. We have applied the binary logistic regression to estimate the CNPs producer by correlating the GNPS data with the species abundance. Usage of The combination of molecular networking and dereplication on online global natural product social networking (GNPS) web platform has proved as a valuable approach for rapid and simultaneous detection of high number of peptides, and rapidly assessing the risk for harmful bloom.

Visualising textual content could be helpful to professionals as well as amateurs across several fields. However, training a text-to-image generator in the mainstream domain requires large amounts of paired text-image and data, which is too expensive to collect since labeling millions of images and videos can be tiresome. GANs like StackGAN and StyleGAN can be considered as solutions to generate images from text. But the images generated may be of low accuracy and resolution, and the entire processing can be highly time-consuming. Moreover, image generation is a notion that is still being researched. Hence, the process of developing a Video Generation model necessitates substantial research. Despite the need for such a model, modern technology has lagged behind the solutions to this problem. This proposal suggests combining two methods, Text modification for Action Definition (TexAD) and SeQuential Image Generation for Video Synthesis (SQIGen). The proposed solution synthesises a sequence of images from textual information feeds and combines these images to create a video. TexAD uses Natural Language Processing and Deep Learning techniques to process, classify and modify text data. SQIGen is an extension of the VQGAN+CLIP neural network architecture that generates a sequence of images from the modified text data

Chagas disease, caused by the protozoan parasite Trypanosoma cruzi, stands as a significant public health challenge in endemic regions of the Americas. Current treatment options, limited and associated with adverse effects, underscore the need for novel therapeutic approaches. This systematic review comprehensively examines existing research on natural products as potential agents against Chagas disease. Employing a systematic search strategy across diverse databases, studies meeting predefined inclusion criteria were assessed for methodological quality and relevance. The synthesis of findings reveals a spectrum of natural products showcasing promising anti-Trypanosoma cruzi properties. The review discusses the limitations of current evidence, highlights gaps in knowledge, and proposes avenues for future research. By elucidating the landscape of natural products in Chagas disease treatment, this review contributes to the ongoing pursuit of innovative and accessible therapeutic strategies.

: Medical gloves, along with masks and gowns, serve as the initial line of defense against poten-tially infectious microorganisms and hazardous substances in the health sector. During the COVID-19 pandemic, medical gloves played a significant role, as they were widely utilized by society in daily activities as a preventive measure. These products demonstrated their value as important personal protection equipment (PPE) and reaffirmed their relevance as infec-tion-prevention tools. This review describes the evolution of medical gloves since the discovery of vulcanization by Charles Goodyear in 1839, which fostered the development of this industry. Regarding the current market, a comparison of the main properties, benefits, and drawbacks of the most widespread types of sanitary gloves is presented. The most usual gloves are produced from natural rubber (NR), polyisoprene (IR), acrylonitrile butadiene rubber (NBR), polychloroprene (CR), polyethylene (PE), and poly(vinyl chloride) (PVC). Furthermore, the environmental impacts of the conventional natural rubber gloves manufacturing process and mitigation strate-gies, such as bioremediation and rubber recycling, are addressed. In order to create new smart medical gloves with improved properties, several biopolymers (e.g. poly(vinyl alcohol) and starch) and additives such as biodegradable fillers (e.g., cellulose and chitin), reinforcing fillers (e.g. silica and cellulose nanocrystals), and antimicrobial agents (e.g., biguanides and quaternary ammonium salts) have been evaluated. This paper covers these smart materials and describes different innovative prototypes of gloves and coatings designed with them.

This paper presents four key results in the philosophy of language. Firstly, it distinguishes between partial and consistent assertion of a sentence, and introduces the concept of an equivocal sentence, which is both partially asserted and partially denied. Secondly, it proposes a novel definition of truth, stating that a true sentence is one that is consistently asserted. This definition avoids the Liar paradox, does not restrict classical logic, and applies to declarative sentences in the language used by any particular person. Thirdly, the paper introduces an epistemic model of language, called assertional language, which formalizes the definition of truth. Finally, it provides an argument for the falsity of so-called Liar sentences. The proposed definition of truth can be viewed as a formal account of the correspondence theory. The epistemic model is a powerful concept that allows for combining different languages in a meaningful way. This model is uniquely capable of reflecting on epistemic inconsistencies, such as logical paradoxes, in a consistent manner and without resorting to non-classical solutions.

Protected areas, such as natural World Heritage sites, RAMSAR wetlands, and Biosphere reserves are ecosystems within landscapes. Each site meets certain criteria that allow it to qualify as heritage or protected. Both climate change and human influence (e.g., incursion, increased tourist visitation) are altering biophysical conditions at many such sites. As a result, conditions at many sites are falling outside the criteria for their original designation. The alternatives are to change the criteria, remove protection from the site, change site boundaries such that the larger or smaller landscape meets the criteria, or manage the existing landscape in some way that reduces the threat. This paper argues for adaptive heritage, an approach that explicitly recognizes changing conditions. We discuss the need to view heritage areas as parts of a larger landscape, and to take an adaptive approach to management of that landscape. We offer five themes of adaptive heritage: 1) treat sites as living heritage, 2) employ innovative governance, 3) embrace transparency and accountability, 4) invest in monitoring and evaluation, and 5) manage adaptively. We offer the Australian Wet Tropics as an example where aspects of adaptive heritage currently are practiced, highlighting the tools being used. This paper offers guidance supporting decisions about natural heritage in the face of climate change and non-climatic pressures. Rather than delisting or lowering standards, we argue for adaptive approaches.

Myxobacteria represent a viable source of chemically diverse and biologically active secondary metabolites. The myxochelins are a well-studied family of catecholate-type siderophores produced by various myxobacterial strains. Here, we report the discovery, isolation and structure elucidation of three new myxochelins N1–N3 from the terrestrial myxobacterium Corallococcus sp. MCy9049, featuring an unusual nicotinic acid moiety. Precursor-directed biosynthesis (PDB) experiments and total synthesis were performed in order to confirm structures, improve access to pure compounds for bioactivity testing and to devise a biosynthesis proposal. The combined evaluation of metabolome and genome data covering myxobacteria supports the notion that the new myxochelin congeners reported here are in fact frequent side products of the known myxochelin A biosynthetic pathway in myxobacteria.

Open domain question answering (OpenQA) tasks have been recently attracting more and more attention from the natural language processing (NLP) community. In this work, we present the first free-form multiple-choice OpenQA dataset for solving medical problems, MedQA, collected from the professional medical board exams. It covers three languages: English, simplified Chinese, and traditional Chinese, and contains 12,723, 34,251, and 14,123 questions for the three languages, respectively. We implement both rule-based and popular neural methods by sequentially combining a document retriever and a machine comprehension model. Through experiments, we find that even the current best method can only achieve 36.7%, 42.0%, and 70.1% of test accuracy on the English, traditional Chinese, and simplified Chinese questions, respectively. We expect MedQA to present great challenges to existing OpenQA systems and hope that it can serve as a platform to promote much stronger OpenQA models from the NLP community in the future.

To sustain water-dependent economic and socio-ecological systems, natural capital and its interactions with other capitals is gaining attention, but a clear understanding of how to manage natural capital sustainably and how to make decisions relevant to water-related ecosystem services is yet to be achieved. In this study, we extended the framing of water-related ecosystem service flows as a cycle, integrating water quantity and quality and capturing the flows of ecosystem services (i.e., green phase) and ecosystem disservices (i.e., red phase), and connecting natural capital, built capital, and beneficiaries. We applied this framework to the Jiulong River watershed in China, using hydrological models to model water quantity and quality based on historical observations and experimental data. Our results showed that, during the green phase, the interactions of natural capital and built capital significantly improved water quality in downstream areas with higher flows. During the red phase, built capital reduced ecosystem disservices by ~10% while natural capital further reduced it by over one half. Our framework can provide information for natural capital management, eco-compensation, and pollutant management relevant to water-related ecosystem services.

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

Course-based Undergraduate Research Experiences (CUREs) in high-enrollment, introductory classes are a 37 potentially transformative approach to retaining more students in STEM majors. We developed and piloted a CURE 38 in the introductory biology courses at the University of Washington. This CURE focuses on analyzing experimental 39 evolution of antibiotic resistance in Escherichia coli and generates data on two topics relevant to clinical practice: 40 compensatory mutations and cross-drug effects. By studying mutations in central cellular machinery that confer drug 41 resistance, students not only gain insight into fundamental cellular phenomena, but also recognize the molecular 42 basis of a medically important form of evolutionary change, connecting genetics, microbiology, and evolution.