Demand Rate Research Articles

The Problem of the Shortage of Fresh Water in Egypt and the Possibilities of Its Solution

The population growth rates that increased after the 1952 revolution accelerated the decline in the supply of fresh water to Egyptians due to the exhaustion of its traditional sources. As a result, at the turn of the 1980s – 1990s, Egypt has overcome the water deficit limit of 1000 m3 per person per year. In order to reduce water stress, the Government of the country in the second half of the twentieth century pursued a policy of birth control and reduction of cultivation of moisture-loving crops. Since the beginning of the 21st century, with the slow introduction of modern irrigation methods, it has focused on increasing the reuse of treated drainage waters in agriculture and the construction of seawater desalination plants to meet the needs of industry and public utilities. The Egyptian Government sees prospects for solving the problem of water scarcity, the growth rate of demand for which in the 21st century will decrease as population growth and economic development slow down, in the achievements of scientific and technological progress and the widespread use of cheap water treatment technologies.

Penerapan Machine Learning Algoritma Regresi Linear Untuk Memprediksi Saham Bank BNI

Indonesia has been growing rapidly, one of which can be seen from the economy and technology in Indonesia, at this time the community is almost entirely using machine power technology as a helper of daily life, and the community has also processed a lot of its finances by way of stock investment, with stock investment, the community believes that stocks are invested safer and more profitable. Shares are securities that show proof of ownership or capital market participation of investors in a company (BNI) and shares have a value that is up and down (volatile). Stocks are very important in a company and stocks are a trigger for rising profits in the company. The rise and fall of stock prices in Indonesia has an adverse effect on companies, especially PT Bank Negara Indonesia (Persero), Tbk. the cause of the rise and fall in stock prices is usually caused by several things, namely the condition and performance of the company, risk, dividends, interest rates, economic conditions, government policies, government issues or other issues, the rate of inflation, supply and demand. Machine learning tools used in predicting stocks, using machine learning, the data obtained is more accurate. Machine learning is an artificial intelligence that can process data that is useful for consideration in making decisions and solving problems. Linear regression algorithm is one of the methods used to predict stock data in Bank Negara Indonesia. Linear regression algorithm tries to model the relationship between two variables by matching the linear equation of the stock data to be studied. One variable is considered the explanatory variable and the other variable is called the dependent variable. Prediction a process for systematically estimating BNI stock data that will appear in the future using data obtained from the past. Thus the company can easily find out the stock data in the future.

Optimum pricing decision for the retailer with warranty and price-dependent variable demand

Buying inventory on a credit basis is an effective pricing plan, and trade credit is a popular component of market transactions that increase demand. In a dynamic situation, most businesses offer different rewards and services to their consumers under certain terms and conditions during commodity sales. The accompanying companies provide a warranty period facility to increase consumer demand for products. The holding cost often increases over time. This study determines a production model wherein the production rate is proportionate to the price-warranty period based on the demand rate with time-dependent holding cost and trade credit policy. This work leads to three vital conventions: (I) the rate of product demand is considered to be price-warranty period dependent; (II) the non-linear function is considered for the rate of replacement failure, wherein the capital of the manufacturer depends on the warranty period; and (III) the rate of inflation is constant; moreover, the present time value of money also measured. This study aims to find the selling price, cycle time, and warranty period by using an algorithm to optimize the total profit function of the manufacturer. The results are validated by solving three numerical examples with their graphical representation based on different situations and the major parameters, and sensitivity analysis is analyzed with important decision-making implications.

Sustainable inventory model with controlled temperature for deteriorating products under preservation technology

The disaster or epidemic has created a terrible situation in front of the whole world. This study saves the Corona vaccine's quality and keeps its demand in mind. This study develops an inventory model for deteriorating products with preservation technology investment under ramp-type demand. Reducing the deterioration rate of items reduces the number of waste items, which is beneficial for the environment. The demand increases at the ramp-type demand rate at the start, and then the market tends to a constant rate until the end of the inventory cycle. Retailers are attempting to reduce emissions, which impacts inventory levels. As a result, the impact of carbon emissions from holding and deterioration costs has been investigated in this study. This study allows for partially backlogging and shortages. The numerical part shows the minimized total cost of this study. The numerical findings indicate that the value of total cost increases due to carbon emissions. The study is analyzed for two cases: Case I shows a difference of 0.45% in total cost due to carbon emission, while Case II shows a difference of 0.20% in total cost due to carbon emission. Carbon emissions and preservation technology have a significant impact on environmental preservation. Finally, numerical examples and graph representation for convexity of total cost and sensitivity analysis are provided to illustrate.

THE EFFECT OF INFLATION ON SBI BANK

The relationship between inflation and the performance of major Indian banks, particularly the State Bank of India (SBI), reveals significant insights into macroeconomic stability and banking sector resilience. High inflation affects SBI's cost structures, loan demand, and non-performing asset (NPA) rates, with adverse impacts on profitability and asset quality. Elevated inflation rates often drive the Reserve Bank of India (RBI) to raise interest rates, indirectly impacting SBI’sloan growth and deposit mobilization.Higher interest rates increase loan costs for customers, suppressing credit demand and increasing default risks. SBI also faces higher operational costs and reduced profit margins under inflationary pressures. However, the bank’s large deposit base and robust capital position offer some resilience against inflationary shocks. Additionally, SBI's adoption of advanced risk management strategies and digitalization helps mitigate inflationary impacts, though prolonged inflationary phases can still strain its financial stability. Addressing these inflation-induced challenges requires active regulatory oversight, efficient risk management, and adaptive fiscal policies to ensure SBI’s sustained growth and stability. Keywords: Inflation, State Bank of India, non-performing assets, Reserve Bank of India, banking sector resilience,macroeconomic stability

Optimal ordering for Product-as-a-Service models with circular economy practices

The Product-as-a-Service (PaaS) model offers the opportunity to implement circular actions such as repairing, reusing, collecting end-of-life products, and recycling. However, adopting circular practices causes more complexities in managing the inventory flow due to repetitive product subscriptions. Accordingly, this paper aims to optimise a PaaS model's order quantity and profit, considering circular economy practices and various quality levels for subscription products. In the proposed model, the subscription firm defines a quality check and repair procedure at the end of each quality period before sending the product to another subscriber. Moreover, the firm recycles end-of-life products and sells the recycled material to the supplier. This study aims to compare a closed-loop PaaS model with the traditional economic order quantity model in terms of operational costs, revenue, and inventory flow. The results show that factors such as the difference between the demand rates of the consecutive periods, the relationship between the recycling capacity and the final collection rate, and the difference between the screening and demand rates have essential roles in alleviating the extra inventory costs related to circular economy actions like reusing, repairing, and recycling.

Clean water consumption of the coal mining operations in East Kalimantan province

Abstract Water consumption of mining workers is a crucial factor in the environmental impact assessment but is often overlooked by the mining company. In addition, water consumption for mining activities is not reflected in some domestic water consumption standards such as SNI and PUPR standards. Therefore, calculating the rate of clean water demand is important to estimate water supply, organize and manage water distribution, and determine the waste water treatment plant (WWTP) capacity. The purpose of this study was to identify clean water consumption of mine workers at the contractor Lati Site of PT. Berau Coal, East Kalimantan Province per person per day. We conducted a direct measurement of water usage using the installed flow meters, and employed assumptions such as piping leakage and roster day-off to calculate clean water consumptions. Compared to the existing standards, clean water consumption at Lati Site was much greater, up to 229 litres per head per day whereas the water usage in the mining camp was 156 litres per head per day.

COMPARATIVE ASSESSMENT OF CRISES AND RECOVERY IN THE ROMANIAN TOURISM MARKET

The Romanian tourism industry has yet to prove that it is a resilient economic sector that can adapt to global crises. This study provides a contextual analysis of the sector's performance during two international crises – the global financial crisis (GFC) and the COVID-19 pandemic (C19). The main question is: How do tourism demand and accommodation supply in the Romanian tourism sector respond to global shocks, and what patterns of resilience or vulnerability do these crises reveal? Previous studies have focussed on tourist satisfaction and the differential impact of tourism activities after EU integration, but have rarely examined the resilience of the sector in the midst of two different global crises. The need for this research stems from its comparative nature — assessing the pre- and post-crisis periods of the GFC and C19 — thus contributing to a comprehensive understanding of the sector's dynamics. This work fills a gap in the existing literature and applies a longitudinal approach to assess Romanian tourism resilience, market confidence and response to economic and health-related shocks. The study applies a quantitative methodological framework and uses time series data from 2002-2022, with descriptive statistics, comparisons and trend analyses serving as the main pillars for interpreting the evolution of tourism demand, accommodation supply and occupancy rates. The analysis shows that the Romanian tourism sector has proven to be resilient, as operational capacity exceeded pre-crisis levels after the COVID-19 crisis, although demand fell sharply and has not fully recovered to date. Occupancy rates fell significantly during both crises and have only partially recovered.

A Data-Driven Optimization Framework for Static Rebalancing Operations in Bike Sharing Systems

Bike sharing systems have been widely deployed in urban cities for first- and last-mile transportation. However, because of the geographical and temporal imbalance of bike demand, bikes need to be reallocated system-wide among stations during the night to maintain a high service level while minimizing demand loss due to stockout or overcapacity. Two technical challenges remain in optimizing the static bike rebalancing operations. One challenge is to accurately predict bike pickup and dropoff demand at each station, considering demand substitution effects and subsequently determining the optimal rebalancing quantity for each station. The other is to efficiently optimize the routing of multiple rebalancing vehicles for large-scale bike sharing systems, considering outlier stations with rebalancing quantities exceeding vehicle capacity. To this end, we propose an end-to-end solution to tackle the aforesaid challenges. Specifically, we first develop deep learning-based predictors that capture the time dependencies of station-level demand, the impact of weather conditions, and the demand substitution effect by nearby stations. Based on the demand rate, a sequential simulation-based demand loss estimator is developed to find the optimal rebalancing quantities that lead to the minimum expected demand loss. Then, a mixed integer linear programming model is formulated to optimize the routing problem of rebalancing vehicles. To address the computational challenge, we propose a data-driven decomposition algorithm to support a multivehicle multivisit rebalancing strategy by decomposing the multivehicle routing problem into smaller and tractable single-vehicle routing problems, which can be solved in parallel. Finally, extensive numerical experiments using real-world data from New York City Citi Bike demonstrate the accuracy of the proposed bike demand predictors, the impact of demand substitution, and the efficiency of the data-driven optimization framework. History: Accepted by Ram Ramesh, Area Editor for Data Science & Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Grant 72201222] and the Hong Kong Research Grants Council [Grants CityU 21500220 and CityU 11504322]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information ( https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0182 ) as well as from the IJOC GitHub software repository ( https://github.com/INFORMSJoC/2022.0182 ). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/ .

Competition and coordination between traditional and internet hospitals

AbstractMotivated by the recent emergence and rapid growth of online healthcare platforms, this paper investigates its impacts on the healthcare ecosystem by taking into consideration of heterogeneous patients, homogeneous service providers and profit-driven hospitals. A game-theoretical model with a queueing framework is developed, and two healthcare systems are considered, including a monopolistic system with either an offline, traditional hospital, or an online, Internet hospital, and a duopolistic system with both kinds of hospitals. Our study finds that in both systems a large population of patients always increases the healthcare supply and demand and the hospital utilization, which leads to an improvement not only in the hospital profitability, but also in the welfare of both patients and service providers. When considering a monopolistic system only, our study suggests that the monopolistic Internet hospital outperforms its counterpart in hospital utilization only when the wait cost is not high (i.e., patients are less sensitive to wait time), especially when the wait cost is medium and the patient-provider ratio is small. When considering a duopolistic system, this study concludes that the decisions of patients, service providers, and hospital managers of both hospitals are interrelated, and that a large population of service providers increases its hospital’s supply, demand, and wage rate, while decreases its wait times. When comparing the two monopolistic with the duopolistic system, the latter outperforms and improves every entity’s utility (i.e., patients, hospital, and society) when there is abundance of service providers with more being allocated to the Internet hospital, whereas the monopolistic system dominates when the patient population is large, and more providers are allocated to the traditional hospital.

Multiobjective Hybrid Al-Biruni Earth Namib Beetle Optimization and Deep Learning based Task Scheduling in Cloud Computing

With the rapid development of computing networks, cloud computing (CC) enables the deployment of large-scale applications and meets the increased rate of computational demands. Moreover, task scheduling is an essential process in CC. The tasks must be effectually scheduled across the Virtual Machines (VMs) to increase resource usage and diminish the makespan. In this paper, the multi-objective optimization called Al-Biruni Earth Namib Beetle Optimization (BENBO) with the Bidirectional-Long Short-Term Memory (Bi-LSTM) named as BENBO+ Bi-LSTM is developed for Task scheduling. The user task is subjected to the multi-objective BENBO, in which parameters like makespan, computational cost, reliability, and predicted energy are used to schedule the task. Simultaneously, the user task is fed to Bi-LSTM-based task scheduling, in which the VM parameters like average computation cost, Earliest Starting Time (EST), task priority, and Earliest Finishing Time (EFT) as well as the task parameters like bandwidth and memory capacity are utilized to schedule the task. Moreover, the task scheduling outcomes from the multi-objective BENBO and Bi-LSTM are fused for obtaining the final scheduling with less makespan and resource usage. Moreover, the predicted energy, resource utilization and makespan are considered to validate the BENBO+ Bi-LSTM-based task scheduling, which offered the optimal values of 0.669J, 0.535 and 0.381.

Does Government Environmental Concern Affect Enterprise Sustainable Development? Evidence from China

As the executor and agent of China’s environmental policy, local governments’ environmental concern reflects local governments’ determination in environmental governance. To figure out how the strengthening environmental concerns affect enterprises’ long-term activities, this study focuses on pharmaceutical manufacturing enterprises due to the enormous and complex composition of emissions. We apply bag of words to summarize relevant environmental words from the annual work reports in local governments to measure environmental concern. The empirical results of the OLS method reveal that the increasing environmental concerns of local governments did decrease the growth rate of chemical oxygen demand (COD) emission authentically. At the same time, it will inhibit the research and experimental development (R&D) activity intensity, but promote production efficiency of pharmaceutical manufacturing enterprises. After that, we discuss the heterogeneity of enterprise ownership, corporate social responsibility and regional regulatory strength of enterprises. Overall, we conclude that environmental concern did reduce COD emission and promote production efficiency, but it also has negative spillover effects. The novel contribution of this paper is that it enriches the trade-off between strengthening environmental compliance costs and long-term production and innovation activities. These results indicate that pharmaceutical manufacturing enterprises prioritize optimizing existing production processes instead of adopting efficient technology when complying with stricter environmental regulation. The reduction of R&D activities may pose risks to the long-term sustainable development of enterprises.

Application of organic silicon quaternary ammonium salt (QSA) to reduce carbon footprint of sewers: Long-term inhibition on sulfidogenesis and methanogenesis

Sulfidogenic and methanogenic processes are undesirable in sewer management, yet the derived problems regarding organic losses are often neglected. Traditional chemical dosing methods aimed at sulfide and methane control commonly involve similar mechanisms of oxidation and/or precipitation. Moreover, previous focuses were centered on elevating control efficacy rather than investigating interactions between dosed chemicals and biofilms. In this work, organic silicon quaternary ammonium salt (QSA) of 75 mg-N/L was firstly applied in laboratory pressurized sewer reactors. After three dosing events, it took 20 days for sulfidogenic activities to recover to 50 % without further elevations. Meantime, methanogenic activities were stable ca. 11 % without significant inclinations to recover. Notably, consumption rate of chemical oxygen demand (COD) was suppressed to 50 % at most, and no microbial resistance to QSA but better control efficacy was observed. Characterizations of physicochemistry, microbial community and metabolism were conducted to elucidate mechanisms. Results showed that QSA was attached on sewer biofilms via electrostatic attraction to exert enduring control efficacy. Biofilms tended to become more hydrophobic and compact after QSA exposure. Microbial analyses indicated that relative abundances of microbes regarding hydrolysis, acidogenesis and methanogenesis were sharply decreased together with down-regulation of pivotal enzymatic activities. Additionally, denitrification batch tests initially suggested that the biodegradability of effluent was significantly enhanced, which ensured the safety of QSA dosing into sewers. Overall, results of this work were expected to lay a theoretical foundation on employing QSA to wastewater management.

ECONOMIC ORDER QUANTITY (EOQ) MODEL: WEIBULL AMELIORATING ITEMS WITH CONSTANT DEMAND RATE

In this study, we investigate the behavior of inventory items having the potential of incurring significant increase in their utilities or values while in stock in accordance to the Weibull distribution law. The amelioration of an inventory occurs when it remains in stock but depletes as a result of constant demand rate. The Weibull amelioration function plays the role of depicting the amelioration state of an inventory whereas the constant demand function describes the different possible stages of market demand. We describe the application of simple, yet analytical, approach for successful derivation and analyses of the underlying inventory system. We furthermore, analyze the model while providing, for the model, the necessary but sufficient and optimal replenishment policy. We use numerical results and examples to better illustrate the solution and successfully carryout the sensitivity analysis and the evaluation of the model.

Experimental Study on the Removal of Pollutants from Domestic Wastewater in a Strongly Constructed Wetland with an Applied Electric Magnetic Field

The addition of physical field enhancement measures to improve the purification effect of vertical flow artificial wetlands has gradually become popular. In this study, a vertical flow artificial wetland system reinforced by electric and magnetic fields was constructed. These fields were first optimized using finite element 3D simulation software to obtain the optimal electric and magnetic field parameters. Then, the pollutant removal effects and changes in microbial community structure were comparatively analyzed. The optimal electromagnetic field parameters (applied voltage of 15 V and applied magnetic field of 20 mT) resulted in significantly enhanced removal rates of chemical oxygen demand (COD), nitrate nitrogen (NH4+-N), total phosphorus (TP), and orthophosphorus (PO43−-P) in wastewater, with rates of 74.47%, 45.44%, 89.85%, and 90.04%, respectively. These rates were notably higher than those observed in the vertical flow artificial wetland system. The microbial community structure analysis revealed that the vertical flow constructed wetland with enhanced electric and magnetic fields exhibited (EM-VFCW) a more diverse and complex microbial community structure. Notably, the abundance of bacteria capable of removing NH4+-N and COD, including Aspergillus, Fusarium, and Actinobacteria, was significantly elevated.

Socio-economic factors affecting spatial inequalities in pregnancy-related ambulance attendances in Greater London

Exploring inequalities in ambulance and pre-hospital demand is important to improve service equity and reduce wider health inequalities. Maternity incidents amongst ambulance demand are a key area of focus because of the specialized care that is needed for patients, as well as the impact of wider determinants of health on pregnancy outcomes. Since there are spatial inequalities amongst pregnant patients who call for an ambulance, the aim of this study is to assess the underlying factors associated with pregnancy related ambulance complaints, to determine why maternity patients utilize the ambulance service. Local indicators of spatial autocorrelation were used to identify clusters of ambulance maternity demand within Greater London (UK). A negative binomial regression model was used to explore associations between socioeconomic, environmental, accessibility and demographic variables. Our results reveal that neighborhoods with low adult skills (i.e. qualifications/English language abilities) have a higher rate of demand. Moreover, our results imply that the demand for ambulance services may not be directly tied to health outcomes; rather, it might be more closely associated with patients' reasons for calling an ambulance, irrespective of the actual necessity. The benefits of identifying factors that drive demand in ambulance services are not just linked to improving equity, but also to reducing demand, ultimately relieving pressure on services if alternative options are identified or underlying causes addressed. Doing so can improve health inequalities by firstly, improving ambulance care equity by directly supporting a better allocation of resources within ambulance systems to target patterns in demand.

Experimental study on the treatment of high COD metal degreasing cleaning wastewater by coagulation and two-stage Fenton oxidation

ABSTRACT In this study, based on the water quality characteristics of metal degreasing cleaning wastewater , wastewater treatment by coagulation combined with two-stage Fenton oxidation was proposed. The results showed that when the dosage of PAC was 5 g/L and the dosage of CPAM was 8 mg/L, with an initial pH of 8, rapid stirring at 350 rpm for 3 min followed by slow stirring at 200 rpm for 15 min, the optimal removal rate of chemical oxygen demand (COD) reached 74.08%. Primary Fenton oxidation is mainly aimed at decomposing macromolecular long-chain pollutants and removing some pollutants. The highest COD removal rate was achieved at 95.07% when the dosage of H2O2 was 4 mol/L, the dosage of FeSO4 was 40 g/L, the oxidation lasted for 60 min, the initial pH was 2, the effluent pH was 8, and the settling time was 90 min. The two-stage Fenton oxidation process provided further advanced treatment. With a reagent ratio of 1:0.5, the effluent COD concentration was reduced to 457 mg/L, meeting the third class standard of the `Integrated Wastewater Discharge Standard' (GB 8978 1996).

Optimal Flexibility Dispatching of Multi-Pumped Hydro Storage Stations Considering the Uncertainty of Renewable Energy

With the continuous increase in the penetration rate of renewable energy, the randomness and flexibility demand in the power system continues to increase. The main grid side of the power system vigorously develops pumped hydro storage (PHS) resources. However, the current PHS station scheduling method of a fixed time period and fixed power has lost a certain flexibility supply. In this paper, an optimal dispatching model of multi-pumped hydro storage stations is proposed to supply flexibility for different regions of the state grid in east China. Firstly, the credible predictable power (CPP) of renewable energy is calculated and the definition of flexibility demand of a power system is given. The calculation model for flexibility demand is established. Secondly, considering the regional allocation constraint in the state grid in east China, a non-centralized model of multi-PHS within the dispatch scope is established. In the model, the constraints of storage capacity of different hydropower conversion coefficients of each PHS station is considered. The flexibility supply model of PHS stations to each region of the state grid in east China is established to realize reasonable flexibility allocation. Then, by combining the PHS station models and the flexibility demand calculation model, the optimal dispatching model for the flexibility supply of multi-PHS stations is established. Finally, based on the network dispatching example, the effectiveness and superiority of the proposed strategy are verified by a case study.

The effects of inflation, shortages, and partial backlogs on products that deteriorate over time in response to varied demand

This study aims to determine an optimal policy to deal with a situation where a retailer should have enough stock to meet customer demands and prevent supply loss and deterioration. In this study, we demonstrate the impact of partial backlogging, shortages, and inflation on decaying items to provide the most relevant solutions to this problem. The model uses a two-parameter Weibull deterioration distribution with an exponential demand rate. Numerical results and sensitivity analysis are combined with a graphical demonstration, and the proposed approach is reliable and accurate, providing a new explanation for a different type of system. This paper determines the total cost and optimal production run time by developing an inventory model. Sensitivity analysis is carried out to demonstrate the proposed model.

Optimal Replenishment Strategy for a High-Tech Product Demand with Non-Instantaneous Deterioration under an Advance-Cash-Credit Payment Scheme by a Discounted Cash-Flow Analysis

This study investigated non-instantaneous deteriorating items because not all products deteriorate immediately. In the high-tech product life cycle, the product demand increases linearly substantially in the growth stage and maintains a near-constant level in the maturity stage. This is a ramp-type demand rate. To satisfy the demand as shortages occur, partial backlogging is necessary. The advance-cash-credit payment scheme, comprising advance, cash, and credit payments, has gained popularity in business transactions to improve cash flow flexibility among supply chain participants. This study explored a partial backlogging inventory model with ramp-type demand for non-instantaneous deteriorating items under generalized payment. The proposed model also incorporated discounted cash flow analysis to account for the time value of the profit function. This study attempted to determine the optimal replenishment strategy to maximize the present value of the total profit. Finally, we conducted a sensitivity analysis to examine the efficacy of the proposed model and gain managerial insights. The optimal total profit rises with an increase in the permissible delay period and sale price but decreases with an increase in ordering and purchase costs. Then, the decision-maker can refer to the managerial insights to choose the appropriate parameter value for the operation.