Specific Model Research Articles

Using a simple radial basis function neural network to predict the glass transition temperature of alkali borate glasses

Artificial Neural Networks (ANN) are powerful machine learning algorithms. In the context of glass science, ANNs have been successfully used to induce composition-property regression models. These models are already influencing the way that we design new glasses, moving away from the traditional “cook and look” approach to computer-aided design. In this work, we induced an ANN model for the glass transition temperature Tg for glasses of the family xM2O⋅(100−x)B2O3, where M = Li, Na, K, Rb, and Cs, using simple radial basis function neural networks (RBF) with only two neurons. Statistically, the results of our methodologies are comparable to other complex models (empirical or physical) available in the literature with more neurons, such as the recent GlassNet and the topological model proposed by Mauro and co-authors (2009). This result supports the idea that a simpler specialized model can perform better than a complex general model for a restricted training domain.

Delineation of Potential Groundwater Zones and Assessment of Their Vulnerability to Pollution from Cemeteries Using GIS and AHP Approaches Based on the DRASTIC Index and Specific DRASTIC

The risk of aquifer contamination is determined by the interaction between the pollutant load and the vulnerability of an aquifer. Owing to the decomposition of bodies and degradation of artefacts, cemeteries may have a negative impact on groundwater quality and suitability for use due to the leaching of organic compounds (e.g., biodegradable organics, pharmaceuticals, and formaldehyde), inorganic compounds (e.g., nitrate and heavy metals), pathogenic bacteria, and viruses. Factors such as burial and soil type, rainfall amount, and groundwater depth may increase aquifer vulnerability to pollutants generated in cemeteries. The potential for groundwater contamination was investigated in two cemeteries of the Soure region in Portugal (Samuel–UC9 and Vinha da Rainha–UC10), using the classic DRASTIC model, followed by some adjustments, depending on the particularities of the locations, resulting in a Final Classification considered as Specific DRASTIC. By combining Remote Sensing (RS), Geographic Information System (GIS), and Analytical Hierarchy Process (AHP), groundwater potential zones (GWPZs) were identified, and aquifer vulnerability was assessed, which included the elaboration of thematic maps using GIS operation tools. The maps allowed for the identification of areas with different susceptibilities to contamination: from “Low” to “Very high” for the DRASTIC index and from “Very Low” to “Very high” for the Specific DRASTIC index. Although the difference between the UC9 and UC10 cemeteries is negligible, UC10 is more vulnerable because of its proximity to the community and critically important mineral water resources (such as Bicanho Medical Spa). The Specific model seems better-suited for describing vulnerability to cemeteries. Although there is limited groundwater quality data for the area, the development of vulnerability maps can identify areas that can be sensitive spots for groundwater contamination and establish procedures for pollution prevention.

ARID1A loss is associated with increased NRF2 signaling in human head and neck squamous cell carcinomas.

Prior to the next generation sequencing and characterization of the tumor genome landscape, mutations in the SWI/SNF chromatin remodeling complex and the KEAP1-NRF2 signaling pathway were underappreciated. While these two classes of mutations appeared to independently contribute to tumor development, recent reports have demonstrated a mechanistic link between these two regulatory mechanisms in specific cancer types and cell models. In this work, we expand upon these data by exploring the relationship between mutations in BAF and PBAF subunits of the SWI/SNF complex and activation of NRF2 signal transduction across many cancer types. ARID1A/B mutations were strongly associated with NRF2 transcriptional activity in head and neck squamous carcinomas (HNSC). Many additional tumor types showed significant association between NRF2 signaling and mutation of specific components of the SWI/SNF complex. Different effects of BAF and PBAF mutations on the polarity of NRF2 signaling were observed. Overall, our results support a context-dependent functional link between SWI/SNF and NRF2 mutations across human cancers and implicate ARID1A inactivation in HPV-negative HNSC in promoting tumor progression and survival through activation of the KEAP1-NRF2 signaling pathway. The tumor-specific effects of these mutations open a new area of study for how mutations in the KEAP1-NRF2 pathway and the SWI/SNF complex contribute to cancer.

Optimization of gluten-free cookie cookies, chocolate flavor, made with different flours using experimental mixture planning

The use of different food matrices can influence the physical properties of the products. In this scenario, the objective was to develop gluten-free cookies, chocolate flavor, made with cocoa bran (FC), rice flour (FA) and corn starch (AM), using the simplex centroid design. Nine tests and seven formulations were developed, using simplex centroid type mixture planning, namely: F1 - 100% FC; F2 – 100% FA; F3 – 100% AM; F4 – 50% HR and 50% FA; F5 – 50% FA and 50% AM; F6 – 50% FC and 50% AM and F7 – 33.33% of each flour; the latter being carried out in triplicate. To characterize the cookies, the average values of mass (g), diameter (mm), thickness before and after baking (mm) and expansion factor (mm) were determined. Cookies made with cocoa bran showed less mass loss and an increase in diameter after baking, in addition to an expansion factor consistent with products rich in fiber. The quadratic predictive model demonstrated good adjustment and predictive capacity on the diameter values after baking (R2- 98.34%), while the special cubic model explained the effect of mixing on the thickness of the cookies before baking (R2- 99.42%), thickness after baking (R2- 98.36%) and expansion factor (R2- 99.20%). This study showed that the results demonstrated here indicate that the preparation of gluten-free cookies, using cocoa bran, presents itself as a viable alternative to the food industry.

Determination of optimal component positioning in THA using 3D preoperative planning.

Advancements in three-dimensional (3D) preoperative planning tools can offer surgeons and design engineers detailed feedback and additional opportunities for clinical advancements. The objective of this study is to use a 3D total hip arthroplasty preoperative planning tool to compare femoral component alignment for three different stem systems. The planning tool in this study used morphology data of femoral bones gathered from a CT database, seven from postoperative patients and 63 from statistical shape models (SSMs), to suggest specific implant sizes and optimal placements in 3D to match each specific bone model. Retrospective validations of predicted stem size and femoral version were first performed by comparing planner-chosen and surgeon-implanted stem sizes and version angles for the seven postoperative patients. Next, the alignment of three different stem systems was evaluated using bone models generated from SSMs, with a quantitative focus on component head positioning. In the validation study, the planner accurately selected stem size and orientation compared to the surgeon for all assessed subjects. In the stem evaluation, the three stem systems yielded different accuracies in component placement, with the newest stem system demonstrating the closest restoration of anatomical head center location. It is evident that new stem designs may have the potential for increased accuracy over their predecessors, demonstrating that new stem designs can offer improved intraoperative and postoperative alignment potential. The 3D preoperative planning tools can provide novel and reliable data to both surgeons and design engineers, which can ultimately improve clinical outcomes and future implant designs.

Effect of the roughness on the photoinduced growth of crystalline tellurium on MoTe2 matrix

In this work, a series of 5 nm thick films of molybdenum oxide (MoO3) were synthesized on SiO2/Si substrates by magnetron sputtering of molybdenum in low vacuum conditions. We used closed-space sublimation (CSS) to tellurize the Mo oxide and to produce pure-phase 1 T′-MoTe2 films by controlling the temperature inside the CSS reactor. Surface analyses showed that tellurium spreads over the surface of the as-grown films, forming localized crystalline precipitates whose distribution depends on the tellularization time. From Raman spectroscopy investigations, we observed that the exposure of these films to visible light leads to a growth of a highly-ordered trigonal phase of tellurium. The intensity evolution of the vibrational modes related to the crystalline phase of tellurium allows us to perform a quantitative analysis of the crystallization. Our results indicate that this process is consistent with the Kolmogorov-Johnson-Mehl-Avrami (KJMA) theory for the overall crystallization process. By the characteristic growth exponent, we introduce a more specific model which takes into account the diffusion and the dissociation of tellurium chains, which is used to probe how the effective growth rate is affected by the roughness of the samples.

Patterns of parental involvement in schools of religious communities. A systematic review

ABSTRACT Research on family involvement has revealed its positive impact on children’s academic and nonacademic achievement over the past two decades. However, little is known about parental involvement in religious schools. During our review, we examined studies focusing on parental involvement with special attention to religious schools. 22 papers met the research criteria out of 123 abstracts screened from 85 databases. Management and decision-making participation in religious schools seem to be less important than in Epstein’s model. Religious schools have developed a special PI model where parents accept decisions based on staff competence, and teachers work to build a parent community and earn the trust of parents by being accountable for the children’s diverse development. Some studies point to inadequate implementation of the ideal model and are critical. The novelty of the analysis is that our analysis was open to schools of all religions. A limitation of the review is that we cannot be sure that all relevant studies were included in the examined databases. As such, further research is needed to better understand this phenomenon.

Intervention Works Conducted to Ensure the Stability of a Slope: A Sustainability Study

Challenges related to sustainability arise in all areas of human activity, but with a significant impact on the environment considering that the construction industry is held accountable for nearly one-third of the world’s final energy consumption. The aim of this paper is to assess through the use of the Bob–Dencsak specific model a sustainable slope design taking into account environmental, economic, and safety variables. Thus, analysis was performed on four intervention works, two versions of reinforced concrete retaining walls and two versions of reinforced soil with a biaxial geogrid, which ensure the stability of a slope that serves as a base for an access road to an ecological landfill located in Alba County, Romania. The study’s analysis points out that reinforced soil retaining walls are far more sustainable, providing the best sustainability indices, which is also supported by the impact of geogrids compared to reinforced concrete, thus resulting in the finding that reinforced concrete is less sustainable, achieving increases of up to 23% for embodied energy and 66% of CO2 emissions in the atmosphere. Finally, the paper provides recommendations for future research on the sustainability assessment of slopes, with the intention of reducing environmental damage, while keeping costs to a minimum.

Fast estimation of generalized linear latent variable models for performance and process data with ordinal, continuous, and count observed variables.

Different data types often occur in psychological and educational measurement such as computer-based assessments that record performance and process data (e.g., response times and the number of actions). Modelling such data requires specific models for each data type and accommodating complex dependencies between multiple variables. Generalized linear latent variable models are suitable for modelling mixed data simultaneously, but estimation can be computationally demanding. A fast solution is to use Laplace approximations, but existing implementations of joint modelling of mixed data types are limited to ordinal and continuous data. To address this limitation, we derive an efficient estimation method that uses first- or second-order Laplace approximations to simultaneously model ordinal data, continuous data, and count data. We illustrate the approach with an example and conduct simulations to evaluate the performance of the method in terms of estimation efficiency, convergence, and parameter recovery. The results suggest that the second-order Laplace approximation achieves a higher convergence rate and produces accurate yet fast parameter estimates compared to the first-order Laplace approximation, while the time cost increases with higher model complexity. Additionally, models that consider the dependence of variables from the same stimulus fit the empirical data substantially better than models that disregarded the dependence.

Viable and Stable Compact Stellar Structures in f(Q,T)$f(\mathcal {Q},\mathcal {T})$ Theory

AbstractThe main objective of this paper is to investigate the impact of gravity on the geometry of anisotropic compact stellar objects, where is non‐metricity and is the trace of the energy‐momentum tensor. In this perspective, the physically viable non‐singular solutions to examine the configuration of static spherically symmetric structures is used. A specific model of this theory to examine various physical quantities in the interior of the proposed compact stars (CSs) is considered. These quantities include fluid parameters, anisotropy, energy constraints, equation of state parameters, mass, compactness, and redshift. The Tolman–Oppenheimer–Volkoff equation is used to examine the equilibrium state of stellar models, while the stability of the proposed CSs is investigated through sound speed and adiabatic index methods. It is found that the proposed CSs are viable and stable in the context of this theory.

The geometry of inflationary observables: Lifts, flows, equivalence classes

The Eisenhart lift allows formulating the dynamics of a scalar field in a potential as pure geodesic motion in a curved field-space manifold involving an additional fictitious vector field. Making use of the formalism in the context of inflation, we show that the main inflationary observables can be expressed in terms of the geometrical properties of a two-dimensional uplifted field-space manifold spanned by the time derivatives of the scalar and the temporal component of the vector. This allows to abstract from specific potentials and models and describe inflation solely in terms of the flow of geometric quantities. Our findings are illustrated through several inflationary examples previously considered in the literature.

Proposal of a Customer Satisfaction Index Model (Part of the Antecedents) Adopted to the Algerian Context

Measuring customer satisfaction is an efficient method for evaluating organisational performance of firms, especially in today’s competitive environment. In the Algerian context, where the industry is developing rapidly and the customer satisfaction and its antecedents have an importance for the most of the Algerian companies, it becomes crucial to explore the factors influencing customer satisfaction in the industrial sector. Therefore, the main objective of this study is to develop a specific model for a customer satisfaction index in the Algerian industrial environment. The inexistence of this type of models in the Algerian context has provided difficulties to companies in detecting what affects the satisfaction of their customers; this research can help them to find out what are the factors that impact on the customer satisfaction and improve it. To achieve this, we have examined previous studies conducted in other countries to identify the antecedents of customer satisfaction. The research methodology employed in this study follows a quantitative approach. We conducted a questionnaire survey among a sample of 203 customers of the BCR brand, which specialises in faucet production. The questionnaire was designed to measure various factors, including corporate image, customer expectations, perceived quality, perceived value, price, and handling of complaints. The collected data was analysed using SPSS software, and the model will be constructed using Smart-PLS software. The findings of the study confirmed that three variables significantly impact customer satisfaction in the studied context: perceived value, price, and perceived quality, specifically focusing on the sustainability dimension. Based on these results, we have developed a customer satisfaction index model, specifically focusing on the antecedents’ part, which incorporates these three variables.

Signature of Scramblon Effective Field Theory in Random Spin Models.

Information scrambling refers to the propagation of information throughout a quantum system. Its study not only contributes to our understanding of thermalization but also has wide implications in quantum information and black hole physics. Recent studies suggest that information scrambling in large-N systems with all-to-all interactions is mediated by collective modes called scramblons. However, a criterion for the validity of scramblon theory in a specific model is still missing. In this work, we address this issue by investigating the signature of the scramblon effective theory in random spin models with all-to-all interactions. We demonstrate that, in scenarios where the scramblon description holds, the late-time operator size distribution can be predicted from its early-time value, requiring no free parameters. As an illustration, we examine whether Brownian circuits exhibit a scramblon description and obtain a positive confirmation both analytically and numerically. Our findings provide a concrete experimental framework for unraveling the scramblon field theory in random spin models using quantum simulators.

Introducing HaNTr – Halloysite Nanotubes Targeting System for The Selective Delivery of Antibiotics

AbstractAntibiotics have been established to induce indiscriminate detrimental effects on the gut commensal bacteria which are vital for human health. This study unprecedently reports the mitigation of this challenge through the targeted delivery of antibiotics to a specific intestinal model pathogen using naturally occurring nanoclay. The designed Halloysite nanotubes targeting (HaNTr) system employs intrinsically mesoporous clay particles, functionalized with antibodies against Escherichia coli (E. coli). Loaded with the antibiotic ciprofloxacin (CIP), the HaNTr particles demonstrate enhanced selectivity of their payload in a human microbiome ex vivo system, preserving the composition of non‐target populations. Furthermore, the HaNTr system exhibits up to a 10‐fold increase in selectivity against E. coli, compared to neat CIP, in a heterogenous culture. This enhanced selectivity is attributed to the sustained and localized release of CIP from the HaNTr particles (≈0.8 ng CIP min−1 mg−1), following their specific binding to target bacteria, as quantitatively measured by high‐throughput imaging flow cytometry. Importantly, HaNTr particles are also shown to be biocompatible with Caco‐2 cells, mimicking the intestinal epithelium. This work highlights the prominent capability of the HaNTr system in alleviating antibiotic‐associated dysbiosis by the targeted delivery of antimicrobials to potentially any microorganism against which the immobilized capture probe can be customized.

Impact of Job Satisfaction, Transformational Leadership, and Work-Life Balance on Employee Performance: A Study in Technology Companies in Indonesia


 This research explores the dynamic interplay of job satisfaction, transformational leadership, work-life balance, and their collective impact on employee performance within the burgeoning technology sector of Indonesia. The study employs a quantitative research design, utilizing Partial Least Squares Structural Equation Modeling (PLS SEM) to analyze data gathered through a questionnaire survey of employees in various technology companies. The findings reveal a robust and positive association between job satisfaction and employee performance, emphasizing the need for organizations to prioritize initiatives that cultivate positive work environments. While the study did not establish a direct link between transformational leadership and employee performance within the specific model, it underscores the nuanced dynamics of leadership impact. Additionally, the research highlights the highly significant and positive relationship between work-life balance and employee performance, emphasizing the crucial role of promoting a healthy balance between work and personal life in enhancing overall employee effectiveness.

Depletion of SMN protein in mesenchymal progenitors impairs the development of bone and neuromuscular junction in spinal muscular atrophy.

Spinal muscular atrophy (SMA) is a neuromuscular disorder characterized by the deficiency of the survival motor neuron (SMN) protein, which leads to motor neuron dysfunction and muscle atrophy. In addition to the requirement for SMN in motor neurons, recent studies suggest that SMN deficiency in peripheral tissues plays a key role in the pathogenesis of SMA. Using limb mesenchymal progenitor cell (MPC)-specific SMN-depleted mouse models, we reveal that SMN reduction in limb MPCs causes defects in the development of bone and neuromuscular junction (NMJ). Specifically, these mice exhibited impaired growth plate homeostasis and reduced insulin-like growth factor (IGF) signaling from chondrocytes, rather than from the liver. Furthermore, the reduction of SMN in fibro-adipogenic progenitors (FAPs) resulted in abnormal NMJ maturation, altered release of neurotransmitters, and NMJ morphological defects. Transplantation of healthy FAPs rescued the morphological deterioration. Our findings highlight the significance of mesenchymal SMN in neuromusculoskeletal pathogenesis of SMA and provide insights into potential therapeutic strategies targeting mesenchymal cells for the treatment of SMA.

Depletion of SMN protein in mesenchymal progenitors impairs the development of bone and neuromuscular junction in spinal muscular atrophy

Spinal muscular atrophy (SMA) is a neuromuscular disorder characterized by the deficiency of the survival motor neuron (SMN) protein, which leads to motor neuron dysfunction and muscle atrophy. In addition to the requirement for SMN in motor neurons, recent studies suggest that SMN deficiency in peripheral tissues plays a key role in the pathogenesis of SMA. Using limb mesenchymal progenitor cell (MPC)-specific SMN-depleted mouse models, we reveal that SMN reduction in limb MPCs causes defects in the development of bone and neuromuscular junction (NMJ). Specifically, these mice exhibited impaired growth plate homeostasis and reduced insulin-like growth factor (IGF) signaling from chondrocytes, rather than from the liver. Furthermore, the reduction of SMN in fibro-adipogenic progenitors (FAPs) resulted in abnormal NMJ maturation, altered release of neurotransmitters, and NMJ morphological defects. Transplantation of healthy FAPs rescued the morphological deterioration. Our findings highlight the significance of mesenchymal SMN in neuromusculoskeletal pathogenesis of SMA and provide insights into potential therapeutic strategies targeting mesenchymal cells for the treatment of SMA.

Comparative analysis of tight-binding models for transition metal dichalcogenides

We provide a comprehensive analysis of the prominent tight-binding (TB) models for transition metal dichalcogenides (TMDs) available in the literature. We inspect the construction of these TB models, discuss their parameterization used and conduct a thorough comparison of their effectiveness in capturing important electronic properties. Based on these insights, we propose a novel TB model for TMDs designed for enhanced computational efficiency. Utilizing MoS_2MoS2 as a representative case, we explain why specific models offer a more accurate description. Our primary aim is to assist researchers in choosing the most appropriate TB model for their calculations on TMDs.

Cluster Size Intelligence Prediction System for Young Women’s Clothing Using 3D Body Scan Data

This study adopts a data-driven methodology to address the challenge of garment fitting for individuals with diverse body shapes. Focusing on young Chinese women aged 18–25 from Central China, we utilized the German VITUS SMART LC3 3D body scanning technology to measure 62 body parts pertinent to fashion design on a sample of 220 individuals. We then employed a hybrid approach, integrating the circumference difference classification method with the characteristic value classification method, and applied the K-means clustering algorithm to categorize these individuals into four distinct body shape groups based on cluster center analysis. Building upon these findings, we formulated specific linear regression models for key body parts associated with each body shape category. This led to the development of an intelligent software capable of automatically calculating the dimensions of 28 body parts and accurately determining the body shape type for young Central Chinese women. Our research underscores the significant role of intelligent predictive systems in the realm of fashion design, particularly within a data-driven framework. The system we have developed offers precise body measurements and classification outcomes, empowering businesses to create garments that more accurately conform to the wearer’s body, thus enhancing both the fit and aesthetic value of the clothing.

ChatGPT - A new milestone in the field of education

With the rapid development of artificial intelligence, natural language processing models like ChatGPT hold creative potential for applications in education. This study focuses on ChatGPT and explores its applications in the field of education. This research employs literature review and case analysis methods to systematically investigate the applications of ChatGPT in instructional assistance, personalized education, and writing support. By analyzing three cases involving ChatGPT's professional competence in medical exams, the construction of personalized learning systems, and writing support, the paper emphasizes ChatGPT's advantages in education, future development directions, as well as ethical and privacy concerns. The study finds that ChatGPT possesses strong semantic understanding and analytical capabilities. Within the context of general applicability, it can provide accurate answers to questions posed by users with certain specialized backgrounds. The paper not only addresses ChatGPT's role as a student's auxiliary tool but also explores collaborative models between ChatGPT and teachers. However, it's important to note that ChatGPT currently has limitations in terms of limited knowledge and inadequate emotional understanding. Additionally, in terms of ethics and privacy, considerations regarding information protection and moral aspects within the educational application of ChatGPT are crucial. In the future, it's worth further exploring specialized models for ChatGPT's tasks in both teacher and student roles, along with enhancing ChatGPT's emotional support capabilities.