A Decision Tree Approach for Assessing and Mitigating Background and Identity Disclosure Risks

The Facebook/Cambridge Analytica data scandal shows a type of privacy threat where an adversary attacks on a massive number of people without prior knowledge about their background information. Existing studies typically assume that the adversary knew the background information of the target individuals. This study examines the disclosure risk issue in privacy breaches without such an assumption. We define the background disclosure risk and re-identification risk based on the notion of prior and conditional probabilities respectively, and integrate the two risk measures into a composite measure using the Minimum Description Length principle. We then develop a decision-tree pruning algorithm to find an appropriate group size considering the tradeoff between disclosure risk and data utility. Furthermore, we propose a novel tiered generalization method for anonymizing data at the group level. An experimental study has been conducted to demonstrate the effectiveness of our approach

Sensing the Pulse of the Pandemic: Geovisualizing the Demographic Disparities of Public Sentiment toward COVID-19 through Social Media

Social media offers a unique lens to observe users emotions and subjective feelings toward critical events or topics and has been widely used to investigate public sentiment during crises, e.g., the COVID-19 pandemic. However, social media use varies across demographic groups, with younger people being more inclined to use social media than the older population. This digital divide could lead to biases in data representativeness and analysis results, causing a persistent challenge in research based on social media data. This study aims to tackle this challenge through a case study of estimating the public sentiment about the COVID-19 using social media data. We analyzed the pandemic-related Twitter data in the United States from January 2020 to December 2021. The objectives are: (1) to elucidate the uneven social media usage among various demographic groups and the disparities of their emotions toward COVID-19, (2) to construct an unbiased measurement for public sentiment based on social media data, the Sentiment Adjusted by Demographics (SAD) index, through the post-stratification method, and (3) to evaluate the spatially and temporally evolved public sentiment toward COVID-19 using the SAD index. The results show significant discrepancies among demographic groups in their COVID-19-related emotions. Female and under or equal to 18 years old Twitter users expressed long-term negative sentiment toward COVID-19. The proposed SAD index in this study corrected the underestimation of negative sentiment in 31 states, especially in Vermont. According to the SAD index, Twitter users in Wyoming (Vermont) posted the largest (smallest) percentage of negative tweets toward the pandemic

Quantum Circuit AutoEncoder

Quantum autoencoder is a quantum neural network model for compressing information stored in quantum states. However, one needs to process information stored in quantum circuits for many tasks in the emerging quantum information technology. In this work, generalizing the ideas of classical and quantum autoencoder, we introduce the model of Quantum Circuit AutoEncoder (QCAE) to compress and encode information within quantum circuits. We provide a comprehensive protocol for QCAE and design a variational quantum algorithm, varQCAE, for its implementation. We theoretically analyze this model by deriving conditions for lossless compression and establishing both upper and lower bounds on its recovery fidelity. Finally, we apply varQCAE to three practical tasks and numerical results show that it can effectively (1) compress the information within quantum circuits, (2) detect anomalies in quantum circuits, and (3) mitigate the depolarizing noise in quantum devices. This suggests that our algorithm is potentially applicable to other information processing tasks for quantum circuits.Comment: 13 pages, 7 figure

Material Removal Mechanism and Force Model of Nanofluid Minimum Quantity Lubrication Grinding

Numerous researchers have developed theoretical and experimental approaches to force prediction in surface grinding under dry conditions. Nevertheless, the combined effect of material removal and plastic stacking on grinding force model has not been investigated. In addition, predominant lubricating conditions, such as flood, minimum quantity lubrication (MQL), and nanofluid minimum quantity lubrication (NMQL), have not been considered in existing force models. In this study, material removal mechanism under different lubricating conditions was researched. An improved theoretical force model that considers material removal and plastic stacking mechanisms was presented. Grain states, including cutting and ploughing, are determined by cutting efficiency (β). The influence of lubricating conditions was also considered in the proposed force model. Simulation was performed to obtain the cutting depth (a g) of each “dynamic active grain.” Parameter β was introduced to represent the plastic stacking rate and determine the force algorithms of each grain. The aggregate force was derived through the synthesis of each single-grain force. Finally, pilot experiments were conducted to test the theoretical model. Findings show that the model’s predictions were consistent with the experimental results, with average errors of 4.19% and 4.31% for the normal and tangential force components, respectively

Biological Bone Micro Grinding Temperature Field under Nanoparticle Jet Mist Cooling

Clinical neurosurgeons used micro grinding to remove bone tissues, and drip irrigation-type normal saline (NS) is used with low cooling efficiency. Osteonecrosis and irreversible thermal neural injury caused by excessively high grinding temperature are bottleneck problems in neurosurgery and have severely restricted the application of micro grinding in surgical procedures. Therefore, a nanoparticle jet mist cooling (NJMC) bio-bone micro grinding process is put forward in this chapter. The nanofluid convective heat transfer mechanism in the micro grinding zone is investigated, and heat transfer enhancement mechanism of solid nanoparticles and heat distribution mechanism in the micro grinding zone are revealed. On this basis, a temperature field model of NJMC bio-bone micro grinding is established. An experimental platform of NJMC bio-bone micro grinding is constructed, and bone micro grinding force and temperatures at different measuring points on the bone surface are measured. The results indicated that the model error of temperature field is 6.7%, theoretical analysis basically accorded with experimental results, thus certifying the correctness of the dynamic temperature field in NJMC bio-bone micro grinding

Leakage current simulations of Low Gain Avalanche Diode with improved Radiation Damage Modeling

We report precise TCAD simulations of IHEP-IME-v1 Low Gain Avalanche Diode (LGAD) calibrated by secondary ion mass spectroscopy (SIMS). Our setup allows us to evaluate the leakage current, capacitance, and breakdown voltage of LGAD, which agree with measurements' results before irradiation. And we propose an improved LGAD Radiation Damage Model (LRDM) which combines local acceptor removal with global deep energy levels. The LRDM is applied to the IHEP-IME-v1 LGAD and able to predict the leakage current well at -30 $^{\circ}$C after an irradiation fluence of $\Phi_{eq}=2.5 \times 10^{15} ~n_{eq}/cm^{2}$. The charge collection efficiency (CCE) is under development

A review on the heat and mass transfer phenomena in nanofluid coolants with special focus on automotive applications

Engineered suspensions of nanosized particles (nanofluids) are characterized by superior thermal properties. Due to the increasing need for ultrahigh performance cooling in many industries, nanofluids have been widely investigated as next-generation coolants. However, the multiscale nature of nanofluids implies nontrivial relations between their design characteristics and the resulting thermo-physical properties, which are far from being fully understood. This pronounced sensitivity is the main reason for some contradictory results among both experimental evidence and theoretical considerations presented in the literature. In this Review, the role of fundamental heat and mass transfer mechanisms governing thermo-physical properties of nanofluids is assessed, from both experimental and theoretical point of view. Starting from the characteristic nanoscale transport phenomena occurring at the particle-fluid interface, a comprehensive review of the influence of geometrical (particle shape, size and volume concentration), physical (temperature) and chemical (particle material, pH and surfactant concentration in the base fluid) parameters on the nanofluid properties was carried out. Particular focus was devoted to highlight the advantages of using nanofluids as coolants for automotive heat exchangers, and a number of design guidelines was suggested for balancing thermal conductivity and viscosity enhancement in nanofluids. This Review may contribute to a more rational design of the thermo-physical properties of particle suspensions, therefore easing the translation of nanofluid technology from small-scale research laboratories to large-scale industrial applications

Spatial–Temporal Land Loss Modeling and Simulation in a Vulnerable Coast: A Case Study in Coastal Louisiana

Coastal areas serve as a vital interface between the land and sea or ocean and host about 40% of the world’s population, providing significant social, economic, and ecological functions. Meanwhile, the sea-level rise caused by climate change, along with coastal erosion and accretion, alters coastal landscapes profoundly, threatening coastal sustainability. For instance, the Mississippi River Delta in Louisiana is one of the most vulnerable coastal areas. It faces severe long-term land loss that has disrupted the regional ecosystem balance during the past few decades. There is an urgent need to understand the land loss mechanism in coastal Louisiana and identify areas prone to land loss in the future. This study modeled the current and predicted the future land loss and identified natural–human variables in the Louisiana Coastal Zone (LCZ) using remote sensing and machine-learning approaches. First, we analyzed the temporal and spatial land loss patterns from 2001 to 2016 in the study area. Second, logistic regression, extreme gradient boosting (XGBoost), and random forest models with 15 human and natural variables were carried out during each five-year and the fifteen-year period to delineate the short- and long-term land loss mechanisms. Finally, we simulated the land-loss probability in 2031 using the optimal model. The results indicate that land loss patterns in different parts change through time at an overall decelerating speed. The oil and gas well density and subsidence rate were the most significant land loss drivers during 2001–2016. The simulation shows that a total area of 180 km2 of land has over a 50% probability of turning to water from 2016 to 2031. This research offers valuable information for decision-makers and local communities to prepare for future land cover changes, reduce potential risks, and efficiently manage the land restoration in coastal Louisiana

Experimental and Modeling Study of the Effects of Sealing Structures in Lost Circulation Prevention and Remediation

Drilling fluid lost circulation leads to non-productive time and increases the overall well cost. In general, wellbore strengthening and lost circulation control are achieved by creating effective sealing structures to inhibit fluid flow through loss conduits such as formation fractures. This research aims at better understanding the effects of sealing structures in fluid loss prevention and remediation, and providing useful references to effectively establishing filtercakes on the wellbore and plugs in the fracture. Recent research on wellbore strengthening disclosed the critical role of filtercake in sealing microfractures during the initial stages of fracture initiation and propagation. The performance of a filtercake to strengthen the wellbore depends on its capability to maintain integrity. In this research, a new parameter –“filtercake rupture resistance” and a new testing method are proposed to simplify the evaluation of the filtercake’s potential to withstand pressure over a small fracture. Experiments were conducted to understand the effects of fluid and filtercake properties on filtercake’s rupture resistance and on the effectiveness of filtercake in reducing fracture sealing time. The effects of filtercake with lost circulation materials (LCMs) in reinforcing fracture sealing were explored and it is recommended to consider the role of filtercake when evaluating the LCMs and designing lost circulation preventive treatment. In addition to studying filtercakes for lost circulation prevention, this research also investigated LCM fracture plugs for fluid loss remediation. When drilling through naturally fractured reservoirs, the remediation of drill-in fluid loss needs to be designed considering both fracture plugging and formation damage. Statistical methods were used to better design the experiments and optimize the LCM implementation schemes, in order to efficiently create the desired fracture plug with less fluid invasion into fractures. The plug structure was visualized by SEM and Micro-CT scan to understand the effects of plug soaking process. A mechanistic model to calculate plug permeability with soaking time was developed for optimizing hesitation schemes. This research presents new understandings about lost circulation and wellbore strengthening, and provides improved recommendations for optimal fluid loss solutions