Space Cost Research Articles

Perda precoce dos elementos 11 e 12 por trauma dental, com intervenção ortodôntica preventiva na odontopediatria: relato de caso

Dental trauma is one that affects the hard tissues embraced, supporting structures or soft tissues in the oral cavity. It is observed more frequently in male children of school age (07 to 12 years old). The most affected teeth in the trauma taken are central and lateral incisors. With regard to tooth avulsions, the protection of children's bone tissue favors a higher frequency in this population than in the adult population. Dental avulsion occurs after displacement of the element out of its alveolus. There is a break in the blood supply to the pulp of the tooth and when this happens the chance of replantation achieved is totally impossible. Thus bringing several harms to the patient such as difficulty in speech, poor aesthetics, or even mesialize or distalize some other acquired element. Space maintainers are used as removable or fixed appliances to treat malocclusion or esthetic defects. They are braces made of acrylics with two extended teeth. Choosing a removable appliance comes with benefits such as better hygiene, satisfactory aesthetics, low cost, and maintenance of the cervico-occlusal space, in addition to the mesio-distal space. The objective of this study was to report a clinical case of avulsion of element 11 and 12 caused by trauma in childhood with the use of a space maintainer. We conclude that it is possible to treat a patient with dentoalveolar trauma with intervention using a space maintainer, since in the present case success was obtained in the selection of therapy, restoring aesthetics and function to the patient. Thus reducing possible orthodontic problems of the child, with follow-up until his/her old age.

Study of microwave hardwood board modification and drying: experiments and technical-economic assessment

The very low permeability of many hardwood species causes problems during lumber drying. These include very long drying times, drying defects, large material losses after drying, high energy consumption, and expensive drying processes. Jarrah (Eucalyptus marginata) is a species which very difficult to dry. Microwave (MW) wood modification and MW drying can provide an increase in wood permeability and solve some hardwood drying problems. Two 60 kW MW conveyor plants at frequencies 2.45 and 0.922 GHz were used for experiments. The study of MW Jarrah (29x87mm) board drying after pre-drying MW modification showed that it is possible to dry boards in the moisture content range from 70 to 10% during 147-159 hours (6 -7 days) using the cycle method of MW power application. Such intensive MW drying does not bring perceptible damage to the wood and provides low variability of moisture content in the board cross sections. The shorter drying times reduce associated capital, space, energy and labor costs whilst the reduction in drying defects can increase yields. A short time of MW drying provides an opportunity to develop conveyor systems for hardwood sawn timber drying. Economic assessment of MW Jarrah board drying showed that a specific drying cost ranges from US$ 50 to $277/m3 at electricity prices from US$0.08 to $0.32/kWh. The electricity costs form the largest share in total specific drying costs - 44 to 82%. Jarrah boards are expensive product and MW drying costs can be acceptable for industry taking into consideration savings provided by fast drying and wood loss reduction.

Intelligent Rapid Asexual Propagation Technology—A Novel Aeroponics Propagation Approach

Various rapid propagation strategies have been discovered, which has facilitated large-scale plant reproduction and cultivar development. These methods, in many plant species, are used to rapidly generate large quantities (900 mini-tubers/m2) of high-quality propagule (free from contamination) at a relatively low cost in a small space. They are also used for plant preservation. This review article aims to provide potential applications for regeneration and clonal propagation. Plant propagation using advanced agrotechnology, such as aeroponics, is becoming increasingly popular among academics and industrialists. The advancement of asexual aeroponic propagation has been achieved through advancements in monitoring and control systems using IoT and smart sensor technology. New sensor technology systems have gained substantial interest in agriculture in recent years. It is used in agriculture to precisely arrange various operations and objectives while harnessing limited resources with minimal human intervention. Modern intelligent technologies and control systems simplify sensor data collection, making it more efficient than manual data collection, which can be slow and prone to errors. Specific ambient variables like temperature, humidity, light intensity, stock solution concentrations (nutrient water), EC (electrical conductivity), pH values, CO2 content, and atomization parameters (frequency and interval) are collected more effectively through these systems. The use of intelligent technologies provides complete control over the system. When combined with IoT, it aids in boosting crop quality and yield while also lowering production costs and providing data directly to tablets and smartphones in aeroponic propagation systems. It can potentially increase the system’s productivity and usefulness compared to the older manual monitoring and operating methods.

Near-Duplicate Text Alignment with One Permutation Hashing

This paper studies the near-duplicate text alignment problem under the constraint of Jaccard similarity. Specifically, given a collection of long texts and a short query text, this problem finds all the subsequences in each text whose Jaccard similarities to the query are no smaller than a given threshold. Near-duplicate text alignment is computationally intensive. This is because there are O(n 2 ) subsequences in a text with n tokens. To remedy this issue, a few recent studies propose to first generate the min-hash sketch of every subsequence in each text and then find all the subsequences whose min-hash sketches are similar to that of the query. They introduce the concept of "compact windows" and show that the O(n 2 k) min-hashes in a text with n tokens can be losslessly compressed in compact windows using O(nk) space, where k is the sketch size. However, the space cost O(nk) is still too high for long texts, especially when the sketch size k is large. To address this issue, we propose to use One Permutation Hashing (OPH) to generate the min-hash sketch and introduce the concept of "OPH compact windows". Although the size of each sketch remains the same, which is O(k), we prove that all the O(n 2 k) min-hashes generated by OPH in a text with n tokens can be losslessly compressed in OPH compact windows using only O(n+k) space. Note the generation of OPH compact windows does not necessitate the enumeration of the O(n 2 k) min-hashes. Moreover, we develop an algorithm to find all the sketches in a text similar to that of the query directly from OPH compact windows, along with three optimizations.We conduct extensive experiments on three real-world datasets. Empirical results show our proposed algorithms significantly outperformed existing methods in terms of index cost and query latency and scaled well.

Integration of the wind and solar power for the dynamic economic emission dispatch with the charging and discharging of plug-in electric vehicles

Wind and solar power are incorporated into the dynamic economic emission dispatch with the plug-in electric vehicles (DEED-PEV) in this paper, seeking to reduce the generation cost and pollutant emission significantly. A Laplacian non-dominated sorting genetic algorithm-II (LNSGA-II) and a constraint handling method (CHM) are proposed for the DEED-PEV. The LNSGA-II applies the Laplace distribution to both the crossover and mutation. The Laplace-based crossover makes each individual carry out unidirectional search towards its partner and enhances the information exchange between each pair of individuals. The Laplace-based mutation makes each individual carry out bi-directional search surrounding itself and adjust the searching range adaptively. Therefore, the LNSGA-II is able to explore and exploit the decision space of the DEED-PEV sufficiently, contributing to the reductions of the generation cost and pollutant emission in the objective space. For the variables of every individual, the CHM limits them within the feasible operating zones. The CHM also limits them within the dynamic boundaries. It further adjusts them according to the power balances. The power generation constraints and up/down ramp rate constraints can be always satisfied while the elimination of the total violation of the other constraints can be expedited. Therefore, the CHM can transform infeasible individuals to feasible ones rapidly. Four strategies are investigated for six PEV charging/discharging scenarios. The first three strategies consider no more than one kind of renewable energy generation while the fourth strategy involves both the wind power and solar power. Experimental results suggest that the fourth strategy is more economical and environment-friendly than the other three strategies. Furthermore, the LNSGA-II outperforms the other three competitor algorithms for six DEED-PEV problems in the light of hypervolume, coverage rate and spacing.

Performance evaluation and comparative study on a novel solar-heat-driven ejection-compression hybrid cooling system with subcooling storage

Refrigeration technology contributes to 10∼15% of global energy consumption, resulting in significant carbon emission. Solar-driven ejection-compression refrigeration system is promising for reducing electricity consumption and carbon emissions. However, existing solar ejection-compression refrigeration systems suffer from drawbacks of low heat utilization efficiency, oversized solar collectors, and thermal leakage due to large temperature differences of storage devices. Addressing these challenges, this study proposes and investigates a new solar-assisted ejector-compressor hybrid refrigeration system with subcooling storage coupled at intermediate temperatures. The system model is established, and the ejector model is experimentally validated. Through system modelling, an energetic and exergetic performance comparative analyses are conducted. The results indicate that the COP of the proposed system is 9.7% higher than that of traditional system combinations, producing the same cooling capacity with the same generating heat. Moreover, considering the stored cooling energy can be fully converted to cooling energy at evaporating temperatures, the COPh of the new system reaches 8.29, 24.5% higher than traditional systems. The cooling storage at 15°C with 0.325 ejector entrainment ratio suggests a reduction of approximately two-thirds in energy storage, lower temperature differences, and reduced thermal leakage, leading to decreased space and economic costs and improved energy performance. Additionally, as the COPh, COPgh, and ηex of MCS mode consistently outperform those of ZCS and HCS modes, the MCS mode is prioritized in system operation. This study underscores that the new system offers superior overall energy efficiency and requires smaller storage devices compared to traditional systems, revealing its promising practical applications.

Nacelle optimisation through multi-fidelity neural networks

PurposeAerodynamic shape optimisation is a complex problem usually governed by transonic non-linear aerodynamics, a high dimensional design space and high computational cost. Consequently, the use of a numerical simulation approach can become prohibitive for some applications. This paper aims to propose a computationally efficient multi-fidelity method for the optimisation of two-dimensional axisymmetric aero-engine nacelles.Design/methodology/approachThe nacelle optimisation approach combines a gradient-free algorithm with a multi-fidelity surrogate model. Machine learning based on artificial neural networks (ANN) is used as the modelling technique because of its ability to handle non-linear behaviour. The multi-fidelity method combines Reynolds-averaged Navier Stokes and Euler CFD calculations as high- and low-fidelity, respectively.FindingsRatios of low- and high-fidelity training samples to degrees of freedom of nLF/nDOFs = 50 and nHF/nDOFs = 12.5 provided a surrogate model with a root mean squared error less than 5% and a similar convergence to the optimal design space when compared with the equivalent CFD-in-the-loop optimisation. Similar nacelle geometries and aerodynamic flow topologies were obtained for down-selected designs with a reduction of 92% in the computational cost. This highlights the potential benefits of this multi-fidelity approach for aerodynamic optimisation within a preliminary design stage.Originality/valueThe application of a multi-fidelity technique based on ANN to the aerodynamic shape optimisation problem of isolated nacelles is the key novelty of this work. The multi-fidelity aspect of the method advances current practices based on single-fidelity surrogate models and offers further reductions in computational cost to meet industrial design timescales. Additionally, guidelines in terms of low- and high-fidelity sample sizes relative to the number of design variables have been established.

A fast dual-module hybrid high-dimensional feature selection algorithm

When dealing with large-scale datasets, high-dimensional feature selection plays a crucial role in improving the performance and interpretability of machine learning models. However, it still faces the problems of the “dimensionality curse” and high computational cost in dealing with high-dimensional datasets. In this paper, we develop a dual-module hybrid feature selection algorithm based on correlation filtering and a multi-objective evolutionary algorithm based on dynamic variation distance (HMOFS-CFDVD), which aims to reduce the computational cost of high-dimensional features and search space. Firstly, a coarse-grained feature filtering method based on correlation is proposed, enabling the algorithm to quickly identify potentially better feature subsets in the later stages. After that, a fine-grained feature selection is further implemented based on the multi-objective evolutionary algorithm with sample variation distance to obtain a high-quality feature subset. This stage incorporates a novel population initialization method with self-regulation, an adaptive evolution strategy, an optimal sample selection mechanism based on dynamic sample distance, and an improved diversity mechanism to enhance the evolutionary performance. Moreover, the feature relevance metric is introduced as a third objective to improve the overall algorithm's performance. Experimental results on 12 high-dimensional feature datasets demonstrate that the proposed HMOFS-CFDVD achieves high accuracy and produces a smaller subset of features.

Thumbnail-Preserving Encryption Technology Based on Digital Processing

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.

Hilbert Curve Projection Distance for Distribution Comparison.

Distribution comparison plays a central role in many machine learning tasks like data classification and generative modeling. In this study, we propose a novel metric, called Hilbert curve projection (HCP) distance, to measure the distance between two probability distributions with low complexity. In particular, we first project two high-dimensional probability distributions using Hilbert curve to obtain a coupling between them, and then calculate the transport distance between these two distributions in the original space, according to the coupling. We show that HCP distance is a proper metric and is well-defined for probability measures with bounded supports. Furthermore, we demonstrate that the modified empirical HCP distance with the Lp cost in the d-dimensional space converges to its population counterpart at a rate of no more than O(n-1/2max{d,p}). To suppress the curse-of-dimensionality, we also develop two variants of the HCP distance using (learnable) subspace projections. Experiments on both synthetic and real-world data show that our HCP distance works as an effective surrogate of the Wasserstein distance with low complexity and overcomes the drawbacks of the sliced Wasserstein distance.

Technical and economic assessment of hybrid PVT solar systems compared to independent solar thermal and photovoltaic collectors in high-temperature zones: A case study in Mexicali, Mexico

This study analyzes the technical and economic feasibility of hybrid photovoltaic/thermal (PVT) solar energy systems, comparing them with independent flat plate solar thermal collectors (FP) and photovoltaic (PV) modules. Using TRNSYS software, different configurations were simulated under the climatic conditions of Mexicali, Baja California, Mexico. The results indicate that, although independent FP and PV systems exhibit better energy performance in terms of energy production and solar fraction, PVT collectors offer higher specific global energy production (electrical + thermal). However, the economic feasibility of PVT systems is limited due to high investment and maintenance costs, coupled with the low cost of thermal and electrical energy in the study region. Independent FP and PV collectors also face similar economic challenges, with return on investment extending beyond the 20-year lifespan of the project. Additionally, the low thermal energy demand for domestic hot water in the studied region negatively impacts the efficient utilization of these systems. This analysis suggests that in contexts where available installation space is limited, PVT systems can be an efficient solution. However, in scenarios with sufficient space and high energy costs, energy generation with separate equipment remains more viable. Moreover, climatic conditions and DHW demand affect the efficiency of the systems. The discrepancy between solar energy generation and hourly DHW demand necessitates the implementation of TEST, which significantly increases costs and impacts the economic viability of the project.

TC-Match: Fast Time-Constrained Continuous Subgraph Matching

Continuously monitoring structural patterns in streaming graphs is a critical task in many real-time graph-based applications. In this paper, we study the problem of time-constrained continuous subgraph matching (shorted as TCSM) over streaming graphs. Given a query graph Q with timing order constraint and a data graph stream G , TCSM aims to report all incremental matches of Q in G for each update of G , where a match should obey both structure constraint (i.e., isomorphism) and timing order constraint of Q. Although TCSM has a wide range of applications, such as cyber-attack detection and credit card fraud detection, we note that this problem has not been well addressed. The state-of-the-art bears the limitations of high index space cost and intermediate result maintenance cost. In this paper, we propose TC-Match, an effective approach to TCSM. First, we design a space and time cost-effective index CSS, which is essentially a k -partite graph structure where a node corresponds to an edge in G. By carefully creating links between nodes, we can encapsulate into CSS the partial embedding and timing order information between edges in G. We theoretically show that CSS has polynomial space and construction time complexities. Second, based on the property of CSS, we develop an efficient incremental matching algorithm with an effective node merging optimization. Extensive experiments show that TC-Match can achieve up to 3 orders of magnitude query performance improvement over the baseline methods, and meanwhile the memory consumption is reduced by 48.7%-86.7%.

Transdisciplinary approaches assessing unmanaged urban green spaces reveal benefits for biodiversity and people

Rapid urbanization is projected for African cities at the cost of urban green space, which could jeopardize biodiversity and human benefits. Studies focusing specifically on human–green space relationships in the Global South are lacking, and the validity of extrapolating results from studies in the Global North remains questionable and cannot provide local context-specific design solutions. This study combines methods and perspectives from ecology and human geography with landscape design to better understand the benefits for biodiversity and people derived from unmanaged green spaces in the City of Tshwane, South Africa. Based on empirical data from two unmanaged green space areas in disadvantaged communities, we identify benefits for biodiversity and people and define guidelines for inclusive trans-disciplinary interventions. We combine information from a vegetation survey, a community survey of 200 respondents and a rapid assessment of multifunctional benefit provision to formulate in holistic landscape design proposals. We show that the sites have biodiversity value and provide habitat for > 169 different plant species, including protected species, and smaller wildlife. Residents use the spaces for utility, passive and active leisure, and > 76% of residents benefit from the use of these spaces. However, the integrity and provision of benefits from green spaces are threatened by pollution, safety concerns, biological invasions, and land conversion. Context-specific designs could be developed by merging methods across disciplines and involving local stakeholders to integrate the multifunctionality of socioecological benefits into landscape interventions. Collaboration across ecology, human geography and landscape design generates multifunctional perspectives of unmanaged green spaces that consider benefits for biodiversity and disadvantaged communities.

Thermodynamic investigation of integrated organic Rankine cycle-ejector vapor compression cooling cycle waste heat recovery configurations for cooling, heating and power production

The present work focuses on vessel engine waste heat recovery (WHR) architectures for cooling, heating and power production based on the combination of an Organic Rankine Cycle (ORC) and a thermally assisted ejector cooling cycle-vapor compression cycle (EVCC), integrated into an ORC-EVCC. Their advantages and disadvantages are analyzed and their performance is evaluated using numerical models developed according to boundary conditions corresponding to a vessel diesel engine WHR micro-scale (100 kWth thermal input) application considering R1233zd(E), R1234yf and R1234ze as working fluids. Ultimately, a parallel ORC- parallel/serial EVCC layout operating with R1233zd(E) is determined as the most promising configuration, considering its superior thermodynamic performance and practical aspects (simplicity, space and weight requirements and cost). The ORC and EVCC are integrated in parallel and operate with the same fluid. Furthermore, the EVCC compressor and ejector are connected in a parallel/serial layout. Under the design point, the net power output of the system is 10.30 kWe in electricity-only mode and 7.68 kWe in CHP mode. In CHP mode, the heating output is 88.97 kWth. In the two cooling modes, electricity and cooling are produced simultaneously by the ORC and EVCC, respectively. The cooling output ranges between approximately 4.48 and 7.82 kWc.

Application of Artificial Intelligence in Solar System Exploration and Beyond

Carl Sagan’s reflections emphasize the boundless potential awaiting humanity in space, regardless of our past achievements or failures. This optimistic view resonates with current space agencies, private space startups, and enthusiasts, all driven by the ambition to make humans a multiplanetary species. Key examples include Elon Musk’s goal to terraform Mars, despite skepticism from some scientists. Space exploration faces numerous challenges, such as dealing with space debris, unpredictable climates on other planets, and the complexities of missions like NASA’s attempts to land on Mars. Space missions confront obstacles in navigation, life detection, and sample testing. Astronauts also encounter physical and mental limitations due to the harsh conditions of space. Artificial Intelligence (AI) has emerged as a crucial tool in addressing these challenges. AI is categorized into supervised learning, unsupervised learning, and semi-supervised learning, with reinforcement learning being a distinct method used for autonomous applications. Neural networks, including convolutional and recurrent neural networks, mimic human behavior for tasks like image inspection and sequential data handling. Deep learning aids in complex pattern recognition, and natural language processing facilitates speech recognition and development. Expert systems enhance decision-making processes. This review explores various AI applications in space missions. AI technologies have been integral to recent missions such as the James Webb Space Telescope, which uses AI for object and galaxy inspection, and the ARTEMIS mission, which employs AI in designing new spacesuits. SpaceX leverages AI for reusable launch vehicles, while the European Space Agency (ESA) uses AI for autonomous spacecraft to cut mission costs. India plans to launch the AI-based Vyommitra robot in space and has utilized AI in the Chandrayaan mission for landing and navigation. Mars exploration benefits significantly from AI, given the planet’s unpredictable weather and cratered surface. AI technologies, like the OASIS system, aid in mission planning and rover navigation. AI also supports space agriculture research, as demonstrated by the UAE’s habitat design efforts. Beyond Mars, AI influences the exploration of asteroids and deep space. AI techniques, such as convolutional neural networks, assist in space dynamics calculations to predict asteroid trajectories and potential Earth impacts. AI has been employed in Voyager missions to seek extraterrestrial life and in the Hubble telescope for imaging exoplanets. AI enhances satellite communication and automates tasks on space stations, saving time and costs. Weather satellites, powered by AI, provide crucial alerts for natural disasters. Despite the challenges, AI offers promising solutions for advancing space exploration. The historical tendency to deem certain challenges insurmountable is gradually shifting, as past impossibilities, like human flight and space travel, have become historical milestones. This reinforces the importance of maintaining hope and persistence in the quest to explore space, as today's dreams may become tomorrow's history.

Fault-tolerant quantum computation using low-cost joint measurements

We introduce a new method to implement joint measurements using a 4-qubit twist defect on a rotated surface code. The proposed method enables us to perform logical S (Phase gate), T (π/8\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\pi /8$$\\end{document} gate), and H (Hadamard) with low overhead. Combined with other universal quantum gates, we can implement fault-tolerant quantum computation at the lattice surgery level beyond the gate level while saving considerable resources. We compare our method with previous methods using benchmark circuits by calculating the space and time costs. The proposed method requires additional lines of physical qubits for each encoded patch. Although it slightly increases the space cost for logical H compared to the previous work, it reduces the time cost. In addition, the proposed method decreases the space cost and time cost by introducing a 4-qubit twist defect for logical S and T. Therefore, the overall space-time cost is reduced.

Qubit-Efficient Randomized Quantum Algorithms for Linear Algebra

We propose a class of randomized quantum algorithms for the task of sampling from matrix functions, without the use of quantum block encodings or any other coherent oracle access to the matrix elements. As such, our use of qubits is purely algorithmic and no additional qubits are required for quantum data structures. Our algorithms start from a classical data structure in which the matrix of interest is specified in the Pauli basis. For N×N Hermitian matrices, the space cost is log(N)+1 qubits and, depending on the structure of the matrices, the gate complexity can be comparable to state-of-the-art methods that use quantum data structures of up to size O(N2), when considering equivalent end-to-end problems. Within our framework, we present a quantum linear system solver that allows one to sample properties of the solution vector, as well as algorithms for sampling properties of ground states and Gibbs states of Hamiltonians. As a concrete application, we combine these subroutines to present a scheme for calculating Green’s functions of quantum many-body systems. Published by the American Physical Society 2024

Compiler Autotuning through Multiple-phase Learning

Widely used compilers like GCC and LLVM usually have hundreds of optimizations controlled by optimization flags, which are enabled or disabled during compilation to improve the runtime performance (e.g., small execution time) of the compiler program. Due to the large number of optimization flags and their combination, it is difficult for compiler users to manually tune compiler optimization flags. In the literature, a number of autotuning techniques have been proposed, which tune optimization flags for a compiled program by comparing its actual runtime performance with different optimization flag combinations. Due to the huge search space and heavy actual runtime cost, these techniques suffer from the widely recognized efficiency problem. To reduce the heavy runtime cost, in this article we propose a lightweight learning approach that uses a small number of actual runtime performance data to predict the runtime performance of a compiled program with various optimization flag combinations. Furthermore, to reduce the search space, we design a novel particle swarm algorithm that tunes compiler optimization flags with the prediction model. To evaluate the performance of the proposed approach, CompTuner, we conduct an extensive experimental study on two popular C compilers, GCC and LLVM, with two widely used benchmarks, cBench and PolyBench. The experimental results show that CompTuner significantly outperforms the six compared techniques, including the state-of-the-art technique BOCA.

Progress of remote sensing inversion research on surface temperature of lakes and reservoirs

Surface temperature of lakes and reservoirs is one of the important indicators of temperature change on the earth's surface, which not only reflects the impact of earth's climate change, but also has great significance for water resource management and ecological environmental protection. However, traditional temperature observation methods are limited by factors such as time, space and economic cost, which make it difficult to provide comprehensive and continuous temperature data. Therefore, the quantitative inversion of the surface temperature of lakes and reservoirs using remote sensing technology has become one of the hot spots in research. Based on this, this paper summarizes the relevant data sources and various research methods of remote sensing quantitative inversion of surface temperature of lakes and reservoirs in recent years, and elaborates and analyzes them, in order to provide reference for the ecological protection of lakes.

Efficient privacy-preserving Gaussian process via secure multi-party computation

Gaussian processes (GPs), known for their flexibility as non-parametric models, have been widely used in practice involving sensitive data (e.g., healthcare, finance) from multiple sources. With the challenge of data isolation and the need for high-performance models, how to jointly develop privacy-preserving GP for multiple parties has emerged as a crucial topic. In this paper, we propose a new privacy-preserving GP algorithm, namely PP-GP, which employs secret sharing (SS) techniques. Specifically, we introduce a new SS-based exponentiation operation (PP-Exp) through confusion correction and an SS-based matrix inversion operation (PP-MI) based on Cholesky decomposition. However, the advantages of the GP come with a great computational burden and space cost. To further enhance the efficiency, we propose an efficient split learning framework for privacy-preserving GP, named Split-GP, which demonstrably improves performance on large-scale data. We leave the private data-related and SMPC-hostile computations (i.e., random features) on data holders, and delegate the rest of SMPC-friendly computations (i.e., low-rank approximation, model construction, and prediction) to semi-honest servers. The resulting algorithm significantly reduces computational and communication costs compared to PP-GP, making it well-suited for application to large-scale datasets. We provide a theoretical analysis in terms of the correctness and security of the proposed SS-based operations. Extensive experiments show that our methods can achieve competitive performance and efficiency under the premise of preserving privacy.