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Securing image transmissions has become critical as the demand for image sharing and storage grows. In some commercial uses, in order to achieve a balance between encryption security and efficiency, some researchers have tried to encrypt only the region of interest of the user in the image. In order to accurately encrypt region of interest images, this paper proposed a region of interest image encryption algorithm based on lifting scheme and object detection. The algorithm automatically identifies the region of interest in the image and encrypts it securely and effectively. Firstly, the region of interest in the image is detected using the object detection model, and the non-maximum suppression algorithm is modified to solve the problem that the detection box outputted by the object detection model does not completely contain the region of interest. Then the existing pixel extraction method for region of interest images is improved to extract the pixels from the region of interest images at a faster speed, which improves the overall efficiency of the algorithm. Finally, based on the thought of wavelet lifting transform, combined with chaotic system, a two-layer hybrid lifting scheme encryption algorithm is proposed to encrypt the extracted pixels. Experimental results and security analysis show that the algorithm proposed in this paper can effectively protect all objects at once with high security. [ABSTRACT FROM AUTHOR]

Nanomaterials (NMs) have found extensive applications in the realm of ornamental plants due to their unique properties. This article comprehensively discusses four main aspects of NM utilization in ornamental plants: 1) providing new insights into challenging problems in tissue culture, 2) exploring their regulatory effects on the growth of ornamental plants, 3) enhancing the resistance to biotic and abiotic stressors, and 4) discussing their widespread application and mechanisms in cut flower harvesting. Furthermore, potential issues and future directions are explored, providing a deeper theoretical basis for the application of novel NMs in the realm of ornamental plants.

Object detection via remote sensing encounters significant challenges due to factors such as small target sizes, uneven target distribution, and complex backgrounds. This paper introduces the K-CBST YOLO algorithm, which is designed to address these challenges. It features a novel architecture that integrates the Convolutional Block Attention Module (CBAM) and Swin-Transformer to enhance global semantic understanding of feature maps and maximize the utilization of contextual information. Such integration significantly improves the accuracy with which small targets are detected against complex backgrounds. Additionally, we propose an improved detection network that combines the improved K-Means algorithm with a smooth Non-Maximum Suppression (NMS) algorithm. This network employs an adaptive dynamic K-Means clustering algorithm to pinpoint target areas of concentration in remote sensing images that feature varied distributions and uses a smooth NMS algorithm to suppress the confidence of overlapping candidate boxes, thereby minimizing their interference with subsequent detection results. The enhanced algorithm substantially bolsters the model's robustness in handling multi-scale target distributions, preserves more potentially valid information, and diminishes the likelihood of missed detections. This study involved experiments performed on the publicly available DIOR remote sensing image dataset and the DOTA aerial image dataset. Our experimental results demonstrate that, compared with other advanced detection algorithms, K-CBST YOLO outperforms all its counterparts in handling both datasets. It achieved a 68.3% mean Average Precision (mAP) on the DIOR dataset and a 78.4% mAP on the DOTA dataset. [ABSTRACT FROM AUTHOR]

Neuroleptic malignant syndrome (NMS) is a severe adverse reaction associated with neuroleptic or antipsychotic drugs. This case report discusses a 43-year-old man with a history of bipolar disorder and polysubstance abuse who presented with altered mental status, autonomic dysfunction, and muscular rigidity. The patient had recently started on ziprasidone, a second-generation antipsychotic, leading to an atypical presentation of NMS. Unlike classic findings associated with NMS induced by first-generation antipsychotics, this case lacked high fever, lead pipe rigidity, or elevated creatine kinase levels greater than 1000 on initial presentation. The delay in diagnosis was attributed to the milder symptoms and absence of typical findings, resulting in extensive diagnostic workup and interventions. The patient responded positively to treatment with lorazepam based on the Woodbury severity stage guidelines. This case underscores the complexity of diagnosing NMS induced by second-generation antipsychotics and highlights the need for awareness and tailored treatment approaches for atypical presentations. [ABSTRACT FROM AUTHOR]

Automatic detection of safety helmet wearing is significant in ensuring safe production. However, the accuracy of safety helmet detection can be challenged by various factors, such as complex environments, poor lighting conditions and small-sized targets. This paper presents a novel and efficient deep learning framework named High-Resolution You Only Look Once (HR-YOLO) for safety helmet wearing detection. The proposed framework synthesizes safety helmet wearing information from the features of helmet objects and human pose. HR-YOLO can use features from two branches to make the bounding box of suppression predictions more accurate for small targets. Then, to further improve the iterative efficiency and accuracy of the model, we design an optimized residual network structure by using Optimized Powered Stochastic Gradient Descent (OP-SGD). Moreover, a Laplace-Aware Attention Model (LAAM) is designed to make the YOLOv5 decoder pay more attention to the feature information from human pose and suppress interference from irrelevant features, which enhances network representation. Finally, non-maximum suppression voting (PA-NMS voting) is proposed to improve detection accuracy for occluded targets, using pose information to constrain the confidence of bounding boxes and select optimal bounding boxes through a modified voting process. Experimental results demonstrate that the presented safety helmet detection network outperforms other approaches and has practical value in application scenarios. Compared with the other algorithms, the proposed algorithm improves the precision, recall and mAP by 7.27%, 5.46% and 7.3%, on average, respectively. [ABSTRACT FROM AUTHOR]

Based on the IEEE 802.16e standard's (672,336) LDPC code and the normalized Min-Sum decoding algorithm, this paper designs and implements an LDPC decoder that optimizes the channel information. The correction factor for check nodes is converted into a correction factor for the initial channel information, replacing the optimization of check node information with that of initial channel information. This achieves decoding performance equivalent to the traditional normalized Min-Sum decoding algorithm. Different correction factor values vary in complexity during FPGA implementation, as they involve different amounts of shift-add operations. For NMS decoding requiring a high number of shift-add operations to achieve optimal correction values, this can be converted into an LDPC decoding algorithm optimized for channel information, reducing computational overhead without sacrificing performance. A partially parallel improved decoder was designed and implemented on an FPGA, and its feasibility was verified using the Vivado simulation platform. [ABSTRACT FROM AUTHOR]

The current centralized defect detection system faces challenges such as large data volume and poor real-time performance， underscoring the pressing need for distributed detection systems， with edge computing as a representative. In response， diverse module algorithms are devised based on the single-stage， lightweight detection model YOLOv8 to boost its accuracy in power scenarios. Firstly， the Mosaic data augmentation algorithm is improved， and a conflict relationship table is introduced to mitigate the damage to original image data information caused by traditional data augmentation algorithms and enhance the diversity of image data. Subsequently， the Res2Net module is employed to replace the original Bottleneck module， reinforcing the model’s multiscale perception while retaining its lightweight design. The adoption of the CIoU-NMS algorithm over the existing NMS （non-maximum suppression） algorithm improves the recall rate and precision of the detection model in the clustering and deduplication stage. Finally， experiments on fourteen defects in power scenarios consistently demonstrate the proposed model’s superior detection accuracy compared to the original model， accompanied by an accelerated detection speed in defect detection power scenarios.

Copyright of Zhejiang Electric Power is the property of Zhejiang Electric Power 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.)

Copyright of Turk Psikiyatri Dergisi is the property of Turk Psikiyatri Dergisi 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.)

Deep learning-based object detection technology often relies on non-maximum suppression (NMS) algorithms to eliminate redundant detections. However, the conventional NMS algorithm struggles with distinguishing between overlapping and small objects due to its simple constraints. While Soft-NMS offers a slight improvement in object detection performance, it still falls short in addressing this challenge. Our proposed solution, adjustable-NMS, represents a significant advancement. While performing comparably to NMS and Soft-NMS on less dense images where objects are easily countable, adjustable-NMS excels in scenarios with higher object density or smaller objects. In such cases, it outperforms both NMS and Soft-NMS, showcasing notably superior object detection capabilities. On average, the improvement achieved with adjustable-NMS reaches an impressive 33.3%. This demonstrates adjustable-NMS’s efficacy in enhancing object detection accuracy, particularly in challenging environments characterized by dense scenes or diminutive objects.

In order to improve the safety needs of personnel in the dark environment under the well, this article adopts the improved YOLOV8 algorithm combined with the ray method to determine whether underground personnel are entering dangerous areas and to provide early warning. First of all, this article introduces the coordinate attention mechanism on the basis of YOLOV8 target detection so that the model pays attention to the location information of the target area so as to improve the detection accuracy of obstruction and small target areas. In addition, the Soft-Non-Maximum Suppression (SNMS) module is introduced to further improve accuracy. The improved model is then combined with the ray method to be deployed and applied under a variety of angles and different scenic information cameras. The experimental results show that the proposed method obtains 99.5% of the identification accuracy and a frame speed of 45 Frames Per Second (FPS) on the self-built dataset. Compared with the YOLOV8 model, it has a higher accuracy and can effectively cope with the changes and interference factors in the underground environment. Further, it meets the requirements for real-time testing in dangerous underground areas. [ABSTRACT FROM AUTHOR]

Introduction and purpose: The number of invented and produced medications has increased over recent years. Besides their positive impact on health, they invariably provide various kinds of side effects. This review is focused on malignant neuroleptic syndrome, a rare condition with no fully confirmed pathogenesis, which at least partially can be explained as an adverse effect of blocking dopamine receptors. Even though it usually affects psychiatric or neurological patients, doctors of all medical specializations should stay alert, as the condition can often lead to premature death. Brief description of knowledge: The main symptoms associated with NMS are hypertonia, hyperpyrexia, autonomic instability and altered consciousness. They can appear in any configuration, sometimes followed by less characteristic manifestations. It should be primarily differentiated from serotonin syndrome, malignant hyperpyrexia and catatonia, mostly through the interview of those who have taken medications and undergone surgeries, as there are not any specific lab tests available. Fundamental treatment methods consist of neuroleptic withdrawal, intensive supportive care, drug administration and eventually ECT. Summary: NMS is an uncommon, severe disease that should be carefully considered in patients treated with neuroleptics. Due to its rarity and sudden onset, it appears to be problematic to conduct widespread research and studies, so there are still no concrete, consistent and unquestioned guidelines on recognizing and treating the condition.

Object detection via remote sensing encounters significant challenges due to factors such as small target sizes, uneven target distribution, and complex backgrounds. This paper introduces the K-CBST YOLO algorithm, which is designed to address these challenges. It features a novel architecture that integrates the Convolutional Block Attention Module (CBAM) and Swin-Transformer to enhance global semantic understanding of feature maps and maximize the utilization of contextual information. Such integration significantly improves the accuracy with which small targets are detected against complex backgrounds. Additionally, we propose an improved detection network that combines the improved K-Means algorithm with a smooth Non-Maximum Suppression (NMS) algorithm. This network employs an adaptive dynamic K-Means clustering algorithm to pinpoint target areas of concentration in remote sensing images that feature varied distributions and uses a smooth NMS algorithm to suppress the confidence of overlapping candidate boxes, thereby minimizing their interference with subsequent detection results. The enhanced algorithm substantially bolsters the model’s robustness in handling multi-scale target distributions, preserves more potentially valid information, and diminishes the likelihood of missed detections. This study involved experiments performed on the publicly available DIOR remote sensing image dataset and the DOTA aerial image dataset. Our experimental results demonstrate that, compared with other advanced detection algorithms, K-CBST YOLO outperforms all its counterparts in handling both datasets. It achieved a 68.3% mean Average Precision (mAP) on the DIOR dataset and a 78.4% mAP on the DOTA dataset.

Based on the IEEE 802.16e standard’s (672,336) LDPC code and the normalized Min-Sum decoding algorithm, this paper designs and implements an LDPC decoder that optimizes the channel information. The correction factor for check nodes is converted into a correction factor for the initial channel information, replacing the optimization of check node information with that of initial channel information. This achieves decoding performance equivalent to the traditional normalized Min-Sum decoding algorithm. Different correction factor values vary in complexity during FPGA implementation, as they involve different amounts of shift-add operations. For NMS decoding requiring a high number of shift-add operations to achieve optimal correction values, this can be converted into an LDPC decoding algorithm optimized for channel information, reducing computational overhead without sacrificing performance. A partially parallel improved decoder was designed and implemented on an FPGA, and its feasibility was verified using the Vivado simulation platform.

Due to the severe underwater environment, research into Underwater Wireless Sensor Networks is at its most undeveloped stage. Due to the restricted sensor batteries and significant variations in water parameters, like temperature, turbulence, flooding, pH, salinity, and others, this research is not as simple as it would seem. To increase node battery life and provide load balancing, several routing protocols are implemented. In this article, we propose the Effective Load Balancing with Sensor Injection routing method, which uses hop distance calculations to enable sensors to connect with sinks through other intermediary sensors. In order to communicate among sensors, localization-based routing protocol is employed. With the use of weight factor calculations, each node determines the minimal distance needed for connection to another node and calculates distance from the lowest hop node. This algorithm balances the load using the sensor injection approach. When weight factor of any node becomes high, it sends request to Network Management System (NMS) for load balancing. NMS directs Autonomous Underwater Vehicle for new sensor injection as neighbour of high factor node. Thus, by using this sensor injection method network lifetime gets increased. Simulation is carried out on NS 2.30 and improved results are obtained in comparison to existing schemes w.r.t., number of dead nodes and alive nodes at every round. [ABSTRACT FROM AUTHOR]

The target detection algorithm for common objects has achieved good results, but the detection accuracy and speed of the target detection algorithm for Unmanned Aerial Vehicle (UAV) need to be improved. Unmanned Aerial Vehicle (UAV) images are characterized by small targets, difficult to identify in the background image, clustering and sparse distribution of targets, etc. Many researchers have proposed the clustering target detection method (ClusDet) for Unmanned Aerial Vehicle (UAV) images. However, due to the large differences in target scales and uneven distribution of targets in UAV images, showing long-tailed distribution, the traditional ClusDet algorithm tends to truncate large and medium targets in the process of clustering; in the detection process, the fixed-threshold NMS method in the ClusDet algorithm is difficult to adaptively detect targets of different sizes, clustering and mutual occlusion. To address the above problems, this paper proposes an adaptive clustered target detection algorithm based on Unmanned Aerial Vehicle (UAV) images under long-tail distribution. The method is divided into three sub-networks: the adaptive clustering sub-network, which outputs several segmented images of small target clustering regions by extracting potential small target clustering regions in Unmanned Aerial Vehicle (UAV) aerial images; the segmentation and filling sub-network, which fills the images with disproportionate aspect ratio for the output of the adaptive clustering network to keep the size of the images within the reasonable range required by the detection network; and the detection sub-network, which detects the targets within the reasonable range required by the detection network by introducing attention mechanism, using variable threshold NMS, and training using sample balancing strategy effectively improve the detection accuracy of targets in the clustered region. Trained in VisDrone 2019 dataset, the simulation results show that the Unmanned Aerial Vehicle (UAV) image adaptive clustering target detection method based on long-tailed distribution has a large improvement in the detection accuracy of small targets, and can effectively improve the detection accuracy of the model for targets in the aggregation region, while the model has good generalization ability. [ABSTRACT FROM AUTHOR]

In this work, we study the coupled nonlinear Maccari systems (NMS) which have been extensively developed in various fields of nonlinear optics, deep water theory, plasma physics, nonlinear optics, and so on. Studying is conducted by implementing the modified extended mapping method (MEMM) which denotes various types of solutions. These solutions including (bright, dark, singular) solitons. In addition, singular periodic, rational type, Weierstrass elliptic and exponential solutions are derived. To show the nature of the extracted solutions, 3D and 2D graphical representations are introduced. [ABSTRACT FROM AUTHOR]

The role of education in the development of pro-EU attitudes is often examined by focusing on the association of educational attainment and the development of pro-EU attitudes. However, student exchanges and youth mobility have also become pillars of the foundation of the European Higher Education Area, figuring out the Maastricht Treaty and the Bologna Declaration. Accordingly, based on a utilitarian approach, the current study investigates the role of participation in learning abroad mobility in the development of positive views on the EU. In doing so, Flash Eurobarometer 478 data was used and multinomial logistic regression analyses were performed by student and non-students groups of youth in the new (NMS) and old (EU15) member states. The findings show that the role of participation depends on the degree of positive views, and it makes a significant contribution to having Very Positive views on the EU among non-students in both national groups and students in the NMS. Findings also demonstrate that participation is more important than consideration in the development of Very Positive views. So, even if respondents could have already been supportive of the EU before participation, participation also takes their positive views on the EU to a higher level. [ABSTRACT FROM AUTHOR]

Neuromedin S (NMS) is a neuroregulatory substance and has many important roles in regulating physiological functions in animal cells, while their specific functions and mechanisms in Leydig cells (LCs) of the testis remain unclear. The current study aims to investigate the role and potential mechanisms of NMS and its receptors in regulating steroidogenesis and proliferation in goat LCs. We found that NMS and its receptors were mainly expressed in LCs of goat testes at different ages (1‐day‐old, 3‐month‐old, and 9‐month‐old), and the highest expressions detected at age three months. NMS addition significantly enhanced the testosterone secretion, STAR, CYP11A1, 3BHSD, and CYP17A1 expressions, cell proliferation, and PCNA expression in vitro cultured goat LCs. Mechanistically, NMS addition increased G1/S cell population, the expressions of CCND1, CDK4 and CDK6, the activities of SOD2 and CAT, and enhanced the mitochondrial fusion, the production of ATP, and mitochondrial membrane potential, while inhibited cellular ROS production, and maintained a low ubiquitination level of mitochondrial proteins. Notably, these effects of NMS addition on goat LCs were suppressed by co‐treatment with NMUR2 knockdown. Therefore, these data suggest that activating NMUR2 with NMS enhances testosterone production and cell proliferation in goat LCs through modulating mitochondrial morphology, function, and autophagy. These findings may provide a novel view of the regulatory mechanisms involved in male sexual maturation. [ABSTRACT FROM AUTHOR]

Automatic pavement crack detection is crucial for reducing road maintenance costs and ensuring transportation safety. Although convolutional neural networks (CNNs) have been widely used in automatic pavement crack detection, they cannot adequately model the long-range dependencies between pixels and easily lose edge detail information in complex scenes. Moreover, irregular crack shapes also make the detection task challenging. To address these issues, an automatic pavement crack detection architecture named STrans-YOLOX is proposed. Specifically, the architecture first exploits the CNN backbone to extract feature information, preserving the local modeling ability of the CNN. Then, Swin Transformer is introduced to enhance the long-range dependencies through a self-attention mechanism by supplying each pixel with global features. A new global attention guidance module (GAGM) is used to ensure effective information propagation in the feature pyramid network (FPN) by using high-level semantic information to guide the low-level spatial information, thereby enhancing the multi-class and multi-scale features of cracks. During the post-processing stage, we utilize α-IoU-NMS to achieve the accurate suppression of the detection boxes in the case of occlusion and overlapping objects by introducing an adjustable power parameter. The experiments demonstrate that the proposed STrans-YOLOX achieves 63.37% mAP and surpasses the state-of-the-art models on the challenging pavement crack dataset. [ABSTRACT FROM AUTHOR]

Object detection is a classic problem in computer vision. The main bottleneck of object detection lies in the fusion of multi-scale features. In this paper, we systematically study the design choices of neural network architecture for real-time object detection, and propose an Align-Yolact to improve the instance segmentation accuracy. Firstly, we propose a weighted bounding box, which improves the accurate positioning of the bounding box. Secondly, we add a bi-directional feature pyramid network to the feature fusion, which improves the mask quality and small target accuracy. Owing to these optimizations and better backbones, we achieve the SOTA results including both detection efficiency and accuracy. [ABSTRACT FROM AUTHOR]

Aiming at the problems of few detecting samples, deformable target sizes and overlapping among targets in the detection of headdresses and seats of Thangka Yidam, we propose an optimised few shot Thangka detection method based on the ResNet and deformable convolution. Firstly, the optimised deep residual network is designed to address the problem of few categories and complicated composition in Thangka images. Then, we replace the 3×3 convolution of the optimised deep residual network with deformable convolution. By introducing the offset of deformable convolution, the receptive field can adapt to the different sizes and shapes of the detection target of Thangka Yidam. Finally, the box regression is achieved through the multi-relation detector, where DT-NMS is proposed to reduce the missed and repeated detection target. Experimental results show that the proposed method has better performance than the SOTA on the COCO dataset. In addition, the AP of 2-way 5-shot on the Thangka dataset is 33.3%, and the AP50 reaches 71.2%, which increases by 4.7% and 5.3%, respectively. [ABSTRACT FROM AUTHOR]

Neuromedin S (NMS) plays various roles in reproductive regulation, while the mechanism by which NMS regulates ovarian steroidogenesis remains unclear. In the current study, we confirmed the enhancement role of NMS in steroidogenesis in goat ovarian granulosa cells (GCs). To further explore the specific mechanism, we conducted a knockdown of NMUR2 in GCs followed by treatment with NMS and determined the effects of NMS treatment on mitochondrial morphology and function. The results found that NMS treatment increased the production of estrogen and up-regulated the expression of STAR, CYP11A1, 3BHSD, and CYP19A1, while the effects of NMS treatment were blocked by the knockdown of NMUR2 in goat GCs. Moreover, NMS treatment enhanced the fusion of mitochondria and up-regulated the expression of OPA1, MFN1, and MFN2, and increased mitochondrial membrane potential, the activity of respiratory chain enzymes and ATP production by maintaining a low expression level of mitochondrial unfolded protein response markers. The effects of NMS treatment on mitochondria were reversed by NMUR2 knockdown and NMS cotreatment. The possible mechanism of the results above was revealed by NMS treatment activating the Hippo pathway effector YAP1 and then managing the expression of phosphorylation PPARGC1A (Ser571). Together, these data showed that NMS promoted the fusion of mitochondria and protected mitochondrial function from mitochondrial unfolded protein response possibly via the NMUR2/YAP1/PPARGC1A pathway, thereby affecting the steroidogenesis of goat GCs. By elaborating the potential mechanism of NMS in regulating estrogen production in goat GCs, our results can serve as the mechanism reference for follicular growth and development. [ABSTRACT FROM AUTHOR]

Copyright of Computer Measurement & Control is the property of Magazine Agency of Computer Measurement & Control 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.)

Lithium, despite being an indispensable agent in the treatment of psychiatric disorders, has a narrow therapeutic index and needs to be carefully administered. Neuroleptic malignant syndrome (NMS) is a rare but potentially fatal complication due to central dopaminergic blockade. This case report illustrates the challenges in lithium therapy particularly related to the development of NMS when further risk factors such as polypharmacy and dehydration are present. We report a case of a 50-year-old man with underlying bipolar affective disorder who was previously able to tolerate olanzapine and lithium well, however developed chronic lithium toxicity due to diminished lithium elimination in acute kidney injury following a two-week history of viral acute gastroenteritis. He also developed NMS which could either be triggered independently by olanzapine; lithium toxicity; or attributed by a synergistic combination from lithium and olanzapine which led to an enhanced neurotoxicity in an already unstable dopaminergic pathway. Fluid therapy and supportive care allowed the patient to recover, and he was discharged well with a lower potency neuroleptic with slow dose titration., Competing Interests: Conflicts of interest: The authors declare no conflicts of interest. This case report did not receive any grant from funding agencies in the public, commercial or not-for-profit sectors., (© the Author(s).)

Urban waterlogging is mainly caused by garbage clogging the sewer manhole covers. If the amount of garbage at a sewer manhole cover can be detected, together with an early warning signal when the amount is large enough, it will be of great significance in preventing urban waterlogging from occurring. Based on the YOLOX algorithm, this paper accomplishes identifying manhole covers and garbage and building a flood control system that can automatically recognize and monitor the accumulation of garbage. This system can also display the statistical results and send early warning information. During garbage identification, it can lead to inaccurate counting and a missed detection if the garbage is occluded. To reduce the occurrence of missed detections as much as possible and improve the performance of detection models, Soft-YOLOX, a method using a new detection model for counting, was used as it can prevent the occurrence of missed detections by reducing the scores of adjacent detection frames reasonably. The Soft-YOLOX improves the accuracy of garbage counting. Compared with the traditional YOLOX, the mAP value of Soft-YOLOX for garbage identification increased from 89.72% to 91.89%. [ABSTRACT FROM AUTHOR]

The method proposed over here in this paper is a vehicle speed estimation technique on moving vehicle under the cctv camera surveillance. For real-time vehicle detection, the YOLO (You Only Look Once) technique is employed, and the centroid approach is used to estimate vehicle speed. The video frame is converted to grayscale so that it may be processed by the computer as 0 and 1. The brightness of the scale is represented by each number. Then, by looking at these statistics, we train the YOLO Convolutional Neural Network to learn to identify the final detection. YOLO reframes object recognition as a single regression issue by taking the entire image and going directly from image pixels to bounding box coordinates and class probabilities. The next step is to compute bounding boxes (boxes that encompass the objects) using IoU (Intersect over Union) and NMS (non-maximum suppression). The IoU indicates how closely the machine's predicted bounding box fits the bounding box of the real item. However, because of the process, a problem of over-identification with a specific object arises. NMS ensures that the best cell is found among all these bounding boxes. Rather than concluding that a single car in the image has numerous causes, NMS chooses the boxes with the highest likelihood of determining the same vehicle. The vehicle centroid values are calculated after the cars have been detected. The distance traveled by vehicle is calculated using the centroid value. The speed of the vehicle is calculated after sorting out the distance that has been covered by the vehicle. YOLO is an effective and efficient strategy that epitomizes the spirit of machine learning in the suggested methodology for our vehicle recognition and speed estimation system. YOLO initially trains with 416*416 photographs, then retrains for 30 epochs at a 10-3 learning rate using 416*416 images. After training, the classifier has a top-one accuracy of 99.4% and a top-five accuracy of 99.3%. [ABSTRACT FROM AUTHOR]

Global patterns of brain injury correlate with motor, cognitive, and language outcomes in survivors of neonatal encephalopathy (NE). However, it is still unclear whether local changes in brain structure predict specific deficits. We therefore examined whether differences in brain structure at 6 months of age are associated with neurodevelopmental outcomes in this population. We enrolled 32 children with NE, performed structural brain MR imaging at 6 months, and assessed neurodevelopmental outcomes at 30 months. All subjects underwent T1-weighted imaging at 3 T using a 3D IR-SPGR sequence. Images were normalized in intensity and nonlinearly registered to a template constructed specifically for this population, creating a deformation field map. We then used deformation based morphometry (DBM) to correlate variation in the local volume of gray and white matter with composite scores on the Bayley Scales of Infant and Toddler Development (Bayley-III) at 30 months. Our general linear model included gestational age, sex, birth weight, and treatment with hypothermia as covariates. Regional brain volume was significantly associated with language scores, particularly in perisylvian cortical regions including the left supramarginal gyrus, posterior superior and middle temporal gyri, and right insula, as well as inferior frontoparietal subcortical white matter. We did not find significant correlations between regional brain volume and motor or cognitive scale scores. We conclude that, in children with a history of NE, local changes in the volume of perisylvian gray and white matter at 6 months are correlated with language outcome at 30 months. Quantitative measures of brain volume on early MRI may help identify infants at risk for poor language outcomes.

Neutrosophic sets deals with inconsistent, indeterminate and imprecise datas. The concept of Neutrosophic Metric Space (NMS) uses the idea of continuous t- norm and continuous t - conorm in intuitionistic fuzzy metric spaces. In this paper, we introduce the definition of subcompatible maps of types (J-1 and J-2). We extend the structure of weak non-Archimedian with the help of subcompatible maps of types (J-1 and J-2) in NMS. Finally, we obtain common fixed point theorems for four subcompatible maps of type (J-1) in weak non-Archimedean NMS.

Abstract Background Research on early object detection methods of crop diseases and pests in the natural environment has been an important research direction in the fields of computer vision, complex image processing and machine learning. Because of the complexity of the early images of tomato diseases and pests in the natural environment, the traditional methods can not achieve real-time and accurate detection. Results Aiming at the complex background of early period of tomato diseases and pests image objects in the natural environment, an improved object detection algorithm based on YOLOv3 for early real-time detection of tomato diseases and pests was proposed. Firstly, aiming at the complex background of tomato diseases and pests images under natural conditions, dilated convolution layer is used to replace convolution layer in backbone network to maintain high resolution and receptive field and improve the ability of small object detection. Secondly, in the detection network, according to the size of candidate box intersection ratio (IOU) and linear attenuation confidence score predicted by multiple grids, the obscured objects of tomato diseases and pests are retained, and the detection problem of mutual obscure objects of tomato diseases and pests is solved. Thirdly, to reduce the model volume and reduce the model parameters, the network is lightweight by using the idea of convolution factorization. Finally, by introducing a balance factor, the small object weight in the loss function is optimized. The test results of nine common tomato diseases and pests under six different background conditions are statistically analyzed. The proposed method has a F1 value of 94.77%, an AP value of 91.81%, a false detection rate of only 2.1%, and a detection time of only 55 Ms. The test results show that the method is suitable for early detection of tomato diseases and pests using large-scale video images collected by the agricultural Internet of Things. Conclusions At present, most of the object detection of diseases and pests based on computer vision needs to be carried out in a specific environment (such as picking the leaves of diseases and pests and placing them in the environment with light supplement equipment, so as to achieve the best environment). For the images taken by the Internet of things monitoring camera in the field, due to various factors such as light intensity, weather change, etc., the images are very different, the existing methods cannot work reliably. The proposed method has been applied to the actual tomato production scenarios, showing good detection performance. The experimental results show that the method in this study improves the detection effect of small objects and leaves occlusion, and the recognition effect under different background conditions is better than the existing object detection algorithms. The results show that the method is feasible to detect tomato diseases and pests in the natural environment.

PLA2G6-dystonia-parkinsonism (PLAN-DP) is characterized by levodopa responsive parkinsonism and dystonia. While neuropsychiatric symptoms and early cognitive decline are also common in this entity there is little information regarding other non-motor symptoms (NMS). Here, we describe a 26-year-old patient with PLAN-DP whose motor symptoms were preceded by mild cognitive impairment and anxiety, and who developed many other NMS as the disease evolved. Furthermore, we reviewed the NMS described in all the PLAN-DP patients published to date. A total of 50 patients with PLAN-DP were identified, 42 of whom developed NMS and in 23 of these cases, NMS preceded the motor symptoms of the disease. Neuropsychiatric symptoms dominated the premotor phase of this condition and cognitive impairment/dementia was the most prevalent NMS. Other NMS were reported infrequently like sleep disorders, autonomic symptoms, pain and hyposmia, and mostly as the disease evolved. NMS are very frequent in PLAN-DP and they may appear before diagnosis or during the course of the disease. Neuropsychiatric symptoms and cognitive decline are the most frequent NMS. The appearance of neuropsychiatric symptoms like depression, anxiety or personality changes prior to a diagnosis of parkinsonism in younger individuals might suggest the presence of PLA2G6 gene mutations. [ABSTRACT FROM AUTHOR]