Search

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]

Focuses on ways to create an effective budgeting process for companies in the paper industry. Impact of assets with high cost on profit; Importance of considering the exposure time to risk in budgeting; Effect of reinvesting less than full depreciation on manufacturing operations.

This paper investigates the theoretical foundations of fiscal sustainability and the policy prescription that when the trajectory of public debt is above a certain threshold, priority should be given to fiscal consolidation. The author contends that the assumption that the government sector is subject to an intertemporal budget constraint can be validated only within a real-exchange economy where money is neutral and public debt entails the absorption of private savings. Conversely, when the economy is viewed as a system of interlocking balance sheets under a paper standard, public debt may prompt higher prices but not an inflation overhang forcing monetization or default. Another justification regards the quality of spending when policymakers' incentives create a deficit bias, but this must be balanced against the need to flexibly provide safe financial assets to offer fiscal support when needed. In conclusion, limiting fiscal action within an intertemporal budget constraint is unwarranted under a paper standard and generates deflationary effects, underlining the urgent need that Europe move decisively toward the creation of a coordinated fiscal policy joined to a permanent capacity to issue EU debt. This is today's most urgently needed reform for the economic, social, and political sustainability of the European Union. [ABSTRACT FROM AUTHOR]

Copyright of Defense & Strategy / Obrana a strategie is the property of Institute for Strategic Studies and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

This paper examines state level lobbying data to determine how budgeting affects the timing, intensity and composition of lobbying expenditures. Using a database of all available lobbying expenditures in the U.S. states ranging up to 25 years, the paper shows interest group lobbying increases substantially during the budgeting process, and the budgeting process is unlike other issues lobbyists face. Spikes in lobbying during budgeting are driven primarily by business groups, and these business groups account for most of the changes on the margin in lobbying activity. Moreover, even groups relatively unaffected by budgets lobby more intensely during legislative budgeting, consistent with the "Christmas tree" effect of appropriations and budget bills. [ABSTRACT FROM AUTHOR]

This paper aimed at a systematic review of the role of FinTech in enhancing the digital transformation of Saudi Arabian banks. Google Scholar was searched with related search terms to finally select 28 papers using the PRISMA process of screening and selection. There were very few papers directly dealing with the use of FinTech as a tool for digital transformation in banks, not specifically in traditional banks. There were a few papers on digital tools used in FinTech. Overall, the digital transformation of any kind of bank using FinTech tools had been slow and a small percentage of the total. The situation was worse in the case of Saudi banks despite the large amounts of budgetary allocations for technologies in banking as a part of its Vision 2030. This was because of the late entry of Saudi Arabia into these concepts. The need for Islamic banks to follow Sharia rules was not found to be a great problem. All these points indicate to the need for greater acceleration of using FinTech tools for the digital transformation of Saudi traditional banks. Some limitations of this review are the low rigour of the available papers due to methodological limitations, discussion papers without empirical data dominating and a wide range of aims provided in the papers. The need for substantially more work on Saudi banks has been highlighted. The validity of the conclusions from this review needs to be verified using empirical research. [ABSTRACT FROM AUTHOR]

Purpose: This paper presents a review of literature, aimed at analyzing and understanding the nexus of knowledge on the topic of government budgetary responses to COVID-19 and identifying gaps for future research directions on crisis budgeting. Design/methodology/approach: A systematic literature review approach was conducted by considering scientific journal articles written in English and published through 2020–2022. The databases used for the literature search in this paper were Scopus and Web of Science, resulting in 41 articles for final review. Findings: This review found that in a crisis, budgetary responses were greatly determined by perceived uncertainties. In the case of the COVID-19 crisis, governments seemed to prioritize economic recovery. While many studies have documented budgetary responses to the crisis, most were written in the beginning of the crisis through documentary content analysis, leaving significant research gaps. Thus, this review offers directions for future research concerning governmental response to perceived uncertainty, logic behind governments' budgeting strategies, sustainable development principles within crisis budgeting and the prioritization of economic considerations in a health crisis. Originality/value: This paper is one of the first to present insights into the state of research regarding the topic of government budgeting during the COVID-19 crisis. In addition, it provides insights from the literature for anticipating future shocks and crises, along with directions for future researchers in developing their research agenda. Peer review: The peer review history for this article is available at: https://publons.com/publon/10.1108/IJSE-01-2023-0057 [ABSTRACT FROM AUTHOR]

The traditional aim in transportation planning is to maximise gains associated with vehicular travel distances or times, indirectly prioritising populations that live near existing or proposed roads—remote populations that first require hours of walking to reach roads are overlooked. In this paper, rural roads optimisation is performed using a new model that estimates proposed roads' accessibility gains, considering reductions in vehicular travel time and reductions in walking time required by remote populations to reach them. This ensures that even the most remote populations that may benefit from new roads are included in their evaluation. When presented with a large number of proposed roads and the requirement of determining a plan within a suitable budget, it is often infeasible to construct all proposed roads. In such instances, subsets of well-performing road-combinations that are evaluated with respect to multiple objectives need to be identified for analysis and comparison–for which multi-objective optimisation approaches can be employed. Traditional optimisation approaches return a small number of road-combination plans only, limited to user-specified budget levels and objective weight sets. This paper presents an innovative heuristic solution approach that overcomes such limitations by returning thousands of well-performing solutions scattered across a budget span, and not limited in number to user-specified objective weight sets at fixed budget levels. The heuristic is employed along with a more traditional weighted-sum integer-linear programming approach to determine high-quality road-combination plans selected from 92 roads recently proposed for construction in Nepal's remote Karnali province. Using these two approaches with inputs from the new multi-modal accessibility model, it is illustrated how rural roads planning can be performed to the benefit of rural populations regardless of their proximity to roads. New planning and analysis benefits of the heuristic are demonstrated by comparing its solutions to those determined by the weighted-sum approach, providing a level of detail and sophistication not previously possible for rural roads planning and analysis. [ABSTRACT FROM AUTHOR]

This paper assesses the growth-enhancing and growth-retarding effects of different budgetary fiscal variables using a balanced panel of 32 middle-income countries for the time period of 2000–2017. Much of previous research requires to be re-evaluated as it ignores the biases associated with either incomplete or wrong specification, or both, of the budget constraint and the money supply. This paper addresses these gaps and obtains more unbiased, consistent and efficient estimates for the fiscal variables, using both static and dynamic panel econometric techniques. Specifically, this paper finds that (1) larger tax and non-tax revenues retard economic growth; (2) higher allocation to public capital expenditure appears to have positive and significant effect on growth; (3) fiscal deficit appears to have neutral-growth effects; and (4) controlling broad money supply corrects for biases in the orthogonal specification. [ABSTRACT FROM AUTHOR]

Copyright of Canadian Journal of Civil Engineering is the property of Canadian Science Publishing and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Participatory budgeting has been advocated as an advanced tool of civic participation and a travelling innovation for more than three decades. This paper provides a bibliometric review of the concurrent body of knowledge on participatory budgeting (PB), explaining how this democratic innovation 'travelled' through time and over different scientific fields. This study was based on a dataset of 396 papers on PB published from 1989 to January 2023. The study finds that research in PB has reached its peak of scholarly attention in pre-COVID-19 pandemic years. The study also finds that the research on PB has migrated from the field of political science to other fields, such as economics, management science, law, urban planning, environmental science, and technology. [ABSTRACT FROM AUTHOR]

Copyright of Zeszyty Prawnicze Biuro Analiz Sejmowej is the property of Kancelaria Sejmu and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

Purpose: This paper aims to investigate how embedding accounting techniques of cost and budgeting within the Australian National Disability Insurance Scheme (NDIS) potentially perpetuates colonial practices for Australian First Nations people living in remote areas. Further, the paper aims to explore how accounting might help to integrate the unique modes of accountability First Nations people have over disability care into the NDIS funding system. Ultimately, the aim is to discern whether accounting practices can be mobilised as a means to decolonising the NDIS framework. Design/methodology/approach: This study uses a qualitative methodology to analyse public hearings from the Australian Disability Royal Commission. Drawing on Bhabha's (1994) concept of the "third space", this study investigates how accounting techniques can be used to potentially decolonise the NDIS. This study also borrows Bhabha's (1994) concept of the third space to explore the potential for decolonising the NDIS through accounting techniques. Findings: Findings show that the accounting techniques pertaining to funding and costs embedded within the NDIS contribute to displacing and disconnecting First Nations people from their cultural practices and ways of life. Further, the analysis reveals that the NDIS funding system could help to decolonise the NDIS space if it were modified to incorporate First Nations' perspectives on accountability for disability care. Originality/value: The case of the NDIS exposes glimpses of colonisation in contemporary Australia, where Western institutional and economic systems dominate over the structure and authority of the practice. In this paper, this study demonstrates that the accounting system used by the NDIS plays a role in marginalising First Nations people. However, accounting, as a technology of negotiation, could also be mobilised to enhance accountability for disability care outcomes and pave the way for decolonising public policies. [ABSTRACT FROM AUTHOR]

By establishing the China International Development Cooperation Agency (CIDCA) in 2018, China has attempted to enhance foreign aid as a diplomatic means and has been more assertive in providing foreign aid through both bilateral and multilateral channels. However, China only publishes aggregated foreign aid statistics in white papers, and data beyond 2019 are not available. This paper aims to estimate China's foreign aid from 2001 to 2022 as a proxy for Official Development Assistance (ODA) defined by the Organisation for Economic Co-operation and Development. The results revealed that China's foreign aid, on a grant-equivalent basis, decreased from US$6.4 billion in 2019 to US$5.0 billion in 2020. China has mobilized a large amount of funding for international cooperation on COVID-19. It is unclear how much of this was financed by regular foreign aid budgets. Thus, two cases were set in the current study. In the upper case, all donated vaccines, estimated at US$3.9 billion, came from other sources while in the lower case, it was assumed that the regular foreign aid budget covered all costs. China's foreign aid in 2022 was estimated at US$7.9 billion in the upper case and US$5.0 billion in the lower case. Compared with major ODA donors, China ranked 6th in the upper case and 13th in the lower case. Currently, the CIDCA is attempting to mobilize new financial resources to expand its activities under the Global Development Initiative. It is important to continue to comprehensively examine how China proceeds in improving its development cooperation. [ABSTRACT FROM AUTHOR]

Financial Portfolio Optimization Problem (FPOP) is a cornerstone in quantitative investing and financial engineering, focusing on optimizing assets allocation to balance risk and expected return, a concept evolving since Harry Markowitz's 1952 Mean-Variance model. This paper introduces a novel meta-heuristic approach based on the Black Widow Algorithm for Portfolio Optimization (BWAPO) to solve the FPOP. The new method addresses three versions of the portfolio optimization problems: the unconstrained version, the equality cardinality-constrained version, and the inequality cardinality-constrained version. New features are introduced for the BWAPO to adapt better to the problem, including (1) mating attraction and (2) differential evolution mutation strategy. The proposed BWAPO is evaluated against other metaheuristic approaches used in portfolio optimization from literature, and its performance demonstrates its effectiveness through comparative studies on benchmark datasets using multiple performance metrics, particularly in the unconstrained Mean-Variance portfolio optimization version. Additionally, when encountering cardinality constraint, the proposed approach yields competitive results, especially noticeable with smaller datasets. This leads to a focused examination of the outcomes arising from equality versus inequality cardinality constraints, intending to determine which constraint type is more effective in producing portfolios with higher returns. The paper also presents a comprehensive mathematical model that integrates real-world constraints such as transaction costs, transaction lots, and a dollar-denominated budget, in addition to cardinality and bounding constraints. The model assesses both equality/inequality cardinality constraint versions of the problem, revealing that the inequality constraint tends to offer a wider range of feasible solutions with increased return potential. [ABSTRACT FROM AUTHOR]

Fiscal rules are argued to be important for sound and sustainable fiscal policies and have been increasingly adopted over the last 20 years. As increased fiscal pressure and fiscal risks urge countries to address the public debt legacy left by recent economic crises, fiscal rules come under greater scrutiny. To inform the debate on fiscal frameworks, this paper presents a comprehensive survey of the empirical literature on the impact of fiscal rules. In particular, we discuss the recent empirical literature that investigates the impact of fiscal rules on various elements related to fiscal performance and beyond. Our survey finds that fiscal rules are associated with improved fiscal performance as approximated by improved budget balances, lower debt and lower public spending volatility. Furthermore, empirical research finds that fiscal rules are related to more accurate budget forecasts and improved sovereign bond ratings. From a macroeconomic perspective, well-designed fiscal rules do not principally undermine public investment, do not increase pro-cyclicality in fiscal policy-making and can support fiscal consolidations. These results, however, also depend on the broader economic and institutional context. Moreover, there is emerging literature that links fiscal rules to macroeconomic and broader political outcomes, such as income inequality and political polarisation. We discuss methodological challenges related to identification and point to avenues for future research. [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]

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]

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]

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]

Many citizens feel excluded from political decision-making, which, in their eyes, is dominated by an unresponsive political elite. Citizens with high populist attitudes perceive the world through a populist 'lens' and therefore yearn for more popular control and for 'the people' to be included in the political process. Participatory budgeting should be particularly suited to address populist demands due to the fact that it is focused on giving citizens actual influence on policy-making. However, so far, no study has examined the effect of participation in a democratic innovation on populist attitudes. This paper empirically assesses if and to what extent participation in a participatory budget affects populist attitudes, and whether citizens with high populist attitudes are affected differently than citizens with low populist attitudes. We analyze panel data on participants of four local participatory budgeting events in the Netherlands before and after participation and find that citizens with high populist attitudes decrease these attitudes significantly after participating in a participatory budget, whereas citizens with low populist attitudes are not significantly affected. Moreover, the significant difference in change between these two groups suggests that citizens with high populist attitudes go 'through the looking glass' and become less populist after participating in a participatory budget. [ABSTRACT FROM AUTHOR]

India's general government debt increased notably due to the Covid-19 pandemic, which necessitates a re-assessment of debt sustainability and the role of fiscal policy. The empirical estimation, using data from 1980-81 to 2021-22, finds evidence of cointegration between general government's expenditure and revenue, satisfying the intertemporal government budget constraint. Despite the recent growth in government debt, the paper finds that primary surplus responds positively to an increase in debt using the fiscal policy response function, implying that India's general government debt is sustainable in the long term. [ABSTRACT FROM AUTHOR]

Imperfect external capital market and internal soft budget constraints are important reasons for the issue of enterprise financing constraints. This paper studies the impact of financing constraints and influencing factors of financing constraints in municipal infrastructure investment and construction for local governments by using the panel data from 2003 to 2018 in China. The results show that, first, there are great financing constraints in municipal infrastructure investment and construction for local government in China. Second, the increase of self-financing capacity and external financing capacity can reduce the level and uncertainty of financing constraints, thus improving the efficiency of municipal infrastructure investment and construction. Third, the expansion of the city scale may reduce the uncertainty of financing constraints, but improve the level of financing constraints thus reducing the efficiency of municipal infrastructure investment and construction. [ABSTRACT FROM AUTHOR]

This proposed system introduces a dynamic coupon generation mechanism empowered by machine learning algorithms, particularly reinforcement learning, to enhance people's engagement and optimize coupon distribution in such a way that the coupon for the subsidy is given to farmers fairly by prioritizing those in need. This process involves key stages, including data gathering, preprocessing, feature enhancement, and autonomous decision-making logic development, taking into account diverse factors such as farmer segments and budget constraints. The system interacts with HTML, PHP, CSS, and JavaScript, with Python supporting the implementation. This paper aims to continuously adapt the model to meet evolving customer behaviors and preferences. This technological approach is essential for optimizing coupons effectively and staying responsive to real-time customer interactions and preferences. [ABSTRACT FROM AUTHOR]

Effective cost management is necessary to implement projects successfully. However, in practice organizations may face unique challenges that need to be addressed. To achieve this goal, the paper explores project cost management challenges, using survey responses from 51 managerial/senior-level staff on the open-ended questions connected to the project cost management processes. Data analysis, supported by process approach, was conducted with the use of systematic coding. The findings reveal that managing projects creates challenges, connected to the project cost management processes such as (1) cost management planning, (2) cost estimation, (3) cost budgeting, and (4) cost control. The research allowed for the discovery of a set of project cost management challenges, definition of their causes and effects, and development of possible countermeasures. Implications and recommendations for future research are given. [ABSTRACT FROM AUTHOR]

The oil and gas industry's inherent volatility, influenced by geopolitical events, supply and demand fluctuations, and economic conditions, poses significant challenges to project schedules and budgets. Accurate project cost prediction is vital for effective project management, particularly in long-term projects in PERTAMINA's downstream sector. Currently, PERTAMINA faces project delays, primarily due to market volatility and lengthy preparatory activities. This time gap results in budget disparities, necessitating an accurate cost escalation method. In current PERTAMINA Downstream project budgeting, cost escalation methods adjust the Owner's Estimate during tender processes but typically only focus on the bidding year, omitting the construction period gap which should also be factored in. This paper analyzes various benchmarks, including gold price, Big Mac index, US Dollar index, and Indonesia's inflation rate, considered as alternative currencies for precise project budgeting, and selects the most suitable one. Results indicate that the Gold Price emerges as the best benchmark for precise cost escalation due to its consistent growth trend, in line with the RAND Study's insights on the impact of omitting scope and risks. [ABSTRACT FROM AUTHOR]

Project bundling has been introduced as an innovative approach that groups several infrastructure projects into a single contract. Project bundling has numerous benefits such as expediting project delivery, capitalizing on economies of scale, and reducing the overall contract cost. However, agencies implementing project bundling also face some challenges, one of which is assessing the feasibility of a certain project delivery method (PDM) of interest. While different PDMs could be used for bundled projects, design-build (DB) has attracted particular interest from project owners as an alternative, innovative PDM due to its numerous benefits related to project performance. While previous research efforts examined different aspects of project bundling in terms of its decision-making factors, opportunities, and challenges, no previous research effort was devoted to studying the feasibility or applicability of the DB delivery method for bundled projects. To that extent, and to fill this gap, this paper develops a scoring framework that assesses the feasibility of the DB delivery for bundled projects. First, a questionnaire survey was distributed to industry experts to quantify the importance of 25 project bundling objectives. Second, principal component analysis (PCA) was implemented to calculate a weight for each project bundling objective. Third, a score for each project bundling objective was calculated and the DB feasibility tiers were identified using a series of statistical tests. Finally, a user-friendly feasibility scoring framework was developed to assign a feasibility score for the DB delivery method based on the user's inputs related to the bundled project of interest. The results showed that the key project bundling objectives that mostly increase the feasibility score of the DB delivery method for bundled projects are (1) Having specific scheduling, sequencing, staging, and phasing; (2) Meeting tight milestones or deadlines (timely completion of individual phases); and (3) Completing the overall project on schedule. Ultimately, this research adds to the body of knowledge by equipping agencies and project owners with a decision-support system that helps them make the go/no-go decision for the DB delivery method for their bundled projects. By making the right PDM decision, project owners can enhance their bundling practices (especially in relation to the PDM selection), reduce the associated risks and challenges, improve the overall schedule of the bundled program, and keep the cost within the identified budgets. [ABSTRACT FROM AUTHOR]

The objective of this paper is to discuss the issues related to the use of Fade Mitigation Techniques (FMT) for the GEOCAST satellite system, considering both transparent and regenerative payloads. The main characteristics of the GEOCAST systems are presented first. Baseline link budgets are established both for the transparent return link and for the regenerative configurations. Then the FMTs designed for GEOCAST are presented. Theoretical performance of the FMTs and their practical implementation are described focusing on the FMT control loop design. Finally, simulation results for system performance with emphasis on physical layer, are presented both for transparent and regenerative configurations. [ABSTRACT FROM AUTHOR]

For many machine learning models, a choice of hyperparameters is a crucial step towards achieving high performance. Prevalent meta-learning approaches focus on obtaining good hyperparameter configurations with a limited computational budget for a completely new task based on the results obtained from the prior tasks. This paper proposes a new formulation of the tuning problem, called consolidated learning, more suited to practical challenges faced by model developers, in which a large number of predictive models are created on similar datasets. In such settings, we are interested in the total optimization time rather than tuning for a single task. We show that a carefully selected static portfolio of hyperparameter configurations yields good results for anytime optimization, while maintaining the ease of use and implementation. Moreover, we point out how to construct such a portfolio for specific domains. The improvement in the optimization is possible due to the more efficient transfer of hyperparameter configurations between similar tasks. We demonstrate the effectiveness of this approach through an empirical study for the XGBoost algorithm and the newly created metaMIMIC benchmarks of predictive tasks extracted from the MIMIC-IV medical database. In the paper, we show that the potential of consolidated learning is considerably greater due to its compatibility with many machine learning application scenarios. [ABSTRACT FROM AUTHOR]

Copyright of Journal of Engineering (17264073) is the property of Republic of Iraq Ministry of Higher Education & Scientific Research (MOHESR) and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)

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]

In recent years, the research field of data collection under local differential privacy (LDP) has expanded its focus fromelementary data types to includemore complex structural data, such as set-value and graph data. However, our comprehensive review of existing literature reveals that there needs to be more studies that engage with key-value data collection. Such studies would simultaneously collect the frequencies of keys and the mean of values associated with each key. Additionally, the allocation of the privacy budget between the frequencies of keys and the means of values for each key does not yield an optimal utility tradeoff. Recognizing the importance of obtaining accurate key frequencies and mean estimations for key-value data collection, this paper presents a novel framework: the Key-Strategy Framework forKey-ValueDataCollection under LDP. Initially, theKey-StrategyUnary Encoding (KS-UE) strategy is proposed within non-interactive frameworks for the purpose of privacy budget allocation to achieve precise key frequencies; subsequently, the Key-Strategy Generalized Randomized Response (KS-GRR) strategy is introduced for interactive frameworks to enhance the efficiency of collecting frequent keys through group-anditeration methods. Both strategies are adapted for scenarios in which users possess either a single or multiple key-value pairs. Theoretically, we demonstrate that the variance of KS-UE is lower than that of existing methods. These claims are substantiated through extensive experimental evaluation on real-world datasets, confirming the effectiveness and efficiency of the KS-UE and KS-GRR strategies. [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]