Accurate Output Research Articles

Evaluating Sequence Alignment Tools for Antimicrobial Resistance Gene Detection in Assembly Graphs

Antimicrobial resistance (AMR) is an escalating global health threat, often driven by the horizontal gene transfer (HGT) of resistance genes. Detecting AMR genes and understanding their genomic context within bacterial populations is crucial for mitigating the spread of resistance. In this study, we evaluate the performance of three sequence alignment tools—Bandage, SPAligner, and GraphAligner—in identifying AMR gene sequences from assembly and de Bruijn graphs, which are commonly used in microbial genome assembly. Efficiently identifying these genes allows for the detection of neighboring genetic elements and possible HGT events, contributing to a deeper understanding of AMR dissemination. We compare the performance of the tools both qualitatively and quantitatively, analyzing the precision, computational efficiency, and accuracy in detecting AMR-related sequences. Our analysis reveals that Bandage offers the most precise and efficient identification of AMR gene sequences, followed by GraphAligner and SPAligner. The comparison includes evaluating the similarity of paths returned by each tool and measuring output accuracy using a modified edit distance metric. These results highlight Bandage’s potential for contributing to the accurate identification and study of AMR genes in bacterial populations, offering important insights into resistance mechanisms and potential targets for mitigating AMR spread.

Evaluating the Enhanced Bathtub Model for Coastal Flood Risk Assessment in Table Bay, South Africa

ABSTRACTCoastal zones are susceptible to increasing pressures from urban development and natural hazards, such as storm events, climate change, and rising sea levels. The GIS‐based enhanced bathtub model (eBTM) enables the identification of areas at risk of flooding as a baseline for disaster management and coastal adaptation. This study aims to establish the methodological robustness of the eBTM for coastal flood modeling, by analyzing eight sites flooded during a recent storm event in Table Bay, Cape Town by comparing eBTM outputs with observed flood extent data collected after the storm. The validation showed that for 74% of the 332 validation points the spatial modeling error was < 6 m and for 56% below 3 m. The root mean square error for the model was 4.88 m, indicating an acceptable level of accuracy of the eBTM outputs for coastal risk assessments where more sophisticated models are unavailable.

Light Output Function and Pulse-Shape Discrimination Capability of p-Terphenyl Organic Scintillator in Wide Neutron Energy Range of 1.1 to 19 MeV

In this work, we studied the light-output properties, efficiency function, as well as the pulse-shape discrimination (PSD) capability of p-Terphenyl scintillator. The selected solid cylindrical scintillation detector has a thickness of 45 mm and a diameter of 45 mm. Recently presented studies of light-output functions have only been measured for low-neutron energies. Our motivation has been to determine the light output function for p-Terphenyl scintillator more accurately over a wider neutron energy range. The measurements have been carried out with mono-energetic neutron beams in the wide energy range from 1.1 to 19 MeV. The neutron–gamma spectrometric system which we developed has been used for the measurement. The input analog signal from the detector was digitized with a fast 12-bits analog to digital converter with a sampling frequency of 1 GHz. Measured data from the detector are processed into the gamma and neutron spectra. The accurate light output function for the p-Therphenyl scintillator has been calculated. The pulse-shape discrimination capability, as well as the detection efficiency, of a p-Terphenyl scintillator are lower in comparison with a NE-213 equivalent detector.

Power or speed: Which metric is more accurate for modelling endurance running performance on track?

This study aimed to compare the accuracy of the power output, measured by a power meter, with respect to the speed, measured by an inertial measurement unit (IMU) and a global navigation satellite system (GNSS) sport watch to determine the critical power (CP) and speed (CS), work over CP (W') and CS (D'), and long-duration performance (i.e., 60 min). Fifteen highly trained athletes randomly performed seven time trials on a 400m track. The CP/CS and W'/D' were defined through the inverse of time model using the 3, 4, 5, 10, and 20 min trials. The 60 min performance was estimated through the power law model using the 1, 3, and 10 min trials and compared with the actual performance. A lower standard error of the estimate was obtained when using the power meter (CP: 2.7 [2.1-3.3] % and W': 13.8 [10.4-17.3] %) compared to the speed reported by the IMU (CS: 3.4 [2.5-4.3] %) and D': 20.7 [16.6-24.7] %) and GNSS sport watch (CS: 3.4 [2.5-4.3] % and D': 20.6 [16.7-24.7] %). A lower coefficient of variation was also observed for the power meter (4.9 [3.7-6.1] %) Regarding the speed reported by the IMU (10.9 [7.1-14.8] %) and GNSS sport watch (10.9 [7.0-14.7] %) in the 60 min performance estimation, the power meter offered lower errors than the IMU and GNSS sport watch for modelling endurance performance on the track.

Efficient Method for Photovoltaic Power Generation Forecasting Based on State Space Modeling and BiTCN

As global carbon reduction initiatives progress and the new energy sector rapidly develops, photovoltaic (PV) power generation is playing an increasingly significant role in renewable energy. Accurate PV output forecasting, influenced by meteorological factors, is essential for efficient energy management. This paper presents an optimal hybrid forecasting strategy, integrating bidirectional temporal convolutional networks (BiTCN), dynamic convolution (DC), bidirectional long short-term memory networks (BiLSTM), and a novel mixed-state space model (Mixed-SSM). The mixed-SSM combines the state space model (SSM), multilayer perceptron (MLP), and multi-head self-attention mechanism (MHSA) to capture complementary temporal, nonlinear, and long-term features. Pearson and Spearman correlation analyses are used to select features strongly correlated with PV output, improving the prediction correlation coefficient (R2) by at least 0.87%. The K-Means++ algorithm further enhances input data features, achieving a maximum R2 of 86.9% and a positive R2 gain of 6.62%. Compared with BiTCN variants such as BiTCN-BiGRU, BiTCN-transformer, and BiTCN-LSTM, the proposed method delivers a mean absolute error (MAE) of 1.1%, root mean squared error (RMSE) of 1.2%, and an R2 of 89.1%. These results demonstrate the model’s effectiveness in forecasting PV power and supporting low-carbon, safe grid operation.

A Review of the Effect of Task Complexity on Syntactic Complexity in Second Language Writing in China

In the 1990s, research on task complexity became prevalent and gradually scholars have investigated how task complexity of writing tasks affects second language learners' writing performance. Skehan synthesized a measure of learners' proficiency in second language writing in terms of complexity, accuracy and fluency (CAF) of linguistic output. Among them, syntactic complexity, which refers to the range of variation and complexity of the form of linguistic output, has received more and more attention from researchers of second language writing in recent years, and has become one of the hot spots of research in the field of second language acquisition. This paper relies on the CNKI to search for studies on effect of task complexity on syntactic complexity of L2 writing in China, and through summarizing, organizing and analyzing these literatures, it can be found that the theoretical guidance of existing studies is mainly based on the cognitive hypothesis, supplemented by the Limited Attentional capacity Model. Researchers have varied task complexity based on the resource-directing and resource-dispersing to investigate its effect on syntactic complexity, but the findings have not been consistent, which may be due to geographic pedagogical differences or subtle differences in study design.

Decision-making around removal of indwelling urinary catheters after pituitary surgery.

Diabetes insipidus (DI) is a common complication following pituitary surgery, causing significant health issues if left untreated. As part of the diagnostic process, accurate urinary output monitoring via indwelling urinary catheters (IDUCs) is essential, despite risks such as urinary tract infections and hindered recovery. Research on IDUC removal after pituitary surgery remains scarce. To explore health professionals' perspectives on IDUC management following pituitary surgery. Employing a qualitative design, semistructured interviews were conducted with 15 professionals in the neurosurgical ward of a Dutch academic hospital. Four themes emerged: Concerns about missing identifying DI, patient-nurse dynamics, workload management, and lack of shared decision making. The findings underscore the need to balance clinical needs with patient care efficiency. There is a need for evidence-based guidelines and a multidisciplinary approach to optimise IDUC management, given the importance of patient-centred care and shared decision-making.

Measuring achievements: Can cohesion policy programmes effectively monitor their performance?

The paper investigates the ability of Cohesion Policy programmes to define accurate policy outputs and to reliably monitor their own performance through accurate indicators. Specifically, the analysis explores the extent to which indicators and output targets set by ERDF programmes for monitoring and evaluation purposes are revised over time, seeking to identify specific patterns related to different areas such as spending categories, typology of regions, etc. Our findings highlight significant challenges faced by programmes in establishing realistic targets, as frequent and substantial changes are introduced to a vast majority of them. However, we also observe that only a small proportion of indicators is modified over time, suggesting relative stability in the overall objectives of programmes. The paper provides useful evidence for the ongoing debate on whether adopting a fully-performance based model, where access to funds is contingent upon achieving results/outputs, would enhance the efficiency and effectiveness of Cohesion Policy.

Analyzing the effects of shading on power output in curved photovoltaic array on airship

This study investigates the use of photovoltaic (PV) arrays on airships, focusing on challenges due to the curved structure that affects solar radiation distribution. A novel Photo-Thermal-Electric Model (PTEM), developed in MATLAB/Simulink, is introduced to simulate PV array behavior under various conditions, including the impact of solar altitude, laying angle, and airship flight attitude on power generation. PTEM's effectiveness is demonstrated through detailed power output graphs, revealing that radiation gradients significantly affect power generation, often leading to overestimations in the Photo-Thermal Model (PTM) model by 3 %–30 %. Comparative analysis shows that PTEM provides more accurate energy output estimates for individual PV modules, especially in areas with pronounced radiation gradients, differing from PTM estimates by hundreds of watts. These findings offer valuable insights for enhancing PV module lifespan and optimizing energy system designs, contributing to more efficient renewable energy solutions for long-endurance aircraft.

An artificial neural network based approach for harmonic component prediction in a distribution line

With the increasing use of nonlinear devices in both generation and consumption of power, it is essential that we develop accurate and quick control for active filters to suppress harmonics. Time delays between input and output are catastrophic for such filters which rely on real-time operation. Artificial Neural Networks (ANNs) are capable of modeling complex nonlinear systems through adjustments in their learned parameters. Once properly trained, they can produce highly accurate predictions at an instantaneous time frame. Leveraging these qualities, various complex control systems may be replaced or aided by neural networks to provide quick and precise responses. This paper proposes an ANN-based approach for the prediction of individual harmonic components using minimal inputs. By extracting and analyzing the nature of harmonic component magnitudes obtained from the survey of a particular area through real-time measurements, a sequential pattern in their occurrence is observed. Various neural network architectures are trained using the collected data and their performances are evaluated. The best-performing model, whose losses are minimal, is then used to observe the harmonic cancellation for multiple unseen cases through a simplified simulation in hardware-in-the-loop. These neural network structures, which produce instantaneous and accurate outputs, are effective in harmonic filtering.

Exploiting the land use to predict shallow landslide susceptibility: A probabilistic implementation of LAPSUS-LS

Due to the significant role of land use on the occurrence of rainfall-induced shallow landslides, this factor is commonly employed as a landslide susceptibility predictor. However, the land use classification is oftentimes very broad, neglecting the proven mechanical and hydrogeological role of the land management on slope stability. Given the necessity of including spatially distributed and management-specific inputs, the process-based landscape evolution model LAPSUS-LS was chosen and adapted to achieve a probabilistic approach which takes into account land management as an input by adopting management-specific values of root cohesion. The model was applied to four test sites in the Oltrepò Pavese (Italy), where different vineyard management techniques play a significant role in triggering landslides. The results for the four test areas had, cumulatively, an Area Under the Roc curve greater than 0.73, with false negative cells being < 1 % of the total for all simulations. In the model’s application, land use practices characterised by higher root cohesion proved to benefit slope stability, whereas tilled vineyards, shrublands and abandoned vineyards were more prone to the formation of shallow landslides. In addition, we found that the inclusion of management-specific input parameters produced more accurate outputs and that in catchments characterised by average slope angles lower than 15°, varying the vineyard management, did not appear to affect the landslide susceptibility. Due to the model’s high dependency on the land use and its ability to include land management, it can take into account the spatial variability of input values such as the root cohesion. Additionally, it can be applied i) to manage current conditions, ii) to explore future land use change, iii) to study less invasive yet beneficial land use management change scenarios and iv) provide farmers of at-risk areas insight on how to improve slope stability.

Generative Artificial Intelligence and Prompt Engineering: A Primer for Orthopaedic Surgeons.

» Generative artificial intelligence (AI), a rapidly evolving field, has the potential to revolutionize orthopedic care by enhancing diagnostic accuracy, treatment planning, and patient management through data-driven insights and personalized strategies.» Unlike traditional AI, generative AI has the potential to generate relevant information for orthopaedic surgeons when instructed through prompts, automating tasks such as literature reviews, streamlining workflows, predicting health outcomes, and improving patient interactions.» Prompt engineering is essential for crafting effective prompts for large language models (LLMs), ensuring accurate and reliable AI-generated outputs, and promoting ethical decision-making in clinical settings.» Orthopaedic surgeons can choose between various prompt types-including open-ended, focused, and choice-based prompts-to tailor AI responses for specific clinical tasks to enhance the precision and utility of generated information.» Understanding the limitations of LLMs, such as token limits, context windows, and hallucinations, is crucial for orthopaedic surgeons to effectively use generative AI while addressing ethical concerns related to bias, privacy, and accountability.

Strategies for designing machine learning models in renewable energy with insufficient data

In the area of renewable energy including wind, solar, and electricity, Machine Learning has established a far-reaching conceptual and algorithmic design framework. However, the design of Machine Learning architectures typically relies on substantial amounts of data. The data acquisition process often involves complex tasks of gathering, cleaning, and labeling data. This challenge is exacerbated in the energy sector due to concerns related to data security and the uniqueness of development initiatives, leading to a potential scarcity of available data. Quite commonly, researchers encounter a vast array of innovative projects that have not been studied in the past and therefore only limited data become available. Therefore, there is an urgent and highly motivated need to develop machine learning strategies that can construct reliable models in the presence of limited data. In this study, three machine learning models tailored for small-scale data scenarios are developed and analyzed. The first model, a fuzzy cluster ensemble approach, focuses on utilizing small training samples treated as the core of clustering. By applying clustering algorithms, one derives membership functions reflecting the structure of data that facilitate accurate predictions. The second model, a kernel integration framework, combines kernel functions with limited training data to calculate activation levels, enabling accurate prediction outputs. For both models, the final predictions are generated by applying weighted aggregations, where the weights are derived from the respective membership grades and activation levels of the data. Additionally, a neural network contrastive learning model is introduced, which enhances the training process by employing pairwise comparisons among data points. This method effectively increases the amount of useable training data, improving the model's ability to make accurate predictions. Together, these approaches provide a robust solution for making reliable predictions even in data-scarce scenarios within renewable energy research. Moreover, the predictive performance of these models is quantified using the Root Mean Square Error criterion. This study also assesses the performance of the developed models by forming a granular characterization of results delivered by the designed models. A comprehensive suite of experimental studies is included, accompanied by an exhaustive comparative analysis. Compared to conventional machine learning models, especially neural networks, the designed models offer reliable results even with limited training data, showing an improvement of around 20 %, thus laying the groundwork for digitization processes in the realm of sustainable energy.

Separated spacecraft attitude algorithm research based on star sensor multi-information fusion

Faced with the problem of poor anti-interference ability of star map recognition technology, in order to improve its anti-interference ability and achieve higher attitude measurement accuracy, this article proposes a star map recognition method based on binary search trees. Star sensors and gyroscopes are combined to solve the problem of the poor real-time performance of star sensors, optimizing the Extended Kalman Filtering (EKF) algorithm. This improves attitude output accuracy and formulates corresponding attitude measurement schemes considering the situation where the star sensor is not visible. The results showed that the improved star pattern recognition algorithm had better adaptability and robustness than the rasterization algorithm, with a high recognition success rate of 95.6% when the star sensor field of view was 3°*3°, while the rasterization algorithm cannot recognize small fields of view. The recognition success rate of the algorithm was 98.6% when the standard deviation of magnitude noise was 2.0 pixels, which was 15.4% higher than the rasterization algorithm. Unlike the EKF algorithm, the optimized EKF algorithm had a higher attitude output accuracy, with an average error of about 0.000 211°; compared to before optimization, it has increased by 93.43%. In the attitude measurement scheme, the relative attitude angle measurement error was small, and the maximum attitude measurement error in the rolling angle direction was 1.8 × 10−3°. This paper adopts a method to achieve high-precision satellite attitude measurement with good adaptability, which can be applied in complex space missions.

Water Demand Forecasting in Multiple District Metered Areas Based on a Multi-Scale Correction Module Neural Network Architecture

Short-term water demand forecasting (STWDF) for multiple spatially and temporally correlated District Metering Areas (DMAs) is an essential foundation for achieving more refined management of urban water supply networks. However, due to the greater uncertainty associated with specific DMA demand compared to overall water usage, accurately predicting STWDF poses significant challenges. This study introduces an innovative network architecture—the multi-scale correction module neural network, built upon Long Short-Term Memory (LSTM) networks and Convolutional Neural Networks (CNN) enhanced with Attention mechanisms—for simultaneous STWDF with a temporal resolution of one hour over a week for 10 DMAs located in a single city in northern Italy. This framework utilizes multivariate corrections to refine and enhance the output accuracy. The results reveal that, in comparison to traditional Gated Recurrent Unit or LSTM models, the proposed model with integrated correction modules, particularly those that leverage inter-DMA correlations, improves performance across all evaluation metrics by an average of 5%-20% per DMA. Additionally, it consistently delivers superior accuracy across three scenarios: single DMA forecasting, total water demand, and extreme conditions, while maintaining stable performance throughout. Furthermore, the interpretability analysis underscores the feasibility of this innovative structure and highlights the contribution of meteorological features to the predictive model in some DMA-level STWDF. The unified input-output framework elegantly simplifies the STWDF process across multiple DMAs, providing new insights and methodologies for future research in this domain.

Automobile Intelligent Vehicle-machine and Human-computer Interaction System based on Big Data

This paper measures the accelerator, brake pedal, clutch, transmission device and steering wheel under different driving conditions in real-time and accurately on the simulation experiment platform. The goal is to conduct human-machine-road system interaction in a virtual simulation of human-vehicle-road systems. Fitting test data establishes the mathematical model of traffic control parameters. In terms of hardware, the distributed architecture of upper and lower computers is utilized. The system communicates point-to-point with the host computer through the RS-232 serial port. The system adopts multi-thread technology and serial communication technology. The simulation system of driving operation is designed with the visual central controller. The system takes 89S52 as the core. The slave program is written in C language. Then, the system establishes a multi-target coordinated obstacle avoidance method based on a multi-sensor information vehicle cooperation collision avoidance method. The multi-vehicle cooperation obstacle avoidance problem is transformed into an optimal control problem under multiple constraints. Simulation analysis shows that the velocity and displacement obtained by the multi-robot collaborative collision avoidance method are in good agreement with the measured values. Compared with the time series algorithm, the output accuracy of the proposed collaborative collision avoidance algorithm is significantly reduced, and the changes in velocity and displacement in the time domain are more stable.

Advanced Design and Implementation of a 2-Channel, Multi-Functional Therapeutic Electrical Stimulator

This research introduces the design, implementation, and rigorous evaluation of a novel 2-channel, multi-functional therapeutic electrical stimulator, meticulously engineered to meet the stringent demands of contemporary clinical applications. The device integrates a high-speed R-2R ladder DAC and a sophisticated pulse generator unit, capable of producing twelve essential current waveforms with fully adjustable parameters, including pulse amplitude, pulse duration, and pulse repetitive frequency. The proposed driving stage unit ensures precise voltage-to-current conversion, delivering stable and accurate output currents even under varying load conditions, which effectively simulate the diverse impedance characteristics of human tissue. Extensive testing confirmed the compliance with international medical standards, notably IEC 60601-1, IEC 60601-1-2, and IEC 60601-2-10. The experimental results underscore the device’s consistent operation within prescribed safety and performance thresholds, with all deviations in pulse parameters remaining well below the permissible limits. Furthermore, the proposed electrical stimulator demonstrated exceptional stability across variable load conditions, as evidenced by minimal amplitude errors and high correlation between waveform characteristics. These findings highlight the proposed device’s robustness and its potential as a versatile tool for a wide range of therapeutic applications, including pain management, muscle stimulation, and nerve rehabilitation, thus marking a significant advancement in the field of therapeutic electrical stimulation.

Uncertainty quantification of acoustic metamaterial bandgaps with stochastic material properties and geometric defects

Acoustic metamaterials are a subject of increasing study and utility. Through designed combinations of geometries with material properties, acoustic metamaterials can be built to arbitrarily manipulate acoustic waves for various applications. Despite the theoretical advances in this field, however, acoustic metamaterials have seen limited penetration into industry and commercial use. This is largely due to the difficulty of manufacturing the intricate geometries that are integral to their function and the sensitivity of metamaterial designs to material batch variability and manufacturing defects. Capturing the effects of stochastic material properties and geometric defects requires empirical testing of manufactured samples, but this can quickly become prohibitively expensive with higher precision requirements or with an increasing number of input variables. This paper demonstrates how uncertainty quantification techniques, and more specifically the use of polynomial chaos expansions and spectral projections, can be used to greatly reduce sampling needs for characterizing acoustic metamaterial dispersion curves. With a novel method of encoding geometric defects in a 1D, interpretable, resolution-independent way, our uncertainty quantification approach allows for both stochastic material properties and geometric defects to be considered simultaneously. Two to three orders of magnitude sampling reductions down to ∼100 and ∼101 were achieved in 1D and 7D input space scenarios, respectively. Remarkably, this reduction in sampling was possible while preserving accurate output probability distributions of the metamaterial performance characteristics (bandgap size and location).

Hybrid artificial neural networks-based approaches predicting the heat transfer and flow characteristics of manifold microchannels

Aiming at meeting the requirement of high heat flux electronics cooling, the geometric complexity of manifold microchannels as an energy-saving technique has been increasing rapidly. It is well worth exploring optimal prediction models for the heat transfer and flow characteristics of manifold microchannels to reduce computing resources and time costs in the optimization process. In this paper, hybrid prediction approaches based on artificial neural networks are proposed for manifold microchannels. Four optimization algorithms, namely, genetic algorithm, particle swarm optimization algorithm, artificial hummingbird algorithm, and zebra optimization algorithm were adopted to enhance the prediction performance. Initially, all prediction models were trained and tested by a numerical database including six input parameters, and the thermal resistance and pumping power were taken as the target outputs. Compared to the original method, hybrid prediction models show higher accuracy and reliability on the numerical data with regression coefficients beyond 0.987. The prediction model optimized by the artificial hummingbird algorithm shows the best performance with the mean average error for thermal resistance and pumping power decreased by 88.7% and 81.4% respectively. Additionally, the runtime of all prediction models is compared and the model optimized by the zebra optimization algorithm takes the longest time, but is still two orders of magnitude shorter than that of the traditional simulation method. Finally, the generalizability of all prediction models is further validated by a database amassed from six experimental studies on manifold microchannels. The results showed that three hybrid models can provide accurate outputs with regression coefficients beyond 0.84 which differs from the poor prediction of the original method. The model optimized by the zebra optimization algorithm shows superior prediction performance for six individual studies, two fluids, three fin shapes, and wide flow rate conditions. The trained hybrid prediction models in this study perform higher accuracy and can be cost-effective and reliable prediction tools for the rapid optimization process of various manifold microchannel applications.

A genetic algorithm approach for flexible power point tracking in partial shading conditions

A novel Flexible Power Point Tracking (FPPT) algorithm based on Genetic Algorithms (GAs) is presented in this study. The algorithm is designed to effectively handle fluctuating solar irradiation levels and partial shading scenarios. The main objective is to enhance power output and tracking accuracy under dynamic environmental conditions, based on extensive simulations. The results showed superior performance for power output, tracking accuracy, and convergence speed than traditional techniques. Its adaptability to changing environmental conditions renders it suitable for real-time implementation, thereby contributing to efficient power generation in PV systems. Simulations demonstrate the efficacy of the algorithm in maximizing power extraction and meeting grid-code requirements under dynamic conditions. The comparative analyses underscore the superiority of the GA-based FPPT algorithm, highlighting its potential to significantly improve power point tracking in photovoltaic systems. This shows significance of adaptive algorithms in modern PV systems, particularly for reducing the effects of partial shading on energy yield. More research is required on the refinement of the algorithm's performance for long-term reliability and scalability in larger PV installations fostering advancements in photovoltaic power point tracking toward sustainable energy systems.