bluuebunny/crossref_metadata_2025_split huggingface dataset:
Size-Up, Size-Down: Accuracy of Component Sizing with Computerized Tomography and Robotic-Assisted Total Knee Arthroplasty
Authors: Henry James P. , Paradis Brienne , Qilleri Aleksandra , Baichoo Nadia , Reinhardt Keith R. , Slover James D. , Danoff Jonathan R. , Germano James A. 
Published: December 2024

AbstractTemplating prior to total knee arthroplasty (TKA) can help to improve surgical efficiency and potentially improve alignment and outcomes. The purpose of this article is to evaluate the ability of computed tomography (CT)-based preoperative templating to accurately predict implant sizes. A total of 724 Stryker MAKO robotic-assisted TKA cases were retrospectively evaluated from a prospectively collected database between January 2020 and October 2023. Cases were performed by one of three adult reconstruction fellowship-trained orthopaedic surgeons from a health system that includes an academic level one trauma center, an ambulatory surgery center, and a community hospital. Out of the 724 cases, 391 were preoperatively templated independently by the surgeon and the company representative (MAKO Product Specialist [MPS]). The remaining 333 cases were only templated prior to incision by the MPS. Final implant sizes of the tibial and femoral components were compared to preoperative templates. The MPS was able to preoperatively predict the final tibial and femoral implants within one size in 97.2 and 97.8% of cases, respectively. A surgeon and MPS combined preoperative templating increased accuracy to predict the final tibial and femoral implants within one size in 98.9 and 99.5% of cases, respectively. Height and weight were positively correlated with the final implant size (p < 0.001). Non-surgeons can reliably predict implanted components in CT-based preoperative templating in the majority of cases, which is further enhanced by surgeon review and adjustments. In no cases in our series were the final size components implanted greater than two sizes larger or smaller. Our findings suggest that there is opportunity to avoid waste by processing fewer trial implants and transporting fewer components. This would likely decrease overall case cost and improve efficiency in the operating room. Level of evidence: III (retrospective cohort).
Fast segment anything model applied in a low-cost camera-based system for oyster growth monitoring
Authors: Batista Vinícius , Biazi Vitorino , Rodrigues Andreia C. M. , Soares Amadeu M. V. M. , Marques Carlos 
Published: December 2024

The increase in the global population has been accompanied by an increase in demand for animal protein, consequently driving the growth of the aquaculture sector as a sustainable solution to satisfy this demand. Observing the growth of animals in farm environments is vital to assess the efficiency of feeding methods, identify health problems or environmental stressors, and help implement sustainable practices. The application of cameras enables remote monitoring of various environments and activities in real time, providing quick insights, improving security measures, and facilitating decision-making based on the collected data. This paper proposes a low-cost camera system and an image processing-based strategy to monitor and measure the size of Pacific oysters inside a tank. The fast segment anything model algorithm is implemented in the Python programming language to perform image segmentation and generate object masks for subsequent processing. The size of oysters is obtained by measuring the distance between the parallel lines of the bounding box that is generated surrounding them. The largest absolute errors and relative errors for the estimates were 3 mm and 5.13%, respectively, when compared to measurements made manually. Furthermore, the width and height have standard deviations of 0.6 and 0.3 mm, respectively, indicating low data dispersion and good repeatability of the algorithm. Since the accuracy of the human eye in estimating a dimension without the aid of a distance measuring instrument tends to be lower than that obtained by the proposed tool, it is believed that the approach can significantly contribute to the management of oyster farm production, providing strategic insights based on animal growth for producers.
Manipulating ambient pressure superconductivity in metal borocarbides through hole doping
Authors: Tian Qing , Zhang Wei , Xia Weiyi , Liu Hanyu , Wang Caizhuang 
Published: December 2024

Abstract
A Ca2B4C4 ternary compound obtained by using a machine learning (ML) guided structure search is found to be metastable with a formation energy of only 18 meV atom−1 above the convex hull but exhibits only marginal superconducting transition temperature (T
c). By replacing Ca with Na, the electronic density of states at the Fermi level is significantly enhanced, increasing the predicted T
c to 21.9 K. Extending this hole-doping strategy to other Ca–B–C compounds, we found that while Na4B2C22 remains superconducting with a T
c of 4.0 K, Na substitution in Ca2B4C8 transforms it from a semiconductor to a superconductor with a T
c of 28.9 K. The flat σ bands, particularly from the 2p
x
and 2p
y
orbitals of carbon, and a Van Hove singularity at the Fermi level, play crucial roles in enhancing the superconductivity. This work introduces a new class of B–C-based superconductors and broadens the compositional space for high-temperature superconducting materials.
SKS-Net: skeleton-strengthening network for surface defect segmentation
Authors: Zuo Haiqiang , Zheng Yubo , Huang Qizhou , Wang Rongdi 
Published: December 2024

Abstract
Surface defect detection is an important task in industrial production. Although significant progress has been made in deep learning-based defect segmentation methods, the low contrast between defect and background and the shape and scale diversity of defects limit the models’ detection accuracy and generalization ability. Therefore, realizing the full automation of surface defect detection still faces many challenges. To overcome these problems, this paper proposes a skeleton-strengthening network called SKS-Net, which provides stable and excellent surface defect detection performance even at low contrast and multi-scale. SKS-Net designs a skeleton-strengthening convolutional module to capture multi-scale features efficiently. The convolution kernel of this convolution module is closer to the shape of the segmentation target, which significantly reduces irrelevant regions and improves the feature extraction capability of the convolution kernel. This convolution module can be directly embedded into existing network structures without adding additional computational overhead. In addition, we design a new feature channel fusion module to extract key information from features at different levels. To improve the training effect, we introduce a multi-scale auxiliary supervision mechanism. The proposed model is evaluated on four different publicly available surface defect datasets and compared with other state-of-the-art models. Results show that SKS-Net performs exceptionally well in terms of accuracy, achieving 66.72% mIoU on the KolektorSDD dataset. The code is publicly available at https://github.com/Wanglaoban3/SKS-Net.git.
Flexible solid-state supercapacitors based on biowaste-derived activated carbon and nanomaterials for enhanced performance
Authors: Koroglu Dilara , Bingol Haluk , Uralcan Betul 
Published: December 2024

Abstract
Supercapacitors are energy storage devices with long cycle life that can harvest and deliver high power. This makes them attractive for a broad range of applications including flexible and lightweight wearable consumer electronics. In this work, we fabricate flexible solid-state supercapacitors with improved capacitance and cycle life. We synthesize activated carbon (AC) from cabbage leaves as a low cost, biowaste-derived active electrode material. To improve mechanical flexibility and conductivity, we incorporate reduced graphene oxide sheets (RGO) and carbon quantum dots (CQDs) into the electrodes. We show that at the optimum AC/RGO/CQD composition, the capacitance of the solid-state supercapacitor is maximized while its scan rate dependence and bending stability are simultaneously improved. We envision that this approach offers significant potential for delivering efficient energy storage devices for consumer electronics.
Experiments of acoustic resonance and vibration mode of kotsuzumi using paper pipe, V-shape pendulum, and carbon paper
Authors: Tabaru Marie , Funakura Tomohisa , Matsutani Akihiro 
Published: December 2024

Abstract
Kotsuzumi is a membranophone used in the traditional Japanese performing art of Noh. So far, there are few studies about kotsuzumi. In this study, to reveal the component of kotsuzumi’s sound, we experimentally verified the acoustic characteristics of kotsuzumi. Acoustic physics experiments were conducted using paper pipes, clay, pendulum, and carbon paper. Loudspeaker and microphone were used to produce and record sound, respectively. First, the characteristics of the acoustic resonance inside the difference cavity shapes of the bodies were measured. Second, vibration modes of the membrane and the body were measured. Finally, a professional performer played kotsuzumi to verify the measured acoustic characteristics. We found that a sound around 200 Hz was due to (0,1) mode of the front membrane of kotsuzumi, a sound around 800 Hz was due to the characteristics of the body as a resonator, and a sound around 1580 Hz was due to the vibration of the kotsuzumi’s body itself.
Electromagnetic design and analysis of a bell-shaped superconducting magnet for head fMRI using a novel hybrid optimization method
Authors: Mei Yunhao , Shang Wangnan , Mo Lei , Qiu Bensheng , Zhang Qing 
Published: December 2024

Abstract
In task-state functional magnetic resonance imaging scans, it is often necessary for subjects to perform certain task actions, which is difficult to achieve in traditional whole-body MRI systems. In this study, a bell-shaped head magnetic resonance superconducting magnet was designed using a novel hybrid optimization method that combines linear programming, genetic algorithm, and nonlinear programming. This magnet design offers the following advantages: (1) the small and compact volume of the magnet structure allows for vertical placement of the system, enabling subjects to be in a sitting position during the scan. (2) The off-center diameter of spherical volume region expands the subject’s field of view. (3) Only the subject’s head is positioned inside the scanning system, freeing the subject’s torso to cooperate in performing various tasks. This paper provides a detailed description of the entire design process, conducts a comprehensive analysis of the electromagnetic performance and material mechanical properties of the designed magnet, and designs a passive quench protection system for the subsequent manufacturing of the magnet, with simulations and discussions on the magnet’s quenching process under the protection of this system.
Study on the mechanism and characteristics of pulsed corona discharge in water
Authors: Kang Zhongjian , Wang Chunbin , Shen Yichao , Yu Yingbo , Gao Chong 
Published: December 2024

Abstract
Plasma pulse is a safe and environmentally friendly unconventional technology for enhancing oil and gas production. However, during the pulsed discharge process, an abnormal discharge phenomenon often occurs, which severely affects the performance of the equipment and is referred to as corona discharge. This paper combines laboratory discharge test data to theoretically analyze and validate through COMSOL simulation the mechanism of corona discharge generation under the same electrode gap distance. A comparative analysis with arc discharge is also conducted to further examine its characteristics, and nonlinear regression analysis is used to study the impedance variation of the plasma channel during corona discharge. Results indicate that the decrease in switch breakdown voltage in the later stages of continuous discharge leads to the generation of corona discharge. The impedance of the plasma channel can be characterized by a time-varying resistance model based on a double exponential function. Finally, validation through theoretical calculations and Simulink simulations achieves a fitting rate of over 99%. The research provides significant guidance for further analysis of corona discharge and the development of suppression methods in plasma pulse technology, contributing to the optimization of the equipment and enhancing its operational performance.
The Halbach and alternating polarity linear magnets array are designed for microparticle separation, based on magnetophoresis
Authors: baghdadi shahriar , malekshahi mazdak rad 
Published: December 2024

Abstract
Particle separation using magnetophoresis requires an inhomogeneous magnetic field which affects the microparticle. The more changes in the magnetic field, the greater the magnetic force. The changes could exist in various ways. In this article, the change to produce a more intensive inhomogeneous magnetic field is generated by rotating the magnets to a specific degree. The analyses are done for three-state magnet arrays. For this purpose, a device is fabricated and the mentioned arrays are embedded in it. First, the results are simulated. The mathematical piecewise algorithm is used to validate the simulation. By examining the states in the fabricated device the experimental results are extracted. Overall, the results demonstrated that the Halbach and alternating polarity linear arrays (both 90, and 180 degrees) have better efficiency and throughput than the regular design array (without magnet rotating). The Halbach designs are illustrated with approximately the same results. However, in alternating polarity linear array, 180 degrees the particle capturing rate stood in a better place than the other design. &#xD;
Negative cumulative residual extropy under concomitants of generalized order statistics based on Cambanis bivariate family with applications
Authors: Husseiny I. A. , Ramadan Magdy Nagy Ahmed Nagy , Mansi Ahmed Hamdi 
Published: December 2024

Abstract
Thermodynamics, statistical mechanics, and quantum mechanics are the branches of physics that use entropy and extropy measures. It also seems to quantify the degree of signal uncertainty in information theory. Recently, extropy has been widely used in the literature as a measure of dispersion or uncertainty. Within generalized Farlie-Gumbel-Morgenstern families, the Cambanis bivariate family holds a prominent position as an efficient and adaptable extended family. Motivated by this, our work gives numerous characterizations of Cambanis copula based on order statistics (OSs). Also, we study the negative cumulative residual extropy and various measures of information associated with it for concomitants generalized OSs.&#xD;Moreover, we study OSs and sequential OSs for each information measure in a computational study. This study offers a few beautiful symmetrical and asymmetric relationships that these information measures satisfy. Additionally, the empirical technique is applied to the Cambanis family to estimate every measure of negative extropy discussed in this work. As a final step, we have examined a bivariate real-world data set for illustrative purposes.
Optical analog to adiabatic passages in two coupled nonlinear waveguides by resonance-locked inverse engineering
Authors: Naim Nida , Deng Li , Niu Yueping , Gong Shangqing 
Published: December 2024

Abstract
We discuss in this paper the optical analog to rapid adiabatic passage (RAP) and two-state stimulated Raman adiabatic passage (STIRAP) in coupled waveguides with nonlinearity. With the increase of the power of light propagating in waveguides, the nonlinearity can significantly prevent the two adiabatic passages from being successfully simulated. Nevertheless, we find that the negative role of the nonlinearity can be avoided under the assistance of the method of resonance-locked inverse engineering. The method implies redesigning the longitudinally varying detuning of the propagation constants. We further point out that the variation of the detuning does not have to be exactly the same as what the method suggests. As long as the detuning has similar trend to what the method shows, the RAP and two-state STIRAP can still be realized in nonlinear waveguides. This makes the waveguide structure more simpler. Based on our study, the applications of two-state adiabatic passages in designing directional couplers and beam splitters are extended from linear to nonlinear waveguides.
Information transmission by random flow of gamma-photons: optimization of data-acquisition time
Authors: Zinnatullin A , Shakhmuratov R , Vagizov F 
Published: December 2024

Abstract
We transmit classical information in the form of Windows-1253 codes for text by modulating the intensity of a random flow of Mössbauer gamma-photons. It is shown that the time required for reliable decoding of information can be significantly reduced by averaging the number of gamma-photons detected in certain time windows. We find the optimal duration of this time experimentally. The proposed protocol allows reliable extraction of information from a very noisy signal.
Task-relevant stimulus design improves P300-based brain–computer interfaces
Authors: Kim Jongsu , Cho Yang Seok , Kim Sung-Phil 
Published: December 2024

Abstract

Objective. In the pursuit of refining P300-based brain–computer interfaces (BCIs), our research aims to propose a novel stimulus design focused on selective attention and task relevance to address the challenges of P300-based BCIs, including the necessity of repetitive stimulus presentations, accuracy improvement, user variability, and calibration demands. Approach. In the oddball task for P300-based BCIs, we develop a stimulus design involving task-relevant dynamic stimuli implemented as finger-tapping to enhance the elicitation and consistency of event-related potentials (ERPs). We further improve the performance of P300-based BCIs by optimizing ERP feature extraction and classification in offline analyses. Main results. With the proposed stimulus design, online P300-based BCIs in 37 healthy participants achieve an accuracy of 91.2% and an information transfer rate (ITR) of 28.37 bits/min with two stimulus repetitions. With optimized computational modeling in BCIs, our offline analyses reveal the possibility of single-trial execution, showcasing an accuracy of 91.7% and an ITR of 59.92 bits/min. Furthermore, our exploration into the feasibility of across-subject zero-calibration BCIs through offline analyses, where a BCI built on a dataset of 36 participants is directly applied to a left-out participant with no calibration, yields an accuracy of 94.23% and the ITR of 31.56 bits/min with two stimulus repetitions and the accuracy of 87.75% and the ITR of 52.61 bits/min with single-trial execution. When using the finger-tapping stimulus, the variability in performance among participants is the lowest, and a greater increase in performance is observed especially for those showing lower performance using the conventional color-changing stimulus. Significance. Using a novel task-relevant dynamic stimulus design, this study achieves one of the highest levels of P300-based BCI performance to date. This underscores the importance of coupling stimulus paradigms with computational methods for improving P300-based BCIs.
A method for dynamically adjusting the difficulty of rehabilitation training tasks driven by attention level
Authors: Chen Raojing , Lv Jian , Qiang Ligang , Liu Xiang 
Published: December 2024

Abstract

Objective. Enhancements in the rehabilitation of motor and cognitive functions are significantly attainable through proactive patient engagement. The difficulty of rehabilitation tasks and the environment in which they are conducted directly impact patient motivation. Consequently, this study introduces a dynamic difficulty adjustment method for rehabilitation training tasks based on attention levels, designed to adjust task difficulty in real-time and augment the focus of participants on their training tasks. Approach. Electroencephalography (EEG) signals from participants were harnessed to train an attention classification model, enabling the acquisition of real-time attention level signals. Task difficulty levels were adjusted based on the fluctuating attention levels. A cohort of 30 participants was engaged to evaluate: (1) the impact on engagement when attention levels are utilized as dynamic difficulty triggers; (2) the influence of various task environments on concentration. The experiment was assessed through EEG signals and questionnaire data, with frequency domain analysis conducted on EEG signals to calculate concentration values and statistical analysis performed on additional data. Main results. The findings reveal that within an identical virtual reality (VR) environment, leveraging attention levels as triggers for difficulty adjustment markedly improves participants’ task concentration. Compared to 2D environments, VR environments substantially enhance participants’ sense of immersion, interest, and flow state, albeit with increased physical exertion during training. The integration of VR and attention level feedback is deemed the most effective strategy. Significance. These exploratory insights indicate that the proposed method paves a novel path for boosting patient engagement in rehabilitation. Immersive rehabilitation training, driven by attention levels, promises a more effective and captivating patient experience. This study advances the field by offering data-driven, personalized rehabilitation approaches, potentially culminating in superior patient outcomes and enhanced quality of life.
Simultaneous encoding of speed, distance, and direction in discrete reaching: an EEG study
Authors: Srisrisawang Nitikorn , Müller-Putz Gernot R 
Published: December 2024

Abstract

Objective. The complicated processes of carrying out a hand reach are still far from fully understood. In order to further the understanding of the kinematics of hand movement, the simultaneous representation of speed, distance, and direction in the brain is explored. Approach. We utilized electroencephalography (EEG) signals and hand position recorded during a four-direction center-out reaching task with either quick or slow speed, near and far distance. Linear models were employed in two modes: decoding and encoding. First, to test the discriminability of speed, distance, and direction. Second, to find the contribution of the cortical sources via the source localization. Additionally, we compared the decoding accuracy when using features obtained from EEG signals and source-localized EEG signals based on the results from the encoding model. Main results. Speed, distance, and direction can be classified better than chance. The accuracy of the speed was also higher than the distance, indicating a stronger representation of the speed than the distance. The speed and distance showed similar significant sources in the central regions related to the movement initiation, while the direction indicated significant sources in the parieto-occipital regions related to the movement preparation. The combination of the features from EEG and source localized signals improved the classification. Significance. Directional and non-directional information are represented in two separate networks. The quick movement resulted in improvement in the direction classification. Our results enhance our understanding of hand movement in the brain and help us make informed decisions when designing an improved paradigm in the future.