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The rapid development of modern manufacturing technology has driven the emergence of human-robot collaboration (HRC) as part of the transformation toward a human-centric intelligent production system. In collaborative work environments, robots are not only required to work efficiently but also to interact safely and responsively with operators. However, most conventional industrial robot systems still use rigid motion controls and are unable to dynamically adapt to human activity around them.This research aims to develop a human-robot collaboration system by integrating computer vision technology to detect operator movement and applying adaptive control algorithms to the robot manipulator. The research methodology includes designing a collaborative workstation, implementing a computer vision-based motion detection system, developing an adaptive control algorithm, and evaluating system performance through various experimental scenarios. Evaluation parameters include task completion time, safe distance, and system response time.The results show that the developed system significantly improves the efficiency and safety of human-robot interaction compared to conventional systems, with shorter task times, optimal safe distances, and faster system response to operator movements.

The rapid development of digitalization and innovation has become a key driver in improving business processes and the competitiveness of organizations worldwide. This study is the first comprehensive bibliometric analysis examining the relationship between digitalization and innovation in business processes, to map the intellectual structure of this field, track the development of its themes, and identify remaining research gaps. This analysis, which utilizes data from Scopus processed using VOSviewer and Biblioshiny software, covers publications from 2010 to 2024 and employs co-occurrence, co-authorship, and thematic evolution techniques. The results show a rapid growth in publications since 2016, peaking at over 110 publications in 2024. Eight key thematic clusters stand out: Industry 4.0, artificial intelligence, robotic process automation, blockchain, drivers, and agile business process management. Despite the field's maturity, it still suffers from high fragmentation, strong geographic concentration, and a reliance on cross-sectoral research designs. As a result, longitudinal insights remain limited, and digital transformation failure rates remain high, reaching up to 70%. This research presents the first quantitative and visual roadmap of global knowledge flows in this domain and underscores the need for longitudinal, geographically inclusive, and people-centric research to move beyond single-point understandings to a sustainable, context-sensitive framework that enhances both the theoretical depth and practical success of digital-based business process innovation

A wall follower robot is a type of autonomous robot that is designed to move by following a wall at a certain distance. This research aims to design and build a Wall follower robot equipped with a Fuzzy-PID control system to improve navigation performance. The robot uses five HC-SR04 ultrasonic sensors to detect the distance to the wall and the surrounding obstacles. The data from the sensor is then processed by a Fuzzy-PID algorithm that combines the advantages of conventional PID control with fuzzy logic, resulting in a more adaptive response to environmental conditions. The test results showed that the robot with Fuzzy-PID control was able to maintain the stability of the distance to the wall more consistently compared to the pure PID control. In addition, the system exhibits better adaptability to complex environmental conditions, such as sharp turns, uneven wall surfaces, and the presence of resistance variations. The application of Fuzzy-PID control has been shown to improve the stability, response speed, and accuracy of the robot's navigation. These findings are expected to contribute to the development of robotic navigation systems for a wide range of practical applications, including automated cleaning robots, environmental exploration, and industrial systems that require reliable autonomous mobility.

Plastic waste detection in indoor environments is an essential challenge in the development of intelligent cleaning systems and robotic automation. Small and medium-sized plastic debris is often difficult to identify using conventional methods due to variations in color, shape, and reflectance. This study proposes an image-processing-based approach that combines thresholding and contour detection techniques to improve the accuracy of detecting plastic objects on floor surfaces. The initial stage involves converting the image into a color space that is more stable under varying illumination, such as HSV or grayscale, to reduce the influence of lighting intensity. Subsequently, adaptive thresholding is applied to separate plastic objects from the background by using dynamic threshold values tailored to the image’s conditions. The segmentation results are refined through morphological operations such as opening and closing, enabling the removal of small noise and enhancing the clarity of object boundaries. The core stage of the system employs contour detection to extract object shapes and areas, allowing the identification of plastic waste based on size, perimeter, and specific geometric characteristics. Experiments were conducted under different lighting conditions and various floor types, and the results demonstrate that the proposed approach successfully detects plastic debris with satisfactory accuracy and relatively fast processing time. Therefore, this method is suitable for implementation in robotic cleaning systems, indoor cleanliness monitoring devices, and other computer vision applications requiring real-time and efficient object detection.

Manual post-harvest paddy stirring requires significant time and labor and often results in uneven mixing, which can affect grain quality. To address this issue, this study designed and implemented a prototype of an Internet of Things (IoT)-based paddy stirring robot to simplify the process and improve efficiency. The system utilizes an ESP32 microcontroller as the main controller, DC motors as the stirring mechanism, and an IoT module for wireless connectivity to a mobile application. The research stages included hardware design, control system programming, IoT platform integration, and performance testing. Testing was conducted to evaluate response time, mixing uniformity, and power consumption. The results showed that the system could be operated remotely via a local Wi-Fi network with an average delay of less than 1 second, enabling real-time control. The prototype successfully stirred 0.3 kg of paddy with a mixing uniformity rate of 92% and an average power consumption of 12 watts. The application of IoT in the paddy stirring mechanism significantly improved time efficiency, reduced manual labor requirements, and maintained grain quality compared to traditional methods. These findings indicate the potential for further development into a large-scale automated paddy processing system with integrated humidity and temperature sensors for real-time quality monitoring, supporting the modernization of post-harvest processing through digital technology.

In the modern industrial world, robots like mechanical arms in automotive factories or packaging lines must move quickly and safely. For this, a real-time operating sistem (RTOS) is needed—think of it as a "super-fast brain" that ensures the robot reacts instantly to commands, without delays or errors. This paper analyzes popular RTOS like FreeRTOS and VxWorks for controlling industrial robots, focusing on evaluating performance (speed of operation) and stability (long-term reliability). We conducted tests in a lab using a simple robot that moves its arm to pick up objects. Performance was measured by response time (how quickly the robot stops when encountering an obstacle, ideally under 10 milliseconds) and throughput (how many tasks it can complete per second). Stability was checked through simulations of disruptions, like heavy loads or interfered sensor signals, using metrics such as error rate and time variation (jitter). Results show that FreeRTOS is more efficient for small, affordable robots, with high performance (average response time of 4 ms) but moderate stability (5-10% errors during overload). In contrast, VxWorks excels in stability (errors <2%, stable for up to 95% of tasks on time) for large factory robots, though it requires stronger hardware. Our analysis uses simple models like graphs and repeated tests, without complex formulas, to prove that the right RTOS can boost production efficiency by up to 25% and reduce accident risks.

This research presents the design, development, and implementation of a mini smart car prototype that operates using Internet of Things (IoT) technology. The system is built around the ESP8266 microcontroller (Amica version), which functions as the core processing unit responsible for handling Wi-Fi communication and data processing. The motion of the car is controlled by an L298 motor driver module that regulates the operation of DC motors. The entire system is powered by a 3.7-volt rechargeable battery, ensuring portability and energy efficiency. The study discusses in detail the hardware configuration, software programming, and integration of IoT-based control through a web or mobile interface. Functional testing of the prototype, named MINIOT, focuses on evaluating the responsiveness, stability, and reliability of remote control operations. The results are expected to show that the system can effectively receive and execute user commands while transmitting real-time telemetry data, such as motor status and connection indicators. This project demonstrates the feasibility of low-cost IoT-based automation for small-scale robotic applications.

This research presents the design, development, and implementation of a mini smart car prototype that operates using Internet of Things (IoT) technology. The system is built around the ESP8266 microcontroller (Amica version), which functions as the core processing unit responsible for handling Wi-Fi communication and data processing. The motion of the car is controlled by an L298 motor driver module that regulates the operation of DC motors. The entire system is powered by a 3.7-volt rechargeable battery, ensuring portability and energy efficiency. The study discusses in detail the hardware configuration, software programming, and integration of IoT-based control through a web or mobile interface. Functional testing of the prototype, named MINIOT, focuses on evaluating the responsiveness, stability, and reliability of remote control operations. The results are expected to show that the system can effectively receive and execute user commands while transmitting real-time telemetry data, such as motor status and connection indicators. This project demonstrates the feasibility of low-cost IoT-based automation for small-scale robotic applications.

Conventional robotic surgical systems, while offering enhanced dexterity and 3D visualization, suffer from a critical limitation: the absence of tactile sensation. This sensory disconnect can lead to inadvertent tissue damage from excessive force application and complicates delicate maneuvers that rely on the surgeon's sense of touch. This research proposes and validates a novel surgical robotic system architecture designed to bridge this sensory gap by integrating high-fidelity 3D visual input with accurate, real-time force feedback from tactile sensors mounted on the end-effector. To rigorously evaluate this innovation, a structured comparative methodology was employed. A cohort of surgeons performed standardized surgical tasks, including suturing and tissue manipulation, on realistic soft-tissue phantoms. The performance of a conventional (visual-only) system was benchmarked against that of the proposed (visual-haptic) system. A comprehensive dataset was collected, which included objective metrics such as task completion time, precision deviation from the ideal tool path, and the magnitude of applied forces. Concurrently, subjective evaluations from the participating surgeons were gathered to assess perceived control, cognitive workload, and overall task confidence. The test data revealed statistically significant improvements when using the visual-haptic system. Participants not only completed tasks with greater speed and accuracy but also applied considerably lower and more consistent forces. The analysis underscores that haptic feedback, enabled by advanced sensor fusion, not only restores a crucial 'sense of touch' to the surgeon but also reduces the incidence of excessive force application, potentially minimizing tissue trauma and improving patient recovery. These findings confirm the hypothesis that haptic-visual integration constitutes a new paradigm in robotic surgery, shifting the paradigm from purely visual guidance to a more intuitive, multi-sensory surgical experience. This study also discusses future challenges and opportunities, including the potential for AI-driven partial autonomy, such as creating virtual safety boundaries or automating sub-tasks, and the development of next-generation sensor technologies to further enhance clinical outcomes.

A lopography or barium enema colostomy is a radiographic examination of the colon using a contrast medium inserted through a colostomy. This examination aims to evaluate the condition of the colon after the colostomy, including detecting the presence of obstructions or masses in the gastrointestinal tract. The contrast medium used is a type of non-ionic soluble water that is inserted through the stoma and anus orifice until it fills the entire colon, characterized by the exit of contrast through the stoma. The examination was carried out using several radiographic projections, including plain photographs of the AP abdomen, as well as post-contrast projections of the Antero Posterior (AP), Right Posterior Oblique (RPO), Left Posterior Oblique (LPO), and lateral. This study aims to find out the procedure for the Lopography examination at the Radiology Installation of Caruban Hospital and the medical reasons for the use of two channels of input of contrast media, namely through the stoma and anus. The method used is qualitative descriptive research with a case study approach. The study was carried out from November to May 2025, with one post-colostomy patient as a sample. Data collection was carried out through direct observation, interviews with medical personnel, and documentation of radiographic procedures. Data analysis includes data reduction, data presentation, and conclusion drawn. The results of the study showed that the procedure of Robotic examination did not require any special preparation before the procedure. The non-ionic contrast medium used is mixed with aquades at a ratio of 1:3 (about 200 cc), and inserted through both the stoma and the anus to obtain a thorough visualization of the colon. The selection of two input paths aims to ensure that there are no blockages or masses that block the contrast flow. The projections used are adjusted to provide maximum information about the structure of the colon after colostomy

Microorganisms are microscopic organisms that cannot be observed directly without the aid of optical instruments. They play an important role in various biological and environmental processes, both beneficial and detrimental. Most microorganisms are unicellular, but some are multicellular. Some microorganisms are also known to have benefits in the fields of food and health. Bamboo shoots or young bamboo are one type of vegetable that is widely consumed by the community, especially in Central Java. Bamboo shoots have a low nutritional content such as water, protein, carbohydrates, minerals, and fat, making them a healthy low-calorie food. In addition, bamboo shoots also contain bioactive compounds such as vitamins, essential amino acids, and antioxidants that are beneficial for body health.Fermentation of bamboo shoots is one method to increase their nutritional value and probiotic content.

Object detection in digital images is a crucial aspect of image processing and computer vision, with applications ranging from surveillance systems and robotics to image-based search. One commonly used approach is template matching, a technique that compares a template image with sections of the target image to identify similar patterns. This study explores the implementation of the template matching method for object recognition in digital images. The process begins with image preprocessing to enhance data quality, followed by a matching procedure using normalized cross-correlation. Experimental results indicate that this method can accurately detect objects under stable lighting and scale conditions. However, its performance decreases when images undergo rotation or scale variations. Therefore, while template matching proves effective under ideal conditions, further methodological development is needed to improve its robustness against geometric transformations.s

In this have a look at, a new mathematical model for FDAE-based smart manage systems is proposed. The model carries fractional derivatives blended with algebraic constraints to symbolize prolonged memory results. We describe a numerical method to solve the proposed device and practice this version to robotics, self-reliant cars, and sensible prosthetics. The Fractional Collocation Method is employed to resolve FDAEs, making sure accuracy and balance. To validate the proposed method, we introduce 3 examples: a simple FDAE demonstrating the accuracy of the numerical solution, a device of FDAEs modeling interdependent dynamic variables with algebraic constraints, and an FDAE with a nonlinear algebraic constraint, highlighting the approach's capability to handle complicated, nonlinear dynamics. Simulation results verify that FDAEs offer a more practical and powerful tool for designing and reading wise manage systems as compared to classical techniques.

The aim of this research is to show how to maintain the existence of the sharia accounting profession in the era of industrial revolution 5.0 in Indonesia. The research method is qualitative with the type of literature study. The results of the research are that strategies for maintaining the existence of the sharia accounting profession in the era of the industrial revolution 5.0 in Indonesia can still be maintained in various ways, namely: (1) Designing assignments that are non-routine and unstructured, where the performance of judgment and wisdom really requires a mindset. humans and special skills that can only be done by humans and cannot be done by technology such as robots or other technological tools; (2) Using Technical Skills and Ethics (TEQ) by understanding relevant technology and acting with ethics and a sense of responsibility not only to stakeholders in the world but to Allah SWT in the afterlife; (3) The duties of sharia accountants as supervisors of sharia-based industries provide great opportunities for sharia accountants to maintain their existence (4). There is no need to worry about the impending threat of industrial revolution 5.0 in the sharia accounting profession because technological developments cannot be prevented. However, what must be done is to adapt and improve the quality of accounting education by providing services both virtual and conventional and (5) Technology will never be able to replace the role of ethics and Islamic religious values ​​and principles in business concepts or transactions based on Islamic sharia.

Minimally invasive surgery offers several advantages, including reduced blood loss, smaller incisions, less pain, and a lower risk of complications than open surgery. This approach enhances patient comfort and supports faster recovery. When guided by optimal path planning, surgical robots can accurately navigate the body to remove malignant tumors with high precision. This study proposes a Modified Particle Swarm Optimization (MPSO) algorithm to determine the optimal path for robotic-assisted minimally invasive surgery targeting brain tumors. The algorithm improves upon standard PSO by modifying the velocity update equation and incorporating an adaptive inertia weight, enhancing convergence speed, global search ability, and solution accuracy. Experimental results show that the proposed MPSO achieves a maximum fitness value of 19.10 in a sparse obstacle environment, outperforming standard PSO and IPSO in quality and in the required number of iterations. The approach effectively balances path efficiency and obstacle avoidance, making it well-suited for complex surgical scenarios. In conclusion, the MPSO-based method provides a reliable and precise solution for robotic surgical navigation, improving outcomes and safety in minimally invasive procedures.

The increasing waste problem requires innovative solutions that are efficient and sustainable. This study aims to design and build an Internet of Things (IoT) based garbage cleaning robot that can be monitored and controlled remotely. This system is designed by utilizing a microcontroller as the main brain, sensors to detect the presence of garbage, and an IoT-based communication module that allows monitoring and control of the robot via mobile devices or the web. Based on the results of the analysis and testing carried out, this study shows that the use of ESP8266 in the RC Trans Robot motor control with the Blynk application has succeeded in significantly increasing system efficiency. The system successfully responds to user input via a mobile application and can control the movement of the robot in real-time. The performance of the robot system controlled via a WiFi network shows good stability in various test scenarios. The robot can operate effectively within a distance that matches the range of the WiFi network, with fast control response and reliable communication.

This research focuses on designing the shortest path to a docking station for the charging system of a robotic coffee delivery system on a cruise ship. With the growing demand for more efficient and innovative services in the maritime industry, autonomous robots are increasingly being used for tasks such as delivering coffee to passengers. However, these robots face unique challenges in the confined and dynamic environment of a ship, particularly in terms of power management and recharging. The goal of this research is to create an efficient charging system using a power bank and Internet of Things (IoT) technology to minimize downtime and ensure continuous robot operation with minimal human intervention. By utilizing sensors such as the MPU 6050 for motion detection and the TCS 3200 for color recognition, the robot can autonomously navigate the shortest path to the docking station for charging, while IoT enables real-time battery capacity monitoring. This helps reduce the time spent on non-operational tasks and improves the overall efficiency of the system. This research not only enhances the robot’s ability to operate autonomously in complex environments but also provides a flexible and scalable solution for the maritime industry to integrate advanced robotics into their service operations. The results of this study contribute to the increased adoption of autonomous systems in the maritime sector, particularly in improving service efficiency on board, reducing the need for human intervention, and ensuring smoother service delivery to passengers.