Management of Penetrating Cranioencephalic Trauma Caused by Sharp Metal Objects—Therapeutic and Evolutionary Aspects: 12 Cases at the Renaissance University Hospital in N’Djamena

Introduction: Cranioencephalic trauma caused by bladed weapons is rare, and that caused by sharp objects is exceptional. The aim of our study was to describe the clinical, therapeutic and evolutionary aspects. Materials and method: This was a descriptive and analytical study over a 48-month period at CHU la Renaissance from January 1, 2018 to December 31, 2021, concerning patients admitted for penetrating cranioencephalic trauma by pointed object. Results: Twelve cases, all male, of penetrating cranioencephalic sharp-force trauma were identified. The mean age was 34 ± 7 years, with extremes of 11 and 60 years. Farmers and herders accounted for 31% and 25% of cases respectively. The average admission time was 47 hours. Brawls were the circumstances of occurrence in 81.2% of cases. Knives (33%), arrows (25%) and iron bars (16.6%) were the objects used. Altered consciousness was present in 43.8% of cases, and focal deficit in 50%. Scannographic lesions were fracture and/or embarrhment (12 cases), intra-parenchymal haematomas (6 cases) and presence of object in place (4 cases). Surgery was performed in 11 patients. Postoperative outcome was favorable in 9 patients. After 12 months, 2 patients were declared unfit. Conclusion: Penetrating head injuries caused by sharp objects are common in Chad. Urgent surgery can prevent disabling after-effects.

Real-Time Object Detection and Face Recognition Application for the Visually Impaired

The advancement of navigation systems for the visually impaired has significantly enhanced their mobility by mitigating the risk of encountering obstacles and guiding them along safe,navigable routes.Traditional approaches primarily focus on broad applications such as wayfinding,obstacle detection,and fall prevention.However,there is a notable discrepancy in applying these technologies to more specific scenarios,like identifying distinct food crop types or recognizing faces.This study proposes a real-time application designed for visually impaired individuals,aiming to bridge this research-application gap.It introduces a system capable of detecting 20 different food crop types and recognizing faces with impressive accuracies of 83.27%and 95.64%,respectively.These results represent a significant contribution to the field of assistive technologies,providing visually impaired users with detailed and relevant information about their surroundings,thereby enhancing their mobility and ensuring their safety.Additionally,it addresses the vital aspects of social engagements,acknowledging the challenges faced by visually impaired individuals in recognizing acquaintances without auditory or tactile signals,and highlights recent developments in prototype systems aimed at assisting with face recognition tasks.This comprehensive approach not only promises enhanced navigational aids but also aims to enrich the social well-being and safety of visually impaired communities.

A Simple and Effective Surface Defect Detection Method of Power Line Insulators for Difficult Small Objects

Insulator defect detection plays a vital role in maintaining the secure operation of power systems.To address the issues of the difficulty of detecting small objects and missing objects due to the small scale,variable scale,and fuzzy edge morphology of insulator defects,we construct an insulator dataset with 1600 samples containing flashovers and breakages.Then a simple and effective surface defect detection method of power line insulators for difficult small objects is proposed.Firstly,a high-resolution featuremap is introduced and a small object prediction layer is added so that the model can detect tiny objects.Secondly,a simplified adaptive spatial feature fusion(SASFF)module is introduced to perform cross-scale spatial fusion to improve adaptability to variable multi-scale features.Finally,we propose an enhanced deformable attention mechanism(EDAM)module.By integrating a gating activation function,the model is further inspired to learn a small number of critical sampling points near reference points.And the module can improve the perception of object morphology.The experimental results indicate that concerning the dataset of flashover and breakage defects,this method improves the performance of YOLOv5,YOLOv7,and YOLOv8.In practical application,it can simply and effectively improve the precision of power line insulator defect detection and reduce missing detection for difficult small objects.

Effect of low-degree astigmatism on the objective visual quality

AIM:To evaluate the effect of low-degree astigmatism on objective visual quality through the Optical Quality Analysis System(OQAS).METHODS:This study enrolled 46 participants(aged 23 to 30y,90 eyes)with normal or corrected-to-normal vision.The cylindrical lenses(0,0.5,0.75,1.0,and 1.25 D)were placed at the axial direction(180°,45°,90°,and 135°)in front of the eyes with the best correction to form 16 types of regular low-degree astigmatism.OQAS was used to detect the objective visual quality,recorded as the objective scattering index(OSI),OQAS values at contrasts of 100%,20%,and 9%predictive visual acuity(OV100%,OV20%,and OV9%),modulation transfer function cut-off(MTFcut-off)and Strehl ratio(SR).The mixed effect linear model was used to compare objective visual quality differences between groups and examine associations between astigmatic magnitude and objective visual quality parameters.RESULTS:Apparent negative relationships between the magnitude of low astigmatism and objective visual quality were observed.The increase of OSI per degree of astigmatism at 180°,45°,90°,and 135°axis were 0.38(95%CI:0.35,0.42),0.50(95%CI:0.46,0.53),0.49(95%CI:0.45,0.54)and 0.37(95%CI:0.34,0.41),respectively.The decrease of MTFcut-off per degree of astigmatism at 180°,45°,90°,and 135°axis were-10.30(95%CI:-11.43,-9.16),-12.73(95%CI:-13.62,-11.86),-12.75(95%CI:-13.79,-11.70),and-9.97(95%CI:-10.92,-9.03),respectively.At the same astigmatism degree,OSI at 45°and 90°axis were higher than that at 0°and 135°axis,while MTFcut-off were lower.CONCLUSION:Low astigmatism of only 0.50 D can significantly reduce the objective visual quality.

A Review on the Application of Deep Learning Methods in Detection and Identification of Rice Diseases and Pests

In rice production,the prevention and management of pests and diseases have always received special attention.Traditional methods require human experts,which is costly and time-consuming.Due to the complexity of the structure of rice diseases and pests,quickly and reliably recognizing and locating them is difficult.Recently,deep learning technology has been employed to detect and identify rice diseases and pests.This paper introduces common publicly available datasets;summarizes the applications on rice diseases and pests from the aspects of image recognition,object detection,image segmentation,attention mechanism,and few-shot learning methods according to the network structure differences;and compares the performances of existing studies.Finally,the current issues and challenges are explored fromthe perspective of data acquisition,data processing,and application,providing possible solutions and suggestions.This study aims to review various DL models and provide improved insight into DL techniques and their cutting-edge progress in the prevention and management of rice diseases and pests.

Development and optimization of object detection technology in pavement engineering: A literature review

Due to the rapid advancement of the transportation industry and the continual increase in pavement infrastructure,it is difficult to keep up with the huge road maintenance task by relying only on the traditional manual detection method.Intelligent pavement detection technology with deep learning techniques is available for the research and industry areas by the gradual development of computer vision technology.Due to the different characteristics of pavement distress and the uncertainty of the external environment,this kind of object detection technology for distress classification and location still faces great challenges.This paper discusses the development of object detection technology and analyzes classical convolutional neural network(CNN)architecture.In addition to the one-stage and two-stage object detection frameworks,object detection without anchor frames is introduced,which is divided according to whether the anchor box is used or not.This paper also introduces attention mechanisms based on convolutional neural networks and emphasizes the performance of these mechanisms to further enhance the accuracy of object recognition.Lightweight network architecture is introduced for mobile and industrial deployment.Since stereo cameras and sensors are rapidly developed,a detailed summary of three-dimensional object detection algorithms is also provided.While reviewing the history of the development of object detection,the scope of this review is not only limited to the area of pavement crack detection but also guidance for researchers in related fields is shared.

Research of Future Directions and Considerations for Implementation of the Forest City Strategy

A “Forest City” (FC) is an urban area that has a significant amount of forest cover. It is now a green urban development strategy that is supported by numerous nations. This essay compares the many FC implementation strategies used in developed and developing countries and explores potential future paths for this tactic. The variations between FC in terms of measurement targets, air purification, street trees, and forestry development are thoroughly compared in this research. This essay goes on to explore FC’s potential in the future regarding policy changes and the environment based on this comparison. Therefore, this essay focuses on the necessity of considering industrial innovation, encouraging biodiversity, lowering greenhouse gas emissions, paying attention to forest restructuring, and being more responsive to the issues provided by urbanization in the future global implementation of FC.

Study of Complex Nitrogen and Oxygen-bearing Molecules toward the High-mass Protostar IRAS 18089–1732

The observation of oxygen(O)-and nitrogen(N)-bearing molecules gives an idea about the complex prebiotic chemistry in the interstellar medium.Recent millimeter and submillimeter wavelength observations have shown the presence of complex O-and N-bearing molecules in the star formation regions.So,the investigation of those molecules is crucial to understanding the chemical complexity in the star-forming regions.In this article,we present the identification of the rotational emission lines of N-bearing molecules ethyl cyanide(C_(2)H_(5)CN)and cyanoacetylene(HC_(3)N),and O-bearing molecule methyl formate(CH_(3)OCHO)toward high-mass protostar IRAS18089–1732 using the Atacama Compact Array.We also detected the emission lines of both the N-and O-bearing molecule formamide(NH_(2)CHO)in the envelope of IRAS 18089–1732.We have detected the v=0 and 1 state rotational emission lines of CH_(3)OCHO.We also detected the two vibrationally excited states of HC_(3)N(v7=1 and v7=2).The estimated fractional abundances of C_(2)H_(5)CN,HC_(3)N(v7=1),HC_(3)N(v7=2),and NH_(2)CHO toward IRAS 18089–1732 are(1.40±0.5)×10^(-10),(7.5±0.7)×10^(-11),(3.1±0.4)×10^(-11),and(6.25±0.82)×10^(-11)respectively.Similarly,the estimated fractional abundances of CH_(3)OCHO(v=0)and CH_(3)OCHO(v=1)are(1.90±0.9)×10^(-9)and(8.90±0.8)×10^(-10),respectively.We also created the integrated emission maps of the detected molecules,and the observed molecules may have originated from the extended envelope of the protostar.We show that C_(2)H_(5)CNand HC_(3)N are most probably formed via the subsequential hydrogenation of the CH_(2)CHCNand the reaction between C_(2)H_(2)and CN on the grain surface of IRAS 18089–1732.We found that NH_(2)CHO is probably produced due to the reaction between NH_(2)and H_(2)CO in the gas phase.Similarly,CH_(3)OCHO is possibly created via the reaction between radical CH_(3)O and radical HCO on the grain surface of IRAS 18089–1732.

Promotion of structural plasticity in area V2 of visual cortex prevents against object recognition memory deficits in aging and Alzheimer's disease rodents

Memory deficit,which is often associated with aging and many psychiatric,neurological,and neurodegenerative diseases,has been a challenging issue for treatment.Up till now,all potential drug candidates have failed to produce satisfa ctory effects.Therefore,in the search for a solution,we found that a treatment with the gene corresponding to the RGS14414protein in visual area V2,a brain area connected with brain circuits of the ventral stream and the medial temporal lobe,which is crucial for object recognition memory(ORM),can induce enhancement of ORM.In this study,we demonstrated that the same treatment with RGS14414in visual area V2,which is relatively unaffected in neurodegenerative diseases such as Alzheimer s disease,produced longlasting enhancement of ORM in young animals and prevent ORM deficits in rodent models of aging and Alzheimer’s disease.Furthermore,we found that the prevention of memory deficits was mediated through the upregulation of neuronal arbo rization and spine density,as well as an increase in brain-derived neurotrophic factor(BDNF).A knockdown of BDNF gene in RGS14414-treated aging rats and Alzheimer s disease model mice caused complete loss in the upregulation of neuronal structural plasticity and in the prevention of ORM deficits.These findings suggest that BDNF-mediated neuronal structural plasticity in area V2 is crucial in the prevention of memory deficits in RGS14414-treated rodent models of aging and Alzheimer’s disease.Therefore,our findings of RGS14414gene-mediated activation of neuronal circuits in visual area V2 have therapeutic relevance in the treatment of memory deficits.

Effectiveness and tolerability of programmed cell death protein-1 inhibitor+chemotherapy compared to chemotherapy for upper gastrointestinal tract cancers

BACKGROUND The combination of programmed cell death protein-1(PD-1)inhibitor and che-motherapy is approved as a standard first-or second-line treatment in patients with advanced oesophageal or gastric cancer.However,it is unclear whether this combination is superior to chemotherapy alone.AIM To assess the comparative effectiveness and tolerability of combining PD-1 inhibitors with chemotherapy vs chemotherapy alone in patients with advanced gastric cancer,gastroesophageal junction(GEJ)cancer,or oesophageal carcinoma.METHODS We searched the PubMed and Embase databases for studies that compared the efficacy and tolerance of PD-1 inhibitors in combination with chemotherapy vs chemotherapy alone in patients with advanced oesophageal or gastric cancer.We employed either random or fixed models to analyze the outcomes of each clinical trial,en-compassing data on overall survival(OS),progression-free survival(PFS),objective response rate,and adverse events(AEs).RESULTS Nine phase 3 clinical trials(7016 advanced oesophageal and gastric cancer patients)met the inclusion criteria.Our meta-analysis demonstrated that the pooled PD-1 inhibitor+chemotherapy group had a significantly longer OS than the chemotherapy-alone group[hazard ratio(HR)=0.76,95%confidence interval(CI):0.71-0.81];the pooled PFS result was consistent with that of OS(HR=0.67,95%CI:0.61-0.74).The count of patients achieving an objective response in the PD-1 inhibitor+chemotherapy group surpassed that of the chemotherapy-alone group[odds ratio(OR)=1.86,95%CI:1.59-2.18].AE incidence was also higher in the combination-therapy group than in the chemotherapy-alone group,regardless of whether≥grade 3 only(OR=1.30,95%CI:1.07-1.57)or all AE grades(OR=1.88,95%CI:1.39-2.54)were examined.We performed a subgroup analysis based on the programmed death-ligand 1(PD-L1)combined positive score(CPS)and noted extended OS and PFS durations within the CPS≥1,CPS≥5,and CPS≥10 subgroups of the PD-1 inhibitor+chemotherapy group.CONCLUSION In contrast to chemotherapy alone,the combination of PD-1 inhibitor and chemotherapy appears to present a more favorable option for initial or subsequent treatment in patients with gastric cancer,GEJ tumor,or oesophageal cancer.This holds true particularly for individuals with PD-L1 CPS scores of≥5 and≥10.

Patterns of Interactions of the Complex City System:Emotional Urban Objects as Triggering Agents-A Secondary Publication

This article presents an analysis of the patterns of interactions resulting from the positive and negative emotional events that occur in cities,considering them as complex systems.It explores,from the imaginaries,how certain urban objects can act as emotional agents and how these events affect the urban system as a whole.An adaptive complex systems perspective is used to analyze these patterns.The results show patterns in the processes and dynamics that occur in cities based on the objects that affect the emotions of the people who live there.These patterns depend on the characteristics of the emotional charge of urban objects,but they can be generalized in the following process:(1)immediate reaction by some individuals;(2)emotions are generated at the individual level which begins to generalize,permuting to a collective emotion;(3)a process of reflection is detonated in some individuals from the reading of collective emotions;(4)integration/significance in the community both at the individual and collective level,on the concepts,roles and/or functions that give rise to the process in the system.Therefore,it is clear that emotions play a significant role in the development of cities and these aspects should be considered in the design strategies of all kinds of projects for the city.Future extensions of this work could include a deeper analysis of specific emotional events in urban environments,as well as possible implications for urban policy and decision making.

Objective Identification and Climatic Characteristics of Heavy-Precipitation Northeastern China Cold Vortexes

The northeastern China cold vortex(NCCV)plays an important role in regional rainstorms over East Asia.Using the National Centers for Environmental Prediction Final reanalysis dataset and the Global Precipitation Measurement product,an objective algorithm for identifying heavy-precipitation NCCV(HPCV)events was designed,and the climatological features of 164 HPCV events from 2001 to 2019 were investigated.The number of HPCV events showed an upward linear trend,with the highest frequency of occurrence in summer.The most active region of HPCV samples was the Northeast China Plain between 40°–55°N.Most HPCV events lasted 3–5 days and had radii ranging from 250 to 1000 km.The duration of HPCV events with larger sizes was longer.About half of the HPCV events moved into(moved out of)the definition region(35°–60°N,115°–145°E),and half initiated(dissipated)within the region.The initial position was close to the western boundary of the definition region,and the final position was mainly near the eastern boundary.The locations associated with the precipitation were mostly concentrated within 2000 km southeast of the HPCV systems,and they were farther from the center in the cold season than in the warm season.

Smart object recommendation based on topic learning and joint features in the social internet of things

With the extensive integration of the Internet,social networks and the internet of things,the social internet of things has increasingly become a significant research issue.In the social internet of things application scenario,one of the greatest challenges is how to accurately recommend or match smart objects for users with massive resources.Although a variety of recommendation algorithms have been employed in this field,they ignore the massive text resources in the social internet of things,which can effectively improve the effect of recommendation.In this paper,a smart object recommendation approach named object recommendation based on topic learning and joint features is proposed.The proposed approach extracts and calculates topics and service relevant features of texts related to smart objects and introduces the“thing-thing”relationship information in the internet of things to improve the effect of recommendation.Experiments show that the proposed approach enables higher accuracy compared to the existing recommendation methods.

Object-Oriented Modeling of the Variation of Acceleration and Deceleration Characteristics in Relation to Speed Bands for Railway Vehicles

Automated operation and artificial intelligence technology have become essential for ensuring the safety, efficiency, and punctuality of railways, with applications such as ATO (Automatic Train Operation). In this study, the authors propose a method to efficiently simulate the kinematic characteristics of railroad vehicles depending on their speed zone. They utilized the function overloading function supported by a programming language and applied the fourth-order Lunge-Kutta method for dynamic simulation. By constructing an object model, the authors calculated vehicle characteristics and TPS and compared them with actual values, verifying that the developed model represents the real-life vehicle characteristics accurately. The study highlights potential improvements in automated driving and energy consumption optimization in the railway industry.

Enhanced Object Detection and Classification via Multi-Method Fusion

Advances in machine vision systems have revolutionized applications such as autonomous driving,robotic navigation,and augmented reality.Despite substantial progress,challenges persist,including dynamic backgrounds,occlusion,and limited labeled data.To address these challenges,we introduce a comprehensive methodology toenhance image classification and object detection accuracy.The proposed approach involves the integration ofmultiple methods in a complementary way.The process commences with the application of Gaussian filters tomitigate the impact of noise interference.These images are then processed for segmentation using Fuzzy C-Meanssegmentation in parallel with saliency mapping techniques to find the most prominent regions.The Binary RobustIndependent Elementary Features(BRIEF)characteristics are then extracted fromdata derived fromsaliency mapsand segmented images.For precise object separation,Oriented FAST and Rotated BRIEF(ORB)algorithms areemployed.Genetic Algorithms(GAs)are used to optimize Random Forest classifier parameters which lead toimproved performance.Our method stands out due to its comprehensive approach,adeptly addressing challengessuch as changing backdrops,occlusion,and limited labeled data concurrently.A significant enhancement hasbeen achieved by integrating Genetic Algorithms(GAs)to precisely optimize parameters.This minor adjustmentnot only boosts the uniqueness of our system but also amplifies its overall efficacy.The proposed methodologyhas demonstrated notable classification accuracies of 90.9%and 89.0%on the challenging Corel-1k and MSRCdatasets,respectively.Furthermore,detection accuracies of 87.2%and 86.6%have been attained.Although ourmethod performed well in both datasets it may face difficulties in real-world data especially where datasets havehighly complex backgrounds.Despite these limitations,GAintegration for parameter optimization shows a notablestrength in enhancing the overall adaptability and performance of our system.

A Secure and Cost-Effective Training Framework Atop Serverless Computing for Object Detection in Blasting

The data analysis of blasting sites has always been the research goal of relevant researchers.The rise of mobile blasting robots has aroused many researchers’interest in machine learning methods for target detection in the field of blasting.Serverless Computing can provide a variety of computing services for people without hardware foundations and rich software development experience,which has aroused people’s interest in how to use it in the field ofmachine learning.In this paper,we design a distributedmachine learning training application based on the AWS Lambda platform.Based on data parallelism,the data aggregation and training synchronization in Function as a Service(FaaS)are effectively realized.It also encrypts the data set,effectively reducing the risk of data leakage.We rent a cloud server and a Lambda,and then we conduct experiments to evaluate our applications.Our results indicate the effectiveness,rapidity,and economy of distributed training on FaaS.

A chaotic hierarchical encryption/watermark embedding scheme for multi-medical images based on row-column confusion and closed-loop bi-directional diffusion

Security during remote transmission has been an important concern for researchers in recent years.In this paper,a hierarchical encryption multi-image encryption scheme for people with different security levels is designed,and a multiimage encryption(MIE)algorithm with row and column confusion and closed-loop bi-directional diffusion is adopted in the paper.While ensuring secure communication of medical image information,people with different security levels have different levels of decryption keys,and differentiated visual effects can be obtained by using the strong sensitivity of chaotic keys.The highest security level can obtain decrypted images without watermarks,and at the same time,patient information and copyright attribution can be verified by obtaining watermark images.The experimental results show that the scheme is sufficiently secure as an MIE scheme with visualized differences and the encryption and decryption efficiency is significantly improved compared to other works.

Few-Shot Object Detection Based on the Transformer and High-Resolution Network

Now object detection based on deep learning tries different strategies.It uses fewer data training networks to achieve the effect of large dataset training.However,the existing methods usually do not achieve the balance between network parameters and training data.It makes the information provided by a small amount of picture data insufficient to optimize model parameters,resulting in unsatisfactory detection results.To improve the accuracy of few shot object detection,this paper proposes a network based on the transformer and high-resolution feature extraction(THR).High-resolution feature extractionmaintains the resolution representation of the image.Channels and spatial attention are used to make the network focus on features that are more useful to the object.In addition,the recently popular transformer is used to fuse the features of the existing object.This compensates for the previous network failure by making full use of existing object features.Experiments on the Pascal VOC and MS-COCO datasets prove that the THR network has achieved better results than previous mainstream few shot object detection.

CAW-YOLO:Cross-Layer Fusion and Weighted Receptive Field-Based YOLO for Small Object Detection in Remote Sensing

In recent years,there has been extensive research on object detection methods applied to optical remote sensing images utilizing convolutional neural networks.Despite these efforts,the detection of small objects in remote sensing remains a formidable challenge.The deep network structure will bring about the loss of object features,resulting in the loss of object features and the near elimination of some subtle features associated with small objects in deep layers.Additionally,the features of small objects are susceptible to interference from background features contained within the image,leading to a decline in detection accuracy.Moreover,the sensitivity of small objects to the bounding box perturbation further increases the detection difficulty.In this paper,we introduce a novel approach,Cross-Layer Fusion and Weighted Receptive Field-based YOLO(CAW-YOLO),specifically designed for small object detection in remote sensing.To address feature loss in deep layers,we have devised a cross-layer attention fusion module.Background noise is effectively filtered through the incorporation of Bi-Level Routing Attention(BRA).To enhance the model’s capacity to perceive multi-scale objects,particularly small-scale objects,we introduce a weightedmulti-receptive field atrous spatial pyramid poolingmodule.Furthermore,wemitigate the sensitivity arising from bounding box perturbation by incorporating the joint Normalized Wasserstein Distance(NWD)and Efficient Intersection over Union(EIoU)losses.The efficacy of the proposedmodel in detecting small objects in remote sensing has been validated through experiments conducted on three publicly available datasets.The experimental results unequivocally demonstrate the model’s pronounced advantages in small object detection for remote sensing,surpassing the performance of current mainstream models.

Detecting the core of a network by the centralities of the nodes

Many networks exhibit the core/periphery structure.Core/periphery structure is a type of meso-scale structure that consists of densely connected core nodes and sparsely connected peripheral nodes.Core nodes tend to be well-connected,both among themselves and to peripheral nodes,which tend not to be well-connected to other nodes.In this brief report,we propose a new method to detect the core of a network by the centrality of each node.It is discovered that such nodes with non-negative centralities often consist in the core of the networks.The simulation is carried out on different real networks.The results are checked by the objective function.The checked results may show the effectiveness of the simulation results by the centralities of the nodes on the real networks.Furthermore,we discuss the characters of networks with the single core/periphery structure and point out the scope of the application of our method at the end of this paper.