Quantitative Strategy Research Articles

A web-based dynamic nomogram for estimating talaromycosis risk in hospitalized HIV-positive patients.

Our study aimed to develop and validate a nomogram to assess talaromycosis risk in hospitalized HIV-positive patients. Prediction models were built using data from a multicentre retrospective cohort study in China. On the basis of the inclusion and exclusion criteria, we collected data from 1564 hospitalized HIV-positive patients in four hospitals from 2010 to 2019. Inpatients were randomly assigned to the training or validation group at a 7:3 ratio. To identify the potential risk factors for talaromycosis in HIV-infected patients, univariate and multivariate logistic regression analyses were conducted. Through multivariate logistic regression, we determined ten variables that were independent risk factors for talaromycosis in HIV-infected individuals. A nomogram was developed following the findings of the multivariate logistic regression analysis. For user convenience, a web-based nomogram calculator was also created. The nomogram demonstrated excellent discrimination in both the training and validation groups [area under the ROC curve (AUC)=0.883 vs. 0.889] and good calibration. The results of the clinical impact curve (CIC) analysis and decision curve analysis (DCA) confirmed the clinical utility of the model. Clinicians will benefit from this simple, practical, and quantitative strategy to predict talaromycosis risk in HIV-infected patients and can implement appropriate interventions accordingly.

Enhancing precision nitrogen management for cotton cultivation in arid environments using remote sensing techniques

Context or problemRemote sensing, particularly through unmanned aerial vehicles (UAVs), has emerged as a pivotal tool in precision agriculture, especially for nitrogen (N) management. Traditional methods, while effective in quantifying crop N status using the Nitrogen Nutrition Index (NNI), fall short in providing quantitative fertilization strategies. MethodsThis study bridges this gap by developing a comprehensive method that leverages multispectral remote sensing data from UAVs to refine N fertilizer management in cotton cultivation within arid environments. By integrating both field observations and UAV-derived multispectral data, we established robust models capable of estimating both leaf and overall cotton nitrogen contents (CNC-leaf and CNC-all), as well as NNI, throughout the growing season. This facilitated real-time calculation of required N fertilizer doses in cotton fields. ResultsWe uniquely applied covariance diagnosis and full subset screening techniques, underscoring the efficacy of vegetation index categories (VIs) in enhancing prediction accuracy. The Random Forest (RF) model exhibited superior performance in predicting plant nitrogen content, particularly in CNC-leaf prediction (Calibration: R²=0.92, RMSE=7.7 g m⁻², MAE=5.5 g m⁻²; Validation: R²=0.60, RMSE=16.5 g m⁻², MAE=12.2 g m⁻²) as opposed to CNC-all prediction (Calibration: R²=0.78, RMSE=117.0 g m⁻², MAE=154.3 g m⁻²; Validation: R²=0.34, RMSE=138.4 g m⁻², MAE=190.4 g m⁻²). The RF model also demonstrated optimal performance in NNI prediction (Calibration: R²=0.93, RMSE=0.05, MAE=0.04; Validation: R²=0.73, RMSE=0.12, MAE=0.10), surpassing the predictions for CNC. ConclusionsUtilizing CNC and NNI estimates derived from the optimized RF model, this study succeeded in generating a comprehensive map detailing the N fertilizer requirement across cotton Fertilization zones were established for different treatments, revealing that biochar application levels primarily determine nitrogen fertilizer needs. As biochar application increases, nitrogen fertilizer demand decreases. Moreover, nitrogen application rates typically increase when irrigation levels reach either 120 % ETc or 60 % ETc. Implications or significanceThis innovative approach not only empowers farmers with intuitive and accurate tools for real-time cotton N management but also fosters enhanced agricultural practices by integrating advanced remote sensing technologies with sophisticated data analysis methods. The findings of this study have significant implications for sustainable and efficient agricultural practices, particularly in arid regions, setting a new precedent in precision nitrogen management.

Leakage Control Mechanisms and Strategies for Hydraulically Driven Controllable Rod Seals

Abstract The primary function of rod seals in hydraulic systems is to prevent the leakage of the hydraulic medium, which would otherwise result in environmental pollution and the loss of energy. However, the leakage control performance of conventional rod seals is limited by the level of product design. As a result, the performance of these seals will gradually degrade during the service process. Furthermore, it is not possible to adaptively adjust the performance of these seals in response to changes in operating conditions. Hydraulically controllable rod seals (HCRS) can be driven hydraulically to adjust the internal stress distribution of the seal to regulate the sealing performance. This may be an effective solution, although the leakage control mechanism and strategy remain unclear. This paper employs a thermoelastohydrodynamic mixed lubrication model to comparatively analyze the behaviors and performances of the HCRS with or without a cavity structure. The leakage control mechanism of the HCRS is revealed, namely that it is achieved by increasing the interface contact pressure and reducing the fluid film thickness through external pressurization. Furthermore, the functional relationship between the sealing performance and the regulating variables under different operating conditions is obtained through multivariate regression analysis, thereby forming the quantitative leakage control strategy of the HCRS.

Joint optimization of empty container repositioning and inventory control applying dynamic programming and simulated annealing

Based on the division of public and exclusive hinterlands, this paper studies the joint optimization problem of empty container repositioning and inventory control under non-stationary demand within the port cluster. This paper established a stochastic mixed integer programming model using distributed robust optimization methods, combined with quantitative and periodic inventory control strategies. After conducting a deterministic transformation, this paper designed a hybrid algorithm of dynamic programming and simulated annealing to solve the model, and compared the various costs under stationary and non-stationary scenarios. The results show that the joint optimization of empty container inventory control and repositioning can always reduce the total cost of empty container management for shipping companies. Periodic inventory control strategies are more suitable for shipping companies to apply to empty container management in non-stationary situations. Sensitivity analysis shows that there is a positive correlation between uncertain demand parameters and the total cost of shipping companies. The superiority of the empty container repositioning mode proposed in this paper under sea-land coordination has also been demonstrated by changing the accessibility parameters between terminals.

Quantitative Pharmacology for Dose and Regimen Determination of Cevostamab Monotherapy in Relapsed/Refractory Multiple Myeloma

Background: In Phase I clinical trials of T-cell engaging bispecific molecules, the recommended dose and schedule are informed by evaluation of priming dose regimens which mitigate cytokine release syndrome (CRS) risk and target doses that impact the balance between efficacy and non-CRS safety. Having a quantitative pharmacology strategy can complement the clinical dose finding of T-cell engaging bispecifics. Here, we present the quantitative pharmacology strategy to inform the dose and schedule of monotherapy cevostamab, an FcRH5xCD3 bispecific antibody that facilitates T cell-directed killing of myeloma cells, and has shown promising activity and manageable safety in an ongoing Phase I study in patients with relapsed/refractory (R/R) multiple myeloma (MM) (GO39775 [NCT03275103]; Trudel et al. ASH 2021). Methods: Complementary quantitative pharmacology approaches to characterize and decouple the pharmacological drivers for efficacy and CRS were used to inform the selection of the priming dose(s) (to mitigate CRS) and target dose (to maximize efficacy) of cevostamab recommended phase 2 dose (RP2D) in R/R MM patients. To identify a target dose with maximal benefit-risk profile, a model-guided clinical empirical dose escalation/expansion data, together with real-time population pharmacokinetics-tumor growth inhibition (PopPK-TGI) and exposure-response (E-R) characterizations across a dose range of 0.15mg − 252mg in study GO39775 for the clinically relevant safety and efficacy endpoints was employed. For the priming dose regimen evaluation, logistic regression analysis was performed to characterize the statistical relationship between cevostamab exposure and CRS. Given the time and exposure dependence of CRS, a mechanistic quantitative systems pharmacology (QSP) model was leveraged to identify a suitable priming dose regimen that can mitigate the CRS risk while enabling the delivery of higher target doses that would provide meaningful clinical benefit in patients with R/R MM. Specifically, the QSP model was utilized to evaluate the effect of various priming regimens on the post-dose IL-6 dynamics. Results: The Sponsor used an integrated framework of clinical evidence and comprehensive modeling and simulation approaches (including QSP, PopPK-TGI, and E-R) for cevostamab across a wide range of doses/schedules in study GO39975. A comprehensive in silico evaluation based on QSP simulations indicated that the 0.3/1.2/3.6mg triple-step priming regimen prior to the target dose falls within the region of lowest post-dose peak IL-6 concentrations suggesting lowered CRS risk for cevostamab. The clinical data, along with the E-R and PopPK-TGI analyses, demonstrate that increasing cevostamab exposure is associated with higher response rates (overall response rate and VGPR+ rate), with the attainment of plateau at the 160mg Q3W dose, implying diminishing gains at doses beyond 160mg Q3W. Conclusions: The model-guided clinical dose escalation/expansion data together with the quantitative pharmacology strategies and insights support that the 0.3/1.2/3.6mg triple-step priming regimen and the Q3W 160mg target dose provides a favorable benefit-risk for the R/R MM population.

THE ROLE OF FINANCIAL LITERACY IN IMPROVING THE LEVEL OF FINANCIAL KNOWLEDGE, FINANCIAL SKILLS AND RESPONSIBILITIES AMONG NON-GOVERNMENT ORGANISATIONS FINANCIAL MANAGERS IN KWAZULU-NATAL

The lack of empirical evidence on key drivers of financial illiteracy among financial managers within non-governmental organisations (NGOs) sector can be the trigger of the current rate of NGOs collapsing in South Africa. Using a quantitative research strategy, this study investigated the role of financial literacy in improving the level of financial knowledge, financial skills and financial responsibility among 53 NGOs finance staff who were purposively and conveniently sampled. The respondents comprise financial managers, accountants, and senior managers and project managers. Data generated were analysed using descriptive and inferential statistics. The results of the study revealed that all the independent variables of the study correlated between .247 and .569. The findings of the study revealed that the ability to prepare a strategic business plan and the respondents’ capability to cope with their organisations’ financial shortfalls have a positive and significant relationship with their level of financial literacy. The study further found that (1) dealing with financial diversification (2) comparing their organisations’ financial performance against the financial objectives (3) writing financial objectives and (4) their ability to raise funds from different funders for their organisations have a positive and significant influence with their financial literacy. This article is the first to explore the financial literacy status among NGOs financial managers in KwaZulu-Natal (KZN). Relying on these empirical findings for the financial literacy variables. This study will fill a gap in understanding the variables identified that drive the financial literacy of NGOs financial managers. These findings will help NGOs financial managers to choose the financial literacy practices model to improve their financial knowledge, skills and responsibilities and eventually enhance the financial viability and sustainability of their entities. This study contributes to the current body of knowledge and further offer insight into constructs for measures of financial literacy among NGOs context.

Exploring an indirect quantification strategy for PFOA in rat tissues via efficient degradation and fluorescence spectroscopy

Perfluorooctanoic acid (PFOA) has aroused global attention due to its persistence, biotoxicity and bioaccumulative properties. Monitoring PFOA levels in biological samples could give significant insights into its toxicity mechanism and health risk evaluation. Herein, to explore the distribution of PFOA in biological tissues, an indirect quantitation strategy for PFOA in biological samples was proposed based on fluorescence spectroscopy. F- ion was produced through highly efficient degradation of PFOA in polar aprotic solvent, and then F- ion was detected with highly selective F- ion fluorescent probe, which enabled the indirect quantitative detection of PFOA in biological samples. The sensitivity for PFOA was exhibited with limit of detection of 3.55 μM. The method’s accuracy and precision were validated by spiked biological sample experiment, with recovery rate ranging from 93.2 % to 105.5 % and relative standard deviation below 9.2 %. The content of PFOA was successfully detected in rats livers, kidneys and lungs tissues. Density function theory was employed to explore defluorination pathway, indicating that heating, alkaline and water conditions played a crucial role in the degradation of PFOA to produce F- ion. Thus, this novel strategy is suitable for quantitative analysis of PFOA in biological samples with high selectivity, sensitivity, recovery ratio, and simple sample preparation, which also provides a strategy for evaluating the levels of per- and polyfluoroalkyl substances in biological samples.

Social Media marketing and restaurants’ brand equity after COVID-19: A revitalizing model

The COVID-19 pandemic has reshaped the establishment of long-term brand equity through social media marketing (SMM) due to the imperative of swiftly adapting to dynamic digital landscapes and heightened competition. This study investigates the impact of SMM on customer-based brand equity (CBBE) for restaurants in the post-COVID-19 era, by reevaluating the SMM model and postulating the effect of electronic word-of-mouth (e-WOM) as a mediating variable rather than an independent one. The study adopts a deductive approach and a descriptive methodologvisualisey where a quantitative survey-based strategy utilizing a five-point Likert scale questionnaire was employed. The validated questionnaire was distributed online to active social media users following local restaurant social media accounts; a convenience sample was utilized. After filtering and screening, 618 responses were analyzed using partial least squares (PLS)-SEM to test the proposed hypotheses. Our findings emphasize that interaction is the most influential factor in shaping restaurants’ CBBE, followed by trendiness and customization. In contrast, entertainment does not significantly affect CBBE. Additionally, e-WOM mediates the relationship between SMM and CBBE. Repositioning e-WOM in the model enhances our comprehension of its impact compared to its previous position, as it cannot be completely controlled by restaurants. In-depth discussions and managerial and theoretical implications are provided.

Risk-oriented source apportionment and implications for mitigation strategies of VOCs in industrial parks: Insights from odor pollution and health risks

A typical industrial park in the upper reaches of the Yangtze River Economic Belt, which is 70 km from the Chongqing urban center, was used to investigate the occurrence and exposure of harmful volatile organic compounds (VOCs). An exposure risk method and a risk-oriented source apportionment approach were performed to assess the inhalation risks and apportion VOC sources, respectively. The quantitative relationships between risk factors and pollution sources were established, identifying key pathogenic and odorous VOCs. The quantitative emission reduction strategies were developed based on risk thresholds. Residents within the industrial parks face potential health risks due to pathogenic VOCs and nuisance odors, and workers in specific sectors experience two to six times higher risks than those in residential areas. Six risk sources were identified in the industrial park, ranked according to their contribution to VOC concentrations as follows: industrial sewage treatment (IST) (32.59 %), natural gas chemical industry (NGCI) (27.77 %), diesel vehicle exhaust (DVE) (12.04 %), pharmaceutical manufacturing industry (PMI) (11.14 %), chemical raw materials manufacturing (CRMM) (9.96 %), and iron and steel industry (ISI) (6.5 %). Among these, NGCI, IST, and CRMM were the top contributors to pathogenic risks, with contributions of 32.13 %, 29.71 %, and 21.71 % to non-carcinogenic risks, and 18.15 %, 19.87 %, and 27.99 % to carcinogenic risks, respectively. DVE produced significantly higher odor pollution compared to other sources, with intensities that were 3 to 10 times greater. The key pathogenic and odorous VOCs differ by source, resulting in varying control priorities for different VOC species. Reducing emissions from these six sources for 20 high-risk species (e.g., acrolein, 2-chlorotoluene, 1,2-dibromoethane, dichloromethane, and p-diethylbenzene) will simultaneously lower pathogenic and odor risks, with cumulative reduction rates ranging from 4.11 % to 93.75 %. This study provides quantitative control targets for VOCs from a health risk perspective, offering valuable guidance for developing risk management policies in industrial parks.

FACTORS INFLUENCING INVESTMENT IN COMMERCIAL REAL ESTATE IN JOS PLATEAU STATE

The investors in real estate are faced with challenges as often stated of knowing if the investment they make will generate returns. The paper aimed to analyse factors influencing investment in commercial real estate in Jos Plateau State, with a view to determining the level of returns of real estate investment. The study adopts survey method using quantitative approach and survey strategy was adopted and data were collected through questionnaire survey. The study adopts multistage sampling because the sampling technique involves taking samples in stages from smaller sampling units. The study revealed that factors that influence residential and commercial real estate were availability of social amenities, return and profit from real estate investment, availability and cost of acquiring land for investment and location advantage of the area. Urban planners and developers should prioritise the inclusion of social amenities in new developments. Collaborate with local communities to identify their specific needs and preferences. Government agencies should invest in infrastructure and programs that support the development and maintenance of social amenities. This includes funding for schools, parks, and public transportation. Real estate investors should consider social amenities as a primary factor when evaluating investment opportunities. Conduct thorough research on the availability and quality of amenities in target neighborhoods.

Pengaruh Kemampuan Menghadapi Konflik dan Kritik Terhadap Hubungan Sosial

Social relationship abilities can be improved by the ability to deal with conflict and criticism. The aim of the research is to reveal the influence of the ability to deal with conflict (X1) with the ability to deal with criticism (X2), using a quantitative associative strategy method. The research location was SMP Negeri 2 Gido, the research population was all class VIII students and the sample was taken purposively as many as 60 people. The data collection instrument was a closed questionnaire which was distributed to a number of respondents. Data were analyzed using multiple linear regression analysis to answer research questions according to the proposed hypothesis. The research results concluded that the ability to deal with conflict (X1) partially had a significant effect on improving social relations (Y). The regression coefficient value for the variable ability to deal with conflict is negative, namely 0.071 and every 1.00% increase in ability will be followed by an increase in social relations of 0.071%. The ability to face criticism (X2) partially has a significant effect on increasing social relationship abilities (Y). The regression coefficient value for the ability to face criticism is positive, namely 1.102 and for every 1.00% increase in the ability to face criticism, it will be followed by an increase in social ability of 1.102%. Variables X1 and X2 together have a significant effect on variable Y and every increase in X1 and the contribution of the influence of variables X1 and it is recommended for school counselors to provide special guidance and counseling services to handle, develop and improve students' social abilities.

An innovative method for short-term forecasting of blockchain cryptocurrency price

Cryptocurrency market sentiment is relatively unstable, which makes cryptocurrency price an attribute of high volatility. Accurate forecasting methods help to clarify the volatility trend of the cryptocurrency price, thereby reducing the investment risk of participants in the cryptocurrency market. Therefore, this research proposed a new method for short-term forecasting of the cryptocurrency price based on a small sample. This study took three typical blockchain cryptocurrencies (Bitcoin, Ethereum, Litecoin) as experimental objects, chose data intervals with different volatility trends in the U.S. stock indices between 2022 and 2023 as sample data, and used grey correlation analysis to select core affecting variables. Furthermore, this study built a grey multivariate convolution model with prioritized accumulating novel information for conducting prediction experiments on blockchain cryptocurrency price. The research findings demonstrate that the proposed model achieves high prediction accuracy in all experiments, and the model accuracy is superior to the comparison models. This study proposes a scientific prediction approach for blockchain cryptocurrency price, which can guide financial investors in developing and analyzing quantitative financial trading strategies to a certain extent. Meanwhile, this study provides a specific reference for relevant government departments to strengthen cryptocurrency regulation, prevent financial risks, and maintain financial stability.

Exploring the Role of Mindfulness-Based Interventions in Enhancing Psychological Capital and Academic Adjustment among Chinese Students

Objectives: This study developed the main aim to investigate the role of mindfulness, psychological capital and intrinsic motivation on the academic performance and student engagement. Methods: This study has employed a serial mediation model and designated the psychological capital as the first mediator and intrinsic motivation as the second mediator. The study measured the variables and the designed associations using the quantitative research strategy and a questionnaire was developed to collect numeric data from the target population. The target population of this study was the students of the education sector in the China. Total 295 valid responses were collected and adopted for data analysis. The study used the SPSS and Amos 24 for data analysis. The structural equational modelling SEM was used to evaluate the regression and association testing. The analysis technique revealed significant relationships between all the variables. Results: The study revealed that mindfulness has a significant impact on the academic performance and the mediation of psychological capital and intrinsic motivation between the mindfulness and dependent variables. Conclusion: The results implied the fact that there is a significant serial mediation exist between all the designed variables.

Quantitative Standardized Expansion Assay: An Artificial Intelligence-Powered Morphometric Description of Blastocyst Expansion and Zona Thinning Dynamics.

Artificial intelligence applied to time-lapse microscopy may revolutionize embryo selection in IVF by automating data collection and standardizing the assessments. In this context, blastocyst expansion dynamics, although being associated with reproductive fitness, have been poorly studied. This retrospective study (N = 2184 blastocysts from 786 cycles) exploited both technologies to picture the association between embryo and inner-cell-mass (ICM) area in µm2, the ICM/Trophectoderm ratio, and the zona pellucida thickness in µm (zp-T) at sequential blastocyst expansion stages, with (i) euploidy and (ii) live-birth per transfer (N = 548 transfers). A quantitative-standardized-expansion-assay (qSEA) was also set-up; a novel approach involving automatic annotations of all expansion metrics every 30 min across 5 h following blastulation. Multivariate regressions and ROC curve analyses were conducted. Aneuploid blastocysts were slower, expanded less and showed thicker zp. The qSEA outlined faster and more consistent zp thinning processes among euploid blastocysts, being more or as effective as the embryologists in ranking euploid embryo as top-quality of their cohorts in 69% of the cases. The qSEA also outlined faster and more consistent blastocyst expansion and zp thinning dynamics among euploid implanted versus not implanted blastocysts, disagreeing with embryologists' priority choice in about 50% of the cases. In conclusion, qSEA is a promising objective, quantitative, and user-friendly strategy to predict embryo competence that now deserves prospective validations.

The Fulfillment of Psychological Contract and Job Satisfaction in Reducing Procrastination of Employees Working in the Public SectorPublic Sector

This research aims to examine the factors that influence procrastination among education staff at a university. The research used mono methods: quantitative method, survey strategy, and cross-sectional research design. The data were collected by questionnaires distributed offline and online via Google Forms to 145 education staff at a state university in Yogyakarta. The data were analyzed using the Structural Equation Modeling equation based on Partial Least Square (SEM PLS). The research found that achievement motivation and organizational commitment influence procrastination directly and through the mediation of fulfilling the psychological contract. Meanwhile, job satisfaction does not significantly affect procrastination, nor does it mediate the influence of achievement motivation and organizational commitment on procrastination. The research results were then discussed according to the characteristics of organizations in the public sector in higher education.

Social Media Shaping Youth’s Perception of Moroccan Female Political Leaders

This paper addresses the role of social media consumption amongst youth and its implications in shaping their perceptions of Moroccan female political leaders. Based on examining previous scholarly works, this research investigates a large body of literature including how social media platforms impacts youth’s engagement in political and civic activities. The role of social media in shaping perceptions about female political leaders is also discussed from a theoretical standpoint. Aiming to identify, the influence of a number of identified form the literature, this paper adopted a quantitative strategy utilizing a survey that was addressed to (N=309) participant targeted on both social media platforms and within university campus. Based on the descriptives, the correlation analysis and regression analysis of the data gathered, this study identified that the level of education, social media exposure, online interactions, political awareness, and cultural factors significantly influence youth’s perceptions of female political leaders. These findings highlight the factors influencing young adults’ perceptions about the Moroccan political landscape and the importance of media education and literacy to enhance critical thinking skills amongst Moroccan youth.

Unlocking HR’s Hidden Treasure on Innovation Capability: The Mediating Role of Knowledge Management Capacity

Effective innovation is possible when one can innovate. Malaysia has serious problems due to a lack of R&D and innovation skills. Furthermore, small and medium-sized firms (SMEs) in developing nations have received less attention in past research on Malaysian innovation. A quantitative strategy is employed to meet the study's goals. A survey approach is used to collect data from the owners, chief executives, and management of ICT SMEs. The research collected and analyzed a total of 200 respondents. The research models and hypotheses were assessed using the Partial Least Squares (PLS) technique. This research comprises the difference between Innovative Centric Human Resource Management (ICHRM) (Acquisition; Development; Egalitarian; Collaborative; Documentation, Information System) and Innovation Capability (Radical; Incremental), also the relationship between ICHRM and Innovation Capability, using Knowledge Management Capacity as a mediator. Based on the findings of this study, the majority of HRM practices are suitable for SMEs' incremental innovation capabilities. This study also presents empirical evidence of the impact of ICHRM on knowledge management capacity on innovation, focusing on the impact of each dimension in ICHRM components that were linked to organizational processes, with a focus on the influence on incremental and radical innovation.

THE INFLUENCE OF THE PROBLEM-BASED LEARNING MODEL ON THE MATHEMATICS LEARNING ACHIEVEMENT IN RELATION AND FUNCTION MATERIAL OF GRADE X STUDENTS AT SMK NEGERI 1 TOMA.

The specialist's point is to decide the impact of the Issue Put together Getting the hang of learning model with respect to the math learning accomplishment of class X understudies at SMK Negeri 1 Toma. This exploration utilizes quantitative strategies with a Semi Trial approach. Inspecting was done utilizing the Group Irregular Examining procedure, where the class was picked arbitrarily from every one of the populaces utilized by the scientist. There was likewise a complete exploration populace of 47. The classes picked by the specialists were the ATPH-X class with 18 understudies as the exploratory class and TKJ-X with 20 understudies as the control class. The instrument utilized is a test. The aftereffects of exploration information handling showed that the typical score for the trial class pretest was 45.48 and the control class was 41.00 and the exploratory class posttest was 78.61 and the control class was 61.75. Speculation testing utilizes the Free Examples Test where the Sig (2-followed) esteem is 0.000 < 0.05. It tends to be reasoned that there is a tremendous contrast in the Posttest of the two classes, implying that the Issue Based Getting the hang of learning model affects understudy learning accomplishment in relationship material and capability. This is impacted by understudies who assume a functioning part in tackling issues connected with regular day to day existence. The findings of this study ought to be taken into consideration by future researchers when choosing a learning model. They propose that: (1) the Problem Based Learning model becomes an alternative for teachers in order for students to maximize their learning outcomes, particularly with regard to the Relationship and Function material; (2) students are advised to improve their ability to understand the material through their knowledge of everyday life; and (3)

4D-FastDIA-based quantitative proteomics reveals protein biomarkers linked to flavor traits between triploid and diploid oysters (Crassostrea angulata)

In this study, the 4D-FastDIA-based quantitative proteomics strategy was employed to identify protein biomarkers linked to flavor traits in triploid and diploid oysters (Crassostrea angulata). A comprehensive analysis identified a total of 61,947 peptides and 9703 protein groups across four sample groups, with 3679 common proteins shared among all groups. Differentially expressed proteins (DEPs) were found to be significantly associated with flavor-related traits, revealing notable differences in amino acids, lipids, and nucleotides between triploid and diploid oysters. Enrichment analysis further elucidated significant pathways involved in metabolic processes, biosynthesis, and immune responses. Among the DEPs, 864 were correlated with flavor quality traits, highlighting key proteins such as S-adenosylmethionine synthase and pyruvate kinase as potential biomarkers for flavor traits. These findings provide valuable insights into the molecular mechanisms underlying flavor differences in oysters and offer potential biomarkers for evaluating and enhancing oyster quality.

Research on Quantitative Investment Strategies of Securities Investment Funds

This paper investigates the quantitative investment strategies of securities investment funds, aiming to explore their applications in financial markets and their impact on investment decision-making. Through a review of relevant literature and empirical data, the paper first outlines the basic concepts of quantitative investing and its importance in securities investment funds. Then, it discusses various quantitative strategies, including multi-factor models and machine learning algorithms, and validates their effectiveness through case studies. The research finds that quantitative investing can enhance the scientific and accurate nature of investment decisions while effectively reducing investment risks. Finally, the paper provides suggestions for optimizing quantitative strategies and discusses future trends, offering practical references for the investment industry.