Reasonable Price Research Articles

Sun exposure and protection habits: Self-reported attitudes, knowledge and behaviours.

As exposure to UV radiation is the primary modifiable environmental risk factor associated with skin cancer, it remains the principal focus of most prevention strategies. Numerous sun protection campaigns have been implemented worldwide; however, their impact on the actual incidence and mortality rates of skin cancer seems to be limited. To create successful skin cancer prevention campaigns, it is important to have a comprehensive understanding of individuals' attitudes and behaviours regarding sun protection. The aim of the current study was to determine and report on the prevalence of self-reported attitudes, knowledge and behaviours regarding two of the major sun protection recommendations-avoidance of sun exposure and use of sunscreens-in an international representative sample across five continents. This cross-sectional study was conducted in 20 countries using a web-based online survey. A total of 50,552 individuals, comprising 25,388 men (50.22%) and 25,164 women (49.78%), participated in the survey. Among them, 83.2% reported having been voluntarily exposed to the sun (for sun-basking reasons) at least once in the last 12 months, and 47.96% acknowledged being exposed to the sun between the hours of 10 AM and 4 PM. The primary reason for non-adherence was that these hours were the most convenient times (32.28%). Only 24.05% reported applying sunscreen every 2 h when outdoors. Forgetfulness was the primary reason as provided by 27.79% of participants. Males and older age groups were less likely to adopt sun-protective behaviours around the world. Forgetfulness and the challenges posed by time constraints seem to be the biggest barriers to proper adherence. These findings should prompt the collaboration with health authorities and the manufacturers to enhance adherence by setting reasonable sunscreen prices and creating formulations that make their application less burdensome.

The Role of Business Management in the Innovative Development of the Cultural Tourism and Education Industry

The Role of Business Management in the Innovative Development of the Cultural Tourism and Education Industry

Effect of the Management Accounting on the Financial Performance of Small and Medium Enterprises in Rwanda: A Case of Small and Medium Entreprises in Gasabo District

Management accounting provides an excellent opportunity for companies to compete in the market by offering superior products and services at reasonable prices to customers. The general objective of this study was to study the effects of management accounting practices on the Financial Performance of Small and Medium Enterprises in Gasabo district, Rwanda. This study adopted a correlation study design. The target population for this study was the 171 small and medium enterprises in Gasabo district Stratified random sampling method was applied to come up with the sample size, since the population in different Small and Medium Enterprises was considered heterogeneous, implying that a simple random sample is unrepresentative. The study therefore involved 171 Small and Medium Enterprises in Gasabo district. The study collected primary data from the respondents. The data collected was both quantitative and qualitative. Qualitative data is descriptive and non-numerical, while quantitative data is numerical. The study found that Budgeting was the most widely used management accounting practices, including budgeting for planning, activity-based budgeting, flexible analysis, zero-based budgeting, and budgeting for controlling costs. costing systems were the second most widely used management accounting practice, including separation of variable, incremental, and fixed costs; plant-wide overhead rate; activity-based costing; target costing; and cost of quality. Information for decision making was also used by respondents, including stock control models, customer satisfaction analysis reports, product profitability analysis reports, risk analysis reports, and investment analysis reports. Strategic analysis was also highly used by respondents. The study further concludes that the most important elements of management accounting practices amongst the Small and Medium Enterprises in Gasabo district are; the management accounting function identifies key factors that influence Financial Performance and management accounting practices have increased return on equity (ROE), which is a measure of how profitable a company is for its shareholders. This is because management accounting practices help companies to identify and improve risky areas, which can lead to increased profits. This study recommends the creation and enhancement of awareness among firms of the importance of Information for decision making practices as this is the most highly used management accounting practice amongst the Small and Medium Enterprises in Gasabo district. Keywords: Management Accounting Practices, Costing Systems, Budgeting, Information for Decision Making, Strategic Analysis, Financial Performance, Small And Medium Enterprises

Vegetable Replenishment and Pricing Planning Based on ARIMA Forecasting

In fresh food supermarkets, vegetables have a short shelf life and can deteriorate quickly, necessitating daily replenishment and pricing decisions based on historical sales to maximize profits. This article, based on the historical sales data of vegetable products from a major supermarket, constructs a replenishment and pricing planning model based on ARIMA forecasting for six types of vegetables, seeking the optimal replenishment and pricing strategy for the next seven days. The results indicate that leafy and flowering vegetables, as well as eggplant-type vegetables, require more replenishment on Fridays and weekends, whereas aquatic root vegetables need more on Thursdays and Fridays. The replenishment of chili vegetables and edible mushrooms fluctuates, and there is a constraint relationship between pricing and replenishment volume. This study aims to assist merchants in making reasonable replenishment and pricing decisions to reduce costs and maximize economic benefits.

A Study of Replenishment And Pricing Strategies For Vegetable Commodities Based On Multiple Linear Regression

Vegetables, which can provide the human body with rich nutrients such as vitamins, minerals and dietary fiber, are indispensable necessities in people's daily life. Therefore, on the one hand, in order to ensure the quality of vegetables to provide fresh vegetables, superstores need to replenish goods in a timely manner; on the other hand, in order to improve the revenue, superstores need to reasonable pricing. This paper takes 251 kinds of vegetables sold in fresh food superstores as the research object, firstly, using Pearson's correlation coefficient to explore the correlation between the sales volume of vegetable single product and category, and obtains that the strongest correlation in vegetable category is cauliflower and edible mushrooms, with a correlation coefficient of -0.9989, and that the strongest correlation in vegetable single product is Hericium erinaceus and fresh zongzi leaves, with a correlation coefficient of 0.9973. Then, this paper set up a multivariate linear regression model to solve the problem for each vegetable. Linear regression model to solve the relationship between total sales volume and cost-plus pricing for each category. Finally, for the total daily replenishment, through the total daily replenishment = total predicted sales / (1 - wastage rate) processing to get the total daily replenishment of vegetables for 7 days, to obtain the cost matrix and markup rate matrix, and then get the pricing of vegetables for 7 days. For example, the replenishment of cauliflower on Monday should be 268.7238 kg, and the pricing is $2.5179/kg.

Reducing carbon emissions in prefabricated buildings supply chains: a focus on component manufacturing processes.

The construction sector accounts for 23% of emissions from global economic activity, with China responsible for nearly 41%. Although China has vigorously promoted the development of prefabricated buildings (PBS) in pursuit of cleaner production, the carbon emissions from prefabricated component factories (PCF) should not be underestimated. So, the focus of this research was on how to promote the decarbonization of PCF. Based on the carbon trading market mechanism, the carbon emissions trading tax and revenue tax collection, the authors established a differential game model consisting of the local government and the PCF, studied the equilibrium solutions under different decision models, and analyzed the roles of the two tax systems, carbon trading revenue, and market preferences. The results are as follows: (1) The PCF's low-carbon technology (LCT) innovation efforts can be directly affected by the carbon price, component price, and tax rate and indirectly affected by influencing the local government's efforts. Besides, the local government and the PCF strategies can be changed through the central government's regulation of carbon prices and tax rates. (2) PCF should set reasonable prices for components, improving economic efficiency and the LCT stocks. (3) Cost-sharing contracts can encourage PCF to increase their LCT innovation, which is conducive to increasing the optimal benefits of the PCF. (4) The local government cannot be motivated by cost-sharing contracts. They can increase their optimal benefits only if the cost-sharing coefficient is below a threshold or if the environmental benefits from low-carbon production are above a specific value.

Research on optimization of improved short-term load composite forecasting model based on AM–CNN–Bi–LSTM

Accurate load prediction is a prerequisite for the design, operation, scheduling, and management of energy systems. In the context of the development of smart grids, the extensive integration of highly volatile distributed energy generation into the power system has brought new challenges to the accuracy, reliability, real-time performance, and intelligence of short-term load forecasting. Therefore, this article proposes a novel short-term power load composite prediction model based on AM–CNN–Bi–LSTM. First, CNN is used to extract relevant feature quantities of power load coupling characteristics. Then, AM is used to evaluate the importance of the feature data, highlighting the features that have a greater impact on the prediction results. Finally, the Bi-LSTM network captures bidirectional temporal information from multiple time steps for prediction. Taking one year of measured data as an example, the error comparison of the prediction results of the composite prediction model overlay shows that compared with other models, the composite prediction model has improved prediction accuracy, feature extraction, generalization ability, and other aspects. The research results improve the accuracy of short-term power load forecasting while providing effective model references for decision-making in power system optimization scheduling, safe operation, and reasonable pricing.

Investigating the Relationship Between Brand Equity and Customer Loyalty the Moderating Effect of Customer Satisfaction in the Telecom Industry

The study explores the connection between brand equity and customer loyalty in the telecom industry. It specifically examines how customer satisfaction plays a moderating role in this relationship. The research employed structural equation modeling and involved 386 Ethio telecom customers. Several aspects were evaluated, including reliability, convergent validity, and sample adequacy. The study's findings highlight a strong positive correlation between brand equity and customer loyalty within the telecom industry. Factors such as positive brand image, perceived quality, brand awareness, and brand associations were found to have significant influences on customer loyalty. Furthermore, customer satisfaction was identified as a moderator, enhancing the positive impact of brand equity on loyalty. Based on the results, the study emphasizes the importance of brand equity and customer satisfaction in fostering loyalty in the telecom industry. It suggests that telecom companies should invest in building a strong brand image, improving perceived quality, increasing brand awareness, and cultivating favorable brand associations. Policymakers and regulators are also encouraged to create a competitive telecom market that promotes innovation, high-quality service delivery, and reasonable pricing. The practical implications of these findings are significant for the industry. They underscore the need for strong brand equity strategies and the delivery of exceptional customer experiences. By prioritizing these aspects, telecom companies can enhance customer loyalty and gain a competitive advantage in the market.

Smallholder farmers’ dissatisfaction with contract farming: a gendered perspective

ABSTRACT This study investigates the drivers of farmers’ dissatisfaction with market-specification contracts for finger millet crop in Kenya. The researcher analysed data from 336 farmers contracted by USOMI, out of which a sub-sample of 146 farmers who grew millet in 2021 was analysed using a probit model and descriptive analysis approach, which showed that asymmetric information related to the role of the contracting firm, lack of adequate understanding of the contract, poor pricing, and input market imperfections are some of the critical constraints and sources of dissatisfaction in the commercialisation of millet farming in Western Kenya. On the other hand, farmers’ satisfaction is driven by access to market and reasonable prices offered by the spot market traders. While the study found out that productivity and pricing were not gendered, it was observed that women were generally satisfied with contract farming. Consequently, contractual schemes should integrate complementary factors such as input provision. While the results do not show explicit benefits to women farmers, they do allow for the backing of contract farming in rural development programs to reduce gender inequalities.

Research on the Purchase Intention of Intangible Cultural Heritage Creative Products based on Tourist Perceived Value

This study aims to explore in depth the purchasing intentions of tourists towards creative products of intangible cultural heritage, aiming to reveal the level of recognition and importance that tourists place on the intangible value carried by intangible cultural heritage during the purchasing process. By using a questionnaire survey, a total of 500 valid samples of tourists from different backgrounds and experiences were collected, allowing for a more comprehensive understanding of their purchasing preferences and decision-making factors. The statistical analysis and discussion of data reveal a series of factors that affect tourist purchase intention. Firstly, uniqueness is an important influencing factor, and tourists tend to purchase products with unique cultural characteristics and historical significance because these products can arouse their interest and sense of identity. Secondly, the expression of cultural symbols also has a significant impact on purchase intention. Tourists are more willing to purchase products that reflect local culture and traditions, as these products not only have aesthetic value but also convey deep cultural connotations. In addition, emotional identification is also one of the important factors affecting purchase intention. Tourists tend to purchase products that can evoke emotional resonance and identification, as these products can stimulate their emotional experiences and memories. Finally, although price factors also have a certain impact on purchase intention, they are not decisive factors. Tourists are more willing to pay reasonable prices for products with rich cultural connotations and emotional experiences. Overall, this study reveals that tourists pay more attention to the cultural connotations, emotional experiences, and emotional identity of intangible cultural heritage creative products when purchasing them, rather than simple material attributes. This provides important references for relevant enterprises and organizations to better understand the needs and preferences of tourists, thereby developing more attractive and competitive intangible cultural heritage creative products.

Research on Superstore Vegetable Pricing and Replenishment Prediction Based on ARIMA Time Series and Particle Swarm Algorithm

With the development of digitalization, it is important to provide accurate and reasonable pricing and replenishment strategies for superstores through market demand and data information. In this paper, we first preprocess the relevant data and draw line graphs to compare the correlation between multiple individual items through Spearman correlation analysis. Secondly, Random Forest feature importance analysis is used to explore the relationship between total sales and cost-plus pricing of each vegetable category, and finally, the target data are tested for grade ratio and a gray model is constructed, and model prediction is carried out by using spsspro, and then the particle swarm algorithm is used to derive the optimal pricing strategy. Meanwhile, by updating the pricing regularly and making accurate replenishment decisions, we can better adapt to market fluctuations, obtain better sales opportunities, and maximize the benefits gained by the superstore itself.

Optimization research on automatic pricing and replenishment decision method for vegetable commodities

This paper addresses the issues of replenishment and pricing of vegetable commodities in fresh food supermarkets. By analyzing sales data and loss rate data, mathematical models are established to solve the following problems. Spearman correlation analysis and cluster analysis methods are employed. Firstly, data preprocessing is conducted by excluding returned vegetable products from a certain supermarkets distribution, and then integrating sales transaction details and wholesale price-related data. Secondly, monthly data is used to succinctly present the sales distribution patterns of various categories and individual items of vegetables through line charts. Finally, the models of Spearman correlation analysis and cluster analysis are respectively applied to illustrate the relationship between the sales volume of 6 major categories of vegetables and 251 individual items. Linear regression models, time series analysis, and XG-Boost regression analysis models are used. This paper requires replenishment plans to be made by category, analyzing the relationship between the total sales volume and cost-plus pricing of various vegetable categories. Firstly, a linear regression model is applied to fit historical sales volume and cost-plus pricing, resulting in a linear functional relationship, indicating that only the sales volume of tomatoes is negatively correlated with cost-plus pricing. Based on historical sales data, the daily replenishment total for each vegetable category within the next week is predicted. Predictive models and time series analysis are employed. Meanwhile, for pricing strategies, the XGBoost algorithm is utilized to provide reasonable pricing strategies. Based on the optimal solution, replenishment plans and pricing strategies for the next week are formulated to ensure the maximization of sales revenue objectives.

Estimation of Soil Loss in the Nanoi River Basin using Geospatial Techniques

Objectives: The main purpose of this research is to identify severe soil loss areas in the Nanoi river basin of Assam, India to suggest appropriate soil management planning in the river basin. Methods: The Revised Universal Soil Loss Equation (RUSLE) integrates geospatial technologies to assess overall soil loss in the Nanoi river basin which provides a faster and more accurate estimation. It is possible to understand soil erosion patterns more thoroughly using RUSLE which makes sustainable soil management easier. Findings: According to the RUSLE equation, the Nanoi river basin experiences an estimated total soil loss of 18,562.5 tons per year, with an average annual soil loss of 0.32 t/h/y. This suggests that approximately 0.64 km2 of the area falls into the Moderate, high and Extreme soil erosion sensitivity zones. Furthermore, a final map is generated to display different areas with varying levels of soil loss rates. Novelty: The geospatial approaches used produce precise findings at a reasonable price and demonstrate the severity of soil loss in the river basin. If soil loss continues at the current rate, there is a high likelihood that certain areas on both sides of the river, particularly in the downstream region of the basin, will experience fluvial hazards, such as drainage congestion and floods. Hence, the results generated will be helpful in the management practices of the river basin. Keywords: Soil loss, Soil erosion, Geospatial, Revised universal soil loss equation, River basin

Genetic Algorithms Application for Pricing Optimization in Commodity Markets

The perishable nature of vegetable commodities poses challenges for superstores, as reselling them is often unfeasible due to their short freshness period. Reliable market demand analysis is crucial for boosting revenue. This study simplifies the pricing and replenishment decision-making process by making reasonable assumptions about the selling time, wastage rate, and replenishment time for vegetable commodities. A single-objective planning model with the objective of profit maximization was constructed by fitting historical data using the nonparametric method of support vector regression (SVR). The study reveals a specific relationship between sales volume and cost-plus pricing for each category and predicts future cost changes using an LSTM model. Combining these findings, we substitute the relationship between sales volume and pricing as well as the LSTM prediction data into the model, and solve it using genetic algorithms in machine learning to derive the optimal replenishment volume and pricing strategy. Practical results show that the method can provide reasonable pricing and replenishment strategies for vegetable superstores, and after careful accounting, we arrive at an expected profit of RMB 22,703.14. The actual profit of the supermarket was RMB 19,732.89. The method, therefore, increases the profit of the vegetable superstore by 13.08%. By optimizing inventory management and pricing decisions, the superstore can better meet the challenges of vegetable commodities and achieve sustainable development.

Charging station service scope division and real-time pricing strategy based on the strongest occupation method

With the rapid construction of charging stations (CSs), charging station operators need to enhance their core competitiveness by precisely planning their service areas and formulating reasonable and effective pricing strategies. However, the regional competition among multiple charging station operators is generally ignored. In the traditional model, the service scope of CSs appears as regular circles, which is inconsistent with the market distribution law. In response to the irregular relationship between the dynamic service scope of CSs and the real-time charging price, a charging station service scope (CSSS) model is proposed by introducing the variable service field strength (SFS). First, the competitiveness of CSs is evaluated quantitatively, and the SFS of CSs is defined to describe the service scope of CSs by the strongest occupation method. Second, the impact of the charging price on the charging demand is analyzed based on the CSSS division model. In addition, the revenue of charging station operators and the stability of the power grid are considered to establish a real-time pricing optimization model. Finally, the numerical simulation is operated in Furong District of Changsha. It is shown that the proposed method effectively achieves more profits for charging stations and decreases the average power deviation of the whole region.

A flow‐based ascending auction to compute buyer‐optimal Walrasian prices

AbstractWe consider a market where a set of objects is sold to a set of buyers, each equipped with a valuation function for the objects. The goal of the auctioneer is to determine reasonable prices together with a stable allocation. One definition of “reasonable” and “stable” is a Walrasian equilibrium, which is a tuple consisting of a price vector together with an allocation satisfying the following desirable properties: (i) the allocation is market‐clearing in the sense that as much as possible is sold, and (ii) the allocation is stable in the sense that every buyer ends up with an optimal set with respect to the given prices. Moreover, “buyer‐optimal” means that the prices are smallest possible among all Walrasian prices. In this paper, we present a combinatorial network flow algorithm to compute buyer‐optimal Walrasian prices in a multi‐unit matching market with truncated additive valuation functions. The algorithm can be seen as a generalization of the classical housing market auction and mimics the very natural procedure of an ascending auction. We use our structural insights to prove monotonicity of the buyer‐optimal Walrasian prices with respect to changes in supply or demand.

Farmer Application

The aim of the program is to provide a platform for farmers to rent modern agricultural machinery and equipment as well as rent storage space for crops. The app will also connect to government servers to track data on crops, fertilizer use and government funds. Records of machinery and equipment will be kept along with details of the crop stored. In India, agriculture is the main source of income for the majority of the population. However, many farmers are not aware of advances in agricultural technology and cannot understand the market value of their products. That's why they often sell products at very low prices. In addition, they also try to follow the government's agricultural plans and reports. To solve these problems, our initiative will create a platform where farmers can get the latest information on vegetables and fruits in every market in India. This will help them sell their products at reasonable prices and improve their financial situation. Additionally, farmers will also receive notifications regarding government agricultural programs and in-app notifications to benefit from these programs. Additionally, our application will help farmers prepare the required products by estimating the amount of vegetables and fruits according to the market. We will also include weather data to support farmers' planning for the next 2-3 days. Overall, our programs focus on providing farmers with new knowledge and technology that will improve their livelihoods and increase their productivity

Constraints of Sugarcane Farmers During Cultivation and Marketing in Kerala, India

The study analysed the constraints faced by farmers during cultivation and marketing of sugarcane in Kerala. A multi-stage random sampling technique was adopted for the data collection and information was obtained from 120 sugarcane farmers and 10 marketing intermediaries. The farmers ranked and used Garrett's ranking technique to analyze the main production and marketing constraints. The major production constraints identified were wild animal and rodent attack, followed by shortage of labour and high labour cost, high cost of inputs and delay in harvesting. Price fluctuations of sugarcane and absence of regulated markets were major marketing constraints. Valuable suggestions were implementing effective measures to mitigate wildlife damage, government should purchase farmers products at reasonable price.

A black-box adversarial attack on demand side management

Demand side management (DSM) contributes to the industry’s transition to renewables by shifting electricity consumption in time while maintaining feasible operations. Machine learning is promising for DSM with reasonable computation times and electricity price forecasting (EPF), which is paramount to obtaining the necessary data. Increased usage of machine learning makes production processes susceptible to so-called adversarial attacks. This work proposes a black-box attack on DSM and EPF based on an adversarial surrogate model that intercepts and modifies the data flow of load forecasts and forces the DSM to result in financial losses. Notably, adversaries can design the data modifications without knowledge of the EPF model or the DSM optimization model. The results show how barely noticeable modifications of the input data lead to significant deterioration of the decisions by the optimizer. The results implicate a significant threat, as attackers can design and implement powerful attacks without infiltrating secure company networks.

Synergizing Sustainability and Financial Prosperity: Unraveling the Structure of Business Profit Growth through Consumer-Centric Strategies—The Cases of Kosovo and Albania

This research investigates the synergistic relationship between sustainability and financial prosperity in businesses, specifically focusing on the impact of consumers on profit growth in Kosovo and Albania. The study aims to understand consumers’ perceptions of their purchases, the factors influencing their choice of businesses, and the types of businesses that effectively support consumers in these countries. Data were collected through a survey completed by 200 consumers and 200 businesses. The analysis, utilizing multivariate analysis of variance, descriptive analysis, and reliability analysis with SPSS, reveals that consumers significantly influence the sustainability of business profit growth. Moving forward, it is recommended that businesses prioritize offering reasonable prices, quality products/services, easy access to products/services, clear information about products/services, and convenient locations. The research has profound implications for businesses, consumers, and countries and suggests the need for further exploration of the impact of consumers on profit growth in diverse contexts.