Original Path Research Articles

PV-YOLO: A lightweight pedestrian and vehicle detection model based on improved YOLOv8

With the frequent occurrence of urban traffic accidents, fast and accurate detection of pedestrian and vehicle targets has become one of the key technologies for intelligent assisted driving systems. To meet the efficiency and lightweight requirements of smart devices, this paper proposes a lightweight pedestrian and vehicle detection model based on the YOLOv8n model, named PV-YOLO. In the proposed model, receptive-field attention convolution (RFAConv) serves as the backbone network because of its target feature extraction ability, and the neck utilizes the bidirectional feature pyramid network (BiFPN) instead of the original path aggregation network (PANet) to simplify the feature fusion process. Moreover, a lightweight detection head is introduced to reduce the computational burden and improve the overall detection accuracy. In addition, a small target detection layer is designed to improve the accuracy for small distant targets. Finally, to reduce the computational burden further, the lightweight C2f module is utilized to compress the model. The experimental results on the BDD100K and KITTI datasets demonstrate that the proposed PV-YOLO can achieve higher detection accuracy than YOLOv8n and other baseline methods with less model complexity.

Examining the Effects of Status Quo Bias on Household/Individual Carbon Consumption

The research investigated the degree of influence status quo bias has on rising carbon emissions. It found that status quo bias strongly regulates the actions of households and individuals when making environmental decisions. Through transportation, individuals lack the incentive to switch to eco- friendly methods and display inertia to their original path. Consumption habits and market interactions also show individuals’ reluctance to new technologies. Results show that the status quo bias plays a significant role as one of the fundamental causes of uncontrollable carbon emissions.

Kinematic Constrained RRT Algorithm with Post Waypoint Shift for the Shortest Path Planning of Wheeled Mobile Robots.

This paper presents a rapidly exploring random tree (RRT) algorithm with an effective post waypoint shift, which is suitable for the path planning of a wheeled mobile robot under kinematic constraints. In the growth of the exploring tree, the nearest node that satisfies the kinematic constraints is selected as the parent node. Once the distance between the new node and the target is within a certain threshold, the tree growth stops and a target connection based on minimum turning radius arc is proposed to generate an initial complete random path. The most significant difference from traditional RRT-based methods is that the proposed method optimizes the path based on Dubins curves through a post waypoint shift after a random path is generated, rather than through parent node selection and rewiring during the exploring tree growth. Then, it is proved that the method can obtain an optimal path in terms of the shortest length. The optimized path has good convergence and almost does not depend on the state of the initial random path. The comparative test results show that the proposed method has significant advantages over traditional RRT-based methods in terms of the sampling point number, the tree node number, and the path node number. Subsequently, an efficient method is further proposed to avoid unknown obstacles, which utilizes the original path information and thus effectively improves the new path planning efficiency. Simulations and real-world tests are carried out to demonstrate the effectiveness of this method.

From Institutions to the Platform Society: An Interdisciplinary Reading of Recent Works by Roberto Esposito

In recent years, reflection on institutions has assumed great prominence in Italian Theory. Roberto Esposito especially, in his three most recent books, has provided a genealogy of institutions, aimed at challenging the prevailing way in which they have been thematized in Western thought. As he sees it, a repressive conception of institutions has dominated in philosophy as well as in sociology and legal studies, which views them as apparatuses that limit the free expansion of individual instincts and life itself. Following an original hermeneutic path, the Italian philosopher seeks to pull through strands of thought that understand the concepts of institution and life to be mutually implicated rather than opposed to each other. This article starts from Roberto Esposito’s work on institutions, clarifying its meaning and perspective. It then draws connections between Esposito’s instituent thought and a few focal points of contemporary sociological thought on institutions. Specifically, it analyzes how Esposito’s post-normative perspective presents similarities with the ‘cognitive turn’ in the institutional thought of new institutionalism, among other sociological schools. The article concludes by showing that Esposito’s work and its connections with sociological thought enable one to reflect critically on a number of practices spawned in the ‘platform society’ by new institutional actors such as Big Tech.

Revisiting modern campuses through spaces for leisure in Izmir, Turkey

ABSTRACT Division of working and leisure spaces as an early product of modern life has still its traces on urban context. Campus spaces, not only universities, reflect such a programmed structure related to modern everyday life. This paper aims to discuss the changing relationship between working and leisure on varying campus spaces, which embrace these two essential components of modern societies. Many campuses in early Turkey follow similar modernist organisational principles in their design and performs an original path in spatial transformation. Industry, education, and public service campuses, which were established in İzmir during the 1950s, constitute the spatial framework of this paper. The paper aims to trace the leisure areas of three campuses and examine this meticulously programmed life idea under the influence of the changing leisure spatial culture in Turkey from the 1950s to today. These spaces are articulated and examined by their dwellings and social interaction spaces. Besides, the social experience and individual perspectives of campus users are documented and merged with the physical analysis of the working and leisure spaces. Discussing those examples in socio-spatial details underlines the transformation and dissolution of campus idea in urban life and exposes the unique experience of campus spaces of İzmir.

Normal spectral emissivity measurement of thermal management materials for electronic components in the 2–14 μm range

Reducing the operating temperature of electronic components is an effective method for enhancing their performance. Selecting suitable thermal management materials is crucial for optimizing cooling efficiency. Normal spectral emissivity, a dimensionless physical quantity, is key in assessing a material’s capability for radiative cooling. This paper outlines the construction of a reflective infrared emissivity measurement apparatus using the IS-50 Fourier Transform Infrared Spectrometer. A gold-coated diffuse integrating sphere was strategically placed in the spectrometer’s sample chamber. By ingeniously leveraging the spectrometer’s original optical path, a dual-sided gold-coated mirror was employed to capture both incident and reflected light, enabling accurate measurements of the normal spectral emissivity of materials used for cooling electronic components. The measurement results for typical materials were consistent with those reported in the literature, satisfyingly so. The uncertainty of the measurement setup was thoroughly evaluated, achieving a combined uncertainty of better than 1 %. This experimental study measured the normal spectral emissivity of various thermal management materials and analyzed the influence of temperature on normal spectral emissivity. These results provide crucial data support for thermal design, simulation analysis, and temperature monitoring in the development of thermal structures for electronic components.

A research on the construction path of ecological city under the concept of carbon neutrality—A case study of Chinese city

<p class="GTC-Norm" style="text-align: justify; text-justify: inter-ideograph; text-indent: 0cm; line-height: 120%; margin: 12.0pt 0cm 6.0pt 0cm;"><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; font-family: 'Times New Roman',serif; color: windowtext;">Under China’s ambitious goal of peaking carbon dioxide emissions by 2030 and achieving carbon neutrality by 2060, we should re-examine the way of urban development, study the key to carbon pollution, change the original urban renewal path, and seek energy-saving and emission reduction. Therefore, this paper takes Xiamen City as an example, through questionnaire survey, mathematical statistics analysis and other methods, combined with the practice of low-carbon city and ecological civilization construction in Xiamen City, this paper makes an in-depth analysis of carbon pollution problems such as transportation carbon emissions, domestic waste disposal carbon emissions and industrial waste carbon emissions in Xiamen City, and puts forward the urban renewal path of Xiamen City to effectively reduce carbon emissions based on the concept of carbon neutrality, which provides a low-carbon plan for Xiamen City</span><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; font-family: 'Times New Roman',serif; mso-fareast-font-family: 等线; mso-fareast-theme-font: minor-fareast; color: windowtext; mso-fareast-language: ZH-CN;">’</span><span lang="EN-US" style="font-size: 10.0pt; line-height: 120%; font-family: 'Times New Roman',serif; color: windowtext;">s urban renewal and is of great significance for Xiamen City to achieve the dual-carbon goal.</span></p>

Simulation of horizontal refraction of continental slope in the southern continental slope of the South China Sea

Abstract Sound waves are significantly influenced by boundaries during their propagation in certain environmental conditions. The extent of this impact is related to the complexity of the boundary, such as in the case of slopes and seamounts. In these areas, sound waves may deviate from their original paths, resulting in three-dimensional effects. Recent experiments and simulations have demonstrated that three-dimensional effects occur when sound waves propagate over seamounts in the South China Sea, leading to larger acoustic shadow regions. However, there are limited studies on 3D acoustic propagation based on measured slope topography or database topography. In this study, the topography of the South China Sea area database is selected, and the measured sound velocity profile and seabed acoustic parameters in the South China Sea are taken into considered. The FOR3D sound field calculation model is used to calculate the N×2D and 3D results. The simulation results show that in the southern continental slope region of the South China Sea, when the sound source frequency is 50 Hz, the acoustic wave exhibits significant horizontal refraction with a refraction angle of approximately 13°.

Splitting probabilities as optimal controllers of rare reactive events.

The committor constitutes the primary quantity of interest within chemical kinetics as it is understood to encode the ideal reaction coordinate for a rare reactive event. We show the generative utility of the committor in that it can be used explicitly to produce a reactive trajectory ensemble that exhibits numerically exact statistics as that of the original transition path ensemble. This is done by relating a time-dependent analog of the committor that solves a generalized bridge problem to the splitting probability that solves a boundary value problem under a bistable assumption. By invoking stochastic optimal control and spectral theory, we derive a general form for the optimal controller of a bridge process that connects two metastable states expressed in terms of the splitting probability. This formalism offers an alternative perspective into the role of the committor and its gradients in that they encode force fields that guarantee reactivity, generating trajectories that are statistically identical to the way that a system would react autonomously.

Navigation Based on Hybrid Decentralized and Centralized Training and Execution Strategy for Multiple Mobile Robots Reinforcement Learning

In addressing the complex challenges of path planning in multi-robot systems, this paper proposes a novel Hybrid Decentralized and Centralized Training and Execution (DCTE) Strategy, aimed at optimizing computational efficiency and system performance. The strategy solves the prevalent issues of collision and coordination through a tiered optimization process. The DCTE strategy commences with an initial decentralized path planning step based on Deep Q-Network (DQN), where each robot independently formulates its path. This is followed by a centralized collision detection the analysis of which serves to identify potential intersections or collision risks. Paths confirmed as non-intersecting are used for execution, while those in collision areas prompt a dynamic re-planning step using DQN. Robots treat each other as dynamic obstacles to circumnavigate, ensuring continuous operation without disruptions. The final step involves linking the newly optimized paths with the original safe paths to form a complete and secure execution route. This paper demonstrates how this structured strategy not only mitigates collision risks but also significantly improves the computational efficiency of multi-robot systems. The reinforcement learning time was significantly shorter, with the DCTE strategy requiring only 3 min and 36 s compared to 5 min and 33 s in the comparison results of the simulation section. The improvement underscores the advantages of the proposed method in enhancing the effectiveness and efficiency of multi-robot systems.

Proxy Tracing: Unbiased Reciprocal Estimation for Optimized Sampling in BDPT

Robust light transport algorithms, particularly bidirectional path tracing (BDPT), face significant challenges when dealing with specular or highly glossy involved paths. BDPT constructs the full path by connecting sub-paths traced individually from the light source and camera. However, it remains difficult to sample by connecting vertices on specular and glossy surfaces with narrow-lobed BSDF, as it poses severe constraints on sampling in the feasible direction. To address this issue, we propose a novel approach, called proxy sampling , that enables efficient sub-path connection of these challenging paths. When a low-contribution specular/glossy connection occurs, we drop out the problematic neighboring vertex next to this specular/glossy vertex from the original path, then retrace an alternative sub-path as a proxy to complement this incomplete path. This newly constructed complete path ensures that the connection adheres to the constraint of the narrow lobe within the BSDF of the specular/glossy surface. Unbiased reciprocal estimation is the key to our method to obtain a probability density function (PDF) reciprocal to ensure unbiased rendering. We derive the reciprocal estimation method and provide an efficiency-optimized setting for efficient sampling and connection. Our method provides a robust tool for substituting problematic paths with favorable alternatives while ensuring unbiasedness. We validate this approach in the probabilistic connections BDPT for addressing specular-involved difficult paths. Experimental results have proved the effectiveness and efficiency of our approach, showcasing high-performance rendering capabilities across diverse settings.

Responses of Soil Moisture to Gully Land Consolidation in Asian Areas with Monsoon Climate

Groundwater resources are essential for sustaining ecosystems and human activities, especially under the pressures of climate change. This study employed Electrical Resistivity Tomography (ERT) to assess the impact of Gully Land Consolidation (GLC) engineering on the groundwater hydrological field of small watersheds in the China Loess Plateau (CLP). Results revealed ample subsurface water storage in backfilled areas, primarily migrating along the original river path owing to topographical limitations. Although the distribution patterns of soil moisture in each backfilling block varied slightly, the boundaries of soil moisture content and variation mainly appeared at depths of 8 m and 20 m underground. Significant moisture variation occurred across the 0–20 m underground layers, suggesting the 8–20 m layer could function as a groundwater collection zone in the study area. Human activities could disturb groundwater, altering migration pathways from the original river path. An optimized “Drainage–Conveyance–Barrier” system is proposed to enhance GLC sustainability, involving upstream groundwater level control, midstream soil moisture management, and downstream hydrological connectivity improvement. These findings carry substantial implications for guiding the planning and execution of GLC engineering initiatives. The novelty of this study lies in its application of ERT to provide a detailed spatial and temporal understanding of soil moisture dynamics in the GLC areas. Future research should focus on factors such as soil types and topographical changes for a comprehensive assessment of GLC’s impact on small watershed groundwater hydrology.

MOMTA-HN: A Secure and Reliable Multi-Objective Optimized Multipath Transmission Algorithm for Heterogeneous Networks

With the rapid development of heterogeneous network technologies, such as mobile edge computing, satellite communications, self-organizing networks, and the wired Internet, satisfying users’ increasingly diversified and complex communication needs in dynamic and evolving network environments has become a critical research topic. Ensuring secure and reliable information transmission is essential for stable network operation in these complex environments. Addressing this challenge, this study proposed a secure and reliable multi-objective optimized multipath transmission algorithm for heterogeneous networks to enhance security and reliability during data transmission. The core principle of this algorithm was that multipath transmission can provide additional protection through redundant paths. This redundancy ensured that even if one path is attacked or fails, alternative paths can maintain data integrity and reachability. In this study, we employed the Optimized Non-dominated Sorting Genetic Algorithm II (ONSGA-II) to determine the range of the initial population and filter suitable paths by optimizing them according to different demand objectives. In the path selection process, we introduced an innovative deletion graph method, which ensures that redundant paths do not share any common links with the original paths, except when there are unique links. This approach enhances the independence of transmission paths and improves the security of the transmission process. It effectively protects against security threats such as single points of failure and link attacks. We have verified the effectiveness of the algorithm through a series of experiments, and the proposed algorithm can provide decision-makers with high-reliability and low-latency transmission paths in heterogeneous network environments. At the same time, we verified the performance of the algorithm when encountering attacks, which is superior to other classical algorithms. Even in the face of network failures and attacks, it can maintain a high level of data integrity and security.

On Klein tunneling of low-frequency elastic waves in hexagonal topological plates

Incident particles in the Klein tunnel phenomenon in quantum mechanics can pass a very high potential barrier. Introducing the concept of tunneling into the analysis of phononic crystals can broaden the application prospects. In this study, the structure of the unit cell is designed, and the low frequency (< 1 kHz) valley locked waveguide is realized through the creation of a phononic crystal plate with a topological phase transition interface. The defect immunity of the topological waveguide is verified, that is, the wave can propagate along the original path in the cases of impurities and disorder. Then, the tunneling phenomenon is introduced into the topological valley-locked waveguide to analyze the wave propagation, and its potential applications (such as signal separators and logic gates) are further explored by designing phononic crystal plates. This research has broad application prospects in information processing and vibration control, and potential applications in other directions are also worth exploring.

Barking Up the Wrong Tree-Motor-Sensory Elements as Prodrome in Autism.

Autism spectrum disorder (ASD) has been intensely investigated since the term was first used over 80 years ago. The prevalence of ASD is constantly rising, and, currently, 1:36 children are diagnosed with this disorder. Despite the intense interest in ASD, the origins of this disorder remain obscure. This article explores motor issues and proprioceptive interoception difficulties as the prodrome of ASD. The importance of early intervention in the prognosis of ASD is common knowledge. Yet, since the communicational and social behaviors typical of ASD are observable only after the age of 18 months, diagnosis and early intervention are delayed. Therefore, the quest into the involvement of sensory-motor difficulties as a source of ASD traits, or at least as a potential early indicator, is warranted, with the intention of enabling early diagnosis and early intervention. This article examines the justification for this new avenue of early diagnosis and intervention and may open up a completely different way of viewing ASD. This new point of view may suggest an original path of assessment and intervention in infancy with this group of clients, possibly leading to improved prognosis for children and their families.

Improving dynamic prognostication of patients with hepatocellular carcinoma by node fusion algorithm equipped survival path mapping.

e13594 Background: Patients with hepatocellular carcinoma (HCC) always require routine surveillance and repeated treatment. Our previous work (1) developed a machine learning methodology called “Survival Path Mapping”, which converted the time series data into a cascading survival map to facilitate dynamic prognosis prediction and treatment planning. However, in previous work, the exponential growth of paths raised challenges of exhausting limited cohort and over-fitting risk. Novel algorithm enhancing data utilization in the Survival Path Mapping was in urgent need. Methods: A node fusion algorithm was designed. First, feature selection algorithm bifurcated each parent-nodes into temporary child-nodes, which constitutes temporary child-nodes set. Then, clustering was performed within temporary child-nodes set according to key features of each child-node. Finally, in each cluster, multiple child-nodes would be merged into one if there was no significant difference in survival. For nodes with bifurcation, a treatment recommendation algorithm was developed. A total of 19446 patients with newly diagnosed primary HCC at Sun Yat-sen University Cancer Center (SYSUCC) between 2008 and 2023 were retrospectively included as internal cohort (divided into internal train and holdout validation cohort); meanwhile 1135 patients from five hospitals were included as multicenter external validation cohort. Dedicated natural language processing program was developed to extract structured features from medical imaging reports. The time series data of patients were converted into data of time slices, with an interval of 3 months. Results: The original survival path mapping (raw-SP) consists of 463 different nodes, while the node fusion survival path mapping (fusion-SP) consisted of 84 nodes (81.8% decrease from raw-SP). The 3-year c-index of fusion-SP were 0.745 (95% CI 0.717-0.772), significantly better (p&lt;0.001) than that of raw-SP: 0.725 (95% CI 0.696-0.755), in the internal holdout validation. Both raw-SP and fusion-SP significantly (p &lt; 0.001) outperformed conventional BCLC and CNLC staging. In the multicenter external validation, the fusion-SP also demonstrated better performance than raw-SP and BCLC staging system. The recommended therapy calculated based fusion-SP system for patients receiving second-line or subsequent treatment had superior or non-inferior efficacy compared to BCLC guideline’s recommendation. Conclusions: The proposed node fusion algorithm solved exponential growth of paths in previous survival path algorithm, improving the accuracy and external validity of dynamic prognosis prediction for HCC patients. The fusion-SP model also retained its transparency and could be used to guide dynamic treatment planning, with its effectiveness in treatment guidance evaluated in prospective trials in the future. 1. Nat Commun 9, 2230; 2018.

Leakage detection of an acoustic emission pipeline based on an improved transformer network

Pipeline leakage detection is an integral part of pipeline integrity management. Combining AE (Acoustic Emission) with deep learning is currently the most commonly used method for pipeline leakage detection. However, this approach is usually applicable only to specific situations and requires powerful signal analysis and computational capabilities. To address these issues, this paper proposes an improved Transformer network model for diagnosing faults associated with abnormal working conditions in acoustic emission pipelines. First, the method utilizes the temporal properties of the GRU and the positional coding of the Transformer to capture and feature extract the data point sequence position information to suppress redundant information, and introduces the largest pooling layer into the Transformer model to alleviate the overfitting phenomenon. Second, while retaining the original attention learning mechanism and identity path in the original DRSN, a new soft threshold function is introduced to replace the ReLU activation function with a new threshold function, and a new soft threshold module and adaptive slope module are designed to construct the improved residual shrinkage unit (ASB-STRSBU), which is used to adaptively set the optimal threshold. Finally, pipeline leakage is classified. The experimental results show that the NDRSN model is able to make full use of global and local information when considering leakage signals and can automatically learn and acquire the important parameters of the input features in the spatial and channel domains. By optimizing the GRU improved Transformer network recognition model, the method significantly reduces the model training time and computational resource consumption while maintaining high leakage recognition accuracy. The average accuracy reached 93.97%. This indicates that the method has good robustness in acoustic emission pipeline leakage detection.

Geophysical characterization of Saraswati River palaeochannel in parts of Yamuna Nagar and Kurukshetra districts of Haryana, India.

Palaeochannels are remnants of rivers or stream channels filled with younger sediments over the period of time. In ancient times, these rivers/channels were thriving in phenomenal conditions, but due to frequent tectonic activities, they lost the direction of their original path and were gradually either lost or buried under thick beds of younger alluvium. Palaeochannels act as reservoirs for fresh groundwater since they are made up of coarser sediments and were formerly flowing rivers. Depending on the groundwater regime and local topography, these could either be saturated or dry. The palaeochannels have high groundwater potential if saturated. These are ideal sites for artificial groundwater recharge, if dry. The identification of palaeochannels becomes quite challenging if they are buried under thick deposits of finer younger sediments. In the present study, an attempt has been made to characterize the Saraswati River Palaeochannel in parts of Yamuna Nagar and Kurukshetra districts of Haryana by using surface and subsurface geophysical methods. Till date, the palaeochannels in this area were mainly discerned on the basis of remote sensing only; therefore, geophysical characterization of these palaeochannels has been attempted in this study. In surface geophysical methods, electrical resistivity surveys, especially gradient resistivity profiling (GRP) and vertical electrical sounding (VES), were conducted in the study area, while electrical and natural gamma logging was used as subsurface geophysical approaches to identify the coarser sands of buried palaeochannels. The main objective of the study was to characterize the Saraswati River palaeochannel and analyze the quality of the groundwater stored in the palaeochannel in the study area. The findings were compared with the well-log data and were found in good agreement.

Line Follower Robot with Obstacle Avoidance

One of the most available robots in applications are the line followers. These robots normally follow either white or a black line. This paper introduces an Autonomous Emergency Indicating Line Follower and Obstacle Avoiding Robot. The robot moves on a specific path determined by the user and detects the obstacle that comes in its way. The vehicle robot stops and diverts its route and returns back to original path, once the obstacle has been overcome. Whenever the robot gets lost from the track, the robot automatically follow the predefined path. The robot is achieved through the implementation of many sensors interacting with the controllers. An experimental study of time taken by the robot for different length obstacle as well as for different speed of the robot is also performed. Key Words: Sensors, Obstacle Detection, Line Follower

RecGraph: recombination-aware alignment of sequences to variation graphs.

Bacterial genomes present more variability than human genomes, which requires important adjustments in computational tools that are developed for human data. In particular, bacteria exhibit a mosaic structure due to homologous recombinations, but this fact is not sufficiently captured by standard read mappers that align against linear reference genomes. The recent introduction of pangenomics provides some insights in that context, as a pangenome graph can represent the variability within a species. However, the concept of sequence-to-graph alignment that captures the presence of recombinations has not been previously investigated. In this paper, we present the extension of the notion of sequence-to-graph alignment to a variation graph that incorporates a recombination, so that the latter are explicitly represented and evaluated in an alignment. Moreover, we present a dynamic programming approach for the special case where there is at most a recombination-we implement this case as RecGraph. From a modelling point of view, a recombination corresponds to identifying a new path of the variation graph, where the new arc is composed of two halves, each extracted from an original path, possibly joined by a new arc. Our experiments show that RecGraph accurately aligns simulated recombinant bacterial sequences that have at most a recombination, providing evidence for the presence of recombination events. Our implementation is open source and available at https://github.com/AlgoLab/RecGraph.