Related Constraints Research Articles

Asteroid material classification based on multi-parameter constraints using artificial intelligence

Material types of asteroids provide key clues to their evolutionary history and contained resources. The Gaia mission has released extensive low-resolution spectral observation data of small Solar System bodies. However, methods for classifying asteroids based on low-resolution space-based spectra are still inadequate, and do not fully leverage the complementary features of spectra and multiple intrinsic attributes of asteroids to achieve precise material classification. Our goal is to propose a method with a higher generalization accuracy for asteroid material classification by integrating multi-source information, identifying optimal feature combinations for model inputs, and deepening the understanding of relationships among asteroid parameters. The effective asteroid photometric, physical, and orbital parameters were screened using the information gain ratio and Spearman's rank correlation coefficient. Then, artificial intelligence techniques were employed to combine asteroid spectra with the selected various parameters for six-class material classification. By comparing five machine learning models, we identified network structures with higher validation accuracy and stable generalization performance. Meanwhile, feature ablation experiments were conducted to determine the input parameter combinations suitable for different scenarios. Finally, based on the statistical results and model outputs, the constraint relationships among asteroid parameters were visualized and analyzed. The proposed AsterRF model achieved a validation accuracy of 92.2<!PCT!>, an improvement of approximately 7.8 percentage points compared to existing methods that use only spectra. V-type asteroids exhibited the highest classification accuracy, followed by A-type and D-type. X-type asteroids had the lowest precision and recall, and were easily confused with C-type. The model generally showed higher classification confidence for S-type asteroids. The top five attributes that the model focused on are the phase slope parameter (G), orbital type, albedo, H magnitude, and effective diameter. Additionally, the correlations between asteroid materials and other parameters were generally below 0.4. Incorporating optimal asteroid parameter combinations can significantly enhance classification accuracy based on spectra. A dual-channel network that processes spectra and parameter inputs separately, and employs a self-attention mechanism for feature fusion is effective in combining multi-source asteroid information. Both the statistical correlations and model performance-based importance rankings of parameters contribute to understanding the constraint relationships among asteroid attributes.

Two-Stage Locating and Capacity Optimization Model for the Ultra-High-Voltage DC Receiving End Considering Carbon Emission Trading and Renewable Energy Time-Series Output Reconstruction

With the load center’s continuous expansion and development of the AC power grid’s scale and construction, the recipient grid under the multi-feed DC environment is facing severe challenges of DC commutation failure and bipolar blocking due to the high strength of AC-DC coupling and the low level of system inertia, which brings many complexities and uncertainties to economic scheduling. In addition, the large-scale grid integration of wind power, photovoltaic, and other intermittent energy sources makes the ultra-high-voltage (UHV) DC channel operation state randomized. The deterministic scenario-based timing power simulation is no longer suitable for the current complex and changeable grid operation state. In this paper, we first start with the description and analysis of the uncertainty in renewable energy (RE) sources, such as wind and solar, and reconstruct the time-sequence power model by using the stochastic differential equation model. Then, a carbon emission trading cost (CET) model is constructed based on the CET mechanism, and the two-stage locating and capacity optimization model for the UHV DC receiving end is proposed under the constraint of dispatch safety and stability. Among them, the first stage starts with the objective of maximizing the carrying capacity of the UHV DC receiving end grid; the second stage checks its dynamic safety under the basic and fault modes according to the results of the first stage and corrects the drop point and capacity of the UHV DC line with the objective of achieving safe and stable UHV DC operation at the lowest economic investment. In addition, the two-stage model innovatively proposes UHV DC relative inertia constraints, peak adjustment margin constraints, transient voltage support constraints under commutation failure conditions, and frequency support constraints under a DC blocking state. In addition, to address the problem that the probabilistic constraints of the scheduling model are difficult to solve, the discrete step-size transformation and convolution sequence operation methods are proposed to transform the chance-constrained planning into mixed-integer linear planning for solving. Finally, the proposed model is validated with a UHV DC channel in 2023, and the results confirm the feasibility and effectiveness of the model.

Guidance Strategy and Fast Robustness Analysis for Space Debris Flyby

Envisioning the scenario of space debris inspection by orbital flyby, this paper investigates the guidance strategy and fast robustness analysis method considering the relative state constraints. First, the corrected state transition matrix is derived to predict and correct the perturbed relative state at the flyby moment. Based on the state transition matrix, a free-flight corridor model is established to characterize the maximum allowed magnitude of the deviation, which can quickly estimate the robustness of the trajectory under uncertainties and circumvent intensive numerical sampling. The corresponding optimization, with four norm-extreme constraints, can be solved by a proposed double-layer algorithm with proven convergence. Numerical simulations demonstrate that the guidance algorithm is effective, and the free-flight corridor can accurately and efficiently reflect the robustness of the nominal trajectory.

A semantic knowledge database-based localization method for UAV inspection in perceptual-degraded underground mine

Abstract In recent years, unmanned aerial vehicles (UAVs) have been applied in underground mine inspection and other similar works depending on their versatility and mobility. However, accurate localization of UAVs in perceptually degraded mines is full of challenges due to the harsh light conditions and similar roadway structures. Due to the unique characteristics of the underground mines, this paper proposes a semantic knowledge database-based localization method for UAVs. By minimizing the spatial point-to-edge distance and point-to-plane distance, the relative pose constraint factor between keyframes is designed for UAV continuous pose estimation. To reduce the accumulated localization errors during the long-distance flight in a perceptual-degraded mine, a semantic knowledge database is established by segmenting the intersection point cloud from the prior map of the mine. The topological feature of the current keyframe is detected in real time during the UAV flight. The intersection position constraint factor is constructed by comparing the similarity between the topological feature of the current keyframe and the intersections in the semantic knowledge database. Combining the relative pose constraint factor of LiDAR keyframes and the intersection position constraint factor, the optimization model of the UAV pose factor graph is established to estimate UAV flight pose and eliminate the cumulative error. Two UAV localization experiments conducted on the simulated large-scale Edgar Mine and a mine-like indoor corridor indicate that the proposed UAV localization method can realize accurate localization during long-distance flight in degraded mines.

A multi-objective dual dynamic genetic algorithm-based approach for thermoelectric optimization of integrated urban energy systems

In order to reduce the loss of energy in the process of conversion and utilization, and to improve the energy utilization effect and economic benefits of urban integrated energy system, a thermoelectricity optimization method of urban integrated energy system based on multi-objective double dynamic genetic algorithm is proposed. The method analyzes the operation characteristics of the urban integrated energy system through its operation model, constructs a multi-objective optimization model for the thermoelectricity of the urban integrated energy system, determines the optimization multi-objective function that minimizes the operation cost, minimizes the carbon emission, minimizes the technical dissatisfaction and the related constraints, and then solves the optimization model by using the dual dynamic genetic algorithm to output the results of the optimization of the thermoelectricity of the urban integrated energy system.The test results show that the algorithm has a crowding distance of 0.75 or above when solving, and the overall unit energy consumption cost reduction ratio is about 22.97 %. The state of charge results of the four energy storage power stations are all below 10 %, and the lowest convergence speed is only 7 seconds, effectively improving energy utilization efficiency.

Study on the Constraint Effect of Vegetation on Ecosystem Services in the Yellow River Basin

Ecosystem services (ESs) serve as the foundation for sustaining human life and development, with vegetation status playing a crucial role in influencing the supply of these services. This study focuses on the Yellow River Basin (YRB), where we quantitatively examined the main ESs indicators from 2010 to 2020. We explored the trends in fractional vegetation cover (FVC) and ESs, as well as the constraint relationship between FVC and total ecosystem services (TES). The findings are as follows. (1) From 2010 to 2020, FVC, landscape aesthetics (LA), soil conservation (SC), food production (FP), and TES in the YRB demonstrated an upward trend, whereas water yield (WY) exhibited a downward trend. (2) A constraint relationship exists between FVC and LA, SC, WY, and TES, with the constraint line taking on a hump-like shape. (3) The threshold value of the constraint line between FVC and LA, SC, WY, and TES are approximately 80%. Below this value, FVC does not impose a constraint effect on LA, SC, WY, and TES, but above 80%, a strong constraint effect emerges, leading to a reduction in LA, SC, WY, and TES. These results offer a valuable data reference for guiding future vegetation restoration and ecological engineering efforts in the region.

Operational long-term management of a salt cavern for industry targeted green H2 production

Hydrogen and its derivatives such as ammonia are increasingly important for reducing carbon emissions in sectors with limited alternatives. Large production systems are generally preferred to reduce costs and for those, advanced management is a cornerstone to the project economic viability. The present work explores the development of an Energy Management System for an industrial low-emission ammonia production platform. The latter is connected to the electrical grid and consists of 1.5 GW of renewable production units, around 500 MW of electrolyzers and storage facilities including especially a salt cavern for H2 storage. The long-term management of the salt cavern is decisive to enhance the profitability while satisfying industry related constraints such as the carbon content of the produced H2. The literature lacks in addressing both aspects simultaneously. Methodological approaches to tackle the long-term management and industrial constraints are proposed and compared. For the considered platform, it is shown that a strategy based on precalculated cost functions in the long term horizon allows relative gains of 2.5% with respect to a state-of-the-art short term management leading to an annual cost saving of approximately $6.2 million in the platform operational expenses. This translates to a reduction of the gap between the state-of-the-art and the optimal a posteriori solution by about 82%.

Changes in demographic components during the COVID-19 pandemic and economic recession in Sri Lanka

The objective of this paper is to investigate the impact of the COVID-19 pandemic and subsequent economic recession on the three demographic components, namely mortality, fertility and migration in Sri Lanka and possible implications. A secondary data analysis of mortality, fertility, migration, and marriage data from the Registrar General’s Department of Sri Lanka and the Ministry of Health was conducted. Although excess mortality (COVID-19 deaths) in 2022 was substantially lower, it was elevated in 2021. In comparison to non-COVID-19 years, the number of deaths has increased substantially during the post-COVID-19 era – 146 thousand deaths in 2019 increased to 194 thousand in 2023 (30.1%). This marked increase could be attributed to the economic recession and related serious constraints faced by the health system in Sri Lanka. Further, a marked decrease in marriages (14.2%) was observed in 2020 due to COVID-19 which recovered to pre-COVID-19 level in 2021. However, instead of experiencing the “babyboom” phenomenon (as observed in some previous pandemics) in the aftermath of the COVID-19 pandemic, the observed data for Sri Lanka indicates a significant downward trend in fertility. Unexpectedly, 319 thousand births were reported in 2019 in Sri Lanka dropping to 257 thousand by 2023, which is a decline of over 62 thousand births (19%). This situation is further complicated by the highest reported outbound migration ever reported in Sri Lanka in 2022, displaying a 250% year-to-year increase. The changes in these demographic components would impact the macroeconomics.

Named Entity Recognition for Equipment Fault Diagnosis Based on RoBERTa-wwm-ext and Deep Learning Integration

Equipment fault diagnosis NER is to extract specific entities from Chinese equipment fault diagnosis text, which is the premise of constructing an equipment fault diagnosis knowledge graph. Named entity recognition for equipment fault diagnosis can also provide important data support for equipment maintenance support. Equipment fault diagnosis text has complex semantics, fuzzy entity boundaries, and limited data size. In order to extract entities from the equipment fault diagnosis text, this paper presents an NER model for equipment fault diagnosis based on RoBERTa-wwm-ext and Deep Learning network integration. Firstly, this model uses the RoBERTa-wwm-ext to extract context-sensitive embeddings of text sequences. Secondly, the context feature information is obtained through the BiLSTM network. Thirdly, the CRF is combined to output the label sequence with a constraint relationship, improve the accuracy of sequence labeling task, and complete the entity recognition task. Finally, experiments and predictions are carried out on the constructed dataset. The results show that the model can effectively identify five types of equipment fault diagnosis entities and has higher evaluation indexes than the traditional model. Its precision, recall, and F1 value are 94.57%, 95.39%, and 94.98%, respectively. The case study proves that the model can accurately recognize the entity of the input text.

Synthesis of dynamic modelling framework and optimal control strategy for virtually coupled train sets with guaranteed safety

Virtual coupling represents a novel railway operational mechanism, wherein several trains share state information and adjust their behaviours based on control objectives, which include safety (collision avoidance) assurance and stability (uniform velocity and constant spacing between trains) maintenance. However, the conflict between safety and stability requirements becomes more apparent, as the closer spacing between trains. Moreover, the hybrid nature of the train platoon operation, which involves both discrete events and continuous dynamics, also poses a significant barrier to the control strategy synthesis. This paper presents the Virtually Coupled Train Sets (VCTS) dynamic modelling framework and strategy synthesis approach to address these problems. A Hybrid Markov Decision Process (HMDP) is adopted for relative longitudinal dynamics modelling of trains, considering the influence of the uncertainty dynamic of trains ahead. A Stackelberg game-based strategy synthesis algorithm is applied for the safe guarantee by overcoming the uncertainty, and a Q-learning method is used for stability strategy selection from the safe strategy. The results showed a 17.95% expansion in the safe state space when compared to the general relative braking scheme (which assumes maximal acceleration during the delay horizon). In a four-train tracking operation scenario, compared with Q-learning without safe constraints, our approach gives similar stability performance; compared with Q-learning with general relative braking safe constraints, our approach not only assurance safety but has a better stability performance.

Structure optimal design of 500 kV dry-type air-core shunt reactor based on response surface method

The hotspot temperature of the reactor is one of the important indices in reactor design, and the appropriate hotspot value can ensure that the reactor will not overheat failure and threaten the safety of the power system. In this paper, the temperature field simulation model of a 500 kV dry-type air-core shunt reactor is established, and the hotspot temperature of the reactor is obtained by COMSOL software. Then, the radial basis function response surface function is used to construct the response surface function of the hotspot temperature and its related variables, and the applicability of the response surface function is tested to analyze the rationality and accuracy of the response surface function. Finally, the optimal solution of the response surface function under the coordination of variables and related constraints is obtained by using the constrained simulated annealing algorithm. The results show that the hotspot temperature of the reactor envelope winding decreases obviously after optimization.

Research on coupling analysis and decoupling control of two-degree-of-freedom PTZ system

Abstract With the development of technology, especially in areas such as robotics, aerospace, photography, and videography, gimbal systems, as an important stabilization device, have been widely used. However, the two-degree-of-freedom gimbal system also faces a series of challenges in practical applications. Among them, one of the most critical issues is the internal coupling phenomenon in the system. The coupling phenomenon refers to the mutual influence and a constraint relationship between different degrees of freedom in the system, which may increase the complexity of the system’s dynamic characteristics and make the design and implementation of control strategies difficult. This study aims at explore more advanced and practical decoupling control strategies through in-depth analysis of the coupling characteristics of the two-degree-of-freedom gimbal system, providing new ideas and methods for the further optimization and application of the gimbal system.

Non-Invasive Assessment of Coronary Microvascular Dysfunction Using Vascular Deformation-Based Flow Estimation.

Non-invasive computation of the index of microcirculatory resistance from coronary computed tomography angiography (CTA), referred to as IMR[Formula: see text], is a promising approach for quantitative assessment of coronary microvascular dysfunction (CMD). However, the computation of IMR[Formula: see text] remains an important unresolved problem due to its high requirement for the accuracy of coronary blood flow. Existing CTA-based methods for estimating coronary blood flow rely on physiological assumption models to indirectly identify, which leads to inadequate personalization of total and vessel-specific flow. To overcome this challenge, we propose a vascular deformation-based flow estimation (VDFE) model to directly estimate coronary blood flow for reliable IMR[Formula: see text] computation. Specifically, we extract the vascular deformation of each vascular segment from multi-phase CTA. The concept of inverse problem solving is applied to implicitly derive coronary blood flow based on the physical constraint relationship between blood flow and vascular deformation. The vascular deformation constraints imposed on each segment within the vascular structure ensure sufficient individualization of coronary blood flow. Experimental studies on 106 vessels collected from 89 subjects demonstrate the validity of our VDFE, achieving an IMR[Formula: see text] accuracy of 82.08 %. The coronary blood flow estimated by VDFE has better reliability than the other four existing methods. Our proposed VDFE is an effective approach to non-invasively compute IMR[Formula: see text] with excellent diagnostic performance. The VDFE has the potential to serve as a safe, effective, and cost-effective clinical tool for guiding CMD clinical treatment and assessing prognosis.

Addressing multi-step inverse heat transfer problems via reduced order models in a cooling process for polymer pipes with sparse measurements

In modern polymer pipe extrusion processes with connected post-processing steps, the intermediate cooling and conditioning process is essential for the resulting product quality. In such cases, computationally efficient models are required for real-time process control and optimization. Unfortunately, heat transfer coefficients are rarely known for actual production processes, leading to an inverse heat transfer problem. Existing methods mainly rely on computationally intensive numerical models or on data intensive neural networks. Both are not suitable, if only sparse measurements are available and a fast execution time is required. In contrast to that, we propose to use a proper orthogonal decomposition and project the governing heat equation onto extracted basis functions via the Galerkin method. This leads to an efficient and accurate reduced order model, which can be used for parameter identification and subsequent process control. Regarding the identification, we address the ill-posed problem with well-known relative constraints, l2 regularization and decomposition of coefficients based on the Nusselt number. Simulation and experimental results show that the heat transfer coefficients are consistently identified and robust against initial parameter guesses despite sparse and noisy temperature measurements. Consequently, the proposed method can be efficiently applied in cooling and conditioning processes in polymer extrusion and related applications such as identifying the heat transfer coefficients and thermal resistance in pressurized surge lines of nuclear power plants and brick walls in melting furnaces respectively.

International students’ experiences in Brisbane during COVID times through a social justice lens

ABSTRACT The article examines the situation of international students living in Brisbane during COVID lockdowns, through a social justice lens. Despite the significant economic contribution of tertiary international students towards the Australian economy, they were excluded from government pandemic support. Help provided by other organisations was limited. International students felt undervalued and excluded, facing challenges due to their lack of income from part-time jobs, mental health issues, constraints in relation to home arrangements, online study, and lack of sense of belonging. Our findings further show that although place was a determinant in the wellbeing of students, social attachment (either spatially or a-spatially) was the most valued support to students. While there has been research on the needs of international students in Australia during lockdowns, no studies yet link these three variables: COVID lockdowns, their effects on international students left in Australia, and considerations of city scale, in a mutually influencing relationship. We followed a qualitative research design, using interviews and focus group discussions with international students and support providers in Brisbane during COVID times. We argue that both distributive and institutional dimensions of justice are important to understand the disadvantaged situation experienced by international students: the institutional COVID restrictions greatly affected students ‘trapped’ in the country, while there was a very limited distribution of resources that did not match the needs of students. We conclude with some recommendations as a result of our research. Practitioner pointers During a global pandemic, government needs to ensure that all people stranded in the country, including international students, are entitled to the same financial assistance. International students should have been consulted about, and appropriately informed through effective ways of communication about the services provided to them during the pandemic. All levels of government and educational institutions dealing with international students, need to fully consult with students and listen to their concerns.

Regulating the Scaling Relations in Ammonia Synthesis through a Light‐Driven Bendable Seesaw Effect on Tailored Iron Catalyst

AbstractAdvancing the energy‐intensive Haber–Bosch process faces significant challenges due to the intrinsic constraints of scaling relations in heterogeneous catalysis. Herein, we reported an approach of bending the “seesaw effect” to regulate the scaling relations over a tailored α‐Fe metallic material (α‐Fe‐110s), realizing highly efficient light‐driven thermal catalytic ammonia synthesis with a rate of 1260 μmol gcatalyst−1 h−1 without additional heating. Specifically, the thermal catalytic activity of α‐Fe‐110s was significantly enhanced by the novel stepped {110} surface, exhibiting a 3.8‐fold increase compared to the commercial fused‐iron catalyst with promoters at 350 °C. The photo‐induced hot electron transfer further accelerates the dinitrogen dissociation and hydrogenation simultaneously, effectively overcoming the limitation of scaling relation over identical sites. Consequently, the ammonia production rate of α‐Fe‐110s was further enhanced by 30 times at the same temperature with irradiation. This work designs an efficient and sustainable system for ammonia synthesis and provides a novel approach for regulating the scaling relations in heterogeneous catalysis.

Quantitative Investigation of Parallel Interactions Between Charged Particles

On the premise that the charged particle is a normal geometry model, an expression of the migration current, displacement current and magnetic induction intensity generated by the charged particle motion is deduced according to the microscopic definition of current intensity, the total current law and the Biot-Savart law. Further calculate the electric field force between two charged particles in vacuum, give the velocity constraint relationship and velocity value criterion of the electric and magnetic field forces, and compare the consistency with the correlation results obtained considering the relativistic effect. It is pointed out that the magnetic field force is comparable to the electric field force only when the charged particle moves near the speed of light.

Ghana's basic education Headteacher leadership: critical discourse analysis of the Headteachers’ handbook

ABSTRACT The Ghana Education Service created the Headteachers' Handbook, a policy document that outlines Headteacher leadership practices. A recent review of Ghana's basic education Headteacher Leadership literature reported that these practices align within a hierarchy-horizontal leadership framework and are enacted within predetermined boundaries specific to the context. Therefore, this paper examined the Handbook's Forward, Introduction, Managing Your School, and Improving the Quality of Learning sections using Fairclough's (2001. Language and Power. 2nd ed. Harlow, UK: Pearson Education) three-stage approach to critical discourse analysis to identify how leadership is constructed and produced. The study shows that the Handbook constructs Headteacher leadership through relational authority, producing responsibilization, accountability, obligations and constraint roles. As such, Headteachers have discourse constraints and limitations when enacting leadership through this predetermined discourse. This paper provides a point of reference for present and potential researchers and Headteachers to theorize about the future of Headteacher leadership in Ghana.

Integrated aircraft routing and cargo routing problem for combination airlines

The combination airlines operate both passenger aircraft and freighter aircraft to meet passenger and cargo demand. At present, combination airlines employ a sequential approach to allocating their capacity for passenger and cargo demand. Nevertheless, implementing an integrated resource allocation procedure has the potential to improve overall resource allocation efficiency. In this paper, we introduce an integrated model to help combination airlines integrate their aircraft routing and cargo routing decisions to maximize the expected overall profits derived from both passenger and cargo demand. We considered the stochastic nature of passenger baggage and proposed a set of individual chance constraints to ensure the robustness of the integrated solution. We reformulate the chance constraints using piecewise linear approximation to ensure solution efficiency. In addition, we proposed a column-and-row generation based solution approach that removes the through-connection related constraints at the beginning of the solution process and then adds the columns and rows during the iterations as needed. We proved that the proposed column-and-row generation approach can obtain an optimal solution for the LP relaxation problem. The model and the solution approach were tested in a number of scenarios obtained from a major Chinese combination airline. The computational results show that the combination airline can improve their expected profits by integrating capacity allocation. The results also demonstrated that the proposed column-and-row generation solution approach can decrease the solution time of the integrated model. These findings indicate that the model and the solution method are useful and efficient tools for combination airlines when planning their aircraft and cargo routes.

Bayesian frameworks for integrating petrologic and geochronologic data

Constraining the absolute time and duration of geologic processes is one of the great challenges and goals in Earth sciences. Increasingly, the integration of geochronologic constraints with petrologic information is being qualitatively applied to understanding the timescales of metamorphic, igneous, tectonic, and fluid-related processes. Many rocks and geochronometers preserve relative age constraints such as compositional zoning or cross cutting relationships. This prior information can be formalized in a Bayesian statistical framework to generate a probabilistic posterior chronology. As we show here, these “age-sequence” models can enhance precision on geochronologic dates and rates and insight into tectonic models. Bayesian modeling of complex, concentrically, zoned monazite from the northern Appalachian orogen was used to develop a detailed temperature-time history through the Acadian (∼405–395 Ma) and Neoacadian (∼380–350 Ma) orogenies with significantly reduced uncertainties (40–70 %). Modeling of zoned monazite from a southern Trans-Hudson orogen granulite yielded durations of 0.5+9/-0.4 Ma and 20+5/-8 Ma for biotite-dehydration melting and suprasolidus conditions, respectively. The relatively short intervals of heating and peak conditions are consistent with a back-arc tectonic setting. A complementary approach, Bayesian change point detection, provides a framework to constrain the timing of compositional changes that can be linked with metamorphic reactions. Applying this approach in the northern Appalachian orogen demonstrates contrasting durations of low-Y monazite crystallization (∼10 vs ∼30 myr) in regions with different pressure-temperature histories. Compositionally distinct monazite domains can be linked with garnet stability, which provides a key constraint on tectonic models. Bayesian statistical analysis represent a powerful tool that can be widely applied to refine the absolute time and duration of geologic processes. A more objective and reproducible set of interpretations are produced by this more formal, although not necessarily complex, statistical analysis.