Test Scenarios Research Articles

Enhanced utility estimation algorithm for discrete choice models in travel demand forecasting

Abstract Recent data-driven discrete choice models in travel demand forecasting have achieved improved predictability. However, such prediction improvements come at the cost of black-box models and lost transparency in travel demand forecasting, which makes scenario testing and transportation planning difficult (if not impossible). Furthermore, these predictability gains have often been modest compared to handcrafted parsimonious models, which benefit from enhanced behavioural interpretability and transparency. This paper introduces a novel bi-level model and estimation framework (DUET) to enhance predictability in traditional utility-based discrete choice models. The proposed model improves the specification process by identifying effective variable transformations and interactions in utility functions. Utilising a genetic algorithm, the upper level of our framework selects feasible functional forms from an extensive array, while the lower level applies iterative singular value decomposition and maximum likelihood estimation to optimise model parameters and prevent overfitting. This approach ensures superior predictability through a general utility functional form that considers extensive variable interactions. Case studies on both synthetic data and the Swissmetro dataset highlight the framework’s effectiveness in improving model performance and uncovering critical behavioural patterns and latent trends. Notably, incorporating interactions among variables in Swissmetro data, our model demonstrates a 6.5% improvement in the Brier score (probabilistic prediction accuracy) compared to the state-of-the-art deep neural network-based discrete choice model.Lastly, our results on transport mode choice data align with existing literature, indicating that younger individuals are less sensitive to travel costs. This confirms the need for targeted pricing policies to encourage public transit use among different age groups.

Online and Cross-User Finger Movement Pattern Recognition by Decoding Neural Drive Information from Surface Electromyogram.

Cross-user variability is a well-known challenge that leads to severe performance degradation and impacts the robustness of practical myoelectric control systems. To address this issue, a novel method for myoelectric recognition of finger movement patterns is proposed by incorporating a neural decoding approach with unsupervised domain adaption (UDA) learning. In our method, the neural decoding approach is implemented by extracting microscopic features characterizing individual motor unit (MU) activities obtained from a two-stage online surface electromyogram (SEMG) decomposition. A specific deep learning model is designed and initially trained using labeled data from a set of existing users. The model can update adaptively when recognizing the movement patterns of a new user. The final movement pattern was determined by a fuzzy weighted decision strategy. SEMG signals were collected from the finger extensor muscles of 15 subjects to detect seven dexterous finger-movement patterns. The proposed method achieved a movement pattern recognition accuracy of ([Formula: see text])% over seven movements under cross-user testing scenarios, much higher than that of the conventional methods using global SEMG features. Our study presents a novel robust myoelectric pattern recognition approach at a fine-grained MU level, with wide applications in neural interface and prosthesis control.

Multi-Target Firefighting Task Planning Strategy for Multiple UAVs Under Dynamic Forest Fire Environment

The frequent occurrence of forest fires in mountainous regions has posed severe threats to both the ecological environment and human activities. This study proposed a multi-target firefighting task planning method of forest fires by multiple UAVs (Unmanned Aerial Vehicles) integrating task allocation and path planning. The forest fire environment factors such high temperatures, dense smoke, and signal shielding zones were considered as the threats. The multi-UAVs task allocation and path planning model was established with the minimum of flight time, flight angle, altitude variance, and environmental threats. In this process, the study considers only the use of fire-extinguishing balls as the fire suppressant for the UAVs. The improved multi-population grey wolf optimization (MP–GWO) algorithm and non-Dominated sorting genetic algorithm II (NSGA-II) were designed to solve the path planning and task allocation models, respectively. Both algorithms were validated compared with traditional algorithms through simulation experiments, and the sensitivity analysis of different scenarios were conducted. Results from benchmark tests and case studies indicate that the improved MP–GWO algorithm outperforms the grey wolf optimizer (GWO), pelican optimizer (POA), Harris hawks optimizer (HHO), coyote optimizer (CPO), and particle swarm optimizer (PSO) in solving more complex optimization problems, providing better average results, greater stability, and effectively reducing flight time and path cost. At the same scenario and benchmark tests, the improved NSGA-II demonstrates advantages in both solution quality and coverage compared to the original algorithm. Sensitivity analysis revealed that with the increase in UAV speed, the flight time in the completion of firefighting mission decreases, but the average number of remaining fire-extinguishing balls per UAV initially decreases and then rises with a minimum of 1.9 at 35 km/h. The increase in UAV load capacity results in a higher average of remaining fire-extinguishing balls per UAV. For example, a 20% increase in UAV load capacity can reduce the number of UAVs from 11 to 9 to complete firefighting tasks. Additionally, as the number of fire points increases, both the required number of UAVs and the total remaining fire-extinguishing balls increase. Therefore, the results in the current study can offer an effective solution for multiple UAVs firefighting task planning in forest fire scenarios.

Warning Signs, Monitoring Tools, and Actions Taken by World-Class Endurance Coaches in Cases of Underperformance.

To describe warning signs, monitoring tools, and training- and non-training-related actions taken by world-class endurance coaches in cases of underperformance. Twelve highly acclaimed male Norwegian coaches known for coaching world-class endurance athletes with a remarkable collection of over 350 Olympic, World, and European Championship medals-primarily with Norwegian athletes-participated in the study. Data collection and analyses followed a 3-step pragmatic qualitative study design, including an initial questionnaire, in-depth interviews, and structured negotiation between researchers and coaches. Reduced performance levels and discrepancies between external (speed or power output) and internal (heart rate, lactate concentrations, and rating of perceived exertion) training-intensity measures in competitions, key training sessions, and testing scenarios, as well as observed changes in emotional state and coach-athlete communication, were highlighted as early warning signs of underperformance. Consistently, daily follow-up, systematic use of training diaries, training-intensity measures, and information from testing scenarios were rated as the most important monitoring tools in both detection and management of underperformance. In cases of underperformance, ruling out medical conditions as underlying causes was followed by a decreased training load (intensity and volume) and actions to reduce life stress to restore athletes' performance levels. The presented data collectively propose applying both objectively and subjectively measured monitoring tools and systems to the early detection and management of underperformance in endurance athletes. In addition, this should be complemented by the holistic and observational role of the coach.

Predictive learning as the basis of the testing effect

A prominent learning phenomenon is the testing effect, meaning that testing enhances retention more than studying. Emergent frameworks propose fundamental (Hebbian and predictive) learning principles as its basis. Predictive learning posits that learning occurs based on the contrast (error) between a prediction and the feedback on that prediction (prediction error). Here, we propose that in testing (but not studying) scenarios, participants predict potential answers, and its contrast with the subsequent feedback yields a prediction error, which facilitates testing-based learning. To investigate this, we developed an associative memory network incorporating Hebbian and/or predictive learning, together with an experimental design where human participants studied or tested English-Swahili word pairs followed by recognition. Three behavioral experiments (N = 80, 81, 62) showed robust testing effects when feedback was provided. Model fitting (of 10 different models) suggested that only models incorporating predictive learning can account for the breadth of data associated with the testing effect. Our data and model suggest that predictive learning underlies the testing effect.

Seaweed-Based Bioplastics: Data Mining Ingredient–Property Relations from the Scientific Literature

Automated analysis of the scientific literature using natural language processing (NLP) can accelerate the identification of potentially unexplored formulations that enable innovations in materials engineering with fewer experimentation and testing cycles. This strategy has been successful for specific classes of inorganic materials, but their general application in broader material domains such as bioplastics remains challenging. To begin addressing this gap, we explore correlations between the ingredients and physicochemical properties of seaweed-based biofilms from a corpus of 2000 article abstracts from the scientific literature since 1958, using a supervised word co-occurrence analysis and an unsupervised approach based on the language model MatBERT without fine-tuning. Using known relations between ingredients and properties for test scenarios, we discuss the potential and limitations of these NLP approaches for identifying novel combinations of polysaccharides, plasticizers, and additives that are related to the functionality of seaweed biofilms. The model demonstrates a valuable predictive ability to identify ingredients associated with increased water vapor permeability, suggesting its potential utility in optimizing formulations for future research. Using the model further revealed alternative combinations that are underrepresented in the literature. This automated method facilitates the mapping of relationships between ingredients and properties, guiding the development of seaweed bioplastic formulations. The unstructured and heterogeneous nature of the literature on bioplastics represents a particular challenge that demands ad hoc fine-tuning strategies for state-of-the-art language models for advancing the field of seaweed bioplastics.

Evaluating the Performance of Large Language Models (LLMs) in Answering and Analysing the Chinese Dental Licensing Examination.

This study aimed to simulate diverse scenarios of students employing LLMs for CDLE examination preparation, providing a detailed evaluation of their performance in medical education. A stratified random sampling strategy was implemented to select and subsequently revise 200 questions from the CDLE. Seven LLMs, recognised for their exceptional performance in the Chinese domain, were selected as test subjects. Three distinct testing scenarios were constructed: answering questions, explaining questions and adversarial testing. The evaluation metrics included accuracy, agreement rate and teaching effectiveness score. Wald χ2 tests and Kruskal-Wallis tests were employed to determine whether the differences among the LLMs across various scenarios and before and after adversarial testing were statistically significant. The majority of the tested LLMs met the passing threshold on the CDLE benchmark, with Doubao-pro 32k and Qwen2-72b (81%) achieving the highest accuracy rates. Doubao-pro 32k demonstrated the highest 98% agreement rate with the reference answers when providing explanations. Although statistically significant differences existed among various LLMs in their teaching effectiveness scores based on the Likert scale, all these models demonstrated a commendable ability to deliver comprehensible and effective instructional content. In adversarial testing, GPT-4 exhibited the smallest decline in accuracy (2%, p = 0.623), while ChatGLM-4 demonstrated the least reduction in agreement rate (14.6%, p = 0.001). LLMs trained on Chinese corpora, such as Doubao-pro 32k, demonstrated superior performance compared to GPT-4 in answering and explaining questions, with no statistically significant difference. However, during adversarial testing, all models exhibited diminished performance, with GPT-4 displaying comparatively greater robustness. Future research should further investigate the interpretability of LLM outputs and develop strategies to mitigate hallucinations generated in medical education.

P-2248. Modeling the Economic Impact of Different Testing Strategies for Meningitis/Encephalitis in Adult Patients from a US Hospital Perspective

Abstract Background Meningitis/encephalitis (ME) is a life-threatening disease whose severity and trajectory vary by etiology. Many times, patients with suspected ME are unnecessarily hospitalized and treated with empiric therapy as a precaution while clinicians determine etiology. ME diagnosis and treatment algorithms vary across hospitals, with some using a battery of rapid diagnostic tests (RDTs) or syndromic testing to inform diagnosis and clinical decisions and others relying on bacterial culture and send-out tests. We modeled the economic impact of 4 ME testing strategies from a US hospital perspective. Methods A simulation model was used to generate a large sample of 20,000 suspected ME cases based on known distributions of ME etiology and cerebrospinal fluid (CSF) analysis results. The model simulated diagnosis, clinical decisions, and resource use and costs to hospitals in 4 testing scenarios: (1) no RDTs, (2) a battery of 4 RDTs for HSV, enterovirus, VZV, and C. neoformans/gattii, (3) BIOFIRE® FILMARRAY® ME panel alone [syndromic testing], and (4) syndromic testing in conjunction with RDTs for HSV and C. neoformans/gattii (Figures 1-2). Mean inpatient resource use (i.e., hospital stay and antimicrobial use) and cost per suspected ME case were estimated. Common costs across testing scenarios were excluded. Sensitivity analyses were conducted. Results The mean (95% CI) cost per suspected ME case was estimated at $19,337 ($19,150-$19,525) with no RDT use, $16,412 ($16,226-$16,598) with the battery of 4 RDTs, $15,465 ($15,285-$15,644) with syndromic testing, and $15,720 ($15,536-$15,903) under conjunction testing. Cost savings were driven primarily by reductions in hospital stay due to reduced time to correct pathogen identification. Performance characteristics of RDTs/syndromic test and time to correct pathogen identification had the largest impact on modeled costs for strategies that include RDT/syndromic testing. Conclusion Using either the battery of 4 RDTs or syndromic testing would result in cost savings for hospitals currently not using RDTs in ME diagnosis and treatment. Syndromic testing would yield even higher savings than the battery of 4 RDTs by further reducing unnecessary hospital stays and inappropriate antimicrobial use. Disclosures Rodrigo Hasbun, MD MPH FIDSA, Biomeriaux: Grant/Research Support|Biomeriaux: Honoraria Kyle Hueth, MS, MLS(ASCP)SC, bioMérieux: Stocks/Bonds (Public Company) Glaucia Paranhos-Baccala, PhD, bioMérieux: Stocks/Bonds (Public Company) Tristan T. Timbrook, PharmD, bioMérieux: Stocks/Bonds (Public Company) Lohit Korrapati, MBA, bioMérieux: Advisor/Consultant Catherine Regan, MPH, bioMérieux: Advisor/Consultant Adrienne Kwok, MPH, bioMérieux: Advisor/Consultant Noam Kirson, PhD, bioMérieux: Advisor/Consultant

EUFCC-340K: A faceted hierarchical dataset for metadata annotation in GLAM collections

Abstract In this paper, we address the challenges of automatic metadata annotation in the domain of Galleries, Libraries, Archives, and Museums (GLAMs) by introducing a novel dataset, EUFCC-340K, collected from the Europeana portal. Comprising over 340,000 images, the EUFCC-340K dataset is organized across multiple facets – Materials, Object Types, Disciplines, and Subjects – following a hierarchical structure based on the Art & Architecture Thesaurus (AAT). We developed several baseline models, incorporating multiple heads on a ConvNeXT backbone for multi-label image tagging on these facets, and fine-tuning a CLIP model with our image-text pairs. Our experiments to evaluate model robustness and generalization capabilities in two different test scenarios demonstrate the dataset’s utility in improving multi-label classification tools that have the potential to alleviate cataloging tasks in the cultural heritage sector. The EUFCC-340K dataset is publicly available at https://github.com/cesc47/EUFCC-340K.

The Random RFPA Method for Modelling Rock Failure

Abstract The random rock failure process analysis (RRFPA) method was developed in this research to characterize the material spatial variability and uncertainty in rock failure modelling. The random field theory (RFT) was integrated with the traditional rock failure process analysis (RFPA) to model rock heterogeneity. In this approach, the variation of rock properties is represented as a function of relative distance, such that the influence of material intrinsic correlation on its fracturing behaviour can be appropriately captured. To validate the theory, 300 RRFPA simulations were conducted to investigate the failure characteristics of rock samples under compressive loading. The results showed that by incorporating a spectrum of material properties, the numerical outcomes exhibited distinct upper and lower bounds of stress across all testing scenarios, closely aligning with the experimental relationships. The histograms for uniaxial compressive strength and elastic modulus showed that both properties followed normal distributions, with the average values of 10.099 MPa and 1.818 GPa, respectively. The corresponding coefficients of variation were 0.450 and 0.038. The localized failure tended to result in a more rapid release of acoustic emission energy, but generated smaller cumulative energy compared to the overall failure pattern. In general, the maximum relative error of the RRFPA model was only 0.66% for uniaxial compressive strength, elastic modulus, and critical axial strain.

LASF: a local adaptive segmentation framework for coronary angiogram segments.

Coronary artery disease (CAD) remains the leading cause of death globally, highlighting the critical need for accurate diagnostic tools in medical imaging. Traditional segmentation methods for coronary angiograms often struggle with vessel discontinuity and inaccuracies, impeding effective diagnosis and treatment planning. To address these challenges, we developed the Local Adaptive Segmentation Framework (LASF), enhancing the YOLOv8 architecture with dilation and erosion algorithms to improve the continuity and precision of vascular image segmentation. We further enriched the ARCADE dataset by meticulously annotating both proximal and distal vascular segments, thus broadening the dataset's applicability for training robust segmentation models. Our comparative analyses reveal that LASF outperforms well-known models such as UNet and DeepLabV3Plus, demonstrating superior metrics in precision, recall, and F1-score across various testing scenarios. These enhancements ensure more reliable and accurate segmentation, critical for clinical applications. LASF represents a significant advancement in the segmentation of vascular images within coronary angiograms. By effectively addressing the common issues of vessel discontinuity and segmentation accuracy, LASF stands to improve the clinical management of CAD, offering a promising tool for enhancing diagnostic accuracy and patient outcomes in medical settings.

A software module to assess the metabolic potential of mutant strains of the bacterium Corynebacterium glutamicum

Technologies for the production of a range of compounds using microorganisms are becoming increas­ingly popular in industry. The creation of highly productive strains whose metabolism is aimed to the synthesis of a specific desired product is impossible without complex directed modifications of the genome using mathematical and computer modeling methods. One of the bacterial species actively used in biotechnological production is Co­rynebacterium glutamicum. There are already 5 whole-genome flux balance models for it, which can be used for me­tabolism research and optimization tasks. The paper presents fluxMicrobiotech, a software module developed at the Institute of Cytology and Genetics of the Siberian Branch of the Russian Academy of Sciences, which implements a se­ries of computational protocols designed for high-performance computer analysis of C. glutamicum whole-genome flux balance models. The tool is based on libraries from the opencobra community (https://opencobra.github.io) within the Python programming language (https://www.python.org), using the Pandas (https://pandas.pydata.org) and Escher (https://escher.readthedocs.io) libraries . It is configured to operate on a ‘file-in/file-out’ basis. The model, environmental conditions, and model constraints are specified as separate text table files, which allows one to pre­pare a series of files for each section, creating databases of available test scenarios for variations of the model. Or vice versa, allowing a single model to be tested under a series of different cultivation conditions. Post-processing tools for modeling data are set up, providing visualization of summary charts and metabolic maps.

Material Characterisation Experiments and Data Preparation for a Finite Element Analysis of the Deep Drawing Process Using AA 1050-O

The use of computer simulation to imitate physical processes has proven to be a time-efficient and cost-effective way of performing scenario testing for process optimisation in different applications. The finite element analysis (FEA) is the dominant numerical simulation method for analysing sheet metal forming processes. It uses mathematical tools and computer-aided engineering software programmes to predict forming processes. To improve the quality of output from the simulation, accurate material characterisation data that correctly model the behaviour of the material when it undergoes deformation must be provided. This paper outlines the stages of conducting material characterisation experiments, such as tensile, hardness, and formability tests, using the aluminium alloy AA1050-O. Sample preparation, the machine setup, and testing procedures for the material characterisation tests are given. Subsequent data preparation methods for input into an FEA software programme are also outlined. Implications of the testing results to a deep drawing process are examined while considering the formation of a rectangular monolithic component measuring 2300 mm by 1400 mm with a drawing depth of approximately 150 mm. The results from the characterisation tests indicate that the forming process for the product can be achieved using cold forming at room temperatures as a 25% strain was recorded before necking against an anticipated uniaxial strain of 5.93%. The aluminium alloy AA1050-O demonstrated a negligible strain rate sensitivity in the forming region, thus eliminating tool velocity from the key process parameters that should be considered during FEA simulations. A 50% increase in hardness was recorded after strain hardening.

Research on ACC Control Strategy for Electric Vehicles Considering Cut-in Behavior

Background: Current adaptive cruise control systems primarily focus on the main target vehicle in the primary lane. However, there may be a delay in updating their targets after a side vehicle merges. This delay can impede the effective utilization of driving data from merging side vehicles, resulting in delayed responses and an increased risk of collision. Objective: Aiming at the problem of untimely control of traditional adaptive cruise control systems in cut-in scenarios, the authors establish a parallel model and collision analysis and design the control strategy of adaptive cruise under parallel behavior to improve the safety, following, and comfort of ACC. Methods: Initially, the vehicle model provided by CarSim is utilized and the power system parameters are adjusted to match the dynamic performance index. The permanent magnet synchronous motor model is then simplified to create an electric vehicle model. Following this, the cruise control system is designed using a PID control method, incorporating a hierarchical control structure. Simultaneously, a cut-in model characterized by sinusoidal acceleration is established, and a collision avoidance strategy based on cut-in behavior is formulated. Building on this foundation, an adaptive cruise control strategy is developed that takes cut-in behavior into account. Results: A test scenario utilizing the CarSim and Simulink co-simulation platforms was developed to simulate and validate the conditions for mode switching and lane cut-in. The ACC system, considering cut-in behavior recognition, demonstrated an ability to control the lead vehicle approximately 1 second earlier than traditional ACC systems. Notably, the distance error improved from - 0.1039 m to 0.040 m, the absolute value of velocity error decreased from 2.65 m/s to 2.02 m/s, the absolute value of main vehicle acceleration reduced by 34%, and the jerk response in acceleration diminished by approximately 14%. Conclusion: The proposed multi-objective cooperative adaptive cruise control system, which considers cut-in behavior, is effective.

Classification of Sleep Disorders Using Support Vector Machine

Sleep disorders become a severe concern in our busy modern lifestyles, which are often overlooked and can cause significant negative impacts on an individual's health and quality of life. This research explores the implementation of machine learning, specifically Support Vector Machine, to facilitate quick and accurate sleep disorder diagnosis. Data shows that sleep deprivation or disturbed sleep is becoming common in society, with 62% of the adult population experiencing dissatisfaction with their sleep quality. This has a significant economic impact and affects the health and productivity sectors. This study uses Kaggle Sleep Health and Lifestyle dataset of 400 data samples, applying Support Vector Machine to classify sleep disorders using three testing scenarios. The results showed an accuracy rate of 92%, confirming that Support Vector Machine can potentially improve the diagnosis of sleep disorders, enabling early intervention and better treatment for patients. Thus, this research contributes to understanding and treating sleep disorders, improving people's overall quality of life.

User Action Recognition Using a Fabric Pressure Distribution Sensor in an Electric Wheelchair for Soil Cultivation

Advancements in robotics technology and information and communication technology have significantly affected agriculture, especially systems that aid workers. Soil cultivation often requires prolonged bending, which results in physical strain. A commercially available work wheelchair reduces back strain, but lacks power assistance and relies on leg movements. In a previous study, we developed an electric wheelchair with a fabric pressure-distribution sensor on the seat. This sensor, combined with a fuzzy controller, aids movement by measuring the center of pressure fluctuations and reducing leg strain. However, it does not recognize user’s actions, such as standing or sitting, and requires the wheelchair to stop. This study introduced a user-action recognition system using a wheelchair seat pressure sensor. This system accurately recognizes user actions through deep learning time-series data. We evaluated data augmentation and multi-user data to enhance the performance. The results show improved prediction accuracy for action recognition in the test scenarios.

Spam and Phishing Whatsapp Message Filtering Application Using TF - IDF and Machine Learning Methods

The rapid development of communication technology has led to an increase in the number of unwanted messages, such as spam and phishing attempts. However, this progress has not been accompanied by sufficient user awareness of the basics of technology use. Additionally, the enforcement of laws regarding internet-based crimes remains unclear, further increasing the risk for users of internet technology to fall victim to such crimes. As one of the media prone to spam and phishing, WhatsApp is the focus of this research, which aims to develop an application capable of filtering spam and phishing messages. The application employs the TF-IDF (Term Frequency-Inverse Document Frequency) method and machine learning using the Random Forest model. It is developed using the MVVM (Model-View-ViewModel) architecture, enabling the separation of business logic from the user interface, thereby improving development and maintenance efficiency. The research findings demonstrate that the combination of TF-IDF and Random Forest achieves high accuracy in classifying spam and phishing messages. Performance evaluation using a confusion matrix reveals an accuracy rate of 92%. For the safe message class, the precision, recall, and F1 scores are 89%, 95%, and 92%, respectively, while for the dangerous message class, the scores are 95%, 88%, and 92%, respectively. Furthermore, the integration of the model and application performed exceptionally well, as evidenced by black-box testing results. All test scenarios were met, successfully detecting test messages with 98% accuracy. Therefore, the developed application provides enhanced protection for WhatsApp users against digital threats.

Anterior Cruciate Ligament Mechanical Response to Load in the Setting of Changes to the Medial Meniscus.

The anterior cruciate ligament (ACL) is a major ligament in the knee joint, and its function is crucial for both the movement and stability of the knee. Our research takes a novel approach by investigating the effect of meniscus tears on the ACL, how such tears will impact the stress on the ACL, and its overall compensation in response to the changes in the meniscus. Hypothesis/Purpose: This study aims to investigate how the ACL compensates for the change in knee joint stability and contact pressures due to partial horizontal cleavage tears (HCTs) in the meniscus, such as partial meniscectomy and partial transplantation on knee joint stability and contact pressures. We hypothesize that HCTs will increase contact pressures and decrease joint stability, thereby inducing compensatory stress on the anterior cruciate ligament (ACL). Method: Seven freshly frozen human cadaveric knees were used in a study to investigate the effects of different meniscal conditions and surgical interventions on the meniscus itself. Four testing scenarios were established: intact knees, knees with partial horizontal cleavage tears (HCTs) of the meniscus, knees with partial meniscectomy, and knees with partial transplantation. Axial loading was applied, and the medial meniscus contact pressures were measured at 0° and 30° of flexion. Additionally, a mathematical 3D finite element model was created to evaluate the behavior of the ACL under different meniscus scenarios, which could not have been measured experimentally. Results: ACL contact pressure and stress analysis across various meniscal conditions demonstrated substantial variability. Horizontal cleavage tears (HCTs) resulted in heightened contact pressures and diminished joint stability, as evidenced by increased ACL stress attributed to compensatory mechanisms in the presence of meniscal tears. Conversely, transplantation procedures exhibited a mitigating effect, maintaining joint mechanics closer to intact conditions and minimizing alterations in ACL forces. These trends persisted at 30 degrees of knee flexion, where significant increases in ACL forces were observed in partial and complete HCT conditions. Conclusions: This study uncovers the biomechanical impacts of meniscal injuries, demonstrating how the ACL compensates for various meniscus conditions. In contrast, transplantation and repair conditions only slightly increase the stress on the ACL, putting much less strain on the ACL and supporting structures of the knee joint than an unrepaired tear.

A Combination of Classification Robust Adaptive Kalman Filter with PPP-RTK to Improve Fault Detection for Integrity Monitoring of Autonomous Vehicles

Real-time integrity monitoring (IM) is essential for autonomous vehicle positioning, requiring high availability and manageable computational load. This research proposes using precise point positioning real-time kinematic (PPP-RTK) as the positioning method, combined with an improved classification adaptive Kalman filter (CAKF) for processing. PPP-RTK enhances IM availability by allowing undifferenced and uncombined observations, enabling individual observation exclusion during fault detection and exclusion (FDE). The CAKF reduces FDE computational load by using a robustness test instead of traditional FDE methods, improving precision and availability in protection level estimation. Epoch-wise weighting adjustments in the robustness test create a more accurate stochastic model, aided by an adaptive unit weight variance (UWV) calculated with a sliding window, achieving a 7–28% UWV reduction. Three test scenarios with up to four simultaneous faults in code and phase observations, ranging from 1 to 200 m and 0.4 to 20 m, respectively, demonstrated successful identification and de-weighting of faults, resulting in maximum positioning errors of 6 mm (horizontal) and 11 mm (vertical). The method reduced FDE computational load by 50–99.999% compared to other approaches.

Genomic exploration of the journey of Plasmodium vivax in Latin America.

Plasmodium vivax is the predominant malaria parasite in Latin America. Its colonization history in the region is rich and complex, and is still highly debated, especially about its origin(s). Our study employed cutting-edge population genomic techniques to analyze whole genome variation from 620 P. vivax isolates, including 107 newly sequenced samples from West Africa, Middle East, and Latin America. This sampling represents nearly all potential source populations worldwide currently available. Analyses of the genetic structure, diversity, ancestry, coalescent-based inferences, including demographic scenario testing using Approximate Bayesian Computation, have revealed a more complex evolutionary history than previously envisioned. Indeed, our analyses suggest that the current American P. vivax populations predominantly stemmed from a now-extinct European lineage, with the potential contribution also from unsampled populations, most likely of West African origin. We also found evidence that P. vivax arrived in Latin America in multiple waves, initially during early European contact and later through post-colonial human migration waves in the late 19th-century. This study provides a fresh perspective on P. vivax's intricate evolutionary journey and brings insights into the possible contribution of West African P. vivax populations to the colonization history of Latin America.