Abstracts:This study presents a systematic review of control methods for electromagnetic force compensation (EMFC) weighing cells, a technology extensively employed in high-accuracy weighing instruments across various industrial and scientific sectors. The research was conducted following a structured methodology applied to recognized scientific databases, covering publications from the past decade. The analysis identifies trends and recurring approaches in control design aimed at ensuring stability, accuracy, and high dynamic performance. The results indicate that achieving higher levels of accuracy requires more robust control methods, where the performance of the electronic subsystem comprising the optical sensor, data acquisition system, and digital controller has a decisive impact compared to mechanical improvements. It is concluded that, in the simulated and experimental evaluations reported in the literature, no study has conducted a complete calibration of the EMFC load cell weighing instrument or validated it in an industrial environment. This gap highlights the need for future research to include validations under real operating conditions and to carry out follow-up assessments that enable evaluation of the instruments metrological drift over a specified time period.

Abstracts:Traumatic brain injury (TBI) is a condition that changes the autonomic system, modulating the heart rate variability (HRV) at all levels of brain lesions. Although fluid percussion injury (FPI) model can reproduce all degrees of severity of clinical TBI, there is still a lack of comprehensive analysis of linear and non-linear HRV metrics following FPI. The present study sought to assess the influence of the FPI model on time-domain (HR, mean NN, SD1, SD2, SDNN, RMSSD, and SD1/SD2) and frequency-domain (LF, HF, and LF/HF). A non-invasive electrocardiogram recording was used in anesthetized and awake male Wistar rats, both before and for three days after moderate FPI. Although a decrease in the SD2 occurred in the anesthetized state, an increase in HFnu led to a reduction in HR during baseline evaluations. Post-TBI analyses revealed that neither the sham nor the TBI groups exhibited HR alterations under the influence of isoflurane; however, both groups showed a decrease in parasympathetic activity (RMSSD, SD1, and HFnu). Under isoflurane anesthesia, only the TBI group exhibited changes in LFnu, HFnu, and LF/HF metrics for three days. In contrast, awake animals experienced an increase in HR for three days post-injury, with a critical period at 24 hours when SD2, LFnu, HFnu, and LF/HF were altered. With few exceptions, the sham group did not exhibit significant differences in the awake state. Therefore, the effects of isoflurane predominate over TBI effects in both time- and frequency-domain metrics, while FPI in awake animals indicates a critical period of altered specific metrics at 24 hours post-injury.

Abstracts:Water consumption Automated Meter Reading (AMR) devices are fundamental to achieving sustainable management in Water Distribution Systems (WDS). However, available solutions are still relatively expensive, and don't feature adequate and synchronized network throughput to attain Leakage Detection and Localization (LDL). As a consequence, AMR installation isn't extended in most cities. As an alternative, we propose the so-called AquOculus Advanced Metering Infrastructure (AMI) system, intended to be a cost-effective solution. This article presents the first results obtained while developing the embryonic AquOculus AMR prototype, consistent with Technology Readiness Level (TRL) 3. It was based on an ESP32 microcontroller and communicated the correct consumed water volume to a remote application via Wi-Fi. An ordinary water meter was leveraged as the main reading instrument, coupled with the developed optoelectronic pulse counter. It doesn't require specific color, metallic, or magnetic parts on the monitored indicator, applying to a wider variety of water meter models. As the water volume counting is indirect, the measurement relies on the factory-calibrated water meter; so the initial validation setup was very simple, using a hairdryer to move the water meter mechanism. Sunlight sensitivity was observed, and the sensor positioning process was demanding. These issues were figured out and discussed for future work. Despite the TRL achieved, this article also addresses the main steps towards the complete AquOculus system. The cost-effective characteristics are expected to boost further studies to allow massive installations by water distribution companies. The developed software repository link was provided for reproducibility.

Abstracts:Heart disease is a critical issue in improving people's health. Medical research and technology are being developed to obtain accurate diagnoses and treatments. This paper contributes to designing an automated diagnosis system to classify electrocardiogram (ECG) signals to detect cardiac diseases. With respect to other related works, it has the following distinctive characteristics: it is feasible to be implemented in real time, capable of detecting different heart pathologies, and effective in performance. The proposed system is based on Fourier series analysis, employing a dynamical state observer to instantaneously obtain salient features and patterns from the ECG harmonic content, whose information is classified through a K-nearest neighbor algorithm (KNN), named as the classifier, which determines the possible disease. The ECG signals used in this paper are obtained from the freely available PhysioNet databases, containing data to diagnose and classify healthy patients, arrhythmia cases, myocardial infarction, and heart failure. The proposed automated procedure is 93% effective in disease detection for the explored databases, highlighting its potential as a classification tool for ECG-based diagnosis.

Abstracts:Switched-capacitor multilevel inverters (SCMLIs) have gained considerable attention in various power conversion applications due to their inherent voltage boosting capability and reduced component count, eliminating the need for auxiliary sources, transformers, or inductors. This paper proposes a novel nine-level compact boost multilevel inverter (NCBMLI) that employs only ten switches, two capacitors, and a single DC input source to achieve a voltage gain of twice the input voltage. The proposed topology is designed for compactness and cost-effectiveness by minimizing the number of active components per voltage level. Further, to operate the proposed NCBMLI a novel fuzzy logic switching method is implemented, offering a flexible alternative to conventional control methods based on static logic circuits and pre-defined lookup tables. This method utilizes rule-based membership functions (MFs) to generate adaptive switching signals, which enhances the overall performance. A detailed comparative analysis is presented to highlight the advantages of the proposed NCBMLI. Furthermore, the effective performance of the proposed NCBMLI is validated through hardware implementation under varying dynamic load conditions and modulation indices.

Abstracts:Early detection of hemodynamic compromise in pediatric patients is critical for timely and effective intervention in intensive care. This study evaluates the use of thermal gradients, specifically the temperature difference between the abdomen and foot, as non-invasive physiological markers to improve prediction of shock. The dataset included thermal gradients, pulse rate, age, and four time-stamped measurements, enabling models to anticipate circulatory deterioration across different prediction horizons. These forecasting windows were examined to assess how far in advance the onset of shock could be reliably predicted. Several machine learning models were compared, and the best approach achieved an AUC of 0.84, with sensitivity of 0.90 and specificity of 0.74. Although methodological differences make direct comparison with previous studies challenging, this performance surpasses that reported in the existing literature. These findings highlight the potential of combining thermal gradients with conventional vital signs to enhance early and reliable risk stratification and support clinical decision-making in pediatric intensive care.

Abstracts:The Move Method refactoring is crucial for mitigating the Feature Envy code smell, which enhances cohesion and reduces coupling by relocating methods to more suitable classes. Existing deep learning approaches often suffer from redundant features, limiting model generalization. To address this, this paper introduces GMove, a novel approach leveraging feature fusion and a hybrid deep learning architecture (Bi-LSTM and CNN branches) to recommend refactoring opportunities. By fusing semantic, structural, and metric features from a constructed 16,828-sample dataset, GMove effectively filters redundant information. Experimental results demonstrate that GMove achieves a high synthetic F1 score of 97.7% and significantly outperforms state-of-the-art refactoring tools, showing an average F1 improvement of 9.7% over the strongest modern baseline, affirming its effectiveness and novel fusion strategy.

Abstracts:Existing point cloud registration methods have achieved significant progress through transformer architecture. However, these methods often overlook the fine-grained structural information in local features, which limits their adaptability to complex scenes. To address this issue, we propose a fine-grained module that enhances the receptive field through hierarchical feature fusion. This approach provides finer-grained feature information and improves the accuracy of point cloud registration. First, a multi-scale hierarchical feature fusion module is designed to capture fine-grained feature and expand the receptive field. Second, this module is integrated into the REGTR backbone network to enhance feature correlation. Finally, an efficient and accurate registration strategy is proposed by enhancing the contribution of high-probability overlapping features. Comprehensive experiments on both indoor (3DMatch, ModelNet40) and outdoor (MCD) benchmarks demonstrate the method's effectiveness. Compared with REGTR baseline, our method achieves relative error reductions of 17.6% and 8.9% on 3DMatch and ModelNet40 respectively, while maintaining competitive computational efficiency. Consistent performance improvement on the outdoor MCD dataset further validates the method's effectiveness across diverse scenarios.

Abstracts:Sequence alignment is a fundamental task in bioinformatics, requiring intensive computational processing that grows quadratically with sequence size. High-Performance Computing (HPC) offers essential solutions to accelerate such tasks. This paper presents a detailed performance analysis of the local alignment Smith-Waterman algorithm, comparing a sequential implementation against parallel versions designed for modern multi-core and nodes architectures. For shared-memory parallelism, an OpenMP version was developed using a wave front strategy to manage data dependencies; for distributed-memory, an MPI version was implemented using a 2D row-based domain decomposition. The evaluation results, using workloads of sequences sizes of 1000, 5000, and 15000, revealed distinct performances. The OpenMP approach proved effective for larger workloads (peak speedup of 1.84x), though inefficient for small workloads (speedup of 0.56x) due to parallelization overhead. In contrast, the MPI approach was consistently outperformed by the sequential version in all tests, demonstrating that the high cost of inter-node communication nullified the gains from distributed computing. The analysis concludes that the choice of a parallel model must carefully balance architectural paradigms with algorithmic characteristics to achieve meaningful performance improvements.

Abstracts:A problem of irreversible data hiding (DH), producing stego images resistant to steganalysis, is considered in spatial domain of gray-scale cover images. Stego image detection error (DE) is maximized when data is hidden (embedded) into noisy-like image areas where pixel values vary significantly. It is proved herein that generalization of the well-known least-significant bit (LSB) substitution to remainder modulo m (RM-m) DH method has an embedding invariant preserved after DH. A new adaptive remainder modulo m (ARM-m) method hiding data first in maximal noisy blocks by RM-m is proposed. ARM-m uses the invariant to construct a block complexity measure for adaptation. Ensemble classifiers and subtractive pixel adjacency matrix (SPAM) with 686 features were used to evaluate stego image DE on 886 images from UCID v.2 database. Compared to the state-of-the-art methods, ARM-4 with 2x2 blocks has DE=41.86% versus 24.42% of the best known method for 1 bit per pixel (bpp) embedding rate (ER). For ER=1.33 bpp, not reachable for known adaptive methods, ARM-4 and ARM-16, both with 8x8 blocks, have DE=27.33% and 27.91%, respectively. ARM-4 is confirmed to be better than other methods also for 2658 gray scale images. RS-diagram steganalysis conducted complies with DE evaluation results.