Low Error Research Articles

Assessment of cutting force coefficient identification methods and force models for variable pitch and helix bull-nose tools

The mechanistic approach is commonly implemented to predict and optimise the cutting forces in milling processes to prevent tool breakages, reduce tool wear, reduce form error, and improve surface quality. To implement this method, the cutting force coefficients (CFCs), that characterise the mechanics of the process, must be calculated. This study compares the accuracy of the predicted cutting forces for variable pitch and helix bull-nose milling tools using a rapid testing (RT) optimisation-based mechanistic CFC identification method that only requires a single angular cut with increasing radial engagement to the traditional mechanistic approach that requires several straight cuts. Along with developing a hybrid technique that combines variation in feed rate and radial engagement. The traditional radial, tangential, and axial (RTA) force model is also compared with the frictional and normal rake face (UV) force model that is independent of the local tool rake and inclination angles which is a necessary for bull nose tools. The RT and the developed hybrid CFC identification method with the UV force model predicted the average Fx, Fy and Fz cutting forces to within 7.1 %, 4.3 %, and 3.8 % error, respectively. These methods were slightly less accurate than the traditional method, however they have significant industrial benefits because they have can be used to identify CFCs with either a single cut, or from any tool-path with chip-load variation, respectively. The RTA force model predicted the average cutting forces similarly to the UV force model, however, the UV force model had lower errors using the rapid RT testing method at the extreme corners of the experimental design space.

Deep feature synthesis approach using selective graph attention for replay attack voice spoofing detection

<span lang="EN-US">As voice-based authentication becomes increasingly integrated into security frameworks, establishing effective defenses against voice spoofing, particularly replay attacks, is more crucial than ever. This paper presents a novel comprehensive framework for replay attack detection that leverages the integration of advanced spectral-temporal feature extraction and graph-based feature processing mechanisms. The proposed system presents the design of a waveform encoder and a novel temporal residual unit for spectral and temporal feature extraction in synchronous. Further, an approach of selective attention graph followed by multi-scale feature synthesis is employed to retain precise and spoof indicative feature vectors at the classification layer. The proposed method addresses the significant challenge of distinguishing genuine speech from replayed recordings. The validation of the proposed model is done on the ASVSpoof2019 dataset to demonstrate the efficacy of the proposed approach. The proposed system outperforms existing methods, achieving a lower equal error rate (EER) of 0.015 and a reduced tandem detection cost function (t-DCF) of 0.503. The comparative outcome exhibits the robustness of the method in identifying replay attacks.</span>

Structured orthogonal random features based on DCT for kernel approximation

Random Fourier features are a popular technique used to improve nonlinear kernel methods in large-scale problems. Recent studies have shown that replacing random Gaussian matrices of random feature maps with appropriately scaled random orthogonal matrices, such as SORF, can significantly improve kernel approximation performance. However, the SORF method theoretically requires zero-padding for datasets whose input feature dimension does not meet d=2p for some p∈N+, resulting in increased memory and computation time. To address this limitation, we propose a new structured orthogonal random features method based on discrete cosine transform (SORF-DCT) to approximate Gaussian kernel functions. The SORF-DCT method does not require the input feature dimension to be d=2p. The key to SORF-DCT is that we combine the DCT matrix with diagonal ”sign-flipping” matrices and scale them appropriately using a diagonal matrix whose diagonal elements follow the chi distribution, resulting in a structured orthogonal matrix whose elements follow the standard Gaussian distribution instead of a random Gaussian matrix. SORF-DCT generates D-dimensional structured orthogonal random features in O(Dlogd) time and O(D) memory by employing fast discrete cosine transform, where d and D represent the input feature dimension and expansion dimension, respectively. We prove that SORF-DCT is an unbiased estimator of the Gaussian kernel function and analyze the concentration error bounds. Experimental results on eleven benchmark datasets show that SORF-DCT achieves lower kernel approximation errors, requires less time, and has comparable nonlinear learning capabilities.

Advancing Mix Design Prediction in 3D Printed Concrete: Predicting Anisotropic Compressive Strength and Slump Flow

Introducing 3D-concrete printing has started a revolution in the construction industry, presenting unique opportunities alongside undeniable challenges. Among these, the major challenge is the iterative process associated with mix design formulation, which results in significant material and time consumption. This research uses machine learning (ML) techniques such as Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), Decision Tree Regression (DTR), Gaussian Process Regression (GPR), and Artificial Neural Network (ANN) to overcome these challenges. A dataset containing 21 mix constituent features and 4 output properties (cast and printed compressive strength, and slump flow) was extracted from the literature to investigate the relationship between mix design and performance. The models were assessed using a range of evaluation metrics, including Mean Absolute Error (MAE), Root Mean Squared Error (RMSE), Mean Squared Error (MSE), and R-squared value. Gaussian Process Regression (GPR) yielded more favorable results. In the case of cast strength, GPR achieved an R2 value of 0.9069, along with RMSE, MSE, and MAE values of 13.04, 170.12, and 9.40, respectively. A similar trend was observed for printed strengths in directions 1, 2, and 3. GPR achieved R-squared values exceeding 0.91 for all directions, accompanied by significantly lower RMSE values (below 4.1). The machine learning models were also validated using four unique mix designs. These mixes were 3D printed and tested for compressive strength and slump flow. GPR's average error was 10.55%, while SVM achieved a slightly lower average error of 9.38%. Overall, this work presents a novel approach for optimizing 3D-printed concrete by enabling the prediction of slump flow and compressive strength directly from the mix design. This approach can facilitate the design and fabrication of 3D-printed concrete structures that fulfill the necessary strength and printability requirements.

Leveraging GIS-based AHP, remote sensing, and machine learning for susceptibility assessment of different flood types in peshawar, Pakistan.

Due to its diverse topography, Pakistan faces different types of floods each year, which cause substantial physical, environmental, and socioeconomic damage. However, the susceptibility of specific regions to different flood types remains unexplored. To the best of our knowledge for the first time, this study employed an integrated approach by leveraging a GIS-based Analytical Hierarchy Process (AHP), remote sensing, and machine learning (ML) algorithms, to assess susceptibility to three different types of flooding in Peshawar, Pakistan. The study first evaluated the degree of susceptibility to riverine, urban, and flash floods using the GIS-based AHP technique, and then employed ML models, (i.e., specifically Random Forest [RF] and Extreme Gradient Boosting [XG-Boost] to analyze multi-type flood susceptibility in the study region. The performance of the ML models was also evaluated, and the XG-Boost model outperforms RF, demonstrating a higher correlation coefficient (R2=0.561-0.922) and lower mean absolute error (MAE=0.042-0.354), and root-mean-square error (RMSE=0.119-0.415) for both training and testing datasets. The superior performance of the XG-Boost was further confirmed by the higher value of the area under the curve (AUC) values, which is relatively higher (0.87) than that of the AHP (0.70) and RF (0.86) models. Based on the relative best performance, the XG-Boost model was chosen for further susceptibility assessment of different types of floods, and the generated flood susceptibility maps revealed that 20.9% of the total area is susceptible to riverine flooding, while 30.27% and 48.68% of the total area is susceptible to urban and flash flooding, respectively. The study's findings are significant, offering valuable insights for relevant stakeholders in guiding future flood risk management and sustainable land use plans in the study area.

Sensitivity analysis of carbon fiber reinforced asphalt pavements: Experimental study and data driven predictive model using machine learning

It is well known that the impact of fibers on reinforcing asphalt pavement (AP) has been extensively investigated. However, the optimal fiber length and content considering various temperatures is still an unresolved issue in this field. To reach this goal, this research aims to study the impact of carbon fiber (CF) reinforcement on the fracture energy and toughness of AP under varying environmental conditions. Several mix designs considering different CF length (10–30 mm) and content (1–3.5 %) at 5 °C and ambient temperatures were evaluated to determine the optimal values. The results demonstrated that while increasing CF length and content generally improves fracture energy and toughness, there is an optimal threshold beyond which additional CF content yields diminishing returns. Notably, the sample with a 1.5 % replacement ratio and 15 mm CF length exhibited superior performance, significantly enhancing the AP’s resistance to fracture due to effective stress distribution and crack bridging. Conversely, samples with the higher fiber content and length showed decreased fracture toughness, due to fiber clustering and reduced workability. Additionally, experimental results indicated higher values at 5 °C for fracture energy and toughness. Advanced machine learning techniques were also utilized to develop predictive models to accurately simulate the fracture behavior of CF-reinforced AP mixtures. Among all models, Gaussian process regression demonstrated superior performance and exhibited lower error in predicting experimental data. Moreover, a sensitivity analysis of this model was conducted to determine how the length and content of CF, as well as temperature, influence fracture energy and toughness, guiding the optimization of CF integration in AP design. These findings provide valuable insights for engineering durable and resilient asphalt pavements capable of withstanding diverse climatic stresses, ultimately contributing to more sustainable and cost-effective infrastructuresolutions.

Explainable Pre-Trained Language Models for Sentiment Analysis in Low-Resourced Languages

Sentiment analysis is a crucial tool for measuring public opinion and understanding human communication across digital social media platforms. However, due to linguistic complexities and limited data or computational resources, it is under-represented in many African languages. While state-of-the-art Afrocentric pre-trained language models (PLMs) have been developed for various natural language processing (NLP) tasks, their applications in eXplainable Artificial Intelligence (XAI) remain largely unexplored. In this study, we propose a novel approach that combines Afrocentric PLMs with XAI techniques for sentiment analysis. We demonstrate the effectiveness of incorporating attention mechanisms and visualization techniques in improving the transparency, trustworthiness, and decision-making capabilities of transformer-based models when making sentiment predictions. To validate our approach, we employ the SAfriSenti corpus, a multilingual sentiment dataset for South African under-resourced languages, and perform a series of sentiment analysis experiments. These experiments enable comprehensive evaluations, comparing the performance of Afrocentric models against mainstream PLMs. Our results show that the Afro-XLMR model outperforms all other models, achieving an average F1-score of 71.04% across five tested languages, and the lowest error rate among the evaluated models. Additionally, we enhance the interpretability and explainability of the Afro-XLMR model using Local Interpretable Model-Agnostic Explanations (LIME) and Shapley Additive Explanations (SHAP). These XAI techniques ensure that sentiment predictions are not only accurate and interpretable but also understandable, fostering trust and reliability in AI-driven NLP technologies, particularly in the context of African languages.

Evaluate Multi-Objective Optimization Model for Product Supply Chain Inventory Control Based on Grey Wolf Algorithm

Enterprise supply chain inventory control plays a critical role in modern enterprise management. Yet achieving multi-objective optimization remains challenging. This paper proposes a deep learning integrated model based on grey wolf optimization (GWO-DLIM) aimed at optimizing inventory holding costs, stockout costs, and order costs. The model integrates multi-layer perceptron (MLP), DeBERTa, and GWO, leveraging the powerful predictive and feature extraction capabilities of deep learning models, combined with the global optimization ability of the grey wolf algorithm, to achieve multi-objective optimization. These results demonstrate that the GWO-DLIM model significantly outperforms other comparative models across all metrics, exhibiting lower prediction errors and root mean square errors, as well as higher service levels. This study provides an effective multi-objective optimization solution for supply chain inventory management, holding significant theoretical and practical implications.

Enhancing cognitive abilities in young adults with ADHD through instrumental music training: a comparative analysis of musicians and non-musicians.

Extensive research highlights the multifaceted benefits of active musical engagement across all ages, from childhood to the elderly. The practice of a musical instrument activates numerous brain regions, enhancing a range of neurocognitive skills. Despite accumulating evidence from various clinical populations, research on the effects of musical training in individuals with ADHD is scarce, with virtually no studies focusing on adults. This study aims to fill the gap by evaluating the impact of long-term instrumental music training on cognitive abilities in young adults (18-35 years) diagnosed with ADHD. Cognitive abilities were compared across groups of 48 musicians (experienced guitar or piano players) and 46 matched non-musicians, all confirmed to have ADHD. The assessments covered cognitive domains such as sustained attention, visuospatial processing, processing speed, graphomotor speed, working memory, auditory recall, response inhibition, and executive function. Evaluation tools included the Digit-Symbol Coding Test, Digit Span Test, Symbol Search Test, Switching Task, and Continuous Performance Test (CPT). Collectively, the results indicated a notable enhancement in cognitive performance in the musician group compared to the non-musician group, including in functions central to the disorder, such as sustained attention and impulse control. Musicians scored higher on the Digit-Symbol Coding, Digit Span, and Symbol Search tests, showed lower error rates and greater consistency in reaction times in the Switching Task, and had fewer commission errors in the CPT. The findings support the integration of specialized musical training in therapeutic and support programs for ADHD, suggesting benefits that may extend into adulthood.

NitroNet – a machine learning model for the prediction of tropospheric NO2 profiles from TROPOMI observations

Abstract. We introduce NitroNet, a deep learning model for the prediction of tropospheric NO2 profiles from satellite column measurements. NitroNet is a neural network trained on synthetic NO2 profiles from the regional chemistry and transport model WRF-Chem, which was operated on a European domain for the month of May 2019. This WRF-Chem simulation was constrained by in situ and satellite measurements, which were used to optimize important simulation parameters (e.g. the boundary layer scheme). The NitroNet model receives NO2 vertical column densities (VCDs) from the TROPOspheric Monitoring Instrument (TROPOMI) and ancillary variables (meteorology, emissions, etc.) as input, from which it reproduces NO2 concentration profiles. Training of the neural network is conducted on a filtered dataset, meaning that NO2 profiles showing strong disagreement (>20 %) with colocated TROPOMI column measurements are discarded. We present a first evaluation of NitroNet over a variety of geographical and temporal domains (Europe, the US West Coast, India, and China) and different seasons. For this purpose, we validate the NO2 profiles predicted by NitroNet against satellite, in situ, and MAX-DOAS (Multi-Axis Differential Optical Absorption Spectroscopy) measurements. The training data were previously validated against the same datasets. During summertime, NitroNet shows small biases and strong correlations with all three datasets: a bias of +6.7 % and R=0.95 for TROPOMI NO2 VCDs, a bias of −10.5 % and R=0.75 for AirBase surface concentrations, and a bias of −34.3 % to +99.6 % with R=0.83–0.99 for MAX-DOAS measurements. In comparison to TROPOMI satellite data, NitroNet even shows significantly lower errors and stronger correlation than a direct comparison with WRF-Chem numerical results. During wintertime considerable low biases arise because the summertime/late-spring training data are not fully representative of all atmospheric wintertime characteristics (e.g. longer NO2 lifetimes). Nonetheless, the wintertime performance of NitroNet is surprisingly good and comparable to that of classic regional chemistry and transport models. NitroNet can demonstrably be used outside the geographic and temporal domain of the training data with only slight performance reductions. What makes NitroNet unique when compared to similar existing deep learning models is the inclusion of synthetic model data, which offers important benefits: due to the lack of NO2 profile measurements, models trained on empirical datasets are limited to the prediction of surface concentrations learned from in situ measurements. NitroNet, however, can predict full tropospheric NO2 profiles. Furthermore, in situ measurements of NO2 are known to suffer from biases, often larger than +20 %, due to cross-sensitivities to photooxidants, which other models trained on empirical data inevitably reproduce.

Navigating space: how fine and gross motor expertise influence spatial abilities at different scales.

Spatial ability, essential for navigating and interacting with the environment, comprises small-scale (e.g., mental rotation) and large-scale (e.g., spatial navigation) skills. Previous research underscores the influence of motor expertise on these abilities, yet comparative studies among different types of movement experts are limited, especially regarding the impact of gross motor skills on large-scale spatial abilities. This case-control study compared small-scale and large-scale spatial abilities among fine movement experts, gross movement experts, and non-movement experts. Ninety participants (30 per group) were assessed through computer-based spatial ability tests, including the Revised Purdue Spatial Visualization Test (PSVT: R), Mental Rotation Test, a navigation task developed in Unity 3D, and Triangle Completion Test (TCT). Fine movement experts excelled in small-scale spatial tasks compared to non-movement experts. Gross movement experts demonstrated superior large-scale spatial abilities, evidenced by lower errors in TCT and higher navigation scores, distinguishing their performance in spatial navigation and orientation from both fine movement experts and non-movement experts. The study highlights the distinct impacts of fine and gross motor expertise on spatial abilities, with gross motor skills particularly benefiting large-scale spatial navigation. These findings suggest potential clinical applications of gross motor training for improving spatial abilities in neurological populations, advocating for further research in immersive virtual environments and exploring lateral dominance effects on spatial performance.

Hybrid Machine Learning Approaches for 5G Traffic Prediction

ABSTRACT Accurate traffic prediction poses great difficulties because of the continuously increasing scale and diversity of 5G network traffic, which is driven by user demands. Moreover, certain characteristics of 5G traffic are constantly changing; thus, simulations using traditional models often lead to incorrect estimations or inefficient utilization of available resources. Consequently, we propose a hybrid machine learning model that integrates support vector machine (SVM) and decision tree algorithms to enhance efficiency of 5G traffic prediction. The structure of the hybrid model dynamically adjusts by adding or removing hidden layers and units within the network to improve prediction performance. The efficacy of the proposed model is evaluated using metrics like mean squared error, mean absolute error, and root mean squared error (RMSE). Findings show that the hybrid model consistently achieves lower error rates than SVM alone. Further performance enhancement of the hybrid model in predicting 5G traffic is also supported by comparisons of R-squared values against signal-to-noise ratios. These outcomes show the potential of the proposed method to improve traffic prediction accuracy in 5G networks, serving as a powerful tool for network control.

Low-overhead quantum computing with the color code

Fault-tolerant quantum computation demands significant resources: large numbers of physical qubits must be checked for errors repeatedly to protect quantum data as logic gates are implemented in the presence of noise. We demonstrate that an approach based on the color code can lead to considerable reductions in the resource overheads compared with conventional methods, while remaining compatible with a two-dimensional layout. We propose a lattice surgery scheme that exploits the rich structure of the color-code phase to perform arbitrary pairs of commuting logical Pauli measurements in parallel while keeping the space cost low. Compared to lattice surgery schemes based on the surface code with the same code distance, and assuming the same amount of time is needed to complete a round of syndrome measurements, our approach yields about a 3× improvement in the space-time overhead, obtained from a combination of a 1.5× improvement in spatial overhead together with a 2× speedup due to the parallelization of commuting logical measurements. Even when taking into account the color code's lower error threshold using current decoders, the overhead is reduced by 10% at a physical error rate of 10−3 and by 50% at 10−4. Published by the American Physical Society 2024

Enhancing positioning accuracy in adjuvant radiotherapy for left breast cancer using cervical-thoracic integrated bracket combined with deep inspiration breath holding.

This study aimed to investigate the accuracy of three fixation methods in patients with left breast cancer receiving whole breast radiotherapy: conventional breast bracket (BB), breast bracket combined with deep inspiration breath holding (DIBH), and cervical-thoracic integrated bracket (CTIB) combined with DIBH. From January 2023 to September 2023, 84 patients who underwent left breast cancer radiotherapy with supraclavicular radiation after conservative surgery were included in this study, of which 25 patients were fixed by conventional BB, 34 patients by BB & DIBH, and 25 patients by CTIB & DIBH. Image registration was conducted around the treatment area, using the sternoclavicular joint and acromioclavicular joint as landmarks. Systematic and random errors were calculated to assess the accuracy of these fixation methods. Compared to the conventional BB group, the CTIB & DIBH group demonstrated significant improvements in accuracy across multiple dimensions, including left-right, superior-posterior, and anterior-posterior directions, as well as rotational errors in the sagittal and coronal planes. The CTIB & DIBH group showed a significant reduction of setup error in the anterior-posterior direction compared to the BB & DIBH group. The displacement of the acromioclavicular joint varied, with the CTIB & DIBH method showing more favorable outcomes. DIBH method exhibited lower setup errors and more effective fixation of the acromioclavicular joint, especially when combined with CTIB, making it a recommended fixation method in adjuvant radiotherapy following breast-conserving surgery.

Predicting COD and TN in A2O+AO Process Considering Influent and Reactor Variability: A Dynamic Ensemble Model Approach

The prediction of the chemical oxygen demand (COD) and total nitrogen (TN) in integrated anaerobic–anoxic–oxic (A2O) and anoxic–oxic (AO) processes (i.e., A2O+AO process) was achieved using a dynamic ensemble model that reflects the dynamics of wastewater treatment plants (WWTPs). This model effectively captures the variability in the influent characteristics and fluctuations within each reactor of the A2O+AO process. By employing a time-lag approach based on the hydraulic retention time (HRT), artificial intelligence (AI) selects suitable input (i.e., pH, temperature, total dissolved solid (TDS), NH3-N, and NO3-N) and output (COD and TN) data pairs for training, minimizing the error between predicted and observed values. Data collected over two years from the actual A2O+AO process were utilized. The ensemble model adopted machine learning-based XGBoost for COD and TN predictions. The dynamic ensemble model outperformed the static ensemble model, with the mean absolute percentage error (MAPE) for the COD ranging from 9.5% to 15.2%, compared to the static ensemble model’s range of 11.4% to 16.9%. For the TN, the dynamic model’s errors ranged from 9.4% to 15.5%, while the static model showed lower errors in specific reactors, particularly in the anoxic and oxic stages due to their stable characteristics. These results indicate that the dynamic ensemble model is suitable for predicting water quality in WWTPs, especially as variability may increase due to external environmental factors in the future.

Lifetime mapping using femtosecond time-resolved photoemission electron microscopy.

Time-resolved photoemission electron microscopy (PEEM) has established itself as a versatile experimental technique to unravel the ultrafast electron dynamics of materials with nanometer-scale resolution. However, the approach of performing PEEM-based, pixel-by-pixel lifetime mapping has not been reported thus far. Herein, we describe in detail the data pre-processing procedure and an algorithm to perform time-trace fittings of each pixel. We impose an energy cutoff for each pixel prior to spectral integration to enhance the robustness of our approach. With the energy cutoff, the energy-integrated time traces show improved statistics and lower fitting errors, thus resulting in a more accurate determination of the fit parameters, e.g., decay time constants. Our work allows us to reliably construct PEEM-based lifetime maps, which potentially shed light on the effects of local microenvironment on the ultrafast processes of the material and allow spatial distributions of lifetimes to be correlated with observables obtained from complementary microscopic techniques, hence enabling a more comprehensive characterization of the material.

A Picture is Worth a Thousand Timesteps: Excess Entropy Scaling for Rapid Estimation of Diffusion Coefficients in Molecular-Dynamics Simulations of Fluids.

In molecular-dynamics simulations of fluids, the Einstein-Helfand (EH) and Green-Kubo (GK) relationships are frequently used to compute a variety of transport coefficients, including diffusion coefficients. These relationships are formally valid in the limit of infinite sampling time: The error in the estimate of a transport coefficient (relative to an infinitely long simulation) asymptotically approaches zero as more dynamics are simulated and recorded. In practice, of course, one can only simulate a finite number of particles for a finite amount of time. In this work, we show that in this pre-asymptotic regime, an approach for estimating diffusion coefficients based upon excess entropy scaling (EES) achieves a significantly lower error than either EH or GK relationships at fixed online sampling time. This approach requires access only to structural information at the level of the radial distribution function (RDF). We further demonstrate that the use of a recently developed RDF mollification scheme significantly reduces the amount of sampling time needed to converge to the long-time value of the diffusion coefficient. We also demonstrate favorable sample-to-sample variances in the diffusion coefficient estimate obtained using EES as compared to those obtained using EH and GK.

Chitosan impregnated sugarcane bagasse biochar for removal of anionic dyes from wastewater

Wastewater treatment is of utmost importance in providing all equitable and safe drinking water. In the present study, a chitosan impregnated sugarcane bagasse biochar SCNC biocomposite has been synthesized for the removal of Congo red (CR) dye from an aqueous solution. The SCNC biocomposite was thoroughly characterized through Brunauer, Emmett and Teller (BET) and N2 adsorption isotherm, point of zero charge (pHPZC), elemental analysis, Scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and Thermogravimetric analysis (TGA) analysis. Moreover, SCNC biocomposite was further employed to remove CR dye from the aqueous solution in batch mode. The SCNC biocomposite could remove more than 95.0% of CR at an initial concentration of (100mgL− 1), adsorbent dosage (0.05 g), time (200 min), pH ~ 3. The SCNC biocomposite achieved maximum adsorption capacity of 170mgg− 1. The equilibrium adsorption data for CR dye were best fitted to the Langmuir isotherm model with R2, 0.999. The kinetic and isotherm were statistically investigated using the chi-square statistic (χ2 ), mean square error (MSE), and the sum of squares error (SSE) Because of the higher correlation coefficient (R2 ≥ 0.999) and lower error functions, the equilibrium CR adsorption isotherms for a single-dye system fit Langmuir and the PSO kinetic model. The thermodynamic studies revealed the spontaneous and endothermic nature of adsorption of CR dye onto SCNC biocomposite. The SCNC biocomposite can be regenerated up to the 5th cycle successfully. The mechanism of CR adsorption onto SCNC was elucidated.

Accurate Acupoint Localization in 2D Hand Images: Evaluating HRNet and ResNet Architectures for Enhanced Detection Performance

Introduction: This research assesses HRNet and ResNet architectures for their precision in localizing hand acupoints on 2D images, which is integral to automated acupuncture therapy. Objectives: The primary objective was to advance the accuracy of acupoint detection in traditional Korean medicine through the application of these advanced deep-learning models, aiming to improve treatment efficacy. Background: Acupoint localization in traditional Korean medicine is crucial for effective treatment, and the study aims to enhance this process using advanced deep-learning models. Methods: The study employs YOLOv3, YOLOF, and YOLOX-s for object detection within a top-down framework, comparing HRNet and ResNet architectures. These models were trained and tested using datasets annotated by technicians and their mean values, with performance evaluated based on Average Precision at two IoU thresholds. Results: HRNet consistently demonstrated lower mean distance errors across various acupoints compared to ResNet, particularly at a 256x256 pixel resolution. Notably, the HRNet-w48 model surpassed human annotators, including medical experts, in localization accuracy. Conclusion: HRNet's superior performance in acupoint localization suggests its potential to improve the precision and efficacy of acupuncture treatments. The study highlights the promising role of machine learning in enhancing traditional medical practices and underscores the importance of accurate acupoint localization in clinical acupuncture.

Two-step fabrication of clear view SLA millifluidic device for long-term in-vitro cultures

Purpose In vitro millifluidic cultures with perfusion are essential tools to analyse and understand the interactions between cells, their matrix and multi-cell populations. The purpose of this paper is to focus on the design and development of a 3D-printed template that can be used for fabrication of a clear view poly (dimethyl siloxane) (PDMS) device. The major objective is to obtain a transparent device prototype that allows perfusion culture of two cell types for multiple days that can be imaged using laser scanning confocal microscopy. Design/methodology/approach The authors used a two-step approach for achieving the final geometric structure at a faster timeline and lower cost. The first part focuses on comparing the fidelity of the printing templates using fused deposition modelling (FDM) and stereolithography (SLA) printers for a range of dimensions. They then show that the complex geometry chip with connection chambers can be printed using low resolution low cost FormLab SLA printer. The final optimized design was then printed using high-resolution Projet 6000 SLA printer to obtain smoother structures. Findings In this work, the authors have shown that the FormLab SLA printer yields significantly lower error for printing complex design geometries as compared to FDM printer. Result shows that FormLab printer can be used to achieve a minimum dimension of 0.5 mm. They then use the printer to optimize the device dimension for the culture chip which requires several iterations of printing and experimenting. They showed the two-step protocol of printing the optimized template in a high-resolution SLA printer and further fabricating a clear view millifluidic PDMS device that is compatible confocal microscopy imaging. They used this culture chip for perfusion culture of two cell type, and the controlled fluidic exchange between the two chambers led to the formation of neuroglia junction. Originality/value One of the major bottlenecks for obtaining complex geometry in mili/microfluidic device by 3D printing is the need of multiple iterations on printing. This makes the tuning of dimension significantly expensive. Another challenge is to obtain a smooth surface of PDMS that leads to a leak proof clear view device compatible for laser based confocal imaging. The combination of two printers plays a crucial role for the rapid prototyping of the imaging device with flow control. The proposed approach lowers the cost for prototyping of in vitro culture chip with complex geometries to improve on biological research demanding multi-chamber fluidic device.