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During the past two decades discussions on urban shrinkage have been increasing in intensity, mainly due to the irresistible trends of migration, economic transformation, and the so-called "siphon effect" of large and mega cities. Yet, very few studies have tried to understand urban shrinkage from the perspective of local microscopic unit evolution. This study develops a novel model for simultaneous simulation of expansion and shrinkage (MSSES) of urban built-up areas. Starting from a microscopic basis, our MSSES can simulate both the processes and the states that lead to spatial expansion or shrinkage. A MaxEnt model is further used to investigate the driving factors of microspatial changes and compute the probabilities of shrinkage (or expansion) of urban built-up areas. Finally, it simulates the explicit layout of the spatial distribution of shrinkage (or expansion) patches with the help of a sorting cellular automata (CA) model. The validity of MSSES is verified using multisourced data from the Wuhan agglomeration, China. We show that (1) MSSES can more accurately simulate urban expansion or shrinkage than the currently widely used neighborhood-based Logistic-CA (NL-CA) and (2) MSSES has advantage in solving the commonly seen problem of "diffusion-coalescence," especially for the simulation of outlying expansion. Against the context of the Wuhan metropolitan area, we also found that overall the shrinkage trend is slowing down significantly, presenting three types of shrinkage at city-scale: remote shrinkage, peripheral shrinkage, and international shrinkage. Our study has contributions to the body of knowledge as well as practical implications for urban management and improving the sustainable development of urban areas in both China and abroad. The city we live in may be experiencing a situation where expansion and shrinkage are happening simultaneously. Hence, the simulation and prediction of temporal and spatial changes in urban expansion and shrinkage hold great importance in formulating urban policies. In this study, we propose a model capable of simultaneously predicting urban growth and shrinkage, and apply this model to the urban morphological changes in the Wuhan metropolitan area from 2000 to 2035. The simulation results demonstrate the effectiveness of this model in predicting the locations of urban expansion and contraction in advance. Furthermore, we discovered that the overall shrinkage trend is projected to significantly decelerate in the future. These findings offer support for intervention in urban planning, guiding the development of urban spaces and advancing the scientific aspects of urban planning. [ABSTRACT FROM AUTHOR]

Based on content analysis (CA), this paper, starting from industrial chain and value chain, constructs a two-dimensional analysis model of the policy tool-industrial chain and policy tool-value chain, respectively. A text analysis of policy documents related to hydrogen energy and fuel cell automobile industry in major areas made before December 2020 shows that environmental policy tools are mostly employed, followed by the demand side and the supply side. Specifically, local policy tools mainly act on the infrastructure links of the industrial chain and the use links of the value chain, accounting for 42.30% and 42.89%, respectively, aiming to demonstrate and promote hydrogen energy and fuel cell vehicles. The policy focus shows a shift from the environment to the demand side. It is necessary to implement supply-oriented policies, optimise the internal structure of policy types, and strengthen the coordination between policy tools, industrial chain and value chain. [ABSTRACT FROM AUTHOR]

Copyright of Refractories / Naihuo Cailiao is the property of Naihuo Cailia (Refractories) Editorial Office and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

The Nekton Maldives Taxonomic Workshop took place at the Maniyafushi Research Station in the Maldives between 12 and 23 February 2023. This workshop had two primary objectives. Firstly, it aimed to identify species from biological samples and underwater imagery collected during the Nekton Maldives Mission in 2022. Secondly, it sought to facilitate training and knowledge exchange sessions between early career researchers from the Maldives and international taxonomists. These sessions were designed to share knowledge and introduce fundamental taxonomy concepts and enhance practical identification skills for common reef benthic groups and major zooplankton taxonomic groups. A total of 24 people from 10 different countries were directly or indirectly involved with the workshop comprising nine taxonomic experts, eleven trainees and four organisers. Collectively, we identified 278 biological specimens including potentially undescribed species of hydroids, black corals, sponges and octocorals, 318 morphotypes for underwater footage and zooplankton composition congruent with previous reports from the Indian Ocean Region. Next steps will involve depositing the specimens into a more a permanent facility to facilitate the process of specimen description and knowledge transfer.

Molten salts such as 2LiF-BeF2 (FLiBe) have been proposed as coolants for advanced nuclear fission and fusion reactors. Critical to the design, licensing and operation of these reactors is characterization and understanding of the chemical behavior and mass transport of activation and fission products, corrosion products, and other solutes in the coolant. Electrochemical techniques are a powerful suite of tools for probing these phenomena. The design of an experimental cell for molten salt electrochemistry is described herein. As a demonstration of this design, details of the experimental methods used to conduct electrochemical experiments with molten FLiBe with addition of LiH are provided. Decommissioning of the cell is considered from the point of view of decontamination and waste generated. Main features of the cell include:•Suitable for operation up to 800 °C; suitable for operation inside and outside of a glovebox.•Enables sweep gas, gas sampling and analysis; enables addition of solid and liquid materials during operation.•Supports a variety of electrode materials and arrangements.

BackgroundUC San Diego Health System (UCSDHS) is the largest academic medical center and integrated care network in US-Mexico border area of California contiguous to the Northern Baja region of Mexico. The COVID-19 pandemic compelled several UCSDHS and local communities to create awareness around best methods to promote regional health in this economically, socially, and politically important border area.PurposeTo improve understanding of optimal strategies to execute critical care collaborative programs between academic and community health centers facing public health emergencies during the COVID-19 pandemic, based on the experience of UCSDHS and several community hospitals (one US, two Mexican) in the US-Mexico border region.MethodsAfter taking several preparatory steps, we developed a two-phase program that included 1) in-person activities to perform needs assessments, hands-on training and education, and morale building and 2) creation of a telemedicine-based (Tele-ICU) service for direct patient management and/or educational coaching experiences.Findings.A clinical and educational program between academic and community border hospitals was feasible, effective, and well received.ConclusionWe offer several policy-oriented recommendations steps for academic and community healthcare programs to build educational, collaborative partnerships to address COVID-19 and other cross-cultural, international public health emergencies.

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.

The effects of Ca addition on the microstructure and mechanical properties of Mg–Zn–Sn–Mn alloys were investigated in this study. The Mg–6Zn–4Sn–1Mn–xCa (x = 0, 0.2, 0.5, 1 and 2 wt%) alloys consist of the α-Mg, Mg2Sn, Mg7Zn3, MgZn, α-Mn, CaMgSn, MgZn2, Mg2Ca and Ca2Mg6Zn3 phases. With increasing Ca content, the eutectic phase and grains were refined, and the morphologies of the CaMgSn phases changed from point-like to needle-like, rod-like and lath-like. At room temperature, small amounts of Ca can improve properties owing to refinement strengthening. The two-stage aged Mg–6Zn–4Sn–1Mn–0.2Ca alloy has the best comprehensive properties at room temperature, with an ultimate tensile strength (UTS), yield strength (YS) and elongation of 407 MPa, 392 MPa and 6.2%, respectively. When the Ca content increased, the number of CaMgSn phases increased, and the morphologies coarsened. Further, it was a brittle phase, resulting in the deterioration of alloy properties. At high temperatures, a small amount of Ca can improve the high-temperature plasticity. The elongations of the as-extruded Mg–6Zn–4Sn–1Mn–0.2Ca alloy were 76.9% (150 °C) and 133.8% (200 °C). A high Ca concentration can significantly improve the high-temperature strengths because of the good thermal stability of the CaMgSn phases, the best UTS and YS are 160 MPa and 136 MPa, respectively, in as-extruded Mg–6Zn–4Sn–1Mn–2Ca alloy at 150 °C.

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 absence of calcium decreases the production of peanuts compared to any other element. To investigate the influence of calcium (Ca) applications on the production and seed quality of four diverse peanut cultivars from different regions—USA (NC-7), Egypt (Giza-6) and Nigeria (Samnut-23 and Samnut-24)—under clay soil conditions, two experiments were carried out at the Agricultural Experimental and Research Station, Cairo University, Egypt, during the two seasons of 2016 and 2017. The experimental design was a randomized complete block in a spilt-plot arrangement with three replications. The main plots were allocated to four peanut cultivars (Giza-6, Samnut-23, Samnut-24 and NC-7), and the sub-plots were devoted to calcium applications (soil application in the form of calcium sulfate dihydrate, foliar application in the form of calcium oxide and the control treatment of distilled water). Results indicated that all four peanut cultivars responded differently to the application of calcium fertilizers. The calcium application significantly enhanced peanut growth, yield components, biological, pod, seed, oil yields, seed oil, free fatty acids and seed calcium percentages in different cultivars. Soil calcium application significantly improved peanut production compared to foliar calcium application. NC-7 cultivar treated with the soil Ca application resulted in the maximum values of biological yield (92.9-ton ha−1), pod yield (6.8-ton ha−1), seed yield (4.4-ton ha−1), oil yield (2247.0 kg ha−1), pod index (203.2 g) and seed index (84.1 g). The interaction between the NC-7 cultivar and soil calcium applications is recommended to attain the best combination, leading to the highest yield and seed quality of peanuts. [ABSTRACT FROM AUTHOR]

The inherent flammability of timber rises the concern of society about the stability and firmness of timber materials when exposed to fire. This study illustrated the fire resistance properties of low-density Neolamarckia cadamba timber to evaluate its charred area (CA), charred depth (CD), mass loss rate (MLR), and charring rate (CR). Three different burning durations - 60, 90, and 120 min - were subjected to the timber test pieces following the NIST PS1-09 2010 standard. The CA and CD of the test pieces were measured using ImageJ before the determination of MLR and CR. N. cadamba showed a statistically significant difference on the top CA (14040.56 mm²) at 90 min compared to its side CA (1957.93 mm²). CD remained statistically unchanged for three burning durations. Top CA increased gradually with burning durations. Side CA and CD, however, were reduced to 1957.93 and 17.84 mm², respectively, for 90 min, before being increased back to 120 min. For CR and MLR, a gradual dropped in trend can be seen in the burning durations. Significant differences were detected when comparing the CR (0.30 mm/min) at 60 min with that in 90 min (0.20 mm/min), whereas the MLR was 2.15 g/m²s at 60 min with 1.42 g/m²s at 120 min. Generally, top CA, side CA, and CD showed an increased trend in the area and depth value. whereas CR and MLR showed the opposite. [ABSTRACT FROM AUTHOR]

Purpose: Near-infrared spectroscopy (NIRS) has been suggested as a non-invasive monitoring technique to set cerebral autoregulation (CA) guided ABP targets (ABPopt) in comatose patients with hypoxic-ischemic brain injury (HIBI) following cardiac arrest. We aimed to determine whether NIRS-derived CA and ABPopt values differ between left and right-sided recordings in these patients. Methods: Bifrontal regional oxygen saturation (rSO2) was measured using INVOS or Fore-Sight devices. The Cerebral Oximetry index (COx) was determined as a CA measure. ABPopt was calculated using a published algorithm with multi-window weighted approach. A paired Wilcoxon signed rank test and intraclass correlation coefficients (ICC) were used to compare (1) systematic differences and (2) degree of agreement between left and right-sided measurements. Results: Eleven patients were monitored. In one patient there was malfunctioning of the right-sided optode and in one patient not any ABPopt value was calculated. Comparison of rSO2 and COx was possible in ten patients and ABPopt in nine patients. The average recording time was 26 (IQR, 22–42) hours. The ABPopt values were not significantly different between the bifrontal recordings (80 (95%-CI 76-84) and 82 (95%-CI 75-84) mmHg) for the left and right recordings, p = 1.0). The ICC for ABPopt was high (0.95, 0.78–0.98, p < 0.001). Similar results were obtained for rSO2 and COx. Conclusion: We found no differences between left and right-sided NIRS recordings or CA estimation in comatose and ventilated HIBI patients. This suggests that in these patients without signs of localized pathology unilateral recordings might be sufficient to estimate CA status or provide ABPopt targets. [ABSTRACT FROM AUTHOR]

Different countries have adopted different policies in their fight against the COVID-19 outbreak. In this work we aim to model these approaches with respect to the first wave of COVID-19 (from January to October 2020) in the five Central Asian republics (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan, and Uzbekistan). By means of a multivariate approach based on principal component analysis and cluster analysis, we derived three clusters. Each of these represents a different strategy adopted by a group of governments in a given period of the first wave: pre-pandemic, initial reaction, and emergency scenarios. The contribution of these results is two-fold: on the one hand they may help us to understand differences and similarities among Central Asian republics during the first wave of the COVID-19 outbreak and guide future quantitative or qualitative studies; on the other hand, our findings suggest that, with the exception of Turkmenistan, the different countries adopted very similar strategies and that deaths, more than cases, pushed governments to impose restrictions. [ABSTRACT FROM AUTHOR]

In this article, we reflect on the pernicious nature of rhetoric aimed at soliciting Black community support for predatory urban development schemes. Highlighting recent examples of Urban One Casino + Resort's development campaign in Richmond, Virginia, and the messaging leveraged by political leaders on behalf of SoFi stadium and the Intuit Dome in Inglewood, California, we find that discursive moves made by public and private stakeholders reflect what we call the "predatory rhetorics of urban development." We argue that these rhetorics intend to enlist divested Black communities as supporters of development projects that concentrate wealth and power in the hands of economic and political elites. They do so by playing on Black desires for social and economic inclusion into American middle-class community life. Four common threads of predatory rhetoric appear across both contexts. They are 1) seizing the real needs and concerns of stigmatized places, 2) relying on representational politics to mitigate issues of trust, 3) the neoliberal framing of American internal colonization as a problem that requires extractive private development solutions and, finally, 4) dissimulating intra-community class interests to consolidate "Black needs." We reflect on the outcomes supported by these rhetorics across both development projects and raise several points of further consideration as we hope for more organized responses to such rhetorics in the future. [ABSTRACT FROM AUTHOR]

Transient receptor potential ankyrin 1 (TRPA1) receptors are major polymodal nociceptors that generate primary pain responses in the peripheral nerve endings of the dorsal root ganglion neurons. Recently, we reported that the activation of TRPA1 receptors by reactive oxygen species (ROS) signaling, which is triggered by Ca2+ influx through T-type Ca2+ channels, contributes to prolonged pain responses induced by jellyfish toxin. In this review, we focus on the characteristics of the TRPA1 receptor involved in intracellular signaling as a secondary pain modulator. Unlike other transient receptor potential receptors, TRPA1 receptors can induce membrane depolarization by ROS without exogenous stimuli in peripheral and central sensory neurons. Therefore, it is important to identify the functional characteristics of TRPA1 receptors to understand pain modulation under several pathogenic conditions such as neuropathic pain syndromes and autoimmune diseases, which are mediated by oxidative signaling to cause chronic pain in the sensory system.

The present study was conducted to examine the changes in calcium and phosphorus levels during Staphylococcus aureus (S. aureus) infection in rabbits. The experimental procedures were carried out with 12 male New Zealand white healthy rabbits, divided into two equal groups - the experimental group (n=6, rabbits infected with S.aureus) and the control group (n=6, uninfected animals). Blood samples were collected at 0 hours before infection, as well on 6, 24, 48, 72 hours and 7, 14 and 21 days after the infection. The amount of calcium in infected rabbits decreased significantly by the 48-th hour (p

The potential effect of the element Ca on the precipitation behavior was investigated in an Mg-rare earth alloy. A combination of metastable β′′′ and β′ precipitates was observed for the peak aging condition at 200°C. Ca addition was found to have no significant effect on the precipitating phases and evolution sequence. Composition analysis showed that the Ca partitioned to both β′′′ and β′ precipitate phases. First-principles calculations indicated that Ca partitions to the rare-earth sublattice in the precipitate phase. This finding suggests the potential of Ca to partially replace costly rare-earth elements in precipitation-hardened Mg-rare earth alloys.

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.

Introduction: Accurate grading identification of tea buds is a prerequisite for automated tea-picking based on machine vision system. However, current target detection algorithms face challenges in detecting tea bud grades in complex backgrounds. In this paper, an improved YOLOv7 tea bud grading detection algorithm TBC-YOLOv7 is proposed. Methods: The TBC-YOLOv7 algorithm incorporates the transformer architecture design in the natural language processing field, integrating the transformer module based on the contextual information in the feature map into the YOLOv7 algorithm, thereby facilitating self-attention learning and enhancing the connection of global feature information. To fuse feature information at different scales, the TBC-YOLOv7 algorithm employs a bidirectional feature pyramid network. In addition, coordinate attention is embedded into the critical positions of the network to suppress useless background details while paying more attention to the prominent features of tea buds. The SIOU loss function is applied as the bounding box loss function to improve the convergence speed of the network. Result: The results of the experiments indicate that the TBC-YOLOv7 is effective in all grades of samples in the test set. Specifically, the model achieves a precision of 88.2% and 86.9%, with corresponding recall of 81% and 75.9%. The mean average precision of the model reaches 87.5%, 3.4% higher than the original YOLOv7, with average precision values of up to 90% for one bud with one leaf. Furthermore, the F1 score reaches 0.83. The model's performance outperforms the YOLOv7 model in terms of the number of parameters. Finally, the results of the model detection exhibit a high degree of correlation with the actual manual annotation results (R2 =0.89), with the root mean square error of 1.54. Discussion: The TBC-YOLOv7 model proposed in this paper exhibits superior performance in vision recognition, indicating that the improved YOLOv7 model fused with transformer-style module can achieve higher grading accuracy on densely growing tea buds, thereby enables the grade detection of tea buds in practical scenarios, providing solution and technical support for automated collection of tea buds and the judging of grades. [ABSTRACT FROM AUTHOR]

With object detection technology, real-time detection of dense scenes has become an important application requirement in various industries, which is of great significance for improving production efficiency and ensuring public safety. However, the current mainstream target detection algorithms have problems such as insufficient accuracy or inability to achieve real-time detection when detecting dense scenes, and to address this problem this paper improves the YOLOv7 model using attention mechanisms that can enhance critical information. Based on the original YOLOv7 network model, part of the traditional convolutional layers are replaced with the standard convolution combined with the attention mechanism. After comparing the optimization results of three different attention mechanisms, CBAM, CA, and SimAM, the YOLOv7B-CBAM model is proposed, which effectively improves the accuracy of object detection in dense scenes. The results on VOC datasets show that the YOLOv7B-CBAM model has the highest accuracy, reaching 87.8%, 1.5% higher than that of the original model, and outperforms the original model as well as other models with improved attention mechanisms in the subsequent results of two other different dense scene practical application scenarios. This model can be applied to public safety detection, agricultural detection, and other fields, saving labor costs, improving public health, reducing the spread and loss of plant diseases, and realizing high-precision, real-time target detection. [ABSTRACT FROM AUTHOR]

To enhance the efficiency of mechanical automatic picking of cherry tomatoes in a precision agriculture environment, this study proposes an improved target detection algorithm based on YOLOv5n. The improvement steps are as follows: First, the K-means++ clustering algorithm is utilized to update the scale and aspect ratio of the anchor box, adapting it to the shape characteristics of cherry tomatoes. Secondly, the coordinate attention (CA) mechanism is introduced to expand the receptive field range and reduce interference from branches, dead leaves, and other backgrounds in the recognition of cherry tomato maturity. Next, the traditional loss function is replaced by the bounding box regression loss with dynamic focusing mechanism (WIoU) loss function. The outlier degree and dynamic nonmonotonic focusing mechanism are introduced to address the boundary box regression balance problem between high-quality and low-quality data. This research employs a self-built cherry tomato dataset to train the target detection algorithms before and after the improvements. Comparative experiments are conducted with YOLO series algorithms. The experimental results indicate that the improved model has achieved a 1.4% increase in both precision and recall compared to the previous model. It achieves an average accuracy mAP of 95.2%, an average detection time of 5.3 ms, and a weight file size of only 4.4 MB. These results demonstrate that the model fulfills the requirements for real-time detection and lightweight applications. It is highly suitable for deployment in embedded systems and mobile devices. The improved model presented in this paper enables real-time target recognition and maturity detection for cherry tomatoes. It provides rapid and accurate target recognition guidance for achieving mechanical automatic picking of cherry tomatoes. [ABSTRACT FROM AUTHOR]

Copyright of Mineralia Slovaca is the property of State Geological Institute of Dionyz Stur and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Plant diseases have become a challenging threat in the agricultural field. Various learning approaches for plant disease detection and classification have been adopted to detect and diagnose these diseases early. However, deep learning entails extensive data for training, and it may be challenging to collect plant datasets. Even though plant datasets can be collected, they may be uneven in quantity. As a result, the problem of classification model overfitting arises. This study targets this issue and proposes an auxiliary classifier GAN (small-ACGAN) model based on a small number of datasets to extend the available data. First, after comparing various attention mechanisms, this paper chose to add the lightweight Coordinate Attention (CA) to the generator module of Auxiliary Classifier GANs (ACGAN) to improve the image quality. Then, a gradient penalty mechanism was added to the loss function to improve the training stability of the model. Experiments show that the proposed method can best improve the recognition accuracy of the classifier with the doubled dataset. On AlexNet, the accuracy was increased by 11.2%. In addition, small-ACGAN outperformed the other three GANs used in the experiment. Moreover, the experimental accuracy, precision, recall, and F1 scores of the five convolutional neural network (CNN) classifiers on the enhanced dataset improved by an average of 3.74%, 3.48%, 3.74%, and 3.80% compared to the original dataset. Furthermore, the accuracy of MobileNetV3 reached 97.9%, which fully demonstrated the feasibility of this approach. The general experimental results indicate that the method proposed in this paper provides a new dataset expansion method for effectively improving the identification accuracy and can play an essential role in expanding the dataset of the sparse number of plant diseases. [ABSTRACT FROM AUTHOR]

The aim of the experiment was to assess the effects of various organic materials on Dactylis glomerata yield, on the content of selected macroelements (K, Ca and Mg) and on K:Ca, K:Mg and K:(Ca + Mg) ratios. As a valuable forage plant, Dactylis glomerata (cocksfoot grass) is a common grass in Poland both in grassland and in arable fields. Its rapid spring growth and its resistance to drought, low temperatures, but also to frequent mowing and pests, makes it a common species in meadows, pastures and grassland, both permanent and alternating. In order to achieve the research goal, a three-year pot experiment was established in a greenhouse. The experiment was conducted in a completely random design, in four replications. In the autumn before the experiment, soil was mixed with organic materials (chicken manure, mushroom substrate and rye straw) and put into pots. To selected units, an additional amount of mineral N was applied in the first year and NPK fertilizers in consecutive years. Mineral fertilizers were applied at the beginning of the growing period. Compared to control, the application of mineral and organic fertilizers resulted in a significant increase in Dactylis glomerata yield. The highest biomass yield (average over the growing periods) was recorded on the unit treated with manure, straw and mineral fertilizers (27.64 g·pot-1) and on the one with mushroom substrate applied together with rye straw and mineral fertilizers (26.47 g·pot-1). The K:(Ca+Mg) ratio in the forage was normal and averaged 0.933, but mineral fertilizers, compared to other treatments, narrowed it. [ABSTRACT FROM AUTHOR]

Object detection in autonomous driving scenarios has become a popular task in recent years. Due to the high-speed movement of vehicles and the complex changes in the surrounding environment, objects of different scales need to be detected, which places high demands on the performance of the network model. Additionally, different driving devices have varying performance capabilities, and a lightweight model is needed to ensure the stable operation of devices with limited computing power. To address these challenges, we propose a lightweight network called BiGA-YOLO based on YOLOv5. We design the Ghost-Hardswish Conv module to simplify the convolution operations and incorporate spatial coordinate information into feature maps using Coordinate Attention. We also replace the PANet structure with the BiFPN structure to enhance the expression ability of features through different weights during the process of fusing multi-scale feature maps. Finally, we conducted extensive experiments on the KITTI dataset, and our BiGA-YOLO achieved a mAP@0.5 of 92.2% and a mAP@0.5:0.95 of 68.3%. Compared to the baseline model YOLOv5, our proposed model achieved improvements of 1.9% and 4.7% in mAP@0.5 and mAP@0.5:0.95, respectively, while reducing the model size by 15.7% and the computational cost by 16%. The detection speed was also increased by 6.3 FPS. Through analysis and discussion of the experimental results, we demonstrate that our proposed model is superior, achieving a balance between detection accuracy, model size, and detection speed. [ABSTRACT FROM AUTHOR]

The area called "Little Cappadocia" within the borders of Nevşehir province is home to about twenty valleys. These valleys, formed by the erosion of tuff material by water over thousands of years, have a unique microclimate in the continental climate. This study investigated the epilithic bryophyte vegetation in the Gomeda Valley, located in the Cappadocia Region (Nevşehir). During the field studies, 30 sample areas were determined on the rocks in the valley. These sample areas were evaluated to identify three epilithic communities: Lewinskya rupestris - Grimmia pulvinata, Grimmia crinitler - Grimmia pulvinatler, and Grimmia anadon - Grimmia pulvinata. 30 moss taxa were also determined. The numerical analysis of the sample areas on the rocks was performed using CA (Cluster Analysis), CoA (Correspondence Analysis), and PCA (Principal Component Analysis) methods included in the PAST (Paleontological Statistics) program. As a result of the study, the class, order, and alliance were determined according to the number of repetitions of the taxa in the communities. Additionally, the life form and life strategy of the communities were analyzed, and their relations with the epilithic substrate and microclimate were investigated. [ABSTRACT FROM AUTHOR]

In order to enhance storage capacity and prevent electrical short circuits in electrochemical storage devices, it is essential and challenging to design and build Lithium ion batteries with flexible solid polymer electrolyte possessing strong ionic conductivity. This study details the use of solution-cast technique to create nano composite doped lithium solid polymer electrolytes (n-LSPEs), from polymer complex of poly (vinylidene fluoride-co-hexafluoropropylene) (PVdF-HFP), polyethylene oxide (PEO), and Lithium trifluoromethanesulfonate (LiCF3SO3), with nano ZrO2 (n-ZrO2). Electrochemical impedance spectroscopy (EIS) was utilised to describe the ionic conductivity values in presence of alternating fields. The ionic conductivity of n-LSPEs are enhanced nearly four times (1.54 × 10─1 mS/cm to 5.996 × 10─1 mS/cm) due to addition of n-ZrO2. By incorporating chronoamperometry (CA) and cyclic voltammetry (CV), we were able to analyse the transference number, ionic strength, and the accumulation of charge carriers in optimized n-LSPE membrane. Scanning electron microscope (SEM) images of n-LSPE membranes reveal a structure that is consistent with interconnecting pores between the polymer complex and the nano filler. The stable monoclinic phase of n-ZrO2 interaction with polymer segmental motion was confirmed by X-ray diffraction (XRD). By examining Fourier transform infrared spectroscopy (FTIR), we performed a quick analysis of the complexation of functional group investigations, bond length and force constant calculations, interaction of functional groups of n-LSPE films. Our FESD's discharge characteristics were investigated at 4.8 mA average discharge current for 25 hours, as indicated by a 0.04C rating. [ABSTRACT FROM AUTHOR]

The analysis here of seven obsidian artifacts from CA-SBR-8334 within the China Lake Naval Weapons Station, as in many of the previous studies, indicated the presence of all Coso Volcanic Field obsidian sources (Shackley 2014, 2015a, 2015b, 2016, 2017, 2019a, 2019b). As typical for sites on the weapons station, the assemblage is dominated by artifacts produced from the West Sugarloaf dome, followed by Sugarloaf and West Cactus Peak

The analysis here of 23 obsidian artifacts from two sites within the China Lake Naval Weapons Station, as in many of the previous studies, indicated the presence of all the Coso Volcanic Field obsidian sources (Shackley 2014, 2015a, 2015b, 2016, 2017, 2019a, 2019b). In this case West Sugarloaf (69.6%) is dominant followed by Sugarloaf (13%) and Joshua Ridge and West Cactus Peak each at 8.7%

Career adaptability (CA) in terms of concern, control, curiosity, and confidence exhibited like a mediator between servant leadership and positive affectivity and is the main focus of the present study's conceptual model. In Sulaymaniyah city in Kurdistan Region of Iraq, hotel workers collected information. Structural equation modeling was used to assess the collected data from five-star hotel workers at Sulaymaniyah. The constructs are meaningful and dependable via exploratory factor analysis with reliability testing. Factor analysis demonstrates high convergent and discriminative validity of the results. A complete mediator linking servant leadership and good effectiveness were found to be the CA. In hotel business, embracing a servant leadership approach may enhance personal resources and improve career adaptability.

Dopamine (DA), a kind of neurotransmitter in animals, has been proven to cause a positive influence on plants during abiotic stress. In the present study, the function of DA on plants under cadmium (Cd) stress was revealed. The yellowing of duckweed leaves under Cd stress could be alleviated by an exogenous DA (10/20/50/100/200 μM) supplement, and 50 μM was the optimal concentration to resist Cd stress by reducing root breakage, restoring photosynthesis and chlorophyll content. In addition, 24 h DA treatment increased Cd content by 1.3 times in duckweed under Cd stress through promoting the influx of Cd2+. Furthermore, the gene expression changes study showed that photosynthesis-related genes were up-regulated by DA addition under Cd stress. Additionally, the mechanisms of DA-induced Cd detoxification and accumulation were also investigated; some critical genes, such as vacuolar iron transporter 1 (VIT1), multidrug resistance-associated protein (MRP) and Rubisco, were significantly up-regulated with DA addition under Cd stress. An increase in intracellular Ca2+ content and a decrease in Ca2+ efflux induced by DA under Cd stress were observed, as well as synchrony with changes in the expression of cyclic nucleotide-gated ion channel 2 (CNGC2), predicting that, in plants, CNGC2 may be an upstream target for DA action and trigger the change of intracellular Ca2+ signal. Our results demonstrate that DA supplementation can improve Cd resistance by enhancing duckweed photosynthesis, changing intracellular Ca2+ signaling, and enhancing Cd detoxification and accumulation. Interestingly, we found that exposure to Cd reduced endogenous DA content, which is the result of a blocked shikimate acid pathway and decreased expression of the tyrosine aminotransferase (TAT) gene. The function of DA in Cd stress offers a new insight into the application and study of DA to Cd phytoremediation in aquatic systems. [ABSTRACT FROM AUTHOR]