Time Consumption Research Articles

LTA‐OM: Long‐term association LiDAR–IMU odometry and mapping

AbstractThis paper focuses on the Light Detection and Ranging (LiDAR)–Inertial Measurement Unit (IMU) simultaneous localization and mapping (SLAM) problem: How to fuse the sensor measurement from the LiDAR and IMU to online estimate robot's poses and build a consistent map of the environment. This paper presents LTA‐OM: an efficient, robust, and accurate LiDAR SLAM system. Employing fast direct LiDAR‐inertial odometry (FAST‐LIO2) and Stable Triangle Descriptor as LiDAR–IMU odometry and the loop detection method, respectively, LTA‐OM is implemented to be functionally complete, including loop detection and correction, false‐positive loop closure rejection, long‐term association (LTA) mapping, and multisession localization and mapping. One novelty of this paper is the real‐time LTA mapping, which exploits the direct scan‐to‐map registration of FAST‐LIO2 and employs the corrected history map to provide direct global constraints to the LIO mapping process. LTA mapping also has the notable advantage of achieving drift‐free odometry at revisit places. Besides, a multisession mode is designed to allow the user to store the current session's results, including the corrected map points, optimized odometry, and descriptor database for future sessions. The benefits of this mode are additional accuracy improvement and consistent map stitching, which is helpful for life‐long mapping. Furthermore, LTA‐OM has valuable features for robot control and path planning, including high‐frequency and real‐time odometry, driftless odometry at revisit places, and fast loop closing convergence. LTA‐OM is versatile as it is applicable to both multiline spinning and solid‐state LiDARs, mobile robots and handheld platforms. In experiments, we exhaustively benchmark LTA‐OM and other state‐of‐the‐art LiDAR systems with 18 data sequences. The results show that LTA‐OM steadily outperforms other systems regarding trajectory accuracy, map consistency, and time consumption. The robustness of LTA‐OM is validated in a challenging scene—a multilevel building having similar structures at different levels. To demonstrate our system, we created a video which can be found on https://youtu.be/DVwppEKlKps.

Research on two stage obstacle-avoidance trajectory planning and trajectory tracking control in curves

The existing obstacle-avoidance trajectory planning and trajectory tracking control algorithms have limitations such as long-time consumption, high failure rate in dynamic traffic environments, and insufficient trajectory tracking accuracy in curved roads. Based on the above problems, this paper designs a two stage obstacle-avoidance trajectory planner based on nonlinear optimization theory. In first stage Part-NLP, only considering the safety obstacle avoidance, a point mass model and linearization constraints are established to quickly solve the initial trajectory. In the second stage Full-NLP, considering smooth soft constraints comprehensively, the initial trajectory is optimized by establishing driving corridors and a lightweight iterative framework. In control module, this paper selects a linear quadratic form lateral trajectory tracking controller, and the parameters were optimized through the carnivorous plant algorithm. The joint simulation results show that in dynamic traffic environment of curved roads, the two stage planner proposed can accurately plan safe and smooth obstacle avoidance trajectories, and there is a significant reduction in time consumption compared to traditional NLP algorithms. The control strategy can accurately track the planned trajectories, with lateral error controlled within plus or minus 0.1 m, heading error controlled within plus or minus 0.15 rad, speed tracking error controlled within plus or minus 0.15 m/s, and vehicle yaw angle error controlled within plus or minus 0.04 rad; the hardware-in-loop test results indicate that the controller can achieve real-time and accurate trajectory tracking.

Surrogate modelling for ice accretion prediction on wind turbine blades based on support vector regression

AbstractIce accretion poses a significant challenge, threatening the safety and operational stability of wind turbines in extreme cold weather. Accurate estimation of ice accretion is essential in minimizing this concern. Data‐driven techniques typically rely on extensive historical datasets, and simulation‐based methods may be time‐consuming. To address these challenges, surrogate modelling for ice accretion prediction is proposed, utilizing support vector regression. This surrogate model undergoes rigorous evaluation to ensure reliability. The surrogate model reduces time consumption by four orders of magnitude compared to simulation, with only a 2.5% decrease in accuracy. The analysis reveals that real‐time pitch angle adjustments, informed by both the surrogate model and meteorological data, can effectively reduce ice accretion on wind turbine blades in cold climates.

Automated Landmark Annotation for Morphometric Analysis of Distal Femur and Proximal Tibia

Manual anatomical landmarking for morphometric knee bone characterization in orthopedics is highly time-consuming and shows high operator variability. Therefore, automation could be a substantial improvement for diagnostics and personalized treatments relying on landmark-based methods. Applications include implant sizing and planning, meniscal allograft sizing, and morphological risk factor assessment. For twenty MRI-based 3D bone and cartilage models, anatomical landmarks were manually applied by three experts, and morphometric measurements for 3D characterization of the distal femur and proximal tibia were calculated from all observations. One expert performed the landmark annotations three times. Intra- and inter-observer variations were assessed for landmark position and measurements. The mean of the three expert annotations served as the ground truth. Next, automated landmark annotation was performed by elastic deformation of a template shape, followed by landmark optimization at extreme positions (highest/lowest/most medial/lateral point). The results of our automated annotation method were compared with ground truth, and percentages of landmarks and measurements adhering to different tolerances were calculated. Reliability was evaluated by the intraclass correlation coefficient (ICC). For the manual annotations, the inter-observer absolute difference was 1.53 ± 1.22 mm (mean ± SD) for the landmark positions and 0.56 ± 0.55 mm (mean ± SD) for the morphometric measurements. Automated versus manual landmark extraction differed by an average of 2.05 mm. The automated measurements demonstrated an absolute difference of 0.78 ± 0.60 mm (mean ± SD) from their manual counterparts. Overall, 92% of the automated landmarks were within 4 mm of the expert mean position, and 95% of all morphometric measurements were within 2 mm of the expert mean measurements. The ICC (manual versus automated) for automated morphometric measurements was between 0.926 and 1. Manual annotations required on average 18 min of operator interaction time, while automated annotations only needed 7 min of operator-independent computing time. Considering the time consumption and variability among observers, there is a clear need for a more efficient, standardized, and operator-independent algorithm. Our automated method demonstrated excellent accuracy and reliability for landmark positioning and morphometric measurements. Above all, this automated method will lead to a faster, scalable, and operator-independent morphometric analysis of the knee.

SchizoBot: Delivering Cognitive Behavioural Therapy for Augmented Management of Schizophrenia

According to WHO, about 1.86 million people in Nigeria and about 24 million people worldwide are living with schizophrenia, having symptoms varying from hallucination to delusion, and distorted speech and thinking. Schizophrenia is a life-long disorder with no cure and thus, patients need continuous management with medications and psychotherapy. However, due to various factors such as the cost of therapy, time consumption, lack of adequate health workers, the unwillingness of patients to engage, and the pandemic, there is a need for an effective alternate medium for providing cognitive behavioural therapy (CBT) to schizophrenia patients. This research aims to develop a chatbot, which is called SchizoBot, delivering CBT for augmented management of schizophrenia. CBT for schizophrenia details, along with FAQs of schizophrenia patients were collected and adopted into a conversational format for pre-processing and model development. The model was developed with artificial neural network (ANN) and trained with the dataset which was split into train-test data to optimize the performance of the model. The result of the ANN showed an accuracy score of 93.97% at 60:40 train-test data split with 200 epochs. This robust system which provides an optimized chatbot platform using ANN as the model classifier for CBT delivery is foreseen to be a windfall to clinicians and patients as an augmentative management tool for schizophrenia. This, therefore, is a relatively low-cost and easily accessible means to significantly improve the health of schizophrenia patients while assisting clinicians in therapy delivery and compensating for the lapses in the administration of CBT to schizophrenia patients.

Question 65 What on‐farm techniques can increase water productivity? General report

AbstractTechnological interventions in tackling water scarcity in agriculture delve into the water productivity issue through available alternative water resources and on‐farm techniques including both structural measures and agronomic practices accompanied by a sound water management approach. On‐farm water management has been addressed under Congress Question 65 through deliberations on irrigation efficiency, soil moisture forecasts and hydrological predictions, efficient and timely distribution of water, controlling non‐beneficial evaporation and reducing non‐returnable losses of irrigation water at existing and state‐of‐the‐art levels using technologies such as SCADA (Supervisory Control and Data Acquisition) and sensor technology. A wide range of land, water and crop‐related important parameters such as soil degradation, soil moisture, zero tillage, conservation agriculture, water demand and supply, future irrigation demand, biologically degradable mulching materials, cost‐effective design of drip irrigation, water quality and cultivation practices through simulation for evaluating environmental impact and adaptation capacity of farmers were addressed. The major outcomes inferred increasing water productivity through optimizing agricultural water use that led to higher crop yields. IoT (Internet of Things) technologies can provide accurate and dynamic information of water consumption and rainfall in real time and have the potential of adapting to climate change scenarios.

FERFusion: A Fast and Efficient Recursive Neural Network for Infrared and Visible Image Fusion

The rapid development of deep neural networks has attracted significant attention in the infrared and visible image fusion field. However, most existing fusion models have many parameters and consume high computational and spatial resources. This paper proposes a fast and efficient recursive fusion neural network model to solve this complex problem that few people have touched. Specifically, we designed an attention module combining a traditional fusion knowledge prior with channel attention to extract modal-specific features efficiently. We used a shared attention layer to perform the early fusion of modal-shared features. Adopting parallel dilated convolution layers further reduces the network’s parameter count. Our network is trained recursively, featuring minimal model parameters, and requires only a few training batches to achieve excellent fusion results. This significantly reduces the consumption of time, space, and computational resources during model training. We compared our method with nine SOTA methods on three public datasets, demonstrating our method’s efficient training feature and good fusion results.

A method for predicting vibration characteristics of track structure based on rail acceleration and deep learning

ABSTRACT The dynamic test on track structure plays a crucial role in evaluating the operational state. Nonetheless, existing testing approaches involve numerous sensors, resulting in high costs and time consumption. This study proposes an economical, high-precision, and swift solution to predict vibration responses and displacement variations in track structures using rail acceleration. Taking the ordinary track structure and the damping track structure as examples, based on the high correlation between the vibration responses of the rail, track slab and tunnel wall, combined with the proposed Bayesian optimized temporal convolutional neural network model (BOA-TCN), and taking the rail acceleration envelope and vibration level as input, the vibration of tunnel wall and the displacement of rail and track slab are estimated. The results demonstrate that the BOA-TCN model effectively solves the limitation of traditional neural network in accurately capturing vibration characteristics in the frequency range of 1 ~ 100 Hz. The absolute error of the total vibration level prediction is significantly reduced, only 0.74 dB. The single point prediction accuracy of track structure displacement is increased by 80%, the absolute error is 50% of the traditional model, and R2 can be increased to more than 90%.

Quantum mechanical study of transition metal hydrides: Comparison of determined molecular properties with experimental data.

This study compares results of four relativistic pseudopotential basis sets, which differ mainly by their size: double-zeta introduced by Hay and Wadt from Los Alamos National Laboratory (LANL2DZ), triple-zeta based on Stuttgart energy-consistent scalar-relativistic pseudopotential (SDD3), its extension with 2fg polarization functions, and combination of Stuttgart pseudopotentials with quintuple-zeta cc-pV5Z base (SDD5). Hydrides of transition metals from Cr to Zn group are chosen as reference molecules. The coupled cluster method (CCSD(T)) is used for evaluation of selected molecular characteristics. Interatomic distances, dissociation energies, vibration modes, and anharmonicity constants are determined and compared with available experimental data. As expected, the accuracy of basis depends mainly on its size. However, only moderate modification of SDD3 basis set significantly improves its accuracy, which becomes comparable to the largest basis set. Nevertheless, the time consumption is significantly lower.

Non-destructive detection method and experiment of pomelo volume and flesh content based on image fusion

Volume and flesh content are crucial factors in assessing the quality of pomelo fruits. However, the lack of accurate and efficient methods for measuring volume and flesh content hampers their application in fruit grading. To address the issue, this study proposes a non-destructive detection method for pomelo fruit volume and edible percent based on image fusion. This method combines the external appearance images and internal X-ray image to construct a three-dimensional model of the pomelo fruit using various slice contour fitting methods. This model enables the acquisition of pomelo volume and flesh thickness information. Subsequently, the flesh content is determined using the grayscale and thickness fitting method (GTFM), which combines the flesh model thickness with the grayscale information obtained from the X-ray image. Extensive experiments were conducted to validate the proposed method, demonstrating its effectiveness. The results indicate a high fitting coefficient (R2) of 0.989 and a mean absolute percentage error (MAPE) of 1.87% for volume measurement, with a time consumption of 486.17 ms. The flesh content measurement yielded an R2 of 0.923, a root mean square error (RMSE) of 2.85%, and a time consumption of 306.71 ms. Compared with existing sorting technology and methods, the volume measurement error is reduced by approximately 1.2%; the flesh content R2 is increased by approximately 0.49 with a halving of the time consumption. The quality assessment of this study is more comprehensive and more accurate.

Intelligent Reduced-Dimensional Scheme of Model Predictive Control for Aero-Engines

Model Predictive Control (MPC) has many advantages in controlling an aero-engine, such as handling actuator constraints, but the computational burden greatly obstructs its application. The current multiplex MPC can reduce computational complexity, but it will significantly decrease the control performance. To guarantee real-time performance and good control performance simultaneously, an intelligent reduced-dimensional scheme of MPC is proposed. The scheme includes a control variable selection algorithm and a control sequence coordination strategy. A constrained optimization problem with low computational complexity is first constructed by using only one control variable to define a reduced-dimensional control sequence. Therein, the control variable selection algorithm provides an intelligent mode to determine the control variable that has the best control effect at the current sampling instant. Furthermore, a coordination strategy is adopted in the reduced-dimensional control sequence to consider the interaction of control variables at different predicting instants. Finally, an intelligent reduced-dimensional MPC controller is designed and implemented on an aero-engine. Simulation results demonstrate the effectiveness of the intelligent reduced-dimensional scheme. Compared with the multiplex MPC, the intelligent reduced-dimensional MPC controller enhances the control quality significantly by 34.06%; compared with the standard MPC, the average time consumption is decreased by 64.72%.

Exploring the Potential of Pretrained CNNs and Time-Frequency Methods for Accurate Epileptic EEG Classification: A Comparative Study.

Prompt diagnosis of epilepsy relies on accurate classification of automated electroencephalogram (EEG) signals. Several approaches have been developed to characterize epileptic EEG data; however, none of them have exploited time-frequency data to evaluate the effect of tweaking parameters in pretrained frameworks for EEG data classification. This study compares the performance of several pretrained convolutional neural networks (CNNs) namely, AlexNet, GoogLeNet, MobileNetV2, ResNet-18 and SqueezeNet for the localization of epilepsy EEG data using various time-frequency data representation algorithms. Continuous wavelet transform (CWT), empirical Fourier decomposition (EFD), empirical mode decomposition (EMD), empirical wavelet transform (EWT), and variational mode decomposition (VMD) were exploited for the acquisition of 2D scalograms from 1D data. The research evaluates the effect of multiple factors, including noisy versus denoised scalograms, different optimizers, learning rates, single versus dual channels, model size, and computational time consumption. The benchmark Bern Barcelona EEG dataset is used for testing purpose. Results obtained show that the combination of MobileNetV2, Continuous Wavelet Transform (CWT) and Adam optimizer at a learning rate of 10^-4, coupled with dual-data channels, provides the best performance metrics. Specifically, these parameters result in optimal sensitivity, specificity, f1-score, and classification accuracy, with respective values of 96.06%, 96.15%, 96.08%, and 96.10%. To further corroborate the efficacy of opted pretrained models on exploited Signal Decomposition (SD) algorithms, the classifiers are also being simulated on Temple University database at pinnacle modeling composition. A similar pattern in the outcome readily validate the findings of our study and robustness of deep learning models on epilepsy EEG scalograms.The conclusions drawn emphasize the potential of pretrained CNN-based models to create a robust, automated system for diagnosing epileptiform. Furthermore, the study offers insights into the effectiveness of varying time-frequency techniques and classifier parameters for classifying epileptic EEG data.

The culture of laziness

The development of science and technology has enabled humans to carry out more or less effortlessly many activities which previously would have entailed much physical effort and expenditure of time. While this development enables people to enjoy more time for leisure, it has also encouraged a culture of physical laziness. The same trend appears in the intellectual field as well. Modern computers can in a few moments, carry out tasks which would have entailed an enormous amount of time and mental effort, earlier. Again, this saving of time enables one to focus on the more creative and insightful activities that are specific to human beings. However, the culture of laziness is widespread. It would appear that there is at the present time, a tendency to even attempt to transfer creative intellectual efforts to machines, through the use of generative AI. The writing up of a good piece of research for publication is one of the creative and enjoyable activities afforded to a scientist. While the use of generative AI to obtain help in deciding the most effective way of “saying what one has to say” can be considered acceptable in situations where the writer’s language skills are not well developed, to attempt to use generative AI to decide on the content of the paper, “what to say”, is to attribute to generative AI an ability it does not have. Recently, scientists and sociologists have written about the deficiencies and dangers of this approach to creating knowledge and understanding. To be meaningful, any generative AI output needs to be moderated and evaluated by human intelligence.

An Integrated Approach to Precedence-Constrained Multi-Agent Task Assignment and Path Finding for Mobile Robots in Smart Manufacturing

Mobile robots play an important role in smart factories, though efficient task assignment and path planning for these robots still present challenges. In this paper, we propose an integrated task- and path-planning approach with precedence constrains in smart factories to solve the problem of reassigning tasks or replanning paths when they are handled separately. Compared to our previous work, we further improve the Regret-based Search Strategy (RSS) for updating the task insertions, which can increase the operational efficiency of machining centers and reduce the time consumption. Moreover, we conduct rigorous experiments in a simulated smart factory with different scales of robots and tasks. For small-scale problems, we conduct a comprehensive performance analysis of our proposed methods and NBS-ISPS, the state-of-the-art method in this field. For large-scale problems, we examine the feasibility of our proposed approach. The results show that our approach takes little computation time, and it can help reduce the idle time of machining centers and make full use of these manufacturing resources to improve the overall operational efficiency of smart factories.

Detour-RS: Reroute Attack Vulnerability Assessment with Awareness of the Layout and Resource

Recent decades have witnessed a remarkable pace of innovation and performance improvements in integrated circuits (ICs), which have become indispensable in an array of critical applications ranging from military infrastructure to personal healthcare. Meanwhile, recent developments have brought physical security to the forefront of concern, particularly considering the valuable assets handled and stored within ICs. Among the various invasive attack vectors, micro-probing attacks have risen as a particularly menacing threat. These attacks leverage advanced focused ion beam (FIB) systems to enable post-silicon secret eavesdropping and circuit modifications with minimal traceability. As an evolved variant of micro-probing attacks, reroute attacks possess the ability to actively disable built-in shielding measures, granting access to the security-sensitive signals concealed beneath. To address and counter these emerging challenges, we introduce a layout-level framework known as Detour-RS. This framework is designed to automatically assess potential vulnerabilities, offering a systematic approach to identifying and mitigating exploitable weaknesses. Specifically, we employed a combination of linear and nonlinear programming-based approaches to identify the layout-aware attack costs in reroute attempts given specific target assets. The experimental results indicate that shielded designs outperform non-shielded structures against reroute attacks. Furthermore, among the two-layer shield configurations, the orthogonal layout exhibits better performance compared to the parallel arrangement. Furthermore, we explore both independent and dependent scenarios, where the latter accounts for potential interference among circuit edit locations. Notably, our results demonstrate a substantial near 50% increase in attack cost when employing the more realistic dependent estimation approach. In addition, we also propose time and gas consumption metrics to evaluate the resource consumption of the attackers, which provides a perspective for evaluating reroute attack efforts. We have collected the results for different categories of target assets and also the average resource consumption for each via, required during FIB reroute attack.

Wastewater Nutrient Recovery via Fungal and Nitrifying Bacteria Treatment

In efforts to reduce the consumption of fossil fuels and promote recycling biowaste, there is an interest in the production of post-hydrothermal liquefaction wastewater (HTL-AP) from the hydrothermal liquefaction (HTL) process that converts wet biomass into biocrude oil. This study explores ways of transforming potentially toxic HTL-AP into a fertilizer source for hydroponic cropping systems. This study specifically investigates the integration of the white-rot fungus Trametes versicolor with nitrifying bacteria (Nitrosomonas and Nitrobacter) to convert the organic nitrogen compounds into inorganic nitrogen while also producing the enzyme laccase, which has been shown to remove toxic compounds. This study aims to increase the concentration of nitrate-N to valorize wastewater as a suitable fertilizer by measuring several parameters, including laccase activity, pH, nitrate-N, and ammonia/ammonium-N concentrations, and analyzes interactions to optimize the conversion process. The data support the claim that the simultaneous inoculation of T. versicolor and nitrifying bacteria significantly increases nitrate-N concentrations in HTL-AP, as it increased by 17 times, or an increase of 32.69 mg/L. In addition, HTL-AP treated with T. versicolor and nitrifying bacteria reduced the treatment time by 120 h, highlighting a reduction in personnel time and energy consumption. Therefore, this research accentuates sustainability through fungal and bacterial treatments to develop eco-friendly hydroponic fertilizers. Future research should explore the potential of utilizing the combination of T. versicolor and nitrifying bacteria for the treatment of other industrial wastewaters.

Combined modulation format identification and optical signal-to-noise ratio monitoring with high accuracy and generalizability based on a proposed fused module few-shot learning algorithm in dynamic coherent optical transmissions

Modulation format identification (MFI) and optical signal-to-noise ratio (OSNR) monitoring are important portions of optical performance monitoring (OPM) for future dynamic optical networks. In this paper, we proposed a fusion module few-shot learning (FMFSL) algorithm as an improvement upon the ordinary few-shot learning algorithms for image recognition with the specialty in adopting a combination of a dilated convolutional group and an asymmetric convolutional group to advance the feature extraction. FMFSL algorithm is applied in MFI and OSNR monitoring in coherent optical communication systems with its performance investigated in both back-to-back and fiber transmission scenarios using small-scale constellation diagrams. The results show that FMFSL algorithm can achieve 100% accuracy in MFI and higher OSNR monitoring accuracy compared to the few-shot learning algorithms Deep Nearest Neighbor Neural Network (DN4) and Prototypical Nets (PN) with 2.14% and 4.28% for 64QAM and 3.38% and 8.06% for 128QAM, respectively, without much increase in time consumption. Furthermore, the trained FMFSL algorithm remains excellent in MFI and OSNR monitoring without retraining while employed in back-to-back transmission scenarios with smaller OSNR intervals and fiber transmission scenarios with different amounts of Kerr nonlinearity, demonstrating its high capabilities in generalization and robustness. FMFSL algorithm provides a potential solution for OPM in future dynamic optical networks as a novel machine learning tool.

Time Consumption and Productivity of Sandat Felling Technique in Agroforestry Private Forests in Probolinggo, Indonesia

Agroforestry is a cropping pattern that is commonly applied to private forest management in Indonesia. Agroforestry based private forest is a land-based silviculture that incorporates forestry plants with agricultural crops, plantation crops, and multi-purpose plants. One of the felling techniques used in agroforestry based private forests is the sandat-felling technique (SFT), which is a rope-assist felling technique. The felling technique was used to protect the remaining stand of the agroforestry based private forest. This technique is an innovation in the harvesting of agroforestry based private forests in Indonesia. The time consumption and productivity of this technique are not yet known. This study aims to assess the working time and productivity of SFT in agroforestry based private forests in Probolinggo, East Java, Indonesia. The observed tree-felling technique included rope installation and tree-felling operations. The performance of the SFT was evaluated by analyzing its working time and productivity. The results of the study showed that the total working time of the SFT was 8.65 minutes tree-1, which consisted of 33.34% for rope installation and 66.66% for felling operation. The productivity of the SFT was 2.02 m3 hour-1.

Unearthing Subtle Cognitive Variations: A Digital Screening Tool for Detecting and Monitoring Mild Cognitive Impairment

Early diagnosis of mild cognitive impairment (MCI) is pivotal in mitigating the risk of cognitive impairment and the onset of dementia. However, prevailing clinical cognitive screening tests and biomarker assessment approaches often suffer from drawbacks such as high cost, invasiveness, time consumption, or subjectivity. In China, existing digital cognitive assessment tools face multiple challenges due to their distinctive cultural, language, and healthcare landscape. Therefore, we embarked on a study to develop a digital cognitive assessment tool, evaluate its efficacy in distinguishing between healthy individuals and MCI patients, and examine its acceptability among Chinese older adults. Through a series of symposiums, programming, and interviews with stakeholders, we iteratively designed the "BrainNursing" mobile application. The system consists of eleven single tasks and three dual tasks, each taking only 1–3 minutes. Subsequently, we conducted statistical comparisons of movement kinetics and physiological signals recorded during cognitive testing in 181 older adults to investigate which parameters could serve as effective digital biomarkers for MCI screening. Leveraging machine learning classifiers and a majority voting principle, we evaluated the classification performance of the BrainNursing system in detecting MCI, yielding an accuracy rate of 90.3%. Furthermore, our analysis of movement kinetics revealed that time, score, and sequence features are crucial in cognitive assessment. Contrasting with their healthy counterparts, MCI patients exhibited decreased heart rate variability, increased sympathetic nervous system activity, and weakened autonomic nervous system regulation in response to stimuli during cognitive testing. Finally, user experience feedback indicated that participants universally perceived BrainNursing as a convenient and user-friendly cognitive screening tool and provided valuable insights for further improvement.

Enhancing multi-scenario data-driven energy consumption prediction in campus buildings by selecting appropriate inputs and improving algorithms with attention mechanisms

Effective building energy prediction is vital for sustainable development, especially with an increasing focus on flexibility and elasticity in building energy usage. However, challenges persist in input and algorithm selection including suitable input selection and the balance of calculation time consumption and prediction precision. This study proposed a method for suitable input selection and introduced a CNN-LSTM-SE model for enhancing prediction precision. The CNN-LSTM-SE model integrates a convolutional neural network (CNN) process and a squeeze-and-excitation (SE) block attention mechanism into a long short-term memory (LSTM) neural network. Experiments across seven buildings demonstrate that selecting inputs improves the prediction performance with a 13.94 % decrease in MAPE (mean absolute percent error), a 3.36 % increase in R2 (coefficient of determination), and an 11.20 % decrease in CV-RMSE (coefficient-of-variation of root mean square error) on average compared to not selecting inputs. Moreover, implementing CNN-LSTM-SE yields a 21.44 % MAPE decrease, a 6.25 % R2 increase, and a 21.82 % CV-RMSE decrease compared to alternative algorithms. Simultaneously employing suitable inputs and CNN-LSTM-SE balances time consumption and prediction precision optimally.