Relative Strength Research Articles

Synergistic regional emission reductions in China: Network evolution, spatial and temporal characteristics, and driving factor

Synergistic regional emission reduction (SRER) is an important part of the strategy to achieve carbon neutrality. In this paper, a system efficiency value-added model is proposed to measure the strength of SRER network relationships for 40,186 sets of city pairs and the SRER levels of 284 cities in China from 2011 to 2021. Subsequently, the evolution characteristics of the SRER network, the spatial and temporal characteristics of urban SRER levels, and the driving factors of the SRER network evolution are further analyzed. The results are as follows: SRER network is shifting from a fan-shaped network pattern of “three-region clustering and sporadic coverage” to a shuttle-shaped nested network pattern of “east-west linkage and multi-region support”. Time series analysis found that urban SRER level in China showed fluctuating growth characteristics during the study period. Spatial analysis reveals that the spatial autocorrelation of urban SRER level is decreasing, and the spatial distribution range is expanding towards the southwest. Emission reduction comparative advantages (CAER), green innovation division of labor (GIDL) and urban economic linkages (LCEL) are driver factors of SRER network evolution. This study proposes an evaluation method for the urban SRER level from network relationships perspective. Subsequently, network evolution characterization, spatial and temporal characterization, and driving factor analysis were conducted. The results of the study provide important theoretical guidance and inspiration for grasping the current status and enhancement paths of China's urban SRER level.

Digital Marketing Of Startup Businesses

This dissertation's overarching goal is to investigate how digital marketing may help new businesses get off the ground and competing. Digital marketing's impact on start-up growth, brand awareness, consumer loyalty, and customer relationship strength would be the subject of future study. There is a lack of literature on this topic; the only relevant study we could locate focused on start-ups and social media, suggesting that the former positively impacts the latter's capacity for innovation. The study relied on a qualitative research strategy based on semi-structured interviews with five startup companies to compile its findings. The study also made use of secondary data culled from online resources, journals, and peer-reviewed papers.Research shows that digital marketing is an effective and innovative strategy for attracting, retaining, and growing a business's clientele. Websites, industry-specific media, and online discussion groups have proven to be the most fruitful avenues for new businesses. It goes on to say that new businesses may make a lot of progress with digital marketing by raising customer awareness, trust, and brand recognition. But, when they first launch, the majority of start-ups are hesitant to use digital marketing methods.The analysis not only highlighted the advantages, but it also revealed the digital marketing tactics that worked best for new businesses. A new channel for reaching consumers and building brands, social media marketing has just arisen. Startups were able to successfully create a strong online presence in large part due to their ability to target certain demographics and customize content to connect with those audiences. Despite the bright potential of digital marketing, the study also shed light on a sobering fact: many new businesses are hesitant to completely commit to digital marketing techniques when they first launch.

Ethnic-Racial (Mis)Match between Mentors and Mentees on Perceived Strength of Relationship

Mentoring programs are popular mechanisms for promoting positive youth development due to developmental research suggesting that youth with strong relationships with a special adult have a lower likelihood of a range of negative outcomes. Community-based mentoring programs are mechanisms for promoting access to mental health support and positive youth outcomes. Youth mentorship programs reveal mixed and modest outcomes, yet youth outcomes are more robust for high-quality relationships. Ethnic-racial identity is hypothesized to affect the relationship quality because shared identities are thought to facilitate trust and empower youth to succeed. However, studies have found that ethnic-racial match does not affect the strength of the dyadic relationship. This study attempts to address these disparate findings by explicitly examining ethnic-racial matching and relationship quality at two-time points. We conducted a two-step hierarchical regression to determine whether ethnic-racial match affected mentee reports of the relationship at follow-up. The results show ethnic-racial match did not significantly predict positive dimensions of the relationship at follow-up. Moreover, same-ethnic-racial matches reported a lower rate of change and slightly less relationship dissatisfaction at time 2 compared to cross-ethnic-racial matches. Results contribute to existing literature showing mixed results in ethnic-racial matching in youth mentorship programs.

Effect of Simulated Eutrophication of Peatlands on the Microbiome of Utricularia vulgaris L.

Global climate change and increasing human impact are the main factors intensifying eutrophication of peatland ecosystems. Due to the high sensitivity of certain groups of microorganisms, they can serve as indicators of the degree of eutrophication and thereby provide much important information for assessment of the state of peatland ecosystems. However, there is still little knowledge of how changes in the fertility of the environment can affect the microbiome of carnivorous plants in these ecosystems. This study was conducted to verify the following hypotheses: (1) the microbiome of carnivorous plant traps reflects the trophic status of the habitat; (2) an increase in the concentration of biogenic compounds causes a greater increase in the size of microbial communities in the aquatic environment than in the traps. An experiment was carried out in laboratory conditions to determine the effect of simulated eutrophication on the microbiome of Utricularia vulgaris L. An experimental increase in habitat fertility caused an increase in the abundance of bacteria, flagellates, testate amoebae, ciliates, and rotifers, while a decrease in abundance was observed for crustaceans. The increase in the concentration of biogenic compounds also modified the taxonomic composition of communities of microorganisms and small metazoa as well as the strength of trophic relationships; as the trophic level increased, the relationships between bacteria and heterotrophic flagellates; bacteria and testate amoebae; and bacteria and ciliates became stronger.

A Bayesian quasi-likelihood design for identifying the minimum effective dose and maximum utility dose in dose-ranging studies.

Most existing dose-ranging study designs focus on assessing the dose-efficacy relationship and identifying the minimum effective dose. There is an increasing interest in optimizing the dose based on the benefit-risk tradeoff. We propose a Bayesian quasi-likelihood dose-ranging design that jointly considers safety and efficacy to simultaneously identify the minimum effective dose and the maximum utility dose to optimize the benefit-risk tradeoff. The binary toxicity endpoint is modeled using a beta-binomial model. The efficacy endpoint is modeled using the quasi-likelihood approach to accommodate various types of data (e.g. binary, ordinal or continuous) without imposing any parametric assumptions on the dose-response curve. Our design utilizes a utility function as a measure of benefit-risk tradeoff and adaptively assign patients to doses based on the doses' likelihood of being the minimum effective dose and maximum utility dose. The design takes a group-sequential approach. At each interim, the doses that are deemed overly toxic or futile are dropped. At the end of the trial, we use posterior probability criteria to assess the strength of the dose-response relationship for establishing the proof-of-concept. If the proof-of-concept is established, we identify the minimum effective dose and maximum utility dose. Our simulation study shows that compared with some existing designs, the Bayesian quasi-likelihood dose-ranging design is robust and yields competitive performance in establishing proof-of-concept and selecting the minimum effective dose. Moreover, it includes an additional feature for further maximum utility dose selection.

Divergent future change in South Atlantic Ocean Dipole impacts on regional rainfall in CMIP6 models

Abstract The South Atlantic Ocean Dipole (SAOD) exerts strong influence on climate variability in parts of Africa and South America. Here we assess the ability of an ensemble of 35 state-of-the-art coupled global climate models to simulate the SAOD impacts on regional rainfall for the historical period (1950 to 2014) and future projections (2015 - 2079). For both periods we consider the peak phase of the dipole which is in austral winter. Observational analysis reveals four regions with spatially coherent SAOD impacts on rainfall; Northern Amazon, Guinea Coast, Central Africa, and Southeast Brazil. The observed rainfall response to the SAOD over Northern Amazon (0.31 mm/day), Guinea Coast (0.38 mm/day), and Southeast Brazil (0.12 mm/day) are significantly underestimated by the modeled ensemble-mean response of 0.10±0.15 mm/day, 0.05±0.15 mm/day, -0.01±0.04 mm/day, respectively. A too southerly rain belt in the ensemble, associated with warmer-than-observed Atlantic cold tongue, leads to better performance of models over Central Africa (46% simulate observations-consistent SAOD-rainfall correlations) and poor performance over the Guinea Coast (only 5.7% simulate observations-consistent SAOD-rainfall correlations). We found divergent responses among the projections of ensemble members precluding a categorical statement on the future strength of the SAOD-rainfall relationship in a high-emissions scenario. Our findings highlight key uncertainties that must be addressed to enhance the value of SAOD-rainfall projections for the affected African and South American countries.

Short-term trends of air quality and pollutant concentrations in Nigeria from 2018–2022 using tropospheric sentinel-5P and 3A/B satellite data

This study presents a comprehensive analysis of atmospheric parameters in Nigeria, utilizing Sentinel-5P and 3A/B offline datasets from April 2018 to December 2022. The research evaluates the concentrations of pollutants such as Nitrogen Dioxide (NO2), Sulphur Dioxide (SO2), Ozone (O3), Formaldehyde (HCHO), Methane (CH4), Carbon Monoxide (CO), and Aerosol Indices (AI) across different periods. The data analysis employs multivariate techniques, including Factor Analysis, Pearson Correlation Coefficient, Scatter Plots, and Dendrogram Clustering, providing insights into the interrelationships and potential sources of pollution. Additionally, the study examines monthly trends, temperature variations, and their impacts on human health and climate. The findings reveal stable levels of NO2, SO2, O3, and HCHO with minimal fluctuations, while CH4 and CO exhibit slight variations, possibly influenced by emission reductions or atmospheric changes. Aerosol optical indices consistently indicate aerosol presence, influenced by local emissions and atmospheric processes. Multivariate analyses highlight shared patterns among atmospheric constituents, shedding light on potential common sources and chemical relationships. The Pearson correlation coefficients illustrate the strength and direction of linear relationships, offering valuable information on pollution dynamics. The study's visualizations, including scatter plots and dendrogram clustering, enhance the understanding of complex interactions. Overall, the research underscores the significance of continuous monitoring and research efforts for informed decision-making and environmental management in Nigeria.

Trace metal transfer to passerines inhabiting wastewater treatment wetlands

Wastewater treatment wetlands are cost-effective strategies for remediating trace metals in industrial effluent. However, biogeochemical exchange between wastewater treatment wetlands and adjacent environments provides opportunities for trace metals to cycle in surrounding ecosystems. The transfer of trace metals to wildlife inhabiting treatment wetlands must be considered when evaluating wetland success. Using passerine birds as bioindicators, we conducted a multi-tissue analysis to investigate the mobilization of zinc, copper, and lead derived from wastewater to terrestrial wildlife in treatment wetlands and surrounding habitat. In addition, we evaluate the strength of relationships between metal concentrations in non-lethal (blood and feathers) and lethal (muscle and liver) sample types for estimation of toxicity risk. From July 2020 to August 2021, 177 passerines of seven species were captured at two wetlands constructed to treat industrial wastewater and two reference wetlands in the coastal plain of South Carolina. Feather, blood, liver, and muscle samples from each bird were analyzed for fourteen metals using inductively coupled plasma mass spectrometry and direct mercury analysis. Passerines inhabiting wastewater treatment wetlands accumulated higher concentrations of zinc in liver, copper in blood, and lead in feathers than passerines in reference wetlands, but neither blood nor feather concentrations were correlated with internal tissue concentrations. Of all the detected metals, only mercury in the blood showed a strong predictive relationship with mercury in internal tissues. This study indicates that trace metals derived from wastewater are bioavailable and exported to terrestrial wildlife and that passerine biomonitoring is a valuable tool for assessing metal transfer from treatment wetlands. Regular blood sampling can reveal proximate trace metal exposure but cannot predict internal body burdens for most metals.

Explicit and Implicit Pattern Relation Analysis for Discovering Actionable Negative Sequences.

Real-life events, behaviors, and interactions produce sequential data. An important but rarely explored problem is to analyze those nonoccurring (also called negative) yet important sequences, forming negative sequence analysis (NSA). A typical NSA area is to discover negative sequential patterns (NSPs) consisting of important nonoccurring and occurring elements and patterns. The limited existing work on NSP mining relies on frequentist and downward closure property-based pattern selection, producing large and highly redundant NSPs, nonactionable for business decision-making. This work makes the first attempt for actionable NSP discovery. It builds an NSP graph representation, quantifies both explicit occurrence and implicit nonoccurrence-based element and pattern relations, and then discovers significant, diverse, and informative NSPs in the NSP graph to represent the entire NSP set for discovering actionable NSPs. A DPP-based NSP representation and actionable NSP discovery method, EINSP, introduces novel and significant contributions to NSA and sequence analysis: 1) it represents NSPs by a determinantal point process (DPP)-based graph; 2) it quantifies actionable NSPs in terms of their statistical significance, diversity, and strength of explicit/implicit element/pattern relations; and 3) it models and measures both explicit and implicit element/pattern relations in the DPP-based NSP graph to represent direct and indirect couplings between NSP items, elements, and patterns. We substantially analyze the effectiveness of EINSP in terms of various theoretical and empirical aspects, including complexity, item/pattern coverage, pattern size and diversity, implicit pattern relation strength, and data factors.

Different but Synergistic Effects of Union and Manager Leadership on Member Job Satisfaction

Existing research has tended to overlook the diverse roles of union leadership in contributing to member attitudes. Drawing on the social information processing theory, this study examines how union leaders’ (shop stewards) service-oriented leadership relates to member job satisfaction. To clarify the mechanism underlying this relationship, this study focuses on union instrumentality as a mediator. The research also examines managers’ ethical leadership as a conditional factor in the relationship between union leaders’ service-oriented leadership and member job satisfaction through union instrumentality. To test our hypothesis, this study analyzed the results of a survey of 603 respondents from two branches of the Korean Metal Workers’ Union. The findings of this study indicate that union instrumentality is the link between service-oriented union leadership and member job satisfaction. Additionally, the strength of the mediated relationship between the aforementioned factors through union instrumentality is contingent on managerial ethical leadership. This study contributes to an integrated understanding of the way in which service-oriented union stewards and ethical managers influence member job satisfaction through their leadership.

Species motif participation provides unique information about species risk of extinction.

Loss of species in food webs can set in motion a cascade of additional (secondary) extinctions. A species' position in a food web (e.g. its trophic level or number of interactions) is known to affect its ability to persist following disturbance. These simple measures, however, offer only a coarse description of how species fit into their community. One would therefore expect that more detailed structural measures such as participation in three-species motifs (meso-scale structures which provide information on a species' direct and indirect interactions) will also be related to probability of persistence. Disturbances affecting the basal resources have particularly strong effects on the rest of the food web. However, how disturbances branch out and affect consumer persistence depends on the structural pattern of species interactions in several steps. The magnitude, for example, the proportion of basal resources lost, will likely also affect the outcome. Here, we analyse whether a consumer's risk of secondary extinction after the removal of basal resources depends on the consumer's motif participation and how this relationship varies with the severity of disturbance. We show that consumer species which participate more frequently in the direct competition motif and less frequently in the omnivory motif generally have higher probability of persistence following disturbance to basal resources. However, both the strength of the disturbance and the overall network structure (i.e. connectance) affect the strength and direction of relationships between motif participation and persistence. Motif participation therefore captures important trends in species persistence and provides a rich description of species' structural roles in their communities, but must be considered in the context of network structure as a whole and of the specific disturbance applied.

THE IMPACT OF CORPORATE SOCIAL NETWORK ON INNOVATION PERFORMANCE - THE CHAIN MEDIATION EFFECT BASED ON KNOWLEDGE ACQUISITION AND ABSORPTIVE CAPACITY

Innovation is the lifeblood and inexhaustible driving force behind the development of an enterprise. With the continuous improvement of scientific and technological levels, the acceleration of technological change, and the changes in market demand, the complexity and uncertainty of innovation are increasing day by day. Enterprises have to acquire knowledge from outside, to obtain new impetus for enterprise development. The rich knowledge resources contained in corporate social networks provide a foundation for future innovation and development. This article takes Chinese enterprises as the research object and uses open innovation theory, social network theory, and knowledge management theory to construct a conceptual model of "social network-knowledge acquisition-absorptive capacity-innovation performance." Through empirical analysis of 549 valid questionnaires, it was found that networks do not all have a positive effect on enterprise innovation performance. This study shows that network size, network centrality, and network heterogeneity have a positive effect on innovation performance. The effect of network relationship strength on innovation performance is not significant. Knowledge acquisition and absorptive capacity play an important role in society. Network size, network centrality, and network heterogeneity have a chain-mediating role in their impact on corporate innovation performance.

The Impact of Innovation on Business Development. The Example of Moderate Innovators and the Visegrad Group Countries

The paper analyzes the relationship between innovation dimensions, according to the European Innovation Scoreboard, and the entrepreneurship rate in a group of moderate innovator countries. Using the Pearson linear correlation, the author conducted a data analysis based on an empirical study using open data from the Summary Innovation Index – European Innovation Scoreboard (SII‑EIS) and Eurostat for 2013–2019. This period covers the moment when all the countries of the Visegrad Group (i.e., the Czech Republic, Hungary, Poland, and Slovakia – the V4) were classified into the same innovation group according to EIS. However, due to the volume limitations of the article, it was decided that Moderate Innovators from 2019 would be included in the comparative analysis, i.e., when the V4 were last collectively classified as Moderate Innovators. The results of the research positively verify the initial research hypothesis that the EIS indicators show a different strength of relationships with the entrepreneurship rate in the V4 and among other economies classified as Moderate Innovators (according to SII–2020).
 The variables that describe the quantity and quality of entrepreneurs’ innovations strongly and positively impact the V4 countries’ entrepreneurship rate. In other countries, the variables derived from the investment attractiveness of economies show a significant and positive correlation with the entrepreneurship rate. Based on the analysis of the results, it can be concluded that there is a strong positive correlation between the entrepreneurship rate and the total innovation index, which is compiled each year based on a set of variables for the European Union countries. The study showed that the entrepreneurship rate in these countries is strongly impacted by indicators representing the following groups: Innovators (small and medium‑sized enterprises (SMEs) with product or process innovations; SMEs with marketing or organizational innovations and SMEs innovating in‑house), Attractive research systems (international scientific co‑publications), Finance and support (Venture capital expenditures), Firm investments (Enterprises that provide training to develop or upgrade their personnel’s ICT skills), and Linkages (Innovative SMEs that collaborate with others). The impact of these factors on the development of entrepreneurship in the V4 means that pro‑innovative activities undertaken in operating enterprises strongly correlate with deciding to start one’s own business. Therefore, it can be concluded that entrepreneurship in these countries has an entirely different development basis than in other countries that are Moderate Innovators, where the factors mentioned above were irrelevant.

Anomaly Detection in Meteorological Data Using a Hierarchical Temporal Memory Model: A Study on the Case of Kazakhstan

In meteorology, which studies atmospheric events, data representing various properties such as temperature, rainfall, and wind speed are collected regularly over a certain period. Unexpected trends in the data may indicate that an abnormal situation is approaching. Therefore, time series (TS) data play an essential role in the early detection of potential meteorological risks. However, applying effective models by considering many complex parameters in performing accurate analysis and anomaly detection (AD) is an important criterion. In this study, machine learning-based AD is performed using a dataset containing meteorological data on different features collected between January 1, 2019, and June 30, 2023, for Kazakhstan, which has the ninth-largest surface area in the world. The Hierarchical Temporal Memory (HTM) model was used for AD, which can provide more accurate forecasts by modeling long-term dependencies and producing effective results in solving TS problems. Detected anomalies are reported at various levels depending on threshold values. In addition, to analyze the ADs more precisely, correlations are calculated using the Spearman model, which allows us to determine the strength and direction of the monotonic relationship between variables. The study's findings show that the HTM is an effective model for AD using TS data on meteorological features.

Country-based modelling of COVID-19 case fatality rate: A multiple regression analysis.

The spread of the severe acute respiratory syndrome coronavirus 2 outbreak worldwide has caused concern regarding the mortality rate caused by the infection. The determinants of mortality on a global scale cannot be fully understood due to lack of information. To identify key factors that may explain the variability in case lethality across countries. We identified 21 Potential risk factors for coronavirus disease 2019 (COVID-19) case fatality rate for all the countries with available data. We examined univariate relationships of each variable with case fatality rate (CFR), and all independent variables to identify candidate variables for our final multiple model. Multiple regression analysis technique was used to assess the strength of relationship. The mean of COVID-19 mortality was 1.52 ± 1.72%. There was a statistically significant inverse correlation between health expenditure, and number of computed tomography scanners per 1 million with CFR, and significant direct correlation was found between literacy, and air pollution with CFR. This final model can predict approximately 97% of the changes in CFR. The current study recommends some new predictors explaining affect mortality rate. Thus, it could help decision-makers develop health policies to fight COVID-19.

The effect of silica nanoparticles on the shock adiabatic relation and tensile strength in polyurethane

This work exploded the effect of silica nanoparticles on shock adiabatic relation and tensile fracture in polyurethane based on atomistic simulations. It is found that the non-uniform interface structures introduced by nanoparticles can increase the shock impedance, which also leading to increased twisting of polyurethane chains and the generation of local hotspots near the nanoparticles. The interface structures near the nanoparticle consists of polyurethane chains with a high gyration radius, and the average gyration radius increases approximately linearly with the increase in nanoparticle content. At lower shock velocity, the potential energy increment initially increases and then decreases with the increase in nanoparticle content. Bond analysis reveal that flexible segments dominate the bending and twisting deformations of polyurethane, while nanoparticles enhance the corresponding deformation degrees. Furthermore, nanoparticles can serve as void nucleation sites for early-stage tensile damage, resulting in a decrease in fracture strength. Differing from metals, the relationship between tensile strength and fracture temperature follows a more linear law. Nanoparticles inhibit the later growth and coalescence of voids by increasing the temperature and steric hindrance. The density and size distribution characteristics of voids are consistent with the changes in potential energy during the shock compression process. Unlike classical spallation, the curled polyurethane chains in the tensile region gradually orient along the shock direction, evolving into a void-fiber structure.

Trust and easy access to home care staff are associated with older adults' sense of security: a Swedish longitudinal study.

Older adults are increasingly encouraged to continue living in their own homes with support from home care services. However, few studies have focused on older adults' safety in home care. This study explored associations between the sense of security and factors related to demographic characteristics and home care services. The mixed longitudinal design was based on a retrospective national survey. The study population consisted of individuals in Sweden (aged 65+ years) granted home care services at any time between 2016 and 2020 (n=82,834-94,714). Multiple ordinal logistic regression models were fitted using the generalised estimation equation method to assess the strength of relationship between the dependent (sense of security) and independent (demographics, health and care-related factors) variables. The sense of security tended to increase between 2016 and 2020, and was significantly associated with being a woman, living outside big cities, being granted more home care services hours or being diagnosed/treated for depression (cumulative odds ratio 2-9% higher). Anxiety, poor health and living alone were most strongly associated with insecurity (cumulative odds ratio 17-64% lower). Aside from overall satisfaction with home care services, accessibility and confidence in staff influenced the sense of security most. We stress the need to promote older adults' sense of security for safe ageing in place, as mandated by Swedish law. Home care services profoundly influence older adults' sense of security. Therefore, it is vital to prioritise continuity in care, establish trust and build relationships with older adults. Given the increasing shortage of staff, integrating complementary measures, such as welfare technologies, is crucial to promoting this sense of security.

Abstract 4824: Multi-level intervention to increase minority cancer patient enrollment to clinical treatment trials- Study design considerations and baseline characteristics from the ACTWONDER2S Study

Abstract Introduction: Black/African American (AA) and Hispanic cancer patients are underrepresented in cancer clinical trials (CCTs). Disparities stem from multiple factors, thus, comprehensive, multi-level interventions (MLI) are needed. Presented here is a conceptual framework for selecting the appropriate study design for MLIs using ACTWONDER2S, a multi-level intervention (MLI) aimed to increase Black/AA and Hispanic patient participation into CCTs, as an illustrative example. Baseline characteristics of the ACTWONDER2S target populations are also provided. Methods: ACTWONDER2S integrates community health educator and digital tools into an MLI targeting community residents and physicians in the Moffitt Cancer Center (MCC) catchment area, as well as MCC patients, physicians and CCT coordinators. A literature review of the relative strengths and weakness of 5 candidate designs for MLIs was conducted, and a cluster stratified randomized design was selected. Geospatial analytics were used to identify clusters of census tracts (“priority zones”) with high Black/AA and Hispanic populations for intervention deployment. Baseline characteristics of the priority zones were described using US Census data and other sources. Results: 14 priority zones were identified, which were matched on population characteristics and randomized into intervention (n=7) and control (n=7) zones. There were no statistically significant differences between the intervention and control priority zones in total population size (paired t-test p-value=0.63), proportions of Black/AA (p=0.133) and Hispanic populations p=0.17), and distance (in miles) from MCC (p=0.64). Approximately 35.8% and 16.5% of the intervention priority zones were Hispanic or Black/AA, respectively. Average distance to MCC, cancer cases, referral and CCT enrollment rates were also similar between groups. Conclusion: Decision frameworks for MLI study design selection are lacking. The framework provided here for ACTWONDER2S can be applied to other studies seeking to evaluate MLIs. Furthermore, the randomization of the priority zones produced optimal target populations for testing the efficacy of the MIL within ACTWONDER2S. Citation Format: Dana E. Rollison, Rossybelle P. Amorrortu, Lindsay N. Fuzzell, Melany A. Garcia, Elliott S. Tapia-Kwan, Yayi Zhao, Steven A. Eschrich, Bob R. Gore, Brian S. Mittman, Nathanael B. Stanley, Susan T. Vadaparampil. Multi-level intervention to increase minority cancer patient enrollment to clinical treatment trials- Study design considerations and baseline characteristics from the ACTWONDER2S Study [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4824.

Community Awareness Model Based on Integrity, Attitude, Knowledge and Perception Towards Solid Waste Management and Environmental Care

Globally, the solid waste management (SWM) and environmental care (EC) are critical issues that require significant attention. Increasing community Awareness is one solution to address these challenges. Community awareness on SWM and EC refers to a community's knowledge and understanding of the importance of proper waste management and environmental care. The objectives in this study are: i) to identify the strength of relationships between variables and ii) to develop a multiple linear regression model for community Awareness, Integrity, Attitude, Knowledge, and Perception. A total of 254 respondents from UiTM Perak Branch, Tapah Campus participated by completing an online questionnaire, with 70.87% female and 29.13% male respondents. The study used a simple random sampling technique and applied correlation analysis and multiple linear regression. The results showed moderate associations (0.4 < r < 0.7) between community Awareness and Integrity, Attitude, Knowledge, and Perception regarding SWM and EC. The multiple linear regression model was found to be significant with a p-value of less than 0.05 where all variables were significantly contributed on community Awareness.

The relationship between exercise intention, behavioural plans, and exercise behaviour among Korean college students using the Health Action Process Approach (HAPA)

ABSTRACT This study was tests whether action plan mediates the relationship between exercise intention and exercise behaviour, and whether this mediation depends on the level of action self-efficacy, maintenance self-efficacy, and action control, using the HAPA model. The participants were 467 university students (269 males, 198 females, mean age 20.99 ± 2.06 years) who completed measures assessing their exercise intentions, action plan, action self-efficacy, maintenance self-efficacy, action control, and exercise behaviour. Data were analysed using hierarchical regression procedures in terms of moderated mediation. The results showed that action plan partially mediated the relationship between exercise intention and exercise behaviour. Action control × maintenance self-efficacy interaction showed that the strength of exercise intention-exercise behaviour relationship progressed linearly through levels of action plan and maintenance self-efficacy. These findings imply that exercise interventions for university students should focus on simultaneously bolstering action plan, action control, and maintenance self-efficacy.