Operational Tasks Research Articles

Cognitive trajectories in longitudinally trained 3xTg-AD mice

Preclinical studies in Alzheimer's disease (AD) often rely on cognitively naïve animal models in cross-sectional designs that can fail to reflect the cognitive exposures across the lifespan and heterogeneous neurobehavioral features observed in humans. To determine whether longitudinal cognitive training may affect cognitive capacities in a well-characterized AD mouse model, 3xTg and wild-type mice (n = 20) were exposed daily to a training variant of the Go-No-Go (GNG) operant task from 3 to 9 months old. At 3, 6, and 9 months, performance on a testing variant of the GNG task and anxiety-like behaviors were measured, while long-term recognition memory was also assessed at 9 months. In general, GNG training improved performance with increasing age across genotypes. At 3 months old, 3xTg mice showed slight deficits in inhibitory control that were accompanied by minor improvements in signal detection and decreased anxiety-like behavior, but these differences did not persist at 6 and 9 months old. At 9 months old, 3xTg mice displayed minor deficits in signal detection, and long-term recognition memory capacity was comparable with wild-type subjects. Our findings indicate that longitudinal cognitive training can render 3xTg mice with cognitive capacities that are on par with their wild-type counterparts, potentially reflecting functional compensation in subjects harboring AD genetic mutations.

SOME ASPECTS OF TRAINING IN THE INTERACTION OF POLICE OFFICERS WHEN USING PHYSICAL FORCE

In their daily work, police offi cers often have to act in extreme situations involving the use of physical force, special means and fi rearms. The success of the performance of operational and offi cial tasks related to the prosecution and detention of the off ender largely depends on the eff ectiveness of interaction between police offi cers. This article discusses the problems of implementing interaction and the main aspects of teaching police offi cers how to interact in situations of operational and offi cial activity. The author points out the need for a more detailed study of organizational and methodological approaches to teaching methods of interaction of police offi cers.

A hierarchical reinforcement learning GPC for flexible operation of ultra-supercritical unit considering economy

With the growing proportion of renewable energy sources in power grid, the demands for deep peak shaving and rapid load regulation of ultra-supercritical (USC) unit to provide more flexible and stable power services is further enhanced. Simultaneously, the load tracking speed is no longer the only significant task in the flexible operation of USC unit, the operational economy is more and more important. To this end, a hierarchical reinforcement learning GPC control scheme with a better economy is proposed and apply to USC unit. Firstly, the model mismatch problem in control process is taken into account, and an integral mode is added into the GPC to eliminate the steady state error during the load regulation. Secondly, the hierarchical reinforcement learning GPC scheme integrated the GPC and twin delayed deep deterministic policy gradient algorithms is presented. The complex flexibility demand problem is decomposed into rapid load regulation and economic operation and then respectively solved in the GPC-based upper layer and reinforcement learning agent-based lower layer. Thirdly, under the flexibility demand, a multi-criteria optimization function including the load tracking cost, main steam throttling loss and coal consumption rate of unit is constructed. Then, via the accurate trajectory tracking by GPC and on-line control sequence optimization by reinforcement learning agent, the operational flexibility and economy of USC unit are simultaneously improved within safe boundaries. Finally, to verify the availability of the proposed hierarchical reinforcement learning GPC strategy, large-scale load variations and disturbance rejection ability tests cover the 30%–100 % rated load of a 1000 MW USC unit are carried out, and the load regulation performance is compared to other strategies. Results confirm that the designed hierarchical reinforcement learning GPC control scheme is profit for the flexible and economic operation capability promotion of USC unit.

A New Productivity Benchmarking System Connected to Well-designed Labour Apprenticeship Framework for Construction Projects

This study primarily identifies the knowledge gaps that must be filled to apply conceptualised methods for rising and benchmarking productivity levels of various construction operational tasks. Accordingly, this study intended to evaluate labour productivity in relation to labour performance using potential apprenticeship instruments and systems. Comprehensive literature analysis and expert consultations were carried out to create a new mechanism with the collection of potential models, methods and systems connected to labour training, performance evaluation and productivity measurements. A total of 23 construction projects in Sri Lanka employed the created approach methodically. The results demonstrate the precise patterns of substantial shifts in labour productivity and performance index values, which have sparked new conceptual mechanisms for construction project planning methods. The outcomes demonstrate that the suggested approach can be used in a way that is reliable, generalisable, productive and sustainable. The study provides project managers, planners and engineers with a new road map and helpful techniques to raise the standard for measuring labour productivity levels across a variety of jobs. The overall study outcomes strengthen the chain relationship among the training elements, labour skills, performance and productivity. Though the study findings were limited to the construction industry and Sri Lankan context, the study outcomes are expected to make a significant impact on the current practices of various industry sectors in many developing countries for achieving higher productivity levels in work operations. This paper discusses some more implications and future scopes of the study elaborately.

World Practice of Applying State Support Measures for Organizations of the Military-Industrial Complex

The article is devoted to an analytical review of the best global practices in the implementation of measures of state support for organizations of the military-industrial complex (MIC). The purpose of the research was to study international experience in this area and determine its applicability in the context of the development of the Russian Federation’s MIC. The relevance of this work is determined by the need to ensure the solution of operational and strategic tasks of the Russian Federation’s military-industrial complex in the conditions of sanctions pressure from certain countries and the importance of assessing the available measures of state support for organizations of the complex. The study analyses the American Defense Manufacturing Community Support Program, which has been implemented in the United States since 2019. The program is based on the development of cooperation between public and private organizations, including enterprises of the military-industrial complex, scientific organizations, as well as local governments. The structure of the program provides for the formation of a community that brings together stakeholders to ensure sustainable development and competitiveness of the defense industrial complex. As part of the study of European experience, the activities of The European Defense Fund, established in the European Union in 2021 on the initiative of the Commission for the Support of Defense Cooperation of the European Union, were examined. The fund is focused on supporting and developing the European defense-industrial complex by financing innovative research, development and joint projects of international importance. The main objectives of the fund include increasing autonomy and competitiveness in the defense industry, as well as promoting integration and cooperation between the member states of the European Union. Within the framework of the article, each practice was analyzed, and conclusions have been drawn about what ideas from international practice could be applied in the development of the defense-industrial complex of the Russian Federation. The results of the study can be used by the Federal Assembly of the Russian Federation, the Government of the Russian Federation, the Ministry of Defense of the Russian Federation, the Foundation for Advanced Studies and other state structures and organizations that determine the policy in the field of defense industry regulation as proposals for further development of state support measures in the Russian Federation.

The Use of Artificial Intelligent Applications In Electrical Power Distribution and Automation Control System

Artificial intelligence has provided great potential and space for the optimization of electrical engineering and it will bring about great improvement not only in economic aspect but also in safety and actual operation control. Since the advent of artificial intelligence it has been widely applied in all fields of life and achieved notable application effect its emergence has even pointed out the direction for the development of many fields and the field of electrical automation control is no exception. In reality, the application of artificial intelligence technology has greatly promoted the progress of the concrete practice of electrical automation and should be paid full attention to by related enterprises and staff. This paper aims to summarize the emergence and development of artificial intelligence and its components and finally summarize the advantages and functions of artificial intelligence in electrical automation control. Advances in machine learning and artificial intelligence (AI) techniques bring new opportunities to numerous intractable tasks for operation and control in modern electric distribution systems. Revolving around the characteristics of distribution system operation and integration of distributed energy resources this paper also illuminates prospects and challenges typified by the privacy explain ability and interpretability of such AI applications in smart grids. KEYWORDS: - Distribution systems, smart buildings, neural networks, Artificial Intelligence, Electrical Engineering, power systems.

Autonomous navigation method of jujube catch-and-shake harvesting robot based on convolutional neural networks

Autonomous navigation of agricultural devices plays an indispensable step for the realization of various field intelligent operation tasks. However, the accurate extraction of navigation path is still a challenging task due to the complex orchard environment with many interference factors. To cope with the challenge, a low-cost vision system based on convolutional neural network and machine learning algorithm is proposed to obtain accurate navigation path for autonomous path detection of catch-and-shake harvesting robot under winter jujube orchard. In the vision system, firstly, an improved light-weight YOLOX-Nano architecture with some tricks including CSP attention block, SPPCSPC-F and ASFF is proposed to detect root points of jujube tree. Results show that the mAP of our model is 84.08 % and model size is only 12.30 MB, which can meet the requirements of embedded deployment. Secondly, a K-means clustering algorithm is applied to divide root points into two classes which belong to left and right tree row. Finally, the navigation line can be determined by geometric relations and least squares. The experimental results showed that the navigation centerline mean pixel errors and average heading angle deviation are 4.49 pixels and 2.55°, respectively. Therefore, this method can provide a new theoretical basis and technical reference for autonomous navigation of winter jujube catch-and-shake harvesting robots.

State-dependent modulation of positive and negative affective valences by a parabrachial nucleus-to-ventral tegmental area pathway in mice.

Appropriately responding to various sensory signals in the environment is essential for animal survival. Accordingly, animal behaviors are closely related to external and internal states, which include the positive and negative emotional values of sensory signals triggered by environmental factors. While the lateral parabrachial nucleus (LPB) plays a key role in nociception and supports negative valences, it also transmits signals including positive valences. However, the downstream neuronal mechanisms of positive and negative valences have not been fully explored. In the present study, we investigated the ventral tegmental area (VTA) as a projection target for LPB neurons. Optogenetic activation of LPB-VTA terminals in male mice elicits positive reinforcement in an operant task and induces both avoidance and attraction in a place-conditioning task. Inhibition of glutamic acid decarboxylase (GAD) 65-expressing cells in the VTA promotes avoidance behavior induced by photoactivation of the LPB-VTA pathway. These findings indicate that the LPB-VTA pathway is one of the LPB outputs for the transmission of positive and negative valence signals, at least in part, with GABAergic modification in VTA.

Rotation tracking control strategy for space flexible structures based on neural network compensation

Flexible space structures play an invaluable role in space operation tasks as large attachments to spacecraft and artificial satellites. Due to the characteristics of flexibility and slenderness, flexible space structures will inevitably cause flexible deformation and vibrate during motion, which will affect the rotation control accuracy. To solve this problem, this paper proposes a rotation tracking control strategy based on sliding mode controller with neural network compensation. The control law is designed according to the dynamic model established by the new flexible deformation description method. Firstly, according to the new flexible deformation description method, the dynamic model of space flexible structures is established by the assumed modal method and Lagrange principle. Through a comparison of the accuracy of the dynamics model deformation, it is found that the new flexible deformation description method has high model accuracy. Based on the space flexible structures’ dynamics model, the sigmoid function is proposed as the convergence law for designing the sliding mode controller. Besides, neural networks are proposed to identify and compensate for the disturbing torque caused by ignoring nonlinear factors. Finally, the effectiveness of the sliding mode controller with neural network compensation is validated by simulation and ground control experiments.

Network slice allocation for 5G V2X networks: A case study from framework to implementation and performance assessment

Empowered by the capabilities provided by fifth generation (5G) mobile communication systems, vehicle-to-everything (V2X) communication is heading from concept to reality. Given the nature of high-mobility and high-density for vehicle transportation, how to satisfy the stringent and divergent requirements for V2X communications such as ultra-low latency and ultra-high reliable connectivity appears as an unprecedented challenging task for network operators. As an enabler to tackle this problem, network slicing provides a power tool for supporting V2X communications over 5G networks. In this paper, we propose a network resource allocation framework which deals with slice allocation considering the coexistence of V2X communications with multiple other types of services. The framework is implemented in Python and we evaluate the performance of our framework based on real-life network deployment datasets from a 5G operator. Through extensive simulations, we explore the benefits brought by network slicing in terms of achieved data rates for V2X, blocking probability, and handover ratio through different combinations of traffic types. We also reveal the importance of proper resource splitting for slicing among V2X and other types of services when network traffic load in an area of interest and quality of service of end users are taken into account.

Spared nerve injury decreases motivation in long-access homecage-based operant tasks in mice.

Neuropathic pain causes both sensory and emotional maladaptation. Preclinical animal studies of neuropathic pain-induced negative affect could result in novel insights into the mechanisms of chronic pain. Modeling pain-induced negative affect, however, is variable across research groups and conditions. The same injury may or may not produce robust negative affective behavioral responses across different species, strains, and laboratories. Here, we sought to identify negative affective consequences of the spared nerve injury model on C57BL/6J male and female mice. We found no significant effect of spared nerve injury across a variety of approach-avoidance conflict, hedonic choice, and coping strategy assays. We hypothesized these inconsistencies may stem in part from the short test duration of these assays. To test this hypothesis, we used the homecage-based Feeding Experimentation Device version 3 to conduct 12-hour, overnight progressive ratio testing to determine whether mice with chronic spared nerve injury had decreased motivation to earn palatable food rewards. Our data demonstrate that despite equivalent task learning, spared nerve injury mice are less motivated to work for a sugar pellet than sham controls. Furthermore, when we normalized behavioral responses across all the behavioral assays we tested, we found that a combined normalized behavioral score is predictive of injury state and significantly correlates with mechanical thresholds. Together, these results suggest that homecage-based operant behaviors provide a useful platform for modeling nerve injury-induced negative affect and that valuable pain-related information can arise from agglomerative data analyses across behavioral assays-even when individual inferential statistics do not demonstrate significant mean differences.

Interaction Design and Research for User Interfaces of Central Bank Digital Currency: An Empirical Study

There is already a consensus among central banks to accelerate the development of Central Bank Digital Currencies (CBDC). To facilitate a smooth money transfer process, the interactive experience of the CBDC user interface design is very important. However, much of the current research has focused on the technical feasibility of the CBDCs, and no scientific research has yet attempted to design an effective CBDC user interface. In this study, a series of CBDC user interfaces were designed to help study the interaction process of CBDCs in a simulated payment transaction context with a focus on the user interface and interactive experience of the CBDCs. The experiment in this study was a 2 (graphical metaphor) × 3 (gesture type) × 2 (visual feedback) mixed factorial design in which a total of 12 CBDC prototypes were created and evaluated based on users’ task performance and a series of subjective evaluations. The results of our empirical study revealed that graphical metaphor user interface design can improve the interface usability and subjective evaluation of CBDCs, and may also trigger the user’s sense of experience with traditional banknotes. Specifically, the use of animated cues for currency movement on the CBDC user interface may help evoke the user’s experience of using cash. In addition, we found that the wave type gesture may cause users to spend more time completing the CBDC operational tasks. Moreover, the animated cue of money moving may have faster performance and a higher degree of rationality. The above-mentioned results may provide an important design reference for CBDC developers to create easy-to-use CBDC services.

Designing electricity distribution networks: The impact of demand coincidence

With the global effort to reduce carbon emissions, clean technologies such as electric vehicles and heat pumps are increasingly introduced into electricity distribution networks. These technologies considerably increase electricity flows and can lead to more coincident electricity demand. In this paper, we analyze how such increases in demand coincidence impact future distribution network investments. For this purpose, we develop a novel model for designing electricity distribution networks, called the distribution network reconfiguration problem with line-specific demand coincidence (DNRP-LSDC). Our analysis is two-fold: (1) We apply our model to a large sample of real-world networks from a Swiss distribution network operator. We find that a high demand coincidence due to, for example, a large-scale uptake of electric vehicles, requires a substantial amount of new network line construction and increases average network cost by 84% in comparison to the status quo. (2) We use a set of synthetic networks to isolate the effect of specific network characteristics. Here, we show that high coincidence has a more detrimental effect on large networks and on networks with low geographic consumer densities, as present in, e.g., rural areas. We also show that expansion measures are robust to variations in the cost parameters. Our results demonstrate the necessity of designing policies and operational protocols that reduce demand coincidence. Moreover, our findings show that operators of distribution networks must consider the demand coincidence of new electricity uses and adapt investment budgets accordingly. Here, our solution algorithms for the DNRP-LSDC problem can support operators of distribution networks in strategic and operational network design tasks.

Mobilization of endocannabinoids by midbrain dopamine neurons is required for the encoding of reward prediction

Brain levels of the endocannabinoid 2-arachidonoylglycerol (2-AG) shape motivated behavior and nucleus accumbens (NAc) dopamine release. However, it is not clear whether mobilization of 2-AG specifically from midbrain dopamine neurons is necessary for dopaminergic responses to external stimuli predicting forthcoming reward. Here, we use a viral-genetic strategy to prevent the expression of the 2-AG-synthesizing enzyme diacylglycerol lipase α (DGLα) from ventral tegmental area (VTA) dopamine cells in adult mice. We find that DGLα deletion from VTA dopamine neurons prevents depolarization-induced suppression of excitation (DSE), a form of 2-AG-mediated synaptic plasticity, in dopamine neurons. DGLα deletion also decreases effortful, cue-driven reward-seeking but has no effect on non-cued or low-effort operant tasks and other behaviors. Moreover, dopamine recording in the NAc reveals that deletion of DGLα impairs the transfer of accumbal dopamine signaling from a reward to its earliest predictors. These results demonstrate that 2-AG mobilization from VTA dopamine neurons is a necessary step for the generation of dopamine-based predictive associations that are required to direct and energize reward-oriented behavior.

Impairments in expression of devaluation in a Pavlovian goal-tracking task, but not a free operant devaluation task, after fentanyl exposure in female rats

In laboratory animals, there are numerous demonstrations that past exposure to drugs of abuse can lead to devaluation impairments weeks after the final drug exposure, with the majority of these demonstrations examining effects of exposure to psychostimulants. There has been minimal investigation into whether prior exposure to opiates can lead to devaluation impairments. Here, we first trained female rats that two separate cuelights predicted two different foods and measured Pavlovian goal-tracking responses (Experiment 1) or trained female rats to press two levers to earn two different foods and measured this operant response (Experiment 2). In both experiments, we subsequently gave the rats injections of fentanyl twice daily for 6 days, and then tested rats for conditioned responses after satiation on one of the foods 48-h after the final injection. We found that rats were impaired in the expression of devaluation in the Pavlovian task after fentanyl exposure, but were unimpaired in the expression of devaluation in the operant task. The pattern of results is most consistent with an impairment in lateral orbitofrontal cortex function, but additional research is needed to determine the neurobiological cause of this pattern of results.

Design of a Novel Exoskeleton with Passive Magnetic Spring Self-locking and Spine Lateral Balancing

This paper proposes a new upper-limb exoskeleton to reduce worker physical strain. The proposed design is based on a novel PRRRP (P-Prismatic; R-Revolute) kinematic chain with 5 passive Degrees of Freedom (DoF). Utilizing a magnetic spring, the proposed mechanism includes a specially designed locking mechanism that maintains any desired task posture. The proposed exoskeleton incorporates a balancing mechanism to alleviate discomfort and spinal torsional effects also helping in limb weight relief. This paper reports specific models and simulations to demonstrate the feasibility and effectiveness of the proposed design. An experimental characterization is performed to validate the performance of the mechanism in terms of forces and physical strain during a specific application consisting of ceiling-surface drilling tasks. The obtained results preliminarily validate the engineering feasibility and effectiveness of the proposed exoskeleton in the intended operation task thereby requiring the user to exert significantly less force than when not wearing it.

Typification of Projects for Development of High-Speed Rail

The emerging demand for improving consumer parameters of rail passenger transport leads to the need to build a dedicated railway infrastructure for passenger trains with speeds exceeding in some sections 250 km/h. The high capital intensity of dedicated infrastructure development projects imposes significant restrictions on their scope and financial feasibility. The geographically determined location of urban agglomerations in European and Asian countries has led to formation of many approaches to the routing of lines for high-speed passenger transportation.The projects for development of dedicated high-speed railways being developed in various countries differ in their technical, technological, and operational characteristics. The use of different approaches has led to different efficiency of the passenger traffic, expressed in the demand of passengers for transportation.A structural analysis of existing high-speed transportation projects allowed revealing general patterns of their development. By the number of operational tasks arising with regard to movement of trains, the phases of traffic development were divided into linear, tree­like and network stages. Thus, when moving from the linear structure of a high-speed rail, an additional problem arises of trains passing from the main track to the secondary one. The transition to the network stage can result in emergence of parallel passages rail tracks between urban agglomerations.To generalise the experience of operating high-speed systems in the world, the article describes the developed method that allows comparing different projects for organising high-speed rail with each other. The comparison is made according to the main characteristics of traffic: travel time between separation points, the traffic speed, the total length of the railway line. Identification of the patterns inherent in various projects for development of high-speed rail will make it possible to compare their technological parameters, to identify the scope of rational use of high-speed lines and areas of competition with other core types of transport.

Digital Future of the Global Hospitality Industry and Hospitality Education: Review of Related Literature

The hospitality and tourism industries are changing dramatically as a consequence of the application of information technology (IT) to managerial, and strategic and operational tasks. This is stimulated by the competition within the industry as well as the development of new, creative technological advancements. Digital revolution constitutes one of the most significant developments in the volatile business market. Hence, utilising digital technology is essential to maintain competitiveness and keep up with the changing world. Advancements of modern technology, has the potential to replace human labour with artificial intelligence and robotics. Thus, hospitality graduates need to be equipped with the right skills and abilities to meet the needs of the evolving hospitality industry. Further, they need to always upgrade themselves with the right competencies to thrive in the job market. With the goal of evaluating recent developments and technology advancements in the hospitality education and industry, this study analyses past research works in the field as well as theories of skill development, particularly for hospitality graduates. Using the content analysis method, the study examined previous research on how hospitality graduates can enhance their skills. Based on the findings, a conceptual framework for fourth-generation technological abilities was developed for the hospitality sector. The proposed conceptual framework can serve as a basis and guideline for future research efforts as well.

Integrating Community Health Workers' Dual Clinic-Community Role in Safety-Net Primary Care: Implementation Lessons from a Pragmatic Diabetes-Prevention Trial.

Over a third of US adults carry a diagnosis of prediabetes, 70% of whom may progress to type 2 diabetes mellitus ("diabetes"). Community health workers (CHWs) can help patients undertake healthy behavior to prevent diabetes. However, there is limited guidance to integrate CHWs in primary care, specifically to address CHWs' dual clinic-based and community-oriented role. Using evidence from CHWs' adaptations of a diabetes-prevention intervention in safety-net hospitals in New York City, we examine the nature, intent, and possible consequences of CHWs' actions on program fidelity. We propose strategies for integrating CHWs in primary care. Case study drawing on the Model for Adaptation Design and Impact (MADI) to analyze CHWs' actions during implementation of CHORD (Community Health Outreach to Reduce Diabetes), a cluster-randomized pragmatic trial (2017-2022) at Manhattan VA and Bellevue Hospital. CHWs and clinicians in the CHORD study, with a focus in this analysis on CHWs. Semi-structured interviews and focus group discussion with CHWs (n=4); semi-structured interviews with clinicians (n=17). Interpretivist approach to explain CHWs' adaptations using a mix of inductive and deductive analysis. CHWs' adaptations extended the intervention in three ways: by extending social assistance, healthcare access, and operational tasks. The adaptations were intended to improve fit, reach, and retention, but likely had ripple effects on implementation outcomes. CHWs' focus on patients' complex social needs could divert them from judiciously managing their caseload. CHWs' community knowledge can support patient engagement, but overextension of social assistance may detract from protocolized health-coaching goals. CHW programs in primary care should explicitly delineate CHWs' non-health support to patients, include multiprofessional teams or partnerships with community-based organizations, establish formal communication between CHWs and clinicians, and institute mechanisms to review and iterate CHWs' work to resolve challenges in their community-oriented role.

고교학점제 도입에 따른 최소 성취수준 보장 지도 운영에 대한 교원 및 전문가 인식 분석

Objectives This study is expected to face many difficulties in operating guidance that guarantees the minimum level of achievement following the introduction of the high school credit system, so it seeks to listen to the opin-ions of teachers and experts, focusing on the case of the Gangwon Special Self-Governing Provincial Office of Education, and propose support measures to prevent failure to reach the minimum level of achievement. Methods To this end, in-depth interviews (2023. 4. - 2023. 6.) was carried out. The questions of the in-depth in-terview consisted of the suitability of the purpose of preventive guidance to guarantee the minimum level of ach-ievement, differences in teachers' perceptions and practices, the actual state of operation and impediments, and the performance of suitable operation plans and policies. Results Through interview analysis on the cases of the Gangwon-do Office of Education’s guidance for guaran-teeing the minimum achievement level of the high school credit system, the results were analyzed from five as-pects: (1) perception, (2) practice, (3) class, (4) evaluation, and (5) environment. Opinions were understood. In ad-dition, implications were drawn by analyzing the current status of guidance for guaranteeing the minimum ach-ievement level of the high school credit system of other city and provincial offices of education, and cases of pre-vention and supplementary guidance for guaranteeing the minimum achievement level for research and prepara-tion schools at the Gangwon Metropolitan Autonomous Office of Education. Conclusions Firstly, as reviewed in the 2022 achievement evaluation results of high schools (107 schools) of the Gangwon-do Office of Education, it was confirmed that more than 40% of the students had academic achieve-ment E. Secondly, as a result of analyzing the operational tasks of research schools and preparatory schools of other city/provincial offices of education and the Gangwon Metropolitan Autonomous Provincial Office of Education and identifying their characteristics, the main factors were methods of selecting low-achieving students, af-ter-school operation of non-attainment prevention guidance, and emotional support programs. Thirdly, measures to improve and support classes and assessments were proposed to have the subject competencies presented in the 2015 revised curriculum based on everyday life and familiar subjects, rather than just achieving the quantita-tive goal of the subject to reach the minimum achievement level.