Optimal Point Research Articles

Response Surface Methodology Approach for Modeling and Performance Optimization of a PEM Fuel Cell

A Polymer Electrolyte Membrane (PEM) fuel cell is a popular green source of electrical energy and is often used in applications like electric vehicles due to its environmentally friendly operation. This type of fuel cell has a low operating temperature, light weight, and negligible emission of greenhouse gases. However, the PEM fuel cell is a complex multivariable system with a large number of input and output factors, and most of the input factors affect output factors directly or indirectly. As a result, it is conventionally quite difficult to determine which input factor has a major effect on a particular output factor. Statistical methods are very popular for finding the individual and interaction effects of input factors on output factors. In this paper, for the first time, a simple and realistic MATLAB SIMULINK model for a PEM fuel cell is presented to conduct various experimental tests. The developed MATLAB SIMULINK model and statistical design of experiments, Response Surface Methodology (RSM), are used to develop metamodels (mathematical models of the simulation model) for the PEM fuel cell to find the individual and interaction effects of various input factors on output factors. The developed metamodels can be used to find the region for optimum operation of the presented fuel cell. The metamodels are validated by conducting four different statistical tests. The optimum point of operation is presented by calculating stationary points from the metamodels.

Numerical investigation on the operation strategy of a chemisorption heat pump system

ABSTRACT This paper presents an analysis of the operating characteristics of a chemisorption heat pump. 1-D transient model of a chemisorption heat pump was developed, for which an analysis of four alternating modes of operation was performed. The performance was analyzed with respect to the operating time of the adsorption and desorption operation modes during alternating operations, and the results showed that the cooling capacity has an optimum point depending on the operating times. It was also confirmed that the coefficient of performance increases as the operating time increases. The system performance was also analyzed according to the flow rates of the secondary fluids supplied to the main components. As the flow rates of the secondary fluids supplied to the main components increased, the cooling capacity improved, and the coefficient of performance was found to be compounded by the secondary flow conditions and the operating times of the adsorption and desorption operation modes. Based on the results, it was found that the cooling capacity, coefficient of performance, and specific cooling power of the chemisorption heat pump can be operated in a large range from 876.7 to 3070.0 W, 0.13 to 0.32, and 39.3 to 137.7 W/kg, respectively, depending on the operating conditions.

Predictive ability of Achilles tendon elastography for frailty in older adults.

The Achilles tendon (AT) is the largest and strongest tendon in the human body, and its elasticity is known to be affected by the aging process. However, the relation between AT stiffness and frailty in older individuals remains uncertain. This study aims to explore the potential of Achilles tendon shear wave elastography (AT-SWE) as a tool for assessing physical frailty in older adults. A total of 148 patients aged 65years and over were included in this cross-sectional study. Patients with heart failure, AT injury, stroke history, active malignancy, and claudication were excluded. All patients underwent a comprehensive geriatric assessment. Physical frailty assessment was performed with the fried frailty phenotype. Achilles tendon elastography was measured by ultrasound. The mean age of the participants was 73.8years and 62.2% were female. 30.4% of the participants were defined as frail. Achilles tendon shear wave elastography measurements were statistically lower in the frail group (p < 0.05). In the multivariate regression analysis, AT-SWE demonstrated a statistically significant association with frailty independent of confounding factors (OR 0.982, 95% CI 0.965-0.999, p value = 0.038). In the ROC curve analysis, the area under the curve for AT-SWE was 0.647 (95% CI, 0.564-0.724, p < 0.01) and the optimum cut-off point was 124.1kilopascals. These findings highlight the value of AT-SWE as a non-invasive and objective tool for predicting frailty in older adults.

Vertical electrical sounding for characterizing subsurface tectonic features and their impacts on groundwater occurrences: a case study of basaltic Jbab uplifting in southern Syria

ABSTRACT Eighteen vertical electrical soundings (VES) were carried out by applying the Schlumberger configuration to characterize the main subsurface tectonic features and determine their impacts on groundwater occurrences of the uplifted Jbab area in Southern Syria. Different interpretative approaches were used to interpret these VES distributed on one longitudinal profile (LP) and four transverse profiles TP1, TP2, TP3, and TP4. The originality of this work is the conjoint use of Pichgin and Habibullaev technique, the fractal concentration-number (C–N) modeling technique, and the 1D inversion technique as an integrated interpretative approach (IIA) in subsurface basalt characterization. This IIA proves its efficacy while interpreting the VES data, in delineating the main subsurface geology and the tectonic conditions and their influences on the groundwater distributions in a basaltic Jbab environment. Several optimum VES points are accordingly proposed and arranged according to their importance for drilling wells for groundwater extraction. The integrated developed geoelectrical technology described in this paper is recommended to be applied for characterizing similar basaltic areas worldwide.

On Necessary Optimality Conditions for Sets of Points in Multiobjective Optimization

AbstractTaking inspiration from what is commonly done in single-objective optimization, most local algorithms proposed for multiobjective optimization extend the classical iterative scalar methods and produce sequences of points able to converge to single efficient points. Recently, a growing number of local algorithms that build sequences of sets has been devised, following the real nature of multiobjective optimization, where the aim is that of approximating the efficient set. This calls for a new analysis of the necessary optimality conditions for multiobjective optimization. We explore conditions for sets of points that share the same features of the necessary optimality conditions for single-objective optimization. On the one hand, from a theoretical point of view, these conditions define properties that are necessarily satisfied by the (weakly) efficient set. On the other hand, from an algorithmic point of view, any set that does not satisfy the proposed conditions can be easily improved by using first-order information on some objective functions. We analyse both the unconstrained and the constrained case, giving some examples.

Experimental COP optimization procedure in an air-based reverse Brayton cycle for cryogenic applications

Sustainable and efficient refrigerants are essential due to increasing regulatory constraints on traditional high-GWP refrigerants. This study investigates the potential of natural refrigerants, specifically air, in Reverse Brayton Cycles (RBC) for low-temperature applications. Unlike carbon dioxide and ammonia, which pose limitations and safety concerns below -40°C, air offers a safer and more versatile solution due to its excellent thermodynamic properties and availability. An experimental RBC was constructed using automotive components like centrifugal compressors, a radial turbine, and inter-coolers. These cost-effective and accessible components shift the refrigeration paradigm. The RBC was tested to optimize the Coefficient of Performance (COP) at -100°C while dissipating 2 kW, a typical scenario for whole-body cryotherapy (WBC). Key control parameters included the pressure ratio of the centrifugal compressors and the position of the stator vanes in the variable geometry turbine (VGT). The optimization process resulted in a COP increase of up to 28%. Additionally, a 1D gas-dynamic model validated these results, suggesting that different component selections could enhance performance by 17% compared to the experimental optimum point. Air-based RBC systems using automotive components can effectively achieve temperatures below -40°C, offering a viable, eco-friendly alternative to traditional refrigerants. This advancement addresses regulatory challenges and contributes to the scientific community by providing a sustainable refrigeration solution using commercially available components and demonstrating improvements through experimental data.

Effects of air to fuel ratio on parameters of combustor used for gas turbine engines: Applications of turbojet, turbofan, turboprop and turboshaft

Combustors of gas turbine engines have generally the highest irreversibility due to the combustion process. Therefore, to observe influence of air to fuel ratio (AFR) on combustor performance is of high importance. In this study, thermodynamic laws are employed to separately determine adiabatic flame temperature (AFT), entropy production, exergy destruction and exergy efficiency of combustor for turbojet (TJ), turbofan (TF), turboprop (TP) and turboshaft (TS) where variables are AFR, combustor inlet temperature (CIT) and combustor pressure ratio (CPR). In this regard, the higher the AFR, the lower the adiabatic flame temperature, however the higher the entropy production for related combustor. Similarly, increasing AFR leads to raise exergy destruction, thereby lowering exergy efficiency. On the other hand, as CIT increases, exergy efficiency of the combustor improves whereas slight effects of CPR on the combustor metrics are observed. Namely, due to variation of AFR, exergy destruction of combustor changes between 0.512 MW and 0.825 MW for TJ, between 1.807 MW and 2.735 MW for TS, between 28.38 MW and 40.49 MW for TF and between 3.102 MW and 4.564 MW for TP. Moreover, exergy efficiency of combustor changes between 59% and 73% for TJ, between 70% and 77% for TS, between 78% and 81% for TF and between 75% and 79% for TP engine through the determined AFR and CIT ranges. These findings indicate that combustor of each gas turbine engine demonstrates different behavior according to the mentioned variables and exergy efficiency of combustor depends on finding the optimum points of combustor variables.

Impact of the Reference Multiple-Time-Point Dosimetry Protocol on the Validity of Single-Time-Point Dosimetry for [177Lu]Lu-PSMA-I&T Therapy.

Internal dosimetry supports safe and effective patient management during radionuclide therapy. Yet, it is associated with high clinical workload, costs, and patient burden, as patient scans at multiple time points (MTPs) must be acquired. Dosimetry based on imaging at a single time point (STP) has continuously gained popularity. However, MTP protocols, used as a reference to judge the validity of STP dosimetry, differ depending on local requirements and deviate from the unknown patient-specific ground truth pharmacokinetics. The aim of this study was to compare the error and optimum time point for different STP approaches using different reference MTP protocols. Methods: Whole-body SPECT/CT scans of 7 patients (7.4-8.9 GBq of [177Lu]Lu-PSMA-I&T) were scheduled at 24, 48, 72, and 168 h after injection. Sixty lesions, 14 kidneys, and 10 submandibular glands were delineated in the SPECT/CT data. Two curve models, that is, a mono- and a biexponential model, were fitted to the MTP data, in accordance with goodness-of-fit analysis (coefficients of variation, sum of squared errors). Three population-based STP approaches were compared: one method published by Hänscheid etal., one by Jackson etal., and one using population-based effective half-lives in the mono- or biexponential curve models. Percentage differences between STP and MTP dosimetry were evaluated. Results: Goodness-of-fit parameters show that a monoexponential function and a biexponential function with shared population-based parameters and physical tail are reasonable reference models. When comparing both reference models, we observed maximum differences of -44%, -19%, and -28% in the estimated absorbed doses for lesions, kidneys, and salivary glands, respectively. STP dosimetry with an average deviation of less than 10% from MTP dosimetry may be feasible; however, this deviation and the optimum imaging time point showed a dependence on the chosen reference protocol. Conclusion: STP dosimetry for [177Lu]Lu-PSMA therapy is promising to boost the integration of dosimetry into clinical routine. According to our patient cohort, 48 h after injection may be regarded as a compromise for STP dosimetry for lesions and at-risk organs. The results from this analysis show that a common gold standard for dosimetry is desirable to allow for reliable and comparable STP dosimetry.

Multi-strategy improved sand cat optimization algorithm-based workflow scheduling mechanism for heterogeneous edge computing environment

Edge computing is one of the predominant technologies which facilitates the option of bringing out the computing resources closer to the location of the end users when they are utilized by them. This facility offered by edge computing technology need to reduce the utilization of network bandwidth and response time with respect to the user’s workflow. In this paper, Multi-Strategy Improved Sand Cat Swarm Optimisation Algorithm (MSISCSOA)-based workflow scheduling mechanism is proposed for handling the challenges of workflow scheduling in cloud-edge computing environment. The core objective of this MSISCSOA-based workflow scheduling algorithm targets on minimizing the execution latency and energy consumption to facilitate timely and on-demand end users’ satisfaction of resources. This MSISCSOA scheme is adopted with the improvement introduced using random variation and elite collaborative strategies, such that well-balanced the trade-off between exploration and exploitation is achieved. This improvement is introduced over Sand Cat Optimization Algorithm (SCOA) using the merits of dynamic random search and joint opposite selection strategies that accelerates the convergence of the algorithm with increased global optimization and searching efficiency. It specifically improved SCOA using random variation for escaping from the local point of optimality. It also used well distributed pareto fronts and population evolution multi-strategy that aids in searching solutions with maximized diversity. The simulation experiments conducted using the datasets of Montage, Cybershake, LIGO and SIPHT an average confirmed minimized execution latency of 21.38 % and energy consumptions of 19.56 %, better than the baseline Ant Colony Optimization Algorithm-Based Workflow Scheduling (IACOAWS), Quadratic Penalty Function-based Particle Swarm Optimization Algorithm (QPF-PSOA), Biogeography Optimization (BBO) Algorithm based Multi-Objective Task Scheduling (BBOAMOTS) and Different Evolution-based Task Clustering and Scheduling (DETCS) approaches used for comparative investigation.

Sequential good lattice point sets for computer experiments

Sequential Latin hypercube designs have recently received great attention for computer experiments. Much of the work has been restricted to invariant spaces. The related systematic construction methods are inflexible while algorithmic methods are ineffective for large designs. For such designs in space contraction, systematic construction methods have not been investigated yet. This paper proposes a new method for constructing sequential Latin hypercube designs via good lattice point sets in a variety of experimental spaces. These designs are called sequential good lattice point sets. Moreover, we provide fast and efficient approaches for identifying the (nearly) optimal sequential good lattice point sets under a given criterion. Combining with the linear level permutation technique, we obtain a class of asymptotically optimal sequential Latin hypercube designs in invariant spaces where the $L_1$-distance in each stage is either optimal or asymptotically optimal. Numerical results demonstrate that the sequential good lattice point set has a better space-filling property than the existing sequential Latin hypercube designs in the invariant space. It is also shown that the sequential good lattice point sets have less computational complexity and more adaptability.

Computational study of gas-dynamic approach for noise reduction in atwo-stroke engine’s exhaust system

BACKGROUND: The traditional approach to designing exhaust mufflers relies heavily on energy dissipation. In the "gas-dynamic" approach, the flow of exhaust gases is equalized by introducing long channel(s) into the muffler to separate impulses and shift them in time. It is assumed that such mufflers will provide noise reduction without creating significant backpressure. AIMS: To evaluate the potential of the "gas-dynamic" approach to reducing the noise level of two-stroke internal combustion engines. MATERIALS AND METHODS: The study was carried out using computational models. The processes in the gas-air tract of the piston engine were calculated using a one-dimensional (1D) model. The noise characteristic was the effective sound pressure at a specified point in the environment, calculated using 2D model of propagation of disturbances in elastic medium. The object of study was a two-stroke gasoline two-cylinder engine RMZ-551i, with a resonator (providing gas-dynamic supercharging) and a muffler. Initially, the parameters and sound pressure level of the engine with the stock muffler at full load and close to nominal engine speed were calculated. Then, the structure of the stock muffler was modified by adding a "tuned" channel between its two chambers. The parameters of the modified muffler were optimized based on the criterion of reducing gas pulsations at the outlet to the atmosphere. The noise reduction of the muffler implementing the gas-dynamic approach was evaluated relative to the stock muffler and expressed in terms of sound pressure levels in dB. Performance and levels of sound pressure were finally calculated over a wide range of engine speeds. RESULTS: According to the computational estimate, the optimal implementation of the gas-dynamic approach in the muffler reduces exhaust noise by 7 dB, while engine power decreases by 2.5%. Calculation of the sound pressure level based on the external speed characteristic showed that at an engine speed of 3000 rpm, the calculated sound pressure exceedsby 8 dB the minimum (99 dB) obtained for the optimally "tuned" muffler at an engine speed of 5000 rpm. It is suggested that the gas-dynamic approach with optimization is also applicable for uniform noise reduction over a wide range of engine speeds, with a more complex structure of the exhaustmuffler. CONCLUSIONS: Theoretical evaluation of a muffler with a tuned channel connecting its two chambers was carried out, with a two-stroke engine RMZ-551i with a stock muffler as a basis for comparison. At the optimum point on the speed characteristic, the exhaust noise was reduced by 7 dB, while the calculated power decrease was insignificant. The authorsnote the suitability of the methodology for rapid assessments and automated computational optimization of mufflers that utilize wave effects. They also point out the limitations of the models used, which require validation or rather calibration based on experimental data, and the need for the development of applied models of acoustic effects and measuring devices for domestic CAE packages.

The effectiveness of a hybrid MPPT controller based on an artificial neural network and fuzzy logic in low-light conditions

Technological advancement and economic progress have made power consumption a big issue. Concern is growing as traditional energy sources dwindle. In the future, numerous fossil fuels will be insufficient to satisfy human requirements. This motivates research into the feasibility of using renewable energy sources. Renewable energy sources offer a multitude of advantages, including their cost-effectiveness, lack of environmental impact, and sustainable nature. Sunlight is currently the most prevalent source of energy because it is both free and readily accessible. Consequently, photovoltaic (PV) energy is gaining importance in the field of electricity generation. Tracking the maximum power point (MPP) in a solar PV system is challenging due to varying meteorological conditions (irradiance and temperature). To maximise the efficiency of a solar power installation, it is essential to monitor the PV array's optimum power point. This analysis compares the perturb and observe (PO), fuzzy logic (FL), and suggested artificial neural network (ANN)-fuzzy strategy for determining the MPP of a PV system with minimal radiation exposure. Simulation results show that at low irradiation levels, the proposed ANN-fuzzy maximum power point tracking (MPPT) unit controller is superior to the FL and PO MPPT controllers in terms of tracking maximum power.

Interim FDG-PET/CT for predicting response of HER2-positive breast cancer to neoadjuvant therapy: ECOG-ACRIN EA1211/DIRECT trial.

TPS614 Background: Strategies to optimize treatment decision-making in early stage HER2-positive breast cancer are a priority. Recent studies suggest that early metabolic changes on FDG-PET/CT predict response to HER2-directed therapy. The TBCRC026 trial showed that participants not obtaining a 40% reduction in SULmax by cycle 1 day 15 (C1D15) following neoadjuvant trastuzumab/pertuzumab (HP) were unlikely to obtain pCR [negative predictive value (NPV) 91%]. This trial aims to validate FDG-PET/CT as a neoadjuvant interim (niFDG-PET/CT) imaging integral biomarker in patients treated with standard HER2-directed regimens (NCT05710328). Methods: Trial design: EA1211/DIRECT is a multicenter, single-arm, primary imaging phase 2 study enrolling patients with stage II/III HER2-positive breast cancer. Patients undergo standard skull base-thigh FDG-PET/CT at baseline and C1D15 and receive standard of care pertuzumab-based neoadjuvant therapy followed by surgery. Eligibility: Age ≥18 years, ECOG performance status 0-2, stage IIa-IIIc, untreated HER2-positive breast cancer with known hormone receptor status, suitable to undergo FDG-PET/CT imaging and neoadjuvant therapy. Statistical methods: ΔSULmaxD15 will be computed as: (D15 SULmax – baseline SULmax)/baseline SULmax. The primary objective is to estimate the NPV of niFDG-PET/CT for pCR using ΔSULmaxD15 of the primary breast cancer at a threshold of 40%. NPV is defined as the probability that pCR will not be achieved by participants with ΔSULmaxD15 &lt;40%. Secondary endpoints include evaluating sensitivity, specificity, and positive predictive value of ΔSULmaxD15 of the primary breast cancer at a threshold of 40% and ability of the niFDG-PET/CT biomarker to predict 3-year event free survival (EFS). Sample size: We expect that 50% of patients will not reach the ΔSULmax threshold of 40% and a pCR rate of 40-60%. The proposed sample size is 210 participants, adjusted to 235 to account for missing data in 10% of cases. Computation of exact, two-sided 95% confidence intervals (CI) for NPV determined the lower limit of the CI is at least 80% when the true NPV is at least 88%. The lower limit is 84% when the true NPV has the value of 91%, which was estimated in the TBCRC026 study. Current status: EA1211/DIRECT was activated in May 2023 across the NCI National Clinical Trials Network (NCTN) and is anticipated to complete in March 2025. Those interested in the clinical trial can email ea1211team@ecog-acrin.org. The EA1211/DIRECT trial is the first step in implementing a Response-Guided Treatment Strategy by validating the ΔSULmax threshold of 40% as the optimum cut point across standard of care HER2-directed neoadjuvant regimens. If EA1211/DIRECT meets its objectives, the results will be used to design clinical utility studies, thus hoping to change practice. Clinical trial information: NCT05710328 .

OPTIMAL PLACEMENT OF UNIFIED POWER FLOW CONTROLLER USING PARTICLE SWARM OPTIMIZATION TECHNIQUE

The load growth in recent times due to advancements in technology and population increase has led to shortage of reactive power in the power system resulting in key issues such as voltage instability, large line losses, power outages among others. In this work, Particle Swarm Optimization (PSO) technique was used for optimal placement of Unified Power Flow Controller (UPFC) for power loss minimization using the IEEE 6-bus system as a test network. The load flow analysis was performed using Gauss-Seidel iterative method with and without inclusion of PSO-based UPFC. The PSO algorithm was used to find the optimum point for UPFC location and determined using the line with the least active power loss among other lines randomly selected by the PSO algorithm, with p.u defined as the voltage statutory limit. The simulation results revealed that the optimum point obtained was line 4-5, and the voltage profile were within the statutory limit with and without optimally placed UPFC. The total active power loss reduced from 495.5 to 273.9 MW, giving a reduction of 44.72 with optimally placed UPFC. The total reactive power loss equally reduced from 1439 to 936.2 MVAr, producing a reduction of 34.94 with optimally placed UPFC. These results obtained showed that PSO-based UPFC placement is suitable for power loss minimization and enhancement of the system voltage profile.

Pemanfaatan Cangkang Kerang Darah Sebagai Katalis dan Filter Rokok Sebagai Adsorben Dry Washing dalam Pembuatan Biodiesel Minyak Kesambi

These three wastes can be used as raw materials for biodiesel in the transesterification and purification processes. The purpose of this study was to determine and analyze the variations of blood clam shell catalysts and cigarette butt adsorbents and to compare the most optimal quality of biodiesel (producing the highest yield) based on SNI 7182:2015. The method used in this research consists of FFA testing, degumming, esterification, tranesterification and purification processes. In the transesterification process, a catalyst was added in the range of 2-6% m/v and 75% methanol, while in the refining process, active charcoal filter cigarette adsorbents were added in the range of 5-10% m/v. The data obtained was subjected to statistical analysis using the Response Surface Methodology (RSM) Central Composite Design (CCD) method. Based on statistical analysis, a linear model equation was obtained and there were no optimum and minimum points. This research also obtained the highest bisodiesel yield of 73.33% at a catalyst concentration of 6% m/v and adsorbent of 5% m/v. Some of the quality characteristics of the biodiesel produced are in accordance with SNI standards.

Indonesian Climatic Factors and Its Effect on Cocoa Productivity

One of the important aspects in cocoa cultivation is productivity, as it is related to the ability of national cocoa producers to meet market demand. Cocoa productivity is affected by many factors, including climate factors. On this basis, this study aims to determine the trend of national cocoa productivity and climatic factors (maximum temperature, minimum temperature, mean temperature, and rainfall) and to determine the influence of climatic factors on cocoa productivity. By applying a descriptive method, this research utilized secondary data with a time span between 1961-2021, which were analyzed using quadratic regression model. From the analysis, it was clear that there had been increasing tendency for national cocoa productivity, maximum temperature, minimum temperature, mean temperature and rainfall from year to year. Meanwhile, the climatic factors that affect cocoa productivity are the maximum temperature, minimum temperature, and mean temperature with an optimum point of 30.53°C; 21.31°C and 25.87°C respectively. Temperature generally has a negative effect on cocoa productivity, although it does not have a big impact. However, continuous exposure to temperature will lead to a more harmful threat to cocoa productivity. This research contributes to the use of non-linear regression analysis, especially quadratic regression model in determining climatic factors that influence cocoa productivity in Indonesia, considering that not many studies have used similar model.

Development of a Novel Codesign Method for Use in Early-Stage High-Performance Craft Design

The performance requirements of modern vessels have increased significantly over time, introducing unique challenges in design and analysis. Driven by competition, such as in the case of racing craft, new high performance vessels require design spaces that push the envelope of hydrodynamic technology. This optimization knowledge resides in the experience of racing experts and hasn’t yet been translated into a naval architecture taxonomy. This paper seeks to bridge the knowledge gap between experienced race manufacturers and naval architects, and in doing so, delineate a design methodology. Modeling risk as a function of vessel speed, as well as coupling the design of the control system in conjunction with its physical design embodiment, allows the overall system to reach a greater point of optimality than what can be accomplished by traditional iterative design processes alone. The approach will be demonstrated utilizing the design of a University of Michigan student-led undergraduate high-speed, electric boat competition team design. The team’s goal is to develop a vessel that has a top speed of 135 mph. The paper will discuss how the team used marine design methodologies integrated with a novel codesign method to create the design that is currently under construction for professional racing use by the team.

Recovery of carotenoids as bioactive compounds from peach pomace by an eco-friendly ultrasound-assisted enzymatic extraction

AbstractThe industrial processing of fruits generates by-products. These by-products serve as a source of valuable bioactive compounds. In this study, carotenoid was extracted from peach pomace (PP) by using the ultrasound-assisted enzymatic extraction (UAEE), an eco-friendly method. The process conditions ensuring the highest carotenoid content and b* color value for UAEE were detected by response surface methodology (RSM). To demonstrate the effectiveness of the ultrasonic process, enzymatic extraction was carried out at the optimum point. Physicochemical (pH, titratable acidity, total soluble solids), color (L*, a*, b*, chroma value (ΔC) and color difference (ΔE), total phenolic compound (TPC) and antioxidant activity analyses (ABTS and FRAP) were carried. When the analysis results evaluated, the highest b* color parameter, TPC (761.10 mg gallic acid/L), ABTS (1933.33 mg Trolox/L) and FRAP (52.66 µmol Trolox/L) results of the extracts was observed with UAEE method. The study shows that ultrasound based upon the cavitation event was increased efficiency of enzymatic reaction with higher extraction yield and this provided in higher amounts of carotenoid and bioactive compounds. In other respects, when obtained carotenoid extracts are used in food formulations compatible with their acidic structure, they will contribute to protection of the product and minimizing color losses.

DATFNets-dynamic adaptive assigned transformer network for fire detection

Fires cause severe damage to the ecological environment and threaten human life and property. Although the traditional convolutional neural network method effectively detects large-area fires, it cannot capture small fires in complex areas through a limited receptive field. At the same time, fires can change at any time due to the influence of wind direction, which challenges fire prevention and control personnel. To solve these problems, a novel dynamic adaptive distribution transformer detection framework is proposed to help firefighters and researchers develop optimal fire management strategies. On the one hand, this framework embeds a context aggregation layer with a masking strategy in the feature extractor to improve the representation of low-level and salient features. The masking strategy can reduce irrelevant information and improve network generalization. On the other hand, designed a dynamic adaptive direction conversion function and sample allocation strategy to fully use adaptive point representation while achieving accurate positioning and classification of fires and screening out representative fire samples in complex backgrounds. In addition, to prevent the network from being limited to the local optimum and discrete points in the sample from causing severe interference to the overall performance, designed a weighted loss function with spatial constraints to optimize the network and penalize the discrete points in the sample. The mAP in the three baseline data sets of FireDets, WildFurgFires, and FireAndSmokes are 0.871, 0.909, and 0.955, respectively. The experimental results are significantly better than other detection methods, which proves that the proposed method has good robustness and detection performance.

Adsorption simulation of 2,4-D pesticide on novel zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complexes using machine learning approach.

The capacity of zinc-based 2-amino-4-(1H-1,2,4-triazole-4-yl)benzoic acid coordination complex (Zn(NH2-TBA)2) and modified Zn(NH-TBA)2COMe complex for removal of 2,4-dichlorophenoxyacetic acid (2,4-D) from aqueous solutions was investigated through adsorption modeling and artificial intelligence tools. Analyzing the adsorption characteristics of pesticides helps in studying the groundwater pollution by pesticides in agriculture area.In this study, Zn(NH2-TBA)2 was synthesized using Schiff base and its surface was modified using acetic anhydride group and their physical characteristics were identified using proton NMR, FTIR, and XRD. NMR results showed maximum modification yield obtained was 65% after 5days. The porous structure and surface area monitored using nitrogen isotherm and BET surface area analysis presented relatively less surface area and porosity after modification. Adsorption modelling indicated that Toth model with a maximum adsorption capacity of 150.8mg/g and 100.7mg/g represents the homogenous adsorption systems which satisfy both low- and high-end boundary of adsorbate concentration in all settings according to the optimum point, while the kinetics and rate of 2,4-D adsorption follow the pseudo-first-order kinetic model in all situations. Artificial neural network (ANN), support vector regression, and particle swarm optimized least squares-support vector regression (PSO-LSSVR) were used for the optimization and modelling of adsorbent mass, adsorbate concentration, contact time, and temperature to develop predictive equations for the simulation of the adsorption efficiency of 2,4-D pesticide. The obtained results exhibited the better performance of ANN and PSO-LSSVR for prediction of adsorption results. The mean square error values of ANN (0.001, 0.012) and PSO-LSSVR (0.121, 0.105) were obtained for Zn(NH2-TBA)2 and Zn(NH-TBA)2COMe, respectively, while their respective coefficient of determination (R2) obtained were 0.999 and 0.988 for ANN and 0.980 and 0.825 for PSO-LSSVR. The study specified that machine learning predictive behavior performed better for Zn(NH2-TBA)2 compared to Zn(NH-TBA)2COMe that is also supported by theoretical kinetics and isotherm models. The research concludes that artificial intelligence models are the most efficient tools for studying the predictive behavior of adsorption data.