Optimal Size Research Articles

Comparison with surgically resected mucinous cystic neoplasm of pancreas and branch-duct type intraductal papillary mucinous neoplasm considering clinico-radiological high-risk features: a reassessment of current guidelines.

To perform a comparative analysis of surgically resected mucinous cystic neoplasm (MCN) of pancreas and branch-duct type intraductal papillary mucinous neoplasms (BD-IPMN) considering clinico-radiological high-risk predictors for malignant tumors using the current management guidelines. 224 patients who underwent surgical resection and had histopathologically confirmed MCNs (benign 73; malignant 17) or BD-IPMNs (benign 110; malignant 24) and had pre-operative CT or MRI were retrospectively reviewed. Tumors classified as either high-grade dysplasia or invasive carcinoma were considered malignant, whereas those with low-grade dysplasia were considered benign. Imaging features were analyzed by two radiologists based on selected high-risk stigmata or worrisome features proposed by prevalent guidelines except tumors with main pancreatic duct dilatation (> 5mm) were excluded. MCNs and BD-IPMNs showed significant differences in aspects like tumor size, location, the presence and size of enhancing mural nodules, the presence of wall or septal thickening, and multiplicity. Multivariate analyses revealed tumor size (OR, 1.336; 95% CI, 1.124-1.660, p = 0.002) and the presence of enhancing mural nodules (OR, 67.383; 95% CI, 4.490-1011.299, p = 0.002) as significant predictors of malignant MCNs. The optimal tumor size differentiating benign from malignant tumor was 8.95cm, with a 70.6% sensitivity, 89% specificity, PPV of 27.6%, and NPV of 96.9%, demonstrating superior specificity than the guideline-suggested threshold of 4.0cm. For malignant BD-IPMNs, the presence of enhancing mural nodules (OR, 15.804; 95% CI, 4.439-56.274, p < 0.001) and CA 19 - 9 elevation (OR, 19.089; 95%CI, 2.868-127.068, p = 0.002) as malignant predictors, with a size of enhancing mural nodule threshold of 5.5mm providing the best malignancy differentiation. While current guidelines may be appropriate for managing BD-IPMNs, our results showed a notably larger optimal threshold size for malignant MCNs than that suggested by current guidelines. This warrants reconsidering existing guideline thresholds for initial risk stratification and management of MCNs.

Cross-Linking CdSO4-Type Nets with Hexafluorosilicate Anions to Form an Ultramicroporous Material for Efficient C2H2/CO2 and C2H2/C2H4 Separation.

A 44.610.8 topology hybrid ultramicroporous material (HUM), {[Cu1.5F(SiF6)(L)2.5]·G}n, (L = 4,4'-bisimidazolylbiphenyl, G = guest molecules), 1, formed by cross-linking interpenetrated 3D four-connected CdSO4-type nets with hexafluorosilicate anions is synthesized and evaluated in the context of gas sorption and separation herein. 1 is the first HUM functionalized with two different types of fluorinated sites (SiF6 2- and F- anions) lining along the pore surface. The optimal pore size (≈5Å) combining mixed and high-density electronegative fluorinated sites enable 1 to preferentially adsorb C2H2 over CO2 and C2H4 by hydrogen bonding interactions with a high C2H2 isosteric heat of adsorption (Qst) of ≈42.3kJmol-1 at zero loading. The pronounced discriminatory sorption behaviors lead to excellent separation performance for C2H2/CO2 and C2H2/C2H4 that surpasses many well-known sorbents. Dynamic breakthrough experiments are conducted to confirm the practical separation capability of 1, which reveal an impressive separation factor of 6.1 for equimolar C2H2/CO2 mixture. Furthermore, molecular simulation and density functional theory (DFT) calculations validate the strong binding of C2H2 stems from the chelating fix of C2H2 between SiF6 2- anion and coordinated F- anion.

Design and sample size determination for multiple-dose randomized phase II trials for dose optimization.

The U.S. Food and Drug Administration (FDA) has launched Project Optimus to shift dose selection from the maximum tolerated dose (MTD) to the dose that produces the optimal risk-benefit tradeoff. One approach highlighted in the FDA's guidance involves conducting a randomized phase II trial following the completion of a phase I trial, where multiple doses (typically including the MTD and one or two doses lower than the MTD) are compared to identify the optimal dose that maximizes the benefit-risk tradeoff. This article focuses on the design of such a multiple-dose randomized trial, specifically the determination of the sample size. We generalized the standard definitions of type I error and power to accommodate the unique characteristics of dose optimization and derived a decision rule along with an algorithm to determine the optimal sample size. The resulting design is referred to as MERIT (Multiple-dosE RandomIzed Trial design for dose optimization based on toxicity and efficacy). Simulation studies demonstrate that MERIT has desirable operating characteristics, and a sample size between 20 and 40 per dosage arm often offers reasonable power and type I errors to ensure patient safety and benefit. To facilitate the implementation of the MERIT design, we provide software, available at https://www.trialdesign.org.

Optimum intra-row spacing and clove size for the economical production of garlic (Allium sativum L.) in Northwestern Highlands of Ethiopia

Abstract Garlic is one of the most important cash crops cultivated by smallholder farmers throughout Ethiopia. However, due to inappropriate agronomic practices including clove size and intra-row spacing, the productivity of the crop is low in the country. Therefore, a field experiment was conducted during the 2019 main cropping season under rain feed conditions to identify the optimum clove size and intra-row spacing for the economical production of garlic in Lay Gayint district, Northwestern Ethiopia. Local garlic cultivar obtained from farmers was used as a test crop. A factorial combination of three clove sizes and four intra-row spacings was used in a randomized complete block design with three replications. The results revealed that the main as well as the interaction effects of clove size and intra-row spacing influenced most of the tested growth and yield parameters of garlic. The longest plant height (73.77 cm) was recorded from garlic plants grown at 5 cm intra-row spacing, while the shortest plant height (66.98 cm) was from plants grown at 12.5 cm. The treatment combination of large-sized clove and 12.5 cm intra-row spacing recorded the longest leaf length (61.07 cm), the highest leaf number (7.79), bulb diameter (4.98 cm), bulb weight (33.83 g), and clove weight (2.71 g) of garlic. However, the treatment combination of large-sized cloves and 7.5 cm intra-row spacing produced the highest marketable (13.44 t ha−1) bulb yield. Based on the partial budget analysis, however, medium-sized cloves (1.5–1.99 g) planted at 10 cm intra-row spacing recorded the highest net benefit (723,205 Eth-Birr ha−1) with an acceptable marginal rate of return (3277.3%). Therefore, growing garlic using medium-sized cloves (1.5–1.99 g) at 10 cm intra-row spacing is recommended for economical production of garlic by smallholder farmers in the study area and areas with similar agroecology.

Research on Energy Loss of Optimization of Inducer–Impeller Axial Fit Dimensions Based on Wave-Piercing Theory

With the development of modern fluid machinery, the energy density of pumps is gradually being improved, and at the same time, higher demands are being placed on the cavitation performance, hence the introduction of the inducer and centrifugal impeller to form a dynamic–dynamic series structure. However, there are strict constraints on the axial size of pumps in fields such as firefighting and aerospace. The traditional empirical formula no longer satisfies the need to fit the axial dimensions between the induced wheel and the impeller at high velocities. Therefore, based on the wave-piercing theory, the drag reduction coefficient is introduced to explore the optimal axial fit size from the perspective of energy characteristics. This paper focuses on the influence of the inducer’s wake on the energy characteristics of downstream impellers, and conducts the following research: by adjusting the axial matching dimensions between the upstream inducer and the centrifugal impeller in the initial model, ten sets of axial distance models with matching dimensions of KD are designed, and the drag reduction coefficient is embedded to determine the optimal axial distance. The results show that the optimal axial distance is 0.2D, which is far lower than the axial distance value of 0.42D obtained from the traditional empirical formula for axial matching dimensions. Meanwhile, this paper uses tangential velocity, the inlet flow angle of the impeller, entropy production theory, and other indicators to analyze the internal energy loss of the high-speed vehicular fire pumps one by one. All of them confirm that the impeller in the high-speed vehicular fire pump has the lowest energy loss and optimal performance at an axial distance of 0.2D. Specifically, at this axial distance, the head can reach 259 m, and the hydraulic efficiency is as high as 83.62%. Thus, the feasibility of determining the axial placement of the impeller using the drag coefficient is validated. This research provides new insights into determining the axial coordination dimensions between the inducer and the impeller.

Optimization of off-grid hybrid renewable energy systems for cost-effective and reliable power supply in Gaita Selassie Ethiopia

This paper explores scenarios for powering rural areas in Gaita Selassie with renewable energy plants, aiming to reduce system costs by optimizing component numbers to meet energy demands. Various scenarios, such as combining solar photovoltaic (PV) with pumped hydro-energy storage (PHES), utilizing wind energy with PHES, and integrating a hybrid system of PV, wind, and PHES, have been evaluated based on diverse criteria, encompassing financial aspects and reliability. To achieve the results, meta-heuristics such as the Multiobjective Gray wolf optimization algorithm (MOGWO) and Multiobjective Grasshopper optimization algorithm (MOGOA) were applied using MATLAB software. Moreover, optimal component sizing has been investigated utilizing real-time assessment data and meteorological data from Gaita Sillasie, Ethiopia. Metaheuristic optimization techniques were employed to pinpoint the most favorable loss of power supply probability (LPSP) with the least cost of energy (COE) and total life cycle cost (TLCC) for the hybrid system, all while meeting operational requirements in various scenarios. The Multi-Objective Grey Wolf Optimization (MOGWO) technique outperformed the Multi-Objective Grasshopper Optimization Algorithm (MOGOA) in optimizing the problem, as suggested by the results. Furthermore, based on MOGWO findings, the hybrid solar PV-Wind-PHES system demonstrated the lowest COE (0.126€/kWh) and TLCC (€6,897,300), along with optimal satisfaction of the village's energy demand and LPSP value. In the PV-Wind-PHSS scenario, the TLCC and COE are 38%, 18%, 2%, and 1.5% lower than those for the Wind-PHS and PV-PHSS scenarios at LPSP 0%, according to MOGWO results. Overall, this research contributes valuable insights into the design and implementation of sustainable energy solutions for remote communities, paving the way for enhanced energy access and environmental sustainability.

Enrichment of Total Flavonoids and Licochalcone A from Glycyrrhiza inflata Bat. Residue Based on a Combined Membrane–Macroporous Resin Process and a Quality-Control Study

Glycyrrhiza inflata Bat. produces a lot of licorice waste after water extraction, which also retains abundant total flavonoids (TFs) and licochalcone A. However, licorice residue is often wasted due to the lack of good utilization of resources in practical applications. This study first screened the optimal membrane pore size and resin type and then explored the mechanism and conditions of the adsorption of TFs on the resin. Then, different combinations and sequences of membrane and macroporous resin (MR) methods were investigated. It was found that using the membrane method for initial purification, followed by the MR method for further purification, yielded the best purification results. Next, response surface methodology was utilized to investigate the resin’s dynamic desorption conditions for TFs. Finally, the TF purity increased from 32.9% to 78.2% (2.38-fold) after purification by a combined membrane–MR process; the purity of licochalcone A increased from 11.63 mg·g−1 to 22.70 mg·g−1 (1.95-fold). This study verified the feasibility of enriching TFs and licochalcone A from licorice residue using a membrane–MR coupling method. In addition, a quality-control method was established using a fingerprinting method on the basis of ultrahigh-performance liquid chromatography (UPLC) to ensure the stability of the enrichment process.

Optimization and performance enhancement of renewable energy microgrid energy system using pheasant bird optimization algorithm

The main aim of this research work is to design and select an optimal renewable energy resource based microgrid (MG) system for rural area electrification of India. MG system consists of diesel generator (DG), battery bank (BB), wind turbine (WT), biomass generator (BMG) and solar photovoltaic (PV) modules with seven different configurations. The optimal system is selected based on technological, economic, environmental, social and reliability parameters. For this objective, a novel algorithm called pheasant bird optimization algorithm (PBOA) is presented and compared with particle swarm optimization (PSO), genetic algorithm (GA), ant colony optimization (ACO), cuckoo search algorithm (CSA) and HOMER Pro. Results show that PV/WT/BMG/BB/DG is an optimal configuration with sizes of 1148 units of PV, 42 units of WT, 20 units of BMG, 36 units of BB and 1 unit of DG. PBOA has determined the optimal resource size for the MG system. In terms of social and reliability parameters for the PV/WT/BMG/BB/DG system, PBOA generated values of 93.58 %, for RF, 0.08754 for EGJC and 0.01 % for LPSP. For the optimal configuration, PBOA estimated NPC, COE, CE values are ₹66185779, ₹9.3 and 650901 kg/yrs respectively. PBOA reaches the minimal NPC of ₹66185779 in just 180 iterations, whereas PSO, ACO, GA, and CSA required 360, 200, 360 and 250 iterations respectively. PBOA also achieved minimal COE at a rate of 1.52, 1.11, 1.11 and 1.94 times faster than PSO, GA, CSA and ACO respectively. The contribution of PV and WT sources to the total generated electricity is approximately 50 % and 35 % respectively. PBOA exhibits faster convergence time and iteration compared to the PSO, ACO, GA and CSA algorithm. The comparison results indicate that the optimal PV/WT/BMG/BB/DG configuration select for rural areas. This research provides valuable information for policy makers and investors on the implementation of MG system in rural regions.

Source reservoir controls on the size, frequency, and composition of large-scale volcanic eruptions.

Large-scale, explosive volcanic eruptions are one of the Earth's most hazardous natural phenomena. We demonstrate that their size, frequency, and composition can be explained by processes in long-lived, high-crystallinity source reservoirs that control the episodic creation of large volumes of eruptible silicic magma and its delivery to the subvolcanic chamber where it is stored before eruption. Melt percolates upward through the reservoir and accumulates a large volume of low-crystallinity silicic magma which remains trapped until buoyancy causes magma-driven fractures to propagate into the overlying crust, allowing rapid magma transfer from the reservoir into the chamber. Ongoing melt percolation in the reservoir accumulates a new magma layer and the process repeats. Our results suggest that buoyancy, rather than crystallinity, is the key control on magma delivery from the source reservoir. They identify an optimum reservoir size for the largest silicic eruptions that is consistent with data from natural systems and explain why larger magnitude eruptions are not observed on Earth.

Analysis of landscape pattern and ecological risk change characteristics in Bosten Lake basin based on optimal scale

Amidst swift socioeconomic progress, urbanization is intensifying, necessitating robust methodologies for environmental quality monitoring. Landscape Ecological Risk (LER) has emerged as a pivotal tool in this context. Extensive research has focused on the land utilization rates within watersheds, scrutinizing them through the lens of land use/land cover (LULC) dynamics. This study delves into the LULC attributes of the Bosten Lake Basin (BLB) across three temporal snapshots: 2000, 2010, and 2020. Grasslands and bare ground made up the vast majority area of the BLB. Selected twelve landscape pattern indices, analyzed their response curves with respect to grain size, determined the optimal grain size to be 150 m, and analysed its optimum amplitude of 10 km × 10 km on the basis of the semi-variance function, thus determined the optimal scale for analyzing the landscape pattern. Exploring scale effect facilitates a nuanced examination of the landscape patterns and land use efficacy within BLB. From 2000 to 2020, the landscape pattern showed less fragmented, less aggregated, and more spatially heterogeneous. The landscape ecological risk index model has been constructed for the investigation of the LER change characteristics of the BLB. Lowest-risk zones and lower-risk zones of LER predominate in BLB. In 2000, 2010 and 2020 the combined lower and lowest risk zones represent 62.02 %, 59.37 % and 58.44 % of the catchment area respectively. Finally, analyzed the spatial autocorrelation variation characteristics of LER. The global Moran' I index of BLB for the three periods from 2000 to 2020 exceeded 0.7. This study establishes theoretical and practical foundation for future multi-scale landscape ecological risk studies in eco-fragile regions.

On the planning of zone-based electric on-demand minibus

An on-demand transportation system can enhance traffic efficiency through its flexible services. In this study, we introduce a planning problem for an on-demand electric minibus service within urban areas. We are considering a zone-based operation, meaning that the on-demand minibus is restricted to picking up or dropping off passengers within a single zone for each trip, and the minibus does not stop in other zones. For this zonal electric minibus system, we plan the service level for each zone, the construction of dedicated charging piles in each zone, and the optimal size of the bus fleet. To model the planning problem, we propose a mixed-integer nonlinear second-order cone program. Additionally, we introduce an adaptive discretization algorithm to expedite the problem-solving process. Our numerical tests illustrate the planning for the on-demand minibus system in Manhattan. Furthermore, these tests underscore the benefits of the proposed on-demand minibus system. The most important findings of this study are: (i) the proposed mode replaces more trips with cars, thus reducing the traffic flow and alleviating traffic congestion; (ii) the service zone division, the setting of the fare, and the number of seats in minibus play an essential role in the revenue of the on-demand minibus system; (iii) considering endogenous zone selecting, number of dedicated charging piles, and mode choice in each zone is essential to the planning of the on-demand minibus system.

GPU-based parallel programming for FEM analysis in the optimization of steel frames

ABSTRACT Optimization of large-scale frame structures consumes a vast amount of time since the analysis of such complex systems contains several iterative processes. Mitigating computational burden and reducing this time to a reasonable level is possible by running GPU (Graphical Processing Unit) processors, which can be found on standard computers. This study presents an algorithm for the acceleration of size optimization of steel frames by using the BBO (Biogeography-Based Optimization) method that is suitable for GPU architecture. The GPU-based parallel algorithm, designed for FEM (Finite Element Method) analysis, is applied to three hypothetical steel-frame case structures with different numbers of members and nodes; and processed on four different computers which are available on the market. The presented case studies revealed that the proposed solution’s efficiency increases as the number of members increases and confirmed the ability of the acceleration algorithm for optimization of large-scale frame structures and provided time efficiency.

Optimal sizing and economic assessment of grid connected active distribution network for reliable rural electrification in India

Majority of rural areas in India have already been electrified, yet the disparity between demand and supply is considerably higher and broadened due to which nation’s rapid financial growth as well as rising standards of living in both the urban as well as rural areas. Hence, the utilisation of renewable resources is rising steadily for the mitigation of such energy crisis. It has become necessity to use a hybrid sustainable energy system by exploiting the strengths of each source in combination with one another to minimise the dependence on the congested power grid. For the efficient use of readily available renewable energy and sustainable rural electrification, this study proposes an effective Active Distribution Network (ADN). A comprehensive techno-economic analysis has been performed on the design of an ADN system that includes the exploitation of solar and biomass energy as sources of power for sustainable rural electrification. In the proposed work, performance assessment elements such as output responses, component costs, feasibility and sensitivity of the project, emissions, inflation rate, and changing the pricing of integrant have been taken into consideration and analysed. Results shows the improvement in rural electrical system while lowering system energy costs along with consideration of environmental impact.

Topical antimicrobial treatment of mesh for the reduction of surgical site infections after hernia repair: a systematic review and meta-analysis.

Use of mesh is essential in hernia repair. A common complication after hernia repair is surgical site infection (SSI), which poses a risk in spreading to the mesh, possibly causing mesh infection. Topical antimicrobial pretreatment of mesh may potentially reduce SSI risk in hernia repair and has shown promising results in in vitro and in vivo studies. Clinical evidence, however, is more important. This systematic review aims to provide an overview of available clinical evidence for antimicrobial pretreated mesh in hernia repair surgery to reduce SSI. We report in accordance with PRISMA guidelines. CENTRAL, EMBASE, CINAHL and PubMed were searched up to October 2023 for studies that investigated the use of antimicrobial pretreated mesh on SSI incidence in adults undergoing hernia repair. The primary outcome was SSI incidence. We also collected data on pathogen involvement, hernia recurrence, and mesh infection. A meta-analysis on SSI risk and GRADE-assessment was performed of eligible studies. We identified 11 eligible studies (n = 2660 patients); 5 randomized trials and 6 cohort studies. Investigated interventions included pre-coated mesh, antibiotic carriers, mesh soaked or irrigated with antibiotic or antiseptic solution. Meta-analysis showed no significant reduction in SSI for antibiotic pretreated polypropylene mesh (RR 0.76 [95% CI 0.27; 2.09]; I2 50%). Data on topical mesh pretreatment to reduce SSI risk after hernia repair is limited. Very low certainty evidence from randomized trials in hernia repair surgery shows no significant benefit for antibiotic mesh pretreatment for SSI reduction, but data are imprecise due to optimal information size not being met.

Integrated assessment of green hydrogen production in California: Life cycle Greenhouse gas Emissions, Techno-Economic Feasibility, and resource variability

This study explores green hydrogen production, focusing on life cycle assessment (LCA) and techno-economic feasibility; specifically, life cycle greenhouse gas emissions and hydrogen production cost. Existing literature lacks a comprehensive exploration of the impact of solar and wind resource variations, ambient conditions, and proton exchange membrane (PEM) electrolyzer sizing on the carbon intensity (CI) and hydrogen production cost. This work explores the relationship between the performance of photovoltaic (PV) and wind systems and the CI of renewable electricity in the U.S. Secondly, this study incorporates the hourly variations in solar and wind resources to assess the variation in hydrogen CI and production costs across California. The study examines how reductions in PEM and Renewable Energy Systems (RES) costs, technological advancements, and the inclusion of co-products’ carbon dioxide equivalent (CO2eq) credits and revenues impact hydrogen CI and production costs. Wind-based systems consistently demonstrate lower CI (0.35 − 1.9 kg CO2eq/kg H2) except for the central regions (CI can reach up to 4.34 kg CO2eq/kg H2), with geographical variations larger than solar-based systems (1.58 − 2.95 kg CO2eq/kg H2). Similar trends are observed for the hydrogen production cost which varies between 1.5 and 15 USD/kg H2 for wind-based systems and between 3.0 and 5.2 USD/kg H2 for the PV-based systems. This study highlights the importance of optimal sizing of PEM electrolyzer to maximize its utilization for hydrogen production and the use of hourly-based models to assess the CI and production costs of hydrogen. Additionally, the geographical variation in the renewable energy resources and the ambient conditions are important factors that affect the hydrogen cost and the CI of both renewable electricity and green hydrogen.

On–off-Grid Optimal Hybrid Renewable Energy Systems for House Units in Iraq

This paper addresses the optimal sizing of Hybrid Renewable Energy Systems (HRESs), encompassing wind, solar, and battery systems, with the aim of delivering reliable performance at a reasonable cost. The focus is on mitigating unscheduled outages on the national grid in Iraq. The proposed On–off-grid HRES method is implemented using MATLAB and relies on an iterative technique to achieve multi-objectives, balancing reliability and economic constraints. The optimal HRES configuration is determined by evaluating various scenarios related to energy flow management, electricity prices, and land cover effects. Consumer requirements regarding cost and reliability are factored into a 2D optimization process. A battery model is developed to capture the dynamic exchange of energy among different renewable sources, battery storage, and energy demands. A detailed case study across fifteen locations in Iraq, including water, desert, and urban areas, revealed that local wind speed significantly affects the feasibility and efficiency of the HRES. Locations with higher wind speeds, such as the Haditha lake region (payback period: 7.8 years), benefit more than urban areas (Haditha city: payback period: 12.4 years). This study also found that not utilizing the battery, particularly during periods of high electricity prices (e.g., 2015), significantly impacts the HRES performance. In the Haditha water area, for instance, this technique reduced the payback period from 20.1 to 7.8 years by reducing the frequency of charging and discharging cycles and subsequently mitigating the need for battery replacement.

Analysis of Grid Performance with Diversified Distributed Resources and Storage Integration: A Bilevel Approach with Network-Oriented PSO

The growing deployment of distributed resources significantly affects the distribution grid performance in most countries. The optimal sizing and placement of these resources have become increasingly crucial to mitigating grid issues and reducing costs. Particle Swarm Optimization (PSO) is widely used to address such problems but faces computational inefficiency due to its numerical convergence behavior. This limits its effectiveness, especially for power system problems, because the numerical distance between two nodes in power systems might be different from the actual electrical distance. In this paper, a scalable bilevel optimization problem with two novel algorithms enhances PSO’s computational efficiency. While the resistivity-driven algorithm strategically targets low-resistivity regions and guides PSO toward areas with lower losses, the connectivity-driven algorithm aligns solution spaces with the grid’s physical topology. It prioritizes actual physical neighbors during the search to prevent local optima traps. The tests of the algorithms on the IEEE 33-bus and the 69-bus and Norwegian networks show significant reductions in power losses (up to 74% for PV, wind, and storage) and improved voltage stability (a 21% reduction in mean voltage deviation index) with respect to the results of classical PSO. The proposed network-oriented PSO outperforms classical PSO by achieving a 2.84% reduction in the average fitness value for the IEEE 69-bus case with PV, wind, and storage deployment. The Norwegian case study affirms the effectiveness of the proposed approach in real-world applications through significant improvements in loss reduction and voltage stability.

A Low-Frequency Fiber Bragg Grating Acceleration Sensor Based on Spring Support and Symmetric Compensation Structure with Flexible Hinges

To measure vibration signals, a low-frequency fiber Bragg grating (FBG) acceleration sensor featuring a flexible hinge with a spring support and symmetric compensation structure has been designed. Based on the mechanical model of the sensor’s structure, the expressions for sensitivity and resonant frequency of the sensor are derived. The structural parameters of the sensor are optimized, and a simulation analysis is conducted using ANSYS 19.2 software. According to the results of simulation analysis and size optimization, the sensor prototype is constructed. Subsequently, its amplitude-frequency response, sensitivity, and temperature characteristics are investigated through vibration experiments. The experimental results show that the resonant frequency of the sensor is 73 Hz, the operating frequency range is 0~60 Hz, and the sensitivity measures 24.24 pm/g. This design meets the requirements for measuring vibration signals at low frequencies.

Constraint‐aware optimization model for plane truss structures via single‐agent gradient descent

AbstractThis study introduces the constraint‐aware optimization model (CAOM), a novel optimization framework designed to optimize the size, shape, and topology of plane truss structures simultaneously. Unlike traditional optimization models, which rely on gradient descent and frequently struggle with managing various constraints due to their dependence on a single optimization agent, CAOM effectively addresses this challenge. It does so by incorporating a variety of assistant modules along with the Adam optimizer, a variant of the gradient descent method. Uniquely, CAOM employs the leaky rectified linear unit (ReLU) activation function beyond its conventional use in neural networks, applying it as a mechanism to integrate constraints and losses seamlessly. The model's effectiveness was validated through two numerical examples and a practical application, demonstrating that CAOM can reduce structural weight by up to 84% compared to unoptimized designs while fully adhering to structural, dimensional, and moveable constraints. Furthermore, the study found that while shape optimization plays a key role for stiffness‐governed structures, size optimization is crucial for strength‐governed structures. Optimizing size, shape, and topology together consistently leads to the most weight‐efficient designs. This emphasizes the significance of a holistic approach in the optimization processes.

Influence of Leaf Area Index Inversion and the Light Transmittance Mechanism in the Apple Tree Canopy

Light plays a crucial role in the growth of fruit trees, influencing not only nutrient absorption but also fruit appearance. Therefore, understanding fruit tree canopy light transmittance is essential for agricultural and forestry practices. However, traditional measurement methods, such as using a canopy analyzer, are time-consuming, labor-intensive, and susceptible to external influences, lacking convenience and automation. To address this issue, we propose a novel method based on point clouds to estimate light transmittance, with the Leaf Area Index (LAI) serving as the central link. Focusing on apple trees, we utilized handheld LiDAR for three-dimensional scanning of the canopy, acquiring point cloud data. Determining the optimal voxel size at 0.015 m via standardized point cloud mean spacing, we applied the Voxel-based Canopy Profile method (VCP) to estimate LAI. Subsequently, we established a function model between LAI and canopy light transmittance using a deep neural network (DNN), achieving an overall correlation coefficient R2 of 0.94. This model was then employed to estimate canopy light transmittance in dwarfed and densely planted apple trees. This approach not only provides an evaluation standard for pruning effects in apple trees but also represents a critical step towards visualizing and intelligentizing light transmittance.