The leaf-scale mass-based photosynthetic optimization model better predicts photosynthetic acclimation than the area-based.

Leaf-scale photosynthetic optimization models can quantitatively predict photosynthetic acclimation and have become an important means of improving vegetation and land surface models. Previous models have generally been based on the optimality assumption of maximizing the net photosynthetic assimilation per unit leaf area (i.e. the area-based optimality) while overlooking other optimality assumptions such as maximizing the net photosynthetic assimilation per unit leaf dry mass (i.e. the mass-based optimality). This paper compares the predicted results of photosynthetic acclimation to different environmental conditions between the area-based optimality and the mass-based optimality models. The predictions are then verified using the observational data from the literatures. The mass-based optimality model better predicted photosynthetic acclimation to growth light intensity, air temperature and CO2 concentration, and captured more variability in photosynthetic traits than the area-based optimality models. The findings suggest that the mass-based optimality approach may be a promising strategy for improving the predictive power and accuracy of optimization models, which have been widely used in various studies related to plant carbon issues.

Optimizing predictive models for evaluating the F-temperature index in predicting the π-electron energy of polycyclic hydrocarbons, applicable to carbon nanocones.

In the fields of mathematics, chemistry, and the physical sciences, graph theory plays a substantial role. Using modern mathematical techniques, quantitative structure-property relationship (QSPR) modeling predicts the physical, synthetic, and natural properties of substances based only on their chemical composition. For a chemical graph, the temperature of a vertex is a local property introduced by Fajtlowicz (1988). A temperature-based graphical descriptor is structured based on temperatures of vertices. Involving a non-zero real parameter ß , the general F-temperature index T ß is a temperature index having strong efficacy. In this paper, we employ discrete optimization and regression analysis to find optimal value(s) of ß for which the prediction potential of T ß and the total π -electron energy E π of polycyclic hydrocarbons is the strongest. This, in turn, answers an open problem proposed by Hayat & Liu (2024). Applications of the optimal values for T ß are presented a two-parametric family of carbon nanocones in predicting their E π with significantly higher accuracy.

Research on prediction method of coal mining surface subsidence based on MMF optimization model.

Coal seam mining causes fracture and movement of overlying strata in goaf, and endangers the safety of surface structures and underground pipelines. Based on the engineering geological conditions of 22,122 working face in Cuncaota No.2 Coal Mine of China Shenhua Shendong Coal Group Co., Ltd. a similar material model test of mining overburden rock was carried out. The subsidence of overburden rock was obtained through the full-section strain data of distributed optical fiber technology, and the characteristics of mining surface subsidence were studied. The Weibull model was used to adjust the mathematical form of the first half of the surface subsidence curve via the MMF function. On this basis, the prediction model of coal seam mining surface subsidence was established, and the parameters of the prediction model of surface subsidence were determined. The test results show that with the advancement of coal seam mining, the fit goodness of the surface subsidence prediction curve based on the MMF optimization model reaches 0.987. Compared with the measured values, the relative error of the surface subsidence prediction model is reduced to less than 10%. The model displays good prediction accuracy. The time required for settlement stability in the prediction model is positively correlated with parameter a and negatively correlated with parameter b. The research results can be further extended to the prediction of overburden "three zones" subsidence, and provide a scientific basis for the evaluation of surface subsidence compression potential in coal mine goaf.

A Cooperative Optimization Model for Variable Approach Lanes at Signaled Intersections Based on Real-Time Flow.

To resolve the congestion caused by imbalanced traffic at intersections, this paper establishes a model of the average delay deviation with the minimization of the average delay in the approach as the optimization objective. Then, the signal control scheme is further optimized based on the variable approach lanes setting. First, we investigate the threshold conditions for setting the VALs under different flows in a single approach direction. The results show that when the ratio of left-turn traffic exceeds the threshold range of 0.20~0.28, the function of the VALs needs to be changed from straight to left-turn. Then, based on the improved Webster's formula, an optimal timing method that aims at minimizing the average vehicle delay, minimizing the queue length, and maximizing the capacity, is proposed. Finally, taking the actual Huangke intersection in the Hefei demonstration area as an example, three schemes are compared and analyzed in the case of a VAL at the intersection. The results show that under the cooperative optimization scheme proposed in this paper, the travel time and the efficiency of the intersection could be reduced by 18.7% and 9.9%, respectively, when compared with the original and Webster's schemes.

Parameter extraction of proton exchange membrane fuel cell based on artificial rabbits' optimization algorithm and conducting laboratory tests.

Proton exchange membrane fuel cell (PEMFC) parameter extraction is an important issue in modeling and control of renewable energies. The PEMFC problem's main objective is to estimate the optimal value of unknown parameters of the electrochemical model. The main objective function of the optimization problem is the sum of the square errors between the measured voltages and output voltages of the proposed electrochemical optimized model at various loading conditions. Natural rabbit survival strategies such as detour foraging and random hiding are influenced by Artificial rabbit optimization (ARO). Meanwhile, rabbit energy shrink is mimicked to control the smooth switching from detour foraging to random hiding. In this work, the ARO algorithm is proposed to find the parameters of PEMFC. The ARO performance is verified using experimental results obtained from conducting laboratory tests on the fuel cell test system (SCRIBNER 850e, LLC). The simulation results are assessed with four competitive algorithms: Grey Wolf Optimization Algorithm, Particle Swarm Optimizer, Salp Swarm Algorithm, and Sine Cosine Algorithm. The comparison aims to prove the superior performance of the proposed ARO compared with the other well-known competitive algorithms.

Using Adaptive Imaging Parameters to Improve PEGylated Ultrasmall Iron Oxide Nanoparticles-Enhanced Magnetic Resonance Angiography.

The PEGylated ultrasmall iron oxide nanoparticles (PUSIONPs) exhibit longer blood residence time and better biodegradability than conventional gadolinium-based contrast agents (GBCAs), enabling prolonged acquisitions in contrast-enhanced magnetic resonance angiography (CE-MRA) applications. The image quality of CE-MRA is dependent on the contrast agent concentration and the parameters of the pulse sequences. Here, a closed-form mathematical model is demonstrated and validated to automatically optimize the concentration, echo time (TE), repetition time (TR) and flip angle (FA). The pharmacokinetic studies are performed to estimate the dynamic intravascular concentrations within 12 h postinjection, and the adaptive concentration-dependent sequence parameters are determined to achieve optimal signal enhancement during a prolonged measurement window. The presented model is tested on phantom and in vivo rat images acquired from a 3T scanner. Imaging results demonstrate excellent agreement between experimental measurements and theoretical predictions, and the adaptive sequence parameters obtain better signal enhancement than the fixed ones. The low-dose PUSIONPs (0.03 mmol kg-1 and 0.05 mmol kg-1) give a comparable signal intensity to the high-dose one (0.10 mmol kg-1) within 2 h postinjection. The presented mathematical model provides guidance for the optimization of the concentration and sequence parameters in PUSIONPs-enhanced MRA, and has great potential for further clinical translation.

Diet affordability: a key dimension in the assessment of sustainable food systems and healthy diets.

A promulgated global shift toward a plant-based diet is largely in response to a perceived negative environmental impact of animal food production, but the nutritional adequacy and economic implications of plant-sourced sustainable healthy dietary patterns need to be considered. This paper reviews recent modeling studies using Linear Programming to determine the respective roles of animal- and plant-sourced foods in developing a least-cost diet in the United States and New Zealand. In both economies, least-cost diets were found to include animal-based foods, such as milk, eggs, fish, and seafood, to meet the energy and nutrient requirements of healthy adults at the lowest retail cost. To model a solely plant-based least-cost diet, the prevailing costs of all animal-sourced foods had to be increased by 1.1 to 11.5 times their original retail prices. This led to the inclusion of fortified plant-based foods, such as fortified soymilk, and a plant-based diet that was considerably (34-45%) more costly. The first-limiting essential nutrients were mostly the vitamins and minerals, with special focus on pantothenic acid, zinc, and vitamin B-12, when transitioning from an animal- and plant-containing least-cost diet to a plant-only based least-cost diet. Modeled least-cost diets based on contemporary food costs include animal-sourced foods, at least for developed high-income US and NZ food economies, and potentially for developing low- and middle-income countries, such as Indonesia. Modeling of least-cost diets that consist exclusively of plant-based foods is feasible, but at a higher daily diet cost, and these diets are often close to limiting for several key nutrients. Diet affordability, as a key dimension of sustainable healthy diets, and the respective economic roles of animal- and plant-sourced foods need to be considered.

A Multi-Strategy Improvement Secretary Bird Optimization Algorithm for Engineering Optimization Problems.

Based on a meta-heuristic secretary bird optimization algorithm (SBOA), this paper develops a multi-strategy improvement secretary bird optimization algorithm (MISBOA) to further enhance the solving accuracy and convergence speed for engineering optimization problems. Firstly, a feedback regulation mechanism based on incremental PID control is used to update the whole population according to the output value. Then, in the hunting stage, a golden sinusoidal guidance strategy is employed to enhance the success rate of capture. Meanwhile, to keep the population diverse, a cooperative camouflage strategy and an update strategy based on cosine similarity are introduced into the escaping stage. Analyzing the results in solving the CEC2022 test suite, the MISBOA both get the best comprehensive performance when the dimensions are set as 10 and 20. Especially when the dimension is increased, the advantage of MISBOA is further expanded, which ranks first on 10 test functions, accounting for 83.33% of the total. It illustrates the introduction of improvement strategies that effectively enhance the searching accuracy and stability of MISBOA for various problems. For five real-world optimization problems, the MISBOA also has the best performance on the fitness values, indicating a stronger searching ability with higher accuracy and stability. Finally, when it is used to solve the shape optimization problem of the combined quartic generalized Ball interpolation (CQGBI) curve, the shape can be designed to be smoother according to the obtained parameters based on MISBOA to improve power generation efficiency.

A fourfold-objective-based cloud privacy preservation model with proposed association rule hiding and deep learning assisted optimal key generation.

Numerous studies have been conducted in an attempt to preserve cloud privacy, yet the majority of cutting-edge solutions fall short when it comes to handling sensitive data. This research proposes a "privacy preservation model in the cloud environment". The four stages of recommended security preservation methodology are "identification of sensitive data, generation of an optimal tuned key, suggested data sanitization, and data restoration". Initially, owner's data enters the Sensitive data identification process. The sensitive information in the input (owner's data) is identified via Augmented Dynamic Itemset Counting (ADIC) based Associative Rule Mining Model. Subsequently, the identified sensitive data are sanitized via the newly created tuned key. The generated tuned key is formulated with new fourfold objective-hybrid optimization approach-based deep learning approach. The optimally tuned key is generated with LSTM on the basis of fourfold objectives and the new hybrid MUAOA. The created keys, as well as generated sensitive rules, are fed into the deep learning model. The MUAOA technique is a conceptual blend of standard AOA and CMBO, respectively. As a result, unauthorized people will be unable to access information. Finally, comparative evaluation is undergone and proposed LSTM+MUAOA has achieved higher values on privacy about 5.21 compared to other existing models.

Developing a fuzzy bi-objective programming and MCDM model for bridge maintenance strategy optimization.

Bridges serve as critical links in road networks, requiring continuous maintenance to ensure proper functionality throughout their lifespan. Given their pivotal role in the urban landscape, connecting various parts of a city, this research presents a multi-objective optimization model for the maintenance and repair of bridges in Babolsar, Iran. The model takes into account budget constraints and aims to minimize the total life cycle and user costs, encompassing traffic delays and vehicle expenses, while maximizing the reliability of the bridge network. Recognizing the inherent complexity of this problem, a multi-objective particle swarm optimization algorithm has been developed for an accurate solution. The study further conducts sensitivity analysis on the objective function concerning the available budget, evaluating key parameters such as hourly costs and the time value of vehicles. The results show that with an increase in the budget level, the number of repairs related to the most costly maintenance has significantly risen. In other words, as the budget expands, the model tends to favor repairs with higher costs because their impact on the bridge's performance is more substantial.

External stakeholder risk response strategies selection in project portfolio.

To improve the effectiveness of external stakeholder risks (ESRs) management in project portfolios (PPs), a portfolio-wide risk response approach is required. However, current research is inadequate to effectively identify response strategies for ESRs, which brings challenges to managing ESRs in PPs. In this context, the purpose of this study is to select an appropriate combination of response strategies for ESRs by considering interactions among ESRs, projects, and response strategies in the PP. A Bayesian influence diagram (BID) coupled with a multi-objective optimization model is deemed suitable for this context. Firstly, a probability-sensitivity matrix is established to determine the key ESRs. Then, a BID is constructed to calculate the expected values of different combinations of response strategies. Finally, integrating stakeholder satisfaction and strategy cost, an optimization model for risk response strategy selection is established to obtain candidate combinations. By combining expected values and candidate combinations, the optimal strategy combination is selected. The proposed model comprehensively considers and evaluates the interactions between risks, projects, and risk responses. This enhances the desirability of expected outcomes and reduces project execution costs.

An integrated simulation-optimization approach for combined allocation of water quantity and quality under multiple uncertainties.

Multiple uncertainties such as water quality processes, streamflow randomness affected by climate change, indicators' interrelation, and socio-economic development have brought significant risks in managing water quantity and quality (WQQ) for river basins. This research developed an integrated simulation-optimization modeling approach (ISMA) to tackle multiple uncertainties simultaneously. This approach combined water quality analysis simulation programming, Markov-Chain, generalized likelihood uncertainty estimation, and interval two-stage left-hand-side chance-constrained joint-probabilistic programming into an integration nonlinear modeling framework. A case study of multiple water intake projects in the Downstream and Delta of Dongjiang River Basin was used to demonstrate the proposed model. Results reveal that ISMA helps predict the trend of water quality changes and quantitatively analyze the interaction between WQQ. As the joint probability level increases, under strict water quality scenario system benefits would increase [3.23, 5.90] × 109 Yuan, comprehensive water scarcity based on quantity and quality would decrease [782.24, 945.82] × 106 m3, with an increase in water allocation and a decrease in pollutant generation. Compared to the deterministic and water quantity model, it allocates water efficiently and quantifies more economic losses and water scarcity. Therefore, this research has significant implications for improving water quality in basins, balancing the benefits and risks of water quality violations, and stabilizing socio-economic development.

Constraint Optimization Model for Dynamic Parking Space Allocation.

Managing car parking systems is a complex process because multiple constraints must be considered; these include organizational and operational constraints. In this paper, a constraint optimization model for dynamic parking space allocation is introduced. An ad hoc algorithm is proposed, presented, and explained to achieve the goal of our proposed model. This paper makes research contributions by providing an intelligent prioritization mechanism, considering user schedule shifts and parking constraints, and assigning suitable parking slots based on a dynamic distribution. The proposed model is implemented to demonstrate the applicability of our approach. A benchmark is constructed based on well-defined metrics to validate our proposed model and the results achieved.

An optimization model for reducing thrombectomy center rotations while maintaining medical accessibility.

BACKGROUND/PURPOSE: This study addresses the delicate balance between healthcare personnel burnout and medical accessibility in the context of endovascular thrombectomy (EVT) services in urban areas. We aimed to determine the minimum number of hospitals providing EVT on rotation each day without compromising patient access. METHODS: Employing an optimization model, we developed shift schedules based on patient coverage rates and volumes during the pre-pandemic (2016-2018) and pandemic (2019-2021) periods. Starting with a minimum of two hospitals on duty per day, we gradually increased to a maximum of eight. Patient coverage rates, defined as the proportion of patients meeting bypass criteria and transported to rotating hospitals capable of EVT, were the primary outcomes. Sensitivity analyses explored the impact of varying patient transport intervals and accumulating patients over multiple years. RESULTS: Results from 7024 patient records revealed patient coverage rates of 92.5% (standard deviation [SD] 2.8%) during the pre-pandemic and 91.4% (SD 2.8%) during the pandemic, with at least two rotating hospitals daily. No significant differences were observed between schedules based on the highest patient volume and coverage rate months. A patient coverage rate of 98.99% was achieved with four rotating hospitals per day during the pre-pandemic period, with limited improvement beyond this threshold. Changing patient transport intervals and accumulating patients over six years (p = 0.83) had no significant impact on coverage rates. CONCLUSION: Our optimization model supports reducing the number of daily rotating hospitals by half while preserving a balance between patient accessibility and alleviating strain on medical teams.

Research on the assignment of elderly care service personnel with time window constraints.

As the population continues to age, there is a growing need for elderly care services. In China, home care is widely embraced as a preferred method of caring for the elderly, and it has a promising commercial outlook. The strategic allocation of Elderly Care Service Personnel (ECSP) is a crucial component of the operational procedures for home care services. By strategically assigning elderly service personnel, it is possible to enhance the satisfaction and simultaneously minimize expenses. The issue of rationalizing the assignment of ECSP in the face of restricted resources is a genuine challenge that requires a solution, given the many types of elderly service demands and the skill requirements and time limits of certain projects. This study presents a strategy for assigning personnel on an hourly basis, taking into account time frame limits. This method involves several steps. Firstly, it involves assessing the specific service needs of the elderly, including the required door-to-door service time, service level, and gender preferences. Secondly, it calculates the service satisfaction and service operation cost of the Home Care Service Platform (HCSP) separately. Finally, it constructs a multi-objective elderly service personnel assignment method. This method aims to minimize the ECSP 's travel time and wasted time, maximize the satisfaction of the elderly by considering priority levels and ECSP scores, and minimize the operation cost of the HCSP. A model is developed to assign ECSP for elderly individuals, with the goal of minimizing travel time and wasted time, maximizing elderly satisfaction by considering priority and service personnel rating, and minimizing operating costs for the HCSP. Additionally, if there are unserved elderly individuals, an optimized path model is constructed using a cross-modified genetic algorithm to obtain the optimal matching result. Ultimately, an arithmetic example is employed to demonstrate the practicality and efficiency of the strategy put forth in this research. The findings demonstrate that the model presented in this research possesses distinct advantages in comparison to other conventional models.

An optimization model for monthly time-step drilling schedule under planned field production.

The field production profile over the yearly horizon is planned for a balance between economy, security, and sustainability of energy. An optimal drilling schedule is required to achieve the planned production profile with minimized drilling frequency and summation. In this study, we treat each possible production process of each well as a dependent time series and the basic unit. Then we ensemble all of them into a tensor. Based on formulated tensor calculation and Lasso regularization, a linear mathematical optimization model for well drilling schedule was developed. The model is aimed at minimizing production profile error while optimizing drilling frequency and summation. Although the model proposed in this work requires more memory consumption to be solved using a computer, it is assured as a linear model and could be numerically globally solved in a stable and efficient way using gradient descent, avoiding complex nonlinear programming problems. Main input data and parameters involved in the model are analyzed in detail to understand the effects of different production parameters on the drilling schedule and production profile. The proposed model in this work can evaluate the manual drilling schedule and automatically generate an optimized drilling schedule for the gas field, significantly reducing development plan formulation time.

A cooperative optimization method for the layout of shared bicycle parking areas and delivery quantity.

With the popularity of shared bicycles in urban areas, more and more residents choose this fast and convenient mode of transportation for short-distance travel. By optimizing the layout of shared bicycle parking areas and delivery quantity, the investment cost of shared bicycle enterprises can be effectively reduced, and the convenience of residents' travel can be improved at the same time. In this paper, we develop a collaborative optimization model for the layout of the shared bicycle parking area and delivery quantity, aiming at minimizing the walking distance of residents and the investment cost of enterprises, while considering the constraints of the parking area's attractive range and the number of bicycles placed. Aiming at the characteristics of this mixed integer nonlinear problem, an improved genetic algorithm incorporating symmetric individual precision control mechanism is designed. Finally, taking the planned area between the Second Ring Road and the Third Ring Road in the northern part of Jin-niu District, Chengdu as the background, the proposed collaborative optimization model for the layout of shared bicycle parking areas and delivery quantity is applied to a real scene. The results show that after optimization, the number of parking areas is reduced by 2, and the total investment cost is reduced by about 12.2%.

Optimal carbon emission reduction path of the building sector: Evidence from China.

To combat climate change, China's building sector, responsible for almost 50 % of national emissions, must undergo a drastic decarbonization transformation. This paper charts the optimal path to achieve this goal, leveraging a combined framework of the Low Emissions Analysis Platform (LEAP) and System Dynamics (SD) for scenario-based forecasting of energy consumption and emissions in 2021-2060. A linear programming model is further developed to identify the lowest-cost combination of 26 building green technologies that align with China's carbon peaking and carbon neutrality targets. Results show that in a business-as-usual scenario, building carbon emissions will peak at 6393 million tons of CO2 in 2041, missing the 2030 carbon peaking target. Key drivers of this shortfall include the high energy intensity for "Transport, Storage and Post" and the large carbon emission factors for "Wholesale, Retail Trades, Hotels, and Catering Services" and "Residential" sectors. Under various technology application scenarios assuming uniform penetration rates, the 2030 carbon peak target appears attainable, though at a considerably high cost. Finally, under optimal technology combinations, building carbon emissions are forecasted to peak in 2030 at 5139 million tons of CO2, a mere 4.4 % increase from 2020. The cost of this optimized combination is projected to represent only 1.5 % of the total GDP in 2060. This scenario also leads to a significantly weaker correlation between energy consumption and carbon emissions in the building sector around 2036, nearly 17 years ahead of the business-as-usual trajectory.

Impact assessment of spatial-temporal distribution of riverine dust on air quality using remote sensing data and numerical modeling.

Soil erosion is a severe problem in Taiwan due to the steep terrain, fragile geology, and extreme climatic events resulting from global warming. Due to the rapidly changing hydrological conditions affecting the locations and the amount of transported sand and fine particles, timely impact evaluation and riverine dust control are difficult, particularly when resources are limited. To comprehend the impact of desertification in estuarine areas on the variation of air pollutant concentrations, this study utilized remote sensing technology coupled with an air pollutant dispersion model to determine the unit contribution of potential pollution sources and quantify the effect of riverine dust on air quality. The images of the downstream area of the Beinan River basin captured by Formosat-2 in May 2006 were used to analyze land use and land cover (LULC) composition. Subsequently, the diffusion model ISCST-3 based on Gaussian distribution was utilized to simulate the transport of PM across the study area. Finally, a mixed-integer programming model was developed to optimize resource allocation for dust control. Results reveal that sand deposition in specific river sections significantly influences regional air quality, owing to the unique local topography and wind field conditions. The present optimal plan model for regional air quality control further showed that after implementing engineering measures including water cover, revegetation, armouring cover, and revegetation, total PM concentrations would be reduced by 51%. The contribution equivalent calculation, using the air pollution diffusion model, was effectively integrated into the optimization model to formulate a plan for reducing riverine dust with limited resources based on air quality requirements.

Circular economy strategies for mitigating metals shortages in electric vehicle batteries under China's carbon-neutral target.

Concerns over supply risks of critical metals used in electric vehicle (EV) batteries are frequently underscored as impediments to the widespread development of EVs. With the progress to achieve carbon neutrality by 2060 for China, projecting the critical metals demand for EV batteries and formulating strategies, especially circular economy strategies, to mitigate the risks of demand-supply imbalance in response to potential obstacles are necessary. However, the development scale of EVs in the transport sector to achieve China's carbon neutrality is unclear, and it remains uncertain to what extent circular economy strategies might contribute to the reduction of primary raw materials extraction. Consequently, we explore the future quantity of EVs in China required to achieve carbon neutrality and quantify the primary supply security levels of critical metals with the effort of battery cascade utilization, technology substitutions, recycling efficiency improvement, and novel business models, by integrating dynamic material flow analysis and national energy technology model. This study reveals that although 18%-30% of lithium and 20%-41% of cobalt, nickel, and manganese can be supplied to EVs through the reuse and recycling of end-of-life batteries, sustainable circular economy strategies alone are insufficient to obviate critical metals shortages for China's EV development. However, the supplementary capacity offered by second-life EV batteries, which refers to the use of batteries after they have reached the end of their first intended life, may prove adequate for China's prospective novel energy storage applications. The cumulative primary demand for lithium, cobalt, and nickel from 2021 to 2060 would reach 5-7 times, 23-114 times, and 4-19 times the corresponding mineral reserves in China. Substantial reduction of metals supply risks apart from lithium can be achieved by the cobalt-free battery technology developments combined with efficient recycling systems, where secondary supply can satisfy the demand as early as 2054.