Effects Of Different Strategies Research Articles

Kidney Segmentations Using CNN models

For medical diagnostic tests, kidney segmentation from high-volume imagery is an important major. Since 3D medical images need a lot of GPU memory, slices and patches are used for training and inference in traditional neural network variant architectures, which necessarily slows down contextual learning. In this research, Mobile Net and Efficient Net CNN models were trained for segmenting human kidney images generated from The Human Biomolecular Atlas Program (HuBMAP). The purpose of this work is to evaluate the effectiveness of different strategies for Glomeruli identification in order to solve the issue. The high size images were decoded to be fitted and trained in the models first, then the CNN models were trained. The CNN models result show that the Efficient Net has the highest accuracy rate with 99.49 %, and Mobile Net with 99.33 %.

Breaking the mold: examining the effectiveness of techniques to reduce motivated reasoning

ABSTRACT Extensive research in the social sciences has shown that people tend to engage in motivated reasoning when processing information. However, one area of inquiry that has been lacking is examining the effectiveness of different strategies that aim to decrease motivated reasoning. In this paper, we examine three strategies that have been shown to decrease motivated reasoning: accuracy motivation, the common ingroup identity model, and self-affirmation. We begin by replicating previous findings examining the effectiveness of each of these strategies in reducing motivated reasoning. We then extend this line of inquiry by comparing the effects of these three strategies against one another to determine if one of them is more effective at reducing motivated reasoning. To test our hypotheses, we used an experimental design and recruited participants from Prolific.

A Data-Driven Solution for Energy Management Strategy of Hybrid Electric Vehicles Based on Uncertainty-Aware Model-Based Offline Reinforcement Learning

Energy management strategy (EMS) is the key technology to improving the fuel efficiency of hybrid electric vehicles (HEV). In recent years, the development of artificial intelligence has enabled tremendous advances by utilizing reinforcement learning (RL) for training and deploying deep neural network-based EMS. However, in contrast to the fields of deep learning such as computer vision and natural language processing which mainly rely on large-scale offline datasets, most RL-based policies must be trained online by trial-and-error with the initial performance being almost arbitrary. Such a paradigm is considered inefficient and unsafe for industrial automation and can only be used to tackle the EMS problems in the simulation world. Considering large historical interactive datasets are readily available in EMS domain, if a RL algorithm can be used to extract a policy purely offline from the prior collected dataset and improve upon data logging policy, the current issues including sample inefficiency, unsafe exploration, and simulation-to-real gap that prevent the widespread use of RL methods could be mitigated to a great extent. To this end, this paper presents a feasible algorithmic framework for model-based offline RL. Unlike vanilla RL approaches without any consideration against distributional shift, a data-driven dynamic model is built before the policy training using RL. After that, two techniques namely conservative MDP and state regularization are augmented which is proved to be effective against model over exploitation. A hardware-in-the-loop (HIL) test is used to evaluate the real-time performance and effectiveness of different strategies on a real controller, and it is found that the proposed naïve data-driven model-based offline RL algorithm can already solve the distributional shift problem inherent in vanilla RL to a large extent. Furthermore, by incorporating the guidance of uncertainty-awareness, a near optimal policy can be obtained by using only the dataset from a sub-optimal controller.

Coordinated suppression strategies for passive and active resonances of the MMC‐HVDC AC‐side system on the medium‐ and high‐frequency bands

AbstractThe medium‐ and high‐frequency resonance between modular multilevel converters (MMCs) and the AC grid has attracted increasing attention due to the frequent occurrence of resonance events in recent years. Therefore, resonance issues are analyzed, and corresponding suppression strategies are proposed in this paper. First, the analytical expression of the MMC AC‐side impedance is established. Then, the mechanism and influencing factors of different types of resonances, including active resonance and passive resonance, are analyzed to provide a more comprehensive perspective. To aid the suppression of these resonances, resonance risk indexes are defined to evaluate the resonance risk, and then the suppression effects of different strategies are compared through analytical calculations. Subsequently, coordinated resonance suppression strategies suitable for different resonance situations are proposed. The key points of the suppression strategies are to suppress the negative damping of MMC impedance at the resonance frequency and improve the integrated impedance of the MMC AC‐side system. Finally, the proposed suppression strategies are verified by simulation results.

A Monte Carlo Assessment of the Effect of Different Ventilation Strategies to Mitigate the COVID-19 Contagion Risk in Educational Buildings

The COVID-19 pandemic outbreak has increased the general awareness of the importance of proper ventilation in the indoor environment to reduce the contagion risk. In particular, attention has been paid to specific categories of buildings, such as schools, due to two factors: (1) high occupancy density and (2) the presence of young and sometimes more susceptible people. Despite the high level of alertness towards the ventilation of classrooms, robust analyses of the effectiveness of the different strategies to mitigate the contagion risk have been difficult to perform. Indeed, the COVID-19 pandemic is still ongoing, and many factors, such as the presence of multiple viral strains, use of facial masks, progression in vaccination, and installation of air purifiers and other sanitization devices, make it difficult to fully quantify the impact of room ventilation by simply analysing available monitoring data. Moreover, mitigation strategies related to ventilation are often dynamic, increasing the complexity of the problem to assess. In this framework, this work proposes a new Monte Carlo method integrated with building performance simulation to evaluate the number of infected occupants under different scenarios, considering also the dynamic boundary conditions. The described approach has been applied to a case study classroom at the Free University of Bozen-Bolzano, Italy, analysing almost 100 different scenarios and discussing the effectiveness of different ventilation strategies traditionally adopted to ensure suitable IAQ according to CO2 concentration limits. Results highlight the importance of combining different solutions (e.g., mixed-mode ventilation and facial masks) to limit the risk for both students and lecturers.

Effective Feedback Strategy for Formative Assessment in an Integrated Medical Neuroscience Course.

Despite the different benefits of formative assessments in an integrated medical curriculum, the effective strategies to provide feedback to medical students to benefit from the different merits of formative assessment are not fully understood. This study aims to determine the effect of different strategies of formative feedback on students' outcomes in a medical neuroscience course. We compared medical students' performance in summative examinations in the academic year that formative feedback was provided using in-person discussion and compared such performances with the academic year when the feedback was provided by written rationales or a combination of written rationales and in-person discussion. We also surveyed medical students' preferences for whether written or in-person formative feedback is a better strategy to provide feedback at the end of each course. ANOVA found a significant difference in summative performance scores for those scoring ≥ 70% when formative feedback was provided by providing a rationale, in-person, and a combination of both ([F (2,80) = 247.60, P < 0.001]. Post hoc analysis revealed a significant and highest performance when feedback was provided using the written rationale approach (***P < 0.05), followed by in-person (**P < 0.05). In contrast, the least performance was recorded when formative feedback was provided using a combination of providing a written rationale for the answers to the questions and in-person discussion of the questions (*P < 0.05). Students' preferred approach for receiving formative feedback for their formative assessment was highest for written rationale (***P < 0.05), followed by in-person or a combination of in-person and written rationale (**P < 0.05). Our results found that medical students preferred a written formative feedback approach, which was associated with better student performance on the summative examination. This study reveals the importance of developing effective strategies to provide formative feedback to medical students for medical students to fully benefit from the merits of formative assessment in an integrated medical school curriculum.

Betweenness Centrality-Based seismic risk management for bridge transportation networks

Although bridges are integral parts of transportation networks, they are vulnerable to natural hazards. The damage incurred during these natural hazards events leads to network disruptions and hinders post-event recovery. Therefore, improving the seismic performance of transportation networks is critical. This paper proposes an origin–destination pair betweenness centrality (BC)-based methodology to rank bridges according to their importance in a complex bridge network. The proposed methodology is an easy-to-use approach that considers user equilibrium and topology and significantly shorten the computational time. In addition, this study proposes two strategies including the retrofit of bridges and construction of new bridges based on the BC importance of bridges to mitigate the seismic risk of bride networks. A well-studied bridge network in California was used to demonstrate the application and the effect of different strategies was compared in this study. Bridges located at the shortest path significantly affect the seismic performance of bridge networks, indicating there is no need to rehabilitate or replace all bridges under budget constraints. Also, constructing five new bridges based on BC importance improved seismic performance of the bridge network much more than retrofitting 21 bridges. Moreover, the proposed methodology presents a significant reduction in computational time (only 1/30 compared with an existing study) in resolving the importance of bridges in a network and can be applied to provide useful information for disaster management teams to mitigate the seismic risk of bridge networks.

A Study on the Effects of Lexical Processing Strategies in Incidental Vocabulary Acquisition While Reading

Research on vocabulary acquisition in SLA has revealed that a large proportion of vocabulary is acquired without overt intention. This paper analyzes the relevance of different lexical processing strategies for incidental vocabulary acquisition while reading involving 56 native Chinese speakers who are studying English at a local university in China. The lexical processing strategies which the participants adopted to analyze unknown words are discussed based on the cognitive processes involved, namely implicit/explicit cognitive processes and top-down/bottom-up cognitive processes. According to the introspective data collected during a think-aloud protocol as well as the results of a subsequent vocabulary retention test, we examine the acquisition effects of different strategies. The results indicate that students can learn vocabulary incidentally through implicit processing, though it has a significantly lower acquisition rate than that of the explicit processing strategies. With regard to the dichotomy of top-down and bottom-up cognitive processes, the bottom-up processing strategy demonstrates better acquisition effects than the top-down strategy. Finally, a multilevel cognitive model of factors that contribute to incidental vocabulary acquisition is developed in an attempt to provide theoretical and practical implications to L2 vocabulary teaching and learning in China.

FACapsnet: A fusion capsule network with congruent attention for cyberbullying detection

As an extension of bullying on social networks, cyberbullying has seriously affected the security of the online social environment and infringed on mental health. Taking appropriate measures to detect online bullying reviews is crucial. Existing studies usually classify the whole content as cyberbullying and non-cyberbullying. However, they do not fully exploit the interaction of multi-dimensional features and precisely distinguish the types of cyberbullying. To automatically extract features of bullying words and further identify fine-grained types of cyberbullying better, we propose a fusion capsule network with congruent attention for cyberbullying detection. In the proposed algorithm, a novel similarity weighting scheme based on word2vec is designed to soft highlight bullying features in word embeddings. Meanwhile, to leverage the respective advantages of extracted multiple subspace features, we construct a novel extensible congruent attention to balance the fusion of complex correlations between different subspace representations and retain the independence of context features. The fused features are updated iteratively with dynamic routing to aggregate and generate fine-grained category capsules for cyberbullying prediction. A series of experiments on the tweets cyberbullying benchmark demonstrate that our architecture matches or exceeds the performance of the compared baseline models and the results of extensive experiments prove the effectiveness of different strategies.

Enclosure Fire with Time-Dependent Changes in Ventilation Factor and Geometry—Field Model Parameters Analysis

The fire scenarios currently used for structural fire design are based on traditional methods that derive from the extrapolation of existing fire test data. The traditional “furnace” geometry test allows a good circulation of the fire gases and a relatively homogeneous temperature distribution throughout the enclosure. These conditions are different from what is observed in real-situation fires. As a result, the need for a field model for an enclosure fire has increased significantly, and appropriate deterministic models are necessary for fire safety engineering. The effects of different strategies involving the opening of doors and windows with changes in the enclosure’s geometry were observed to gain an insight into the impact of how ventilation and geometry influence changes during the development of a fire. This article presents and discusses the results of the field model compared with similar tests and experiments described in the technical and scientific literature. Generating data using a field model reduces experimental costs and facilitates sensitivity analyses of the parameters of problems.

Recruitment and Retention Strategies for Underrepresented Populations and Adults With Arthritis in Behavioral Interventions: A Scoping Review.

To identify strategies used to recruit and retain underrepresented populations and populations with arthritis or fibromyalgia (FM) into behavioral programs targeting exercise, physical activity, or chronic disease self management. Five bibliographic databases were searched for articles published between January 2000 and May 2022. The search focused on strategies and best practices for recruiting and retaining underrepresented populations or populations with arthritis or FM into disease self-management or physical activity/exercise programs. Abstracts and full-text articles were screened for inclusion by 2 independent reviewers, and 2 reviewers extracted data from included articles. Of the 2,800 articles, a total of 43 publications (31 interventions, 8 reviews, 4 qualitative/descriptive studies) met criteria and were included. The majority of studies focused on physical activity/exercise (n=36) and targeted African American (n=17), Hispanic (n=9), or arthritis populations (n=7). Recruitment strategies that were frequently used included having race- or community-matched team members, flyers and information sessions in areas frequented by the population, targeted emails/mailings, and word of mouth referrals. Retention strategies used included having race- or community-matched team members, incentives, being flexible, and facilitating attendance. Most studies used multiple recruitment and retention strategies. This scoping review highlights the importance of a multifaceted recruitment and retention plan for underrepresented populations and populations with arthritis or FM in behavioral intervention programs targeting exercise, physical activity, or chronic disease self management. Additional research is needed to better understand the individual effects of different strategies and the costs associated with the various recruitment/retention methods in underrepresented populations and populations with arthritis.

Multidimensional evaluation of teaching strategies for pharmacology based on a comprehensive analysis involving 21,269 students.

Background: Given the limitations of traditional pharmacology pedagogical method, diverse novel teaching methods have been widely explored. In this study, we performed a network meta-analysis (NMA) to evaluate the effects of different strategies in pharmacology education. Methods: Literature databases were searched from their inception to November 2022, and the studies were screened according to predefined inclusion and exclusion criteria to extract important information. Outcomes, including theoretical test scores, experimental test scores, subjective test scores, satisfaction scores, and the proportion of satisfaction, were analyzed using R software (version 3.6.1) and STATA (version 15). The NMA was conducted with a random-effects model under the Bayesian framework to calculate odds ratios (ORs) or mean differences (MDs) with associated 95% credible intervals (95% CIs). Surface under the cumulative ranking curve (SUCRA) probability values were calculated to rank the teaching methods examined. Results: A total of 150 studies involving 21,269 students were included. This NMA systematically evaluated 24 teaching strategies, such as problem-based learning (PBL), team-based learning (TBL), case-based learning (CBL) and flipped classrooms (FC), etc., The results of the NMA showed that, PBL combined with CBL was most likely to improve students' theoretical and subjective test scores (SUCRA = 75.49 and 98.19%, respectively), TBL was most likely to improve the experimental test score (SUCRA = 92.38%) and the satisfaction score (SUCRA = 88.37%), while FC had the highest probability of being the best option for improving the proportion of satisfaction (SUCRA = 84.45%). Conclusion: The current evidence indicates that TBL, PBL combined with CBL, and FC might be optimal strategies for pharmacology education since they have a more beneficial effect on students.

Experimental study on the control effects of different strategies on particle transportation in a conference room: Mechanical ventilation, baffle, portable air cleaner, and desk air cleaner

To control the spread and transmission of airborne particles (especially SARS-CoV-2 coronavirus, recently) in the indoor environment, many control strategies have been employed. Comparisons of these strategies enable a reasonable choice for indoor environment control and cost-effectiveness. In this study, a series of experiments were conducted in a full-scale chamber to simulate a conference room. The control effects of four different strategies (a ventilation system (320 m3/h) with and without a baffle, a specific type of portable air cleaner (400 m3/h) and a specific type of desk air cleaner (DAC, 160 m3/h)) on the transportation of particles of different sizes were studied. In addition, the effects of coupling the ventilation strategies with five forms of indoor airflow organization (side supply and side or ceiling return, ceiling supply and ceiling or side return, floor supply and ceiling return) were evaluated. The cumulative exposure level (CEL) and infection probability were selected as evaluation indexes. The experimental results showed that among the four strategies, the best particle control effect was achieved by the PAC. The reduction in CEL for particles in the overall size range was 22.1% under the ventilation system without a baffle, 34.3% under the ventilation system with a baffle, 46.4% with the PAC, and 10.1% with the DAC. The average infection probabilities under the four control strategies were 11.3–11.8%, 11.1–11.8%, 9.1–9.5%, and 18.2–19.7%, respectively. Among the five different forms of airflow organization, the floor supply and ceiling return mode exhibited the best potential ability to remove particles.

Evaluating Prescription Pattern and Effectiveness of Antihypertensive Drugs in Non-Operated Aortic Dissection Patients

Aortic dissection (AD) is a life-threatening disease. However, the effectiveness of different strategies of antihypertensive therapies in non-operated AD patients is still unclear. Patients were classified into five groups (groups 0-4) based on the number of classes of antihypertensive drugs, including β-blockers, renin-angiotensin system (RAS) agents (angiotensin-converting enzyme inhibitors (ACEIs), angiotensin II receptor blockers (ARBs), and the renin-inhibitors), calcium channel blockers (CCBs), and other antihypertensive drugs, were prescribed within 90 days after discharge. The primary endpoint was a composite outcome of re-hospitalization associated with AD, referral for aortic surgery, and all-cause death. A total of 3932 non-operated AD patients were included in our study. The most prescribed antihypertensive drugs were CCBs, followed by β-blockers and ARBs. Within group 1, compared to other antihypertensive drugs, patients using RAS agents (aHR, 0.58; p = 0.005) had a significantly lower risk of occurrence of the outcome. Within group 2, the risk of composite outcomes was lower in patients using β-blockers + CCBs (aHR, 0.60; p = 0.004) or CCBs + RAS agents (aHR, 0.60; p = 0.006) than in those using RAS agents + others. For non-operated AD patients, RAS agents, β-blockers, or CCBs should be given in a different strategy of combinations to reduce the hazard of AD-related complications compared to other agents.

The manufacturers’ strategy selection of carbon emission reduction and pricing under carbon trading policy and consumer environmental awareness

Carbon trading policy and consumer environmental awareness are increasingly important to manufacturers’ carbon emission reduction and pricing. To analyze their strategy selection of carbon emission reduction and pricing, this paper develops three game models, where two manufacturers could choose no cooperation (NC), only cooperation in carbon emission reduction (SC), or simultaneous cooperation in carbon emission reduction and pricing (CC). By solving these models and comparing their environmental R&amp;amp;D levels, net carbon emissions, and profits, the paper finds strategy selection for manufacturers and its conditions. Results show that from the view of the environmental R&amp;amp;D level and supply chain’s profit, NC and SC may be the optimal strategy and the second-best strategy, respectively. From the net carbon emission point of view, CC and SC should be the optimal strategy and the second-best strategy, respectively. As to manufacturers’ profits, CC should be the optimal strategy, and NC or SC should be the second-best strategy. From comprehensive views, none of these strategies could be the optimal strategy, but SC may be the second-best strategy. This paper contributes in three aspects. First, this paper designs three strategies of carbon emission reduction and pricing for two manufacturers. Second, this paper takes the initial carbon emission allowances of the government as one of decision variables. Finally, this paper investigates the effects of different strategies and finds strategy selections for manufacturers from a single view and comprehensive views.

Digital Interventions for Social Participation in Adults with Long-term Physical Conditions: A Systematic Review.

This review aims to identify and evaluate digital interventions for social participation in the growing population of adults with long-term physical conditions. Articles were sourced from MEDLINE, EMBASE, CINAHL and PsycINFO databases using subject headings and keywords related to "social participation" and "digital technology". Studies that adopted digital technology interventions to improve social participation in adults with long-term physical conditions were included. Data on study methodology, participant and digital intervention characteristics, and findings related to social participation were extracted. The search yielded a total of 4646 articles and 14 articles met criteria for final review with five randomized controlled trials, two non-randomized clinical trials and seven one-group pretest-posttest clinical trials. Studies were organized based on the digital intervention strategy implemented to improve social participation: group support (n = 4), individual skill training or counseling (n = 6), education and support (n = 3), and mixed intervention (n = 1). The group support interventions developed a social network among participants through videoconference, app, or virtual reality platform. Three studies reported positive improvements in different aspects of social participation. Individual skill training or counseling mainly utilized phone calls to help participants cope with activity participation and interpersonal relationship issues. Only two studies demonstrated benefits for social participation. The education and support intervention, which used messages and website information to increase participants' knowledge and provide support, showed positive findings in three studies. This review suggests digital interventions for improving social participation in adults with long-term physical conditions are feasible and the effectiveness of different strategies may vary.Registration: This review was prospectively registered on the International Prospective Register of Systematic Reviews (PROSPERO) (registry number: CRD42021254105).

Deep learning method for risk identification of autonomous bus operation considering image data augmentation strategies

Objective The autonomous bus is a key application scenario for autonomous driving technology. Identifying the risk of autonomous bus operation is of great significant to improve road traffic safety and promote the large-scale application of autonomous driving technology. Methods For the purpose of risk identification, the actual operation data for autonomous buses in Shanghai were converted into 3 kinds of grayscale images and 1 kind of radar image from a temporal–spatial perspective, and a deep learning convolutional neural network, AlexNet, was applied for image recognition. This article uses several image data augmentation strategies to address the problem of uneven distribution of samples and compares the effectiveness of different strategies. Results The optimal accuracy (ACC) of the risk identification was 90.4%, the optimal true positive rate (TPR) was 83.7%, and the optimal false negative rate (FPR) was 94.58%. The accuracy of risk identification using AlexNet was higher based on the sample containing Fourier images. In addition, risk identification accuracy based on grayscale images was higher than that based on radar images. Conclusions Autonomous buses were found to be vulnerable to risks in areas such as turning sections and intersections. In addition, the results show that the Fourier transform, an image augmentation strategy, can effectively solve the uneven sample distribution problem, and the length of the input time series has an impact on the accuracy of the risk identification.

Time and type of pruning affect tree growth and yield in high-density olive orchards

The effect of different strategies and the time of pruning on olive vegetative-reproductive behavior was studied in a 5-year-old high-density orchard (1,250 trees ha−1, cv. ‘Ascolana tenera’). The treatments were: a) winter lateral pruning and topping, b) summer lateral pruning and winter topping, c) winter lateral pruning and summer topping and d) summer lateral pruning and topping. Selective spring pruning maintaining a conical shape of the canopy was used as the Control. Topping significantly maintained tree height recorded at the end of the 3-year trial in comparison to the Control (3.84 ± 0.45 m), in particular summer treatments maintained the final tree height at 3.0 ± 0.26 m. Summer topping and lateral pruning significantly reduced canopy volume compared to winter pruning and the Control. There was less vegetative re-sprouting in the control and, on the average, less for summer than for winter pruning in the same year of pruning. There were no significant differences between the treatments regarding the seasonal vegetative growth of 1-year-old mixed shoots. Fruit yield per tree was significantly higher with time-split pruning (part in winter and part in summer) with respect to conical-shape pruning of the canopy. Results suggest that the proposed management of pruning with winter lateral pruning and summer topping contributes to good vegetative-reproductive balance of young trees with high yield and a more compact shape.

Effect of different application strategies of universal adhesive used for immediate and delayed dentin sealing on the microtensile bond strength of self-adhesive resin cement to dentin with and without aging.

Bond strength of indirect restorations is a very important issue that should be given special attention. Immediate dentin sealing (IDS) technique has been suggested in recent years. The aim of the present study was to investigate the effect of different strategies of universal adhesive application for immediate and delayed dentin sealing (DDS) on the microtensile bond strength (μTBS) of self-adhesive resin cement with and without aging. In this experimental study, 24 healthy human third molars were selected. After exposing the occlusal dentin, the teeth were randomly divided into two groups of 12 based on the All-Bond Universal adhesive application strategy (etch-and-rinse or self-etch). Each group was further subdivided into two (n=6) based on IDS or DDS technique. Then composite blocks were cemented on the occlusal surface with self-adhesive resin cement. After cutting the samples into 1 mm2 cross-sections, half of the samples of each subgroup were subjected to µTBS test after one week, and the other half were tested under μTBS after 10,000 thermal cycles. Data were analyzed using three-way ANOVA (P<0.05). μTBS was significantly affected by all three factors of bond strategy, sealing technique and aging. There was also a significant interaction between the three factors. Immediate dentin sealing improved μTBS. The etch-and-rinse strategy resulted in higher μTBS, while aging led to a decrease in μTBS. Key words:Adhesives, dental bonding, dentin, universal, sealing.

Study on the image restoration strategy and effect of corporates in crisis situations based on text mining

A corporate's reputation may be significantly impacted by how it handles emergencies. To investigate the effect of different coping strategies on corporate image restoration, we collected 47 representative events of corporates in different industries as well as relevant comments from Weibo. Based on the image restoration theory proposed by Benoit, the effects of different strategies on corporate reputation restoration were analyzed by using sentiment analysis and the topic model. We found that the two strategies of corrective action and mortification can usually better alleviate the negative emotions of the public. On the contrary, if corporates respond with strategies of denial, evading responsibility, or reducing offensiveness, there could be strong feelings of dissatisfaction. The case study of the food industry also finds that even if the effect of alleviating the negative emotions of the public is not significant, an appropriate response could shift the attention of some consumers and to some extent serve the purpose of restoring the corporate reputation.