Optimization Tool Research Articles

RAPID OPTIMIZATION OF DISINFECTANTS BY PHOTOGRAPHIC IMAGE-BASED ANALYSIS AND DESIGN OF EXPERIMENTS

The current pandemic of coronavirus disease 2019 (COVID-19) introduced the need for the development and optimization of new alcohol-water based disinfectant formulations. Moreover, the limited supply chain of traditional active ingredients such as ethanol (EtOH) has excessively increased the base formula cost, thus, novel platforms are needed to design new strategies for disinfectant development. In this work, we devise a novel quantification method of disinfectant turbidity and foam thickness based on photographic image analysis of disinfectant to optimize formula preparation, which improves critical physicochemical parameters related to colloidal stability. Next, the numerical data obtained from the pixel photograph's values were ordered by applying a 24 factorial design, considering each disinfectant ingredient, followed by an analysis of variance (ANOVA) and counter with contour and surface plots, respectively. Furthermore, our novel method was validated using a linear regression test, consequently outlining the method's error value. Our results suggest that the photographic image analysis supported by the statistical model correlated satisfactorily with the real physicochemical behavior of disinfectant, showing that the EtOH-H2O system plays a crucial role in turbidity and foam height control. In addition, we predicted by Minitab Optimizer Tool the physicochemical and aesthetic conditions of the disinfectant, having an error of 5%. Our current approach opens up a novel path to incorporate novel active ingredients for a rapid formulation and potentially scalable method to fabricate disinfectants. Keywords: image analysis, factorial design, disinfectant, foaming, active ingredients, optimization

Microscale underfill dynamics and void formation of high-density flip-chip packaging: Experiments and simulations

This research focuses on developing high-fidelity experimental and numerical models to analyze microscale underfill dynamics and void formation in high-density flip-chip packaging. Three underfilling scenarios are investigated, namely, point type, I-shaped line type, and L-type line type. The point-type underfilling validates the numerical model against experimental results, while the I-shaped and L-type line-type underfilling explore grid independence, void formation, and critical parameters such as filling position, contact angle, and liquid viscosity. Results indicate that contact angle and viscosity significantly influence filling efficiency and interface evolution. A smaller contact angle accelerates the process, reducing interface jumping motions. Viscous effects are quantified, revealing dimensionless filling time convergence. The use of low-viscosity surrogate fluids enhances numerical simulation efficiency. Sub-bump-sized, bump-sized, and sup-bump-sized voids are observed, identifying three void formation scenarios representing different underfilling flow mechanisms. This study provides insight into microscale flip-chip underfill physics and establishes validated models for next-generation high-density flip-chip products. These models can be further refined and integrated into optimization tools for automated process design, contributing to improved assembly yield and reliability of emerging electronic packages through physics-based understanding and modeling.

Jellyfish search algorithm for economic load dispatch under the considerations of prohibited operation zones, load demand variations, and renewable energy sources

<span lang="EN-US">This paper suggests a modified version of the former economic load dispatch (MELD) problem with the integration of wind power plant (WPP) and solar power plants (SPP) into thermal units (TUs). The target of the whole study is to cut the total producing electricity cost (TPEC) as much as possible. Three meta-heuristic algorithms, including particle swarm optimization (PSO), jellyfish search (JS) and salp swarm algorithm (SSA), are applied to solve the MELD. The real performance of these optimization tools is tested on the first system with six thermal units considering prohibited zones, and the second system with the combination of the first system and one solar, and two WPPs. In addition, the variation of load demand in 24 hours per day is also taken into account in the second system. JS is proved to be the most effective method for dealing with MELD. Furthermore, JS can also reach lower or the same TPEC as other previous algorithms. Hence, JS is a recommended to be a strong computing method for dealing with the MELD problem. </span>

Automatic smooth map generation of internal combustion engines via local-global model based calibration technique

The addition of new sensors and actuators to the engine, to reduce fuel consumption and emissions besides improving the engine operation, complicates the control commands stored in the engine control unit (ECU). Substitution of mechanical actuators with electronic ones increases the engine’s degrees of freedom and the number of control parameters, which results in the increased engine calibration time and cost. The aim of this paper is to take advantage of optimization techniques to achieve optimal values of control parameters in a fast and automated way. In this regard, it requires replacing the real engine with the virtual model and implementing the model-based calibration by coupling the virtual engine model with optimization algorithms. In this study, deep neural network (DNN) modeling and genetic algorithm (GA, NSGA-II) optimization are used for model-based calibration. The effect of all input control parameters, including ignition angle, continuously variable valve timing, etc., on all output control parameters including, brake-specific fuel consumption, emissions level, knock limit, combustion stability, etc., are investigated simultaneously by a valid global model, which is a remarkable achievement in the model-based calibration. Dynamic lag of some actuators delays the execution of control commands sent from ECU. To avoid abrupt variations in the actuators values, smoothness of the engine maps is considered in the calibration process. To reduce fuel consumption, decrease emission levels and attain smooth maps, the calibration of control parameters is performed by local-multi-objective optimization and global-single-objective optimization. Local-global model-based calibration presented in this study reduces 3.7% of the brake-specific fuel consumption and 7%–10% of emissions level at breakpoints of the engine map compared to manual calibration. In addition, the calibration time and costs while producing better engine performance can be reduced by automating the calibration process. Finally, calibrated maps are stored as a lookup table (LUT) in ECU. Generating an optimal lookup table involves the pre-calculation of several points that cover the calculation domain and allow the interpolation for other points. Selecting the optimal points for exact calculation is of great importance in the size and accuracy of LUT. In this study, an optimization tool is also presented to generate accurate and efficient LUT.

Implementation of corporate social responsibility in the context of integration with the enterprise management information system

Purpose. To develop recommendations for the corporate digital responsibility (CDR) strategy aimed at increasing the level of integration of corporate social responsibility (CSR) and information systems (IS) of the company. To develop a mathematical model of coordination decisions regarding CSR with production and business activities of companies. Methodology. The introduction of the methods of: logical generalization made it possible to define CDR adapted to Ukrainian realities; analysis and synthesis – to point out the need to integrate CSR with IS; comparative analysis – to determine directions, tasks, requirements for the implementation of CDR; logical abstraction – to substantiate the need to implement strategy for CDR implementation, develop its stages, tasks and goals of each stages; mathematical analysis – to develop mathematical model of coordination of management decisions. Findings. A definition of CDR, adapted to Ukrainian reality, which became the conceptual basis of the presented research, is proposed. The need for integration of CSR and IS and the formation of a new mechanism – CDR, as a result of the evolution of CSR, is substantiated. The directions, tasks, and requirements for the implementation of CDR are defined. The need to implement the strategy for the implementation of CDR are substantiated, its stages, tasks and goals of each stage are developed. It is indicated that the integration of CSR and IS can take place iteratively, with management gaining experience and finding optimal methods and tools. Originality. A mathematical model that allows one not only to coordinate various management decisions, but also to forecast their short-term and long-term results, is developed. Practical value. The proposed approach to CDR strategy formation will increase the effectiveness of CDR and improve the integration of CSR and IS. The developed mathematical model will allow one not only to solve the problem of coordination of management decisions, but will also help to economically spend IT resources and speed up the delivery of results.

Interventions of Bioelectronics and Biosensors on Oncology, Infectious and Neurological Diseases

With mental health being an exponential issue in the world, mental health care has begun to utilize more technological advancements within their work. With AI techniques becoming more normalized within society, the mental health field has started incorporating Machine Learning (ML) systems and Deep Learning (DL) models into their relevant professional interventions. Psychiatry specialists have begun to use ML and DL techniques for detecting psychological disorders, providing personalized mental health support, raising the effiency rate for clinical applications, and preventing additional diseases. With all the assistance gained from AI use, many limitations have begun to emerge, specifically related to the data privacy of customers, with the information derived from platforms being misabused by scientists and technologists. All of these following components will be discussed in depth within this publication, by analyzing the efficacy of AI platforms, optimal diagnostic tools, in conjunction with the rise of AI-based platforms that can help identify optimal solutions to patients diagnosed with neurodegenerative diseases. Ultimately, this research publication aims to shed light on various ethical contradictions, legal frameworks and psychiatric interventions that can enable researchers to better expose the traction of Machine Learning tools for future researchers to adopt.

Enhancing the Zebra Optimization Algorithm with Chaotic Sinusoidal Map for Versatile Optimization

In this study, the Chaotic Sinusoidal Map (CSM)-enhanced Zebra Optimization Algorithm (CZOA)is introduced. CZOA combines CSM's integration strengths with ZOA's optimization skills. ZOA already exhibitsgreat optimization capabilities, but the addition of CSM increases its potential even more. This addition greatlystrengthens ZOA's exploration and exploitation skills and increases its flexibility for various optimization tasks.CZOA outperforms both the original ZOA and contemporary optimisation methods on 23 benchmark functions,including high-dimensional (FD), multimodal (MM), and unimodal (UM) challenges. Using the chaos of CSM toinvestigate regional optimal and determine better convergence and exploration-exploitation equilibrium are shownby CZOA, which also shows more profitable solution locations. CZOA demonstrates its resilience and versatilitythrough multiple benchmark activities, underscoring its potential as an adaptable optimisation tool. CZOAbecomes a potent metaheuristic by combining biological inspiration and chaotic dynamics to solve difficultoptimisation problems. Inspired by the natural behaviour of zebras, the Zebra Optimisation Algorithm (ZOA) is arelatively new optimisation technique. It makes use of a herd behaviour mechanism and the ideas of leadership andfollowing, in which members of the population—zebras in this case—cooperate to solve optimisation issues in thebest possible ways

Use of responsible artificial intelligence to predict health insurance claims in the USA using machine learning algorithms

Aim: This study investigates the potential of artificial intelligence (AI) in revolutionizing healthcare insurance claim processing in the USA. It aims to determine the most effective machine learning (ML) model for predicting health insurance claims, leading to cost savings for insurance companies. Methods: Six ML algorithms were used to predict health insurance claims, and their performance was evaluated using various metrics. The algorithms examined include support vector machine (SVM), decision tree (DT), random forest (RF), linear regression (LR), extreme gradient boosting (XGBoost), and k-nearest neighbors (KNN). The research involves a performance assessment that encompasses key metrics. Additionally, a feature importance analysis is conducted to illuminate the critical variables that exert influence on the prediction of insurance claims. Results: The findings demonstrate that the XGBoost and RF models outperformed the other algorithms, displaying the highest R-squared values of 79% and 77% and the lowest prediction errors. The feature importance analysis underscores the pivotal role of variables such as smoking habits, body mass index (BMI), and blood pressure levels in the domain of insurance claim prediction. These results emphasize the degree to which these variables should be included in the formulation of insurance policies and pricing strategies. Conclusions: This study supports the transformative potential of AI, with specific emphasis on the XGBoost model, in extending the precision and efficiency of healthcare insurance claim processing. The identification of key variables and the mitigation of prediction errors not only signal the potential for substantial cost savings but also affirm the potential to integrate AI into healthcare insurance processes. This research supports the value of the utilization of AI as an emerging tool for process optimization and data-informed decision-making within the healthcare insurance domain.

Industry 4.0 implementation for multinationals: a case study

PurposeMultinational business deliver value via multiple sites with similar operational capacities. The age of the Fourth Industrial Revolution (4IR) delivers significant opportunities for the deployment of digital tools for business optimization. Therefore, this study aims to study the Industry 4.0 implementation for multinationals.Design/methodology/approachThe key objective of this research is multi-site systems integration using a reproducible, modular and standardized “Cyber Physical System (CPS) as-a-Service”.FindingsA best practice reference architecture is adopted to guide the design and delivery of a pioneering CPS multi-site deployment. The CPS deployed is a cloud-based platform adopted to enable all manufacturing areas within a multinational energy and petrochemical company. A methodology is developed to quantify the system environmental and sustainability benefits focusing on reduced carbon dioxide (CO2) emissions and energy consumption. These results demonstrate the benefits of standardization, replication and digital enablement for multinational businesses.Originality/valueThe research illustrates the ability to design a single system, reproducible for multiple sites. This research also illustrates the beneficial impact of system reuse due to reduced environmental impact from lower CO2 emissions and energy consumption. The paper assists organizations in deploying complex systems while addressing multinational systems implementation constraints and standardization.

Fair and green hyperparameter optimization via multi-objective and multiple information source Bayesian optimization

AbstractIt has been recently remarked that focusing only on accuracy in searching for optimal Machine Learning models amplifies biases contained in the data, leading to unfair predictions and decision supports. Recently, multi-objective hyperparameter optimization has been proposed to search for Machine Learning models which offer equally Pareto-efficient trade-offs between accuracy and fairness. Although these approaches proved to be more versatile than fairness-aware Machine Learning algorithms—which instead optimize accuracy constrained to some threshold on fairness—their carbon footprint could be dramatic, due to the large amount of energy required in the case of large datasets. We propose an approach named FanG-HPO: fair and green hyperparameter optimization (HPO), based on both multi-objective and multiple information source Bayesian optimization. FanG-HPO uses subsets of the large dataset to obtain cheap approximations (aka information sources) of both accuracy and fairness, and multi-objective Bayesian optimization to efficiently identify Pareto-efficient (accurate and fair) Machine Learning models. Experiments consider four benchmark (fairness) datasets and four Machine Learning algorithms, and provide an assessment of FanG-HPO against both fairness-aware Machine Learning approaches and two state-of-the-art Bayesian optimization tools addressing multi-objective and energy-aware optimization.

Intrinsic and post-hoc XAI approaches for fingerprint identification and response prediction in smart manufacturing processes

AbstractIn quest of improving the productivity and efficiency of manufacturing processes, Artificial Intelligence (AI) is being used extensively for response prediction, model dimensionality reduction, process optimization, and monitoring. Though having superior accuracy, AI predictions are unintelligible to the end users and stakeholders due to their opaqueness. Thus, building interpretable and inclusive machine learning (ML) models is a vital part of the smart manufacturing paradigm to establish traceability and repeatability. The study addresses this fundamental limitation of AI-driven manufacturing processes by introducing a novel Explainable AI (XAI) approach to develop interpretable processes and product fingerprints. Here the explainability is implemented in two stages: by developing interpretable representations for the fingerprints, and by posthoc explanations. Also, for the first time, the concept of process fingerprints is extended to develop an interpretable probabilistic model for bottleneck events during manufacturing processes. The approach is demonstrated using two datasets: nanosecond pulsed laser ablation to produce superhydrophobic surfaces and wire EDM real-time monitoring dataset during the machining of Inconel 718. The fingerprint identification is performed using a global Lipschitz functions optimization tool (MaxLIPO) and a stacked ensemble model is used for response prediction. The proposed interpretable fingerprint approach is robust to change in processes and can responsively handle both continuous and categorical responses alike. Implementation of XAI not only provided useful insights into the process physics but also revealed the decision-making logic for local predictions.

Два метода локального поиска в планировании открытых горных работ

This paper aims to investigate ways of planning open pit mining using local search (genetic algorithms are not disscussed in the paper). Open pit mining is a complex and multifaceted problem that requires efficient planning to optimize mineral extraction. The Local search methods are a powerful optimization tool to search for local optima in the space of lage size. This paper reviews two of the most commonly used local search approaches for open pit mining planning.

Towards a Reliable Design of Geopolymer Concrete for Green Landscapes: A Comparative Study of Tree-Based and Regression-Based Models

The design of geopolymer concrete must meet more stringent requirements for the landscape, so understanding and designing geopolymer concrete with a higher compressive strength challenging. In the performance prediction of geopolymer concrete compressive strength, machine learning models have the advantage of being more accurate and faster. However, only a single machine learning model is usually used at present, there are few applications of ensemble learning models, and model optimization processes is lacking. Therefore, this paper proposes to use the Firefly Algorithm (AF) as an optimization tool to perform hyperparameter tuning on Logistic Regression (LR), Multiple Logistic Regression (MLR), decision tree (DT), and Random Forest (RF) models. At the same time, the reliability and efficiency of four integrated learning models were analyzed. The model was used to analyze the influencing factors of geopolymer concrete and determine the strength of their influencing ability. According to the experimental data, the RF-AF model had the lowest RMSE value. The RMSE value of the training set and test set were 4.0364 and 8.7202, respectively. The R value of the training set and test set were 0.9774 and 0.8915, respectively. Therefore, compared with the other three models, RF-AF has a stronger generalization ability and higher prediction accuracy. In addition, the molar concentration of NaOH was the most important influencing factors, and its influence was far greater than the other possible factors including NaOH content. Therefore, it is necessary to pay more attention to NaOH molarity when designing geopolymer concrete.

Temperature description and heat extraction optimization of enhanced geothermal system via failure volume ratio

It is difficult to quantitatively measure the temperature characteristics to evaluate the heat extraction in Enhanced Geothermal Systems (EGS). This study aims to define a new evaluation index called failure volume ratio to describe the temperature distribution in rocks and optimize heat extraction. In the established three-dimensional thermal-hydrologic coupling model, the cumulative heat production and production temperature are interpreted and predicted by the proposed index. A set of orthogonal experiments is designed to quantitatively find the key factors affecting EGS. Results show that the failure volume ratio can provide a more rational and concise explanation for changes in the temperature and heat of production during EGS heat extraction. The ratio when the production temperature begins to decrease reflects how much heat has been extracted at the maximum production temperature. In a given model, we only need to calculate the failure volume ratio which reflects both cumulative heat production and production temperature changes, which is useful for a combined system of above-ground power generation and underground heat extraction. Based on the analysis of variance, the injection flow has the greatest impact on EGS. Its optimal value in this model is 80 kgs−1. This study provides a quantitative tool for reservoir evaluation and geothermal system optimization.

Establishing an efficient membrane bioreactor for simultaneous pollutant removal and purple bacteria production under salinity stress

Recovering resources from wastewater to alleviate the energy crisis has become the prevailing trend of technological development. Purple phototrophic bacteria (PPB), a group of fast-growing microbes, have been widely noticed for their potential in producing value-added products from waste streams. However, saline contents in these waste streams, such as food processing wastewater pose a big challenge, which not only restrain the pollutant removal efficiency, but also hinder the growth of functional microbes. To overcome this, a photo anaerobic membrane bioreactor cultivating PPB (PPB-MBR) was constructed and its performance upon long-term salinity stress was investigated. PPB-MBR achieved desirable pollutants removal performance with the average COD and NH4+ removal efficiency being 87% (±8%, n = 87) and 89% (±10%, n = 87), respectively during long-term exposure to salinity stress of 1–80 g NaCl L−1. PPB were predominant during the entire operation period of 87 days (60%–80%), obtaining maximum biomass yield of 0.67 g biomass g−1 CODremoved and protein productivity of 0.18 g L−1 d−1 at the salinity level of 20 g NaCl L−1 and 60 g NaCl L−1, respectively. The sum of value-added products in proportion to the biomass reached 58% at maximum at the salinity level of 60 g NaCl L−1 with protein, pigments and trehalose contributing to 44%, 8.7%, and 5%, respectively. Based on economic analysis, the most cost-saving scenario treating food processing wastewater was revealed at salinity level of around 20 g NaCl L−1. However, more optimization tools are needed to boost the production efficiency so that the profit from value-added products can outweigh the additional cost by excess salinity in the future implication.

ECONOMIC AND ANALYTICAL BASIS OF DIGITAL TRANSFORMATION OF ADMINISTRATION PROCESSES IN CONSTRUCTION ENTERPRISES

An effective management system at an enterprise should be built on the use of optimal tools. Data Science is one of the ways to optimize the management system of a business entity through the use of analytical procedures that will help to obtain high-quality and timely information content to prevent risks in the internal business environment. Analytical tools allow identifying problematic aspects of the company's activities, assessing the impact of factors and developing ways to eliminate the negative impact of factors with further development of a development strategy. Interpretation of the results of the analysis of activities using predictive analytics helps to improve the efficiency of the management system at the enterprise. The conceptual foundations of updating business processes and management systems for a construction enterprise on the basis of digitalization (digital administration) are substantiated, which are positioned as a means for the enterprise to achieve competitive advantages in the multi-project field of construction development. Analytical IT tools should be applied to a sufficient amount of qualitative and operational information, which will allow for effective integration of research results into the management process at an enterprise. The article is devoted to solving an urgent scientific and applied problem of developing theoretical foundations and methodology, which includes information models, methods, cognitive mechanisms that allow for the analysis, selection, optimization, and evaluation of the effectiveness of the introduction of modern concepts of information modeling and integrated implementation of a construction project.

Tank Cleaning Process Optimization For Loading Tanks MT Bauhinia Guna Cargo Change

Suboptimal implementation of tank cleaning will produce suboptimal results and can affect loading failure. These less than optimal results are influenced by several things. Tank Cleaning is a tank cleaning carried out on a tanker which serves to reduce the risk of damage to the cargo. Wall wash test is a tank cleanliness test for chemical tankers after completing tank cleaning. In the loading process, chemical tankers must prepare their loading tanks in accordance with the correct procedures. In every process of preparing this tank must always pay attention to the standards that have been determined from the surveyor or based on IMO (International Maritime Organization). When the Chemical ship will load, the ship's tank will be checked by the cargo surveyor, if there is dirt or residual cargo before the ship can be declared a failure in the tank cleaning process and as a result the ship will be ordered out of the Jetty, ordered to re-clean. This causes company losses because there will be delays in the loading and discharging process of the ship and the company gets complaints from the charter party. The method used to carry out the research is descriptive qualitative. Cutting the time for tank cleaning can result in poor tank cleaning results, poor quality materials also result in less than optimal results, and equipment that lacks maintenance will result in less than optimal tool performance. When the Chemical ship will load, the ship's tank will be checked by the cargo surveyor, if there is dirt or residual cargo before the ship can be declared a failure in the tank cleaning process and the ship will be ordered out of the Jetty and ordered to re-clean. This causes losses to the company because there will be delays in the loading and discharging process of the ship and the company can get complaints from the charter party. The success or failure of tank cleaning and wall wash tests depends on the discipline of the crew, adequate tools and materials, and procedures that are carried out properly.

Theory and verification of moiré fringes for x-ray three-phase grating interferometer

Dual-phase and three-phase grating x-ray interference is a promising new technique for grating-based x-ray differential phase contrast imaging. Dual-phase grating interferometers have been relatively completely studied and discussed. In this paper, the corresponding imaging fringe formula of the three-phase grating interferometer is provided. At the same time, the similarities and differences between the three-phase grating interferometer and the dual-phase grating interferometer are investigated and verified, and that the three-phase grating interferometer can produce large-period moiré fringes without using the analyzing grating is demonstrated experimentally. Finally, a simple method of designing three-phase grating and multi-grating imaging systems from geometric optics based on the thin-lens theory of gratings is presented. These theoretical formulas and experimental results provide optimization tools for designing three-phase grating interferometer systems.

A novel techno-economical layout optimization tool for floating wind farm design

Abstract. Over the past few years, the offshore wind sector has been subject to renewed yet growing interest from the industry and from the research sphere, with a particular focus on a recently developed concept, the floating offshore wind (FOW). Because of its novelty, floating research material is found in limited quantity. This paper focuses on the layout optimization of a floating offshore wind farm (FOWF) considering multiple parameters and engineering constraints, combining floating-specific parameters together with economic indicators. Today’s common wind farm layout optimization codes do not take into account either floating-specific technical parameters (anchors, mooring lines, inter-array cables (IACs), etc.) or non-technical parameters (operational expenditure, OPEX; capital expenditure, CAPEX; and other techno-economic project parameters). In this paper, a multi-parametric objective function is used in the optimization of the layout of a FOWF, combining the annual energy production (AEP) together with the costs that depend on the layout. The mooring system and the collection system including the inter-array cables and the offshore substation are identified as layout-dependent and therefore modeled in the optimization loop. Using ScotWind site 10 as a study case, it was found with the predefined technical and economic assumptions that the profit was increased by EUR 34.5 million compared to a grid-based layout. The main drivers were identified to be the AEP, followed by the anchors and the availability associated with the failures of inter-array cables.

Bayesian cell therapy process optimization.

Optimizing complex bioprocesses poses a significant challenge in several fields, particularly in cell therapy manufacturing. The development of customized, closed, and automated processes is crucial for their industrial translation and for addressing large patient populations at a sustainable price. Limited understanding of the underlying biological mechanisms, coupled with highly resource-intensive experimentation, are two contributing factors that make the development of these next-generation processes challenging. Bayesian optimization (BO) is an iterative experimental design methodology that addresses these challenges, but has not been extensively tested in situations that require parallel experimentation with significant experimental variability. In this study, we present an evaluation of noisy, parallel BO for increasing noise levels and parallel batch sizes on two in silico bioprocesses, and compare it to the industry state-of-the-art. As an in vitro showcase, we apply the method to the optimization of a monocyte purification unit operation. The in silico results show that BO significantly outperforms the state-of-the-art, requiring approximately 50% fewer experiments on average. This study highlights the potential of noisy, parallel BO as valuable tool for cell therapy process development and optimization.