Proactive Model Research Articles

Proactive control affects task conflict beyond contingency learning.

Various models of the Stroop task suggest that proactive task control adaptation accounts for the modulation of task conflict in different conditions of the Stroop task, for example, when task conflict is very frequent or very infrequent. Other researchers have argued that a contingency learning of colour-word associations is the main contributor to the modulations of the Stroop effect. In this work, we constructed a design that controls for confounds that are suspected to rule out the role of control adaptation in the Stroop task. We focused on one type of conflict-task conflict and tested whether colour-naming of neutral-words (where task conflict is present) differed from colour-naming of neutral-symbols (where task conflict is not present) in four different conditions: mostly words-congruent, mostly words-incongruent, mostly words-neutral, or mostly non-words-shape. Importantly, the conditions used for the task conflict marker were identical in all four conditions. We found that the marker of task conflict (reaction time [RT] for neutral-words > RT for neutral-symbols) was significant in the mostly non-words-shape condition, where proactive task control is relaxed, but not in the mostly words conditions, where proactive task control is activated, with no difference between these three words conditions. These findings suggest that control adaptation is the main contributor to the modulations of the Stroop effect. The relevance of the results to the current literature is discussed and the results are explained in light of the proactive control-task conflict (PC-TC) model.

A study of engine room smoke detection based on proactive machine vision model for intelligent ship

Fire disaster causes damage to ships, pollute the environment, and threatens people's lives, so making early detection and reasonable decision is essential for avoiding catastrophic accidents. Smoke, as the main feature of fire, plays a significant role in early fire identification. However, challenged by the complex engine room (E/R) environment and shooting issues, the recognition performances of current smoke detection methods based on machine vision are unsatisfactory at the early fire stage. To address the issues, this paper proposes a proactive machine vision model based on the fusion of the transfer learning method and proactive perception technology for smoke detection. Firstly, a smoke images database is established based on similar environments (indoor and ship fire scenes) for the transfer learning module training, where more smoke features are extracted and learned from complex scenes. Afterward, real-time images are input into the trained model for local significance analysis which is applied as a triggering criterion for active smoke perception. When the significance indicator reaches the trigger condition, the proactive perception technology adopts reinforcement learning and proactive vision for further identifying detailed information about scenarios. By this processing, the internal and external parameters of the camera are adjusted to narrow and focus the targets of interest in the current scene. Finally, the ship E/R workshop and distribution box fire cases are selected to validate the proposed method. The experiment results indicate that the proposed model outperforms the existing techniques in accuracy as it has the potential to detect earlier smoke features.

Research on the scheduling method of ground resource under uncertain arrival time

We present a two-stage scheduling approach including proactive and reactive scheduling to solve the ground resource scheduling problem with uncertain arrival time. In the first stage, an integer programming model is constructed to minimize the delay and transfer costs. After solving this model, we obtain a baseline scheduling plan that considers the service arrival time uncertainty. In the second stage, the feasibility of the subsequent benchmark plan is evaluated based on the current state of the services and resources. The reactive scheduling model is enabled when trigger conditions are met. Moreover, an improved adaptive large neighborhood search is designed to solve the proactive scheduling model effectively. Real data from an international airport in South China is used as a test case to compare different scheduling strategies. The results show that it is difficult to handle the uncertainty of the problem with the benchmark plan that simply considered buffer time. Compared with rolling time-domain scheduling, the average transfer cost of the scheduling strategy proposed in this paper increased slightly, but the average service delay cost can be reduced significantly. Algorithm-wise, instances of different scales are designed to verify the effectiveness of the improved adaptive large neighborhood search algorithm. The efficiency of the algorithm scheme is better than that of the Gurobi solver scheme in medium to large-scale problems. Therefore, the forward and reactive strategies can better handle the uncertainty of airport ground protection services as they can simultaneously guide the allocation and utilization of airport ground protection resources.

Creative adaptability and negative emotions of employees during a crisis: the role of servant leadership

The main aim of this study is to examine the role of servant leadership (SL) in mitigating employees’ negative emotions (NE) while enhancing their creative adaptability (CA) during a crisis. A proactive personality model for employees was used as a mediator in these relationships. Additionally, the moderating effect of leader gender was explored in the link between the SL and NE of employees. Data were collected from 315 aviation employees during the pandemic crisis, with empirical findings revealing that SL had a negative impact on NE and a positive impact on CA among employees. The findings also confirmed the mediating role of proactive personality in these associations. Leader gender moderated the impact of SL on NE. The implications of these findings are discussed.

Addressing challenges in telephone triage for outpatient oncology care: A data-centred digital routing solution.

591 Background: As cancer care shifts to a predominately outpatient model, there is growing recognition that optimizing communication between patients and their care providers between scheduled visits can improve efficiency and patient outcomes. Princess Margaret Cancer Centre (PM), which sees over 80,000 patients annually, operates with many parallel telephone triage lines as the primary means of patients-provider communication. When a patient needs to contact their care team, they are instructed to leave a voicemail and wait for a response. In a high-volume setting this model presents many challenges to the patient experience, provider efficiency, and patient safety. The purpose of this pilot project was to design, implement, and assess the feasibility of a digital channel for patients to reach their care team. Methods: We used a human-centred design approach to develop a new digital triage service. First, we analyzed manually collected triage call data over a one-year period to understand the most common patient queries of the telephone triage service. Through co-design, a new digital-triage service was conceptualized and prototyped. Subsequently, we tested a data-centred digital routing algorithm for patient concerns and symptoms. To evaluate implementation and assess feasibility, we collected usage data, satisfaction surveys, and conducted staff shadowing and interviews. We used PDSA cycles to refine user experience and clinical workflows. Results: Patient adoption of digital triage increased continuously over 9 months, with volumes surpassing voicemail after four months: over the last four months digital triage has encompassed on average 63% of the total request volumes. The response rate to patient surveys was 21%. Patients indicated being very satisfied or satisfied (74.4%), neutral (6.7%), and dissatisfied or very dissatisfied (18.9%). Interviews with leading triage staff suggest the digital channel improves triage efficiency and redirecting/escalation by providing structured and complete patient queries. Conclusions: The pilot highlights the feasibility and acceptability of a digital-triage service in a comprehensive cancer care setting. By leveraging digital tools and real-time data, PM addressed the challenges of telephone triage, enabling a more efficient process to respond to patients’ needs, while demonstrating high adoption and patient satisfaction. Subsequent work will expand the use of digital triage to all disease sites across the cancer centre, and create foundational workflows and technology for proactive care models, such as Remote Patient Monitoring.

Needs before deeds: psychological need satisfaction as a mechanism linking inclusive leadership to organizational citizenship behavior

PurposeDrawing upon self-determination theory (SDT) and the proactive motivation model, this study examined how inclusive leadership is related to organizational citizenship behavior (OCB) through psychological need satisfaction (PNS).Design/methodology/approachData were collected from a large Dutch private company in the financial sector (N = 264) and analyzed using structural equation modeling (SEM).FindingsInclusive leadership positively influenced all three PNS dimensions (autonomy, competence and relatedness). Both autonomy and relatedness fully mediated the relationship between inclusive leadership and OCB. However, this was not the case for competence, although additional analyses revealed the serial mediation of all three PNS dimensions.Originality/valueBy highlighting the mediating role of PNS, this study contributes to the inclusive leadership literature by helping unravel the underlying process through which leaders influence team outcomes. The findings emphasize the importance of inclusive leaders in satisfying employees' individual psychological needs, so that they can redirect their attention toward prosocial behaviors.

Bringing mental health to the frontlines: A proactive team-based model for healthcare workers during the COVID-19 pandemic

ObjectiveA novel team-based service was developed at the beginning of the pandemic in which sixty liaisons were assigned to provide proactive, tailored psychological support for healthcare workers (HCWs) across three of NewYork-Presbyterian's Weill Cornell affiliated hospitals. MethodThe program took the proactive approach of bringing mental health awareness to every department and major division that interfaced with COVID-19 patients. Virtual and in-person team-based “town hall” meetings were offered to provide psychoeducation, facilitate discussion, foster adaptive coping and social cohesion, and identify employees who would benefit from further individualized support. ResultsThe program's success was reflected in the number of town halls (1000+) and attendees (6000+) and in qualitative feedback from departments who requested ongoing services. ConclusionsThis article presents the development, implementation, challenges, and opportunities in designing a team-based support model for HCWs. This model may be useful for organizations that seek to develop similar programs.

Análise de situações de trabalho na fiscalização sanitária de medicamentos da agência reguladora federal

Abstract This study analyzes the work situations experienced by professionals in the sanitary fiscalization of medicines at the Brazilian Health Regulatory Agency, during the COVID-19 pandemic. This is a single-case study, qualitative in nature, with an ergological approach. The data were collected from 2020 to 2021. During this period, 18 interviews and 3 online work meetings were conducted. The discursive practices were systematized in ergodialogical maps, resulting in three categories: the works experienced in the sanitary enforcement of medicines; the variabilities of the organizational context and the renormalizations facing the pandemic. The fragilities of the information systems, the outdated regulatory framework, the performance model, the fragmentation of the work, among others, have caused barriers to the professional’s activity. The conformation of a new way of performing the sanitary fiscalization of medicines involves the combination of interventions aimed at prevention, detection, and response to the consumption of medicines that are harmful to health. The necessary transformation goes beyond the restricted function of control and punishment and consolidates itself as a new proactive and dynamic action model, based on health risk management and the implementation of Good Fiscalization Practices.

Analysis of work scenarios in the sanitary enforcement of medicines from the federal regulatory agency.

This study analyzes the work situations experienced by professionals in the sanitary fiscalization of medicines at the Brazilian Health Regulatory Agency, during the COVID-19 pandemic. This is a single-case study, qualitative in nature, with an ergological approach. The data were collected from 2020 to 2021. During this period, 18 interviews and 3 online work meetings were conducted. The discursive practices were systematized in ergodialogical maps, resulting in three categories: the works experienced in the sanitary enforcement of medicines; the variabilities of the organizational context and the renormalizations facing the pandemic. The fragilities of the information systems, the outdated regulatory framework, the performance model, the fragmentation of the work, among others, have caused barriers to the professional's activity. The conformation of a new way of performing the sanitary fiscalization of medicines involves the combination of interventions aimed at prevention, detection, and response to the consumption of medicines that are harmful to health. The necessary transformation goes beyond the restricted function of control and punishment and consolidates itself as a new proactive and dynamic action model, based on health risk management and the implementation of Good Fiscalization Practices.

A brief outline of the digitalization of medicine

The article proposes an analysis of modern digital tools in the field of public health. The expediency of improving the experimental legal regime in the field of digital innovations is substantiated. The main reasons for the technological breakthrough in the field of digital innovations in medicine are investigated. An assessment is given of possible options for further technological development of the healthcare sector in the direction of “artificial intelligence”. At the same time, specific mechanisms for the legal regulation of technological solutions introduced into practice are proposed. A special place is given to strategic planning documents that determine and justify, from the point of view of law, the course towards the digital transformation of the healthcare sector. Particular attention is paid to the issue of transition to a new proactive model of medical care using artificial intelligence systems. At the same time, the lack of legal regulation of issues of legal liability in the case of the use of artificial intelligence systems and robotics is taken into account.

Going beyond the role: How employees' perception of corporate social responsibility fuels proactive customer service performance

Corporate social responsibility (CSR) and employees' proactive performance are essential in the hospitality industry, given their positive impact on corporate performance. However, little focus has been given to the influence of CSR on employees' proactive performance. This study utilizes the social information processing theory and proactive motivation model to examine the impact of employees' perceived CSR on their proactive customer service performance. We also investigate the employees' motivational states as mediators, including employees' organization-based self-esteem, felt obligation, and experienced meaningfulness. Empirical evidence is from 438 frontline employees in boutique hotels in China. Results indicate that employees' perceived CSR significantly influences their proactive customer service performance. Specifically, felt obligation and experienced meaningfulness play the mediating roles between employees' perceived CSR and their proactive customer service performance, while organization-based self-esteem does not. This study contributes to the growing body of literature on CSR and employee proactivity, offering practical implications for managers and policymakers who aim to improve employee performance through CSR programs.

From job crafting to job quitting? Testing a wise proactivity perspective

PurposeThis study investigates when and for whom job crafting may turn into job quitting. The authors hypothesize that approach job crafting relates more positively to turnover intentions and subsequent voluntary job changes among employees with (a) high (vs low) need for career challenges and (b) those with high (vs low) self-esteem.Design/methodology/approachData were collected from 575 employees of a large public organization in the Netherlands with two measurement moments three months apart. Hypotheses were tested using cross-lagged regression analyses and path modeling.FindingsSupporting the hypotheses, approach crafting related positively to an increase in turnover intentions only among employees with high need for challenge or high self-esteem. Moreover, via turnover intentions at Time 1, approach crafting related positively to the voluntary job change at Time 2 for employees with (a) high need for challenge, as well as those with (b) high self-esteem. These findings held after controlling for avoidance crafting.Research limitations/implicationsThis study has been conducted in a relatively homogenous sample. Future research may test the predictions in a more heterogeneous sample, including participants from different cultural and economic contexts.Practical implicationsThe authors advise human resource (HR) professionals to facilitate the job crafting efforts of employees with a high need for challenge and those with high self-esteem because these groups are particularly at risk of voluntarily quitting their jobs. Adopting insights from the wise proactivity model may help ensure that job crafting benefits both employees and employers.Originality/valueThis study brings clarity to the inconsistent relationships between job crafting and job quitting by using the wise proactivity model as an explanatory framework.

A real-time discovery method for vehicle companion via service collaboration

PurposeVehicle companion is one of the most common companion patterns in daily life, which has great value to accident investigation, group tracking, carpooling recommendation and road planning. Due to the complexity and large scale of vehicle sensor streaming data, existing work were difficult to ensure the efficiency and effectiveness of real-time vehicle companion discovery (VCD). This paper aims to provide a high-quality and low-cost method to discover vehicle companions in real time.Design/methodology/approachThis paper provides a real-time VCD method based on pro-active data service collaboration. This study makes use of dynamic service collaboration to selectively process data produced by relative sensors, and relax the temporal and spatial constraints of vehicle companion pattern for discovering more potential companion vehicles.FindingsExperiments based on real and simulated data show that the method can discover 67% more companion vehicles, with 62% less response time comparing with centralized method.Originality/valueTo reduce the amount of processing streaming data, this study provides a Service Collaboration-based Vehicle Companion Discovery method based on proactive data service model. And this study provides a new definition of vehicle companion through relaxing the temporal and spatial constraints for discover companion vehicles as many as possible.

A Comparative Analysis of Collaborative Natural Resource Governance in Two Protected Areas in Zambia

In Zambia, early models of natural resource governance were based on state-centric approaches to conservation and later, to some degree, based on Community-Based Natural Resource Management (CBNRM) models. Both of the models delivered poorly in terms of improved biodiversity management, enhanced rural livelihoods, and rights-based benefits. A lack of productive dialogue, involvement, and participation of local communities in natural resource governance resulted in considerable conflicts between protected area managers and local communities, with substantial local political and socio-economic costs. Through a mixed-methods approach using a questionnaire, focus group discussions, and key informant interviews, the Environmental Governance Systems (EGS) framework was applied to compare how interactions among political, economic, and civil society actors influence resource use and the state of resources in the state-led Kaingu chiefdom and the community-managed Kaindu Community Conservancy. Results show limited communication, cooperation, and coordination among the actors in both cases. Conflicting interests over the use of land, wildlife, forests, and fisheries among actors have led to strained relationships, limited interactions, and many negative outcomes in both cases. Both protected areas exhibit a top-down structure of natural resources governance with limited community participation, conflictual relationships among actors, corruption, lack of transparency, and low accountability. The CBNRM structures and processes need to be changed legislatively to improve local ownership and a sense of responsibility and legitimacy by restructuring the constitutions of CBNRM organizations and developing their human resource, financial, and logistical capacities. The study proposes a proactive transformative model for mitigating negative impacts on the state of resources and resource use.

Increase Entrepreneurial Intentions: Proactive Personality, Entrepreneurship Education, and Academic Support as Predictors

This study conceptualizes entrepreneurship education and academic support as a moderator to understand the mechanism of the relationship between proactive personality and student entrepreneurial intentions. This study proposes a different proactive personality model as a variable influencing students' entrepreneurial intentions, with entrepreneurship education and academic support as a moderating variable. This influence model has never been studied, especially in Semarang. Some previous researchers tend to explore the influence of these factors separately. Observations from 192 students at ten universities in Semarang were used to test the hypothesized model. The study used a quota sampling technique and data collection was carried out by distributing questionnaires. The effect moderation model was tested using tiered regression analysis, with the Moderated Regression Analysis (MRA) interaction test. The results showed that the effect of proactive personality and entrepreneurial intentions of students was stronger when entrepreneurship education and academic support were included in the interaction. These findings are consistent with existing theory and studies. In addition, as a managerial implication, this study provides advice to tertiary institutions as educational institutions to consider proactive personality, entrepreneurship education, and academic support as necessary factors to encourage student interest in entrepreneurship. Entrepreneurship teaching methods should also be explored further.

The role of lung volume recruitment therapy in neuromuscular disease: a narrative review.

Respiratory muscle weakness results in substantial discomfort, disability, and ultimately death in many neuromuscular diseases. Respiratory system impairment manifests as shallow breathing, poor cough and associated difficulty clearing mucus, respiratory tract infections, hypoventilation, sleep-disordered breathing, and chronic ventilatory failure. Ventilatory support (i.e., non-invasive ventilation) is an established and key treatment for the latter. As survival outcomes improve for people living with many neuromuscular diseases, there is a shift towards more proactive and preventative chronic disease multidisciplinary care models that aim to manage symptoms, improve morbidity, and reduce mortality. Clinical care guidelines typically recommend therapies to improve cough effectiveness and mobilise mucus, with the aim of averting acute respiratory compromise or respiratory tract infections. Moreover, preventing recurrent infective episodes may prevent secondary parenchymal pathology and further lung function decline. Regular use of techniques that augment lung volume has similarly been recommended (volume recruitment). It has been speculated that enhancing lung inflation in people with respiratory muscle weakness when well may improve respiratory system "flexibility", mitigate restrictive chest wall disease, and slow lung volume decline. Unfortunately, clinical care guidelines are based largely on clinical rationale and consensus opinion rather than level A evidence. This narrative review outlines the physiological changes that occur in people with neuromuscular disease and how these changes impact on breathing, cough, and respiratory tract infections. The biological rationale for lung volume recruitment is provided, and the clinical trials that examine the immediate, short-term, and longer-term outcomes of lung volume recruitment in paediatric and adult neuromuscular diseases are presented and the results synthesised.

Alternative Reimbursement Models for Health Providers in High-Performance Sport: Stakeholder Experiences and Perceptions

BackgroundValue-based healthcare provider reimbursement models have been proposed as an alternative to traditional fee-for-service arrangements that can align financial reimbursement more closely to the outcomes of value to patients and society. This study aimed to investigate stakeholder perceptions and experiences of different reimbursement systems for healthcare providers in high-performance sport, with a focus on fee-for-service versus salaried provider models.MethodsThree in-depth semi-structured focus group discussions and one individual interview were conducted with key stakeholders across the Australian high-performance sport system. Participants included healthcare providers, health managers, sports managers and executive personnel. An interview guide was developed using the Exploration, Preparation, Implementation, Sustainment framework, with key themes deductively mapped to the innovation, inner context and outer context domains. A total of 16 stakeholders participated in a focus group discussion or interview.ResultsParticipants identified several key advantages of salaried provider models over fee-for-service arrangements, including: the potential for more proactive and preventive models of care; enhanced inter-disciplinary collaboration; and the ability for providers to have a deeper understanding of context and how their role aligns with a broader set of priorities for an athlete and the organisation. Noted challenges of salaried provider models included the potential for providers to revert to reactive care delivery when not afforded adequate capacity to provide services, and difficulties for providers in demonstrating and quantifying the value of their work.ConclusionsOur findings suggest that high-performance sporting organisations seeking to improve primary prevention and multidisciplinary care should consider salaried provider arrangements. Further research to confirm these findings using prospective, experimental study designs remains a priority.

Intelligent deep learning‐aided future beam and proactive handoff prediction model in Unmanned Aerial Vehicle‐assisted anti‐jamming Terahertz communication system

SummaryWireless communications often suffer from legitimate transmissions regarding malicious jamming attacks launched through the smart jammer. The drone or unmanned aerial vehicle (UAV) communication networks derived with reconfigurable intelligent surfaces (RIS) increase the issues of beam selection and proactive handoff in terahertz (THz). Thus, a new heuristic strategy is designed for efficient and incorporated optimization of the beamforming vector and anti‐jamming transmit power allocation in undefined environments. Here, the transmit power allocation and beamforming matrix of UAV are optimized with the developed hybrid heuristic algorithm of the Hybrid Crow Black Widow Search Optimization (HCBWSO) algorithm for maximizing the system achievable rate. Here, the HCBWSO algorithm is implemented to integrate with the Crow search algorithm (CSO) and Black Widow Optimization (BWO). The second contribution is to adopt RIS into THz–UAV communications, a new Enhanced Deep Temporal Convolutional Network (EDTCN) for predicting the future beam and proactive handoff of UAVs based on their prior analysis of the UAV locations, where the HCBWSO algorithm is utilized for recommending EDTCN. Here, the training of the EDTCN needs to be done with the collection of UAV information from the DEEPMIMO dataset for predicting the future beams and, also, tracking the location of the UAV. EDTCN helps in increasing the possibility of expanding the UAV coverage and also increases the consistency of the THz communication system. Thus, the prediction of the future beam increases the coverage area of the UAV and also maximizes the system rate in the THz communication system.

Activity as a predictor of the subjective well-being of Chinese adolescents

From the mid-1990s, there has been a certain scale of discussion in Chinese academic circles around the concept of mental health. In these conditions, the study of the subjective well-being of Chinese adolescents has gained great importance, which has overall determined the relevance of the research topic. The article is dedicated to the issue of subjective well-being of Chinese adolescents and contains the following research objective - to identify the internal factors of subjective well-being of Chinese adolescents in their relationship with other elements of personality structure. Special attention is paid to studying the influence of activity on the subjective well-being of Chinese adolescents. The diagnostic methods used in the study were: the "Psychological Well-being Scale" by C. Riff, the Proactive Attitudes Scale, and the General Self-Efficacy Questionnaire by R. Schwarzer. The obtained data were analyzed using mathematical-statistical methods (calculation of mean values, standard deviation, Shapiro-Wilk criterion, non-parametric Kruskal-Wallis dispersion analysis, regression and cluster analysis). As a result of the empirical research, it was found that more than half of all surveyed Chinese adolescents have high proactivity, characterized by traits such as initiative, decisiveness, self-confidence, the ability to quickly adapt to changes in the environment, plan, and actively build their future. Thus, adolescents with a proactive behavior model evaluate their subjective well-being higher than adolescents with low activity. The study concludes on the high role of proactivity in shaping the subjective well-being of Chinese adolescents, which can be considered in the development of measures to improve the psychological well-being of Chinese adolescents. The results of the study are consistent with other contemporary research data on mental well-being, the high level of which is ensured by voluntary regulation and proactive activities in educational activities.

Proactive and dynamic load balancing model for workload spike detection in cloud

Dynamic Load Balancing (DLB) has been an ever-green research topic since the beginning of cloud computing. DLB model distributes the available cloud resources (i.e., CPU, RAM, Storage, BW, etc.) among the VMs using a systematic approach to ensure efficient resource utilization. Overloading, an abnormal condition, occurs when the host encounters a resource shortage due to the high workload of Federated Learning. VM migration and consolidation are reliable load-balancing techniques to fix dynamic overloading issues. As part of the migration, the oversubscribed host VMs are moved to the other hosts till the load is regulated. Besides the high workload, the sudden spike in workload also leads the hosts to instant overloading. The former DLB models are efficient in handling general overloading conditions. Still, they are now suffering from limitations in taking the spike overloading due to 1) a sudden spike in workloads, 2) instant overloading, 3) recurrent migrations, and 4) SLA violations. To address these limitations in handling spike overloading, in this paper, we proposed a proactive and dynamic load balancing model (DLBM), which is designed to handle spike overloading with optimal solutions. The offered dynamic load balancing model with a spike detection algorithm helps detect workload spikes in advance to prevent the hosts from overloading and SLA violations. Experimental conducted using the Cloudsim tool and the planet lab workload dataset prove that the proposed DLBM identified the spikes in workload and efficiently controlled the recurrent migrations.