Optimal Consumption Level Research Articles

The role of health in consumption and portfolio decision-making: Insights from state-dependent models

In this paper, we study an optimal consumption and asset allocation problem accounting for the fact that individuals’ utility differs across various health states. Our study is based on the assumption that an individual’s health status evolves through a semi-Markov process, where the transition probabilities are contingent on both the present state and the duration spent in that state. The optimal form of stock investment and of consumption is determined analytically. The optimal consumption level is significantly shaped by the integration of health-related factors and can be represented as the inverse of the marginal utility function with respect to time and state price density. Additionally, we introduce a Lagrangian multiplier that can be derived by solving a fixed point problem. While our optimal solutions are applicable to a wide range of utility functions, we provide numerical illustrations specifically using the power utility function in a model encompassing three distinct health states. Transition probabilities are estimated from real data collected by the China Insurance Regulatory Commission (CIRC), using a high-order polynomial Perks formula. We find that the investor’s consumption is higher when health is good, but lower when care is needed.

Mechanisms of Flavonoids and Their Derivatives in Endothelial Dysfunction Induced by Oxidative Stress in Diabetes.

Diabetic complications pose a significant threat to life and have a negative impact on quality of life in individuals with diabetes. Among the various factors contributing to the development of these complications, endothelial dysfunction plays a key role. The main mechanism underlying endothelial dysfunction in diabetes is oxidative stress, which adversely affects the production and availability of nitric oxide (NO). Flavonoids, a group of phenolic compounds found in vegetables, fruits, and fungi, exhibit strong antioxidant and anti-inflammatory properties. Several studies have provided evidence to suggest that flavonoids have a protective effect on diabetic complications. This review focuses on the imbalance between reactive oxygen species and the antioxidant system, as well as the changes in endothelial factors in diabetes. Furthermore, we summarize the protective mechanisms of flavonoids and their derivatives on endothelial dysfunction in diabetes by alleviating oxidative stress and modulating other signaling pathways. Although several studies underline the positive influence of flavonoids and their derivatives on endothelial dysfunction induced by oxidative stress in diabetes, numerous aspects still require clarification, such as optimal consumption levels, bioavailability, and side effects. Consequently, further investigations are necessary to enhance our understanding of the therapeutic potential of flavonoids and their derivatives in the treatment of diabetic complications.

Effects of Smallanthus sonchifolius Flour on Metabolic Parameters: A Systematic Review.

Smallanthus sonchifolius, popularly known as yacon, is a member of the Asteraceae family. Due to its medicinal and edible value, yacon is consumed by different populations. Yacon is unique due to its high fructo-oligosaccharide and inulin content, as well as flavonoids, sesquiterpene lactones, and phenolic acids. Roots can be used to produce flour, which is less perishable and can be applied in various industrial products. This systematic review focuses on the effects of yacon flour on metabolic parameters. PubMed, Cochrane, Embase, Science Direct, Scopus, Web of Science, and Google Scholar databases were consulted, and PRISMA guidelines were followed in the selection of the studies. In total, 526 articles were found in the databases, and of these, only 28 full texts were eligible for inclusion. After applying the inclusion and exclusion criteria, seven studies were finally included. The results showed that the use of yacon flour can reduce glycemia, HbA1c, advanced glycation ends, plasma lipids, body fat mass, body weight, and waist circumference and improve intestinal microbiota and the antioxidant status. Further exploration of the effects of yacon flour is warranted, and additional clinical trials are necessary to determine the optimal daily consumption levels required to assist in improving metabolic parameters.

Optimal Configuration of Wind–Solar–Thermal-Storage Power Energy Based on Dynamic Inertia Weight Chaotic Particle Swarm

The proposed approach involves a method of joint optimization configuration for wind–solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated from the combination of wind and solar energy is analyzed quantitatively by using the average complementarity index (ACI) to determine the optimal ratio of wind and solar installations. We constructed a multi-objective optimization configuration model for the WSTS power generation systems, considering the equivalent annual income and the optimal energy consumption level as objective functions of the system. We solved the model using the chaotic particle swarm optimization algorithm with linearly decreasing dynamic inertia weight. To validate the effectiveness of the proposed approach, we conducted a simulation using the 2030 power energy base planning data of a particular region in Inner Mongolia. The results demonstrate that the proposed method significantly improves the annual income, enhances the consumption of wind–solar energy, and boosts the power transmission capacity of the system.

Sodium and water dynamics in the progression of chronic kidney disease: mechanisms and clinical significance.

Lifestyle modifications can postpone the progression of chronic kidney disease toward its terminal stage.This mini-review aims to explore the impact of salt and water intake on the progression of chronic kidney disease (CKD) and provide insights into the optimal consumption levels to preserve the glomerular filtration rate. We reviewed relevant literature to examine the association between salt and water consumption and CKD progression. Our analysis includes discussions on the pathophysiology, findings from clinical trials, and recommended intake guidelines. Sodium intake, often linked to cardiovascular risk and CKD progression, has shown a complex J-shaped association in some studies, leading to uncertainty about the ideal salt intake level. Sodium and fluid retention are key factors contributing to hypertension, a well-established risk factor for CKD progression. Low-sodium diets have demonstrated promise in reducing blood pressure and enhancing the effects of renin-angiotensin-aldosterone system inhibitors in non-dialysis CKD patients. However, a debate persists regarding the independent effect of salt restriction on CKD progression. Despite medical recommendations, salt consumption remains high among CKD patients. Additionally, the role of water consumption in CKD remains controversial despite its established benefits for CKD prevention in the general population. Lifestyle modifications involving salt and water intake can influence the progression of CKD. While low-sodium diets have shown potential for mitigating hypertension and proteinuria in non-dialysis CKD patients, their independent impact on CKD progression warrants further investigation. The role of water consumption in CKD remains uncertain, and there is a need for additional research in this area. Clinicians should consider individualized dietary recommendations for CKD patients to help preserve the glomerular filtration rate and improve overall outcomes.

Energy scheduling in a smart energy hub system with hydrogen storage systems and electrical demand management

The energy price uncertainty in energy markets is the main challenge for achieving economic modeling of energy consumption and generation. This paper proposes optimal energy scheduling in a smart energy hub (SEH) under the uncertainty of electricity prices in the day-ahead. The proposed optimization approach is implemented by a two-layer interval approach for minimizing energy generation cost. In first layer optimization, demand side management (DSM) for electrical demand subject to the optimal level of electrical consumption is implemented. In second-layer optimization, the interval approach is used for modeling uncertainty. The energy generation cost is converted to bi-objective functions including average and deviation costs with conflicting nature than each other. Also, the multi-performance of the hydrogen storage system based on electrical and natural gas generation is considered for managing uncertainties in second layer optimization. Since the interval optimization approach is proposed in the second layer, minimizing the average cost and deviation cost are formulated as bi-objective functions by the augmented epsilon-constraint method. In the following, the LINMAP procedure is utilized to achieve maximum compatibility and the best trade-off consequence in the Pareto frontier of the multi-objective functions. Finally, the proposed optimization approach is modeled as a numerical simulation in the case studies to verify obtained results. The participation of the DSM and the hydrogen storage systems leads to minimizing the average and deviation costs by 3.03 % and 3.16 % in comparison with non-participation.

Study on accurate regulation of power consumption in self-supporting time of off-grid system in Haikou

In practical engineering, Off-grid PV systems have failed to meet user requirements due to the absence of precise local weather parameters and unverified battery charging times. In this study, off-grid solar street lights in a village in Haikou were taken as an example. A three-year monitoring period was conducted using a local PV station. It was observed that in this area, the lowest solar radiation is typically recorded from December to January. Over three years, there were four instances of continuous overcast and rainy weather, with the longest period lasting up to 9 days. During these 9 days, the daily average power generation per kilowatt of PV module fluctuated between 0.46 and 0.92 kW. Based on this data, a theoretical formula for the optimal daily power consumption was derived, and it was determined that 180W and 240W were the optimal power consumption levels for continuous overcast and rainy weather. The charging time for the batteries was verified to be 4 days. Finally, practical validation was conducted using the village's solar street lights, confirming that the optimal power consumption during continuous overcast and rainy days was 180W, and during continuous overcast days, it was 230W. These research findings hold significant importance. Highlights Through theory and demonstration, it is concluded that: Based on years of meteorological monitoring, it has been determined that the maximum self-sufficient duration for the off-grid power station system in the Haikou area is 9 days. Under the condition of satisfying SST, the formula of optimal daily power consumption of off-grid PV system load and optimal days of battery full charge is derived through theoretical analysis. The actual engineering demonstration shows that the optimal daily consumption power of the street light off-grid system is about 180W when the load is in continuous rainy days. Under continuous overcast conditions, the optimal daily consumption power of the load is about 240 W. The best time for a fully charged battery is four days.

Macroeconomic consequences of alternative reforms to the health insurance system in the United States

AbstractThis paper presents a dynamic general equilibrium model designed to examine the macroeconomic effects and welfare implications of alternative reforms to the US health insurance system. Specifically, it scrutinizes the extent to which health care reform can mitigate inefficiencies stemming from market imperfections in the health insurance industry. The model considers a stochastic overlapping generations framework, incorporating heterogeneous agents who are subject to uncertain health shocks. These individuals make optimal decisions regarding labor supply, health insurance, and medical services. Given that the optimal levels of medical consumption and hours worked are endogenous, this setting encapsulates general equilibrium effects. The model is calibrated to US data, and numerical simulations suggest that suitable adjustments to the present health insurance system can broaden coverage and enhance welfare. This improvement is achieved by reducing adverse selection, improving overall health status, and lessening tax distortions on labor supply.

Breastfeeding and Inborn Errors of Amino Acid and Protein Metabolism: A Spreadsheet to Calculate Optimal Intake of Human Milk and Disease-Specific Formulas.

Human milk (HM) offers important nutritional benefits. However, except for phenylketonuria (PKU), there are little data on optimal levels of consumption of HM and a special formula free of disease-related amino acids (SF-AA) in infants with inborn errors of metabolism of amino acids and proteins (IEM-AA-P). We designed a spreadsheet to calculate the amounts of SF-AA and HM required to cover amino acid, protein, and energy needs in patients with the nine main IEM-AA-P in infants aged under 6 months. Upon entering the infant's weight and the essential amino acid or intact protein requirements for the specific IEM, the spreadsheet calculates the corresponding required volume of HM based on the amino acid concentration in HM. Next, the theoretical daily fluid intake (typical range, 120-200 mL/kg/day) is entered, and the estimated daily fluid intake is calculated. The required daily volume of SF-AA is calculated as the difference between the total fluid intake value and the calculated volume of HM. The spreadsheet allows for the introduction of a range of requirements based on the patient's metabolic status, and includes the option to calculate the required volume of expressed HM, which may be necessary in certain conditions such as MMA/PA and UCD. In cases in which breastfeeding on demand is feasible, the spreadsheet determines the daily amount of SF-AA divided over 6-8 feeds, assuming that SF-AA is administered first, followed by HM as needed. Intake data calculated by the spreadsheet should be evaluated in conjunction with data from clinical and nutritional analyses, which provide a comprehensive understanding of the patient's nutritional status and help guide individualized dietary management for the specific IEM.

On the COP26 and coal's phase-out agenda: Striking a balance among the environmental, economic, and health impacts of coal consumption

Economic and environmental policy actions are often substitutionary in their impacts, as one man's food could be another's poison. One of the critical emphases at the recent Conference of Parties 26 (COP26) is the need for coal to be phased out in the energy consumption basket of nations to achieve environmental sustainability, but this could be at the expense of the positive performance of other socio-economic fundamentals. The best bet could then be to maintain an optimal consumption level to strike a balance. Relying on this, we examine the environmental, economic, and health impacts of coal consumption in the world's highest coal-consuming countries, putting the latter's threshold level into consideration. In summary, we find that there is a trade-off between pushing for a sustainable environment through a reduction in coal consumption and achieving better growth and health status. This implies that phasing out of coal totally will have severe economic and health costs. However, based on our threshold regression model results, it is most reasonable to maintain a lower level of coal use in the overall energy mix of these countries. This will definitely yield a relatively low level of carbon, but will still assure a certain level of economic growth and health performance. As such, reducing the intensity of coal gradually and simultaneously providing a substitute that can also serve economic and health needs are encouraged.

Association between dairy consumption and the risk of diabetes: A prospective cohort study from the China Health and Nutrition Survey.

Diet is closely related to the risk of diabetes; yet the relationship between dairy consumption and the risk of diabetes is unclear with conflicting evidence from previous studies. This study used data from the Chinese Health and Nutrition Survey to investigate the association between dairy consumption and diabetes. A total of 15,512 adults were included; dairy consumption at each survey was assessed by the 3-day 24-h recall and weighed food record methods, and diabetes occurrence was derived from self-reported information. The association between dairy consumption and diabetes was explored using Cox regression and further stratified with BMI and energy intake. Results indicated that 12,368 (79.7%) participants had no dairy consumption, while 2,179 (14.0%) and 947 (6.1%) consumed dairy at 0.1–100 and >100 g/day, respectively. After adjusting for potential confounders, dairy consumption of 0.1–100 g/day was associated with lower risk of diabetes in all participants (HR 0.53, 95% CI:0.38 −0.74; P < 0.001) and males (HR 0.50, 95% CI: 0.31–0.80; P = 0.004). According to the restricted cubic splines (RCS), the protective effect on diabetes was significant in the total population with dairy consumption ranging from 25 to 65 g/day (HR <1, P = 0.025). In the stratified analysis, consuming 30–80 g/day was associated with reduced diabetes risk among the ≤ 2,000 kcal/day energy intake group (HR <1, P = 0.023). In conclusion, dairy consumption was inversely associated with a reduced diabetes risk in Chinese population. Further studies are required to examine the optimal level of dairy consumption for preventing diabetes in the Chinese population.

Nudges and peak pricing: A common pool resource energy conservation experiment

Using a contextualised common pool resource framework, individual energy consumption choices are studied. Individuals are nudged towards the socially optimal level of consumption by the use of a happy (sad) face if they are underconsuming (overconsuming). A price is set to incentivise a second group to choose the level of consumption observed in the nudge treatment in order to quantify the nudge via an equivalent price. Across all 10 periods, consumption is significantly lower in treatment groups compared to control groups without nudges and prices. The price treatment leads to an average level of consumption above the Nash equilibrium. There are implications for policy makers as the nudge treatment performs as well, on average, as an equivalent price without the implied loss of welfare, and is understood and integrated into subjects’ decision making quicker than an equivalent price. However, there is a tendency for both the nudge and the price to reinforce existing consumption behaviour as those who overconsume continue to overconsume.

The optimal behavior of renewable energy resources and government's energy consumption subsidy design from the perspective of green technology implementation

Carbon emissions are a major problem in the industrial sector due to energy consumption. As a solution, this research first investigates the impact of energy consumption and shortfall on green technology implementation from 1000 textile firms in Punjab, Pakistan and for energy shortfall incorporates subsidy variables to make green technology implementation possible via a binary logistic regression model. Second, we develop a simulation-based optimization model to measure the optimal behavior of energy consumption parameters to clean the environment under green technology implementation. The results depict that renewable energy resources (coal, corn cobs, and wood) positively affect green technology implementation. Interestingly, energy shortfall (electricity and gas) is also impacted positively due to the move of industries towards renewable resources during this shortage period. We find a higher quantity of wood in optimal behavior analysis instead of coal and cobs due to its ease of availability. An optimal subsidy on electricity shortfall is found to be greater than the natural gas shortfall subsidy due to its higher shortfall. This study helps the government to decide the optimal energy shortfall for saving energy usage and to manage the optimal level of subsidies in order to overcome energy usage problems. It also benefits the textile industry in deciding over the optimal energy consumption level to make the environment clean in terms of green technology implementation.

ВИКОРИСТАННЯ ДИНАМІЧНОЇ ТАРИФІКАЦІЇ ДЛЯ ОПТИМІЗАЦІЇ ТЕХНІКО-ЕКОНОМІЧНИХ ПОКАЗНИКІВ MICROGRID НА ЛОКАЛЬНИХ РИНКАХ ЕЛЕКТРОЕНЕРГІЇ

Features of development of electric power industry of Ukraine from the point of view of the formation of the perspective structure of generating capacities of UES of Ukraine and development of renewable energy sources (RES) are characterized. In the analysis of the problem of introduction of RES in the energy system of our country and the use of advanced tariff systems, three levels are identified: the highest level - the UES of Ukraine; middle level – regional energy companies; lower-level - local energy supply systems. It is shown that the study of choosing a strategy for better implementation of dynamic electricity prices, as well as trends in business models for local electricity markets with a combination of physical, communication, information, and business levels are relevant. The peculiarities of Microgrid development as a basic element of the functioning of local electricity markets under the condition of formation of optimal modes of generation and consumption of electricity using dynamic charging, which can provide a significant part of imbalance compensation due to the widespread introduction of RES. The article considers the formation of dynamic prices at the level of local power systems (Microgrid), which have features of both the operation and implementation of business models. The elements of the business model for the local electricity market, which is proposed for the sole owner of all generators and consumers, are analyzed when Microgrid is considered as an independent element of the local market, where a dynamic balance between supply and demand. It is determined that at the level of local energy supply systems charging must be based on dynamic models that will take into account the dynamic nature of generation and regulation of electricity consumption in the system and stimulate appropriate desired actions by the consumer. It is proposed to use the tariff calculation not for the time interval, but for the state, as well as to control the optimal levels of generation and consumption of electricity to use the reference tariff and the formation of reference profiles of generation and consumption of electricity. Modified Frieze reactive power was used to estimate the discrepancy between current and optimal power regimes. References 37, table 1.

Analysis of Food Factory Energy Consumption Pattern for Application of Factory Energy Management System(FEMS)

Recently, the Korean government is actively promoting support and investment for climate technology innovation with the goal of becoming carbon neutral by 2050. For this reason, there is a growing interest and necessity for energy saving and efficiency improvement. As an alternative, various solutions have been proposed that introduce a Factory Energy Management System(FEMS) to achieve energy savings through partial system improvement. In the food industry, the scale of energy consumption is gradually increasing due to the rapid improvement of dietary standards. Therefore, energy saving in the food factory is important, but there is no application of the FEMS. In the case of some small factories, it is difficult to ascertain even the amount of energy consumed for actual production. In this study, the energy consumption pattern was analyzed to estimate the amount of energy saved when the FEMS is applied to a food factory. The optimal level of energy consumption for the food processing was derived by predicting the energy demand and supply required in food factories and analyzing the basic unit per product output. In addition, the energy consumption pattern was predicted through data mining to calculate the amount of energy savings, and the accuracy of the prediction model was evaluated.

Assessment of PV Integration in the Industrial and Residential Sector under Energy Market Conditions

Abstract The paper assesses the integration of solar photovoltaic technology in the industrial and residential sectors under energy market conditions. The aim is to determine and compare the payback period for the use of solar photovoltaic technology between the industrial and residential sectors, taking into account the application of optimal load scheduling and level of direct consumption. The industrial sector is represented by a glassware company. Installing PV technologies for a larger area and power is cheaper than installing low-power solar photovoltaic technologies. In addition, direct consumption has significant benefit due to high network service tariffs.

ВИБІР ОПТИМАЛЬНОЇ СТРУКТУРИ ПРИХОВАНИХ ШАРІВ ШТУЧНОЇ НЕЙРОННОЇ МЕРЕЖІ ДЛЯ АНАЛІЗУ ЕФЕКТИВНОСТІ ЕНЕРГОСПОЖИВАННЯ

A method for optimal structure selection of hidden layers of the artificial neural network (ANN) is proposed. Its main idea is the practical application of several internal structures of ANN and further calculation of the error of each hidden layer structure using identical data sets for ANN training. The method is based on the alternate comparison of the expected result values and the actual results of the feedforward artificial neural networks with a different number of inner layers and a different number of neurons on each layer. The method afforces searching the optimal internal structure of ANN for usage in the development of "smart" house systems and for calculation of the optimal energy consumption level in accordance with current conditions, such as room temperature, presence of people, and time of the day. The usage of the presented method allows to reduce the time spent on choosing the effective structure of the artificial neural network at the initial stages of research and to pay more attention to the relationship between the input and output data, as well as to such important parameters of the ANN learning process, as a number of training iterations, minimal training error, etc. The software has been developed that allows to carry out the processes of training, testing, and obtaining the output results of the algorithm of the artificial neural network, such as the expected value of power consumption and operating time of each individual appliance. The disadvantage of the approach used in finding the optimal internal structure of the artificial neural network is that each subsequent structure is created on the basis of the most efficient of the previously created structures without analyzing other structures that showed worse results with fewer hidden layers. It was found that to improve the solution of this problem it is necessary to create a mechanism which will be based on the analysis of input data, output data, will analyze the internal relationships between parameters and will optimize the network structure at each stage using certain logical rules according to the results obtained in the previous step. It is established that this problem is a nonlinear programming problem that can be solved in the further development of this study.

Determining an Optimal Water Mix for Hong Kong

In this paper, we adapt the decision-making model proposed by Leroux and Martin (2016) to include an additional water resources type to make it suitable for Hong Kong’s current and proposed water supply mix. More precisely, we broaden the model to account for purchased water (from China) which is currently the primary source of water for Hong Kong. In addition, purchased water price uncertainty has been integrated, which differs from the uncertainty on water inflows and quality. Harvested stormwater and manufactured water, although not currently used in Hong Kong, may be sources for the future and are therefore considered in a 4-pronged scenario. The objective is to derive the optimal shares for each water supply. We obtain the optimal water mix and consumption levels for several scenarios, the current scenario, the proposed 4-pronged scenario, a drop in water quality, and self-sufficiency. The 4-pronged scenario advocates for a large-scale development of desalination plants in Hong Kong; however, it is also significantly more expensive and therefore results in a lower optimal water consumption which has implications regarding the current water tariff approach.

Regional Collaborative Electricity Consumption Management: an Urban Operations Research Model

Electricity generation can be a major source of pollution. In a compact region where pollutants can easily transfer from one city to another, a unilateral response—on the part of one city to improve its environmental conditions—is often ineffective. This paper develops an urban operations research model for collaborative management of a reduction in electricity consumption. This model internalizes the external costs of electricity consumption in a region; derives the optimal level of electricity consumption; and sets up a scheme to compensate for the externalities of electricity consumption. This analysis is the first urban operations research model for collaborative electricity consumption management, which internalizes the external costs of electricity consumption. This study is also the first attempt to derive the optimal level of electricity consumption within regional collaboration. This is the first time that a scheme to compensate for the externalities is proposed to ensure that agreed-upon optimality principle could be maintained throughout the entire duration of cooperation.

Competitiveness of Solar and Wind Power Plants in the Countries of the Commonwealth of Independent States

Techno-economic indicators of renewable (solar and wind) and non-renewable energy sources are systematized, taking into account the interval of their uncertainty for the conditions of the countries of the Commonwealth of Independent States (CIS). The main attention was paid to Russia and the countries of Central Asia (Kazakhstan, Kyrgyzstan, Tajikistan, Turkmenistan and Uzbekistan), where there are the most favorable conditions for the development of solar and wind energy. A comparison of renewable and non-renewable energy sources by the criterion of the cost of generated electricity has been made. It is shown that the payment for greenhouse gas emissions increases the competitiveness of solar and wind power plants in the energy markets. Under favorable conditions for renewable energy, solar and wind installations in some areas can produce cheap electricity at a cost of 3–5 cents/(kW×h). With such values, they can be competitive without additional measures to stimulate their implementation. Using the mathematical model REM-2 (Renewable Energy Model), energy sources of different types were compared taking into account system effects. The power supply system consists of photovoltaic converters, wind turbines that enable the possibility of short-term accumulation of electricity and power consumption from a backup power source. Modeling of photoelectric converters and wind turbines operating modes was performed by time (hours) for different values of solar radiation arrival and wind speed. The optimal ratios between electricity production by photovoltaic converters and wind turbines, as well as the optimal level of electricity consumption from the network under different climatic and economic conditions, were determined. The economic efficiency of joint use of solar and wind energy in the CIS countries, primarily in Russia (with the exception of the northern regions) and the countries of Central Asia, is shown.