Initial Step Research Articles

Exploration of data for analysis using boundary line methodology

The boundary line model has been proposed for interpretation of the plot of a biological response (such as crop yield) against a potentially-limiting variable from observations in a large set of scenarios across which other factors show uncontrolled variation. Under this model the upper bound of the distribution of data represents the limiting effect of the potential factor on the response. Methods have been proposed to fit this model, but we propose that an initial exploratory data analysis step is needed to evaluate evidence that (i) the model is plausible and (ii) that any limiting upper bound is exhibited by the data set (which could, in principle, not include any cases where the factor is limiting). We propose a statistic based on the density of observations in upper sections of early convex hull peels of the data plot. We evaluate this approach using various data sets, some of which have been used for boundary line analysis in previous studies.

Joint analysis of interaction and psychological characteristics in english teaching based on multimodal integration

The intersection of psychology and English teaching is profound, as the application of psychological principles not only guides specific English instruction but also elevates the overall quality of teaching. This paper takes a multimodal approach, incorporating image, acoustics, and text information, to construct a joint analysis model for English teaching interaction and psychological characteristics. The novel addition of an attention mechanism in the multimodal fusion process enables the development of an English teaching psychological characteristics recognition model. The initial step involves balancing the proportions of each emotion, followed by achieving multimodal alignment. In the cross-modal stage, the interaction of image, acoustics, and text is facilitated through a cross-modal attention mechanism. The utilization of a multi-attention mechanism not only enhances the network’s representation capabilities but also streamlines the complexity of the model. Empirical results demonstrate the model’s proficiency in accurately identifying five psychological characteristics. The proposed method achieves a classification accuracy of 90.40% for psychological features, with a commendable accuracy of 78.47% in multimodal classification. Furthermore, the incorporation of the attention mechanism in feature fusion contributes to an improved fusion effect.

Viral Immunogenicity Prediction by Machine Learning Methods.

Since viruses are one of the main causes of infectious illnesses, prophylaxis is essential for efficient disease control. Vaccines play a pivotal role in mitigating the transmission of various viral infections and fortifying our defenses against them. The initial step in modern vaccine design and development involves the identification of potential vaccine targets through computational techniques. Here, using datasets of 1588 known viral immunogens and 468 viral non-immunogens, we apply machine learning algorithms to develop models for the prediction of protective immunogens of viral origin. The datasets are split into training and test sets in a 4:1 ratio. The protein structures are encoded by E-descriptors and transformed into uniform vectors by the auto- and cross-covariance methods. The most relevant descriptors are selected by the gain/ratio technique. The models generated by Random Forest, Multilayer Perceptron, and XGBoost algorithms demonstrate superior predictive performance on the test sets, surpassing predictions made by VaxiJen 2.0-an established gold standard in viral immunogenicity prediction. The key attributes determining immunogenicity in viral proteins are specific fingerprints in hydrophobicity and steric properties.

Hydrodynamic features of pulsed solvent extractor for separation of two metals by using the antagonistic effect of solvents

Separating copper and cobalt ions is crucial due to the industry’s strategic reliance on both these elements. When the extraction process is able to significantly increase the separation factor, it becomes favorable to separate two ions. However, the presence of Cu(II) ions together with Co(II) hinders the achievement of optimum efficiency when using commonly available extractants. This study conducted the separation of the two elements using both batch and continuous methods in a pilot plant pulsed column equipped with a disc and doughnut structure. The initial step involved optimizing the key variables to maximize the separation factor using the central composite design procedure. The optimization of Cyanex272, Cyphos IL 101 concentrations, and the pH value of the aqueous phase were all adjusted to 0.024 M, 0.046 M, and 7.3, correspondingly. In the following step, the hydrodynamic characteristics and extraction performance were examined in the pulsed column of the pilot plant. The findings indicated that the presence of Cyphos IL 101 resulted in an increased separation factor and efficiency within the column. As a result, the ionic liquid enhances performance without encountering any operational issues. This additive is considered an environmentally friendly solvent and does not cause any negative impacts. Consequently, it is suggested for utilization in continuous industrial processes.

Language-based game theory in the age of artificial intelligence.

Understanding human behaviour in decision problems and strategic interactions has wide-ranging applications in economics, psychology and artificial intelligence. Game theory offers a robust foundation for this understanding, based on the idea that individuals aim to maximize a utility function. However, the exact factors influencing strategy choices remain elusive. While traditional models try to explain human behaviour as a function of the outcomes of available actions, recent experimental research reveals that linguistic content significantly impacts decision-making, thus prompting a paradigm shift from outcome-based to language-based utility functions. This shift is more urgent than ever, given the advancement of generative AI, which has the potential to support humans in making critical decisions through language-based interactions. We propose sentiment analysis as a fundamental tool for this shift and take an initial step by analysing 61 experimental instructions from the dictator game, an economic game capturing the balance between self-interest and the interest of others, which is at the core of many social interactions. Our meta-analysis shows that sentiment analysis can explain human behaviour beyond economic outcomes. We discuss future research directions. We hope this work sets the stage for a novel game-theoretical approach that emphasizes the importance of language in human decisions.

Assessing the Legal Protection of Intangible Cultural Heritage in Saudi Arabia: A Critical Analysis in the Context of the 2003 UNESCO Convention

Saudi Arabia boasts a diverse and abundant cultural heritage that reflects a fusion of pre-Islamic and Islamic civilizations, serving as a precious legacy for future generations. Confronted with the challenges arising from globalization and rapid development, preserving intangible cultural heritage has become increasingly challenging, particularly in the absence of comprehensive heritage policies. The initial steps toward conservation were taken in 1972 when legislation was enacted to protect historical and cultural sites. However, it was not until 2014 that a new law was introduced to address the gaps left by the 1972 law. Unfortunately, this legal protection predominantly centered on tangible aspects of cultural heritage, leaving the equally important intangible cultural heritage neglected and unprotected. This study aims to evaluate the existing legal mechanisms in Saudi Arabia for the preservation and protection of intangible cultural heritage. It identifies the existing deficiencies and obstacles in the current cultural heritage framework regarding the preservation of intangible cultural heritage in Saudi Arabia. Our analysis focuses on how Saudi Arabia aligns with the principles and guidelines established by the 2003 UNESCO Convention for the Safeguarding of Intangible Cultural Heritage.

Characterization of Silica Nanoparticles from Pumice as an Aerogel Adsorbent

Pumice is often found around the banks of rivers, this stone is a type of igneous rock formed from volcanic eruptions. One of the compounds contained in pumice is silica. Therefore, the synthesis of silica nanoparticles from pumice using the sol gel method was used because it is simpler and more efficient in terms of cost and processing time. the initial step was by reacting pumice powder and NaOH at 70°C - 80°C then synthesized by adding 2M HCI to form a gel or white precipitate, soaking in ethanol, teos, hexane solutions was then synthesized to become an aerogel. Silica synthesis results into silica aerogels were characterized by FTIR and XRF. From FTIR silanol and siloxane functional groups were found, and the SiO2 composition increased to 93,299% after synthesis.

Fractionation and purification of a high-impurity, alkaline-pretreated, corn stover lignin with simple renewable solvents

Corn stover has been identified as a key resource in the transition from petrochemicals to sustainably sourced energy and materials; however, the as-recovered lignin from alkaline pretreatment contains unusually high levels of residual impurities, which must be removed to maximize potential application. Thus, the Aqueous Lignin Purification using Hot Agents (ALPHA) process was applied to a corn stover lignin containing ∼10 % carbohydrates and ∼3 % ash. Lignin fractions ∼100 times cleaner than the feed lignin and of controlled molecular weight (i.e., as low as one-fifth and as high as triple the absolute molecular weight (Mw) of the feed) were obtained. The ALPHA solvent was a hot (60 or 75 °C) solution of 80 % ethanol combined with the feed lignin at an unusually low, 3:1 solvent-to-feed lignin ratio – conditions that create the liquid–liquid equilibrium (LLE) phase split needed to practice ALPHA. Lignin fractionation then commenced by adding water to the original solution, decreasing the ethanol content in increments of 3–4 wt%. Surprisingly, the highest Mw fractions did not precipitate until the solution contained just below 50 % ethanol, by which point almost half of the starting lignin of near-average Mw had already precipitated. Another unexpected result was that the lignin waste stream from the initial purification step could be increased from 40 to 98 % lignin in a single equilibrium stage simply by re-processing that waste with another 80 % ethanol solution at the conditions given above. In summary, the efficacy of ALPHA for purifying and fractionating an impurities-laden corn stover lignin to ultraclean levels in a scalable manner requiring only aqueous bioethanol solutions has been demonstrated.

Demographic and Clinical Characteristics of Refugee Children Utilizing Healthcare Services of Türkiye (2021-2022): A Single-Centre Study

BACKGROUND: The number of refugees in the world is increasing due to ongoing wars, political instability, violence, poverty and climate change. Türkiye has the world’s largest refugee population and half of the refugees are children. The aim of this study is to describe healthcare utilization patterns of the pediatric refugee population admitted to emergency, outpatient and inpatient services to enable hospitals to be better prepared to treat large numbers of refugee children. METHODS: This was a retrospective observational study using data from medical records of refugee children who had visited the _University of Health Sciences Sancaktepe Training and Research Hospital_, Istanbul, in pediatric emergency, outpatient and inpatient services between 1 June 2021 and 1 June 2022. RESULTS: 4383 refugee children including emergency (81.9%), outpatient (15.1%) and inpatient clinics (3%) were analyzed. The most frequently seen diseases for each service were upper respiratory infection and gastroenteritis in emergency; vitamin D deficiency and malnutrition in outpatient; lower respiratory infection, fever and intoxication in inpatient services. Most of the visits were non-urgent visits. COVID-19 disease was found in 1.3% of all children. CONCLUSION: Up to now, there is little data available on the health status of these pediatric refugees. This study demonstrates that refugee children’s visits to the Emergency Department (ED) are an initial first step for accessing healthcare. Identifying the reasons for non-emergency ED use might provide additional insights and new approaches to help serve the healthcare needs of this vulnerable population.

Heat 'n Beat: A Universal High-Throughput End-to-End Proteomics Sample Processing Platform in under an Hour.

Proteomic analysis by mass spectrometry of small (≤2 mg) solid tissue samples from diverse formats requires high throughput and comprehensive proteome coverage. We developed a nearly universal, rapid, and robust protocol for sample preparation, suitable for high-throughput projects that encompass most cell or tissue types. This end-to-end workflow extends from original sample to loading the mass spectrometer and is centered on a one-tube homogenization and digestion method called Heat 'n Beat (HnB). It is applicable to most tissues, regardless of how they were fixed or embedded. Sample preparation was divided into separate challenges. The initial sample washing and final peptide cleanup steps were adapted to three tissue sources: fresh frozen (FF), optimal cutting temperature (OCT) compound embedded (FF-OCT), and formalin-fixed paraffin embedded (FFPE). Third, for core processing, tissue disruption and lysis were decreased to a 7 min heat and homogenization treatment, and reduction, alkylation, and proteolysis were optimized into a single step. The refinements produced near doubled peptide yield when compared to our earlier method ABLE delivered a consistently high digestion efficiency of 85-90%, reported by ProteinPilot, and required only 38 min for core processing in a single tube, with the total processing time being 53-63 min. The robustness of HnB was demonstrated on six organ types, a cell line, and a cancer biopsy. Its suitability for high-throughput applications was demonstrated on a set of 1171 FF-OCT human cancer biopsies, which were processed for end-to-end completion in 92 h, producing highly consistent peptide yield and quality for over 3513 MS runs.

Molecular driving forces for water adsorption in MOF-808: A comparative analysis with UiO-66.

Metal-organic frameworks (MOFs), with their unique porous structures and versatile functionality, have emerged as promising materials for the adsorption, separation, and storage of diverse molecular species. In this study, we investigate water adsorption in MOF-808, a prototypical MOF that shares the same secondary building unit (SBU) as UiO-66, and elucidate how differences in topology and connectivity between the two MOFs influence the adsorption mechanism. To this end, molecular dynamics simulations were performed to calculate several thermodynamic and dynamical properties of water in MOF-808 as a function of relative humidity (RH), from the initial adsorption step to full pore filling. At low RH, the μ3-OH groups of the SBUs form hydrogen bonds with the initial water molecules entering the pores, which triggers the filling of these pores before the μ3-OH groups in other pores become engaged in hydrogen bonding with water molecules. Our analyses indicate that the pores of MOF-808 become filled by water sequentially as the RH increases. A similar mechanism has been reported for water adsorption in UiO-66. Despite this similarity, our study highlights distinct thermodynamic properties and framework characteristics that influence the adsorption process differently in MOF-808 and UiO-66.

Symptom Management in Children Who Are Neurologically Impaired for the Primary Care Medical Home.

Children with neurologic impairment are a growing population of pediatric patients who require care from a large team of physicians to maintain their health. These children often have similar clinical patterns and symptoms that occur because of their neurologic impairment. Families often seek care first from their primary care home to identify and guide initial steps in management. Identifying the symptoms outlined in the 4 cases in this article will help alleviate consequences of delayed care for these patients and provide opportunities for shared decision-making with the family's goals of care for their child. [Pediatr Ann. 2024;53(3):e82-e87.].

Multivariate analysis and GIS approaches for modeling and mapping soil quality and land suitability in arid zones

Assessing soil quality marks the initial step in precision farming and agricultural management. Developing countries like Egypt face numerous hurdles in ensuring food security due to increasing populations and limited agricultural resources. A geographic information system (GIS) and multivariate analysis were utilized in the current work to evaluate and map a soil quality index (SQI). Moreover, the land suitability of the land for two plantations of the tree's oak (Quercus robur), and pine (Pinus silvestris), respectively was assessed using a parametric approach. A total of 82 soil profiles were selected to fulfill the objectives of the study. Based on the samples' PC scores, and agglomerative hierarchical clustering (AHC, the data was divided into two clusters: Cluster I and Cluster II, which collectively account for approximately 57% and 43% of the total data, respectively.. . The findings indicated that land suitability for planting Q. robur planted identified 2.14% of the research area as highly suitable (S1), 37.98% as moderately suitable (S2), and 59.89% as not suitable (N). Furthermore, the assessment of suitability for P. silvestris indicated that 50.88% of the investigated area was classified into: S1, 48.73% as S2, and 0.39% as N, which means it is not suitable for conservation activities. The research identified that soil depth beside excessive salinity and calcium carbonate as the primary soil constraints in the area in both clusters. The average soil depth, ECd and CaCO3 were 113.62 ± 12.41, 17.27 ± 10.23, 16.83 ± 6.57 in Cluster 1 and 45.43 ± 15.21, 22.42 ± 12.43, 21.55 ± 5.63 in Cluster II. The study demonstrates that integrating multivariate analysis with GIS enables a precise and streamlined assessment of the Soil Quality Index (SQI). Soil suitability modelling underscores the importance of implementing efficient management practices to attain agricultural sustainability in arid regions, particularly amidst intensive land utilization pressures

A materials informatics framework based on reduced‐order models for extracting structure–property linkages of additively manufactured continuous fiber‐reinforced polymer composites

AbstractThe innovative combination of additive manufacturing (AM) and continuous fiber‐reinforced polymer composites (CFRPCs) confers products with the dual advantages of integrated manufacturing and designability of properties, but lack an efficient and reliable method for property prediction. This study presents a materials informatics framework using reduced‐order models and machine learning (ML) to extract the structure–property (SP) linkages between microstructures and macroscopic elastic properties of AM‐CFRPCs. The initial step involves generating microstructural 2D cross‐sections and representative volume elements (RVEs) with random fiber and pore distributions based on the minimum potential method. Then, finite element (FE) calculations are performed on RVEs to obtain nine macroscopic elastic properties. Following that, the quantification and dimensionality reduction of the 2D cross‐sectional dataset are conducted separately using two‐point spatial correlations and principal component analysis (PCA). Finally, a Bayesian optimized composite kernel support vector regression (CK‐SVR) algorithm is used to effectively establish complex mapping relationships between the reduced‐order representations of the microstructures and the mechanical properties. Despite the reduced‐order dataset containing only 3–6 variables, the framework generates an interpretable model exhibiting excellent accuracy with all predicted R2 values surpassing 0.91. Therefore, this framework presents a prospective solution for expediting the design and optimization of AM‐CFRPCs.Highlights A materials informatics scheme is proposed to predict the 9 elastic properties of AM‐CFRPCs. Microstructures are quantified and dimensionally reduced by two‐point statistics and PCA. SP linkages are established between 2D cross‐sections and 3D macromechanical properties. Modified CK‐SVR exhibits higher prediction accuracy compared to conventional models.

How has the COVID-19 pandemic affected the delivery of preventive healthcare? An interrupted time series analysis of adults in English primary care from 2018 to 2022

ObjectiveOffering advice and support for smoking, obesity, excess alcohol, and physical inactivity is an evidence-based component of primary care. The objective was to quantify the impact of the pandemic on the rate of advice or referral for these four risk factors. MethodsA retrospective cohort study using primary care data from 1847 practices in England and 21,191,389 patients contributing to the Oxford Clinical Informatics Digital Hub. An interrupted time series analysis was undertaken with a single change point (March 2020). Monthly trends were modelled from 1st January 2018 – 30th June 2022 using segmented linear regression. ResultsThere was an initial step reduction in advice and referrals for smoking, obesity, excess alcohol, and physical inactivity in March 2020. By June 2022, advice on smoking (slope change −0.02 events per hundred patient years/month (EPH/month); 95% confidence interval (CI) -0.17, 0.21), obesity (0.06 EPH/month; 95% CI 0.01, 0.12), alcohol (0.02 EPH/month; 95% CI -0.01, 0.05) and physical inactivity (0.05 EPH/month; 95% CI 0.01, 0.09) had not returned to pre-pandemic levels. Similarly, smoking cessation referral remained lower (0.01 EPH/month; 95% CI -0.01, 0.09), excess alcohol referral returned to similar levels (0.0005 EPH/month; 95% CI 0.0002, 0.0008), while referral for obesity (0.14 EPH/month; 95% CI 0.10, 0.19) and physical inactivity (0.01 EPH/month; 95% CI 0.01, 0.02) increased relative to pre-pandemic rates. ConclusionAdvice and support for smoking, and advice for weight, excess alcohol and physical inactivity have not returned to pre-pandemic levels. Clinicians and policy makers should prioritise preventive care in COVID-19 recovery plans.

Mystic Christianity and Cosmic Integration: On a Pilgrim Trail with John Moriarty

This essay takes initial steps on a journey with an Irish eco-spiritual philosopher, the late John Moriarty. As a gateway into his broader oeuvre and way of thinking, we explore Moriarty’s image of the Christian mystical Easter journey—the Triduum Sacrum—as a vision for humanity and the planet. After briefly reviewing his spiritual biography, we consider Moriarty’s re-framing of the story as a journey to the bottom of a symbolic Grand Canyon, a mystical trail beyond historical time to a primordial unity before the evolution of the species. There, the total integration of the natural ecumene is experienced. For Moriarty, this journey leads not only into the past, but prefigures a pilgrimage that everyone can—and should—take. Analyzing primarily his own writing, we highlight the intercultural roots and ecumenical connections of Moriarty’s work, which draws extensively on spiritual traditions and contemporary debates from across the world. On that basis, we sign-post directions for further research into a potential post-Christian ecology as a new way of thinking about the earth and our role on it, based on an attitude of Gelassenheit.

The Effect of Nanoparticles on Drug Distribution in The Mathematical Model of Blood Flow

This research examines the influence of nanoparticles in the distribution of drugs in healthy blood flow on linear, angular velocity and blood temperature. Construction and simplification of a blood flow model based on boundary layer equations, dimensionless variables, stream functions, and similarity variables. The initial step is to establish a dimensional blood flow model. Using dimensionless variables, the equation is simplified into a dimensionless equation. A similarity equation is generated by converting the non-dimensional equation. The nanoparticles used are . At the linear velocity and temperature of blood flow is in the highest position. At the angular velocity of blood flow, the position of blood flow with nanoparticles is in the uppermost position. This research is used to estimate the velocity and temperature of blood flow with the influence of nanoparticles as drug distribution.

Transcriptional analysis of oxidative and nitrosative stress on oral opportunistic Candida albicans.

The yeast Candida albicans is one of the most aggressive opportunistic pathogens in immunocompromised patients. The ability of the yeast to withstand stresses and radicals is of great concern. In the present study, four isolates of C. albicans were taken from patients with oral candidiasis and grown on RPMI for 24 hours at 37°C. Then, they were exposed to various concentrations of oxidative (H2O2) and nitrosative (HNO3) stress for two hours, and gene expression rates were measured through RT-PCR. After initial biofilm formation steps and growth validation, RNA extracted from the yeast and gene expression status were evaluated. Upon treatment with H2O2, the gene expression profile for ALS1, MLH1, and EXO1 showed approximately a fold increase in expression. While within HNO3 the yeast gene expression exhibited a dramatic increase in ALS1 up to 217 folds, while others such as MLH1, HWP1, and ERG11 showed a one-fold increase in the expression rate. The findings of this research indicate a considerable expression activity within the biofilm of Candida albicans, increased rate of DNA mismatch repair and break fixation may indicate the ability of the yeast to tolerate high concentrations of free radicals. It paves the way toward understanding the pathogenicity of the yeast and its survival capability inside macrophages. The study also revealed that the biofilm strategy of the yeast is more active within these stresses.

Quantifying the Effects of Known Unknowns on Inferred High-redshift Galaxy Properties: Burstiness, IMF, and Nebular Physics

The era of the James Webb Space Telescope ushers stellar population models into uncharted territories, particularly at the high-redshift frontier. In a companion paper, we apply the Prospector Bayesian framework to jointly infer galaxy redshifts and stellar population properties from broadband photometry as part of the UNCOVER survey. Here we present a comprehensive error budget in spectral energy distribution (SED) modeling. Using a sample selected to have photometric redshifts higher than 9, we quantify the systematic shifts stemming from various model choices in inferred stellar mass, star formation rate (SFR), and age. These choices encompass different timescales for changes in the star formation history (SFH), nonuniversal stellar initial mass functions (IMF), and the inclusion of variable nebular abundances, gas density, and ionizing photon budget. We find that the IMF exerts the strongest influence on the inferred properties: the systematic uncertainties can be as much as 1 dex, 2–5 times larger than the formal reported uncertainties in mass and SFR, and importantly, exceed the scatter seen when using different SED fitting codes. Although the assumptions on the lower end of the IMF induce degeneracy, our findings suggest that a common practice in the literature of assessing uncertainties in SED-fitting processes by comparing multiple codes is substantively underestimating the true systematic uncertainty. Highly stochastic SFHs change the inferred SFH by much larger than the formal uncertainties, and introduce ∼0.8 dex systematics in SFR averaged over a short timescale and ∼0.3 dex systematics in average age. Finally, employing a flexible nebular emission model causes ∼0.2 dex systematic increase in mass and SFR, comparable to the formal uncertainty. This paper constitutes an initial step toward a complete uncertainty estimate in SED modeling.

Daily natural gas load prediction method based on APSO optimization and Attention-BiLSTM.

As the economy continues to develop and technology advances, there is an increasing societal need for an environmentally friendly ecosystem. Consequently, natural gas, known for its minimal greenhouse gas emissions, has been widely adopted as a clean energy alternative. The accurate prediction of short-term natural gas demand poses a significant challenge within this context, as precise forecasts have important implications for gas dispatch and pipeline safety. The incorporation of intelligent algorithms into prediction methodologies has resulted in notable progress in recent times. Nevertheless, certain limitations persist. However, there exist certain limitations, including the tendency to easily fall into local optimization and inadequate search capability. To address the challenge of accurately predicting daily natural gas loads, we propose a novel methodology that integrates the adaptive particle swarm optimization algorithm, attention mechanism, and bidirectional long short-term memory (BiLSTM) neural networks. The initial step involves utilizing the BiLSTM network to conduct bidirectional data learning. Following this, the attention mechanism is employed to calculate the weights of the hidden layer in the BiLSTM, with a specific focus on weight distribution. Lastly, the adaptive particle swarm optimization algorithm is utilized to comprehensively optimize and design the network structure, initial learning rate, and learning rounds of the BiLSTM network model, thereby enhancing the accuracy of the model. The findings revealed that the combined model achieved a mean absolute percentage error (MAPE) of 0.90% and a coefficient of determination (R2) of 0.99. These results surpassed those of the other comparative models, demonstrating superior prediction accuracy, as well as exhibiting favorable generalization and prediction stability.