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Image vaccine against steganography in encrypted domain
Abstract This paper investigates on the defense against steganography, and the overall purpose of the study is to design a satisfactory defense scheme in encrypted domain. Image vaccine against steganography is an effective technique to discover the utilization of steganography with extremely high detection accuracy. However, the image owner and vaccine provider are not the same person usually. To meet the requirements of steganography defense and privacy protection simultaneously, this paper proposes a vaccine scheme against steganography for encrypted images. After encrypting the entire data of a original image using a stream cipher, the vaccine data can be injected into the image without knowing the image content. With an encrypted image containing vaccine data, one can decrypt it to obtain the vaccinated image. When steganography is executed on vaccinated image, the utilization of steganography can be discovered in encrypted domain. Experimental results show that the detection accuracy of our scheme on steganography is 100% for all cases. That means the utilization of steganography can be always detected using our scheme. Integrate image vaccine into the imaging process of digital cameras in IoT systems is a potential practical application of our scheme. Non-universal detection mechanism is the potential limitations of this study, and it may be solved by pre-processing original image instead of injecting specific data.
Extensive off-fault damage around the 2023 Kahramanmaraş earthquake surface ruptures
Abstract Quantifying coseismic fault offsets for surface ruptures of major earthquakes is important for earthquake cycle and slip-rate studies, and thus for earthquake hazard assessments. However, measurements of such offsets generally underestimate fault slip due to inelastic deformation and secondary fault offsets, i.e., off-fault damage. Here, we use satellite synthetic aperture radar images to quantify off-fault damage in the two 2023 Kahramanmaraş (Türkiye) magnitude 7.8 and 7.6 earthquakes. We first derive three-dimensional coseismic surface displacements and show that on average ~35% of the coseismic slip is accommodated by off-fault damage within 5–7 km of the coseismic surface ruptures. Fault sections exhibiting geometrical complexities (e.g., bends and step-overs) experienced a higher level of off-fault damage than simpler fault sections. Our results highlight the importance of extending off-fault damage assessments to several km away from fault ruptures and indicate that fault offset measurements may underestimate slip-rate estimations by as much as a third.
Fundus camera-based precision monitoring of blood vitamin A level for Wagyu cattle using deep learning
Constraining the equation of state in neutron-star cores via the long-ringdown signal
Abstract Multimessenger signals from binary neutron star (BNS) mergers are promising tools to infer the properties of nuclear matter at densities inaccessible to laboratory experiments. Gravitational waves (GWs) from BNS merger remnants can constrain the neutron-star equation of state (EOS) complementing constraints from late inspiral, direct mass-radius measurements, and ab-initio calculations. We perform a series of general-relativistic simulations of BNS systems with EOSs constructed to comprehensively cover the high-density regime. We identify a tight correlation between the ratio of the energy and angular-momentum losses in the late-time portion of the post-merger signal, called the long ringdown, and the EOS at the highest pressures and densities in neutron-star cores. Applying this correlation to post-merger GW signals significantly reduces EOS uncertainty at densities several times the nuclear saturation density, where no direct constraints are currently available. Hence, the long ringdown can provide stringent constraints on material properties of neutron stars cores.
Multi-scale comparison of the formation mechanisms in landscape genes of traditional villages
The overlooked impacts of freshwater scarcity on oceans as evidenced by the Mediterranean Sea
Dynamic behavior of solitons in nonlinear Schrödinger equations
Carbon pricing drives critical transition to green growth
Efficacy and safety of selective laser trabeculoplasty for uveitic glaucoma
Reframing the filter bubble through diverse scale effects in online music consumption
Enhancing urban air quality prediction using time-based-spatial forecasting framework
Abstract Air quality forecasting plays a pivotal role in environmental management, public health and urban planning. This research presents a comprehensive approach for forecasting the Air Quality Index (AQI). The proposed Time-Based-Spatial (TBS) forecasting framework is integrated with spatial and temporal information using machine learning techniques on data collected from a wide range of cities. The TBS employs Convolutional Neural Networks (CNNs) to capture spatial dependencies based on normalized latitude and longitude coordinates of the cities. Simultaneously, time series model, specifically the ARIMA (AutoRegressive Integrated Moving Average) was employed to capture temporal dependencies using pollutant concentration readings over time. The dataset included information such as date, time, pollutant concentrations and AQI was further preprocessed and divided into training and testing sets. The CNN was configured to utilize the normalized latitude and longitude grid, while the ARIMA model concurrently processed the pollutant concentrations. The model was trained on the training dataset, and a 6 hour forecast is generated for each test instance. The outcomes demonstrate the TBS model’s ability to accurately predict AQI values. The integration of CNNs and time series model allowed for an clearer and deeper understanding of geographical and pollutant concentration factors that contribute to air quality variations.
Personalized metronomic radiopharmaceutical therapy through injection profile optimization via physiologically based pharmacokinetic (PBPK) modeling
Enhancing counterfeit RFID tag classification through distance based cognitive risk control
AI-driven video summarization for optimizing content retrieval and management through deep learning techniques
The impact of action descriptions on attribution of moral responsibility towards robots
Ecological overview of hard ticks (Ixodida: Ixodidae) in Nagasaki prefecture of western Japan during winter 2021–2022
Evaluation of an aquatic liverwort and terrestrial moss as biomonitors of heavy metals associated with particulate matter
Environmental and health risk assessment of polycyclic aromatic hydrocarbons and toxic elements in the red sea using Monte Carlo simulation
Abstract This research evaluates the environmental and health risks linked to potentially toxic elements (PTEs) and PAHs along the western coast of the Gulf of Suez, Egypt. This study investigated the concentration of 16 PAH compounds in the Suez Gulf, revealing significantly higher levels than the EU (0.20 µg/L) and US (0.030 µg/L) standards. The average total PAH concentration across eight locations was significantly higher, with the Suez area having the highest concentration at 479 µg/L. Pyrene (Pyr) was the dominant PAH with a concentration of 443 µg/L in Suez, while acenaphthylene (Ace) had the lowest concentration at 0.120 µg/L in Northern Zaafarana. Carcinogenic PAHs (CAR) ranged from 8.67 µg/L at Ras Gharib to 29.62 µg/L at Suez, highlighting the urgent need for regulatory measures. Confirmatory ratios pointed to industrial and shipping influences as petrogenic sources. Elevated total organic carbon (TOC) levels in Suez Bay indicated aggravated organic pollution, exacerbated by oil rigs and refineries. The ecological risk assessment highlighted substantial risks, particularly in Suez, necessitating immediate interventions to combat PAH contamination and preserve the environmental balance of the Red Sea. The dominant metals in water samples were arranged in descending order as follows: Pb > Fe > Cr > Cu > Zn > Mn > Cd > Ni. The study evaluated environmental and human health risks using a multifaceted approach, including cluster analysis, principal component analysis, and various indices (HPI, RI, MI, HQ, HI, and CR). Most water samples exhibited high pollution risks, surpassing permissible limits for HPI (> 100) and MI (> 6). Notably, HI oral values indicated significant non-carcinogenic risks for adults and children. While HI values for adults suggested low-risk dermal contact, those for children showed a substantial proportion in the high-risk category. Most water samples displayed CR values exceeding 1 × 10–4 for Cd, Cr, and Pb, indicating vulnerability to carcinogenic effects in both age groups. Monte Carlo simulations reinforced these findings, revealing a significant carcinogenic impact on children and adults. The identified clusters, reflective of industrial, petroleum-related, and urban runoff contamination sources, were consistently validated and clarified through PCA, enhancing the reliability of the findings. In light of these results, urgent and comprehensive water treatment measures are imperative to mitigate carcinogenic and non-carcinogenic health risks. These insights provide a foundation for implementing targeted management strategies to effectively address the challenges of heavy metal contamination in the Red Sea.