Search Results (144)

Background/Objectives: Dorsal screw protrusion or intra-articular screw penetration at the distal radius can cause extensor tendon injuries or articular surface damage. Despite the use of various views, the detection of screw misplacement remains limited in 2D fluoroscopy. This study compares the sensitivity of 2D and 3D fluoroscopy for detecting screw misplacement at the distal radius. Methods: Volar locking plates were placed in six cadaveric forearms, and dorsal or intra-articular screw misplacement was induced. For each screw position, images were acquired by 2D and 3D fluoroscopy and assessed by three blinded observers. Sensitivity and specificity, inter-rater agreement, and observer confidence were evaluated. The dose area product (DAP) was measured separately for 2D and 3D fluoroscopy. Results: Three-dimensional fluoroscopy showed higher sensitivities for detecting dorsal (97.22%) and intra-articular (95.83%) screw misplacements than two-dimensional fluoroscopy. In 2D fluoroscopy, sensitivity for detecting dorsal screw protrusions improved from 63.89 to 75.00–77.78% with the inclusion of tangential views. For intra-articular penetrations, sensitivity in 2D fluoroscopy increased from 79.17 to 83.33% with the addition of oblique views. Observer confidence was higher in 3D fluoroscopy. DAP was significantly higher in 3D (42.4 ± 0.4 cGycm²) compared to 2D fluoroscopy (14.2 ± 3.7 cGycm²) (p < 0.0001). Conclusions: Compared to 2D fluoroscopy, 3D fluoroscopy improves the detection of screw misplacement at the distal radius. However, its routine use is constrained by increased radiation exposure and limited availability. If 3D fluoroscopy is not accessible, the addition of dorsal tangential and oblique views may improve the sensitivity of 2D fluoroscopy. Full article

Recent advances in Machine Learning have significantly improved anomaly detection in industrial screw driving operations. However, most existing approaches focus on binary classification of normal versus anomalous operations or employ unsupervised methods to detect novel patterns. This paper introduces a comprehensive dataset of screw driving operations encompassing 25 distinct error types and presents a multi-tiered analysis framework for error-specific classification. Our results demonstrate varying detectability across different error types and establish the feasibility of multi-class error detection in industrial settings. The complete dataset and analysis framework are made publicly available to support future research in manufacturing quality control. Full article

A continuous mixing process with a twin-screw extruder was investigated for graphite-based negative electrode pastes for high-power applications. In the extrusion-based mixing process, the first kneading concentration is one of the key processing parameters for systematic optimization of relevant electrode paste properties like viscosity and particle size distribution. For different active materials at a constant electrode paste composition, a clear correlation of increasing kneading concentration with decreasing viscosity can be observed up to a certain reversal point, initiating a change in the trend and the rheological behavior, thus indicating a process limit. The fundamental effects causing this change and the associated impact on materials and battery performance were evaluated by applying further analytical methods and electrochemical characterization. It is revealed that the change in viscosity is associated with enhanced de-agglomeration of the carbon black additive and with partial particle grinding of the active material and thus a partial change in the interlayer distance of graphene layers and, correspondingly, the electrochemical behavior of the active material. Beyond this, correlations between processing parameters and product properties are presented. Furthermore, indicators are suggested with which monitoring of the machine parameters enables the detection of changes in the electrode paste characteristics. Full article

Volumetric error decoupling is a critical prerequisite for effective error compensation. In this paper, the forward volumetric error model is established using the screw theory. Additionally, the Jacobian matrix based on the product of exponential is derived to construct the linear relationship between the volumetric error and the axis motion and decouple the volumetric error model. To address the limitation of compensation motion, a step-by-step decoupling method is proposed, where attitude and position errors are compensated sequentially. After detecting the actual geometric errors of the grinding machine, the volumetric error can be determined, and the compensation motion commands for each axis are calculated to correct the volumetric error. The simulation result shows that the mean value of the comprehensive error ranges can be reduced from 19.7 μm to 1.8 μm, demonstrating the effectiveness of the proposed method. Full article

Background/Objectives: In the twin-screw wet granulation (TSWG) process, accurate measurement of residence time distribution (RTD) is critical, as it characterizes material transport kinetics and mixing behavior. It plays a critical role in evaluating the homogeneity and stability of the granulation process and optimizing process parameters. It is necessary to overcome the limitations arising from the complex and time-consuming procedures of conventional RTD determination methods. Methods: This study proposes a new RTD detection method based on image processing. It uses black dye as a tracer to obtain RTD curve data, and the effects of process parameters such as tracer dosage, screw speed, and feeding rate on the RTD were investigated. Results: The results show that the established method can accurately determine RTD and that the tracer dosage has no significant effect on the detection results. Further analysis revealed that the screw speed is negatively correlated with the mean residence time (MRT). As the speed increases, not only does the MRT shorten, but its distribution also decreases. Similarly, an increase in the feeding rate also leads to a decrease in the MRT and distribution, but it is worth noting that lower feeding rates are beneficial for achieving a state close to mixed flow, while excessively high feeding rates are not conducive to sufficient mixing of materials in the extruder. Conclusions: The RTD detection method provides a reliable parameter basis and theoretical guidance for the in-depth study of the TSWG process and the development of quality control strategies. Full article

Background/Objectives: One of the surgical interventions applied in the cervical region is the pedicle screw method. The cervical pedicle screw is stronger than any other screw method; however, use of the cervical pedicle screw is limited due to the variability in the anatomy of the cervical vertebrae and the risks to the neurological and vascular structures in this region. This study aimed to determine the morphological features of subaxial cervical vertebrae of the adult Turkish population and to provide guidance for the pedicle screwing method. Methods: In our study, pedicle analyses were examined in the subaxial neck vertebrae of a total of 60 patients, 30 male and 30 female, using computed tomography images. In subaxial vertebrae (C3–C7), bilateral pedicle width, pedicle axis length, pedicle transverse angle, sagittal and transverse diameter of vertebral foramen, and the distance between two pedicles were measured. Results: Pedicle widths that did not fit the commonly used 3.5 mm pedicle screw were detected in both male and female patients. The mean bilateral pedicle width in male patients was found to be greater than in female patients. When the parameter results were compared according to the levels, it was found that the pedicle width, pedicle axis length, transverse diameter, and the distance between the two pedicles increased statistically significantly. Conclusions: We think that the data obtained from the study will help determine the appropriate screwing (screw selection) in subaxial vertebra pedicle surgery and increase the success of the surgical procedure. Full article

Rice plays a crucial role in global agricultural production, but various foreign objects often mix in during its processing. To efficiently and accurately detect small foreign objects in the rice processing pipeline, ensuring food quality and consumer safety, this study innovatively proposes a YOLOv-MA-based foreign object detection algorithm for rice, leveraging deep learning techniques. The proposed algorithm adaptively enhances multi-scale feature representation across small, medium, and large object detection layers by incorporating the multi-scale dilated attention (MSDA) mechanism. Additionally, the adaptive spatial feature fusion (ASFF) module is employed to improve multi-scale feature fusion in rice foreign object detection, significantly boosting YOLOv8’s object detection capability in complex scenarios. Compared to the original YOLOv8 model, the improved YOLOv-MA model achieves performance gains of 3%, 3.5%, 2%, 3.9%, and 4.2% in mean Average Precision (mAP@[0.5:0:95]) for clods, corn, screws, stones, and wheat, respectively. The overall mAP@[0.5:0:95] reaches 90.8%, reflecting an improvement of 3.3%. Furthermore, the proposed model outperforms SSD, FCOS, EfficientDet, YOLOv5, YOLOv6, YOLOv7, YOLOv8, YOLOv9, YOLOv11, and YOLOv12 in overall performance. Thus, this model not only reduces the burden of manual inspection but also provides an efficient and high-precision solution for rice foreign object detection. Full article

Objectives: To introduce a novel method for apical lesion sampling using a protected paper point device and to evaluate its effectiveness and robustness during the sampling process in vitro. Methods: A prototype for apical sample collection was developed as an adaptation of the Micro-Apical Placement System—the device features a highly tapered screw head with a thin, hollow, stainless-steel tube and an internal wire piston. Standardized 5 mm paper points (ISO 10; PD Dental, Switzerland) served as carrier material. The prototype was tested using 30 × 3D-printed, single-rooted tooth models inoculated using two bacterial strains (Staphylococcus epidermidis and Escherichia coli) to simulate apical and intraradicular bacterial infections, respectively. The sampling process involved collecting and analyzing samples at specific timepoints, focusing on the presence or absence of E. coli contamination. Following sample collection, cultural detection of bacterial presence was performed by incubating the samples on agar plates to confirm the presence of E. coli. Samples were collected as follows: S0 (sterility control of the prototype), P0 (sterility control of the tooth model), P1 (apical sample collected with the CAPS (controlled apical sampling) device, and P2 (contamination control sample to check for the presence of E. coli inside the root canal). Results: Handling of the CAPS prototype was straightforward and reproducible. No loss of paper points or complications were observed during sample collection. All sterility samples (P0, S0) were negative for tested microorganisms, confirming the sterility of the setup. P2 samples confirmed the presence of E. coli in the root canal in all trials. The P1 samples were free from contamination in 86.67% of trials. Conclusions: The CAPS method for apical sampling demonstrated advances in the successful and precise sample collection of apically located S. epidermidis and will be a useful tool for endodontic microbiological analysis. Its user-friendly design and consistent performance highlight its potential for clinical application, contributing to more accurate microbial diagnostics and later patient-specific therapeutic approaches in endodontic treatments. Full article

This study provides the design, analysis, and prototype fabrication of a remotely controlled actuation system for railcar-mounted sensors. Frequent railway inspections are essential for detecting and preventing major defects that could lead to train derailments or accidents. Integrating supplemental automated inspection systems into existing trains can aid inspection crews without interfering with standard railway operations. However, many sensors and cameras require protection during transit, motivating the need for a deployable mounting assembly. The feasibility of a deployable sensor system was successfully assessed by creating and demonstrating a functional prototype mounting assembly that can be used with future automated inspection systems. Typical loads and accelerations experienced by a train were used to design a lead screw and stepper motor system capable of working within desired tolerances. Optimized inputs controlling this motion with an Arduino Uno were found through the iterative testing of digital signals and direct port manipulation. Further research testing in a field-like environment is suggested. Full article

Aiming to address the problems of many temperature control disturbances and the hysteresis of control output in existing rice noodle extruders, a temperature control method for a rice noodle extruder based on adaptive particle swarm optimization (APSO) optimization model predictive control (MPC) was designed. Firstly, the temperature control principle of the rice noodle extruder is analyzed by combining the structure of the rice noodle extruder. The temperature balance equation of the barrel is constructed by thermodynamic analysis, and the temperature prediction model is established. The APSO algorithm is further selected to perform the adaptive parameter identification of the model based on the collected input/output data. Then, aiming at high-precision temperature control, the objective function is constructed by combining the temperature prediction value and the reference trajectory, and the objective function is optimized to obtain the optimal control sequence. At the same time, the feed rate is selected as feedforward, the feed rate change is monitored by detecting the feed screw speed, and the optimal control sequence is corrected to eliminate the interference caused by the fluctuation of the feed rate. The experimental results show that the maximum temperature overshoot under different parameter combinations is 7.75%, the steady-state error is within ±1 °C, and the longest adjustment time is 1228 s. Compared with fuzzy PID control, it has stronger adaptability and higher control accuracy. Full article

In smart meters, loose contact at screw terminals can lead to prolonged overheating and arcing, posing significant fire hazards. To mitigate these risks through early fault detection, this study proposes a data-driven framework integrating the Local Outlier Factor (LOF) and Multiple Linear Regression (MLR) algorithms. Voltage differentials, extracted from operational data collected via a simulated multi-meter metering enclosure, are leveraged to diagnose terminal contact degradation. Specifically, LOF identifies arc faults, characterized by abrupt and transient voltage deviations, by detecting outliers in voltage differentials, while MLR quantifies contact resistance through regression analysis, enabling precise loose contact detection, a condition associated with gradual and persistent voltage changes due to increased resistance. Extensive validation demonstrates the framework’s robustness, outperforming conventional centralized methods in diagnostic accuracy and adaptability to diverse load conditions. Full article

(1) Background: postoperative surgical-site infections are a significant complication in small-animal surgical procedures, with detected rates ranging from 0.8% to 18.1%, depending on the type of surgery. The sterilization process of surgical instruments is a crucial factor in infection control, but the shelf life of surgical instruments and the best packaging method are not precisely defined due to the multiple variables that influence them. This study aimed to assess the shelf life of surgical instruments stored under controlled environmental conditions in a veterinary hospital and compare two packaging methods: using a self-sealing single pouch versus a self-sealing double pouch. (2) Methods: a sample of 400 non-sterile screws was divided into three groups: Group 1 (175 screws in single pouches), Group 2 (175 screws in double pouches), and Group 3 (50 non-sterile screws as a control group to verify the microbial culture efficiency). Microbiological tests were conducted at 1, 7, 15, 30, 60, 182, and 390 days post-sterilization. (3) Results: no bacterial growth was detected on screws packaged in single and double pouches up to 182 days. However, after 390 days, bacterial growth was observed in one screw packaged in a single pouch. In Group 3, only two screws turned out to be bacteriologically positive. (4) Conclusions: no statistical difference was found between the two groups; however, the detection of a single positive screw in the single-pouch group raised a potential clinical consideration, suggesting the need for further studies based on events and time. Full article

This study introduces a computational framework for understanding the symmetry and asymmetry of human movement by integrating Laban Movement Analysis (LMA). By conceptualizing movement refinement as a structured computational process, we model the golf swing as a series of state transitions where perceptual invariants guide biomechanical optimization. The golf club’s motion is analyzed using the instantaneous screw axis (ISA) and inertia tensor revealing how expert golfers dynamically adjust movement by detecting and responding to invariant biomechanical structures. This approach extends Gibson’s ecological theory by proposing that movement execution follows an iterative optimization process analogous to a Turing machine updating its states. Furthermore, we explore the role of symmetry in motor control by aligning Laban’s X-scale with structured computational transitions, demonstrating how movement coordination emerges from dynamically balanced affordance–action couplings. This insight gained from the study suggests that AI-driven sports training and rehabilitation can leverage symmetry-based computational principles to enhance motion learning and real-time adaptation in virtual and physical environments. Full article

Purpose: The aim of this study was to evaluate the long-term clinical results of two different implant–abutment connection types (screw-retained/Morse locking taper), marginal bone loss, and complications in prosthetic restorations. Materials and Methods: In 2017–2018, 579 implants and 242 implant-supported restorations applied to 137 patients were included in the study. Patients were recalled every six months, clinical evaluations were accomplished, and complications were recorded. When examining the distribution of prosthetic restorations by type, it was determined that 38 (15.70%) were single crowns, 136 (56.19%) were fixed partial cement-retained bridge restorations, 53 (21.90%) were fixed partial screw-retained bridge restorations, and 15 (6.19%) were overdenture prostheses. Findings: Overall, complications included eighteen (21.68%) retention losses, nineteen (22.89%) instances of screw loosening, twenty-one (25.30%) veneer ceramic fractures, three (3.61%) acrylic base fractures, fourteen (16.87%) cases of peri-implantitis, and eight (9.64%) implant losses. Conclusions: Differences in complication rates were observed between implants with different implant–abutment connection designs. While no significant differences were found regarding annual mesial and distal marginal bone loss for implants with conical locking connections, a significant difference was detected in those with screw-retained connections. In both implant groups, mesial and distal marginal bone loss progressed gradually over the follow-up period. Full article

Objectives: Hot-melt extrusion (HME) offers a solvent-free, scalable approach for manufacturing pharmaceutical co-crystals (CCs), aligning with the industry’s shift to continuous manufacturing (CM). However, challenges like undefined yield optimization, insufficient risk management, and limited process analytical technology (PAT) integration hinder its industrial application. This study aimed to develop a proof-of-concept HME platform for CCs, assess process risks, and evaluate PAT-enabled monitoring to facilitate robust production. Methods: Using carbamazepine (CBZ) and nicotinamide (NIC) as model compounds, an HME platform compatible with PAT tools was established. A systematic risk assessment identified five key risk domains: materials, machinery, measurement, methods, and other factors. A Box–Behnken design of experiments (DoE) evaluated the impact of screw speed, temperature, and mixing sections on CC quality. Near-infrared (NIR) spectroscopy monitored CBZ-NIC co-crystal formation in real time during HME process. Results: DoE revealed temperature and number of mixing sections significantly influenced particle size (D₅₀: 2.0–4.0 μm), while screw speed affected efficiency. NIR spectroscopy detected a unique CC absorption peak at 5008.3 cm⁻¹, enabling real-time structural monitoring with high accuracy (R² = 0.9999). Risk assessment highlighted material attributes, process parameters, and equipment design as critical factors affecting CC formation. All experimental batches yielded ≥ 94% pure CCs with no residual starting materials, demonstrating process reproducibility and robustness. Conclusions: Overall, this work successfully established a continuous hot-melt extrusion (HME) process for manufacturing CBZ-NIC co-crystals, offering critical insights into material, equipment, and process parameters while implementing robust in-line NIR monitoring for real-time quality control. Additionally, this work provides interpretable insights and serves as a basis for future machine learning (ML)-driven studies. Full article