Laboratory Environment Research Articles

Robust pollination for tomato farming using deep learning and visual servoing

Abstract In this work, we propose a novel approach for tomato pollination that utilizes visual servo control. The objective is to meet the growing demand for automated robotic pollinators to overcome the decline in bee populations. Our approach focuses on addressing this challenge by leveraging visual servo control to guide the pollination process. The proposed method leverages deep learning to estimate the orientations and depth of detected flower, incorporating CAD-based synthetic images to ensure dataset diversity. By utilizing a 3D camera, the system accurately estimates flower depth information for visual servoing. The robustness of the approach is validated through experiments conducted in a laboratory environment with a 3D printed tomato flower plant. The results demonstrate a high detection rate, with a mean average precision of 91.2 %. Furthermore, the average depth error for accurately localizing the pollination target is impressively minimal, measuring only 1.1 cm. This research presents a promising solution for tomato pollination, showcasing the effectiveness of visual-guided servo control and its potential to address the challenges posed by diminishing bee populations in greenhouses.

Insecticidal efficacy of green synthesized silver nanoparticles, Bacillus thuringiensis and Triazophos against Pectinophora gossypiella

Pink bollworm (Pectinophora gossypiella) poses a significant threat to cotton crops worldwide, resulting in economic losses surpassing sustainable levels for farmers since 2011. This study examines different approaches to controlling pink bollworm infestations, specifically silver nanoparticles (AgNPs), bio-formulations of Bacillus thuringiensis (Bt), and a commercial insecticide (Triazophos), in both laboratory and field environments. AgNPs were synthesized using neem plant leaf extract (Azadirachta indica). The formation of AgNPs was confirmed through UV-visible spectroscopy, Fourier transform infrared (FTIR) spectroscopy, and a zeta sizer analyzer. In the laboratory, the effectiveness of individual and combined applications of AgNPs, Bt, and Triazophos was tested against 2nd and 4th instar pink bollworm larvae. Mortality rates were measured at 1, 2, and 5 days after application. The results showed that the treatment combining AgNPs and Triazophos (40ppm + 50ppm) achieved a larval mortality rate of over 90%, surpassing the other treatments. In field conditions, the combination of Triazophos and AgNPs at 40ppm (100ml + 50ml) proved most effective in managing P. gossypiella populations. These findings underscore the potential of nanoparticles as efficient larvicidal agents for controlling P. gossypiella. Furthermore, they emphasize the importance of reevaluating past management strategies for cotton crops given emerging alternatives to traditional insecticides. Future research directions may involve further optimizing nanoparticle formulations and investigating their broader ecological impacts on cotton ecosystems.

REVEALING ANOMALIES BY NETWORK PACKET FLOODING ON BUILT FTP AND OPENSSH SERVERS IN CONTROLLED LAB ENVIRONMENT

This scientific paper investigates the effects of network packet flooding on FTP (port 21) and SSH (port 22) protocols, aiming to reveal and document anomalies in server behavior under high-load conditions. By simulating packet flooding in a controlled lab environment, an analysis on vulnerabilities and anomalies unique to each protocol is conducted in order to the improve defensive capabilities. The results provide guidance on best practices to secure FTP and OpenSSH services against malicious traffic, such as Distributed Denial of Service (DDoS) attacks, supporting wider network security strategies.

Abstract IA024: Validation of liquid biopsy approaches for the care of children with cancer

Abstract Pediatric cancers are driven by a unique set of mutations, destinct from the variants that typically drive adult cancers. Therefore, widely available liquid biopsy assays are often not relevant for detection, quantificaiton, or profiling of circulating tumor DNA in childhood cancer. Custom liquid biopsy approaches have been developed by the Crompton lab and others and these assays demonstrate that ctDNA levels provide valuable prognostic information and can guide some therapeutic choices in pediatrics. Implementation of liquid biopsies into national phase 3 trials for children with cancer will require translation of these approaches into a clinical molecular laboratory environment. Citation Format: Brian Crompton. Validation of liquid biopsy approaches for the care of children with cancer [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr IA024.

Less gets more attention: A novel human-centered MR remote collaboration assembly method with information recommendation and visual enhancement

Mixed reality remote collaboration assembly is a type of computer-supported collaborative assembly work that uses mixed reality technology to enable spatial information and collaboration status sharing among geographically distributed collaborators, including remote experts and local users. However, due to the abundance of mixed virtual and real-world information in the MR space and the limitations imposed by narrow field-of-view augmented reality (AR) glasses, users face challenges in effectively focusing on relevant and valuable visual information. Our research aims to enhance users' visual attention to critical guidance information in MR collaborative assembly tasks, thereby improving the clear expression of instructions and facilitating the transmission of collaborative intention. We developed the Information Recommendation and Visual Enhancement System (IRVES) through an assembly process information hierarchy division mechanism, a content-based information recommendation system, and a gesture interaction-based information visual enhancement method. IRVES can leverage the guidance expertise and preferences of remote experts to recommend information to filter out irrelevant information and present the key information that the remote expert conveys to the local user in an intuitive way through visual enhancement. We conducted a user study experiment of a collaborative assembly task of a small engine in a laboratory environment. The experimental results indicate that IRVES outperforms traditional MR remote collaborative assembly methods (VG3DV) in terms of time performance, operational errors, cognitive performance and user experience. Our research contributes a human-centered information visualization approach for remote experts and local users, providing a novel method and idea for designing visual information interfaces in MR remote collaboration assembly tasks.

Photocatalytic degradation of microcystins from a field-collected cyanobacterial assemblage by 3D printed TiO2 structures using artificial versus solar irradiation

Microcystins (MCs) from freshwater cyanobacteria cause adverse effects to humans and ecological receptors through multiple exposure routes requiring adaptable and diverse treatment technologies. Photocatalysis of MCs using TiO2 is a promising technology; however, TiO2 photocatalysts as unbound nanoparticles in suspension are impractical to deploy. 3D Printing (3DP) provides a means to immobilize TiO2, producing deployable photocatalyst structures with extensive geometric freedom. The objective of this proof-of-concept experiment was to incrementally increase the environmental complexity (e.g., broad-spectrum fluorescent lamps and outdoor solar; filtered cell lysate and algal assemblage as the source of MCs) while comparing photocatalysis rates of MCs by 3DP TiO2 structures using polylactic acid (PLA) as the binder. Degradation half-lives of MCs were shorter in TiO2 embedded in 3DP PLA relative to PLA-only controls with differences in half-lives ranging from 3.6 to 10h. The one exception was the outdoor solar and an algal assemblage, where significant differences could not be discerned due to the already rapid photolysis rates. Ultimately, photocatalysis rates (half-life = 1.9–11.6h) were comparable to those previously published for TiO2 3DP structures in a laboratory environment and TiO2 fixed-films (half-life = 2–13h) demonstrating feasibility of 3DP to immobilize TiO2 photocatalysts under a range of conditions. This is the first time that MC concentrations from a field-collected HAB were photocatalytically degraded in both solar simulated light and sunlight using a custom-made advanced photocatalytic nano-composite with enhanced performance through high surface area design enabled by 3D printing. These data inform future development of scalable, retrievable, and operationally flexible structures with immobilized TiO2.

A novel 'stop-hole' strategy to extend the fatigue life of orthodontic thermoformed retainers: an in vitro study.

This study aimed to investigate the potential to limit crack propagation in thermoformed retainers (TRs) and extend their fatigue life by placing a 'stop-hole'. Thirty-two TRs, fabricated from 1mm thick, round polyethylene terephthalate glycol (PETG) blanks of ICONIC®, underwent testing within a custom-built fatigue tester. After initial crack growth of 3mm, the TRs were divided into three groups: a control group (C) and two experimental groups based on the stop-hole placement method. In the first experimental group, stop-holes were placed using a 1mm round carbide bur on a slow-speed handpiece (ES), while the second group utilized a heated 1-mm-width ball-ended CPITN probe (EH). Following the placement of a stop-hole at the crack-tip, all 32 TRs resumed testing until the final crack length of 10mm was reached. Stop-hole placement significantly prolonged the delay in crack re-initiation in both experimental groups compared to the control group (P < .001 for both). The mean re-initiation times for the ES and EH groups were 104.50 and 129.50min, respectively, whereas the control group was 28.94min. Consequently, this discrepancy was reflected in the total testing times, with both experimental groups requiring significantly longer time to reach the final crack length (P < .001). There was no significant difference in the testing times between the two experimental groups. This is an in vitro study. The stop-hole methodology significantly extended the fatigue life of a TR in a controlled laboratory environment.

Proto-DS: A Self-Supervised Learning-Based Nondestructive Testing Approach for Food Adulteration with Imbalanced Hyperspectral Data

Conventional food fraud detection using hyperspectral imaging (HSI) relies on the discriminative power of machine learning. However, these approaches often assume a balanced class distribution in an ideal laboratory environment, which is impractical in real-world scenarios with diverse label distributions. This results in suboptimal performance when less frequent classes are overshadowed by the majority class during training. Thus, the critical research challenge emerges of how to develop an effective classifier on a small-scale imbalanced dataset without significant bias from the dominant class. In this paper, we propose a novel nondestructive detection approach, which we call the Dice Loss Improved Self-Supervised Learning-Based Prototypical Network (Proto-DS), designed to address this imbalanced learning challenge. The proposed amalgamation mitigates the label bias on the most frequent class, further improving robustness. We validate our proposed method on three collected hyperspectral food image datasets with varying degrees of data imbalance: Citri Reticulatae Pericarpium (Chenpi), Chinese herbs, and coffee beans. Comparisons with state-of-the-art imbalanced learning techniques, including the Synthetic Minority Oversampling Technique (SMOTE) and class-importance reweighting, reveal our method’s superiority. Notably, our experiments demonstrate that Proto-DS consistently outperforms conventional approaches, achieving the best average balanced accuracy of 88.18% across various training sample sizes, whereas the Logistic Model Tree (LMT), Multi-Layer Perceptron (MLP), and Convolutional Neural Network (CNN) approaches attain only 59.42%, 60.38%, and 66.34%, respectively. Overall, self-supervised learning is key to improving imbalanced learning performance and outperforms related approaches, while both prototypical networks and the Dice loss can further enhance classification performance. Intriguingly, self-supervised learning can provide complementary information to existing imbalanced learning approaches. Combining these approaches may serve as a potential solution for building effective models with limited training data.

The Application of PID Control in DC Motor Control Systems

Abstract. The application of Proportional-Integral-Derivative (PID) control in direct current (DC) motor control systems is essential for achieving high levels of precision and reliability in both industrial and robotic applications. As DC motors are widely utilized in environments requiring accurate speed and torque control, this study seeks to address common challenges such as overshoot, instability, and sluggish response times that can compromise performance. The research is centered on the development and implementation of a PID-based control strategy using MATLAB/Simulink as the primary tool for simulation and testing. The study involves detailed simulation models and experimental validation, using data collected from controlled lab environments to evaluate the effectiveness of various tuning methods. These methods include adjusting the proportional, integral, and derivative gains to optimize motor responsiveness and system stability. The results indicate that with appropriate tuning, PID controllers can significantly enhance DC motor performance, leading to reduced overshoot, faster response times, and greater overall system reliability. The research concludes that adaptive PID control strategies, which dynamically adjust parameters in response to changing operating conditions, provide a robust solution for improving DC motor control in complex, dynamic, and nonlinear environments. This study lays the groundwork for future innovations in automation and precision-driven industries, offering insights that could be applied across a wide range of applications.

An Investigation of Indoor Environment Quality on Occupants’ Thermal Responses, Health, and Productivity: A Study Based on Physiological Data in Occupied Office Space

This study explores the impact of Indoor Environment Quality (IEQ) on the health and productivity of office workers in an office building in Fujisawa, Kanagawa, Japan. Previous studies have shown that IEQ can affect the physiological responses of occupants, such as of skin temperature, heart rate, and metabolic rate, which are indicators of health and productivity. However, most studies took place in controlled laboratory environments, which may not accurately represent real-life experiences. The study collected subjective and objective data from actual occupied office space, including on perceptions of IEQ, health, and productivity, and measurements of IEQ parameters such as on the thermal environment, light environment, indoor air quality, and acoustics. The study used correlation and linear regression methods to examine the relationship between IEQ, physiological data, and subjective responses to health and productivity. The stable thermal environment and low physical intensity of office work may contribute to the weak correlation between physiological data, thermal responses, and health–productivity variables. The results of this study can provide insights into how IEQ affects the psychological responses, well-being, and performance of office workers in real-world settings.

Classification of Hyperspectral Images of Explosive Fragments Based on Spatial–Spectral Combination

The identification and recovery of explosive fragments can provide a reference for the evaluation of explosive power and the design of explosion-proof measures. At present, fragment detection usually uses a few bands in the visible light or infrared bands for imaging, without fully utilizing multi-band spectral information. Hyperspectral imaging has high spectral resolution and can provide multidimensional reference information for the fragments to be classified. Therefore, this article proposed a spatial–spectral joint method for explosive fragment classification by combining hyperspectral imaging technology. In a laboratory environment, this article collected hyperspectral images of explosion fragments scattered in simulated scenes. In order to extract effective features from redundant spectral information and improve classification accuracy, this paper adopted a classification framework based on deep learning. This framework used a convolutional neural network–bidirectional long short-term memory network (CNN-BiLSTM) as the spectral information classification model and a U-shaped network (U-Net) as the spatial segmentation model. The experimental results showed that the overall accuracy exceeds 95.2%. The analysis results indicated that the method of spatial–spectral combination can accurately identify explosive fragment targets. It validated the feasibility of using hyperspectral imaging for explosive fragment classification in laboratory environments. Due to the complex environment of the actual explosion site, this study still needs to be validated in outdoor environments. Our next step is to use airborne hyperspectral imaging to identify explosive fragments in outdoor environments.

Compact Plasma Ionization for Ion Mobility Spectrometry Using a 4.3 MHz Miniature Tesla Coil.

In this study, a low-cost 4.3 MHz plasma ionization source for ion mobility spectrometry (IMS), utilizing a miniaturized Tesla coil, is presented. This compact design, combined with a 3D printed cyclic olefin copolymer (COC) housing, delivers a stable and directed plasma suitable for ionization in IMS applications. The 3D printed housing ensures chemical resistance and low off-gassing, which are crucial for maintaining sample integrity. The Tesla coil produces a consistent sine wave at 4.3 MHz, and when connected to stainless steel screw electrodes it generates a stable plasma capable of ionizing analytes such as limonene, MTBE, nicotine, 2-octanone, and propofol. Measurements were conducted in both positive and negative ion modes. The results demonstrate the Tesla coil's effectiveness as a low-cost and reliable ionization source for IMS, offering comparable performance to traditional Ni63 β-emitters. This advancement in plasma ionization technology could facilitate more accessible and flexible IMS systems for diverse analytical applications. The integration of 3D printing in the development of this ionization source underscores the potential for customized, low-cost analytical instrumentation, promoting innovation in laboratory environments and commercial applications.

Direct characterisation of AM-to-PM phenomena in fast Si and InGaAs photodiodes

The direct measurement method of AM-to-PM phenomena in fast silicon and InGaAs photodiodes is described. The setup is simple, relatively inexpensive and allows fast and precise measurements not only in a laboratory environment. During sample tests, the authors have found that the influence of bias voltage on the phase shift of an optical signal conversion is significant. The reported effect together with the influence of modulation depth on phase shift (AM-to-PM conversion) has a negative impact on an optical signal reception especially in coherent applications. The authors show that, with our proposed setups, it is possible to find optimal bias voltage and optimal optical power in order to reduce electrical phase noise of the photodetector.

Unveiling Efficiency: A Research Inquiry into Technical Staff Utilization in South Indian Clinical Laboratory using Workload Indicators Staffing Need (WSN) Analysis.

Background Effective clinical laboratories are the need of the hour, they play a significant role in healthcare, providing tests for diagnosis, treatment monitoring. Having skilled and adequate staffing is crucial for clinical laboratories to operate efficiently, manage their workload effectively, and deliver test results in a timely manner. Despite being critical for healthcare delivery, ensuring adequate staffing levels in clinical laboratories remains a complex challenge for healthcare providers worldwide due to resource constraints and workforce shortages. Methods This study evaluates the effectiveness of clinical laboratory staff utilization by considering workload, work pressure, and staffing requirements. We utilized the Workload Indicators of Staffing Need (WISN) method to assess the staffing in the clinical laboratory of a tertiary care hospital. Annual hospital statistics were collected for two years from June 2021 to May 2023 to calculate the average number of days (234) worked annually, percentage of workload, distribution of activity time measurement units, and the ratio of needed, surplus, and existing staff. The findings aim to provide valuable insights for optimizing staffing levels and ensuring an efficient laboratory environment. Result The analysis found a significant workforce gap in the laboratory between the current staff numbers and the calculated staffing needs. The WISN ratios ranged from 0.2 to 0.7, indicating the existing 33 staff members face a high workload burden. The calculated ideal staffing level was 46.33 personnel. These results demonstrate staff shortage and excess workload pressure on the current laboratory employees. Conclusion The results highlight the importance of optimizing staffing levels in clinical laboratories to ensure quality service delivery. The WISN methodology can be a useful tool in healthcare facilities for making evidence -based decisions for staff allocation, maximizing the utilization of employee skill sets, and establishing standard staffing benchmarks tailored to the needs of clinical laboratories.

Compact Digital Immunoassay Platform Integrating ELISA with a Lateral Flow Strip

Background/Objectives: On-site diagnosis of infection in their early stages requires assays with high sensitivities that are compact and easy to operate out of the laboratory and hospital environments. However, current assay technologies fall short of these requirements and require highly skilled technicians to set up, operate, and interpret the results. Methods: To address these challenges, we developed and evaluated a Point-of-Care-Testing (PoCT) immunoassay platform called the D-strip. The D-strip platform combines the capabilities of a digital enzyme-linked immunoassay (ELISA) with a lateral flow assay (LFA). The D-strip sample flow cell is composed of the same components found in conventional LFAs, and its high sensitivity is due to its efficient implementation of ELISA. The fully integrated platform is simple and requires minimal user intervention to operate. Results: The D-strip exhibited a sample-to-result time of 15 min with a limit of detection (LOD) of 1.7 × 103 copies/mL for severe acute respiratory syndrome coronavirus 2 (SARS-2-CoV) antigen. The LOD of the D-strip is 488-fold higher than that for conventional LFAs and is comparable to a clinical laboratory test. Conclusions: The D-strip is a compact and highly sensitive immunoassay platform with a strong potential for application as a confirmatory assay outside the clinical laboratory.

A sensing system and solving method for dynamic detection of relative pose of hydraulic support group

The precise detection of the pose of hydraulic support groups is crucial for the construction of intelligent coal mines. Addressing current challenges in comprehensive position sensing of support groups, such as one-sided descriptions, missing information, and limited detection capabilities, this paper, based on the concept of digital twin, proposes a sensing system and solving method for dynamic detection of the relative pose of hydraulic support group. Initially, a relative pose detection model, based on the six-point localization principle, is proposed by combining existing sensor information. Then, a sensing system is designed, incorporating a photoelectric detection device for monitoring key position parameters and virtual sensors for determining pose information. Consequently, a method for dynamically deriving relative positions, driven by the fusion of real-virtual iterative solving, real-virtual error feedback, and iterative condition optimization, is established. Finally, a relative pose dynamic detection system for a hydraulic support prototype is constructed in a laboratory environment. Experimental results demonstrate that the sensor system and the relative position detection method designed in this paper achieve a relative position accuracy of over 95.32 % for the hydraulic support, with a dynamic reconstruction time of less than 0.2 s. This verifies the rationality of the sensor system and the feasibility and accuracy of the dynamic deduction method. To sum up, this work provides a theoretical foundation and technical support for the autonomous perception of hydraulic support group, autonomous frame adjustment between frames, and overall straightness adjustment. It also lays the groundwork for further research and the advancement of information-physical systems in the fully-mechanized mining face.

LarvaeCountAI: a robust convolutional neural network-based tool for accurately counting the larvae of Culex annulirostris mosquitoes.

Accurate counting of mosquito larval populations is essential for maintaining optimal conditions and population control within rearing facilities, assessing disease transmission risks, and implementing effective vector control measures. While existing methods for counting mosquito larvae have faced challenges such as the impact on larval mortality rate, multiple parameters adjustment and limitations in availability and affordability, recent advancements in artificial intelligence, particularly in AI-driven visual analysis, hold promise for addressing these issues. Here, we introduce LarvaeCountAI, an open-source convolutional neural network (CNN)-based tool designed to automatically count Culex annulirostris mosquito larvae from videos captured in laboratory environments. LarvaeCountAI does not require videos to be recorded using an advanced setup; it can count larvae with high accuracy from videos captured using a simple setup mainly consisting of a camera and commonly used plastic trays. Using the videos enables LarvaeCountAI to capitalise on the continuous movement of larvae, enhancing the likelihood of accurately counting a greater number of larvae. LarvaeCountAI adopts a non-invasive approach, where larvae are simply placed in trays and imaged, minimising any potential impact on larval mortality. This approach addresses the limitations associated with previous methods involving mechanical machines, which often increase the risk of larval mortality as larvae pass through multiple sections for counting purposes. The performance of LarvaeCountAI was tested using 10 video samples. Validation results demonstrated the excellent performance of LarvaeCountAI, with an accuracy ranging from 96.25% to 99.13% across 10 test videos and an average accuracy of 97.88%. LarvaeCountAI represents a remarkable advancement in mosquito surveillance technology, offering a robust and efficient solution for monitoring larval populations. LarvaeCountAI can contribute to developing effective strategies for reducing disease transmission and safeguarding public health by providing timely and accurate data on mosquito larvae abundance.

Should I Evaluate My Augmented Reality System in an Industrial Environment? Investigating the Effects of Classroom and Shop Floor Settings on Guided Assembly.

Numerous prior studies have investigated real-time assembly instructions using Augmented Reality (AR). However, most such experiments were conducted in laboratory settings with simplistic assembly tasks, failing to represent real-world industrial conditions. To ascertain to what extent results obtained in a laboratory environment may differ from studies in actual industrial environments, we carried out a user study with 32 manufacturing apprentices. We compared assembly task execution results in two settings, a classroom and an industrial workshop environment. To facilitate the experiments, we developed AR-guided manual assembly systems for simple and more complex assets. Our findings reveal a significantly improved task performance in the industrial workshop, reflected in faster task completion times, fewer errors, and subjectively perceived higher flow. This contradicted participants' subjective ratings, as they expected to perform better in the classroom environment. Our results suggest that the actual manufacturing environment is critical in evaluating AR systems for real-world industrial applications.

Synthesis, Characterization, Insecticidal Activity and Antibacterial Evaluation of Some Heterocyclic Compounds Containing 1,2,3-Triazoles Moiety

A series of 1,2,3-triazole compounds having thymol or paracetamol moieties have been designed via employing ‘click’ chemistry with copper-catalyzed azide-alkyne cycloaddition reactions. Additionally, the insecticidal characterizations of many designed products were perceived with respect to second & fourth of Spodoptera littoralis instar larvae. Numerous insecticides are revealed as novelties. As a result, numerous triazole derivatives were designed chemically to assist as analogues a comprehensive class of insecticides. With LC50 value of 0.321 mg/L for second larvae instar and LC50=0.627 mg/L four instar larvae, components 10 display the highest insecticidal bioactivity. This article deliberates how to find novel compounds that could be employed as insecticidal components in the future. A few biological and biochemical properties of the compounds produced in a laboratory environment were also examined. This work also addresses the process of finding new compounds that could be used as insecticidal products in the future. Ultimately, the antibacterial effectiveness of each developed component contrary to both Gram-positive & Gram-negative bacteria was observed.

A new field-test for assessing the medial and lateral hamstring strength at long-muscle length

This research investigates the influence of tibial rotations with knee flexion (KF) on the electromyographic (EMG) activities of hamstring muscles (HM) groups and the strength ratio between the medial and lateral rotation of the tibia. A cross-sectional design was employed to assess muscle activity, isometric strength and reliability. The research was conducted in a controlled laboratory environment. Thirty-six amateur male athletes were recruited as volunteers. The measures included peak muscle activity of the semitendinosus and biceps femoris long head, the knee flexors’ isometric strength ratio and reliability. The isometric strength ratios of medial (MR) to lateral (LR) tibial rotations were 0.94 ± 0.17 at 90°, and 0.93 ± 0.10 at both 60° and 30° of KF angulation. Tibial position significantly influenced knee flexion strength as well as HM activity, irrespective of KF angulation. Specifically, biceps femoris activity increased by 33.6% in LR compared to MR, while semitendinosus activity increased by 22.6% in MR compared to LR. The Knee-Rotation test (KR-test) can be a valuable tool for evaluating both HM groups (ICC > 0.87), and identifying the primary target for strengthening purposes during the injury prevention process. It provides insights for effective rehabilitation and training interventions.