Group Contribution Research Articles

AI-enhanced chemical paradigm: From molecular graphs to accurate prediction and mechanism

The development of accurate and interpretable models for predicting reaction constants of organic compounds with hydroxyl radicals is vital for advancing quantitative structure-activity relationships (QSAR) in pollutant degradation. Methods like molecular descriptors, molecular fingerprinting, and group contribution methods have limitations, as traditional machine learning struggles to capture all intramolecular information simultaneously. To address this, we established an integrated graph neural network (GNN) with approximately 12 million learnable parameters. GNN represents atoms as nodes and chemical bonds as edges, thus transforming molecules into a graph structures, effectively capturing microscopic properties while depicting atom connectivity in non-Euclidean space. Our datasets comprise 1401 pollutants to develop an integrated GNN model with Bayesian optimization, the model achieves root mean square errors of 0.165, 0.172, and 0.189 on the training, validation, and test datasets, respectively. Furthermore, we assess molecular structure similarity using molecular fingerprint to enhance the model's applicability. Afterwards, we propose a gradient weight mapping method for model explainability, uncovering the key functional groups in chemical reactions in artificial intelligence perspective, which would boost chemistry through artificial intelligence extreme arithmetic power.

Swelling of Polypropylene Separators and Its Effect on the Lithium-Ion Battery Performance

The safety of lithium-ion batteries (LIBs) depends on separators -- porous materials providing ionic transport between the electrodes while preventing them from direct contact. Many commercial separator materials are polymers, e.g., polypropylene. Polypropylene has a non-polar structure, so it tends to interact favorably with non-polar solvents, such as carbonates used in LIBs as electrolyte solvents. These favorable interactions result in two effects: separator plasticization (softening) [1] and swelling [2]. Both swelling and plasticization affect the battery performance and cause safety concerns.Here we present a model which predicts the swelling of a porous polymer separator based on the molecular structures of the polymer and the solvent. The model is based on Flory's theory of polymer solutions and the Flory–Huggins parameter for polymer–solvent interactions. We introduced two additional parameters to Flory's theory, taking into account the complex structure of the porous polymers. These two parameters can be obtained from experimental data on two solvents. The Flory–Huggins parameter is calculated based on the UNIFAC-FV group contribution method for a given polymer–solvent pair. Our model predicts swelling of polypropylene separators in various solvents, matching the experimental data [3] quite well. By relating the swelling of the separator to the change in its porosity and tortuosity, we estimated the increase of cell impedance as a result of the swelling.Finally, the plasticization of the separator makes it less persistent to mechanical impacts, increasing the chances of failure. Since the solvent-induced plasticization of the separator is correlated with its swelling [3], our model can also be utilized to assess this effect. Therefore, our model can be used to quickly evaluate various polymer–solvent pairs for application in lithium-ion batteries.1. Sheidaei, A.; Xiao, X.; Huang, X.; Hitt, J. Mechanical behavior of a battery separator in electrolyte solutions. J. Power Sources 2011, 196, 8728−8734.2. Cannarella, J.; Liu, X. Y.; Leng, C.; Sinko, P.; Gor, G. Y.; Arnold, C. B. Mechanical properties of a battery separator under compression and tension. J. Electrochem. Soc. 2014, 161, F3117−F3122.3. Gor, G. Y.; Cannarella, J.; Leng, C. Z.; Vishnyakov, A.; Arnold, C. B. Swelling and softening of lithium-ion battery separators in electrolyte solvents. J. Power Sources 2015, 294, 167−172.4. Maksimov, A. V.; Molina, M.; Maksimova, O. G.; Gor, G. Y. Prediction of Swelling of Polypropylene Separators and Its Effect on the Lithium-Ion Battery Performance. ACS Appl. Polym. Mater. 2023, 5(3), 2026-2031.

The sustainability of two departmental health insurance units in Senegal: A qualitative study

Despite decades of implementation, community-based health insurance (CBHI) in Africa has not effectively achieved Universal Health Coverage (UHC). As a response, from 2014 to 2017, a Belgian-Senegalese project organized an alternative solution: Departmental Health Insurance Units (UDAM). Professionals managed UDAMs at a departmental level, and healthcare providers charged a flat-rate fee. While research on sustainability is scarce in Africa, this study aims to understand the factors that explain why, four years after the end of the project, UDAMs are sustained. This qualitative research used Schell et al. (2013) conceptual framework on factors influencing sustainability. The data came from 13 months of field observations, a documentation review, 120 interviews at the local level and nine at the regional and national level, and a focus group. We carried out a thematic analysis according to the conceptual framework. The results show that central political support has strengthened over time. UDAMs have managed to stabilize their funding through State subsidies and social marketing strategies to improve membership. UDAMs kept their management fees at 13%, below the 25% standard proposed by the West African Economic and Monetary Union. Multiple partnerships have been established at international, national, and local levels. Building on the professionalization of its staff, UDAMs have strengthened their organizational capacity. Internal controls and a complaints system have been organized to improve UDAMs’ accountability. Many communication activities were carried out before the end of the project to prepare the exit strategy. With their extensive coverage (penetration rate over 60%), UDAMs impact the health system (between 2014 and 2021, they paid 2.5 million EUR to healthcare providers). Innovations have been implemented, such as organizing a group contribution based on collective work in fields of culture supported by the communities and local stakeholders. As part of its UHC strategy, Senegal decided at the end of 2022 to transition its 676 communal CBHI into 46 departmental units. The sustainability of two UDAMs has demonstrated the relevance of this model. However, sustainability can only be assured if the State prioritizes the health sector and donors place greater emphasis on alignment and sustainability. We need to study how it is organized to scale up nationally.

PH dependent physicochemical signatures of amino acids in aqueous buffers: Thermodynamic analysis

The physicochemical signatures such as apparent molar volume (V2,Φ), isoentropic compressibilities (KS,2,φ), and dilution enthalpies of a series of amino acids (AAs) glycine (Gly), L-alanine (Ala), DL-α-amino-n-butyric acid (ABA), L-valine (Val) and L-leucine (Leu) have been measured at various pH values (pH = 2, 5 7 and 9) at a buffer molality of 0.1 mol kg−1 of water at 298.15 K. The data obtained from high sensitivity densimeter and sound analyser along with ultrasensitive isothermal titration calorimetry has been analyzed to evaluate the values of standard partial molar quantities such as for volume at infinite dilution (V2,m0), compressibility KS, 2,m0 and dilution enthalpy (ΔdilH0) of the AAs together with standard partial molar quantities of transfer of these solutes from water to aqueous buffer systems at these pH values. The linear correlation of V2,m0 for this series of AAs against number of carbon atoms in their alkyl chain has been used to evaluate the contribution of zwitterionic end groups (NH3+, COO–), the CH2 group, and other parts of alkyl chains of the AAs to the overall value of V2,m0. The results of standard partial molar transfer properties on volumes, compressibilities and enthalpies of dilution from water to aqueous buffer systems at various pH values have been connected and explained in terms of ion-polar, ion-hydrophilic, hydrophobic-hydrophobic and ion-hydrophobic interactions of the participating groups. These thermodynamic signatures when compared at different pH values suggest that at electrostriction of water dominates at pH 2. Such physicochemical properties of constituents of proteins such as amino acids have not been reported earlier. Thus, the results demonstrate that the interaction of individual amino acid residues with the buffer components does not lead to significant change in their physicochemical properties thereby permitting their use in obtaining mechanistic insights into systems of biological interest consisting of these constituents.

QSPR analysis to predict some quantum chemical properties of 2‐phenylindol derivatives as anticancer drugs using molecular descriptor and genetic algorithm multiple linear regression

AbstractThermodynamic properties of molecules provide fundamental information for binding in fragment‐based drug discovery programs and for understanding complex interactions. Experimental measurements of some thermodynamic properties are not always feasible. Quantum mechanical methods and group contribution methods are the major toolboxes to obtain some theoretical quantities. In drug discovery and development, computational studies can reduce the costs and risks of bringing a new medicine to market. Computational methods have been widely used to optimize promising new ligands by estimating their binding affinity to proteins using the thermodynamic ligand binding parameters (Gibbs energy, enthalpy, entropy, and heat capacity). In this article, the relationship between molecular descriptors and quantum chemical properties, such as the Gibbs free energy (ΔG kJ mol−1) and enthalpy of formation (ΔHf kJ mol−1) of 2‐phenylindole derivatives as anticancer drugs is studied. These properties were calculated at the DFT‐B3LYP/6‐311G (d,p) level of quantum chemistry by Gaussian 09 software. Molecular descriptors were calculated by Dragon 5.4 software, and the stepwise multiple linear regression (MLR) and the Genetic algorithm (GA) techniques were used to select the best descriptors and build QSPR models. To evaluate the predictive ability of developed quantitative structure‐property relationship (QSPR) models, different internal and external validation methods were adopted. The predictive powers of the models were found to be satisfactory. The models revealed that 3D matrix‐based descriptors (H3D, SEig) are useful to predict the Gibbs free energy and enthalpy of formation of the investigated compounds respectively.

Problems of assessment and analysis of mortality rates in the context of the rules of the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10)

Purpose of the study: to study changes in the structure of causes of death among those who died aged 18 years and older according to medical death certificates (MDC) and to discuss the problems of assessing the contribution of certain groups of diseases and conditions to the mortality of the population. Materials and methods. The source of information was the electronic database of the Main Directorate of the Civil Registry Office (USR registry office of the Moscow Region) of the MSS for those who died at the age of 18 and older for 2019, 2021 and 2022. one of the subjects of the Russian Federation. Results: All 3 years 99 % of deaths occur in 12 classes of the International Statistical Classification of Diseases and Related Health Problems, Tenth Revision (ICD-10); 3 classes (diseases of the circulatory system – DCS, nervous system – DNS and neoplasms) accounted for 70 % of deaths in 2019, 58 % in 2021 and 62 % in 2022 (the share decreased due to COVID-19). The share of DCS was 40 %, 32 % and 37.7 %; neoplasms – 15.3 % 10.9 % and 13.5 %. The greatest decrease in mortality was noted in the class of mental and behavioural disorders (MBD; 8 times: 58.4 in 2019 and 7.4 per 100 thousand in 2022). The proportion of male deaths over all 3 years was higher in the classes of injury and poisoning (IP), infectious diseases (ID), diseases of the respiratory system (DRS) and digestive system diseases (DDS). The highest age of the deceased was registered according to the classes of DNS and diseases of the genitourinary system (DGS); the youngest age in ID and IP classes. For all 3 years, more than 60 % of deaths in a hospital are registered in the classes of ID and DDS, and in 2021 and 2022 from COVID-19. Deaths at home predominate in the class of DNS and ill-defined conditions (class R). For individual codes in the classes of DCS, DNS, MBD, DGS, representing non-prohibited but unspecified causes of death, significant changes (hundreds and thousands of cases) were revealed in dynamics, which is probably due to a change in approaches to coding primary cause of death and affects the structure of mortality by classes and individual reasons. Conclusions. Significant annual changes in the number of deaths from individual causes, differences in demographic characteristics by ICD classes require clarification of the criteria for using individual 4-digit codes and subsequent analysis of mortality for clinically related groups of diseases.

Contribution of tRNA sequence and modifications to the decoding preferences of E. coli and M. mycoides tRNAGlyUCC for synonymous glycine codons.

tRNA superwobbling, used by certain bacteria and organelles, is an intriguing decoding concept in which a single tRNA isoacceptor is used to decode all synonymous codons of a four-fold degenerate codon box. While Escherichia colirelies on three tRNAGly isoacceptors to decode the four glycine codons (GGN), Mycoplasma mycoidesrequires only a single tRNAGly. Both organisms express tRNAGly with the anticodon UCC, which are remarkably similar in sequence but different in their decoding ability. By systematically introducing mutations and altering the number and type of tRNA modifications using chemically synthesized tRNAs, we elucidated the contribution of individual nucleotides and chemical groups to decoding by the E. coli and M. mycoides tRNAGly. The tRNA sequence was identified as the key factor for superwobbling, revealing the T-arm sequence as a novel pivotal element. In addition, the presence of tRNA modifications, although not essential for providing superwobbling, was shown to delicately fine-tune and balance the decoding of synonymous codons. This emphasizes that the tRNA sequence and its modifications together form an intricate system of high complexity that is indispensable for accurate and efficient decoding.

Contribution of surface roughness and oxygen-containing groups to the interfacial shear strength of carbon fiber/epoxy resin composites

The interfacial shear strength (IFSS) between carbon fibers (CFs) and the matrix is crucial to the performance of CF-reinforced polymer composites. To evaluate the contribution of mechanical interlocking and chemical anchoring at the interfaces of a polyacrylonitrile-based CF (TORAYCA T800SC-12000-10E)-reinforced epoxy resin (EP: bisphenol A type epoxy resin and tetrafunctional epoxy resin) composites, the surface roughness and content of oxygen-containing functional groups of the CFs were respectively altered by ammonia treatment and electrochemical oxidation. The results showed that ammonia treatment increased the surface roughness without much change to the surface elemental composition, while electrochemical oxidation increased the number of surface oxygen groups without changing the surface roughness. The IFSS of CF/EP composites was tested by the micro-droplet method. The relationships between IFSS, and surface roughness and oxygen content were obtained by linear fitting. The results showed that in the interfacial bonding of CF to epoxy resin, the contribution of chemical anchoring to the IFSS is larger than that of mechanical interlocking.

Investigation of the solubility of anticancer drugs in the supercritical solvent for development of innovative drug delivery systems; artificial intelligence paradigms (MLP-ANN) and thermodynamic correlations

The solubility of four established anticancer drugs, including busulfan, decitabine, temozolomide, and tamoxifen, has already been documented in supercritical CO2 (scCO2) to develop advanced formulations. In the current research, their supercritical solubility was theoretically evaluated by some empirical models, such as PR and SRK (cubic equations of state), UNIQUAC and modified Wilson’s models (expanded liquid models), and an artificial intelligent paradigm. The critical properties of these drugs were calculated using approved group contribution methods. Among the empirical models, the Belghait model provided the best fit to the experimental data, yielding an AARD% value within the range of 5.45–9.37 and an approximate Radj value of 0.990. Comparing SRK and PR models, the SRK model exhibited higher accuracy in correlating solubility data of all the mentioned drugs, achieving mean AARD% and Radj values in the range of 9.32–15.37 and 0.98–0.996, respectively. The UNIQUAC model outperformed the modified Wilson model in accurately correlating the solubility data for all the specified drugs, achieving mean AARD% values of 5.04–6.48 and Radj values in the range of 0.981 to 0.991. Moreover, the artificial neural network model offered the highest accuracy in predicting the solubility of the desired drugs. Notably, approximately 99.7% of its predicted data can fit with the experimental data, indicating its extremely good performance.

Development and selection of working fluid pairs for cascade high-temperature heat pump based on computer-aided molecular design and group contribution method

Development and selection of working fluid pairs for cascade high-temperature heat pump based on computer-aided molecular design and group contribution method

Mechanistic insights to sorptive removal of four sulfonamide antibiotics from water using magnetite-functionalized biochar

Magnetite-functionalized biochar (MBC) is a promising engineered material for remediation of antibiotic-contaminated fields. However, sorption mechanisms of ionizable organic compounds such as sulfonamide antibiotics (SAs) on MBC are still unclear. This study employed four representative SAs including sulfamethazine (SMT), sulfamerazine (SMR), sulfadiazine (SDZ), and sulfamethoxazole (SMX), to compare the difference in sorption on MBC. Results showed that the sorption capacities and affinities of the four SAs varied with their substituents, hydrophobic properties, and dissociation constants (pKa). Synergistic effect during co-pyrolysis with Fe3+ enhanced the sorption performance of MBC towards SAs compared to original BC. Spectral methods confirmed structural changes of MBC such as the variance in oxygen-containing groups and defective/graphitized phases. Results of modeling pH-dependent sorption revealed that H-bonding or π-bond assisted H-bonding determined the sorption affinities and capacities of SAs. In particular, the SAs with lower pKa were thermodynamically favorable to form H-bonding with MBC via proton exchange with water molecules. Quantum calculation results quantified the contributions of H-bonding strengths and found that the energies of H-bonding were correlated with affinities of SAs. Moreover, contributions of oxygen-containing groups instead of minerals dominated the H-bonding energies. Mechanistic insights from this study can be valuable in exploring engineered BC composites for practical application in field remediation.Graphical

Biological nitrogen fixation and the role of soil diazotroph niche breadth in representative terrestrial ecosystems

Biological nitrogen fixation (BNF) driven by diazotrophs is a fundamental process underpinning the primary productivity of terrestrial ecosystems and plays a pivotal role in the global nitrogen cycle. However, the relationships between BNF function and soil diazotroph taxa with different niche breadths remain unclear. This study investigated the N2 fixation rate and community structure of diazotrophs in soils collected from representative terrestrial ecosystems in China—agricultural, desert, forest, grassland and wetland—and explored the specific contributions of groups of diazotrophs with different niche breadths to the N2 fixation rate. Among all the ecosystems, the N2 fixation rate, nifH gene abundance and diazotroph diversity were highest in the wetland ecosystems. The N2 fixation rate was significantly (P < 0.05) positively correlated with diazotroph diversity, indicating a relationship between microbial community diversity and ecosystem functionality. Furthermore, the N2 fixation rate and Shannon index were significantly (P < 0.05) positively correlated with the abundances of diazotrophs classified as specialists and opportunists. Structural equation modeling indicated that geographical factors and climatic factors affected the abundance of specialists and opportunists directly or indirectly through soil physicochemical properties, which in turn affected N2 fixation rates. Collectively, these valuable insights into the intrinsic relationship between nitrogen-fixing function and groups of soil diazotroph taxa with different niche breadths advance our understanding of BNF across terrestrial ecosystems.

Revisiting the Total Hildebrand and Partial Hansen Solubility Parameters of Analgesic Drug Meloxicam

The reported total Hildebrand solubility parameter (δ2) value of meloxicam, as calculated based on the group contribution method proposed by Fedors, was compared with those estimated based on the maximum solubility peaks observed in different aqueous cosolvent systems at T = 298.15 K. Thus, the observed δ2 values varied from (19.8 to 29.1) MPa1/2, respectively. Moreover, the Hansen solubility parameters (HSPs) and the total Hildebrand solubility parameter were also determined by using the Bustamante regression method with the reported experimental solubility values of meloxicam in 31 neat solvents (30 organic solvents and water), obtaining the values: δd = 19.9 MPa1/2, δp = 16.9 MPa1/2, δh = 5.7 MPa1/2, and δT = 26.7 MPa1/2. Furthermore, the HSPs of meloxicam were also estimated based on the Hoftyzer–van Krevelen group contribution method, obtaining the values: δd = 17.9 MPa1/2, δp = 20.3 MPa1/2, and δh = 9.2 MPa1/2, and the total solubility parameter as: δT = 28.6 MPa1/2. In addition, the Kamlet–Abboud–Taft linear solvation energy relationship (KAT-LSER) model was also employed to evaluate the role of different intermolecular interactions on the dissolution of meloxicam in different solvents that varied in terms of polarity and hydrogen bonding capability.

The Fundamental Role of Oxime and Oxime Ether Moieties in Improving the Physicochemical and Anticancer Properties of Structurally Diverse Scaffolds

The present review explores the critical role of oxime and oxime ether moieties in enhancing the physicochemical and anticancer properties of structurally diverse molecular frameworks. Specific examples are carefully selected to illustrate the distinct contributions of these functional groups to general strategies for molecular design, modulation of biological activities, computational modeling, and structure–activity relationship studies. An extensive literature search was conducted across three databases, including PubMed, Google Scholar, and Scifinder, enabling us to create one of the most comprehensive overviews of how oximes and oxime ethers impact antitumor activities within a wide range of structural frameworks. This search focused on various combinations of keywords or their synonyms, related to the anticancer activity of oximes and oxime ethers, structure–activity relationships, mechanism of action, as well as molecular dynamics and docking studies. Each article was evaluated based on its scientific merit and the depth of the study, resulting in 268 cited references and more than 336 illustrative chemical structures carefully selected to support this analysis. As many previous reviews focus on one subclass of this extensive family of compounds, this report represents one of the rare and fully comprehensive assessments of the anticancer potential of this group of molecules across diverse molecular scaffolds.

Implementing and scaling authentic assessment through a pedagogical technology partnership

Authentic assessment has been identified as an effective strategy to transform assessment, with a focus on measuring students’ success in skill-relevant and real-life situations (Wiggins, 1998). However, effectively implementing activities that can help foster authentic assessment, such as peer feedback, group work, or team-based learning, especially in online and hybrid courses and large cohorts, is a consistent challenge. (Shank, 2009) To combat this challenge, the higher education institution Deakin University partnered with the pedagogical technology company FeedbackFruits, with the initial focus on developing a solution aimed at optimising group work. Due to the positive impact of the partnership, the two organisations continued their collaboration and worked together on implementing faculty-wide authentic assessment strategies with pedagogical technology, across a wide range of course sizes and modalities. The first collaborative project was meant to build on FeedbackFruits’s two solutions, Peer Review, for streamlining peer feedback, and Group Member Evaluation, enabling group member evaluation. Deakin University signalled the need for a pedagogical feature that would ensure fairness in group work. The two organisations developed the Group Contribution Grading feature that based the students’ grades on the contribution of each individual to the group deliverable and allowed the instructor to later manually adjust the grade. With increased visibility for teachers and between students on their performance and contribution to group work, students had more opportunities for autonomy and self-regulation. The results of the initial collaboration encouraged Deakin University to continue collaborating with FeedbackFruits on a wider scope. Since FeedbackFruits’s pedagogical technology included multiple tools that supported a wide range of learning approaches, the new project introduced the possibility to link different elements of different assignment types to customise the setup flow of assignments. As a result, educators could create complete, diversified activities more quickly and easily, as well as access the combined analytics in one place. Following the success of the past projects, the two organisations, together with several other higher education institutions, developed an AI-powered tool, Automated Feedback, that provided instant formative feedback to students on “lower-level” academic writing concerns, such as citation, academic style, grammar, and structure, freeing up time for the instructors to provide personalised feedback on “higher-order” concerns such as comprehension and critical argumentation of concepts. The most recent collaborative project involved leveraging the team-based learning (TBL) framework to provide additional learning experiences in established authentic assessment, with plans to scale up across STEM faculty. Following the principles for defining and measuring authentic assessment (Schultz et al., 2021), the institution used the team-based learning Tool 4 to conduct intra-group evaluation after authentic group projects. The collaboration between Deakin University and FeedbackFruits resulted in a robust ecosystem of co-developed authentic assessment technology that has prioritised the lived experience of educators and continuous improvement. Combining the expertise of higher education practitioners and pedagogical technology developers enabled a highly engaging, innovative learning experience for all students.

Sensitivity analysis by differential importance measure for unsupervised fault diagnostics

Fault diagnostic approaches based on supervised classifiers are difficult to apply to safety-critical or new design systems, because they require the availability of labelled data collected when the systems operate abnormally, which is a rare situation. To address this challenge, we develop a novel unsupervised method for fault diagnostics based on a fault detection module and on sensitivity analysis . Specifically, the Differential Importance Measure (DIM) is originally used to quantify how much a signal is, or a set of signals are, responsible for the variation of the system health state. The proposed method is tested on simulated data from a wind turbine and on real data from a gas turbine. The advantages of the proposed fault diagnostic method are: (1) it can be developed using only normal condition data (2) it allows identifying the component responsible for the abnormality by quantifying the contribution of groups of signals to the variation of the system health state; (3) it is capable of distinguishing the abnormalities caused by changes in external conditions from those caused by components malfunctions; (4) it can be used in combination with any fault detection technique.

Peer Groups, Success and Punishment as Correlates and Contributors to Pupils’ Academic Performance in Mathematics in Bamenda II Subdivision, Cameroon

The study set out to determine the relative contributions of peer groups, pupils’ success and punishment as motivating factors in mathematics classrooms on pupils’ academic performance in mathematics in Bamenda II Subdivision, Cameroon. Three specific objectives, three research questions and three hypotheses guided the study. The study adopted the descriptive survey and correlational research designs. One hundred and fifty (150) primary three pupils from two functional public primary schools in Bamenda II Subdivision constituted the population of the study. Using the Krejcie and Morgan table, and employing the stratified proportionate sampling technique, 108 pupils were selected to form the sample of the study. Data was collected using a questionnaire. The questionnaire was validated by experts and its reliability of 0.82 assured using the Cronbach Alpha. The data was analyzed using Pearson product moment correlation (r), the coefficient of determination (r2) to answer the research questions and the two tailed p-value for correlation to test the hypotheses at 0.05 level of significance. The findings of the study revealed that peer groups, pupils’ success and punishment in mathematics classrooms all contribute significantly to their academic performance in mathematics. It was therefore recommended that punishment should be carefully utilized by the classroom teachers in a bid to keep them focused on the subject both in and out of the classroom. Furthermore, teachers should always use very easy questions either during set induction or during the introduction of new mathematical concepts—questions within the reach of all pupils. This is because perceived success in mathematics by pupils has proven to be a good motivating factor to students’ in the subject. Lastly, group counseling should be done by both the school counselors and the classroom teachers, aimed at encouraging pupils to assist each other academically, especially in mathematics.

Peer groups for medical professionals in Ukraine: experience of implementing an innovative format of continuing professional development

The training of medical professionals does not end with graduation – continuing professional development (CPD) should be integrated in the professional life of each medical professional. Peer groups, an innovative format of CPD, were launched in the Netherlands in 1979. Subsequently, in 1980-1990, this format spread to other European countries and became an important method of quality improvement in primary health care. Research on peer groups is rare, and especially for Eastern Europe. The purpose of this study, which is unique for the region and Ukraine, is to describe the context and results of implementation and functioning of the first peer groups in Ukraine among doctors, nurses, and primary care managers. The methodology of the study is a case study, which was implemented through (a) data analysis – a description of the experience that was independently prepared by the facilitators of the newly created peer groups, reflected in text and graphic information on posters, and voiced their stories orally during the Congress of Peer Group Facilitators (October 2021), (b) analysis of facilitators’ publications on social media that highlight their experience, (c) analysis of semi-structured interviews during supervision and organizational meetings on the implementation and development of peer groups, (d) analysis of an expert interview with a Swiss medical doctor who was also involved in the development of peer groups in Ukraine. The results of the study are as follows: more than half of the trained facilitators have created their own peer groups; such groups have been functioning for several years despite COVID-19 and Russia’s full-scale invasion of the sovereign territory of Ukraine. Facilitators and participants of peer groups emphasize not only the educational contribution of peer groups to their profession, but also the social and psychological one. Barriers to the creation and sustainability of peer groups include lack of management support, lack of time, motivation and formal incentives (CPD points). Peer groups as one of the approaches to continuous professional development are gradually creating a culture of learning in healthcare facilities, bringing more trust in the interaction of professionals with each other and improving the quality of care provided.

Modeling Mixtures with PCP-SAFT: Insights from Large-Scale Parametrization and Group-Contribution Method for Binary Interaction Parameters

Vapor/liquid equilibria of mixtures are of utmost importance in the design of chemical processes. Because of the combinatorial complexity of mixtures, the available experimental data are small when considering the large molecular space. To fill this knowledge gap, molecular equations of state like PCP-SAFT show promise due to their explicit consideration of intermolecular interactions that can be transferred to mixtures. In this work, we comprehensively assess and exploit PCP-SAFT for modeling phase equilibria of mixtures. First, we provide binary interaction parameters for 7861 binary systems for which pure-component parameters and experimental data are available. Bubble and dew point pressures are described with a median deviation of 2.3 %. Secondly, we adjust a matrix of binary group/group interaction parameters for the homosegmented and heterosegmented group-contribution (GC) methods for PCP-SAFT. Among 1389 mixtures that can be described with the GC methods, the median deviation in bubble and dew point pressures are 6.4 % for the homosegmented approach and 5.1 % for the heterosegmented approach. The detailed analysis shows the importance of hydrogen bonds in mixtures of non-self-associating components with self-associating components. The parametrization is only possible by introducing a fast numerical method to calculate the derivative of bubble and dew point pressures with respect to arbitrarily many model parameters. The approach leverages reverse mode automatic differentiation (backpropagation), the same method used in machine learning to regress millions of model parameters to large datasets.

Development of the cumene oxidation process: Rigorous design, optimization, and control

This work first proposes a rigorous process model for the industrial production of cumene hydroperoxide (CHP) through cumene oxidation. The research scope covers process design, optimization, and controllability studies. The study began with a comprehensive analysis and supplementation of the required physical properties. The group contribution method or COSMO-based calculations are used to estimate the required mixture properties. With a detailed description of the thermodynamics, the kinetic parameters are re-regressed using the literature data. Subsequently, alternative configurations of the process are designed and compared, including the single-reactor (1R) and three-reactor (3R) designs. After thorough analysis, it is found that employing a 3R-design increases per-pass conversion by 4.8 % and CHP yield by 4.0 %, while reducing reboiler duty by 20.5 % compared to the single-reactor process. Finally, alternative control structures are proposed for the 3R-design. The control strategy employing pressure scheduling in the reactors and controlling the bottom temperature of the stripper (i.e. CS3) is found to be effective in rejecting the specified feed disturbances (i.e. ± 10 % flowrate change and 2 % impurity of CHP, acetone, phenol, and water in the cumene feed, respectively).