Minimum Cost Research Articles

Mothers with low incomes view both individual and structural interventions as potentially helpful for supporting early child development

Many developmental psychologists aspire to conduct research that informs interventions and policies to prevent income-related disparities in child development. Among growing researcher discussion about the value of interventions that target “structural” and resource-related correlates of income inequality and child development (e.g., housing, food, material goods, cash), rather than individual, person-centered correlates (e.g., parenting behaviors), the perspectives of mothers with low incomes may provide important context. 281 mothers with young children and low incomes rated various structural and individual interventions, framed as having minimal costs and entry barriers, for their perceived helpfulness. Analyses were pre-registered. Overall, mothers rated all interventions very highly, though they rated structural interventions as slightly more helpful than individual interventions. Mothers rated interventions they used in the past as less helpful than those they hadn’t previously used. An exploratory qualitative analysis revealed mothers’ desires for supports in other intervention domains beyond those addressed in our survey. Together, mothers’ responses indicated that they did not see individual interventions as inherently unhelpful due to a focus on individual states, knowledge, and skills. Implications for developmental psychology and intervention science are discussed.

Energy Efficiency for Faster-than-Nyquist Data Transmission Using Processing Algorithms with Decision Feedback

One of the ways to increase the volume of transmitted information is to increase the bit rate above the Nyquist barrier. However, an increase in bit rate in the case of FTN (Faster-Than-Nyquist) signals leads to an increase in energy costs for receiving information on channels with limited bandwidth, for example, in Digital Video Broadcasting satellite systems like DVB-S2/S2X. It is possible to minimize energy losses by using the processing algorithm “maximum likelihood sequence estimation”. However, the computational complexity of this algorithm is extremely high, which limits its use, especially in terrestrial mobile satellite terminals. We propose a new bit-by-bit decision feedback algorithm with maximum likelihood ratio estimation of subsequent symbols in the observation interval. This algorithm provides minimal energy costs comparable to the method “maximum likelihood sequence estimation” at speeds 2–3 times higher than the Nyquist barrier. At the same time, the complexity is two orders of magnitude less. It is shown by simulation for a channel with additive noise that energy losses in relation to the potential bit error rate (BER) are less than 4.5 dB. In the presence of Rayleigh fading, the application of the proposed algorithm makes it possible to provide the processing of FTN signals for double bit rates in urban areas with energy costs equal to 12 dB when using an equalizer. We give numerical estimations of the increase in computational complexity for the proposed processing algorithm. It is shown that an increase in the bit rate by 1.5 times leads to an increase in the computational complexity by more than an order of magnitude. The same conclusion can be reformulated in another form: for the proposed algorithm, each decibel of energy gain is achieved by increasing the number of computational operations by 1.5×105. It is experimentally shown that additional energy losses due to non-ideal phase and timing synchronization are no more than 1 dB when the proposed algorithm is applied in a fading channel. The energy costs in fading channels relative to a stationary channel for twice the Nyquist rate are equal to 13.8 dB when using an equalizer.

Stochastic Techno-Economic Optimization of Hybrid Energy System with Photovoltaic, Wind, and Hydrokinetic Resources Integrated with Electric and Thermal Storage Using Improved Fire Hawk Optimization

In this paper, a stochastic techno-economic optimization framework is proposed for three different hybrid energy systems that encompass photovoltaic (PV), wind turbine (WT), and hydrokinetic (HKT) energy sources, battery storage, combined heat and power generation, and thermal energy storage (Case I: PV–BA–CHP–TES, Case II: WT–BA–CHP–TES, and Case III: HKT–BA–CHP–TES), with the inclusion of electric and thermal storage using the 2m + 1 point estimate method (2m + 1 PEM) utilizing real data obtained from the city of Espoo, Finland. The objective function is defined as planning cost minimization. A new meta-heuristic optimization algorithm named improved fire hawk optimization (IFHO) based on the golden sine strategy is applied to find the optimal decision variables. The framework aims to determine the best configuration of the hybrid system, focusing on achieving the optimal size for resources and storage units to ensure efficient electricity and heat supply simultaneously with the lowest planning cost in different cases. Also, the impacts of the stochastic model incorporating the generation and load uncertainties using the 2m + 1 PEM are evaluated for different case results compared with the deterministic model without uncertainty. The results demonstrated that Case III obtained the best system configuration with the lowest planning cost in deterministic and stochastic models and. This case is capable of simply meeting the electrical and thermal load with the contribution of the energy resources, as well as the CHP and TESs. Also, the IFHO superiority is proved compared with the conventional FHO, and particle swarm optimization (PSO) achieves the lowest planning cost in all cases. Moreover, incorporating the stochastic optimization model, the planning costs of cases I–III are increased by 4.28%, 3.75%, and 3.57%, respectively, compared with the deterministic model. Therefore, the stochastic model is a reliable model due to its incorporating the existence of uncertainties in comparison with the deterministic model, which is based on uncertain data.

Hierarchical optimization for the energy management of a greenhouse integrated with grid-tied photovoltaic–battery systems

This study addresses the challenges of high energy consumption and environmental concerns in traditional greenhouse operations by exploring an integrated greenhouse with grid-tied photovoltaic (PV)-battery systems. A two-layer hierarchical optimization framework is proposed for effective energy management. In the upper layer, greenhouse operations are optimized with the Energy Consumption Minimization (ECM) strategy and the Energy Cost Minimization (ECoM) strategy, ensuring suitable climate conditions while reducing energy use and costs. In the lower layer, the scheduling of the hybrid energy system is optimized with the Self-consumption Maximization (SCM) strategy and the Total Cost Minimization (TCM) strategy, aiming to maximize the self-consumption of PV power and minimize total costs, including energy costs, battery aging costs, and carbon emission costs, while meeting the greenhouse’s electricity demand. A sensitivity analysis is conducted to investigate the impact of electricity prices, feed-in tariffs, battery capacity, and PV array area on hybrid energy system scheduling. Results reveal that the ECoM strategy reduces costs by 6.98% compared to the ECM strategy, and the TCM strategy reduces total costs by 43.50% compared to the SCM strategy. Battery capacity and PV array area are found to have a more significant influence on total costs than electricity prices and feed-in tariffs. This study can offer crucial insights for realizing cost-effective and environmentally friendly greenhouse operations, contributing to the advancement of sustainable agriculture.

Cost-effectiveness analysis of acalabrutinib in treatment of patients with relapsed/refractory form of mantle cell lymphoma who previously received at least one line of therapy

Objective: to conduct a clinical and economic study of acalabrutinib for the treatment of adult patients with relapsed/refractory form of mantle cell lymphoma who previously received at least one line of therapy in the Russian Federation.Material and methods. Acalabrutinib and ibrutinib were considered as compared treatment options. Based on the adjusted indirect comparison, they were found to be comparable in terms of overall survival and progression-free survival. Therefore, a cost minimization analysis method was selected for the study. Based on results of ACE-LY-004 clinical trial a distributed survival model was developed. The model was used to calculate drug costs over a 3-year horizon. To explore uncertainty in the results of economic evaluations we conducted a sensitivity analysis. A number of patients in the hypothetical cohort who could be further treated with a less expensive drug was determined during a missed opportunity analysis. Both mean life years gained and mean progression-free life years were calculated too.Results. Based on simulations over a 3-year horizon, the mean life years gained were 2.45, and the mean progression-free life years were 1.75. The average costs of acalabrutinib therapy both over 1 year and in total over 3 years were 36.6% lower compared to ibrutinib. So, in the 1st year, the costs for acalabrutinib were 3,546,237,60 rubles, for ibrutinib – 5,591,391,00 rubles (a difference of 2,045,153,40 rubles). Using acalabrutinib for 3 years was associated with costs of 7,252,980,60 rubles compared to 11,435,852,60 rubles for ibrutinib (a decrease of 4,182,872,00 rubles). Univariate multi-way sensitivity analysis showed the robustness of modeling results to price fluctuations in the range ±10%. For a hypothetical cohort of 100 patients, using acalabrutinib instead of the more expensive ibrutinib within the same budget will provide therapy to additional 57 patients per year.Conclusion. The results of the study demonstrated that acalabrutinib compared to ibrutinib for the treatment of adult patients with relapsed/ refractory form of mantle cell lymphoma can reduce costs for the 1st year of therapy by 2,045,153,40 rubles (36.6%), and by 4,182,872,00 rubles (36.6%) for 3 years. Thus, acalabrutinib is the cost-saving option for previously treated adult patients with relapsed/refractory mantle cell lymphoma in the Russian Federation.

Development of higher order deterministic approaches of forward and inverse uncertainty quantification for nonlinear engineering systems

This paper introduces an innovative numerical scheme that may accurately quantify the parameter and response distributions with minimal computational costs, with specific applications to the numerical computation of multiphase flows under various fluid conditions. The probabilistic approach of uncertainty quantification, e.g., the Monte Carlo simulation, although known to be effective in estimating the parameter/response distribution for highly nonlinear problems, is usually not applicable to multi-physics and multi-scale integrated systems due to its high computational cost. The objective of this study is first, to reduce the computing demand for complex physical system calculations and, at the same time, to accurately predict actual probability distributions by developing a higher order deterministic approach to uncertainty quantification. To achieve this, instead of executing simulation codes multiple times to estimate the probability distribution, the first to fourth moments of the probability density function are derived directly by employing a second order Taylor expansion form of the responses along with Bayesian statistics. This characterizes the parameter and response distributions computed by inverse and forward uncertainty quantification based on their mean value, mode, covariance, skewness, and kurtosis. The parameter distribution is estimated by the numerically calculated higher moments of the parameters with a posteriori probability density function being derived by Bayesian inference. The parameter distribution is then mapped into the second order approximated response function to compute the characteristics of the response distribution through both analytical calculation and numerical approximation of the moments of the responses. The proposed approach is numerically demonstrated through simulations of two benchmark problems: MIT pressurizer and FEBA tests. The prediction of the responses is improved by inverse uncertainty quantification as numerical calculations with the a posteriori model parameters are in better agreement with the measured data over a wide range of transient periods. Additionally, the response distribution estimated by the proposed approach of forward uncertainty quantification accurately predicts the distribution computed by the Monte Carlo simulation.

Energy management in integrated energy system with electric vehicles as mobile energy storage: An approach using bi-level deep reinforcement learning

The integrated energy system with electric vehicle charging station via vehicle-to-grid aims to offer a proactive solution for low-carbon development of both energy and transportation sectors. However, achieving optimal energy efficiency with minimal operational costs in such a complex system is challenging due to the high randomness of electric vehicle travel patterns. This work proposes a reinforcement learning-based energy management framework to optimize the coordinated scheduling of integrated energy and vehicle-to-grid. The problem is modeled as a Markov Decision Process and optimized using the bi-level soft actor-critic algorithm, where the upper-level agent supervises the scheduling of the integrated energy system, and lower-level agent sets the electric vehicles’ charging and discharging strategies based on upper-level decision. Numerical simulations demonstrated that by adopting a bi-level reinforcement learning approach, the proposed algorithm effectively enhances energy exchange between integrated energy and electric vehicle charging station, reducing operational costs by 8 % compared to other multi-agent algorithms. Furthermore, when compared to the Mixed-Integer Linear Programming model, the proposed algorithm achieves similar optimality with significantly less computation time, maintaining a margin of error of approximately 4 %. Additionally, integrating electric vehicles as mobile energy storage within this framework can lead to a further 10 % reduction in operating costs.

Inventory Optimization Model with Quadratic Demand Pattern and Non-instantaneous Deterioration Rate with Mixed Cash and Advance Payment Scheme

Objectives: To develop a mathematical model for cost minimization by examining a scenario involving a seasonal product retail market, evaluating the outcome, and providing solutions to unanswered queries of existing models are the highlights of this present work. Methods: 1. Using dynamic differential equations with initial and boundary conditions, we mathematically model a retail market scenario for perishable commodities and determine the retailer's total cost. 2. We consider a quadratic function of time to precisely describe the maintenance cost and demand function. Also, a combination of cash and advance payment schemes is implemented in our model. 3. Our model was numerically formulated using a dataset deduced from earlier research. We have included a sensitivity analysis and a numerical example with a graphical depiction of the total cost function that highlights the most and least influential factors. 4. Based on the Hessian matrix condition and the calculus derivative approach, we developed an algorithm to calculate the optimal cost. Findings: This model provides retailers with insights for effective stock management while optimizing the cost of retailing perishable commodities with non-linear demand patterns. Sensitivity analysis showed that the two factors that have the biggest impact on retailers' overall costs are non-linear demand and holding cost parameters. The smaller optimal inventory cycle time and the large value of time at which a shortage happens raise the overall cost. Additionally, because of higher demand, the time-dependent deterioration rate has the least impact on overall cost. Additionally, a three-dimensional graph of cost minimization is offered to illustrate the convexity of total cost. Novelty: Implementation of Quadratic time-dependent demand and non-linear holding costs under a mixed cash advance payment structure, along with time-dependent deterioration and partial backlog shortages. Keywords: Quadratic Demand pattern, Non­instantaneous decay rate, Cash and Advance Payment scheme, Partial Backlog Shortages

Towards a sustainable energy future: Modeling Morocco’s transition to renewable power with enhanced OSeMOSYS model

Developing countries encounter numerous challenges, such as limited access to reliable electricity and a heavy reliance on imported fossil fuels. To satisfy the rising energy demand, which is essential for economic growth, these regions are shifting towards sustainable energy solutions. Solar and wind power have emerged as key and secure energy sources. This research develops an enhanced OSeMOSYS energy system model to examine long-term energy supply strategies, using Morocco as a case study. The proposed model addresses the specific needs of decision-makers in developing countries, enabling the achievement of renewable energy targets and optimal temporal resolution. A baseline and three alternative scenarios were developed, exploring both a restructuring plan to address existing gaps and a forward-looking policy targeting nearly 100 % renewable energy. These scenarios are assessed in terms of installed capacity, generation mix, investment costs, variable and fixed costs, storage capacity utilization, and carbon emissions. The results indicate that a 92 % integration rate for renewables is feasible at an additional cost of $32 billion, thanks to new energy efficiency measures reducing demand by 15 % between 2030 and 2050 compared to baseline forecasts. Furthermore, an ambitious energy strategy by 2050 could achieve the lowest emissions rate of 0.29 Mt, paving the way for complete decarbonization. Technologically, investment in pumped-storage hydroelectric plants is the most viable backup option for a country dependent on natural gas imports. Our findings emphasize that a diversified energy mix and robust energy efficiency plans are essential for a rapid and feasible transition of the country’s energy system. This paper offers an enhanced energy model to help decision-makers in developing countries set targets on the share of renewables in total installed capacity, thereby increasing their integration rate at minimal additional cost.

Deep Neural Network with a Characteristic Analysis for Seal Stroke Recognition

Seal characters are derived from ancient Chinese pictographs, naturally inheriting pictographic characteristics and complex structures. As the essential components of seal characters, seal strokes play a vital role in seal character recognition, composition and writing, so accurate recognition of seal strokes can greatly promote the investigation of seal characters. Inspired by curve fitting, we propose a new model called the characteristic analysis neural network (CANN) for seal stroke recognition. Instead of indiscriminate grasping of feature information in regular neural networks, we design an efficient approximation technique based on the piecewise Bezier curves that can effectively facilitate structural compression and lossless feature extraction. The feature extraction capability of Bezier approximation helps the methodology achieve impressive recognition accuracy not only on the seal strokes but also on any curve-based symbols. Furthermore, the hierarchical structure of the deep learning strategy is inherited and improved for better performance with high generalisation. Experiments conducted on different types of strokes verify that CANN obtains superior performance on both seal strokes and other smooth symbols. The robustness and the effectiveness of CANN are also demonstrated with minimal learning cost compared to other state-of-art models.

The Impact of Digital Financial Innovation on Entrepreneurial Financial Performance through Digital Financial Knowledge: An Analytical Study of the Opinions of a sample of Workers of Iraqi Private Banks in -AL Najaf AL-Ashraf

The study aims to measure and analyze the impact of digital financial innovation on entrepreneurial financial performance through digital financial knowledge. The study community was represented by employees of Iraqi private banks, a sample of this community was selected, the sample size was (91) individuals, the questionnaire was adopted as a data collection tool, the method of modeling small squares (Partial Least Squares) was used in statistical analysis, using the (Smart-PLS) program, and after conducting the data analysis process and obtaining results, several conclusions were reached, including digital financial innovation has a positive moral impact on entrepreneurial financial performance. Based on the conclusions reached, a number of recommendations were formulated, most notably the need for banks to use the internet and mobile phone to provide banking services, as the use of these methods leads to customers receiving banking services in a timely manner and with minimal effort and cost.

Bi-Objective Mixed Integer Nonlinear Programming Model for Low Carbon Location-Inventory-Routing Problem with Time Windows and Customer Satisfaction

In order to support a low-carbon economy and manage market competition, location–inventory–routing logistics management must play a crucial role to minimize carbon emissions while maximizing customer satisfaction. This paper proposes a bi-objective mixed-integer nonlinear programming model with time window constraints that satisfies the normal distribution of stochastic customer demand. The proposed model aims to find Pareto optimal solutions for total cost minimization and customer satisfaction maximization. An improved non-dominated sorting genetic algorithm II (IMNSGA-II) with an elite strategy is developed to solve the model. The model considers cost factors, ensuring that out-of-stock inventory is not allowed. Factors such as a carbon trading mechanism and random variables to address customer needs are also included. An entropy weight method is used to derive the total cost, which is comprised of fixed costs, transportation costs, inventory costs, punishment costs, and the weight of carbon emissions costs. The IMNSGA-II produces the Pareto optimal solution set, and an entropy–TOPSIS method is used to generate an objective ranking of the solution set for decision-makers. Additionally, a sensitivity analysis is performed to evaluate the influence of carbon pricing on carbon emissions and customer satisfaction.

Implementation of the Gravity Location Models Method in Determining Regional Warehouse Locations to Minimize Goods Transport Costs at PT

Supply Chain is a network of companies that work together to create and deliver a product to the end user. PT X is a company engaged in manufacturing whose products are steel pipes, not only steel pipes are produced. Supply chain management at PT X is considered inefficient because it has to supply four warehouses or depots at once, this can cost a lot of shipping costs in the PT X Supply Chain process. Therefore, supply chain management needs to be carried out using the Gravity Location Model method which aims to minimize the costs of transporting goods at PT X. The aim of this research is to determine the variables needed to determine the location point of PT X regional warehouse using the Gravity Location Model method and to determine the regional warehouse location and optimal shipping costs using the Gravity Location Model method at PT X. From the results of research that has been carried out, the variables used to determine the location points for PT X regional warehouse construction include X and Y, Xn and Yn, dn, Cn, Dn, and TC as well as calculating cost minimization through a comparison of 20 iterations, it was found that the minimum cost was in the 11th iteration, namely IDR 386.955,50. with coordinates X = -4.13 and Y = 115,18.

Solar–Hydrogen-Storage Integrated Electric Vehicle Charging Stations with Demand-Side Management and Social Welfare Maximization

The reliable operation of a power system requires a real-time balance between supply and demand. However, it is difficult to achieve this balance solely by relying on supply-side regulation. Therefore, it is necessary to cooperate with effective demand-side management, which is a key strategy within smart grid systems, encouraging end-users to actively engage and optimize their electricity usage. This paper proposes a novel bi-level optimization model for integrating solar, hydrogen, and battery storage systems with charging stations (SHS-EVCSs) to maximize social welfare. The first level employs a non-cooperative game theory model for each individual EVCS to minimize capital and operational costs. The second level uses a cooperative game framework with an internal management system to optimize energy transactions among multiple EVCSs while considering EV owners’ economic interests. A Markov decision process models uncertainties in EV charging times, and Monte Carlo simulations predict charging demand. Real-time electricity pricing based on the dual theory enables demand-side management strategies like peak shaving and valley filling. Case studies demonstrate the model’s effectiveness in reducing peak loads, balancing energy utilization, and enhancing overall system efficiency and sustainability through optimized renewable integration, energy storage, EV charging coordination, social welfare maximization, and cost minimization. The proposed approach offers a promising pathway toward sustainable energy infrastructure by harmonizing renewable sources, storage technologies, EV charging demands, and societal benefits.

Distributed and Multi-Agent Reinforcement Learning Framework for Optimal Electric Vehicle Charging Scheduling

The increasing number of electric vehicles (EVs) necessitates the installation of more charging stations. The challenge of managing these grid-connected charging stations leads to a multi-objective optimal control problem where station profitability, user preferences, grid requirements and stability should be optimized. However, it is challenging to determine the optimal charging/discharging EV schedule, since the controller should exploit fluctuations in the electricity prices, available renewable resources and available stored energy of other vehicles and cope with the uncertainty of EV arrival/departure scheduling. In addition, the growing number of connected vehicles results in a complex state and action vectors, making it difficult for centralized and single-agent controllers to handle the problem. In this paper, we propose a novel Multi-Agent and distributed Reinforcement Learning (MARL) framework that tackles the challenges mentioned above, producing controllers that achieve high performance levels under diverse conditions. In the proposed distributed framework, each charging spot makes its own charging/discharging decisions toward a cumulative cost reduction without sharing any type of private information, such as the arrival/departure time of a vehicle and its state of charge, addressing the problem of cost minimization and user satisfaction. The framework significantly improves the scalability and sample efficiency of the underlying Deep Deterministic Policy Gradient (DDPG) algorithm. Extensive numerical studies and simulations demonstrate the efficacy of the proposed approach compared with Rule-Based Controllers (RBCs) and well-established, state-of-the-art centralized RL (Reinforcement Learning) algorithms, offering performance improvements of up to 25% and 20% in reducing the energy cost and increasing user satisfaction, respectively.

A sequential multiple attribute three-way group decision-making method to heterogeneous MAGDM problems with unknown weight information

As a decision model to depict the human cognitive process, three-way decision (3WD) provides a reasonable semantic interpretation for solving practical multi-attribute group decision-making (MAGDM) problems. At the same time, there often exist different types of information due to the subjective and objective criteria in practical decision-making problems, thus it is necessary to develop some efficient frameworks to deal with the MAGDM problems with heterogeneous information. In this paper, based on 3WD theory and sequential strategy, we propose a sequential multiple attribute three-way group decision-making (SMA3WGD) method for the heterogeneous MAGDM problem with unknown weight information to achieve this goal. At first, we compute the weights of decision-makers by using the consensus degree between each of the decision-makers in a group. Then, we calculate the collective evaluation result of multiple decision-makers by utilizing the mathematical planning method with two objectives of minimum difference and high similarity between group evaluation values and individual evaluation values. Meanwhile, a nested sequence of the set of attributes is constructed according to the attribute values offered by decision-makers that have different degrees of consensus on different attributes. On this basis, a multilevel granular structure for the heterogeneous MAGDM problems is constructed, and then the compute of the cost functions and conditional probability is explored in a single level of granular structure, and the thresholds and three-way decision rules of alternatives are further determined depending on the principle of cost minimization. Furthermore, by using a sequential strategy, we obtain a sequential three-way decision result of the heterogeneous MAGDM problems under the multilevel granular structure. At last, the practicality and advantages of the suggested method are demonstrated via a blockchain service provider selection case study, a parameter sensitivity analysis along with a comparison analysis.

Birding specialization and satisfaction in Australian birders – a Big Year is not a big issue

ABSTRACT Birding as a non-consumptive, nature-related outdoor activity is becoming increasingly popular. Here, using an online survey of Australian birders, we focussed on birding as a construct of recreation specialization and considered three aspects: perceived level of skill/knowledge, behaviour, and involvement. We analysed demographic effects (age, degree and payment for birding services) in relation to level of birding specialization, as well as leisure satisfaction for the first time in Australian birders. We further studied the “Big Year”. Birders can be classified into three groups: casual/novice, intermediate and advanced. Their satisfaction with birding as a leisure activity was generally high. More highly skilled and committed birders reported higher satisfaction, as did birders who participated more frequently. We also found that paid birders reported higher skill/knowledge compared to unpaid birders. Most respondents never participated in a Big Year, but if they did, it was most often in their local geographic area. From a theoretical viewpoint, leisure activity satisfaction should be studied on the general (trait) level as well as on the situational place level in situ. As a managerial implication, institutionalizing a local Big Year in a nearby birding hotspot (with minimal travelling time and costs) may be an enhancing and satisfying task.

Performance analysis of agricultural machinery with aid digital agriculture

Digital agriculture has increasingly gained strength in the countryside, as it is considered a byproduct of growing globalization. As the search for production optimization, cost reduction, and productivity increase continues, these tools become the great allies of the producer in the final agricultural production process.Therefore, agricultural operations must also follow this evolution in the development of technological improvements, which carry positive aspects for large-scale production. In this context, the objective of this work was to evaluate the performance of seeders-fertilizers with different input distribution mechanisms. Since one of the biggest expenses in the production process is the acquisition of seeds and fertilizer, any savings in production costs become a significant advantage for the producer, who will have a greater net income. The experimental design used to evaluate the data followed the premises of Statistical Process Control (SPC), and the results were assessed using individual value control charts. It was concluded that the production process was negatively affected by the performance of the seeders-fertilizers, as in one area, more seeds were falling than necessary, increasing the cost per hectare and not adhering to the principles of cost minimization and profit maximization. Conversely, in the second area evaluated, the machinery's performance resulted in fewer seeds being deposited than planned. Consequently, according to the results, the producer lost the opportunity to produce 5.85 sc/ha without reducing costs in the operation, as the seeder simply did not deposit the seeds, thereby reducing the gain per area, which could have been more productive, increasing final profitability.

Cost-Minimization Analysis for Subcutaneous Daratumumab in the Treatment of Newly Diagnosed Multiple Myeloma in Three Gulf Countries

Background: The second most common hematologic cancer worldwide is multiple myeloma (MM), with incidence and mortality rates that have more than doubled over the past 30 years. The safety and efficacy of daratumumab regimens in the treatment of newly diagnosed MM (NDMM) is demonstrated in clinical trials. Objective: To assess the financial effects of the adoption of subcutaneous daratumumab (dara-SC) rather than intravenous daratumumab (dara-IV) for the treatment of NDMM in three Gulf countries (Qatar, Oman and the United Arab Emirates; UAE), a cost-minimization model was constructed. Methods: We performed static cost minimization analyses from a societal perspective to evaluate the costs and possible reductions in resource utilization associated with a shift from dara-IV infusion to dara-SC injection for NDMM patients over a 5-year time horizon. The model included 2 scenarios: the current scenario in which 100% of patients with NDMM are treated with dara-IV infusion and a future scenario in which dara-SC injection is gradually adopted over the modeled time horizon. The model differentiated precisely between autologous stem cell transplantation (ASCT)–eligible and ASCT-ineligible NDMM patients in terms of their number in each group and the associated therapeutic regimens. One-way sensitivity analyses were also conducted. Results: The model showed that the use of dara-SC in NDMM patients who were eligible or ineligible for ASCT resulted in lower non-drug costs, including premedication drug costs, adverse-effect costs, administration costs, medical staff costs, and indirect costs. The resulting total savings over the 5-year time horizon of the model for Hamad Medical Corporation, Sultan Qaboos University Hospital/Royal Hospital, Sheikh Shakhbout Medical City (SSMC), and Tawam Hospital were QAR −2 522 686, OMR −143 214, AED −30 010 627, and AED −5 003 471, respectively. Conclusion: The introduction of dara-SC as a front-line treatment for NDMM patients in Qatar (Hamad Medical Corporation), Oman (Sultan Qaboos University Hospital, Royal Hospital-MOH), and the UAE (SSMC and Tawam Hospital) can help save resources and minimize constraints on the healthcare system.

THE ROLE OF SOCIAL MEDIA-ADVERTISING IN THE DEVELOPMENT OF GEORGIAN COMPANIES

Guliko Katamadze E-mail: qatamadze.guliko@bsu.edu.ge Associated Professor, Batumi Shota Rustaveli State University Batumi, Georgia https://orcid.org/0000-0002-5198-3890 Abstract . The use of social media advertising as a means of marketing communication is gaining strategic importance for companies operating in Georgia. Social media-advertising research is all the more relevant for those firms that are trying to deliver a marketing "message" to a wide audience with minimal costs and brand positioning in the market. The relevance of the problem is determined by the fact that the influence of social media advertising on the development of Georgian companies, their branding process and the effectiveness of the returns received from the investment of their marketing activities has not been properly studied. The purpose of the research is to develop the best mechanism for using social media advertising for companies operating in Georgia, to determine the most favorable form for use as a marketing communication tool based on user behavior. Methods of data collection and observation were used to develop the paper. We used the collection method to assess the possibility of using social media advertising by companies operating in Georgia, and the observation method - to determine the potential of their social media space, based on which the possibilities of increasing the effectiveness of companies through social media advertising were identified. As a result of the research, it is clear that Georgian companies use social media advertising to improve their brand, to attract a new target audience. Social media advertising has a strategic importance in increasing the effectiveness of companies operating in Georgia at all stages of their operation. Accordingly, we can refer to the complex combination of traditional and electronic businesses as a result of using social media advertising, which will help to increase the marketing efforts of companies operating in Georgia and, as a result, their competitiveness and operational efficiency. Keywords: Social media-advertising, marketing effectiveness, social media-space, business. JEL classification: M31, M37