Aspects Of Deployment Research Articles

Devising a method for integrated dataset formation and selecting a model for recognizing the technical condition of unmanned aerial vehicle

The object of this study is the process of forming a training dataset for diagnosing the technical condition of unmanned aerial vehicles (UAVs) using machine-learning algorithms. UAV flights are extremely important for various aspects of troop deployment. Combat UAV flights are performed under the influence of negative factors that cause flight special cases (FSC), which hinder the execution of combat missions, lead to mission failures, and result in the aircraft damage or loss. The available capabilities of autopilots are not enough for control under complex conditions, and in certain situations, the human operator cannot timely recognize a flight special case, including evaluation of the destructive impact of enemy’s electronic warfare systems on communication channels and operation of UAV. Therefore, the urgent issue is the intellectualization of onboard control systems, particularly towards recognizing the current technical state of UAV using artificial intelligence methods. To design such systems, labeled datasets are required. The procedure for forming datasets that consider the specificity of UAV construction and their combat use under adversarial conditions is not defined, necessitating the development of an appropriate method. Based on the well-known CRISP-DM methodology, a method for dataset formation has been proposed for subsequent use in artificial intelligence systems that use various machine-learning methods. This method differs from existing ones by considering the specificity of combat mission execution under adversarial conditions, which allowed for an 8.0 % increase in the accuracy of recognizing special cases in UAV flights by the onboard system. It also enabled timely detection of electronic warfare impacts on UAV and evaluation of the effectiveness of radio signal receivers jamming

Unveiling hidden aspects of GPS deployment on wildlife: A multistep and transdisciplinary approach to urban wild boar monitoring

Studies of free-ranging wildlife often involve individual tracking by sequentially recording animals’ positions over a continuous and extended period. Automatic, programmable, operational continuously, and user-friendly thanks to the development of intuitive software, GPS (Global Positioning System) enable the acquisition of large quantities of data, day and night, regardless of field and weather conditions, while allowing for levels of spatial and temporal resolution in the location data never before achieved in wildlife tracking studies. However, GPS collars deployment on wild fauna does not directly translate into scientific outcomes. This article delves into the hidden aspects of telemetry programs, offering a reflective account of our transdisciplinary experience (between researchers and wildlife managers) in GPS tracking of urban wild boar. The described protocol and its discussion aim to outline the necessary conditions to benefit from GPS programs. The program first requires a common construction of the protocol, which meets the objectives of each partner. Second, raw data collection and transformation into information involve four steps. Finally, both technical and human-related dimensions are to be anticipated and considered for further analyses.•Transdisciplinary research requires a common construction of the protocol in line with the research question.•Technical constraints and negotiations between partners need to be considered.•Multiple steps are required to leverage the scientific advantages of the monitoring.

Targeting Machine Learning and Artificial Intelligence Algorithms in Health Care to Reduce Bias and Improve Population Health.

Policy Points Artificial intelligence (AI) is disruptively innovating health care and surpassing our ability to define its boundaries and roles in health care and regulate its application in legal and ethical ways. Significant progress has been made in governance in the United States and the European Union. It is incumbent on developers, end users, the public, providers, health care systems, and policymakers to collaboratively ensure that we adopt a national AI health strategy that realizes the Quintuple Aim; minimizes race-based medicine; prioritizes transparency, equity, and algorithmic vigilance; and integrates the patient and community voices throughout all aspects of AI development and deployment.

Portable Instrumentation for Ambient Ionization and Miniature Mass Spectrometers.

We critically evaluate the current status of portable mass spectrometry (pMS), particularly where this aligns with ambient ionization. Assessing the field of pMS can be quite subjective, especially in relation to the portable aspects of design, deployment, and operation. In this review, we discuss what it means to be portable and introduce a set of criteria by which pMS and ambient ionization sources can be assessed. Moreover, we consider the recent literature in terms of the most popular and significant advances in portable instrumentation for ambient ionization and miniature mass spectrometers. Finally, emerging trends and exciting future prospects are discussed and some recommendations are offered.

Unveiling the Black Box: Bringing Algorithmic Transparency to AI

Overall, algorithmic transparency is an important aspect of responsible AI development and deployment. Ensuring that AI systems are transparent and accountable will help build trust and confidence in these systems and ensure that they are used ethically and effectively. Artificial intelligence (AI) has emerged as a cutting-edge domain that is fundamentally redefining different areas of daily experiences, such as health care, transport, finance, education, and others. The systems are not created for making a judgment like human judgment of natural language, spotting patterns and problem-solving; rather AI produces machines that also have intelligence level same as that of human beings. AI having more influence over us, it is to be considered the ethical directions of these tools and see that they operate under principles of transparency and accountability. The element regarding algorithmic transparency, which means the process of understanding the functioning and explanation of how AI systems make their decisions is the one that is most crucial. The issue of algorithm transparency is of fundamental importance for many considerations. AI systems are not only supported by fairness but also by their non-discrimination. If we do not know how a system of AI arrives at the decisions made, it becomes impossible to determine if the provided results meet equal treatment for everybody. If used in delicate areas like recruitment, credit, and legal system- where the AI-machine must make choices which are life changing, then this aspect is very important. On top of fairness, algorithmic transparency is also an important factor for accountability. If we are ignorant about what an artificial intelligence algorithm does and what is the source of its decision-making process, we are unable to track and classify the mistakes or mishaps of the system. This has always mattered when central to the operation of systems with high stake, such as those used in self-driving vehicles or in health care. Algorithmic transparency may be reached using different instruments. The transparent AI systems can be made by a more transparent design, for example, the simple modelling tools, that use interpretable models. Another method is designing technologies and techniques that can help people why the artificial systems difficult to be decoded but easy to understand which they can utilize in making decisions. Therefore, algorithmic transparency is a key factor of the AI made responsibly and used by the society. It is crucial that AI machines are both transparent and accountable since this will lead to people building trust in the system and accepting its ethical and practical implications. This paper examines regulation of algorithmic transparency in the EU, specifically provisions under the General Data Protection Regulation (GDPR), it aims to situate analysis of the GDPR's provisions on explainability of AI systems within broader technology ethics and policy discourse. The paper's scope is limited to EU regulations applicable to AI data processing transparency.

Different deployments of attentional breadth selectively predict UFOV task performance in older adults

The Useful Field of View task (UFOV) is a strong and reliable predictor of crash risk in older drivers. However, while the functional domain of attention is clearly implicated in UFOV performance, the potential role of one specific attentional process remains unclear: attentional breadth (the spatial extent of the attended region around the point of visual fixation). The goal of the present study was to systematically test the role of two distinct aspects of attentional breadth, maintaining a specific breadth of attention and resizing the attended region, in UFOV performance. To this end, 135 older adults completed the UFOV and modified Navon tasks to measure their efficiency in maintaining, contracting, and expanding the breadth of attention. We then examined individual-difference associations between these aspects of attentional breadth deployment and UFOV performance. We found that performance on UFOV Subtask 2 was associated with efficient contraction of attentional breadth (i.e., resizing the attended region to a smaller area), while Subtask 3 performance was associated with the efficiency of expanding attentional breadth (i.e., resizing the attended region to a larger area). The selectivity of these relationships appears to implicate these specific deployments of attentional breadth in how people complete the task, as it suggests that these relationships are not simply attributable to shared variance in a broader domain of cognitive functioning. The implications of these results for our understanding of UFOV, as well as future research directions that test the relative contributions of different cognitive processes in predicting task performance, are discussed.

Global path planning for autonomous construction vehicles in building construction: A comparative study with a focus on vehicle kinematic characteristics

Technological advancements in robotics and automation have led to a growing emphasis on integration into construction industry from a global research context. In particular, autonomous construction vehicles (ACVs) stand out as a pivotal innovation. One key aspect of successful deployment of ACVs on construction sites is the path planning technology, especially the global path planning (GPP) methods. Thus, this study conducted a comparative study of the six GPP algorithms from various underlying principles, including graph search methods (e.g. Dijkstra and A*), sampling-based methods (e.g. rapidly exploring random tree (RRT) and rapidly exploring random tree star (RRT*)), evolutionary computation (e.g. genetic algorithms (GAs)), and artificial intelligence (e.g. deep Q learning (DQN)), to reveal how GPP algorithms perform on the travel of ACVs. These algorithms were evaluated with consideration of the kinematic characteristics of typical construction vehicles, including truck-trailers and excavators, within a typical unstructured real-world building construction site and based on two tasks and eight criteria encompassing efficiency and safety. The findings were analysed from three perspectives, i.e. the planned global paths and travel paths of ACVs, the performance of algorithms in each metric, and the overall performance of each algorithm, to identify the strengths of each GPP algorithm. GAs excel in generating smoother travel paths than others, whereas the global paths from RRT and DQN exhibit unpredictable changes, leading to a meandering travel paths for ACVs. The quality of global paths induces a greater impact on differential-wheeled excavators compared to front-wheel steering truck-trailers. Practical guidance can therefore be provided to select and improve the GPP algorithms and thereby to enhance the safety and efficiency of ACVs on construction sites.

Status Quo and Gap Analysis of Heliostat Field Deployment Processes for Concentrating Solar Tower Plants

Abstract Deployment of the solar field of a concentrating solar power plant is one of many factors that are integral to the success of a project. Knowledge transfer from outside the industry is limited due to the unique nature of heliostats, which redirect sunlight to a receiver with high precision while maintaining a high level of reflectivity. Moreover, learning from project to project can be limited due to the site-specific nature of projects, as the market includes several developers, each with their own unique design. In this paper, we discuss the state of the art in heliostat field deployment. We cover all the key aspects of deployment from project assessment to a fully functioning system, which include site selection, layout development, supply chain, assembly, site preparation and construction, calibration, and operations and maintenance. We then perform a gap analysis on field deployment and recommend priorities for future research.

Strategy to systematically design and deploy the ITER plasma control system: A system engineering and model-based design approach

The paper details the process of developing the ITER Plasma Control System (PCS), that is, how to design and deploy it systematically, in the most efficient and effective manner. The integrated nature of the ITER PCS, with its multitude of coupled control functions, and its long-term development, calls for a different approach than the design and short-term deployment of individual controllers. It requires, in the first place, a flexible implementation strategy and system architecture that allows system re-configuration and optimization throughout its development. Secondly, a model-based system engineering approach is carried out, for the complete PCS development, i.e. both its design and deployment. It requires clear definitions for both the PCS role and its functionality, as well as definitions of the design and deployment process itself. The design and deployment process is shown to allow tracing the relationships of the many individual design and deployment aspects, such as system requirements, assumed operation use-cases and response models, and eventually verification and functional validation of the system design. The functional validation will make use of a dedicated PCS simulation platform that includes the description of the control function design as well as plant, actuator and sensor models that enable the simulation of these functions. By establishing a clear understanding of the interconnected steps involved in designing, implementing, commissioning, and operating the system, a more systematic approach is achieved. This ensures the completion of a comprehensive design that can be deployed efficiently, hence preventing the loss of precious operational time needed to debug and retune control functions and more importantly avoiding tokamak discharge disruptions.

Resource analysis for quantum-aided Byzantine agreement with the four-qubit singlet state

In distributed computing, a Byzantine fault is a condition where a component behaves inconsistently, showing different symptoms to different components of the system. Consensus among the correct components can be reached by appropriately crafted communication protocols even in the presence of byzantine faults. Quantum-aided protocols built upon distributed entangled quantum states are worth considering, as they are more resilient than traditional ones. Based on earlier ideas, here we establish a parameter-dependent family of quantum-aided weak broadcast protocols. We compute upper bounds on the failure probability of the protocol, and define and illustrate a procedure that minimizes the quantum resource requirements. Following earlier work demonstrating the suitability of noisy intermediate scale quantum (NISQ) devices for the study of quantum networks, we experimentally create our resource quantum state on publicly available quantum computers. Our work highlights important engineering aspects of the future deployment of quantum communication protocols with multi-qubit entangled states.

Crossover Boosted Grey Wolf Optimizer‐based framework for leader election and resource allocation in Intrusion Detection Systems for MANETs

AbstractMobile Ad hoc Networks (MANETs) is a self‐organizing networks without having a fixed infrastructure for making them susceptible to security threats. Intrusion Detection Systems (IDS) promotes security in MANETs by identifying malicious activities. Leader election is a fundamental aspect of IDS deployment, impacting resource allocation and system efficiency. This article presents a novel approach, the Crossover Boosted Grey Wolf Optimizer (CBGWO), for leader election and resource allocation in MANET‐based IDS. The proposed CBGWO algorithm integrates the Grey Wolf Optimizer (GWO) with innovative crossover operators that have an ability to enhance the capabilities of exploration and exploitation in the optimization process. The leader election problem is solved through applying multi‐objective optimization by considering energy consumption, reputation, and communication overhead. Objective functions are defined to maximize energy efficiency while maintaining a balanced distribution of leadership roles. Extensive simulations are conducted, varying network densities and the percentage of selfish nodes. Results demonstrate the effectiveness of the CBGWO‐based model in balancing energy consumption, prolonging network lifespan, and enhancing intrusion detection by comparing different state‐of‐the‐art models such as PCA‐FELM, CTAA‐MPSO, FLS‐RE, LEACH, DCAIDS, WOA‐GA, and VOELA. The proposed model achieved an energy consumption of 4.31 J, network lifetime of 560.482 ms, and average intrusion detection latency of 0.12 s, respectively. The proposed model outperforms than existing random and connectivity‐based leader election methods that is evaluated by taking main consideration of energy efficiency and network survivability. This research contributes to the field by introducing a robust algorithm for leader election in MANET‐based IDS, addressing challenges posed by network dynamics and resource constraints. The CBGWO‐based approach showcases its potential to achieve effective leader election and efficient resource allocation, thereby enhancing the security and sustainability of MANETs.

Insights from semi-structured interviews on integrating artificial intelligence in clinical chemistry laboratory practices

BackgroundArtificial intelligence (AI) is gradually transforming the practises of healthcare providers. Over the last two decades, the advent of AI into numerous aspects of pathology has opened transformative possibilities in how we practise laboratory medicine. Objectives of this study were to explore how AI could impact the clinical practices of professionals working in Clinical Chemistry laboratories, while also identifying effective strategies in medical education to facilitate the required changes.MethodsFrom March to August 2022, an exploratory qualitative study was conducted at the Section of Clinical Chemistry, Department of Pathology and Laboratory Medicine, Aga Khan University, Karachi, Pakistan, in collaboration with Keele University, Newcastle, United Kingdom. Semi-structured interviews were conducted to collect information from diverse group of professionals working in Clinical Chemistry laboratories. All interviews were audio recorded and transcribed verbatim. They were asked what changes AI would involve in the laboratory, what resources would be necessary, and how medical education would assist them in adapting to the change. A content analysis was conducted, resulting in the development of codes and themes based on the analyzed data.ResultsThe interviews were analysed to identify three primary themes: perspectives and considerations for AI adoption, educational and curriculum adjustments, and implementation techniques. Although the use of diagnostic algorithms is currently limited in Pakistani Clinical Chemistry laboratories, the application of AI is expanding. All thirteen participants stated their reasons for being hesitant to use AI. Participants stressed the importance of critical aspects for effective AI deployment, the need of a collaborative integrative approach, and the need for constant horizon scanning to keep up with AI developments.ConclusionsThree primary themes related to AI adoption were identified: perspectives and considerations, educational and curriculum adjustments, and implementation techniques. The study’s findings give a sound foundation for making suggestions to clinical laboratories, scientific bodies, and national and international Clinical Chemistry and laboratory medicine organisations on how to manage pathologists’ shifting practises because of AI.

Locational Role Analysis of Energy Storage Systems Based on Optimal Capacity Needs and Operations under High Penetration of Renewable Energy

As the need for energy storage systems (ESSs) capacity is increasing due to high accommodation of renewable resources, it is crucial to analyze in which location and for what purpose the ESSs are required to achieve the highest efficiency. Investors and system operators can place and operate the ESSs as expected based on this analysis. Therefore, this study assesses the specific roles of ESSs in a grid system based on their optimal capacity needs, locations, and operations. A long-term simulation model using mixed-integer programming is proposed to obtain these optimal solutions, such as ESS capacity and operational schedules for energy and reserves. Four-week operational simulations are performed for each month using data from the California Independent System Operator. ESSs are placed at sites with solar photovoltaic (PV) systems or wind farms, at baseload generator buses, and at load buses to verify the role of ESSs, depending on the locational differences. The detailed roles are analyzed from the aspects of flexible capacity supply, reserve deployments, time-shifting renewable and thermal energy generation, and costs. The results show that the ESSs on the baseload generation side provide flexibility by time-shifting baseload generation and turn on baseload generators, even when the net load is small. For instance, the required capacity of the flexible thermal generators, such as natural gas turbine generators, is about 3004 MW without the ESS operations in May. When 450 MW ESSs colocated with solar PVs are operated, the required flexible capacity of the thermal generators is lowered to 2404 MW. Moreover, ESSs are highly utilized as a downward reserve provider, although their costs for reserves are higher than thermal generators.

Artificial Intelligence Applications in Prostate Cancer Management: Success Stories and Future Ahead

Summary This review summarizes and provides an outlook for developments around the use of artificial intelligence (AI) in the diagnosis and treatment of prostate cancer. We searched existing literature on the design and development of new AI-based systems using a non-systematic approach. Areas targeted by AI include the diagnosis, Gleason scoring, biomarker identification, and prognosis of prostate cancer (PCa) from digitised histopathology, segmentation, detection, and classification of PCa from magnetic resonance imaging, AI applications for prostate ultrasound, AI in radiotherapy for PCa including synthetic computed tomography generation and treatment planning and AI in measuring and improving surgical outcomes and education. Recent work has focused on deep learning techniques. Algorithms have achieved results that outperform or are similar to those of experts. However, few proposed algorithms are clinically oriented and can be practically deployed. Future progress needs to be made in data availability, prospective evaluation, regulation, responsible AI, explainability, and practical aspects of clinical deployment.

Near-Space Communications: The Last Piece of 6G Space–Air–Ground–Sea Integrated Network Puzzle

This article presents a comprehensive study on the emerging near-space communications (NS-COM) within the context of space–air–ground–sea integrated network (SAGSIN). Specifically, we firstly explore the recent technical developments of NS-COM, followed by the discussions about motivations behind integrating NS-COM into SAGSIN. To further demonstrate the necessity of NS-COM, a comparative analysis between the NS-COM network and other counterparts in SAGSIN is conducted, covering aspects of deployment, coverage, channel characteristics, and unique problems of NS-COM network. Afterward, the technical aspects of NS-COM, including channel modeling, random access, channel estimation, array-based beam management, and joint network optimization, are examined in detail. Furthermore, we explore the potential applications of NS-COM, such as structural expansion in SAGSIN communication, civil aviation communication, remote and urgent communication, weather monitoring, and carbon neutrality. Finally, some promising research avenues are identified, including stratospheric satellite-to-ground direct links for mobile terminals, reconfigurable multiple-input multiple-output and holographic multiple-input multiple-output, federated learning in NS-COM networks, maritime communication, electromagnetic spectrum sensing and adversarial game, integrated sensing and communications, stratospheric-satellite-based radar detection and imaging, NS-COM-assisted enhanced global navigation system, NS-COM-assisted intelligent unmanned system, and free-space optical communication. Overall, this paper highlights that the NS-COM plays an indispensable role in the SAGSIN puzzle, providing substantial performance and coverage enhancement to the traditional SAGSIN architecture.

Practicability analysis of online deep reinforcement learning towards energy management strategy of 4WD-BEVs driven by dual-motor in-wheel motors

Deep reinforcement learning (DRL) has emerged as a promising approach for optimizing energy management strategies (EMS) in new energy vehicles. Nevertheless, existing studies typically focus on evaluating the performance of a particular algorithm, overlooking critical implementation details and lacking comprehensive analysis of their real-world applicability. In this paper, we mitigate this issue by conducting a thorough practicability analysis of DRL-based EMS methods. First, we theoretically analyze the benefits and limitations of existing DRL-based EMS approaches based on taxonomy towards their practicability. Subsequently, a novel EMS method that leverages model-based DRL algorithms that other researchers typically underestimate is proposed. The method newly introduces an uncertainty-aware model-based algorithm known as Probabilistic Ensembles with Trajectory Sampling (PETS) and is validated utilizing a four-wheel-drive (4WD) battery electric vehicle (BEV). After that, we conduct a comprehensive practicability analysis of three state-of-the-art DRL algorithms considering critical aspects for real-world deployment, e.g., hyperparameter sensitivity and algorithm transferability. The results demonstrate that even though the on-policy DRL achieves better asymptotic rewards and the off-policy DRL possesses better convergence, the proposed model-based DRL, PETS-based EMS, outperforms others regarding superior robustness and promising transferability across different extents of relevance between tasks. Besides, energy consumption results demonstrate that the model-based EMS can achieve a considerable 96.6% optimality compared to the baseline dynamic programming (DP). Thus, motivated by the challenges of applying DRL algorithms to real-world EMS, our systematic investigation and new insights contribute to advancing the practical employment of DRL-based EMS for BEVs.

CHESSIoT: A model-driven approach for engineering multi-layered IoT systems

Context:The current technology revolution, which places the highest value on people’s welfare, is frequently seen as being mainly supported by Internet of Things (IoT) technologies. IoT is regarded as a powerful multi-layered network of systems that integrates several heterogeneous, independently networked (sub-)systems working together to achieve a shared purpose. Objective:In this article, we present CHESSIoT, a model-driven engineering environment that integrates high-level visual design languages, software development, safety analysis, and deployment approaches for engineering multi-layered IoT systems. With CHESSIoT, users may conduct different engineering tasks on system and software models under development to enable earlier decision-making and take prospective measures, all supported by a unique environment. Methodology:This is achieved through multi-staged designs, most notably the physical, functional, and deployment architectures. The physical model specification is used to perform both qualitative and quantitative safety analysis by employing logical Fault-Trees models (FTs). The functional model specifies the system’s functional behavior and is later used to generate platform-specific code that can be deployed on low-level IoT device nodes. Additionally, the framework supports modeling the system’s deployment plan and run-time service provisioning, which would ultimately be transformed into deployment configuration artifacts ready for execution on remote servers. Results:To showcase the effectiveness of our proposed approach, as well as the capability of the supporting tool, a multi-layered Home Automation system (HAS) scenario has been developed covering all its design, development, analysis, and deployment aspects. Furthermore, we present the results from different evaluation mechanisms which include a comparative analysis and a qualitative assessment. The evaluation mechanisms target mainly completeness of CHESSIoT by addressing specific research questions.

FIDES: An ontology-based approach for making machine learning systems accountable

Although the maturity of technologies based on Artificial Intelligence (AI) is rather advanced nowadays, their adoption, deployment and application are not as wide as it could be expected. This could be attributed to many barriers, among which the lack of trust of users stands out. Accountability is a relevant factor to progress in this trustworthiness aspect, as it allows to determine the causes that derived a given decision or suggestion made by an AI system. This article focuses on the accountability of a specific branch of AI, statistical machine learning (ML), based on a semantic approach. FIDES, an ontology-based approach towards achieving the accountability of ML systems is presented, where all the relevant information related to a ML-based model is semantically annotated, from the dataset and model parametrisation to deployment aspects, to be exploited later to answer issues related to reproducibility, replicability, definitely, accountability. The feasibility of the proposed approach has been demonstrated in two scenarios, real-world energy efficiency and manufacturing, and it is expected to pave the way towards raising awareness about the potential of Semantic Technologies in different factors that may be key in the trustworthiness of AI-based systems.

PLS performance analysis of the vertical UWOC system with perfect and imperfect CSI.

Although underwater wireless optical communication (UWOC) receives much interest lately, security issues associated with it get little attention. In this work, it is the first attempt to investigate the physical layer security (PLS) performance of the vertical UWOC system with perfect and imperfect channel state information (CSI). Specifically speaking, the communication between two legitimate peers in the presence of an external eavesdropper is studied from the information-theoretic security perspective. Assuming that turbulence-induced fading over the vertical UWOC links is respectively subject to cascaded lognormal (LN) and Gamma-Gamma (GG) distributions for weak and moderate/strong turbulence conditions, and the angular pointing error is randomized by the Beckmann distribution, the composite cascaded statistical fading models are derived with the comprehensive effects of path loss, underwater turbulence, angular pointing errors, and channel estimation error. On the basis of these models, analysis frameworks of the probability of strictly positive secrecy capacity (SPSC), secrecy outage probability (SOP), and average secrecy capacity (ASC) are further obtained for this UWOC system, which are confirmed by Monte Carlo (MC) simulations. Furthermore, the effects including the number of layers, the level of channel estimation error, the link distance, the location of the eavesdropper, the quality of the main and eavesdropping channels on this system are analyzed for different water conditions. The presented results give valuable insights into the practical aspects of deployment of UWOC networks.

Gambaran Regulasi Emosi pada Ibu Bekerja yang Mengalami Konflik Peran Ganda

This study aims to determine the description of emotional regulation in working mothers who experience dual role conflict where there is a conflict between work outside the home and household work conflicts. The purpose of this study is to provide an overview of emotional regulation by working mothers who experience dual role conflict and to determine the factors that influence emotional regulation of working mothers who experience dual role conflict. The researcher uses a qualitative approach by focusing on a phenomenon, where mothers experience a dual role conflict between working and being a housewife. The respondents of this study were 3 mothers who worked as employees and had also become housewives who had children and significant others or informants from each respondent amounted to one person. Data collection was done by structured interviews. The results showed that the three respondents could bring up aspects of emotional regulation including aspects of situation selection, situation modification, attention deployment, cognitive change and response modulation. While the factors in the regulation of emotions include gender, cognitive and social support.