“赫拉”(Hera)任务是由欧空局(ESA)主导并与美国国家航空航天局(NASA)合作开展的行星防御任务之一,通过对“双小行星重定向测试”(Double Asteroid Redirection Test,DART)任务撞击的“迪蒂莫斯”(Didymos)双星小行星系统开展近距离探测、物理特性表征及精确定轨等工作,从而实现撞击后小行星偏转评估、撞击坑产生和演化评估、撞击过程能量传递效率的评估,以此验证撞击防御的有效性并为后续合理有效的撞击防御方案设计提供重要的数据支撑。此外,任务通过撞击翻出的小行星表层以下物质的探测进一步研究太阳系早期小行星形成过程等科学问题。本文概述“赫拉”探测任务探测器设计、载荷配置、立方星设计、任务过程等工程实施特点,针对未来中国小行星防御技术的发展和规划提出思考和建议。
In this paper,the proton structure function F_(2)^(p)(x,Q^(2))at small-x is investigated using an analytical solution to the Balitsky–Kovchegov(BK)equation.In the context of the color dipole description of deep inelastic scattering(DIS),the structure function F_(2)^(p)(x,Q^(2))is computed by applying the analytical expression for the scattering amplitude N(k,Y)derived from the BK solution.At transverse momentum k and total rapidity Y,the scattering amplitude N(k,Y)represents the propagation of the quark-antiquark dipole in the color dipole description of DIS.Using the BK solution we extracted the integrated gluon density xg(x,Q^(2))and then compared our theoretical estimation with the LHAPDF global data fits,NNPDF3.1sx and CT18.Finally,we have investigated the behavior of F_(2)^(p)(x,Q^(2))in the kinematic region of 10^(-5)≤x≤10^(-2)and 2.5 GeV^(2)≤Q^(2)≤60 GeV^(2).Our predicted results for F_(2)^(p)(x,Q^(2))within the specified kinematic region are in good agreement with the recent high-precision data for F_(2)^(p)(x,Q^(2))from HERA(H1 Collaboration)and the LHAPDF global parametrization group NNPDF3.1sx.
Breast cancer remains a significant global health concern,with early detection being crucial for effective treatment and improved survival rates.This study introduces HERA-Net(Hybrid Extraction and Recognition Architec-ture),an advanced hybrid model designed to enhance the diagnostic accuracy of breast cancer detection by leveraging both thermographic and ultrasound imaging modalities.The HERA-Net model integrates powerful deep learning architectures,including VGG19,U-Net,GRU(Gated Recurrent Units),and ResNet-50,to capture multi-dimensional features that support robust image segmentation,feature extraction,and temporal analysis.For thermographic imaging,a comprehensive dataset of 3534 infrared(IR)images from the DMR(Database for Mastology Research)was utilized,with images captured by the high-resolution FLIR SC-620 camera.This dataset was partitioned with 70%of images allocated to training,15%to validation,and 15%to testing,ensuring a balanced approach for model development and evaluation.To prepare the images,preprocessing steps included resizing,Contrast-Limited Adaptive Histogram Equalization(CLAHE)for enhanced contrast,bilateral filtering for noise reduction,and Non-Local Means(NLMS)filtering to refine structural details.Statistical metrics such as mean,variance,standard deviation,entropy,kurtosis,and skewness were extracted to provide a detailed analysis of thermal distribution across samples.Similarly,the ultrasound dataset was processed to extract detailed anatomical features relevant to breast cancer diagnosis.Preprocessing involved grayscale conversion,bilateral filtering,and Multipurpose Beta Optimized Bihistogram Equalization(MBOBHE)for contrast enhancement,followed by segmentation using Geodesic Active Contours.The ultrasound and thermographic datasets were subsequently fed into HERA-Net,where VGG19 and U-Net were applied for feature extraction and segmentation,GRU for temporal pattern recognition,and ResNet-50 for classification.The performance assessment of HERA-Net on both imaging modalities demons
Background: Gestational diabetes mellitus (GDM) is one of the most common pathologies in pregnancy. Unfortunately, both clinicians and patients are often reluctant to begin insulin therapy, a phenomenon that has been known as psychological insulin resistance (PIR). Objectives: To assess the barriers of initiating insulin among GDM pregnant women. Patients and Methods: An observational cross-sectional study was conducted in the GDM clinic, Diabetes Center in Hera’a General Hospital, Makkah, Saudi Arabia in a period of 4 months. A self-administered validated questionnaire was adopted. It included socio-demographic data of women, perceived (personal, social, pharmacological, occupational and misconception) barriers towards insulin therapy and possible solutions to overcome these barriers. Results: A total of 164 pregnant women with gestational diabetes were included in the study. The age of 36.4% of them exceeded 35 years. Among personal barriers, preferring other treatment methods over insulin (56.4%) and unaware of insulin dose control method (45.4%) were commonly reported. Regarding family barriers, 23.6% reported past family experience of insulin-related complications. Concerning side effects, fear of hypoglycemia (59.4%) and fear of weight gain (50.9%) were most frequently reported barriers against use of insulin. Regarding misconceptions about insulin injections, 26% believed that insulin is addictive;the injection will continue for life. Among work-related barriers, irregular eating times during working hours and long working hours (55.2%) were barriers for insulin use. Facilitating access to healthcare services (94%), engage the patient in decision-making and development of the treatment plan (91.6%), activate virtual clinics and social media for remote follow-up (86.6%) and organize social support groups for pregnant women who use insulin to share their experiences were the most frequently reported possible solutions to initiate and commit to insulin therapy. Conclusion: Various barriers were identified a
