Search Results (80)

Lower gastrointestinal bleeding (LGIB) is a frequent and potentially life-threatening clinical condition. Over the past two decades, several prognostic scoring systems have been developed to stratify risk and guide the management of patients with LGIB. This comprehensive review aims to summarize and compare the current evidence on the utility, accuracy, and limitations of key LGIB scoring systems, including the Glasgow-Blatchford Score (GBS), AIMS65, ABC score, Oakland score, SALGIB, CHAMPS, and Rockall score. We conducted a structured literature review of studies evaluating these scores in adult patients with LGIB. For each scoring system, we analyzed its origin, components, intended use, and predictive performance regarding clinical outcomes such as severe bleeding, transfusion requirement, in-hospital mortality, rebleeding, and safe discharge. Comparative analyses of diagnostic accuracy were extracted where available. Our findings indicate that while no single score offers comprehensive predictive accuracy across all outcomes, certain tools are particularly effective for specific endpoints. The Oakland and GBS scores are useful for identifying patients at low risk who may be managed safely as outpatients. The ABC and CHAMPS scores demonstrate superior performance in predicting mortality, especially in elderly or comorbid populations. SALGIB, a newer score developed in Vietnam, shows promising performance for early triage but requires further validation. The Rockall score, although historically valuable in upper GI bleeding, offers limited applicability in LGIB due to its reliance on post-endoscopic findings. In conclusion, multiple prognostic tools are now available to support early decision-making in LGIB. Their optimal use requires understanding their strengths, limitations, and appropriate clinical contexts. Integrating these scores into routine practice, along with clinical judgment, can enhance patient outcomes and resource allocation. Full article

This study addresses the challenges of high-power consumption and complexity in conventional infrared (IR) gas sensors by integrating metamaterials and gold coatings into IR radiation sources to reduce radiation loss. In addition, emitter design optimization and material selection were employed to minimize conduction loss. Our metasurface exhibited superior performance, achieving a narrower full width at half maximum at 4197 and 3950 nm, resulting in more confined emission spectral ranges. This focused emission reduced energy waste at unnecessary wavelengths, improving efficiency compared to traditional blackbody emitters. At 300 °C, the device consumed only 6.8 mW, while maintaining temperature uniformity and a fast response time. This enhancement is promising for the operation of such sensors in IoT networks with ultra-low power consumption and at suitably low costs for widespread demands in high-technology farming. Full article

COVID-19-associated invasive fungal infections (CAIFIs) contribute to increased mortality and morbidity rates. This study explores the epidemiology, laboratory parameters, radiological characteristics, treatments, and 30-day mortality risks of CAIFI in critically ill intubated patients while also evaluating factors associated with prolonged mechanical ventilation (PMV) in this population. Adults admitted to a tertiary hospital from 1 April 2021 to 31 March 2022 who were diagnosed with severe COVID-19, required invasive mechanical ventilation, and developed invasive fungal infection (IFI) during hospitalization were analyzed in this retrospective cohort study. Among 150 patients, 65 (43.3%) required PMV, with an in-hospital mortality rate of 64%. Candida albicans (47%) and Aspergillus fumigatus (27%) were the most prevalent pathogens. Multivariate analysis revealed that COVID-19 vaccination (adjusted odds ratio, aOR = 0.155, 95% confidence interval, 95% CI = 0.029–0.835, p = 0.030) and higher serum protein levels (aOR = 0.900, 95% CI = 0.819–0.989, p = 0.028) were significantly associated with a reduced risk of PMV. Meanwhile, elevated glucose levels (hazard ratio, HR = 1.047, 95% CI = 1.003–1.093, p = 0.036) and an increased neutrophil-to-lymphocyte ratio (HR = 1.024, 95% CI = 1.009–1.039, p = 0.002) were correlated with a greater 30-day mortality risk. Tracheostomy emerged as a protective factor, significantly reducing the risk of 30-day mortality (HR = 0.273, 95% CI = 0.127–0.589, p = 0.001). In this single-center study, patients with CAIFI exhibit a high mortality rate. Clinicians should maintain vigilance for IFI in critically ill COVID-19 patients with mechanical ventilation. Full article

Vitexin and isovitexin are natural flavone C-glucosides that have numerous benefits for human health. However, their low oral bioavailability and poor gastrointestinal absorption dramatically restrict their potential medicinal uses. To overcome this challenge, chitosan-coated alginate microcapsules were prepared for intragastrical administration to rabbits. An LC-MS/MS method was developed and validated for the simultaneous determination of vitexin and isovitexin in the plasma of treated rabbits, using salicylic acid as the internal standard. Raw rabbit plasma samples were deproteinized using acetonitrile as a precipitation agent. Chromatographic separation was performed on a reversed-phase C18 column (100 mm × 4.6 mm, 3.5 µm), with an isocratic mobile solvent system comprising methanol and 0.1% acetic acid (40:60) as the mobile phase. All the analytes and the internal standard were ionized on a triple quadrupole mass spectrometer and electrospray ionization, operating in negative mode and multiple reaction monitoring. The analytical approach developed underwent validation in terms of system suitability, specificity, selectivity, LLOQ of 2 ng/mL, linearity (2.0–200 ng/mL, R² > 0.99), accuracy (the intra- and inter-day from 94 to 110% with the relative standard deviations no more than 8.7%, precision with the recoveries from 97% to 102%, matrix effect (90–100%), carry-over, dilution integrity (2 times), and stability at room and frozen temperature for up to 1 month, and all the parameters met FDA and EMA requirements for bioanalytical methods. The validated procedure was applied to measure the absorption of vitexin and isovitexin from encapsulated extracts in a pilot pharmacokinetic study on rabbit plasma. Compared to the raw traditional extracts, the microcapsules enhanced the bioavailability of vi-texin and isovitexin regarding C_max and AUC values. Full article

Microplastic (MP) pollution has recently emerged as a critical global environmental issue. Laundry wastewater is a significant contributor to MP pollution, containing high concentrations of MPs. Although coagulation has recently been widely applied to remove MPs from such wastewater, its efficiency remains poor, and the removal mechanisms are not yet fully elucidated. In this study, the occurrence and characteristics of MPs in raw domestic laundry wastewater were investigated. The coagulation process was combined with ultrafiltration (UF) membrane filtration to enhance MP removal. The results showed that the concentrations of MPs in laundry wastewater ranged from 9000 to 11,000 particles/L, with fibrous particles constituting the majority (42.6%) and polyester accounting for 68.2% of detected MPs. Using aluminium chloride and ferric chloride as coagulants, maximum removal efficiencies of 91.7 and 98.3% were achieved, respectively. Mechanistic analysis revealed that charge neutralization played a dominant role during coagulation. Fourier transform infrared spectroscopy further demonstrated the formation of new functional groups, substituted benzene rings, and the presence of Fe-O and Al-O bonds, indicating the interaction between MPs and coagulants. Furthermore, the UF membrane was used to remove fibrous MPs and MPs with low densities. These MPs had not been removed with pre-coagulation. The removal efficiency of these MPs reached 96 ± 2%, reducing their concentration to only 60 particles/L in the UF permeate. These findings highlight the synergistic potential of coagulation and UF membrane filtration for effective MP removal and provide a valuable reference for advancing wastewater treatment technologies targeting MP pollution. Full article

In long-haul WDM (wavelength division multiplexing) optical communication systems utilizing the DP-16QAM modulation scheme, traditional methods for removing chaos have exhibited poor performance, resulting in a high bit error rate of ${10}^{-2}$ between the original signal and the removed chaos signal. To address this issue, we propose DeepChaos+, a machine learning-based approach for chaos removal in WDM transmission systems. Our framework comprises two key points: (1) DeepChaos+ automatically generates a dataset that accurately reflects the features of the original signals in the communication system, which eliminates the need for time-consuming data simulation, streamlining the process significantly; (2) it allows for the training of a lightweight model that provides fast prediction times while maintaining high accuracy. This allows for both efficient and reliable signal reconstruction. Through extensive experiments, we demonstrate that DeepChaos+ achieves accurate reconstruction of the original signal with a significantly reduced bit error rate of approximately ${10}^{-5}$. Additionally, DeepChaos+ exhibits high efficiency in terms of processing time, facilitating fast and reliable signal reconstruction. Our results underscore the effectiveness of DeepChaos+ in removing chaos from WDM transmission systems. By enhancing the reliability and efficiency of chaotic secure channels in optical fiber communication systems, DeepChaos+ holds the potential to improve data transmission in high-speed networks. Full article

This paper proposes an output feedback-based control for uncertain electro-hydrostatic systems (EHSs) to satisfy high output tracking precision under the influences of unknown mismatched and matched uncertainties and unstructured dynamical behavior. In this configuration, an extended state observer (ESO) is first employed to obtain unmeasured states and suppress the adverse effect of matched uncertainty. Meanwhile, the influence of unstructured dynamical behavior is approximated by employing a radial basis function neural network (RBFNN)-based technique. With the unmeasured states observed, matched uncertainty, and system dynamics compensated, the robust backstepping sliding mode control is accordingly established and the lumped mismatched uncertainty is then suppressed through disturbance observer-based adaptive law. Interestingly, the proposed control methodology requires only output feedback but can address the whole system dynamics. The stability of the closed-loop system is theoretically proven through a Lyapunov theorem and the effectiveness of the proposed methodology is demonstrated through comparative simulations. Full article

Configuring green power transmissions for heavy-industry marines is treated as a crucial request in an era of global energy and pollution crises. Following up on this hotspot trend, this paper examines the effectiveness of a modified optimization-based energy management strategy (OpEMS) for a dual proton exchange membrane fuel cells (dPEMFCs)-battery-ultra-capacitors (UCs)-driven hybrid electric vessels (HEVs). At first, the summed power of the dual PEMFCs is defined by using the equivalent consumption minimum strategy (ECMS). Accordingly, a map search engine (MSE) is proposed to appropriately split power for each FC stack and maximize its total efficiency. The remaining power is then distributed to each battery and UC using an adaptive co-state, timely determined based on the state of charge (SOC) of each device. Due to the strict constraint of the energy storage devices’ (ESDs) SOC, one fine-corrected layer is suggested to enhance the SOC regulations. With the comparative simulations with a specific rule-based EMS and other approaches for splitting power to each PEMFC unit, the effectiveness of the proposed topology is eventually verified with the highest efficiency, approximately about 0.505, and well-regulated ESDs’ SOCs are obtained. Full article

This article aims to examine the hedging effect of green bonds in the US market, the European market, and the Chinese market on carbon price risk in the European Union Emission Trading System (EU ETS) from 2021 to 2023. By using daily datasets extracted from Bloomberg and the Vector Error Correction Model (VECM), the research provides evidence of the hedging effect of green bonds in all three markets on carbon price risk in the EU ETS. The paper concludes that the hedging ratio is positive for green bonds in the EU and China, while the figure for the US market is negative. Moreover, there is a positive effect of oil prices on carbon returns in EU ETS. Meanwhile, the opposite is found for stock prices. Full article

This article proposes a novel real-time optimization-based energy management strategy (EMS) for proton membrane exchange fuel cell (PEMFC)-battery-supercapacitors-driven hybridized-electric tramways (HETs). The proposed algorithm is derived based on an enhanced extremum seeking (ES) algorithm, with a new equivalent state-of-charge (SOC) and a new adaptive co-state introduced. Thereby, optimized reference power for each power source can be distributed appropriately when using three components. The workability and prominent of the proposed technique are demonstrated through comparative simulations with fuzzy-rule-based EMS (FEMS) and equivalent consumption minimization strategy (ECMS) in two case studies: with and without considering the supercapacitors, as an important factor in the EMS design to stabilize the SOC of energy storage devices (ESDs). Briefly, under the proposed ES-based method, the PEMFC power can be regulated such that high-efficiency can be performed, approximately by 46.7%. Subsequently, the hydrogen consumption is reduced about 31.2% compared to a comparative fuzzy-based EMS. Besides, the supplements’ SOCs at the end of a driving cycle are also regulated to be equal to the initial ones. Full article

Early forecasting of vehicle flow speeds is crucial for sustainable traffic development and establishing Traffic Speed Forecasting (TSF) systems for each country. While online mapping services offer significant benefits, dependence on them hampers the development of domestic alternative platforms, impeding sustainable traffic management and posing security risks. There is an urgent need for research to explore sustainable solutions, such as leveraging Global Positioning System (GPS) probe data, to support transportation management in urban areas effectively. Despite their vast potential, GPS probe data often present challenges, particularly in urban areas, including interference signals and missing data. This paper addresses these challenges by proposing a process for handling anomalous and missing GPS signals from probe vehicles on parallel multilane roads in Vietnam. Additionally, the paper investigates the effectiveness of techniques such as Particle Swarm Optimization Long Short-Term Memory (PSO-LSTM) and Genetic Algorithm Long Short-Term Memory (GA-LSTM) in enhancing LSTM networks for TSF using GPS data. Through empirical analysis, this paper demonstrates the efficacy of PSO-LSTM and GA-LSTM compared to existing methods and the state-of-the-art LSTM approach. Performance metrics such as Root Mean Square Error (RMSE), Mean Absolute Error (MAE), and Median Absolute Error (MDAE) validate the proposed models, providing insights into their forecasting accuracy. The paper also offers a comprehensive process for handling GPS outlier data and applying GA and PSO algorithms to enhance LSTM network quality in TSF, enabling researchers to streamline calculations and improve supposed model efficiency in similar contexts. Full article

In Vietnam, chest radiography (CXR) is used to refer people for GeneXpert (Xpert) testing to diagnose tuberculosis (TB), demonstrating high yield for TB but a wide range of CXR abnormality rates. In a multi-center implementation study, computer-aided detection (CAD) was integrated into facility-based TB case finding to standardize CXR interpretation. CAD integration was guided by a programmatic framework developed for routine implementation. From April through December 2022, 24,945 CXRs from TB-vulnerable populations presenting to district health facilities were evaluated. Physicians interpreted all CXRs in parallel with CAD (qXR 3.0) software, for which the selected TB threshold score was ≥0.60. At three months, there was 47.3% concordance between physician and CAD TB-presumptive CXR results, 7.8% of individuals who received CXRs were referred for Xpert testing, and 858 people diagnosed with Xpert-confirmed TB per 100,000 CXRs. This increased at nine months to 76.1% concordant physician and CAD TB-presumptive CXRs, 9.6% referred for Xpert testing, and 2112 people with Xpert-confirmed TB per 100,000 CXRs. Our programmatic CAD-CXR framework effectively supported physicians in district facilities to improve the quality of referral for diagnostic testing and increase TB detection yield. Concordance between physician and CAD CXR results improved with training and was important to optimize Xpert testing. Full article

Termites’ digestive systems, particularly in lower termites with the presence of protozoa, are unique ecological niches that shelter a diverse microbiota with a variety of functions for the host and the environment. In 2012, the metagenomic DNA (5.4 Gb) of the prokaryotes that freely live in the gut of the lower termite Coptotermes gestroi were sequenced. A total of 125,431 genes were predicted and analyzed in order to mine lignocellulolytic genes. however, the overall picture of the structure, diversity, and function of the prokaryotic gut microbiota was not investigated. In the present study, these 125,431 genes were taxonomically classified by MEGAN and functionally annotated by the Kyoto Encyclopedia of Genes and Genomes (KEGG) and by the Carbohydrate-Active enZYmes (CAZy) and HMMER databases. As a result, 95,751 bacterial genes were classified into 35 phyla. The structure of the bacteria, typified by a high ratio of Firmicutes to Bacterioidetes, was distinct from the structure of the entirety of the bacteria in the lower or higher termites’ guts. The archaea (533 genes) were distributed into 4 phyla, 10 classes, 15 orders, 21 families, 47 genera, and 61 species. Although freely living in the guts, the prokaryotic community was formed, developed, and adapted to exhibit unique interactions in order to perform mutual roles of benefit to their hosts. Methanobacteriales, accounting for 61% of the archaea symbionts, seem to play an important role in methanogenesis. Concomitantly, bacterial methanotrophs in the gut utilize methane and combine with other bacterial groups, including potential lignocellulolytic degraders, acetogens, sulfur bacteria, and nitrogen-recycling bacteria, to efficiently convert wood with little nitrogen into acetates via certain pathway modules specified by prokaryotes that freely live in the gut. This forms an important energy source for the termites. Furthermore, bacteria carry 2223 genes involved in the biosynthesis of 17 antibiotic groups. The gut bacteria also possess genes for the degradation of 18 toxic aromatic compounds, of which four are commercial pesticides against termites commonly used for the preservation of wooden constructions. Eight of the eighteen pathways were the first to be reported from the termite gut. Overall, this study sheds light on the roles of the freely living bacteria and archaea in the C. gestroi gut, providing evidence that the gut microbiome acts as the second host genome, contributing both nutrients and immunity to support the host’s existence, growth, and development. Full article

This study proposes a low-cost and reliable smart fire alarm system that utilizes ultraviolet (UV) detection technology with an aspherical lens to detect fires emitting photons in the 185–260 nm range. The system integrates the aspherical lens with an accelerator and a digital compass to determine the fire source’s direction, allowing for safe evacuation and effective firefighting. Artificial intelligence is employed to reduce false alarms and achieve a low false alarm rate. The system’s wide detection range and direction verification make it an effective fire detection solution. Upon detecting a fire, the system sends a warning signal via Wi-Fi or smartphone to the user. The proposed system’s advantages include early warning, a low false alarm rate, and detection of a wide range of fires. Experimental results validate the system’s design and demonstrate high accuracy, reliability, and practicality, making it a valuable addition to fire management and prevention. The proposed system utilizes a parabolic mirror to collect UV radiation into the detector and a simple classification model that uses Fourier transform algorithm to reduce false alarms. The results showed accuracies of approximately 95.45% and 93.65% for the flame and UVB lamp, respectively. The system demonstrated its effectiveness in detecting flames in the range of up to 50 m, making it suitable for various applications, including small and medium-sized buildings, homes, and vehicles. Full article

Growth factors affect farm owners, environmental conditions, nutrient adaptation, and resistance to chrysanthemum diseases. Healthy chrysanthemum plants can overcome all these factors and provide farms owners with a lot of income. Chrysanthemum white rust disease is a common disease that occurs worldwide; if not treated promptly, the disease spreads to the entire leaf surface, causing the plant’s leaves to burn, turn yellow, and fall prematurely, reducing the photosynthetic performance of the plant and the appearance of the flower branches. In Korea, chrysanthemum white rust disease most often occurs during the spring and autumn seasons, when temperature varies during the summer monsoon, and when ventilation is poor in the winter. Deep neural networks were used to determine healthy and unhealthy plants. We applied the Raspberry Pi 3 module to recognize white rust and test four neural network models. The five main deep neural network processes utilized for a dataset of non-diseased and white rust leaves include: (1) data collection; (2) data partitioning; (3) feature extraction; (4) feature engineering; and (5) prediction modeling based on the train–test loss of 35 epochs within 20 min using Linux. White rust recognition is performed for comparison using four models, namely, DenseNet-121, ResNet-50, VGG-19, and MobileNet v2. The qualitative white rust detection system is achieved using a Raspberry Pi 3 module. All models accomplished an accuracy of over 94%, and MobileNet v2 achieved the highest accuracy, precision, and recall at over 98%. In the precision comparison, DenseNet-121 obtained the second highest recognition accuracy of 97%, whereas ResNet-50 and VGG-19 achieved slightly lower accuracies at 95% and 94%, respectively. Qualitative results were obtained using the Raspberry Pi 3 module to assess the performance of the seven models. All models had accuracies of over 91%, with ResNet-50 obtaining a value of 91%, VGG-19 reaching a value of 93%, DenseNet-121 reaching 95%, SqueezeNet obtaining over 95%, MobileNet obtaining over 96%, and MobileNetv2-YOLOv3 reaching 92%. The highest accuracy rate was 97% (MobileNet v2). MobileNet v2 was validated as the most effective model to recognize white rust in chrysanthemums using the Raspberry Pi 3 system. Raspberry Pi 3 module was considered, in conjunction with the MobileNet v2 model, to be the best application system. MobileNet v2 and Raspberry Pi require a low cost for the recognition of chrysanthemum white rust and the diagnosis of chrysanthemum plant health conditions, reducing the risk of white rust disease and minimizing costs and efforts while improving floral production. Chrysanthemum farmers should consider applying the Raspberry Pi module for detecting white rust, protecting healthy plant growth, and increasing yields with low-cost. Full article