Construction Of Diagrams Research Articles

Galvanostatic performance of single-particle Li-ion battery materials: a rapid diagram diagnosis assisted by a Python / C++ software

Abstract Herein, a novel open-source software for predicting the performance of single particles of electrode materials under galvanostatic charging conditions is presented. The model improves previous work by incorporating different thermodynamic approaches to describe the interaction between intercalated ions, and the software provides tools for fitting different kinetic parameters and generating potential/capacity profiles or concentration/distance profiles to be analyzed. These features allow for detailed studies of the factors limiting the charging rate of active materials in which ions are intercalated. The diagnostic diagram is constructed through simulations of the capacity reached for a given potential cut-off, represented in the domain of two dimensionless parameters. These parameters represent kinetic and particle-size limitations for Li-ion storage. The present tool aims to facilitate the analysis of single-particle experiments, taking advantage of two different computational languages, popular in scientific computing: Python and C++. The software integrates a Python interface, for user-friendly interaction, with a C++ computational core allowing parallelization via OpenMP, for high computational performance. The software supports different thermodynamic approaches:} Langmuir-Frumkin intercalation model and equilibrium potentials derived from experimental data. The inclusion of experimental insertion isotherms allows for the construction of realistic capacity diagrams. The open-source software is available at https://github.com/fernandezfran/galpynostatic/

Development of Novel Women's Friendly Antifungal Microemulsion Loaded Gel for Vulvovaginal Infections

Aim: The research was carried out to develop the microemulsion-loaded gel of curcu-min, alkylpolyglucoside, and tea tree oil to treat vulvovaginal candidiasis infection. Methods: Screening of oils, surfactants, and co-surfactants was done based on solubility studies and the construction of pseudo-ternary phase diagrams with curcumin. The microemulsion was characterized for globule size, zeta potential, viscosity, and thermodynamic stability. Ex-vivo studies were carried out using Franz diffusion cells. The antifungal activity of microemulsion-loaded hydrogel was evaluated using the cup plate method using Candida albicans ATCC 10231 in glucose yeast agar medium. Results: The selected micro-emulsion consisted of curcumin 35μg/mL, IPM+TTO (1:1) 0.1 mL, Milcoside 100 + Acconon MC 8-2 EP NF 0.6-0.3 mL, phosphate buffer pH 4 160 mL showed maximum thermodynamic stability and exhibited lowest particle size and highest intensity. The viscosity of microemulsion-loaded gel was 11.2 pa.s. The surface tension of the microemulsion was measured by tensiometer and was found to be 26.07 mN/m. Antimicrobial susceptibility test-ing assay was done according to NCCLs assay protocol, and the EC50 value of our formulation was found to be 0.4465 μg/mL. In-vitro drug release and ex-vivo permeation studies showed 67.9% release in 420 minutes and 83.02% release in 360 minutes, respectively. An in-vitro irrita-tion study concluded that there was no redness or irritation on goat mucosa. Conclusion: The texture analysis test showed adhesiveness at -172.46 g s at - 4.60 adhesive force, and the peak load was 13.40 g. The microemulsion-loaded gel formulation can be a promis-ing alternative to the marketed formulations available for vaginal yeast infections.

System Dynamics Modeling: Technological Solution to Evaluating Cold-Chain Meat Packaging Scenarios

A cold-chain meat packaging project was developed for a meat product company in northwestern Mexico that moves high volumes of fresh meat into national and international markets. The objective of the present research is to evaluate the supply process for three types of thermo-shrinkable polyethylene bags to provide a technological solution for high-volume meat packaging based on a graphical user interface. A system dynamics (SD) methodology is developed in seven stages to generate a technological solution: (1) system mapping; (2) causal diagram construction; (3) stock, flow modeling, and equations; (4) model simulation; (5) model validation; (6) scenarios and multicriteria analysis; and (7) graphical user interface development. The main result for the company was a technological solution that could communicate with decision-makers and the proposed graphical user interface. Future optimistic and pessimistic scenarios were self-evaluated based on the current situation related to three thermo-shrinkable bags used for selling high volumes of fresh meat. In these solutions, previously simulated costs and savings can be implemented in a real situation. Quantitative graphical user interface data can be observed to adequately manage box and bag inventories and minimize costs. Using SD enables the development of technological solutions in complex environments with robust simulations and models that offer data to people interested in the system under study.

Artificial Neural Network-Based Non-Linear Modeling and Simulation of CaO-SiO2-Al2O3-MgO Blast Furnace Slag Viscosity

Blast furnace slags are formed by CaO-SiO2-Al2O3-MgO systems and have several physical characteristics, one of which is viscosity. Viscosity is an important variable for the operation and blast furnace performance. This work aimed to model viscosity through linear and non-linear models in order to obtain a model with precision and accuracy. The best model constructed was a non-linear model by artificial neural networks that presented 23 nodes in the first hidden layer and 24 nodes in the second hidden layer with 6 input variables and 1 output variable named ANN 23-24. ANN 23-24 obtained better statistical evaluations in relation to 11 different literature equations for predicting viscosity in CaO-SiO2-Al2O3-MgO systems. ANN 23-24 was also subjected to numerical simulations in order to demonstrate the validation of the non-linear model and presented applications such as viscosity prediction, calculation of the inflection point in the viscosity curve by temperature, the construction of ternary diagrams with viscosity data, and the construction of iso-viscosity curves.

Explicit construction of Penrose diagrams for black hole to white hole transition with spacelike thin shells

In this article, we explicitly construct the coordinates associated with the Penrose diagram in spacetimes connected via a spacelike thin shell in the following two examples: the generalized black-to-white hole bounce with mass difference and the Schwarzschild-to-de Sitter transition. We point out the issue of the first junction condition in the Penrose diagram constructed by cutting and pasting analytically known metrics with spherical symmetry by a static spacelike thin shell. With the goal of a global conformal coordinate chart associated with the corresponding Penrose diagram without discontinuity at the thin shell, we give a procedure consisting of three conformal transformations that serve different purposes. The first two of them are used to generate a continuous coordinate patch covering the entire thin shell, and therefore, the Penrose diagram can be constructed properly by patches with overlapping. The third transformation removes any coordinate singularity reintroduced by the first two transformations at the event horizons.

A high-throughput framework for lattice dynamics

We develop an automated high-throughput workflow for calculating lattice dynamical properties from first principles including those dictated by anharmonicity. The pipeline automatically computes interatomic force constants (IFCs) up to 4th order from perturbed training supercells, and uses the IFCs to calculate lattice thermal conductivity, coefficient of thermal expansion, and vibrational free energy and entropy. It performs phonon renormalization for dynamically unstable compounds to obtain real effective phonon spectra at finite temperatures and calculates the associated free energy corrections. The methods and parameters are chosen to balance computational efficiency and result accuracy, assessed through convergence testing and comparisons with experimental measurements. Deployment of this workflow at a large scale would facilitate materials discovery efforts toward functionalities including thermoelectrics, contact materials, ferroelectrics, aerospace components, as well as general phase diagram construction.

Using generative neural networks when training digital engineers to improve the quality of their training

The problem and the aim of the study. Modernized higher education provides great prospects for artificial intelligence technologies to be introduced when training engineers for knowledge-intensive and high-tech industries. Applying generative neural networks makes it possible to meet the requirements both for programs to train relevant specialists and for the level of developed skills key to Industry 4.0 and for further Industry 5.0. The purpose of the study is to identify the possibilities of using generative neural networks when training digital engineers to improve the quality of their professional training. Research methods. The authors consider the generative neural network as an open resource with which it is possible to design, individualize and fill educational content in accordance with the professional interests of the participants in the interaction. The choice of service is justified by the type of information being processed and the engineer's work function being implemented. 68 bachelors of Vyatka State University are involved (Russian Federation). Training program: 08.03.01 – Construction. Focus: hydraulic engineering, industrial and civil engineering, Digital technologies for the examination of construction sites and real estate management. The assessment of the quality (level) of training is carried out with the help of materials from the fund of complex qualification tasks. For statistical processing of data, Pearson's chi-squared test is used. Results. The directions of work of students of the experimental group studying the discipline "Information Technology", including interactive communication supported by neural networks: generation of images and 3D-models, search for original titles, selection of a list of references, construction of diagrams and algorithms, etc. Statistically significant differences were revealed in the changes that occurred in the pedagogical system (χ2 = 9.095; p < 0.05). Conclusion. The summary are formulated that neural network services contribute to improving the quality of training due to the following didactic capabilities: presentation of information in various forms, automation of calculations, analysis of large amounts of data, decision support, etc. Difficulties are also highlighted: constantly updated features of services, the English-language interface, compliance with copyright and ethics of interaction on the network

Research of the distribution symmetrical components in an electrical system with a diesel generator at an unsymmetrical load

Purpose. Investigation symmetrical components of current and voltage in microgrid elements with an autonomous synchronous generator of limited power, which works in parallel with a network transformer under the condition of non-metric load. Methodology. During the research, visual programming methods were used in the Simulink/Matlab package, methods of electrical engineering theory in terms of determining symmetrical component currents and voltages in observation branches, methods of synthesizing schemes for replacing elements of electric power systems for symmetrical component currents, methods of data processing and construction diagrams using specialized software tools.. Findings. As a result the analysis known studies, it was established that under the condition of researching low-power generating systems in conditions of asymmetry, insufficient attention is paid to the nature of the asymmetric load. Using the well-known structure of a microgrid, which includes a synchronous generator, a machine load, and an external network transformer, its model was built, in which, by introducing asymmetric loads of various types, a series of experiments was implemented with the registration of voltages and currents of forward, reverse, and zero sequences in the branches of the network. The analysis of the results obtained during the research established that the positive sequence voltage in the case of a change in the active load does not change its level, under the same circumstances, in the case of an active-capacitive and active-inductive load, the voltage change has the opposite character. In the case of increasing asymmetry of an active-inductive nature, the positive sequence voltage decreases, and in the case of an active-capacitive one, it increases. The specified changes in some cases exceed 3%. Positive sequence currents in the case of active load imbalance differ slightly. In the case of a positive sequence current of the transformer, its continuous growth is observed, which in the case of active-capacitive and active-inductive loads exceeds the active one by an average of 1.5 times. Negative sequence currents in the studied model, which are formed by an asymmetrical load, are completely closed to the transformer, causing zero-sequence voltages, and the nature asymmetrical load significantly affects the value of these currents, a 61% increase was noted. Originality. It was established that in a microgrid, which includes a synchronous generator, a machine load, a symmetrical active load of comparable power to the generator, an external network transformer, in addition to the fact of asymmetry caused by an asymmetrical load, the character of an asymmetrical load (active, active-inductive or active-capacitive). Practical value. In the case of using in microgrids devices that provide an increase in the quality of electrical energy, for example, symmetrical devices, it is necessary, in addition to the asymmetry factor, to take into account the natural load that causes the latter.

Evaluating the Impact of Edge-Seal on the Performance of Double-Glass Solar Photovoltaic Modules

Solar energy is a vital component of the renewable energy landscape. Nevertheless, photovoltaic (PV) modules face numerous challenges during operation due to environmental stress factors, which can lead to various degradation issues such as delamination, encapsulant discoloration, corrosion of cell metallization, and potential-induced degradation. Ethylene-vinyl acetate (EVA), despite being a prominent encapsulant material, is notably vulnerable to moisture. Upon degradation, EVA releases acetic acid, severely impacting the long-term performance of PV modules. This study investigates the effectiveness of using a polyisobutylene-based edge-seal to minimize moisture ingress in double-glass modules. One-cell mini-modules encapsulated with EVA, with and without edge-seal, are subjected to damp heat testing (85°C / 85% RH) for up to 5000 hours and their performance are evaluated though current-voltage characteristics. Mini-modules without edge-seal exhibit a significant 70% loss in power, primarily due to a 37% decrease in short-circuit current, a 56% decrease in fill factor, and a staggering 650% increase in series resistance. However, mini-modules with edge-seal see only a 33% loss in power, driven mainly by a 21% decrease in fill factor and a 76% increase in series resistance. The use of edge-seal does not completely prevent but effectively reduces moisture ingress and mitigates its detrimental effects on module performance. Additionally, the Network Structural Equation Modeling approach is applied to analyze current-voltage characteristics, enabling the identification of statistically significant relationships, the construction of degradation pathway diagrams, and the determination of key factors contributing to power degradation. This analysis reveals increased series resistance and reduced fill factor as primary causes of power degradation for both mini-module configurations. Although the encapsulant materials exhibit minimal degradation in optical, chemical, and thermo-chemical properties, the presence of moisture within the module construction can still cause corrosion of cell metallization. This results in a decline in power performance even without substantial acetic acid formation. This study highlights the critical importance of preventing moisture ingress to enhance the durability and reliability of PV modules, ensuring their optimal performance throughout their intended service lifetime.

ЩОДО ОДНОГО ПІДХОДУ ДО ВИРІШЕННЯ ПИТАННЯ АВТОМАТИЗОВАНОГО БУДУВАННЯ СХЕМИ ПОСИЛАНЬ РЕЛЯЦІЙНИХ БАЗ ДАНИХ

Introduction. The current trend of continuous integration of various areas of scientific and practical human activity contributes to the constant increase in the complexity of problems, the successful solution of which becomes possible only through the use of large volumes of information, as a rule, geographically distributed across multiple database files. In this case, the need to know the locations and features of the interaction of its components becomes of fundamental importance when organizing the most convenient and efficient ways to access it. In some cases, to meet such needs, you can use the services of existing products (Microsoft Access, Microsoft SQL Server, MySQL, FileMaker Pro, Oracle Database, dBASE, etc.). Sometimes, using a specialized SQL language or its dialects, it is more convenient to create a “handmade” one that will satisfy the stated requirements. If the user desires or needs to know the location of the information used in the work, understand the logic of the mechanism for obtaining it, and evaluate the effectiveness of the ways of its delivery to constantly monitor its current and future nomenclature and quantity, its developments can help, which can provide more detailed information, than known means. As experience shows, obtaining an integral and effective solution to this set of closely interrelated issues becomes possible only with the availability of a purely specialized software tool for the automated construction of link diagrams, which is a multifunctional, operational, easy-to-learn, and easy-to-use user environment. Endowed with the ability at any time and with minimal user participation to visualize on the screen the current structure of the created database with the required degree of clarity, detail, and ease of perception, such an environment should become a means of operational analysis and assessment of ways of possible modification of the database itself in the direction of creating a more effective one in practical terms, regarding the variant of its structure. Purpose. Research and practical testing of the existing capabilities of approaches, methods, algorithms, and software modules that were developed during the creation of a universal information software technology for working with undirected graphs of the “mixed forest” type. To evaluate the efficiency and feasibility of using these approaches to improve a set of integrated software tools for the automated construction of relational database link diagrams with minimal user participation in this process. Methods. Database systems, apparatus of graph-tree theory. Result. A new approach is proposed, and the result of its software implementation is presented in the form of a “pilot” version of a set of software tools for the automated construction of relational database link schemes. The basis of the approach is techniques, methods, algorithms, etc. which were created and implemented programmatically in a new, universal information software technology for working with (temporarily) undirected graphs of the “sour cream forest” type. , Software package (SP) makes “formal” the user’s participation in the process of building link diagrams. By greatly reducing the time required for this and providing more than 90% savings in the technical resources of the computer used, SP makes it possible to take into account data tables, the total number of fields of which is limited only by the latter’s capabilities. A positive conclusion about the suitability of such an approach will become an incentive for its further improvement in the form of specialized information software technology as an effective means of solving the problems of constructing relational database link schemes.

Exploring the key pathogenic mechanisms and potential intervention targets for Sophorae Flavescentis radix in managing bone metastasis of lung cancer based on network pharmacology and molecular docking techniques.

Lung cancer often metastasizes to the bone, which significantly complicates treatment and worsens patient prognosis. Thus, new therapeutic strategies need to be established. Using network pharmacology and bioinformatics analysis, this study sought to determine the molecular targets and associated mechanisms of the traditional Chinese medicine (TCM) Sophorae Flavescentis radix in the treatment of lung cancer bone metastasis. The active components of Sophorae Flavescentis radix were screened using the TCM Systems Pharmacology (TCMSP) platform based on drug-likeness and oral bioavailability. The target genes of these active compounds were obtained from the DrugBank database. Differentially expressed genes (DEGs) between primary and bone metastatic lung cancer samples were screened in the GSE175601 dataset from the Gene Expression Omnibus (GEO) database using GEO2R. The intersecting DEGs from both groups were used to construct a Venn diagram to identify the candidate target genes. The expression and prognostic relevance of these genes were validated in The Cancer Genome Atlas (TCGA) database. The GeneMania and Search Tool for Recurring Instances of Neighbouring Genes (STRING) databases were used to generate the protein-protein interaction networks. Molecular docking was performed using the PubChem, Protein Data Bank (PDB), and CB-DOCK2 databases. A Gene Set Enrichment Analysis (GSEA) was conducted to explore the possible mechanisms of action. In the TCMSP database, 28 active compounds and 227 target genes of the Sophorae Flavescentis radix were identified. In total, 952 DEGs related to lung cancer bone metastasis were found in the GSE175601 dataset from the GEO database. Five common DEGs were identified via Venn diagram construction (i.e., F10, JUN, AKR1B1, MMP1, and CCND1). MMP1 was selected as the candidate gene. MMP1 was upregulated in lung cancer tissues, and patients with low MMP1 expression had better survival rates than those with high MMP1 expression (P<0.05). MMP1 has an affinity of -8.9 with luteolin. The GSEA results suggested that MMP1 might influence biological processes in lung cancer by participating in pathways such as chemokine signaling, apoptosis, Wingless/Integrated (Wnt) signaling, tumor protein p53-regulated cell cycle arrest, Hedgehog signaling, and mitogen-activated protein kinase signaling. Patients with lower MMP1 levels had prolonged overall survival and may serve as a novel predictive biomarker for lung cancer. Sophorae Flavescentis radix appears to exert therapeutic effects on lung cancer bone metastasis by inhibiting MMP1 expression and modulating the abnormal activation of the Wnt pathway. Our findings further extend the understanding of the pathogenic mechanisms and potential therapeutic interventions of Sophorae Flavescentis radix in lung cancer bone metastasis, providing a theoretical basis for clinical diagnosis and treatment research.

MICE tourism product value chain

MICE tourism is a segment of the tourism industry that, due to its singularity, requires specific attention. This article offers a holistic approach to the channel of distribution of MICE tourism and targets two main objectives: the proposal of a conceptual framework that depicts all stakeholders, flows of action and possibilities of intermediation and study of the process of intermediation in order to analyze its present status and future development. To achieve our goal in this study, we used research methods - literature analysis, study of scientific publications, scientific research, and also used the construction of tables and diagrams of interaction between the main stakeholders of MICE tourism. When analyzing materials and writing this article, the following methods were used: general scientific methods of cognition (analysis, synthesis, systematization of theoretical data), system content analysis. The results observe a slow but steady process of disintermediation in which intermediaries are being bypassed in order to reduce costs. However, it also reaches the conclusion that total disintermediation is not feasible due to the high standards of quality service that the MICE market demands.

Analysis of Thermophysical Properties of Electro Slag Remelting and Evaluation of Metallographic Cleanliness of Steel.

Improving the competitiveness of steel companies is linked to sustainable, quality-compliant steel production. Therefore, new steel production technologies contributing to increased cleanliness of steel are continuously being developed and optimized. One way to achieve a high steel quality is to use electro slag remelting (ESR) technology. In this paper, the principle of ESR technology and the importance of fused slags for optimizing the process are outlined. The aim of this work was to analyze the main thermophysical properties of steel and fused slags used in the ESR process. Determination of the properties of steel and slags was performed using the FactSage calculation software, which involved the calculation of the liquid and solid temperature of steel and slags, the calculation and construction of quaternary diagrams, and the calculation of viscosity. The resulting quaternary diagrams revealed the substantial influence of chemical composition on melting temperatures of slags. In order to validate the acquired results, a CrNiMoV-type steel was subjected to investigation of its metallographic cleanliness and evaluation of its mechanical properties; the ESR process was shown to significantly improve the cleanliness of the steel and improve the mechanical properties of the steel compared to its cleanliness and quality when produced via vacuum degassing (VD) technology. During the ESR process, the average size of non-metallic inclusions was reduced from 20 μm to 10 μm, and the maximum size of non-metallic inclusions was reduced from 50 μm to 28 μm. The mechanical properties of the steel produced using ESR technology were impacted as follows: the ductility increased by 10%, contraction increased by 18%, notched toughness at 20 °C increased by 46%, and at -40 °C (respectively -50 °C) it increased by 30%.

What physical examinations are observed during an in-person GP consultation? Automatic extraction using a text-based approach

ObjectivesTeleconsultation is anticipated to have a long-term role in primary care. However, conducting virtual physical examinations is a well-known limitation. To anticipate unmet needs general practitioners (GPs) and patients may experience during teleconsultation, this study aims to automatically identify physical examinations typically conducted during in-person GP consultation. Material and MethodsThis study utilizes 281 GP in-person consultations (de-identified transcripts & video recordings) within UK general practices, where 169 eligible ones were included in this study. We propose an automated text-based approach using regular expressions on keywords in GP-patient consultation dialogue (e.g., “roll up your sleeves”) to identify physical examinations (e.g. blood pressure measurement). This approach involves the construction of conceptual diagrams to visually inspect the relationship between keywords and physical examinations, syntax analysis to identify patterns between keywords and generate regular expressions, and the use of these regular expressions in consultation transcripts to detect potential instances of physical examinations, where matching video frames were subsequently retrieved. The performance of our automated text-based approach is compared to manual classification by 2 independent researchers using 5-fold cross-validation (precision, recall, and F1-score). ResultsAmong the 169 eligible GP in-person consultations, 133 (79%) required a physical examination, while the other 33 visits were for psychological reasons. Out of these 133 consultations, a total of 283 physical examinations were observed, with 21 instances conducted behind a curtain. We identified 42 distinct types of physical examinations from these 283 instances, grouped into 10 physical examination categories based on body areas and physical artefacts. The most frequent category of physical examinations is Vital Signs 26.80% (76/283). Overall, blood pressure measurement (also belonging to the Vital Signs category) is the most frequent physical examination at 59.2% (45/76). The comparison between manual classification and the regular expression model demonstrates an average precision of 88.3%, recall of 78.9%, and an F1-score of 83.3% from 5-fold cross-validation, providing significant insights into the frequency and types of physical examinations conducted during in-person GP consultations. ConclusionBy using regular expressions in consultation dialogues between GPs and patients, we can automatically identify physical examinations in GP consultations with a precision of 88.3%. Findings from this study, i.e. physical examinations during in-person GP consultations, provide insights into areas where GPs and patients may need support during teleconsultation.

Thermodynamic study of water/solvent, bitumen/solvent, water/bitumen, and water/bitumen/solvent systems: From CPA EoS parametrization towards phase diagram construction

Various fluid phase equilibria happen in solvent-aided steam-assisted gravity drainage (SA-SAGD). An appropriate fluid model is a prerequisite for reservoir simulation, SA-SAGD operation analysis, and the design of various phase behavior experiments. Computational thermodynamics is a powerful tool for phase behavior studies in this application. We used the Cubic Plus Association Equation of State (CPA EoS) to predict equilibrium properties due to the presence of molecules with highly directional attractive forces. Calibration of the thermodynamic model is performed by parametrizing its unknown parameters versus two-phase equilibrium data. The model’s prediction capability of three-phase experimental data is evaluated, and it is used to construct the phase diagram of water/multicomponent solvent and water/multicomponent solvent/bitumen systems. For the water/multicomponent solvent system, the Txy diagram is obtained at 25 bar, and various phase equilibria regions are specified. These phase regions are presented on ternary diagrams for water/multicomponent solvent/bitumen systems at 25 bar and 475 K, 480 K, and 485 K. The steam/solvent coinjection analysis shows that at the same pressure, temperature, and steam/solvent composition, the multicomponent solvent/steam coinjection yields more aqueous phase compared to pure solvent/steam. This results in the transfer of more latent heat to bitumen, significantly contributing to its viscosity reduction. Analysis of the SA-SAGD operating conditions shows that a multicomponent solvent outperforms pure solvents in promoting steam condensation. The results aids in designing experiments, reducing the risk of failure of experiments, and saving time and resources in phase behavior studies.

Design and Evaluation of Clove Oil-Based Self-Emulsifying Drug Delivery Systems for Improving the Oral Bioavailability of Neratinib Maleate.

Neratinib maleate (NM), a tyrosine kinase inhibitor, is used in the treatment of breast cancer. NM is orally administered at a high dose of 290 mg due to its low solubility and poor dissolution rate at pH > 3, as well as gut-wall metabolism limiting its bioavailability. Self-emulsifying drug delivery systems (SEDDSs) of NM were developed in the current study to improve its oral bioavailability. The oily vehicle (clove oil) was selected based on the solubility of NM, while the surfactant and the cosurfactant were selected based on the turbidimetric analysis. Three different sets were screened for surfactant selection in the preparation of SEDDS formulations, the first set containing Cremophor® EL alone as the surfactant, the second set containing a mixture of Cremophor® EL (surfactant) and Caproyl® PGMC (cosurfactant), and the third set containing a mixture of Cremophor® EL (surfactant) and Capmul® MCM C8 (cosurfactant). Propylene glycol was used as the cosolubilizer in the preparation of SEDDSs. A series of studies, including the construction of ternary phase diagrams to determine the zone of emulsification, thermodynamic stability studies (involving dilution studies, freeze-thaw, and heating-cooling studies), turbidimetric analysis, and physicochemical characterization studies were conducted to identify the two most stable combinations of SEDDSs. The two optimized SEDDS formulations, TP16 and TP25, consisted of clove oil (45% w/w) and propylene glycol (5% w/w) in common but differed with respect to the surfactant or surfactant mixture in the formulations. TP16 was prepared using a mixture of Cremophor® EL (surfactant) and Caproyl® PGMC (cosurfactant) in a 4:1 ratio (50% w/w), while TP25 contained only Cremophor® EL (50% w/w). The mean globule sizes were 239.8 ± 77.8 nm and 204.8 ± 2.4 nm for TP16 and TP25, respectively, with an emulsification time of <12 s for both formulations. In vitro drug dissolution studies performed at different pH conditions (3.0, 4.5, 6.8) have confirmed the increase in solubility and dissolution rate of the drug by TP16 and TP25 at all pH conditions compared to plain NM. An oral pharmacokinetic study in female Wistar rats showed that the relative bioavailability (Frel) values of TP16 and TP25 over the plain NM were 2.18 (p < 0.05) and 2.24 (p < 0.01), respectively.

Contribution of artificial intelligence for understanding animal rabies epidemiology in Morocco: What are the perspectives of an innovative and predictive approaches?

Rabies is a major zoonotic disease legally notifiable in Morocco and elsewhere. Given the burden of rabies and its impact on public health, several national control programs have been implemented since 1986, without achieving their expected objectives.The aim of this study was to design a predictive analysis of rabies in Morocco. The expected outcome was the construction of probabilistic diagrams that can guide actions for the integrated control of this disease, involving all stakeholders, in the country. Such modeling is an essential step in operational epidemiology to optimize expenditure of public funds allocated to the integrated strategy for fighting this disease.The methodology employed combined the use of geospatial analysis tools (kriging) and artificial intelligence models (Machine Learning). In order to investigate the link between the risk of rabies within a territorial municipality (commune) and its socio-economic situation, the following data were analyzed: (1) health data: reported animal cases of rabies between 2004 and 2021 and data obtained through the ArcGIS kriging tool (Geospatial data); (2) demographic and socio-economic data. We compared several Machine Learning models. Of these, the “Imbalanced-Xgboost” model associated with kriging yielded the best results. After optimizing this model, we mapped our results for better visualization.The obtained results complement and consolidate previous study in this field with greater accuracy, showing a strong correlation between a commune's socio-economic status, its geographical location and its risk level of rabies. From this, 399 out of the 1546 communes have been identified as high-risk areas, accounting for 25.8% of the total number of communes. Under this risk-based approach, the results of these analyses make it practical to take targeted decisions for rabies prevention and control, as well as canine population control, in a territorial commune according to its risk level. Such an approach allows obvious optimized distribution of financial resources and adaptation of the control actions to be taken.The study highlights also the importance of using innovative technologies to refine epidemiological approaches and fill gaps in field data. Through this study, we hope to contribute to eradication of rabies in Morocco by providing reliable data and practical recommendations for control actions against rabies.

Emergence of vortex state in the S=1 Kitaev-Heisenberg model with single-ion anisotropy

The search for Kitaev spin liquid states has recently broadened to include a number of honeycomb materials with integer spin moments. The qualitative difference with their spin-1/2 counterparts is the presence of single-ion anisotropy (SIA). This motivates our investigation of the effects of SIA on the ground state of the spin-1 Kitaev-Heisenberg (KH) model using the density-matrix renormalization group which allows construction of detailed phase diagrams around the Kitaev points. We demonstrate that positive out-of-plane SIA induces an in-plane vortex state without the need for off-diagonal interactions. Conversely, negative SIA facilitates the emergence of a state in the presence of antiferromagnetic Heisenberg interactions, whereas a state can emerge for ferromagnetic Heisenberg coupling. These findings, pertinent even for weak SIA, not only enhance our theoretical understanding of the spin-1 KH model but also suggest experimental prospects for observing these novel magnetic states in material realizations. Published by the American Physical Society 2024

Instructional framing for guiding the construction of explanatory diagrams in a science classroom: a metafunction perspective

ABSTRACT This study investigates how to guide students’ drawing diagram activities as constructing explanations in science classrooms from the metafunctions in systemic functional language (SFL). Although a drawing-to-learn approach requires sufficient support, there is still a lack of understanding regarding the pedagogical considerations for guiding students on what and how to draw during their activities. Guided by the metafunctions in SFL, I analysed a case of one teacher’s teaching practices of employing drawing-diagram activities in a general science classroom. I found that the teacher translated the objects to draw from the familiar to the targeted visual meaning for the students (ideational). To provide a clearer understanding of the roles of diagrams, she had reflective discussions with the students about how other people could interpret their diagrams and what would be important to represent iteratively through sharing the diagrams using a visualiser (interpersonal). She also often provided several examples of organising diagrams at the beginning of or during the activities to brainstorm ideas for the compositions (textual). This practical knowledge framed by the metafunctions is aimed at providing a better understanding of how teachers can deploy a drawing-to-learn approach and intervene during drawing activities to facilitate students’ science learning in general science classrooms.

AIPHAD, an active learning web application for visual understanding of phase diagrams

Phase diagrams provide considerable information that is vital for materials exploration. However, the determination of multidimensional phase diagrams typically requires a significant investment of time, cost, and human resources owing to the necessity of numerous experiments or simulations. Machine learning and artificial intelligence techniques present a viable solution for expediting phase diagrams investigations. Additionally, effective visualization is critical for understanding phase diagrams. This study reports the development of AIPHAD (Artificial Intelligence technique for PHAse Diagram), an open-source web application to assist in the investigation and visual understanding of phase diagrams using active learning. AIPHAD employs PDC (Phase Diagram Construction) algorithm, which operates on the principle of uncertainty sampling in active learning. The AIPHAD application facilitates the examination of five diagram types: two-variable diagrams, three-variable diagrams, ternary sections, ternary phase diagrams, and quaternary sections. The efficacy of the application is demonstrated in the study of the Fe-Ti-Sn ternary system, where it efficiently identified the presence of the Heusler phase. The integration of machine learning tools with traditional materials science approaches showcased in this study has the potential to drive groundbreaking advancements in materials exploration and discovery.