Accurate Event Detection Research Articles

LEVERAGING EPILEPSY DETECTION ACCURACY THROUGH BURST ENERGY INTEGRATION IN CWT AND DECISION TREE CLASSIFICATION OF EEG SIGNALS

The biomedical field plays a pivotal role in advancing healthcare by leveraging technological innovations to enhance diagnostics and treatment strategies. In the context of neurological disorders, particularly epilepsy, automated EEG signal processing stands out as a critical facet of biomedical research. The ability to analyze and interpret vast amounts of electroencephalogram (EEG) data using sophisticated techniques, such as Continuous Wavelet Transform (CWT), contributes significantly to the timely and accurate detection of epileptic events. Automated EEG signal processing, which uses advanced algorithms and pattern recognition methods, enables the identification of subtle yet crucial patterns indicative of epileptic activity. This paper presents an in-depth exploration of epilepsy identification using the CWT on the CHB-MIT Scalp EEG database. The study uses nine complex mother wavelets from the Gaussian and Morlet families to look at 8920 EEG segments, including 197 seizure events. The performance of each wavelet in detecting epileptic convulsions within EEG signals is rigorously evaluated. Our study shows that the complex Gaussian wavelet of order 5 (cgau5) emerged as the optimal choice, with a sensitivity of 97.58% and a precision of 97.93%. To improve epilepsy detection, we introduce burst energy, a novel engineered feature. This method gets accurate information about brain activity from the CWT scalogram by detecting ictal and ictal-free EEGs at different energy levels. The use of burst energy has a significant impact on classification performance, highlighting its potential for improved accuracy in epilepsy identification. This comprehensive study contributes valuable insights into selecting appropriate wavelets and introduces an innovative feature for more effective EEG-based epilepsy detection.

Accurate detection of gait events using neural networks and IMU data mimicking real-world smartphone usage

Wearable technologies such as inertial measurement units (IMUs) can be used to evaluate human gait and improve mobility, but sensor fixation is still a limitation that needs to be addressed. Therefore, aim of this study was to create a machine learning algorithm to predict gait events using a single IMU mimicking the carrying of a smartphone. Fifty-two healthy adults (35 males/17 females) walked on a treadmill at various speeds while carrying a surrogate smartphone in the right hand, front right trouser pocket, and right jacket pocket. Ground-truth gait events (e.g. heel strikes and toe-offs) were determined bilaterally using a gold standard optical motion capture system. The tri-dimensional accelerometer and gyroscope data were segmented in 20-ms windows, which were labelled as containing or not the gait events. A long-short term memory neural network (LSTM-NN) was used to classify the 20-ms windows as containing the heel strike or toe-off for the right or left legs, using 80% of the data for training and 20% of the data for testing. The results demonstrated an overall accuracy of 92% across all phone positions and walking speeds, with a slightly higher accuracy for the right-side predictions (∼94%) when compared to the left side (∼91%). Moreover, we found a median time error <3% of the gait cycle duration across all speeds and positions (∼77 ms). Our results represent a promising first step towards using smartphones for remote gait analysis without requiring IMU fixation, but further research is needed to enhance generalizability and explore real-world deployment.

Disruption Dynamics and Charge Transfer of a Single Attoliter Emulsion Droplet Revealed by Combined Fast-Scan Sinusoidal Voltammetry and Short Time Fourier Transform Analysis.

Single-entity electrochemistry has gained significant attention for the analysis of individual cells, nanoparticles, and droplets, which is leveraged by robust electrochemical techniques such as chronoamperometry and cyclic voltammetry (CV) to extract information about single entities, including size, kinetics, mass transport, etc. For an in-depth understanding such as dynamic interaction between the electrode and a single entity, the unconventional fast electrochemical technique is essential for time-resolved analysis. This fast experimental technique is unfortunately hindered by a substantial nonfaradaic response. In this work, we introduce fast-scan sinusoidal voltammetry (FSSV) combined with a short-time Fourier transform (STFT) for analyzing single emulsion droplets. Utilizing ultramicroelectrode and fast potential sweeps up to apparent 200 V/s, we achieved high temporal resolution (8 ms per voltammogram) to capture the current signals during droplet collisions. STFT analysis reveals the amplitude and phase changes, allowing for the accurate detection of collision events even in the absence of redox species. By adopting an algorithm of drift-free baseline subtraction, a conventional CV shape was obtained in FSSV. The reacted charge from the single-entity voltammogram at every 8 ms was also plotted. This method effectively addresses limitations in traditional techniques, providing insights into emulsion dynamics such as droplet contact and droplet breakdown.

Quantifying population-level neural tuning functions using Ricker wavelets and the Bayesian bootstrap

BackgroundExperience changes visuo-cortical tuning. In humans, re-tuning has been studied during aversive generalization learning, in which the similarity of generalization stimuli (GSs) with a conditioned threat cue (CS+) is used to quantify tuning functions. Previous work utilized pre-defined tuning shapes (generalization and sharpening patterns). This approach may constrain the ways in which re-tuning can be characterized since the tuning patterns may not match the prototypical functions. New methodThe present study proposes a flexible and data-driven method for precisely quantifying changes in tuning based on the Ricker wavelet function and the Bayesian bootstrap. This method was applied to EEG and psychophysics data from an aversive generalization learning paradigm. ResultsThe Ricker wavelet model fitted the steady-state visual event potentials (ssVEP), alpha-band power, and detection accuracy data well. A Morlet wavelet function was used for comparison and fit the data better in some situations, but was more challenging to interpret. The pattern of re-tuning in the EEG data, predicted by the Ricker model, resembled the shapes of the best fitting a-priori patterns. Comparison with existing methodsAlthough the re-tuning shape modeled by the Ricker function resembled the pre-defined shapes, the Ricker approach led to greater Bayes factors and more interpretable results compared to a-priori models. The Ricker approach was more easily fit and led to more interpretable results than a Morlet wavelet model. ConclusionThis work highlights the promise of the current method for capturing the precise nature of visuo-cortical tuning, unconstrained by the implementation of a-priori models.

Fall Detection Based on Data-Adaptive Gaussian Average Filtering Decomposition and Machine Learning

Falls are a significant health concern leading to increased morbidity and healthcare costs, especially for the elderly. Early and accurate detection of fall events is critical for timely intervention and preventing severe complications. This study presents a novel approach to triaxial accelerometer signals by employing data-adaptive Gaussian average filtering (DAGAF) decomposition in conjunction with machine learning techniques for fall detection. The triaxial accelerometer signals from the FallAllD dataset were decomposed into intrinsic mode functions (IMFs) and a residual component, from which feature vectors were extracted to train support vector machine (SVM) and k-nearest neighbor (kNN) classifiers. Experimental results demonstrate that the combination of the first and the third IMFs with the residual component yields the highest classification accuracy of 96.34%, with SVM outperforming kNN across all performance metrics. This approach significantly improves fall detection accuracy compared to using raw accelerometer signals, highlighting its potential in enhancing wearable fall detection systems. The proposed DAGAF decomposition method not only enhances feature extraction but also provides a promising advancement in the field, suggesting its potential to increase the reliability and accuracy of fall detection in practical applications.

Enhancing fault detection in multivariate industrial processes: Kolmogorov–Smirnov non-parametric statistical approach

The accurate detection of abnormal events in modern process plants is extremely important. The detection of faults of small magnitude and in a noisy process environment is still a challenge that many industries face. In this paper, a Kolmogorov–Smirnov (KS) based non-parametric statistical fault indicator is proposed to identify a variety of sensor faults in process plants. The data collected from most modern process plants are randomly varying and non-Gaussian, due to which the multivariate scheme based on Independent Component Analysis (ICA) is considered. The KS-based indicator is amalgamated with the ICA multivariate model, which yields a novel ICA-KS-based fault detection (FD) scheme. The KS statistical indicator compares any two distributions and checks if they are similar or dissimilar. The potential of the KS indicator is extended in the FD domain, where the residuals of training and testing data are compared in a sliding window. The ability of the proposed ICA-KS-based FD strategy is validated on a simulated Distillation column (DC) process and the benchmark Tennessee Eastman (TE) process to identify a variety of sensor faults. The simulation results demonstrate the effectiveness of the detection performance of the ICA-KS scheme, which outperforms conventional FD-based methods with a high detection rate.

Improved method for drone sound event detection system aiming at the impact of background noise and angle deviation

Drones pose a hidden threat to public safety, and effective and accurate detection technology for the drone that exist in the environment is imminent, in which how to weaken the influence of target sound source localization deviation and strong background interference noise on the detection task is the key to improve the accuracy of drone sound event detection. In this paper, a method has been proposed to improve the accuracy of drone acoustic event detection, named as the linear shrinkage-subspace projection-power spectral density filter method (LSP). This method mainly including covariance matrix reconstruction, steering vector recalibration, and filter coefficient redesign method. Firstly, based on Minimum Variance Distortionless Response, the linear shrinkage method is used to suppress the interference and noise components in the signal plus interference covariance matrix, and the sample covariance matrix is reconstructed to eliminate background interference noise. Then, the correlation between the steering vector and the eigenvector is used to eliminate the angle correlation term, and the subspace projection method is combined to recalibrate the steering vector, so as to improve the ability of the beamforming method to resist the angle deviation and realize the correction of the target source positioning deviation. Next, a redesign method for wiener filter coefficients based on the estimated power spectral density is used to further weaken background interference noise. In order to verify the accuracy of the proposed method, a complete drone sound event detection system is constructed by combining the deep learning drone sound event detection classifier, and the evaluation is carried out according to different angular deviations and interference sound distances. In addition, a new evaluation criterion is proposed, named as the Machine-Human Extreme Hearing Distance Rate (MHDR), which analogizes the system's detection ability with the ear's auditory detection ability. The research results of this article indicate that the detection accuracy of the detection system shows satisfactory accuracy when the proposed method is applied to circular microphone array, that improved by 15.12 % compared to existing methods. The proposed method improves the detection accuracy of the drone acoustic event detection task under the influence of sound source position deviation and strong background speech interference, and provides a reference for the development of anti-drone technology.

Correlation analysis of wind power and meteorology based on the ramp detection method with adaptive sliding window

Abstract Since the penetration of wind power rises rapidly, the coupling between wind power and meteorology strengthens the dependence of the new power system on meteorology. The fluctuation of wind power and the ramp events become key challenges that affect the safe and stable operation of the power system. To balance the accuracy and efficiency of wind power ramp event detection, an adaptive sliding time window-based ramp event detection method is proposed, which includes three major steps: dataset preprocessing and compression, wind power ramp trend pattern extraction considering bump event screening, and wind power ramp detection based on adaptive sliding time window. The correlation between the ramp event of wind power and meteorological factors like temperature, wind speed and direction are analyzed, and the feasibility of the proposed method is verified. The results show that wind speed is the decisive meteorological factor influencing ramp events, and wind power is also affected by wind direction. However, the correlation between wind power and temperature is weak, which only works in a few ramp events.

ToPick: Time-of-Pickup Measurement for the Elderly using Wearables.

The ability to pick up objects off the floor can degrade over time with elderly individuals, leading to a reduced quality of life and an increase in the risk of falling. Healthcare professionals have expressed an interest in monitoring the decline in pickup ability of a subject over extended periods of time and intervening when it becomes hazardous to the subject's health. The current means of evaluating pickup ability involving in-clinic patient visits is both time and financially expensive. There is a clear need for a cost-effective, remote means of pickup evaluation to ease the burden on both patients and physicians. To address these challenges, we introduce a Time-of-Pickup (ToP) solution, called ToPick, designed for the automatic assessment of pickup ability over time. The practical performance of ToPick is evident, demonstrated by a minimal median error of approximately 100 milliseconds in evaluating 20 pickup events among 10 elderly individuals. Furthermore, ToPick exhibits a high level of reliability, achieving perfect accuracy, precision, and recall scores for pickup event detection. We actualize our research findings by designing an application intended for adoption by both healthcare practitioners and elderly individuals. The app aims to reduce both time and financial costs while enabling mobile treatment for users.

Machine learning model with output correction: Towards reliable bradycardia detection in neonates

Bradycardia is a commonly occurring condition in premature infants, often causing serious consequences and cardiovascular complications. Reliable and accurate detection of bradycardia events is pivotal for timely intervention and effective treatment. Excessive false alarms pose a critical problem in bradycardia event detection, eroding trust in machine learning (ML)-based clinical decision support tools designed for such detection. This could result in disregarding the algorithm’s accurate recommendations and disrupting workflows, potentially compromising the quality of patient care. This article introduces an ML-based approach incorporating an output correction element, designed to minimise false alarms. The approach has been applied to bradycardia detection in preterm infants. We applied five ML-based autoencoder techniques, using recurrent neural network (RNN), long-short-term memory (LSTM), gated recurrent unit (GRU), 1D convolutional neural network (1D CNN), and a combination of 1D CNN and LSTM. The analysis is performed on ∼440 hours of real-time preterm infant data. The proposed approach achieved 0.978, 0.73, 0.992, 0.671 and 0.007 in AUC-ROC, AUC-PRC, recall, F1 score, and false positive rate (FPR) respectively and a false alarms reduction of 36% when compared with methods without the correction approach. This study underscores the imperative of cultivating solutions that alleviate alarm fatigue and encourage active engagement among healthcare professionals.

A machine learning-based algorithm for automated detection of frequency-based events in recorded time series of sensor data

Automated event detection methods, vital for monitoring technical systems via sensor data, are highly sought after in the automotive industry for tracing events in time series data. For assessing the active vehicle safety systems, a diverse range of driving scenarios is conducted. These scenarios involve the recording of the vehicle's behavior using external sensors, enabling the evaluation of operational performance. In such setting, automated detection methods not only accelerate but also standardize and objectify the evaluation by avoiding subjective, human-based appraisals in the data inspection. This study introduces a novel method for identifying frequency-based events in time series data. To this aim, the time series data is mapped to representations in the time-frequency domain, known as scalograms. After filtering scalograms to enhance relevant parts of the signal, an object detection model is trained to detect the desired event objects in the scalograms. By leveraging the accuracy of object detection networks in localizing objects, precise time intervals of events are identified. For the analysis of unseen time series data, events can be detected in their scalograms with the trained object detection model and are thereafter mapped back to the time series data to mark the corresponding time interval. The algorithm, evaluated on unseen datasets, achieves a precision rate of 0.97 in event detection, providing sharp time interval boundaries whose accurate indication by human visual inspection is challenging. Incorporating this method into the vehicle development process enhances the accuracy and reliability of event detection, which holds major importance for rapid testing analysis.

BERT-based language model for accurate drug adverse event extraction from social media: implementation, evaluation, and contributions to pharmacovigilance practices.

Social media platforms serve as a valuable resource for users to share health-related information, aiding in the monitoring of adverse events linked to medications and treatments in drug safety surveillance. However, extracting drug-related adverse events accurately and efficiently from social media poses challenges in both natural language processing research and the pharmacovigilance domain. Recognizing the lack of detailed implementation and evaluation of Bidirectional Encoder Representations from Transformers (BERT)-based models for drug adverse event extraction on social media, we developed a BERT-based language model tailored to identifying drug adverse events in this context. Our model utilized publicly available labeled adverse event data from the ADE-Corpus-V2. Constructing the BERT-based model involved optimizing key hyperparameters, such as the number of training epochs, batch size, and learning rate. Through ten hold-out evaluations on ADE-Corpus-V2 data and external social media datasets, our model consistently demonstrated high accuracy in drug adverse event detection. The hold-out evaluations resulted in average F1 scores of 0.8575, 0.9049, and 0.9813 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. External validation using human-labeled adverse event tweets data from SMM4H further substantiated the effectiveness of our model, yielding F1 scores 0.8127, 0.8068, and 0.9790 for detecting words of adverse events, words in adverse events, and words not in adverse events, respectively. This study not only showcases the effectiveness of BERT-based language models in accurately identifying drug-related adverse events in the dynamic landscape of social media data, but also addresses the need for the implementation of a comprehensive study design and evaluation. By doing so, we contribute to the advancement of pharmacovigilance practices and methodologies in the context of emerging information sources like social media.

A novel method for accurate division of the gait cycle into seven phases using shank angular velocity

BackgroundAccurate detection of gait events is crucial for gait analysis, enabling the assessment of gait patterns and abnormalities. Inertial measurement unit (IMU) sensors have gained traction for event detection, mainly focusing on initial contact (IC) and toe-off (TO) events. However, effective detection of other key events such as heel rise (HR), feet adjacent (FA), and tibia vertical (TBV) is essential for comprehensive gait analysis. Research questionCan a novel IMU-based method accurately detect HR, TO, FA, and TBV events, and how does its performance compare with existing methods? MethodsWe developed and validated an IMU-based method using cumulative mediolateral shank angular velocity (CSAV) for event detection. A dataset of nearly 25,000 gait cycles from healthy adults walking at varying speeds and footwear conditions was used for validation. The method’s accuracy was assessed against force plate and motion capture data and compared with existing TO detection methods. ResultsThe CSAV method demonstrated high accuracy in detecting TO, FA, and TBV events and moderate accuracy in HR event detection. Comparisons with existing TO detection methods showcased superior performance. The method's stability across speed and shoe variations underscored its robustness. SignificanceThis study introduces a highly accurate IMU-based method for detecting gait events needed to divide the gait cycle into seven phases. The effectiveness of the CSAV method in capturing essential events across different scenarios emphasizes its potential applications. Although HR event detection can be further improved, the precision of the CSAV method in TO, FA, and TBV detection advance the field. This study bridges a critical gap in IMU-based gait event detection by introducing a method for subdividing the swing phase into its subphases. Further research can focus on refining HR detection and expanding the method’s utility across diverse gait contexts, thereby enhancing its clinical and scientific significance.

Accuracy, concurrent validity, and test-retest reliability of pressure-based insoles for gait measurement in chronic stroke patients.

Wearables are potentially valuable tools for understanding mobility behavior in individuals with neurological disorders and how it changes depending on health status, such as after rehabilitation. However, the accurate detection of gait events, which are crucial for the evaluation of gait performance and quality, is challenging due to highly individual-specific patterns that also vary greatly in movement and speed, especially after stroke. Therefore, the purpose of this study was to assess the accuracy, concurrent validity, and test-retest reliability of a commercially available insole system in the detection of gait events and the calculation of stance duration in individuals with chronic stroke. Pressure insole data were collected from 17 individuals with chronic stroke during two measurement blocks, each comprising three 10-min walking tests conducted in a clinical setting. The gait assessments were recorded with a video camera that served as a ground truth, and pressure insoles as an experimental system. We compared the number of gait events and stance durations between systems. Over all 3,820 gait events, 90.86% were correctly identified by the insole system. Recall values ranged from 0.994 to 1, with a precision of 1 for all measurements. The F1 score ranged from 0.997 to 1. Excellent absolute agreement (Intraclass correlation coefficient, ICC = 0.874) was observed for the calculation of the stance duration, with a slightly longer stance duration recorded by the insole system (difference of -0.01 s). Bland-Altmann analysis indicated limits of agreement of 0.33 s that were robust to changes in walking speed. This consistency makes the system well-suited for individuals post-stroke. The test-retest reliability between measurement timepoints T1 and T2 was excellent (ICC = 0.928). The mean difference in stance duration between T1 and T2 was 0.03 s. We conclude that the insole system is valid for use in a clinical setting to quantitatively assess continuous walking in individuals with stroke.

Abstract 3492: Comprehensive analysis of hematological malignancies with optical genome mapping and Bionano VIA™Software

Abstract The current standard for analysis of samples with suspected hematological cancer is karyotyping, fluorescence in situ hybridization (FISH), and/or chromosomal microarray (CMA). However, these methods have several limitations for the detection of structural variants (SVs) or copy number variants (CNV) that are smaller than 5 Mbp (karyotyping). Additionally, they are limited in their ability to detect low allele fraction (LAF) events and cannot detect the absence of heterozygosity (AOH) or the loss of heterozygosity (AOH/LOH - FISH and karyotyping). Optical genome mapping (OGM) from Bionano can enable accurate detection of SV and CNV events of sizes down to 5 kbp, with high sensitivity to detect LAF events down to 5% variant allele fraction. Bionano’s VIA™ software provides visualization and analysis support for OGM SV events including an improved Circos plot and ideogram for whole genome review. VIA incorporates CNV calling from OGM data using the SNP-FASST3 algorithm which also allows for accurate copy number calling at allele fractions as low as 10%. CNV detection performance was evaluated using 280 CNV events ranging in size from 175 kbp to 8 Mbp on the 22 autosomal chromosomes. Sensitivity and PPV were calculated for each size range separately. Using SNP-FASST3, VIA also adds the ability to detect AOH/LOH events from OGM data. By analyzing locations where known SNPs disrupt the specific nucleotide sequence motif used by the DLE-1 labeling enzyme, a B-allele frequency can be calculated and enable AOH/LOH detection at low allelic fractions. AOH/LOH performance using SNP-FASST3 was evaluated using a cohort of constitutional and cancer samples analyzed with orthogonal methods as well as with simulated events. Sensitivity for 20-25 Mbp AOH events was 92% with PPV greater than 90% at 25% aberrant cell fraction. The VIA 7.0 release also includes the ability to create a customizable decision tree using supplied reference annotation files for CNV, AOH and SV event pre-classification. VIA resources include a panel of target variants derived from various medical societies for clinical analysis guidelines specific to Acute Myeloid Leukemia (AML), Myelodysplastic Syndrome (MDS) and an aggregation of all hematological malignancy guidelines (pan-heme). These methods, combined with tools for expert review and reclassification of variants, allow users to conduct whole genome interpretation and report results. These new features in VIA provide a streamlined workflow for analyzing heterogeneous hematological cancer samples and demonstrate the analytical performance of updated approaches for detection of CNV and AOH/LOH regions at low allele fractions using optical genome mapping alone. Citation Format: Jacob Wisotsky, Aliz Raski, Westley Sherman, Megan Roytman, Samer Al-Saffar, Jen Hauenstein, Andy Wing Chun Pang, Viren Wasnikar, Neil Miller. Comprehensive analysis of hematological malignancies with optical genome mapping and Bionano VIA™Software [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3492.

Time series procession for monitoring land disturbance caused by surface coal mining in China

Surface mining has been subject to increased criticism and pressure due to its potential to cause environmental damage. China accounts for more than half of world coal consumption, and this continued demand for coal, combined with low costs, has accelerated surface coal mining worldwide. However, it isn't easy to understand the impact of surface mining in China. Therefore, combined with POI, the Exposed Coal Frequency Index (ECFI) was constructed without any mining information in China. A LandTrendr algorithm was used to identify past mining disturbances and reclamation events (1986–2021). While examining these events, surface destruction due to coal mining in China was revealed. Based on the results, (1) the detection accuracy of statistical surface mining sites by province reaches 91.8%, while the detection accuracy of disturbance and reclamation events exceeds 72.59 %. Also, by comparing the mine data, our results provide more accurate information regarding both time and spatial consistency. (2) 253.61 km2 of surface mining sites were identified in 14 provinces or autonomous regions during 1986–2021. Of these, Inner Mongolia, Ningxia, Shanxi and Xinjiang ranked in the top four occupying 89.0% of the total mining site. (3) China has cumulatively disturbed 545.84 km2 and reclaimed 169.41 km2 including more comprehensive human activities. In the last 12 years, large-scale mining and reclamation have accounted for 51.63% and 75.17% of the cumulative area, respectively. (4) Generally, reclamation in China can be completed within four years. In contrast, reclamation time intervals and the proportion of reclamation varied widely between mining areas. Clearly, this study provides the most accurate data on surface coal mining disturbances in China, which will be beneficial for future studies in spatial geography related to mining. It also includes more policies and management creation for mines.

Short-time acoustic indices for monitoring urban-natural environments using artificial neural networks

Urban-natural environments, proximal to rapidly urbanizing cities, provide essential ecosystem functions that benefit both city residents and ecological communities. With escalating urbanization, the resilience of these ecosystems is being progressively challenged, highlighting the need for robust monitoring mechanisms. Acoustic monitoring has emerged as an unobtrusive method to evaluate the status of these environments, capitalizing on indicators that reflect both landscape features and specific acoustic events. Despite potentially offering significant insights, this approach generates a large volume of acoustic data, introducing complexities in subsequent analyses. To mitigate this, we propose integrating artificial neural networks with acoustic indices to enhance data analysis. Our approach emphasizes the usefulness of short-time acoustic indices, computed over finite-duration analysis windows, to enhance polyphonic sound event detection accuracy. Empirical results support the performance of our approach, registering both an F1-Score and an error rate of 0.614. Overall, this study delineates a novel paradigm geared towards enhancing or preserving the biological diversity of urban-natural environments in areas with population growth and urban development.

Importance of Quality of Medical Record: Differences in Patient Safety Incident Inquiry Results According to Assessment for Quality of Medical Record.

Medical record review is the gold standard method of identifying adverse events. However, the quality of medical records is a critical factor that can affect the accuracy of adverse event detection. Few studies have examined the impact of medical record quality on the identification of adverse events. In this study, we analyze whether there were differences in screening criteria and characteristics of adverse events according to the quality of medical records evaluated in the patient safety incident inquiry in Korea. Patient safety incident inquiry was conducted in 2019 on 7500 patients in Korea to evaluate their screening criteria, adverse events, and preventability. Furthermore, medical records quality judged by reviewers was evaluated on a 4-point scale. The χ 2 test was used to examine differences in patient safety incident inquiry results according to medical record quality. Cases with inadequate medical records had higher rates of identified screening criteria than those with adequate records (88.8% versus 55.7%). Medical records judged inadequate had a higher rate of confirmed adverse events than those judged adequate. "Drugs, fluids, and blood-related events," "diagnosis-related events," and "patient care-related events" were more frequently identified in cases with inadequate medical records. There was no statistically significant difference in the preventability of adverse events according to the medical record quality. Lower medical record quality was associated with higher rates of identified screening criteria and confirmed adverse events. Patient safety incident inquiry should specify medical record quality evaluation questions more accurately to more clearly estimate the impact of medical record quality.

IMANoBAS: An Improved Multi-Mode Alert Notification IoT-based Anti-Burglar Defense System

Burglary involves forced or unauthorized entry, which leads to damage or loss of property having monetary or emotional value and, more severely, puts lives at risk. The dire need for the safety of lives and properties has attracted so much research on burglary alert system using Internet of Things (IoT) technology. Most of the research focused on alerting the users of burglary attempts using any or a combination of two notification methods: SMS, call, and email. This study emphasizes three-mode notification that combines SMS, call, and email using the application of IoT technology in a burglary alert system, which uses a Passive Infrared (PIR) sensor for burglar detection to ensure that Homeowners or authorized personnel get alerts in events of imminent attempt to break-ins. The study also details the sensor integration with its supporting components, such as the central hub or microcontroller, buzzer, LED, and network interface in the development of the system. The software was developed to facilitate seamless integration with the hardware, ensuring timely and accurate event detection and subsequent alert generation using Arduino IDE programming language, a framework based on the C++ language. The system effected the 3-mode notification to ensure that users get notification in case of an imminent break-in since the failure of the three modes simultaneously is extremely rare. The system’s performance based on its responsiveness on the 3-mode notifications was evaluated, and an average of 83.56% responsiveness was obtained, indicating an acceptable response time.

Identifying conditions leading to power quality events in Arctic Norway: Feature selection

Higher penetration of renewable energy and increased electricity consumption makes delivering a reliable power supply a complex task. Power system operators are nevertheless required to provide a reliable power supply with a power quality within specified limits. Power quality events can cause malfunctioning of sensitive equipment for consumers in the distribution network. Communities in the periphery of radial distribution networks, such as rural and island communities, often experience more challenges with power quality than other areas. This study focuses on a small island community in the Norwegian Arctic that exemplifies the challenges of power reliability. It explores an alternative approach to utilise the existing electrical infrastructure. By analysing one year of measurements from an electrical substation near a large industry complex, combined with weather forecast data, machine learning models are employed to recognise conditions leading to low power quality. Although these models show satisfactory performance, some data is missing to fully understand the causes of such events. To address this, feature selection techniques are applied to identify the most significant data features and find new important features. Through this process, a refined dataset is obtained, enabling more accurate detection of power quality events in the Arctic community. This research not only enhances the detection capability but also provides valuable insights into the specific data features that contribute to identifying power quality events, offering opportunities for targeted mitigation strategies and generalisation to other locations.