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Human-robot collaborative disassembly lines are widely used by remanufacturing companies to disassemble end-of-life (EOL) products. When disassembling large-sized EOL products, each workstation on a disassembly line is generally divided into multiple operating positions, so that different operators can disassemble the same product at their respective positions at the same time, thereby greatly improving efficiency. This paper focuses on a position-constrained human-robot collaborative disassembly planning (PC-HRCDP) problem for the above-mentioned lines, including three subproblems of disassembly sequence planning, disassembly line balancing and robot path planning. A multi-objective mixed integer programming model for PC-HRCDP is developed to solve smallscale instances. Furthermore, a multi-objective multi-fidelity optimisation (MO-MFO) algorithm is proposed to solve large-scale instances. Comprehensive experiments are conducted based on 10 problem instances generated in this study. Experimental results show that the proposed MO-MFO is better than a high-fidelity optimisation algorithm in terms of running time. In addition, benefiting from the strategy of MO-MFO to allocate the limited high-fidelity computational budget to solutions in the two stages of multi-objective optimisation and optimal sampling, MO-MFO is significantly better than the existing representative multi-fidelity optimisation algorithms in terms of the hyper-volume and the inverted generational distance. [ABSTRACT FROM AUTHOR]

Inspired by the global budget concept in health care, we propose an innovative subsidy scheme in which governments do not commit the subsidy amount in advance but determine it by the end of the subsidy period. The performance of the traditional and proposed subsidy schemes is evaluated in an oligopoly without competition, where each manufacturer decides its product quality and production quantity under the diseconomies of scale in production. Both types of schemes can take into account product quality or not. Our results show that compared to the traditional scheme, the proposed scheme drives manufacturers to reduce production quantity but improves product quality and brings them higher profits. Furthermore, the scheme results in greater social welfare when the subsidy budget is large. Finally, we conduct robustness checking by considering the market competition and absence of the diseconomies of scale, respectively. The result shows that the main findings in the basic model continue to hold, while there are additional findings. In the first scenario, as the market competition intensity increases, our proposed scheme becomes more preferable to the manufacturers and the government. In the second scenario, subsidy schemes that take product quality into account are never preferable. [ABSTRACT FROM AUTHOR]

We study a production lot-sizing problem inspired by a veterinary pharmaceutical plant in which demands are uncertain. First, we develop a deterministic capacitated lot-sizing model for the production of animal pesticides, performed in three machine-specific stages. Second, we propose a traditional robust optimisation formulation following the popular budget-of-uncertainty approach. Third, we derive a novel fragility-based approach that circumvents well-known issues with traditional robust optimisation approaches, such as the estimation of budgets of uncertainty, the over-conservatism of robust solutions and the sensitivity of solutions to the decision maker's risk attitude. The fragility-based approach is grounded in the idea of minimising violations, over the full uncertainty support, from a user-specified cost target. It avoids the estimation of budgets of uncertainty and produces less conservative solutions via explicit modelling of constraint violation. We demonstrate the effectiveness of our approach on instances built upon real data provided by our industrial partner, a major player in the Brazilian veterinary pharmaceutical sector. The results show that our fragility-based approach reduces average total costs across all instances and maintains greater model stability under different target estimations. It also preserves cost savings when bottlenecks are introduced in production and when inventory costs and capacities are varied. [ABSTRACT FROM AUTHOR]

This paper presents novel models for the Integrated Procurement and Lot-Sizing Problem with multiple customers and backlogging. Since allowing backlog in a traditional cost minimisation model involves dealing with intangible costs of not fulfilling the demand on time, we propose optimising service-levels while keeping the costs minimal using a budget constraint. The motivation of this study emanates from a manufacturing company that assembles commercial and industrial refrigeration equipment, using both purchased materials and in-house products. Instead of considering an aggregate demand, we consider the specific demand from various customers. This allows us to incorporate demand fulfilment decisions into the model in case of stock-outs by deciding which customers will have their orders backlogged. The fill-rate, or β service-level, is considered both globally and for customers and products individually. Computational experiments show that the service-level-driven models improve service when compared to the traditional cost model and also end up enforcing service equity among different customers at the expense of a deterioration in the global fill-rate. [ABSTRACT FROM AUTHOR]

Historically, advertising allocation decisions have operated mostly at a macro level, comprising determination of the total budget followed by apportionment among media channels. Owing to the rapid and sustained rise of digital media, an additional decision now operates at a micro level, namely, which specific customers to target with advertising. In the macro case, optimal control theory provides a powerful framework for firm profit maximization, specifically allowing for ad response, cost per medium, and discount rate, all in the presence of multiple competing brands. However, optimal control theory has never been applied to the situation of microtargeting individual customers. Consequently, in this study the author shows how optimal control theory can be adapted for application to individual customers. The author estimates a multinomial logit model with individual-specific advertising response parameters. In turn, these parameters are used to determine the optimal number of exposures each customer should receive for each advertising medium. An empirical example demonstrates that using the optimal microtargeting method improves firm profits over existing ad scheduling methods by between 150% and 183%. [ABSTRACT FROM AUTHOR]

Due to the novelty of the format, web series have been little studied. To address this gap, this research identifies and compares the nature, experiences, and practices of professional and amateur web series filmmakers. Thirty semi-structured interviews were conducted with Colombian professionals (15) and amateurs (15) covering 1) their training and experience; 2) their working practices and routines; 3) their creative processes; and 4) the financial aspects of their productions (salaries and budgets). The main findings suggest that web series production is similar for both types of filmmakers in most of these aspects and has evolved from both television practices and media industry logics. Moreover, both groups of filmmakers only innovate in terms of the technological possibilities and distribution of their web series, and their creativity is regulated and dependent on the approval of influential social and industry actors. [ABSTRACT FROM AUTHOR]

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]

Biosecurity activities primarily include pre‐border and border quarantine, post‐border surveillance and post‐border eradication. Budget allocated to quarantine and surveillance activities ultimately influence the expenditure and success rate of eradication campaigns. Optimal portfolio allocation examined in previous research is susceptible to potential severe uncertainties existing in ecology and in the behaviour of invasive species itself. These uncertainties, together with a limited budget, make it difficult for decision makers to allocate the total management budget to each biosecurity activity in a robust manner. Info‐gap decision theory is applied to model the severe uncertainty in invasive species management, and robust optimize the total management cost. This research shows that using a combination of pre‐border and border quarantine (to reduce the incursion probability) and post‐border surveillance (to enable early detection and rapid response), enables decision makers to be more robust to potential uncertainty. Further, it is reported that investment in quarantine that is more cost‐effective should outweigh that in surveillance, in line with precautionary principle. Increasing the estimated population threshold for surveillance detection also gains more robustness. Synthesis and applications: Portfolio allocation options developed in this research provide decision makers with a way to manage the invasive species spatially, cost‐effectively and confidently by allocating the total management budget in a robust manner. The methods outlined in this research can not only be applied to invasive species, but also the conservation of endangered species that are constrained by severe uncertainty in ecological modelling and limited resources. [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]

Aim Methods Results Conclusions Shocks to the food system (such as extreme weather events, wars, and pandemics) are felt by institutional food systems. For hospitals, these shocks affect the quantity, quality, and variety of foods that can be offered to patients. One strategy to buffer the hospital food supply from external threats is to prioritise ingredients produced locally. Thus, the aim of the current research is to describe the country of origin of all foods purchased by a large, metropolitan healthcare organisation and to identify opportunities for improving the locality of the food supply.This study was of a cross‐sectional, observational design. The country of origin for all foods procured over a 1‐year period by a large, urban healthcare organisation was determined by proportion of food budget spend. State of origin was identified for fresh fruit, vegetables, and meat. The organisation was in Queensland, Australia and utilised a cook‐fresh, room‐service foodservice model. Descriptive analysis was used to to determine the number of items and the proportion of budget spend on all foods produced in Australia, and by food category. Similar descriptive statistics were generated to determine the proportion of the budget spend on fresh fruits, vegetables and meats produced in Queensland.Over the 1‐year period, 659 individual food items were purchased by the hospital foodservice, and 502 food items were included in the anlaysis. In total, 53% of the food budget was spent on Australian foods (100% Australian ingredients) and almost all fruit (73%) and vegetables (91%) were Australian grown. Procuring fresh fruit (28%), vegetables (35%), and meat (46%) from within the state was less common, and this may reflect the primary states of production across Australia, and seasonal variability of the food supply.Findings offer priority areas for improving the locality of the food supply. Future research to determine if procuring more foods locally has benefits to consistency of the food supply is warranted. [ABSTRACT FROM AUTHOR]

Glaciers, distinct from the Greenland and Antarctic ice sheets, play a crucial role in Earth's climate system by affecting global sea levels, freshwater availability, nutrient and energy budgets and regional climate patterns. Accurate measurements of glacier mass changes are needed to understand and project glacier evolution and its related impacts on the climate system. Two distinct methods allow to measure glacier mass changes at high spatial resolution. Remotely sensed surface elevation data provides volume change estimates over large glacierized regions for multi-annual to decadal time periods. Field glaciological measurements provide annually to seasonally resolved information on glacier mass change for a small sample of the world's glaciers. By combining the two methods we provide annual time series of individual glacier mass changes and related uncertainties spanning the hydrological years from 1976 to 2023. The per-glacier time series can then be seamlessly integrated into annually resolved global regular grids of glacier mass changes at user-specified spatial resolution. Our results undergo a leave-one-out cross-validation confirming uncertainty estimates at the glacier level to be in the conservative side. Our dataset provides a new baseline for future glacier change modelling assessments and their impact on the world's energy, water, and sea-level budget. The present annual mass change time-series for the individual glaciers and the derived global gridded annual mass change product at a spatial resolution of 0.5° latitude and longitude will be made available from the WGMS webpage. During the review process, the dataset is temporarily available from URL: https://user.geo.uzh.ch/idussa/Dussaillant_etal_ESSD_data/. [ABSTRACT FROM AUTHOR]

Copyright of Environmental & Social Management Journal / Revista de Gestão Social e Ambiental is the property of Environmental & Social Management Journal 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 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]

Various stress-releasing phenomena, such as episodic tremor and slip (ETS) and low-frequency earthquakes, occur at the downdip seismogenic zone in southwest Japan. However, it is unclear how much net stress and slip deficit accumulate at these depths during the interseismic phase. Here, we perform both elastic and viscoelastic earthquake-cycle forward models and reassess the locking state in Nankai from a synthesized perspective with the aid of geodetic modeling results. Our results suggest that the overestimation of the locking depth due to ignoring Earth's viscoelasticity is much smaller (less than 5 km) in this early interseismic subduction zone compared to that (~ 10 km) of late-interseismic margins. Considering viscoelastic modeling results and other physical arguments, the preferred steady-state viscosities for the continental and oceanic mantle are 5 × 1019 Pa s and 1020 Pa s, respectively. We find a clear trade-off between the full locking depth and the width of the transition zone when explaining both horizontal and vertical geodetic data, demanding other data to further resolve this inherent ambiguity. Unlike in Cascadia, partial megathrust locking in Nankai likely penetrates into the ETS zone, leaving no intervening gap between the shallow megathrust, where hosts large earthquakes, and the ETS zone. Assuming locking extends into the downdip of the ETS zone (i.e., 40 km), we propose a preferred viscoelastic locking model with a full locking depth of 18 km and a broad transition zone spanning a 22-km depth range. In this model, the downdip half portion of the transition zone corresponds to the ETS zone, which can accumulate certain slip deficit in a largely creeping and partially locked state. However, most of the accumulated slip deficit in the ETS zone may be accommodated aseismically simultaneously by stress-releasing phenomena, leaving limited to no budget to release during future megathrust earthquakes. We suggest that precise documentation of total slip during slow slip events, along with refinement of viscoelastic locking models, will provide new insights into the net slip budget available in the ETS zone. This will help assess the potential of future coseismic and/or postseismic slip penetrating into the ETS zone in Nankai, Cascadia and other subduction zones. [ABSTRACT FROM AUTHOR]

This commentary documents how federal funding agencies are changing the criteria by which they distribute taxpayer money intended for scientific research. Increasingly, STEMM (Science, Technology, Engineering, Mathematics, and Medicine) funding agencies are requiring applicants for funding to include a plan to advance DEI (“Diversity, Equity, and Inclusion”) in their proposals and to dedicate a part of the research budget to its implementation. These mandates undermine the academic freedom of researchers and the unbiased generation of knowledge needed for a well-functioning democracy. Maintaining excellence in science is fundamental to the continuation of the U.S. as a global economic leader. Science provides a basis for solving important global challenges such as security, energy, climate, and health. Diverting funding from science into activities unrelated to the production of knowledge undermines science’s ability to serve humankind. When funding agencies politicize science by using their power to further a particular ideological agenda, they contribute to public mistrust in science. Hijacking science funding to promote DEI is thus a threat to our society. [ABSTRACT FROM AUTHOR]

Aim: In 2022, in Italy, general practitioners (GPs) have been allowed to prescribe SGLT2i in Type 2 Diabetes (T2D) under National Health Service (NHS) reimbursement. In the pivotal clinical trial named DECLARE-TIMI 58, dapagliflozin reduced the risk of hospitalization for heart failure, CV death and kidney disease progression compared to placebo in a population of T2D patients. This study evaluated the health and economic impact of dapagliflozin for T2D patients who had or were at risk for atherosclerotic cardiovascular disease in the Italian GPs setting. Methods: A budget impact model was developed to assess the health and economic impact of introducing dapagliflozin in GPs setting. The analysis was conducted by adopting the Italian NHS perspective and a 3-year time horizon. The model estimated and compared the health outcomes and direct medical costs associated with a scenario with dapagliflozin and other antidiabetic therapies available for GPs prescription (scenario B) and a scenario where only other antidiabetic therapies are available (scenario A). Rates of occurrence of cardiovascular and renal complications as well as adverse events were captured from DECLARE-TIMI 58 trial and the literature, while cost data were retrieved from the Italian tariff and the literature. One-way sensitivity analyses were conducted to test the impact of model parameters on the budget impact. Results: The model estimated around 442.000 patients eligible for the treatment with dapagliflozin in the GPs setting for each simulated year. The scenario B compared to scenario A was associated with a reduction in the occurrence of cardiovascular and renal complication (−1.83%) over the 3 years simulated. Furthermore, the scenario A allowed for an overall cost saving of 102,692,305€: 14,521,464€ in the first year, 33,007,064€ in the second and 55,163,777€ in the third. The cost of cost of drug acquisition, the probability of cardiovascular events and the percentage of patients potentially eligible to the treatment were the factor with largest impact on the results. Conclusions: The use of dapagliflozin in GPs setting reduce the number of CVD events, kidney disease progression and healthcare costs in Italy. These data should be considered to optimize the value produced for the T2D patients who had or were at risk for atherosclerotic cardiovascular disease. [ABSTRACT FROM AUTHOR]

Injection molding is one of the dominant methods for mass‐producing short fiber reinforced plastics renowned for their exceptional specific properties. In the utilization of such composite components, optimization of process parameters significantly influences material characteristics and part performance. However, in industrial practice, this process often relies on intuition and iterative experimentation. Prior studies have introduced data‐efficient optimization methods but faced limitations in adopting minor variations in the product development cycle. This study introduces a multi‐fidelity optimization framework aimed at efficiently addressing new problems by leveraging previously acquired knowledge from analogous domains, particularly accommodating alterations in material scenarios. Two data‐driven frameworks are explored: 1) Gaussian process‐based and 2) neural network‐based, each employing distinct information‐transferring techniques, hierarchical Kriging and transfer learning, respectively. Bayesian optimization of process parameters under limited data budget, which is typical in realistic industrial settings, is performed. The results highlight the efficiency of the proposed framework, demonstrating superior performance not only in data‐driven modeling but also in optimization efficiency compared to conventional single‐fidelity approaches. The Pearson correlation coefficient is utilized to assess the applicability of the multi‐fidelity framework in handling the inherent ambiguity of the similarity of problem scenarios. The proposed method is believed to be adaptable and versatile, offering potential application across various challenges in process optimization. [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]

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]

Friendship is a unique and underexplored area of human sociality. Research suggests that humans have preferences for characteristics in their friends that maximize the benefits of these relationships. Yet, whereas more friends might increase friendship benefits, humans also have limited time, resources, and energy to invest in finding high-quality friends, making it likely that the nature of these preferences differs depending on the resources an individual has available to invest in this goal. Across two studies (total N = 693), we investigated how this trade-off may function by investigating the nature of friendship preferences. In Study 1, we utilized the budget paradigm method from behavioral economics to investigate the necessities and luxuries in friendship preferences. In Study 2, we replicated these preferences with a novel method and extended our investigation into understanding the hierarchical nature of these preferences. Taken together, our results provide a promising starting point for research investigating trade-offs between necessities and luxuries in friendship preferences. [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]

Medical-legal partnership (MLP) integrates the unique expertise of lawyers into collaborative clinical environments. MLP teams meet the needs of individual patients while also detecting structural problems at the root of health inequities and advancing solutions at the institutional, community, and system levels. Yet MLPs today operate in limited settings and survive on scant budgets. Expanding their impact requires secure funding. Financing MLPs as health care can do the following: (1) help address inequity at the point of care; (2) enable expert diagnosis and treatment of nonmedical drivers of health; (3) enhance team-based practice in health care organizations; (4) offer another way for clinicians to participate in advocacy; and (5) bolster a broader movement to increase access to justice. [ABSTRACT FROM AUTHOR]

This article examines William Gladstone's Irish policy through the prism of his commitment to 'sound' finance, a fiscal programme involving low taxation, minimal government expenditure, balanced budgets and free trade. These prescriptions, it argues, served as an unintentional stimulus to Irish nationalism while also encouraging Gladstone's receptivity to Home Rule, because by the 1880s he had become convinced that the cost of governing Ireland within the framework of the Union was imperilling 'sound' finance throughout the United Kingdom. Viewed from the vantage of fiscal policy, it concludes, Gladstone's approach to Ireland was characterized by ideological rigidity rather than political adaptability. [ABSTRACT FROM AUTHOR]

Information on the benefits of green budget tagging (Green budget tagging refers to itemization and linking of every budget item with climate action, mitigation, adaptation, or both) in developing countries could help boost the uptake of this practice and in turn help these same developing countries to mobilize climate-related development finance. We apply double differences in means econometric analytical approach to evaluate the association between green budget tagging (GBT) and target outcome variables of climate-related finance and CO2 emissions. The results show that all 32 developing countries that have undertaken green budget tagging are also associated with larger inflows of climate-related finance. Among the adopters, climate-related finance inflows have been significantly larger for countries with externally-initiated green budget tagging than for those with internally-initiated tagging. The results also show that a minimum level of climate-related finance is required to achieve CO2 emissions reduction. However, the current mean yearly inflow of climate-related development finance falls short of the level that drives emissions reduction. These findings point to the importance of green budget tagging and to the need to upscale climate-related finance inflows to propel improvement in environmental quality. Although green budget tagging is a useful tool for mobilizing external development finance, current external finance inflows are insufficient to achieve ambitious emission reductions. Immense effort is therefore required to mobilize the adequate scale of climate-related finance from both external and other sources. Green budget tagging can be a crucial tool for mobilizing climate-related finance and improving environmental quality in developing countries. To entice climate-related development finance, developing countries need to clearly define climate-related projects in their national budgets. Climate finance mobilization policies designed for reducing emissions are not complete unless they emphasize and promote the scale of inflows. Hence, policymakers should put greater effort into mobilizing adequate finance (from both external and domestic sources) to drive emissions reduction. [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]

Cloud computing has gradually become one of the most popular platforms for executing scientific applications due to its elastic and on-demand resource provisional capabilities. But, how to effectively schedule a set of parallel workflows to minimize the makespan under their individual budget constraints remains a critical problem. This work proposes a Clustering-assisted Gradient-Based Optimizer (C-GBO) to improve the performance for scheduling workflows in cloud environments. First, it designs a novel individual encoding mechanism including task-VM mapping and task-priority sub-strings to further optimize the makespan by updating both sub-strings simultaneously, especially each element representing task execution order in a task-priority sub-string can take any values within the pre-specified range but not subject to control dependencies among tasks. Second, to address the original GBO's easiness of falling into local optima brought by its only one best guiding solution, it divides individuals into different groups as their position information by the K-means algorithm and selects the best guiding solution for each group with 50% probability from their own clusters locally, such that the search diversity is improved and the cross evolution is reduced. Third, a Gaussian disturbance-based elite enhancement strategy is developed by introducing a Gaussian disturbance operation to a certain number of each elite individual so as to fully exploit these individuals and increase the quality of the global best solution. Our proposed C-GBO algorithm is testified and compared with six peers on datasets with different scales through WorkflowSim. The results demonstrate that our proposed C-GBO achieves the best results in all the involved algorithms, i.e., obtaining the success rate of 100% on all datasets, and performs better in the average makespan that is at most 88.47% shorter than its peers in most cases. [ABSTRACT FROM AUTHOR]

Risk budgeting is one of the recent and successful strategies for asset portfolio selection. In this strategy, risk budgets are associated with assets, and the amount of investment is adjusted so that the contribution of each asset to the portfolio risk is proportional to its risk budget. To the best of our knowledge, no specific method has been presented in the literature to systematically determine the value of risk budgets. To fill this research gap, in this article, we consider the risk budgets as decision variables and present a bi-level programming model where the upper level decides the risk budgets and the lower level determines the risk budgeting portfolio. Three approaches are introduced to solve the model. The first is a single-level reformulation of the bi-level model, the second is a novel gradient-based algorithm, and the third is the particle swarm optimization algorithm. Moreover, the k-means clustering method is utilized to determine the assets involved in the portfolio. Computational results over real-world datasets demonstrate the significance of the bi-level model. In addition, the results confirm the proficiency of our gradient-based algorithm from both solution quality and running time. [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]

Copyright of International Journal of Professional Business Review (JPBReview) is the property of AOS: ESTRATEGIA & INOVACAO 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.)

BAföG is intended to exploit educational potential and promote equal opportunities. To this end, it has two main control levers: allowances and benefit rates. However, adjustments to both figures have been determined by political debates and budgetary considerations rather than on the basis of scientific evidence on the achievement of objectives. Without a causal impact evaluation, however, neither the effectiveness nor the efficiency of the funding can be determined. In view of the objectives and the scope of the funds used (€3 billion per year), causal evaluations appear more than overdue. This article proposes a solution to this problem with five key points as an impetus for political and scientific discussion. [ABSTRACT FROM AUTHOR]

Copyright of Anatolian Journal of General Medical Research is the property of Galenos Yayinevi Tic. LTD. STI 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.)

We study intertemporal choice through a novel and flexible framework that accounts for savoring of future consumption and memories of past consumption. The model uses standard intertemporal budget constraints but enriches preference structures with utility from anticipation, remembering, and experience. We also present an internal commitment mechanism that ensures dynamic consistency. We provide a revealed preference characterization of this model and apply it to quarterly consumption data from Spanish households. Utility from anticipation is important—and time inconsistency not strictly needed—to rationalize consumption patterns in the data. [ABSTRACT FROM AUTHOR]

What can organizations do to minimize wasteful year‐end spending? I introduce a two‐period model to derive optimal budget roll‐over and audit rules. A principal tasks an agent with using a budget to fulfill the organization's spending needs, which are private information of the agent. The agent can misuse funds for private benefit. The optimal rules allow the agent to roll‐over a share of the unused funds, but not necessarily the full share, and in most cases to audit only sufficiently large spending. The optimal audit rule can change once fund roll‐over is allowed. Strategically underfunding the agent can be optimal. [ABSTRACT FROM AUTHOR]

With the intensification and rising frequency of disasters in recent years, implementing disaster preparedness measures has become increasingly important. However, local governments must be prepared for disasters, with their limited resources of personnel and budget, to make the redressal plans relevant for facing problems such as population decline. This study aims to enhance resilience in the field of disaster management by facilitating identification of problems in the disaster management plan of local governments, and promotion of mutual understanding among those involved. For this purpose, a tool has been developed to evaluate local disaster management measures from the viewpoints of both local governments and residents, and to promote smooth communication and coordination among the stakeholders. A questionnaire survey was conducted using an index for self-assessment by local government officials, which was formulated based on the literature of the United Nations Office for Disaster Risk Reduction. Based on the survey results, the elements of resilience were classified into three groups depicting the abilities to collaborate and organize, adapt to the environment, and manage for the next generation. The evaluation was conducted in four local governments. In two regions, the residents' awareness and preparedness for a disaster were surveyed. The survey results revealed that the questionnaire should be improved; nevertheless, the authors were able to identify the problems of local governments and the fields that the residents do not know sufficiently about. The results indicate the possibility to apply this method in other regions in the future. [ABSTRACT FROM AUTHOR]

The construction industry has always been prone to uncertainties due to its dynamic nature, but the COVID-19 pandemic highlighted the need for a deeper understanding of its present challenges and their implications for the future of the industry. Despite numerous studies conducted on this topic, a comprehensive understanding of the factors stemming from COVID-19 that disrupted the industry is still lacking. This study aims to bridge that knowledge gap by identifying the challenges, categorizing them into four areas (cost overruns, schedule overruns, contractual implications, and health and safety issues), ranking them based on their frequency of occurrence in previous studies, and outlining management strategies to lessen their detrimental impacts on projects. A systematic literature review was conducted, and 284 publications were shortlisted for further review. The review revealed 26 challenges, of which shortage of labor, suspension or slowing of ongoing projects, force majeure confusion, and workplace safety concerns were among the most frequently cited, and offered strategies, such as workforce investment, supplier management, legal consultation, and transparent communication, for managing them. This research provides valuable insights for stakeholders and project managers in the construction sector that will help them comprehend and effectively manage the pandemic-induced challenges. [ABSTRACT FROM AUTHOR]

This paper uses a newly assembled dataset on various types of social protection spending in 154 countries during the COVID‐19 pandemic in 2020 and 2021 to analyze the effect of the electoral cycle on the size and composition of the social protection stimulus budget. The analysis shows that the longer the time since the last election in a country—and thus the sooner the next election date—the larger the share of the social protection pandemic budget allocated to social assistance and income protection, and the lower the share allocated to job retention schemes. The electoral cycle appears to have impacted the size of social assistance spending only in countries with high political competition. [ABSTRACT FROM AUTHOR]

Uncertainty and variability in demand and supply processes make it difficult for companies to make inventory management decisions. In this study, a model is developed that will provide the maximum service level of a pharmaceutical warehouse under the budget constraint, taking into account stochastic demand. Due to stochastic demand, the chance constraint programming approach is used to achieve the desired service level at different levels. In this study, the problem of a pharmaceutical warehouse that supplies medicines to pharmacies and hospitals is considered as a realworld problem. The model is designed as a dynamic programming model based on periods. Since there are thousands of drugs in the pharmaceutical warehouse, as the number of products increases, it becomes difficult to find the appropriate solution in an acceptable time. The model is first solved as a mixed integer linear programming model in Lingo. A genetic algorithm (GA) approach is then proposed for large-scale problems. The simulation optimization method also applied to the problem and compared with the optimization method and GA. The GA approach yields better results in the shortest time as the number of periods increases. The developed integrated model demonstrated a numerical example in a pharmaceutical warehouse and was solved using three different approaches. This study is of great importance in terms of providing results that will enable managers to decide the amount of items they should keep in their warehouses by using their budgets in the most efficient way. Nine different scenarios have been derived with various chance constraint risk factors and budget values. Scenario analysis has revealed that the budget has a significant impact on the results at a 95% confidence level. If a pharmaceutical warehouse increases its budget by 10%, it can reduce its total annual inventory carrying costs by 70%. [ABSTRACT FROM AUTHOR]

Scholars have long regarded government fiscal stress as a crucial factor driving public service outsourcing; however, further investigation is still needed to determine whether service outsourcing in turn helps governments improve fiscal conditions. Public choice theory suggests that outsourcing services can lead to efficiency improvement and cost savings. Nevertheless, transaction costs theory implies that the costs associated with service outsourcing may offset or even outweigh its potential benefits. Moreover, the extent of transaction costs depends on contextual factors such as market competition, bureaucrat support, and government management capacity. Empirically, we employ an instrumental variable estimation approach to examine the impact of service outsourcing on the budget balances and debt levels of U.S. municipalities. We find that service outsourcing improves government fiscal conditions, with contextual factors playing an important role in moderating this effect. Additionally, outsourcing services to different types of contractors has varying implications for government fiscal conditions. [ABSTRACT FROM AUTHOR]

Background: The COVID-19 vaccination programme in South Africa was rolled out in February 2021 via five delivery channels- hospitals, primary healthcare (PHC), fixed, temporary, and mobile outreach channels. In this study, we estimated the financial and economic costs of the COVID-19 vaccination programme in the first year of roll out from February 2021 to January 2022 and one month prior, in one district of South Africa, the West Rand district. Methods: Financial and economic costs were estimated from a public payer's perspective using top-down and ingredient-based costing approaches. Data were collected on costs incurred at the national level and from the West Rand district. Total cost and cost per COVID-19 vaccine dose were estimated for each of the five delivery channels implemented in the district. In addition, we estimated vaccine delivery costs which we defined as total cost exclusive of vaccine procurement costs. Results: Total financial and economic costs were estimated at US$8.5 million and US$12 million, respectively; with a corresponding cost per dose of US$15.31 (financial) and US$21.85 (economic). The two biggest total cost drivers were vaccine procurement which contributed 73% and 51% to total financial and economic costs respectively, and staff time which contributed 10% and 36% to total financial and economic costs, respectively. Total vaccine delivery costs were estimated at US$2.1 million (financial) and US$5.7 million (economic); and the corresponding cost per dose at US$3.84 (financial) and US$10.38 (economic). Vaccine delivery cost per dose (financial/economic) was estimated at US$2.93/12.84 and US$2.45/5.99 in hospitals and PHCs, respectively, and at US$7.34/20.29, US$3.96/11.89 and US$24.81/28.76 in fixed, temporary and mobile outreach sites, respectively. Staff time was the biggest economic cost driver for vaccine delivery in PHCs and hospitals while per diems and staff time were the biggest economic cost drivers for vaccine delivery in the three outreach delivery channels. Conclusion: This study offers insights for budgeting and planning of COVID-19 vaccine delivery in South Africa's public healthcare system. It also provides input for cost-effectiveness analyses to guide future strategies for maximizing vaccination coverage in the country. [ABSTRACT FROM AUTHOR]