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BACKGROUND: Bioactive glass is a multifunctional synthetic composite material that releases active ions slowly and exhibits certain biological activities after affinity with tissues. Their versatility stems from the versatility of their preparation processes and components, allowing them to be applied in different clinical scenarios. OBJECTIVE: To review the main application forms, application fields of bioactive glass, as well as the influence of doping different elements on its function. METHODS: A literature search was conducted across WanFang Medical Database, CNKI Database, PubMed Database, and Web of Science Database, using the search terms “bioactive glass, slow-release ions, bone tissue engineering, composite scaffold, tissue regeneration and repair, biomedical engineering” in Chinese and English. The timeframe was limited from 2000 to 2023. Finally, 88 articles were included for review. RESULTS AND CONCLUSION: (1) In terms of application forms, bioactive glass can be fabricated as coatings, particles, bone cements, and scaffolds according to needs. Coatings have the potential to enhance the biological activity of implants, yet they are susceptible to instability as a result of degradation. Particles offer a viable solution for the repair of irregular bone defects; however, particles produced through traditional methods often possess limited functionality. Bone cement provides the benefits of minimal invasiveness and injectability, yet its application is restricted to smaller bone defects. Scaffolds exhibit excellent mechanical properties and are commonly used for larger-sized bone defects, yet they have limited toughness. (2) In terms of applications, bioactive glass can be used in a variety of tissue regeneration and repair and disease treatment fields, including dentistry, orthopedics, soft tissue engineering, and cancer. (3) In terms of element doping, the addition of specific elements to bioactive glass not only improves its mechanical properties but also endows it with special biological functions such as bioactivity, degradability, and antibacterial properties. (4) Biologically active glass is a versatile material that can be used in different forms and functions by adjusting the preparation process and element doping to meet various clinical needs in bone tissue engineering and is widely used in the field of biomedical engineering. [ABSTRACT FROM AUTHOR]

The gardenia flower not only has extremely high ornamental value but also is an important source of natural food and spices, with a wide range of uses. To support the development of gardenia flower products, this study used headspace gas chromatography–ion mobility spectrometry (HS-GC–IMS) technology to compare and analyze the volatile organic compounds (VOCs) of fresh gardenia flower and those after using four different drying methods (vacuum freeze-drying (VFD), microwave drying (MD), hot-air drying (HAD), and vacuum drying (VD)). The results show that, in terms of shape, the VFD sample is almost identical to fresh gardenia flower, while the HAD, MD, and VD samples show significant changes in appearance with clear wrinkling; a total of 59 volatile organic compounds were detected in the gardenia flower, including 13 terpenes, 18 aldehydes, 4 esters, 8 ketones, 15 alcohols, and 1 sulfide. Principal component analysis (PCA), cluster analysis (CA), and partial least-squares regression analysis (PLS-DA) were performed on the obtained data, and the research found that different drying methods impact the VOCs of the gardenia flower. VFD or MD may be the most effective alternative to traditional sun-drying methods. Considering its drying efficiency and production cost, MD has the widest market prospects. [ABSTRACT FROM AUTHOR]

Wildfires are serious hazards for the environment, and WFSD (Wildfire Smoke Detection) is a challenge for ensuring optimal response and mitigation efforts. Hence, this study suggests an attention-based YOLOv5 (You Only Look Once) network for detecting smoke instances within video frames, particularly ECA (Efficient Channel Attention), GAM (Global Attention Module) and CA (Coordinate Attention). Here, an open-source wildfire smoke dataset divided into train, validation and test set is used for experimentation. The comprehensive research and evaluations show that the incorporation of attention mechanisms successfully enhances the accuracy and robustness of the YOLOv5 model for WFSD. In the training among the attention modules, GAM appears as the most effective, attaining an improved 95% F1 score on the dataset. This research provides the impact of attention mechanisms on object detection in the context of wildfire smoke. The findings of the research paper contribute to improving the capabilities of deep learning models for emergency response and environmental monitoring. The proposed methodology not only outperforms regular YOLOv5 but also sets up a benchmark for future research of WFSD. [ABSTRACT FROM AUTHOR]

Abstract\nNOVELTY STATEMENT\nHIGHLIGHTSIn recent years, heavy metal pollution has become a global environmental problem and poses a great threat to the health of people and ecosystems. Therefore, strategies for the effective remediation of Cd from contaminated soil are urgently needed. In this study, ryegrass was utilized as a remediation plant, and its remediation potential was enhanced through the application of Citric Acid (CA) in conjunction with Bacillus megaterium (B. megaterium). The P3 treatment (CA + Bacillus megaterium) exhibited a significantly higher efficiency in promoting cadmium extraction by ryegrass, resulting in a 1.79-fold increase in shoot cadmium accumulation compared to the control group (CK) with no Bacillus megaterium or CA. Moreover, the P3 treatment led to an increased abundance of Actinobacteriota, Acidobacteriota, and Patescibacteria in the rhizosphere. The concentration of amino derivatives (such as betaine, sulfolithocholylglycine, N-alpha-acetyl-lysine, glycocholic acid, arginyl-threonine) showed significant upregulation following the P3 treatment. In summary, this study proposes a viable approach for phytoremediation of soil contaminated with cadmium by harnessing the mobilizing abilities of soil bacteria.Our aim was to gain a comprehensive understanding of the mechanisms involved in phytoremediation. These findings contribute to the existing knowledge by providing insights into the mechanism of phytoremediation in Cd-contaminated soil. They are expected to serve as a theoretical foundation for further elucidation of the phytoremediation mechanisms employed in Cd-contaminated soil.CA + Bacillus megaterium increased Cd accumulation in ryegrass by 1.79 times compared to CK.CA + Bacillus megaterium also significantly improved the activity of soil enzymes.CA + Bacillus megaterium significantly improved soil bacterial communities.CA + Bacillus megaterium significantly increased metabolites in the soil. [ABSTRACT FROM AUTHOR]

Zinc (Zn) deficiency is a widespread problem in agricultural soils all over the globe. As an essential element, Zn plays a decisive role in realization of high yields of wheat, the changes in activities of Zn dependent enzymes and its accumulation in various plant parts of wheat varieties of differential susceptibility. The aim of the study was to examine the activities of Zn dependent enzymes, i.e. superoxide dismutase (SOD), carbonic anhydrase (CA); chlorophyll and grain protein content and to evaluate the pattern of Zn transport in two wheat varieties (UP2628-Zn efficient and UP262-Zn inefficient). All these parameters were evaluated in a pot experiment under different modes of Zn applications viz., control (C), soil application (S), foliar application (F), soil+foliar application (S+F). The accumulation of Zn in different plant parts of both wheat varieties under different Zn supply regimes was monitored in a separate autoradiography experiment where radiolabelled Zn was used. An increased activity of SOD, CA and total chlorophyll content in leaves, high protein content in grains and 65Zn accumulation was observed under S+F mode of Zn application. Zinc efficient variety (UP2628) showed higher SOD, CA activities, chlorophyll content in leaves and grain protein content as compared to UP262. Both soil application + foliar spray of Zn and cultivation of Zn efficient variety (UP2628) can be best approach for Zn biofortification of wheat. [ABSTRACT FROM AUTHOR]

This paper investigates the issue of translating genderisms from Uzbek into English. It investigates and compares the approaches taken by both native and foreign translators to address this issue through a case study based on Uzbek author Abdulhamid Sulaymon o’g’li Cho’lpon’s unfinished dilogy, Night and Day. Originally written between 1933 and 1934 and translated into English by native translators Muminov and Khamidov in 2014 and foreign translator Fort in 2019, Cho’lpon’s work was specifically chosen, because it depicts women's repression in the male hegemonic cultures of Central Asian countries during the nineteenth century, particularly in the Uzbek context during the colonial period. Based on both the content analysis (CA) and critical discourse analysis (CDA), the researchers extracted the genderisms in the source text (ST) and compared them to their representation in the target text (TT). The CDA results revealed that genderisms in the ST manifest themselves in women's reliance on men, the limitations on their independence, their low status and powerlessness, and their abusive treatment and threats by men. Analysis of the translations showed that native translators mostly omitted genderisms in translation, attempting to protect their culture by avoiding the introduction of negative views of their nation. On the other hand, the foreign translator kept the genderisms in the TT, trying to realize the author's original goal of using genderism to encourage independence and selfconfidence in women. The results of this study serve to reduce the problems of expressing gender representation in translations from many Turkic languages into English. [ABSTRACT FROM AUTHOR]

Abstract Different chemical solutions can significantly change the contact angle (CA) of soil, but few studies have studied the change rule and action mechanism of the CA from the mineral composition of soil essence. In unsaturated soil mechanics, the CA is an important parameter to calculate the wet suction between soil particles in unsaturated soil. When the chemical composition of the soil pore liquid changes, the CA will also change, which will affect the wet suction and other parameters, thus changing the macroscopic mechanical properties of the soil. In this study, the CA of air-solution-mineral phases with different solution components (pH, type and concentration of salt solution) of different minerals (quartz, orthoclase and plagioclase) were measured. The results show that the CAs of quartz, orthoclase and plagioclase all rise first and then drop with the rise of pH. The peak CAs are pH = 5, pH = 4 and pH = 3, respectively. Quartz, orthoclase and plagioclase all have valley values in different concentrations of NaCl and KCl solutions. In CaCl2 solution, only quartz has valley value, while orthoclase and plagioclase increase monotonously. Quartz in soil plays a main role in the influence of soil CA, followed by orthoclase and plagioclase. The CA of different minerals in different chemical solutions is mainly controlled by electric double layer interaction and functional groups interaction. In different salt solution environment, in addition to the above two effects, the mineral CA is also affected by the surface tension.

Abstract Background The quality of maize kernels is significantly enhanced by amino acids, which are the fundamental building blocks of proteins. Meanwhile, calcium (Ca) and magnesium (Mg), as important nutrients for maize growth, are vital in regulating the metabolic pathways and enzyme activities of amino acid synthesis. Therefore, our study analyzed the response process and changes of amino acid content, endogenous hormone content, and antioxidant enzyme activity in kernels to the coupling addition of sugar alcohol-chelated Ca and Mg fertilizers with spraying on maize. Result (1) The coupled addition of Ca and Mg fertilizers increased the Ca and Mg content, endogenous hormone components (indole-3-acetic acid, IAA; gibberellin, GA; zeatin riboside, ZR) content, antioxidant enzyme activity, and amino acid content of maize kernels. The content of Ca and Mg in kernels increased with the increasing levels of Ca and Mg fertilizers within a certain range from the filling to the wax ripening stage, and significantly positively correlated with antioxidant enzyme activities. (2) The contents of IAA, GA, and ZR continued to rise, and the activities of superoxide dismutase (SOD) and catalase (CAT) were elevated, which effectively enhanced the ability of cells to resist oxidative damage, promoted cell elongation and division, and facilitated the growth and development of maize. However, the malondialdehyde (MDA) content increased consistently, which would attack the defense system of the cell membrane plasma to some extent. (3) Leucine (LEU) exhibited the highest percentage of essential amino acid components and a gradual decline from the filling to the wax ripening stage, with the most substantial beneficial effect on essential amino acids. (4) CAT and SOD favorably governed essential amino acids, while IAA and MDA negatively regulated them. The dominant physiological driving pathway for the synthesis of essential amino acids was “IAA-CAT-LEU”, in which IAA first negatively drove CAT activity, and CAT then advantageously controlled LEU synthesis. Conclusion These findings provide a potential approach to the physiological and biochemical metabolism of amino acid synthesis, and the nutritional quality enhancement of maize kernel.

The object detection model in UAV aerial image scenes faces challenges such as significant scale changes of certain objects and the presence of complex backgrounds. This paper aims to address the detection of small objects in aerial images using NATCA (neighborhood attention Transformer coordinate attention) YOLO. Specifically, the feature extraction network incorporates a neighborhood attention transformer (NAT) into the last layer to capture global context information and extract diverse features. Additionally, the feature fusion network (Neck) incorporates a coordinate attention (CA) module to capture channel information and longer-range positional information. Furthermore, the activation function in the original convolutional block is replaced with Meta-ACON. The NAT serves as the prediction layer in the new network, which is evaluated using the VisDrone2019-DET object detection dataset as a benchmark, and tested on the VisDrone2019-DET-test-dev dataset. To assess the performance of the NATCA YOLO model in detecting small objects in aerial images, other detection networks, such as Faster R-CNN, RetinaNet, and SSD, are employed for comparison on the test set. The results demonstrate that the NATCA YOLO detection achieves an average accuracy of 42%, which is a 2.9% improvement compared to the state-of-the-art detection network TPH-YOLOv5. [ABSTRACT FROM AUTHOR]

Surface defects of steel plates unavoidably exist during the industrial production proceeding due to the complex productive technologies and always exhibit some typical characteristics, such as irregular shape, random position, and various size. Therefore, detecting these surface defects with high performance is crucial for producing high-quality products in practice. In this paper, an improved network with high performance based on You Only Look Once version 5 (YOLOv5) is proposed for detecting surface defects of steel plates. Firstly, the Space to Depth Convolution (SPD-Conv) is utilized to make the feature information transforming from space to depth, helpful for preserving the entirety of discriminative feature information to the greatest extent under the proceeding of down-sampling. Subsequently, the coordinate attention mechanism is introduced and embedded into the bottleneck of C3 modules to effectively enhance the weights of some important feature channels, in favor of capturing more important feature information from different channels after SPD-Conv operations. Finally, the Spatial Pyramid Pooling Faster module is replaced by the Spatial Pyramid Pooling Fully Connected Spatial Pyramid Convolution module to further enhance the feature expression capability and efficiently realize the multi-scale feature fusion. The experimental results on NEU-DET dataset show that, compared with YOLOv5, the mAP and mAP50 dramatically increase from 51.7, 87.0 to 61.4, 92.6%, respectively. Meanwhile, the frame rate of 250 FPS implies that it still preserves a well real-time performance. Undoubtedly, the improved algorithm proposed in this paper exhibits outstanding performance, which may be also used to recognize the surface defects of aluminum plates, as well as plastic plates, armor plates and so on in the future. [ABSTRACT FROM AUTHOR]

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Reducing the influence of significant noise signal components on the obtained raw ECG signal is essential for precise identification of cardiac arrhythmias (CA), which frequently present as irregularities in heart rate or rhythm. Preprocessing is used to remove noise signals and baseline drift from the ECG wave that is recorded using the internet of things (IoT). After that, the denoised signal is subjected to dimensionality reduction and feature extraction. In order to determine whether classification method is more effective in detecting cardiac arrhythmias, this study compares two methods: an adaptive neuro-fuzzy inference system and artificial feed-forward neural networks trained with the back-propagation learning algorithm. An Adaptive Neuro Fuzzy Inference System analyses ICA features obtained by non-parametric power spectral estimates, and an Artificial Neural Network (ANN) classifier uses the ECG signal's morphological and statistical aspects to identify patterns. The creation of artificial feed-forward neural networks provides a rich framework for studying the Back Propagation Algorithm. Sensitivity, specificity, accuracy, and positive predictiveivity are some of the performance characteristics that are thoroughly examined. An overall accuracy of 97.79%, sensitivity of 99.82%, specificity of 99.68%, and positive predictivity of 98.58% were seen in the results of the Artificial Neural Feed Forward Network (ANFFN). The Adaptive Neuro Fuzzy Inference System (ANFIS) outperforms these metrics with an astounding overall accuracy of 99.62%, specificity of 98.63%, and positive predictivity of 99.46%. With a classification accuracy of 99.82%, ANFIS demonstrates to be the most effective classifier for identifying cardiac arrhythmias. [ABSTRACT FROM AUTHOR]

Cardiovascular diseases (CVD) stand as the leading cause of global mortality, claiming millions of lives annually. An electrocardiogram (ECG) records the heart's electrical activity based on the Internet of Things (IoT), crucial in detecting cardiac arrhythmias (CA), characterized by irregular heart rates and rhythms. Signals from the MIT-BIH Arrhythmia Physio net database are analyzed. This chapter aims to propose a hybrid approach merging Genetic Algorithm-Support Vector Machine (GSVM) and Particle Swarm Optimization-Support Vector Machine (PSVM) for CA classification. The study introduces an algorithm for categorizing ECG beats into six groups using Independent Component Analysis (ICA)-derived features. Optimal SVM settings are determined using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) on ICA features computed via non-parametric power spectral estimation. The research delves into the origins and methodologies of GA and PSO. Simulation results comparing GSVM and PSVM are presented, emphasizing PSVM's superior performance in accuracy, sensitivity, specificity, and positive predictivity. Detailed performance metrics, including Sensitivity, Specificity, Positive Predictivity, and Accuracy percentages, are scrutinized and compared against the top classifier. The findings endorse PSVM's superiority over GSVM, indicating enhanced performance across multiple evaluation criteria. [ABSTRACT FROM AUTHOR]

Background: The quality of maize kernels is significantly enhanced by amino acids, which are the fundamental building blocks of proteins. Meanwhile, calcium (Ca) and magnesium (Mg), as important nutrients for maize growth, are vital in regulating the metabolic pathways and enzyme activities of amino acid synthesis. Therefore, our study analyzed the response process and changes of amino acid content, endogenous hormone content, and antioxidant enzyme activity in kernels to the coupling addition of sugar alcohol-chelated Ca and Mg fertilizers with spraying on maize. Result: (1) The coupled addition of Ca and Mg fertilizers increased the Ca and Mg content, endogenous hormone components (indole-3-acetic acid, IAA; gibberellin, GA; zeatin riboside, ZR) content, antioxidant enzyme activity, and amino acid content of maize kernels. The content of Ca and Mg in kernels increased with the increasing levels of Ca and Mg fertilizers within a certain range from the filling to the wax ripening stage, and significantly positively correlated with antioxidant enzyme activities. (2) The contents of IAA, GA, and ZR continued to rise, and the activities of superoxide dismutase (SOD) and catalase (CAT) were elevated, which effectively enhanced the ability of cells to resist oxidative damage, promoted cell elongation and division, and facilitated the growth and development of maize. However, the malondialdehyde (MDA) content increased consistently, which would attack the defense system of the cell membrane plasma to some extent. (3) Leucine (LEU) exhibited the highest percentage of essential amino acid components and a gradual decline from the filling to the wax ripening stage, with the most substantial beneficial effect on essential amino acids. (4) CAT and SOD favorably governed essential amino acids, while IAA and MDA negatively regulated them. The dominant physiological driving pathway for the synthesis of essential amino acids was "IAA-CAT-LEU", in which IAA first negatively drove CAT activity, and CAT then advantageously controlled LEU synthesis. Conclusion: These findings provide a potential approach to the physiological and biochemical metabolism of amino acid synthesis, and the nutritional quality enhancement of maize kernel. [ABSTRACT FROM AUTHOR]

Introduction: Trials evaluating the role of intravascular imaging in percutaneous coronary intervention (PCI) for complex coronary artery disease have yielded mixed results. This study aimed to compare the outcomes of intravascular imaging specifically intravascular ultrasound (IVUS) with those from conventional coronary angiography in complex PCI. Methods: Comprehensive electronic search of MEDLINE, EMBASE, and Cochrane databases was performed until March 2023 for randomized clinical trials (RCTs) comparing intravascular imaging with coronary angiography in patients undergoing complex PCI. Complex PCI was defined per each study, and included PCI for American College of Cardiology/American Heart Association (ACC/AHA) type B2/C lesions, unprotected left main coronary artery disease, or multivessel stenting. The primary study outcome was major adverse clinical events (MACE). Results: The meta-analysis included 10 RCTs with a total of 6615 patients (3576 in the intravascular imaging group and 3039 in the coronary angiography group). The weighted mean-follow up was 28.9 months. Compared with coronary angiography, intravascular imaging reduced MACE (8% vs. 13.3%; relative risk [RR] 0.63; 95% confidence interval [CI] 0.54–0.73), cardiac death (RR 0.47; 95% CI 0.31–0.73), definite/probable stent thrombosis (RR 0.48; 95% CI 0.24–0.97), target vessel revascularization (RR 0.62; 95% CI 0.46–0.83), and target lesion revascularization (RR 0.61; 95% CI 0.47–0.79). There was no difference between both groups in all-cause death (RR 0.79; 95% CI 0.53–1.18) and myocardial infarction (RR 0.80; 95% CI 0.61–1.04). Conclusion: In patients undergoing complex PCI, intravascular imaging—specifically IVUS—reduced MACE by decreasing the incidence of cardiac death, stent thrombosis, and target vessel and target lesion revascularization. [ABSTRACT FROM AUTHOR]

Aiming at the problems of incomplete dehazing, color distortion, and loss of detail and edge information encountered by existing algorithms when processing images of underground coal mines, an image dehazing algorithm for underground coal mines, named CAB CA DSConv Fusion gUNet (CCDF-gUNet), is proposed. First, Dynamic Snake Convolution (DSConv) is introduced to replace traditional convolutions, enhancing the feature extraction capability. Second, residual attention convolution blocks are constructed to simultaneously focus on both local and global information in images. Additionally, the Coordinate Attention (CA) module is utilized to learn the coordinate information of features so that the model can better capture the key information in images. Furthermore, to simultaneously focus on the detail and structural consistency of images, a fusion loss function is introduced. Finally, based on the test verification of the public dataset Haze-4K, the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are 30.72 dB, 0.976, and 55.04, respectively, and on a self-made underground coal mine dataset, they are 31.18 dB, 0.971, and 49.66, respectively. The experimental results show that the algorithm performs well in dehazing, effectively avoids color distortion, and retains image details and edge information, providing some theoretical references for image processing in coal mine surveillance videos. [ABSTRACT FROM AUTHOR]

The diffused bit generator (DBG) is an entropy generator that generates a stream of random bits. DBG is realized using a linear feedback shift register (LFSR) and cellular automata (CA). LFSR and CA are realized using flip-flops and XOR gates. The flip-flops required are designed using true single-phase clock (TSPC)-based logic to minimize the area and power of the LFSR and CA circuits. The XOR gates used in the circuits are realized either using a 12-transistor (12T) or a 6-transistor (6T). To further reduce power dissipation in the DBG circuit, low-power techniques are incorporated. In the proposed 8-bit DBGs, the adaptive voltage level at source (AVLS) is incorporated to reduce power consumption. The circuits are realized using Cadence Virtuoso in CMOS 180 nm technology, and Specter is used for simulation and power analysis. The DBG functionality is assessed for frequencies ranging up to 1 GHz with a supply voltage of 1.8 V. From the results, it was inferred that at 1 GHz the proposed-1 DBG (using a 12T XOR gate) is 94.05% power efficient; while, the proposed-2 DBG (using a 6T XOR gate) is 90.27% power efficient when compared to the reference circuits. Implementation of the proposed-1 DBG was also carried out in CMOS 45 nm technology, the functionality was verified and the power was analyzed. A similar trend of power reduction was observed. [ABSTRACT FROM AUTHOR]

Cinnamon is one of the most popular spices worldwide, and volatile organic compounds (VOCs) are its main metabolic products. The misuse or mixing of cinnamon on the market is quite serious. This study used gas chromatography-ion migration spectroscopy (GC-IMS) technology to analyze the VOCs of cinnamon samples. The measurement results showed that 66 VOCs were detected in cinnamon, with terpenes being the main component accounting for 45.45%, followed by aldehydes accounting for 21.21%. The content of esters and aldehydes was higher in RG-01, RG-02, and RG-04; the content of alcohols was higher in RG-01; and the content of ketones was higher in RG-02. Principal component analysis, cluster analysis, and partial least squares regression analysis can be performed on the obtained data to clearly distinguish cinnamon. According to the VIP results of PLS-DA, 1-Hexanol, 2-heptanone, ethanol, and other substances are the main volatile substances that distinguish cinnamon. This study combined GC-IMS technology with chemometrics to accurately identify cinnamon samples, providing scientific guidance for the efficient utilization of cinnamon. At the same time, this study is of great significance for improving the relevant quality standards of spices and guiding the safe use of spices. [ABSTRACT FROM AUTHOR]

Accurate identification of Mycobacterium tuberculosis (M. tuberculosis) is a critical step in the diagnosis of tuberculosis. Existing object detection methods struggle with the challenges posed by the varied morphology and size of M. tuberculosis in sputum smear images, which makes precise targeting difficult. To solve these problems, an improved YOLOv8s model is proposed. Specifically, an additional detection head is added to focus on small target information. Second, a multi-scale feature fusion module is introduced to adapt the model to different sizes of M. tuberculosis. In addition, a convolutional layer is added to the Coordinate Attention (CA) module to extract more advanced semantic features. Finally, a self-attention mechanism is added after the CA module to enhance the model's ability to accurately understand and localize the varied morphology of M. tuberculosis. Our model performed well with an average precision of 85.7 % when tested on a publicly available dataset. This clearly demonstrates the effectiveness of our proposed model in M. tuberculosis detection.

Abstract Introduction Trials evaluating the role of intravascular imaging in percutaneous coronary intervention (PCI) for complex coronary artery disease have yielded mixed results. This study aimed to compare the outcomes of intravascular imaging specifically intravascular ultrasound (IVUS) with those from conventional coronary angiography in complex PCI. Methods Comprehensive electronic search of MEDLINE, EMBASE, and Cochrane databases was performed until March 2023 for randomized clinical trials (RCTs) comparing intravascular imaging with coronary angiography in patients undergoing complex PCI. Complex PCI was defined per each study, and included PCI for American College of Cardiology/American Heart Association (ACC/AHA) type B2/C lesions, unprotected left main coronary artery disease, or multivessel stenting. The primary study outcome was major adverse clinical events (MACE). Results The meta-analysis included 10 RCTs with a total of 6615 patients (3576 in the intravascular imaging group and 3039 in the coronary angiography group). The weighted mean-follow up was 28.9 months. Compared with coronary angiography, intravascular imaging reduced MACE (8% vs. 13.3%; relative risk [RR] 0.63; 95% confidence interval [CI] 0.54–0.73), cardiac death (RR 0.47; 95% CI 0.31–0.73), definite/probable stent thrombosis (RR 0.48; 95% CI 0.24–0.97), target vessel revascularization (RR 0.62; 95% CI 0.46–0.83), and target lesion revascularization (RR 0.61; 95% CI 0.47–0.79). There was no difference between both groups in all-cause death (RR 0.79; 95% CI 0.53–1.18) and myocardial infarction (RR 0.80; 95% CI 0.61–1.04). Conclusion In patients undergoing complex PCI, intravascular imaging—specifically IVUS—reduced MACE by decreasing the incidence of cardiac death, stent thrombosis, and target vessel and target lesion revascularization.

To investigate the effect of calcium (Ca) application on post-harvest fruit quality in Nanfeng tangerines, this study sprayed three calcium fertilizers (Calbit, Larry 8 Calcium, and Suspended Calcium) before harvesting. The fruit quality was assessed over a storage period of 0–60 d at a controlled room temperature of 20 ± 5 °C. The evaluation methods included principal component analysis (PCA) and linearly weighted summation. Pre-harvest calcium treatments increased the fruit calcium content and maintained higher firmness and shear. Compared to the control group, fruits treated with Calbit exhibited decreased levels of decay, weight loss, and respiration rates. Additionally, they demonstrated higher values of L*, b*, C*, and H° and lower values of a* and CCI. Moreover, the Larry 8 Calcium and Calbit treatments increased the levels of soluble solids, total soluble sugars, titratable acids, and VC content in the fruit. They accelerated the decomposition of tartaric, oxalic, and citric acids in the pulp, promoting the accumulation of sucrose and glucose. PCA and comprehensive evaluation scores indicated that the comprehensive scores assessing the storage quality of Nanfeng tangerine fruits treated with pre-harvest Larry 8 Calcium and Calbit were higher than those of the control group. The highest composite quality scores for Calbit-treated fruits were observed at 40 and 60 d, and the highest scores for Larry 8 Calcium were recorded at other intervals. These findings suggest that pre-harvest calcium application improved the post-harvest fruit quality of Nanfeng tangerines, with Larry 8 Calcium and Calbit emerging as favorable options. [ABSTRACT FROM AUTHOR]

This study aimed to investigate the changes in Paraoxonase (PON), Carbonic Anhydrase (CA), and β-glucosidase levels of different aged rats and scopolamine-induced memory impairment rats. This study used young, adult, and middle-aged male Wistar Albino rats. Scopolamine was administered as a single dose/multiple doses and a Morris water maze (MWM) was used for spatial learning testing in rats. Enzyme-linked immunosorbent assays (ELISAs) were used to determine serum and liver PON and β-glucosidase levels. The CA enzyme activity was assayed following the hydration of CO2. As a result of the comparison of age-related and scopolamine-related changes in PON and β-glucosidase levels in liver and serum samples, no significant age-related and scopolamine-effective changes were observed in serum, while liver PON and liver β-glucosidase levels were found to change significantly. CA activity studies, on the other hand, showed that adults have the lowest CA activity compared to young and middle-aged groups and scopolamine inhibited CA activity in vivo. We found that adult rats modeled with memory impairment had statistically lower levels of liver PON and liver β-glucosidase. CA activity was also found to be significantly reduced. β-glucosidase and CA should be further investigated in terms of neurodegenerative disease risk factors, just like PON, whose importance has been determined by numerous studies in the literature. [ABSTRACT FROM AUTHOR]

Polydimethylsiloxane (PDMS) is recognized as an excellent stretchable substrate in wearable electronics, due to its desirable properties such as tensile properties, transparency, thermal stability, non-toxicity, and good biocompatibility. However, its limited fracture toughness and susceptibility to cracking significantly reduce the material's overall durability. In this study, the solution casting method was applied to prepare the nanofibers composites combined stretchable PDMS and aligned electrospun cellulose acetate (CA) to improve its mechanical properties and keep its transparency. The results showed that composites containing 3 wt% loadings of electrospun CA nanofibers exhibited a light transmittance exceeding 85% within the visible light range. Specifically, the PDMS/CA-400 composites exhibited maximum improvements in comparison to pure PDMS. Notably, the tensile strength increased significantly from 2.1 to 3.0 MPa, while the toughness increased from 0.93 to 2.49 MJ/m³. In addition, the tensile strength of PDMS/CA-400 composites with pre-cut cracks increased from 0.2 to 1.4 MPa, and the fracture toughness increased from 14.78 to 174.42 kJ/m³, which were respectively 7 and 12 times compared to pure PDMS. Scanning electron microscope images showed that PDMS formed good interfacial interaction with CA nanofibers. This study introduces a novel method utilizing electrospun nanofibers to create transparent and fracture-resistant stretchable composites, offering promising enhancements for the durability of wearable electronic devices. Highlights • Explore the impact of the speed of receiving rollers on the degree of fiber arrangement. • PDMS/CA-400 composite material shows good light transmittance and high fracture resistance. • The tight interface between the fiber and the matrix improves the optical transmission and mechanical properties of the composite materials. [ABSTRACT FROM AUTHOR]

Due to the issues of remote sensing object detection algorithms based on deep learning, such as a high number of network parameters, large model size, and high computational requirements, it is challenging to deploy them on small mobile devices. This paper proposes an extremely lightweight remote sensing aircraft object detection network based on the improved YOLOv5n. This network combines Shufflenet v2 and YOLOv5n, significantly reducing the network size while ensuring high detection accuracy. It substitutes the original CIoU and convolution with EIoU and deformable convolution, optimizing for the small-scale characteristics of aircraft objects and further accelerating convergence and improving regression accuracy. Additionally, a coordinate attention (CA) mechanism is introduced at the end of the backbone to focus on orientation perception and positional information. We conducted a series of experiments, comparing our method with networks like GhostNet, PP-LCNet, MobileNetV3, and MobileNetV3s, and performed detailed ablation studies. The experimental results on the Mar20 public dataset indicate that, compared to the original YOLOv5n network, our lightweight network has only about one-fifth of its parameter count, with only a slight decrease of 2.7% in mAP@0.5. At the same time, compared with other lightweight networks of the same magnitude, our network achieves an effective balance between detection accuracy and resource consumption such as memory and computing power, providing a novel solution for the implementation and hardware deployment of lightweight remote sensing object detection networks. [ABSTRACT FROM AUTHOR]

The main objective of this study is to assess the monitoring system of surface water quality in Can Tho using principal component analysis (PCA) and cluster analysis (CA). Water samples were monthly collected in 2020 at 35 sampling sites and then analyzed for 18 parameters including temperature, pH, turbidity, dissolved oxygen (DO), biological oxygen demand (BOD), chemical oxygen demand (COD), total suspended solids (TSS), ammonium (NH4 + -N), nitrate (NO3ˉ-N), nitrite (NO2ˉ-N), orthophosphate (PO4 3 ˉ-P), chromium (Cr), fluoride (F- ), arsenic (As), lead (Pb), mercury (Hg) and coliform. These values were compared with the national technical regulation on surface water quality and calculated water quality index (WQI). The findings indicated that surface water quality in Can Tho was contaminated by organic matters, suspended solids, and microorganisms. TSS, BOD and COD were high in the dry season, while high level of coliform was found in the wet season. The results of computed WQI showed that the surface water quality was from good to excellent. The PCA results show that 77.2% of water quality fluctuations in the study area were potentially affected by domestic, agricultural, industrial activities and hydrological factors (rainwater runoff, riverbank erosion). Moreover, the results of PCA revealed that water quality parameters could be diminished from 18 to 14 parameters without the loss of important for evaluating surface water quality. CA suggested that total sampling sites and frequencies in the current monitoring program are likely to reduce from 35 to 27 sites and 12 to 6-7 times/year, respectively. It is suggested that these findings can provide essential scientific information for local environmental managers in adjusting the surface water quality monitoring network in Can Tho city, Vietnam. [ABSTRACT FROM AUTHOR]

Background: Condyloma acuminatum (CA), which is a highly contagious sexually transmitted illness generated by human papillomavirus (HPV) infection, is characterized by abnormal proliferation of keratinocytes resulting in verrucous lesions. Although solute carrier family 30 member 1 (ZNT1) is highly expressed in CA tissues, the function of ZNT1 in CA remains unclear. Methods: HPV transfection was performed in HaCaT to simulate the CA pathological environment. The mRNA and protein levels were monitored using RT-qPCR and immunoblotting. Cell viability was found using the MTT test. Cell invasion and migration were probed using the transwell and wound healing. Results: ZNT1 expression was up-regulated in CA tissues, and HPV transfection increased the expression of ZNT1. Overexpression of ZNT1 promoted the proliferation, migration and invasion of Human immortalized keratinocyte (HaCaT) transfected with HPV. Meanwhile, ZNT1 knockdown repressed the proliferation, migration and invasion of HaCaT that HPV transfected. Further research displayed that ZNT1 promoted the proliferation, migration and invasion of HaCaT transfected with HPV through the PI3K/Akt pathway. Conclusion: Our research confirmed that ZNT1 regulated the proliferation, migration and invasion of HaCaT transfected with HPV through the PI3K/Akt pathway, providing a new target for the effective remedy of CA. [ABSTRACT FROM AUTHOR]

Chromosome analysis (CA) and chromosomal microarray analysis (CMA) have been successfully used to diagnose genetic disorders. However, many conditions remain undiagnosed due to limitations in resolution (CA) and detection of only unbalanced events (CMA). Optical genome mapping (OGM) has the potential to address these limitations by capturing both structural variants (SVs) resulting in copy number changes and balanced rearrangements with high resolution. In this study, we investigated OGM's concordance using 87 SVs previously identified by CA, CMA, or Southern blot. Overall, OGM was 98% concordant with only three discordant cases: (1) uncalled translocation with one breakpoint in a centromere; (2) uncalled duplication with breakpoints in the pseudoautosomal region 1; and (3) uncalled mosaic triplication originating from a marker chromosome. OGM provided diagnosis for three previously unsolved cases: (1) disruption of the SON gene due to a balanced reciprocal translocation; (2) disruption of the NBEA gene due to an inverted insertion; (3) disruption of the TSC2 gene due to a mosaic deletion. We show that OGM is a valid method for the detection of many types of SVs in a single assay and is highly concordant with legacy cytogenomic methods; however, it has limited SV detection capabilities in centromeric and pseudoautosomal regions. [ABSTRACT FROM AUTHOR]

Land surface temperature (LST) is a crucial parameter influencing the thermal environment of urban and natural areas. Land use/land cover (LULC) patterns significantly impact LST, highlighting the need for a comprehensive understanding of their relationship. This study delves into the intricate interplay between LULC and LST in Pune city, India, employing a spatiotemporal analysis approach. The study utilizes satellite imagery from 2000, 2010, and 2022 to map LULC changes with Maximum likelihood, support vector machine and Random tree classification and their evaluation with accuracy and Kappa coefficient. Additionally, projected LULC for 2030 is prepared to assess future LST scenarios. Winter and summer LST data are analyzed to capture the seasonal dynamics of LST-LULC interactions. Pune has undergone significant LULC transformations, with the proportion of built-up areas increasing from 7% in 2000 to 47% in 2022, while barren land has decreased from 41% to 37%. Built-up areas exhibit consistently lower LST values during winter, while barren land tends to have the highest LST. In contrast, during summer, built-up and barren areas experience higher LST values, while water bodies and vegetation display relatively lower LST values due to their cooling effects. Mean LST in Pune has increased from 34.59°C in 2000 to 39.38℃ in 2022, reflecting the changing thermal dynamics associated with LULC alterations. The expansion of built-up areas has contributed to temperature increases of up to 5.5℃ in specific locations, highlighting the urban heat island effect. The study's findings emphasize the need for sustainable land use planning and development strategies to mitigate the adverse impacts of urbanization on LST. Policymakers, urban planners, and environmentalists can utilize these insights to develop informed land use policies, zoning regulations, and climate-smart urban development strategies. [ABSTRACT FROM AUTHOR]

Bearing fault diagnosis plays an important part in preventing rotating equipment faults, especially in the field of ultra-low-speed bearing fault diagnosis. Due to their low fault frequency and insignificant fault characteristics, it is difficult to realise the fault diagnosis of ultra-low-speed bearings using traditional methods; therefore, based on acoustic emission (AE) signals, this study proposes an ultra-low-speed bearing recognition model with EfficientNet as the backbone feature extraction network and successfully achieves bearing fault diagnosis under small-sample variable working conditions combined with transfer learning. The coordinate attention (CA) mechanism is introduced into the EfficientNet backbone feature extraction network to improve the ability of the model to extract detailed position information. The AdamW optimisation algorithm is introduced to improve the generalisation ability of the model. Combined with the idea of transfer learning, the data under different working conditions are trained and tested to form a high-performance and lightweight small-sample variable condition bearing recognition model called EfficientNet-CA-AdamW (EfficientNet-CAA). Comparison experiments show that the EfficientNet-CAA model proposed in this study has an accuracy of 99.81% for ultra-low-speed bearing recognition when the training samples are sufficient. Furthermore, the recognition accuracy is smoother and the loss function is significantly lower compared with convolutional neural network (CNN) models such as AlexNet, VGG-16, ResNet-34, ShuffleNet-V2 and EfficientNet-B0. In small-sample variable condition fault recognition, it has more powerful advantages compared with the other models. The recognition accuracy under variable conditions can reach more than 98%, which is significantly higher than that of the other models, and effectively improves the bearing fault recognition accuracy under small-sample variable conditions. In this study, the CA mechanism and the AdamW optimisation algorithm are introduced to lessen the difficulty of extracting detailed features and address the lack of generalisation ability of the EfficientNet model, which provides an idea for the application of the deep learning model to small-sample bearing fault diagnosis under variable working conditions. [ABSTRACT FROM AUTHOR]

Climate change is a change in the usual weather found in a place. The climate change has a major impact not only on natural disasters of the Earth but also on human health. The climate crisis is then no longer a future concern. It includes both the global warming driven by human emissions of greenhouse gases (GHG), and the resulting large-scale shifts in weather patterns. Global warming can occur from a variety of causes, both natural and human induced. The primary GHG in Earth's atmosphere, listed in decreasing order of average global mole fraction, are: water vapor (H2O), carbon dioxide (CO2), methane (CH4), nitrous oxide (N2O), and ozone (O3). Today, scientists around the world continue to try and solve the puzzle of climate change. It is clear that to address climate change, the amount of CO2 released into the atmosphere by industrial process has to be reduced because once it is added to the atmosphere, it can continue to affect climate for thousands of years. For such a purpose, an approach to intervention using expression vectors for any protein targeting to the cell plasma membrane via the glycosylphosphatidylinositol, GPI, anchor is suggested. The resulting GPI-anchored proteins would be useful for studying intermolecular interactions, especially gene-environment interactions, in investigating the potential impact of any chemical compounds on any genes of interest and could be used for carbonic anhydrase (CA)-based CO2-capture (environmental application). This approach would be crucial not only for capturing CO2via GPI and CA but also for the production of CA enzyme as well as its stabilization and therefore useful for combating the global warming of climate change. [ABSTRACT FROM AUTHOR]

Luvic Greyzemic Phaeozems have been farmed for centuries in Western Ukraine. We investigate their stocks of major nutrients and stoichiometry under arable crops receiving conventional NPK fertilisation and an unfertilised control. Total carbon stocks increase only with parallel increases in total N, P, Ca and Mg. Likewise, total N increases along with parallel increases in total Ca and Mg, P and Ca, P and C. Under winter barley, application of the background fertilisation (N23P60K60) + N97 (urea) before sowing, with or without nitrapyrin as a nitrogen stabiliser, improved the status of C, N, Ca and Mg to P in the 0–20 cm soil layer. The best ratio of total C, N, Ca and Mg to P and the best C:N, C:P, N:P, and Ca:N ratios were achieved with the background fertilisation plus nitrapyrin and urea or ammonium nitrate. The system cannot hold onto nutrients, nitrogen in particular, applied in excess of the harmonic stoichiometry. [ABSTRACT FROM AUTHOR]

The gardenia flower not only has extremely high ornamental value but also is an important source of natural food and spices, with a wide range of uses. To support the development of gardenia flower products, this study used headspace gas chromatography–ion mobility spectrometry (HS-GC–IMS) technology to compare and analyze the volatile organic compounds (VOCs) of fresh gardenia flower and those after using four different drying methods (vacuum freeze-drying (VFD), microwave drying (MD), hot-air drying (HAD), and vacuum drying (VD)). The results show that, in terms of shape, the VFD sample is almost identical to fresh gardenia flower, while the HAD, MD, and VD samples show significant changes in appearance with clear wrinkling; a total of 59 volatile organic compounds were detected in the gardenia flower, including 13 terpenes, 18 aldehydes, 4 esters, 8 ketones, 15 alcohols, and 1 sulfide. Principal component analysis (PCA), cluster analysis (CA), and partial least-squares regression analysis (PLS-DA) were performed on the obtained data, and the research found that different drying methods impact the VOCs of the gardenia flower. VFD or MD may be the most effective alternative to traditional sun-drying methods. Considering its drying efficiency and production cost, MD has the widest market prospects.

The CO2-assisted oxidative dehydrogenation of propane (ODP) was investigated over titania based composite metal oxides, 10% MxOy-TiO2 (M: Zr, Ce, Ca, Cr, Ga). It was found that the surface basicity of composite metal oxides was significantly higher than that of bare TiO2 and varied in a manner which depended strongly on the nature of the MxOy modifier. The addition of metal oxides on the TiO2 surface resulted in a significant improvement of catalytic performance induced by a synergetic interaction between MxOy and TiO2 support. Propane conversion and propylene yield were strongly influenced by the nature of the metal oxide additive and were found to be superior for the Cr2O3-TiO2 and Ga2O3-TiO2 catalysts characterized by moderate basicity. The reducibility of the latter catalysts was significantly increased, contributing to the improved catalytic performance. This was also the case for the surface acidity of Ga2O3-TiO2 which was found to be higher compared with Cr2O3-TiO2 and TiO2. A general trend was observed whereby catalytic performance increased significantly with decreasing the primary crystallite size of TiO2. DRIFTS studies conducted under reaction conditions showed that the adsorption/activation of CO2 was favored on the surface of composite metal oxides. This may be induced by the improved surface basicity observed with the MxOy addition on the TiO2 surface. The Ga2O3 containing sample exhibited sufficient stability for about 30 h on stream, indicating that it is suitable for the production of propylene through ODP with CO2 reaction. [ABSTRACT FROM AUTHOR]

The coaxial electrospinning method is widely used in a wide range of applications, including medical devices and sensing technology. This study proposes a novel optical dual sensor for simultaneous detection of oxygen (O2) and ammonia (NH3) based on coaxial electrospinning method to produce core-shell fiber membrane doped fluorescent dyes. The O2 (core) and NH3 (shell) sensitive dye membranes were successfully fabricated using coaxial electrospinning method by dissolving a polymer matrix, cellulose acetate (CA), with platinum (II) meso-tetrakis (pentafluorophenyl) porphyrin (PtTFPP) and Eosin-Y, respectively. The optical dual sensor was illuminated by an UV LED to monitor the intensity change and wavelength shift in the presence of selected analyte gases. The experimental data show that the sensitivities of optical dual sensor were found to be 6.4 and 3.2 for O2 and NH3, respectively. The response and recovery times of O2 and NH3 sensing probes were measured to be 12 s/29 s and 65 s/66 s, respectively. Also, when exposed to NH3 gas gradually from 0 to 500 ppm, the wavelength shift data of Eosin-Y was started at 569.5 nm, 573.9 nm, 578.4 nm, 579.4 nm, 580.8 nm, and 582.2 nm, respectively. In applications, the proposed optical dual sensor based on coaxial electrospinning method can detect O2 and NH3 gases simultaneously.

Fritillaria is a well-known health-promoting food, but it has many varieties and its market circulation is chaotic. In order to explore the differences in volatile organic compounds (VOCs) among different varieties of Fritillaria and quickly and accurately determine the variety of Fritillaria, this study selected six varieties of Fritillaria and identified and analyzed their volatile components using gas chromatography-ion mobility spectrometry (GC-IMS), establishing the characteristic fingerprints of VOCs in Fritillaria. In all samples, a total of 76 peaks were detected and 67 VOCs were identified. It was found that the composition of VOCs in different varieties of Fritillaria was similar, but the content was different. Combined with chemometric analysis, the differences between VOCs were clearly shown after principal component analysis, cluster analysis, and partial least-squares discriminant analysis. This may provide theoretical guidance for the identification and authenticity determination of different varieties of Fritillaria.

The object detection model in UAV aerial image scenes faces challenges such as significant scale changes of certain objects and the presence of complex backgrounds. This paper aims to address the detection of small objects in aerial images using NATCA (neighborhood attention Transformer coordinate attention) YOLO. Specifically, the feature extraction network incorporates a neighborhood attention transformer (NAT) into the last layer to capture global context information and extract diverse features. Additionally, the feature fusion network (Neck) incorporates a coordinate attention (CA) module to capture channel information and longer-range positional information. Furthermore, the activation function in the original convolutional block is replaced with Meta-ACON. The NAT serves as the prediction layer in the new network, which is evaluated using the VisDrone2019-DET object detection dataset as a benchmark, and tested on the VisDrone2019-DET-test-dev dataset. To assess the performance of the NATCA YOLO model in detecting small objects in aerial images, other detection networks, such as Faster R-CNN, RetinaNet, and SSD, are employed for comparison on the test set. The results demonstrate that the NATCA YOLO detection achieves an average accuracy of 42%, which is a 2.9% improvement compared to the state-of-the-art detection network TPH-YOLOv5.

Aiming at the problems of incomplete dehazing, color distortion, and loss of detail and edge information encountered by existing algorithms when processing images of underground coal mines, an image dehazing algorithm for underground coal mines, named CAB CA DSConv Fusion gUNet (CCDF-gUNet), is proposed. First, Dynamic Snake Convolution (DSConv) is introduced to replace traditional convolutions, enhancing the feature extraction capability. Second, residual attention convolution blocks are constructed to simultaneously focus on both local and global information in images. Additionally, the Coordinate Attention (CA) module is utilized to learn the coordinate information of features so that the model can better capture the key information in images. Furthermore, to simultaneously focus on the detail and structural consistency of images, a fusion loss function is introduced. Finally, based on the test verification of the public dataset Haze-4K, the Peak Signal-to-Noise Ratio (PSNR), Structural Similarity (SSIM), and Mean Squared Error (MSE) are 30.72 dB, 0.976, and 55.04, respectively, and on a self-made underground coal mine dataset, they are 31.18 dB, 0.971, and 49.66, respectively. The experimental results show that the algorithm performs well in dehazing, effectively avoids color distortion, and retains image details and edge information, providing some theoretical references for image processing in coal mine surveillance videos.

Cinnamon is one of the most popular spices worldwide, and volatile organic compounds (VOCs) are its main metabolic products. The misuse or mixing of cinnamon on the market is quite serious. This study used gas chromatography-ion migration spectroscopy (GC-IMS) technology to analyze the VOCs of cinnamon samples. The measurement results showed that 66 VOCs were detected in cinnamon, with terpenes being the main component accounting for 45.45%, followed by aldehydes accounting for 21.21%. The content of esters and aldehydes was higher in RG-01, RG-02, and RG-04; the content of alcohols was higher in RG-01; and the content of ketones was higher in RG-02. Principal component analysis, cluster analysis, and partial least squares regression analysis can be performed on the obtained data to clearly distinguish cinnamon. According to the VIP results of PLS-DA, 1-Hexanol, 2-heptanone, ethanol, and other substances are the main volatile substances that distinguish cinnamon. This study combined GC-IMS technology with chemometrics to accurately identify cinnamon samples, providing scientific guidance for the efficient utilization of cinnamon. At the same time, this study is of great significance for improving the relevant quality standards of spices and guiding the safe use of spices.

To investigate the effect of calcium (Ca) application on post-harvest fruit quality in Nanfeng tangerines, this study sprayed three calcium fertilizers (Calbit, Larry 8 Calcium, and Suspended Calcium) before harvesting. The fruit quality was assessed over a storage period of 0–60 d at a controlled room temperature of 20 ± 5 °C. The evaluation methods included principal component analysis (PCA) and linearly weighted summation. Pre-harvest calcium treatments increased the fruit calcium content and maintained higher firmness and shear. Compared to the control group, fruits treated with Calbit exhibited decreased levels of decay, weight loss, and respiration rates. Additionally, they demonstrated higher values of L*, b*, C*, and H° and lower values of a* and CCI. Moreover, the Larry 8 Calcium and Calbit treatments increased the levels of soluble solids, total soluble sugars, titratable acids, and VC content in the fruit. They accelerated the decomposition of tartaric, oxalic, and citric acids in the pulp, promoting the accumulation of sucrose and glucose. PCA and comprehensive evaluation scores indicated that the comprehensive scores assessing the storage quality of Nanfeng tangerine fruits treated with pre-harvest Larry 8 Calcium and Calbit were higher than those of the control group. The highest composite quality scores for Calbit-treated fruits were observed at 40 and 60 d, and the highest scores for Larry 8 Calcium were recorded at other intervals. These findings suggest that pre-harvest calcium application improved the post-harvest fruit quality of Nanfeng tangerines, with Larry 8 Calcium and Calbit emerging as favorable options.

This study aimed to investigate the changes in Paraoxonase (PON), Carbonic Anhydrase (CA), and β-glucosidase levels of different aged rats and scopolamine-induced memory impairment rats. This study used young, adult, and middle-aged male Wistar Albino rats. Scopolamine was administered as a single dose/multiple doses and a Morris water maze (MWM) was used for spatial learning testing in rats. Enzyme-linked immunosorbent assays (ELISAs) were used to determine serum and liver PON and β-glucosidase levels. The CA enzyme activity was assayed following the hydration of CO2. As a result of the comparison of age-related and scopolamine-related changes in PON and β-glucosidase levels in liver and serum samples, no significant age-related and scopolamine-effective changes were observed in serum, while liver PON and liver β-glucosidase levels were found to change significantly. CA activity studies, on the other hand, showed that adults have the lowest CA activity compared to young and middle-aged groups and scopolamine inhibited CA activity in vivo. We found that adult rats modeled with memory impairment had statistically lower levels of liver PON and liver β-glucosidase. CA activity was also found to be significantly reduced. β-glucosidase and CA should be further investigated in terms of neurodegenerative disease risk factors, just like PON, whose importance has been determined by numerous studies in the literature.

Due to the issues of remote sensing object detection algorithms based on deep learning, such as a high number of network parameters, large model size, and high computational requirements, it is challenging to deploy them on small mobile devices. This paper proposes an extremely lightweight remote sensing aircraft object detection network based on the improved YOLOv5n. This network combines Shufflenet v2 and YOLOv5n, significantly reducing the network size while ensuring high detection accuracy. It substitutes the original CIoU and convolution with EIoU and deformable convolution, optimizing for the small-scale characteristics of aircraft objects and further accelerating convergence and improving regression accuracy. Additionally, a coordinate attention (CA) mechanism is introduced at the end of the backbone to focus on orientation perception and positional information. We conducted a series of experiments, comparing our method with networks like GhostNet, PP-LCNet, MobileNetV3, and MobileNetV3s, and performed detailed ablation studies. The experimental results on the Mar20 public dataset indicate that, compared to the original YOLOv5n network, our lightweight network has only about one-fifth of its parameter count, with only a slight decrease of 2.7% in mAP@0.5. At the same time, compared with other lightweight networks of the same magnitude, our network achieves an effective balance between detection accuracy and resource consumption such as memory and computing power, providing a novel solution for the implementation and hardware deployment of lightweight remote sensing object detection networks.

Electrochemical water oxidation over high-efficiency (Ca, Mg, or Y) doped lithium metal (Ni) phosphate electrocatalysts

This study examines urban growth to support sustainability by investigating urban sprawl in the Vila Velha Urban Agglomeration (UA). If the spatial area of Vila Velha UA increases by 24.36%, from 22.98 km2 in 1994 to 51.64 km2 in 2024, urban sprawl may affect surrounding areas. Landsat satellite images from 1994, 2004, 2014, and 2024 were analyzed to study the spatiotemporal pattern of urban growth. Shannon's entropy index identified urban sprawl in Vila Velha UA. The QGIS software's MOLUSCE (Modules for Land Use Change Simulations) plug-in, utilizing Cellular Automata (CA) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP) models, forecasted urban expansion for 2034. The built-up area is projected to increase from 51.64 km2 in 2024 to 62.31 km2 in 2034. Shannon's entropy index indicated a high rate of urban sprawl. Remote sensing and machine learning techniques are crucial for understanding spatial trends, forecasting future expansion, and informing sustainable urban planning. These methods facilitate an accurate assessment of urbanization dynamics, aiding in developing strategies to manage and mitigate sprawl's adverse impacts. This study offers a unique approach to defining urban growth, emphasizing the need for zoning regulations, green space conservation, and infrastructure development to curb urban sprawl and promote sustainable growth in Vila Velha. • Urban Sprawl Growth and Prediction using Remote Sensing and Machine learning techniques. • Cellular Automata (CA) and Artificial Neural Network-Multi Layer Perceptron (ANN-MLP) models for Lulc Change analysis. • Impact of urban due improper urban growth. [ABSTRACT FROM AUTHOR]

Alkali metals and alkaline earth metals (AAEM) are important factors to be considered in coal gasification process, and understanding the release behavior of AAEM under the interaction of volatile-char is the key to realize efficient thermal conversion of coal. In this study, four typical O-containing functional groups (—COOH,—OH, C O,—OCH 3) were used to react with coal char in a fixed-bed reactor, the reaction temperature was set up at 750，800，850，900 ℃ to survey the release characteristics of Ca by different oxygen functional groups during the interaction of volatile-char at different temperatures from a quantitative point of view. The result show that the Ca release rate of coal char is not high in the whole reaction temperature range, and the interaction of O-containing compounds-coal char at lower temperature will increase the O content (C—O and C O) of coal char, the content of Ca in interactive coal chars was higher than that of the coal char. At lower temperature, the order of compounds inhibiting Ca release ability is: C 6 H 5 CHO≈C 6 H 5 COOH > C 10 H 8 O 2 > C 6 H 5 OCH 3. In addition, it also seems that O-containing compounds (except C 6 H 5 COOH) can obviously promote the conversion of ammonium acetate-soluble Ca to water-soluble Ca during the interaction with char. As the temperature increases, the change trend of O-containing functional groups on the surface of sample after interaction is completely opposite to that at lower temperature, the O-containing model compounds undergo de-oxidation, cracking and polymerization in the effect of coal char and AAEM, causing the formation of polycyclic aromatic hydrocarbon on the surface of the char, which hinders the diffusion of Ca from the char to the gas phase. This work is good for enriching and developing the study on the release characteristics of alkali earth metals during the interaction of volatile-char, and provides a theoretical basis for the development of related industries. • The effects of O-containing functional groups on Ca release during volatile-char interaction was studied. • O-containing species inhibit Ca release follows the order: C 6 H 5 CHO ≈ C 6 H 5 COOH > C 10 H 8 O 2 > C 6 H 5 OCH 3. • The possible mechanism of O-containing compounds-char interaction induced Ca release was proposed. [ABSTRACT FROM AUTHOR]