Efficient Resource Allocation Research Articles

Leveraging Community-based Approaches for Enhancing Resource Allocation in Fog Computing Environment

Efficient resource allocation in fog computing environments is essential to address the increasing demand for high-performance and adaptable network services. Traditional methods lack granular differentiation based on traffic characteristics often resulting in suboptimal bandwidth utilization and elevated latency. To enhance network efficiency, this study applies a community-based resource allocation approach leveraging the Louvain algorithm to dynamically cluster network nodes with similar traffic demands. By forming communities based on bandwidth and latency needs, this approach enables a targeted resource distribution, aligning each community with optimized pathways that address specific requirements. The results indicate notable performance gains, including a 14% increase in bandwidth utilization affecting the download and a reduction in latency by an average of 23% for time-sensitive applications. These improvements highlight the effectiveness of the proposed approach in managing diverse network demands, improving data flow stability, and enhancing the overall performance of fog computing infrastructures. These findings underscore the potential for community-based resource allocation to support scalable, adaptable, and secure resource management, positioning it as a viable solution to meet the complex needs of IoT and other distributed network systems.

Years of life lost due to cancer in Ecuador

BackgroundCancer is the leading cause of death worldwide. In the Americas, it is also one of the leading causes of death. In Ecuador, studies on the burden of disease are limited and none analyze or estimate the burden of all types of cancer in a single study. Therefore, the aim of this study is to estimate the years of life lost prematurely due to cancer in Ecuador from 2014 to 2022. MethodsCross-sectional observational analysis study. The databases of general deaths from the years 2014 to 2022, population projections from 2014 to 2021 and the population census for the year 2022 of the Republic of Ecuador were used. Deaths registered with ICD-10 code C00-C96 were included as deaths from cancer. The mortality rate and years of life lost prematurely due to cancer were estimated, considering the life tables of the Coale-Demeny West model and a standard time discount of 3 % was implemented without weighting by age. ResultsAn increase in mortality and premature years of life lost was observed in the period 2014–2019, but in 2020 a reduction in mortality was seen in several types of cancer. In 2021 and 2022 mortality increased again, reaching the maximum peak of mortality and premature years of life lost in the entire study. Approximately 1,3 million years of life lost prematurely due to cancer were estimated. The types of cancer with the greatest number of years of life lost prematurely were malignant tumors of the stomach (188.180), uterus (117.142), which include both uterine cancer and cervical cancer, and leukemia (107.440). In men, in descending order, these were tumors of the stomach (101.112), prostate (67.624) and leukemia (55.654), while in women, these were malignant tumors of the uterus (117.142), breast (100.217) and stomach (87.067). ConclusionsThe results of this study provide a broader basis for debate on public health policies, efficient allocation of resources, and enable monitoring of cancer and prevention strategies over time.

Assessing the critical success factors for the sustainable construction project management of Saudi Arabia

ABSTRACT The construction sector in Saudi Arabia faces significant challenges in achieving sustainability in project management, encompassing activities from design to project completion. This research aims to identify and evaluate the critical success factors (CSFs) essential for sustainable construction project management in Saudi Arabia. Through an extensive literature review, 24 CSFs were identified, reflecting the sector’s complex dynamics. A detailed survey of industry experts assessed the importance of these factors using a Likert scale, with further analysis conducted through the Relative Importance Index (RII) and Analytic Hierarchy Process (AHP). The CSFs were categorized into five groups: human resources and stakeholder requirements, sustainable project delivery and stakeholder engagement, strategic planning and alignment, project management practices and leadership, and compliance and governance. Stakeholders emphasized the adoption of modern tools and processes to overcome challenges and improve project outcomes. The study highlighted five key CSFs: effective strategic planning, adherence to anti-corruption measures, efficient resource allocation, project managers’ expertise, and robust policy enforcement. These factors are pivotal in advancing sustainable construction project management in Saudi Arabia. The study provides a structured framework for evaluating CSFs and offers practical insights to stakeholders for promoting sustainability within the construction sector, particularly in Saudi Arabia and the Middle East.

Strategic evaluation of European Inland transport companies for European green deal compliance: A multi-criteria decision making-analysis.

The purpose of this study is to evaluate the readiness of European inland transport companies for the European Green Deal by analyzing the causal relationships between key factors essential for EGD implementation. The research identifies six critical factors: Investment in Green Technologies, Policy Alignment and Compliance, Stakeholder Collaboration, Resource Management, Employee Training and Engagement, and Emission Reduction Initiatives. This research offers the first integrated evaluation of EGD readiness utilizing a combination of fuzzy-DEMATEL, multiple regression analysis, and Structural Equation Modeling aimed at uncovering systemic interactions among the factors in the system. The study revealed Resource Management and Employee Training as significant drivers in enhancing the readiness, highlighting the role of human capital and resource optimization in environmental sustainability. Expert panel, consisting of 19 industry representatives selected through purposive sampling, provided insights on the effective relationships which offer practical strategies for managers to prioritize resource management and employee engagement to achieve Green Deal compliance and guiding more strategic and efficient allocation of resources to enhance alignment with European Green Deal stipulations.

Advancing public safety and housing solutions: A comprehensive framework for machine learning and predictive analytics in urban policy optimization

Urban areas worldwide face mounting challenges, including public safety concerns, housing shortages, and efficient resource allocation. Addressing these complexities requires innovative solutions that integrate technology and policy frameworks. This paper proposes an integrated framework leveraging machine learning (ML) and predictive analytics to optimize urban policy-making and provide actionable insights for sustainable urban development. The framework combines real-time data streams with advanced predictive modeling to enhance decision-making in urban governance. Key features include the ability to assess and respond to emerging trends in public safety by analyzing crime patterns, resource deployment, and community risk factors. Similarly, the framework addresses housing shortages by evaluating demand-supply dynamics, forecasting population growth, and identifying areas requiring immediate intervention. A critical review of existing models underscores the limitations of traditional urban policy approaches in managing socioeconomic disparities and environmental influences. Building upon these insights, the proposed framework integrates diverse data sources, such as demographic information, environmental metrics, and public sentiment analysis, to create a holistic view of urban systems. This multidimensional approach ensures that policy recommendations are context-sensitive, equitable, and inclusive. Moreover, the framework emphasizes stakeholder engagement, fostering collaboration among policymakers, urban planners, community leaders, and data scientists. It prioritizes scalability and adaptability, allowing its application across diverse urban contexts and geographic locations. This adaptability ensures that the framework remains relevant as urban environments evolve and new challenges arise. By addressing the interplay between data-driven insights and policy optimization, this research aims to bridge the gap between theoretical models and practical implementation. The framework holds the potential to redefine urban governance by enabling proactive decision-making, equitable resource allocation, and sustainable development. Future research should explore real-world applications of this framework to validate its efficacy and refine its components further.

Technological diversification and innovation performance: ownership structure

PurposeThis study explores the contingent relationship between technological diversification (TD) and innovation performance, particularly in the context of China’s high-tech firms. It aims to understand how the structure of firm ownership influences the allocation of innovation resources and, consequently, the efficiency of innovation activities.Design/methodology/approachThe research employs a fixed-effect regression model to analyze a unique panel dataset of 1,139 Chinese listed high-tech firms over a nine-year period. The dataset includes firm-year observations from 19 industries, allowing for a comprehensive analysis of the relationships between TD, innovation performance and ownership structure.FindingsThis study finds an inverted U-shaped relationship between TD and innovation performance, indicating that while a certain level of TD can enhance innovation performance, excessive diversification can lead to diminishing returns. The findings also reveal that ownership concentration amplifies this relationship, suggesting that firms with more concentrated ownership structures can quickly reach optimal levels of innovation performance but are also more susceptible to the “over-diversification trap.” In contrast, equity restriction and ownership type (state-owned vs privately owned enterprises) moderate the relationship, indicating that firms with these structures may maintain optimal innovation performance levels for longer periods.Originality/valueThis research provides novel insights by confirming a nonlinear, inverted U-shaped relationship between TD and innovation performance, specifically within the context of Chinese high-tech firms. By integrating corporate governance theory, our study advances understanding of how ownership structure – through ownership concentration, equity restriction and ownership type – moderates the TD-innovation performance relationship. This perspective offers a deeper understanding of resource allocation and innovation efficiency in emerging markets. The findings hold practical implications for managers and policymakers, underscoring the importance of aligning TD strategies with firm ownership structures to optimize innovation outcomes.

Enhancing Product UpgradaProduct Modular Enhancement Framework (PMEF): A New Approach to Product Upgradationtion: A Study on theModular Enhancement Framework (MEF)

Background In today’s rapidly evolving and highly competitive market, the ability to upgrade existing products is essential for companies to maintain their market position and meet changing customer needs. By discovering new methods, we can implement changes more swiftly and with fewer resources, minimizing the risk of widespread issues and ensuring that upgrades are more manageable. Objective The objective is to develop a simple product enhancement framework for product upgradation. This framework aims to offer greater flexibility, faster implementation, and efficient resource allocation, making it an attractive option for companies looking to enhance their product offerings. Methods An analysis was conducted on the data provided by leading manufacturers worldwide regarding their recent product upgrades. This analysis encom- passed the product upgradation process, the outcomes of these upgrades, market research reports, competitor analysis, customer feedback, and internal company documents. From the data gathered, we identified gaps in the product upgradation process and the reasons for failures, leading to the development of a universal framework. Results The proposed Product Modular Enhancement Framework (PMEF) provides notable benefits in terms of flexibility, efficiency, and scalability. Its modular structure allows for independent upgrades, minimizing the risk of widespread issues and facilitating quicker implementation. Conclusion The PMEF represents a promising approach to product upgradation, offering a structured and innovative solution to meet the demands of a dynamic market. While the framework presents several benefits, it also requires meticulous planning and execution to address challenges such as module integration and resource allocation.

The impact of carbon emissions trading on green total factor productivity based on evidence from a quasi-natural experiment

Based on a balanced panel dataset of 272 prefecture-level cities from 2000 to 2022, this paper systematically investigates the impact of the carbon emissions trading system on green total factor productivity and its underlying mechanisms from an integrated perspective of overall, dynamic, and spatial dimensions. The findings reveal that (1) the carbon emissions trading system significantly enhances regional total factor productivity, primarily by optimizing resource allocation efficiency and strengthening regional competitiveness. (2) From a dynamic perspective, the policy effect exhibited a U-shaped relationship: from 2013 to 2018, green total factor productivity was suppressed due to underdeveloped market mechanisms and the policy environment; after 2018, with market maturation and policy stabilization, the policy effects improved significantly. (3) Spatial effect analysis indicates that the emissions trading system positively influences pilot regions but generates a siphon effect on nonpilot regions, leading to regional performance divergence, although the overall impact remains positive. (4) Heterogeneity analysis reveals that the policy has more pronounced effects in regions with higher carbon intensity, stricter environmental regulations, better infrastructure, and richer resource endowments, reflecting significant regional disparities in policy effectiveness. This study provides empirical evidence and theoretical insights to optimize carbon trading policies and achieve regional green development.

Open government data and firm value: A quasi‐natural experiment from China

AbstractThe Chinese government is actively promoting the utilisation of open government data (OGD) to unlock the intrinsic value of data assets. This study considers the establishment of local OGD platforms as a quasi‐natural experiment. We conducted our research using a sample of China's A‐share listed firms from 2007 to 2021 and employed the staggered difference‐in‐difference (DID) model to examine the impact of OGD on firm value. The empirical analysis reveals a significant positive effect of the establishment of OGD platforms on firm value, which is achieved by alleviating financing constraints, promoting corporate risk‐taking, and enhancing resource allocation efficiency as well as talent agglomeration. Heterogeneity analysis shows that OGD has a more pronounced impact on the value of firms operating in highly competitive industries, firms with weak internal controls, and firms located in non‐central cities. The findings presented in this paper provide valuable insights for promoting the deep integration of government data with enterprise management.

Acute Concurrent Exercise Improves Inhibitory Control Without Mediating the Role of Lactate: An Event-Related Potential Study

BackgroundConcurrent exercise (CE), an emerging exercise modality characterized by sequential bouts of aerobic (AE) and resistance exercise (RE), has demonstrated acute benefits on executive functions (EFs) and neuroelectric P3 amplitude. However, the effect of acute CE on inhibitory control, a sub-component of EFs, and P3 amplitude remains inconclusive. Moreover, exploring the mechanisms underlying the effects of acute exercise on EFs contributes to scientific comprehension, with lactate recognized as a crucial candidate positively correlated with EFs. Therefore, this study aimed to determine the effects of acute CE on inhibitory control via behavioral and event-related potential approaches and to examine its potential mediational role on lactate.MethodsSeventy-eight adults (mean age = 22.95, SD = 1.75 years) were randomly assigned to either a CE, AE, or control (CON) group. Participants in the CE group engaged in 12-min of AE (40–59% of heart rate reserve [HRR]) coupled with 13-min of RE (1 set, with 75% of 10-repetition maximum, and 12 repetitions of 8 movements). The AE group participated in 25 min of AE (40–59% HRR). Prior to and following exercise onset, participants in both the CE and AE groups completed a 5-min warm-up and cool-down. Participants in the CON group read books for 35 min. Lactate concentrations were measured at timepoints of 0-, 17-, and 30-min relative to the treatment onset. Response time (RT) and accuracy in the Stroop test, as well as P3 amplitude, were assessed before and after the treatment.ResultsThe results revealed that both the CE and AE groups had significantly shorter RTs compared to the CON group, with no significant differences in accuracy among groups. A decrease in P3 amplitude was observed for the CE group compared to the AE and CON groups. The mediating effects of lactate between acute exercise and inhibitory control were insignificant.ConclusionsThe findings suggest that both CE and AE improve inhibitory control and CE potentially enhances the efficient allocation of attention resources. The lack of a significant mediating effect of lactate warrants further investigation.Trial registration: ClinicalTrials.gov, NCT06370286. Registered 12 April 2024—Retrospectively registered, https://clinicaltrials.gov/study/NCT06370286.

Adaptive Terrain Modeling for Side-Slope Surfaces

Three-dimensional site modeling is an important aspect of Building Information Modeling (BIM), especially in mountainous areas. Accurate site modeling is essential for efficient construction planning and resource allocation. A key issue in site modeling is how to accurately calculate the shape of side-slopes. It involves three sub-problems: geometric representation of side-slopes, determination of fill/cut types, and intersection of side-slopes surface with the terrain surface. To address this, a two-stage method for constructing side-slope models adaptive to terrain is proposed. In the first stage, a marching algorithm along polylines is used to calculate the intersection points of the site boundary polylines with the terrain surface. These intersection points are used to segment the boundary polylines. A rule-based approach is then applied to automatically determine the fill/cut type for each segment. Subsequently, the equations of the side-slopes passing through each segment are derived using geometric principles. In the second stage, a marching algorithm along the plane is used to trace the intersection lines of side-slopes with the terrain. Finally, the side-slopes are rendered with precision by integrating the equations of each segment with the determined intersection lines. The effectiveness of the method is verified through illustrative examples. Algorithm efficiency analysis and 3D modeling illustrations have demonstrated that this method not only boasts accuracy and swift computation but also excels in the level of automation achieved in the modeling process.

Spatial–Temporal Dynamics and Drivers of Crop Water Footprint in Xinjiang, China

Efficient allocation and utilization of water resources are critical for the sustainable development of agriculture in arid regions, particularly those heavily reliant on irrigation. Xinjiang, one of China’s major agricultural regions, faces significant challenges in managing water resources due to its arid climate and dependence on irrigation. This study investigates the spatial–temporal dynamics of crop water footprint (CWF) and its driving factors in Xinjiang. Unlike previous studies on Xinjiang that primarily focus on total water footprint, this research emphasizes the crop blue water footprint (CWFB) to provide a more precise assessment of agricultural water allocation and consumption. Using the CROPWAT 8.0 model, the CWF of 14 prefectures in Xinjiang were analyzed for the period 2000–2020. Focusing primarily on the crop blue water footprint (CWFB), the study employed the Logarithmic Mean Divisia Index (LMDI) model to identify key drivers and their mechanisms. Results reveal that Xinjiang’s average annual CWF is 179.02 Gm3, with CWFB contributing 90.22% and the crop green water footprint (CWFG) accounting for. 10.05%. The CWFB showed an initial increase followed by stabilization, with Southern Xinjiang being the largest contributor, trailed by Northern and Eastern Xinjiang. Among the 14 prefectures, the top seven accounted for 90.46% of CWFB. Cotton, wheat, and maize were the major crops, comprising 47.80%, 23.14%, and 21.45% of the total blue water footprint, respectively. This study identifies the dominant role of economic effect and water use efficiency effect in driving changes in CWFB through its analysis of the driving factors. Understanding the spatial–temporal changes and key drivers of blue water consumption helps regions adjust cropping structures and agricultural water resource allocation patterns to ensure sustainable agricultural development. The findings not only offer valuable implications for policymakers and stakeholders in Xinjiang but also provide references for other arid and semiarid regions facing similar challenges in agricultural water resource management.

How Enterprise Resilience Affects Enterprise Sustainable Development—Empirical Evidence from Listed Companies in China

With the frequent occurrence of various emergencies, the stable operation of enterprises has been seriously affected, and the research of resilience has received more and more attention in various fields. Enterprise resilience (En_RES) is not only related to corporate survival but is also the key to determining whether a company can realize long-term development. To explore the impact of En_RES on enterprise sustainable development (En_SD), this paper conducts an empirical test using panel regression models based on the data of A-share listed companies in China from 2004 to 2022. It is found that En_RES has a significant positive contribution to En_SD, which is more obvious when the degree of environmental uncertainty is lower, the degree of information sharing is higher, and the degree of business complexity is higher. The mechanism test analysis finds that En_RES can further contribute to En_SD by reducing the bankruptcy risk, improving credit availability, and optimizing resource allocation efficiency. This paper innovatively analyzes and verifies the impact of En_RES on En_SD and its functioning mechanism from the perspective of microenterprises, which not only enrich the theoretical relationship between En_RES and En_SD but also provide important references for enterprises to pay attention to and develop resilience in practice, which can help enterprises better cope with challenges, grasp opportunities, and make contributions to the sustainable development of enterprises.

An evaluation of financial losses due to lumpy skin disease outbreaks in dairy farms of northern Thailand

Lumpy skin disease (LSD) poses a significant threat to the cattle industry, resulting in adverse economic consequences in affected countries. This study aims to estimate the financial losses due to LSD outbreaks in dairy farms in northern Thailand. Based on a retrospective study, data was collected using a standardized questionnaire from 100 farms affected by LSD outbreaks (outbreak farms) and 33 farms that did not experience LSD outbreaks (non-LSD outbreak farms) in two dairy farming areas that experienced LSD outbreaks between June and December 2021. In outbreak farms, the average total financial losses was 727.38 USD per farm, significantly higher than the 349.19 USD per farm observed in non-LSD outbreak farms. The primary cause of financial loss in outbreak farms was mortality. Reductions in milk sold due to a drop in milk production, and the need to discard milk because of the withdrawal time of antibiotics used for treating secondary infections on affected cattle, also contributed substantially to the financial losses. On farms without LSD outbreaks, the main expenses were related to vaccination and disease prevention, amounting to 130.66 USD and 218.53 USD per farm, respectively. LSD outbreaks negatively affect all farms in the outbreak areas, as both outbreak farms and non-LSD outbreak farms had to bear prevention costs. In the post-outbreak phase, the primary activities focused on continued monitoring of new LSD cases and conducting surveillance, carried out collaboratively by farmers and livestock authorities. This is the first study in Thailand providing valuable insights into the financial implications of LSD outbreaks for farmers, highlighting the substantial financial consequences of the disease. The findings from this study are beneficial for decision making, efficient resource allocation and the development of effective mitigation strategies.

Social Protection in Developing Countries: Legal, Economic, and Social Trends

This paper investigates the evolution of social protection frameworks in developing countries, distinguishing between contributory and tax-financed systems. Through a comparative analysis of social insurance, social assistance, public works, and community-based employment programs, this study assesses their effectiveness in mitigating poverty, managing risks, and fostering resilience. A unique aspect of this research lies in its integration of economic, historical, and cultural factors, offering a comprehensive evaluation of social protection beyond conventional economic assessments. Key findings reveal the critical balance between efficiency and equity in resource allocation, underscoring the importance of addressing dependency and compliance in policy execution. By presenting a nuanced framework that bridges technical and social dimensions, this paper contributes to the growing discourse on sustainable social policies in the context of developing nations. It provides policymakers with actionable insights for crafting inclusive, long-term social protection strategies that align with both immediate relief and sustainable development goals.

How do digital knowledge resources affect the carbon emission intensity of the pig industry?

Purpose By exploring the impact of digital knowledge resources (DKR) on the carbon emission intensity of the pig industry (PCEI), this study aims to reveal the role of DKR in reducing PCEI. Design/methodology/approach Based on provincial panel data in China from 2011 to 2021, this study uses the entropy and Intergovernmental Panel on Climate Change coefficient methods to calculate the evaluation index system of DKR and PCEI, respectively. Empirical analysis using a panel fixed-effects model examines the influence of DKR on PCEI and its underlying mechanisms. Findings DKR can significantly reduce PCEI. This conclusion still holds even after undergoing endogeneity treatment and a series of robustness tests. Mechanism test results indicate that DKR can operate indirectly through the mediation mechanism of rural human capital (RHC) and pig breeding technology innovation (PTI), while environmental regulation intensity (ERI) plays a positive moderating role in the relationship between DKR and PCEI. The magnitude of the impact of DKR on PCEI depends on ERI. Further studies found that the impact of DKR on PCEI has obvious heterogeneity characteristics, and the promotion effect is more obvious in regions with good integration degrees and high development potential. Practical implications This paper divides DKR into three dimensions: digital technology knowledge (DTK), digital management knowledge (DMK) and digital application knowledge (DAK), providing a new framework for research and enriching the understanding of the relationship between DKR and PCEI. Furthermore, the research results reveal the application potential of DKR in the pig industry, particularly in terms of resource allocation efficiency. This is of great significance for promoting low-carbon development in the pig industry and provides insights for the low-carbon transformation of other industries. In addition, the study emphasizes the moderating effect of ERI on the mechanism of carbon reduction in the pig industry through DKR. This offers a new perspective for understanding the relationship between knowledge management and environmental governance, providing a reference basis for policy formulation in related fields. Originality/value This paper further enriches the role of DKR in the livestock industry. Integrating DKR with traditional industries promotes knowledge innovation, information distribution and utilization and scientific decision-making. This has significant value in promoting the development and application of carbon reduction technologies, enhancing industrial competitive advantages, and other aspects.

Equity and efficiency of public health resource allocation in China

ObjectiveTo analyze the equity and efficiency of public health resource allocation in China from 2018 to 2022, and to provide a scientific basis for promoting the development of public health resources in China.MethodsData on public health resources of 31 provincial-level administrative regions in mainland China were extracted from 2018 to 2022, and descriptive analysis, Theil index, and health resource agglomeration degree (HRAD) were used to analyze the equity, and data envelopment analysis (DEA) was used to analyze the efficiency.ResultsThe Theil index of public health resource allocation by population in China ranges from 0.006 to 0.049, and the Theil index contribution rate of the number of beds, health technicians, licensed (assistant) physicians, and registered nurses within region is less than 28.76%. The Theil index of public health resource allocation by geography in China ranges from 0.274 to 0.624, and the Theil index contribution rate of the number of institutions, the number of beds, health technicians, licensed (assistant) physicians, and registered nurses within region is more than 77.05%. The HRAD of public health resources is more than 2.18 in the eastern and central regions, while the HRAD of public health resources is less than 1 in the northeastern and western regions. The overall HRAD/population agglomeration degree (PAD) is less than 1 in the northeastern and eastern regions, and greater than 1 in the central and western regions overall. The scale efficiency and overall efficiency of public health resource allocation in China are not 1, the returns to scale are decreasing, and the DEA is relatively ineffective from 2019 to 2022.ConclusionThe equity by geography is slightly worse than the equity by population, and the inequity by population mainly comes from between regions, while the inequity by geography mainly comes from within regions. There is a contradiction in the equity by population and by geography allocation in the eastern and western regions, and a congruence in the equity by population and by geography allocation in the northeastern and central regions. The DEA of public health resource allocation in China is relatively ineffective in 2019–2022.

Comparative Analysis of Open and Closed Nasal Fractures in Trauma Settings: Mechanisms, Intent, Surgical Interventions, and Outcomes

Objectives: This study aimed to explore nasal fracture patterns, trauma mechanism and intent, treatment approaches, and mortality rates, offering insights for clinical practice and prevention in trauma settings. Design: This retrospective analysis was carried out using trauma data from the National Trauma Data Bank (NTDB) for the years 2013 to 2016. Main outcome measures: Trauma mechanism and mortality rates between closed and open fractures were conducted. Results: This study involved 122,574 closed and 9704 open nasal fractures to elucidate demographic, hospital, and clinical characteristics. Significant risk factors for open nasal fractures included a higher injury severity score, self-inflicted intent, unintentional causes, and firearm mechanism compared to assault injuries. Conclusions: Significant factors associated with open nasal fractures include injury severity, self-inflicted intent, trauma type, and firearm mechanisms, which notably increase the likelihood of open fractures. Findings highlight the need for targeted prevention, efficient resource allocation, and risk screening to enhance the management of complex facial traumas in the national trauma system.

Europe's extra-territorial mineral trade and clean energy metamorphosis in a landscape of multifaceted risks.

Despite modest reserves of energy transition minerals (ETMs) like copper, nickel, and aluminium, European nations largely depend on imports, a dynamic often marred by financial uncertainties, price volatility, and geopolitical tensions. The purpose of this study is to investigate the response of extra-territorial ETM imports to the clean energy objectives of 24 European nations, taking into account financial stress, mineral price volatility, and geopolitical instability. We analyse monthly data from January 2005 to October 2022, using the quantiles via moments (QvM) and the linear models multiway fixed effects (LMFE) approaches to account for heterogeneity, spatial dependence, heteroscedasticity, and endogeneity in the data. Our results indicate that during flourishing market conditions, Europe's extra-territorial mineral imports are positively associated with the clean energy transition. Financial stress, however, negatively affects these imports across various market conditions. Moreover, the EU's mineral imports are adversely influenced by both individual and combined effects of financial stress and mineral price volatility, whereas the impacts of geopolitical risk velocity and financial stress are minimal. Using the LMFE method, we find the influence of geopolitical risk intensification to be insignificant on its own, but when combined with financial stress, it has a negative effect, particularly when addressing endogeneity. Hence, our conclusion highlights the adverse effects of multifarious risk factors on Europe's mineral trade, necessitating the efficient allocation of financial resources and strategic risk management to expedite the region's energy transition.

Energy-efficient task offloading and efficient resource allocation for edge computing: a quantum inspired particle swarm optimization approach

Energy-efficient task offloading and efficient resource allocation for edge computing: a quantum inspired particle swarm optimization approach