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With the worldwide awareness of sustainability, biomass-derived carbon electrode materials for supercapacitors have attracted growing attention. In this research, for the first time, we explored the feasibility of making use of the carbon byproduct from hydrothermal liquefaction (HTL) of microalgae, termed herein as algae-derived carbon (ADC), to prepare sustainable carbon electrode materials for high-performance supercapacitor development. Specifically, we investigated carbon activation with a variety of activating reagents as well as N- and Fe-doping of the obtained ADC with the intention to enhance its electrochemical performance. We characterized the structure of the activated and doped ADCs using scanning electron microscope (SEM), Raman spectroscopy, Fourier transform infrared spectroscopy (FTIR), X-ray photoelectron spectroscopy (XPS), and BET surface area and pore analysis, and correlated the ADCs’ structure with their electrochemical performance as evaluated using cyclic voltammetry (CV), galvanostatic charge/discharge (GCD), impedance, and cycle stability through an assembled symmetric two-electrode cell with 1 M H2SO4 as electrolyte. It was found that the ADC that is activated using KOH (KOH-ADC) showed the best electrochemical performance, and its specific capacitance was 14.1-fold larger with respect to that of the raw ADC and reached 234.5 F/g in the GCD test at a current density of 0.5 A/g. The KOH-ADC also demonstrated excellent capacitance retention (97% after 10,000 cycles at a high current density of 10 A/g) for stable long-term operations. This research pointed out a promising direction to develop sustainable electrode materials for supercapacitors from the carbon byproduct produced after HTL processing of algae.

A supercapacitor is an electrical energy storage system with high power output. With worldwide awareness of sustainable development, developing cost-effective, environmentally friendly, and high-performance supercapacitors is an important research direction. The use of sustainable components like wood biochar in the electrode materials for supercapacitor uses holds great promise for sustainable supercapacitor development. In this study, we demonstrated a facile and powerful approach to prepare meso-microporous carbon electrode materials for sustainable and high-performance supercapacitor development by electrospinning polyacrylonitrile (PAN) with F-treated biochar and subsequent aerogel construction followed by stabilization, carbonization, and carbon activation. The resultant carbon nanofibrous aerogel electrode material (ENFA-FBa) exhibited exceptional specific capacitance, attributing to enormously increased micropore and mesopore volumes, much more activated sites to charge storage, and significantly greater electrochemical interaction with electrolyte. This electrode material achieved a specific capacitance of 407 F/g at current density of 0.5 A/g in 1 M H2SO4 electrolyte, which outperformed the state-of-the-art specific capacitance of biochar-containing electrospun carbon nanofibrous aerogel electrode materials (

A sequential modular hydrodynamic model integrated with detailed reaction kinetics (SMHM-RK) was developed and validated to predict tar and syngas components produced by the steam gasification of biomass in a fluidized bed gasifier. The simulations showed that the prediction accuracy is sensitive to both models for hydrodynamics and reaction kinetics. The simulations showed that the tar composition predicted by the SMHM-RK was more close to the measured values than those predicted by the well-mixed hydrodynamic model integrated with the same reaction kinetics (WMHM-RK). The predictions showed that the total tar decreased, but the polycyclic aromatic tar compounds increased with the increase in gasification temperature. There was an optimum steam-to-biomass ratio (SBR) for minimizing tar formation. The simulations found that the contents of total tar and heavy tar compounds decreased by increasing the SBR from 0.3 to 0.9, and then increased by further increasing the SBR. The injection of a small amount of oxygen in steam gasification cannot reduce tar formation. The injection of oxygen in steam gasification changed the reaction pathways of naphthalene to produce more naphthalene in the syngas. The de-volatilization rate affects pyrolytic volatile compositions and subsequent tar formation. Therefore, biomass devolatilization and homogeneous gas reactions should be solved simultaneously to accurately predict the syngas and tar composition.

Sea surface temperature (SST) is crucial in ocean research and marine activities. It makes predicting SST of paramount importance. While SST is highly affected by different oceanic, atmospheric, and climatic parameters, few papers have investigated time-series SST prediction based on multiple features. This paper utilized multi features of air pressure, water temperature, wind direction, and wind speed for time-series hourly SST prediction using deep neural networks of convolutional neural network (CNN), long short-term memory (LSTM), and CNN–LSTM. Models were trained and validated by different epochs, and feature importance was evaluated by the leave-one-feature-out method. Air pressure and water temperature were significantly more important than wind direction and wind speed. Accordingly, feature selection is an essential step for time-series SST prediction. Findings also revealed that all models performed well with low prediction errors, and increasing the epochs did not necessarily improve the modeling. While all models were similarly practical, CNN was considered the most suitable as its training speed was several times faster than the other two models. With all this, the low variance of time-series data helped models make accurate predictions, and the proposed method may have higher errors while working with more variant features.

Increasing global demand for travel has drawn public attention to the tourism industry. This industry needs the design of intelligent systems based on new concepts to facilitate better service delivery. To this end, this study proposes a ubiquitous tourist system based on context-awareness, multicriteria decision making (MCDM), and augmented reality (AR) using a geospatial information system (GIS). This system provides two services to the user. First, it recommends a hotel in the vicinity of the user based on their preferences. Subsequently, it modifies the information property to augment the information concerning the visited object using AR technology. This system offers the advantage of adapting its models based on the user and their environment using context-awareness, thereby facilitating increased system automation during service delivery. Furthermore, this system enables personalization based on user needs. Our system was evaluated via a usability test using a Likert scale based on two system aspects, namely, system design, and user acceptance of the result. The output of this test yielded an average score of 4.112. The proximity of this score to the highest level of the Likert scale indicates the acceptance of the system by users.

One of the main global problems that affect human health is the development of bacterial resistance to different drugs. As a result, the growing number of multidrug-resistant pathogens has contributed to an increase in resistant infections and represents a public health problem. The present work seeks to investigate the chemical composition and antibacterial activity of the essential oil of Syzygium cumini leaves. To identify its chemical composition, gas chromatography coupled to mass spectrometry was used. The antibacterial activity test was performed with the standard strains Escherichia coli ATCC 25922, Pseudomonas aeruginosa ATCC 25853 and Staphylococcus aureus ATCC 25923 and multidrug-resistant clinical isolates E. coli 06, P. aeruginosa 24 and S. aureus 10. The minimum inhibitory concentration (MIC) was determined by serial microdilution as well as the verification of the modulating effect of the antibiotic effect. In this test, the oil was used in a subinhibitory concentration. The test reading was performed after 24 h of incubation at 37 °C. The results show that the major chemical constituent is α-pinene (53.21%). The oil showed moderate activity against E. coli ATCC 25922, with the MIC of 512 µg/mL; there was no activity against the other strains. The oil potentiated the effect of antibiotics demonstrating possible synergism when associated with gentamicin, erythromycin and norfloxacin against E. coli 06 and S. aureus 10.

Cymbopogon winterianus, known as “citronella grass”, is an important aromatic and medicinal tropical herbaceous plant. The essential oil of C. winterianus (EOCw) is popularly used to play an important role in improving human health due to its potential as a bioactive component. The present study aimed to identify the components of the essential oil of C. winterianus and verify its leishmanicidal and trypanocidal potential, as well as the cytotoxicity in mammalian cells, in vitro. The EOCw had geraniol (42.13%), citronellal (17.31%), and citronellol (16.91%) as major constituents. The essential oil only exhibited significant cytotoxicity in mammalian fibroblasts at concentrations greater than 250 μg/mL, while regarding antipromastigote and antiepimastigote activities, they presented values considered clinically relevant, since both had LC50 < 62.5 μg/mL. It can be concluded that this is a pioneer study on the potential of the essential oil of C. winterianus and its use against the parasites T. cruzi and L. brasiliensis, and its importance is also based on this fact. Additionally, according to the results, C. winterianus was effective in presenting values of clinical relevance and low toxicity and, therefore, an indicator of popular use.

Weeds are an important source of natural products; with promising biological activity. This study investigated the anti-kinetoplastida potential (in vitro) to evaluate the cytotoxicity (in vitro) and antioxidant capacity of the essential oil of Rhaphiodon echinus (EORe), which is an infesting plant species. The essential oil was analyzed by GC/MS. The antioxidant capacity was evaluated by reduction of the DPPH radical and Fe3+ ion. The clone Trypanosoma cruzi CL-B5 was used to search for anti-epimastigote activity. Antileishmanial activity was determined using promastigotes of Leishmania braziliensis (MHOM/CW/88/UA301). NCTC 929 fibroblasts were used for the cytotoxicity test. The results showed that the main constituent of the essential oil was γ-elemene. No relevant effect was observed concerning the ability to reduce the DPPH radical; only at the concentration of 480 μg/mL did the essential oil demonstrate a high reduction of Fe3+ power. The oil was active against L. brasiliensis promastigotes; but not against the epimastigote form of T. cruzi. Cytotoxicity for mammalian cells was low at the active concentration capable of killing more than 70% of promastigote forms. The results revealed that the essential oil of R. echinus showed activity against L. brasiliensis; positioning itself as a promising agent for antileishmanial therapies.

The coronavirus disease 2019 (COVID-19) pandemic is still ongoing, with no signs of abatement in sight. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), which is the causative agent of this pandemic and has claimed over 5 million lives, is still mutating, resulting in numerous variants. One of the newest variants is Omicron, which shows an increase in its transmissibility, but also reportedly reduces hospitalization rates and shows milder symptoms, such as in those who have been vaccinated. As a result, many believe that Omicron provides a natural vaccination, which is the first step toward ending the COVID-19 pandemic. Based on published research and scientific evidence, we review and discuss how the end of this pandemic is predicted to occur as a result of Omicron variants being surpassed in the community. In light of the findings of our research, we believe that it is most likely true that the Omicron variant is a natural way of vaccinating the masses and slowing the spread of this deadly pandemic. While the mutation that causes the Omicron variant is encouraging, subsequent mutations do not guarantee that the disease it causes will be less severe. As the virus continues to evolve, humans must constantly adapt by increasing their immunity through vaccination.

(1) Background: Candida is a genus of yeasts with notable pathogenicity and significant ability to develop antimicrobial resistance. Gossypium hirsutum L., a medicinal plant that is traditionally used due to its antimicrobial properties, has demonstrated significant antifungal activity. Therefore, this study investigated the chemical composition and anti-Candida effects of aqueous (AELG) and hydroethanolic (HELG) extracts obtained from the leaves of this plant. (2) Methods: The extracts were chemically characterized by UPLC–QTOF-MS/MS, and their anti-Candida activities were investigated by analyzing cell viability, biofilm production, morphological transition, and enhancement of antifungal resistance. (3) Results: The UPLC–QTOF-MS/MS analysis revealed the presence of twenty-one compounds in both AELG and HELG, highlighting the predominance of flavonoids. The combination of the extracts with fluconazole significantly reduced its IC50 values against Candida albicans INCQS 40006, Candida tropicalis INCQS 40042, and C. tropicalis URM 4262 strains, indicating enhanced antifungal activity. About biofilm production, significant inhibition was observed only for the AELG-treated C. tropicalis URM 4262 strain in comparison with the untreated control. Accordingly, this extract showed more significant inhibitory effects on the morphological transition of the INCQS 40006 and URM 4387 strains of C. albicans (4) Conclusions: Gossypium hirsutum L. presents promising antifungal effects, that may be potentially linked to the combined activity of chemical constituents identified in its extracts.

Amburana cearensis (Allemão) ACSm. belongs to the Fabaceae family and occurs in the Brazilian semiarid, Argentina, Paraguay, Bolivia, and Peru. Numerous studies that portray its ethnobotany, use in popular medicine, chemical composition, and biological activities exist in the literature. This review aimed to provide an overview of the chemical composition, ethnopharmacology, and biological activities associated with A. cearensis and its isolated constituents. Information was collected from internet searches in the Scopus, Medline, PubMed, Google Scholar, and ScienceDirect databases were performed covering publications from 1997–2020. An ethnopharmacological literature analysis revealed that A. cearensis is used to treat a wide range of respiratory disorders in addition to intestinal, circulatory, and inflammatory problems. Coumarins, flavonoids, phenolic glycosides, phenolic acids, phenylpropanoid derivatives, and triterpenoids, among others, have been reported as active compounds, with High-Performance Liquid Chromatography (HPLC) being the main analytical technique used. The A. cearensis extracts and compounds presented several biological activities, including antimicrobial, antinociceptive, anti-inflammatory, antioxidant, neuroprotective, and myorelaxant activities, among others. This review provides a useful bibliography for future investigations and A. cearensis applications; however, future studies should focus on its toxic effects and the mechanisms of action of its extracts and isolated constituents to guide clinical applications.

The objective of this research is to develop an machine learning (ML) -based system that evaluates the performance of high school students during the semester and identify the most significant factors affecting student performance. It also specifies how the performance of models is affected when models run on data that only include the most important features. Classifiers employed for the system include random forest (RF), support vector machines (SVM), logistic regression (LR) and artificial neural network (ANN) techniques. Moreover, the Boruta algorithm was used to calculate the importance of features. The dataset includes behavioral information, individual information and the scores of students that were collected from teachers and a one-by-one survey through an online questionnaire. As a result, the effective features of the database were identified, and the least important features were eliminated from the dataset. The ANN accuracy, which was the best accuracy in the original dataset, was reduced in the decreased dataset. On the contrary, SVM performance was improved, which had the highest accuracy among other models, with 0.78. Moreover, the LR and RF models could provide the same performance in the decreased dataset. The results showed that ML models are influential for evaluating students, and stakeholders can use the identified effective factors to improve education.

The search for new antibacterial agents has become urgent due to the exponential growth of bacterial resistance to antibiotics. Nitrogen-containing heterocycles such as 1,8-naphthyridine derivatives have been shown to have excellent antimicrobial properties. Therefore, the purpose of this study was to evaluate the antibacterial and antibiotic-modulating activities of 1,8-naphthyridine derivatives against multi-resistant bacterial strains. The broth microdilution method was used to determine the minimum inhibitory concentration (MIC) of the following compounds: 7-acetamido-1,8-naphthyridin-4(1H)-one and 3-trifluoromethyl-N-(5-chloro-1,8-naphthyridin-2-yl)-benzenesulfonamide. The antibiotic-modulating activity was analyzed using subinhibitory concentrations (MIC/8) of these compounds in combination with norfloxacin, ofloxacin, and lomefloxacin. Multi-resistant strains of Escherichia coli, Pseudomonas aeruginosa, and Staphylococcus aureus were used in both tests. Although the compounds had no direct antibacterial activity (MIC ≥ 1.024 µg/mL), they could decrease the MIC of these fluoroquinolones, indicating synergism was obtained from the association of the compounds. These results suggest the existence of a structure–activity relationship in this group of compounds with regard to the modulation of antibiotic activity. Therefore, we conclude that 1,8-naphthyridine derivatives potentiate the activity of fluoroquinolone antibiotics against multi-resistant bacterial strains, and thereby interesting candidates for the development of drugs against bacterial infections caused by multidrug resistant strains.

The increase in antibiotic resistance and the emergence of new bacterial infections have intensified the research for natural products from plants with associated therapy. This study aimed to verify the antibacterial and antioxidant activity of crude extracts of the genus Plectranthus species, being the first report on the modulation of aminoglycosides antibiotic activity by Plectranthus amboinicus extracts. The chemical composition was obtained by chemical prospecting and High-Performance Liquid Chromatography with diode arrangement detector (HPLC/DAD). The antibacterial activities of the extracts alone or in association with aminoglycosides were analyzed using the microdilution test. The antioxidant activity was evaluated by 2,2-diphenyl-1-picrylhydrazyl (DPPH) free radical scavenging. The phytochemical prospection allowed the flavonoids, saponins, tannins and triterpenoids to be identified. Quercetin, rutin, gallic acid, chlorogenic acid, caffeic acid, catechin, kaempferol, glycosylated kaempferol, quercitrin, and isoquercitrin were identified and quantified. The principal component analysis (PCA) observed the influence of flavonoids and phenolic acids from Plectranthus species on studied activities. Phytochemical tests with the extracts indicated, especially, the presence of flavonoids, confirmed by quantitative analysis by HPLC. The results revealed antibacterial activities, and synergistic effects combined with aminoglycosides, as well as antioxidant potential, especially for P. ornatus species, with IC50 of 32.21 µg/mL. Multivariate analyzes show that the inclusion of data from the antioxidant and antibacterial activity suggests that the antioxidant effect of these species presents a significant contribution to the synergistic effect of phytoconstituents, especially based on the flavonoid contents. The results of this study suggest the antibacterial activity of Plectranthus extracts, as well as their potential in modifying the resistance of the analyzed aminoglycosides.

IoT, as an emerging technology along with GIS, can result in advanced and user-friendly features in Smart Cities. In order to investigate the capabilities offered by these technologies, this paper provides an overview of GIS and IoT integration focusing on applications and architecture. Specifically, this paper starts with investigating the role of GIS and IoT separately and jointly in different domains. Then, a review of GIS and IoT integration studies is provided to examine how GIS could be used in IoT architecture. The results showed that the capabilities of GIS in dealing with geospatial data and attributes along with offering visualization and analyzing tools make it possible to develop an integrated system benefiting from real-time data collection and real-time monitoring provided by IoT. The presented details would assist researchers in future studies on utilizing GIS and IoT at the same time.

Dengue fever is a dangerous infectious endemic disease that affects over 100 nations worldwide, from Africa to the Western Pacific, and is caused by the dengue virus, which is transmitted to humans by an insect bite of Aedes aegypti. Millions of citizens have died as a result of dengue fever and dengue hemorrhagic fever across the globe. Envelope (E), serine protease (NS3), RNA-directed RNA polymerase (NS5), and non-structural protein 1 (NS1) are mostly required for cell proliferation and survival. Some of the diterpenoids and their derivatives produced by nature possess anti-dengue viral properties. The goal of the computational study was to scrutinize the effectiveness of diterpenoids and their derivatives against dengue viral proteins through in silico study. Methods: molecular docking was performed to analyze the binding affinity of compounds against four viral proteins: the envelope (E) protein, the NS1 protein, the NS3 protein, and the NS5 protein. Results: among the selected drug candidates, triptolide, stevioside, alepterolic acid, sphaeropsidin A, methyl dodovisate A, andrographolide, caesalacetal, and pyrimethamine have demonstrated moderate to good binding affinities (−8.0 to −9.4 kcal/mol) toward the selected proteins: E protein, NS3, NS5, and NS1 whereas pyrimethamine exerts −7.5, −6.3, −7.8, and −6.6 kcal/mol with viral proteins, respectively. Interestingly, the binding affinities of these lead compounds were better than those of an FDA-approved anti-viral medication (pyrimethamine), which is underused in dengue fever. Conclusion: we can conclude that diterpenoids can be considered as a possible anti-dengue medication option. However, in vivo investigation is recommended to back up the conclusions of this study.

The treatment of viral disease has become a medical challenge because of the increasing incidence and prevalence of human viral pathogens, as well as the lack of viable treatment alternatives, including plant-derived strategies. This review attempts to investigate the trends of research on in vitro antiviral effects of curcumin against different classes of human viral pathogens worldwide. Various electronic databases, including PubMed, Scopus, Web of Science, and Google Scholar were searched for published English articles evaluating the anti-viral activity of curcumin. Data were then extracted and analyzed. The forty-three studies (published from 1993 to 2020) that were identified contain data for 24 different viruses. The 50% cytotoxic concentration (CC50), 50% effective/inhibitory concentration (EC50/IC50), and stimulation index (SI) parameters showed that curcumin had antiviral activity against viruses causing diseases in humans. Data presented in this review highlight the potential antiviral applications of curcumin and open new avenues for further experiments on the clinical applications of curcumin and its derivatives.

Croton heliotropiifolius Kunth (Euphorbiaceae), whose occurrence has already been registered in the most varied Brazilian biomes, is commonly found in the Chapada do Araripe, Ceará. The species is traditionally used to treat fungal, parasitic, and degenerative diseases. This study investigated the chemical composition and pharmacological potential (antioxidant, antifungal, antiparasitic, and cytotoxic) of an aqueous extract obtained from the roots of C. heliotropiifolius. Following a qualitative phytochemical screening, the chemical constituents were identified by ultra-efficiency liquid chromatography coupled witha quadrupole/time-of-flight system (UPLC-QTOF). The antioxidant potential was verified by thin-layer chromatography (TLC). The direct and combined antifungal activity of the extract against opportunistic Candida strains was investigated using the microdilution method. The minimal fungicidal concentration (MFC) was determined by subculture, while the modulation of the morphological transition (fungal virulence) was evaluated by light microscopy. The in vitro antiparasitic activity was analyzed using epimastigotes of Trypanosoma cruzi and promastigotes of Leishmania braziliensis and Leishmania infantum, while cytotoxicity was determined in cultures of mouse fibroblasts. The phytochemical analysis identified the presence of acids, terpenes, flavonoids, lignans, and alkaloids. Among these constituents, the presence of polar and non-polar phenolic compounds with known antioxidant action was highlighted. While the extract showed clinically ineffective antifungal effects, it could enhance the effectiveness of fluconazole, in addition to inhibiting the morphological transition associated with increased virulence in Candida strains. Although the extract showed low cytotoxicity against fibroblasts, it also had weak antiparasitic effects. In conclusion, Croton heliotropiifolius is a source of natural products with antifungal and antioxidant potential.

Products of natural origin remain important in the discovery of new bioactive molecules and are less damaging to the environment. Benzaldehyde is a product of the metabolism of plants, and similarly to oxygenated terpenes, it can have antibacterial activity against Staphylococcus aureus and toxic action against Drosophila melanogaster; we aimed to verify these activities. The broth microdilution tests determined the minimum inhibitory concentration (MIC) of benzaldehyde alone and in association with antibiotics and ethidium bromide (EtBr). Toxicity against Drosophila melanogaster was determined by fumigation tests that measured lethality and damage to the locomotor system. The results indicated that there was an association of norfloxacin and ciprofloxacin with benzaldehyde, from 64 μg/mL to 32 μg/mL of ciprofloxacin in the strain K6028 and from 256 μg/mL to 128 μg/mL of norfloxacin in the strain 1199B; however, the associations were not able to interfere with the functioning of the tested efflux pumps. In addition, benzaldehyde had a toxic effect on flies. Thus, the results proved the ability of benzaldehyde to modulate quinolone antibiotics and its toxic effects on fruit flies, thus enabling further studies in this area.

In this study, asthma-prone area modeling of Tehran, Iran was provided by employing three ensemble machine learning algorithms (Bootstrap aggregating (Bagging), Adaptive Boosting (AdaBoost), and Stacking). First, a spatial database was created with 872 locations of asthma patients and affecting factors (particulate matter (PM10 and PM2.5), ozone (O3), sulfur dioxide (SO2), carbon monoxide (CO), nitrogen dioxide (NO2), rainfall, wind speed, humidity, temperature, distance to street, traffic volume, and a normalized difference vegetation index (NDVI)). We created four factors using remote sensing (RS) imagery, including air pollution (O3, SO2, CO, and NO2), altitude, and NDVI. All criteria were prepared using a geographic information system (GIS). For modeling and validation, 70% and 30% of the data were used, respectively. The weight of evidence (WOE) model was used to assess the spatial relationship between the dependent and independent data. Finally, three ensemble algorithms were used to perform asthma-prone areas mapping. According to the Gini index, the most influential factors on asthma occurrence were distance to the street, NDVI, and traffic volume. The area under the curve (AUC) of receiver operating characteristic (ROC) values for the AdaBoost, Bagging, and Stacking algorithms was 0.849, 0.82, and 0.785, respectively. According to the findings, the AdaBoost algorithm outperforms the Bagging and Stacking algorithms in spatial modeling of asthma-prone areas.

Peganum harmala, a well-known medicinal plant, has been used for several therapeutic purposes as it contains numerous pharmacological active compounds. Our study reported an anti-parasitic activity of P. harmala seed extract against Acanthamoeba triangularis. The stress induced by the extract on the surviving trophozoites for Acanthamoeba encystation and vacuolization was examined by microscopy, and transcriptional expression of Acanthamoeba autophagy-related genes was investigated by quantitative PCR. Our results showed that the surviving trophozoites were not transformed into cysts, and the number of trophozoites with enlarged vacuoles were not significantly different from that of untreated control. Molecular analysis data demonstrated that the mRNA expression of tested AcATG genes, i.e., ATG3, ATG8b, and ATG16, was at a basal level along the treatment. However, upregulation of AcATG16 at 24 h post treatment was observed, which may indicate an autophagic activity of this protein in response to the stress. Altogether, these data revealed the anti-Acanthamoeba activity of P. harmala extract and indicated the association of autophagy mRNA expression and cyst formation under the extract stress, representing a promising plant for future drug development. However, further identification of an active compound and a study of autophagy at the protein level are needed.

The intravascular parasitic worm Schistosoma mansoni is a causative agent of schistosomiasis, a disease of great global public health significance. Praziquantel is the only drug available to treat schistosomiasis and there is an urgent demand for new anthelmintic agents. Adopting a phenotypic drug screening strategy, here, we evaluated the antiparasitic properties of 46 commercially available cardiovascular drugs against S. mansoni. From these screenings, we found that amiodarone, telmisartan, propafenone, methyldopa, and doxazosin affected the viability of schistosomes in vitro, with effective concentrations of 50% (EC50) and 90% (EC90) values ranging from 8 to 50 µM. These results were further supported by scanning electron microscopy analysis. Subsequently, the most effective drug (amiodarone) was further tested in a murine model of schistosomiasis for both early and chronic S. mansoni infections using a single oral dose of 400 mg/kg or 100 mg/kg daily for five consecutive days. Amiodarone had a low efficacy in chronic infection, with the worm and egg burden reduction ranging from 10 to 30%. In contrast, amiodarone caused a significant reduction in worm and egg burden in early infection (>50%). Comparatively, treatment with amiodarone is more effective in early infection than praziquantel, demonstrating the potential role of this cardiovascular drug as an antischistosomal agent.

The species Cordia verbenacea DC (Boraginaceae), known as the whaling herb and camaradinha, is a perennial shrub species native to the Atlantic Forest. Its leaves are used in folk medicine as an anti-inflammatory, analgesic, antiulcerogenic and curative agent, in the form of teas or infusions for internal or topical use. The present study aimed to verify the cytotoxicity of the essential oil and the leishmanicidal and trypanocidal potential of C. verbenacea. The essential oil was characterized by GC-MS. The in vitro biological activity was determined by anti-Leishmania and anti-Trypanosoma assays. The cytotoxixity was determined using mammalian fibroblasts. The C. verbenacea species presented α-pinene (45.71%), β-caryophyllene (18.77%), tricyclo[2,2,1-(2.6)]heptane (12.56%) as their main compounds. The essential oil exhibited strong cytotoxicity at concentrations below 250 μg/mL (LC50 138.1 μg/mL) in mammalian fibroblasts. The potent anti-trypanosome and anti-promastigote activities occurred from the concentration of 62.5 μg/mL and was considered clinically relevant. The results also demonstrate that at low concentrations (C. verbenacea managed to be lethal for these activities. This can be considered an indication of the power used in daily human consumption. Therefore, it can be concluded that the essential oil of C. verbenacea contains a compound with remarkable antiparasitic activities and requires further research.

Depressive disorder is a recurrent illness that affects large numbers of the general population worldwide. In recent years, the goal of depression treatment has moved from symptomatic response to that of full remission. However, treatment-resistant depression is a major challenge in the treatment of depression or depression-related disorders. Consensus opinion, therefore, suggests that effective combined aggressive initial treatment is the most appropriate strategy. This study aimed to evaluate the effects of quercetin (QUR) and/or ascorbic acid (AA) on Phenobarbital-induced sleeping mice. QUR (50 mg/kg) and/or AA (25 mg/kg) with or without intraperitoneally pre-treated with GABA receptor agonist (diazepam: 2 mg/kg, i.p.) or antagonist (Flumazenil: 2.5 mg/kg, i.p.) to underscore the effects, as well as the possible involvement of the GABA receptor in the modulatory action of QUR and AA in sleeping mice. Additionally, an in silico study was undertaken to predict the involvement of GABA receptors in the sleep mechanism. Findings suggest that the pretreatment of QUR and AA modulated the onset and duration of action of the standard drugs in experimental animals. The acute administration of QUR and/or AA significantly (p < 0.05) reversed the DZP-mediated onset of action and slightly reversed the duration of sleep time in comparison to the vehicle (control) group. A further combination of QUR or AA with the FLU resulted in an enhancement of the onset of action while reducing the duration of action, suggesting a FLU-like effect on the test animals. In in silico studies, AA and QUR showed good to moderate binding affinities with GABAA and GABAB receptors. Both QUR and AA produced a stimulatory-like effect on mice, possibly through the GABAA and GABAB receptor interaction pathways. Further studies are necessary to verify this activity and clarify the exact mechanism of action(s) involved.

GPS-equipped vehicles are an effective approach for acquiring urban population movement patterns. Attempts have been made in the present study in order to identify the population displacement pattern of the study region using taxis’ origin and destination data, and then model the parameters affecting the population displacement pattern and provide an ultimate model in order to predict pick-up and drop-off locations. In this way, the passenger pick-up and drop-off locations have been identified in order to obtain the population movement pattern. In this study, Moran’s I index was used to measure the spatial autocorrelation, and hot spot analysis was used to analyze spatial patterns of pick-up and drop-off locations. Effective parameters modeling was performed using the Poisson regression. The results of the spatiotemporal distribution map for pick-up and drop-off locations indicated a similarity in patterns and equal results for some locations. Results also indicated different features of spatial distribution during different hours of the day. Spatial autocorrelation analysis results indicated a low probability of randomness in the general spatial distribution of the locations. The result of modeling the parameters shows the positive effect of the parameters on the pattern of population movement, and according to the p-value of 0.000, Poisson regression is significant for the pick-up and drop-off locations. The modeling results also highlighted the importance of movement patterns in recognizing urban hot spots, which is valuable for policymakers and urban planners.

A lack of required data resources is one of the challenges of accepting the Augmented Reality (AR) to provide the right services to the users, whereas the amount of spatial information produced by people is increasing daily. This research aims to design a personalized AR that is based on a tourist system that retrieves the big data according to the users’ demographic contexts in order to enrich the AR data source in tourism. This research is conducted in two main steps. First, the type of the tourist attraction where the users interest is predicted according to the user demographic contexts, which include age, gender, and education level, by using a machine learning method. Second, the correct data for the user are extracted from the big data by considering time, distance, popularity, and the neighborhood of the tourist places, by using the VIKOR and SWAR decision making methods. By about 6%, the results show better performance of the decision tree by predicting the type of tourist attraction, when compared to the SVM method. In addition, the results of the user study of the system show the overall satisfaction of the participants in terms of the ease-of-use, which is about 55%, and in terms of the systems usefulness, about 56%.

Cannabis plant has long been execrated by law in different nations due to the psychoactive properties of only a few cannabinoids. Recent scientific advances coupled with growing public awareness of cannabinoids as a medical commodity drove legislation change and brought about a historic transition where the demand rose over ten-fold in less than five years. On the other hand, the technology required for cannabis processing and the extraction of the most valuable chemical compounds from the cannabis flower remains the bottleneck of processing technology. This paper sheds light on the downstream processing steps and principles involved in producing cannabinoids from Cannabis sativa L. (Hemp) biomass. By categorizing the extraction technology into seed and trichome, we examined and critiqued different pretreatment methods and technological options available for large-scale extraction in both categories. Solvent extraction methods being the main focus, the critical decision-making parameters in each stage, and the applicable current technologies in the field, were discussed. We further examined the factors affecting the cannabinoid transformation that changes the medical functionality of the final cannabinoid products. Based on the current trends, the extraction technologies are continuously being revised and enhanced, yet they still fail to keep up with market demands.

In this paper, land subsidence susceptibility was assessed for Shahryar County in Iran using the adaptive neuro-fuzzy inference system (ANFIS) machine learning algorithm. Another aim of the present paper was to assess if ensembles of ANFIS with two meta-heuristic algorithms (imperialist competitive algorithm (ICA) and gray wolf optimization (GWO)) would yield a better prediction performance. A remote sensing synthetic aperture radar (SAR) dataset from 2019 to 2020 and the persistent-scatterer SAR interferometry (PS-InSAR) technique were used to obtain a land subsidence inventory of the study area and use it for training and testing models. Resulting PS points were divided into two parts of 70% and 30% for training and testing the models, respectively. For susceptibility analysis, eleven conditioning factors were taken into account: the altitude, slope, aspect, plan curvature, profile curvature, topographic wetness index (TWI), distance to stream, distance to road, stream density, groundwater drawdown, and land use/land cover (LULC). A frequency ratio (FR) was applied to assess the correlation of factors to subsidence occurrence. The prediction power of the models and their generated land subsidence susceptibility maps (LSSMs) were validated using the root mean square error (RMSE) value and area under curve of receiver operating characteristic (AUC-ROC) analysis. The ROC results showed that ANFIS-ICA had the best accuracy (0.932) among the models (ANFIS-GWO (0.926), ANFIS (0.908)). The results of this work showed that optimizing ANFIS with meta-heuristics considerably improves LSSM accuracy although ANFIS alone had an acceptable result.

With the development of Internet of Things (IoT) applications, applying the potential and benefits of IoT technology in the health and environment services is increasing to improve the service quality using sensors and devices. This paper aims to apply GIS-based optimization algorithms for optimizing IoT-based network deployment through the use of wireless sensor networks (WSNs) and smart connected sensors for environmental and health applications. First, the WSN deployment research studies in health and environment applications are reviewed including fire monitoring, precise agriculture, telemonitoring, smart home, and hospital. Second, the WSN deployment process is modeled to optimize two conflict objectives, coverage and lifetime, by applying Minimum Spanning Tree (MST) routing protocol with minimum total network lengths. Third, the performance of the Bees Algorithm (BA) and Particle Swarm Optimization (PSO) algorithms are compared for the evaluation of GIS-based WSN deployment in health and environment applications. The algorithms were compared using convergence rate, constancy repeatability, and modeling complexity criteria. The results showed that the PSO algorithm converged to higher values of objective functions gradually while BA found better fitness values and was faster in the first iterations. The levels of stability and repeatability were high with 0.0150 of standard deviation for PSO and 0.0375 for BA. The PSO also had lower complexity than BA. Therefore, the PSO algorithm obtained better performance for IoT-based sensor network deployment.

Parasites may affect the dynamics of bird populations. Plasmodium, Leucocytozoon and Haemoproteus are well-known avian haematozoa that can trigger decreased productivity and high mortality in domesticated birds. In this study, we evaluated the prevalence of avian blood parasites (Plasmodium, Leucocytozoon and Haemoproteus) against 335 birds of 8 species in the Yazd province in central Iran. To detect blood parasites, Giemsa-stained blood smears were prepared. Of the birds, 11.64% (39/335) were infected with at least one parasite genus, particularly Haemoproteus (32.6%; 23/335). The total prevalence values for Plasmodium, Haemoproteus and Leucocytozoon were 1.7, 6.8 and 2.9%, respectively. Plasmodium had lower prevalence rates of 1.7% (6/335). Among birds, pigeons, hens and ducks have the highest prevalence of Haemoproteus, Leucocytozoon and Plasmodium parasites at 1.7%, 6.8% and 2.9%, respectively. Results from this research extend our knowledge on the incidence of avian blood parasites in domesticated birds living in central Iran. The overall low incidence of avian blood parasites in birds was found in the Yazd province, Iran.

This study aimed to prepare forest fire susceptibility mapping (FFSM) using a ubiquitous GIS and an ensemble of adaptive neuro fuzzy interface system (ANFIS) with genetic (GA) and simulated annealing (SA) algorithms (ANFIS-GA-SA) and an ensemble of radial basis function (RBF) with an imperialist competitive algorithm (ICA) (RBF-ICA) model in Chaharmahal and Bakhtiari Province, Iran. The forest fire areas were determined using MODIS satellite imagery and a field survey. The modeling and validation of the models were performed with 70% (183 locations) and 30% (79 locations) of forest fire locations (262 locations), respectively. In order to prepare the FFSM, 10 criteria were then used, namely altitude, rainfall, slope angle, temperature, slope aspect, wind effect, distance to roads, land use, distance to settlements and soil type. After the FFSM was prepared, the maps were designed and implemented for web GIS and mobile application. A receiver operating characteristic (ROC)- area under the curve (AUC) index was used to validate the prepared maps. The ROC-AUC results showed an accuracy of 0.903 for the ANFIS-GA-SA model and an accuracy of 0.878 for the RBF-ICA model. The results of the spatial autocorrelation showed that the occurrence of fire in the study area has a cluster distribution and most of the spatial dependence is related to the distance to settlement, soil and rainfall variables.

Most existing augmented reality (AR) applications are suitable for cases in which only a small number of real world entities are involved, such as superimposing a character on a single surface. In this case, we only need to calculate pose of the camera relative to that surface. However, when an AR health or environmental application involves a one-to-one relationship between an entity in the real-world and the corresponding object in the computer model (geo-referenced object), we need to estimate the pose of the camera in reference to a common coordinate system for better geo-referenced object registration in the real-world. New innovations in developing cheap sensors, computer vision techniques, machine learning, and computing power have helped to develop applications with more precise matching between a real world and a virtual content. AR Tracking techniques can be divided into two subcategories: marker-based and marker-less approaches. This paper provides a comprehensive overview of marker-less registration and tracking techniques and reviews their most important categories in the context of ubiquitous Geospatial Information Systems (GIS) and AR focusing to health and environmental applications. Basic ideas, advantages, and disadvantages, as well as challenges, are discussed for each subcategory of tracking and registration techniques. We need precise enough virtual models of the environment for both calibrations of tracking and visualization. Ubiquitous GISs can play an important role in developing AR in terms of providing seamless and precise spatial data for outdoor (e.g., environmental applications) and indoor (e.g., health applications) environments.

Hemp (Cannabis sativa L.) is an emerging high-value specialty crop that can be cultivated for either fiber, seed, or cannabidiol (CBD). The demand for hemp and its products has been consistently on the rise in the 21st century. The United States of America (USA) has reintroduced hemp and legalized its production as an agricultural commodity through the 2018 Federal Farm Bill. Although there is a renewed interest in the adoption of hemp due to the emerging market, its production in the United States (US) remains limited partly because of unclear agronomic guidance and fertilization recommendations. This review article provides information on the current agronomic management practices that are available in the literature and identifies the future research needs for cultivating this multipurpose crop to address the growing market demands. Hemp production could be beneficial if managed properly. Hemp fertilizer requirements vary in accordance with the type of hemp grown (seed, fiber, or CBD), soil, environmental conditions and requires a wide range of macro- and micronutrients. Integrating management practices in hemp cultivation intended to build soil health is promising since the hemp cropping system is suitable for crop rotation, cover cropping, and livestock integration through animal waste applications. Hemp also has significant environmental benefits since it has the potential to remediate contaminated soils through phytoremediation, convert high amounts of atmospheric CO2 to biomass through bio-sequestration, and hemp biomass for bioenergy production. This review identifies that most of the agronomic research in the past has been limited to hemp fiber and, to some extent, hemp seed but not CBD hemp. With the increase in the global markets for hemp products, more research needs to be conducted to provide agronomic guidelines for sustainable hemp production.

Two bimetallic Fe-Cu and Fe-Ca oxygen carriers were studied for chemical looping gasification (CLG) of biomass. The SEM results indicated that there was no obvious agglomeration on the bimetallic Fe-Cu oxygen carrier supported on Al2O3 and Fe-Ca oxygen carrier after five redox cycles while agglomeration occurred on CuO supported on Al2O3 due to the low melting point of CuO. The XRD results indicated the presence of copper-ferrite and calcium-ferrite phases in the bimetallic materials. The two bimetallic oxygen carriers can be re-oxidized with air to form a crystalline that is similar to the fresh materials. The Fe-Ca oxide became active at 360 °C which was lower than 380 °C for the Fe-Cu oxygen carrier. The high thermal stability and redox reactivity of bimetallic Fe-Cu and Fe-Ca oxygen carriers make the bimetallic oxygen carriers more suitable for recycling during CLG. The method for preparing Fe-Cu oxygen carriers had no significant impact on biomass conversion efficiency but had significant effect on the quality of syngas. Proper control of the biomass/oxygen carrier mass ratio is critical to achieve high selectivity towards gasification instead of combustion. The Fe-Ca oxygen carrier could achieve higher selectivity towards gasification than the Fe-Cu oxygen carrier.

Currently, new technologies have enabled the design of smart applications that are used as decision-making tools in the problems of daily life. The key issue in designing such an application is the increasing level of user interaction. Mixed reality (MR) is an emerging technology that deals with maximum user interaction in the real world compared to other similar technologies. Developing an MR application is complicated, and depends on the different components that have been addressed in previous literature. In addition to the extraction of such components, a comprehensive study that presents a generic framework comprising all components required to develop MR applications needs to be performed. This review studies intensive research to obtain a comprehensive framework for MR applications. The suggested framework comprises five layers: the first layer considers system components; the second and third layers focus on architectural issues for component integration; the fourth layer is the application layer that executes the architecture; and the fifth layer is the user interface layer that enables user interaction. The merits of this study are as follows: this review can act as a proper resource for MR basic concepts, and it introduces MR development steps and analytical models, a simulation toolkit, system types, and architecture types, in addition to practical issues for stakeholders such as considering MR different domains.

In this study, an innovative green biorefinery system was successfully developed to process the green biomass into multiple biofuels and bioproducts. In particular, fresh giant miscanthus was separated into a solid stream (press cake) and a liquid stream (press juice) using a screw press. The juice was used to cultivate microalga Chlorella vulgaris, which was further thermochemically converted via thermogravimetry analysis (TGA) and pyrolysis-gas chromatography/mass spectrometry (Py-GC/MS) analysis, resulting in an approximately 80% conversion. In addition, the solid cake of miscanthus was pretreated with dilute sulfuric acid and used as the feedstock for bioethanol production. The results showed that the miscanthus juice could be a highly nutritious source for microalgae that are a promising feedstock for biofuels. The highest cell density was observed in the 15% juice medium. Sugars released from the miscanthus cake were efficiently fermented to ethanol using Saccharomyces cerevisiae through a simultaneous saccharification and fermentation (SSF) process, with 88.4% of the theoretical yield.