Factorial Model Research Articles

Microencapsulation and controlled release of Bacillus clausii through a novel non-digestible carbohydrate formulation as revolutionizing probiotic delivery

Probiotics have gained significant attention in recent years due to the growing awareness of physical health and well-being. However, maintaining high concentrations of probiotics throughout the product's shelf life and during the gastrointestinal tract is crucial for ensuring their health-promoting effects. After determining an optimal formulation through a fractional factorial model, this study optimizes probiotic Bacillus Clausii delivery through spray-drying microencapsulation using a novel maltodextrin-alginate-inulin (MDX-ALG-IN) formulation (optimized ratio: 7:2:1). Notably, this formulation exclusively comprises non-digestible carbohydrates, marking a novel approach in probiotic encapsulation. Achieving a high Product Yield (51.06 %) and Encapsulation Efficiency (80.53 %), the study employed SEM for morphological analysis, revealing an irregular form and extensive surface in dentations characteristic of maltodextrin involvement. With a low moisture content of 3.02 % (±0.23 %) and 90.52 % solubility, the powder displayed exceptional properties. Probiotic viability remained robust, surviving up to 60 % even after 180 days at 4 °C, 25 °C, and 37 °C. Thermal characterization unveiled microcapsule resilience, exhibiting a glass transition temperature (Tg) at 138.61 °C and a melting point of 177.28 °C. The study systematically addresses crucial aspects of microencapsulation, including formulation optimization, morphological characteristics, and powder properties. Notably, the MDX-ALG-IN microcapsules demonstrated stability in simulated gastrointestinal conditions, indicating potential application for supplements and complex food matrices. In summary, this research contributes to microencapsulation understanding, emphasizing the MDX-ALG-IN formulation's efficacy in preserving probiotic viability across production stages and simulated digestive processes.

The framing of information nudge and students effort: A behavioral economics approach

One of the important fields for achieving the goals of the higher education system is to use new methods to influence students' motivation. Therefore, the main goal of this study is to investigate the impact of the framing of information nudge on students' efforts using the behavioral economics approach, taking into account the theories of social psychology. To investigate this goal, a 2*2*2 factorial model including; two levels of framing effect (positive or negative), two levels of regulatory focus (promotion or prevention) and two levels of test difficulty (difficult or easy) were considered. The independent variables of this study are the negative frame, the promotion focus and the difficulty of the test and the dependent variable is the anticipated effort in a hypothetical exam. The random sampling of this research included 400 students of Ilam and Allameh Tabatabai University. The results of the estimation of the ordinary least squares model showed that the promotion focus frame increases students' effort compared to the prevention focus frame. The negative frame increases student effort relative to the positive frame, but without controlling for regulatory focus, it has no independent effect on student effort. The results of the estimation of interaction variables also showed that the variable of negative frame * promotion focus has a negative and significant coefficient. This result shows that the joint condition of negative frame and promotion focus increases students' effort less than negative frame and prevention focus. In other words, the negative frame increases students' effort both in the promotion focus and in the prevention focus, but the effect of the negative frame is greater in the prevention focus. It also confirms that students experience regulatory fit under the common conditions of prevention focus and negative frame, but regulatory fit is not confirmed under the common conditions of promotion focus and positive frame. In order to strengthen students' motivation and performance, policy makers and planners in higher education can use behavioral economics tools such as framing of information nudge appropriate to subjects such as exam preparation, education plan, choosing a field at higher levels of education, etc.

A new factorial sensitivity model for analyzing the impacts of climatic factors on crop water footprint

The impact of climate change on the crop water footprint (WF) is highly uncertain, hindering effective agricultural water use in response to climate change. This study proposes a factor sensitivity analysis method, which can screen out statistically significant climatic factors and interactions on crop WF under various uncertainties and determine water resource management measures to mitigate drought for different crops. A special case study was conducted in Heilongjiang Province, China. The results showed that (1) the annual change in crop WF showed a downward trend from 1988 to 2018. The crop WF was dominated by green water footprint (WFgreen), and the average occupancy rate of WFgreen in crops was 58.7%–74.1 %; the spatial distribution of WF has latitude zonality. (2) Drought has different effects on WF of different crops, and WF of soybean is susceptible to drought. Wind speed, sunshine hours, and humidity have a greater impact on crop WF in most growth stages. (3) The effect of climatic factors on crop WF varies in different months. The rice WF is mainly affected by the climate in May, and there is an interaction between May humidity and May rain. The WF of maize and soybeans are affected primarily by the climate in July, especially sunshine hours. The proposed approach attempts to analyze that crop WF is affected by not only an individual climatic factor but also their interactions. Crop water management practices should be adjusted based on the results to mitigate the adverse impact of climatic conditions on crop WF during different growing months.

Psychometric properties of the CEMA-A questionnaire: motives for lying.

Previous research on the motives for lying lacks factorial models that allow grouping of motives into specific categories. The objective of this study is to confirm the factorial structure of the questionnaire of motives for lying (CEMA-A). Participants were 1,722 adults residing in the Canary Islands (Spain) who completed the CEMA-A and the Eysenck Personality Questionnaire (EPQ-R). The four-dimensional structure of the questionnaire was confirmed (χ2 = 1460.97, df = 325, p = 0.001; CFI = 0.94; TLI = 0.93; NFI = 0.93; NNFI = 0.93; RMSEA = 0.05, CI = 0.051-0.057; SRMR = 0.04). The four factors of the CEMA-A were Intrapersonal Motivation-Emotionality, Interpersonal Motivation-Sociability, Egoism/Hardness Motivation, and Malicious Motivation, with an internal consistency between 0.79 and 0.91. Invariance analyses confirmed the equivalence of the instrument for men and women. The CEMA-A factors positively correlated with Neuroticism and Psychoticism, and negatively with Dissimulation. Extraversion was not related to any of the factors, and only displayed a low negative correlation with Intrapersonal Motivation-Emotionality. Analysis of variance showed that men scored higher in Egoism/Hardness and Malicious Motivation. The CEMA-A has proven capable of apprehending the motives for lying and has adequate psychometric criteria for use in various populations.

From the factory model to the classroom-workshop: Learning from practice in the fields of health and education

ABSTRACT In this article, we engage in a dialogue about structural competencies based on the proposal of the classroom-workshop, aimed at educational and work environments in the fields of health and education. Our starting point is the notion that practical experiences do not merely represent a collection of specialised technical knowledge, as they are part of the complexity of the social game. Drawing from the theoretical frameworks of Pierce, Gadamer, Freire, and others, the proposal of the classroom-workshop recovers the power of collective praxis. Learning from and with others becomes central in the social game in order to collectively appropriate knowledge by making the autonomy of learning as horizontal as possible. By centering the relational world of learning and working, the classroom-workshop recovers the circular structure of understanding that enables a reflective appropriation of the territory – understood as the social space where life takes place – while de-instrumentalizing the reading of theoretical traditions and resignifiying the practice of writing. It establishes connections, links, and anchors that enable a dialogue between theory and practice, so often dissociated and fragmented.

Development and Validation of Cyberbullying Measurement Instrument: A Mixed Method Approach

This research is motivated by the increasing increase in cyber bullying and the negative impacts it causes. This research aims to develop a cyberbullying measurement instrument using a mixed methods approach involving three stages. Theme exploration stage, namely the qualitative stage with open questionnaire data collection techniques to explore themes surrounding Cyberbullying. At this stage the data analysis technique applied is open coding (65 participants). The testing stage is the content validity stage where the instrument is assessed by experts using the Aiken formula. This stage also includes factor analysis with an exploratory factor analysis (EFA) approach which helps identify the factorial structure underlying the instrument, relationships between items, and grouping items based on dimensions (270 participants). Scale validation stage where at this stage confirmatory factor analysis (CFA) was carried out to test and validate the factorial model identified through EFA (789 Participants). Sampling technique uses convenience sampling technique. The results of the research show that based on the CFA test, the cyber bullying scale showed that all items had loading factor values >0.5 and construct reliability >0.7, as well as AVE results >0.5, so that all the items used were forming factors of the latent variable. The items used are valid and reliable items so they can be relied upon as research measuring tools. Reliable Cyberbullying measurement instruments can more precisely identify cyberbullying behavior and thus can assist in a better understanding of this phenomenon, so that prevention and intervention efforts can be targeted more effectively.

Transformation of mechanisms of sustainable development of food industry enterprises: preconditions for winning the competition by upgrading the business model of the food factory

The specific features of the development of the food industry on the basis of disruptive innovation, where fundamentally new conditions and opportunities are created to enter new segments of the sales market are studied. The innovativeness of the food factory involves creating business models to defeat or even undermine competitors through higher profitability and business security.Emphasis will be paid to the essential elements of the meaningful foundation of the business model, аmong them: a special way of running a business, which includes a scheme of interaction with other market participants; creation of value for the customer, suggesting a set of unique resources, capabilities and processes; offering value, considering the network nature of relationships in the market and the role of the food factory in this network.

The influence of the laser cutting process parameters on the quality of the cut edge

The article describes the influence of the parameters of laser cutting on the cut edge quality of 10 mm S355J2 structural steel. The cutting process was carried out with a fiber laser. The basic evaluation criteria were measurements of perpendicularity tolerance, average profile height of roughness Rz5, visual evaluation, and measurements of hardness HV10. Moreover, the factorial regression models were prepared to investigate the standardized effects of the process parameters on the cutting quality. The variable process parameters were laser beam power, cutting velocity and working gas pressure. For regression modeling, low and high levels of these factors were considered representing the effective range of each parameter. The research presented in this paper proves that there are statistical correlations between the examined factors that have a significant impact on the achieved cutting effect. By the analysis of the process control parameters, it is possible to achieve an optimal level of quality that facilitates further technological operations on a given cutting product.

Improving Problem-Solving and Communication Skills in Automotive Vocational Education through the Development of Teaching Factory Model with Problem-Based Learning (TEFA-PBL) Concept

Improving Problem-Solving and Communication Skills in Automotive Vocational Education through the Development of Teaching Factory Model with Problem-Based Learning (TEFA-PBL) Concept

Layer factor analysis in convolutional neural networks for explainability

Explanatory methods that focus on the analysis of the features encoded by Convolutional Neural Networks (CNNs) are of great interest, since they help to understand the underlying process hidden behind the black-box nature of these models. However, to explain the knowledge gathered in a given layer, they must decide which of the numerous filters to study, further assuming that each of them corresponds to a single feature. This, coupled with the redundancy of information, makes it difficult to ensure that the relevant characteristics are being analyzed. The above represents an important challenge and defines the aim and scope of our proposal. In this paper we present a novel method, named Explainable Layer Factor Analysis for CNNs (ELFA-CNNs), which models and describes with quality convolutional layers relying on factor analysis. Regarding contributions, ELFA obtains the essential underlying features, together with their correlation with the original filters, providing an accurate and well-founded summary. Through the factorial parameters we gain insights about the information learned, the connections between channels, and the redundancy of the layer, among others. To provide visual explanations in a similarly way to other methods, two additional proposals are made: a) Essential Feature Attribution Maps (EFAM) and b) intrinsic features inversion. The results prove the effectiveness of the developed general methods. They are evaluated in different CNNs (VGG-16, ResNet-50, and DeepLabv3+) on generic datasets (CIFAR-10, imagenette, and CamVid). We demonstrate that convolutional layers adequately fit a factorial model thanks to the new metrics presented for factor and fitting residuals (D1, D>, and Res, derive from covariance matrices). Moreover, knowledge about the deep image representations and the learning process is acquired, as well as reliable heat maps highlighting regions where essential features are located. This study effectively provides an explainable approach that can be applied to different CNNs and over different datasets.

Higher global gross primary productivity under future climate with more advanced representations of photosynthesis.

Gross primary productivity (GPP) is the key determinant of land carbon uptake, but its representation in terrestrial biosphere models (TBMs) does not reflect our latest physiological understanding. We implemented three empirically well supported but often omitted mechanisms into the TBM CABLE-POP: photosynthetic temperature acclimation, explicit mesophyll conductance, and photosynthetic optimization through redistribution of leaf nitrogen. We used the RCP8.5 climate scenario to conduct factorial model simulations characterizing the individual and combined effects of the three mechanisms on projections of GPP. Simulated global GPP increased more strongly (up to 20% by 2070-2099) in more comprehensive representations of photosynthesis compared to the model lacking the three mechanisms. The experiments revealed non-additive interactions among the mechanisms as combined effects were stronger than the sum of the individual effects. The modeled responses are explained by changes in the photosynthetic sensitivity to temperature and CO2 caused by the added mechanisms. Our results suggest that current TBMs underestimate GPP responses to future CO2 and climate conditions.

DESIGN OF EXPERIMENTS AND OPTIMIZATION OF AMORPHOUS SOLID DISPERSION OF A BCS CLASS IV ANTI-PLATELET DRUG THROUGH FACTORIAL DESIGN

Objective: The aim of this study was to evaluate and optimize the amorphous solid dispersion of a low-soluble BCS Class IV anti-platelet drug using factorial design in line with the Quality by Design (QbD) principle. Methods: Ticagrelor, a low-soluble anti-platelet agent, was used as the model drug for the current study. A solid dispersion technique was explored to improve the dissolution of ticagrelor. The extent of amorphization of ticagrelor with the solid dispersion approach was evaluated with powder X-Ray diffraction (p-XRD) and differential scanning calorimetry (DSC). The principle of factorial design (FD) was adopted to optimize the formulation of ticagrelor solid dispersion. Design Expert® 13 (Stat-Ease Inc., Minneapolis, MN, USA) was explored for the Design of experiments (DoE) and Statistical evaluation. The experiments were designed with three factors at two levels (a 23-factor design) and two responses. The significance of the model was evaluated by analysis of variance (ANOVA) and fit statistics. Various statistical parameters such as sequential p-values, lack of fit, squared correlation coefficient (R2), adjusted R2, and adequate precision were considered in fit statistics. Results: The crystalline ticagrelor has completely amorphized, as indicated by the powder x-ray diffraction (p-XRD) and differential scanning calorimetry (DSC) of the solid dispersion of ticagrelor prepared with copovidone VA 64 and vitamin E TPGS through solvent evaporation technique. An increase in ticagrelor dissolution by 10.7 fold was possible through solid dispersion technology. The lack of fit F-values of 0.11 and 0.00 in the factorial model for response dissolution at 10 min and disintegration time, respectively, are indicative of a good fit. The ANOVA and the fit statistics for the selected factorial model were found to be significant. Conclusion: A solid dispersion technique with carrier copovidone VA 64 and vitamin E TPGS could enhance the dissolution of ticagrelor significantly, to an extent of 10.7 fold. Factorial design is an important tool in optimizing the amorphous solid dispersion of ticagrelor and establishing the design space.

Estimate of lysine nutritional requirements for Japanese quail breeders.

Japanese quail breeders are the basis for genetic improvement and multiplication for commercial layers, however, there have been no known studies on the optimal lysine level for these birds. Thus, study the egg output response to the lysine (Lys) supply using different e-functions and evaluate the that best fit, have allowed the partition the lysine requirements for maintenance, both weight and egg output maximum. The objectives of this study were to identify the responses to various Lys levels, identify the functions related to these responses and determine the ideal Lys intake amount for Japanese quail breeders. A completely randomized design of seven treatments with seven replicated was used. Treatments consisted of diet supplementation by Lys in concentrations of 16.8, 11.8, 8.4, 6.7, 5.0, 3.4, and 1.7 g/kg. Six exponential models were adjusted. The level of Lys was found to affect bird responses (P<0.001). The birds responded to the levels provided, allowing for the creation of a lysine response curve. A monomolecular function with four parameters was balanced against the statistics of adjustment and selection of models. It was possible to estimate the level of lysine required for maintenance as 133 ±2 mg/kg BW0.67, and based an average of 41% efficiency, 22 mg Lys produced 1 g of egg output (EO). The daily intake calculated by the monomolecular factorial model was 284 mg Lys for a bird with 0.170 kg body weight and production of 10 g EO/day. The four-parameter monomolecular function proposed in this study is adequate for interpreting the animal response and calculating lysine intake for breeders.

Subjective and Personal Characteristics of Women with Breast Cancer: A Factorial Model of Disease Risks and the Course of Disease.

The purpose of this study was to evaluate psychological factors influencing the incidence of breast cancer in women. Basic beliefs, coping strategies, hardiness, subjective control, personal helplessness and independence were studied in 184 women, of which 80 women diagnosed with breast cancer, 104 women without a cancer diagnosis. All study participants completed psychodiagnostic methods and sociodemographic questionnaires. First, we analyzed differences in the psychological characteristics of women depending on the presence of an oncological diagnosis (F - Fisher's test). Second, we conducted factor analysis (principal component analysis with Varimax with Kaiser Normalization) to obtain psychological risk factors for breast cancer in women. Distinctive psychological characteristics of women with breast cancer have been discovered. Women with breast cancer underestimate their worth and importance (p=0.021), as well as their luck (p=0.006) and the ability to control events in their lives (p=0.019). Women with breast cancer are very independent (arrogance) (p=0.000), rarely use confrontation-type coping (p=0.05), positive reappraisal (p=0.05), and planful problem-solving (p=0.035). Women diagnosed with breast cancer have an external locus of control (p=0.002), and are low hardiness (p=0.029). The listed features that distinguish women with breast cancer from cancer-free women were considered as probabilistic psychological risk factors for breast cancer. As a result of factor analysis, a model was obtained that included psychological factors associated with breast cancer in women: Independence (arrogance) (28.88% of variance), Belief system (24.83% of variance), Maladaptive coping (20.17% of variance). Extreme independence, beliefs about one's failure, insignificance and inability to control life, as well as maladaptive coping are probabilistic psychological risk factors for breast cancer among Russian women.

Model of teaching factory in vocational high school fashion program students

The Fashion Design expertise program applies a teaching factory learning system to productive subjects, including Industrial Clothing. Judging from the planning and implementation, the teaching factory learning model in this subject has not run optimally. This study aims to: to determine the effectiveness of the teaching factory model for students of fashion as a learning model at State Vocational High School 1 Beringin. This type of research is descriptive qualitative with research and development methods. This research was conducted at State Vocational High School 1 Beringin, with the research subjects being teachers and students of the Fashion Design Expertise Program. The object of this research is teaching factory activities in productive subjects. Research on the development of the Teaching Factory learning model went through three stages, namely: 1) Needs analysis, 2) Product development, and 3) Expert Validation. The results of this study indicate that the Teaching Factory model has been effectively used in productive subjects such as industrial clothing with an assessment of 98.6% by material experts and 88% by design experts. After going through the validation stage by material experts and design experts, it can be concluded that the Teaching Factory learning model product developed has been effectively used as a learning model in productive subjects for students of the Dressmaking Expertise Program at Beringin 1 State Vocational High School

A Unified Formal Framework for Factorial and Probabilistic Topic Modelling

Topic modelling has become a highly popular technique for extracting knowledge from texts. It encompasses various method families, including Factorial methods, Probabilistic methods, and Natural Language Processing methods. This paper introduces a unified conceptual framework for Factorial and Probabilistic methods by identifying shared elements and representing them using a homogeneous notation. The paper presents 12 different methods within this framework, enabling easy comparative analysis to assess the flexibility and how realistic the assumptions of each approach are. This establishes the initial stage of a broader analysis aimed at relating all method families to this common framework, comprehensively understanding their strengths and weaknesses, and establishing general application guidelines. Also, an experimental setup reinforces the convenience of having harmonized notational schema. The paper concludes with a discussion on the presented methods and outlines future research directions.

Technical feasibility of the use of topsoil in the composition of substrates of Caatinga forest species

Mining generates great environmental impacts, in addition to a large amount of waste. In order to comply with environmental legislation, the production of seedlings is one of the actions carried out, which requires a large volume of substrates, generally commercial. The topsoil it is a residue with physical-chemical characteristics with potential for agricultural use, present in large quantities, reaching 33 thousand m3. This research aimed to know the technical feasibility of topsoil added to commercial substrate for the production of seedlings of four native Caatinga species. The experiment was carried out in a completely randomized design, in a 5 x 4 factorial model, with five topsoil levels (0, 25, 50, 75 and 100%) and four species [‘Canafístula’ (Peltophorum dubium), ‘Farinha Seca’ (Albizia hasslerii), ‘Timbaúba’ (Enterolobium contortisiliquum) and ‘Ipê Branco’ (Tabebuia roseo-alba)]. Considering the Dickson quality index, for the species ‘Canafístula’ and ‘Ipê Branco’, 74.1 and 36.1% are the maximum limits of topsoil and, for ‘Farinha Seca’ and ‘Timbaúba’, up to 100% can be used. In general, the addition of topsoil to the substrate will allow savings of 77% in substrate costs, making its use in the production of forest seedlings economically viable and environmentally correct.

A multidimensional factorial enviro-economic model: Approaches of retrospective decomposition and prospective projection for energy systems

Understanding the patterns of energy usage through simulation and prediction from multiple perspectives is crucial for alleviating energy-environmental conflicts and achieving sustainable energy system development. Despite their importance, multidimensional interactions among the production, demand, and supply sides have not been systematically characterized, thus representing a significant research gap. This paper aims to address this gap by developing a a multidimensional factorial enviro-economic (MFEE) model, which offers an in-depth investigation of Guangdong's energy system. This model, designed for retrospective decomposition (1997–2017) and prospective projections (2035), uniquely links historical and future analysis from systematic and sectoral perspectives through structural decomposition analysis, sectoral evolution analysis, and the Biproportional Scaling RAS method. Our findings reveal that the final demand level and Research & development (R & D) are the principal drivers of increased energy usage. We also found that the wholesale and retail sector have the most significant impact on demand and input sides, accounting for over 70 %. Furthermore, the interaction of key sectors from the production-based perspective notably influences system symbiosis. To effectively manage these dynamics, our study recommends regulating and controlling measures for the circulation sector that consider the benefits of both the energy sector and the accommodation and catering sector. This comprehensive analysis, therefore, offers scientifically-supported guidance for sustainable energy system development.

Exploratory Factorial Model of Attitude Toward Violence in the Covid-19 Era

Exploratory Factorial Model of Attitude Toward Violence in the Covid-19 Era

Development of a cloud intelligent machine monitoring and control system

Facing the trend of Industry 4.0, the cloud-based supervisory control and data acquisition (SCADA) system employing cloud computing and IoT technology can help the manufacturing industry reduce software investment and system maintenance costs. However, manufacturers may need to install new sensors and controllers, the connection of SCADA system and shop floor machine controller, monitoring dashboard design and implementation usually need to outsource to an experienced system integration company, which may impede medium-sized manufacturing enterprises (SMEs). This paper aims to develop a cloud-based intelligent machine monitoring and control system (CIM-MCS) framework, the service structure, and approach to deploying the CIM-MCS in a public cloud infrastructure platform and service provider. The package diagram is proposed for building the CIM-MCS’s virtual factory model to improve modeling efficiency and data stability. CIM-MCS and its SCADA application in a leading automatic filling and packaging production line show that the CIM-MCS is easy to implement. The transmission time is short and acceptable for practical application. The integration of CIM-MCS with a cloud-based advanced planning scheduling system has the advantage of real-time monitoring, production progress reporting, scheduling, and dispatching and achieves the goal of anytime, anywhere, anyone, and any platform operating an intelligent factory.