Object Regions Research Articles

Influence of region, experience, and subjective norm on the use of e-learning: Lesson from the insurance industry in Indonesia

The effectiveness and efficiency of e-learning system in industry significantly depend on users’ acceptance and adoption. This is specifically determined by external and internal factors represented by subjective norms (SN) and experience (XP), both believed to affect users’ perceived usefulness (PU) and perceived ease of use (PEOU). Users’ acceptance of e-learning system is influenced by the immensity of region, often hampered by inadequate infrastructure support. Therefore, this study aimed to investigate behavioral intention to use e-learning in the Indonesian insurance industry by applying Technology Acceptance Model (TAM). To achieve this objective, Jabotabek and Non-Jabotabek regions were used as moderating variables in all related hypotheses. An online survey was conducted to obtain data from 800 respondents who were Indonesian insurance industry employees. Subsequently, Structural Equation Model (SEM) was used to evaluate the hypotheses, and Multi-Group Analysis (MGA) to examine the role of region. The results showed that out of the seven hypotheses tested, only one was rejected. Furthermore, XP had no significant effect on PU, and the most significant correlation was found between PEOU and PU. In each relationship path model, the role of region (Jabodetabek and Non Jabodetabek) had no significant differences. These results were expected to provide valuable insights into the components of e-learning acceptability for the development of a user-friendly system in the insurance industry.

Weakly Supervised Underwater Object Real-time Detection Based on High-resolution Attention Class Activation Mapping and Category Hierarchy

Recently, deep learning-based underwater object detection technology has achieved remarkable success. However, the accuracy and completeness of dataset instance annotation are crucial for its success. The quality of underwater images is low, severe objects clustering, and occlusion, acquiring object's annotations demands substantial time and labor costs, while mis annotation and missed annotation can also degrade model performance and limit their application in practical scenarios. To address this issue, this paper presents a novel weakly supervised underwater object real-time detection method, which is divided into two subtasks: weakly supervised object localization and real-time object detection. In the weakly supervised object localization task, we design a novel category hierarchy structure network that integrates the high-resolution attention-class activation mapping algorithm to obtain high-quality object class activation maps, weaken background interference, and obtain more complete object regions. The parameterized spatial loss module is devised to enable the model to escape from local optimal solutions, thus accurately and efficiently obtaining object pseudo-detection annotation boxes. For the real-time object detection task, the single-stage detector YOLOv7 is selected as the basic detection model, and an object perception loss function is designed based on the class activation map to jointly supervise the training process. A method for filtering noisy pseudo-supervision information is proposed to enhance the pseudo-supervision information involved in training. Ablation experiments and multi-method comparison experiments were conducted on the URPC and RUOD datasets, and the results verify the effectiveness of the proposed strategy, and our model exhibits significant advantages in detection performance and detection efficiency compared to current mainstream and advanced models.

Clustering of agro-industrial complex of the Republic of Kazakhstan: prerequisites, distinguishing features, correlation matrix

The role of cluster formations in increasing the competitiveness of products and accelerating the innovative development of the agrarian economy of Kazakhstan is revealed. Theoretical and practical principles, prerequisites and methodological approaches to the creation of agro-industrial clusters are defined, their distinctive features are revealed. Purpose - economic interests of clustering in agro-industrial complex in the context of regions of the republic are considered. The selection of objects in 17 regions with the highest economic indicators of financial and economic activity was carried out. Methods – k-means - for preliminary reduction of data dimensionality by means of factor analysis; rapid cluster analysis - in order to combine economic entities into cluster components on the basis of similar characteristics. Results - primary data were collected, correlation matrix was constructed and the presence of generalized factors was assessed to reduce the dimensionality of the studied attributes. Multivariate statistical study of agricultural products production by regions for 2023 was carried out. The variant with 5 clusters, the order and map of location of 17 administrative-territorial units of regional significance were adopted, the dendrogram of intergroup linking of regions was developed. Conclusions - cluster analysis allows to distribute the totality of objects (regions) of the Republic of Kazakhstan into five cluster formations, connecting in them the enterprises having similar structure and production specialization. The identified groups can be used for rating assessment of agro-industrial cluster that promotes effective integration of resources and knowledge. It has been established that the application of the cluster approach contributes to increasing productivity and sustainability of the agricultural sector, ensuring long-term development and competitive advantages.

Quantification and sensitivity assessment of Chinese provincial ecological compensation in the perspective of carbon deficit redistribution

The ecological compensation mechanism is a tool for managing regional development and promoting the green economy. This paper proposes a revised model for carbon emissions and absorption based on IPCC, then analyses the spatial and temporal variations of carbon emissions, absorption, and deficit in 31 provinces of China from 2001 to 2019. A model was developed to redistribute carbon deficits and compensate for ecological imbalances, with the aim of eliminating background differences between regions. The concept of ecological compensation sensitivity was proposed, and a redundancy analysis (RDA) was conducted to detect possible influencing factors. Results indicate that: (1) The carbon deficits were relatively substantial in the centre and developed coastal regions, which were the subject regions of China’s ecological compensation. Meanwhile, the northwest and southwest regions were the object regions of ecological compensation, and the compensated object regions tended to extend towards the southeast. The majority of compensated subject areas are generally less sensitive than compensated object regions. (2) The graph of carbon deficit volume over time shows that China’s two carbon targets are closely aligned. The distribution of China’s ecological compensation amounts follows the pattern of provincial economic development levels, with higher compensation amounts concentrated in the central and coastal regions. There is a positive correlation between GDP, year, and population size with carbon emission and carbon deficit. Additionally, there is a positive correlation between year, region, and carbon absorption. (3) This compensation model can strongly incentivise the compensation subject area to proactively adjust its economic development model to cope with the significant compensation pressure. Additionally, it can fully encourage the compensation recipient area to continue adhering to the green economic development model. The conclusions of the study hold significant reference value for promoting the development of a green, low-carbon economy.

A Portrait of the Rotation of Ultra-cool Dwarfs Revealed by TESS

This study presents the results of a search for rotation signatures in 250 Gaia Data Release 3 ultra-cool dwarfs (UCDs) with TESS light curves (LCs). We identified 71 targets with unambiguous periodicities, of which 61 present rotation signatures and a single-source behavior, with periods between 0.133 and 5.81 days. Five UCDs show double-dip features, namely variations with two periods, one approximately double or half the other. The remaining 10 UCDs with unambiguous variability present a likely nonsingle behavior. We also found 20 UCDs showing complex behavior in their LCs, with noticeable fluctuations and irregular structure, with a few exhibiting apparent changes in their temporal structure. The remaining 159 targets show noisy LCs corresponding to low-amplitude signals, whose temporal variation cannot be easily identified. The distribution of the UCDs with rotation signature in a color–magnitude diagram points to a lack of rotating objects within about 11.5 < M G < 12.5 and G–G RP < 1.5, separating them into two regimes, one mainly composed of less massive late-M stars with P rot ≥ 1.0 day, and another mainly composed of more massive early-M stars with P rot < 1.0 day. It is important to emphasize that by separating stars into age intervals, one observes that UCDs with P rot ≥ 1.0 day tend to be located in regions of younger objects, and, in contrast, those with P rot < 1.0 day are mainly concentrated in regions of older objects. Whether this trend of stars contrasting the sample separation is physical or produced by observational biases is a question to be verified in future studies.

THE THEORY OF TRANSFORMATION OF INTELLECTUAL PROPERTY LEGAL REGIMES USING BLOCKCHAIN TECHNOLOGIES: INTERNATIONAL CONTEXT

This article is dedicated to the study of the theory of transformation of intellectual property legal regimes through blockchain technologies on an international scale. It focuses on three thematic blocks: firstly, defining the essence of blockchain technologies; secondly, analysing international blockchain technology standards; and thirdly, exploring variations in the use of blockchain technologies for the registration of intellectual property objects. The subject of the study is the social relations in the field of intellectual property and the mechanisms for their regulation. Methodology. The article employs scientific methods of cognition and doctrinal resolution of issues related to the use of digital tools in the regulation of social relations. This encompasses formal-logical and dialectical methodologies. A variety of empirical techniques, including synthesis, deduction, and statistical analysis, were employed. Results. The study reveals the absence of a direct indication of the use or intent to use distributed ledger technology, otherwise known as blockchain technology. The study demonstrates the significance of utilising blockchain technology in maintaining the customs register to account for intellectual property objects protected by law. This ensures the impossibility of unauthorised alteration or deletion of information about intellectual property objects, as provided by distributed ledger technology. These legal relations emerge in the course of entering information into the aforementioned registers, making modifications (updates), and providing information to interested parties. The use of blockchain technologies in the formation of the register of issued certificates of state registration of copyright for a work is justified.

A Cross-Level Iterative Subtraction Network for Camouflaged Object Detection

Camouflaged object detection (COD) is a challenging task, aimed at segmenting objects that are similar in color and texture to their background. Sufficient multi-scale feature fusion is crucial for accurately segmenting object regions. However, most methods usually focus on information compensation, overlooking the difference between features, which is important for distinguishing the object from the background. To this end, we propose the cross-level iterative subtraction network (CISNet), which integrates information from cross-layer features and enhances details through iteration mechanisms. CISNet involves a cross-level iterative structure (CIS) for feature complementarity, where texture information is used to enrich high-level features and semantic information is used to enhance low-level features. In particular, we present a multi-scale strip convolution subtraction (MSCSub) module within CIS to extract difference information between cross-level features and fuse multi-scale features, which improves the feature representation and guides accurate segmentation. Furthermore, an enhanced guided attention (EGA) module is presented to refine features by deeply mining local context information and capturing a broader range of relationships between different feature maps in a top-down manner. Extensive experiments conducted on four benchmark datasets demonstrate that our model outperforms the state-of-the-art COD models in all evaluation metrics.

Search and recovery network for camouflaged object detection

Camouflaged object detection aims to accurately identify objects blending into the background. However, existing methods often struggle, especially with small object or multiple objects, due to their reliance on singular strategies. To address this, we introduce a novel Search and Recovery Network (SRNet) using a bionic approach and auxiliary features. SRNet comprises three key modules: the Region Search Module (RSM), Boundary Recovery Module (BRM), and Camouflaged Object Predictor (COP). The RSM mimics predator behavior to locate potential object regions, enhancing object location detection. The BRM refines texture features and recovers object boundaries. The COP fuse multilevel features to predict final segmentation maps. Experimental results on three benchmark datasets show SRNet's superiority over SOTA models, particularly with small and multiple objects. Notably, SRNet achieves performance improvements without significantly increasing model parameters. Moreover, the method exhibits promising performance in downstream tasks such as defect detection, polyp segmentation and military camouflage detection.

Reverse cross-refinement network for camouflaged object detection

Due to the high intrinsic similarity between camouflaged objects and the background, camouflaged objects often exhibit blurred boundaries, making it challenging to distinguish the boundaries of objects. Existing methods still focus on the overall regional accuracy but not on the boundary quality and are not competent to identify camouflaged objects from the background in complex scenarios. Thus, we propose a novel reverse cross-refinement network called RCR-Net. Specifically, we design a diverse feature enhancement module that simulates the correspondingly expanded receptive fields of the human visual system by using convolutional kernels with different dilation rates in parallel. Also, the boundary attention module is used to reduce the noise of the bottom features. Moreover, a multi-scale feature aggregation module is proposed to transmit the diverse features from pixel-level camouflaged edges to the entire camouflaged object region in a coarse-to-fine manner, which consists of reverse guidance, group guidance, and position guidance. Reverse guidance mines complementary regions and details by erasing already estimated object regions. Group guidance and position guidance integrate different features through simple and effective splitting and connecting operations. Extensive experiments show that RCR-Net outperforms the existing 18 state-of-the-art methods on four widely-used COD datasets. Especially, compared with the existing top-1 model HitNet, RCR-Net significantly improves the performance by ∼16.4% (Mean Absolute Error) on the CAMO dataset, showing that RCR-Net could accurately detect camouflaged objects.

Fast and robust object region segmentation with self-organized lattice Boltzmann based active contour method

Fast and robust object region segmentation with self-organized lattice Boltzmann based active contour method

DERE-Net: A dual-encoder residual enhanced U-Net for muscle fiber segmentation of H&E images

Accurate segmentation of hematoxylin-eosin (H&E) muscle fiber images is crucial for the diagnosis of weightless muscle atrophy. However, uneven contrast, blurred fiber boundaries and adhesions in H&E images are still challenges for accurate segment complete muscle fiber. Although the existing single-encoder based U-shaped networks can extract local information about the target in H&E images, ignores remote dependencies between muscle fibers and low-level detail features, resulting in poor segmentation accuracy when facing H&E images with complex contrast. Therefore, we propose a dual-encoder residual enhanced U-Net segmentation model (DERE-Net) for effective segment muscle fibers in H&E images. DERE-Net uses Transformer and residual CNN to extract global context information and local features of muscle fibers in parallel, and fuses these two features to enable the model to accurately identify the muscle fibers. The multi-scale semantic information fusion (MSIF) module also utilizes the differences between multi-scale features to recover undetected muscle fiber, thus improving the network’s ability to recognize the object regions. In addition, the attention module is added to the skip connection, making the muscle fiber regions information more prominent and improving the robustness of the model. To demonstrate the DERE-Net effect, the comparative experiments are conducted on our own muscle fiber H&E image dataset, GLAS and MoNuSeg. DERE-Net achieved excellent performance on the muscle fiber H&E dataset (DSC 0.919), GLAS dataset (DSC 0.912), and MoNuSeg dataset (DSC 0.821). These results indicate that DERE-Net can accurately predict muscle fiber regions, providing a new method for accurate diagnosis of muscle atrophy in the future.

Перспективы использования блокчейна в сфере интеллектуальной собственности

Despite the potential of the blockchain in solving the problems facing intellectual property law, its use in this area has boundaries, which are determined both by the properties of the technology itself and by the characteristics of the results of intellectual activity and rights to them. These boundaries relate to the use of blockchain for registration and accounting of intellectual property objects, their tokenisation, protection of the interests of copyright holders, the State and society in the sphere of their turnover. The purpose of the article is to explore the limits of the use of blockchain in the field of intellectual property, considering the risks of such use, including the problems of duplication of information about objects in different distributed registries, the inconsistency of this information and its inconsistency with the real situation. The authors conclude that the solution to the problem is possible by adequate legal regulation in this area, as well as by expanding the powers of Rospatent through the mandate to authorise the entry of information on intellectual rights into blockchain information systems.

On the Interpretation of Mid-infrared Absorption Lines of Gas-phase H2O as Observed by JWST/MIRI

Rovibrational absorption lines of H2O in the 5–8 μm wavelength range selectively probe gas against the mid-infrared continuum-emitting background of the inner regions of young stellar objects and active galactic nuclei and deliver important information about these warm, dust-obscured environments. JWST/Mid-Infrared Instrument (MIRI) detects these lines in many lines of sight at a moderate spectral resolving power of R ∼ 3500 (full width at half-maximum of 85 km s−1). Based on our analysis of high-resolution SOFIA/EXES observations, we find that the interpretation of JWST/MIRI absorption spectra can be severely hampered by the blending of individual transitions and the lost information on the intrinsic line width or the partial coverage of the background continuum source. In this paper, we point out problems such as degeneracy that arise in deriving physical properties from an insufficiently resolved spectrum. This can lead to differences in the column density by 2 orders of magnitude. We emphasize the importance of weighting optically thin and weak lines in spectral analyses and provide recipes for breaking down the coupled parameters. We also provide an online tool to generate the H2O absorption line spectra that can be compared to observations.

Advancing 3D point cloud understanding through deep transfer learning: A comprehensive survey

The 3D point cloud (3DPC) has significantly evolved and benefited from the advances of deep learning (DL). However, the latter faces various issues, including the lack of data or annotated data, the existence of a significant gap between training data and test data, and the requirement for high computational resources. To that end, deep transfer learning (DTL), which decreases dependency and costs by utilizing knowledge gained from a source data/task in training a target data/task, has been widely investigated. Numerous DTL frameworks have been suggested for aligning point clouds obtained from several scans of the same scene. Additionally, DA, which is a subset of DTL, has been modified to enhance the point cloud data’s quality by dealing with noise and missing points. Ultimately, fine-tuning and DA approaches have demonstrated their effectiveness in addressing the distinct difficulties inherent in point cloud data. This paper presents the first review shedding light on this aspect. it provides a comprehensive overview of the latest techniques for understanding 3DPC using DTL and domain adaptation (DA). Accordingly, DTL’s background is first presented along with the datasets and evaluation metrics. A well-defined taxonomy is introduced, and detailed comparisons are presented, considering different aspects such as different knowledge transfer strategies, and performance. The paper covers various applications, such as 3DPC object detection, semantic labeling, segmentation, classification, registration, downsampling/upsampling, and denoising. Furthermore, the article discusses the advantages and limitations of the presented frameworks, identifies open challenges, and suggests potential research directions.

Cross-region feature fusion with geometrical relationship for OCR-based image captioning

Automatically generating a readable sentence that describes the text-contained image is a challenging task. Compared to traditional image captioning algorithms, OCR-based image captioning focuses on reading OCR tokens in images and understanding them with the image content to generate descriptions. However, existing research mainly concentrate on improving the quantity and quality of obtaining OCR tokens and exploring their spatial relationships while lacking investigation into how to effectively join OCR tokens with image content. This paper proposes a cross-region feature fusion with a geometrical relationship Transformer(CFGR-Transformer) for OCR-based image captioning. The network first establishes the associations between the OCR and object regions of the image by constructing relative geometric relationships, including width/height difference, distance, IOU(Intersection over Union), inclusion relationships, and angles offset, and then incorporates intra-region and cross-region features to aggregate entities from different modalities by a multi-head attention mechanism based on relative relationships. Benefiting from the guidance of the relative relationship, visual entities like OCR tokens and object regions can consider multiple relative relationships as the attention weight for feature fusion within each subspace. Extensive experiments conducted on the TextCaps dataset demonstrate the effectiveness of the proposed CFGR-Transformer method. In particular, our results on the online testing of TextCaps achieve an improvement in CIDEr score from 93.0% to 98.2%.

Mathematical Modeling of the Heat Transfer Process in Spherical Objects with Flat, Cylindrical and Spherical Defects

This paper proposes a method for determining the optimal parameters for the thermal testing of plant tissues of fruits and vegetables containing surface and subsurface defects in the form of areas of plant tissues with different thermophysical characteristics. Based on well-known mathematical models for objects of predominantly flat, cylindrical and spherical shapes containing flat, spherical and cylindrical regions of defects, numerical solutions of three-dimensional, non-stationary temperature fields were found, making it possible to measure the power and time of the thermal exposure of the sample surface to the radiation from infrared lamps using the finite element method. This made it possible to ensure the reliable detection of a temperature contrast of up to 4 °C between the defect and defect-free regions of the test object using modern thermal imaging cameras. In this case, subsurface defects can be detected at a depth of up to 3 mm from the surface. To determine the parameters of mathematical models of temperature fields, such as thermal conductivity and a coefficient of the thermal diffusivity of plant tissues, a new method of a pulsed heat flux from a flat heater is proposed; this differs in the method of processing experimental data and makes it possible to determine the required characteristics with high accuracy during the active stage of the experiment in a period not exceeding 1–3 min.

Object Registration Techniques For 3D Particle Tracking

Image based 3D particle tracking is currently the most widely used technique for volumetric velocity measurements. Inspecting the flow-field around an object is however, hampered by the latter, obstructing the view across it. In this study, the problem of measurement limitations due to the above is addressed. The present work builds upon the recent proposal from Wieneke and Rockstroh (2024), whereby the information of the occluded lines of sight can be incorporated into the particle tracking algorithm. The approach, however, necessitates of methods that accurately evaluate the shape and position of the object within the measurement domain. Methods of object marking and the following 3D registration of a digital object model (CAD) are discussed. For the latter, the Iterative Closest Point (ICP) registration algorithm is adopted. The accuracy of object registration is evaluated by means of experiments, where marking approaches that include physical and optically projected markers are discussed and compared. Three objects with growing level of geometrical complexity are considered: a cube, a truncated wing and a scaled model of a sport cyclist. The registered CAD representations of the physical objects are included in aerodynamic experiments, and the flow field is measured by means of large-scale particle tracking using helium filled soap bubbles. Three operating regimes are studied and compared: monolithic, partitioned and object-aware (OA) monolithic. The results indicate that object registration enables a correct reconstruction of particle tracers and strongly reduces the domain clipping typical of the monolithic approach. Furthermore, the dynamical use of all views in the OA monolithic method offers clear benefits compared to the partitioned approach, namely a lower occurrence of ghost particles. Finally, the combined visualization of the object and the surrounding flow pattern offers means of insightful data inspection and interpretation, along with posing a basis for PIV data assimilation at the fluid-solid interface.

FETR: Feature Transformer for vehicle-infrastructure cooperative 3D object detection

3D object detection plays a crucial role in the perception system of autonomous vehicles, however, the vehicle’s field of view is restricted due to obstructions from nearby vehicles and buildings. Vehicle-infrastructure cooperation can compensate for the issue of visibility, but due to discrepancies in timestamps between vehicle and infrastructure sensors as well as data transmission delays, there is typically a time asynchrony between vehicle and infrastructure data. Therefore, Feature Transformer (FETR) has been introduced, which is a vehicle-infrastructure cooperative 3D object detection model utilizing Transformer as a Feature Predictor. The Transformer Predictor is capable of predicting features of future frame based on the current frame features, efficiently addressing the problem of time asynchrony. Additionally, to enhance the precision of 3D object detection, we have introduced a plug-and-play module named Mask Feature Enhancement (MFE), MFE employs a mask to amplify the features in the object region while simultaneously diminishing the features of the surrounding environment, enlarging the difference between object features and environmental features, thereby improving the detection effect. Experimental results show that FETR attains a 68.15 BEV-mAP (IoU=0.5) on the DAIR-V2X dataset, with a 200ms latency, and the data transmission is merely 6.0×104 bytes, constituting just 4.2% of the original point cloud data, outperforming current vehicle-infrastructure cooperative models in terms of both precision and data transmission.

A Comparative Study Between Ultrasound-Guided Ilioingunial/Iliohypogastric Nerve Block versus Transverse Abdominis Plane Block in Patients Undergoing Oblique Inguinal Hernia Repair

Abstract Background Poorly controlled acute pain after abdominal surgery is related to somatic pain signals derived from the abdominal wall and is associated with a variety of unwanted post-operative consequences, including patient suffering, distress, respiratory complications, delirium, myocardial ischemia and prolonged hospital stay. Objective Regional anesthesia by (Ultrasound guided (US) Transversus abdominis plane block (TAP) or Ultrasound guided ilioinguinal and iliohypogastric nerves (IIN+IHN) block may be used as components of multimodal anesthesia with a comparable effect. In the present study, Bupivacaine is used to compare between them following inguinal hernia repair. Patients and Methods The study was conducted on 60 randomly chosen patients aged above 18 years, American Society of Anesthesiologists (ASA) class I or II scheduled for elective unilateral open inguinal hernia repair under general anesthesia, in Ain Shams University Hospitals after approval of the medical ethical committee. They were allocated in two groups. TAP block Group: received general anesthesia and (0.2 mL/kg of 0.25% bupivacaine per side), by midaxillary approach, under ultrasound guidance, the probe was placed transversely between the iliac crest and costal margin. Ilioinguinal/ iliohypogastric nerves block group: received general anesthesia, with (0.2 mL/kg of 0.25% bupivacaine per side). The inferior portion of linear ultrasound transducer will be placed over the anterior superior iliac spine with the superior margin of the transducer pointed directly in an oblique plane toward the umbilicus. The superior margin of the ultrasound transducer was slowly rotated superiorly and inferiorly until the fascial plane between the internal oblique and transverse abdominis muscle is identified. Results The results of the study revealed that patients received IIN+IHN block had significantly lower pain scores at 8, 12, 16, 20, and 24 hrs after operation, delayed time for rescue analgesia and decreased total need of analgesic in the first 24hrs post-operative compared to patients who received TAP nerves block. Conclusion IIN+IHN block was effective in reducing postoperative pain scores for 4-6 hours, lowering total 24-h postoperative opioid and analgesic consumption and delaying the need for rescue analgesia after inguinal hernia repair under general anesthesia, compared to TAP nerves block. It will be more useful to study in high-risk hypertensive and cardiac patients and to correlate with plasma catecholamine levels, which reflect the stress response and it is necessary to evaluate postoperative pain on movement in farther studies.

A study of two young multipolar planetary nebulae: Hen 2-73 and Hen 2-96

We perform an infrared (IR) spectral and visible morphological study of two young planetary nebulae (YPNe) Hen 2-73 and Hen 2-96 using archival Spitzer Space Telescope and Hubble Space Telescope (HST) observations to understand their dust properties and nebular structures. High-resolution HST images of these nebulae show several bipolar lobes and ionised tori in the central regions of both objects. The presence of these multi-lobe structures suggests that the formation process of these nebulae is complex. To search for a possible link between the central sources and multipolar appearances of these objects, the Transiting Exoplanet Survey Satellite (TESS) observations are used to examine whether their central stars (CSs) exhibit periodic photometric variations. In the TESS observations, the CS light curve of Hen 2-96 shows a photometric variation with a period of 2.23 h. The IR spectra of these two YPNe suggest that the nebulae have mixed dust environments, which are associated with the presence of dense tori created by central binary interactions in these objects. Two three-dimensional models are constructed to study the complex nebular structures of the YPNe. These simulations suggest that the number of multipolar YPNe may be larger than observed. In addition, we analyse the spectral energy distributions of these nebulae to study their gas, dust, and photospheric components.