Error Rate Reduction Research Articles

Impact of a Bundle of Interventions on the Spectrum of Parenteral Drug Preparation Errors in a Neonatal and Pediatric Intensive Care Unit.

Background/Objectives: This study aimed to evaluate the impact of a bundle of interventions on the error rates in preparing parenteral medications in a neonatal and pediatric intensive care unit (NICU/PICU). Methods: We conducted a prospective interventional study in a NICU/PICU in a tertiary university hospital as a follow-up to a prior study in the same setting. A clinical pharmacist and a pharmacy technician (PT) analyzed the workflow of drug preparation on the ward, identified high-alert medications, and defined a bundle of five interventions, which include the following: Drug Labeling: 1. EN ISO-DIVI labeling; Training: 2. Standardized preparation process on the ward; 3. eLearning Program; 4. Expert Consultations; and Location of Preparation: 5. Transfer of the preparation of high-alert medications and standardized preparations to the central pharmacy. After implementing the bundle of interventions, we observed the preparation process on the ward to evaluate if the implementation of the interventions had an impact on the quality of the drug preparation. Results: We observed 262 preparations in the NICU/PICU. Each single step of the preparation process was defined as an error opportunity. We defined seven error categories with an overall error opportunity of 1413. In total, we observed 11 errors (0.78%). The reduction in the overall error rate from 1.32% in the former study to 0.78% per preparation opportunity demonstrated that the implemented interventions were effective in enhancing medication safety. Conclusions: This study provides evidence that a bundle of interventions, including standardizing drug labeling, enhancing training, and centralizing the preparation of high-alert medications, can reduce medication errors in NICU/PICU settings.

Enhancing real-time conflict identification using trajectory data: Exploring the impact of interactions among traffic flow state variables

Objective This study aims to address the limitations of using historical crash data and trajectory data for crash and conflict identification. Specifically, it focuses on enhancing real-time conflict identification by investigating the influence of traffic flow state variables and their interactions on conflicts. Methods Vehicle trajectory data from HighD were processed, allowing extraction of traffic flow state and corresponding conflict during a specific time interval (10 s). Logistic regression models were further used to verify the impact of variables, including interaction terms, on the conflicts for different lane categories (inner, middle, and outer lanes). Additionally, machine learning techniques were employed to compare conflict identification performance including or excluding variable interactions. Results The interaction terms of the traffic flow state variables have significant effects on the conflicts for different categories of lanes. It is therefore essential to consider both the individual effects of traffic variables and their interaction effects to analyze conflict risk. Considering variable interactions leads to improved conflict identification accuracy and reduced identification error rates in comparison to the condition where interaction items are not taken into account. Conclusions The interaction terms of traffic flow state variables significantly affect and enhance conflict identification, improving accuracy and reducing error rates. These findings contribute to advancing the high-precision identification of real-time conflict identification, with implications for improving road safety measures.

Hybrid Intelligent Anomaly Detection System Using Attention based Deep Learning Approach for Cyber Attacks Prevention

Objectives: Network Intrusion Detection System (NIDS) plays an important role in finding and preventing cyber-attacks, which helps to improve the entire security posture of an organization’s network infrastructure. The development of Deep Learning (DL) techniques possess the ability of IDS to detect attacks without delay and protects from intrusions even in real-time environment. Methods: The present study proposes an improved IDS framework called Enhanced Gated Recurrent Unit Hyper-Model combined Attention Bidirectional Long-Short Term Memory (EGHAB) approach, to effectively address the detection of attacks with maximum accuracy and minimal error rate. The proposed model is enhanced by using methods like numericalization and normalisation for pre-processing the input data, Genetic Algorithm (GA) for extracting intricate features from the input data, and the EGHAB classifier, which works on the principles of both Gated Recurrent Unit (GRU) and Bidirectional Long-Short Term Memory (BiLSTM) models along with attention mechanism. Findings: The EGHAB model efficiently learns the abnormal behaviour of network and classified the attack and non-attack data using the publicly available NSL-KDD dataset and a generated real time data set, scapy. And it achieved 99.94% accuracy over NSL KDD dataset during multiclass classification and 93.3% on real time data set with reduced Error rate. Additionally, to examine the efficacy, the proposed approach is compared with other existing methods and proved its improved performance. Novelty: A combined ensemble classifier with GRU, BiLSTM and attention mechanism is designed to predict the attacks in earlier stage rather than due over. The model achieved better accuracy and reduced false assumption using anomaly prediction mechanism. Keywords: Cyber Attack, Anomaly Detection, Deep learning, Gated Recurrent Unit, Bi­ Directional Long­-Short Term Memory, Attention mechanism

A hybrid detection model for acute lymphocytic leukemia using support vector machine and particle swarm optimization (SVM-PSO).

Leukemia, a hematological disease affecting the bone marrow and white blood cells (WBCs), ranks among the top ten causes of mortality worldwide. Delays in decision-making often hinder the timely application of suitable medical treatments. Acute lymphoblastic leukemia (ALL) is one of the primary forms, constituting approximately 25% of childhood cancer cases. However, automated ALL diagnosis is challenging. Recently, machine learning (ML) has emerged as an important tool for building detection models. In this study, we present a hybrid detection model that improves the accuracy of the detection process by combining support vector machine (SVM) and particle swarm optimization (PSO) approaches to automatically identify ALL. We use SVM to represent a two-dimensional image and complete the classification process. PSO is employed to enhance the performance of the SVM model, reducing error rates and enhancing result accuracy. The input images are obtained from two public datasets (ALL-IDB1 and ALL-IDB2), and public online datasets are utilized for training and testing the proposed model. The results indicate that our hybrid SVM-PSO model has high accuracy, outperforming stand-alone algorithms and demonstrating superior performance, an enhanced confusion matrix, and a higher detection rate. This advancement holds promise for enhancing the quality of technical software in the medical field using machine learning.

Predicting e-commerce product prices through the integration of variational mode decomposition and deep neural networks

Product prices frequently manifest nonlinear and nonstationary time-series attributes, indicating potential variations in their behavioral patterns over time. Conventional linear models may fall short in adequately capturing these intricate properties. In addressing this, the present study leverages the adaptive and non-recursive attributes of the Variational Mode Decomposition (VMD) methodology. It employs VMD to dissect the intricate time series into multiple Intrinsic Mode Functions (IMF). Subsequently, a method rooted in the minimum fuzzy entropy criterion is introduced for determining the optimal modal number (K) in the VMD decomposition process. This method effectively mitigates issues related to modal confusion and endpoint effects, thereby enhancing the decomposition efficacy of VMD. In the subsequent phase, deep neural networks (DNN) are harnessed to forecast the identified modes, with the cumulative modal predictions yielding the ultimate e-commerce product price prognostications. The predictive efficacy of the proposed Variational Mode Decomposition-deep neural network (VMD-DNN) decomposition model is assessed on three public datasets, wherein the mean absolute percentage error (MAPE) on the E-commerce Price Prediction Dataset and Online Retail Dataset is notably low at 0.6578 and 0.5414, respectively. This corresponds to a remarkable error reduction rate of 66.5% and 70.4%. Moreover, the VMD-DNN decomposition model excels in predicting e-commerce product prices through DNN, thereby amplifying the VMD decomposition capability by 4%. The VMD-DNN model attains superior results in terms of directional symmetry, boasting the highest Directional Symmetry (DS) score of 86.25. Notably, the forecasted trends across diverse price ranges closely mirror the actual trends.

A-181 The Effectiveness Module to Reduce Laboratory Error Reporting Rates

Abstract Background In a hospital in central Taiwan, the number of tests conducted in 2022 was 1.88 million, there were 26 cases of laboratory error reports, with an error rate of 14 cases per million. Error reports may lead to diagnostic errors, posing a significant risk to patient safety. Therefore, our team aims to establish a simple and universal module that can be applicable to various laboratory operations to minimize errors. Methods Initially, we conducted an analysis of error causes, identifying 25 cases of human error and 1 case of computer system error. Human errors were categorized according to the laboratory process, including 4 specimen identification errors, 3 procedural errors, 9 typing errors, 7 validation of analytical data errors, and 2 instances of failure to communicate issues during handover. The proximate cause of human error was non-compliance with SOPs, and the root cause was inadequate SOPs leading to inconsistent practices. We reviewed the workflow, implemented corrective actions, revised SOPs, and provided training for personnel. Additionally, three important strategies were introduced: 1.High-Frequency Audits: Corrective actions were included in the internal audit checklist, initially increasing the frequency to at least once a month for the first 3 months, followed by at least once every 6 months within the next 6 months. If no further occurrences were noted, audits could be conducted annually or not. Each audit should be completed within 20 minutes to avoid excessive burden, with direct observation of operations and random questioning being the preferred audit methods. 2. Audit deficiencies should be documented: Personnel were requested to conduct cause analysis and improvements. This not only expressed the supervisor's commitment to improvement but also provided an opportunity to discover reasons why countermeasures cannot not be implemented, allowing for strategic adjustments until successful implementation. 3.Case-Based Training: Benchmark cases were developed into teaching materials, and compiled errors regularly for looking for patterns or trends to identity key steps. Make these key steps the focus of your educational training. Results In 2023, 1.94 million tests were conducted, there were 4 error reports, with an error rate of 3 cases per million, all were attributed to human error. In another hospital in southern Taiwan, laboratory error reports were primarily due to human error. From June 2022 to July 2023, a total of 3.46 million tests were conducted, there were 14 error reports and an error rate of 5 cases per million. After implementing the same improvement module from August to December 2023, a total of 1.56 million tests were conducted, there were 2 error reports and an error rate of 2 cases per million, all were attributed to human error. Conclusions We applied this improvement module to two hospitals in central and southern Taiwan, resulting in a significant reduction in error rate. The key factors of effectiveness were utilizing high-frequency audits to ensure sustained results, and demonstrating the positive attitude of supervisors during the process. This motivated personnel to demand higher standards from themselves, ultimately showcasing remarkable improvements.

Enhancing Wireless Communication Efficiency Using Dynamic Nonlinear Distortion Adaptive Optimization Analysis

Abstract Modern communication networks require improved signal quality and spectrum efficiency. Modern wireless communication relies heavily on the multiple input multiple output (MIMO) technology to improve spectrum efficiency and achieve faster data rates. However, the performance of MIMO systems is significantly hampered by non-linear distortions in power amplifiers (PA), especially under dynamic operating conditions. Traditional methods to reduce these distortions often lack the adaptability required for effective optimization. The proposed dynamic nonlinear distortion adaptive optimization analysis (DNDAOA) therefore introduces an adaptive approach that dynamically adjusts the pre-distortion signals using real-time feedback mechanisms. DNDAOA effectively reduces non-linear distortions and thus improves the performance of MIMO systems. Simulation results show that DNDAOA significantly increases spectrum efficiency, reduces error rates, and improves signal quality. This method is widely used in areas such as wireless telecommunications, internet of things (IoT), autonomous vehicles, and smart infrastructure, driving advances in connectivity, reliability, and data integrity.

Multicarrier dual‐index permuted chaos shift keying system for efficiency improvement in chaotic communications

AbstractWe propose a multicarrier chaotic system that incorporates dual‐index and M‐ary modulation techniques. The proposed system improves the energy and spectrum efficiencies with a tradeoff in computational complexity. In addition, it applies index modulation to select the chaotic signal and permuted chaotic signals along with ‐ary modulated symbols. As a result, the spectral and energy efficiencies are increased in chaotic systems. Theoretical expressions are derived to calculate the bit error rate of the proposed system under additive white Gaussian noise and Rayleigh fading channels. The equations are validated through Monte Carlo simulations, whose results demonstrate a reduction in bit error rate for the proposed system. The data rate, spectral efficiency, energy efficiency, and computational complexity of the proposed system are analyzed and compared with those of existing multicarrier chaotic systems.

A Machine Learning Approach to Optimal Group Formation Based on Previous Academic Performance

In today’s educational institutions, student performance can vary widely due to differences in cognition, motivation, and environmental factors. These variations create challenges in achieving optimal learning outcomes. To address these challenges, Optimal Group Formation (OGF) has emerged as a promising research area. Optimal Group Formation (OGF) aims to form student groups that maximize learning efficiency based on past academic performance. Group formation problems are inherently complex and time-consuming, but their applications are extensive, spanning from manufacturing systems to educational contexts. This paper introduces a machine learning-based model designed to create optimal student groups using academic records as the primary input. The goal is to enhance overall group performance and reduce error rates by organizing students into cohesive, efficient teams. What sets this research apart is its focus on educational group formation, leveraging machine learning to improve collaborative learning outcomes. The paper also reviews prior research, emphasizing the importance of Optimal Group Formation (OGF) in various fields and its relevance in education. The model’s effectiveness is demonstrated through comparative analysis, showcasing its potential to improve group dynamics in both theoretical and lab-based courses. Ultimately, the aim is to improve educational outcomes by ensuring that student groups are optimally balanced and structured.

Secondary analysis of hand-offs in internal medicine using the I-PASS mnemonic

BackgroundMiscommunications account for up to 80% of preventable medical errors. Mnemonics like I-PASS (Illness severity, Patient summary, Actions list, Situation awareness, Synthesis) have demonstrated a positive impact on reducing error rates. Currently, physicians at our hospital do not follow a specific structure during hand-offs. We aimed to compare current hand-offs without prior training to a gold standard and the I-PASS tool in terms of content and sequence.MethodsThis study is a secondary analysis of data collected during a simulation study of a Friday evening hand-off to the night resident at University Hospitals of Geneva. Thirty physicians received a hand-off of four patients and managed two other patients through nursing pages at the start of the night shift, generating six sign-outs each, totaling 177 sign-outs. A focus group of three senior doctors defined the gold standard (GS) by consensus on the essential content of each sign-out. The analysis focused on the rates of relevance (ratio of information considered relevant by the GS) and completeness (proportion of transmitted elements out of all expected elements of the GS), and the distribution and sequence of the first four I-PASS categories.ResultsRelevance and completeness rates were 37.2% ± 0.07 and 51.9% ± 0.1, respectively, with no significant difference between residents and supervisors. There was a positive correlation between total hand-off time and relevance (residents: R2 = 0.62; supervisors: R2 = 0.67) and completeness (residents: R2 = 0.32; supervisors: R2 = 0.56). The distribution of I-PASS categories was highly skewed in both the GS (I = 2%, P = 72%, A = 17%, S = 9%) and participants (I = 6%, P = 73%, A = 14%, S = 7%), with significant differences in categories A (p = 0.046) and I (p ≤ 0.001). Sequences of I-PASS categories generally followed a P-A-S-I pattern. The first S category was frequently absent, and only one participant began by announcing the case severity as suggested by I-PASS.ConclusionWe identified gaps between current medical sign-outs in our institution's general internal medicine division and the I-PASS structure. We recommend implementing the I-PASS mnemonic, emphasizing the "I" category at the start and the "S" category to anticipate and prevent complications. Future studies should assess the impact of this recommendation, adapt the mnemonic elements to the context, and introduce specific hand-off training for senior medical students.

A Hybrid Machine Learning Approach: Analyzing Energy Potential and Designing Solar Fault Detection for an AIoT-Based Solar–Hydrogen System in a University Setting

This research aims to optimize the solar–hydrogen energy system at Kangwon National University’s Samcheok campus by leveraging the integration of artificial intelligence (AI), the Internet of Things (IoT), and machine learning. The primary objective is to enhance the efficiency and reliability of the renewable energy system through predictive modeling and advanced fault detection techniques. Key elements of the methodology include data collection from solar energy production and fault detection systems, energy potential analysis using Transformer models, and fault identification in solar panels using CNN and ResNet-50 architectures. The Transformer model was evaluated using metrics such as Mean Absolute Error (MAE), Mean Squared Error (MSE), and an additional variation of MAE (MAE2). Known for its ability to detect intricate time series patterns, the Transformer model exhibited solid predictive performance, with the MAE and MAE2 results reflecting consistent average errors, while the MSE pointed to areas with larger deviations requiring improvement. In fault detection, the ResNet-50 model outperformed VGG-16, achieving 85% accuracy and a 42% loss, as opposed to VGG-16’s 80% accuracy and 78% loss. This indicates that ResNet-50 is more adept at detecting and classifying complex faults in solar panels, although further refinement is needed to reduce error rates. This study demonstrates the potential for AI and IoT integration in renewable energy systems, particularly within academic institutions, to improve energy management and system reliability. Results suggest that the ResNet-50 model enhances fault detection accuracy, while the Transformer model provides valuable insights for strategic energy output forecasting. Future research could focus on incorporating real-time environmental data to improve prediction accuracy and developing automated AIoT-based monitoring systems to reduce the need for human intervention. This study provides critical insights into advancing the efficiency and sustainability of solar–hydrogen systems, supporting the growth of AI-driven renewable energy solutions in university settings.

Optimized multi-head self-attention and gated-dilated convolutional neural network for quantum key distribution and error rate reduction

ABSTRACT Quantum key distribution (QKD) is a secure communication method that enables two parties to securely exchange a secret key. The secure key rate is a crucial metric for assessing the efficiency and practical viability of a QKD system. There are several approaches that are utilized in practice to calculate the secure key rate. In this manuscript, QKD and error rate optimization based on optimized multi-head self-attention and gated-dilated convolutional neural network (QKD-ERO-MSGCNN) is proposed. Initially, the input signals are gathered from 6G wireless networks which face obstacles to channel. For extending maximum transmission distances and improving secret key rates, the signals are fed to the variable velocity strategy particle swarm optimization algorithm, then the signals are fed to MSGCNN for analysing the quantum bit error rate reduction. The MSGCNN is optimized by intensified sand cat swarm optimization. The performance of the QKD-ERO-MSGCNN approach attains 15.57%, 23.89%, and 31.75% higher accuracy when analysed with existing techniques, like device-independent QKD utilizing random quantum states, practical continuous-variable QKD and feasible optimization parameters, entanglement and teleportation in QKD for secure wireless systems, and QKD for large scale networks methods, respectively.

Deep learning-based energy prediction and tangent search remora optimization-based secure multi-path data communication mechanism in WSN

ABSTRACT Wireless Sensor Network (WSN) has been exploited in numerous regions which can be hardly accessed by humans. However, it is essential to convey the information accumulated by the sensing devices or nodes to the Base Station (BS) for further processing. Multipath routing protocols are found to address these challenges and provide reliable communication. This paper aims to find an optimal path to the gateway with minimum energy consumption and reduced error rate while meeting the end-to-end delay requirements. In this research, an effective multipath routing based on energy prediction and hybrid optimization is developed. Here, a Deep Q-Network (DQN) is applied to predict the energy, and the process is augmented by the usage of a proposed Tangent Search Remora Optimization (TSRO) algorithm. Further, the multipath routing is executed using the TSRO algorithm, considering a fitness function formulated using various factors, like residual energy, distance, throughput, reliability, trust factors, predicted energy, Link Life Time (LLT), delay, and traffic intensity. The devised TSRO-routing is scrutinized for its competence based on trust, throughput, energy, distance, and delay and has achieved superior values of energy of 0.402 J, throughput at 25.056Mbps, trust at 84.975, and minimal distance of 29.964 m, and delay of 0.750 ms.

Locomotion trajectory optimization for quadruped robots with kinematic parameter calibration and compensation

It is significant to improve the locomotion trajectory accuracy of a quadruped robot for more applications. A compensation approach with kinematic parameter calibration is proposed to enhance the trajectory accuracy. The motion equation of the foot ends in coordinated gait is given. The related kinematic error model is deduced. The model for identifying the joint parameter errors is implemented via the trajectory error in the Z-direction. The trunk trajectory accuracy enhancement is facilitated by compensating the joint parameter error with joint angles as tuning variables. The performance of the methodology is validated in the simulation system by predetermining ten random sets of joint variable errors. The results indicate that both the trajectory and the attitude accuracy of the calibrated robot can be significantly improved. In practical experiments, it is shown that an error reduction rate of 80% in positional deviation and 58% in attitude error can be achieved.

The influence of sample size and sampling design on estimating population-level intra specific trait variation (ITV) along environmental gradients.

Understanding the relationship between intraspecific trait variability (ITV) and its biotic and abiotic drivers is crucial for advancing population and community ecology. Despite its importance, there is a lack of guidance on how to effectively sample ITV and reduce bias in the resulting inferences. In this study, we explored how sample size affects the estimation of population-level ITV, and how the distribution of sample sizes along an environmental gradient (i.e., sampling design) impacts the probabilities of committing Type I and II errors. We investigated Type I and II error probabilities using four simulated scenarios which varied sampling design and the strength of the ITV-environment relationships. We also applied simulation scenarios to empirical data on populations of the small mammal, Peromyscus maniculatus across gradients of latitude and temperature at sites in the National Ecological Observatory Network (NEON) in the continental United States. We found that larger sample sizes reduce error rates in the estimation of population-level ITV for both in silico and Peromyscus maniculatus populations. Furthermore, the influence of sample size on detecting ITV-environment relationships depends on how sample sizes and population-level ITV are distributed along environmental gradients. High correlations between sample size and the environment result in greater Type I error, while weak ITV-environmental gradient relationships showed high Type II error probabilities. Therefore, having large sample sizes that are even across populations is the most robust sampling design for studying ITV-environment relationships. These findings shed light on the complex interplay among sample size, sampling design, ITV, and environmental gradients.

Reinforcement learning-based automated modulation switching algorithm for an enhanced underwater acoustic communication

Acoustic communication is the preferred method for underwater communication due to its superior propagation characteristics, including longer range, lower attenuation, better penetration, and adaptability to water density. However, challenges like varying water depths, temperature gradients, noise, and multipath propagation require innovative solutions beyond fixed-data rate systems and single modulation schemes. To address these challenges, we propose a reinforcement learning (RL)-based automated modulation switching algorithm designed to enhance data transmission efficiency. This approach leverages the UNETStack software and the Frequency Hopping-Binary Frequency Shift Key (FH-BFSK) modulation, extending its capabilities by incorporating Amplitude Shift Keying (ASK), Phase Shift Keying (PSK), and Orthogonal Frequency Division Multiplexing (OFDM) modulation techniques. We designed a cost-effective, software-controlled acoustic modem using commercial off-the-shelf (COTS) components, enabling full-duplex underwater communication. The RL algorithm utilizes a Q-matrix guided by a greedy policy to assess network conditions, such as data volume and data rates. It continuously monitors underwater environments to select the most suitable modulation scheme dynamically. Experimental validation demonstrates a 3.648 % improvement in Received Signal Strength Indicator (RSSI), a 32 % reduction in bit error rate at a constant 7 dB Signal-to-Noise Ratio (SNR), and a 5 % increase in utility at 10 dB SNR when using OFDM selected by the reinforcement learning algorithm, compared to FH-BFSK.

HeadShift: Head Pointing with Dynamic Control-Display Gain

Head pointing is widely used for hands-free input in head-mounted displays (HMDs). The primary role of head movement in an HMD is to control the viewport based on absolute mapping of head rotation to the 3D environment. Head pointing is conventionally supported by the same 1:1 mapping of input with a cursor fixed in the centre of the view, but this requires exaggerated head movement and limits input granularity. In this work, we propose to adopt dynamic gain to improve ergonomics and precision, and introduce the HeadShift technique. The design of HeadShift is grounded in natural eye-head coordination to manage control of the viewport and the cursor at different speeds. We evaluated HeadShift in a Fitts’ Law experiment and on three different applications in VR, finding the technique to reduce error rate and effort. The findings are significant as they show that gain can be adopted effectively for head pointing while ensuring that the cursor is maintained within a comfortable eye-in-head viewing range.

Optimizing Electric Vehicle (EV) Charging with Integrated Renewable Energy Sources: A Cloud-Based Forecasting Approach for Eco-Sustainability

As electric vehicles (EVs) are becoming more common and the need for sustainable energy practices is growing, better management of EV charging station loads is a necessity. The simple act of folding renewable power from solar or wind in an EV charging system presents a huge opportunity to make them even greener as well as improve grid resiliency. This paper proposes an innovative EV charging station energy consumption forecasting approach by incorporating integrated renewable energy data. The optimization is achieved through the application of SARLDNet, which enhances predictive accuracy and reduces forecast errors, thereby allowing for more efficient energy allocation and load management in EV charging stations. The technique leverages comprehensive solar and wind energy statistics alongside detailed EV charging station utilization data collected over 3.5 years from various locations across California. To ensure data integrity, missing data were meticulously addressed, and data quality was enhanced. The Boruta approach was employed for feature selection, identifying critical predictors, and improving the dataset through feature engineering to elucidate energy consumption trends. Empirical mode decomposition (EMD) signal decomposition extracts intrinsic mode functions, revealing temporal patterns and significantly boosting forecasting accuracy. This study introduces a novel stem-auxiliary-reduction-LSTM-dense network (SARLDNet) architecture tailored for robust regression analysis. This architecture combines regularization, dense output layers, LSTM-based temporal context learning, dimensionality reduction, and early feature extraction to mitigate overfitting. The performance of SARLDNet is benchmarked against established models including LSTM, XGBoost, and ARIMA, demonstrating superior accuracy with a mean absolute percentage error (MAPE) of 7.2%, Root Mean Square Error (RMSE) of 22.3 kWh, and R2 Score of 0.87. This validation of SARLDNet’s potential for real-world applications, with its enhanced predictive accuracy and reduced error rates across various EV charging stations, is a reason for optimism in the field of renewable energy and EV infrastructure planning. This study also emphasizes the role of cloud infrastructure in enabling real-time forecasting and decision support. By facilitating scalable and efficient data processing, the insights generated support informed energy management and infrastructure planning decisions under dynamic conditions, empowering the audience to adopt sustainable energy practices.

PULMO: Precise utterance-level modeling for speech anti-spoofing

In recent years, most state-of-the-art approaches for spoofed speech detection have been based on convolutional neural networks (CNNs). Most neural networks, including CNNs, are trained in minibatch units, where all input data in each minibatch must have the same shape. Therefore, for minibatch training, each utterance is first either padded or truncated because utterances are variable-length sequences and thus cannot be directly fed into networks in minibatch units. However, modeling either a padded or truncated utterance, rather than the original one, makes it unfeasible to capture the entire context as is: padding could propagate even unwanted information, like artifacts, in the original utterance, and truncation inevitably loses some information. With these information distortions, model could get stuck in a suboptimal solution. To fill this gap, we proposeÚ a method for precise utterance-level modeling that enables minibatch-wise utterance-level modeling of variable-length utterances while minimizing the information distortions. The proposed method comprises sequence segmentation followed by segment aggregation. Sequence segmentation feeds variable-length utterances in the minibatch unit by decomposing each of them into fixed-length segments, which enables parallel processing of variable-length utterances without the uncertainty in input length. Segment aggregation plays a role in aggregating the segment embeddings by utterance to encode the entire information of each utterance. The experimental results of the evaluation trials of ASVspoof 2019 and 2021 indicate that the proposed method shows up to 84.9 % and 97.6 % relative equal error rate reductions on logical and physical access scenarios, respectively. Furthermore, the proposed method reduced the FLOPs for an epoch by 6 %.

Error-Free Arduino Communication: Integrating Hamming Code for UART Serial Transmission

Serial communication is a fundamental method for data transfer in electronic devices, particularly in Arduino-based systems. However, existing protocols, such as Universal Asynchronous Receiver/Transmitter (UART), often lack robust error detection mechanisms, leading to potential data integrity issues. This study aims to address the knowledge gap regarding error detection in UART communication by implementing Hamming Code, a well-established method for detecting and correcting single-bit errors. The research employs a systematic approach, including data encoding before transmission and decoding with error correction at the receiver end. The results demonstrate that the integration of the Hamming Code significantly enhances the reliability of data transmission, reducing error rates and improving overall system performance. The implications of this research extend to various applications requiring high data integrity, such as industrial control systems and Internet of Things (IoT) devices. By providing a practical solution to the challenges of error detection in serial communication, this study contributes to the advancement of reliable communication systems in modern technology.