Improvement In Speed Research Articles

Virtual Reality Interventions for Older Adults With Mild Cognitive Impairment: Systematic Review and Meta-Analysis of Randomized Controlled Trials.

Alzheimer disease is incurable, but it is possible to intervene and slow down the progression of dementia during periods of mild cognitive impairment (MCI) through virtual reality (VR) technology. This study aimed to analyze the effects of VR interventions on older adults with MCI. The examined outcomes include cognitive abilities, mood, quality of life, and physical fitness, including general cognitive function, memory performance, attention and information processing speed, executive function, language proficiency, visuospatial abilities, depression, daily mobility of individuals, muscle performance, and gait and balance. A total of 4 web-based databases (Web of Science, PubMed, Embase, and Ovid) were searched up to December 30, 2023, for randomized controlled trials assessing the self-reported outcomes of VR-based technology on cognition, mood, quality of life, and physical fitness in older adults (aged ≥55 years) with MCI. Two reviewers independently screened the search results and reference lists of the identified papers and related reviews. Data on the intervention components and delivery and behavioral change techniques used were extracted. A meta-analysis, risk-of-bias sensitivity analysis, and subgroup analysis were performed where appropriate to explore potential moderators. The Grading of Recommendations, Assessment, Development, and Evaluations (GRADE) approach was used to assess the quality of evidence. This review analyzed 18 studies involving 722 older adults with MCI. VR was delivered through different immersion levels with VR cognitive training, VR physical training, or VR cognitive-motor dual-task training. VR interventions showed significant improvements in memory (standardized mean difference [SMD] 0.2, 95% CI 0.02-0.38), attention and information processing speed (SMD 0.25, 95% CI 0.06-0.45), and executive function (SMD 0.22, 95% CI 0.02-0.42). VR without therapist involvement improved memory as well as attention and information processing speed. VR cognitive training also resulted in significant improvements in attention and information processing speed in older adults with MCI (SMD 0.31, 95% CI 0.05-0.58). In addition, immersive VR had a significant impact on improving attention and information processing speed (SMD 0.25; 95% CI 0.01-0.50) and executive function (SMD 0.25; 95% CI 0.00-0.50). However, the effects of the intervention were very small in terms of general cognitive function, language proficiency, visuospatial abilities, depression, daily living ability, muscle performance, and gait and balance. Quality of evidence varied, with moderate ratings for certain cognitive functions and low ratings for others, based on the GRADE approach. VR interventions can improve memory, attention and information processing speed, and executive function in older adults with MCI. The quality of evidence is moderate to low, and further research is needed to confirm these findings and explore additional health-related outcomes.

Feasibility and Usage of a Virtual Assistant Device in Cognitively Impaired Homebound Older Adults.

Social technology in older adults can improve self-rated health; however, there can also be difficulties using it. Our study aimed to evaluate the feasibility and acceptance of virtual assistant device (VAD) use in cognitively impaired homebound older adults. 52 newly referred Meals on Wheels clients aged 60 and older were recruited for a three-phase study: 6 weeks of meals alone (control), followed by 6 weeks of meals+Alexa Echo Show 8 (AES8) basic usage, and lastly 6 weeks of meals+AES8 advanced usage. Technology acceptance with the AES8 was significantly higher by the end of the study and participants anecdotally enjoyed playing music, setting reminders, and accessing spiritual content. There were also associations with improvements in memory, depression, and gait speed, despite no specific health programming. Thus, we believe use of VADs for cognitively impaired homebound older adults have future potential to benefit their cognitive and physical health. ClinicalTrials.gov ID: NCT04581317. Impact Statement: We certify that this work is novel because we were able to highlight improvements in the feasibility of use of a voice-activated virtual assistant device, technology acceptance, and some health indicators for underserved, cognitively impaired homebound older adults.

TinyHLS: a novel open source high level synthesis tool targeting hardware accelerators for artificial neural network inference.

In recent years, wearable devices such as smartwatches and smart patches have revolutionized biosignal acquisition and analysis, particularly for monitoring electrocardiography (ECG). However, the limited power supply of these devices often precludes real-time data analysis on the patch itself. This paper introduces a novel Python package, tinyHLS (High Level Synthesis), designed
to address these challenges by converting Python-based AI models into platform-independent hardware description language (HDL) code accelerators. Specifically designed for convolutional neural networks (CNNs), tinyHLS seamlessly integrates into the AI developer's workflow in Python TensorFlow Keras. Our methodology leverages a template-based hardware compiler that ensures flexibility, efficiency, and ease of use. In this work, tinyHLS is first-published featuring templates for several layers of neural networks, such as dense, convolution, max and global average pooling. In the first version, rectified linear unit (ReLU) is supported as activation. It targets one-dimensional data, with a particular focus on time series data. The generated accelerators are validated in detecting atrial fibrillation (AF) on electrocardiogram (ECG) data, demonstrating significant improvements in processing speed (62-fold) and energy efficiency (4.5-fold). Quality of code and synthesizability are ensured by validating the outputs with commercial ASIC design tools. Importantly, tinyHLS is open-source and does not rely on commercial tools, making it a versatile solution for both academic and commercial applications. The paper also discusses the integration with an open-source RISCV and potential for future enhancements of tinyHLS, including its application in edge servers and cloud computing. The source code is available on GitHub: https://github.com/Fraunhofer-IMS/tinyHLS.

Effects of interactive boxing-cycling on dual-task walking and prefrontal cortex activation in older adults with cognitive frailty: A randomized controlled trial.

Older adults with cognitive frailty often have impaired dual-task walking and prefrontal cortex (PFC) activation. Combining cycling with interactive boxing offers an innovative and interesting dual-task training to challenge both physical and cognitive skills. This study investigated the effects of interactive boxing-cycling on this population. Thirty-nine participants were assigned to the interactive boxing-cycling group (n = 20) or the stationary cycling group (n = 19) for 36 sessions over 12 weeks. The outcomes included dual-task walking performance, PFC activation, cognitive function, and frailty scores. Results showed the interactive boxing-cycling group had greater improvements in dual-task walking speed and cost and beneficially reduced oxygenated hemoglobin levels compared to the stationary group. Furthermore, both groups improved in frailty and cognition, but the interactive boxing-cycling group had better effects on general and specific cognitive function. Interactive boxing-cycling is an efficient intervention to enhance dual-task walking and PFC activation in older adults with cognitive frailty.

Adaptive Filtering for Channel Estimation in RIS-Assisted mmWave Systems

The advent of millimeter-wave (mmWave) massive multiple-input multiple-output (MIMO) systems, coupled with reconfigurable intelligent surfaces (RISs), presents a significant opportunity for advancing wireless communication technologies. This integration enhances data transmission rates and broadens coverage areas, but challenges in channel estimation (CE) remain due to the limitations of the signal processing capabilities of RIS. To address this, we propose an adaptive channel estimation framework comprising two algorithms: log-sum normalized least mean squares (Log-Sum NLMS) and hybrid normalized least mean squares-normalized least mean fourth (Hybrid NLMS-NLMF). These algorithms leverage the sparse nature of mmWave channels to improve estimation accuracy. The Log-Sum NLMS algorithm incorporates a log-sum penalty in its cost function for faster convergence, while the Hybrid NLMS-NLMF employs a mixed error function for better performance across varying signal-to-noise ratio (SNR) conditions. Our analysis also reveals that both algorithms have lower computational complexity compared to existing methods. Extensive simulations validate our findings, with results illustrating the performance of the proposed algorithms under different parameters, demonstrating significant improvements in channel estimation accuracy and convergence speed over established methods, including NLMS, sparse exponential forgetting window least mean square (SEFWLMS), and sparse hybrid adaptive filtering algorithms (SHAFA).

Real-Time Neuropsychological Testing for Hydrocephalus: Ultra-Fast Neuropsychological Testing During Infusion and Tap Test in Patients with Idiopathic Normal-Pressure Hydrocephalus

Background/Objectives: Ventriculoperitoneal shunting is a validated procedure for the treatment of idiopathic normal-pressure hydrocephalus. To select shunt-responsive patients, infusion and tap tests can be used. Only gait is evaluated after the procedure to establish a potential improvement. In this study, we present our Hydro-Real-Time Neuropsychological Testing protocol to assess the feasibility of performing an ultra-fast assessment of patients during the infusion and tap test. Methods: We tested 57 patients during the infusion and tap test to obtain real-time feedback on their cognitive status. Data were obtained immediately before the infusion phase (T0), when the pressure plateau was reached (T1), and immediately after cerebrospinal fluid subtraction (T2). Based on cerebrospinal fluid dynamics, 63.15% of the patients presented a resistance to outflow > 12 mmHg/mL/min, while 88% had a positive tap test response. Results: Compared to T0, cerebrospinal fluid removal significantly improved performance on tasks exploring executive functions (counting backward, p < 0.001; verbal fluency, p < 0.001). Patients were significantly faster at counting backward at T2 vs. T1 (p < 0.05) and at T2 vs. T0 (p < 0.001) and were significantly faster at counting forward at T2 vs. T1 (p < 0.005), suggesting an improvement in speed at T2. There was a significantly smaller index at T1 vs. T0 (p = 0.005) and at T2 vs. T0 (p < 0.001), suggesting a more marked improvement in patients’ executive abilities at T2 and a smaller improvement at T1. Regarding verbal fluency, patients were worse at T1 vs. T0 (p < 0.001) and at T2 vs. T0 (p < 0.001). Conclusions: Patients’ performance can be monitored during the infusion and tap test as significant changes in executive functions are observable. In future, this protocol might help improve patients’ selection for surgery.

Comparison of Maitland Passive Joint Mobilization and Self-Rehabilitation on Function, Activity, and Quality of Life in Beninese Patients With Hip Osteoarthritis: A Pragmatic Randomized Trial.

Orthopedic manual therapy is currently considered as an alternative approach for treating hip osteoarthritis. However, studies assessing its efficacy in low-income countries in Sub-Saharan Africa are scarce. Investigating the effectiveness of Maitland passive joint mobilization (Maitland PJM) compared to self-rehabilitation in patients with hip osteoarthritis in Benin, a low-income country, in Sub-Saharan Africa. This was a pragmatic, single-blind, two-arm randomized trial involving 66 participants randomly assigned into two groups (Maitland PJM group, n=33, and self-rehabilitation group, n=33). Both interventions took 5weeks. The primary outcome (pain) was evaluated using numerical rating scale. Secondary outcomes (passive hip range of motion, muscles strength, walking, and quality of life) were assessed using goniometry, medical research council, 10m walk test, and short-form 36. Participants were assessed at baseline, after interventions and 3-month follow-up. Forty-nine patients (Maitland PJM group, n=22, self-rehabilitation, n=27) completed the sessions. Within-group analyses showed a significant improvement in body function, walking speed, and quality of life (p<0.05) after interventions and at 3-month follow-up within both groups. No significant differences in any outcomes were observed between the Maitland PJM and self-rehabilitation groups, after interventions and at the 3-month follow-up (p>0.05). Maitland passive joint mobilization and self-rehabilitation similarly improved function, walking speed, and quality of life in Beninese patients with hip osteoarthritis. PACTR201911553990405.

EXPLORING THE IMPACT OF ATTENDANCE IN AN OUTDOOR WALKING PROGRAM ON HEALTH IN OLDER ADULTS WITH MOBILITY LIMITATIONS

Abstract Walking in an outdoor environment offers numerous health benefits for older adults with mobility limitations. Effects may vary, however, depending on the level of attendance. This study aimed to investigate whether a dose-response relationship existed between attendance in an outdoor walk group (OWG) program and improvement in health outcomes among older adults with mobility limitations. We analyzed data from a randomized controlled trial conducted across four cities in Canada and focused specifically on community-dwelling older adults assigned to a 10-week OWG program (n=79, age=74.7±6.6 years, 72% female). Participants were categorized into three groups based on attendance tertiles, with the first, second, and third tertiles corresponding to attending 0–9, 10–15, and 16–20 outdoor walking sessions in total. Outcomes were operationalized as change in scores on measures of walking endurance, comfortable and fast gait speed, balance, lower extremity strength, walking self-confidence, and emotional well-being pre- to post-intervention. Results from multiple linear regression models showed that, compared to those who attended 0–9 OWG sessions, participants who attended 16–20 sessions exhibited a 52.7-meter greater improvement in walking endurance (95% CI:12.3, 93.1); 0.15-meter/second greater improvement in comfortable gait speed (95% CI:0.00, 0.29); and 0.17-meter/second greater improvement in fast gait speed (95% CI:0.02, 0.33), after adjusting for sex and study site. No significant dose-response relationships for balance, lower extremity strength, walking self-confidence, and emotional well-being were observed. The role of attendance highlighted in this study provides critical information relevant to the design and implementation of outdoor walking interventions aimed at improving health among older adults.

Impact of plyometric training on sprint and agility performance in college-level futsal athletes: a comparative study using hurdle jumps and box jumps

The primary objective of this study is to analyze the effects of plyometric training on sprint and agility performance in college athletes, focusing on hurdle jumps and box jumps as interventions. A total of 45 college athletes were selected based on their participation in regular sports training. They were divided into three groups: Group Hurdle Jump (GHJ), Group Box Jump (GBJ), and a Control Group (GCR). The plyometric training program lasted for 12 weeks, with the GHJ and GBJ groups completing specific plyometric exercises, while the GCR group followed their usual training regimen without additional interventions. The results showed significant improvements in both sprint speed and agility in the intervention groups. In the GHJ group, sprint performance improved from 3.18 ± 0.32 seconds (pretest) to 2.79 ± 0.29 seconds (posttest) (p = 0.01), while agility improved from 12.21 ± 0.67 seconds to 10.20 ± 0.43 seconds (p = 0.01). Similarly, the GBJ group demonstrated improvements in sprint performance from 3.15 ± 0.43 seconds to 2.98 ± 0.13 seconds (p = 0.05), and agility performance from 12.43 ± 0.82 seconds to 11.01 ± 0.39 seconds (p = 0.05). In contrast, the Control Group (GCR) did not show statistically significant changes in either sprint or agility performance, with p-values of 0.34 and 0.63, respectively. These findings suggest that targeted plyometric exercises, particularly hurdle jumps and box jumps, play a crucial role in enhancing sprint and agility performance in college athletes. The study also highlights the effectiveness of the Smartspeed timing system in accurately measuring performance changes in these parameters. Further research is needed to explore the long-term impacts of such training programs on various sports disciplines.

TRAINING MODEL OF EVASION TECHNIQUES IN DOUBLE ART CATEGORY OF PENCAK SILAT BASED ON ATTACK MACHINE

Pencak silat, as a traditional martial art, has evolved into a modern combat sport that is now featured in various national and even international events. This research has developed 11 training models for evasion techniques in the double category of pencak silat using the Attack Machine and an Instrument Test to measure the speed of evasion techniques, named the Attack Machine Test. This research was conducted using the Research and Development method. The research procedure followed the ADDIE model, which includes Analysis, Design, Development, Implementation, and Evaluation. Data collection involved 30 active students specializing in the art category, particularly doubles, using testing and measurement techniques as the Instrument Test for collecting pretest and posttest data (Attack Machine Test). The data analysis presented includes descriptive quantitative analysis and qualitative analysis. The results of this study show that the average pretest score was 58.3 and the posttest score was 67.5, with standard deviations of 21.104 and 17.555, respectively. In the t-test table for Equality of Means, with a significance level of &lt; 0.05 for the One-Sided p Mean Difference Two-Sided p Std. Error Difference, it indicates the significant improvement in training effectiveness and reaction speed in the double category.

Optimizing SAQ performance in football atheletes exploring the influence of high-intensity interval training

Abstract Objectives. The major goal of this study was to investigate the impact of a twelve-week high-intensity interval training (HIIT) on male football players’ speed, agility, and quickness (SAQ) capabilities. Also, this study is to clarify the physiological and biomechanical mechanisms affected by HIIT. This work suggests the sustainability of this HIIT plan over a long period of time. Method. A total of thirty male college football players were split into two groups. One group underwent High-Intensity Interval Training (HIIT) and another group continued with their regular activities. This study was carried out for 12 weeks without changing the usual habits of the participants. The Illinois Agility Test, the Ruler Drop Test, and the Flying 30-meter Test were conducted both before and after the testing time. The T-tests were employed for statistical analysis by comparing both groups. Result. It was discovered that there were no significant gains in quickness (p &gt; 0.05), agility (p &gt; 0.05), or speed (p &gt; 0.05) by comparing the control group to the experimental group. The results of the t-test did not indicate any significant difference between the experimental and control group (p &gt; 0.05). Conclusion. The research shows the effectiveness of HIIT program for the improvement of Speed, Agility, and Quickness (SAQ) of male football players. While the current study did not find significant improvements in SAQ, further research is needed to explore the potential benefits of HIIT for football players. Factors such as sample size, study duration and individual differences might have influenced the results. Key words high intensity interval training, speed, agility, quickness, football players

Model-Free Generalized Super-Twisting Fast Terminal Sliding Mode Control for Permanent Magnet Synchronous Motors

Permanent Magnet Synchronous Motors (PMSMs) are nonlinear, multi-parameter systems that exhibit structural symmetry but are susceptible to parameter variations and external disturbances. These challenges can disrupt the inherent symmetrical characteristics of PMSM dynamics during real-world operations, posing difficulties for achieving efficient control. To address this issue, this paper proposes a Model-Free Generalized Super-Twisting Algorithm Fast Terminal Sliding Mode Control (MFFTSMC-GSTA) method. First, a novel ultra-local model incorporating PMSM uncertainties is established, and the MFFTSMC-GSTA controller is designed to address the system’s complex dynamic behavior. By integrating the generalized super-twisting algorithm with the nonsingular fast terminal sliding mode algorithm, the proposed controller ensures finite-time convergence and effectively mitigates chattering. Second, an extended sliding mode disturbance observer is developed to estimate the unknown components of the ultra-local model and provide feedforward compensation, further enhancing system robustness and dynamic performance. The experimental results show that the total harmonic distortion (THD) value of the proposed control method is 1.38%, demonstrating significant improvements in response speed and robustness for motor speed control, and verifying the algorithm’s superior performance under complex operating conditions.

Web-based Vulnerability Analysis and Detection

Background: In today’s digital world, web-based applications are widely used across organizations, making them a primary target for breaches, hacking attempts, data theft, and unauthorized access. Ensuring robust security through vulnerability analysis and detection is critical to mitigate emerging threats and prevent potential exploits. Objective: This research aims to develop and demonstrate a novel approach to web-based vulnerability assessment by combining advanced automated scanning tools with human expertise. The study introduces a modular, flexible, and efficient scanning tool capable of detecting critical vulnerabilities in web applications. Methods: The proposed tool is built using Python and Selenium and employs a modular architecture with regular expression-based detection techniques. The scanner identifies vulnerabilities such as SQL injection, cross-site scripting (XSS), file inclusions, command injection, and more. A ranking framework is introduced to prioritize fixes based on exploiting potential, severity, and patch availability. The tool's performance was evaluated on real-world web applications to assess its accuracy, detection speed, and effectiveness compared to traditional methods. Results: Experimental results highlight significant improvements in vulnerability detection accuracy and scanning speed. The tool successfully identified a broad range of vulnerabilities, including complex and dynamic ones, outperforming traditional scanning techniques. The modular design and automation enabled faster and more flexible vulnerability detection across diverse web platforms. Conclusion: This research provides a holistic and practical approach to web-based vulnerability assessment, combining advanced automated scanning with human analysis for optimal results. The introduced tool offers a reliable solution for detecting and prioritizing vulnerabilities, empowering organizations to enhance their cybersecurity defenses. By improving detection accuracy and efficiency, this study contributes to advancing web security practices and provides actionable insights for organizations navigating the evolving digital landscape.

HDNLS: Hybrid Deep-Learning and Non-Linear Least Squares-Based Method for Fast Multi-Component T1ρ Mapping in the Knee Joint

Non-linear least squares (NLS) methods are commonly used for quantitative magnetic resonance imaging (MRI), especially for multi-exponential T1ρ mapping, which provides precise parameter estimation for different relaxation models in tissues, such as mono-exponential (ME), bi-exponential (BE), and stretched-exponential (SE) models. However, NLS may suffer from problems like sensitivity to initial guesses, slow convergence speed, and high computational cost. While deep learning (DL)-based T1ρ fitting methods offer faster alternatives, they often face challenges such as noise sensitivity and reliance on NLS-generated reference data for training. To address these limitations of both approaches, we propose the HDNLS, a hybrid model for fast multi-component parameter mapping, particularly targeted for T1ρ mapping in the knee joint. HDNLS combines voxel-wise DL, trained with synthetic data, with a few iterations of NLS to accelerate the fitting process, thus eliminating the need for reference MRI data for training. Due to the inverse-problem nature of the parameter mapping, certain parameters in a specific model may be more sensitive to noise, such as the short component in the BE model. To address this, the number of NLS iterations in HDNLS can act as a regularization, stabilizing the estimation to obtain meaningful solutions. Thus, in this work, we conducted a comprehensive analysis of the impact of NLS iterations on HDNLS performance and proposed four variants that balance estimation accuracy and computational speed. These variants are Ultrafast-NLS, Superfast-HDNLS, HDNLS, and Relaxed-HDNLS. These methods allow users to select a suitable configuration based on their specific speed and performance requirements. Among these, HDNLS emerges as the optimal trade-off between performance and fitting time. Extensive experiments on synthetic data demonstrate that HDNLS achieves comparable performance to NLS and regularized-NLS (RNLS) with a minimum of a 13-fold improvement in speed. HDNLS is just a little slower than DL-based methods; however, it significantly improves estimation quality, offering a solution for T1ρ fitting that is fast and reliable.

Comparison of Two Fourier-Based Methods for Simulating Inlet Distortion Unsteady Flows in Transonic Compressors

The aerodynamic performance of transonic compressors, particularly the stall margin, is significantly influenced by inlet distortion. While time-marching methods accurately simulate such unsteady flows, they can be time-consuming. To enhance the computational efficiency, two Fourier-based methods are proposed in this paper: the time-accurate method with interface filtering and the time–space collocation (TSC) method. The time-accurate method with interface filtering ignores the rotor–stator interaction effects, enabling a larger time step and faster convergence. In contrast, the TSC method accounts for harmonics of conservative variables and transforms the unsteady simulation into multiple steady-state calculations, thereby reducing computational costs. The two Fourier-based methods are validated using NASA Stage 67 and a two-stage transonic fan. Near the peak efficiency point, the results from both methods closely match that of URANS simulation and experimental data. The time-accurate method with interface filtering demonstrates a speed enhancement of 4 to 5 times as a result of a reduction in the iteration steps. In contrast, the TSC method exhibits a speed improvement of at least 20 times in two specific cases, attributable to the significantly smaller mesh size and iteration steps employed in the TSC method compared to the URANS method. Near the stall point, more harmonics for inlet distortion are necessary in TSC simulation to accurately capture flow separation. In the two-stage transonic fan simulations, the strong rotor–stator interaction effects lead to deviations from the URANS simulation; nevertheless, the Fourier-based simulations accurately reflect the trend of the stall margin under total pressure distortion. Overall, the Fourier-based methods show promising potential for engineering applications in estimating the performance degradation of compressors subjected to inlet distortion.

Enhancing Microcalcification Detection in Mammography with YOLO-v8 Performance and Clinical Implications.

Background: Microcalcifications in the breast are often an early warning sign of breast cancer, and their accurate detection is crucial for the early discovery and management of the disease. In recent years, deep learning technology, particularly models based on object detection, has significantly improved the ability to detect microcalcifications. This study aims to use the advanced YOLO-v8 object detection algorithm to identify breast microcalcifications and explore its advantages in terms of performance and clinical application. Methods: This study collected mammograms from 7615 female participants, with a dataset including 10,323 breast images containing microcalcifications. We used the YOLO-v8 model for microcalcification detection and trained and validated the model using five-fold cross-validation. The model's performance was evaluated through metrics such as accuracy, recall, F1 score, mAP50, and mAP50-95. Additionally, this study explored the potential applications of this technology in clinical practice. Results: The YOLO-v8 model achieved an mAP50 of 0.921, an mAP50-95 of 0.709, an F1 score of 0.82, a detection accuracy of 0.842, and a recall rate of 0.796 in breast microcalcification detection. Compared to previous similar deep learning object detection techniques like Mask R-CNN, YOLO-v8 has shown improvements in both speed and accuracy. Conclusions: YOLO-v8 outperforms traditional detection methods in detecting breast microcalcifications. Its multi-scale detection capability significantly enhances both speed and accuracy, making it more clinically practical for large-scale screenings. Future research should further explore the model's potential in benign and malignant classification to promote its application in clinical settings, assisting radiologists in diagnosing breast cancer more efficiently.

Development of an Optimized LC-MS Workflow for Host Cell Protein Characterization to Support Upstream Process Development.

Host cell proteins (HCPs) coexpressed during the production of biotherapeutics can affect the safety, efficacy, and stability of the final product. As such, monitoring HCP populations and amounts throughout the production and purification process is an essential part of the overall quality control framework. Mass spectrometry (MS) is used as an orthogonal method to enzyme-linked immunosorbent assays (ELISA) for the simultaneous identification and quantification of HCPs, particularly for the analysis of downstream processes. In this study, we present an MS-based analytical protocol with improvements in both speed and identification performance that can be implemented for routine analysis to support upstream process development. The protocol adopts a streamlined sample preparation strategy, combined with a high-throughput MS analysis pipeline. The developed method identifies and quantifies over 1000 HCPs, including 20 proteins listed as high risk in the literature, in a clarified cell culture sample with repeatability and precision shown for digest replicates. In addition, we explore the effects of varying standard spike-ins and changes to the data processing pipeline on absolute quantification estimates of the HCPs, which highlight the importance of standardization for wider use in the industry. Data are available via ProteomeXchange with the identifier PXD053035.

Deep learning of optimal exercise boundaries for American options

Efficiently determining a price and optimal exercise boundary for an American option is a critical subject in the financial sector. This study introduces a novel application of long short-term memory neural networks to solve a relevant Volterra equation, enhancing the accuracy and efficiency of American option pricing. The proposed approach outperforms traditional numerical techniques, including finite difference methods, binomial trees, and Monte Carlo methods, delivering an impressive speed improvement by a factor of thousands while maintaining industry-accepted accuracy levels. It exhibits computational speeds up to about a hundred times faster than the state-of-the-art method used by Andersen et al. [High-performance American option pricing, J. Comput. Finance 20(1) (2016), pp. 39–87] when evaluating numerous options. The proposed network, trained on a reasonable range of parameters related to the Black–Scholes model, swiftly determines option prices and exercise boundaries, serving as a practical closed-form solution.

Low-Resource Chinese Named Entity Recognition via CNN-based Multitask Learning

Named entity recognition (NER) is a fundamental subtask for information extraction that aims to locate and classify named entities in unstructured text into predefined categories. Recently, large-scale language models (LLMs) have achieved SOTA performance on a variety of natural language processing tasks. However, because NER is a sequence labeling task in nature while LLMs is a text-generation model, the performance of LLMs on NER is still significantly below supervised baselines, and NER remains a difficult task. Meanwhile, the word boundary and semantic information of Chinese words are usually quite vague, as words contained in Chinese texts are not separated by spaces. Thus, the NER task still requires supervised learning paradigm and heavily relies on large amounts of labeled data, such as entity type and boundary information. However, the cost of labeling data can be prohibitively large, and the purely supervised approaches usually suffer from poor generalization capability. In this article, we propose a multitask learning-based bidirectional iterated dilated convolution model, BCNN-CWS, for low-resource NER via leveraging word boundary information of Chinese word segmentation (CWS) task. Specifically, to efficiently recognize named entities, an iterated dilated convolutional model with a limited number of layers is implemented. In addition, a bidirectional causal convolution mechanism is presented for contextual information extraction. Results of extensive experiments on public Chinese datasets demonstrate that BCNN-CWS achieves superior performance over state-of-the-art models, and it yields up to about 50% speed improvement over existing methods. It is worth noting that BCNN-CWS can be further improved by combining with a pretrained model. Received: 25 Spetember 2024 | Revised: 4 November 2024 | Accepted: 28 November 2024 Conflicts of Interest The authors declare that they have no conflicts of interest to this work. Data Availability Statement The data that support the findings of this study are openly available in GitLab at https://github.com/jiangfeng13/BCNN-CWS Author Contribution Statement Tao Wu: Conceptualization, Methodology, Writing – original draft, Writing – review &amp; editing, Visualization, Supervision. Xinwen Cao: Resources, Data curation. Feng Jiang: Software, Validation, Formal analysis, Investigation, Writing – original draft. Canyixing Cui: Data curation, Writing -review &amp; editing. Xuehao Li: Resources. Xingping Xian: Supervision, Project administration, Funding acquisition.

Comparison Between LSTM, GRU and VARIMA in Forecasting of Air Quality Time Series Data

Air quality forecast is essential in alerting the public, especially those who have respiratory diseases, to take necessary precautions beforehand. The public can be forewarned of any worsening of air quality and be aware of the importance of reducing air pollution. In recent years, forecasting techniques based on deep learning algorithms such as recurrent neural network (RNN) have seen improvements in both accuracy and execution speed. Long short-term memory (LSTM) network and gated recurrent unit (GRU) are among the most popular variants of RNN. In this study, the hourly PM2.5 concentrations at five selected air quality monitoring stations, provided by the Department of Environment Malaysia, are forecasted using LSTM, GRU and vector autoregressive integrated moving average (VARIMA) models respectively. Data containing missing, negative and zero values are pre-processed using an interpolation technique before being split into training and test sets on an 80:20 ratio basis. Optimal combinations of hyperparameter values are selected via manual tuning based on the 10-fold growing window cross-validation results. The model performance is evaluated based on RMSE, MAE and MAPE. The results demonstrate that neural network models significantly outperform the multivariate time series model in which the LSTM and GRU models have comparable performance in forecasting the hourly PM2.5 concentration, with a slightly better prediction in the west coast region for LSTM and the east coast region for GRU. However, due to the complex architecture of neural networks, the computational time to train both LSTM and GRU models is three times longer than that for VARIMA. Additionally, it is observed that a higher percentage of interpolated values leads to lower prediction errors.