In this research, we introduce and analyze a mathematical model for online social networks, incorporating two distinct delays. These delays represent the time it takes for active users within the network to begin disengaging, either with or without contacting non-users of online social platforms. We focus particularly on the user prevailing equilibrium (UPE), denoted as $P^{*}$, and explore the role of delays as parameters in triggering Hopf bifurcations. In doing so, we find the conditions under which Hopf bifurcations occur, then establish stable regions based on the two delays. Furthermore, we delineate the boundaries of stability regions wherein bifurcations transpire as the delays cross these thresholds. We present numerical simulations to illustrate and validate our theoretical findings. Through this interdisciplinary approach, we aim to deepen our understanding of the dynamics inherent in online social networks. Full article

Millet is a small-seeded cereal crop with big potential. There are many different cultivars of proso millet (Panicum miliaceum L.) with different characteristics, bringing forth the issue of sorting which are important for growers, processors, and consumers. Current methods of grain cultivar detection and classification are subjective, destructive, and time-consuming. Therefore, there is a need to develop nondestructive methods for sorting the cultivars of proso millet. In this study, the feasibility of using near-infrared (NIR) hyperspectral imaging (900–1700 nm) to discriminate between different cultivars of proso millet seeds was evaluated. A total of 5000 proso millet seeds were randomly obtained and investigated from the ten most popular cultivars in the United States, namely Cerise, Cope, Earlybird, Huntsman, Minco, Plateau, Rise, Snowbird, Sunrise, and Sunup. To reduce the large dimensionality of the hyperspectral imaging, principal component analysis (PCA) was applied, and the first two principal components were used as spectral features for building the classification models because they had the largest variance. The classification performance showed prediction accuracy rates as high as 99% for classifying the different cultivars of proso millet using a Gradient tree boosting ensemble machine learning algorithm. Moreover, the classification was successfully performed using only 15 and 5 selected spectral features (wavelengths), with an accuracy of 98.14% and 97.6%, respectively. The overall results indicate that NIR hyperspectral imaging could be used as a rapid and nondestructive method for the classification of proso millet seeds. Full article

Background and Objectives: Coronary artery anomalies (CAAs) represent a group of rare cardiac abnormalities with an incidence of up to 1.2%. The aim of this retrospective study was to conduct a comprehensive epidemiological assessment of the prevalence of hypoplastic coronary arteries using coronary computed tomography angiography (CCTA) in patients with diagnosed CAAs and individuals presenting with cardiovascular manifestations in the north-eastern region of Romania. This study was motivated by the limited investigation of the CAAs conducted in this area. Methods: We analyzed data collected from 12,758 coronary computed tomography angiography (CCTA) records available at the “Prof. Dr. George I.M. Georgescu” Cardiovascular Diseases Institute, spanning the years 2012 to 2022. Results: Among 350 individuals with CAAs (2.7% of the total cohort), 71 patients (20.3% of the anomaly presenting group and 0.5% of the entire CCTA cohort) exhibited at least one hypoplastic coronary artery. The mean age of individuals diagnosed with hypoplastic coronary artery disease (HCAD) was 61 years, while the age distribution among them ranged from 22 to 84 years. Nearly equal cases of right and left dominance (33 and 31, respectively) were observed, with only 7 cases of co-dominance. Conclusions: HCAD may be considered underexplored in current published research, despite its potentially significant implications ranging to an increased risk of sudden cardiac arrest. The specific prevalence of HCAD among CAAs might be higher than previously reported, possibly reflecting better diagnostic accuracy of CCTA over classic coronary imaging. The absence of standard diagnostic and therapeutic protocols for HCAD underscores the necessity of a personalized approach for such cases. Full article

This paper proposes a novel methodology to improve stability in a transmission system under critical conditions of operation when additional loads that take the system to the verge of stability are placed in weak bus bars according to the fast voltage stability index (FVSI). This paper employs the Newton–Raphson method to calculate power flows accurately and, based on that information, correctly calculate the FVSI for every transmission line. First, the weakest transmission line is identified by considering $N-1$ contingencies for the disconnection of transmission lines, and then all weak nodes associated with this transmission line are identified. Following this, critical scenarios generated by stochastically placed loads that will take the system to the verge of instability will be placed on the identified weak nodes. Then, the methodology will optimally size and place a single static VAR compensator SVC in the system to take the transmission system to the conditions before the additional loads are connected. Finally, the methodology will be validated by testing the system for critical contingencies when any transmission line associated with the weak nodes is disconnected. As a result, this paper’s methodology found a single SVC that will improve the system’s stability and voltage profiles to similar values when the additional loads are not connected and even before contingencies occur. The methodology is validated on three transmission systems: IEEE 14, 30, and 118 bus bars. Full article

Evapotranspiration (ET) is a major component of the water budget in Bottomland Hardwood Forests (BHFs) and is driven by a complex intertwined suite of meteorological variables. The understanding of these interdependencies leading to seasonal variations in ET is crucial in better informing water resource management in the region. We used structural equation modeling and AIC modeling to analyze drivers of ET using Eddy covariance water flux data collected from a BHF located in the Russel Sage Wildlife Management Area (RSWMA). It consists of mature closed-canopy deciduous hardwood trees with an average canopy height of 27 m. A factor analysis was used to characterize the shared variance among drivers, and a path analysis was used to quantify the independent contributions of individual drivers. In our results, ET and net radiation (R_n) showed similar variability patterns with Vapor Pressure Deficit (VPD) and temperature in the spring, summer, and autumn seasons, while they differed in the winter season. The path analysis showed that R_n has the strongest influence on ET variations via direct and indirect pathways. In deciduous forests like BHFs, our results suggest that ET is more energy dependent during the growing season (spring and summer) and early non-growing season (autumn) and more temperature dependent during the winter season. Full article

Nuclear fusion is a potential source of energy that could supply the growing needs of the world population for millions of years. Several experimental thermonuclear fusion devices try to understand and control the nuclear fusion process. A very interesting diagnostic called Thomson scattering (TS) is performed in the Spanish fusion device TJ-II. This diagnostic takes images to measure the temperature and density profiles of the plasma, which is heated to very high temperatures to produce fusion plasma. Each image captures spectra of laser light scattered by the plasma under different conditions. Unfortunately, some images are corrupted by noise called stray light that affects the measurement of the profiles. In this work, we propose the use of deep learning models to reduce the stray light that appears in the diagnostic. The proposed approach utilizes a Pix2Pix neural network, which is an image-to-image translation based on a generative adversarial network (GAN). This network learns to translateimages affected by stray light to images without stray light. This allows for the effective removal of the noise that affects the measurements of the TS diagnostic, avoiding the need for manual image processing adjustments. The proposed method shows a better performance, reducing the noise up to $98\%$ inimages, which surpassesprevious works that obtained $85\%$ for the validation dataset. Full article

Point cloud registration is an important task in computer vision and robotics which is widely used in 3D reconstruction, target recognition, and other fields. At present, many registration methods based on deep learning have better registration accuracy in complete point cloud registration, but partial registration accuracy is poor. Therefore, a partial point cloud registration network, HALNet, is proposed. Firstly, a feature extraction network consisting mainly of adaptive graph convolution (AGConv), two-dimensional convolution, and convolution block attention (CBAM) is used to learn the features of the initial point cloud. Then the overlapping estimation is used to remove the non-overlapping points of the two point clouds, and the hybrid attention mechanism composed of self-attention and cross-attention is used to fuse the geometric information of the two point clouds. Finally, the rigid transformation is obtained by using the fully connected layer. Five methods with excellent registration performance were selected for comparison. Compared with SCANet, which has the best registration performance among the five methods, the RMSE(R) and MAE(R) of HALNet are reduced by 10.67% and 12.05%. In addition, the results of the ablation experiment verify that the hybrid attention mechanism and fully connected layer are conducive to improving registration performance. Full article

In order to assess the geochemical effects of retrograde metamorphic rehydration, fluid metasomatism, and the fluid-mobile elements (FMEs) budget in the case of oceanic and continental subduction, we report the petrography, bulk, and in situ LA-ICP-MS trace-element data for the two poorly studied ophiolites in the northern (Khara-Nur, Eastern Sayan, Russia) and central (Alag-Khadny accretionary wedge, SW Mongolia) parts of the peri-Siberian orogenic framing. Both complexes are relics of the ancient oceanic mantle, which was subjected to processes of partial melting, metasomatism, and retrograde metamorphism. Typical mineral assemblages include olivine + orthopyroxene + chlorite + tremolite ± secondary olivine (640–800 °C), olivine + antigorite ± secondary clinopyroxene (<640 °C), and olivine + chrysotile ± secondary clinopyroxene (<250 °C) and are stable at pressures up to 2 GPa. Hydration and partial serpentinization of mantle peridotites lead to tremolite formation after orthopyroxene, followed by olivine replacement by antigorite. Serpentine-group minerals (antigorite and chrysotile) were distinguished by Raman spectroscopy, and the contents of incompatible elements (mobile and immobile in fluids) in metamorphic minerals (tremolite, antigorite, and chrysotile) were examined in situ by LA-ICP-MS. The behavior of conservative HFSE (Zr, Nb, Ta, and Ti) and—in part—HREE does not distinguish between the two types (oceanic and continental) of subduction environments. Different patterns of FMEs (Cs, Rb, Ba, U, Sb, Pb, Sr, and LREE) enrichment in metaperidotites reflect variations in the slab fluid composition, which was primarily governed by the contrasting nature of subducted lithologies. The affinity of Alag-Khadny to the subduction of a continental margin is recorded by increased FME contents and selective enrichment by some moderately mobile elements, such as U, Th, and LREE, with respect to the oceanic-type subduction environment of Khara-Nur. Distinct patterns of FME enrichment in tremolite and antigorite from two complexes indicate different sequences of fluid-induced replacement, which was controlled by Opx composition. We demonstrate that evaluation of the initial composition of precursor minerals affected by multi-stage melting and melt metasomatism should be considered with care to estimate the differential fluid overprint and associated elemental uptake from subduction fluids. Full article

Globalization opens up economies and encourages the free movement of persons and factors of production. Diaspora investors and workers earn income in the process and make remittances to the migrating countries. We examine the impact of the remittance inflow on the macroeconomic performance of top emigrating countries, which comprise nine emerging and two advanced economies. We conduct group and individual country analyses with distinct econometric models (Feasible Quasi Generalized Least Squares and Dynamic Common Correlated Effects) using data between 1987 and 2021. The results reveal positive impact of remittance inflows on nominal GDP and nominal GDP per capita and on real GDP and real GDP per capita, although evidence on the latter is weaker. In all, the emigrating countries can benefit from diaspora remittance in terms of improved productivity and macroeconomic performance. We therefore recommend better systems to facilitate remittance receipt and policies to channel such flows more into investment activities. Full article

Abundant tuff mineral resources offer a promising solution to the shortage of fly ash (FA) and silica fume (SF) resources as emerging supplementary cementitious materials. However, a lack of clarity on its hydration mechanism has hindered its practical engineering application. In this study, high SiO₂-content tuff powder (TP) was examined to assess the mechanical and workability performance of mortar specimens with varying particle sizes of the TP as complete replacements for FA or SF. Microscopic analysis techniques, including X-ray diffraction (XRD), differential thermal analysis (DTG), and energy-dispersive X-ray spectroscopy (EDS), were employed to elucidate the hydration mechanism of the TP and its feasibility as a substitute for SF or FA. Results indicated that TP primarily functions as nuclei and filler, promoting cement hydration, with smaller particle sizes amplifying the hydration ability and increasing Ca(OH)₂ and C-S-H gel content. The specimens with TP (median particle size 7.58 μm) demonstrated 9.2% and 29.9% higher flexural and compressive strengths at 28 days, respectively, compared to the FA specimens of equal mass. However, fluidity decreased by 23.1% accordingly. Due to TP’s smaller specific surface area compared to SF, the TP specimens exhibited higher fluidity but with decreased strength relative to the SF specimens. Overall, TP shows potential as a replacement for FA with additional measures to ensure workability. Full article

Musculoskeletal injuries such as equine osteoarthritis, osteoarticular defects, tendonitis/desmitis, and muscular disorders are prevalent among sport horses, with a fair prognosis for returning to exercise or previous performance levels. The field of equine medicine has witnessed rapid and fruitful development, resulting in a diverse range of therapeutic options for musculoskeletal problems. Staying abreast of these advancements can be challenging, prompting the need for a comprehensive review of commonly used and recent treatments. The aim is to compile current therapeutic options for managing these injuries, spanning from simple to complex physiotherapy techniques, conservative treatments including steroidal and non-steroidal anti-inflammatory drugs, hyaluronic acid, polysulfated glycosaminoglycans, pentosan polysulfate, and polyacrylamides, to promising regenerative therapies such as hemoderivatives and stem cell-based therapies. Each therapeutic modality is scrutinized for its benefits, limitations, and potential synergistic actions to facilitate their most effective application for the intended healing/regeneration of the injured tissue/organ and subsequent patient recovery. While stem cell-based therapies have emerged as particularly promising for equine musculoskeletal injuries, a multidisciplinary approach is underscored throughout the discussion, emphasizing the importance of considering various therapeutic modalities in tandem. Full article

This study used DC magnetron sputtering technology to fabricate Sm-Fe films and systematically investigated the phase transition behavior of Sm-Fe films with different Fe ratios. It was found that at higher Fe content, the films consisted of Sm₂Fe₁₇ or SmFe₇ phases; as Fe content decreased, the films were mainly composed of SmFe₃ or SmFe₂ phases; at higher Sm content, the films primarily consisted of Sm phase. Sm is prone to volatilization at high temperatures, so Ta was used as a capping layer to effectively suppress Sm volatilization, successfully synthesizing pure SmFe₂ phase films at a nearly 1:2 ratio. The magnetic properties and magnetostrictive behavior of the SmFe₂ films were investigated, revealing that pure-phase SmFe₂ films exhibit good perpendicular magnetic anisotropy and magnetostriction properties. The larger stress along the perpendicular-to-film direction, resulting from the absence of substrate-induced constraints, contributes to the excellent perpendicular magnetic anisotropy of the films. This study successfully synthesized pure-phase SmFe₂ films and discovered a new method for fabricating perpendicularly anisotropic films. The research findings are of great significance for the efficient synthesis of desired films with high phase formation temperatures containing volatile elements. Full article

The partial discharge (PD) event in high-voltage insulation releases energy, exerts mechanical pressure, and generates elastic waves. Detecting and locating these PD events through short-duration acoustic pulses is well established, particularly in gas-insulated systems and oil-insulated transformers. However, its full potential remains untapped in solid insulation systems, where the propagation capability of the acoustic pulse and the acoustic reflections pose fundamental challenges to the acoustic emission (AE) detection technique. This study investigates the influence of reflections and multiple paths on the propagating acoustic pulse in polymeric insulating materials using a finite element method (FEM) in COMSOL. It was observed that the reflections from the boundary influence the propagating pulse’s shape, peak magnitude, and arrival time. An analytical MATLAB model further quantifies the impact of multiple propagation paths on the shape, magnitude, and arrival time of the pulse travelling in a cylinder. Additionally, a Perfect Matched Layer (PML) was implemented in the COMSOL model to eliminate the reflections from the boundary, and it revealed that the acoustic pulse magnitude decreases with distance following the inverse square law. In essence, the models aid in measuring how reflections contribute to the observed signals, facilitating the precise identification of the source of the PD event in the tested system. Full article

Bubble growth processes are highly complex processes, which are not only dependent on the foaming process parameters (temperature, pressure and blowing agent concentration) but also on the type and structure of the polymer used. Since the elongational viscosity at the bubble wall during bubble growth also depends on these influencing factors, the so-called transient elongational viscosity plays a key role in describing the gas bubble growth behavior in polymer melts. The model-based description of the transient elongational viscosity function is difficult due to its dependence on time, Hencky strain and strain rate. Therefore, representative viscosities or shear viscosity models are usually used in the literature to predict the bubble growth behavior. In this work, the transient equibiaxial elongational viscosity function at the bubble wall during bubble growth is described holistically for the first time. This is achieved by extending the so-called molecular stress function (MSF) model by superposition principles (temperature, pressure and blowing agent concentration) and by using the elongational deformation behavior (Hencky strain and strain rate) at the bubble wall during the initial, and thus viscosity-driven, bubble growth process. Therefore, transient uniaxial elongational viscosity measurements are performed and the non-linear MSF model parameters of the two investigated polymers PS (linear polymer chains) and PLA (long-chain branched polymer chains) are determined. By applying the superposition principles and by changing the strain mode parameter to the equibiaxial case in the MSF model, the transient equibiaxial viscosity master curve is obtained and used to describe the bubble growth process. The results show that the extended MSF model can fully predict the transient equibiaxial elongational viscosity function at the bubble wall during bubble growth processes. The bubble growth behavior over time can then be realistically described using the defined transient equibiaxial elongational viscosity function at the bubble wall. This is not possible, for example, with a representative viscosity and therefore clearly demonstrates the influence and importance of knowing the transient deformation behavior that prevails at the bubble wall during bubble growth processes. Full article

Based on the need for radiobiological databases, in this work, we mined experimental ionizing radiation data of human cells treated with X-rays, γ-rays, carbon ions, protons and α-particles, by manually searching the relevant literature in PubMed from 1980 until 2024. In order to calculate normal and tumor cell survival α and β coefficients of the linear quadratic (LQ) established model, as well as the initial values of the double-strand breaks (DSBs) in DNA, we used WebPlotDigitizer and Python programming language. We also produced complex DNA damage results through the fast Monte Carlo code MCDS in order to complete any missing data. The calculated α/β values are in good agreement with those valued reported in the literature, where α shows a relatively good association with linear energy transfer (LET), but not β. In general, a positive correlation between DSBs and LET was observed as far as the experimental values are concerned. Furthermore, we developed a biophysical prediction model by using machine learning, which showed a good performance for α, while it underscored LET as the most important feature for its prediction. In this study, we designed and developed the novel radiobiological ‘RadPhysBio’ database for the prediction of irradiated cell survival (α and β coefficients of the LQ model). The incorporation of machine learning and repair models increases the applicability of our results and the spectrum of potential users. Full article

Background and objectives: Hepatocellular carcinoma (HCC) is one of the most common malignancies worldwide and is caused by multiple factors. To explore novel targets for HCC treatment, we comprehensively analyzed the expression of HomeoboxB13 (HOXB13) and its role in HCC. Materials and Methods: The clinical significance of HCC was investigated using open gene expression databases, such as TIMER, UALCAN, KM, OSlihc, and LinkedOmics, and immunohistochemistry analysis. We also analyzed cell invasion and migration in HCC cell lines transfected with HOXB13-siRNA and their association with MMP9, E2F1, and MEIS1. Results: HOXB13 expression was higher in fibrolamellar carcinoma than in other histological subtypes. Its expression was associated with lymph node metastasis, histological stage, and tumor grade. It was positively correlated with immune cell infiltration of B cells (R = 0.246), macrophages (R = 0.182), myeloid dendritic cells (R = 0.247), neutrophils (R = 0.117), and CD4+ T cells (R = 0.258) and negatively correlated with immune cell infiltration of CD8+ T cells (R = −0.107). A positive correlation was observed between HOXB13, MMP9 (R = 0.176), E2F1 (R = 0.241), and MEIS1 (R = 0.189) expression (p < 0.001). The expression level of HOXB13 was significantly downregulated in both HepG2 and PLC/PFR/5 cell lines transfected with HOXB13-siRNA compared to that in cells transfected with NC siRNA (p < 0.05). Additionally, HOXB13 significantly affected cell viability and wound healing. Conclusions: HOXB13 overexpression may lead to poor prognosis in patients with HCC. Additional in vivo studies are required to improve our understanding of the biological role and the exact mechanism of action of HOXB13 in HCC. Full article

Background: Long-term rhythm monitoring (LTRM) can detect undiagnosed atrial fibrillation (AF) in patients at risk of AF and stroke. Circulating microRNAs (miRNAs), which have been shown to play a role in atrial electrical and structural remodelling, could help to select patients who would benefit most from LTRM. The aim of this study was to investigate whether patients with diabetes mellitus (DM) and hypertension and screen-detected subclinical AF (SCAF) using an insertable cardiac monitor (ICM) have significantly different plasma baseline levels of five selected miRNAs playing a role in the modulation of atrial electrical and structural remodelling (miR-21-5p, miR-29b-3p, miR-150-5p, miR-328-3p, and miR-432-5p) compared to those without SCAF. Methods: This study was performed at the outpatient clinic of a secondary academic teaching hospital between December 2013 and November 2015. Eligible patients were ≥65 years of age with DM and hypertension but without known heart diseases. All patients received an ICM. On the day of ICM implantation, blood samples for the measurement of plasma levels of the five miRNAs were drawn. In this post hoc analysis, we investigated their expression by reverse transcription-quantitative polymerase chain reaction. MiRNA plasma levels in patients with and without newly detected SCAF were compared. Results: We included 82 consecutive patients (median age of 71.3 years (IQR 67.4–75.1)), who were followed for a median of 588 days (IQR: 453–712 days). Seventeen patients (20.7%) had ICM-detected SCAF. Plasma levels of miR-328-3p, miR-29b-3p, miR-21-5p, miR-432-5p, and miR-150-5p were slightly but not significantly different in patients with incident SCAF compared with patients without. Conclusions: In patients with hypertension and DM, newly detected SCAF was not significantly associated with changes in expression levels of miR-21-5p, miR-29b-3p, miR-150-5p, miR-328-3p, and miR-432-5p. Full article

Brain tissue segmentation plays a critical role in the diagnosis, treatment, and study of brain diseases. Accurately identifying these boundaries is essential for improving segmentation accuracy. However, distinguishing boundaries between different brain tissues can be challenging, as they often overlap. Existing deep learning methods primarily calculate the overall segmentation results without adequately addressing local regions, leading to error propagation and mis-segmentation along boundaries. In this study, we propose a novel mis-segmentation-focused loss function based on a two-stage nnU-Net framework. Our approach aims to enhance the model’s ability to handle ambiguous boundaries and overlapping anatomical structures, thereby achieving more accurate brain tissue segmentation results. Specifically, the first stage targets the identification of mis-segmentation regions using a global loss function, while the second stage involves defining a mis-segmentation loss function to adaptively adjust the model, thus improving its capability to handle ambiguous boundaries and overlapping anatomical structures. Experimental evaluations on two datasets demonstrate that our proposed method outperforms existing approaches both quantitatively and qualitatively. Full article

Traditional methods for assessing the cleanliness of liquid metal are characterized by prolonged detection times, delays, and susceptibility to variations in sampling conditions. To address these limitations, an online cleanliness-analyzing system grounded in the method of the electrical sensing zone has been developed. This system facilitates real-time, in situ, and quantitative analysis of inclusion size and amount in liquid metal. Comprising pneumatic, embedded, and host computer modules, the system supports the continuous, online evaluation of metal cleanliness across various metallurgical processes in high-temperature environments. Tests conducted with gallium liquid at 90 °C and aluminum melt at 800 °C have validated the system’s ability to precisely and quantitatively detect inclusions in molten metal in real time. The detection procedure is stable and reliable, offering immediate data feedback that effectively captures fluctuations in inclusion amount, thereby meeting the metallurgical industry’s demand for real-time analyzing and control of inclusion cleanliness in liquid metal. Additionally, the system was used to analyze inclusion size distribution during the hot-dip galvanizing process. At a zinc melt temperature of 500 °C, it achieved a detection limit of 21 μm, simultaneously providing real-time data on the size and amount distribution of inclusions. This represents a novel strategy for the online monitoring and quality control of zinc slag throughout the hot-dip galvanizing process. Full article

Research on wildlife monitoring methods is a crucial tool for the conservation of rare wildlife in China. However, the fact that rare wildlife monitoring images in field scenes are easily affected by complex scene information, poorly illuminated, obscured, and blurred limits their use. This often results in unstable recognition and low accuracy levels. To address this issue, this paper proposes a novel wildlife identification model for rare animals in Giant Panda National Park (GPNP). We redesigned the C3 module of YOLOv5 using NAMAttention and the MemoryEfficientMish activation function to decrease the weight of field scene features. Additionally, we integrated the WIoU boundary loss function to mitigate the influence of low-quality images during training, resulting in the development of the NMW-YOLOv5 model. Our model achieved 97.3% for mAP50 and 83.3% for mAP50:95 in the LoTE-Animal dataset. When comparing the model with some classical YOLO models for the purpose of conducting comparison experiments, it surpasses the current best-performing model by 1.6% for mAP50:95, showcasing a high level of recognition accuracy. In the generalization ability test, the model has a low error rate for most rare wildlife species and is generally able to identify wildlife in the wild environment of the GPNP with greater accuracy. It has been demonstrated that NMW-YOLOv5 significantly enhances wildlife recognition accuracy in field environments by eliminating irrelevant features and extracting deep, effective features. Furthermore, it exhibits strong detection and recognition capabilities for rare wildlife in GPNP field environments. This could offer a new and effective tool for rare wildlife monitoring in GPNP. Full article

Detecting cracks in the pavement is a vital component of ensuring road safety. Since manual identification of these cracks can be time-consuming, an automated method is needed to speed up this process. However, creating such a system is challenging due to factors including crack variability, variations in pavement materials, and the occurrence of miscellaneous objects and anomalies on the pavement. Motivated by the latest progress in deep learning applied to computer vision, we propose an effective U-Net-shaped model named DepthCrackNet. Our model employs the Double Convolution Encoder (DCE), composed of a sequence of convolution layers, for robust feature extraction while keeping parameters optimally efficient. We have incorporated the TriInput Multi-Head Spatial Attention (TMSA) module into our model; in this module, each head operates independently, capturing various spatial relationships and boosting the extraction of rich contextual information. Furthermore, DepthCrackNet employs the Spatial Depth Enhancer (SDE) module, specifically designed to augment the feature extraction capabilities of our segmentation model. The performance of the DepthCrackNet was evaluated on two public crack datasets: Crack500 and DeepCrack. In our experimental studies, the network achieved mIoU scores of 77.0% and 83.9% with the Crack500 and DeepCrack datasets, respectively. Full article