Fetal lower urinary tract obstruction (LUTO) and pleural effusion are conditions that can disrupt fetal growth and lead to fetal death. LUTO inhibits the formation of amniotic fluid, which is vital for lung development, while pleural effusions can compress the fetal heart, potentially causing fatal cardiac failure. To manage these conditions, a fetal shunt (vesico-amniotic shunt) is placed inside the fetal bladder. This paper presents a study on a new design incorporating a Tesla valve in the shunt. Six groups of Tesla valves with loop angles of 50 degrees and 60 degrees, and different end dimensions, are examined and evaluated in terms of the urine flow rate from the fetal bladder into the amniotic cavity, the pressure buildup between the two sides, and their potential in developing fetal bladder muscles. A mathematical method is used to compare diode characteristics, analyze flow rates, identify the Tesla valve angle, determine the Reynolds number, and assess diodicity. The Computational Fluid Dynamics (CFD) method is also employed to verify calculation results and simulate fluid behavior inside the Tesla valve. Combining the calculations and simulations, a 50-degree Tesla valve with specific dimensions showed the best performance and will be the optimal design for the fetal shunt.

The demand for work safety protection in Human-Robot Interaction (HRI) work cells is rapidly increasing, driven by the projected 34.3% Compound Annual Growth Rate (CAGR) of the global Collaborative Robot (Cobot) market from 2020 to 2030 [1]. According to IRF-World Robotics 2023, it is reported that there are nearly 4 million industrial robots in operation worldwide, with approximately 10% of them being cobot [2]. A NIOSH report highlighted 61 robot-related fatalities between 1992 and 2015, with an expectation of further rising due to the increasing use of industrial robots and cobots in the US work environment [3]. A recent study in [4] delved into 355 robot accidents documented by KOSHA between 2009 and 2019, revealing that 95% occurred in manufacturing businesses. Pinch and crush incidents accounted for 52% of the accidents, while impacts and collisions accounted for 36%, and the remaining 12% involved falls, flying objects, trips/slips, cuts, burns, etc. These findings align with US data reported in [5].The rising integration of cobot units among major manufacturers emphasizes the critical need for enhancing cobot safety in manufacturing. Owing to safety considerations and regulatory requirements, existing cobots frequently operate at significantly reduced speeds and are restricted from undertaking complex interaction tasks in shared workspace. This limitation has curtailed the full potential utilization and productivity of cobots in manufacturing. This paper introduces a novel 3D sensing framework designed to address these limitations by enabling safety assurance in workspaces requiring close human-robot interaction. The framework generates 3D human pose information and relays it to the robot for real-time safety monitoring. Our methodology begins with data collection from a single RGB-D camera capturing human-robot interactions in a manufacturing environment. Human shape and pose are predicted using deep neural networks, which then incorporate depth information and undergo 3D geometric transformations to deduce size, shape, and translation. This process produces a reconstructed 3D avatar with pose, size, and location. Following 3D human posture estimation, this data is then integrated into a virtual environment with a real robot for real-time monitoring. Results demonstrate successful reconstruction of 3D human geometry within human-robot collaboration settings. By integrating both the reconstructed mesh and real-time robot state into a unified virtual environment, we achieved real-time, offline, continuous monitoring of the critical distance between robot and human throughout operation. These distance measurements provide crucial data for developing collision detection, prediction, and avoidance capabilities when incorporated into the robot control feedback loop.

Industrial workers often engage in repetitive lifting tasks. This type of continual loading on their arms throughout the workday can lead to muscle or tendon injuries. A non-intrusive system designed to assist a worker’s arms would help alleviate strain on their muscles, thereby preventing injury and minimizing productivity losses. The goal of this project is to develop a wearable soft robotic arm enhancement device that supports a worker’s muscles by sharing the load during lifting tasks, thereby increasing their lifting capacity, reducing fatigue, and improving their endurance to help prevent injury. The device should be easy to use and wear, functioning in relative harmony with the user’s own muscles. It should not restrict the user’s range of motion or flexibility. The human arm consists of numerous muscles that work together to enable its movement. However, as a proof of concept, this project focuses on developing a prototype to enhance the biceps brachii muscle, the primary muscle involved in pulling movements during lifting. Key components of the prototype include a soft robotic muscle or actuator analogous to the biceps, a control system for the pneumatic muscle actuator, and a method for securing the soft muscle to the user’s arm. The McKibben-inspired pneumatic muscle was chosen as the soft actuator for the prototype. A hybrid control algorithm, incorporating PID and model-based control methods, was developed. Electromyography (EMG) and pressure sensors were utilized as inputs for the control algorithms. This paper discusses the design strategies for the device and the preliminary results of the feasibility testing. Based on the results, a wearable EMG-controlled soft robotic arm augmentation could effectively enhance the endurance of industrial workers engaged in repetitive lifting tasks.

Municipal solid waste (MSW) represents a large, low-cost biomass resource for bioenergy and bioproduct production. However, its heterogeneity and compositional variability limit its direct use as a conversion-ready feedstock. This study evaluated three mechanical pre-screening technologies vibratory screening, trommel screening, and a dynamic disc screen (DDS) for the recovery of the organic fraction of MSW (OFMSW) and removal of plastic contaminants from unprocessed black bag MSW. All systems separated MSW into fines and overs fractions based on particle size and density, with compositional analysis confirming enrichment of biodegradable organics in the fines and preferential concentration of plastics in the overs. Vibratory screening produced an organic fines fraction with 45.6% purity and 63.1% OFMSW recovery, whereas trommel screening achieved higher purity (70.5%) but substantially lower recovery (12.2%) on a dry basis. In contrast, DDS screening consistently outperformed the other technologies, achieving fines purities ranging from 77.8% to 99.0%, with OFMSW recovery varying between 25.3% and 99.1% across five batches. Across all DDS batches, plastic decontamination efficiency remained high (85.7-95.5%), demonstrating robust contaminant removal over a range of feed compositions and operating conditions. DDS performance was also validated under extended operation using a larger waste mass (~3700 pounds), confirming its scalability and process stability. Process optimization showed that DDS motor speed strongly influenced separation performance, with higher speeds reducing recovery of dense organic materials in the fines fraction. Overall, DDS-based mechanical pre-screening offers a scalable and effective approach for producing high-purity, low-contaminant OFMSW suitable for downstream biochemical valorization and sustainable MSW management.

This paper discusses the design and testing of a mast system constructed to collect ticks from rugged terrain in remote areas via a mobile robot. The goal of this project is to develop a robotic tick collection system that can be used to collect ticks in diverse tick habitats, such as shrubs, weeds, and grass of various heights, and in different environmental conditions. The

This paper discusses the design and testing of a mast system constructed to collect ticks from rugged terrain in remote areas via a mobile robot. The goal of this project is to develop a robotic tick collection system that can be used to collect ticks in diverse tick habitats, such as shrubs, weeds, and grass of various heights, and in different environmental conditions. The