Search Results (58)

The Goddess Temple at Niuheliang, located in Chaoyang City, Liaoning Province, is the earliest known temple excavated in China, offering profound insights into Neolithic religious architecture. Built during the Neolithic era, this sacred site reflects a deliberate integration of geographical features and early spiritual beliefs. The temple demonstrates a mythologically inspired architectural landscape, shaped by the local terrain and animal symbolism. Its design principles are evident in three main aspects. First, the alignment of the temple along the central axis of Niuheliang Mountain and its bird-shaped architecture—resembling an eagle and an owl—may embody the belief in sacred birds as intermediaries between humans and deities. Second, the goddess head within the temple mirrors the contours of Bear-Headed Mountain (Xiongshoushan 熊首山), suggesting a deliberate visual alignment between the goddess image and the form of the mountain. Third, the bear-shaped clay sculpture inside the temple conceptually links to Bear-Headed Mountain, potentially reflecting a widespread belief in the Celestial Bear (Tianxiong 天熊). This fusion of topography and myth exemplifies a distinctive approach to constructing sacred space in early Chinese religious culture, where the natural environment was not merely a backdrop but an active medium for expressing cosmological ideas. The Niuheliang Goddess Temple thus stands as a purposefully created mythological world, revealing the ancestors’ complex and sophisticated engagement with the natural landscape and spiritual beliefs. Full article

Field-scale pest monitoring requires accurate pest recognition and classification techniques. However, there are two main challenges in practical pest detection tasks. First, both intra-species morphological variation across developmental stages and inter-species size differences create challenges for models adapting to multi-scale features. Second, biological camouflage reduces target-background contrast, increasing the difficulty of model recognition. To address these issues, this paper proposes an improved pest detection model, IP-YOLOv8, based on YOLOv8s. First, a multi-scale feature fusion architecture is introduced, establishing a cross-layer feature interaction mechanism that effectively integrates shallow detailed features and deep semantic features, significantly enhancing the model’s multi-scale representation ability. Second, a dynamic detection head is designed to address the diverse morphology of pests. This head adapts the receptive field through a dynamic sampling mechanism, allowing the model to accurately capture pest features of varying scales and shapes. Finally, to tackle the issue of camouflage background confusion, an edge feature fusion module is proposed to enhance target contour information, thereby addressing the blurring of edge features caused by camouflage. Experimental results demonstrate that IP-YOLOv8 outperforms YOLOv8s on the IP102 dataset, achieving improvements of 2.2% in mAP50, 1.3% in mAP50:95, 3.1% in precision, and 1.5% in recall. This method effectively adapts to complex field pest detection tasks, providing strong technical support for precision agriculture. Full article

In this study, a dual-band dual-beam shared-aperture reflector antenna based on a Cassegrain configuration is designed using a frequency-selective surface (FSS) subreflector. The antenna generates two shaped beams that operate at different frequencies and can spatially overlap. One beam contour can be independently optimized by properly designing the shape of the main reflector. The contour of the second beam is defined by optimizing the unit cell and geometry of the FSS-based subreflector once the shape of the main reflector is set. The reflector antenna design is cast as the optimization of a suitably defined cost function aimed at yielding the desired directivity performance in the regions of coverage. In order to validate the proposed solution, a set of numerical experiments was conducted using most of China and Shaanxi province as benchmark examples. Full article

Mechanical metamaterials, especially the cells with a negative Poisson’s ratio (NPR), have received much attention since they offer more deformability potential in morphing wings. This paper proposes a strategy for regulating the deformation of metamaterial cells based on the deformation form of the wing planform. The deformation of the wing shape was achieved through this strategy, with the main control factor of NPR. In light of the strategy, taking bi-directional re-entrant anti-tetrachiral (BRATC) metamaterial cells with NPR as an example, a scheme for BRATC metamaterial cells to regulate NPR is proposed. Driven by the same increase in wingspan (Δspan = 5%), the wing models, which are constructed based on the BRATC metamaterial cells with NPR characteristics at the different chord length increment at wing root (Δchord = 20%, 25%, and 30%), achieved an acceptable object-contour shape error (K = 1.29%, 1.40%, and 2.10%) with corresponding relative area increases (Ar = 15.5%, 18.13%, and 20.75%). Finally, the feasibility of the method is verified by experimentally measuring the deformation of the wing model. Full article

This paper presents the results of wind tunnel experiments, where lift and drag coefficients were studied at various angles of attack and flow speeds, alongside numerical simulations conducted in ANSYS. The main objectives of this study are to investigate the aerodynamic characteristics and self-starting capabilities of three-bladed Darrieus rotors with asymmetrical blades and assess their efficiency. This study presents results on pressure distribution, velocity contours, and the impact of the angle of attack on pressure and aerodynamic characteristics. The results show that blades with asymmetric shapes achieve maximum values of lift and drag coefficients at angles of attack between 180° and 210°, with peak coefficients of Cx = 1.38 and Cy = 2.84, respectively. These findings indicate high effectiveness of the blades at low wind speeds, making them promising for use in WEIs where good starting characteristics and high power output are especially important. A good correlation was found between experimental data and numerical simulation results. This study contributes to the development of recommendations for optimizing the design and operating parameters of wind-driven powerplants, which in turn can improve their reliability and economic efficiency. Thus, the paper aims to expand the knowledge in the field of wind power engineering and to develop technologies to facilitate a wider adoption of wind-driven powerplants in the energy infrastructure of different regions. Full article

The body contour market has grown rapidly in recent years, due to persistent requests for noninvasive treatments for localized fat adiposities, cellulite, and skin laxity. A variety of different methods are now available to improve body shaping. This review aims to provide an exhaustive compendium of the main recommendations for the optimal use of an innovative device delivering microwaves (MWs) for unwanted fat and cellulite reduction (Onda Coolwaves, DEKA, Florence, Italy), resulting from the experiences of the most expert international users. The availability of this new technology has led to an increasing number of treated patients and clinical studies. However, what is still missing, to the best of our knowledge, is an evaluation of the long-term efficacy and safety of this method. Based on the most recent data available, this compendium focuses on the ideal parameters, patient selection, and treatment methodology for providing safe and effective treatment protocols. Future research findings may suggest changes to the conclusions or recommendations in this report. Full article

Delay partial differential equations have significant applications in numerous fields, such as population dynamics, control systems, neuroscience, and epidemiology, where they are required to efficiently model the effects of past states on current system behavior. This work presents an RBF-based localized meshless method for the numerical solution of delay partial differential equations. In the suggested numerical scheme, the localized meshless method is combined with the Laplace transform. The main attractive features of the localized meshless method are its simplicity, adaptability, and ease of implementation for complex problems defined on complex shaped domains. In a localized meshless scheme, a linear system of equations is solved. The Laplace transform, which is one of the most powerful techniques for solving integer- and non-integer-order problems, is used to represent the desired solution as a contour integral in the complex plane, known as the Bromwich integral. However, the analytic inversion of contour integral becomes very laborious in many situations. Therefore, a contour integration method is utilized to numerically approximate the Bromwich integral. The aim of utilizing the Laplace transform is to handle the costly convolution integral associated with the Caputo derivative and to avoid the effects of time-stepping techniques on the stability and accuracy of the numerical solution. We also discuss the convergence and stability of the suggested scheme. Furthermore, the existence and uniqueness of the solution for the considered model are studied. The efficiency, efficacy, and accuracy of the proposed numerical scheme have been demonstrated through numerical experiments on various problems. Full article

Cytology, a type of pathological examination, involves sampling cells from the human body and observing the morphology of the nucleus, cytoplasm, and cell arrangement. In developing classification AI technologies to support cytology, it is essential to collect and utilize a diverse range of images without bias. However, this is often challenging in practice because of the epidemiologic bias of cancer types and cellular characteristics. The main aim of this study was to develop a method to generate cytological diagnostic images from image findings using text-to-image technology in order to generate diverse images. In the proposed method, we collected Papanicolaou-stained specimens derived from the lung cells of 135 lung cancer patients, from which we extracted 472 patch images. Descriptions of the corresponding findings for these patch images were compiled to create a data set. This dataset was then utilized to finetune the Stable Diffusion (SD) v1 and v2 models. The cell images generated by this method closely resemble real images, and both cytotechnologists and cytopathologists provided positive subjective evaluations. Furthermore, SDv2 demonstrated shapes and contours of nuclei and cytoplasm that were more similar to real images compared to SDv1, showing superior performance in quantitative evaluation metrics. When the generated images were utilized in the classification tasks for cytological images, there was an improvement in classification performance. These results indicate that the proposed method may be effective for generating high-quality cytological images, which enables the image classification model to learn diverse features, thereby improving classification performance. Full article

Droplet quality in drop-on-demand (DoD) Electrohydrodynamic (EHD) inkjet printing plays a crucial role in influencing the overall performance and manufacturing quality of the operation. The current approach to droplet printing analysis involves manually outlining/labeling the printed dots on the substrate under a microscope and then using microscope software to estimate the dot sizes by assuming the dots have a standard circular shape. Therefore, it is prone to errors. Moreover, the dot spacing information is missing, which is also important for EHD DoD printing processes, such as manufacturing micro-arrays. In order to address these issues, the paper explores the application of feature extraction methods aimed at identifying characteristics of the printed droplets to enhance the detection, evaluation, and delineation of significant structures and edges in printed images. The proposed method involves three main stages: (1) image pre-processing, where edge detection techniques such as Canny filtering are applied for printed dot boundary detection; (2) contour detection, which is used to accurately quantify the dot sizes (such as dot perimeter and area); and (3) centroid detection and distance calculation, where the spacing between neighboring dots is quantified as the Euclidean distance of the dot geometric centers. These stages collectively improve the precision and efficiency of EHD DoD printing analysis in terms of dot size and spacing. Edge and contour detection strategies are implemented to minimize edge discrepancies and accurately delineate droplet perimeters for quality analysis, enhancing measurement precision. The proposed image processing approach was first tested using simulated EHD printed droplet arrays with specified dot sizes and spacing, and the achieved quantification accuracy was over 98% in analyzing dot size and spacing, highlighting the high precision of the proposed approach. This approach was further demonstrated through dot analysis of experimentally EHD-printed droplets, showing its superiority over conventional microscope-based measurements. Full article

In this study, a novel method combining contour analysis with deep CNN is applied for fire detection. The method was made for fire detection using two main algorithms: one which detects the color properties of the fires, and another which analyzes the shape through contour detection. To overcome the disadvantages of previous methods, we generate a new labeled dataset, which consists of small fire instances and complex scenarios. We elaborated the dataset by selecting regions of interest (ROI) for enhanced fictional small fires and complex environment traits extracted through color characteristics and contour analysis, to better train our model regarding those more intricate features. Results of the experiment showed that our improved CNN model outperformed other networks. The accuracy, precision, recall and F1 score were 99.4%, 99.3%, 99.4% and 99.5%, respectively. The performance of our new approach is enhanced in all metrics compared to the previous CNN model with an accuracy of 99.4%. In addition, our approach beats many other state-of-the-art methods as well: Dilated CNNs (98.1% accuracy), Faster R-CNN (97.8% accuracy) and ResNet (94.3%). This result suggests that the approach can be beneficial for a variety of safety and security applications ranging from home, business to industrial and outdoor settings. Full article

In this study, we discuss the dynamical behaviors and extract new interesting wave soliton solutions of the two significant well-known nonlinear partial differential equations (NPDEs), namely, the Korteweg–de Vries equation (KdVE) and the Jaulent–Miodek hierarchy equation (JMHE). This investigation has applications in pattern recognition, fluid dynamics, neural networks, mechanical systems, ecological systems, control theory, economic systems, bifurcation analysis, and chaotic phenomena. In addition, bifurcation analysis and the chaotic behavior of the KdVE and JMHE are the main issues of the present research. As a result, in this study, we obtain very effective advanced exact traveling wave solutions with the aid of the proposed mathematical method, and the solutions involve rational functions, hyperbolic functions, and trigonometric functions that play a vital role in illustrating and developing the models involving the KdVE and the JMHE. These new exact wave solutions lead to utilizing real problems and give an advanced explanation of our mentioned mathematical models that we did not yet have. Some of the attained solutions of the two equations are graphically displayed with 3D, 2D, and contour panels of different shapes, like periodic, singular periodic, kink, anti-kink, bell, anti-bell, soliton, and singular soliton wave solutions. The solutions obtained in this study of our considered equations can lead to the acceptance of our proposed method, effectively utilized to investigate the solutions for the mathematical models of various important complex problems in natural science and engineering. Full article

Background: Orbital floor fractures (OFFs) represent an interesting chapter in maxillofacial surgery, and one of the main challenges in orbit reconstruction is shaping and cutting the precise contour of the implants due to its complex anatomy. Objective: The aim of the retrospective study was to demonstrate, through pre- and postoperative volumetric measurements of the orbit, how the use of a preformed titanium mesh based on the stereolithographic model produced with 3D printers (“In-House” reconstruction) provides a better reconstruction volumetric compared to the intraoperatively shaped titanium mesh. Materials and Methods: The patients with OFF enrolled in this study were divided into two groups according to the inclusion criteria. In Group 1 (G1), patients surgically treated for OFF were divided into two subgroups: G1a, patients undergoing orbital floor reconstruction with an intraoperatively shaped mesh, and G1b, patients undergoing orbital floor reconstruction with a preoperative mesh shaped on a 3D-printed stereolithographic model. Group 2 (G2) consisted of patients treated for other traumatic pathologies (mandible fractures and middle face fractures not involving orbit). Pre- and postoperative orbital volumetric measurements were performed on both G1 and G2. The patients of both groups were subjected to the measurement of orbital volume using Osirix software (Pixmeo SARL, CH-1233 Bernex, Switzerland) on the new CT examination. Both descriptive (using central tendency indices such as mean and range) and regressive (using the Bravais–Pearson index, calculated using the GraphPad program) statistical analyses were performed on the recorded data. Results: From 1 January 2017 to 31 December 2021, of the 176 patients treated for OFF at the “Magna Graecia” University Hospital of Catanzaro 10 fulfilled the study’s inclusion criteria: 5 were assigned to G1a and 5 to G1b, with a total of 30 volumetric measurements. In G2, we included 10 patients, with a total of 20 volumetric measurements. From the volumetric measurements and statistical analysis carried out, it emerged that the average of the volumetric differences of the healthy orbits was ±0.6351 cm³, the standard deviation of the volumetric differences was ±0.3383, and the relationship between the treated orbit and the healthy orbit was linear; therefore, the treated orbital volumes tend to approach the healthy ones after surgical treatment. Conclusion: This study demonstrates that if the volume is restored within the range of the standardized mean, the diplopia is completely recovered already after surgery or after one month. For orbital volumes that do not fall within this range, functional recovery could occur within 6 months or be lacking. The restoration of the orbital volume using pre-modeled networks on the patient’s anatomical model, printed internally in 3D, allows for more accurate reconstructions of the orbital floor in less time, with clinical advantages also in terms of surgical timing. Full article

Based on the well-known role of peritumour characterization in cancer imaging to improve the early diagnosis and timeliness of clinical decisions, this study innovated a state-of-the-art approach for peritumour analysis, mainly relying on extending tumour segmentation by a predefined fixed size. We present a novel, adaptive method to investigate the zone of transition, bestriding tumour and peritumour, thought of as an annular-like shaped area, and detected by analysing gradient variations along tumour edges. For method validation, we applied it on two datasets (hepatocellular carcinoma and locally advanced rectal cancer) imaged by different modalities and exploited the zone of transition regions as well as the peritumour ones derived by adopting the literature approach for building predictive models. To measure the zone of transition’s benefits, we compared the predictivity of models relying on both “standard” and novel peritumour regions. The main comparison metrics were informedness, specificity and sensitivity. As regards hepatocellular carcinoma, having circular and regular shape, all models showed similar performance (informedness = 0.69, sensitivity = 84%, specificity = 85%). As regards locally advanced rectal cancer, with jagged contours, the zone of transition led to the best informedness of 0.68 (sensitivity = 89%, specificity = 79%). The zone of transition advantages include detecting the peritumour adaptively, even when not visually noticeable, and minimizing the risk (higher in the literature approach) of including adjacent diverse structures, which was clearly highlighted during image gradient analysis. Full article

Using a single RGB camera to obtain accurate body dimensions, rather than measuring these manually or via more complex multicamera systems or more expensive 3D scanners, has a high application potential for the apparel industry. We present a system that estimates upper human body measurements using a hybrid set of techniques from both classic computer vision and recent machine learning. The main steps involve (1) using a camera to obtain two views (frontal and side); (2) isolating in the image pair a set of main body parts; (3) improving the image quality; (4) extracting body contours and features from the images of body parts; (5) indicating markers on these images; (6) performing a calibration step; and (7) producing refined final 3D measurements. We favour a unique geometric shape, that of an ellipse, to approximate human body main horizontal cross-sections. We focus on the more challenging parts of the body, i.e., the upper body from the head to the hips, which, we show, can be well represented by varying an ellipse’s eccentricity for each individual. Then, evaluating each fitted ellipse’s perimeter allows us to obtain better results than the current state-of-the-art methods for use in the fashion and online retail industry. In our study, we selected a set of two equations, out of many other possible choices, to best estimate upper human body section circumferences. We experimented with the system on a diverse sample of 78 female participants. The results for the upper human body measurements in comparison to the traditional manual method of tape measurements, when used as a reference, show ±1 cm average differences, which are sufficient for many applications, including online retail. Full article

This explorative study examines intonation contours in neutral and non-neutral statements of Paraguayan Spanish, a variety shaped by extensive contact with Guarani, a co-official language of Paraguay. Paraguayan Spanish displays both lexical and syntactic borrowings from Guarani, along with innovative intonation patterns not found in other Spanish varieties. Previous but still limited research on yes/no and wh-questions in this variety suggests the emergence of a unique intonational system, possibly of a hybrid nature, in both Spanish monolinguals and Spanish–Guarani bilinguals. To date, no comprehensive description of intonation patterns in Paraguayan Spanish statements exists. The present study addresses this gap by analyzing data obtained through a Discourse Completion Task, covering broad-focus statements, contrastive focus, exclamatives, and statements of the obvious. Data were collected in 2014 from two monolingual speakers, eleven bilingual Spanish-dominant speakers, and eight bilingual Guarani-dominant speakers. The intonation is formalized using the Autosegmental–Metrical model of intonational phonology and the Spanish Tones and Break Indices labeling system. The findings reveal three main realizations of nuclear accents (L+H*, H+L*, and innovative >H+L*) in neutral and non-neutral declarative sentences, lengthening of syllables, diverse syntactical strategies, and lexical borrowings. The study contributes to the understanding of a lesser-studied Spanish variety and offers insights into theoretical aspects of contact linguistics. Full article