Investigating the Effects of AL049796.1 Silencing in Inhibiting High Glucose-Induced Colorectal Cancer Progression.

Patients with colorectal cancer (CRC) and diabetes share many risk factors. Despite a strong association between diabetes and CRC being widely studied and confirmed, further genetic research is needed. This study found higher AL049796.1 and TEA domain transcription factor 1 (TEAD1) levels (both mRNA and protein) in CRC tissues of diabetic patients compared with nondiabetics, but no significant difference in miR-200b-3p levels. A positive correlation between AL049796.1 and TEAD1 protein existed regardless of diabetes status, whereas miR-200b-3p was only negatively correlated with TEAD1 protein in nondiabetic CRC tissues. In vitro experiments have shown that high glucose (HG) treatment increased AL049796.1 in CRC cells, and AL049796.1 silencing reduced HG-induced proliferation, migration and invasion, as well as connective tissue growth factor, cysteine-rich angiogenic inducer 61, and epidermal growth factor receptor protein expression. Mechanistic investigations indicated that AL049796.1 could mitigate suppression of miR-200b-3p on TEAD1 posttranscriptionally by acting as a competitive binder. In vivo, subcutaneous CRC tumors in streptozotocin (STZ)-induced mice grew significantly faster; AL049796.1 silencing did not affect the growth of subcutaneous CRC tumors but significantly reduced that of STZ-induced mice. Our study suggests that AL049796.1 independently contributes to the risk of CRC in diabetic patients, highlighting its potential as both a therapeutic target and a novel biomarker for CRC among individuals with diabetes.

Chinese undergraduates' mental health help-seeking behavior: the health belief model.

The detection rate of mental health problems among undergraduates has recently risen significantly. However, undergraduates underutilize mental health services; approximately a third only of undergraduates in need of treatment use school counseling resources. Based on a social psychological theoretical framework, the health belief model, factors of undergraduates' willingness to seek help when dealing with psychological problems were investigated. A cross-sectional online questionnaire and a snowball sampling method with 446 undergraduates investigated perceived susceptibility, perceived severity, perceived behavioral benefits, perceived barriers, self-efficacy, and cues to action to understand how students' mental health-seeking behaviors are affected. We found that perceived susceptibility (p < 0.01), perceived severity (p < 0.01), perceived benefits (p < 0.01), perceived barriers (p < 0.01), self-efficacy (p < 0.01), and cues to action (p < 0.01) significantly correlated with behavioral intention. Encouragement or counseling from others would be more likely to motivate undergraduates to seek mental health help. In addition, we used a bias-corrected Bootstrap approach to test the significance of the mediating effect, the mediation effect of cues to action between undergraduates' perceived susceptibility and mental health help-seeking behavior was utterly significant [mediation effect value of 0.077, with an SE value of 0.027 and a 95% CI (0.028, 0.133)]. It demonstrated that those who perceived themselves to be at high risk of developing a mental illness and who had received encouragement or counseling to seek mental health help were more likely to be motivated to seek mental health help. Multiple regression analyses indicated that self-efficacy (Z = 5.425, p < 0.01) and cues to action (Z = 6.673, p < 0.01) independently influenced behavioral intentions. Encouragement or counseling from others would be more likely to motivate undergraduates to seek mental health help.

A Retrospective Study of the Effect of Spinopelvic Parameters on Fatty Infiltration in Paraspinal Muscles in Patients With Lumbar Spondylolisthesis.

OBJECTIVE: The effect on fat infiltration (FI) of paraspinal muscles in degenerative lumbar spinal diseases has been demonstrated except for spinopelvic parameters. The present study is to identify the effect of spinopelvic parameters on FI of paraspinal muscle (PSM) and psoas major muscle (PMM) in patients with degenerative lumbar spondylolisthesis. METHODS: A single-center, retrospective cross-sectional study of 160 patients with degenerative lumbar spondylolisthesis (DLS) and lumbar stenosis (LSS) who had lateral full-spine x-ray and lumbar spine magnetic resonance imaging was conducted. PSM and PMM FIs were defined as the ratio of fat to its muscle cross-sectional area. The FIs were compared among patients with different pelvic tilt (PT) and pelvic incidence (PI), respectively. RESULTS: The PSM FI correlated significantly with pelvic parameters in DLS patients, but not in LSS patients. The PSM FI in pelvic retroversion (PT > 25°) was 0.54 ± 0.13, which was significantly higher in DLS patients than in normal pelvis (0.41 ± 0.14) and pelvic anteversion (PT < 5°) (0.34 ± 0.12). The PSM FI of DLS patients with large PI ( > 60°) was 0.50 ± 0.13, which was higher than those with small ( < 45°) and normal PI (0.37 ± 0.11 and 0.36 ± 0.13). However, the PSM FI of LSS patients didn't change significantly with PT or PI. Moreover, the PMM FI was about 0.10-0.15, which was significantly lower than the PSM FI, and changed with PT and PI in a similar way of PSM FI with much less in magnitude. CONCLUSION: FI of the PSMs increased with greater pelvic retroversion or larger pelvic incidence in DLS patients, but not in LSS patients.

High Thermal Performance Ultraviolet (368 nm) AlGaN-Based Flip-Chip LEDs with an Optimized Structure.

In this study, we have fabricated a 368 nm LED with an epitaxial Indium Tin Oxide (ITO) contact layer. We analyze the thermal performance of the flip-chip LED with a symmetric electrode and metal reflective layer, applying ANSYS to build a coupled electro-thermal finite element model (FEM) of the temperature distribution in the multiple quantum wells (MQWs). We compare our system with the traditional Au-bump flip-chip LED and a flip-chip LED with a Distributed Bragg Reflector (DBR) layer. The simulation results have shown that the flip-chip LED with a metal reflective layer and symmetric electrode exhibits better heat dissipation performance, particularly at high input power. The influence of the insulating layer on the LED chip junction temperature is also examined. The simulation data establish an effect due to the thermal conductivity of the insulating layer in terms of heat dissipation, but this effect is negligible at an insulation layer thickness ≤1 µm.

Autonomous Atmospheric Water Harvesting over a Wide RH Range Enabled by Super Hygroscopic Composite Aerogels.

Sorption-based atmospheric water harvesting (SAWH) offers a sustainable strategy to address the global freshwater shortage. However, obtaining sorbents with excellent performance over a wide relative humidity (RH) range and devices with fully autonomous water production remains challenging. Herein, magnesium chloride (MgCl2) is innovatively converted into super hygroscopic magnesium complexes(MC), which can effectively solve the problems of salt deliquescence and agglomeration. The MC are then integrated with photothermal aerogels composed of sodium alginate and carbon nanotubes (SA/CNTs) to form composite aerogels, which showed high water uptake over a wide RH range, reaching 5.43 and 0.27 kg kg-1 at 95% and 20% RH, respectively. The hierarchical porous structure enables the as-prepared SA/CNTs/MC to exhibit rapid absorption/desorption kinetics with 12 cycles per day at 70% RH, equivalent to a water yield of 10.0 L kg-1 day-1. To further realize continuous and practical freshwater production, a fully solar-driven autonomous atmospheric water generator is designed and constructed with two SA/CNTs/MC-based absorption layers, which can alternately conduct the water absorption/desorption process without any other energy consumption. The design provides a promising approach to achieving autonomous, high-performance, and scalable SAWH.

An Information-Assisted Deep Reinforcement Learning Path Planning Scheme for Dynamic and Unknown Underwater Environment.

An autonomous underwater vehicle (AUV) has shown impressive potential and promising exploitation prospects in numerous marine missions. Among its various applications, the most essential prerequisite is path planning. Although considerable endeavors have been made, there are several limitations. A complete and realistic ocean simulation environment is critically needed. As most of the existing methods are based on mathematical models, they suffer from a large gap with reality. At the same time, the dynamic and unknown environment places high demands on robustness and generalization. In order to overcome these limitations, we propose an information-assisted reinforcement learning path planning scheme. First, it performs numerical modeling based on real ocean current observations to establish a complete simulation environment with the grid method, including 3-D terrain, dynamic currents, local information, and so on. Next, we propose an information compression (IC) scheme to trim the mutual information (MI) between reinforcement learning neural network layers to improve generalization. A proof based on information theory provides solid support for this. Moreover, for the dynamic characteristics of the marine environment, we elaborately design a confidence evaluator (CE), which evaluates the correlation between two adjacent frames of ocean currents to provide confidence for the action. The performance of our method has been evaluated and proven by numerical results, which demonstrate a fair sensitivity to ocean currents and high robustness and generalization to cope with the dynamic and unknown underwater environment.

RAP Vol: Robust Adversary Populations With Volume Diversity Measure.

Deep reinforcement learning (DRL) algorithms have made remarkable achievements in various fields, but they are vulnerable to changes in environment dynamics. This vulnerability easily leads to poor generalization, low performance, and catastrophic failures in unseen environments, which severely hinders the application of DRL in real-world scenarios. The robustness via adversary populations (RAP) algorithm addresses this issue by introducing a population of adversaries that perturb the protagonist. However, the low data utilization efficiency and lack of population diversity greatly limit the generalization performance. This article proposes robust adversary populations with volume diversity measure (RAP Vol) to address these drawbacks. In the proposed joint adversarial training framework, we use the training data to update all adversaries rather than only a single adversary, leading to a higher data utilization efficiency and a fast convergence speed. In the proposed population diversity iterative improvement mechanism, the vectors representing adversaries span a high-dimensional region. The volume of this region is utilized to measure and enhance population diversity via its square. The ablation experiments have verified the effectiveness of our proposed method in improving the robustness against variations in environment dynamics. Also, the influence of various factors (such as adversary population size and diversity weight) on the robustness has been investigated.

Generative AI-based Data Completeness Augmentation Algorithm for Data-driven Smart Healthcare.

In the decade, artificial intelligence has achieved great popularity and applications in medicine and healthcare. Various AI-based algorithms have shown astonishing performance. However, in various data-driven smart healthcare algorithms, the problem of incomplete dataset remains a huge challenge. In this paper, we propose a data completeness enhancement algorithm based on generative AI (i.e., GenAI-DAA) to solve the problems of the in-sufficient data for model training, the data imbalance, and the biases of the training samples. We first construct the cognitive field of the generative models and effectively understand the state of incomplete cognition in generative models. Secondly, on this basis, we propose a quest algorithm for abnormal samples in the cognitive field based on local outlier factor. By fine-grained value evaluation, abnormal samples are given more refined attention. Finally, integrating the above process through multiple cognitive adjustments, GenAI-DAA gradually improves the cognitive ability. GenAI-DAA can be summarized as "Quest-→Estimate-→Tune-up". We have conducted extensive experiments to demonstrate the effectiveness of our proposed algorithm, and shown widely applications to some typical data-driven smart healthcare algorithms.

AUV Path Planning Considering Ocean Current Disturbance Based on Cloud Desktop Technology.

In the field of ocean energy detection, Autonomous Underwater Vehicles (AUVs) offer significant advantages in terms of manpower, resource, and energy efficiency. However, the unpredictable nature of the ocean environment, particularly the real-time changes in ocean currents, poses navigational risks for AUVs. Therefore, effective path planning in dynamic environments is crucial for AUVs to perform specific tasks. This paper addresses the static path planning problem and proposes a model called the noise net double DQN network with prioritized experience replay (N-DDQNP). The N-DDQNP model combines a noise network and a prioritized experience replay mechanism to address the limited exploration and slow convergence speed issues of the DQN algorithm, which are caused by the greedy strategy and uniform sampling mechanism. The proposed approach involves constructing a double DQN network with a priority experience replay and an exploration mechanism using the noise network. Second, a compound reward function is formulated to take into account ocean current, distance, and safety factors, ensuring prompt feedback during the training process. Regarding the ocean current, the reward function is designed based on the angle between the current direction and the AUV's heading direction, considering its impact on the AUV's speed. As for the distance factor, the reward is determined by the Euclidean distance between the current position and the target point. Furthermore, the safety factor considers whether the AUV may collide with obstacles. By incorporating these three factors, the compound reward function is established. To evaluate the performance of the N-DDQNP model, experiments were conducted using real ocean data in various complex ocean environments. The results demonstrate that the path planning time of the N-DDQNP model outperforms other algorithms in different ocean current scenarios and obstacle environments. Furthermore, a user console-AUV connection has been established using spice cloud desktop technology. The cloud desktop architecture enables intuitive observation of the AUV's navigation posture and the surrounding marine environment, facilitating safer and more efficient underwater exploration and marine resource detection tasks.

Two-in-One Polymeric NO-Donor Prodrugs Mediate Precision and Synergistic Prostate Cancer Treatment.

Chemotherapy predominates in clinical treatment of prostate cancer (PCa), while irreversible resistance to chemotherapeutics and severe side effects hinder the therapeutic efficacy, especially in castration-resistant PCa (CRPC). Herein, a bombesin (BBN)-decorated two-in-one prodrug (T-NO/E2-PMs) incorporating a polymeric nitric oxide (NO) donor and acetal-linked 17ß-estradiol (E2) in one backbone is developed, aiming to inhibit androgen receptor (AR) expression, reprogram the tumor microenvironment of CRPC, and enhance estradiol-mediated hypoxic CRPC therapy. Following efficient internalization mediated by BBN, T-NO/E2-PMs releases estradiol and NO in response to the unique intracellular environments. Both in vitro and in vivo studies demonstrate that the T-NO/E2-PMs nano-prodrug along with NO release potently downregulates AR levels to reverse CRPC and further enhances the chemo-sensitization of estradiol to PCa PC-3 cell apoptosis and the inhibition of metastasis. Collectively, this two-in-one nano-prodrug strategy offers a promising platform for construction of advanced nanomedicine to boost the therapeutic efficacy.

In Situ Grown Silver-Polymer Framework with Coordination Complexes for Functional Artificial Tissues.

Self-sensing actuators are critical to artificial robots with biomimetic proprio-/exteroception properties of biological neuromuscular systems. Existing add-on approaches, which physically blend heterogeneous sensor/actuator components, fall short of yielding satisfactory solutions, considering their suboptimal interfaces, poor adhesion, and electronic/mechanical property mismatches. Here, a single homogeneous material platform is reported by creating a silver-polymer framework (SPF), thus realizing the seamless sensing-actuation unification. The SPF-enabled elastomer is highly stretchable (1200%), conductive (0.076 S m-1 ), and strong (0.76 MPa in-strength), where the stretchable polymer matrix synthesis and in situ silver nanoparticles reduction are accomplished simultaneously. Benefiting from the multimodal sensing capability from its architecture itself (mechanical and thermal cues), self-sensing actuation (proprio-deformations and external stimuli perceptions) is achieved for the SPF-based pneumatic actuator, alongside an excellent load-lifting attribute (up to 3700 times its own weight), substantiating its advantage of the unified sensing-actuation feature in a single homogenous material. In view of its human somatosensitive muscular systems imitative functionality, the reported SPF bodes well for use with next-generation functional tissues, including artificial skins, human-machine interfaces, self-sensing robots, and otherwise dynamic materials.

MetaMP: Metalearning-Based Multipatch Image Aesthetics Assessment.

Image aesthetics assessment (IAA) is a subjective and complex task. The aesthetics of different themes vary greatly in content and aesthetic results, whether they are in the same aesthetic community or not. In aesthetic evaluation tasks, the pretrained network with direct fine-tune may not be able to quickly adapt to tasks on various themes. This article introduces a metalearning-based multipatch (MetaMP) IAA method to adapt to various thematic tasks quickly. The network is trained based on metalearning to obtain content-oriented aesthetic expression. In addition, we design a complete-information patch selection scheme and a multipatch (MP) network to make the fine details fit the overall impression. Experimental results demonstrate the superiority of the proposed method in comparison with the state-of-the-art models based on aesthetic visual analysis (AVA) benchmark datasets. In addition, the evaluation of the dataset shows the effectiveness of our metalearning training model, which not only improves MetaMP assessment accuracy but also provides valuable guidance for network initialization of IAA.

Macromolecular NO-Donor Micelles for Targeted and Augmented Chemotherapy against Prostate Cancer.

Mitoxantrone (MTO) is clinically utilized for treating hormone-refractory prostate cancer (PCa), however, the therapeutic outcome is far from optimal due to the lack of proper drug carrier as well as the inherent MTO detoxification mechanisms of DNA lesion repair and anti-oxidation. Herein, a bombesin-installed nanoplatform combining the chemotherapeutic MTO and the chemotherapeutic sensitizer of nitric oxide (NO) is developed based on MTO-loaded macromolecular NO-donor-containing polymeric micelles (BN-NMMTO ) for targeted NO-sensitized chemotherapy against PCa. BN-NMMTO actively target and accumulates in PCa sites and are internalized into the tumor cells. The macromolecular NO-donor of BN-NMMTO undergoes a reductive reaction to unleash NO upon intracellular glutathione (GSH), accompanying by micelle swelling and MTO release. The targeted intracellular MTO release induces DNA lesion and reactive oxygen species (ROS) generation in tumor cells without damage to the normal cells, and MTO's cytotoxicity is further augmented by NO release via the inhibition of both DNA repair and anti-oxidation pathways as compared with traditional MTO therapies.

Disc degeneration contributes to the denser bone in the subendplate but not in the vertebral body in patients with lumbar spinal stenosis or disc herniation.

BACKGROUND CONTEXT: It is commonly believed that decreased bone quality would lead to endplate degeneration and arthritic changes in the facet joints, and thus accelerated disc degeneration (DD). However, some more detailed studies of vertebral bone structure have found that bone mineral density (BMD) in the vertebral body is increased rather than decreased in moderate or greater disc degeneration. The relationship between BMD and DD still needs further study. MRI-based vertebral bone quality scores have been shown to be effective in reflecting BMD, rendering a new way to evaluate the changes of vertebral body bone with DD using MRI alone. PURPOSE: To evaluate MRI-based vertebral bone quality and Pfirrmann grades in patients with lumbar spinal stenosis or disc herniation, and to identify if DD is associated with denser bone around the endplate. STUDY DESIGN/SETTING: A single-center, retrospective cohort study. PATIENT SAMPLE: A total of 130 patients with lumbar disc herniation and lumbar spinal stenosis from January 2019 to November 2020 who had a complete dual-energy X-ray absorptiometry scan and noncontrast lumbosacral spine MRI data. OUTCOME MEASURES: The vertebral bone quality score (VBQ) and sub-endplate bone quality score (EBQ) was calculated as a ratio of the signal intensity of the vertebral bodies and sub-endplate regions to the signal intensity of the cerebrospinal fluid at L3 on the mid-sagittal T1-weighted MRI images, respectively. The Pfirrmann grades of the lumbar discs were assessed as well. METHODS: The age, gender, body mass index, and T-score of the lumbar spine of the patients were collected. The degeneration grades of the lumbar discs were evaluated according to the Pfirrmann classification. VBQ and EBQ were measured through T1-weighted lumbar MRI. The VBQ and EBQ scores were compared between cranial and caudal sides. The correlation between MRI-based bone quality and DD was calculated. A linear regression model was used to examine the association between DD and adjacent EBQ and VBQ. RESULTS: This study included 569 lumbar segments from 130 inpatients. Cranial and caudal EBQ decreased with the increase of the Pfirrmann grade. The discs with Pfirrmann grade 5 had significantly lower caudal EBQ than the discs with Pfirrmann grades 2, 3, and 4. In the osteoporosis patients, the Pfirrmann grades negatively correlated both with the cranial EBQ and caudal EBQ. Pfirrmann grade greater than 4 was an independent contributor to the cranial EBQ, whereas greater than 3 was an independent contributor to the caudal EBQ. CONCLUSIONS: Disc degeneration grades correlated with the EBQ but not with the VBQ. In patients with lumbar spinal stenosis or disc herniation, DD contributes to the denser bone in the sub-endplate, but not in the whole vertebral body.

A Tiny Model for Fast and Precise Ship Detection via Feature Channel Pruning.

It is of great significance to accurately detect ships on the ocean. To obtain higher detection performance, many researchers use deep learning to identify ships from images instead of traditional detection methods. Nevertheless, the marine environment is relatively complex, making it quite difficult to determine features of ship targets. In addition, many detection models contain a large amount of parameters, which is not suitable to deploy in devices with limited computing resources. The two problems restrict the application of ship detection. In this paper, firstly, an SAR ship detection dataset is built based on several databases, solving the problem of a small number of ship samples. Then, we integrate the SPP, ASFF, and DIOU-NMS module into original YOLOv3 to improve the ship detection performance. SPP and ASFF help enrich semantic information of ship targets. DIOU-NMS can lower the false alarm. The improved YOLOv3 has 93.37% mAP, 4.11% higher than YOLOv3 on the self-built dataset. Then, we use the MCP method to compress the improved YOLOv3. Under the pruning ratio of 80%, the obtained compressed model has only 6.7 M parameters. Experiments show that MCP outperforms NS and ThiNet. With the size of 26.8 MB, the compact model can run at 15 FPS on an NVIDIA TX2 embedded development board, 4.3 times faster than the baseline model. Our work will contribute to the development and application of ship detection on the sea.

A DBULSTM-Adaboost Model for Sea Surface Temperature Prediction.

Sea surface temperature (SST) is an important parameter to measure the energy and heat balance of sea surface. The change of sea surface temperature has an important impact on the marine ecosystem, marine climate and marine environment. Therefore, sea surface temperature prediction has become an significant research direction in the field of ocean. This article proposes a DBULSTM-Adaboost model based on ensemble learning. The model is composed of Deep Bidirectional and Unidirectional Long Short Term Memory (DBULSTM) and Adaboost strong learner. DBULSTM can capture the forward and backward dependence of time series, and the DBULSTM model is integrated with Adaboost strong learner to reduce the variance and bias of prediction and realize the short and medium term prediction of SST at a single point scale. Experimental results show that the model can improve the accuracy and stability of SST prediction. Experiments on the East China Sea and South China Sea with different prediction lengths show that the model is almost superior to other classical models in different sea areas and at different prediction levels. Compared with full-connected LSTM (FC-LSTM) model, the root-mean-square error is reduced by about 0.1.

Self-Sustained Programmable Hygroelectronic Interfaces for Humidity-Regulated Hierarchical Information Encryption and Display.

The emerging moisture-driven energy generation (MEG) technology opens up new possibilities for humidity-responsive materials, devices, and interdisciplinary opportunities in fields like information security. However, such potential remains untapped. Here, an original MEG structure with a hygroionic energy-conversion route by selective coating of ionic hygroscopic hydrogels on a carbon black surface is reported. The hygroionic route features a process in which the scavenged energy is stored in the electrical double layers formed at the interfaces between the ionic hydrogel and the carbon nanoparticles. The resultant electrical field developed across the hydrogel-coated wet carbon and the rest of the dry carbon area is thus durably lasted. Based on this unique structure, hygroelectronic information interfaces (HEII) for humidity-regulated information encryption and display are put forward by devising hydrogel patterns on a carbon platform. Further by tuning the hygroscopicity of the ionic hydrogels and incorporating encoding methods (e.g., Morse code), it is demonstrated that the HEII platform is programmable to carry different information in certain humidity ranges. Unlike those conventional anti-counterfeiting methods that optically reveal the hidden information once the required stimulus is provided, the new HEII serves as a hierarchical solution for high-security encryption and display.

Exploration of Laser Marking Path and Algorithm Based on Intelligent Computing and Internet of Things.

Nowadays, laser processing technology is being used more and more in various fields, and the requirements for laser control procedures are getting higher and higher. This paper aims to study the path generation problem of laser marking technology in order to improve the efficiency of laser marking as well as the protection of the marking material. Therefore, we creatively propose two-path generation methods, namely, sawtooth parallel and contour parallel, and design the boundary curve offset algorithm and domain partition intersection algorithm for the computer simulation of the two marking paths, respectively. Through the simulation, we discussed the efficiency and marking quality of the two path generation methods and gave the conclusion that the efficiency of the sawtooth parallel path generation method is greater than that of the contour parallel path generation method under specific parameters.

Enriching Facial Anti-Spoofing Datasets via an Effective Face Swapping Framework.

In the era of rapid development of the Internet of things, deep learning, and communication technologies, social media has become an indispensable element. However, while enjoying the convenience brought by technological innovation, people are also facing the negative impact brought by them. Taking the users' portraits of multimedia systems as examples, with the maturity of deep facial forgery technologies, personal portraits are facing malicious tampering and forgery, which pose a potential threat to personal privacy security and social impact. At present, the deep forgery detection methods are learning-based methods, which depend on the data to a certain extent. Enriching facial anti-spoofing datasets is an effective method to solve the above problem. Therefore, we propose an effective face swapping framework based on StyleGAN. We utilize the feature pyramid network to extract facial features and map them to the latent space of StyleGAN. In order to realize the transformation of identity, we explore the representation of identity information and propose an adaptive identity editing module. We design a simple and effective post-processing process to improve the authenticity of the images. Experiments show that our proposed method can effectively complete face swapping and provide high-quality data for deep forgery detection to ensure the security of multimedia systems.