Search Results (64)

In the U-Space environment, seamless communication between key stakeholders—such as U-Space Service Providers (USSP), Common Information Service Providers (CISP), and drone operators—is very important for the safe and efficient management of Unmanned Aerial Vehicle (UAV) operations. A major challenge in this context is minimizing communication latency, which directly affects the performance of time-sensitive services. This study investigates latency issues by evaluating two communication protocols: push–pull (using REST-API and ZeroMQ) and publish–subscribe (using AMQP and MQTT). Through a case study focused on drone detection, the research examines latency across critical operational activities, including conformance monitoring, flight plan confirmation, and the transmission of alerts via the USSP system under varying message intervals and payload sizes. The results indicate that while message interval has a significant influence on latency, message size has a minimal effect. Furthermore, the push–pull protocols consistently deliver lower and more stable latency compared to publish–subscribe protocols under the tested conditions. Both approaches, however, achieve latency levels that align with EASA’s operational requirements for U-Space systems. Full article

In inventory management, storage capacity constraints complicate multi-item lot-sizing decisions. As the number of items increases, deciding how much of each item to order without exceeding capacity becomes more difficult. Dynamic programming works efficiently for a single item, but when capacity constraints are nearly minimal across multiple items, novel heuristics are required. However, previous heuristics have mainly focused on inventory bound constraints. Therefore, this paper introduces push and pull heuristics to solve the multi-item uncapacitated lot-sizing problem under near-minimal capacities. First, a dynamic programming approach based on a network flow model was used to generate the initial replenishment plan for the single-item lot-sizing problem. Next, under storage capacity constraints, the push operation moved the selected replenishment quantities from the current period to subsequent periods to meet all demand requirements. Finally, the pull operation shifted the selected replenishment quantities from the current period into earlier periods, ensuring that all demand requirements were satisfied. The results of the random experiment showed that the proposed heuristic generated solutions whose performance compared well with the optimal solution. This heuristic effectively solves all randomly generated instances representing worst-case conditions, ensuring robust operation under near-minimal storage. For large-scale problems under near-minimal storage capacity constraints, the proposed heuristic achieved only small optimality gaps while requiring less running time. However, small- and medium-scale problems can be solved optimally by a Mixed-Integer Programming (MIP) solver with minimal running time. Full article

Background: The aim of this study was to characterise the performance, perceptual, and wellness responses to a barbell back squat overreaching training protocol. Methods: Eight trained male participants (age = 24.6 ± 2.8 years; relative to body mass back squat one repetition maximum (1-RM) = 1.9 ± 0.4; training experience = 7.0 ± 3.2 years) participated in a 5-day squat OR protocol (SqOR), followed by a 14-day taper. SqOR consisted of five sets of barbell back squats using 80% of daily adjusted 1-RM. A 40% velocity loss threshold was used to determine the set end point. For performance, isometric mid-thigh pull (IMTP) peak force (PF), and countermovement jump (CMJ) PF and jump height; for perceptual, perceived recovery scale (PRS); and for wellness, Hooper Wellness Index (HWI), were recorded at baseline, each day of SqOR, and at select intervals during the taper (POST 1 d, 2 d, 7 d, and 14 d). Follow-up back squat 1-RM testing was conducted at POST 7 d and POST 14 d to determine strength-performance changes relative to baseline. Results: Back squat 1-RM increased by 4.8% at POST 7 d and 5.2% at POST 14 d. IMTP PF increased by 10.3% at POST 7 d and 11.4% at POST 14 d relative to the baseline. CMJ PF and jump height decreased during SqOR but returned to baseline by POST 7 d. PRS and HWI worsened during SqOR, with the greatest impairment occurring on day 3 (PRS = −41.5%; HWI = 34.4%), and did not return to baseline until POST 14 d and POST 2 d, respectively. Conclusions: These findings demonstrate that a short-term period of planned OR improves muscular strength performance, but the duration of the taper influences when peak strength improvements are observed. Full article

University innovation plays an increasingly significant role in regional industrial development. In this paper, we study how local industrial initiatives affect the university innovation activities. We link preferred initiatives from Chinese provincial Five-Year Plans to university–industry patent data. Using difference-in-differences design, we document that local preferred industrial initiatives significantly enhance university innovation. These initiatives increase public Research and Development (R&D) funding (government-push effect) and facilitate regional university–industry collaboration (market-pull effect). The effects exhibit heterogeneity across university administrative affiliation, university research capacity, and industry technology intensity. Furthermore, regional industrial comparative advantages and university technology transfer capabilities strengthen the innovation-enhancing effects. Finally, this paper demonstrates that university research capacity strengthens the effectiveness of industrial initiatives on firm output. These findings underscore the synergy among industrial initiatives, university innovation, and local development. Full article

Environmental risks often stem from contamination driven by chemical stressors introduced from multiple sources, either geogenic or anthopogenic. Differentiating between anthropogenic chemical anomalies and those inherent to the environment is crucial. This distinction is essential for defining feasible remediation objectives. This study applied univariate and multivariate statistical techniques to analyse geochemical data from over 7000 topsoil samples in Campania (Southern Italy), over an area of approximately 13,600 km². A key step in the methodology was applying Normal Score Transformation (NST), which stabilized the variance of the dataset, pulling the extreme outliers back to normal ranges, making it more suitable for multivariate analysis. Principal Component Analysis (PCA) was performed, and four components were selected; the spatialization of their scores revealed four primary independent sources controlling geochemical variability across the region. Specifically, two distinct volcanic districts were identified, plus a siliciclastic and an anthropogenic component. The integration of RGB composite maps further refined this differentiation, emphasising the coexistence or the predominance of one component over the other. The methodological approach demonstrated here provides valuable insights for environmental risk assessment and remediation planning in geochemically complex and anthropized regions. Full article

The coastal zone consists of diverse littoral habitats, which we categorize into two primary types: linear and areal. Investigating linear littoral habitats is crucial for resolving the ‘coastal squeeze’ phenomenon in coastal and marine protected areas and in seaside resorts. Our research aims to identify the critical conditions for the conversion of defunct seaside military training areas as brownfields into coastal protected areas and small-scale seaside resorts and their sustainable planning and management. The development of seaside tourism facilities is taking place both on the coast and in the hinterland, but the coast is used for tourism much more intensively than the hinterland. It is challenging to ‘pull’ tourists away from the linear beach to the areal hinterland. We argue that the distinctiveness of the resource use conflicts in coastal and hinterland tourism lies in an essential difference between the system’s linear and areal littoral habitats, as 78% of summer visitors in Pajūris Regional Park in Lithuania come for active leisure in nature. The results of our study show that combining the GIS interpretation algorithms, supported by the innovative conjoining of DPSIR and Delphi analytical tools, ensures site-tailored integrated management of the linear waterfront and the areal hinterland. Full article

This paper presents a path-planning approach for tethered robots. The proposed planner finds paths that minimize the tether tension due to tether–obstacle and tether–floor interaction. The method assumes that the tether is managed externally by a tether management system and pulled by the robot. The planner is initially formulated for ground robots in a 2D environment and then extended for 3D scenarios, where it can be applied to tethered aerial and underwater vehicles. The proposed approach assumes a taut tether between two consecutive contact points and knowledge of the coefficient of friction of the obstacles present in the environment. The method first computes the visibility graph of the environment, in which each node represents a vertex of an obstacle. Then, a second graph, named the tension-aware graph, is built so that the tether–environment interaction, formulated in terms of tension, is computed and used as the cost of the edges. A graph search algorithm (e.g., Dijkstra) is then used to compute a path with minimum tension, which can help the tethered robot reach longer distances by minimizing the tension required to drag the tether along the way. This paper presents simulations and a real-world experiment that illustrate the characteristics of the method. Full article

Background: Prostheses are becoming more advanced and biomimetic with time, providing additional capabilities to their users. However, prosthetic sensation lags far behind its natural limb counterpart, limiting the use of sensory feedback in prosthetic motion planning and execution. Without actionable sensation, prostheses may never meet the functional requirements to match biological performance. Methods: We propose an approach for upper limb prosthetic grasp security feedback, delivered to the wearer through direct nerve stimulation proportional to the likelihood of objects slipping from grasp. This proportional feedback is based on a linear regression of the sensors embedded in a prosthetic hand to predict slip before it occurs. Four participants with transhumeral amputation performed pulling tasks with their prosthetic hand grasping an object at predetermined grip forces, attempting to pull the object with as much force as possible without slip. These trials were performed with two different prediction notification paradigms. Results: At lower grasp forces, where slip was more likely, a strong, single impulse notification of impending slip reduced the incidence of object slip by a median of 32%, but the maximum achieved pull forces did not change. At higher grasp forces, where slip was less likely, the maximum achieved pull forces increased by a median of 19% across participants when provided with a stimulation strength inversely proportional to the grasp security, but slip incidence was unchanged. Conclusions: These results suggest that this approach may be effective in recreating a lost sense of grip stability in the missing limb that can be incorporated into motor planning and ultimately prevent unanticipated object slips. Full article

The Czochralski method is the primary technique for single-crystal silicon production. However, anomalous states such as crystal loss, twisting, swinging, and squareness frequently occur during crystal growth, adversely affecting product quality and production efficiency. To address this challenge, we propose an enhanced multimodal fusion classification model for detecting and categorizing these four anomalous states. Our model initially transforms one-dimensional signals (diameter, temperature, and pulling speed) into time–frequency domain images via continuous wavelet transform. These images are then processed using a Dense-ECA-SwinTransformer network for feature extraction. Concurrently, meniscus images and inter-frame difference images are obtained from the growth system’s meniscus video feed. These visual inputs are fused at the channel level and subsequently processed through a ConvNeXt network for feature extraction. Finally, the time–frequency domain features are combined with the meniscus image features and fed into fully connected layers for multi-class classification. The experimental results show that the method can effectively detect various abnormal states, help the staff to make a more accurate judgment, and formulate a personalized treatment plan for the abnormal state, which can improve the production efficiency, save production resources, and protect the extraction equipment. Full article

Background/Objectives: Becoming an elite canoe slalom athlete requires thousands of hours of training, spread over many years. It is difficult to assess the correct balance between flatwater and whitewater training because differences in the paddle forces on these terrains are not known. The aim of this study was to describe paddle forces during canoe slalom training on flatwater and whitewater courses for the C1 canoe category. Methods: Paddle forces for twenty C1 canoe slalom athletes were quantified during all-out figure-of-eight tests on a flatwater course and during race simulations on a whitewater course. Paddle forces were measured using strain gauges embedded in the paddle shaft and quantified by their force, impulse, and stroke durations. Results: The mean force during the pull phase of the paddle strokes was not significantly different between the flatwater and whitewater courses; however, the longer pull phase durations led to a greater pull phase impulse when paddling on the whitewater course. Conclusions: This study indicates that training for all-out runs on a whitewater course is more demanding for canoe slalom athletes than performing all-out trials on a flatwater figure-of-eight course. This evidence may help to develop effective training plans that are essential to reach the highest levels of the sport. Full article

Data on the strength ratio between agonist and antagonist muscles are frequently examined in sports testing, given its correlation with athletic performance. The purpose of this study was to determine the agonist-to-antagonist ratio of upper body strength in female and male elite Swedish track and field throwers using a new push (bench press) and pull (supine bench row) test device, and to determine its reliability. The study involved eight female and nine male athletes, aged 19–29 years, engaging, respectively, in discus, hammer, and shot put competitions at both national and international levels. The athletes’ maximum isometric force was assessed during the bench press (push) and supine bench row (pull) exercises, respectively, using a custom-built test device. The test–retest reliability of the device was also examined. The total push-to-pull strength ratio for the female throwers was 1.15, whereas male throwers demonstrated a ratio of 1.22. Total push and pull force for the female throwers was significantly less than for the male throwers (5511 N vs. 8970 N, p < 0.001). Intraclass correlation coefficients ranged from 0.93 to 0.96 for the bench press and supine bench row exercise, indicating that the push and pull test device was highly reliable. The main findings of this study were that elite female and male discus, hammer, and shot put throwers exhibited 15% and 22% more pushing (bench press) than pulling (supine bench row) strength. Push and pull strength in the female throwers ranged from 47% to 71% of that of the male throwers. The push and pull test device is a reliable tool in establishing the agonist-to-antagonist ratio of upper body strength of athletes. Coaches and athletes may benefit from examining upper body push and pull strength ratios for training planning and prescription. Full article

The Demand-Driven Material Resource Planning (DDMRP) method is one of the newer methods of inventory management in an enterprise. Its creation was initiated by a change in the business environment and the characteristics of today’s supply chains. DDMRP brings a combined pull/push approach to inventory management based on creating strategic stacks in the supply chain and managing inventory at these strategic points based on customer orders. The DDMRP system provides a simple methodology that is easy to apply, even in smaller businesses, without the need for advanced information systems. However, a simple methodology also has its limitations because, in many cases, intuitive and subjective approaches are used to set inventory management parameters (variability factor, running time factor, seasonality factor, thresholds, etc.). Simplified parameter determination may, under certain conditions, lead to some storage tanks being too high or too low for certain periods of time. We know from classical inventory management, in the conditions of setting stack parameters in DDMRP, that the deficiency can be eliminated by the use of statistical–analytical approaches and optimisation techniques. This article deals with the issue of setting optimal values of storage tanks in DDMRP, while the correctness of the methodology is verified through simulation of the demand-driven planning process. The correctness and usability of the proposed approaches in sizing strategic reservoirs in DDMRP was confirmed through the results of stimulation experiments. Full article

Several studies on robotic cotton harvesters have designed their end-effectors and harvesting algorithms based on the approach of harvesting a single cotton boll at a time. These robotic cotton harvesting systems often have slow harvesting times per boll due to limited computational speed and the extended time taken by actuators to approach and retract for picking individual cotton bolls. This study modified the design of the previous version of the end-effector with the aim of improving the picking ratio and picking time per boll. This study designed and fabricated a pullback reel to pull the cotton plants backward while the rover harvested and moved down the row. Additionally, a YOLOv4 cotton detection model and hierarchical agglomerative clustering algorithm were implemented to detect cotton bolls and cluster them. A harvesting algorithm was then developed to harvest the cotton bolls in clusters. The modified end-effector, pullback reel, vacuum conveying system, cotton detection model, clustering algorithm, and straight-line path planning algorithm were integrated into a small red rover, and both lab and field tests were conducted. In lab tests, the robot achieved a picking ratio of 57.1% with an average picking time of 2.5 s per boll. In field tests, picking ratio was 56.0%, and it took an average of 3.0 s per boll. Although there was no improvement in the lab setting over the previous design, the robot’s field performance was significantly better, with a 16% higher picking ratio and a 46% reduction in picking time per boll compared to the previous end-effector version tested in 2022. Full article

An accurate calculation of the indicator diagram of a pumping unit is the key factor in analyzing the performance of an oilfield production and operation and in preparing and optimizing an oilfield development plan. Aiming at the problems of the poor stability of the conventional load-displacement sensor method and the wave equation method, owing to the influence of an alternating load on the force sensor and the difficulty in measuring the crank angle using the electrical parameter method, a new soft sensing method employing the input electrical parameters of the motor and the beam inclination has been proposed to obtain the indicator diagram. At first, this method is established based on the beam angle of the pumping unit, which is easily measured using the suspension point displacement mathematics calculation model and the torque factor. Subsequently, the electric motor input parameters, the parameters of the four-bar linkage, and the relationship between the polished rod load have been established. Finally, the motor and the beam angle of the measured electrical parameters have been substituted into the calculation of the suspension point displacement and load value and pull in accordance with the guidelines to eliminate the singularity mutation values. After processing the measured data through a Butterworth filter, the indicator diagram is obtained. The results of the engineering experiment and application show that the average relative error of the method is less than 3.95%, and the maximum relative error remains within 2% for 6 months, which verifies the stability of the soft sensing method. Full article

Gantry-type dual-axis platforms can be used to move heavy loads or perform precision CNC work. Such gantry systems drive a single axis with two linear motors, and under heavy loads, a high driving force is required. This can generate a pulling force between the drive shafts in the coupling mechanism. In these situations, when a synchronization error becomes too large, mechanisms can become deformed or damaged, leading to damaged equipment, or in industrial settings, an additional power consumption. Effectively and accurately acquiring the synchronized movement of the platform is important to reduce energy consumption and optimize the system. In this study, a fractional-order fuzzy PID controller (FOFPID) using Oustaloup’s recursive filter is used to control a synchronous X–Y gantry-type platform. The optimized controller parameters are obtained by the measurement of control errors in a simulated environment. Four optimization methods are tested and compared: particle swarm optimization, invasive weed optimization, a gray wolf optimizer, and biogeography-based optimization. The systems were tested and compared in order to optimize the control parameters. Each of the four algorithms is simulated on four contour shapes: a circle, bow, heart, and star. The simulations and control scheme of the experiments are implemented using MATLAB, and the reference paths were planned using non-uniform rational B-splines (NURBS). After running the simulations to determine the optimal control parameters, each set of acquired control parameters is also tested and compared in the experiments and the results are recorded. Both the simulations and experiments show good results, and the tracking of the X–Y platform showed improved performance. Two performance indices are used to determine and validate the relative performance of the models and results. Full article