Search Results (223)

Gas bubbles are one of the main disturbances encountered when dispensing drugs of microliter volumes using portable miniaturized systems based on piezoelectric diaphragm micropumps. The presence of a gas bubble in the pump chamber leads to the inaccurate administration of the required dose due to its impact on the flowrate. This is particularly important for highly concentrated drugs such as insulin. Different types of sensors are used to detect gas bubbles: inline on the fluidic channels or inside the pump chamber itself. These solutions increase the complexity, size, and cost of the microdosing system. To address these problems, a radically new approach is taken by utilizing the sensing capability of the piezoelectric diaphragm during micropump actuation. This work demonstrates the workflow to build a self-sensing micropump based on artificial intelligence methods on an embedded system. This is completed by the implementation of an electronic circuit that amplifies and samples the loading current of the piezoelectric ceramic with a microcontroller STM32G491RE. Training datasets of 11 micropumps are generated at an automated testbench for gas bubble injections. The training and hyper-parameter optimization of artificial intelligence algorithms from the TensorFlow and scikit-learn libraries are conducted using a grid search approach. The classification accuracy is determined by a cross-training routine, and model deployment on STM32G491RE is conducted utilizing the STM32Cube.AI framework. The finally deployed model on the embedded system has a memory footprint of 15.23 kB, a runtime of 182 µs, and detects gas bubbles with an accuracy of 99.41%. Full article

In recent decades, the use of off-road vehicles (ORVs) for challenging outdoor trips has increased significantly worldwide, impacting soil, vegetation, and wildlife. This study was conducted in Sde Zin, Israel, a hyper-arid desert zone. The area has a high concentration of trails created unintentionally over the years by ORVs. The study sought to examine whether the degraded trails will be restored naturally or if there is a need for active intervention. Five ORV trails were selected, with a plot of 40 × 15 m in each trail, comprising three subplot treatments: one session of disk tillage, no tillage, and an adjacent control subplot. Soil and vegetation parameters were measured for two consecutive years. The results indicated that the measured soil parameters did not differ between treatments except for the degree of soil compaction, which was a significant factor in plant survival and restoration. The highest H′ Shannon diversity was found in the disk-tillage treatment, where the plant assemblage differed from that of the non-tillage and control subplots. The conclusion derived from this study is that active management to prevent soil compaction is needed in severely degraded desert areas to stimulate soil and vegetation restoration processes. Full article

Low-frequency oscillations of blood components have been observed when the plasma is diluted by crystalloid fluid. The present study explores whether oscillations also occur during the infusion of hyper-oncotic albumin 20%. For this purpose, the hemoglobin-derived plasma dilution, plasma colloid osmotic pressure, and plasma albumin concentration were measured on 15 occasions over 5 h in 72 volunteers. All of them received 3 mL/kg of albumin 20% over 30 min in various clinical settings. Quality checks excluded 35% of the concentration–time curves, leaving 137 for analysis. Fourier transforms applied to the residuals after curve-fitting showed that the dominating frequency was 144 ± 42 min (mean ± SD), corresponding to 0.007 Hz and a wave amplitude of 1.8 ± 0.9%. The highest percentile of the amplitudes corresponded to a “peak-to-peak” variation in the plasma volume by 6%, which corresponds to a fluctuation of 180 mL, or 45% of the maximum volume expansion following the infusion of albumin 20%. Differences between settings (volunteers, surgery, postoperative, and post-burn) were small. In conclusion, oscillations with very low frequency occurred after infusion of albumin 20%. They varied the plasma volume by 3.6% and by up to 6% in the percentile with the highest amplitudes. The oscillations are large enough to affect measurements of cardiovascular function. Full article

Background: Asthma is a heterogeneous disease characterized by variable bronchial obstruction, hyper-responsiveness, and inflammation. Evaluating the immunological changes following pneumococcal immunization in patients with different asthma endotypes is of great importance. This study aimed to evaluate the effects of PCV13 on the clinical parameters and the changes over time in the levels of the main cytokines in asthma patients. Methods: This was a single-center, open-label, non-randomized, prospective, cohort, controlled study of 31 patients aged 18 to 80 with a known diagnosis of asthma. The study subjects were given one injection of PCV13. Their clinical parameters and serum concentrations of certain Th1/Th2/Treg cytokines were assessed over a year following the vaccination. Results: Compared to the pre-vaccination period, there was an 81.5% reduction in the number of patients with asthma exacerbations (p < 0.001), a 76.5% increase in the number of patients free from hospitalization (p < 0.001), and an improvement in the level of asthma control. Positive changes were observed both in patients with T2-high and T2-low asthma; however, only those with T2-low asthma showed a significant improvement in the level of asthma control. Significant changes were reported for IFN-γ: its serum concentrations increased six weeks following the vaccination (p < 0.05), primarily in patients with T2-high asthma. Conclusions: In asthma patients, immunization with PCV13 was clinically effective, irrespective of the asthma endotype. Its clinical effects were accompanied by a reduction in the rates of exacerbations and hospitalizations and an increase in IFN-γ serum levels. This finding suggests that this cytokine plays an important role in restoring immune response in asthma patients. Full article

Salt lakes and the surrounding saline soils distributed across northwestern China and Inner Mongolia impose severe physicochemical corrosion on cement-based concrete. Understanding the corrosion products and mechanisms are crucial scientific and technological factors in ensuring the durability and service life of concrete structures in these regions. In this study, various analytical techniques—including X-ray diffraction, thermogravimetric–differential thermal analysis, X-ray fluorescence, and scanning electron microscopy coupled with energy-dispersive spectroscopy—were employed to systematically analyze the corrosion products of ordinary Portland cement (OPC) and high-performance concrete (HPC) specimens after eight years of field exposure in the Qarhan Salt Lake area of Qinghai. The study provided an in-depth understanding of the physicochemical corrosion mechanisms involved. The results showed that, after eight years of exposure, the corrosion products comprised both physical corrosion products (primarily sodium chloride crystals), and chemical corrosion products (associated with chloride, sulfate, and magnesium salt attacks). A strong correlation could be observed between the chemical corrosion products and the strength grade of the concrete. In C25 OPC, the detected corrosion products included gypsum, monosulfate-type calcium sulfoaluminate (AFm), Friedel’s salt, chloro-ettringite, brucite, magnesium oxychloride hydrate 318, calcium carbonate, potassium chloride, and sodium chloride. In C60 HPC, the identified corrosion products included Kuzel’s salt, Friedel’s salt, chloro-ettringite, brucite, calcium carbonate, potassium chloride, and sodium chloride. Among them, sulfate-induced corrosion led to the formation of gypsum and AFm, whereas chloride-induced corrosion resulted in chloro-ettringite and Friedel’s salt. Magnesium salt corrosion contributed to the formation of brucite and magnesium oxychloride hydrate 318, with Kuzel’s salt emerging as a co-corrosion product of chloride and sulfate attacks. Furthermore, a conversion phenomenon was evident between the sulfate and chloride corrosion products, which was closely linked to the internal chloride ion concentration in the concrete. As the chloride ion concentration increased, the transformation sequence of sulfate corrosion products occurred in the following order: AFm → Kuzel’s salt → Friedel’s salt → chloro-ettringite. There was a gradual increase in chloride ion content within these corrosion products. This investigation into concrete durability in salt-lake ecosystems offers technological guidance for infrastructure development and material specification in hyper-saline environments. Full article

This review focuses on the potential health risks of artificial food additives, especially their effects on lung health. Preservatives, synthetic colorants, and flavor enhancers, which are commonly used in processed foods, play roles in worsening respiratory diseases, such as asthma and chronic obstructive pulmonary disease (COPD). These additives cause oxidative stress, systemic inflammation, and immune dysregulation, often through the gut-lung axis. The preservatives sodium nitrite and sulfites have the risk of causing bronchial hyper-responsiveness and allergic reactions. The synthetic colorant, Ponceau 4R, is also related to immune-mediated lung inflammation. Flavoring agents such as diacetyl contribute to occupational respiratory diseases like bronchiolitis obliterans. In animal models, prenatal exposure to additives, such as titanium dioxide (E171), might disrupt the development of respiratory neural networks, with long-term consequences. Ultra-processed foods (UPFs), which also contain a high concentration of additives, lead to systemic inflammation and impair lung function. Despite their wide usage, the use of these additives has become a warning sign due to their safety issue, particularly in sensitive people like children, pregnant women, and patients with pre-existing respiratory and chronic conditions. The review highlights the serious need for strict regulation and further research on the long-term effects of food additives on respiratory health. Policymakers should ban these food additives that are more harmful to human health. As an alternative to artificial additives, natural flavors and colors from fruits and vegetables, safe preservatives, and minimally processed ingredients can be used. Full article

Phytoremediation has been reported as a more energy-efficient, and therefore cost-effective, method of environmental restoration compared to traditional remediation methods for heavy-metal-contaminated soils. Biochar has been shown to have variable effects on remediation potential in heavy-metal-contaminated soils. The objective of this study was to evaluate the effects of soil contamination level (i.e., low, medium, and high), industrial hemp (Cannabis sativa L.) cultivar (i.e., ‘Carmagnola’ and ‘Jinma’), biochar rate (i.e., 0, 2, 5, and 10% by volume), and their interactions on root tissue Cd, Pb, and Zn concentrations and uptakes; whole-plant Cd, Pb, and Zn uptakes; and translocation factors after 90 days of hemp growth in contaminated soil from the Tar Creek Superfund Site near Picher, Oklahoma. Hemp removal of Cd, Pb, and Zn differed between soil contamination levels (p < 0.01), but was unaffected (p > 0.05) by the hemp cultivar or biochar rate, except for total Zn uptake. Total Zn uptake was affected (p = 0.02) by the biochar rate in the medium- and high-contaminated soils, where total plant Zn uptake in the high-contaminated soil was numerically the largest with 10% biochar (0.28 mg cm⁻²) and in the medium-contaminated soil was numerically the largest with 2% biochar (0.07 mg cm⁻²), but was unaffected (p > 0.05) by the biochar rate in the low-contaminated soil. The translocation factor for Zn uptake in the low and medium soils was >1, indicating industrial hemp as a potential Zn hyper-accumulator up to a threshold soil contamination level. Results demonstrate that biochar amendment has the potential to enhance hemp’s remediation capability of heavy-metal-contaminated soils. Full article

The ideal bacteriophages (phages) for the treatment of bacterial disease (phage therapy) would bypass bacterial evolution to phage resistance. However, this feature (called a hyper-aggression feature) has never been observed to our knowledge. Here, we microbiologically characterize, fractionate, genomically classify, and perform electron microscopy of the newly isolated Bacillus thuringiensis phage 0524phi7-1, which we find to have this hyper-aggression feature. Even visible bacterial colonies are cleared. Phage 0524phi7-1 also has three other features classified under hyper-aggression (four-feature-hyper-aggressive phage). (1) Phage 0524phi7-1 forms plaques that, although sometimes beginning as semi-turbid, eventually clear. (2) Clear plaques continue to enlarge for days. No phage-resistant bacteria are detected in cleared zones. (3) Plaques sometimes have smaller satellite plaques, even in gels so concentrated that the implied satellite-generating phage motion is not bacterial host generated. In addition, electron microscopy reveals that phage 0524phi7-1 (1) is a myophage with an isometric, 91 nm-head (diameter) and 210 nm-long contractile tail, and (2) undergoes extensive aggregation, which inhibits typical studies of phage physiology. The genome is linear double-stranded DNA, which, by sequencing, is 157.103 Kb long: family, Herelleviridae; genus, tsarbombavirus. The data suggest the hypothesis that phage 0524phi7-1 undergoes both swimming and hibernation. Techniques are implied for isolating better phages for phage therapy. Full article

Precipitation events have been occurring more frequently in the hyper-arid region of the Taklamakan Desert (TD) under recent climate change. However, in this water-limited environment, the microphysical characteristics of precipitation, as well as their link to rainfall intensity, remain unclear. To address this, this study utilizes dual-frequency precipitation radar (DPR) data of the Global Precipitation Measurement (GPM) satellite from 2014 to 2023 to analyze the microphysical characteristics of different precipitation types (stratiform and convective) in the TD during the summer. The results show that liquid water path (LWP) is a key factor influencing precipitation type: when LWP is insufficient, stratiform precipitation is more likely to occur (84.1%), while convective precipitation is difficult to occur (15.9%). Microphysical process analysis indicates that in convective precipitation, abundant low-level moisture leads to the growth of liquid particles primarily through the collision–coalescence process (59.7%), resulting in larger raindrop diameters (1.7 mm) and lower concentrations (31.9 mm⁻¹ m⁻³). In contrast, stratiform precipitation, with limited LWP, primarily involves the melting and breaking-up of high-level ice-phase particles, leading to smaller raindrop diameters (1.2 mm) and higher concentrations (34.3 mm⁻¹ m⁻³). The warm rain process plays a significant role in raindrop formation in both types of precipitation. The greater (lesser) the amount of LWP, the larger (smaller) the contribution of collision–coalescence (break-up) processes, and the larger (smaller) the raindrop diameter and precipitation intensity. Full article

Accurate and objective regional landslide risk assessment is crucial for the precise prevention of regional disasters. This study proposes an integrated landslide risk assessment via a landslide susceptibility model based on intelligent optimization algorithms. By simulating the process of rime frost formation, it effectively selects features and assigns weights, overcoming the overfitting issue faced by XGBoost in handling high-dimensional features. By integrating the concepts of landslide susceptibility, dynamic landslide factors, and social vulnerability, an integrated landslide risk index was developed. Further investigation was conducted on how landslide susceptibility results influence risk, identifying regions with varying levels of landslide risk due to spatial heterogeneity in geological background, natural environment, and socio-economic conditions. This study’s results demonstrate that the RIME-XGBoost landslide susceptibility model exhibits superior stability and accuracy, achieving an AUC score of 0.947, which represents an improvement of 0.064 compared to the unoptimized XGBoost model, while the accuracy shows a maximum increase of 0.15 relative to other models. Additionally, an analysis using cloud theory indicates that the model’s expectation and hyper-entropy are minimized. High-risk-level areas, constituting only 1.26% of the total area, are predominantly located in densely populated, economically developed urban regions, where roads and rivers are the key influencing factors. In contrast, low-risk areas, which cover approximately 72% of the total area, are more broadly distributed. The landslide susceptibility predictions notably influence high-risk regions with concentrated populations. Full article

In order to rapidly and nondestructively detect pesticide residues on tomato leaves, fluorescence spectroscopy and hyperspectral techniques were used to study the nondestructive detection of three different concentrations of benzyl-pyrazolyl esters on the surface of tomato leaves, respectively. In this study, fluorescence spectrum acquisition and hyperspectral imaging processing of tomato leaf samples with and without pesticides were conducted, and spectral data from regions of interest of hyperspectral images were extracted. The data in the spectral raw bands were optimized using convolutional smoothing (S-G), standard normal variable transformation (SNV), multiplicative scatter correction (MSC), and baseline calibration (baseline) algorithms, respectively. In order to improve the operating rate of discrimination, a continuous projection algorithm (SPA) was used to extract the characteristic wavelengths of the fluorescence spectra and hyperspectral data of pesticide residues, and algorithms such as the least-squares support vector machine (LSSVM) algorithm and least partial squares regression (PLSR) were used to build a quantitative model, while algorithms such as the convolutional neural network (BPNN) algorithm and decision tree algorithm (CART) were used to build a qualitative model. According to the results, R² of the model of hyperspectral data after SG-SNV preprocessing and PLSR modeling reached 0.9974, RMSEC reached 0.0221, and RMSEP reached 0.0565. R² of the model of fluorescence spectral data after SG-MSC preprocessing and SVM modeling reached 0.9986, RMSEC reached 0.2496, and RMSEP reached 0.4193. Qualitative analysis was established based on the characteristic wavelengths of hyper-spectrum and fluorescence spectrum extracted by the SPA algorithm, and the accuracy of the training sets of the optimal qualitative model reached 94.9% and 95.7%, respectively, and the accuracy of the test sets both reached 100%. After comparison, the quantitative model of data based on fluorescence spectrum for pesticide residue detection in tomato leaves proved to have a better effect, and the qualitative model showed higher accuracy in discrimination. Therefore, the fluorescence spectral and hyperspectral imaging techniques applied to tomato leaf pesticide detection enjoy a promising application prospect. Full article

China produces the largest amount of CO₂ emissions since 2007 and is the second largest economy in the world since 2010, and the Yangtze River Delta (YRD) area plays a crucial role in promoting low-carbon development in China. Analyzing its evolutionary characteristics of spatial and temporal patterns and its decoupling effect is of great importance for the purpose of low-carbon development. However, this analysis relies on the estimation of CO₂ emissions. Recently, neural network-based models are widely used for CO₂ emission estimation. To improve the performance of neural network models, the Aquila Optimizer (AO) algorithm is introduced to optimize the hyper-parameter values in the back-propagation (BP) neural network model in this research due to the appealing searching capability of AO over traditional algorithms. Such a model is referred to as the AO-BP model, and this paper uses the AO-BP model to estimate carbon emissions, compiles a city-level CO₂ emission inventory for the YRD region, and analyzes the spatial dependence, spatial correlation characteristics, and decoupling status of carbon emissions. The results show that the CO₂ emissions in the YRD region show a spatial distribution pattern of “low in the west, high in the east, and developing towards the west”. There exists a spatial dependence of carbon emissions in the cities from 2001 to 2022, except for the year 2000, and the local spatial autocorrelation test shows that high-high is concentrated in Shanghai and Suzhou, and low-low is mainly centered in Anqing, Chizhou, and Huangshan in southern Anhui. Furthermore, there exist significant regional differences in the correlation levels of CO₂ emissions between cities, with a trend of low in the west and high in the east in location, and a decreasing and then increasing trend in time. From 2000 to 2022, the decoupling of carbon emissions and economic growth shows a steadily improving trend. Full article

Background: Although computational models are advancing air quality prediction, achieving the desired performance or accuracy of prediction remains a gap, which impacts the implementation of machine learning (ML) air quality prediction models. Several models have been employed and some hybridized to enhance air quality and air quality index predictions. The objective of this paper is to systematically review machine and deep learning techniques for spatiotemporal air prediction challenges. Methods: In this review, a methodological framework based on PRISMA flow was utilized in which the initial search terms were defined to guide the literature search strategy in online data sources (Scopus and Google Scholar). The inclusion criteria are articles published in the English language, document type (articles and conference papers), and source type (journal and conference proceedings). The exclusion criteria are book series and books. The authors’ search strategy was complemented with ChatGPT-generated keywords to reduce the risk of bias. Report synthesis was achieved by keyword grouping using Microsoft Excel, leading to keyword sorting in ascending order for easy identification of similar and dissimilar keywords. Three independent researchers were used in this research to avoid bias in data collection and synthesis. Articles were retrieved on 27 July 2024. Results: Out of 374 articles, 80 were selected as they were in line with the scope of the study. The review identified the combination of a machine learning technique and deep learning techniques for data limitations and processing of the nonlinear characteristics of air pollutants. ML models, such as random forest, and decision tree classifier were among the commonly used models for air quality index and air quality predictions, with promising performance results. Deep learning models are promising due to the hyper-parameter components, which consist of activation functions suitable for nonlinear spatiotemporal data. The emergence of low-cost devices for data limitations is highlighted, in addition to the use of transfer learning and federated learning models. Again, it is highlighted that military activities and fires impact the O₃ concentration, and the best-performing models highlighted in this review could be helpful in developing predictive models for air quality prediction in areas with heavy military activities. Limitation: This review acknowledges methodological challenges in terms of data collection sources, as there are equally relevant materials on other online data sources. Again, the choice and use of keywords for the initial search and the creation of subsequent filter keywords limit the collection of other relevant research articles. Full article

This paper presents numerical works on estimating some logistic models using particle swarm optimization (PSO). The considered models are the Verhulst model, Pearl and Reed generalization model, von Bertalanffy model, Richards model, Gompertz model, hyper-Gompertz model, Blumberg model, Turner et al. model, and Tsoularis model. We employ data on commercial and rural banking assets in Indonesia due to their tendency to correspond with logistic growth. Most banking asset forecasting uses statistical methods concentrating solely on short-term data forecasting. In banking asset forecasting, deterministic models are seldom employed, despite their capacity to predict data behavior for an extended time. Consequently, this paper employs logistic model forecasting. To improve the speed of the algorithm execution, we use the Cauchy criterion as one of the stopping criteria. For choosing the best model out of the nine models, we analyze several considerations such as the mean absolute percentage error, the root mean squared error, and the value of the carrying capacity in determining which models can be unselected. Consequently, we obtain the best-fitted model for each commercial and rural bank. We evaluate the performance of PSO against another metaheuristic algorithm known as spiral optimization for benchmarking purposes. We assess the robustness of the algorithm employing the Taguchi method. Ultimately, we present a novel logistic model which is a generalization of the existence model. We evaluate its parameters and compare the result with the best-obtained model. Full article

In the current work, chloro(meso-tetrakis(phenyl)porphyrin) manganese(III) [Mn(TPP)Cl] was synthesized following two steps: the preparation of meso-tetraphenylporphyrin (H_⁠2TPP) and the insertion of manganese into the free porphyrin H₂TPP. The compounds were characterized using SEM, FT-IR, UV, TGA/DTA, and XRD analyses. Manganese(III) meso-porphyrins exhibited hyper-type electronic spectra with a half-vacant metal orbital with symmetry, such as [dπ:dxz and dyz]. The thermal behavior of [Mn(TPP)(Cl)] changed (three-step degradation process) compared to the initial H₂TPP (one-step degradation process), confirming the insertion of manganese into the core of the free porphyrin H₂TPP. Furthermore, [Mn(TPP)Cl] was used to degrade calmagite (an azo dye) using H₂O₂ as an oxidant. The effects of dye concentration, reaction time, H₂O₂ dose, and temperature were investigated. The azo dye solution was completely degraded in the presence of [Mn(TPP)(Cl)]/H₂O₂ at pH = 6, temperature = 20 °C, C₀ = 30 mg/L, and H₂O₂ = 40 mL/L. The computed low activation energy (Ea = 10.55 Kj/mol) demonstrated the efficiency of the proposed catalytic system for the azo dye degradation. Overall, based on the synthesis process and the excellent catalytic results, the prepared [Mn(TPP)Cl] could be used as an effective catalyst for the treatment of calmagite-contaminated effluents. Full article