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In recent years, the security of cloud storage has become a topic attracting significant attention due to a series of features such as large storage space, high availability, and low cost. Although traditional plain text images can withstand external attacks, the usability of images is completely lost. In order to balance the usability and privacy of images, some scholars have proposed the thumbnail-preserving encryption (TPE) scheme. The ideal TPE algorithm can keep the same thumbnail before and after encryption, which reduces the time cost and strengthens the resistance to attacks, but the existing schemes cannot fulfill the above criteria. In this paper, we propose a new TPE scheme that combines bit-transform encryption and improved hierarchical encryption. By constructing a chaotic system, both encryption and decryption times are shortened, while the randomness of the selected cells is enhanced. In addition, the Hamming distance is introduced to classify and scramble the binary encryption units. The experimental results show that when the number of thumbnail chunks is 16 × 16, the encryption and decryption time decreases to 4 s, and the SSIM value after encryption is close to 1, which indicates that the thumbnail before and after satisfying the encryption basically remains the same, and when the number of chunks is gradually increased, the success rate of the face detection tends to be close to 0. In addition, as the number of experimental iterations increases, the encryption effect improves with an increasing ability to resist attacks. [ABSTRACT FROM AUTHOR]

The exponential increase in photovoltaic (PV) arrays installed globally, particularly given the intermittent nature of PV generation, has emphasized the need to accurately forecast the predicted output power of the arrays. Regardless of the length of the forecasts, the modeling of PV arrays is made difficult by their dependence on weather. Typically, the model projections are generated from datasets at one location across a couple of years. The purpose of this study was to compare the effectiveness of regression models in very short-term deterministic forecasts for spatiotemporal projections. The compiled dataset is unique given that it consists of weather and output power data of PVs located at five cities spanning 3 and 6 years in length. Gated recurrent unit (GRU) generalized the best for same-city and cross-city predictions, while long short-term memory (LSTM) and ensemble bagging had the best cross-city and same-city predictions, respectively. [ABSTRACT FROM AUTHOR]

Wireless electric vehicle charging technology is developing in the direction of high power levels. However, more generated heat brought by higher power will accelerate the system's aging and can even lead to damage. An excellent thermal design for the magnetic coupler can reduce each part's maximum temperature, ensuring long-term operation reliability. Therefore, in this article, the magnetic coupler's thermal estimation and design are studied based on a 6.6 kW wireless electric vehicle charging system. First, the calculation method of internal resistance of a litz coil, core loss, and eddy current loss of a shielding aluminum plate are studied. Considering the influence of thermal fields on material properties, each part's power loss calculation formula is further modified to improve the accuracy. After that, heat dissipation research is carried out. The heat dissipation measures, such as filling the surface of the shielding aluminum plate with thermal conductive silicone grease, are proposed. Finally, the effectiveness of the heat dissipation measures is verified by simulation and experiments. The experiment shows that the error between the power loss value of each part calculated by simulation and measured by the experiment is less than 15%, and the maximum temperature of the magnetic coupler is controlled below 80 °C. [ABSTRACT FROM AUTHOR]

Countries have started to aggressively undertake energy structure transformation strategies in order to reach the objective of carbon neutrality. Both clean and efficient coal energy use and clean energy use will be crucial to the process of changing the energy structure since the two cannot be totally replaced within a short period of time. In this study, we quantify emotions as an irrational factor, combine them with an evolutionary game using RDEU theory, and build an evolutionary game model between government regulators and energy consumers. We then analyze how low-carbon emotions of decision-makers affect their choice of strategy and the transformation of the energy structure. The findings support that by affecting the relative importance of each strategic choice, emotions have a profound impact on the evolutionary steady state of the system. Appropriate stress and anxiety can increase decision-makers' feelings of responsibility, while pleasant emotions frequently support strategic conduct. The main countermeasures are as follows: Allow government regulators and energy consumers to properly release positive information, with government regulators forming subsidies and energy consumers actively cooperating and promoting low-carbon activities. This will properly guide the low-carbon sentiment of game subjects to keep them realistically pessimistic. [ABSTRACT FROM AUTHOR]

Cross-modal vehicle localization is an important task for automated driving systems. This research proposes a novel approach based on LiDAR point clouds and OpenStreetMaps (OSM) via a constrained particle filter, which significantly improves the vehicle localization accuracy. The OSM modality provides not only a platform to generate simulated point cloud images, but also geometrical constraints (e.g., roads) to improve the particle filter's final result. The proposed approach is deterministic without any learning component or need for labelled data. Evaluated by using the KITTI dataset, it achieves accurate vehicle pose tracking with a position error of less than 3 m when considering the mean error across all the sequences. This method shows state-of-the-art accuracy when compared with the existing methods based on OSM or satellite maps. [ABSTRACT FROM AUTHOR]

Quality improvement is crucial for manufacturing, and existing research has paid less attention to the influence of regulatory factors and irrational factors of decision makers. Considering the impact of the reward and punishment strategy of the shared platform on quality decision-making, this paper introduces prospect theory and mental account theory into the process of multi-agent evolutionary game of shared manufacturing, constructs a co-evolutionary game model of shared manufacturing quality synergistic improvement under the dynamic reward and punishment mechanism, and analyzes the dynamic evolution law of each game agent. The research results show that: (1) The synergistic improvement of shared manufacturing quality is the consequence of the combined action of numerous interrelated and interacting factors, rather than the linear effect of a single element. (2) Although the combination of multiple incentive and punishment methods can significantly alter the effect of shared manufacturing quality synergy, there are certain effectiveness gaps. (3) The subsidy mechanism can effectively compensate for the effectiveness gap of the reward and punishment mechanism, and it can also strengthen the internal driving force of shared manufacturing quality coordination. The main management insights are as follows: (1) Consider strong external regulation to be the framework constraint, and positive internal control to be the detail specification. (2) Create a reliable reward and punishment mechanism and dynamically alter the intensity of rewards and penalties. (3) To close the effectiveness gap, strengthen the subsidy mechanism as an essential addition to the incentive and punishment mechanisms. This study can give a new reference path for quality improvement of shared manufacturing, allowing shared manufacturing to play a more constructive role in supporting the transformation and development of the manufacturing industry. [ABSTRACT FROM AUTHOR]

Shared manufacturing provides a new path for the transformation and development of the manufacturing industry, but challenges such as low quality and poor positivity for quality improvement limit the positive role of shared manufacturing. Considering the influences of heterogeneous emotions of subjects on quality decision making, the theory of rank-dependent expected utility (RDEU) and evolutionary game theory were integrated to establish an evolutionary game model of shared manufacturing quality innovation synergy with multi-agent participation and analyze how sentiment affects motivation for quality improvement. The study showed that: (1) emotions, an irrational factor, can significantly change the stable state of the evolution of the shared manufacturing quality innovation synergetic system by influencing the decision-making behavior of decision makers; (2) in terms of the specific microscopic influence mechanism, rationality is the key to ensuring that the behavioral decisions of decision makers do not enshrine large systemic deviations. (3) In terms of the mechanism of heterogeneous emotions, when one party is optimistic, the deepening of the other party's pessimism tends to bring positive effects; when one party is pessimistic, the deepening of the other party's optimism tends to bring negative effects. The main management insights are as follows: (1) correctly recognizing and treating heterogeneous emotions of decision makers and regulating the formation and role of heterogeneous emotions of decision makers; (2) appropriately creating an atmosphere of pessimistic emotions, and guiding shared manufacturing to pay attention to manufacturing quality innovation synergy; (3) appropriately releasing favorable information about quality innovation synergy, and continuously promoting high-quality development of shared manufacturing. This study broadens the path of quality improvement in shared manufacturing and the scope of application of emotion theory in a certain sense. [ABSTRACT FROM AUTHOR]

The accurate assessment of left ventricular systolic function is crucial in the diagnosis and treatment of cardiovascular diseases. Left ventricular ejection fraction (LVEF) and global longitudinal strain (GLS) are the most critical indexes of cardiac systolic function. Echocardiography has become the mainstay of cardiac imaging for measuring LVEF and GLS because it is non-invasive, radiation-free, and allows for bedside operation and real-time processing. However, the human assessment of cardiac function depends on the sonographer's experience, and despite their years of training, inter-observer variability exists. In addition, GLS requires post-processing, which is time consuming and shows variability across different devices. Researchers have turned to artificial intelligence (AI) to address these challenges. The powerful learning capabilities of AI enable feature extraction, which helps to achieve accurate identification of cardiac structures and reliable estimation of the ventricular volume and myocardial motion. Hence, the automatic output of systolic function indexes can be achieved based on echocardiographic images. This review attempts to thoroughly explain the latest progress of AI in assessing left ventricular systolic function and differential diagnosis of heart diseases by echocardiography and discusses the challenges and promises of this new field. [ABSTRACT FROM AUTHOR]

To rapidly rehabilitate mangrove forests, exotic mangrove species characterized by high growth rates have been introduced in China, which would undoubtedly affect the nutrient status, nutrient acquisition and utilization strategies of mangrove plants, but the mechanism remains unclear. Qi'ao Island (a suburb of Zhuhai City) has the largest continuous exotic mangrove forests in China, where a mass collection of mangrove soils, plant tissues and tidewater was conducted. Ecological stoichiometric ratios and isotopic compositions were then analyzed to evaluate the ecosystem-scale nutrient status and compare the nutrient acquisition and utilization strategies of native Kandelia obovata (KO) and exotic Sonneratia apetala (SA) species. Soil and foliar C:N:P stoichiometries indicated that there is high P availability but N limitations, while further isotopic evidence indicated that native KO and exotic SA responded differently to the N limitation status. First, native KO seemed to prefer NO3−, while exotic SA preferred NH4+, according to the Δ15Nleaf–root (leaf–root δ15N difference) as well as the relationships between foliar δ15N and soil-extracted NH4+ δ15N, and between N and heavy metal contents. This suggested possible inter-specific competition between native KO and exotic SA, leading to different N species' preferences to maximize resource utilization. Next, native KO likely adopted the "conservative" strategy to ensure survival with reduced investment in N-rich growth components but root systems leading to lower growth rates and higher N use efficiency (NUE) and intrinsic water use efficiency (iWUE), while exotic SA adopted the "aggressive" strategy to ensure fast growth with heavy investment in N-rich growth components, leading to rapid growth and lower NUE and iWUE, and showing signs of invasiveness. Further, native KO is more responsive to aggravated N limitation by enhancing NUE. This study will provide insights into the adaptation of different mangrove species to nutrient limitations and the risks associated with large-scale plantations of exotic mangrove species. [ABSTRACT FROM AUTHOR]

Pharmaceutical cocrystal provides an alternative modification strategy for the formulation development of drugs owning to their potential ability to improve the physicochemical properties of active pharmaceutical ingredients (APIs) efficiently by changing inter-molecular interactions between raw materials. Isoniazid (INH) is an indispensable main drug for the treatment of tuberculosis, but its tablet formulation is unstable and prone to degradation. In the present study, the monohydrate cocrystal of INH and protocatechuic acid (PA) was prepared by solvent evaporation using PA as cocrystal former to optimize the properties of INH. The parent materials and corresponding 1:1 molar ratio INH-PA monohydrate cocrystal have been characterized by the terahertz time-domain (THz-TDS) and Raman spectroscopy. The THz absorption spectra displayed that there were obvious differences between the peaks of experimental cocrystal and the parent materials, and the same situation was found in Raman vibrational spectra. In addition, density functional theory (DFT) was applied to simulating and optimizing the structure of INH-PA monohydrate cocrystal and supplied corresponding vibrational modes. Our results provided a unique method to characterize the formation of INH-PA monohydrate cocrystal at the molecular-level and a lot of information about cocrystal structure and intra-molecular and/or inter-molecular hydrogen bond interactions in the emerging pharmaceutical cocrystal fields. [ABSTRACT FROM AUTHOR]

Shale oil exploration has been a key area of onshore oil and gas exploration in China in recent years. In this study, organic geochemistry and element geochemistry are united to study the shale oil and source rock in the Lucaogou formation of Jimusar sag, in order to reveal the paleoclimate, paleoenvironment, source of organic matter, and factors affecting organic matter accumulation and shale oil generation. The shale oil in the study area is mainly accumulated in two strata with good reservoir properties and oiliness, known as the upper sweet spot and lower sweet spot. Indexes of biomarkers and sensitive elements revealed the warm and semi-arid paleoclimate during Lucaogou formation, and the water column was brackish to salty. Water stratification caused a suboxic to anoxic environment in the deep-water column and coincided with the anoxic photic zone phenomenon. Compared with the lower sweet spot, the more humid climate, deeper and fresher water, and stronger water stratification characterize the upper sweet spot during sedimentation. This made the photic zone with freshwater more suitable for the reproduction of algae in the upper sweet spot. Meanwhile, the organic matter was well-preserved in the anoxic zone. Volcanic ash caused algae bloom, which promoted primary productivity and ensured the supply of organic matter. The composition and distribution pattern of biomarkers prove that phytoplankton is the most important source of organic matter in the study area and the contribution of higher plants is insignificant. The relationship between parameters of paleoproductivity and the redox condition versus total organic carbon (TOC) suggests that compared with the preservation conditions, the input of organic carbon is the most important controlling factor of organic matter accumulation in the study area. [ABSTRACT FROM AUTHOR]

The co-crystallization of active pharmaceutical ingredients (APIs) has received increasing attention due to the modulation of the relative physicochemical properties of APIs such as low solubility, weak permeability and relatively inferior oral bioavailability. Crystal engineering plays a decisive role in the systematic design and synthesis of co-crystals by means of exerting control on the inter-molecular interactions. The characterization and detection of such co-crystal formations plays an essential role in the field of pharmaceutical research and development. In this work, nitrofurantoin (NF), melamine (MELA) and their hydrated co-crystal form were characterized and analyzed by using terahertz time-domain spectroscopy (THz-TDS) and Raman vibrational spectroscopy. According to the experimental THz spectra, the hydrated co-crystal form has characteristic absorption peaks at 0.67, 1.05, 1.50 and 1.73 THz, while the THz spectra for the two raw parent materials (NF and MELA) are quite different within this spectral region. Similar observations were made from the experimental Raman vibrational spectra results. Density functional theory (DFT) calculation was performed to help determine the major vibrational modes of the hydrated co-crystal between nitrofurantoin and melamine, as well as identify the structural changes due to inter- and/or intra-molecular hydrogen bonding motifs between NF and MELA. The results of the theoretical frequency calculations corroborate the THz and Raman experimental spectra. The characteristic bands of the NF–MELA-hydrated co-crystal between nitrofurantoin and melamine were also determined based on the DFT simulated calculation. The reported results in this work provide us with a wealth of structural information and a unique vibrational spectroscopic method for characterizing the composition of specific co-crystals and inter-molecular hydrogen bonding interactions upon pharmaceutical co-crystallization. [ABSTRACT FROM AUTHOR]

Regulating the microstructure of powder metallurgy (P/M) nickel-based superalloys to achieve superior mechanical properties through heat treatment is a prevalent method in turbine disk design. However, in the case of dual-performance turbine disks, the complexity and non-uniformity of the heat treatment process present substantial challenges. The prediction of yield strength is typically derived from the analysis of microstructures under various heat treatment regimes. This method is time-consuming, expensive, and the accuracy often depends on the precision of microstructural characterization. This study successfully employed a coupled method of Artificial Neural Network (ANN) and finite element analysis (FEA) to reveal the relationship between the heat treatment process and yield strength. The coupled method accurately predicted the location specified and temperature-dependent yield strength based on the heat treatment parameters such as holding temperatures and cooling rates. The root mean square error (RMSE) and mean absolute percentage deviation (MAPD) for the training set are 50.37 and 3.77, respectively, while, for the testing set, they are 50.13 and 3.71, respectively. Furthermore, an integrated model of FEA and ANN is established using a Abaqus user subroutine. The integrated model can predict the yield strength based on temperature calculation results and automatically update material properties of the FEA model during the loading process simulation. This allows for an accurate calculation of the stress-strain state of the turbine disk during actual working conditions, aiding in locating areas of stress concentration, plastic deformation, and other critical regions, and provides a novel reliable reference for the rapid design of the turbine disk.