The Pathologies of Modernity:Liberalism,Nihilism,Conservatism,Postmodernism,Intersectionality/Identity Politics,and Secular Humanism

In this article,Mocombe highlights what he calls the pathological-pathogenic purposive-rationalities(liberalism,nihilism,conservatism,postmodernism,intersectionality/identity politics,and secular humanism)that emerge out of modernity as constituted by the West under American hegemony.In the place of these pathological-pathogenic responses to the vagaries of modernity,Mocombe calls for an antihumanism,associated with what he calls libertarian communism,as a panacea to the aforementioned malaises.

The Intersectionality Framework and Its Contribution to Nursing Knowledge Related to the Prevention of Mother to Child Transmission (PMTCT) Program

Nurses work within a multidimensional scope of practice, which includes a reactive approach of treating health problems, a proactive stance of health promotion, and disease prevention. The purpose of this paper is to discuss the contribution of the intersectionality framework in building nursing knowledge related to the PMTCT of HIV and show how the intersectionality framework can contribute to the improvement of nursing practice, policy, and research related to PMTCT of HIV. A comprehensive literature review was conducted to be able to discuss the contribution of intersectionality framework in nursing knowledge. Intersectionality is a nice perspective that gives insight into different identities that link and work together to provide inequalities. Intersectionality is a well-suited approach in a nursing profession where we strive to offer efficient and better care to our patients or clients;an excellent tool to explore more on the roots of inequality, oppression, and discrimination.

Safe Efficient Policy Optimization Algorithm for Unsignalized Intersection

Unsignalized intersections pose a challenge for autonomous vehicles that must decide how to navigate them safely and efficiently.This paper proposes a reinforcement learning(RL)method for autonomous vehicles to navigate unsignalized intersections safely and efficiently.The method uses a semantic scene representation to handle variable numbers of vehicles and a universal reward function to facilitate stable learning.A collision risk function is designed to penalize unsafe actions and guide the agent to avoid them.A scalable policy optimization algorithm is introduced to improve data efficiency and safety for vehicle learning at intersections.The algorithm employs experience replay to overcome the on-policy limitation of proximal policy optimization and incorporates the collision risk constraint into the policy optimization problem.The proposed safe RL algorithm can balance the trade-off between vehicle traffic safety and policy learning efficiency.Simulated intersection scenarios with different traffic situations are used to test the algorithm and demonstrate its high success rates and low collision rates under different traffic conditions.The algorithm shows the potential of RL for enhancing the safety and reliability of autonomous driving systems at unsignalized intersections.

{1012}twin–twin intersection-induced lattice rotation and dynamic recrystallization in Mg–6Al–3Sn–2Zn alloy

This study investigated the formation mechanism of new grains due to twin–twin intersections in a coarse-grained Mg–6Al–3Sn–2Zn alloy during different strain rates of an isothermal compression.The results of electron backscattered diffraction investigations showed that the activated twins were primarily{1012}tension twins,and 60°<1010>boundaries formed due to twin–twin intersections under different strain rates.Isolated twin variants with 60°<1010>boundaries transformed into new grains through lattice rotations at a low strain rate(0.01 s^(−1)).At a high strain rate(10 s^(−1)),the regions surrounded by subgrain boundaries through high-density dislocation arrangement and the 60°<1010>boundaries transformed into new grains via dynamic recrystallization.

The prediction of projectile-target intersection for moving tank based on adaptive robust constraint-following control and interval uncertainty analysis

To improve the hit probability of tank at high speed,a prediction method of projectile-target intersection based on adaptive robust constraint-following control and interval uncertainty analysis is proposed.The method proposed provides a novel way to predict the impact point of projectile for moving tank.First,bidirectional stability constraints and stability constraint-following error are constructed using the Udwadia-Kalaba theory,and an adaptive robust constraint-following controller is designed considering uncertainties.Second,the exterior ballistic ordinary differential equation with uncertainties is integrated into the controller,and the pointing control of stability system is extended to the impact-point control of projectile.Third,based on the interval uncertainty analysis method combining Chebyshev polynomial expansion and affine arithmetic,a prediction method of projectile-target intersection is proposed.Finally,the co-simulation experiment is performed by establishing the multi-body system dynamic model of tank and mathematical model of control system.The results demonstrate that the prediction method of projectile-target intersection based on uncertainty analysis can effectively decrease the uncertainties of system,improve the prediction accuracy,and increase the hit probability.The adaptive robust constraint-following control can effectively restrain the uncertainties caused by road excitation and model error.

Detection Algorithm of Laboratory Personnel Irregularities Based on Improved YOLOv7

Due to the complex environment of the university laboratory,personnel flow intensive,personnel irregular behavior is easy to cause security risks.Monitoring using mainstream detection algorithms suffers from low detection accuracy and slow speed.Therefore,the current management of personnel behavior mainly relies on institutional constraints,education and training,on-site supervision,etc.,which is time-consuming and ineffective.Given the above situation,this paper proposes an improved You Only Look Once version 7(YOLOv7)to achieve the purpose of quickly detecting irregular behaviors of laboratory personnel while ensuring high detection accuracy.First,to better capture the shape features of the target,deformable convolutional networks(DCN)is used in the backbone part of the model to replace the traditional convolution to improve the detection accuracy and speed.Second,to enhance the extraction of important features and suppress useless features,this paper proposes a new convolutional block attention module_efficient channel attention(CBAM_E)for embedding the neck network to improve the model’s ability to extract features from complex scenes.Finally,to reduce the influence of angle factor and bounding box regression accuracy,this paper proposes a newα-SCYLLA intersection over union(α-SIoU)instead of the complete intersection over union(CIoU),which improves the regression accuracy while increasing the convergence speed.Comparison experiments on public and homemade datasets show that the improved algorithm outperforms the original algorithm in all evaluation indexes,with an increase of 2.92%in the precision rate,4.14%in the recall rate,0.0356 in the weighted harmonic mean,3.60%in the mAP@0.5 value,and a reduction in the number of parameters and complexity.Compared with the mainstream algorithm,the improved algorithm has higher detection accuracy,faster convergence speed,and better actual recognition effect,indicating the effectiveness of the improved algorithm in this paper and its potential for practical application in laboratory scenarios.

Advancements in machine learning for material design and process optimization in the field of additive manufacturing

Additive manufacturing technology is highly regarded due to its advantages,such as high precision and the ability to address complex geometric challenges.However,the development of additive manufacturing process is constrained by issues like unclear fundamental principles,complex experimental cycles,and high costs.Machine learning,as a novel artificial intelligence technology,has the potential to deeply engage in the development of additive manufacturing process,assisting engineers in learning and developing new techniques.This paper provides a comprehensive overview of the research and applications of machine learning in the field of additive manufacturing,particularly in model design and process development.Firstly,it introduces the background and significance of machine learning-assisted design in additive manufacturing process.It then further delves into the application of machine learning in additive manufacturing,focusing on model design and process guidance.Finally,it concludes by summarizing and forecasting the development trends of machine learning technology in the field of additive manufacturing.

Wetting front migration model of ion-adsorption rare earth during the multi-hole unsaturated liquid injection

In the process of ion-adsorption rare earth ore leaching,the migration characteristics of the wetting front in multi-hole injection holes and the influence of wetting front intersection effect on the migration distance of wetting fronts are still unclear.Besides,wetting front migration distance and leaching time are usually required to optimize the leaching process.In this study,wetting front migration tests of ionadsorption rare earth ores during the multi-hole fluid injection(the spacing between injection holes was 10 cm,12 cm and 14 cm)and single-hole fluid injection were completed under the constant water head height.At the pre-intersection stage,the wetting front migration laws of ion-adsorption rare earth ores during the multi-hole fluid injection and single-hole fluid injection were identical.At the postintersection stage,the intersection accelerated the wetting front migration.By using the Darcy’s law,the intersection effect of wetting fronts during the multi-hole liquid injection was transformed into the water head height directly above the intersection.Finally,based on the Green-Ampt model,a wetting front migration model of ion-adsorption rare earth ores during the multi-hole unsaturated liquid injection was established.Error analysis results showed that the proposed model can accurately simulate the infiltration process under experimental conditions.The research results enrich the infiltration law and theory of ion-adsorption rare earth ores during the multi-hole liquid injection,and this study provides a scientific basis for optimizing the liquid injection well pattern parameters of ion-adsorption rare earth in situ leaching in the future.

A Novel Foreign Object Detection Method in Transmission Lines Based on Improved YOLOv8n

The rapid pace of urban development has resulted in the widespread presence of construction equipment andincreasingly complex conditions in transmission corridors. These conditions pose a serious threat to the safeoperation of the power grid.Machine vision technology, particularly object recognition technology, has beenwidelyemployed to identify foreign objects in transmission line images. Despite its wide application, the technique faceslimitations due to the complex environmental background and other auxiliary factors. To address these challenges,this study introduces an improved YOLOv8n. The traditional stepwise convolution and pooling layers are replacedwith a spatial-depth convolution (SPD-Conv) module, aiming to improve the algorithm’s efficacy in recognizinglow-resolution and small-size objects. The algorithm’s feature extraction network is improved by using a LargeSelective Kernel (LSK) attention mechanism, which enhances the ability to extract relevant features. Additionally,the SIoU Loss function is used instead of the Complete Intersection over Union (CIoU) Loss to facilitate fasterconvergence of the algorithm. Through experimental verification, the improved YOLOv8n model achieves adetection accuracy of 88.8% on the test set. The recognition accuracy of cranes is improved by 2.9%, which isa significant enhancement compared to the unimproved algorithm. This improvement effectively enhances theaccuracy of recognizing foreign objects on transmission lines and proves the effectiveness of the new algorithm.

关于两个两点Witten-Kontsevich关联子公式等价的一个注记

We prove the equivalence between two explicit expressions for two-point Witten-Kontsevich correlators obtained by Bertola-Dubrovin-Yang and by Zograf, respectively.

A Workable Solution for Reducing the Large Number of Vehicle and Pedestrian Accidents Occurring on a Yellow Light

Traffic intersections are incredibly dangerous for drivers and pedestrians. Statistics from both Canada and the U.S. show a high number of fatalities and serious injuries related to crashes at intersections. In Canada, during 2019, the National Collision Database shows that 28% of traffic fatalities and 42% of serious injuries occurred at intersections. Likewise, the U.S. National Highway Traffic Administration (NHTSA) found that about 40% of the estimated 5,811,000 accidents in the U.S. during the year studied were intersection-related crashes. In fact, a major survey by the car insurance industry found that nearly 85% of drivers could not identify the correct action to take when approaching a yellow traffic light at an intersection. One major reason for these accidents is the “yellow light dilemma,” the ambiguous situation where a driver should stop or proceed forward when unexpectedly faced with a yellow light. This situation is even further exacerbated by the tendency of aggressive drivers to inappropriately speed up on the yellow just to get through the traffic light. A survey of Canadian drivers conducted by the Traffic Injury Research Foundation found that 9% of drivers admitted to speeding up to get through a traffic light. Another reason for these accidents is the increased danger of making a left-hand turn on yellow. According to the National Highway Traffic Safety Association (NHTSA), left turns occur in approximately 22.2% of collisions—as opposed to just 1.2% for right turns. Moreover, a study by CNN found left turns are three times as likely to kill pedestrians than right turns. The reason left turns are so much more likely to cause an accident is because they take a driver against traffic and in the path of oncoming cars. Additionally, most of these left turns occur at the driver’s discretion—as opposed to the distressingly brief left-hand arrow at busy intersections. Drive Safe Now proposes a workable solution for reducing the number of accidents occurring during a yellow light at intersections. We believe this fairly simple solution will save lives, prevent injuries, reduce damage to public and private property, and decrease insurance costs.

Research of Fly over as a Solution to Congestion of Intersection Junction(Case Study:Jalan Jatingaleh Semarang)

In the next few years traffic will happen most of the time.This was triggered by the growing rate of vehicles against the road capacity which is not balance.All the time the congestion in the city of Semarang has been occurred at peak hours.Congestion also occurred in between Teuku Umar and Setia Budi road Jatingaleh because of a plot intersection(Kesatrian intersection,PLN intersection and Jatingaleh intersection)with the Toll Road.Jatingaleh is located in the southern city of Semarang which is a central meeting point between the upper and lower Semarang where the vehicle flows in through a combination of local current and regional traffic,and the flow of vehicles coming in and out from highway.The main cause of the problems that occurred in the area of Jatingaleh is due to the numbers of vehicles movement that occurs at the intersections.With the above issues,it is necessary to analyse the existing conditions and look into some solutions.Before carrying out an analysis a field surveys at peak hours for example morning(06:00 to 08:00 am)and for the afternoon(04:00 to 06:00 pm)should be conducted,then the number of vehicles is counted manually with“short-breakcounting”according to types of vehicles.From the analysis we found that the degree of saturation(DS)is 1.61 between Teuku Umar and Setia Budi road during the morning peak hours and 1.56 during the afternoon peak hours.This means that the capacity of the existing road is no longer able to accommodate the traffic flow.One of the solutions for the congestion that occurs at the intersection of Jatingaleh is to apply the efficiency of the intersection that is not in a plot with a Fly over,Underpass and the combination of Fly Over-Underpass.Base on the flow reduction calculation with 3 comparative modeling it shows that the Fly Over is the most technically efficient to be applied in this research.

Six-year trends and intersectional correlates of meeting 24-Hour Movement Guidelines among South Korean adolescents:Korea Youth Risk Behavior Surveys,2013-2018

Background:Grounded in intersectionality theory,this study examined the 6-year prevalence trend and correlates in meeting Canada’s 24-Hour Movement Guidelines(Guidelines hereafter)in a nationally representative adolescent samples of South Korea(officially the Republic of Korea).Methods:Self-reported,annually repeated cross-sectional data collected between 2013 and 2018 were used(n=372,433,12-17 years old,47.9%females).Adolescents were categorized as meeting or not meeting different sets of physical activity,screen time(ST),and sleep recommendations within the Guidelines,separately for weekdays and weekend days.Intersectional correlates included sex and social class(i.e.,family economic status,parental education level,and academic performance).Multiple logistic regression analyses were performed.Results:Overall,the proportion of adolescents meeting physical activity,ST,and sleep recommendations were 5.3%,60.3%,and 10.2%on weekdays and 5.3%,28.2%,and 46.4%on weekend days,respectively.Between 2013 and 2018,no substantial changes were observed for meeting the physical activity or sleep recommendation,while meeting the ST recommendation was markedly lower in 2018.The proportion of meeting all 3 recommendations were 0.5%for weekdays and 0.8%for weekend days.Overall,compared to female adolescents,male adolescents were consistently associated with more favorable patterns of meeting different sets of recommendations,regardless of social class.Among females only,social class appeared to be not important or even detrimental in meeting different sets of recommendations.Being male,compounded with social class,was associated with meeting the ST recommendation.Conclusion:Less than 1%of Korean adolescents met the overall Guidelines.Intersectionality-based analysis and intervention may be important in promoting healthy active lifestyles among South Korean adolescents.

Proactive Platooning Based on C-V2X to Relieve Congestion at a Signalized Intersection

Nowadays urban traffic congestion becomes a major issue due to the increase in vehicles that lead to more commuting time,accidents,traffic violations,and high fuel consumption.Platooning is a good way to solve traffic congestion because it drives with a smaller inter-vehicle distance than human-driving.This paper proposes proactive platooning based on C-V2X(cellular vehicle-to-everything)to relieve congestion.Proactive platooning reduces the inter-vehicle distance and increases the throughput of signalized intersections through integrating the networked control platooning and computation-centralized platooning.Model and simulation experiments of proactive platooning were conducted to verify the impact of inter-vehicle distance on platooning latency,platooning safety,and traffic throughput.Simulation results show that the optimal inter-vehicle distance under proactive platooning is less than half of human-driving at a signalized intersection.

TOA positioning algorithm of LBL system for underwater target based on PSO

For the underwater long baseline(LBL)positioning systems,the traditional distance intersection algorithm simplifies the sound speed to a constant,and calculates the underwa-ter target position parameters with a nonlinear iteration.However,due to the complex underwater environment,the sound speed changes with time and space,and then the acoustic propagation path is actually a curve,which inevitably causes some errors to the traditional distance intersection positioning algorithm.To reduce the position error caused by the uncertain underwater sound speed,a new time of arrival(TOA)intersection underwater positioning algorithm of LBL system is proposed.Firstly,combined with the vertical layered model of the underwater sound speed,an implicit positioning model of TOA intersection is constructed through the constant gradient acoustic ray tracing.And then an optimization function based on the overall TOA residual square sum is advanced to solve the position parameters for the underwater target.Moreover,the particle swarm optimization(PSO)algorithm is replaced with the tra-ditional nonlinear least square method to optimize the implicit positioning model of TOA intersection.Compared with the traditional distance intersection positioning model,the TOA intersec-tion positioning model is more suitable for the engineering practice and the optimization algorithm is more effective.Simulation results show that the proposed methods in this paper can effectively improve the positioning accuracy for the underwater target.

Automatic Lane-Level Intersection Map Generation using Low-Channel Roadside LiDAR

A lane-level intersection map is a cornerstone in high-definition(HD) traffic network maps for autonomous driving and high-precision intelligent transportation systems applications such as traffic management and control, and traffic accident evaluation and prevention. Mapping an HD intersection is time-consuming, labor-intensive, and expensive with conventional methods. In this paper, we used a low-channel roadside light detection and range sensor(LiDAR) to automatically and dynamically generate a lane-level intersection, including the signal phases, geometry, layout, and lane directions. First, a mathematical model was proposed to describe the topology and detail of a lane-level intersection. Second, continuous and discontinuous traffic object trajectories were extracted to identify the signal phases and times. Third, the layout, geometry, and lane direction were identified using the convex hull detection algorithm for trajectories. Fourth, a sliding window algorithm was presented to detect the lane marking and extract the lane, and the virtual lane connecting the inbound and outbound of the intersection were generated using the vehicle trajectories within the intersection and considering the traffic rules. In the field experiment, the mean absolute estimation error is 2 s for signal phase and time identification. The lane marking identification Precision and Recall are96% and 94.12%, respectively. Compared with the satellite-based,MMS-based, and crowdsourcing-based lane mapping methods,the average lane location deviation is 0.2 m and the update period is less than one hour by the proposed method with low-channel roadside LiDAR.

Recognition model and algorithm of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain

In order to improve the recognition rate and accuracy rate of projectiles in six sky-screens intersection test system,this work proposes a new recognition method of projectiles by combining particle swarm optimization support vector and spatial-temporal constrain of six sky-screens detection sensor.Based on the measurement principle of the six sky-screens intersection test system and the characteristics of the output signal of the sky-screen,we analyze the existing problems regarding the recognition of projectiles.In order to optimize the projectile recognition effect,we use the support vector machine and basic particle swarm algorithm to form a new recognition algorithm.We set up the particle swarm algorithm optimization support vector projectile information recognition model that conforms to the six sky-screens intersection test system.We also construct a spatial-temporal constrain matching model based on the spatial geometric relationship of six sky-screen intersection,and form a new projectile signal recognition algorithm with six sky-screens spatial-temporal information constraints under the signal classification mechanism of particle swarm optimization algorithm support vector machine.Based on experiments,we obtain the optimal penalty and kernel function radius parameters in the PSO-SVM algorithm;we adjust the parameters of the support vector machine model,train the test signal data of every sky-screen,and gain the projectile signal classification results.Afterwards,according to the signal classification results,we calculate the coordinate parameters of the real projectile by using the spatial-temporal constrain of six sky-screens detection sensor,which verifies the feasibility of the proposed algorithm.

Design on the Prestressed Concrete Frame Beam-column

A bidirectional ribbed concrete beam slab structure was widly adopted for the upper space of industrial buildings.To maintain ample space and minimize the presence of conventional columns,a bidirectional prestressed concrete beam is often employed.The intersection node of the prestressed concrete frame beam column was characterized by a high density of steel reinforcement,significant structural loads,and complex construction requirements.To ensure the quality,safety,and progress of prestressed frame beamcolumn intersection nodes during construction,this article proposed a new technology for constructing such nodes,which includes setting the tensioning and haunching ends of nodes at different positions,using ABAQUS finite element software to optimize the design of cross-sectional dimensions,conducting stress analysis simulations.

Novel Distance Measures on Hesitant Fuzzy Sets Based on Equal-Probability Transformation and Their Application in Decision Making on Intersection Traffic Control

The purpose of this study is to reduce the uncertainty in the calculation process on hesitant fuzzy sets(HFSs).The innovation of this study is to unify the cardinal numbers of hesitant fuzzy elements(HFEs)in a special way.Firstly,a probability density function is assigned for any given HFE.Thereafter,equal-probability transformation is introduced to transform HFEs with different cardinal numbers on the condition into the same probability density function.The characteristic of this transformation is that the higher the consistency of the membership degrees in HFEs,the higher the credibility of the mentioned membership degrees is,then,the bigger the probability density values for them are.According to this transformation technique,a set of novel distance measures on HFSs is provided.Finally,an illustrative example of intersection traffic control is introduced to show the usefulness of the given distance measures.The example also shows that this study is a good complement to operation theories on HFSs.

Mechanical analysis of the femoral neck dynamic intersection system with different nail angles and clinical applications

BACKGROUND The femoral neck dynamic intersection system(FNS)is mechanically more stable than other internal fixation techniques.Current studies have confirmed that the structural design of FNS has good biomechanical properties in European and American populations.However,whether the suitability of the FNS's 130°main nail angle design for Asian populations has been thoroughly investigated remains unclear.AIM To compare the biomechanical stability differences among different main nail angles of the FNS in the treatment of femoral neck fractures in Asian populations.METHODS Computed tomography data of the femur of healthy adult male volunteers were imported into Mimics software to create a three-dimensional model of the femur.The model was adapted to the curve using Geomagic software and imported into Solidworks software to construct the Pauwels I femoral neck fracture model and design the FNS internal fixation model using different main nail angles.Afterward,the models were assembled with the FNS fracture model and meshed using the preprocessing Hypermesh software.Subsequently,they were imported into Abaqus software to analyze and evaluate the biomechanical effects of different angles of the FNS main nail on the treatment of femoral neck fractures.RESULTS The peak displacement of the proximal femur under different angles of FNS fixation under stress was 7.446 millimeters in the 120°group and 7.416 millimeters in the 125°group;in the 130°,135°,and 140°FNS fixation groups,the peak displacement was 7.324 millimeters,8.138 millimeters,and 8.246 millimeters,respectively.In the 120°and 125°FNS fixation groups,the maximum stresses were concentrated at the main nail and the anti-rotation screw,which intersected the fracture line of the femur neck,resulting in peak stresses of 200.7 MPa and 138.8 MPa,respectively.Peak stresses of 208.8 MPa,219.8 MPa,and 239.3 MPa were observed on the angular locking plate distal to the locking screw in the 130°,135°,and 140°fixation groups.CONCLUSION FNS has significant stress distribution properties,a minimal proximal femoral displacement,and an optimal stability for treating femoral neck fractures in Asian populations when performed with a 130°main nail angle.