Trajectory Characteristics Research Articles

Factors that influence university graduates’ choice of postgraduate study

ABSTRACT Enrolments in university certificate and postgraduate programmes have grown considerably during recent decades. This study assesses the relationship between socio-demographic characteristics, characteristics of the university trajectory and the salary received after graduation and the decision to pursue postgraduate studies. Data for the analysis were obtained from a survey of graduates of 11 Chilean universities during 2021. Multinomial logistic regression was applied to estimate the effect of the various characteristics. The decision to enrol in a certificate or graduate programme was associated primarily with the field of the undergraduate degree programme, family income and current salary. The decision to enrol in a postgraduate degree programme was associated with parents’ education level, field of undergraduate study and academic performance, and current wage level. First-generation university graduates and those from low-income families were less likely to continue postgraduate studies. Overall social inequality among university graduates was reproduced in postgraduate education and increased in Education and Social Sciences programmes.

Fractional Lévy Stable Motion from a Segmentation Perspective

The segmentation analysis of the Golding–Cox mRNA dataset clarifies the description of these trajectories as a Fractional Lévy Stable Motion (FLSM). The FLSM method has several important advantages. Using only a few parameters, it allows for the detection of jumps in segmented trajectories with non-Gaussian confined parts. The value of each parameter indicates the contribution of confined segments. Non-Gaussian features in mRNA trajectories are attributed to trajectory segmentation. Each segment can be in one of the following diffusion modes: free diffusion, confined motion, and immobility. When free diffusion segments alternate with confined or immobile segments, the mean square displacement of the segmented trajectory resembles subdiffusion. Confined segments have both Gaussian (normal) and non-Gaussian statistics. If random trajectories are estimated as FLSM, they can exhibit either subdiffusion or Lévy diffusion. This approach can be useful for analyzing empirical data with non-Gaussian behavior, and statistical classification of diffusion trajectories helps reveal anomalous dynamics.

Clinical presentations and diagnostic application of proposed biomarkers in psychiatric-onset prodromal dementia with Lewy bodies.

Research criteria for the diagnosis of prodromal dementia with Lewy bodies (DLB) include three clinical subtypes: mild cognitive impairment with Lewy bodies (MCI-LB), delirium-onset prodromal DLB, and psychiatric-onset prodromal DLB. Late-onset psychiatric manifestations are at a higher risk of developing dementia, but its relation to prodromal DLB remains unclear. In addition to the risk of severe antipsychotic hypersensitivity reactions, accurate discrimination from non-DLB cases is important due to the potential differences in management and prognosis. This article aims to review a rapidly evolving psychiatric topic and outline clinical pictures of psychiatric-onset prodromal DLB, including the proposed biomarker findings of MCI-LB: polysomnography-confirmed rapid eye movement sleep behaviour disorder, cardiac [123I]metaiodobenzylguanidine scintigraphy, and striatal dopamine transporter imaging. We first reviewed clinical pictures of patients with autopsy-confirmed DLB. Regarding clinical reports, we focused on the patients who predominantly presented with psychiatric manifestations and subsequently developed DLB. Thereafter, we reviewed clinical studies regarding the diagnostic applications of the proposed biomarkers to patients with late-onset psychiatric disorders. Clinical presentations were mainly late-onset depression and psychosis; however, other clinical manifestations were also reported. Psychotropic medications before a DLB diagnosis may cause extrapyramidal signs, and potentially influences the proposed biomarker findings. These risks complicate clinical manifestation interpretation during the management of psychiatric symptoms. Longitudinal follow-up studies with standardised evaluations until conversion to DLB are needed to investigate the temporal trajectories of core features and proposed biomarker findings. In patients with late-onset psychiatric disorders, identification of patients with psychiatric-onset prodromal DLB provides the opportunity to better understanding the distinct prognostic subgroup that is at great risk of incident dementia. Advances in the establishment of direct biomarkers for the detection of pathological α-synuclein may encourage reorganising the phenotypic variability of prodromal DLB.

Memorializing Women on the Move: A register of migrant women landmarks in Europe.

This dataset was developed by COST Action 19112 Women on the Move (WEMov), which engages in unveiling women migrants' presence and participation in the construction of Europe. The dataset was built as a register of toponyms and monuments in the political and public landscape in Europe - such as street names, school names and parks, as well as statues and memorials - that celebrate women migrants. With the dataset we want to discover how women migrants are remembered and what kind of landmarks present these individuals who have had an episode of migration for a variety of reasons. Moreover, our aim is to make these landmarks and the stories of women migrants visible by presenting the results of the dataset in an interactive map on our website. At the moment, the dataset includes 1000 landmarks. The collection of data was based on voluntary work of scholars and students from over 40 different European countries. We have aimed for broad geographical coverage; however, some areas are better represented than others due the nature of data collection. The collection of data is an ongoing process and therefore the dataset in Nakala repository, to which this data note refers, presents the situation in July 2023. Updated versions of the dataset will be made available in Nakala and we will download new landmarks to our interactive map on a regular basis. The selected landmarks and migrant trajectories feature cross-community or cross-cultural migration. They show both typical and exceptional forms of mobility and present women of different age, profession, social status and migration status. This intersectionality of the project and the dataset highlights not only the richness of these landmarks and their value for scholarship but also the wide spectrum of migrant women and their contribution to society.

Features of career trajectories of graduates of Altai universities (using the example of a sociological study in Barnaul)

Special attention was paid to the employment of graduates of higher educational institutions in Soviet times, when planned state policy distributed graduates to various state-owned enterprises depending on the education they received. It is important to note that on the periphery of the centuries there has been some shift in the idea of a career. If earlier a career was understood as moving up the career ladder, today a career is planned as mastering a variety of practices that complement and develop a person (self-development, a high level of extracurricular, additional activities). Thus, a logical question arises: how do graduates imagine their future careers and whether they think about it at all. How aware are graduates of their employment opportunities in principle and does the university contribute to students' awareness of careers and career opportunities. The selection method was a quota sample with elements of spontaneous selection of respondents. We selected two methods for the study: qualitative (in-depth interview) and quantitative (questionnaire). Together, these methods give the researcher the most accurate idea of the subject under study. Thus, the tools for conducting empirical sociological research were developed based on this methodology. Data analysis has shown that a well-chosen profession increases self-confidence, life satisfaction, and reduces problems related to physical and mental health. And the personal level of "quality of life", responsible for satisfaction, has a significant impact on social, objective indicators of quality of life. Theoretical approaches are analyzed to describe the formation and content of graduates' career trajectories. The psychological approach focuses on the study of self-perception, self-regulation of consciousness, and the process of choice. Pedagogical is based on the formation of certain professional interests and inclinations, the suitability of an individual for a particular profession. Sociologists and economists consider the career trajectories of graduates from the point of view of the need for this profession in society and taking into account the socio-economic relations of the specialized workforce within the organization. In this context, the scheme of professional specialization, types of career guidance and career growth, all kinds of careers of an employee and a graduate are considered. Personality-oriented, sociological, managerial-organizational, biomedical, pedagogical, legal regulation, complex (differentiated) approaches to career guidance are disclosed.

Trajectory planning and control of spacecraft avoiding dynamic debris swarm

The continuous accumulation of space debris poses a significant threat to spacecraft on orbit. This paper introduces a dynamic trajectory planning and control framework aimed at ensuring the safe traversal of spacecraft through space debris swarms. Initially, a threat model is established, considering the movement of the debris swarm and the constraints of spacecraft detection range. This model utilizes a dynamically updated, redefined Gaussian mixture model to characterize the threat posed by the debris swarm to spacecraft, with proximity to debris correlating to increased threat levels. Building upon this threat model, four guidance rules are defined, including target guidance, minimal threat, threat threshold, and local path optimization, to facilitate the generation of the shortest collision-free local trajectory for spacecraft. Combining the concept of receding horizon control, an online real-time planning and control framework is formulated for spacecraft navigating through debris swarms. Finally, numerical and semi-physical simulations verify that the proposed framework can stably generate spacecraft safe trajectories and control commands under the threat of numerous dynamic space debris, showing the characteristics of optimal planning trajectory, rapid computation speed and robust safety features.

Stimulus-response signaling dynamics characterize macrophage polarization states

The functional state of cells is dependent on their microenvironmental context. Prior studies described how polarizing cytokines alter macrophage transcriptomes and epigenomes. Here, we characterized the functional responses of 6 differentially polarized macrophage populations by measuring the dynamics of transcription factor nuclear factor κB (NF-κB) in response to 8 stimuli. The resulting dataset of single-cell NF-κB trajectories was analyzed by three approaches: (1) machine learning on time-series data revealed losses of stimulus distinguishability with polarization, reflecting canalized effector functions. (2) Informative trajectory features driving stimulus distinguishability ("signaling codons") were identified and used for mapping a cell state landscape that could then locate macrophages conditioned by an unrelated condition. (3) Kinetic parameters, inferred using a mechanistic NF-κB network model, provided an alternative mapping of cell states and correctly predicted biochemical findings. Together, this work demonstrates that a single analyte's dynamic trajectories may distinguish the functional states of single cells and molecular network states underlying them. A record of this paper's transparent peer review process is included in the supplemental information.

Critical radius prediction for small radius curved section of two-lane secondary highways based on speed characteristics

Small radius curved sections of two-lane secondary highways have low technical indicators, complex alignments, and frequent accidents. In order to improve the safety of traffic operation, the article investigates the curved sections of two-lane secondary highways with different radii and obtains the travelling speeds of different vehicle types at different section locations. By studying the trajectory characteristics and speed characteristics of vehicles travelling in curves, the velocity difference ratio, which responds to the continuity of driving speed, was defined. Then, based on this, this paper investigates the vehicle motion law of small radius curved road sections. The results show that the speed change of large cars in small radius curves is similar to a “U" shape, while the speed change of small cars is similar to a “V" shape. The speed adjustments of the larger cars occur mainly within the range of gentle curves, whereas the speed changes of the smaller cars are large throughout the range of curves. There is a significant exponential function relationship between curve radius and 85 % percentile speed, from which the curve radius threshold can be predicted. This provides a basis for engineering construction of curved road sections and optimization of curve design indicators, which greatly improves the traffic safety level of curved road sections.

Firefighting Water Jet Trajectory Detection from Unmanned Aerial Vehicle Imagery Using Learnable Prompt Vectors.

This research presents an innovative methodology aimed at monitoring jet trajectory during the jetting process using imagery captured by unmanned aerial vehicles (UAVs). This approach seamlessly integrates UAV imagery with an offline learnable prompt vector module (OPVM) to enhance trajectory monitoring accuracy and stability. By leveraging a high-resolution camera mounted on a UAV, image enhancement is proposed to solve the problem of geometric and photometric distortion in jet trajectory images, and the Faster R-CNN network is deployed to detect objects within the images and precisely identify the jet trajectory within the video stream. Subsequently, the offline learnable prompt vector module is incorporated to further refine trajectory predictions, thereby improving monitoring accuracy and stability. In particular, the offline learnable prompt vector module not only learns the visual characteristics of jet trajectory but also incorporates their textual features, thus adopting a bimodal approach to trajectory analysis. Additionally, OPVM is trained offline, thereby minimizing additional memory and computational resource requirements. Experimental findings underscore the method's remarkable precision of 95.4% and efficiency in monitoring jet trajectory, thereby laying a solid foundation for advancements in trajectory detection and tracking. This methodology holds significant potential for application in firefighting systems and industrial processes, offering a robust framework to address dynamic trajectory monitoring challenges and augment computer vision capabilities in practical scenarios.

An online method for ship trajectory compression using AIS data

Abstract Vessel trajectories from the Automatic Identification System (AIS) play an important role in maritime traffic management, but a drawback is the huge amount of memory occupation which thus results in a low speed of data acquisition in maritime applications due to a large number of scattered data. This paper proposes a novel online vessel trajectory compression method based on the Improved Open Window (IOPW) algorithm. The proposed method compresses vessel trajectory instantly according to vessel coordinates along with a timestamp driven by the AIS data. In particular, we adopt the weighted Euclidean distance (WED), fusing the perpendicular Euclidean distance (PED) and synchronous Euclidean distance (SED) in IOPW to improve the robustness. The realistic AIS-based vessel trajectories are used to illustrate the proposed model by comparing it with five traditional trajectory compression methods. The experimental results reveal that the proposed method could effectively maintain the important trajectory features and significantly reduce the rate of distance loss during the online compression of vessel trajectories.

Planning Socially Expressive Mobile Robot Trajectories.

Many mobile robotics applications require robots to navigate around humans who may interpret the robot's motion in terms of social attitudes and intentions. It is essential to understand which aspects of the robot's motion are related to such perceptions so that we may design appropriate navigation algorithms. Current works in social navigation tend to strive towards a single ideal style of motion defined with respect to concepts such as comfort, naturalness, or legibility. These algorithms cannot be configured to alter trajectory features to control the social interpretations made by humans. In this work, we firstly present logistic regression models based on perception experiments linking human perceptions to a corpus of linear velocity profiles, establishing that various trajectory features impact human social perception of the robot. Secondly, we formulate a trajectory planning problem in the form of a constrained optimization, using novel constraints that can be selectively applied to shape the trajectory such that it generates the desired social perception. We demonstrate the ability of the proposed algorithm to accurately change each of the features of the generated trajectories based on the selected constraints, enabling subtle variations in the robot's motion to be consistently applied. By controlling the trajectories to induce different social perceptions, we provide a tool to better tailor the robot's actions to its role and deployment context to enhance acceptability.

Protecting privacy and enhancing utility: A novel approach for personalized trajectory data publishing using noisy prefix tree

In recent years, the widespread adoption of location-based software has significantly improved people’s daily lives. However, this convenience has brought about an increasingly severe concern: the risk of data privacy breaches. To tackle this issue, a novel scheme for personalized trajectory data publishing is proposed, leveraging a noisy prefix tree structure. The scheme begins by constructing multiple trajectory equivalence classes based on the spatiotemporal characteristics of trajectories, followed by the calculation of distinct Hilbert curve orders for each equivalence class. Subsequently, the Hilbert curve is employed to partition the location points within each equivalence class, with the aid of a scoring function that selects optimal center points to replace other location points effectively. Additionally, appropriate encoding levels are determined for each partitioned region, facilitating the conversion of the center points into binary encoded characters using Hilbert-Geohash. This conversion process safeguards against data privacy leakage by obfuscating the original latitude and longitude coordinates. Ultimately, a noisy prefix tree is constructed to store the binary encoding in its nodes. To ensure data privacy preservation, a novel privacy budget allocation approach is introduced, applying suitable Laplace noise to each node. The effectiveness of the proposed algorithm is confirmed through experimental comparisons with existing schemes, employing real datasets and demonstrating its notable performance in terms of both data privacy and utility.

Association of AIS and Radar Data in Intelligent Navigation in Inland Waterways Based on Trajectory Characteristics

Intelligent navigation is a crucial component of intelligent ships. This study focuses on the situational awareness of intelligent navigation in inland waterways with high vessel traffic densities and increased collision risks, which demand enhanced vessel situational awareness. To address perception data association issues in situational awareness, particularly in scenarios with winding waterways and multiple vessel encounters, a method based on trajectory characteristics is proposed to determine associations between Automatic Identification System (AIS) and radar objects, facilitating the fusion of heterogeneous data. Firstly, trajectory characteristics like speed, direction, turning rate, acceleration, and trajectory similarity were extracted from ship radar and AIS data to construct labeled trajectory datasets. Subsequently, by employing the Support Vector Machine (SVM) model, we accomplished the discernment of associations among the trajectories of vessels collected through AIS and radar, thereby achieving the association of heterogeneous data. Finally, through a series of experiments, including overtaking, encounters, and multi-target scenarios, this research substantiated the method, achieving an F1 score greater than 0.95. Consequently, this study can furnish robust support for the perception of intelligent vessel navigation in inland waterways and the elevation of maritime safety.

Model-free visual servoing based on active disturbance rejection control and adaptive estimator for robotic manipulation without calibration

PurposeVisual feedback control is a promising solution for robots work in unstructured environments, and this is accomplished by estimation of the time derivative relationship between the image features and the robot moving. While some of the drawbacks associated with most visual servoing (VS) approaches include the vision–motor mapping computation and the robots’ dynamic performance, the problem of designing optimal and more effective VS systems still remains challenging. Thus, the purpose of this paper is to propose and evaluate the VS method for robots in an unstructured environment.Design/methodology/approachThis paper presents a new model-free VS control of a robotic manipulator, for which an adaptive estimator aid by network learning is proposed using online estimation of the vision–motor mapping relationship in an environment without the knowledge of statistical noise. Based on the adaptive estimator, a model-free VS schema was constructed by introducing an active disturbance rejection control (ADRC). In our schema, the VS system was designed independently of the robot kinematic model.FindingsThe various simulations and experiments were conducted to verify the proposed approach by using an eye-in-hand robot manipulator without calibration and vision depth information, which can improve the autonomous maneuverability of the robot and also allow the robot to adapt its motion according to the image feature changes in real time. In the current method, the image feature trajectory was stable in the camera field range, and the robot’s end motion trajectory did not exhibit shock retreat. The results showed that the steady-state errors of image features was within 19.74 pixels, the robot positioning was stable within 1.53 mm and 0.0373 rad and the convergence rate of the control system was less than 7.21 s in real grasping tasks.Originality/valueCompared with traditional Kalman filtering for image-based VS and position-based VS methods, this paper adopts the model-free VS method based on the adaptive mapping estimator combination with the ADRC controller, which is effective for improving the dynamic performance of robot systems. The proposed model-free VS schema is suitable for robots’ grasping manipulation in unstructured environments.

Revolutionizing motor dysfunction treatment: A novel closed-loop electrical stimulator guided by multiple motor tasks with predictive control

Functional electrical stimulation (FES) has been demonstrated as a viable method for addressing motor dysfunction in individuals affected by stroke, spinal cord injury, and other etiologies. By eliciting muscle contractions to facilitate joint movements, FES plays a crucial role in fostering the restoration of motor function compromised nervous system. In response to the challenge of muscle fatigue associated with conventional FES protocols, a novel biofeedback electrical stimulator incorporating multi-motor tasks and predictive control algorithms has been developed to enable adaptive modulation of stimulation parameters. The study initially establishes a Hammerstein model for the stimulated muscle group, representing a time-varying relationship between the stimulation pulse width and the root mean square (RMS) of the surface electromyography (sEMG). An online parameter identification algorithm utilizing recursive least squares is employed to estimate the time-varying parameters of the Hammerstein model. Predictive control is then implemented through feedback corrections based on the comparison between predicted and actual outputs, guided by an optimization objective function. The integration of predictive control and roll optimization enables closed-loop control of muscle stimulation. The motor training tasks of elbow flexion and extension, wrist flexion and extension, and five-finger grasping were selected for experimental validation. The results indicate that the model parameters were accurately identified, with a RMS error of 3.83 % between actual and predicted values. Furthermore, the predictive control algorithm, based on the motor tasks, effectively adjusted the stimulus parameters to ensure that the stimulated muscle groups can achieve the desired sEMG characteristic trajectory. The biofeedback electrical stimulator that was developed has the potential to assist patients experiencing motor dysfunction in achieving the appropriate joint movements. This research provides a foundation for a novel intelligent electrical stimulation model.

Relationship between the longitudinal trajectory of the triglyceride-glucose index and the development of CKD: an 8-year retrospective longitudinal cohort study.

The triglyceride-glucose (TyG) index, a simple surrogate marker of insulin resistance, is significantly associated with chronic kidney disease (CKD). However, there is limited research on the longitudinal trajectory of TyG index over time and its relationship with CKD. To analyse the characteristics of the longitudinal trajectory of the TyG index over time and its association with the development of CKD in a health check-up population. Participants who underwent at least three annual health check-ups at the Health Management Center of Sichuan Provincial People's Hospital from 2015 to 2022 were included in this retrospective cohort study. The TyG index was calculated as ln [fasting triglycerides (mg/dL) × fasting glucose (mg/dL)/2]. The latent class mixed model (LCMM) was used to identify the TyG index trajectory of the study population. A Cox proportional hazard model was used to estimate the CKD incidence risk in different quartile groups and the association of changes in the TyG index trajectory with the development of CKD. A total of 4,921 participants were included in this study, and they were divided into four groups according to the quartiles of the baseline TyG index: Q1 (5.43-6.66), Q2 (6.67-7.04), Q3 (7.05-7.43), and Q4 (7.43-9.97). There was no difference in the risk of CKD occurrence among the TyG groups. Three different TyG index trajectories were identified in this study: a high-level group, middle-level stable group and low-level stable group, respectively. The incidence rate of CKD in the high-level TyG index trajectory group was 2.399 times greater than that in the low-level stable trajectory group (HR=2.399, 95% CI 1.167-4.935). Individuals with long-term exposure to high TyG index levels had a significantly greater risk of CKD. Routine monitoring of the TyG index and its longitudinal trend will aid in the risk stratification of CKD in the general population and will be helpful for CKD prevention and targeted management.

Transfer-Learning-Enhanced Regression Generative Adversarial Networks for Optimal eVTOL Takeoff Trajectory Prediction

Electric vertical takeoff and landing (eVTOL) aircraft represent a crucial aviation technology to transform future transportation systems. The unique characteristics of eVTOL aircraft include reduced noise, low pollutant emission, efficient operating cost, and flexible maneuverability, which in the meantime pose critical challenges to advanced power retention techniques. Thus, optimal takeoff trajectory design is essential due to immense power demands during eVTOL takeoffs. Conventional design optimizations, however, adopt high-fidelity simulation models in an iterative manner resulting in a computationally intensive mechanism. In this work, we implement a surrogate-enabled inverse mapping optimization architecture, i.e., directly predicting optimal designs from design requirements (including flight conditions and design constraints). A trained inverse mapping surrogate performs real-time optimal eVTOL takeoff trajectory predictions with no need for running optimizations; however, one training sample requires one design optimization in this inverse mapping setup. The excessive training cost of inverse mapping and the characteristics of optimal eVTOL takeoff trajectories necessitate the development of the regression generative adversarial network (regGAN) surrogate. We propose to further enhance regGAN predictive performance through the transfer learning (TL) technique, creating a scheme termed regGAN-TL. In particular, the proposed regGAN-TL scheme leverages the generative adversarial network (GAN) architecture consisting of a generator network and a discriminator network, with a combined loss of the mean squared error (MSE) and binary cross-entropy (BC) losses, for regression tasks. In this work, the generator employs design requirements as input and produces optimal takeoff trajectory profiles, while the discriminator differentiates the generated profiles and real optimal profiles in the training set. The combined loss facilitates the generator training in the dual aspects: the MSE loss targets minimum differences between generated profiles and training counterparts, while the BC loss drives the generated profiles to share analogous patterns with the training set. We demonstrated the utility of regGAN-TL on optimal takeoff trajectory designs for the Airbus A3 Vahana and compared its performance against representative surrogates, including the multi-output Gaussian process, the conditional GAN, and the vanilla regGAN. Results showed that regGAN-TL reached the 99.5% generalization accuracy threshold with only 200 training samples while the best reference surrogate required 400 samples. The 50% reduction in training expense and reduced standard deviations of generalization accuracy achieved by regGAN-TL confirmed its outstanding predictive performance and broad engineering application potential.

Predicting Vessel Trajectories Using ASTGCN with StemGNN-Derived Correlation Matrix

This study proposes a vessel position prediction method using attention spatiotemporal graph convolutional networks, which addresses the issue of low prediction accuracy due to less consideration of inter-feature dependencies in current vessel trajectory prediction methods. First, the method cleans the vessel trajectory data and uses the Time-ratio trajectory compression algorithm to compress the trajectory data, avoiding data redundancy and providing feature points for vessel trajectories. Second, the Spectral Temporal Graph Neural Network (StemGNN) extracts the correlation matrix that describes the relationship between multiple variables as a priori matrix input to the prediction model. Then the vessel trajectory prediction model is constructed, and the attention mechanism is added to the spatial and temporal dimensions of the trajectory data based on the spatio-temporal graph convolutional network at the same time as the above operations are performed on different time scales. Finally, the features extracted from different time scales are fused through the full connectivity layer to predict the future trajectories. Experimental results show that this method achieves higher accuracy and more stable prediction results in trajectory prediction. The attention-based spatio-temporal graph convolutional networks effectively capture the spatio-temporal correlations of the main features in vessel trajectories, and the spatio-temporal attention mechanism and graph convolution have certain interpretability for the prediction results.

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

A Study of Multi-Step Sparse Vessel Trajectory Restoration Based on Feature Correlation

Observational signatures of charged rotating traversable wormhole: shadows and light rings with different accretions

In this work, we investigate the optical appearance of qualitatively new observational features of accretion disk images around the charged rotating traversable wormhole (TWH) space-time for different spin, throat, and charge values. To accomplish this, we first consider the Hamilton–Jacobi method to derive the geodesic equations for the motion of photons and study the effects of parameters on the photon orbit in the observer’s sky. We found that each parameter affects the size and shape of the wormhole (WH) shadow and flatness is observed in the shadow because of spin and other parameters. To produce shadow images of sufficient visual quality but within manageable computational times, we adopt the ray-trace procedure and characterize the significant features of light trajectories on the observer’s screen, depending on the interaction between the space-time structure and the accretion disk. In addition, we consider the static spherically symmetric accretion flow model to observe the specific intensity around the traversable WH space-time geometry. It is found that the intensity and positions of the photon ring vary with respect to the involved parameters. In future observation, this type of study may provide a fertile playground to test the nature of compact objects, specifically the WH in the strong-field regime.