Selection Issues Research Articles

A Simple Access to γ- and ε-Keto Arenes via Enzymatic Divergent C─H Bond Oxyfunctionalization.

Performing divergent C─H bond functionalization on molecules with multiple reaction sites is a significant challenge in organic chemistry. Biocatalytic oxyfunctionalization reactions of these compounds to the corresponding ketones/aldehydes are typically hindered by selectivity issues. To address these challenges, the catalytic performance of oxidoreductases is explored. The results show that combining the peroxygenase-catalyzed propargylic C─H bond oxidation with the Old Yellow Enzyme-catalyzed reduction of conjugated C─C triple bonds in one-pot enables the regio- and chemoselective oxyfunctionalization of sp3 C─H bonds that are distant from benzylic sites. This enzymatic approach yielded a variety of γ-keto arenes with diverse structural and electronic properties in yields of up to 99% and regioselectivity of 100%, which are difficult to achieve using other chemocatalysis and enzymes. By adjusting the C─C triple bond, the carbonyl group's position can be further tuned to yield ε-keto arenes. This enzymatic approach can be combined with other biocatalysts to establish new synthetic pathways for accessing various challenging divergent C─H bond functionalization reactions.

Implementation of Monarch Butterfly Optimization for Feature Selection in Coronary Artery Disease Classification Using Gradient Boosting Decision Tree

Coronary artery disease, a prevalent type of cardiovascular disease, is a significant contributor to premature mortality globally. Employing the classification of coronary artery disease as an early detection measure can have a substantial impact on reducing death rates caused by this ailment. To investigate this, the Z-Alizadeh dataset, consisting of clinical data from patients afflicted with coronary artery disease, was utilized, encompassing a total of 303 data points that comprise 55 predictive attribute features and 1 target attribute feature. For the purpose of classification, the Gradient Boosting Decision Tree (GBDT) algorithm was chosen, and in addition, a metaheuristic algorithm called monarch butterfly optimization (MBO) was implemented to diminish the number of features. The objective of this study is to compare the performance of GBDT before and after the application of MBO for feature selection. The evaluation of the study's findings involved the utilization of a confusion matrix and the calculation of the area under the curve (AUC). The outcomes demonstrated that GBDT initially attained an accuracy rate of 87.46%, a precision of 83.85%, a recall of 70.37%, and an AUC of 82.09%. Subsequent to the implementation of MBO, the performance of GBDT improved to an accuracy of 90.26%, a precision of 86.82%, a recall of 80.79%, and an AUC of 87.33% with the selection of 31 features. This improvement in performance leads to the conclusion that MBO effectively addresses the feature selection issue within this particular context.

Fuzzy-based optimization of AODV routing for efficient route in wireless mesh networks

The performance of any communication system heavily relies on the efficient routing of interventions. This article addresses the significant issue of routing protocol selection for optimal path determination in networks. Particularly, when wireless communication occurs among mobile nodes with limited resources, such as batteries, the routing problem becomes even more challenging. This article proposes the Fuzzy Control Energy Efficient (FCEE) routing protocol to overcome these challenges. The FCEE protocol combines the Ad-Hoc On-Demand Distance Vector (AODV) protocol with fuzzy logic techniques to enhance network lifetime and performance. The proposed approach introduces a memory-based channel integrated with fuzzy logic methodologies, which effectively restricts the forwarding of unnecessary broadcast packets based on the energy availability of the operating node. Through extensive simulations, demonstrate the promising capabilities of FCEE as a routing protocol for wireless mesh networks. To further assess the effectiveness of the FCEE protocol, the proposed article compares it with two existing routing protocols: AODV and Intelligent Routing AODV (IRAODV). The simulation results shows that the FCEE routing protocol significantly enhances the reliability of the conventional AODV, providing improved link connectivity and longer route lifetimes. Additionally, our proposed protocol enhances the quality of service (QoS) for mesh routing, with an average throughput of 351.374 (Kbps) compared to 90 (Kbps) for IRAODV and 39 (Kbps) for AODV. Moreover, FCEE exhibits superior energy efficiency with an average energy consumption of 14, while IRAODV and AODV consume 40 and 90 joules, respectively. In conclusion, the FCEE routing protocol demonstrates its potential to address the challenges of efficient routing in wireless mesh networks. By leveraging fuzzy logic and integrating it with AODV, FCEE enhances network lifetime, performance, and energy efficiency, making it a promising solution for future wireless communication systems.

A STUDY ON DEMAND FOR ROMAN SCRIPT FOR KOKBOROK LANGUAGE BY THE INDIGENOUS TIPRA PEOPLE OF TRIPURA

This article delves into the complex issue of script selection for the Kokborok language in Tripura, where the imposition of the Bengali script against the desires of the indigenous people has ignited a profound struggle. We explore how this decision holds implications that extend far beyond language preservation, encompassing the preservation of indigenous identity, cultural rights, and resilience against cultural assimilation. It is argued that the movement advocating for the adoption of the Roman script for Kokborok exemplifies the unwavering determination of the indigenous Tipra people to safeguard their linguistic heritage. The Roman script, widely understood regionally and globally, not only transcends linguistic barriers but also fosters a sense of communal cohesion and empowerment. This movement is part of a broader narrative of resistance against the erosion of indigenous languages and cultures, resonating with similar struggles worldwide. The successful adoption of the Roman script not only validates cultural rights but also contributes to a more inclusive and compassionate world where linguistic diversity is celebrated and preserved for generations to come.

Index of active participation in society

The concept of Active citizenship is gaining importance. Ignoring the issues of selection of dimensions and items, the paper addresses methodological issues and aggregation of indicators of multidimensional active participation index (PI) by two methods. Policy makers and researchers can take advantages of the proposed methods of arithmetic aggregation (Method-1) with normally distributed transformed scores or multiplicative aggregation (Method-2) without scaling and choosing weights. Thus, the Method-2 avoids the problems of rank robustness specific to selection of weights. Multiplicative aggregation proposed in Method-2 can be converted to additive model by taking logarithms. Normal distributions of two or more groups by Method-1 are likely to give rise to lower value of Ginis coefficient indicating equality. Avoiding major limitations of ordinal scores, both methods satisfy desired properties, analysis under parametric set up for meaningful comparisons including testing of statistical hypothesis, identification of critical dimensions, ranking of the dimensions by elasticity, assessment of progress/decline of PI, etc. Method-2 offers more generalized approach satisfying time reversal test and formation of chain indices. However, test of normality is required for this method unlike the Method-1 which ensures normally distributed scores.

Alcohol culture and corporate risk-taking

Using data on Chinese firms from 2004 to 2019, this study reveals a positive association between alcohol culture and corporate risk-taking. The positive relationship is robust across different measures of risk-taking, estimation methods, and after considering potential sample selection and endogeneity issues. Two mechanisms emerge behind this positive relationship: social networks and trustful social environments; these mediating variables explain corporate risk-taking well. In addition, stronger formal institutions (market development, government efficiency, and the legal environment) and religion weaken the positive effects of alcohol culture on risk-taking. Meanwhile, these positive effects are positively moderated by collectivism and language diversity. Finally, influenced by alcohol culture, CEOs play an essential role in risk-taking.

A Degron Blocking Strategy Towards Improved CRL4CRBN Recruiting PROTAC Selectivity.

Small molecules inducing protein degradation are important pharmacological tools to interrogate complex biology and are rapidly translating into clinical agents. However, to fully realise the potential of these molecules, selectivity remains a limiting challenge. Herein, we addressed the issue of selectivity in the design of CRL4CRBN recruiting PROteolysis TArgeting Chimeras (PROTACs). Thalidomide derivatives used to generate CRL4CRBN recruiting PROTACs have well described intrinsic monovalent degradation profiles by inducing the recruitment of neo-substrates, such as GSPT1, Ikaros and Aiolos. We leveraged structural insights from known CRL4CRBN neo-substrates to attenuate and indeed remove this monovalent degradation function in well-known CRL4CRBN molecular glues degraders, namely CC-885 and Pomalidomide. We then applied these design principles on a previously published BRD9 PROTAC (dBRD9-A) and generated an analogue with improved selectivity profile. Finally, we implemented a computational modelling pipeline to show that our degron blocking design does not impact PROTAC-induced ternary complex formation. We believe that the tools and principles presented in this work will be valuable to support the development of targeted protein degradation.

A New Improved Prediction of Software Defects Using Machine Learning-based Boosting Techniques with NASA Dataset

Predicting when and where bugs will appear in software may assist improve quality and save on software testing expenses. Predicting bugs in individual modules of software by utilizing machine learning methods. There are, however, two major problems with the software defect prediction dataset: Social stratification (there are many fewer faulty modules than non-defective ones), and noisy characteristics (a result of irrelevant features) that make accurate predictions difficult. The performance of the machine learning model will suffer greatly if these two issues arise. Overfitting will occur, and biassed classification findings will be the end consequence. In this research, we suggest using machine learning approaches to enhance the usefulness of the CatBoost and Gradient Boost classifiers while predicting software flaws. Both the Random Over Sampler and Mutual info classification methods address the class imbalance and feature selection issues inherent in software fault prediction. Eleven datasets from NASA's data repository, "Promise," were utilised in this study. Using 10-fold cross-validation, we classified these 11 datasets and found that our suggested technique outperformed the baseline by a significant margin. The proposed methods have been evaluated based on their abilities to anticipate software defects using the most important indices available: Accuracy, Precision, Recall, F1 score, ROC values, RMSE, MSE, and MAE parameters. For all 11 datasets evaluated, the suggested methods outperform baseline classifiers by a significant margin. We tested our model to other methods of flaw identification and found that it outperformed them all. The computational detection rate of the suggested model is higher than that of conventional models, as shown by the experiments..

Quantitative structure-property relationship modelling for predicting retention indices of essential oils based on an improved horse herd optimization algorithm

ABSTRACT The horse herd optimization algorithm (HOA), one of the more contemporary metaheuristic algorithms, has demonstrated superior performance in a number of challenging optimization tasks. In the present work, the descriptor selection issue is resolved by classifying different essential oil retention indices using the binary form, BHOA. Based on internal and external prediction criteria, Z-shape transfer functions (ZTF) were tested to verify their efficiency in improving BHOA performance in QSPR modelling for predicting retention indices of essential oils. The evaluation criteria involved the mean-squared error of the training and testing datasets (MSE), and leave-one-out internal and external validation (Q 2). The degree of convergence of the proposed Z-shaped transfer functions was compared. In addition, K-fold cross validation with k = 5 was applied. The results show that ZTF, especially ZTF1, greatly improves the performance of the original BHOA. Comparatively speaking, ZTF, especially ZTF1, exhibits the fastest convergence behaviour of the binary algorithms. It chooses the fewest descriptors and requires the fewest iterations to achieve excellent prediction performance.

Fermatean fuzzy covering-based rough set and their applications in multi-attribute decision-making

Rough set theory (RST) is a common and effective tool for data processing. In order to extend the range of data that RST and its related theories can handle, scholars have proposed many innovations on the basis of Pawlak’s model. This paper focuses on solving the multi-attribute decision-making (MADM) issue in Fermatean fuzzy environment and interval-valued Fermatean fuzzy environment by using Fermatean fuzzy β-covering rough set (CFFRS) model and interval-valued Fermatean fuzzy β-covering set (CIVFFS) model, respectively. Firstly, we give the definitions of Fermatean fuzzy β-neighborhood and interval-valued Fermatean fuzzy β-neighborhood, and then we construct CFFRS model and CIVFFRS model, respectively. Furthermore, the related properties and the complementary concepts of these two models are also discussed. Next, in order to generalize MADM approaches in Fermatean fuzzy and interval-valued Fermatean fuzzy environments, from the point of view of comparison among the advantages of the alternatives, we establish two TOPSIS methodologies based on CFFRS and CIVFFRS, respectively. Moreover, we discuss the effect of value of precision parameter β for decision-making results by two examples and conclude that the construction of fuzzy β-neighborhood is the most important influence factor in decision-making results. And then, we give a principle for the selection of the precision parameter β, and suggest the treatment when there are alternatives in decision that cannot be compared. To demonstrate the decision-making processes of novel methodologies, aiming at an electric vehicle charging stations selection issue in an India city, including Raniganj, Jamuria, Kulti, and Burnpur, the decision-making results based on CFFRS and CIVFFRS models show that Jamuria appears to be the best location to build an electric vehicle charging station. Finally, we compare the decision-making results to other existing methods, through the Spearman ranking correlation coefficient and Pearson correlation coefficient methods, we verify the effectiveness of novel approaches.

Uniform moduli characterization of asphalt mixtures under dynamic and static loading conditions

The modulus is a crucial material parameter reflecting stiffness characteristics of asphalt mixture in the elastic stage. Under specific conditions such as identical temperatures, loading velocities, and stress amplitudes, the moduli under the same stress state are theoretically identical. However, the moduli were artificially divided into dynamic moduli and static moduli based on loading methods during test, which brought uncertainties and disunity for estimating the stiffness performance of asphalt mixture. In this study, the dynamic and static moduli under distinct temperatures, loading velocities, and stress states were measured. The relationship between dynamic and static moduli was established by applying loading velocities as independent variable and moduli as the dependent variable. Through Sigmoidal function and WLF function fitting, the uniform characterization master curves of dynamic and static moduli under distinct temperatures, loading velocities, and stress states were established. The moduli of asphalt mixture under distinct test temperatures, loading velocities, and stress states can be obtained by the models, which provide material parameters for mechanical response analysis of asphalt pavements under distinct service environments. This resolves the issue of inconsistent moduli parameter selection.

Ensemble learning of multi-kernel Kriging surrogate models using regional discrepancy and space-filling criteria-based hybrid sampling method

Kriging surrogate model has been widely used to simulate expensive models in engineering application. Ensemble of multi-kernel Kriging surrogate models can integrate the information of different kernel functions and enhance the predictive robustness. The performance of the ensemble Kriging model is dependent on the collection of samples. However, the traditional sampling methods primarily concentrate on the accuracy of individual-kernel Kriging, disregarding the difference among different kernel functions. To deal with the issue of sample selection in the context of ensemble Kriging, a regional discrepancy and space-filling criteria-based hybrid sampling (RSHS) method is proposed in this paper. According to the bias-variance decomposition, the expected leave-one-out cross validation error is introduced to compute the weight of each Kriging component. Considering the disagreement of different kernel-based Kriging models, the combination of regional correlation and regional predictive error is derived to measure the regional discrepancy for pursuing local accuracy. The space-filling criterion is also employed to ensure the even coverage across the entire space for global exploration. Therefore, the hybrid sampling criterion is developed to select sample point sequentially by maximizing the regional discrepancy with subject to the constraint of space-filling criterion. The performance of the proposed method is validated by six benchmark examples and an engineering application of airfoil shape model. The results demonstrate the RSHS sampling method can provide promising accuracy and robustness for ensemble Kriging metamodeling.

Utilizing indocyanine green video angiography to bridge intracranial aneurysm treatment gaps in low- and middle-income countries: a mini-review.

Intracranial aneurysms, affecting 2%-5% of the population, pose a significant challenge to neurosurgeons due to their potential to cause subarachnoid haemorrhage and high mortality rates. Intraoperative angiography is necessary for effective surgical planning and indocyanine green video angiography (ICG-VA) has emerged as a useful tool for real-time visualization of aneurysmal blood flow, aiding in better planning for potential blood flow and detection of aneurysm remnants. This mini narrative review explores the application of ICG-VA in intracranial aneurysm surgery. Compared with conventional dye-based angiography, ICG-VA is safer, more effective and more cost-effective. It can assess haemodynamic parameters, cerebral flow during temporary artery occlusion, completeness of clipping and patency of branch vessels. However, implementing ICG-VA in low- and middle-income countries presents challenges such as financial constraints, limited access to training and expertise, patient selection and consent issues. Addressing these obstacles requires capacity-building, training programmes for neurosurgeons and multidisciplinary teams, technology transfer, equipment donations, public-private partnerships, continued research and development, reducing conventional dye usage, reducing ICG wastage, exploring mechanisms to reuse ICG dyes and advocating for increased government funding and healthcare budgets.

Description length guided nonlinear unified Granger causality analysis.

Most Granger causality analysis (GCA) methods still remain a two-stage scheme guided by different mathematical theories; both can actually be viewed as the same generalized model selection issues. Adhering to Occam's razor, we present a unified GCA (uGCA) based on the minimum description length principle. In this research, considering the common existence of nonlinearity in functional brain networks, we incorporated the nonlinear modeling procedure into the proposed uGCA method, in which an approximate representation of Taylor's expansion was adopted. Through synthetic data experiments, we revealed that nonlinear uGCA was obviously superior to its linear representation and the conventional GCA. Meanwhile, the nonlinear characteristics of high-order terms and cross-terms would be successively drowned out as noise levels increased. Then, in real fMRI data involving mental arithmetic tasks, we further illustrated that these nonlinear characteristics in fMRI data may indeed be drowned out at a high noise level, and hence a linear causal analysis procedure may be sufficient. Next, involving autism spectrum disorder patients data, compared with conventional GCA, the network property of causal connections obtained by uGCA methods appeared to be more consistent with clinical symptoms.

К ВОПРОСУ О РОЛИ ПСИХОСОЦИАЛЬНЫХ ФАКТОРОВ НА ЗДОРОВЬЕ РАБОТАЮЩЕГО НАСЕЛЕНИЯ С ПОЗИЦИЙ ПРОМЫШЛЕННОЙ И ВОССТАНОВИТЕЛЬНОЙ МЕДИЦИНЫ

The article contains data from a review of foreign literature on the prevention of diseases of the working population from the perspective of industrial and restorative medicine. Scientists have identified the most significant measures as: impact on the social determinants of mental health (standard of living and working conditions; measures to prevent and promote health and mental health, including measures to reduce stigma and discrimination; increasing access to evidence-based care through the development of services occupational health and safety, including access to industrial and restorative medicine services. Work intensity is most often considered the main risk factor for constant stress in the workplace/production place, as it correlates with the body's reactions to the adverse effects of production factors. Analysis of the combination of high production demands with social vulnerability at work/work with symptoms of depression and/or anxiety showed a high risk of mental disorder depending on psychosocial factors, regardless of the social status, health status, verbal intelligence and personal qualities of the employee. There is a need for closer attention to issues of gender selection in various professions as part of the study of the gender aspect as a social determinant of health in the context of maintaining/restoring the health of workers. The discrepancy between the efforts made and the rewards received at work is another psychosocial factor that negatively affects the mental health of the population, which also contributes to the abuse of alcohol and various medications that affect cognitive abilities, and reduces the quality of sleep. High personal self-esteem is thought to be a protective resource that reduces the risk of psychological distress, but there is a paucity of research linking self-esteem to neurobiological adaptability.

Agri-food supply chain under live streaming and government subsidies: Strategy selection of subsidy recipients and sales agreements

To ensure the security of grain reserves and maintain the economic and social benefits related to agricultural products, the government should subsidize the agri-food supply chain members. At the same time, the agri-food supplier urgently needs live streaming to expand marketing channels. Based on this, this paper investigates the issue of strategy selection of the agri-food supply chain considering live-streaming under government subsidy. Thus, four scenarios are developed based on different combinations of situations where the choice of the target of government subsidies (agri-food supplier or streamer) and both parties in the supply chain enter into different agreements (commission or sharing). For this, the optimal decision of the government, agri-food supplier, and streamer in four situations are given, and the optimal strategies of all parties are compared and analysed. The theoretical results were verified through numerical experiments, and sensitivity analyses were conducted to identify further insights. The research conclusions are as follows. (i) The streamer is willing to pay more live-streaming effort for agricultural products with good fresh-keeping quality, and vice versa; (ii) If the government values the safety and freshness of agricultural products, the government should subsidize the agri-food supplier. In contrast, if the government focuses on the agricultural products’ visibility, sales and popularity, the government should subsidize the streamer; (iii) In a given situation, there is the all-win strategy among all parties, i.e., the government subsidizes the agri-food supplier and the agri-food supplier enter into a sharing agreement with the streamer; (iv) The all-win region between the government and supply chain members will gradually shrinks with uncertainty increases. Overall, this research provides a theoretical basis for optimizing the supply chain system associated with live broadcast assistance to agriculture under government subsidies.

Enhanced ammonia/amines sensitivity at room temperature using plasma polymerized polyvinyl acetate-reduced graphene oxide composite film sensors

Amines and ammonia can harm the environment, even in small concentrations. Efficient amine sensors are required to monitor the quality of food and air. A growing number of industries need sensors that can recognize a particular gas. Here, we demonstrate composite films made of plasma polymerized vinyl acetate (PVAc) and reduced graphene oxide (rGO) to address the crucial issues of limited selectivity, long-term instability, and temperature instability in chemoresistive gas sensors. Change in resistance of the sensor was studied in presence of analyte gas. An inductively linked plasma polymerization setup prepares PVAc films under carefully calibrated deposition conditions. Several samples are analysed to determine the ideal PVAc to rGO ratio for the composite film. Compared to individual PVAc and rGO films, final samples have demonstrated a sensitivity to ammonia and amines up to 15 times higher. The sensitivity of measuring ammonia/amines ranges from 628 for trimethylamine to a maximum of 3389 for methylamine. The sensitivity of measuring ammonia is 1621, while the sensitivity of measuring dimethylamine is 2041. The redox reaction, charge transfer, and swelling of polymer films explain potential sensing processes. Ammonia/amine selectivity has been enhanced by up to 160 compared to other volatile organic compounds (VOCs) using PVAc-rGO composite films. Based on unique selectivity, these films may also successfully distinguish between ammonia and other amines. It is shown that fluctuations in temperature between 30 °C and 150 °C did not affect the sensitivity of PVAc-rGO films. These films remain sensitive for far longer than three months. As a result, composite films of PVAc-rGO may be used to fabricate exceptionally sensitive and reliable gas sensors for ammonia/amines environmental monitoring.

Variables selection using [formula omitted] penalty

The determination of a tuning parameter by the generalized information criterion (GIC) is considered an important issue in variable selection. It is shown that the GIC and the L0 penalized objective functions are equivalent, leading to a new L0 penalized maximum likelihood method for high-dimensional generalized linear models in this article. Based on the technique of the well-known discrete optimization problem in theoretical computer science, a two-step algorithm for local solutions is proposed. The first step optimizes the L0 penalized objective function under a given model size, where only a maximum likelihood algorithm is needed. The second step optimizes the L0 penalized objective function under a candidate set of model sizes, where only the GIC is needed. As the tuning parameter can be fixed, the selection of the tuning parameter can be ignored in the proposed method. The theoretical study shows that the algorithm is polynomial and any resulting local solution is consistent. Thus, it is not necessary to use the global solution in practice. The numerical studies show that the proposed method outperforms its competitors in general.

Optimization of Deep Generative Intrusion Detection System for Cloud Computing: Challenges and Scope for Improvements

The large amount of data and its exponential increase result in security problems which subsequently cause damage to cloud computing and its environments. The Intrusion detection system (IDS) is among the systems that monitor and analyse data for malicious attacks in the cloud environment. High volume, high redundancy, and high dimensionality of network traffic in cloud computing make it difficult to detect attacks by contemporary techniques. To improve the performance of IDS features selection and data imbalance issues need to be resolved. This paper includes techniques and surveys of cloud-based IDS with ML techniques and IDS performance on the different types of cloud-based datasets. It also analyses the gaps and scope for enhancement of evaluation parameters of IDS. It provides a cloud-based IDS system which will produce a good performance result as compared to the other contemporary system. Moreover, this paper offers a current overview of cloud-based IDS, Data imbalance technique, Dataset and proposed cloud IDS system architecture.

Optimization of BESS placement, technology selection, and operation in microgrids for minimizing energy losses and CO2 emissions: A hybrid approach

This research paper addresses the issue of placement, technology selection and operation of BESS energy storage systems (BESS) in microgrids under a variable distributed generation (DG) and energy demand scenario for an average year of operation. To address this issue, a mixed-integer nonlinear programming (MINLP) model was formulated. The model considers two different objective functions, which are optimized using a mono-objective approach: (i) total energy losses and (ii) total CO2 emissions. The MINLP model was solved using a master–slave methodology based on metaheuristic algorithms. In the master stage, the Chu & Beasley parallel genetic algorithm (PCBGA) was used to solve the BESS placement and technology selection problem, while in the slave stage, the vortex search algorithm (VSA) was used to solve the BESS operation problem. A matrix-based successive approximation power flow was also used in this stage to determine the objective functions and evaluate the technical and operational constraints defined in the mathematical model. To verify the results, the 33-node test system was adjusted to the generation and demand characteristics of the city of Medellín. In addition, the proposed methodology was compared with two optimization techniques used in the master stage, namely the Parallel Monte Carlo (PMC) method and the Parallel Particle Swarm Optimizer (PPSO). Simulation results obtained using MATLAB software for the test scenario showed that the CBGA-VSA methodology outperformed the other techniques in terms of solution quality, repeatability and processing times to minimize total annual operating costs.