搜索结果：cash

Electronic cash (e-cash) is a digital alternative to physical currency that allows anonymous transactions between users and merchants. Typically, coins in an e-cash scheme are only dispensed through a central bank. A drawback of this approach is that the bank is always on the critical path during withdrawals, and if a reliable connection to the bank is temporarily unavailable, users may be unable to withdraw coins in a timely fashion. As with physical currency, there are benefits to supporting a decentralized infrastructure where withdrawals can be performed without involving the bank in the critical path. We propose the design of a new cryptographic bearer token that can be dispensed by automatic teller machines (ATM) in a fully offline e-cash scheme. Such bearer tokens provide anonymity, unforgeability and untraceability, i.e., users cannot be tracked by their spending activities or the locations of withdrawal. We formalize the requirements of an e-cash scheme with multiple issuers and propose an efficient design building on top of the compact e-cash protocol of Camenisch et al. (EUROCRYPT 2005). Our construction leverages an unforgeable and doubly-anonymous voucher that allows a

Owing to the importance of project cash flow, which comprises an entire history of all cash inflows and cash outflows, to economic survival of firms, it is vital to coping with project scheduling issues considering resource constraints in circumstances involving cash flow. Furthermore, since appropriate project management is subject to the innate uncertainties involved in most projects, they are required to be appraised respecting their profound impact. In this paper, a new comprehensive multi-mode multi-objective linear programming model with two conflicting objectives, which are maximizing final cash flow for profit optimization and shortening the duration of project execution, considering improving assumptions, that is, payments delays, project finance constraints, initial capital, different types of interest rates, credit limit to assuage financial distress, credit line usage, is presented in an uncertain environment. Since the model is considered as multi-objective with uncertain parameters, a new extended interval valued fuzzy - Torabi and Hassini (IVF-TH) approach is proposed to tackle the problem. The presented mixed integer linear programming (MILP) model is solved applyin

Although discrete choice (choice-based conjoint) analysis has become a widely used technique for the elicitation of consumer preferences and hence a foundation for product design, to the best of our knowledge, there exists neither free and open-source nor commercial software that covers the game-theoretic simulation of competitive reactions among firms based on discrete choice models to improve decision making beyond traditional product (line) optimization. The R package cash (conjoint + Nash) does not only provide functions to fill this gap but comprises an entire simulation pipeline including the upstream processes of discrete choice analysis itself. cash ranges from preference generation, choice design, error and response simulation, through Bayesian model estimation and evaluation, to Nash equilibrium computation. Doing so, it partly draws from established R packages concerned with discrete choice analysis. While the structure of cash generally aims towards end-to-end simulation as well as simulation of competitive dynamics based on real data, all its key elements mentioned above may be of use independently of each other.

Even though research has repeatedly shown that non-cash incentives can be effective, cash incentives are the de facto standard in crowdsourcing contests. In this multi-study research, we quantify ideators' preferences for non-cash incentives and investigate how allowing ideators to self-select their preferred incentive -- offering ideators a choice between cash and non-cash incentives -- affects their creative performance. We further explore whether the market context of the organization hosting the contest -- social (non-profit) or monetary (for-profit) -- moderates incentive preferences and their effectiveness. We find that individuals exhibit heterogeneous incentive preferences and often prefer non-cash incentives, even in for-profit contexts. Offering ideators a choice of incentives can enhance creative performance. Market context moderates the effect of incentives, such that ideators who receive non-cash incentives in for-profit contexts tend to exert less effort. We show that heterogeneity of ideators' preferences (and the ability to satisfy diverse preferences with suitably diverse incentive options) is a critical boundary condition to realizing benefits from offering ideato

Digital banking is among the technological innovations currently reverberating the cyber wave. this study seeks to assess communication, awareness and acceptance of it among the residents of south-east and south-south, nigeria. the survey objectives were to ascertain awareness level of the south-east and south-south residents towards digital banking during the cash crunch, determine the acceptance level of digital banking among the south-east and south-south residents, find out the role of communication in awareness and acceptance of digital banking during the cash crunch in south-east and south-south nigeria, and assess the usage of digital banking amidst cash crunch in south-east and south nigeria. the study methodology is a sample survey which allowed researchers to administer questionnaires on 385 respondents out of the 50,166,807 study population. the findings showed that awareness level of digital banking was good (36%) in south-east and south-south nigeria during the cash crunch but it level of acceptance and usage improved more (37%) after the cash crunch. the study also ascertained that communication contribute significantly (59%) towards the usage and acceptance of digita

The payback period is unambiguously defined for conventional investment projects, projects in which a series of cash outflows is followed by a series of cash inflows. Its definition for nonconventional projects is more challenging, since their balances (cumulative cash flow streams) may have multiple break-even points. Academics and practitioners offer a few contradictory recipes to manage this issue, suggesting to use the first break-even point of the balance, the last break-even point of the balance, or the moment in time at which the cumulative sum of net cash inflows first exceeds the total sum of net cash outflows. In this paper, we show that the last break-even point of the project balance is the only definition of the payback period consistent with a set of economically meaningful axioms. An analogous result is established for the discounted payback period.

The consumer store is ubiquitous and plays an important role in our everyday lives. It is an open question why stores usually have such short life cycles (typically around 3 years in China). This paper proposes a theoretical framework based on an equilibrium in style supply of stores and style demand of consumers to characterize store cash flow (revenue), leading to a strong explanation of this puzzle. In our model, we derive that the preference shifting of consumers is the main reason for the cash flow decreasing to its break-even line over time, while the visibility broadening leads to initial growth, resulting in rainbow-shaped cash flow and its life cycle. Moreover, the intensified spatial competition will lead to an unexpected decrease in the store's cash flow, or even closure. We calibrate our model with proprietary data of three Chinese stores from three representative industries and study the relationship between customers' preference shifting and cash flow. To our knowledge, there have been no prior attempts to quantitatively model the life cycle of the store.

This paper investigates the impact of sanctions on Tornado Cash, a smart contract protocol designed to enhance transaction privacy. Following the U.S. Department of the Treasury's sanctions against Tornado Cash in August 2022, platform activity declined sharply. We document a significant and sustained reduction in transaction volume, user diversity, and overall protocol utilization after the sanctions were imposed. Our analysis draws on transaction data from three major blockchains: Ethereum, BNB Smart Chain, and Polygon. We further examine developments following the partial lifting and eventual removal of sanctions by the U.S. Office of Foreign Assets Control (OFAC) in March 2025. Although activity partially recovered, the rebound remained limited. The Tornado Cash case illustrates how regulatory interventions can affect decentralized protocols, while also highlighting the challenges of fully enforcing such measures in decentralized environments.

Urban Air Mobility (UAM) envisions aerial corridors for Unmanned Aerial Vehicles (UAVs) to reduce ground traffic congestion by supporting 3D mobility, such as air taxis. A key challenge in these high-mobility aerial corridors is ensuring reliable connectivity, where frequent handovers can degrade network performance. To resolve this, we present a Context-Aware Smart Handover (CASH) protocol that uses a forward-looking scoring mechanism based on UAV trajectory to make proactive handover decisions. We evaluate the performance of the proposed CASH against existing handover protocols in a custom-built simulator. Results show that CASH reduces handover frequency by up to 78% while maintaining low outage probability. We then investigate the impact of base station density and safety margin on handover performance, where their optimal setups are empirically obtained to ensure reliable UAM communication.

Organizations use cash management models to control balances to both avoid overdrafts and obtain a profit from short-term investments. Most management models are based on control bounds which are derived from the assumption of a particular cash flow probability distribution. In this paper, we relax this strong assumption to fit cash management models to data by means of stochastic and linear programming. We also introduce ensembles of random cash management models which are built by randomly selecting a subsequence of the original cash flow data set. We illustrate our approach by means of a real case study showing that a small random sample of data is enough to fit sufficiently good bound-based models.

A new class of risk measures called cash sub-additive risk measures is introduced to assess the risk of future financial, nonfinancial and insurance positions. The debated cash additive axiom is relaxed into the cash sub additive axiom to preserve the original difference between the numeraire of the current reserve amounts and future positions. Consequently, cash sub-additive risk measures can model stochastic and/or ambiguous interest rates or defaultable contingent claims. Practical examples are presented and in such contexts cash additive risk measures cannot be used. Several representations of the cash sub-additive risk measures are provided. The new risk measures are characterized by penalty functions defined on a set of sub-linear probability measures and can be represented using penalty functions associated with cash additive risk measures defined on some extended spaces. The issue of the optimal risk transfer is studied in the new framework using inf-convolution techniques. Examples of dynamic cash sub-additive risk measures are provided via BSDEs where the generator can locally depend on the level of the cash sub-additive risk measure.

In the literature on risk measures, cash subadditivity was proposed to replace cash additivity, motivated by the presence of stochastic or ambiguous interest rates and defaultable contingent claims. Cash subadditivity has been traditionally studied together with quasi-convexity, in a way similar to cash additivity with convexity. In this paper, we study cash-subadditive risk measures without quasi-convexity. One of our major results is that a general cash-subadditive risk measure can be represented as the lower envelope of a family of quasi-convex and cash-subadditive risk measures. Representation results of cash-subadditive risk measures with some additional properties are also examined. The notion of quasi-star-shapedness, which is a natural analogue of star-shapedness, is introduced, and we obtain a corresponding representation result via the lower envelope of normalized, quasi-convex and cash-subadditive risk measures.

In this paper, I conduct a policy exercise about how much the introduction of a cash transfer program as large as a Norwegian-sized lottery sector to the United States would affect startups. The key results are that public cash transfer programs (like lottery) do not increase much the number of new startups, but increase the size of startups, and only modestly increase aggregate productivity and output. The most important factor for entrepreneurs to start new businesses is their ability.

Institutional investors have been increasing the allocation of the illiquid alternative assets such as private equity funds in their portfolios, yet there exists a very limited literature on cash flow forecasting of illiquid alternative assets. The net cash flow of private equity funds typically follow a J-curve pattern, however the timing and the size of the contributions and distributions depend on the investment opportunities. In this paper, we develop a benchmark model and present two novel approaches (direct vs. indirect) to predict the cash flows of private equity funds. We introduce a sliding window approach to apply on our cash flow data because different vintage year funds contain different lengths of cash flow information. We then pass the data to an LSTM/ GRU model to predict the future cash flows either directly or indirectly (based on the benchmark model). We further integrate macroeconomic indicators into our data, which allows us to consider the impact of market environment on cash flows and to apply stress testing. Our results indicate that the direct model is easier to implement compared to the benchmark model and the indirect model, but still the predicted cash fl

Due to complexity and dynamics of construction work, resource, and cash flows, poor management of them usually leads to time and cost overruns, bankruptcy, even project failure. Existing approaches in construction failed to achieve optimal control of resource flow in a dynamic environment with uncertainty. Therefore, this paper introducess a model and method to adaptive control the resource flows to optimize the work and cash flows of construction projects. First, a mathematical model based on a partially observable Markov decision process is established to formulate the complex interactions of construction work, resource, and cash flows as well as uncertainty and variability of diverse influence factors. Meanwhile, to efficiently find the optimal solutions, a deep reinforcement learning (DRL) based method is introduced to realize the continuous adaptive optimal control of labor and material flows, thereby optimizing the work and cash flows. To assist the training process of DRL, a simulator based on discrete event simulation is also developed to mimic the dynamic features and external environments of a project. Experiments in simulated scenarios illustrate that our method outperfo

Humanitarian and disaster management actors have increasingly adopted cash transfer to reduce the sufferings and vulnerability of the survivors. Case transfers have also been used as a critical instrument in the current COVID-19 pandemic. Unfortunately, academic work on humanitarian and disaster-cash transfer related issues remains limited. This article explores how NGOs and governments implement humanitarian cash transfer in a post-disaster setting using an exploratory research strategy. It asks What are institutional constraints and opportunities faced by humanitarian emergency responders in ensuring an effective humanitarian cash transfer and how humanitarian actors address such institutional conditions. We introduced a new conceptual framework, namely humanitarian and disaster management ecosystem for cash transfer. This framework allows non-governmental actors to restore complex relations between the state, disaster survivors or citizen, local market economy and civil society. Mixed methods and multistage research strategy were used to collect and analyze primary and secondary data. The findings suggest that implementing cash transfers in the context of post tsunamigenic earth

The Combined Algorithm Selection and Hyperparameters optimization (CASH) problem is one of the fundamental problems in Automated Machine Learning (AutoML). Motivated by the success of ensemble learning, recent AutoML systems build post-hoc ensembles to output the final predictions instead of using the best single learner. However, while most CASH methods focus on searching for a single learner with the best performance, they neglect the diversity among base learners (i.e., they may suggest similar configurations to previously evaluated ones), which is also a crucial consideration when building an ensemble. To tackle this issue and further enhance the ensemble performance, we propose DivBO, a diversity-aware framework to inject explicit search of diversity into the CASH problems. In the framework, we propose to use a diversity surrogate to predict the pair-wise diversity of two unseen configurations. Furthermore, we introduce a temporary pool and a weighted acquisition function to guide the search of both performance and diversity based on Bayesian optimization. Empirical results on 15 public datasets show that DivBO achieves the best average ranks (1.82 and 1.73) on both validation

We consider a singular control model of cash reserve management, driven by a diffusion under ambiguity. The manager is assumed to have maxmin preferences over a set of priors characterized by $κ$-ignorance. A verification theorem is established to determine the firm's cost function and the optimal cash policy; the latter taking the form of a control barrier policy. In a model driven by arithmetic Brownian motion, we use Dynkin games to show that an increase in ambiguity leads to higher expected costs under the worst-case prior and a narrower inaction region. The latter effect can be used to provide an ambiguity-driven explanation for observed cash management behavior. Our findings can be applied to broader applications of singular control in managing inventories under ambiguity.

In the digital transformation era, integrating digital technology into every aspect of banking operations improves process automation, cost efficiency, and service level improvement. Although logistics for ATM cash is a crucial task that impacts operating costs and consumer satisfaction, there has been little effort to enhance it. Specifically, in Vietnam, with a market of more than 20,000 ATMs nationally, research and technological solutions that can resolve this issue remain scarce. In this paper, we generalized the vehicle routing problem for ATM cash replenishment, suggested a mathematical model and then offered a tool to evaluate various situations. When being evaluated on the simulated dataset, our proposed model and method produced encouraging results with the benefits of cutting ATM cash operating costs.