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Many South African municipalities suffer servicedelivery problems that can usually be ascribed to inadequate management, skills, and budgets. The reasons for these, in turn, invariably include weak economic base, unwise spending of available funds, and difficulty in recruiting and retaining skilled staff. The White Paper on Local Government of 1998, to which Valli Moosa, the then Minister for Provincial Affairs and Constitutional Development, referred as "almost [being] regarded as a 'mini- Constitution' for local government", together with the Municipal Demarcation Act of the same year, spelled out the framework in terms of which the local government system would be transformed. Municipalities, covering the entire country "wall-towall", were thereafter established and powers and functions were assigned to them. After more than two decades, there can no longer be any doubt that many municipalities are, to a significant extent, failing in their primary duty of delivering services. The author sought to investigate to what extent this failing is due to flaws in the 'mini-Constitution'. By examining aspects of the performance of municipalities, the article assesses key assumptions made by the drafters of the White Paper in respect of a number of key attributes for service delivery. These attributes include sufficient skills and funding, prudent budgeting and effective spending, good leadership, adequate systems and data, stability of the senior leadership, and the presence of trust and credibility. The article finds that many of the assumptions were flawed, with severe consequences for service delivery. [ABSTRACT FROM AUTHOR]

Background: University students typically face acute financial pressure, which can adversely impact mental health, wellbeing, and academic outcomes. This scoping review of qualitative and quantitative studies aimed to identify distinct money-management behaviours, and psychological determinants, to inform future interventions. Methods: Two electronic databases were searched for observational studies focusing on money-management behaviours and their correlates (in quantitative studies) or reflections on experiences of such behaviours (qualitative studies). Of 789 unique papers identified, 12 papers, reporting 10 distinct studies (six quantitative, two qualitative, two mixed-methods), were entered into review. We inductively categorised all behaviours and psychological correlates, and narratively synthesised findings. Results: We documented 15 distinct money-management behaviours, which fitted five higher-order categories: budgeting, saving (i.e., building funds), spending, borrowing, and settling debts. Twenty-two distinct potential correlates were observed, which fitted six categories: personality characteristics, financial beliefs and knowledge, attitudes, affective responses, self-efficacy and control, and social influences. Financial beliefs and knowledge, attitudes, self-efficacy and control, and social support from parents and peers were generally associated with 'better' money management practices. Conclusion: Heterogeneity in behaviours and correlates studied precluded definitive conclusions. Future studies should more comprehensively adopt theories and concepts from behavioural science, to distinguish between different money-management behaviours, identify which behaviours have most impact on students, and establish which specific determinants are most related to which money-management behaviours. [ABSTRACT FROM AUTHOR]

A System-of-Systems (SoS) is characterized both by independence and by inter-dependency. This inter-dependency, while allowing an SoS to achieve its objectives, also means that failures can cascade throughout the SoS. An SoS needs to be resilient to deal with the impact of complex internal and external environments. In this paper, we propose a resilience analysis method of an SoS based on a hierarchy structure. Firstly, we establish a hierarchy structure, which is ranked from high to low as capability level, activity level and system level. Then, Bayesian Networks (BNs) and Dynamic Bayesian Networks (DBNs) are used to analyze the resilience of the SoS. A resilience-based system importance metric is introduced, which is used in the budget allocation optimization problem during the development domain of an SoS. This paper proposes a mathematical programming model aimed at optimizing SoS resilience by optimally using budget to the subsystem. The application of the proposed approach is demonstrated using a case study: a Next Generation Air Transportation setting. The study results provide evidence that the proposed inter-dependency analysis based on Bayesian theory and the SoS resilience design approach can assist SoS system engineers in increasing expected SoS resilience during the development domain. [ABSTRACT FROM AUTHOR]

We study the problem of mass screening of heterogeneous populations under limited testing budget. Mass screening is an essential tool that arises in various settings, e.g., the COVID-19 pandemic. The objective of mass screening is to classify the entire population as positive or negative for a disease as efficiently and accurately as possible. Under limited budget, testing facilities need to allocate a portion of the budget to target sub-populations (i.e., proactive screening) while reserving the remaining budget to screen for symptomatic cases (i.e., reactive screening). This paper addresses this decision problem by taking advantage of accessible population-level risk information to identify the optimal set of sub-populations for proactive/reactive screening. The framework also incorporates two widely used testing schemes: Individual and Dorfman group testing. By leveraging the special structure of the resulting bilinear optimization problem, we identify key structural properties, which in turn enable us to develop efficient solution schemes. Furthermore, we extend the model to accommodate customized testing schemes across different sub-populations and introduce a highly efficient heuristic solution algorithm for the generalized model. We conduct a comprehensive case study on COVID-19 in the US, utilizing geographically-based data. Numerical results demonstrate a significant improvement of up to 52% in total misclassifications compared to conventional screening strategies. In addition, our case study offers valuable managerial insights regarding the allocation of proactive/reactive measures and budget across diverse geographic regions. Author summary: The problem of mass screening of heterogeneous populations is challenging, especially when we do not have enough tests to check everyone all at once. Therefore, under limited testing budget, testing facilities need to allocate a portion of the testing budget to target sub-populations (i.e., proactive screening) while reserving the remaining budget to screen for symptomatic cases (i.e., reactive screening). Our study provides a strategy to make those decision efficiently by taking advantage of risk information and utilizing optimization models. We conduct a comprehensive case study on COVID-19 in the US and numerical results demonstrate a significant improvement compared to conventional screening strategies. The results of this paper will benefit health administrators and policy-makers by providing managerial insights regarding optimal allocation of testing kits and testing designs across diverse geographic regions. [ABSTRACT FROM AUTHOR]

This paper examines the Scandinavian states' staffing of middle and senior‐ranking positions within the United Nations (UN)‐system, in comparison to other members of the Organization of Economic Cooperation and Development (OECD). It is true that the Scandinavian states are overrepresented per the size of their population in both staffing categories; however, when it comes to a comparison of financial contributions to the UN regular budget, the three states are underrepresented compared to the rest of the OECD in staffing funded by that mechanism. Yet, the strong reputation of the Scandinavian states and their practitioners has still enabled them to disproportionately staff senior UN positions. To explain this, this paper utilises Pierre Bourdieu's concepts of capital, field, and habitus to explain how the three states' reputation within the UN‐system gives them an advantage in staffing senior positions. [ABSTRACT FROM AUTHOR]

Any tool, apparatus, machine, appliance, implant, reagent for in vitro usage, software, material, or other like or related item that the producer intends to be used, either alone or in combination, for medical purposes is considered a medical device. A thorough review was conducted for the purpose of revealing the medical device data and medical device industry in the current paper. Some of the data incorporated in this paper has been taken from Government Websites, Press Releases, Media Reports, Deloitte Report, Union Budget 2022-23 and dully citation was mentioned. [ABSTRACT FROM AUTHOR]

Wind from all directions Wind loads on membrane structures are not regulated by standards. The variety of shapes of the typically open and thus flow‐exposed surfaces on both sides cannot be captured using geometric variables in tables, as is common for typical building forms in Eurocode EN 1991‐1‐4. The recommended individual wind tunnel tests are only feasible for larger projects with the corresponding budgets and planning periods. Therefore, some recommendations for typical membrane roofs have been collected in the literature, and at the same time, computer‐based wind simulations have developed to the extent that they are now also offered in market‐standard FEM software packages. This paper aims to explain the accuracy and comparability of the various approaches using case studies. The paper presents the analysis of standard approaches, literature values, wind tunnel tests, and computer‐based methods with a comparison of calculation results and provides insights into the development of load assumptions in membrane construction. [ABSTRACT FROM AUTHOR]

ERP provides a powerful guarantee and a basic platform for the centralized management of group funds. This paper uses Likert's five-point method to design questionnaires, obtain original data, and apply structural equation models. Then it establishes a framework for centralized management of corporate group funds in an ERP environment, collects data through questionnaires, and explores the framework for centralized management of corporate group funds under the ERP environment. It uses linear regression models to describe experts' recognition of the capital management framework by establishing a structural equation model to test the key factors influencing the construction of a centralized fund management system. Results show that the factors influencing the construction of the centralized fund management framework include: risk control, fund budget control, fund management decision-making system, online banking, financial, logistics and other modules in formation integration, and fund monitoring. This paper can help companies not only sort out the contexts, and strengthen capital management, but also improve the efficiency and effectiveness of financial monitoring, and increase the risk control of monetary capital management. It has important practical significance for strengthening group capital management and reducing capital costs. [ABSTRACT FROM AUTHOR]

Although adaptive cancer therapy shows promise in integrating evolutionary dynamics into treatment scheduling, the stochastic nature of cancer evolution has seldom been taken into account. Various sources of random perturbations can impact the evolution of heterogeneous tumors, making performance metrics of any treatment policy random as well. In this paper, we propose an efficient method for selecting optimal adaptive treatment policies under randomly evolving tumor dynamics. The goal is to improve the cumulative "cost" of treatment, a combination of the total amount of drugs used and the total treatment time. As this cost also becomes random in any stochastic setting, we maximize the probability of reaching the treatment goals (tumor stabilization or eradication) without exceeding a pre-specified cost threshold (or a "budget"). We use a novel Stochastic Optimal Control formulation and Dynamic Programming to find such "threshold-aware" optimal treatment policies. Our approach enables an efficient algorithm to compute these policies for a range of threshold values simultaneously. Compared to treatment plans shown to be optimal in a deterministic setting, the new "threshold-aware" policies significantly improve the chances of the therapy succeeding under the budget, which is correlated with a lower general drug usage. We illustrate this method using two specific examples, but our approach is far more general and provides a new tool for optimizing adaptive therapies based on a broad range of stochastic cancer models. Author summary: Tumor heterogeneities provide an opportunity to improve therapies by leveraging complex (often competitive) interactions of different types of cancer cells. These interactions are usually stochastic due to both individual cell differences and random events affecting the patient as a whole. The new generation of cancer models strive to account for this inherent stochasticity, and adaptive treatment plans need to reflect it as well. In optimizing such treatment, the most common approach is to maximize the probability of eventually stabilizing or eradicating the tumor. In this paper, we consider a more nuanced version of success, maximizing the probability of reaching these therapy goals before the cumulative burden from the disease and treatment exceed a chosen threshold. Importantly, our method allows computing such optimal treatment plans efficiently and for a range of thresholds at once. If used on a high-fidelity personalized model, our general approach could potentially be used by clinicians to choose the most suitable threshold after a detailed discussion of a specific patient's goals (e.g., to include the trade-offs between toxicity and quality of life). [ABSTRACT FROM AUTHOR]

Limited research has been directed toward coastal protection infrastructure compared to other types of infrastructure, despite the increasing global population in low-elevated coastal regions and the threats posed by climate change. This paper presents a risk-based asset management framework for coastal protection structures that improves accuracy with each inspection. The framework consists of five components: the Coastal Asset Inventory (CAI), Inspection and Condition Assessment (ICA) module, Backward Markovian Deterioration Model (BMDM), Forward Markovian Deterioration Model (FMDM), and Intervention Policy Engine (IPE). The framework addresses challenges in accurately predicting coastal structure deterioration due to uncertainties in wave loading conditions and the need for frequent inspections. It is applied to rubble-mound breakwaters in Alexandria, Egypt. The BMDM and FMDM models are developed based on inspection data, and the IPE optimizes interventions considering structural condition, risk thresholds, and budget constraints. Results showed that long-term deterioration estimates occur at an accelerated rate with an increase in inspection points, triggering earlier interventions. However, the framework proves reliable even with only two inspection points, allowing asset managing agencies to implement the model based on the structural condition at the year of construction and a minimum of two inspections. The proposed risk-based asset management framework provided a comprehensive approach to managing coastal protection infrastructure, reducing risks to life and property. By accurately predicting deterioration and optimizing intervention decisions, the framework can greatly assist in the effective management and maintenance of coastal assets. This is vital in ensuring the safety of coastal populations facing global climate change and demographic growth. Coastal protection structures are crucial for safeguarding coastal populations in the face of climate change and demographic growth. This research paper introduces a risk-based asset management framework for coastal protection structures, with practical applications for coastal management agencies and practitioners. The framework addresses the challenges of predicting coastal structure deterioration and optimizing intervention decisions. The framework consists of five components: the Coastal Asset Inventory (CAI), Inspection and Condition Assessment (ICA), Backward Markovian Deterioration Model (BMDM), Forward Markovian Deterioration Model (FMDM), and Intervention Policy Engine (IPE). It has been successfully applied to rubble-mound breakwaters in Alexandria, Egypt. The practical implications are significant: The framework enables asset managers to make informed decisions on coastal infrastructure maintenance and interventions, reducing risks to life and property. It provides a comprehensive approach to managing coastal protection infrastructure and ensuring the safety of coastal populations. By accurately predicting deterioration and optimizing interventions, the framework supports effective management and maintenance of coastal assets. Its reliability and flexibility make it a valuable tool for coastal management practitioners, promoting sustainable development and safeguarding coastal areas. With its potential for broader application, this risk-based asset management framework offers practical solutions for coastal protection worldwide. [ABSTRACT FROM AUTHOR]

The real-time sharing of traffic data can offer improved services to users and timely respond to environmental changes. However, this data often involves individuals' sensitive information, raising substantial privacy concerns. It is imperative to find ways to protect the privacy of the shared traffic data while maintaining its ongoing data utility. In this paper, a Differential Privacy-based scheme with Spatial Correlation for Real-time traffic data (named as DP-SCR) is proposed. DP-SCR not only ensures the high data utility of shared traffic data, but also provides strong privacy protection. Specifically, DP-SCR is designed to adhere to w-event ε -differential privacy, ensuring a high level of privacy protection. Subsequently, a novel adaptive allocation based on spatial correlation prediction is proposed to optimize the privacy budget allocation in differential privacy. In addition, a feasible dynamic clustering algorithm is developed to minimize the relative perturbation error, which further improves the quality of shared data. Finally, the analyses demonstrate that DP-SCR provides w-event privacy for the shared data of each section, and the spatial correlation is a more pronounced characteristic of the traffic data than other characteristics. Meanwhile, experiments conducted on real-world data show that the MAR and MER of the predicted data in DP-SCR are smaller than those in other baseline DP-based schemes. It indicates that the DP-SCR scheme proposed in this paper can provide more accurate shared data. [ABSTRACT FROM AUTHOR]

This paper studies multilateral trading problems in which agents' valuations for items are interdependent. Assuming that each agent's information has a greater marginal effect on her own valuation than on the other agents' valuations, the paper identifies a necessary and sufficient condition for the existence of trading mechanisms satisfying efficiency, ex-post incentive compatibility, ex-post individual rationality, and ex-post budget balance. The paper presents a trading mechanism that satisfies the four properties when the necessary and sufficient condition holds and shows that this mechanism maximizes the ex-post budget surplus among all efficient, ex-post incentive compatible, and ex-post individually rational trading mechanisms. The paper examines an environment where each agent can possess at most one unit of an item, and her information about the item is one-dimensional. It then extends the results to two general environments: the multiple units environment and the multidimensional information environment. [ABSTRACT FROM AUTHOR]

This paper builds on an intensive and unique survey of all Czech municipalities (n = 6,258) regarding the geoparticipatory spatial tools used in the decision-making processes of local administrations and the Index of Geoparticipation derived from the dataset. Furthermore, we have collected and analysed data regarding 238 participatory budgeting events in the Czech Republic from the years 2012–2020. This paper is divided into two sections. We investigate the relationships among the municipalities' membership in the Healthy Cities of the Czech Republic network (HCCZ), as well as the municipalities' levels of indebtedness and their performances in the Index of Geoparticipation and their usage of participatory budgeting. Our primary theory states that membership in HCCZ and higher values in the Index of Geoparticipation will support the use of participatory budgeting, while indebtedness will discourage municipalities from implementing participatory budgeting. We deepen the knowledge of participatory budgeting in the Czech Republic, while focusing on the qualitative analysis of projects that have been approved and implemented through the tool of participatory budgeting at the municipal level. Additionally, we examine what type of projects the funds are allocated to and how the characteristics of these projects change over time. [ABSTRACT FROM AUTHOR]

Extensive uncertainty can affect the efficiency and fairness of consensus in the consensus reaching process (CRP), but few scholars have studied consensus modeling that focuses on fairness and efficiency in uncertain environments. Additionally, the weight of the decision maker (DM) in the CRP is influenced by multiple factors. Therefore, this paper proposes robust consensus models (EFCMs) focusing on fairness and efficiency under uncertain costs to address these issues. Firstly, this paper constructs multiple uncertainty sets to describe the uncertainty of the unit adjustment cost. Secondly, the fair utility level and opinion adjustment distance are used to measure the fairness and efficiency of reaching consensus, respectively. Furthermore, this paper uses a data-driven method based on the KDE method combined with trust propagation in social networks to determine the DMs' weights jointly. Finally, this paper also applies the proposed models to the carbon emission reduction negotiation process between the government and enterprises. The experimental results and sensitivity analysis show that the consensus cost budget and the DMs' jealous preference behavior particularly affects the efficiency of reaching consensus, which provides a theoretical basis for solving practical decision making problems. [ABSTRACT FROM AUTHOR]

This paper aims to empower cardiovascular (CV) researchers by promoting diversity, equity, and inclusion (DE&I) principles throughout the research cycle. It defines DE&I and introduces practical strategies for implementation in recruitment, retention, and team dynamics within CV research. Evidence-based approaches supporting underrepresented populations' participation are outlined for each research phase. Emphasizing the significance of inclusive research environments, the paper offers guidance and resources. We invite CV researchers to actively embrace DE&I principles, enhancing research relevance and addressing longstanding CV health disparities. [ABSTRACT FROM AUTHOR]

What is known about the topic? Little is known about the state of mental health reform in Australia. This article describes the struggle to develop the systems of accountability necessary to assess national progress. What does this paper add? We provide some historical context regarding mental health reform and consider recent efforts in particular, before then describing current key opportunities. What are the implications for practitioners? Many people working in mental health are struggling to deliver quality services to Australians. This paper considers the broad policy issues which have led to this situation. This is useful for practitioners who can then better respond and participate in processes of systemic reform. Opportunities to engage now in key policy formulation are identified in the article. [ABSTRACT FROM AUTHOR]

Budgets are often thought of as boring, invoking the tedium of bookkeeping. The summer of 2020 suggested otherwise. As America's plague of police brutality combined with the death-dealing blows of the COVID-19 pandemic and a wave of urban uprising gripped US cities, activists turned their organising attention to municipal budgeting. From Seattle to Atlanta, demands rang out for cities to #defund the police, rethink public safety and adopt budgets for the people. Since then, the people's budget movement has grown in strength at the municipal level, including in Los Angeles, Chicago, Louisville, Jacksonville, Minneapolis and Nashville, among other cities. What should urban studies scholars make of these struggles and from the aspirations and visions that impel them? This paper uses a case study of the Nashville People's Budget Coalition (NPBC) to examine how municipal budgeting processes and public financing have become new sites of theorisation, debate and political intervention. We demonstrate how the people's budget movement offers a new calculus for municipal budgeting that radically reconceptualises the logics of value and care that underpin economic thought and public accounting practices. We conclude by considering avenues through which a scholarly agenda for economic democracy in solidarity with movement organisers could be expanded. [ABSTRACT FROM AUTHOR]

Pre-disaster protection strategies are essential for enhancing the resilience of electric power systems against natural disasters. Considering the budgets for protection strategies, the dependency of other infrastructure systems on electricity, and the uncertainty of disaster scenarios, this paper develops risk-neutral and risk management models of strategies for pre-disaster protection. The risk-neutral model is a stochastic model designed to maximize the expected value of resilience (EVR) of the integrated system. The risk management model is a multi-objective model prioritizing the minimization of risk metrics as a secondary goal alongside maximizing the EVR. A case study conducted on the energy infrastructure systems in the Greater Toronto Area (GTA) validates the effectiveness of the models. The findings reveal the following: (i) increasing the budget enhances the EVR of the integrated system; however, beyond a certain budget threshold, the incremental benefits to the EVR significantly diminish; (ii) reducing the value of the downside risk often results in an increase in the EVR, with the variation in Pareto-optimal solutions between the two objectives being non-linear; and (iii) whether for the risk-neutral or risk management protection strategies, there are reasonable budgets when considering disaster intensity and the cost of protection measures. The models can help decision-makers to select effective protection measures for natural disasters. [ABSTRACT FROM AUTHOR]

Optimal facility repair planning is a challenge, especially as it applies to a portfolio of facilities constrained by a limited budget. This paper discusses a linear programming optimization method to develop improved maintenance and repair strategies. This method introduces a utility metric, termed component "endurance," used to determine repair decisions to help maximize the financial health of a portfolio of facilities. A predictive model is used to calculate this endurance metric to measure the impact of repair decisions at a specified future date. This future impact is integrated into an optimization framework to guide repair decisions with the aim of avoiding excessive deterioration of the most valuable components while remaining within budget. This approach shows appreciable benefits over traditional component condition ranking approaches, including increased portfolio health and reductions in deferred maintenance. [ABSTRACT FROM AUTHOR]

The hydrocarbon extraction process is complex and involves numerous design variables and mitigating risk. Numerous time-consuming simulations are required to maximize objective functions such as NPV from a particular field while contemplating a significant representation of uncertainty scenarios and various production strategies. Production strategies searches may result in a high-dimensional search space which can yield sub-optimal reservoir economical exploration. As a solution, appropriate optimization algorithms selection and tuning may provide good solutions with lesser simulations. This paper presents a methodology to calibrate, develop, and select optimization algorithms for oil production strategy applications while quantifying the dimension and optimum location effects. Global optimum location altered the best method to be selected. It presents a novel algorithm (ASLHC) and a modification of the Nelder-Mead method (NMNS) to improve its high dimensionality performance. Performances of six pre-calibrated techniques were compared using novel normalized mathematical functions. Optimizations were limited to a 500 evaluation functions computational budget. The PSO, ASLHC, NMNS, and IDLHC were selected and implemented to perform production strategy improvements regarding two parameterizations of the reservoir management variables for a real reservoir model with restricted platform. Results showed the implemented algorithms successfully improved NPV by at least 8% at each of the 24 real-case optimizations. After upscaling the selected techniques for a 115 variable parameterization, the NMNS and IDLHC demonstrated good resilience against local convergence and each technique kept improving during all iterations of the process. An optimization method recommendation chart is presented based on the computational budget of the application. [ABSTRACT FROM AUTHOR]

This paper extends the Cournot duopoly model by allowing the government to impose firm-dependent specific taxes or subsidies while keeping the budget balanced. It considers two possible government goals: maximizing the social surplus and maximizing the consumer surplus. It shows that, with identical firms, the best government policy is not to intervene. In the case of cost asymmetry, social surplus and consumer surplus maximization goals require opposite strategies: to maximize the social surplus, the government should tax the high-cost firm driving the economy toward monopoly and increasing productive efficiency at the expense of lower production. In the case of consumer surplus maximization, the tax should be imposed on the low-cost firm reducing the gap between the firms' outputs. Such a strategy, however, increases productive inefficiency and reduces the social surplus. [ABSTRACT FROM AUTHOR]

Experimental investigations of flow past two (one above other) horizontal cylinders have been presented in this paper. The experimental data captured over a rough-bed with double cylinders having three different diameters, were used for the analysis. Firstly, general characteristics such as streamwise velocity, Reynolds shear stress and turbulence intensity profiles have been elucidated at different downstream locations from cylinder position; then some advanced analysis such as length scales, turbulent kinetic energy (TKE) fluxes and budget have been investigated to exhibit their variations. Length scale profiles exhibit higher values in near bed; indicating comparatively larger eddies' downstream of both cylinders. TKE budget profiles indicate highly negative pressure energy diffusion rate together with a sufficient TKE production rate, stabilized by the amplified TKE diffusion and dissipation rates. To determine one of the most prime behaviors of turbulence characteristics, namely TKE dissipation rate more accurately, the concept of structure function has been employed. Primarily velocity gradient method elucidates TKE dissipation rate, then it has been verified by Kolmogorov's two-thirds law using second order structure function and Kolmogorov's − 5/3 law using the method of power spectra. [ABSTRACT FROM AUTHOR]

The global climate budget for 1.5°C necessitates distributing future CO2 emissions to individual countries. In Germany, this translates to 3.1 billion tons (January 2022). All sectors must engage to meet this target, particularly the lignite (i.e. low-grade brown coal) sector. This entails leaving significant reserves untapped. Also, economic factors are already driving a decline in coal consumption, aided by Germany's renewable expansion and rising CO2 prices. Forecasts suggest lignite phase-out by 2030, prompting reevaluation of mining plans without devastation of villages. The paper proposes new mining plans for the main lignite regions Rhineland and Lusatia. [ABSTRACT FROM AUTHOR]

Sample average approximation (SAA), the standard approach to stochastic mixed-integer programming, does not provide guidance for cases with limited computational budgets. In such settings, variance reduction is critical in identifying good decisions. This paper explores two closely related ensemble methods to determine effective decisions with a probabilistic guarantee. (a) The first approach recommends a decision by coordinating aggregation in the space of decisions as well as aggregation of objective values. This combination of aggregation methods generalizes the bagging method and the "compromise decision" of stochastic linear programming. Combining these concepts, we propose a stopping rule that provides an upper bound on the probability of early termination. (b) The second approach applies efficient computational budget allocation for objective function evaluation and contributes to identifying the best solution with a predicted lower bound on the probability of correct selection. It also reduces the variance of the upper bound estimate at optimality. Furthermore, it adaptively selects the evaluation sample size. Both approaches provide approximately optimal solutions even in cases with a huge number of scenarios, especially when scenarios are generated by using oracles/simulators. Finally, we demonstrate the effectiveness of these methods via extensive computational results for "megascale" (extremely large scale) stochastic facility location problems. History: Accepted by Pascal Van Hentenryck, Area Editor for Computational Modeling: Methods & Analysis. Funding: This work was supported by The Office of Naval Research [Grant N00014-20-1-2077] and the Air Force Office of Scientific Research [Grant FA9550-20-1-0006]. 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.2021.0324) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2021.0324). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Friendship‐and‐love affords bonding that satisfies what can be called "transcendental preferences"—in contradistinction of "substantive preferences" afforded by, for example, food, clothes, and shelter. Substantive preferences involve ordinary "substantive cost," whereas transcendental preferences involve "bonding cost" that includes heartaches, obsession, and emotional turmoil. What about the cost of gifts such as flowers, time, and other carriers of friendship‐and‐love? The greater is the expenditure on gifts, the greater the bonding cost. This paper investigates the following question: How should we model bonding cost, which includes the cost of gift, in relation to substantive cost? Given bonding cost and substantive cost share the same budget, neoclassical economists treat them as commensurable and, hence, transcendental and substantive preferences make up a unidimensional objective function. This treatment, however, originates the "gift anomaly": If people easily substitute between the two genera of preferences, why do they consider the demand of payments for visiting their grandmothers—or payments for voting and sexual intercourse—as repugnant (taboo)? To solve the gift anomaly, this paper is critical of the standard economist's entry point. This paper proposes bonding and substantive costs as incommensurable and, corollary, transcendental and substantive preferences as incommensurable as well. This paper further shows how, without undermining the incommensurability thesis, the incommensurability is up to a limit: the two genera of costs and, corollary, the two genera of preferences are still linked via the income effect—as opposed to the substitution effect. [ABSTRACT FROM AUTHOR]

Universities face an uncertain funding environment and turbulent marketplace. Financial scenario modelling offers a potential mechanism to assist in navigating a way forward. Our previous paper on UK universities' practice found some variation in the sophistication of the approaches taken, but the overall impression was of a relatively simple approach, with spreadsheets generally viewed as having sufficient functionality to meet current strategic needs. This subsequent paper offers guidance on the construction of financial scenario models. It addresses the key variables that universities may wish to include, and it offers advice on how models might be formulated and evolve. The characteristics of differing approaches taken by universities in meeting their own scenario modelling requirements are explored by identifying three forms of model: basic, intermediate and advanced. The contribution of this paper is to enable universities, in the UK and beyond, to evaluate and improve their own financial scenario modelling practices. [ABSTRACT FROM AUTHOR]

The study explores the divergence in data protection enforcement strategies among national agencies. Whereas the literature on cross-national enforcement practices is scarce, this study develops a scale for data protection enforcement strategies and measures and compares enforcement choices across agencies. Based on survey responses from 18 DPAs, interviews with DPA employees, and secondary sources on GPDR enforcement, the paper clusters DPAs based on enforcement strategies, analyzes cross-national variations, and investigates misalignments between strategy and actions. Using Fuzzy-Set Qualitative Comparative Analysis, the paper tests how bureaucratic and political contexts – organizational capacities, budget sources, and issue saliency – impact enforcement choices. Almost half of the studied DPAs reflect high deterrence by their strategy, but for many of them, lack of resources and expertise inhibits the translation of strategy into practice. This study provides a starting point for understanding the national impacts of Europeanization post-GDPR, adding empirical support for theorizing about enforcement across the EU. [ABSTRACT FROM AUTHOR]

Interferometric synthetic aperture radar (InSAR) processing techniques have been widely used to derive surface deformation or retrieve terrain elevation. Over the development of the past few decades, most research has mainly focused on its application, new techniques for improved accuracy, or the investigation of a particular error source and its correction method. Therefore, a thorough discussion about each error source and its influence on InSAR-derived products is rarely addressed. Additionally, InSAR is a challenging topic for beginners to learn due to the intricate mathematics and the necessary signal processing knowledge required to grasp the core concepts. This results in the fact that existing papers about InSAR are easy to understand for those with a technical background but difficult for those without. To cope with the two issues, this paper aims to provide an organized, comprehensive, and easily understandable review of the InSAR error budget. In order to assist readers of various backgrounds in comprehending the concepts, we describe the error sources in plain language, use the most fundamental math, offer clear examples, and exhibit numerical and visual comparisons. In this paper, InSAR-related errors are categorized as intrinsic height errors or location-induced errors. Intrinsic height errors are further divided into two subcategories (i.e., systematic and random error). These errors can result in an incorrect number of phase fringes and introduce unwanted phase noise into the output interferograms, respectively. Location-induced errors are the projection errors caused by the slant-ranging attribute of the SAR systems and include foreshortening, layover, and shadow effects. The main focus of this work is on systematic and random error, as well as their effects on InSAR-derived topographic and deformation products. Furthermore, because the effects of systematic and random errors are greatly dependent on radar wavelengths, different bands are utilized for comparison, including L-band, S-band, C-band, and X-band scenarios. As examples, we used the parameters of the upcoming NISAR operation to represent L-band and S-band, ERS-1 and Sentinel-1 to represent C-band, and TerraSAR-X to represent X-band. This paper seeks to bridge this knowledge gap by presenting an approachable exploration of InSAR error sources and their implications. This robust and accessible analysis of the InSAR error budget is especially pertinent as more SAR data products are made available (e.g., NISAR, ICEYE, Capella, Umbra, etc.) and the SAR user-base continues to expand. Finally, a commentary is offered to explore the error sources that were not included in this work, as well as to present our thoughts and conclusions. [ABSTRACT FROM AUTHOR]

The main and primary goal of the research in this paper is to analyze the actual current situation in domestic civil engineering, by determining the causes of missing deadlines and exceeding budgets of civil engineering projects. Since the results of this research also indicate the most likely problems that could arise during the construction of a project, the findings and conclusions could be used in risk assessment and drawing up dynamic plans for actual projects, which would improve the situation in domestic civil engineering and increase productivity of this important industry. The results obtained in this paper provide a modern approach to the problem of risk in construction, provide a realistic insight into risk factors on civil engineering projects in the Republic of Serbia and the surrounding area, and facilitate the establishment of a better correlation between theory and practice in project management and risk management. [ABSTRACT FROM AUTHOR]

We consider in this paper the budgeted minimum s - t cut problem. Suppose that we are given a directed graph G = (V , A) with two distinguished nodes s and t, k non-negative cost functions c 1 , ... , c k : A → Z + , and k - 1 budget bounds b 1 , ... , b k - 1 . The goal is to find a s - t cut C satisfying the budget constraints c h (C) ⩽ b h , for h = 1 , ... , k - 1 , and whose cost c k (C) is minimum. In this paper we discuss the linear relaxation of the problem and introduce a strict partial ordering on its solutions. We give a necessary and sufficient condition for which it has an integral optimal minimal (with respect to this ordering) basic solution. We also show that recognizing whether this is the case is NP-hard. [ABSTRACT FROM AUTHOR]

The implicit trace estimation problem asks for an approximation of the trace of a square matrix, accessed via matrix-vector products (matvecs). This paper designs new randomized algorithms, XTrace and XNysTrace, for the trace estimation problem by exploiting both variance reduction and the exchangeability principle. For a fixed budget of matvecs, numerical experiments show that the new methods can achieve errors that are orders of magnitude smaller than existing algorithms, such as the Girard--Hutchinson estimator or the Hutch++ estimator. A theoretical analysis confirms the benefits by offering a precise description of the performance of these algorithms as a function of the spectrum of the input matrix. The paper also develops an exchangeable estimator, XDiag, for approximating the diagonal of a square matrix using matvecs. [ABSTRACT FROM AUTHOR]

Identifying optimal maintenance policies for transportation infrastructure such as bridges, is a challenging task that requires taking into account many aspects relating to budget availability, resource allocation and traffic rerouting. In practice, it is difficult to accurately quantify all of the aforementioned factors; accordingly, it is equally difficult to obtain network-scale optimal maintenance policies. This paper presents an approach to evaluate the costs associated with deviations from optimal bridge-level maintenance policies, specifically focusing on delays in maintenance actions. Evaluating the cost of maintenance delays is performed using a reinforcement learning (RL) approach that relies on a probabilistic deterioration model to describe the deterioration in the structural components. The RL framework provides estimates for the total expected discounted maintenance costs associated with each maintenance policy over time, allowing comparison of maintenance policies where maintenance actions are delayed against an optimal maintenance policy. The comparisons are performed by probabilistically quantifying the ratio of expected costs associated with each maintenance policy. This ratio represents the trade-offs between performing or delaying maintenance actions over time. Moreover, the proposed approach is scalable, making it applicable to bridges with numerous structural elements. Example of application using the proposed framework is demonstrated using inspection data from bridges in the Quebec province of Canada. [ABSTRACT FROM AUTHOR]

Renewable energy, such as wind power and photovoltaic power, has uncertain and intermittent characteristics and zero marginal cost characteristics. The traditional power market mechanism is difficult to adapt to the new power system with a high proportion of renewable energy, and the original market system needs to be reformed. This paper discusses the application of a VCG auction mechanism in the electricity market, proposes a two-stage VCG market-clearing model based on the VCG mechanism, including the day-ahead market and the real-time market, and discusses the nature of the VCG mechanism. In order to address the discrepancy between the actual output of stochastic generator sets in the real-time market and their pre-scheduled output in the day-ahead market due to prediction deviations, a method for calculating punitive costs is proposed. A reallocation method based on market entities' contributing factors to budget imbalance is proposed to address the issue of budget imbalance under the VCG mechanism, in order to achieve revenue and expenditure balance. Through an example, the incentive compatibility characteristics of the VCG mechanism are verified, the problems of the locational marginal pricing (LMP) mechanism in the stochastic electricity market with a high proportion of renewable energy are analyzed, the electricity prices of the LMP mechanism and the VCG mechanism under different renewable energy proportions are compared, and the redistribution of the budget imbalance of the VCG mechanism is analyzed. [ABSTRACT FROM AUTHOR]

Budget allocation in project risk response is a vital issue in project risk management since it relates to a reasonable utilization of strict project budgets and effective mitigations of the risks that typify projects. This paper presents an integrated method based on an optimization model and fault tree analysis for allocating a risk response budget from a preventive and protective perspective. The proposed method consists of three main steps. The first step is to analyse and calculate risk probabilities and risk losses which involves identifying risk causes that may trigger a risk event to occur using fault tree analysis. It also identifies consequences once the risk event occurs, evaluates the occurrence probabilities of risk causes and expected financial losses of consequences. The second step is to build a relationship between the budget allocated to risk response strategies and the corresponding response effects. The third step is to construct an optimization model aiming at minimizing the total risk cost. We present proofs for the optimal risk response strategy in special cases of the budget allocation model. Furthermore, a detailed computational experiment is performed to gain insights into the three-phased budget allocation model for more general cases. The results show that an optimal risk response budget can be determined and the structure of the risk network has a significant impact on the preferred risk response strategy. [ABSTRACT FROM AUTHOR]

Understanding and quantifying the global methane (CH4) budget is important for assessing realistic pathways to mitigate climate change. Emissions and atmospheric concentrations of CH4 continue to increase, maintaining CH4 as the second most important human-influenced greenhouse gas in terms of climate forcing after carbon dioxide (CO2). The relative importance of CH4 compared to CO2 for temperature change is related to its shorter atmospheric lifetime, stronger radiative effect, and acceleration in atmospheric growth rate over the past decade, the causes of which are still debated. Two major challenges in reducing uncertainties in the factors explaining the well-observed atmospheric growth rate arise from diverse, geographically overlapping CH4 sources and from the uncertain magnitude and temporal change in the destruction of CH4 by short-lived and highly variable hydroxyl radicals (OH). To address these challenges, we have established a consortium of multi-disciplinary scientists under the umbrella of the Global Carbon Project to improve, synthesise and update the global CH4 budget regularly and to stimulate new research on the methane cycle. Following Saunois et al. (2016, 2020), we present here the third version of the living review paper dedicated to the decadal CH4 budget, integrating results of top-down CH4 emission estimates (based on in-situ and greenhouse gas observing satellite (GOSAT) atmospheric observations and an ensemble of atmospheric inverse-model results) and bottom-up estimates (based on process-based models for estimating land-surface emissions and atmospheric chemistry, inventories of anthropogenic emissions, and data-driven extrapolations). We present a budget for the most recent 2010–2019 calendar decade (the latest period for which full datasets are available), for the previous decade of 2000–2009 and for the year 2020. The revision of the bottom-up budget in this edition benefits from important progress in estimating inland freshwater emissions, with better accounting of emissions from lakes and ponds, reservoirs, and streams and rivers. This budget also reduces double accounting across freshwater and wetland emissions and, for the first time, includes an estimate of the potential double accounting that still exists (average of 23 Tg CH4 yr-1). Bottom-up approaches show that the combined wetland and inland freshwater emissions average 248 [159–369] Tg CH4 yr-1 for the 2010–2019 decade. Natural fluxes are perturbed by human activities through climate, eutrophication, and land use. In this budget, we also estimate, for the first time, this anthropogenic component contributing to wetland and inland freshwater emissions. Newly available gridded products also allowed us to derive an almost complete latitudinal and regional budget based on bottom-up approaches. For the 2010–2019 decade, global CH4 emissions are estimated by atmospheric inversions (top-down) to be 575 Tg CH4 yr-1 (range 553–586, corresponding to the minimum and maximum estimates of the model ensemble). Of this amount, 369 Tg CH4 yr-1 or ~65 % are attributed to direct anthropogenic sources in the fossil, agriculture and waste and anthropogenic biomass burning (range 350–391 Tg CH4 yr-1or 63–68 %). For the 2000–2009 period, the atmospheric inversions give a slightly lower total emission than for 2010–2019, by 32 Tg CH4 yr-1 (range 9–40). Since 2012, global direct anthropogenic CH4 emission trends have been tracking scenarios that assume no or minimal climate mitigation policies proposed by the Intergovernmental Panel on Climate Change (shared socio-economic pathways SSP5 and SSP3). Bottom-up methods suggest 16 % (94 Tg CH4 yr-1) larger global emissions (669 Tg CH4 yr-1, range 512–849) than top-down inversion methods for the 2010–2019 period. The discrepancy between the bottom-up and the top-down budgets has been greatly reduced compared to the previous differences (167 and 156 Tg CH4 yr-1 in Saunois et al. (2016, 2020), respectively), and for the first time uncertainty in bottom-up and top-down budgets overlap. The latitudinal distribution from atmospheric inversion-based emissions indicates a predominance of tropical and southern hemisphere emissions (~65 % of the global budget, <30° N) compared to mid (30° N–60° N, ~30 % of emissions) and high-northern latitudes (60° N–90° N, ~4 % of global emissions). This latitudinal distribution is similar in the bottom-up budget though the bottom-up budget estimates slightly larger contributions for the mid and high-northern latitudes, and slightly smaller contributions from the tropics and southern hemisphere than the inversions. Although differences have been reduced between inversions and bottom-up, the most important source of uncertainty in the global CH4 budget is still attributable to natural emissions, especially those from wetlands and inland freshwaters. We identify five major priorities for improving the CH4 budget: i) producing a global, high-resolution map of water-saturated soils and inundated areas emitting CH4 based on a robust classification of different types of emitting ecosystems; ii) further development of process-based models for inland-water emissions; iii) intensification of CH4 observations at local (e.g., FLUXNET-CH4 measurements, urban-scale monitoring, satellite imagery with pointing capabilities) to regional scales (surface networks and global remote sensing measurements from satellites) to constrain both bottom-up models and atmospheric inversions; iv) improvements of transport models and the representation of photochemical sinks in top-down inversions, and v) integration of 3D variational inversion systems using isotopic and/or co-emitted species such as ethane as well as information in the bottom-up inventories on anthropogenic super-emitters detected by remote sensing (mainly oil and gas sector but also coal, agriculture and landfills) to improve source partitioning. The data presented here can be downloaded from https://doi.org/10.18160/GKQ9-2RHT (Martinez et al., 2024). [ABSTRACT FROM AUTHOR]

This paper discusses the financial determinants of the efforts of local government units (LGUs) to invest in the area of energy transition (ET). The main objective is to verify the links between the scale, directions, and funding sources of LGUs' investments in ET and their budgetary situation described by the level of income independence, budget result, and debt level. The general research hypothesis assumes that the ET investment activity of LGUs is associated with their financial situation. The analysis covers the period 2019–2022 and uses data from the questionnaire-based survey conducted in 2023 among 181 LGUs in the Podkarpackie region in southern Poland. Non-parametric tests were employed to verify the association between the LGUs' ET investment activity and their financial situation: the Chi2 test, the Mann–Whitney U test, the Kruskal–Wallis test, and the Kendall's Tau correlation. Financial aspects were shown to influence the scale of municipal ET investments, as well as the type of projects implemented. Among the analysed financial indicators, the debt level was the most important constraint for LGUs to make their own ET investments. The debt level not only determined the scale of municipal ET investment, but also influenced decisions on the type of investments. [ABSTRACT FROM AUTHOR]

Warranty is a standard and essential policy for today's products. The period and cost of warranty are majorly affected by the reliability of products. Therefore, validation and verification of product reliability are required to control warranty costs and sustain the brand reputation. In this paper, we propose a modified reliability acceptance sampling plan (MRASP) for product reliability validation. The operating characteristic function of the MRASP is derived by considering the accelerated time‐to‐failure data with Type‐II censoring, which follows a Weibull distribution. To minimize the number of failures required for the accelerated life test and consider the sampling risk constraints, we constructed an optimization model to determine the optimal plan design of the proposed MRASP. Following the optimal plan design, practitioners can set the number of failures required in the accelerated life test to estimate the lifetime capability of the submitted lot and decide the lot disposition using the acceptance benchmark. In addition, the lifetime capability estimation can be used to infer the warranty return rate and warranty cost of the lot, which is valuable for practitioners to formulate the warranty budget. Finally, we illustrate a real‐world case to demonstrate the practicality and applicability of the proposed method. [ABSTRACT FROM AUTHOR]

Addressing current socio-economic crises strains public budgets and may threaten fiscal sustainability. Particularly in the welfare sector, where high expenditures meet poor controllability, efficient resource usage is essential to ensure future governments' capability to act while alleviating current problems. Consequently, this paper asks: why are some countries more efficient in translating social expenditure into welfare outcomes? To answer this question, it is argued that efficiency is a matter of institutional structures and their vertical policy-process integration (VPI): efficiency depends on institutional structures' capability to (1) ensure policymakers' responsibility and to (2) provide coordinated feedback, thus pushing for considerate and informed resource use. Analysing the effect of VPI on the relationship between welfare efforts and social outcomes in 21 OECD countries over three decades, the results show that VPI can not only turn 'less' into 'more', but it also compensates for performance losses in the face of spending cuts. [ABSTRACT FROM AUTHOR]

Purpose: Stereolithography (SLA) additive manufacturing (AM) technique has enabled the production of inconspicuous and aesthetically pleasing orthodontics that are also hygienic. However, the staircase effect poses a challenge to the application of invisible orthodontics in the dental industry. The purpose of this study is to implement chemical postprocessing technique by using isopropyl alcohol as a solvent to overcome this challenge. Design/methodology/approach: Fifteen experiments were conducted using a D-optimal design to investigate the effect of different concentrations and postprocessing times on the surface roughness, material removal rate (MRR), hardness and cost of SLA dental parts required for creating a clear customized aligner, and a container was constructed for chemical treatment of these parts made from photocurable resin. Findings: The study revealed that the chemical postprocessing technique can significantly improve the surface roughness of dental SLA parts, but improper selection of concentration and time can lead to poor surface roughness. The optimal surface roughness was achieved with a concentration of 90 and a time of 37.5. Moreover, the dental part with the lowest concentration and time (60% and 15 min, respectively) had the lowest MRR and the highest hardness. The part with the highest concentration and time required the greatest budget allocation. Finally, the results of the multiobjective optimization analysis aligned with the experimental data. Originality/value: This paper sheds light on a previously underestimated aspect, which is the pivotal role of chemical postprocessing in mitigating the adverse impact of stair case effect. This nuanced perspective contributes to the broader discourse on AM methodologies, establishing a novel pathway for advancing the capabilities of SLA in dental application. [ABSTRACT FROM AUTHOR]

This paper proposes two sequential metamodel‐based methods for level‐set estimation (LSE) that leverage the uniform bound built on stochastic kriging: predictive variance reduction (PVR) and expected classification improvement (ECI). We show that PVR and ECI possess desirable theoretical performance guarantees and provide closed‐form expressions for their respective sequential sampling criteria to seek the next design point for performing simulation runs, allowing computationally efficient one‐iteration look‐ahead updates. To enhance understanding, we reveal the connection between PVR and ECI's sequential sampling criteria. Additionally, we propose integrating a budget allocation feature with PVR and ECI, which improves computational efficiency and potentially enhances robustness to the impacts of heteroscedasticity. Numerical studies demonstrate the superior performance of the proposed methods compared to state‐of‐the‐art benchmarking approaches when given a fixed simulation budget, highlighting their effectiveness in addressing LSE problems. [ABSTRACT FROM AUTHOR]

This paper discusses a case study of a zone-picking system in a distribution centre. In particular, we designed a basic simulation model for analysing the system using Simio and connected the model to the MySQL database using the existing and new steps in Simio. A ranking and selection problem was subsequently formulated to determine the capacity of the system that maximises the expected throughput while satisfying the constraints on: (1) the expected maximum utilisation, (2) expected time-averaged number of boxes in the main conveyor, and (3) expected flow time. To solve the problem under a limited simulation budget, we implemented an optimal computing budget allocation procedure, which may work in the presence of stochastic constraints. The experimental results demonstrate that our approach can more efficiently and effectively determine the capacity of the system than the equal allocation scheme. [ABSTRACT FROM AUTHOR]

Existing methods for filter pruning mostly rely on specific data-driven paradigms but lack the interpretability. Besides, these approaches usually assign layer-wise compression ratios automatically only under given FLOPs by neural architecture search algorithms or just manually, which are short of efficiency. In this paper, we propose a novel interpretable task-inspired adaptive filter pruning method for neural networks to solve the above problems. First, we treat filters as semantic detectors and develop the task-inspired importance criteria by evaluating correlations between input tasks and feature maps, and observing the information flow through filters between adjacent layers. Second, we refer to the human neurobiological mechanism for the better interpretability, where the retained first layer filters act as individual information receivers. Third, inspired by the phenomenon that each filter has a deterministic impact on FLOPs and network parameters, we provide an efficient adaptive compression ratio allocation strategy based on differentiable pruning approximation under multiple budget constraints, as well as considering the performance objective. The proposed method is validated with extensive experiments on the state-of-the-art neural networks, which significantly outperforms all the existing filter pruning methods and achieves the best trade-off between neural network compression and task performance. With ResNet-50 on ImageNet, our approach reduces 75.49% parameters and 70.90% FLOPs, only suffering from 2.31% performance degradation. [ABSTRACT FROM AUTHOR]

The aim of this paper is to explore how efficient is the Fish School Search (FSS) algorithm in building a Decision Support System (DSS) for healthcare facility managers in order to optimize both the number of beds in their facilities and their budget. We have used a finite capacity queueing model with phase-type service distribution combined with a compartmental model and associated cost model. The Fish School Search algorithm was used to optimize the queueing model. To illustrate the proposed approach, we used the available data reported by a department of geriatric medicine of a hospital. Our findings showed that by encoding the whole information provided by the queueing system and the cost model in a fish, the Fish School Search is a potentially beneficial tool for optimizing healthcare resources and the facility's budget. The model can be extended to different medical facilities with respect to the objective parameters. [ABSTRACT FROM AUTHOR]

With the growing use of artificial intelligence, search engine providers are increasingly pushing advertisers to use automated bidding strategies based on machine learning. Such automated decision‐making systems leave advertisers in the dark about the data being used and how they can influence the outcome of the decision‐making process. Previous literature on artificial intelligence lacks an understanding of the dangers related to artificially intelligent systems and their lack of transparency. In response, our paper addresses the inherent risks of the automated optimization of advertisers' bidding strategies in search engine advertising. The selected empirical case of a service company therefore demonstrates how data availability can trigger a long‐term decline in advertising performance and how search engine advertising performance metrics develop before and after an event of data scarcity. Based on data collected for 525 days, difference‐in‐differences analysis shows that the algorithmic approach has a considerable and lasting negative impact on advertising performance. Furthermore, the empirical case indicates that self‐regulated learning can initialize a downward spiral that gradually impairs advertising performance. Thus, the aim of this study is to increase awareness regarding automated decision‐making dangers in search engine advertising and help advertisers take preventive measures to reduce the risks of algorithm missteps. [ABSTRACT FROM AUTHOR]

For financial index tracking, it is desirable to build a sparse portfolio of a small number of assets to save transaction costs. For all we know, a majority of the pertinent literatures on sparse index tracking are mainly concentrated on the penalized least squares estimation under the cardinality and no short-selling constraints. Nevertheless, the return series of financial index often exhibit outliers, and thus the above literatures may fail to produce a robust solution for index tracking. In this paper, we indeed to provide a general procedure to build robust portfolio that can undertake stock selection and capital allocation for financial index tracking. To be more realistic, we further take the full investment constraint (or budget constraint) into consideration. Numerical simulations indicate that the proposed method has good resistance to heavy-tailed error and outlier contamination. Finally, the out-of-sample performance of the new portfolios is compared empirically by tracking the SSE 50 index and FTSE China A50 index. [ABSTRACT FROM AUTHOR]

Mobile Crowdsensing (MCS) has shown the greatest potential that allows smart devices to collect and share different sensing data. Mobile users (or participants) send the desired sensing data to the service providers and collect rewards. However, the reward needs to be given such as, it does not increase platform costs. On the other hand, the unsatisfactory reward may reduce the interest of the participant which may degrade the quality data. Therefore, increasing sensing data quality with a constrained budget is a crucial challenge. There has been extensive research on the reward mechanism for MCS, but, most of the work is on the basic assumption that participant will complete their assigned task positively. In this paper, we propose an efficient user selection mechanism for Mobile Crowdsensing System (MCS) by considering the Probability of Success (PoS) of users (i.e. participant may fail to complete the assigned task with some probability). For the selection of an efficient user, the proposed mechanism also accounts the parameters like data quality and platform cost. We also propose a reward calculation model for the selected users. Minimizing the platform cost with a constrained budget is an NP-hard problem. To provide a sub-optimal solution to this problem Chaotic Krill-Herd optimization algorithm is used. The extensive simulation results reveal that the proposed method outperforms the existing work by considerable margins. [ABSTRACT FROM AUTHOR]