Cost Information Research Articles

Three Dimensional Microwave Data Inversion in Feature Space for Stroke Imaging.

Microwave imaging is a promising method for early diagnosing and monitoring brain strokes. It is portable, non-invasive, and safe to the human body. Conventional techniques solve for unknown electrical properties represented as pixels or voxels, but often result in inadequate structural information and high computational costs. We propose to reconstruct the three dimensional (3D) electrical properties of the human brain in a feature space, where the unknowns are latent codes of a variational autoencoder (VAE). The decoder of the VAE, with prior knowledge of the brain, acts as a module of data inversion. The codes in the feature space are optimized by minimizing the misfit between measured and simulated data. A dataset of 3D heads characterized by permittivity and conductivity is constructed to train the VAE. Numerical examples show that our method increases structural similarity by 14% and speeds up the solution process by over 3 orders of magnitude using only 4.8% number of the unknowns compared to the voxel-based method. This high-resolution imaging of electrical properties leads to more accurate stroke diagnosis and offers new insights into the study of the human brain.

A special role for anterior cingulate cortex, but not orbitofrontal cortex or basolateral amygdala, in choices involving information.

Subjects are often willing to pay a cost for information. In a procedure that promotes paradoxical choices, animals choose between a richer option followed by a cue that is rewarded 50% of the time (No Info) vs. a leaner option followed by one of two cues that signal certain outcomes: one always rewarded (100%) and the other never rewarded, 0% (Info). Since decisions involve comparing the subjective value of options after integrating all their features, preference for information may rely on cortico-amygdalar circuitry. To test this, male and female rats were prepared with bilateral inhibitory Designer Receptors Exclusively Activated by Designer Drugs (DREADDs) in the anterior cingulate cortex, orbitofrontal cortex, basolateral amygdala, or null virus (control). We inhibited these regions after stable preference was acquired. We found that inhibition of the anterior cingulate cortex destabilized choice preference in female rats without affecting latency to choose or response rate to cues. A logistic regression fit revealed that previous choice predicted current choice in all conditions, however previously rewarded Info trials strongly predicted preference in all conditions except in female rats following anterior cingulate cortex inhibition. The results reveal a causal, sex-dependent role for the anterior cingulate cortex in decisions involving information.

Activity Based Costing-Sebuah Kajian

The unit cost calculation method with Activity Based Costing (ABC) is an efficient system. This system is certainly a system that is far more accurate than the traditional system. It is important for a company to know the units of the product or service it produces. Unit cost information becomes important for managerial decision making. The importance of the right decision certainly requires the company to use the right calculation method. Thus this study conducts a study of the ABC method that can be utilized properly by companies. This research method is a literature study conducted to gain more understanding of Activity based costing (ABC) research. This study carried out the stages starting from selecting topics, collecting literature, to reviewing the literature. The results of this study indicate that the application of this method requires a large commitment and resources from the organization and if this is fully implemented then the quality of the use of this method is very beneficial for companies in decision making

Toward more ethically oriented humanitarian logistics operations: An exploratory research

Ethical standards in humanitarian logistics have been frequently overlooked in the literature. This research aims to explore the interaction between logistics decisions and ethical standards implementation in the context of humanitarian relief operations. The paper adopts a data triangulation approach to collect and validate the primary and secondary data through a literature review, focus group, and interview. The results revealed that logistics decisions and processes affect the implementation of ethical standards. These decisions are physical distribution, facility location, procurement, outsourcing, deprivation cost analysis, visibility and information flow, supply chain collaboration, staff selection and training, types of relief items, staff security, and localization of logistics activities. This research can provide practitioners with insight into a complete set of ethical standards and inform their understanding of which factors affect their implementation. The novelty of this research comes from combining two disciplines, humanitarian logistics and social work, to compile a complementary list of ethical standards in humanitarian operations. It shows how logistics decisions can affect implementing ethical standards and hence achieving core values of social work.

Content Filtering with Inattentive Information Consumers

We develop a model of content filtering as a game between the filter and the content consumer, where the latter incurs information costs for examining the content. Motivating examples include censoring misinformation, spam/phish filtering, and recommender systems acting on a stream of content. When the attacker is exogenous, we show that improving the filter’s quality is weakly Pareto improving, but has no impact on equilibrium payoffs until the filter becomes sufficiently accurate. Further, if the filter does not internalize the consumer’s information costs, its lack of commitment power may render it useless and lead to inefficient outcomes. When the attacker is also strategic, improvements in filter quality may decrease equilibrium payoffs.

Exploring the Impact of Artificial Intelligence in Healthcare

The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications. AI's impact is observed across various domains, including detecting clinical conditions in medical imaging, early diagnosis of coronavirus disease 2019 (COVID-19), virtual patient care utilizing AI-powered tools, electronic health record management, enhancing patient engagement and treatment compliance, reducing administrative burdens for healthcare professionals (HCPs), drug and vaccine discovery, identification of medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. However, the integration of AI in healthcare encounters several technical, ethical, and social challenges, such as privacy concerns, safety issues, autonomy and consent, cost considerations, information transparency, access disparities, and efficacy uncertainties. Effective governance of AI applications is imperative to ensure patient safety, accountability, and to bolster HCPs' confidence, thus fostering acceptance and yielding significant health benefits. Precise governance is essential to address regulatory, ethical, and trust concerns while advancing the adoption and implementation of AI in healthcare. With the onset of the COVID-19 pandemic, AI has sparked a healthcare revolution, signaling a promising leap forward to meet future healthcare demands.

Exploring the Impact of Artificial Intelligence in Healthcare

The integration of artificial intelligence (AI) applications has revolutionized healthcare. This study conducts a comprehensive literature review to elucidate the multifaceted role of AI in healthcare, focusing on key aspects including medical imaging and diagnostics, virtual patient care, medical research and drug discovery, patient engagement and compliance, rehabilitation, and administrative applications. AI's impact is observed across various domains, including detecting clinical conditions in medical imaging, early diagnosis of coronavirus disease 2019 (COVID-19), virtual patient care utilizing AI-powered tools, electronic health record management, enhancing patient engagement and treatment compliance, reducing administrative burdens for healthcare professionals (HCPs), drug and vaccine discovery, identification of medical prescription errors, extensive data storage and analysis, and technology-assisted rehabilitation. However, the integration of AI in healthcare encounters several technical, ethical, and social challenges, such as privacy concerns, safety issues, autonomy and consent, cost considerations, information transparency, access disparities, and efficacy uncertainties. Effective governance of AI applications is imperative to ensure patient safety, accountability, and to bolster HCPs' confidence, thus fostering acceptance and yielding significant health benefits. Precise governance is essential to address regulatory, ethical, and trust concerns while advancing the adoption and implementation of AI in healthcare. With the onset of the COVID-19 pandemic, AI has sparked a healthcare revolution, signaling a promising leap forward to meet future healthcare demands.

Abstract P483: Food Purchase Priorities and Healthy Food Purchase Supports and Barriers for U.S. General and Low-Income Consumers: A Quantitative Survey

Introduction: Identifying priorities and attributes that influence food purchase decisions will inform interventions and strategies to promote healthy eating. Aim: This study aimed to identify food purchase priorities and healthy food purchase supports and barriers for the general population and those with limited household income. Methods: Participants were recruited by a market research company in May 2023 and were required to be ≥ 18 years old, not work in healthcare/medical/nutrition or advertising/public relations/marketing/market research and be the primary/shared food decision maker for the household. Recruitment quotas ensured age, income, gender and geographic region approximated 2022 US census data (n=1,182) (Census-representative group, CRG). A sample with limited income and participation in federal food or healthcare programs (low-income group, LIG) was also recruited (n=506). The survey was administered online in May and June 2023. Differences between LIG and CRG were assessed using z-tests (p < 0.05). Results: Taste was most commonly rated by CRG as very or extremely important in household food purchasing (87%). Availability, price, ingredients and nutritional value were also important to at least two-thirds of CRG. Taste was similarly important to LIG (85%), but they were more likely to rate price and less likely to rate availability, nutritional value and ingredients as important. When prompted to choose the most important factor, price and taste were the most common responses, chosen by similar proportions of CRG, while price was the most important factor for LIG, followed by taste. CRG versus LIG were more likely to prioritize personal taste over cost, health and nutrition information on labels, buying local and selecting according to nutrition, brand and origin/sourcing information. CRG were less likely than LIG to prefer a lower cost and usually select according to price. LIG were more likely than CRG to state that nothing supports them in selecting healthier foods and less likely to have found support from good-tasting recipes, advance meal or grocery list preparation, cooking equipment access, following influencers, recommendations from organizations or consulting a dietitian/nutritionist. Conversely, LIG were more likely to identify price and availability as barriers to selecting healthier foods, and less likely to see long preparation time, small servings sizes or nothing as barriers. Conclusions: Cost is a strong barrier to healthy eating, especially with limited income, along with taste, while nutrition is not a leading influence on food choice. Strategies to enable healthy eating among low-income households must prioritize taste and cost.

Analysis of the Importance and Performance of Cost Information in Pricing Decisions in Ready-to-Eat Food Retail MSE in Vitória-ES

Currently, decision-making is the major challenge for managers in the day-to-day operations of the company. It depends on various factors to be assertive and achieve positive results. We can say that pricing has become one of the fundamental decisions to be made. With that said, this work aimed to examine the relationship between the importance attributed and the effective knowledge of cost information for decision-making in setting prices in Micro and Small Enterprise (MSE) of ready-to-eat foods in Vitória-ES, from the perspective of managers. To do this, the methodology adopted was through quantitative methods and bibliographic research, based on the daily reality of small and medium-sized enterprises. We can analyze through raw data, collected with the help of standardized tools and a specific target audience. Firstly, the work presents a brief account of the importance of micro and small retail companies of ready-to-eat foods for the economy, the importance of cost accounting in pricing, and the conceptualization of the Slack Matrix used in the analysis of the raw results obtained, followed by the methodology and interpretation of the data collected from the managers.

A Non-Iterative Coordinated Scheduling Method for a AC-DC Hybrid Distribution Network Based on a Projection of the Feasible Region of Tie Line Transmission Power

AC-DC hybrid distribution grids realize power transmission through tie lines. Accurately characterizing the power exchange capacity between regional grids while ensuring safe grid operation is the basis for the coordinated scheduling of resources in interconnected distribution grids. However, most of the current AC/DC hybrid models are linear, and it is challenging to ensure the accuracy criteria of the obtained feasible regions. In this paper, a two-stage multi-segment boundary approximation method is proposed to characterize the feasible region of hybrid distribution grid tie line operation. Information such as security operation constraints are mapped to the feasible region of the boundary tie line to accurately characterize the transmission exchange capacity of the tie line. To avoid the limitations of linear models, the method uses a nonlinear model to iteratively search for boundary points of the feasible region. This ensures high accuracy in approximating the real feasible region shape and capacity limitations. A convolutional neural network (CNN) is then utilized to map the given boundary and cost information to obtain an estimated equivalent operating cost function for the contact line, overcoming the inability of previous methods to capture nonlinear cost relationships. This provides the necessary cost information in a data-driven manner for the economic dispatch of hybrid AC-DC distribution networks. Numerical tests demonstrate the effectiveness of the method in improving coordination accuracy while preserving regional grid privacy. The key innovations are nonlinear modeling of the feasible domain of the contact line and nonlinear cost fitting for high-accuracy dispatch.

ANALISIS PENETAPAN HARGA POKOK PRODUKSI (HPP) PADA USAHA MIKRO KECIL JALANGKOTE SALAHUTU DI KOTA MAKASSAR

This study aims to: know and analyze the Determination of Cost of Goods Produced (COGS) in Jalangkote Salahutu Micro Small Enterprises (MSEs) in Makassar City. This study is based on analyzing the determination of the cost of goods produced (COGS) at UMK Jalangkote Salahutu in Makassar City whether the costs involved are in accordance with the selling price that has been set. The data collection techniques used were interviews and documentation. Data analysis techniques use qualitative descriptive analysis. This technique begins with collecting the required data in the form of data and cost information and other supporting evidence, conducting interviews with business owners or owners, processing interview results, and administrative data obtained based on the analysis of the data used and calculating it with the full costing method, describing the method of determining the cost of goods produced in MSEs, and drawing conclusions. Based on the results of the study that: (1) Cost of Goods Produced that has been carried out by researchers shows that the cost of goods produced in UMK Jalangkote Salahutu products is Rp. 2,979 in Jalangkote products, Rp. 5,338 in Spring Roll products, Rp. 5,354 in Croquette products, Rp. 5,265 in Bikang Doang products. While the selling price set by the company is Rp. 7,000. (2) Looking at the comparison between the cost of goods produced and the selling price of the product, the business owner earns a profit of Rp. 4,021 on Jalangkote products, Rp. 1,662 on Spring Roll products, Rp. 1,646 on Croquette products, Rp. 1,753 on Bikang Doang products.Keywords: Cost of Goods Produced

Cost Estimating of Providing Desired Food Basket for the Iranian Society and Its Situation in the Last 3 Years

Background: Food security has always been emphasized as a primary goal of sustainable development policies. Events, such as the COVID-19 outbreak, climate change, wars, and unjust (cruel) sanctions against countries threaten global food security. Therefore, considering the importance of nutrition and food consumption in budget planning and household shopping, we indirectly examined the cost of providing an optimal food basket for the Iranian community, in total and by separating its components based on the average price of selected food items in urban areas from year 1399-1401 in the Solar Hijri calendar (2020 to 2022). Methods: In this cross-sectional costing study, the country’s desired food basket was used as a reference for cost measurement of an acceptable food basket, and information on food prices and household expenses was obtained from the Statistical Center of Iran and the Central Bank. The cost of providing an optimal food basket for the Iranian community, in total and for each component, was calculated for an individual and a family of four. After extracting the cost information of providing an optimal food basket in these three years, the data were compared and analyzed to study the trend. Results: Based on the costs of providing an optimal household basket, the average food cost for a family of four in 1399 (2020- 2021) was about 3.2 million Tomans, which increased by 27% to 4.365 million Tomans in 1400 (2021-2022), and in 1401 (October 2022), it reached 6.785 million Tomans, a 35% increase compared to 1400 (2021-2022). Conclusion: It is suggested to support vulnerable groups and target subsidies according to different income levels. The salaries and wages should be revised in line with inflation food prices and household expenses, and also policymakers and planners are suggested to monitor the prices.

An evolutionary analysis of supply chain collaborative information sharing based on prospect theory.

In order to delve into the dynamic evolution process and influencing factors of information sharing decisions among stakeholders under supply chain collaboration, this study constructs an evolutionary game model with suppliers and retailers as the primary entities. Within this model, a combined approach of game theory and prospect theory is employed, integrating prospect value functions and weight functions to create an information sharing prospect value matrix. A comprehensive analysis is conducted on the strategic choices and benefits of entities considering the psychological perception of information sharing, and critical factors influencing the stability of information sharing evolution results are explored through numerical simulations using Matlab. The key findings of this study are as follows: Firstly, from the perspective of supply chain collaboration, the probability of entities evolving into information sharing is negatively correlated with the cost of information sharing and positively correlated with the benefits generated by information coordination. Secondly, looking at supply chain collaboration, entities are more likely to engage in information sharing behavior when they exhibit a lower level of risk aversion, indicating greater rationality, when facing profits; conversely, they are more likely to participate in information sharing when they display a higher degree of risk preference, indicating less rationality, in the face of losses. Furthermore, the lesser sensitivity of suppliers and retailers to losses is more likely to drive the system towards an information-sharing state. Based on the primary findings mentioned above, this study offers recommendations for enhancing trust, constructing information exchange platforms, and adjusting psychological awareness. These suggestions contribute to improving information sharing among entities within the supply chain, thus enhancing the overall efficiency and collaboration of the supply chain.

Enhancing the profitability of pay what you want: A study of suggested prices

AbstractA suggested price is oftentimes utilized in practice to enhance the profitability of pay what you want (PWYW). In this paper, we focus on this suggested price strategy. We conduct five experiments to explore factors that impact buyers' payments after seeing the suggested price. In Study 1, we show a relationship between the suggested price and payments. Intuitively, a higher suggested price results in increased payments. Studies 2A and 2B demonstrate that the relationship between the suggested price and payments is mediated by cost estimation, while Studies 3A and 3B display that the relationship is moderated by the presence of a charitable element. Interestingly, in the case with a charitable element, increasing the suggested price does not stimulate an increment in payments on a par with the case without a charitable element. This paper proposes that sellers who use PWYW could employ a suggested price to furnish buyers with pertinent cost information, but utilizing PWYW with a suggested price may not be suitable for products with a charitable component.

The impact of the agricultural digital economy on China's agricultural export growth: Examining mechanisms from supply‐ and demand‐side path perspectives

AbstractThe deep integration of the agricultural digital economy and foreign trade is an important driving force for promoting high–quality economic development. Based on a theoretical analysis of the impact mechanisms of the agricultural digital economy on agricultural exports, this study empirically examines the impact and mechanism of the agricultural digital economy on agricultural exports using Chinese agricultural export data from 2011 to 2016. The relevant findings are threefold. (1) The agricultural digital economy significantly enhances the growth of China's agricultural exports, and the result remains robust after a series of robustness tests. (2) The heterogeneity analysis reveals that the agricultural digital economy has a greater role in promoting exports from quality frontier firms, primary agricultural products and exports to developed countries. (3) The mechanism analysis demonstrates that the agricultural digital economy promotes exports of agricultural products on the supply side most strongly through quality–enhancing and the information cost effects, improving product quality and reducing information expenditure to achieve export growth. On the demand side, domestic demand traction and external–demand creation effects exert a local market effect and stimulate import market demand to promote export growth. [EconLit Citations: F746.12].

A Policy Gradient Algorithm for the Risk-Sensitive Exponential Cost MDP

We study the risk-sensitive exponential cost Markov decision process (MDP) formulation and develop a trajectory-based gradient algorithm to find the stationary point of the cost associated with a set of parameterized policies. We derive a formula that can be used to compute the policy gradient from (state, action, cost) information collected from sample paths of the MDP for each fixed parameterized policy. Unlike the traditional average cost problem, standard stochastic approximation theory cannot be used to exploit this formula. To address the issue, we introduce a truncated and smooth version of the risk-sensitive cost and show that this new cost criterion can be used to approximate the risk-sensitive cost and its gradient uniformly under some mild assumptions. We then develop a trajectory-based gradient algorithm to minimize the smooth truncated estimation of the risk-sensitive cost and derive conditions under which a sequence of truncations can be used to solve the original, untruncated cost problem. Funding: This work was supported by the Office of Naval Research Global [Grant N0001419-1-2566], the Division of Computer and Network Systems [Grant 21-06801], the Army Research Office [Grant W911NF-19-1-0379], and the Division of Computing and Communication Foundations [Grants 17-04970 and 19-34986].

Non-contact weigh-in-motion approach with an improved multi-region of interest method

Vehicle weight monitoring is increasingly essential for traffic safety and infrastructure maintenance. Compared with traditional weigh-in-motion techniques, the non-contact weigh-in-motion (NC-WIM) has the advantages of non-intrusive installation, low cost, and rich information conveyance. However, most existing procedures for NC-WIM tend to be computationally intensive and time-consuming. To improve the data processing efficiency in NC-WIM, a multi-region of interest (ROI) method is proposed. With this method, the latency for tire deformation extraction can be decreased from more than 1 min to less than 0.15 s. Meanwhile, the multi-ROI method is robust to daytime lighting variations and works well under both backlighting and frontlighting on cloudy and sunny days. Based on the outcomes of this study, the ability of NC-WIM systems to cope with heavy traffic and complex illumination can be significantly enhanced. The improved NC-WIM is promising to provide long-term data for studies such as structural health monitoring, pavement design, and low-carbon transportation.

Comparison of the out-of-pocket costs of Medicare-funded telepsychiatry and face-to-face consultations: A descriptive study.

Telepsychiatry items in the Australian Medicare Benefits Schedule (MBS) were expanded following the COVID-19 pandemic. However, their out-of-pocket costs have not been examined. We describe and compare patient out-of-pocket payments for face-to-face and telepsychiatry (videoconferencing and telephone) MBS items for outpatient psychiatric services to understand the differential out-of-pocket cost burden for patients across these modalities. out-of-pocket cost information was obtained from the Medical Costs Finder website, which extracted data from Services Australia's Medicare claims data in 2021-2022. Cost information for corresponding face-to-face, video, and telephone MBS items for outpatient psychiatric services was compared, including (1) Median specialist fees; (2) Median out-of-pocket payments; (3) Medicare reimbursement amounts; and (4) Proportions of patients subject to out-of-pocket fees. Medicare reimbursements are identical for all comparable face-to-face and telepsychiatry items. Specialist fees for comparable items varied across face-to-face to telehealth options, with resulting differences in out-of-pocket costs. For video items, higher proportions of patients were not bulk-billed, with greater out-of-pocket costs than face-to-face items. However, the opposite was true for telephone items compared with face-to-face items. Initial cost analyses of MBS telepsychiatry items indicate that telephone consultations incur the lowest out-of-pocket costs, followed by face-to-face and video consultations.

IRS Enforcement and Voluntary Tax Disclosure

ABSTRACT Although voluntary tax disclosures can improve firms’ information environments and reduce financial statement users’ costs of processing complex tax information, they can also inform tax authorities about tax avoidance strategies. This study explores the content of voluntary tax disclosures during quarterly earnings announcements and conference calls to examine the effect of IRS enforcement on managers’ voluntary tax disclosure decisions. We estimate a negative relation between IRS enforcement and voluntary tax disclosures that is concentrated in the subset of firms with high levels of tax avoidance. These results are consistent with managers having concerns about the potential costs of voluntary disclosures to tax authorities. In supplemental analysis, we find results consistent with voluntary tax disclosures benefiting analysts, but at the cost of increasing IRS attention. Collectively, our results suggest an unintended negative effect of IRS enforcement on the information environment. Data Availability: Data are available from the sources cited in the text. JEL Classifications: H25; M41; M48.

Leveraging trust for joint multi-objective and multi-fidelity optimization

In the pursuit of efficient optimization of expensive-to-evaluate systems, this paper investigates a novel approach to Bayesian multi-objective and multi-fidelity (MOMF) optimization. Traditional optimization methods, while effective, often encounter prohibitively high costs in multi-dimensional optimizations of one or more objectives. Multi-fidelity approaches offer potential remedies by utilizing multiple, less costly information sources, such as low-resolution approximations in numerical simulations. However, integrating these two strategies presents a significant challenge. We propose the innovative use of a trust metric to facilitate the joint optimization of multiple objectives and data sources. Our methodology introduces a modified multi-objective (MO) optimization policy incorporating the trust gain per evaluation cost as one of the objectives of a Pareto optimization problem. This modification enables simultaneous MOMF optimization, which proves effective in establishing the Pareto set and front at a fraction of the cost. Two specific methods of MOMF optimization are presented and compared: a holistic approach selecting both the input parameters and the fidelity parameter jointly, and a sequential approach for benchmarking. Through benchmarks on synthetic test functions, our novel approach is shown to yield significant cost reductions—up to an order of magnitude compared to pure MO optimization. Furthermore, we find that joint optimization of the trust and objective domains outperforms sequentially addressing them. We validate our findings with the specific use case of optimizing particle-in-cell simulations of laser-plasma acceleration, highlighting the practical potential of our method in the Pareto optimization of highly expensive black-box functions. Implementation of the methods in existing Bayesian optimization frameworks is straightforward, with immediate extensions e.g. to batch optimization possible. Given their ability to handle various continuous or discrete fidelity dimensions, these techniques have wide-ranging applicability in tackling simulation challenges across various scientific computing fields such as plasma physics and fluid dynamics.