Multistage Process Research Articles

Prevalence, risk factors, and parental perspectives of dental caries in children in Odisha: A mixed-method study

Background/objectiveDespite numerous advances in dentistry, the frequency of dental caries remains high, especially among children. Therefore, the study's objectives were to assess the prevalence of dental caries in school-aged children between ages 6–14 years, to identify the risk factors associated with dental caries and to explore parents' understanding and awareness of preventing dental caries among children. MethodA mixed method study was conducted in the rural areas of Cuttack and Jagatsinghpur districts in Odisha, India. Children were chosen from schools using a multistage sampling process. Pretested and standardised questionnaires were utilized to collect data from children, while interviews with parents and teachers were employed to collect qualitative data. The DMFT/DEFT index was calculated for children. Data were analysed using frequency, percentage and binary multiple logistic regression analysis. The interviews were analysed using thematic analysis. ResultA total of 419 children, 14 parents, and 12 teachers participated in the study. The findings of the current study reported the prevalence of dental caries in 60 % of children. In addition, age, a high frequency of sugar and chocolate consumption, and poor socioeconomic status were found to be associated with a high risk of dental caries. Furthermore, the results of the thematic analysis indicated parent and teacher awareness and understanding of dental caries; however, it highlights the need for improved oral health practices. ConclusionThese insights from qualitative and quantitative findings can guide the development and improvement of preventive interventions to combat dental caries through implementing oral health programs in schools, thereby fostering better oral health and overall well-being among the younger population.

Dispersivity calculation in digital twins of multiscale porous materials using the micro-continuum approach

The micro-continuum method is a novel approach to simulate flow and transport in multiscale porous materials. For such materials, the domain can be divided into three sub-domains depending on the local porosity ε: fully resolved solid phase, for which ε = 0, fully resolved pores, for which ε = 1.0, and unresolved pores, for which 0 < ε < 1.0. For such domains, the flow can be solved using the Darcy-Brinkman-Stokes (DBS) equation, which offers a seamless transition between unresolved pores, where flow is described by Darcy’s law, and resolved pores, where flow is described by the Stokes equations. Species transport can then be modelled using a volume-averaged equation. In this work, we present a derivation of the closure problem for the micro-continuum approach. Effective dispersivity tensors can then be calculated through a multi-stage process. First, high resolution images are chosen for characterizing the structure of the unresolved pores. Porosity, permeability and effective dispersivity for the unresolved parts are calculated by solving a closure problem based on Direct Numerical Simulation (DNS) in the high-resolution images. The effective dispersivity is then expressed as a function of the Péclet number, which describes the ratio of advective to diffusive transport. This relationship, along with porosity and permeability, is then integrated into the multiscale domain and the effective dispersivity tensor for the full image is calculated. Our novel method is validated by comparison with the numerical solution obtained for a fully-resolved simulation in a multiscale 2D micromodel. It is then applied to obtain an effective dispersivity model in digital twins for two multiscale materials: hierarchical ceramic foams and microporous carbonate rocks.

Comprehensive Transcriptome Analysis Expands lncRNA Functional Profiles in Breast Cancer.

Breast cancer is a heterogeneous disease that arises as a multi-stage process involving multiple cell types. Patients diagnosed with the same clinical stage and pathological classification may have different prognoses and therapeutic responses due to alterations in molecular genetics. As an essential marker for the molecular subtyping of breast cancer, long non-coding RNAs (lncRNAs) play a crucial role in gene expression regulation, cell differentiation, and the maintenance of genomic stability. Here, we developed a modular framework for lncRNA identification and applied it to a breast cancer cohort to identify novel lncRNAs not previously annotated. To investigate the potential biological function, regulatory mechanisms, and clinical relevance of the novel lncRNAs, we elucidated the genomic and chromatin features of these lncRNAs, along with the associated protein-coding genes and putative enhancers involved in the breast cancer regulatory networks. Furthermore, we uncovered that the expression patterns of novel and annotated lncRNAs identified in breast cancer were related to the hormone response in the PAM50 subtyping criterion, as well as the immune response and progression states of breast cancer across different immune cells and immune checkpoint genes. Collectively, the comprehensive identification and functional analysis of lncRNAs revealed that these lncRNAs play an essential role in breast cancer by altering gene expression and participating in the regulatory networks, contributing to a better insight into breast cancer heterogeneity and potential avenues for therapeutic intervention.

Contextual coherence increases perceived numerosity independent of semantic content.

Number perception emerges from multiple stages of visual processing. Understanding how systematic biases in number perception occur within a hierarchy of increasingly complex feature representations helps uncover the multistage processing underlying our visual number sense. Recent work demonstrated that reducing coherence of low-level visual attributes, such as color and orientation, systematically reduces perceived number. Here, we ask when in the visual processing hierarchy coherence affects numerosity perception and specifically whether the coherence effect is exclusive to low-level visual features or instead whether it can be driven by contextual or semantic relationships. We tested adults in an ordinal numerical comparison task with contextual coherence mathematically manipulated using a statistical model of visual object co-occurrence. Across several experiments, we found that arrays with high contextual coherence were perceived as numerically larger than arrays with low contextual coherence. This contextual coherence effect was not attenuated even when we reduced objects to texforms (unrecognizable images that preserve midlevel visual features) or removed semantic content from the images through box scrambling and diffeomorphic warping. Together, these results suggest that visual coherence derived from natural statistics of object co-occurrence systematically alters perceived numerosity at low-level visual processing, even before later stages at which items can be explicitly categorized and identified. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Interpretation and utility of the Animal Health Loss Envelope as part of the Global Burden of Animal Diseases analytical process.

The Global Burden of Animal Diseases (GBADs) programme follows a multistage process to produce disease burden estimates in livestock and farmed aquatic animal production systems. The GBADs programme has broad goals of inclusivity, transparency and rigour. Meeting those goals means providing users of all levels of technical expertise with a clear explanation of the programme's output. In this way, the meaning and limitations of those results are clearly communicated, minimising the risk of misinterpretation. The first published estimates of disease burden have been calculated at farm level using a new metric called the Animal Health Loss Envelope. This metric estimates the cost of lost productivity and expenditure on disease control for profit-maximising producers by comparing current system performance to a hypothetical ‘ideal health'scenario. This ideal is a farm-specific concept and is critically different from an ideal health state when physiologically defined. The metric and its key concepts are described in this article.

Development of Quality Indicators to Evaluate the Quality of Care for People with Differences of Sex Development.

Achieving evidence-based, high-quality medical care is the overarching goal of healthcare quality management. Quality indicators (QIs) serve as proxies to show whether good quality is reached or not. This article describes the development of QI for the evaluation of healthcare quality in the area of differences of sex development (DSD). Following the model of Donabedian, the aim was to develop QI to assess defined relevant aspects of the quality of structures, processes, and outcomes of care in DSD. Ten DSD clinical centres and two self-advocacy groups in Germany included in the DSDCare project were involved in the development of the QI and a benchmarking system. The development of the QI involved several structured steps: analysis of guidelines and recommendations, literature review, qualitative interviews with key stakeholders in the field of DSD, and patients or their carers. QIs were discussed in a multi-stage systematic consensus process and assessed in terms of their relevance, feasibility, and practicability. In a multi-stage systematic consensus process involving medical and psychological experts from a range of disciplines, people with DSD and their families, and representatives of self-advocacy groups, we have developed a set of 37 QIs (22 structure, seven process, and eight outcome quality). The QIs serve to evaluate care in the field of DSD and may add to the German criteria for certification of Centres for Rare Conditions formulated by the National Action League for People with Rare Diseases (NAMSE) in this area of expertise. We have succeeded in developing and jointly adopting a set of QIs that consider a wide range of perspectives on the quality of care for people with DSD and their families. These QIs have been found to be relevant, feasible, and practicable, and they are now used for a yearly quality benchmarking in the participating DSD centres.

Towards a gapless 1 km fractional snow cover via a data fusion framework

Accurate quantification of snow cover facilitates the prediction of snowmelt runoff, the assessment of freshwater availability, and the analysis of Earth’s energy balance. Existing fractional snow cover (FSC) data, however, often suffer from limitations such as spatial and temporal gaps, compromised accuracy, and coarse spatial resolution. These limitations significantly hinder the ability to monitor snow cover dynamics effectively. To address these formidable challenges, this study introduces a novel data fusion framework specifically designed to generate high-resolution (1 km) daily FSC estimation across vast regions like North America, regardless of weather conditions. It achieved this by effectively integrating the complementary spatiotemporal characteristics of both coarse- and fine-resolution FSC data through a multi-stage processing pipeline. This pipeline incorporates innovative strategies for bias correction, gap filling, and consideration of dynamic characteristics of snow cover, ultimately leading to high accuracy and high spatiotemporal completeness in the fused FSC data. The accuracy of the fused FSC data was thoroughly evaluated over the study period (September 2015 to May 2016), demonstrating excellent consistency with independent datasets, including Landsat-derived FSC (total 24 scenes; RMSE=6.8–18.9 %) and ground-based snow observations (14,350 stations). Notably, the fused data outperforms the widely used Interactive Multi-sensor Snow and Ice Mapping System (IMS) daily snow cover extent data in overall accuracy (0.92 vs. 0.91), F1_score (0.86 vs. 0.83), and Kappa coefficient (0.80 vs. 0.77). Furthermore, the fused FSC data exhibits superior performance in accurately capturing the intricate daily snow cover dynamics compared to IMS data, as confirmed by superior agreement with ground-based observations in four snow-cover phenology metrics. In conclusion, the proposed data fusion framework offers a significant advancement in snow cover monitoring by generating high-accuracy, spatiotemporally complete daily FSC maps that effectively capture the spatial and temporal variability of snow cover. These FSC datasets hold substantial value for climate projections, hydrological studies, and water management at both global and regional scales.

Automatic Generation Algorithm of Movie and TV Scripts Based on ChatGPT

A movie or TV script is a meticulously crafted document that serves as the blueprint for a visual storytelling project. It outlines the dialogue, actions, and scene descriptions that will unfold on screen, guiding actors, directors, and crew members in bringing the story to life. Each script is divided into scenes and acts, with clear instructions on character entrances and exits, camera angles, and pacing. This paper introduces a novel framework for enhancing the quality of movie and TV scripts through the integration of the Combinational Multi-Stage Genetic Optimization (CMSGO) model with ChatGPT, a state-of-the-art language generation model. The CMSGO model utilizes iterative optimization techniques to systematically refine and enhance script elements such as coherence, dialogue flow, character development, and overall narrative structure. The proposed CMSGO model comprises the Combinational model with the genetic optimization function. The function CMSGO model examines the fitness function with the Multi-Stage Optimization process. The proposed CMSGO model uses the estimation of features in the Multi-stage optimization model with the computation of features related to the scripts. Through 20 generations of optimization, the CMSGO model demonstrates its effectiveness in improving script quality, as evidenced by a steady increase in average script quality scores. Additionally, the multi-stage optimization approach targets specific aspects of script quality, allowing for targeted adjustments to parameters related to character motivations, plot coherence, and tone. Viewer opinions further validate the efficacy of the generated scripts, with positive evaluations across various aspects such as audience engagement, coherence, emotional impact, and originality. The proposed framework offers a robust and data-driven approach to scriptwriting, enabling the creation of high-quality movie and TV scripts that captivate and resonate with audiences, thus enriching the overall viewing experience.

Benzo-12-crown-4 and β-diketone bi-functionalized ionic liquids for highly selective lithium extraction from alkaline solution

Indeed, the efficient and sustainable recovery of lithium from highly concentrated sodium-containing mother liquors of Li2CO3 and salt-lake brines remains a significant challenge. Herein, two typical Li+ recognition receptors, namely benzo-12-crown-4 and thenoyltrifluoroacetone, were separately introduced into the cationic and anionic parts to successfully synthesize novel bi-functionalized task-specific ionic liquids [(B12C4)Cnim][TTA] (n = 4, 6, 8). A new, green, and high-efficiency extraction system has been developed by dissolving [(B12C4)C6im][TTA] in commercial room-temperature ionic liquid 1-hexyl-3-methyl-imdazolium bis(trifluromethylsulfonyl)imide [C6mim][NTf2], without the addition of any other co-extractants. The newly developed [(B12C4)C6im][TTA]-[C6mim][NTf2] extraction system exhibited a wide pH working range (4.0–11.0), high lithium extraction efficiency (95.4 %), as well as superior separation factors of Li/Na (βLi/Na = 2154.0) and Li/K (βLi/K=757.2). The extraction mechanism involving cation exchange was determined through slope analysis, ion chromatography, UV–vis spectroscopy and FT-IR spectra measurement, indicating that one [(B12C4)C6im][TTA] molecule can interact with two lithium ions and form a 1:2 complex. Moreover, the [(B12C4)C6im][TTA]-[C6mim][NTf2] extraction system also demonstrated fast extraction kinetics, facile stripping and regeneration, along with good cycling performance. Furthermore, a multi-stage countercurrent extraction process to extract lithium from the simulated mother liquor of Li2CO3 was evaluated. When the volume ratio (O/A) of oil phase (O) to water phase (A) was 1.0, only two theoretical extraction stages were needed to extract 91.4 % of lithium into the organic phase. Thus, the [(B12C4)C6im][TTA]-[C6mim][NTf2] extraction system is promising for lithium extraction from mother liquor of Li2CO3 and salt-lake brines. This study also opens the avenue for designing cationic and anionic bi-functionalized ionic liquids for metal ion separation.

Enhanced low-light image fusion through multi-stage processing with Bayesian analysis and quadratic contrast function

This manuscript introduces an innovative multi-stage image fusion framework that adeptly integrates infrared (IR) and visible (VIS) spectrum images to surmount the difficulties posed by low-light settings. The approach commences with an initial preprocessing stage, utilizing an Efficient Guided Image Filter for the infrared (IR) images to amplify edge boundaries and a function for the visible (VIS) images to boost local contrast and brightness. Utilizing a two-scale decomposition technique that incorporates Lipschitz constraints-based smoothing, the images are effectively divided into distinct base and detail layers, thereby guaranteeing the preservation of essential structural information. The process of fusion is carried out in two distinct stages: firstly, a method grounded in Bayesian theory is employed to effectively combine the base layers, so effectively addressing any inherent uncertainty. Secondly, a Surface from Shade (SfS) method is utilized to ensure the preservation of the scene's geometry by enforcing integrability on the detail layers. Ultimately a Choose Max principle is employed to determine the most prominent textural characteristics, resulting in the amalgamation of the base and detail layers to generate an image that exhibits a substantial enhancement in both clarity and detail. The efficacy of our strategy is substantiated by rigorous testing, showcasing notable progressions in edge preservation, detail enhancement, and noise reduction. Consequently, our method presents significant advantages for real-world applications in image analysis.

Multi-stage process for mixed microbial culture production of polyhydroxyalkanoates from sugarcane stillage: Assessment of external nutrient supplementation

Sugarcane stillage is an abundant wastewater from ethanol production. It has drawn considerable interest as a potential feedstock for biotechnological processes aiming at the recovery of energy and value-added products. In this study we explored the use of stillage for the production of polyhydroxyalkanoates (PHA), which are microbial bioplastics with potential to replace conventional plastics in specific applications. As stillage is nutrient deficient, we investigated the impact of external nutrient supplementation on the selection of PHA-producing mixed cultures. Cultures selected under four different total-chemical-oxygen-demand-to-nitrogen ratios (COD.t/N) (15, 30, 40 and 80) were evaluated according to their PHA production performance and microbial composition. The experimental results demonstrated that the most favorable PHA production was achieved for the biomass selected under strict carbon-limiting conditions, with a COD.t/N ratio of 15. This resulted in an overall PHA volumetric productivity of 2.10 g PHA L−1 d−1, a maximum intracellular PHA content of 0.65 g PHA g VSS−1 and a PHA storage yield of 0.71 g CODPHA g CODH.Org−1. The resulting PHA was a copolymer of 3-hydroxybutyrate (73 %mol) and 3-hydroxyvalerate (27 %mol) monomers. The microbial consortium was enriched by members of the Brevundimonas genus, known PHA producers, with a relative abundance of 26.4 %.

Predicting error for machining thin-walled blades considering initial error

A multistage machining process is employed to machine the blades with low stiffness. Nonetheless, machining errors can be transferred and accumulate throughout the multistage machining process, complicating the precise prediction of the final accuracy of thin-walled blades. Consequently, this paper introduces a machining accuracy prediction model for thin-walled blades that takes into account initial error. The machining error prediction model of thin-walled blades is developed using Gaussian process regression optimized by the sparrow search algorithm (SSA-GPR) with the initial contour error, depth of cut, feed per tooth, and spindle speed as inputs, and the machining error as the output. And the results show that the prediction accuracy of the SSA-GPR is 6.73 % higher than that of the Gaussian process regression (GPR), 13.73 % higher than that of the back propagation neural network (BPNN), and 32.32 % higher than that of the support vector machine regression (SVR). The influence of the initial error and milling parameters on the machining error is analyzed through the length-scales of the Gaussian kernel function. The findings indicate that the depth of cut, feed per tooth and initial error significantly affect the machining error, whereas the spindle speed has a minor impact on the machining error. Furthermore, the 3D graph based on the SSA-GPR shows that the increase of the initial error will increase the machining error of thin-walled blades. This research provides a theoretical foundation for the process optimization of thin-walled blades.

Finite element solution of coupled multiphysics reaction-diffusion equations for fracture healing in hard biological tissues

This study proposes a computational framework to investigate the multi-stage process of fracture healing in hard tissues, e.g., long bone, based on the mathematical Bailon-Plaza and Van der Meulen formulation. The goal is to explore the influence of critical biological factors by employing the finite element method for more realistic configurations. The model integrates a set of variables, including cell densities, growth factors, and extracellular matrix contents, managed by a coupled system of partial differential equations. A weak finite element formulation is introduced to enhance the numerical robustness for coarser mesh grids, complex geometries, and more accurate boundary conditions. This formulation is less sensitive to mesh quality and converges smoothly with mesh refinement, exhibiting superior numerical stability compared to previously available strong-form solutions. The model accurately reproduces various stages of healing, including soft cartilage callus formation, endochondral and intramembranous ossification, and hard bony callus development for various sizes of fracture gap. Model predictions align with the existing research and are logically coherent with the available experimental data. The developed multiphysics simulation clarifies the coordination of cellular dynamics, extracellular matrix alterations, and signaling growth factors during fracture healing.

Electrical excitability of cancer cells-CELEX model updated.

The normal functioning of every cell in the body depends on its bioelectric properties and many diseases are caused by genetic and/or epigenetic dysregulation of the underlying ion channels. Metastasis, the main cause of death from cancer, is a complex multi-stage process in which cells break away from a primary tumour, invade the surrounding tissues, enter the circulation by encountering a blood vessel and spread around the body, ultimately lodging in distant organs and reproliferating to form secondary tumours leading to devastating organ failure. Such cellular behaviours are well known to involve ion channels. The CELEX model offers a novel insight to metastasis where it is the electrical excitation of the cancer cells that is responsible for their aggressive and invasive behaviour. In turn, the hyperexcitability is underpinned by concomitant upregulation of functional voltage-gated sodium channels and downregulation of voltage-gated potassium channels. Here, we update the in vitro and in vivo evidence in favour of the CELEX model for carcinomas. The results are unequivocal for the sodium channel. The potassium channel arm is also broadly supported by existing evidence although these data are complicated by the impact of the channels on the membrane potential and consequent secondary effects. Finally, consistent with the CELEX model, we show (i) that carcinomas are indeed electrically excitable and capable of generating action potentials and (ii) that combination of a sodium channel inhibitor and a potassium channel opener can produce a strong, additive anti-invasive effect. We discuss the possible clinical implications of the CELEX model in managing cancer.

Preparation and Electrical Performance Analysis of a GaAs‐Based Tritium Battery

A nuclear battery is a promising candidate for small power supply sources in the military and commercial fields, but its output power and energy conversion efficiency need to be improved. This paper mainly describes a design, preparation, and electrical performance analysis of a GaAs-based tritium battery. The design of the tritium battery uses a multistage process with Monte Carlo and Matlab simulations. A titanium tritide source was prepared by a high-temperature tritium absorption device, and a GaAs semiconductor transducer was developed using a metal-organic chemical vapor deposition method. The D/Ti ratio and T/Ti ratio of the deuterium/tritium titanium films were 1.9 and 1.7, respectively. Two kinds of GaAs-based PIN junction semiconductor transducers were proposed and irradiated with the prepared tritium source. Their electrical properties were measured in situ and analyzed qualitatively. Under the irradiation of a 0.61-Ci tritium source, the short-circuit current of the device was 0.3 to 0.38 μA, the open-circuit voltage was 35 to 63 mV, the peak power was 2.8 to 6.4 nW, and the energy conversion efficiency of the GaAs semiconductor transducer was about 1.86%. It was found that an air gap between the GaAs semiconductor transducer and the radioactive source caused serious loss of beta particle energy, resulting in low output power and low energy conversion efficiency of the nuclear battery. The open-circuit voltage of the devices with a SiO2 passivation layer on the surface decreased both in a dark environment and in light illumination, but SiO2 passivation did not reduce surface recombination as expected. The research work in this paper will provide some valuable reference for the preparation and performance optimization of nuclear batteries.

Forecasting the life cycle of reinforced concrete structures in severe climatic conditions

The article considers the problem of design forecasting of the life cycle of reinforced concrete structures operated in severe climatic conditions using the methodology of limit states. The authors propose to use the reliability index β, conditionally regulated by the norms for design limit states, as a criterion index of the functional suitability of structures. The kinetics of the reliability index β during cyclic freezing and thawing is evaluated by means of multifactor periodic experimental control of strength and deformability indices in frozen and thawed states. Testing was conducted using prismatic (100×100×400 mm) and cubic (100 mm) specimens of the B25 mature concrete (180 days), F270 frost resistance grade. In this case, the parametric failure of an element was identified by the moment when the criterion parameter reached the threshold value. The threshold is extablished (with the required level of reliability) by normative functional models of the limit state, transformed to a form describing the dependence of the concrete resistance parameter on the value of the estimated force and section parameters. Test intervals were determined considering the specificity of concrete frost degradation as a multistage process of structural adaptability, as well as microcrack formation and accumulation. Statistical aspects of resistance parameters were identified from experimental data using the maximum likelihood method and Pearson’s criterion (χ2) in assessing their consistency with the theoretical distribution. Additional transient design situations that consider the thermal-moisture state of the structures are considered. The presented experimental data confirmed the feasibility of the proposed approach.

Design, synthesis, biological evaluation, and molecular modeling simulations of new phthalazine-1,4-dione derivatives as anti-Alzheimer's agents.

The development of targeted phthalazine-1,4-dione acetylcholinesterase (AChE) inhibitors for treating Alzheimer's disease involved the synthesis of 32 compounds via a multistage process. Various analytical techniques confirmed the compounds' identities. Thirteen compounds were found to inhibit AChE by more than 50% without affecting butyrylcholinesterase (BChE). Among these, three compounds, 8m, 8n, and 8p, exhibited extraordinary activity similar to donepezil, a reference AChE inhibitor. During enzyme kinetic studies, compound 8n, displaying the highest AChE inhibitory activity, underwent evaluation at three concentrations (2 × IC50, IC50, and IC50/2). Lineweaver-Burk plots indicated mixed inhibition activity for compound 8n against AChE, suggesting a combination of competitive and noncompetitive characteristics. Additionally, effective derivatives 8m, 8n, and 8p exhibited high blood-brain barrier (BBB) permeability in in vitro parallel artificial membrane permeability assay tests. Molecular docking studies revealed that these compounds bind to the enzyme's active site residues in a position similar to donepezil. Molecular dynamic simulations confirmed the stability of the protein-ligand system, and the chemical reactivity characteristics of the compounds were investigated using density functional theory. The compounds' wide energy gaps suggest stability and therapeutic potential. This research represents a significant step toward finding a potential cure for Alzheimer's disease. However, further research and testing are required to determine the compounds' safety and efficacy. The unique structure of phthalazine derivatives makes them suitable for various biological activities, and these compounds show promise for developing effective drugs for treating Alzheimer's disease. Overall, the development of these targeted compounds is a crucial advancement in the search for an effective treatment for Alzheimer's disease.

Development of neighborhood networks in suburban areas for persuading women to adhere to breast cancer screening programs

Introduction: The purpose of this study was to determine the effectiveness of the development of neighborhood networks in suburban areas for increasing adherence to breast cancer screening. Methods: This community-based intervention study was conducted among Iranian urban women aged 30 to 65 in 2021. The samples were chosen using a multi-stage random sampling process. Women were randomly assigned to intervention group (n=271) and control group (n=155). Following a three-month intervention period, participants were asked about their breast cancer screening behaviors to gauge the program’s success. Cognitive factors including perceived social support, perceived benefits and perceived barriers toward breast cancer screening, subjective norms, behavioral intention, and breast cancer screening behaviors were collected. The data were analyzed with SPSS statistical software version 21. Results: After the intervention, the results showed an improvement in the intention of referring to mammography centers, perceived benefits of early diagnosis of breast cancer, receiving social support, breast cancer screening behaviors, and a decrease in barriers to early diagnosis of breast cancer compared to the control group (P&lt;0.05). Conclusion: Harnessing the power of social networks alongside educational initiatives and strong community support can provide a safe, affordable, and effective means of boosting participation in Breast Cancer Screening (BCS).

Reduction of organic contaminants from industrial effluent using the advanced oxidation process, chemical coagulation, and green nanotechnology

Municipal wastewater treatment systems use the chemical oxygen demand test (COD) to identify organic contaminants in industrial effluents that impede treatment due to their high concentration. This study reduced the COD levels in tannery wastewater using a multistage treatment process that included Fenton oxidation, chemical coagulation, and nanotechnology based on a synthetic soluble COD standard solution. At an acidic pH of 5, Fenton oxidation reduces the COD concentration by approximately 79%. It achieves this by combining 10 mL/L of H2O2 and 0.1 g/L of FeCl2. Furthermore, the author selected the FeCl3 coagulant for the coagulation process based on the best results of comparisons between different coagulants. At pH 8.5, the coagulation dose of 0.15 g/L achieved the maximum COD removal efficiency of approximately 56.7%. Finally, nano bimetallic Fe/Cu was used to complete the degradation and adsorption of the remaining organic pollutants. The XRD, SEM, and EDX analyses proved the formation of Fe/Cu nanoparticles. A dose of 0.09 g/L Fe/Cu NPs, 30 min of contact time, and a stirring rate of 200 rpm achieve a maximum removal efficiency of about 93% of COD at pH 7.5. The kinetics studies were analyzed using pseudo-first-order P.F.O., pseudo-second-order P.S.O., and intraparticle diffusion models. The P.S.O. showed the best fit among the kinetic models, with an R2 of 0.998. Finally, the authors recommended that technique for highly contaminated industrial effluents treatment for agriculture or industrial purposes.

Multi-pass deep-drawing process and mould design for thin-walled hollow cylindrical parts

Thin-walled hollow cylindrical parts are widely employed in various industries, including automobiles, ships, and aviation, due to their increased demand and the escalating quality requirements for their formation. The production of these parts primarily relies on a multi-stage deep drawing process. However, during the deep drawing process, the gradual reduction in the side wall thickness creates a susceptible area at the bottom rounded transition, where local over-thinning may occur, thereby increasing the risk of cracking. Furthermore, the deep-drawing process becomes more intricate for thin-walled hollow cylindrical parts with a large height-to-diameter ratio, amplifying the likelihood of defects during actual production. This paper combines CAE simulation and empirical design to comprehensively investigate and analyze the multi-stage deep drawing process and mold design for thin-walled hollow cylindrical parts. The aim is to optimize the deep-drawing process and mold parameters, enhance the products qualification rate, and provide valuable guidance for the practical production of relevant enterprises.