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Logo detection is a technology that identifies logos in images and returns their locations. With logo detection technology, brands can check how often their logos are displayed on social media platforms and elsewhere online and how they appear. It has received a lot of attention for its wide applications across different sectors, such as brand identity protection, product brand management, and logo duration monitoring. Particularly, logo detection technology can offer various benefits for companies to help brands measure their logo coverage, track their brand perception, secure their brand value, increase the effectiveness of their marketing campaigns and build brand awareness more effectively. However, compared with the general object detection, logo detection is more challenging due to the existence of both small logo objects and large aspect ratio logo objects. In this paper, we propose a novel approach, named Discriminative Semantic Feature Pyramid Network with Guided Anchoring (DSFP-GA), which can address these challenges via aggregating the semantic information and generating different aspect ratio anchor boxes. More specifically, our approach mainly consists of two components, namely Discriminative Semantic Feature Pyramid (DSFP) and Guided Anchoring (GA). The former is proposed to fuse semantic features into low-level feature maps to obtain discriminative representation of small logo objects, while the latter is further integrated into DSFP to generate large aspect ratio anchor boxes for detecting large aspect ratio logo objects. Extensive experimental results on four benchmarks demonstrate the effectiveness of the proposed DSFP-GA. Moreover, we further conduct visual analysis and ablation studies to illustrate the strength of the proposed DSFP-GA when detecting both small logo objects and large aspect logo objects. [ABSTRACT FROM AUTHOR]

Recent investigations found that hydrothermal activity and sulfide mineralization occurs along the Southwest Indian Ridge (SWIR). The Longqi and Duanqiao hydrothermal fields between 49° E and 53° E of the SWIR are two prospective mineralization areas discovered by Chinese scientists. With the aim to determine the mineralogical and chemical characteristics of sulfide minerals, we have conducted detailed studies for samples from the two areas using an optical microscope, X‐ray diffractometer, scanning electron microscope, and electron microprobe. The mineralization processes in the Longqi area are divided into three main stages: (1) the low‐medium‐temperature stage: colloform pyrite (Py I) + marcasite → euhedral pyrite (Py II), (2) the high‐temperature stage: isocubanite (±exsolved chalcopyrite) + pyrrhotite → coarse‐grained chalcopyrite (Ccp I), and (3) the medium–low‐temperature stage: sphalerite + fine‐grained chalcopyrite inclusions (Ccp II) → aggregates of anhedral pyrite (Py III) ± marcasite → Fe‐oxide (‐hydroxide) + amorphous silica. The mineralization processes in the Duanqiao area are divided into two main stages: (1) the medium–high‐temperature stage: subhedral and euhedral pyrite (Py I′) → coarse‐grained chalcopyrite (Ccp I′) and (2) the medium–low‐temperature stage: sphalerite → fine‐grained chalcopyrite (Ccp II′) + chalcopyrite inclusions (Ccp II′) → silica‐cemented pyrite (Py II′) + marcasite → Fe‐oxide + amorphous silica. We suggest that the fine‐grained chalcopyrite inclusions in sphalerite from Longqi and Duanqiao were formed by co‐precipitation and replacement mechanisms, respectively. Primary sphalerites from both fields are enriched in Fe (avg. 5.84 wt% for the Longqi field vs. avg. 3.69 wt% for the Duanqiao field), Co (avg. 185.56 ppm for the Longqi field vs. 160.53 ppm for the Duanqiao field), and Cd (avg. 1950 ppm for the Longqi field vs. avg. 525.26 ppm for the Duanqiao field). Cu contents in pyrite from the Duanqiao field (Py I′: avg. 849.23 ppm and Py II′: avg. 1191.11 ppm) tend to be higher than those from the Longqi field (Py I: avg. 26.67 ppm, Py II: avg. 445 ppm, and Py III: avg. 179.29 ppm). Chalcopyrite from both fields is enriched in Zn (Ccp I: avg. 3226.67 ppm, Ccp II: avg. 9280 ppm, Ccp I′: avg. 848 ppm, Ccp II′ (inclusions): avg. 1098 ppm, and Ccp II′ (fine‐grained): avg. 1795 ppm). The varying contents of Zn in the different pyrite and chalcopyrite generations may result from the zone refining process. An integrated study of the mineralogy and mineralogical chemistry suggests that the hydrothermal fluids of the Longqi area are likely conditioned with higher temperatures and relatively lower fO2 and fS2 than those of the Duanqiao area, but in contrast to the former, the latter is much affected by the compositions of the surrounding rocks. [ABSTRACT FROM AUTHOR]

Logo detection is one of the crucial branches in computer vision due to various real-world applications, such as automatic logo detection and recognition, intelligent transportation, and trademark infringement detection. Compared with traditional handcrafted-feature-based methods, deep learning-based convolutional neural networks (CNNs) can learn both low-level and high-level image features. Recent decades have witnessed the great feature representation capabilities of deep CNNs and their variants, which have been very good at discovering intricate structures in high-dimensional data and are thereby applicable to many domains including logo detection. However, logo detection remains challenging, as existing detection methods cannot solve well the problems of a multiscale and large aspect ratios. In this paper, we tackle these challenges by developing a novel long-range dependence involutional network (LDI-Net). Specifically, we designed a strategy that combines a new operator and a self-attention mechanism via rethinking the intrinsic principle of convolution called long-range dependence involution (LD involution) to alleviate the detection difficulties caused by large aspect ratios. We also introduce a multilevel representation neural architecture search (MRNAS) to detect multiscale logo objects by constructing a novel multipath topology. In addition, we implemented an adaptive RoI pooling module (ARM) to improve detection efficiency by addressing the problem of logo deformation. Comprehensive experiments on four benchmark logo datasets demonstrate the effectiveness and efficiency of the proposed approach. [ABSTRACT FROM AUTHOR]

To study the effects of different storage methods and time on content of nutrients in biogas slurry of straw, two storage methods were carried out on biogas slurry between open storage and airtight storage conditions at normal atmospheric temperature. The contents of N, P, K, and organic matter in biogas slurry of straw were determined in different storage times. The results showed that; during the process of biogas slurry storage, little change occurred in the content of the organic matter while the total content of N. P. K significantly declined; up to 50 days, the total content of N. P. K reduced to nearly 80%-90%. Because the contents of N, P. K in biogas slurry reduced less in airtight storage conditions so that a better result was found on airtight storage methods than open storage methods in fertilizer field of biogas slurry of straw. [ABSTRACT FROM AUTHOR]

Objective: The aim of our study is to develop and validate a radiomics model based on ultrasound image features for predicting carpal tunnel syndrome (CTS) severity. Methods: This retrospective study included 237 CTS hands (106 for mild symptom, 68 for moderate symptom and 63 for severe symptom). There were no statistically significant differences among the three groups in terms of age, gender, race, etc. The data set was randomly divided into a training set and a test set in a ratio of 7:3. Firstly, a senior musculoskeletal ultrasound expert measures the cross-sectional area of median nerve (MN) at the scaphoid-pisiform level. Subsequently, a recursive feature elimination (RFE) method was used to identify the most discriminative radiomic features of each MN at the entrance of the carpal tunnel. Eventually, a random forest model was employed to classify the selected features for prediction. To evaluate the performance of the model, the confusion matrix, receiver operating characteristic (ROC) curves, and F1 values were calculated and plotted correspondingly. Results: The prediction capability of the radiomics model was significantly better than that of ultrasound measurements when 10 robust features were selected. The training set performed perfect classification with 100% accuracy for all participants, while the testing set performed accurate classification of severity for 76.39% of participants with F1 values of 80.00, 63.40, and 84.80 for predicting mild, moderate, and severe CTS, respectively. Comparably, the F1 values for mild, moderate, and severe CTS predicted based on the MN cross-sectional area were 76.46, 57.78, and 64.00, respectively.. Conclusion: This radiomics model based on ultrasound images has certain value in distinguishing the severity of CTS, and was slightly superior to using only MN cross-sectional area for judgment. Although its diagnostic efficacy was still inferior to that of neuroelectrophysiology. However, this method was non-invasive and did not require additional costs, and could provide additional information for clinical physicians to develop diagnosis and treatment plans. [ABSTRACT FROM AUTHOR]

Background: A major challenge of prospective cohort studies is attrition in follow-up surveys. This study investigated attrition in a prospective cohort comprised of medical graduates in China. We described status of attrition, identified participants with higher possibility of attrition, and examined if attrition affect the estimation of the key outcome measures.Methods: The cohort study recruited 3,620 new medical graduates from four medical universities in central and western China between 2015 and 2019. Online follow-up surveys were conducted on an annual basis. Follow-up status was defined as complete (meaning that the participant completed all the follow-up surveys) and incomplete, while incomplete follow-up was further divided into 'always-out', 'rejoin' and 'other'. Multivariable logistic and linear regressions were used to examine factors predicting attrition and the influence on the outcome measures of career development.Results: 2364 (65.3%) participants completed all follow-up surveys. For those with incomplete follow-up, 520 (14.4%) were 'always-out', 276 (7.6%) rejoined in the 2020 survey. Willingness to participate in residency training (OR=0.80, 95%CI[0.66 - 0.98]) and willingness to provide sensitive information in the baseline survey predicted a lower rate of attrition (providing scores for university entrance exam OR=0.82, 95%CI[0.69 - 0.97]]; providing contact information (OR=0.46, 95%CI[0.32 - 0.66]); providing household income (OR=0.60, 95%CI[0.43 - 0.84]). Participants with compulsory rural service (OR=1.52, 95%CI[1.05 - 2.19]) and those providing university entrance scores (OR=1.64, 95%CI[1.15-2.33)) were more likely to rejoin in the follow-up survey. These factors associated with follow-up status did not have significant impact on key outcome measures of career development.Conclusions: Graduates who were unwilling to participate in residency training or not providing sensitive information should be targeted early in the cohort study to reduce attrition. More information about the study should be provided to those graduates early to facilitate their understanding of the meaning in participation. On the contrary, medical graduates with compulsory rural service and those who provided university entrance scores were more likely to rejoin in the cohort. The research team should invest more effort in contacting those graduates and returned them to the cohort. [ABSTRACT FROM AUTHOR]

Background: A major challenge of prospective cohort studies is attrition in follow-up surveys. This study investigated attrition in a prospective cohort comprised of medical graduates in China. We described status of attrition, identified participants with higher possibility of attrition, and examined if attrition affect the estimation of the key outcome measures. Methods: The cohort study recruited 3,620 new medical graduates from four medical universities in central and western China between 2015 and 2019. Online follow-up surveys were conducted on an annual basis. Follow-up status was defined as complete (meaning that the participant completed all the follow-up surveys) and incomplete, while incomplete follow-up was further divided into 'always-out', 'rejoin' and 'other'. Multivariable logistic and linear regressions were used to examine factors predicting attrition and the influence on the outcome measures of career development. Results: 2364 (65.3%) participants completed all follow-up surveys. For those with incomplete follow-up, 520 (14.4%) were 'always-out', 276 (7.6%) rejoined in the 2020 survey. Willingness to participate in residency training (OR=0.80, 95%CI[0.66 - 0.98]) and willingness to provide sensitive information in the baseline survey predicted a lower rate of attrition (providing scores for university entrance exam OR=0.82, 95%CI[0.69 - 0.97]]; providing contact information (OR=0.46, 95%CI[0.32 - 0.66]); providing household income (OR=0.60, 95%CI[0.43 - 0.84]). Participants with compulsory rural service (OR=1.52, 95%CI[1.05 - 2.19]) and those providing university entrance scores (OR=1.64, 95%CI[1.15-2.33)) were more likely to rejoin in the follow-up survey. These factors associated with follow-up status did not have significant impact on key outcome measures of career development. Conclusions: Graduates who were unwilling to participate in residency training or not providing sensitive information should be targeted early in the cohort study to reduce attrition. More information about the study should be provided to those graduates early to facilitate their understanding of the meaning in participation. On the contrary, medical graduates with compulsory rural service and those who provided university entrance scores were more likely to rejoin in the cohort. The research team should invest more effort in contacting those graduates and returned them to the cohort. [ABSTRACT FROM AUTHOR]

The article focuses on a research paper on geology of hot spring geyserite cesium deposit in Targejia in Tibet, China. It discusses the geochemical analysis of the acidic and alkaline spring water area in Targejia. It also reflects on the geological setting of the deposits whcih suggests eruptive fountains, hot springs and sediments of the springs the mining area.

An eco-friendly material, activated carbon cloth (ACC) was used as the heterogeneous catalyst in activation of peroxydisulfate (PDS) for the efficient degradation of organic pollutant in water. Besides, the effects of several parameters in the ACC/PDS process including initial pH, PDS concentration, reaction temperature, stirring speed and co-existing anions were investigated. Under optimum conditions, almost complete removal (98.6%) of AO7 in 60 min and 67.4% of total organic carbon (TOC) removal within 180 min were obtained, accompanied by the remarkable destruction of azo band and naphthalene ring on AO7. The electron paramagnetic resonance and radical quenching experiments were carried out to identify the reactive radicals in the ACC/PDS process. Surface characteristic techniques such as XRD, BET, SEM, FTIR, XPS were applied to analysis the change of crystal structure, surface area, surface morphology, functional groups on the surface of fresh and spent ACC samples. Hydroxyl groups (C‒OH) and π-π transitions significantly affected the catalytic activity of ACC. The intermediate products of AO7 oxidation were identified by LC-MS and the corresponding degradation pathway was proposed. Image 1 • Activated carbon cloth (ACC) in monolithic form was applied to catalyze PDS. • Effects of reaction parameters on AO7 removal were evaluated. • SO 4.•−, HO• and •C were identified in ACC/PDS system • Surface function groups are responsible for the adsorption and activation of ACC. [ABSTRACT FROM AUTHOR]

Lead halide perovskite, as an emerging semiconductor, provides a fire‐new opportunity for high‐definition display and solid‐state lighting. Earthshaking improvements are implemented in green, red, and near‐infrared perovskite light‐emitting diodes (PeLEDs). However, blue PeLEDs are still far behind in performance, which restricts the development of PeLEDs in practical applications. Herein, a facile energy cascade channel strategy via one‐step self‐organized and controllable 2D/3D perovskite preparation by introducing guanidine hydrobromide (GABr) is developed that greatly improves the efficiency of blue PeLEDs. The 2D/3D perovskite structure boosts the energy cascade to induce energy transfer from the wide into the narrow bandgap domains and inhibit free charge diffusion, which increases the density of electrons and holes, and enhances the radiative recombination. Profiting from this energy cascade channels, the external quantum efficiency of blue PeLEDs, emitting at 492 nm, is considerably enhanced from 1.5% of initial blue device to 8.2%. In addition, device operating stability under ambient conditions is also improved by 2.6‐fold. The one‐step self‐organized 2D/3D hybrid perovskites induced by GABr pave a new and simple route toward high‐performance blue emission PeLEDs. [ABSTRACT FROM AUTHOR]