Handling Tasks Research Articles

Vision-based manipulation of transparent plastic bags in industrial setups

IntroductionThis paper addresses the challenges of vision-based manipulation for autonomous cutting and unpacking of transparent plastic bags in industrial setups, contributing to the Industry 4.0 paradigm. Industry 4.0, emphasizing data-driven processes, connectivity, and robotics, enhances accessibility and sustainability across the value chain. Integrating autonomous systems, including collaborative robots (cobots), into industrial workflows is crucial for improving efficiency and safety.MethodsThe proposed system employs advanced Machine Learning algorithms, particularly Convolutional Neural Networks (CNNs), for identifying transparent plastic bags under diverse lighting and background conditions. Tracking algorithms and depth-sensing technologies are integrated to enable 3D spatial awareness during pick-and-place operations. The system incorporates vacuum gripping technology with compliance control for optimal grasping and manipulation points, using a Franka Emika robot arm.ResultsThe system successfully demonstrates its capability to automate the unpacking and cutting of transparent plastic bags for an 8-stack bulk-loader. Rigorous lab testing showed high accuracy in bag detection and manipulation under varying environmental conditions, as well as reliable performance in handling and processing tasks. The approach effectively addressed challenges related to transparency, plastic bag manipulation and industrial automation.DiscussionThe results indicate that the proposed solution is highly effective for industrial applications requiring precision and adaptability, aligning with the principles of Industry 4.0. By combining advanced vision algorithms, depth sensing, and compliance control, the system offers a robust method for automating challenging tasks. The integration of cobots into such workflows demonstrates significant potential for enhancing efficiency, safety, and sustainability in industrial settings.

A low-cost printed circuit board-based centrifugal microfluidic platform for dielectrophoresis

In recent decades, electrokinetic handling of microparticles and biological cells found many applications ranging from biomedical diagnostics to microscale assembly. The integration of electrokinetic handling such as dielectrophoresis (DEP) greatly benefits microfluidic point-of-care systems as many modern assays require cell handling. Compared to traditional pump-driven microfluidics, typically used for DEP applications, centrifugal CD microfluidics provides the ability to consolidate various liquid handling tasks in self-contained discs under the control of a single motor. Therefore, it has significant advantages in terms of cost and reliability. However, to integrate DEP on a spinning disc, a major obstacle is transferring power to the electrodes that generate DEP forces. Existing solutions for power transfer lack portability and availability or introduce excessive complexity for DEP settings. We present a concept that leverages the compatibility of DEP and inductive power transfer to bring DEP onto a rotating disc without much circuitry. Our solution leverages the ongoing advances in the printed circuit board market to make low-cost cartridges (<$1) that can employ DEP, which was validated using yeast cells. The resulting DEPDisc platform solves the challenge that existing printed circuit board electrodes are reliant on expensive high-voltage function generators by boosting the voltage using resonant inductive power transfer. This work includes a device costing less than $100 and easily replicable with the information provided in the Supplementary material. Consequently, with DEPDisc we present the first DEP-based low-cost platform for cell handling where both the device and the cartridges are truly inexpensive.

The challenges clinical supervisors experience when supervising students in the workplace

PurposeThis article aims to provide a deeper understanding of the challenges clinical supervisors’ experience when supervising students’ professional learning in the workplace.Design/methodology/approachQualitative interviews were conducted with 13 healthcare professionals, who are experienced clinical supervisors. They were employed either in a hospital ward or in one of two municipal care units.FindingsThe findings showed that clinical supervisors must navigate a complex landscape of challenges to ensure students’ professional learning. They had to balance their regular duties with the time and effort required for supervision. Prominent challenges were ensuring students met their learning objectives and determining when to allow students to handle tasks independently. The supervisors also faced challenges that were beyond their control. Their workplace conditions were not designed to accommodate students effectively. They struggled with negative attitudes from colleagues towards student supervision and they wanted the clinic managers to be more involved. The gap between the university’s educational demands and the realities of the workplace also presented supervisors with a significant challenge.Originality/valueApart from unique empirical material depicting clinical supervisors’ experiences of student supervision, the value of this study lies in the recommendation that supervisors have clear requirements from higher educational institutions, but of equal importance is that these requirements align with the conditions in the workplace. Further studies are necessary to understand the role that workplace conditions play for clinical supervisors in creating supportive learning environments that facilitate students in their journey to becoming established professionals.

Explainable AI-Enhanced Human Activity Recognition for Human–Robot Collaboration in Agriculture

This study addresses a critical gap in human activity recognition (HAR) research by enhancing both the explainability and efficiency of activity classification in collaborative human–robot systems, particularly in agricultural environments. While traditional HAR models often prioritize improving overall classification accuracy, they typically lack transparency in how sensor data contribute to decision-making. To fill this gap, this study integrates explainable artificial intelligence, specifically SHapley Additive exPlanations (SHAP), thus enhancing the interpretability of the model. Data were collected from 20 participants who wore five inertial measurement units (IMUs) at various body positions while performing material handling tasks involving an unmanned ground vehicle in a field collaborative harvesting scenario. The results highlight the central role of torso-mounted sensors, particularly in the lumbar region, cervix, and chest, in capturing core movements, while wrist sensors provided useful complementary information, especially for load-related activities. The XGBoost-based model, selected mainly for allowing an in-depth analysis of feature contributions by considerably reducing the complexity of calculations, demonstrated strong performance in HAR. The findings indicate that future research should focus on enlarging the dataset, investigating the use of additional sensors and sensor placements, and performing real-world trials to enhance the model’s generalizability and adaptability for practical agricultural applications.

Reinforcement Learning of a Six-DOF Industrial Manipulator for Pick-and-Place Application Using Efficient Control in Warehouse Management

This study investigates the integration of reinforcement learning (RL) with optimal control to enhance precision and energy efficiency in industrial robotic manipulation. A novel framework is proposed, combining Deep Deterministic Policy Gradient (DDPG) with a Linear Quadratic Regulator (LQR) controller, specifically applied to the ABB IRB120, a six-degree-of-freedom (6-DOF) industrial manipulator, for pick-and-place tasks in warehouse automation. The methodology employs an actor–critic RL architecture with a 27-dimensional state input and a 6-dimensional joint action output. The RL agent was trained using MATLAB’s Reinforcement Learning Toolbox and integrated with ABB’s RobotStudio simulation environment via TCP/IP communication. LQR controllers were incorporated to optimize joint-space trajectory tracking, minimizing energy consumption while ensuring precise control. The novelty of this research lies in its synergistic combination of RL and LQR control, addressing energy efficiency and precision simultaneously—an area that has seen limited exploration in industrial robotics. Experimental validation across 100 diverse scenarios confirmed the framework’s effectiveness, achieving a mean positioning accuracy of 2.14 mm (a 28% improvement over traditional methods), a 92.5% success rate in pick-and-place tasks, and a 22.7% reduction in energy consumption. The system demonstrated stable convergence after 458 episodes and maintained a mean joint angle error of 4.30°, validating its robustness and efficiency. These findings highlight the potential of RL for broader industrial applications. The demonstrated accuracy and success rate suggest its applicability to complex tasks such as electronic component assembly, multi-step manufacturing, delicate material handling, precision coordination, and quality inspection tasks like automated visual inspection, surface defect detection, and dimensional verification. Successful implementation in such contexts requires addressing challenges including task complexity, computational efficiency, and adaptability to process variability, alongside ensuring safety, reliability, and seamless system integration. This research builds upon existing advancements in warehouse automation, inverse kinematics, and energy-efficient robotics, contributing to the development of adaptive and sustainable control strategies for industrial manipulators in automated environments.

A Soft Wearable Robot with an Adjustable Twisted String Actuator and a Two‐Stage Transmission Mechanism for Manual Handling Tasks

A manual handling task is one of the most common causes of back injuries, accounting for nearly 31.9% of the total work‐related injuries. To promote a safe working environment for workers, wearable robots (wearables) are rapidly emerging to fulfill various needs in human‐robot interactive tasks. Although numerous studies have successfully developed wearables to assist humans, they are often limited to supporting a single degree of freedom (DoF) of the human body with a single actuator. However, as humans tend to use multiple parts of their bodies, additional actuators and mechanisms to transmit force/motion are necessary to realize multi‐DoF, which increases the volume, price, and complexity of the wearables. To address these issues, a multi‐DoF wearable robot (WeaRo) with an adjustable twisted string actuator (ATSA) and a two‐stage transmission mechanism (2TM) is proposed. By introducing the novel ATSA and 2TM, the proposed WeaRo achieves multi‐DoF (lumbar and arm) with only a single electric motor and ATSA. Experimental results demonstrate that the proposed WeaRo effectively reduces the maximal voluntary contraction (%MVC) of lumbar, biceps, and triceps muscles by a maximum of 18.2, 29.1, and 27.0%, respectively without constraining users’ movements. Additionally, the fabric‐based design ensures a lightweight solution weighing 5.2 kg including batteries.

Industrial Robot Selection using TOPSIS Method: A Comparative Analysis

The days when humans and robots have not yet interacted in daily activities are gone. In fact, robots are on their way to changing their applications from industry to contributing to the well-being of people in everyday life. These robots are called "social robots" (SR). Unlike robots that simply describe what helps them, social robots aim to establish social interactions and improve the socialization of human beings. While many industrial robots are primarily used by manufacturing companies for hazardous or menial tasks, social robots have become a popular choice due to their quality and the positive impact they have on productivity and profitability. Industrial robots have anthropological features and are generally reprogrammable machines. Their mechanical arms are crucial components, and they possess other, albeit less prominent, features such as decision-making abilities, a range of emotions, the ability to respond to inputs, and communication skills. They are widely used in various industries, including material handling, assembly, and machines for applications like material handling, assembly, and machines for applications. These robots play an essential role in increasing efficiency and quality. Robots in manufacturing companies are highly valued for their ability to automate tasks and be reprogrammed for different functions. They possess various features optimized for specific handling tasks, including the ability to move in two or more axes and respond to different sensory inputs. This comprehensive set of features makes them suitable for a wide range of applications, including assembly, welding, material handling, loading, packaging, and inspection, where endurance, speed, and accuracy are required. The selection of industrial robots is a decision-making process based on the needs of production. Making the right decision is crucial for productivity and success. Choosing the wrong robot can lead to issues such as inefficiency or the inability to perform specific tasks within a multi-scheduled production. In the worst-case scenario, a completely unsuitable robot can render the entire company unusable. The complexity of the selection process is amplified by the diversity of robot manufacturers, as well as the significant variations in manufacturing jobs. Manufacturers themselves have recognized the importance of addressing these challenges, particularly in relation to the demands and intensity of specific tasks. Unity of ideal solution (TOPSIS). is prioritized by, this is a multi-criteria decision Analytical method. TOPSIS Abbreviation of (PIS). Select The short geometric distance alternative is positive The best solution is, basically The Great Solution of Thought (Nis) To be negative Distance is geometric. TOPSIS The assumption is even higher is, is coming or going The benchmarks are increasing. Scaling problems or Many in the criteria Parameters Mostly Improper Dimensions Due to normalization Generally required. Alternative taken as load capacity, Maximum tip speed, Memory capacity, Manipulator reach, Repeatability, Positioning accuracy. Evaluation parameter taken as Industrial robot 1, Industrial robot 2, Industrial robot 3, Industrial robot 4, Industrial robot 5, Industrial robot 6, Industrial robot 7. From the result it is seen that Industrial robot 3 is got the first rank where as is the Industrial robot 1 is having the lowest rank. Keywords: Industrial Robot, Manufacturing industry, Tools or Facilitator teachers.

A Lightweight Powered Elbow Exoskeleton for Manual Handling Tasks

A Lightweight Powered Elbow Exoskeleton for Manual Handling Tasks

Effectiveness of Sensors-Based Augmented Feedback in Ergonomics to Reduce Adverse Biomechanical Exposure in Work-Related Manual Handling-A Rapid Review of the Evidence.

Manual handling is a major risk factor for work-related musculoskeletal disorders and one of the leading causes of disability-adjusted life years globally, necessitating multifaceted risk reduction measures. One potential intervention for manual handling tasks is work technique training assisted by augmented feedback on biomechanical exposures. However, there is a research gap regarding its effectiveness specifically for manual handling tasks in both real work environments and controlled settings, as well as its ability to induce retained reductions in biomechanical exposure. The gap was investigated using a rapid review comprising a literature search using two databases and 11 reviews/overviews to identify studies from the past 20 years, up to studies published by 1 June 2024. Sixteen studies were identified, with 14 of them being of high or moderate methodological quality and were included. Three studies were conducted in real work environments and eleven in controlled settings. Most studies (n = 9) used auditory feedback, followed by vibration feedback (n = 6). In real work environments, the evidence for the effectiveness of sensor-based augmented feedback in reducing biomechanical exposure during administration was considered to be inconsistent and very limited directly after administration. For longer periods after administration, ranging from one week to more than six months, there is currently no evidence demonstrating the effectiveness of the feedback. In controlled settings, there was strong evidence for its effectiveness during and immediately after administration, and limited evidence for effectiveness up to six months post-administration when considering the tasks included in the training. Future research needs are discussed.

CRTF-MoeICP: A robust coarse-to-fine reflector-based LiDAR indoor positioning algorithm

The reflector-based Light Detection and Ranging (LiDAR) positioning method is susceptible to environmental interferences, resulting in instability. This instability not only reduces movement accuracy but also poses safety hazards. To solve the above problems in the application of LiDAR sensors in the field of indoor positioning, we propose a Coarse Registration algorithm based on the Triangular Feature (CRTF) and a fine registration algorithm based on Multi-level outlier elimination and Iterative Closest Point (MoeICP) for the reflector-based LiDAR positioning. The proposed coarse-to-fine positioning algorithm CRTF-MoeICP addresses the issue of reflector-based LiDAR positioning failure arising from the improper selection of the initial transformation matrix and outlier interference in indoor structured industrial environments. The experiment results show that the CRTF-MoeICP algorithm can ensure the stable registration of the LiDAR point cloud and the reflector map by completely removing all outliers, greatly improving the indoor positioning stability of LiDAR sensors. Besides, the proposed algorithm can be realized by LiDARs with different performance, and improve the static positioning repeatability to ±3 mm. The high precision and stable positioning results improve the motion accuracy, ensuring that the Automatic Guided Vehicle (AGV) can accurately and stably complete the handling task.

Dynamic Modelling of the Spine for the Estimation of Vertebral Joint Torques using Gordon’s Method

World Health Organization (WHO) recognised musculoskeletal disorder (MSD) as the main contributor to disability worldwide, with low back pain as the major disorder globally. The occupational disorder normally occurs during lifting. The weight of the load and manual handling tasks during lifting has an impact on the spine and joint torque. The purpose of this study is to propose a dynamic model of the spine that can estimate the vertebral joint torques. This study is a bimodal approach that consists of the experimental and theoretical parts. Ten healthy UniMAP students (10 males) participated in this study. The subjects were required to lift a 3kg weight plate for kinematics and EMG data collection. Retro-reflective markers were attached to the subject body, and then, the data was collected and stored in QTM software. Kinematic data was processed using C-Motion Visual3D. Eight Trigno Wireless Sensors were attached on the back muscles (left and right erector spinae, latissimus dorsi, external oblique and internal oblique). The EMG data were stored in EMG Acquisition software and subsequently, were processed using EMG Analysis software. Gordon’s method was used to develop a mathematical model of the spine. The model comprises of five kinematic chains which connected three lumbar, two thoracic and one cervical. The model calculated the value of joint torque on flexion/extension movement using Matlab and Microsoft Excel. When calculated on L5, the model gives an estimation within 0 – 30 kgm2s-2. The model was further used to estimate value of L3, L1, MAI and T2. The estimate average value of joint torque at L3 is within 5 – 25 kgm2s-2, MAI is within 0 – 6 kgm2s-2 and T2 is within 0 – 1 kgm2s-2. The average RMS values show the highest muscle activity on the right internal oblique muscle (1519 µV), followed by the right external oblique (1166 µV) and left external oblique (418 µV). The results obtained gives an insight on the value of joint torque that have been applied by the spine and the most activated back muscles during lifting.

A mixed reality system for the evaluation of the effects of exoskeletons on cognitive load during manual handling task

A mixed reality system for the evaluation of the effects of exoskeletons on cognitive load during manual handling task

Analisis Pelaksanaan Tugas Ramp Handling dalam Menjaga On Time Performance (OTP) Pada Maskapai Garuda Indonesia di Bandara Soekarno Hatta Cengkareng

Soekarno-Hatta Airport is the main gateway for international and domestic air travel in Indonesia. As one of the busiest airports in Southeast Asia, Soekarno-Hatta Airport handles more than 60 million passengers per year (Soekarno-Hatta Airport Authority, 2023). According to the operational management theory by Suryanto (2020), efficiency in managing all operational processes, including ramp handling, is crucial to ensure smooth and punctual flights. With high traffic volume, the main challenge is to maintain On Time Performance (OTP) to minimize flight delays. This study aims to determine the implementation of ramp handling tasks during pre-flight activities, as well as to determine the obstacles experienced by Ramp Handling officers in pre-flight activities at Soekarno Hatta Cengkareng International Airport and to determine how efforts are made by PT Gapura Angkasa in improving the quality of On Time Performance (OTP) at Soekarno Hatta International Airport, Cengkareng. This study uses a qualitative method, namely through interviews, observations, and documentation of ramp handling personnel units, this study was conducted for 1 month at Soekarno Hatta International Airport The results of this study are that ramp handling officers at Soekarno-Hatta Cengkareng International Airport are responsible for checking the readiness of ground equipment, flight documents, and coordinating with the ground crew team. The use of checklists ensures that all pre-flight steps are carried out correctly and efficiently, which is important for smooth boarding and loading.

Flexible Sensor-based Whole-body Biomechanics of Exoskeleton-assisted Patient Handling

The issue of work-related musculoskeletal disorders (WMSDs) stemming from manual handling tasks remains a significant economic and societal burden, with healthcare workers being one of the most persistently affected groups. Exoskeletons have seen growing implementation for aiding manual handling tasks in industrial sectors. Traditional measurements and analyses of joint kinematics and dynamics commonly utilize optical motion capture systems and force plates. Nevertheless, the limitations of optical motion capture systems include their dependence on marker visibility and the necessity for a controlled lab environment. An innovative wireless technology featuring miniaturized, skin-mounted sensors, BioStamp nPoint (MC10, Inc., MA, USA) exhibits significant potential in addressing the challenges mentioned above. Therefore, the objective of this study was to explore a flexible sensor-based approach to assessing the effects of a back exoskeleton on joint kinematics during patient handling.

Ergonomic Risk Assessment and Mitigation Strategies for Retail Workers: Insights from a Case Study in Dungun

Manual handling activities contribute to 40% of musculoskeletal disorders (MSDs), leading to long-term discomfort, disability, medical costs, and financial hardships for workers, while employers face lower productivity and compensation costs. An ergonomic assessment was conducted among employees at ABC Hardware in Dungun to identify ergonomic risk factors, determine the likelihood of harm affecting workers' musculoskeletal health, and recommend control measures to improve workers' health and well-being. Utilizing a combination of interviews, observations, and questionnaires based on established ergonomics guidelines, the research highlights the prevalence of MSDs among workers engaged in manual handling tasks, emphasizing the need for ergonomic interventions and awareness to enhance workplace safety and employee well-being. Findings from the ergonomic assessment revealed significant discomfort and risk factors related to awkward postures, repetitive motions, forceful exertion, and static positions. Specific ergonomic issues identified include discomfort in the upper back, thighs, and knees among storekeepers, and neck and lower back pain among warehouse workers. Service desk representatives were found to experience discomfort related to static postures and repetitive motions. The study recommends advanced ergonomic assessments such as Rapid Entire Body Assessment (REBA), Occupational Repetitive Assessment (OCRA), and Rapid Upper Limb Assessment (RULA), along with improvements in ergonomic practices and tools. Implementing job rotation, providing ergonomic equipment, and conducting training on proper lifting techniques are essential to address the identified risks. This research highlights the critical role of ergonomic practices in preventing MSDs and improving overall workplace safety.

Enhancing Privacy Protection for Time-Series Signals in Ergonomics Studies via Data Synthesis

Electromyography (EMG) signal analysis is critical in understanding and treating work-related musculoskeletal disorders (WMSDs). Despite the increasing use of EMG signals combined with machine learning to assess biomechanical risks in various occupational settings, a significant shortage of extensive EMG datasets hinders progress. This shortage is primarily due to stringent data management plans and the limited availability of EMG datasets representing occupational tasks. To address this, our research leverages diffusion models to synthesize EMG signals tailored to manual material handling (MMH) tasks, aiming to enrich occupational EMG data repositories while maintaining data privacy. Using a conditional diffusion model with a residual U-Net architecture, we synthesized EMG signals for MMH activities such as pulling, pushing, and lifting. The synthesized data, evaluated across time and frequency domains, demonstrated fidelity to the original EMG signals, capturing distinct patterns and amplitudes characteristic of different tasks. Our findings highlight the potential of diffusion models in generating high-fidelity EMG data, providing a novel solution to the data scarcity challenge in occupational health research.

Holographic visual cues for mission task elements

AbstractMission task elements (MTEs) are commonly used to evaluate the handling qualities (HQs) of rotorcraft. However, the traditional approach of physical ground courses faces challenges due to evolving rotorcraft designs and flight profiles. Infrastructure limitations and lack of flexibility for research pose significant obstacles, particularly for high-speed tasks. The emergence of new air vehicles, such as eVTOLs in the civil sector and future vertical lift configurations in the military domain, requires an adapted visual cueing process for previously unaddressed mission profiles. This paper proposes an innovative solution: an augmented reality (AR) system utilizing a head-mounted display to create virtual MTE courses called holographic visual cues (HVCs). A piloted simulation campaign performed at DLR’s AVES simulator compares the effectiveness of HVCs with dome projection-based visual cues, evaluating the performance of the AR system. The study investigated the impact of holographic visual cues on pilot handling qualities, workload ratings, and task performance, finding that although these cues typically do not significantly alter pilot ratings, individual responses varied, highlighting both the technology’s potential and the need for further refinement.

Risk factors of musculoskeletal symptoms among industrial workers in Peninsular Malaysia

Objectives. The prevalence rate of work-related musculoskeletal disorders (WMSDs) globally is notably high. There are a limited number of studies investigating WMSDs and their associated risk factors. However, there are currently no data available for WMSDs among industrial workers in Peninsular Malaysia. This study aimed to identify the prevalence of WMSDs and associated risk factors among industrial workers experiencing WMSDs through their daily working tasks. Methods. A quantitative study using a questionnaire was conducted among industrial workers from rehabilitation centres and factories in Peninsular Malaysia. The analysis of 232 participant narratives aimed to identify the correlation between job tasks and musculoskeletal pain, especially in case of repetitive and heavy handling tasks. Results. The prevalence of WMSDs among industrial workers stands at 93.1%. The results also indicate that the most affected part of the body was the lower back, with 62.1% for 7 days or more in the last year, caused by industrial workers’ job tasks. The prominent risk factors associated with body parts include gender, age, working hours and most difficult tasks with MSDs, especially in the lower back. Conclusion. This survey helps us to understand whether the workers are experiencing any discomfort, pain or disability related to workplace activities.

Preliminary architecture design for human-in-the-loop control of robotic equipment in remote handling tasks: Case study on the NEFERTARI project

In this work, we present a general control architecture for robotic systems dedicated to the remote handling of in-vessel components in fusion machines. This control architecture will be tested for the inspection and maintenance of the first wall components of the RFX-mod2 experiment, in the scope of the New Equipment For the Experimental Research and Technological Advancement for the Rfx Infrastructure (NEFERTARI) project. The architecture is split into low-level and high-level layers. The former is used for the low-level control of the robotics systems and to manage safety; the latter is used for the high-level planning and implements several sub-modules, such as a Virtual Reality (VR) environment for the visualisation of the robot’s digital twin. Moreover, an Application Programming Interface (API) is intended to connect the two layers, and the communication between modules and layers is provided by the ROS2 framework. A typical usage of such a framework involves a human operator who is teleoperating the real robot, data from motor encoders are used as inputs for the dynamic model module. This module is used to compute the forward dynamics in real-time, providing an accurate simulation of the robot (digital twin) in the virtual environment.

The Many Faces of a Teacher: Exploring School Teachers’ Experiences with Multiple Responsibilities Amidst Post-Pandemic Times

This qualitative study aimed to explore the experiences of public elementary school teachers in Carmen District, Davao del Norte, Philippines, in handling multiple responsibilities during the post-pandemic period. Ten teachers were selected as participants using purposive sampling techniques. The study identified four main themes characterizing teachers' experiences: Testing teachers' versatility, Doing countless responsibilities, Excelling amidst trials, and Challenged with Multiple Responsibilities. Additionally, two coping mechanisms emerged from the data: Stress management and One task at a time system. Furthermore, the study uncovered insights related to educational management, including themes such as Work-life balance and Handling Task Conflict Effectively. The findings provide valuable insights into the nuanced challenges faced by public elementary school teachers in the post-pandemic context and offer implications for educational policy and practice.