Search Results (293)

Background: Accurately detecting the onset of saturated boiling in herbal medicine extraction processes is critical for improving production efficiency and reducing energy consumption. However, the traditional monitoring methods based on temperature suffer from time delays. To address the challenge, acoustic emission (AE) signals were used in this study owing to its sensitivity to bubble behavior. Methods: An AE signal acquisition system was constructed for herbal extraction monitoring. Characteristics of AE signals at different boiling stages were analyzed in pure water systems with and without herbs. The performance of AE-based and temperature-based recognition of boiling stages was compared. To enhance applicability in different herb extraction systems, multivariate statistical analysis was adopted to compress spectral–frequency information into Hotelling’s T² and SPE statistics. For real-time monitoring, a self-adaptive decision-making boiling judgment method (BoilStart) was proposed. To evaluate the robustness, the performance of BoilStart under different conditions was investigated, including extraction system mass and heating medium temperature. Furthermore, BoilStart was applied to a lab-scale extraction process of Dabuyin Wan, which is a practical formulation, to assess its performance in energy conservation and efficiency improvement. Results: AE signal in the 75–100 kHz frequency band could reflect the boiling states of herbal medicine extraction. It was more sensitive to the onset of saturated boiling than the temperature signal. Compared with SPE, Hotelling’s T² was identified as the optimal indicator with higher accuracy. BoilStart could adaptively monitor saturated boiling across diverse herbal systems. The absolute error of BoilStart’s boiling determination ranged from 1.5 min to 2.0 min. The increasing-temperature time was reduced by about 22–36%. For the extraction process of Dabuyin Wan, after adopting BoilStart, the increasing-temperature time was reduced by about 29%, and the corresponding energy consumption was lowered by about 26%. Conclusions: The first AE-based method for precise boiling state detection in herbal extraction was established. BoilStart’s model-free adaptability met industrial demands for multi-herb compatibility. This offered a practical solution to shorten ineffective heating phases and reduce energy consumption. Full article

Electrocardiograms (ECGs) are critical for cardiovascular disease diagnosis, but their accuracy is often compromised by electrode motion (EM) artifacts—large, nonstationary distortions caused by patient movement and electrode-skin interface shifts. These artifacts overlap in frequency with genuine cardiac signals, rendering traditional filtering methods ineffective and increasing the risk of false alarms and misdiagnosis, particularly in wearable and ambulatory ECG applications. To address this, we propose the Feature-Shuffle Multi-Head Attention Autoencoder (FMHA-AE), a novel architecture integrating multi-head self-attention (MHSA) and a feature-shuffle mechanism to enhance ECG denoising. MHSA captures long-range temporal and spatial dependencies, while feature shuffling improves representation robustness and generalization. Experimental results show that FMHA-AE achieves an average signal-to-noise ratio (SNR) improvement of 25.34 dB and a percentage root mean square difference (PRD) of 10.29%, outperforming conventional wavelet-based and deep learning baselines. These results confirm the model’s ability to retain critical ECG morphology while effectively removing noise. FMHA-AE demonstrates strong potential for real-time ECG monitoring in mobile and clinical environments. This work contributes an efficient deep learning approach for noise-robust ECG analysis, supporting accurate cardiovascular assessment under motion-prone conditions. Full article

One of the main challenges faced by DC microgrid (DCMG) is their low inertia, which leads to rapid and significant voltage fluctuations during load or generation changes. These fluctuations can negatively impact sensitive loads and protection devices. Previous studies have addressed this by enabling battery converters to mimic the behavior of synchronous generators (SGs), but this approach becomes ineffective when the converters or batteries reach their current or energy limits, leading to a loss of inertia and potential system instability. In interconnected multi-microgrid (MMG) systems, the presence of multiple batteries offers the potential to enhance system inertia, provided there is a coordinated control strategy. This research introduces a hierarchical control method that combines decentralized and centralized approaches. Decentralized control allows individual converters to emulate SG behavior, while the centralized control uses Internet of Things (IoT) technology to enable real-time coordination among all Energy Storage Units (ESUs). This coordination improves inertia across the DCMMG system, enhances energy management, and strengthens overall system stability. IoT integration ensures real-time data exchange, monitoring, and collaborative decision-making. The proposed scheme is validated through MATLAB simulations, with results confirming its effectiveness in improving inertial response and supporting the integration of renewable energy sources within DCMMGs. Full article

Background: Obesity remains a major global health challenge, and a significant proportion of bariatric surgery patients continue to experience dysfunctional emotional eating and body image concerns after surgery. Neurofeedback training (NFT) has been investigated as a potential intervention for maladaptive eating behaviours, but evidence in post-bariatric populations is still limited. Methods: Thirty-six patients who underwent sleeve gastrectomy were included, divided into an NFT group (N = 18) and a control group (N = 18). Assessments were performed at baseline and after 10 NFT sessions, using the Eating Disorder Inventory (EDI) and the Body Uneasiness Test (BUT). The intervention aimed to enhance alpha and theta waves with real-time feedback. Results: Compared with the control group, the NFT group showed significant improvements; specifically, reductions were observed in EDI subscales such as Drive for Thinness (p = 0.023, d = 0.51), Bulimia (p = 0.008, d = 0.92), Body Dissatisfaction (p = 0.015, d = 0.52), Ineffectiveness (p = 0.002, d = 0.89), Perfectionism (p = 0.006, d = 0.70), Interpersonal Distrust (p = 0.008, d = 0.82), and Interoceptive Awareness (p = 0.001, d = 0.91). Significant reductions were also found in BUT subscales including Weight Phobia (p = 0.041, d = 0.84), Body Image Concern (p = 0.039, d = 0.90), Avoidance (p = 0.027, d = 0.83), Compulsive Self-Monitoring (p = 0.013, d = 0.83), and Depersonalisation (p = 0.033, d = 0.85). Conclusions: The data indicate that NFT may help reduce emotional eating and related psychological factors in post-bariatric patients in the short term. However, studies with larger samples and longer follow-ups are needed to confirm its effectiveness and assess its clinical applicability. Full article

Per-polyfluoroalkyl substances (PFASs) are a class of persistent organic pollutants that have been detected in several environmental matrices. Photoelectrocatalysis (PEC) was employed to remove PFASs contained in natural groundwater collected in the Veneto region (Italy), where a massive PFAS contamination was present. Nine PFASs were detected and monitored throughout the process. By varying the magnitude of the applied cell voltage (no bias and 4, 6, and 8 V) the optimal condition was assessed to be 4 V, resulting in a total PFAS removal of about 87%. The presence of H₂O₂ was ineffective on the reaction kinetic, while NaCl inhibited the oxidation of PFASs. The E_EO (Electrical Energy per Order of Magnitude) analysis revealed that PEC is more energy-efficient than both traditional photolysis and most advanced oxidation techniques discussed in published research. Full article

Plant parasitic nematodes are a significant agricultural threat, causing substantial economic losses. Methyl bromide, a commonly used nematicide, has been banned due to its harmful environmental and human health effects. As an alternative, the expression of the programmed cell death (PCD) gene CED4 from Caenorhabditis elegans in transgenic plants has been proposed to control nematode populations. In this study, the interaction between CED4 and other proteins was analyzed, and peptide sequences representing interaction domains were identified. Efficacy assays demonstrated that specific peptides—particularly Peptides 2 and 3 (N-terminal α/β domain) and Peptide 12 (C-terminal HD-2 domain)—induced significant mortality in C. elegans, while other peptides were ineffective. The study further investigated whether these peptides, along with modified CED4-like peptides (2a, 3a, and 12a), induce PCD in C. elegans via the activation of the nematode’s endogenous PCD pathway. Testing was conducted on wild-type and mutant strains of C. elegans (ced-4 and ced-3 mutants). Nematode survival was monitored over 34 days, revealing that c3 mutants survived exposure to CED4-like peptides, suggesting that the peptides trigger PCD through the activation of the endogenous cell death pathway. These findings support the potential use of CED4-based peptides as a novel strategy for nematode control. Full article

Impaired visibility, a major global environmental threat, is a result of light scattering by atmospheric particulate matter. While digital photographs are increasingly used for daytime visibility estimation, such methods are largely ineffective at night owing to the different scattering effects. Here, we introduce an image-based algorithm for inferring nighttime visibility from a single photograph by analyzing the forward scattering index and optical thickness retrieved from glow effects around light sources. Using photographs crawled from social media platforms across mainland China, we estimated the nationwide visibility for one year using the proposed algorithm, achieving high goodness-of-fit values (R² = 0.757; RMSE = 4.318 km), demonstrating robust performance under various nighttime scenarios. The model also captures both chronic and episodic visibility degradation, including localized pollution events. These results highlight the potential of using ubiquitous smartphone photography as a low-cost, scalable, and real-time sensing solution for nighttime atmospheric monitoring in urban areas. Full article

Project monitoring is an important element in project management that aims to ensure project implementation in accordance with the plan, schedule, budget, and objectives that have been set. The ineffectiveness of project monitoring can cause various problems, such as delays, cost overruns, inappropriate quality of results, and poor communication between stakeholders. To address these issues, technological advances such as YOLO (You Only Look Once) and Reinforcement Learning (RL) offer innovative solutions through real-time visual detection and data-driven automated decision making. This research aims to develop a web-based project monitoring system that integrates YOLO to detect activities in the field, such as workers and heavy equipment, and RL to provide optimal recommendations for resource management. The implementation of the system is expected to increase efficiency, reduce risk, and support more accurate decision making. Based on previous research, the adoption of AI technology in project monitoring is proven to reduce operational costs and increase productivity. This web-based system is designed to provide flexibility and accessibility, allowing users to monitor projects in real-time through an interactive interface. The expected outcome of this research is the creation of an effective technological solution to improving the efficiency of construction project management, as supported by the findings of previous research that shows the great potential of AI in the construction sector. Full article

Partial discharge (PD) is a critical indicator of insulation degradation in high-voltage DC systems, necessitating accurate diagnosis to ensure long-term reliability. Conventional AC-based diagnostic methods, such as phase-resolved partial discharge analysis (PRPDA), are ineffective under DC conditions, emphasizing the need for waveform-based analysis. This study presents a novel clustering framework for DC PD pulses, leveraging multi-level wavelet decomposition and statistical feature extraction. Each signal is decomposed into multiple frequency bands, and 70 distinctive waveform features are extracted from each pulse. To mitigate feature redundancy and enhance clustering performance, principal component analysis (PCA) is employed for dimensionality reduction. Experimental data were obtained from multiple defect types and measurement distances using a 22.9 kV cross-linked polyethylene (XLPE) cable system. The proposed method significantly outperformed conventional time-frequency (T-F) mapping techniques, particularly in scenarios involving signal attenuation and mixed noise. Propagation-induced distortion was effectively addressed through multi-resolution analysis. In addition, field noise sources such as HVDC converter switching transients and fluorescent lamp emissions were included to assess robustness. The results confirmed the framework’s capability to distinguish between multiple PD types and noise sources, even in challenging environments. Furthermore, optimal mother wavelet selection and correlation-based feature analysis contributed to improved clustering resolution. This framework supports robust PD classification in practical HVDC diagnostics. The framework can contribute to the development of real-time autonomous monitoring systems for HVDC infrastructure. Future research will explore incorporating temporal deep learning architectures for automated PD-type recognition based on clustered data. Full article

To prepare for a boosted sterile insect technique (SIT) field trial in Saint-Joseph, Reunion island, we compared the attractiveness of two adult mosquito traps for Aedes albopictus and Aedes aegypti. In addition, we explored the co-occurrence of these species in their usual larval habitats. Two traps were compared with two conditions each using a Latin square design: BG Sentinel trap baited with carbon dioxide (CO₂) with/without addition of BG Lure and ovi-sticky trap with/without hay. The ovi-sticky traps proved ineffective. For both Aedes species, CO₂-baited traps were equally effective at catching females when baited with the lure or not. In contrast, they were more attractive to males than for females with the lure. Aedes aegypti larvae were found in four of six vacoas (Pandanus utilis), and one of four anthropogenic breeding sites. In vacoas, the densities of Aedes albopictus and Aedes aegypti larvae were negatively correlated, whereas the correlation was positive between chironomids and Aedes aegypti. The abundance of adults and larvae varied according to weather conditions. Finally, CO₂-baited traps were used without lure for the entomological monitoring to assess the effectiveness of the area-wide boosted SIT intervention to reduce costs and logistics. Full article

Background: Immunotherapy has revolutionized oncology care, but clinical response to immune checkpoint inhibitors (ICIs) remains unpredictable, and treatment carries substantial risks and costs. The EpiSwitch^® CiRT blood test is a novel 3D genomic assay that stratifies patients by probability of ICI benefit using a binary, blood-based classification: high (HPRR) or low (LPRR) probability of response. Methods: This interim analysis of the ongoing PROWES prospective real-world evidence study evaluates the clinical utility of CiRT in 205 patients with advanced solid tumors. The primary endpoint was treatment decision impact, assessed by pre-/post-test physician surveys. Secondary endpoints included treatment avoidance, time to ICI initiation, concordance with clinical response, early discontinuation rates, and exploratory health economic modeling. Longitudinal use, resistance monitoring, and equity analysis by social determinants of health (SDoH) were also explored. Results: CiRT results influenced clinical decision-making in a majority of cases. LPRR status was associated with higher rates of treatment avoidance and early discontinuation due to immune-related adverse events (IrAEs). In contrast, HPRR patients experienced greater clinical benefit and longer ICI exposure. CiRT classification was not associated with short-term imaging-based response outcomes, supporting its role as an independent predictor. Given that ICI therapy and supportive care can cost more than $850,000 per patient, CiRT offers potential value in avoiding ineffective treatment and associated toxicities. Conclusions: CiRT demonstrates meaningful clinical utility as a non-invasive, predictive tool for guiding immunotherapy decisions across tumor types. It enables more precise treatment selection, improves patient outcomes, and supports value-based cancer care. Full article

Banana (Musa spp.) crops face severe yield and economic losses due to foliar diseases such as Moko disease and Black Sigatoka. In Ecuador, Moko outbreaks have increasingly devastated banana plantations, threatening one of the country’s most important export commodities and putting significant pressure on local producers and the national economy. Traditional field inspection methods are labor-intensive, subjective, and often ineffective for timely disease detection and containment. In this study, we propose an improved deep learning-based segmentation approach using YOLOv8 architectures to automatically detect and segment Moko and Black Sigatoka infections from unmanned aerial vehicle (UAV) imagery. Multiple YOLOv8 configurations were systematically analyzed and compared, including variations in backbone depth, model size, and hyperparameter tuning, to identify the most robust setup for field conditions. The final optimized configuration achieved a mean precision of 79.6%, recall of 80.3%, mAP@0.5 of 84.9%, and mAP@0.5:0.95 of 62.9%. The experimental results demonstrate that the improved YOLOv8 segmentation model significantly outperforms previous classification-based methods, offering precise instance-level localization of disease symptoms. This study provides a solid foundation for developing UAV-based automated monitoring pipelines, contributing to more efficient, objective, and scalable disease management strategies. Full article

Online monitoring systems continuously collect massive multivariate time-series data from converter transformers. Accurate outlier detection in these data is essential for identifying sensor faults, communication errors, and incipient equipment failures, thereby ensuring reliable condition assessment and maintenance decisions. However, the complex characteristics of transformer monitoring data—including non-Gaussian distributions from diverse operational modes, high dimensionality, and multi-scale temporal dependencies—render traditional outlier detection methods ineffective. This paper proposes a Multi-View Clustering-based Outlier Detection (MVCOD) framework that addresses these challenges through complementary data representations. The framework constructs four complementary data views—raw-differential, multi-scale temporal, density-enhanced, and manifold representations—and applies four detection algorithms (K-means, HDBSCAN, OPTICS, and Isolation Forest) to each view. An adaptive fusion mechanism dynamically weights the 16 detection results based on quality and complementarity metrics. Extensive experiments on 800 kV converter transformer operational data demonstrate that MVCOD achieves a Silhouette Coefficient of 0.68 and an Outlier Separation Score of 0.81, representing 30.8% and 35.0% improvements over the best baseline method, respectively. The framework successfully identifies 10.08% of data points as outliers with feature-level localization capabilities. This work provides an effective and interpretable solution for ensuring data quality in converter transformer monitoring systems, with potential applications to other complex industrial time-series data. Full article

Ulcerative colitis (UC) is a chronic, relapsing inflammatory disease of the colon, often associated with gut microbial dysbiosis. Although anti-TNF-α agents, such as Adalimumab (Cinnora^®), are used to treat moderate-to-severe UC, the treatment response is highly variable. Identifying early microbial biomarkers of response could help support personalized therapeutic strategies and prevent unnecessary exposure to ineffective treatments. However, the long-term effects of anti-TNF therapy on both stool and mucosal microbiota remain poorly understood. This prospective longitudinal study included 23 corticosteroid-refractory or -dependent UC patients who started Adalimumab after endoscopy-confirmed flare-ups. Stool samples and inflamed colonic biopsies were collected at baseline, and 3 and 6 months. Microbiota profiling was performed using 16S rRNA sequencing. Microbial changes were analyzed over time and compared between responders (Mayo score 0–1) and non-responders (Mayo score ≥ 2). Sixty percent of patients achieved clinical remission. In responders, stool microbiota showed increased Bacteroidetes and decreased Proteobacteria abundances, along with an enrichment of beneficial taxa including Faecalibacterium prausnitzii, Bifidobacterium, and Akkermansia muciniphila. Mucosal microbiota exhibited persistent dysbiosis, characterized by an increase in Proteobacteria and a reduced Firmicutes/Proteobacteria ratio. Notably, responders showed distinct compartment-specific microbial changes, with a decrease in Gammaproteobacteria in stool and an increase in Corynebacterium in tissue. Adalimumab induces divergent microbial changes in stool and mucosa. While stool microbiota trends toward eubiosis in responders, persistent mucosal dysbiosis may reflect asymptomatic inflammation. These findings underscore the importance of niche-specific microbiome profiling in UC and support its integration into personalized treatment monitoring. Full article

Background/Objectives: Newborn heart rate is an integral part of resuscitation algorithms, but the impact of ECG monitoring on resuscitative interventions and clinical outcomes has been identified as a knowledge gap. The objective was to evaluate the impact of routine use of dry-electrode ECG in all newborns immediately after birth on time to start positive pressure ventilation (PPV) when indicated. Methods: We conducted a randomized clinical trial from June 2019 to November 2021 at Stavanger University Hospital, Norway. Dry-electrode ECG sensors were applied immediately after birth to all newborns ≥ 34 weeks’ gestation. Randomization determined whether the heart rate display was visible or masked. Time of birth was registered in an observation app. Time to start ventilation was calculated from video recordings. Results: In total, 7343 newborns ≥ 34 weeks’ gestation were enrolled, 4284 in the intervention and 3059 in the control group, and 3.7% and 3.8% received ventilation, respectively. In 171/275 (62%) of the newborns the exact time of birth and a video of the resuscitation were available, for 98 in the intervention and 73 in the control group. Ventilation was provided within 60 s to 44/98 (45%) in the intervention and 24/73 (33%) in the control group, p = 0.12. Time from birth to start of PPV was a median of 66 (44, 102) s in the intervention and 84 (49, 148) s in the control group, p = 0.058. Resuscitated newborns were apneic (74%) or breathing ineffectively (26%) at the start of PPV, and only 36% had a heart rate < 100 beats per minute. Conclusions: The use of dry-electrode ECG heart rate monitoring did not change the proportion of newborns that received ventilation within 60 s after birth, but early termination due to employee protests to video recordings rendered the trial inadequately powered to detect a difference. Breathing status was likely a more important determinant of starting ventilation than bradycardia. Full article