Decision Level Research Articles

‘The system is engineered to do this’: Multilevel Disempowerment and Climate Injustice in Regulating Colorado’s Oil and Gas Development

Abstract Continuing to emit greenhouse gases (GHGs) moves our planet closer to crossing critical tipping points, making ongoing development of fossil fuels an act of climate injustice. Yet, the United States has doubled down on unconventional oil and gas (UOG), becoming the top global producer of hydrocarbons. Significant segments of the public resist UOG drilling, mobilizing to limit or stop production due to intersecting climate, environmental, and public health concerns. However, regulatory conflicts complicate the power of public resistance and problematize the role of the state in facilitating industry. UOG production has rapidly expanded with little capacity for public participation in decision-making. We contend this is a climate injustice—where procedural injustices across levels of governance make it difficult for the public to prevent fossil fuel extraction. We examine these injustices across state legislative, judicial, and executive governance processes in Colorado where residents have actively resisted UOG production. We demonstrate how the public consistently faces procedural inequities and power disadvantages across multiple sites and levels of decision-making, resulting in multilevel disempowerment. In this high-stakes context, our policy ethnography illustrates how these multilevel procedural injustices can facilitate rapid expansion of UOG production, where state-sanctioned activities also constitute broader acts of climate violence and injustice.

Multi-scale Knowledge Transfer Vision Transformer for 3D vessel shape segmentation

In order to facilitate the robust and precise 3D vessel shape extraction and quantification from in-vivo Magnetic Resonance Imaging (MRI), this paper presents a novel multi-scale Knowledge Transfer Vision Transformer (i.e., KT-ViT) for 3D vessel shape segmentation. First, it uniquely integrates convolutional embeddings with transformer in a U-net architecture, which simultaneously responds to local receptive fields with convolution layers and global contexts with transformer encoders in a multi-scale fashion. Therefore, it intrinsically enriches local vessel feature and simultaneously promotes global connectivity and continuity for a more accurate and reliable vessel shape segmentation. Furthermore, to enable using relatively low-resolution (LR) images to segment fine scale vessel shapes, a novel knowledge transfer network is designed to explore the inter-dependencies of data and automatically transfer the knowledge gained from high-resolution (HR) data to the low-resolution handling network at multiple levels, including the multi-scale feature levels and the decision level, through an integration of multi-level loss functions. The modeling capability of fine-scale vessel shape data distribution, possessed by the HR image transformer network, can be transferred to the LR image transformer to enhance its knowledge for fine vessel shape segmentation. Extensive experimental results on public image datasets have demonstrated that our method outperforms all other state-of-the-art deep learning methods.

Ensemble-based multimodal medical imaging fusion for tumor segmentation

The use of multimodal medical imaging is on the rise, both in academic and clinical settings. There was a meteoric growth in the use of multimodal imaging analysis (MIA) with the addition of ensemble learning techniques, which had particular advantages in the medical field. We provide an algorithmic framework that allows supervised MIA and Cross-Modality Fusing at the preprocessing phase algorithms for classification and decision-making levels, drawing inspiration from the current triumphs of deep learning approaches in medical imaging. We presented a method for picture segmentation that makes use of sophisticated convolutional neural networks to identify lesions produced by tumors in soft tissues. To do this, MRI tomography and PET scans are combined to provide multi-modal images. Networks trained with multimodal images outperform their single-modal counterparts. When it relates to tumor segmentation, fusing photos throughout the neural network (i.e., within the convolutional layer or totally connected layers) yields better results than photographs that merge the network’s output. The proposed approach employs four pre-trained models, specifically VGG 19, ResNet 50,SqueezeNet, as well as DenseNet 121. Using a dataset of ISL images, the pre-trained models are fine-tuned. Subsequently, the ensemble learning technique is employed to combine the predictions generated by the three models. Here, ensemble is based on a weighted voting method. Impressive results were obtained with the proposed ensemble method: 98.1% accuracy, 97.5% F1 score, and 90.8% Kappa score. The ensemble method outperformed individual models and existing approaches for multimodal medical fusion and classification, with a Jaccard score of 93.8% and a recall of 98.2% demonstrate its effectiveness for multimodal medical fusion and classification.

Analytical and Clinical Interference of Sample Hemolysis in Evaluating Blood Biochemical and Endocrine Parameters in Cows.

Hemolysis is a common cause of errors in laboratory tests as it affects blood parameters and leads to a positive or negative bias. This study aims to examine the relationship between the level of hemolysis (expressed as cell-free hemoglobin concentration, g/L) and the variability of metabolic and endocrine parameters and to determine the threshold level of hemolysis that causes an analytically and clinically significant bias for the twenty most frequently examined blood parameters in cows. Paired blood samples of 10 mL each were obtained from 30 cows. One was subjected to mechanical trauma and plasma was extracted directly from the other. Hemolyzed and non-hemolyzed samples from the same animal were mixed to obtain final samples with cell-free hemoglobin concentrations of 0, 1, 2, 4, 6, 8, and 10 g/L. Metabolic and endocrine parameters were measured in the samples and their deviation and the linear equation between the level of hemolysis and the deviation were determined. The following threshold values of hemolysis were determined, which correspond to the acceptable analytical (lower value) and clinical (upper value) levels of parameter variability: BHB 0.96 and 4.81; NEFA 0.39 and 3.31; GLU 0.38 and 3.90; ALB 1.12 and 6.11; TPROT 1.40 and 6.80; UREA 6.62 and 20.1; TBIL 0.75 and 5.65; AST 0.11 and 2.18; GGT 1.71 and 8.90, LDH 0.01 and 0.11, ALP 0.97 and 2.95; TGC 1.56 and 15.5; CHOL 1.29 and 8.56; Ca 5.68 and 25.7; P 0.57 and 8.43; Mg 1.10 and 8.47; INS 1.15 and 3.89; T3 8.19 and 15.6; T4 8.97 and 18.5; and CORT 2.78 and 11.22 g/L cell-free hemoglobin. Three decision levels are available for each metabolic and endocrine parameter: if hemolysis is below the lower (analytical) threshold value, results can be reported without restriction; if hemolysis is between the lower and upper thresholds, the results can be issued with guidance in the form of corrective linear equations; and if hemolysis is above the upper (clinical) threshold, the results and sample must be discarded. This method contributes to an optimal approach to hemolysis interference with metabolic profile parameters in blood samples from cows.

THE ANALYSIS STUDY OF OSTEOPOROSIS AND FRACTURE RISK : A TEN YEARS SYSTEMATIC REVIEW

Background: Microarchitecture degeneration and reduced bone mass, which increase fragility and fracture susceptibility, are hallmarks of osteoporosis, a systemic skeletal disorder. Disease has a substantial effect on fracture healing as well as bone mass and microstructure. More accurate fracture risk assessment and a wider range of treatment choices are the results of research advances. Effective treatment regimens for osteoporosis have been aided by the fracture risk assessment tool (FRAX), which has had a substantial impact on clinical decision-making and intervention levels. Methods: Following PRISMA 2020 guidelines, this systematic review concentrated on full-text English literature published between 2014 and 2024. Editorials and review articles that appeared in the same journal as the submission were not accepted without a DOI. A number of websites, including ScienceDirect, PubMed, and SagePub, were utilized to gather the literature. Result: The study looked at more than 4,000 publications using reputable sources including Science Direct, SagePub, and PubMed. After it was decided that eight publications needed greater investigation, a more extensive review of the entire literature was carried out. Conclusion: Studies have shown that osteoporosis can lead to fractures, with the Fracture Risk Assessment Tool (FRAX) and Bone Mineral Density (BMD) testing being used to assess fracture risk in women. Studies have shown that bone anabolic treatments can reduce fracture risk, but the certainty of these results is low due to the small number of studies.

Fusion of Google Street View, LiDAR, and Orthophoto Classifications Using Ranking Classes Based on F1 Score for Building Land-Use Type Detection

Building land-use type classification using earth observation data is essential for urban planning and emergency management. Municipalities usually do not hold a detailed record of building land-use types in their jurisdictions, and there is a significant need for a detailed classification of this data. Earth observation data can be beneficial in this regard, because of their availability and requiring a reduced amount of fieldwork. In this work, we imported Google Street View (GSV), light detection and ranging-derived (LiDAR-derived) features, and orthophoto images to deep learning (DL) models. The DL models were trained on building land-use type data for the Greater Toronto Area (GTA). The data was created using building land-use type labels from OpenStreetMap (OSM) and web scraping. Then, we classified buildings into apartment, house, industrial, institutional, mixed residential/commercial, office building, retail, and other. Three DL-derived classification maps from GSV, LiDAR, and orthophoto images were combined at the decision level using the proposed ranking classes based on the F1 score method. For comparison, the classifiers were combined using fuzzy fusion as well. The results of two independent case studies, Vancouver and Fort Worth, showed that the proposed fusion method could achieve an overall accuracy of 75%, up to 8% higher than the previous study using CNNs and the same ground truth data. Also, the results showed that while mixed residential/commercial buildings were correctly detected using GSV images, the DL models confused many houses in the GTA with mixed residential/commercial because of their similar appearance in GSV images.

Dynamic vaccination strategies in dual-strain epidemics: A multi-agent-based game-theoretic approach on scale-free hybrid networks

This paper proposes a multi-agent-based game-theoretic framework to explore the dynamics of decision-making among agents in a situation involving a dual-strain epidemic, where the emergence of the second strain is directly related to the first strain. It considers the impact of imperfect vaccination on dual strains within the SIRV framework, in a hybrid network that combines scale-free and grid-based structures. This research presents three models based on the level of decision-making of agents: Individual Decision Making (IDM), Collective Decision Making (CDM), and Stochastic Decision Making (SDM) model. In the IDM model, agents can continuously update their strategies based on the infection and vaccination statuses of their immediate neighbors. The CDM model offers a macro-level approach where agents’ willingness to vaccinate varies uniformly, while the SDM model maintains a constant level of willingness for vaccination across agents. This study demonstrates that agents using the IDM model tend to initiate vaccination earlier in scenarios of high infection rates, effectively mitigating the spread of the disease in subsequent epidemic seasons. However, when both infection and vaccination rates heavily influence decision-making, there is a noticeable decrease in vaccination uptake, thereby aggravating the spread of infection. Additionally, the authors found that in scenarios of high infection, an over reliance on vaccination without adequate initial uptake can jeopardize disease control efforts. Another important finding of this study is that despite the CDM and SDM models showing a quicker emergence of the second strain and reaching equilibrium points faster than the IDM model, the overall infection rate remains lower in the IDM model. This research underscores the complexity of managing dual-strain epidemics through vaccination strategies and highlights the significance of micro-level decision-making in epidemic control.

"Digitalization and Coaching for the Pre-University Education"

Abstract Education in pre-university education, once with the COVID-19 pandemic, can no longer be done only in the classic system, because the impact that digitization had during the pandemic on the didactic process was a major one, there was practically a reset of the way of teaching- learning-evaluation. The current work addresses a highly relevant and important topic: digitization in pre-university education, more specifically in pre-university education in Bucharest, emphasizing coaching for teachers to acquire digital skills. The research carried out consists in the application of a questionnaire, on a representative sample made up of the directors of pre-university education units in Bucharest regarding how important, timely and necessary this process of digitization is, how digital skills can be acquired by teachers, and what are the methods, its resources and limits, how necessary is a digitalization strategy at each decision-making level of pre-university education. The application of the questionnaire validates the research hypothesis, namely that the teachers consider digitization a tool that improves the didactic process, indicates coaching as a tool for acquiring digital skills and the fact that there is a need for a strategy regarding digitization at all levels of pre-university education, from the national level to the level of each unit separate schooling. The conclusion is that digitization is used much more after the post-pandemic period as a main teaching-learning-evaluation tool in the pre-university environment, because teachers are constantly preparing, using coaching, for the acquisition of new digital skills, and the pre-university education system, as a whole, it needs a more accurate planning of how digitization can influence the educational process. The contribution of this research consists in creating a strategy model regarding digitization at the level of a school unit.

LA-ViT: A Network With Transformers Constrained by Learned-Parameter-Free Attention for Interpretable Grading in a New Laryngeal Histopathology Image Dataset.

Grading laryngeal squamous cell carcinoma (LSCC) based on histopathological images is a clinically significant yet challenging task. However, more low-effect background semantic information appeared in the feature maps, feature channels, and class activation maps, which caused a serious impact on the accuracy and interpretability of LSCC grading. While the traditional transformer block makes extensive use of parameter attention, the model overlearns the low-effect background semantic information, resulting in ineffectively reducing the proportion of background semantics. Therefore, we propose an end-to-end network with transformers constrained by learned-parameter-free attention (LA-ViT), which improve the ability to learn high-effect target semantic information and reduce the proportion of background semantics. Firstly, according to generalized linear model and probabilistic, we demonstrate that learned-parameter-free attention (LA) has a stronger ability to learn highly effective target semantic information than parameter attention. Secondly, the first-type LA transformer block of LA-ViT utilizes the feature map position subspace to realize the query. Then, it uses the feature channel subspace to realize the key, and adopts the average convergence to obtain a value. And those construct the LA mechanism. Thus, it reduces the proportion of background semantics in the feature maps and feature channels. Thirdly, the second-type LA transformer block of LA-ViT uses the model probability matrix information and decision level weight information to realize key and query, respectively. And those realize the LA mechanism. So, it reduces the proportion of background semantics in class activation maps. Finally, we build a new complex semantic LSCC pathology image dataset to address the problem, which is less research on LSCC grading models because of lacking clinically meaningful datasets. After extensive experiments, the whole metrics of LA-ViT outperform those of other state-of-the-art methods, and the visualization maps match better with the regions of interest in the pathologists' decision-making. Moreover, the experimental results conducted on a public LSCC pathology image dataset show that LA-ViT has superior generalization performance to that of other state-of-the-art methods.

Hierarchical Flight Control System for Tilt-Rotor Drones

This paper presents a hierarchical flight control system for tilt-rotor drones. The offered approach performs high-level mission goals by gradually confirming them into machine-level instructions. The learned data from numerous sensors is spread backside to the greater levels for sensitive decision making. Each vertical take-off and landing drone is linked through regular wireless communication rules for accessible multi-agent facility. The proposed flight control system has been effectively employed on several small tilt-rotor drones and validated in some applications. Solutions from waypoint navigation, a probabilistic chase-evasion competition and vision-based object chasing show the capability of the recommended method for intelligent flying drones.

The impact of proactive personality and self-esteem on career decision making self-efficacy of university students in Botswana: A mediation analysis

The university period is where students make career related decisions and preparations to transition from the school to professional settings, however, studies show that some students face some degree of career indecisiveness and decision making difficulties. Therefore, this study examined the impact of individual proactive personality and self-esteem on the career decision making self-efficacy (CDMSE) of students. Data was collected from a sample of 120 participants from 5 different universities across Botswana. The Rosenberg self-esteem scale, Career decision self-efficacy Short form (CDSE-SF) and the Proactive Personality (PP) scales were administered to assess the variables under study. The data collected was analyzed using the Pearson’s correlation, regression analysis and Hayes’ mediation model. The results indicate that proactive personality had a significant positive impact on the career decision making self-efficacy levels of students. Secondly, self-esteem had a significant positive correlation and impact with the career decision making self-efficacy of student. Moreover, mediation analysis indicate that proactive personality significantly mediated the relationship between self-esteem and career decision making self-efficacy of university students. Further analysis reveal a significant positive correlation of student’s age with their self-esteem levels but no gender disparities were found. In conclusion, students with higher levels of proactive personality and self-esteem are more likely to have higher levels of career decision making self-efficacy. Further results, implications and limitations are discussed.

Review of the Factors Leading to Corruption in Russia

This article examines the major factors leading to corruption in Russia. We explore the historical, economic, social, political, and cultural factors influencing the formation and strengthening of corrupt practices. To this end, the impact of the Soviet era and periods of transformation on management structures and public mentality, creating the preconditions for corruption, is examined in the context of history. In the economic aspect, the lack of competition, inefficiency of the labor market and weakened property protection, which contribute to corruption schemes in public institutions, are discussed. The educational and cultural systems are investigated in the context of the formation of the mentality of society and its attitude towards corruption. Insufficient attention to ethics in education and the normalization of corrupt practices in culture can help maintain corruption. When analyzing the structural features of public administration, problems in the system of control, transparency and responsibility are identified that pose a threat to the effective fight against corruption. Political aspects, including the lack of separation of powers and the presence of corrupt political elites, which may contribute to corruption at the decision-making level, are also explored. We proposed theories of historical/political influences on corruption and the theory of the control and responsibility system.

What is the potential of sugarcane borer in reducing sorghum fitness and grain production?

AbstractPest attack is an important biotic threat to sorghum productivity, and one of the main insect pests of grain sorghum is sugarcane borer (SCB) Diatraea saccharalis. Despite the importance of this insect species, little is known about the relationship between SCB infestation and reduction in sorghum grain yield, as well as on the gain threshold and economic injury level for decision making of pest control. This study evaluated the influence of SCB infestation on plant performance and grain production in three commercial grain sorghum hybrids. Hybrids AG1090, BRS 373, and DKB 590 were planted in three crop seasons in Sete Lagoas, Brazil, and six treatments were assessed in the experiments, consisting of three grain sorghum hybrids with and without the insecticide chlorantraniliprole at 52.5 g a.i. ha−1 under SCB natural infestation. The parameters evaluated on sorghum plants were the length of tunnels, plant height, length and weight of panicles, stem borer injury, and grain yield. The infestation of SCB was higher when sorghum plants were not treated with insecticide, resulting in lower productivity. The use of insecticide increased plant height, reduced tunnel length, and increased panicle length and weight. Hybrids only affected panicle weight. The hybrid that presented the highest panicle weight when using the insecticide was AG 1090. Hybrids AG 1090 and DKB 590 showed reduced grain production with increasing gallery length, whereas BRS 373 did not show a significant correlation, which may suggest a certain level of tolerance to SCB attack. This is the first study that determines the gain threshold and economic injury level and that effectively shows how much grain yield is lost by SCB attack in grain sorghum hybrids.

Construction and Application of Regional Carbon Performance Evaluation Index System: The Case of Chinese Provinces

As global warming becomes increasingly severe, reducing carbon emissions and promoting low-carbon development has become an international consensus. Against this backdrop, evaluating regional carbon performance helps better understand the carbon emission status, emission reduction capabilities, and low-carbon development levels, providing a scientific basis for formulating targeted carbon emission reduction policies. This study constructed a “5E” regional carbon performance evaluation index system from five dimensions: economy, effectiveness, efficiency, environmentality, and equity. Then, this study evaluated and analyzed the carbon performance of 30 provinces in China from 2008 to 2021 using the entropy weight TOPSIS method. The research results indicated that (1) during the sample period, China’s carbon performance ranged from 0.416 to 0.504, exhibiting a steady upward trend; the highest score among the first-level indicators was Effectiveness, while the lowest was Economy; (2) in terms of carbon performance among China’s three major regions, it showed a decreasing pattern from east to west, with the growth potential of the central and western regions being greater than that of the eastern region; (3) in 2033, the carbon performance of China in the eastern region, the central region, and the western region will reach 0.602, 0.612, 0.613, and 0.582, respectively. A carbon performance evaluation carries significant practical and strategic implications. Our study can provide a reference for policymakers to assess carbon emission performance and improve carbon management efficiency and decision-making levels.

Optimising Agricultural Waste Supply Chains for Sustainable Bioenergy Production: A Comprehensive Literature Review

One of the United Nations’ 17 Sustainable Development Goals is to “Ensure access to affordable, sustainable, and clean energy for all.” Additionally, the growing concerns about climate change and energy security have heightened the importance of exploring alternative energy sources to replace fossil fuels. The utilisation of agricultural waste for bioenergy production has acquired significant attention due to its potential to mitigate environmental impacts and provide renewable energy sources. However, the major obstacle to producing bioenergy is managing the supply chain while considering economic, environmental, and social factors in an optimal way. This paper presents a comprehensive overview of the literature on the management of agriculture waste supply chains, specifically related to the use of modelling and optimisation techniques for planning. The first section describes different stages of the supply chain and various technologies for converting biomass to bioenergy. This is followed by a synopsis of the literature reviewed based on decision levels, objective functions, modelling methodologies, and optimisation approaches. Finally, the review highlights limitations and gaps in current research and the areas with potential for further exploration.

#2388 Relevance of anti-PLA2R levels in therapy decision and prediction of therapy outcome in patients with membranous nephropathy

Abstract Background and Aims Detection of anti-phospholipase A2 receptor (PLA2R) antibodies in patients with primary membranous nephropathy (MN) supports diagnosis as well as disease monitoring. An individualised therapy approach was introduced for anti-PLA2R positive MN patients at the Radboud University Medical Center. Immunosuppressive treatment (cyclophosphamide combined with steroids) was stopped when anti-PLA2R results by indirect immunofluorescence testing (IIFT) became negative. Here, we evaluated the relevance of anti-PLA2R levels for therapeutic decisions and the outcome of MN, comparing qualitative and quantitative detection methods. Method Stored serum samples were retrieved for beginning of treatment (baseline), decision point, and follow-up. Anti-PLA2R levels were determined qualitatively by IIFT as well as quantitatively by enzyme-linked immunosorbent assay (ELISA) and chemiluminescence immunoassay (ChLIA) at baseline as well as after treatment and correlated to immunological remission and persistence. Results For an evaluation of the relevance of anti-PLA2R levels at the start of therapy, serum samples of 60 patients were available. Patients sampled at the beginning of the therapy were grouped according to tertiles of anti-PLA2R levels determined by ChLIA (n[lowest tertile] = 20, n[middle tertile] = 20, n[highest tertile] = 20). Lower anti-PLA2R levels were seen in patients with relapsing disease. Higher anti-PLA2R levels were associated with more severe proteinuria. Patients in the lowest tertile of anti-PLA2R were more likely to develop immunological remission after 8 weeks of therapy. In total, 90% vs 75% vs 45% of patients in the tertiles turned anti-PLA2R negative after being treated for 8 weeks. Moreover, in this subgroup of patients who showed immunological remission after 8 weeks of therapy, only 11% of the patients in the lowest tertile needed renewed immunosuppressive therapy, while this percentage increased in the middle and highest tertiles (11% vs 53% vs 33%, p=0.033). For the assessment of anti-PLA2R levels before and after therapy, samples at baseline and week 8 of treatment were examined. At baseline, 50/50 (100%) tested positive in IIFT and ChLIA as well as 48/50 (94%) in ELISA. After 8 weeks on treatment, immunological remission based on IIFT was found in 37/51 (73%) patients. At this point, the overall agreement compared to IIFT was 92% for ChLIA and 82% for ELISA. Yet the availability of quantitative levels provided further insight into this group of patients in whom treatment was discontinued. With ChLIA, anti-PLA2R titers >5 RU/mL after 8 weeks of therapy were found in 4% of IIFT negative patients with persistent remission, vs 55% of IIFT negative patients with early relapse (p=0.006). Conclusion Individualised treatment of MN patients with cyclophosphamide and steroids has been recently introduced. In this respect, the quantitative determination of anti-PLA2R levels adds further value. Of the examined quantitative methods, ChLIA demonstrated the highest agreement with IIFT. Additional studies are needed to evaluate the impact on clinical decision making and outcome.

Explaining crowdworker behaviour through computational rationality

ABSTRACT Crowdsourcing has transformed whole industries by enabling the collection of human input at scale. Attracting high quality responses remains a challenge, however. Several factors affect which tasks a crowdworker chooses, how carefully they respond, and whether they cheat. In this work, we integrate many such factors into a simulation model of crowdworker behaviour rooted in the theory of computational rationality. The root assumption is that crowdworkers are rational and choose to behave in a way that maximises their expected subjective payoffs. The model captures two levels of decisions: (i) a worker's choice among multiple tasks and (ii) how much effort to put into a task. We formulate the worker's decision problem and use deep reinforcement learning to predict worker behaviour in realistic crowdworking scenarios. We examine predictions against empirical findings on the effects of task design and show that the model successfully predicts adaptive worker behaviour with regard to different aspects of task participation, cheating, and task-switching. To support explaining crowdworker actions and other choice behaviour, we make our model publicly available.

The Role of Emotional Intelligence in Leadership Effectiveness and Organisational Behavior

Objectives: The primary objective of this study is to investigate the significance of emotional intelligence (EI) in relation to leadership efficacy and organizational conduct. Specifically, the study aims to explore the constituent elements of emotional intelligence, their influence on leadership behavior, and their correlation with organizational behavior. Additionally, the study seeks to examine the impact of emotional intelligence on fostering a favorable organizational climate, improving communication, facilitating decision-making, resolving conflicts, and promoting employee engagement. Methods: This study employs a quantitative research methodology, utilizing a self-administered survey instrument. The sample consists of 60 participants holding managerial positions in various organizations. Statistical methodologies such as explanatory diagrams and correlation analysis are used to analyze the data and investigate the relationships between emotional intelligence, managerial competence, organizational behavior, and employee satisfaction. Results: The results of the study highlight a positive association between emotional intelligence and managerial competence, organizational behavior, and employee satisfaction. The findings suggest that higher levels of emotional intelligence are correlated with more effective leadership, improved organizational climate, better communication, enhanced decision-making, conflict resolution, and higher levels of employee engagement. Conclusion: In conclusion, this study emphasizes the importance of emotional intelligence in leadership effectiveness and organizational behavior. The findings underscore the positive impact of emotional intelligence on various aspects of organizational conduct and employee satisfaction. Additionally, the study suggests avenues for future research, including longitudinal inquiries, intercultural variances, exploring intervening and moderating factors, developing interventions, and conducting comparative assessments.

Leadership Style and Social Behavior of Social Workers in China

This study examined the influence of leadership style on the social behave of social workers across different districts and organizations in China. The empirical measurement involved the participation of social work stations and associations, which included the Social Workers Association and various district social work stations in Hunan. The study sample consisted of 488 individuals who were social work learners and professionals. These participants were picked from various districts and training sites, and they were required to undergo self-evaluation tests. Findings reveal that while social workers generally exhibit average levels of empathy, professionalism, ethical decision-making, collaboration, and resilience, significant variations exist based on ae and educational background. Older social workers demonstrate higher levels of these traits compared to younger counterparts. However, barriers such as burnout, limited resources, organizational constraints, and lack of recognition hinder leadership development and positive social behavior. The study seeks to enhance leadership development initiatives and foster positive social behavior in the field of social work in China through a holistic strategy.

Model-driven decision support: A community-based meta-implementation strategy to predict population impact

PurposeStandard tools for public health decision making such as data dashboards, trial repositories, and intervention briefs may be necessary but insufficient for guiding community leaders in optimizing local public health strategy. Predictive modeling decision support tools may be the missing link that allows community level decision makers to confidently direct funding and other resources to interventions and implementation strategies that will improve upon the status quo. MethodsWe describe a community-based model-driven decision support (MDDS) approach that requires community engagement, local data, and predictive modeling tools (agent-based modeling in our case studies) to improve decision-making on implementing strategies to address complex public health problems such as overdose deaths. We refer to our approach as a meta-implementation strategy as it provides guidance to a community on what intervention combinations and their required implementation strategies are needed to achieve desired outcomes. We use standard implementation measures including the Stages of Implementation Completion to assess adoption of this meta-implementation approach. ResultsUsing two case studies, we illustrate how MDDS can be used to support decision making related to HIV prevention and reductions in overdose deaths at the city and county level. Even when community acceptance seems high, data acquisition and diffuse responsibility for implementing specific strategies recommended by modeling are barriers to adoption. ConclusionsMDDS has the capacity to improve community decision makers use of scientific knowledge by providing projections of the impact of intervention strategies under various scenarios. Further research is necessary to assess its effectiveness and the best strategies to implement it.