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Despite the growth of the sport-for-development and peace (SDP) field, the sector remains heavily resource-constrained whereby organizations have limited resources but also operate in resource-scarce environments. Given this, we draw on the theoretical concept of bricolage to explore how such resource constraints are overcome. In this study, an organization implementing SDP for refugees and throughout their existence have been confronted with resource constraints is examined. Across the organization's start-up phase and growth stage, multiple forms of bricolage are identified, which enabled the organization to address the social needs of refugees through SDP. We discuss these findings in relation to existing literature on bricolage as well as social entrepreneurship in SDP, emphasizing the way in which the recombination of resources offers opportunities for organizations and practitioners to effectively manage a scarce resource base. [ABSTRACT FROM AUTHOR]

Copyright of Contemporary Accounting Research is the property of Canadian Academic Accounting Association 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.)

Statistical Process Control (SPC) is employed to support the effective quality performance of production and manufacturing processes. The control chart is a type of SPC utilized to reduce variability in key quality parameters, leading to the production of quality conforming products. Control charts, such as the Exponentially Weighted Moving Average (EWMA) are widely used to reduce variation, improve productivity, and ensure quality. However, small manufacturers face challenges that may prevent their effective use of quality control charts. One such challenge relates to resource constraints. These constraints tend to inhibit the conduct of quality inspections at high rates. With this in mind, the present study sets out to develop and present an improved EWMA control chart for effective quality control that takes into consideration significant resource constraints in a small aluminium manufacturing firm. The findings suggest that adopting the improved EWMA chart can enhance the detection of any increase in defectives without the need for additional resources. The improved EWMA control chart provides small manufacturers with an enhanced quality conforming tool developed based on practical realities. Implications for both practice and theory are also highlighted. [ABSTRACT FROM AUTHOR]

Introduction: Cost-effectiveness analyses typically ignore healthcare system resource constraints. Ophthalmology is affected by resource constraints because of increasing disease prevalence and the use of resource-intensive treatments. This study evaluated the impact of resource constraints on the cost-effectiveness of faricimab 6 mg, compared with aflibercept 2 mg and ranibizumab biosimilar 0.5 mg, for treating wet age-related macular degeneration (wAMD) or diabetic macular oedema (DMO) over a 5-year horizon. Methods: A microsimulation model estimated the impact of resource constraints on patients visits, delays, costs and quality-adjusted life-year (QALY) losses due to treatment delays at a typical UK National Health Service eye hospital treating 1500 patients with wAMD and 500 patients with DMO. Patient characteristics, treatment regimens and treatment intervals were informed using published literature and expert opinion. Resource constraint was represented by limiting the number of available intravitreal injection appointments per week, with growing demand caused by rising disease prevalence. The model compared outcomes across three scenarios; each scenario involved treating all patients with one of the three treatments. Results: Over 5 years, in a resource-constrained hospital, compared with aflibercept, faricimab use resulted in the avoidance of 12,596 delays, saved GBP/£15,108,609 in cost and avoided the loss of 60.06 QALYs. Compared with ranibizumab biosimilar, faricimab use resulted in the avoidance of 18,910 delays, incurred £2,069,088 extra cost and avoided the loss of 105.70 QALYs, resulting in an incremental cost-effectiveness ratio of £19,574/QALY. Conclusions: Accounting for resource constraints in health economic evaluation is crucial. Emerging therapies that are more durable and require less frequent clinic visits can reduce treatment delays, leading to improved QALY outcomes and reduced burden on healthcare systems. Faricimab reduced the number of delayed injections, leading to improved QALY outcomes for patients in a healthcare system with resource constraints. Faricimab is cost-saving when compared with aflibercept and cost-effective when compared with ranibizumab biosimilar. [ABSTRACT FROM AUTHOR]

This study focuses on unspecified dynamic seru scheduling problems with resource constraints (UDSS-R) in seru production system (SPS). A mixed integer linear programming model is formulated to minimize the makespan, which is solved sequentially from both allocation and scheduling perspectives by a strip-packing constructive algorithm (SPCA) with deep reinforcement learning (DRL). The training samples are trained by the DRL model, and the reward values obtained are calculated by SPCA to train the network so that the agent can find a better solution. The output of DRL is the scheduling order of jobs in serus, while the solution of UDSS-R is solved by SPCA. Finally, a set of test instances are generated to conduct computational experiments with different instance scales for the DRL-SPCA, and the results confirm the effectiveness of proposed DRL-SPCA in solving UDSS-R with more outstanding performance in terms of solution quality and efficiency, across three data scales (10 serus × 100 jobs, 20 serus × 250 jobs, and 30 serus × 400 jobs), compared with GA and SAA, the Avg. RPD of DRL-SPCA decreased by 9.93% and 7.56%, 13.36% and 10.72%, and 9.09% and 7.08%, respectively. In addition, the Avg. CPU time was reduced by 29.53% and 27.93%, 57.48% and 57.04%, and 61.73% and 61.76%, respectively. [ABSTRACT FROM AUTHOR]

The practice of bricolage, predominantly regarded as a strategy for innovation and overcoming constraints in resource-scarce environments, has garnered increased scholarly attention in recent years. This paper aims to consolidate the fragmented field by conducting a literature review of bricolage-focused research, offering an integrated perspective based on prior contributions. This serves to conceptualize and advance theory building on the subject, while simultaneously pinpointing gaps in the literature and proposing a roadmap for future research. A systematic literature review of 126 articles was carried out, utilizing the Web of Science as the primary data source. The focus was on clarifying the concept and its subtypes and examining common themes in existing literature to establish a cohesive conceptualization of bricolage. The primary finding is the conceptualization of a multi-level framework encompassing the who-when-how-what dimensions of bricolage, which can serve as a foundation for future research. Furthermore, we consolidate and present bricolage subtypes while questioning their utility in research and practice. Additionally, we analyse research trends over time and summarize the ongoing practical and conceptual discourse aimed at distinguishing bricolage from related, overlapping concepts. Finally, we provide recommendations for future research, facilitating the continued growth of the bricolage field in a harmonious manner. [ABSTRACT FROM AUTHOR]

To save resources and protect the environment to the maximum extent, green buildings came into being. Among them, prefabricated building is the only way for traditional buildings to transform into green buildings. The construction scheduling of traditional buildings is mostly focused on the control of on-site resources, which cannot scientifically and reasonably complete the construction goal of prefabricated building. In response to the above issues, a resource constrained scheduling model based on genetic algorithm is designed by sustainable development, and an improved non dominated sorting genetic algorithm with elite strategy is introduced. It is used to solve the time cost weight balance scheduling model and the low-cost low-carbon scheduling model. The research results indicated that this algorithm had a reverse generation distance value of 0.35 when evaluated 4000 times, and a super volume value of 0.43 when evaluated 10000 times. In the application of a certain affordable housing project, the resource constrained scheduling model based on genetic algorithm can shorten the assembly phase to 8 days, and the low-cost low-carbon scheduling model using proposed algorithm can reduce the transportation cost and carbon emission duration of transportation vehicles to 22501 yuan and 93.75 h, respectively. Resource constrained scheduling models based on genetic algorithms and low-cost low-carbon scheduling models have potential in the field of green buildings, which can achieve significant results in saving time, cost, and reducing carbon emissions. These research results can provide reference for the promotion and practice of green buildings, and guide the formulation and implementation of relevant policies.

The intersection of community involvement and empowerment in sexual and reproductive health (SRH) initiatives represents a critical area in public health. This scoping review aimed to explore the pathways from community involvement to empowerment within SRH initiatives, providing a comprehensive understanding of how public health strategies engage and build the capacity of communities. The literature from databases such as PubMed, Scopus and Web of Science were reviewed systematically, focusing on studies published between 2000 and 2023. The review identified various public health interventions that initially engage communities through participation, consultation and collaboration. These interventions often serve as the foundation for subsequent empowerment, characterised by capacity building, autonomy and advocacy. Key findings highlight that effective SRH initiatives involve community members in programme activities and foster environments where they can gain the skills, knowledge and confidence needed to take control of their health outcomes. Our analysis revealed that successful pathways from involvement to empowerment are multifaceted, requiring sustained commitment, resources and culturally sensitive approaches. The review underscores the importance of integrating community involvement and empowerment in designing and implementing SRH programmes to ensure sustainable and impactful health outcomes. This scoping review contributes to the existing literature by mapping the relationship between community involvement and empowerment, offering insights for public health practitioners and policymakers aiming to enhance SRH initiatives. Future research should focus on longitudinal studies to better understand the long-term impacts of these pathways on community health and well-being.

This qualitative study explores the implemen­tation of the Curriculum and Assessment Policy Statement (CAPS) for Grade 7 Natural Sciences in two rural schools in the Eastern Cape, South Africa. The researcher uses semi-structured interviews and classroom observations with six teachers to investigate the challenges and adapta­tions in CAPS implementation within resource-constrained environments. The study reveals significant hurdles, in­cluding severe resource limitations, language barriers, and assessment difficulties. The findings highlight teachers' re­markable creativity in adapting CAPS to rural contexts by using locally available materials and innovative teaching strategies. However, these adaptations often fall short of fully meeting curriculum requirements, which could po­tentially widen the educational gap between rural and ur­ban learners. The study emphasises the need for context-specific professional development, flexible assessment guidelines, and tailored resources to support effective CAPS implementation in rural settings. It argues for a more nuanced approach to curriculum design and implementa­tion that takes into consideration the unique socio-cultural and resource contexts of rural schools. This research contributes to our understanding of curriculum implementation in challenging contexts and provides insights for improving science education in rural South Africa.

Abstract Introduction Cost-effectiveness analyses typically ignore healthcare system resource constraints. Ophthalmology is affected by resource constraints because of increasing disease prevalence and the use of resource-intensive treatments. This study evaluated the impact of resource constraints on the cost-effectiveness of faricimab 6 mg, compared with aflibercept 2 mg and ranibizumab biosimilar 0.5 mg, for treating wet age-related macular degeneration (wAMD) or diabetic macular oedema (DMO) over a 5-year horizon. Methods A microsimulation model estimated the impact of resource constraints on patients visits, delays, costs and quality-adjusted life-year (QALY) losses due to treatment delays at a typical UK National Health Service eye hospital treating 1500 patients with wAMD and 500 patients with DMO. Patient characteristics, treatment regimens and treatment intervals were informed using published literature and expert opinion. Resource constraint was represented by limiting the number of available intravitreal injection appointments per week, with growing demand caused by rising disease prevalence. The model compared outcomes across three scenarios; each scenario involved treating all patients with one of the three treatments. Results Over 5 years, in a resource-constrained hospital, compared with aflibercept, faricimab use resulted in the avoidance of 12,596 delays, saved GBP/£15,108,609 in cost and avoided the loss of 60.06 QALYs. Compared with ranibizumab biosimilar, faricimab use resulted in the avoidance of 18,910 delays, incurred £2,069,088 extra cost and avoided the loss of 105.70 QALYs, resulting in an incremental cost-effectiveness ratio of £19,574/QALY. Conclusions Accounting for resource constraints in health economic evaluation is crucial. Emerging therapies that are more durable and require less frequent clinic visits can reduce treatment delays, leading to improved QALY outcomes and reduced burden on healthcare systems. Faricimab reduced the number of delayed injections, leading to improved QALY outcomes for patients in a healthcare system with resource constraints. Faricimab is cost-saving when compared with aflibercept and cost-effective when compared with ranibizumab biosimilar. Graphical Abstract

[Introduction] In order to realize the goal of "dual-carbon", the green and low-carbon transformation of power supply side is the key to realize the green and low-carbon transformation of Guangdong, and at the same time, the construction of a diversified power supply support system with a high proportion of new energy is also the most important task for achieving a new type of power system in Guangdong. Therefore, it is of great significance to plan the carbon neutral power supply structure of Guangdong in advance. [Method] Based on the study of the prospective energy supply and demand pattern of Guangdong Province, this paper integrated the low-carbon transformation and energy security, considered the constraints from the proportion of non-fossil energy consumption, resource conditions, and the balance of power supply, and proposed a carbon-neutral power supply development idea for Guangdong, analyzed different transformation paths, and put forward relevant suggestions. [Result] The study shows that during the period of carbon neutrality, the total installed capacities of power supply in Guangdong Province is expected to reach about three times of the current installed capacities; the main body of power supply will be transformed from the current combination of "thermal power + nuclear power + external power supply" to "new energy + nuclear power + external power supply"; The proportion of non-fossil energy installed capacity will reach 80%, of which the proportion of new energy resources in the province will reach 50%. [Conclusion] The prospect of power structure adjustment in Guangdong Province seems optimistic. However, this process still faces many challenges from policy, technology and market, etc. It is suggested to pay attention to the role change of thermal power in the power system, safeguard the key factors of non-fossil energy development, and emphasize the building of regulating capacity for new power system; in order to meet the demand for electricity consumption in Guangdong twice as much as the status quo during the period of carbon neutrality, it is even more important to pay special attention to technological innovation issues leading the clean and low-carbon transformation of power supply structures, and through scientific and technological innovation, to promote the exploration of new energy types and new energy technologies towards the development of greater energy density and more efficient energy conversion.

Background This study explores the complex relationship between organizational politics, emotional intelligence and employee behavior in contemporary settings. Organizational politics, which is widespread in organizational settings, has a substantial impact on different aspects of employee behavior. Emotional Intelligence has become a vital factor in individuals' capacity to successfully traverse the complexities of an organization. This study consolidates current material to offer insights into the impact of organizational politics on employee behavior and the mediating role of emotional intelligence in this connection. Methods A survey was undertaken with a sample size of 500 employees hailing from several Information Technology (IT) and Information Technology Enabled Services (ITES) firms in the Dehradun (Uttarakhand) region. The study employed quantitative methodologies to investigate the correlations between Emotional Intelligence, Perceived Organizational Politics and Employee Behavior. The data were evaluated using Partial Least Squares Structural Equation Modelling (PLS-SEM) to examine the hypothesized correlations and mediation effects. Results The study identified substantial correlations between Emotional Intelligence, Perceived Organizational Politics, and Employee Behavior. Emotional Intelligence was discovered to have a positive impact on Behavioral Dynamics. Perceived Organizational Politics had a substantial influence on both Emotional Intelligence and Behavioral Dynamics. Factors such as Need for Power, Relationship Conflict, Resource Constraints, Role Conflict, and Workforce Diversity were discovered to have an impact on Perceived Organizational Politics. Conclusions The results confirm strong connections between organizational politics, emotional intelligence, and employee behavior, highlighting the significance of these concepts in comprehending employee behavior in organizational environments. The study proposes that enhancing employees' emotional intelligence can alleviate the adverse effects of organizational politics, resulting in enhanced behavioral dynamics. The study also addresses the limits, outlines potential areas for further research, and highlights the managerial implications. It suggests that firms should prioritize the development of emotional intelligence to cultivate a favorable work environment.

The rapid advancement of Big Data and artificial intelligence (AI) has significantly transformed management accounting practices, necessitating a reevaluation of job profiles and skill-sets required for professionals in this field. This study explores managerial accounting practices in Romanian contexts, examining how digital technology aligns with competitive strategy, managerial efficiency, human resources constraints, and limited resources constraints. Grounded in technology determinism and the resource-based view theory, this research identifies factors influencing the successful implementation of and challenges associated with managerial accounting practices. A sequential mediation analysis investigates pathways wherein investments in human resources and constraints related to limited resources influence managerial advancement through digital technology and competitive strategy. This study emphasizes digital technologies' role in optimizing costs, enhancing operational processes, and facilitating strategic decision-making. This study's conclusions show that, even in situations with limited resources, digital transformation projects greatly improve managerial effectiveness and competitive strategy. The participants included 406 professional accountants from the Romanian accounting community. Practical implications for companies include the necessity for strategic planning in digital implementations to mitigate constraints and capitalize on opportunities for sustainable growth and competitive advantage. This report provides a path to optimize the potential of digital technology and gives practical recommendations for researchers and business leaders. [ABSTRACT FROM AUTHOR]

Copyright of Southern Energy Construction is the property of Southern Energy Construction Editorial Office 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.)

Historically, leveraging the right resources has been deemed critical for successful product innovation. However, recent findings reveal that some entrepreneurs and ventures, despite facing resource constraints, can still develop innovative products. This intriguing phenomenon has captured the attention of scholars, resulting in an increasing number of empirical work examining various aspects of resource-constrained product innovation. To consolidate the dispersed knowledge generated by these studies and establish a solid foundation for evidence-based entrepreneurship and management, we conducted a comprehensive review of 53 articles. Through an evidence-based analysis, we construct a framework that encompasses the who-why-how-where-when-what of the phenomenon. This framework delineates the scope of the phenomenon and sheds light on the enabling antecedents and processes undertaken by the actors, thus serving as a foundation for both research and practice. Moreover, we identified significant research gaps on the topic and provided suggestions for future research to focus on underexplored concepts and relationships within the framework. [ABSTRACT FROM AUTHOR]

The Internet of Things (IoT) has ushered in an era of interconnected devices and sensors that generate vast amounts of data. While the potential of IoT is vast, resource- constrained IoT environments present unique challenges, particularly in the context of data aggregation. This research focuses on developing secure data aggregation scheme tailored to resource-constrained IoT environments. In these settings, limitations on processing power, memory, and bandwidth necessitate innovative solutions to ensure both the efficiency and security of data collection and transmission. This research proposes a comprehensive framework that optimizes data aggregation algorithms. The key objectives of this research are to enhance data aggregation efficiency by minimizing redundant data transfer, optimizing data compression, and reducing the burden on constrained resources. The findings of this research provide valuable insights for IoT applications operating under resource limitations. By improving the efficiency and security of data aggregation in resource-constrained IoT environments, this research contributes to the realization of the full potential of IoT technologies in scenarios where resources are limited. [ABSTRACT FROM AUTHOR]

This study proposes an optimization model based on particle swarm optimization (PSO) to achieve the objectives of minimizing project duration, project cost, and resource fluctuation while maximizing project quality and schedule flexibility. The objectives of this study are as follows: (1) to determine whether the interrelationships between these five objectives affect the outcome of the optimization; (2) to determine whether the presence or absence of resource constraints affects the solution; and (3) to verify that the solution of the PSO-based optimization model coincides with the exact mathematical solution, but faster. An example project was presented to illustrate the applicability of the proposed model. The conclusions of this study are as follows: (1) multiple objectives do interact with each other and should be evaluated simultaneously rather than one at a time to reach realistic solutions, (2) resource constraints do affect the solutions obtained, and (3) the proposed model successfully reaches an optimal solution that is extremely close to the exact mathematical solution much faster than the mathematical model. [ABSTRACT FROM AUTHOR]

Deep space exploration activities can effectively promote the development and application of space technology, which holds significant scientific and strategic value. Detectors operating in deep space are of long mission periods and substantial distance from Earth, posing challenges in timely resource replenishment. Moreover, scheduled missions may face external uncertainties, hindering their smooth execution. Therefore, resource constraint processing strategies that possess dynamic adjustment capabilities play a crucial role in ensuring the successful execution of deep space detectors. Existing methods mainly focus on studying fixed mission sequences, and the efficiency of planning methods is not high. Specifically, the single uncertainty is only considered, and there is a lack of detection and processing mechanisms, rendering the requirements of deep space detectors inadequate. In this paper, we propose a reverse chain search algorithm, aiming to efficiently plan missions subject to resource constraints and let the mission sequence to possess a certain degree of dynamic repair capability. The results have demonstrated that the resource allocated to each mission is within the resource value range. Compared with the classic algorithm, the proposed algorithm has fewer iterations and whose maximum flow value is accurate. Meanwhile, the potential resource constraints problem during execution can be alerted with the detection and warning system. The missions that were warned are repaired by a dynamic resource repair system, and the proposed algorithm is of higher efficiency and a certain ability to resist external uncertainties. Overall, this paper lays a solid foundation for the processing of resource constraints for deep space detectors. [ABSTRACT FROM AUTHOR]

We extend the equivariance and invariance conditions for construction of optimal designs to multiple-group mixed models and, hence, derive the support of optimal designs for first- and second-order models on a symmetric square. Moreover, we provide a tool for computation of D- and L-efficient exact designs in multiple-group mixed models by adapting the algorithm of Harman et al. (Appl Stoch Models Bus Ind, 32:3–17, 2016). We show that this algorithm can be used both for size-constrained problems and also in settings that require multiple resource constraints on the design, such as cost constraints or marginal constraints. [ABSTRACT FROM AUTHOR]

Background: Neonatal care interventions are crucial for reducing infant mortality rates, particularly in low-resource settings where access to advanced medical facilities is limited. Implementing these interventions poses significant challenges due to resource constraints and infrastructural limitations. Materials and Methods: This clinical study investigated the challenges, successes, and strategies involved in implementing advanced neonatal care interventions in a low-resource setting. A retrospective analysis was conducted on the data collected from a neonatal care unit in a resource-limited area over a period of two years. The study assessed the availability of medical equipment, healthcare personnel training, and the efficacy of interventions in improving neonatal health outcomes. Results: The analysis revealed that despite resource constraints, significant strides were made in implementing advanced neonatal care interventions. Availability of essential medical equipment increased by 30%, and healthcare personnel received targeted training programs resulting in a 25% improvement in neonatal survival rates. Strategies such as task-shifting and community outreach programs played a pivotal role in overcoming infrastructural limitations. Conclusion: Implementing advanced neonatal care interventions in low-resource settings is challenging but feasible with targeted strategies. While resource constraints remain a barrier, innovative approaches such as task-shifting and community involvement can significantly improve neonatal health outcomes. Continued investment in infrastructure, training, and community engagement is essential for sustainable progress in reducing neonatal mortality rates in resource-limited areas.

In this research, we present several extensions for the resource-constrained project scheduling problem with alternative subgraphs (RCPSP-AS). First of all, we investigate more complex variants of the alternative project structure. More precisely, we consider nested alterative subgraphs, linked alternative branches, multiple selection, caused and closed choices, and split choices. Secondly, we introduce non-renewable resources in the RCPSP-AS in order to implicitly avoid certain combinations of alternatives given a limited availability of this resource over the complete project horizon. We formulate both the basic RCPSP-AS and its extensions as an ILP model and solve it using Gurobi. The computational experiments are conducted on a large set of artificial project instances as well as three case studies. The results show the impact of the different extensions on the project makespan and the computational complexity. We observe that combinations of the proposed extensions might imply complex alternative project structures, resulting in an increasing computational complexity or even infeasible solutions. The analysis of the three case studies shows that it is hard to find feasible solutions with a small time limit or optimal solutions with a larger time limit for projects with a realistic size in terms of the number of activities or alternatives. [ABSTRACT FROM AUTHOR]

Modern information technology is driving the rapid development of the information and automation industry through the combination of computer technology, communication technology, and control technology. Inspired by nature’s cluster movement, such as birds, people proposed the concept of Multi-Agent Systems (MASs). This paper deals with the formation control problem for a class of heterogeneous multi–agent systems with time-delays. First, the formation control of heterogeneous multi–agent systems with communication delay in fixed and switching topologies is investigated. A heterogeneous multi–agent system model with input delay and communication delay is established. Furthermore, based on this model, the formation control protocol is designed and the stability of the proposed control protocol in fixed and switched topologies is analyzed. The first– and second-order multi–agent control system was formulated for both fixed and switched topology, respectively. Then, using the new LMI toolbox, the feasibility of the parameters is obtained and the example is created by MATLAB. The simulation results show that the proposed formation control protocol with delay in this paper can make the systems converge to the expected value under fixed and switched hierarchies.

This research aims to study a real-world example of the unrelated parallel machine scheduling problem (UPMSP), considering job-splitting, inventories, shortage, and resource constraints. Since the nature of the studied optimization problem is NP-hard, we applied a metaheuristic algorithm named Grey Wolf Optimizer (GWO). The novelty of this study is fourfold. First, the model tackles the inventory problem along with the shortage amount to avoid the late fee. Second, due to the popularity of minimizing completion time (Makespan), each job is divided into small parts to be operated on various machines. Third, renewable resources are included to ensure the feasibility of the production process. Fourth, a mixed-integer linear programming formulation and the solution methodology are developed. To feed the metaheuristic algorithm with an initial viable solution, a heuristic algorithm is also fabricated. Also, the discrete version of the GWO algorithm for this specific problem is proposed to obtain the results. Our results confirmed that our proposed discrete GWO algorithm could efficiently solve a real case study in a timely manner. Finally, future research threads are suggested for academic and industrial communities. [ABSTRACT FROM AUTHOR]

An integrated problem for unrelated parallel machine scheduling with additional resources is considered in this study. In addition to resources such as machines or workbenches used in the production environment, there is also the use of additional resources. Additional resources can be used both in the setup and process stage. With the use of a common server as an additional resource in the setup stage, machine scheduling problem with common server arises. With the use of additional resources in the process phase, resourceconstrained machine scheduling problem occurs. By integrating these two separate problems, a single problem is defined, and a mathematical model is developed. Due to the complexity of the problem, metaheuristic methods are proposed for solving large-scale problems. The proposed methods are tested in randomly derived problems. The computation results show that the mathematical model for small-sized problems and simulation annealing algorithm for medium and large-sized problems achieved better results. [ABSTRACT FROM AUTHOR]

Entrepreneurial growth—firm growth via the introduction of new market offerings or expansion into new markets—is an important topic for entrepreneurship scholars and practitioners alike. Any firm that wants to exploit opportunities for entrepreneurial growth needs resources and capabilities that it can use to develop new market offerings or to enter new markets. However, many firms face resource and capability constraints, and research has shown that strategic partnerships can provide external pathways for firms to exploit growth opportunities despite their resource and capability constraints. All the extant external growth pathways have in common that they require firms to have some resources and capabilities, which are valuable for partners and can be jointly appropriated with them. An alternative pathway for firms to leverage external resources and capabilities—especially knowledge-based ones—that has received little attention in the literature on growth is short-term contracting of professional service firms such as accounting firms, marketing agencies, or R&D consultancies. Hence, we investigate the role of service intermediaries—professional service firms that facilitate the exchange of services among other firms—as external managers who support their clients to access and leverage a broad range of required resources and capabilities from third parties. We conducted a nested multi-case study of two service intermediaries that enabled two small, wineries from North Macedonia to successfully seize entrepreneurial growth opportunities in markets abroad despite their resource and capability deficits. We identify seven support mechanisms—need articulating, social embedding, linking, governing, clarifying, renegotiating, and mediating—through which the service intermediaries orchestrated complementary external resources and capabilities on behalf of the wineries, thereby enabling the two firms to successfully develop two new product lines for and enter two new geographic markets each. We also identify process differences depending on the stage of the opportunity evaluation process, target market characteristics, and external stakeholder involvement for which we postulate three propositions about the influence of mechanisms on the growth opportunity development. Our study offers novel insights and makes a contribution to research on entrepreneurial growth and resource orchestration. Plain English Summary: Many firms face resource and capability constraints that inhibit their entrepreneurial growth. One approach that firms can use to overcome these constraints is by leveraging external resources and capabilities. However, constrained firms often are unable to establish relationships with and leverage resources and capabilities from external providers. Our study demonstrates how service intermediaries can orchestrate the complementary external resources and capabilities of constrained firms to enable the exploitation of growth opportunities in new markets. The findings of our study can provide policymakers, especially ones focused on regional competitiveness and economic growth, with a framework to foster the creation of intermediaries, which in turn can enable resource- and capability-constrained firms to exploit growth opportunities. In addition, constrained firms may use our findings of this study as a guiding framework when leveraging complementary external resources—independent of whether they orchestrate these resources themselves or engage external resource orchestration managers. [ABSTRACT FROM AUTHOR]

Serial passaging is a fundamental technique in experimental evolution. The choice of bottleneck severity and frequency poses a dilemma: longer growth periods allow beneficial mutants to arise and grow over more generations, but simultaneously necessitate more severe bottlenecks with a higher risk of those same mutations being lost. Short growth periods require less severe bottlenecks, but come at the cost of less time between transfers for beneficial mutations to establish. The standard laboratory protocol of 24-h growth cycles with severe bottlenecking has logistical advantages for the experimenter but limited theoretical justification. Here we demonstrate that contrary to standard practice, the rate of adaptive evolution is maximized when bottlenecks are frequent and small, indeed infinitesimally so in the limit of continuous culture. This result derives from revising key assumptions underpinning previous theoretical work, notably changing the metric of optimization from adaptation per serial transfer to per experiment runtime. We also show that adding resource constraints and clonal interference to the model leaves the qualitative results unchanged. Implementing these findings will require liquid-handling robots to perform frequent bottlenecks, or chemostats for continuous culture. Further innovation in and adoption of these technologies has the potential to accelerate the rate of discovery in experimental evolution. [ABSTRACT FROM AUTHOR]

We present a multi-agent system where agents can cooperate to solve a system of dependent tasks, with agents having the capability to explore a solution space and make inferences, as well as query for information under a limited budget. Re-exploration of the solution space only happens when an older solution expires. We investigate the effects of task dependencies, increasing the speed of the agents, the complexity of the problem space, and the query budgets available to agents. Specifically, we evaluate trade-offs between the agent's speed and query budget. We observe that increasing the speed of a single agent improves the system performance to a certain point only and increasing the number of faster agents may not improve the system performance due to task dependencies. Favouring faster agents during budget allocation enhances the system performance, in line with the "Matthew effect". [ABSTRACT FROM AUTHOR]

This paper explores the relationship between resource constraints and innovation of new firms. Drawing upon the relevant literature, we incorporate resource constraints as the antecedent to the bricolage-innovation link. Compared to prior studies that treated resource constraints as a one-dimensional variable, we operationalize it along two dimensions: knowledge constraints and financial constraints. Our argument posits that knowledge constraints and financial constraints act as catalysts for innovation in new firms, with bricolage serving as a mediating role. To test our hypotheses, we conducted a survey involving 183 entrepreneurs in the United States. The data analysis demonstrates that bricolage fully mediates the relationship between knowledge constraints and innovation and partially mediates the relationship between financial constraints and innovation. Theoretical and practical implications are discussed.

The ant colony algorithm, a heuristic algorithm that mimics ants’ foraging, can optimize problem-solving. Applying this algorithm to the design of innovation and entrepreneurship projects in colleges and universities can enhance the effectiveness and feasibility of these projects. Based on an analysis of the college innovation and entrepreneurship project design process, the article explores the entrepreneurial path through the use of big data analysis. Then, it discusses the ant colony algorithm’s basic concept and mathematical model and proposes a two-population ant colony algorithm for multi-objective resource-constrained project scheduling problems. When compared to the traditional algorithm, the improved ant colony algorithm runs 25–33 ms faster, and Company A’s operating costs drop by 1,176,000 yuan from 2020 to 2023. This shows that the improved ant colony algorithm suggested in this paper can make university innovation and entrepreneurship programs more efficient at allocating resources.

The 14th Five-Year Plan for China proposes to promote the urbanization process from a county perspective, presenting an unprecedented opportunity to develop county urbanization. However, in the context of relatively limited resources, insufficient funds, and comparatively lower land values in Chinese counties, how to promote county urbanization remains the primary challenge, which has not been studied yet. This study first analyzes the necessity of promoting county urbanization from four dimensions: national development strategy, industrial policy development, local government status, and enterprise development demands using literature research and survey interviews. Based on field research conducted in 32 counties in China, the strengths, weaknesses, opportunities, and threats (SWOT) analysis is carried out on the county urbanization process in this study. By analyzing the developmental status and challenges of major urban areas, expansion areas of counties, and their surrounding countryside, this paper proposes a "three-level gradient integration" concept for the spatial integration of county urbanization. Furthermore, the mechanism of multi-agent linkage to promote county urbanization was explored based on the theory of urban governance and the characteristics of county urbanization. Subsequently, relying on the analysis of policies, such as land acquisition and integrated land improvement, and adopting modes, such as "Investor + Engineering Procurement Construction + Operation" (investor + EPC + O) and renovate–operate–transfer (ROT), we propose a pathway for promoting county urbanization through the linkage of government, enterprises, and residents. This study provides insight into promoting the county urbanization process. Additionally, each country faces the common issue of how to use limited resources to promote regional development, and this article can provide valuable insights and inspiration for addressing this shared issue by the principle of adapting to local conditions and adhering to the concept of efficient market and proactive government. [ABSTRACT FROM AUTHOR]

The object of the research work was to evolve a methodology to maximize the availability of aircraft. Analysis of repairable systems, including the selection of critical systems, the goodness of fit tests, and optimization of preventive maintenance, was formulated. The rotables that underwent periodic preventive maintenance and preventive replacement, contributing towards major downtime of aircraft were investigated for scope of improvement to maximize availability. The applicability and suitability of data were tested and validated. Estimation of parameters was carried out using Crow's Maximum Likelihood Estimator. The optimized preventive maintenance model was implemented on one of the aircraft fleets. The proposed methodology of optimizing the preventive maintenance, mean time between failure, and forecast of critical rotables was found effective and resulted in significant improvement in the availability of the aircraft. [ABSTRACT FROM AUTHOR]

In front of escalating resource limitations in agriculture, this study emphasizes the critical need for efficient crop cultivation. Focusing particularly on tomatoes, a staple in global diets, the research aims to sustenably enhance productivity. Recognizing productivity as a key economic indicator, the study highlights the pivotal role of energyefficient practices in addressing challenges posed by climate change and depleting natural resources. By optimizing energy use in agricultural processes, this research not only improves economic efficiency but also positions the sector for resilience and innovation in a resource-constrained environment. Through an in-depth analysis of data from Eurostat covering the 2014-2022 period, we trace the trends in greenhouse tomato cultivation across Europe, evaluating changes in cultivation areas and productivity. This research offers insights into how different European countries have adapted their agricultural practices in response to environmental and economic pressures and in the same time, it underscores the need for continued innovation and adaptation in agricultural practices to meet the demands of a growing global population under increasingly constrained environmental conditions. [ABSTRACT FROM AUTHOR]

This paper focuses on internal corporate social responsibility (CSR) (i.e. shareholder responsibility and employee responsibility) changes within emerging-market multinational companies (EMNCs) with a resource constraints perspective and attention-based view, providing a finely variegated analysis of and new insights into the internationalization–CSR relationship. Specifically, as EMNCs' resource constraints change from high to low and the primary focus shifts during internationalization, their dominant logic moves from efficiency to legitimacy; correspondingly, their internal CSR shows a U-shaped change—first declining and then increasing. This nonlinear model is supported by an empirical test of Chinese listed MNCs from 2013 to 2018. On this basis, the moderating roles of state-owned background and innovation-oriented strategy are examined, as both affect the resource constraints and attention structure of EMNCs during internationalization. We also provide a sensitivity analysis of internationalization's impact on each dimension of internal CSR and an in-depth discussion of our findings. [ABSTRACT FROM AUTHOR]

In this study, a multi-period, multi-objective mathematical model is presented for selecting oil and gas investment projects based on the Sustainable Development Goals (SDGs) approach, considering resource limitations. Based on the economic dimension of sustainability, the profit from the implementation of petrochemical projects is maximized, and based on the environmental dimension, the amount of greenhouse gas emissions, energy consumption, and produced waste is minimized. According to the social dimension, the number of job opportunities, the number of people covered by insurance, the job satisfaction of employees, the project’s impact on the regional economy, and the number of lost workdays is examined. The uncertainty of the strategic and operational parameters of the model has also been considered, and to deal with the uncertainty, fuzzy possibility programming (FPP) is used. The model is solved in GAMS optimization software with a two-stage approach based on fuzzy programming and the best-worst group decision-making method (BWM). Numerical results confirm the effectiveness of the proposed model and show that SDG will lead to a significant improvement in economic, environmental, and social dimensions without significantly reducing the profits of the selected oil and gas projects. [ABSTRACT FROM AUTHOR]

This paper presents a state-of-the-art review of different approaches for Neural Architecture Search targeting resource-constrained devices such as microcontrollers, as well as the implementations of on-device learning techniques for them. Approaches such as MCUNet have been able to drive the design of tiny neural architectures with low memory and computational requirements which can be deployed effectively on microcontrollers. Regarding on-device learning, there are various solutions that have addressed concept drift and have coped with the accuracy drop in real-time data depending on the task targeted, and these rely on a variety of learning methods. For computer vision, MCUNetV3 uses backpropagation and represents a state-of-the-art solution. The Restricted Coulomb Energy Neural Network is a promising method for learning with an extremely low memory footprint and computational complexity, which should be considered for future investigations.

This article sets out a data-driven approach for targeting environmental policies optimally in order to combat deforestation. We focus on the Amazon, the world's most extensive rainforest, where Brazil's federal government issued a "Priority List" of municipalities in 2008—a blacklist to be targeted with more intense environmental monitoring and enforcement. First, we estimate the causal impact of the Priority List on deforestation (along with other relevant treatment effects) using "changes-in-changes" due to Athey and Imbens (2006), finding that it reduced deforestation by 43 |$\%$| and cut emissions by almost 50 million tons of carbon. Second, we develop a novel framework for computing targeted optimal blacklists that draws on our treatment effect estimates, assigning municipalities to a counterfactual list that minimizes total deforestation subject to realistic resource constraints. We show that the ex post optimal list would result in carbon emissions over 10 |$\%$| lower than the actual list, amounting to savings of more than |$ \$ $| 1.2 billion (34 |$\%$| of the total value of the Priority List), with emissions over 23 |$\%$| lower on average than a randomly selected list. The approach we propose is relevant both for assessing targeted counterfactual policies to reduce deforestation and for quantifying the impacts of policy targeting more generally. [ABSTRACT FROM AUTHOR]