Optimal Point Research Articles

A general framework for the design of efficient passive pitch systems

Mitigating the impact of variable inflow conditions is critical for a wide range of engineering systems such as drones or wind and tidal turbines. Passive control systems are of increasing interest for their inherent reliability, but a mathematical framework to aid the design of such systems is currently lacking. To this end, in this paper a two-dimensional rigid foil that passively pitches in response to changes in the flow velocity is considered. Both an analytical quasi-steady model and a dynamic low-order model are developed to investigate the pivot point position that maximizes unsteady load mitigation. The paper focuses on streamwise gusts, but the proposed methodology would apply equally to any change in the inflow velocity (speed and/or direction). The quasi-steady model shows that the force component in any arbitrary direction can be kept constant if the pivot lies on a particular line, and that the line coordinates depend on the gust and the foil characteristics. The dynamic model reveals that the optimum distance of the pivot location from the foil increases with decreasing inertia. For a foil at small angles of incidence, the optimum pivot point is along the extended chord line. This knowledge provides a methodology to design optimum passively pitching systems for a plethora of applications, including flying and swimming robotic vehicles, and provides new insights into the underlying physics of gust mitigation.

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 <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 .

Security-Communication-Computation Tradeoff of Split Decisions for Edge Intelligence

Security and privacy are of paramount importance to data-driven edge intelligence services. By offloading computation-intensive model portions to the edge server, split inference can empower low-latency and energy-efficient model inference at resource-constrained devices. The split decision is critical to the communication and computation performances of split inference. However, its impact on security issues has yet to be studied. This article first investigates the security-communicationcomputation tradeoff of split decisions in the inference phase. With the emphasis on security concerns, we summarize the threat models of split inference and illustrate two passive attack mechanisms for recovering the input data and private labels. Case studies are shown to validate the security-communicationcomputation tradeoff and investigate the selection of optimal candidate split points according to the specific device capabilities and service requirements.

A multi-energy production system utilizing an absorption refrigeration cycle, and a PEM electrolyzer powered by geothermal energy: Thermoeconomic assessment and optimization

This study aims to optimize a multi-energy production system that utilizes sustainable geothermal energy as its primary power source. The system comprises several subsystems, including a geothermal well, an organic rankine cycle (ORC) unit, an absorption chiller, and a proton exchange membrane (PEM) electrolyzer. The study examined the impact of variations in seven key parameters, including turbine inlet temperature, evaporator pinch point temperature, pump inlet temperature, evaporator inlet temperature, evaporator inlet mass flow rate, turbine efficiency, and pump efficiency, on the overall system performance. The Response Surface Methodology (RSM) was employed to find the optimum point of the system. To facilitate the practical implementation of the system, the research team assessed its viability in 10 different Iranian cities, all known for their substantial geothermal energy potential. Among the cities, Zahedan emerged as the most suitable location for deploying the system, given its robust energy production potential. The results showed that January, with a production of 626.4 MW h of electricity, and November, with a production of 622.8 MWh of electricity, are the best months for the system to operate in Zahedan city. The environmental aspect of the system also yielded noteworthy results. By setting up the proposed system in Zahedan city, it is possible to prevent the emission of 1472.411 tons of carbon dioxide by producing 7217.7 MWh of electricity throughout the year at a cost of 35337.86 $. Additionally, the proposed system can help expand 7 ha of green space, providing the electricity needed by 2349 people throughout the year.

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

The 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.

Design, Simulation and Optimization of a Novel Transpired Tubular Solar Air Heater

In this paper, a novel tubular solar air heater is introduced. In this air heater, the hot boundary layer is drawn into the absorber tube and can provide thermal energy at moderate temperatures. Several different cases were simulated and a correlation was proposed to predict the collector’s effectiveness as a function Rayleigh number and Reynolds number. An equation was derived to find the effectiveness of this collector. Finally, a real case was studied with non-uniform solar flux distribution, as well as radiation heat loss. Good agreement was found between the results and those derived by the proposed analytical method. For different suction values, the first-law and the second-law efficiencies were calculated. Based on the exergy analysis, exergy destruction in absorption is the dominant factor that is unavoidable in low-temperature collectors. It was shown that there is an optimum suction value at which the second-law efficiency is maximized. At the optimum point, temperature rise can reach 54 K, which is hardly possible with a flat plate collector. Based on the exergy analysis, the relation between tube wall temperature and air outlet temperature in their dimensionless forms at the optimum working condition was derived, and it was shown that effectiveness at the optimum working condition is around 0.5. This means that the air temperature rise shall be half of the temperature difference between collector wall and the ambient temperatures. A high outlet temperature besides the low cost of construction and maintenance are the main advantages of this air heater. With such a high temperature rise, this type of collector can increase the use of solar energy in domestic applications.

Enhancing green hydrogen production via improvement of an integrated double flash geothermal cycle; Multi-criteria optimization and exergo-environmental evaluation

The environmental advantages of hydrogen as a clean energy carrier are more prominent when it is produced utilizing renewable resources. In this regard, a novel geothermal energy driven electricity generation system integrated to hydrogen production plant is developed and investigated in this research. In the developed plant, a PEM electrolyzer is employed for hydrogen generation such that its required electricity is provided by an improved double-flash geothermal cycle. A self-superheater is applied for superheating the vapor at the steam turbine inlet using the geothermal resource to enhance the hydrogen production capacity. To evaluate feasibility of such superheating process and to examine its effects on hydrogen production, thermodynamic models are developed based on first and second laws. Also, environmental considerations are considered in evaluation of the proposed plant performance based on exergo-environmental indices. The influences of first and second flashing pressure, geothermal source temperature and current density of water electrolyzer on energy and exergy efficiency, hydrogen production rate, and environmental damage index are investigated. After carrying out a parametric study, the optimum operation point of the plant is determined via a two-objective optimization based on the hydrogen rate and environmental damage index (EDI). It is found that, under optimum operation, the system can produce 25.48 kg/h of hydrogen, while its environmental damage index is calculated to be as 0.00645.

Integrating low salinity water, surfactant solution, and functionalized magnetite nanoparticles with natural acidic groups for enhanced oil recovery: Interfacial tension study

In recent years, nanoparticles (NPs) have become a popular choice for improving oil recovery through enhanced oil recovery (EOR) methods. NPs can enhance oil recovery by reducing interfacial tension (IFT), altering wettability, and decreasing the mobility ratio. In this study, magnetic NPs with acidic groups (tartaric acid and malic acid), which are natural acids found in fruits such as grapes and apples, were synthesized and functionalized to reduce the IFT between oil and water. Various characterizations tests, including zeta potential (ZP), Fourier transform infrared spectroscopy (FTIR), X-ray diffraction analysis (XRD), dynamic light scattering (DLS), field emission scanning electron microscopy (FE-SEM), and energy dispersive X-ray analysis (EDAX), were used to determine the properties of NPs. Afterwards, the IFT behaviour of crude oil and aqueous solutions, containing surfactant sodium dodecyl sulfate (SDS), two types of salts namely sodium chloride (NaCl) and sodium sulfate (Na2SO4), and functionalized magnetite NPs with tartaric acid and malic acid were investigated. Two crude oils were used in this study: light (A) and heavy (B). The results showed that increasing salt concentration decreases the IFT for crude oils down to an optimum point (20000 ppm for both salts), after which the IFT increases with increasing salt concentration. The presence of functionalized Fe3O4@Tartaric acid NPs (300 ppm) in the aqueous solutions containing salts at their optimum concentration (20000 ppm) and SDS (300 ppm) can decrease the IFT more significantly than that of Fe3O4 and Fe3O4@Malic acid for both crude oils. This is due to higher stability and higher ZP of functionalized Fe3O4@Tartaric acid NPs compared to Fe3O4@Malic. The IFT for oil A decreases from 20.8 mN/m to 2.87 mN/m for light crude oil and from 32.96 mN/m to 1.91 mN/m for heavy crude oil. The IFT for heavy crude oil was more reduced than that of light crude oil, which was due to the presence of asphaltene, resin, and other surface-active agents in heavy crude oil, which can act as a natural surfactant in oil and help reducing the IFT.

Wastewater treatment from a typical multisystem hospital using chemically modified banana peels: Taguchi parametric optimization and characterization

The study aims to develop an eco-friendly solution for addressing contaminants in hospital wastewater. To achieve this, banana peel, a cost-effective and easily available agricultural waste, was chemically modified using sulfuric acid and sodium hydroxide. The hospital wastewater was analyzed using an Atomic Absorption Spectrometer to identify the presence of heavy metals. The physicochemical properties were measured and compared against the standards set by the World Health Organization (WHO). Scanning electron microscopy and Fourier-transform infrared spectroscopy were used to characterize the chemically modified banana peel. Batch adsorption was conducted with the wastewater using the prepared adsorbent. The effect of contact time, mixing rate, temperature, and adsorbent dosage was examined to know the removal efficiency of the adsorbent. The experimental design incorporates Taguchi OA design to optimize the process. The total heavy metals discovered in the wastewater were As, Cd, Fe, Cu, Ni, Cr, Pb, Mn and Zn. The percentage of Cd, Fe, Cu, Ni, Cr and Pb removed were 79.5%, 87.9%, 56.9%, 89.0%, 88.5% and 93.5%, respectively, at temperature, adsorbent dosage, mixing rate and contact time of 30 °C, 3 g, 700 rpm and 60 min, respectively. The physicochemical properties remain within the acceptable WHO range except the turbidity. At optimum point, the percentage removal of Cd, Fe, Cu, Ni, Cr, Pb and Zn were 64.3%, 80.5%, 51.4%, 82.3%, 72.4%, 86.6% and 72.1%, respectively; while, the conductivity, pH, TDS, and turbidity value were, respectively, 20.57 mS/cm, 6.8, 10.16 mg/l, and 86.31 NTU. Characterization revealed the existence of active functional groups on the adsorbent surface enhancing the hospital wastewater treatment and change in its morphological structure after the treatment was noticed. In conclusion, the modified banana peel was effective for the treatment of hospital wastewater.

Abstract PO1-01-01: ECOG-ACRIN EA1211: Interim FDG-PET/CT for predicting response of HER2-positive breast cancer to neoadjuvant therapy (DIRECT Trial)

Abstract Background: Strategies to optimize treatment decision-making in early stage HER2-positive breast cancer are a current priority for the oncology and patient advocacy community. Recent studies suggest that early metabolic changes on FDG-PET/CT (imaging biomarker) 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%]. The ECOG-ACRIN EA1211/DIRECT 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). 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. 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%, in patients treated with neoadjuvant HER2-directed therapy. NPV is defined as the probability that pCR will not be achieved by participants with ΔSULmaxD15 < 40%. Secondary endpoints include evaluating the sensitivity, specificity, and positive predictive value of ΔSULmaxD15 of the primary breast cancer at a threshold of 40% and the 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 accrual is anticipated to complete in March 2025. Those interested in the clinical trial can email ea1211team@ecog-acrin.org. Conclusion: 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, and thus hoping to change practice. Citation Format: Heather Jacene, Constantine Gatsonis, Roisin Connolly, Brian Burnette, Erica Stringer-Reasor, Maeve Hennessy, Justin Romanoff, Alexander Taurone, Ciara O'Sullivan, H. T. Carisa Le-Petross, Courtney Lawhn Heath, Vered Stearns, Amy Fowler, Shou-Ching Tang, Karla A Sepulveda, Angela DeMichele, David Mankoff, Antonio Wolff. ECOG-ACRIN EA1211: Interim FDG-PET/CT for predicting response of HER2-positive breast cancer to neoadjuvant therapy (DIRECT Trial) [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-01-01.

The prognostic role of the change in albumin-derived neutrophil-to-lymphocyte ratio during neoadjuvant chemoradiotherapy in patients with locally advanced rectal cancer.

The prognosis of patients with locally advanced rectal cancer (LARC) has improved with the adoption of a multidisciplinary treatment approach combining neoadjuvant chemoradiotherapy (nCRT) and total mesorectal excision (TME). Developing real-time, sensitive biomarkers to monitor systemic changes during nCRT is of paramount importance. Although the association between albumin-derived neutrophil-to-lymphocyte ratio (Alb-dNLR) and prognosis in various cancers is established, its prognostic value in LARC patients undergoing nCRT is not well-studied. This study enrolled a cohort of 618 LARC patients, stratifying them into two groups according to their change in Alb-dNLR (∆Alb-dNLR) values, using an optimal cut-off point: a low ∆Alb-dNLR group (≤ 0.90) and a high ∆Alb-dNLR group (> 0.90). The prognostic significance of ∆Alb-dNLR was evaluated using a Cox proportional hazards model. The 5-year overall survival (OS) rates were 75.2% in the low ∆Alb-dNLR group (≤ 0.90) and 85.9% in the high ∆Alb-dNLR group (>0.90) (P < 0.001). The 5-year disease-free survival (DFS) rates were 71.2% and 80.6%, respectively (P = 0.016). Multivariate analyses demonstrated that both ∆Alb-dNLR and pre-Alb-dNLR were independent prognostic factors for OS (P ≤ 0.001), while ∆Alb-dNLR was demonstrated as an independent prognostic factor for DFS (P = 0.016). A predictive nomogram, incorporating the ∆Alb-dNLR subgroup, demonstrated enhanced performance (concordance index [C-index] of 0.720 for OS and 0.690 for DFS) compared to the pre-treatment Alb-dNLR subgroup (C-index of 0.700 for OS and of 0.680 for DFS). Therefore, ∆Alb-dNLR shows significant potential as a usable and prognostic biomarker for predicting OS and DFS in LARC patients undergoing nCRT.

Postsurgical utility of copeptin for the prediction of postoperative arginine vasopressin deficiency.

Arginine vasopressin deficiency (AVD) following neurosurgical procedures for pituitary disorders is common and can delay discharge. Copeptin, a stable surrogate marker of arginine vasopressin, may predict postoperative AVD. The authors' aim was to assess the optimal postoperative sampling time and cut-point concentration of copeptin to predict the development of postsurgical AVD. Adults without preexisting AVD who were undergoing surgery for a pituitary lesion between February 2020 and April 2022 were eligible for study inclusion. Two samples were drawn from each patient postoperatively to assess the copeptin concentration using an immunofluorescent assay. Samples were denoted as "early" (within 6 hours of extubation) or "postoperative day 1" (POD1; within 10-30 hours of extubation). Patients were evaluated for the development of AVD. One hundred ninety-two patients (54.2% female) with a median age of 54.5 years (IQR 39.8-67.0 years) were included in the study. The median copeptin concentration at both time points was significantly lower in those with AVD (transient or permanent; n = 22, 11.5%) than in those without (early: 4.9 vs 18.7 pmol/L, p < 0.001; POD1: 3.4 vs 4.9 pmol/L, p < 0.001) but did not differ in those who developed transient versus permanent AVD. The optimal copeptin cut point for the prediction of AVD was < 8.5 pmol/L for early samples (sensitivity 0.70, specificity 0.80, positive predictive value [PPV] 0.29, negative predictive value [NPV] 0.96) and < 4.3 pmol/L for POD1 samples (sensitivity 0.82, specificity 0.63, PPV 0.22, NPV 0.96). In early samples, a copeptin cutoff of 22.9 pmol/L increased the sensitivity for the detection of AVD to 95% with an NPV of 99%. The proportion of patients who had AVD was higher (60.0% vs 8.8%, p < 0.001) and the copeptin concentration lower (early: 4.3 vs 17.0 pmol/L, p < 0.001; POD1: 2.7 vs 4.9 pmol/L, p < 0.001) among those who had undergone surgery for a craniopharyngeal duct pathology versus a pituitary adenoma. Although copeptin was lower in patients with persistent Cushing's disease than in those in remission, the difference did not reach statistical significance (early p = 0.11, POD1 p = 0.52). Furthermore, the copeptin concentration could not predict the development of syndrome of inappropriate secretion of antidiuretic hormone. Patients without AVD who had received stress dose steroids intraoperatively had lower median early copeptin (11.7 vs 19.1 pmol/L, p = 0.027). In early postoperative copeptin samples, the optimal copeptin cut point for AVD diagnosis was < 8.5 pmol/L, and a level > 22.9 pmol/L had predicative utility in excluding AVD. Caution should be used when interpreting copeptin results, as patients administered glucocorticoids intraoperatively without AVD had lower median copeptin concentrations.

Antifungal evaluation of saponins extracted from quinoa husk (Chenopodium quinoa Willd) against Botrytis cinerea in strawberry

Saponins are widely recognized for their biological properties, which has led to the development of several research projects applicable to the agricultural sector. In this sense, the aim of this study was to evaluate the in vitro and in vivo antifungal effect of saponins extracted from quinoa husks, using the crude extract, and incorporated in a formulation against Botrytis cinerea in strawberries. The extraction of saponins by the maceration method was studied, where the effect of temperature, time, and ethanol concentration on the extraction of saponins was evaluated; the process was optimized using a central composite design (CCD). The in vitro antifungal effect of the crude extract of saponins was evaluated by the minimum inhibitory concentration (MIC), while the in vitro and in vivo antifungal effect of the formulation was determined by the mycelial inhibition percentage and control percentage, respectively. The optimum extraction point of saponins was obtained at 49.8 °C, 0.367 h, and 47.4% ethanol. On the other hand, the antifungal activity against Botrytis cinerea was dependent on the concentration of saponins; in the in vitro evaluation the MIC of the crude extract was 8.25 mg mL-1 of saponins and the mycelial inhibition percentage of the formulation was higher than 90%. In the in vivo evaluation of the formulation, the percentage of control ranged from 63.03 to 76.14%, compared to 61.5% control exhibited by the chemical fungicide Carbendazim.

Integrated Optimal Control for Electrolyte Temperature With Temporal Causal Network and Reinforcement Learning.

The electrowinning process is a critical operation in nonferrous hydrometallurgy and consumes large quantities of power consumption. Current efficiency is an important process index related to power consumption, and it is vital to operate the electrolyte temperature close to the optimum point to ensure high current efficiency. However, the optimal control of electrolyte temperature faces the following challenges. First, the temporal causal relationship between process variables and current efficiency makes it difficult to estimate the current efficiency accurately and set the optimal electrolyte temperature. Second, the substantial fluctuation of influencing variables of electrolyte temperature leads to difficulty in maintaining the electrolyte temperature close to the optimum point. Third, due to the complex mechanism, building a dynamic electrowinning process model is intractable. Hence, it is a problem of index optimal control in the multivariable fluctuation scenario without process modeling. To get around this issue, an integrated optimal control method based on temporal causal network and reinforcement learning (RL) is proposed. First, the working conditions are divided and the temporal causal network is used to estimate current efficiency accurately to solve the optimal electrolyte temperature under multiple working conditions. Then, an RL controller is established under each working condition, and the optimal electrolyte temperature is placed into the controller's reward function to assist in control strategy learning. An experiment case study of the zinc electrowinning process is provided to verify the effectiveness of the proposed method and to show that it can stabilize the electrolyte temperature within the optimal range without modeling.