Inefficient Resource Utilization Research Articles

Time-Sensitive Learning for Heterogeneous Federated Edge Intelligence

Real-time machine learning (ML) has recently attracted significant interest due to its potential to support instantaneous learning, adaptation, and decision making in a wide range of application domains, including self-driving vehicles, intelligent transportation, and industry automation. In this paper, we investigate real-time ML in a federated edge intelligence (FEI) system, an edge computing system that implements federated learning (FL) solutions based on data samples collected and uploaded from decentralized data networks, e.g., Internet-of-Things (IoT) and/or wireless sensor networks. FEI systems often exhibit heterogenous communication and computational resource distribution, as well as non-i.i.d. data samples arrived at different edge servers, resulting in long model training time and inefficient resource utilization. Motivated by this fact, we propose a time-sensitive federated learning (TS-FL) framework to minimize the overall run-time for collaboratively training a shared ML model with desirable accuracy. Training acceleration solutions for both TS-FL with synchronous coordination (TS-FL-SC) and asynchronous coordination (TS-FL-ASC) are investigated. To address the straggler effect in TS-FL-SC, we develop an analytical solution to characterize the impact of selecting different subsets of edge servers on the overall model training time. A server dropping-based solution is proposed to allow some slow-performance edge servers to be removed from participating in the model training if their impact on the resulting model accuracy is limited. A joint optimization algorithm is proposed to minimize the overall time consumption of model training by selecting participating edge servers, the local epoch number (the number of model training iterations per coordination), and the data batch size (the number of data samples for each model training iteration). Motivated by the fact that data samples at the slowest edge server may exhibit special characteristics that cannot be removed from model training, we develop an analytical expression to characterize the impact of both staleness effect of asynchronous coordination and straggler effect of FL on the time consumption of TS-FL-ASC. We propose a load forwarding-based solution that allows a slow edge server to offload part of its training samples to trusted edge servers with higher processing capability. We develop a hardware prototype to evaluate the model training time of a heterogeneous FEI system. Experimental results show that our proposed TS-FL-SC and TS-FL-ASC can provide up to 63% and 28% of reduction, in the overall model training time, respectively, compared with traditional FL solutions.

3D Printing of Microfluidic-assisted Liposomes Production for Drug Delivery and Nanobiomedicine: A Review.

Numerous techniques exist for the production of liposomes; however, these methods need to be revised due to their incapacity to achieve precise management of the dimensions and uniformity of liposomes and their inefficient utilization of reagents and resources. One particular challenge lies in replicating accurate form and size control seen in biological cells, as accomplishing this level of precision through macroscale approaches proves exceptionally arduous. The advent of microfluidic technology tackles this problem by lowering liposome synthesis to a centimeter-level chip, drastically cutting related costs, and enhancing liposome manufacturing efficiency and mobility. Although various microfluidic technologies for micro or nanoparticle preparation have been established, manufacturing microfluidic devices poses challenges due to their high cost and time-consuming nature. However, a promising and cost-effective solution lies in additive production, commonly guided by 3D printing. This innovative technique has demonstrated significant potential and has been successfully applied to create microfluidic chips. Here, we will explore using 3D printing to produce microfluidic devices specifically designed for liposome production. Moreover, the biomedical applications of the liposomes produced by 3D printing-fabricated chips will be fully discussed.

Navigating uncertainty: Managing herbivore communities enhances Savanna ecosystem resilience under climate change

Abstract Savannas are characterized by water scarcity and degradation, making them highly vulnerable to increased uncertainties in water availability resulting from climate change. This poses a significant threat to ecosystem services and rural livelihoods that depend on them. In addition, the lack of consensus among climate models on precipitation change makes it difficult for land managers to plan for the future. Therefore, Savanna rangeland management needs to develop strategies that can sustain Savanna resilience and avoid tipping points under an uncertain future climate. Our study aims to analyse the impacts of climate change and rangeland management on degradation in Savanna ecosystems of southern Africa, providing insights for the management of semi‐arid Savannas under uncertain conditions worldwide. To achieve this, we simulated the effects of projected changes in temperature and precipitation, as predicted by 10 global climate models, on water resources and vegetation (cover, functional diversity, tipping points (transition from grass‐dominated to shrub‐dominated vegetation)). We simulated three different rangeland management options (herbivore communities dominated by grazers, by browser and by mixed feeders), each with low and high animal densities, using the ecohydrological model EcoHyD. Our results identified intensive grazing as the primary contributor to the increased risk of degradation in response to changing climatic conditions across all climate change scenarios. This degradation encompassed a reduction in available water for plant growth within the context of predicted climate change. It also entails a decline in the overall vegetation cover, the loss of functionally important plant species and the inefficient utilization of available water resources, leading to earlier tipping points. Synthesis and applications. Our findings underscore that, in the face of climate uncertainty, farmers' most effective strategy for securing their livelihoods and ecosystem stability is to integrate browsers and apply management of mixed herbivore communities. This management approach not only significantly delays or averts tipping points but also maintained greater plant functional diversity, fostering a more robust and resilient ecosystem that acts as a vital buffer against adverse climatic conditions.

Income distribution and sustainable development nexus: Fostering poverty reduction and integrated sustainability amidst multi-dimensional regional integration

This study investigates the impact of income distribution as poverty reduction on sustainable development (SD), by taking the moderating role multidimensional regional integration (MRII) in fostering the integrated sustainability across 64 Belt and Road Initiative (BRI) countries from 2005 to 2020. Utilising Sys-GMM methodology, the findings support the notion that countries within the BRI are theoretically aligned with a sustainable development path as suggested by economic theory. Moreover, MRII significantly contributes to the integrated sustainability path. The direct channel analysis reveals a negative yet significant effect of income distribution on sustainable development, indicating an increase in disparity and capital management system. Conversely, the indirect channel suggests a reduction in disparity and regionally integrated BRI boost the sustainable poverty reduction. This study identifies positive contributions from macroeconomic conditions, institutional quality, and population size to BRI countries' sustainable development. Additionally, it suggests the inefficient utilisation of resources by BRI countries, particularly in government consumption. The study underscores the need for enhanced regional policies aimed at increasing income and reducing poverty to foster sustainable development. Upcoming research should investigate the unexplored effects of household consumption, financial inclusion, financial aid, and energy poverty influence on integrated sustainability, regarding global financial integration.

When rain does not run, a fingerprint of uncoordinated water resources development

The management of water resources in a basin can be challenging due to the sharing of water resources among multiple stakeholders and conflicting interests. This often results in imbalanced water resource development across the basin. While small and medium-sized projects are efficiently implemented in the headwater and upstream areas, the commissioning of large reservoirs faces delays due to the substantial investment required and regulatory processes involved. Also, the lack of coordination and synchronization in water resource development between upstream and downstream regions can lead to water scarcity and inefficient utilization of resources in the lower part of the basin. In this study, we assess the occurrence of water scarcity by comparing the frequency of hydrological and meteorological droughts before and after the detected abrupt change in river flow. To facilitate this assessment, we introduced two new indices: the positive to negative phases of hydrological and meteorological droughts. These indices highlight the increasing occurrence of hydrological droughts, which may not necessarily be linked to meteorological droughts during the identified periods. Furthermore, we propose a Monotone drought analysis method to examine the correlation between meteorological and hydrological droughts and address the impact of anthropogenic activities. We applied this analysis in the Sefid-Rud basin, the largest sub-basin in the southern Caspian Sea situated in Iran, which hosts several dams and reservoirs while more dams are in the construction and planning phase. The assessment took place in 24 gauges across the basin from 1978 to 2017 to detect and evaluate the alterations in flow regimes. Our analysis revealed that the unbalanced development of upstream water resources, driven by uncoordinated water infrastructure projects implemented by various decision-makers, has resulted in a decline of available water at critical points within the basin, which will be aggravated with the commissioning of new dams.

The effects of a mixture of small peptide chelating minerals and inorganic minerals on the production performance and tissue deposition of broiler chickens.

Due to the limited bioavailability of inorganic trace minerals, their utilization in poultry production has led to problems such as environmental contamination and inefficient resource utilization. It was investigated whether replacing inorganic trace minerals (ITM) with a blend of organic small peptide-chelated trace minerals (MIX) would improve production performance, selected biochemical parameters, antioxidant capacity, mineral deposition in liver, heart, and tibia, as well as mineral content in feces of broilers. A total of 432 healthy 21-day-old 817 broilers were randomly divided into 4 groups with 6 replicates per group and 18 chickens per replicate. The control group received a basal diet supplemented with 1,000 mg/kg of inorganic trace minerals as sulfate. The experimental groups received basal diets supplemented with 200, 400, and 600 mg/kg of mixed trace mineral elements (50% sulfate +50% small peptide-chelate) for a trial period of 30 days, divided into two stages: 21-35 days and 36-50 days. The results indicate that on the 50th day, compared with the 1,000 mg/kg ITM group, the levels of serum cholesterol, urea nitrogen, and malondialdehyde in the 200, 400, and 600 mg/kg MIX groups decreased (p < 0.01), while the levels of serum glutathione peroxidase in the 200, 400, and 600 mg/kg MIX groups increased (p < 0.05). Compared to the ITM group, the addition of organic small peptide chelated trace minerals mixed with inorganic trace minerals can reduce the levels of zinc and manganese in feces (p < 0.01). Furthermore, the iron content in the heart and tibia of the 600 mg/kg MIX group also significantly decreased (p < 0.05). There were no differences in growth performance and slaughter performance among the groups (p > 0.05). This study shows that replacing inorganic minerals with low-dose MIX (200, 400, and 600 mg/kg) can reduce the levels of zinc and manganese in feces, with no negative impact on growth and slaughter performance.

Innovative Deep Learning Architecture for the Classification of Lung and Colon Cancer From Histopathology Images

The increasing prevalence of colon and lung cancer presents a considerable challenge to healthcare systems worldwide, emphasizing the critical necessity for early and accurate diagnosis to enhance patient outcomes. The precision of diagnosis heavily relies on the expertise of histopathologists, constituting a demanding task. The health and well‐being of patients are jeopardized in the absence of adequately trained histopathologists, potentially leading to misdiagnoses, unnecessary treatments, and tests, resulting in the inefficient utilization of healthcare resources. However, with substantial technological advancements, deep learning (DL) has emerged as a potent tool in clinical settings, particularly in the realm of medical imaging. This study leveraged the LC25000 dataset, encompassing 25,000 images of lung and colon tissue, introducing an innovative approach by employing a self‐organized operational neural network (Self‐ONN) to accurately detect lung and colon cancer in histopathology images. Subsequently, our novel model underwent comparison with five pretrained convolutional neural network (CNN) models: MobileNetV2‐SelfMLP, Resnet18‐SelfMLP, DenseNet201‐SelfMLP, InceptionV3‐SelfMLP, and MobileViTv2_200‐SelfMLP, where each multilayer perceptron (MLP) was replaced with Self‐MLP. The models’ performance was meticulously assessed using key metrics such as precision, recall, F1 score, accuracy, and area under the receiver operating characteristic (ROC) curve. The proposed model demonstrated exceptional overall accuracy, precision, sensitivity, F1 score, and specificity, achieving 99.74%, 99.74%, 99.74%, 99.74%, and 99.94%, respectively. This underscores the potential of artificial intelligence (AI) to significantly enhance diagnostic precision within clinical settings, portraying a promising avenue for improving patient care and outcomes. The synopsis of the literature provides a thorough examination of several DL and digital image processing methods used in the identification of cancer, with a primary emphasis on lung and colon cancer. The experiments use the LC25000 dataset, which consists of 25,000 photos, for the purposes of training and testing. Various techniques, such as CNNs, transfer learning, ensemble models, and lightweight DL architectures, have been used to accomplish accurate categorization of cancer tissue. Various investigations regularly show exceptional performance, with accuracy rates ranging from 96.19% to 99.97%. DL models such as EfficientNetV2, DHS‐CapsNet, and CNN‐based architectures such as VGG16 and GoogleNet variations have shown remarkable performance in obtaining high levels of accuracy. In addition, methods such as SSL and lightweight DL models provide encouraging outcomes in effectively managing large datasets. In general, the research emphasizes the efficacy of DL methods in successfully diagnosing cancer from histopathological pictures. It therefore indicates that DL has the potential to greatly improve medical diagnostic techniques.

Efficient inverse design of optical multilayer nano-thin films using neural network principles: backpropagation and gradient descent.

Optical multilayer thin films have a wide range of applications due to their ability to manipulate transmissive or reflective wavelengths by adjusting the thickness of composed layers, enabling diverse uses. Although their light weight, flexible nature and ease of fabrication position them as promising components for future devices, determining their optimal layer thickness for the desired functionality demands extensive simulations, leading to inefficient utilization of computational resources and time. To overcome these challenges, inverse design methods, leveraging machine learning and deep learning, are being explored. However, these methods necessitate learning processes, despite the presence of well-established formulas that elucidate these phenomena. Furthermore, deriving accurate answers for conditions not included in the learning process proves to be challenging. This paper introduces an innovative inverse design approach that utilizes the backpropagation of a networked transfer matrix, effectively explaining the characteristics of optical multilayer thin films. By exploiting the chain rule of the network, this method calculates gradients to discern how each layer thickness influences the outcomes. Consequently, the optimal thickness is determined without the need for an additional learning process. Mathematical elucidation of the operational principle of this approach is precisely described. Optimization of computing resource utilization through network configuration reduces the calculation time compared to conventional methods. The efficacy of this method is demonstrated through its application in the inverse design of transmissive and reflective films, verifying its potential for enhancing efficiency and accuracy in optical multilayer thin-film design and manufacturing processes.

INSTITUTIONAL ASPECTS AND RISKS IN THE DIGITAL ECONOMY: WAYS TO REDUCE UNCERTAINTY FOR ECONOMIC AGENTS

In the rapidly evolving landscape of the modern world, the inexorable shift towards a digital revolution is reshaping traditional economic models across the globe, including the Republic of Uzbekistan. This article delves into the impediments posed by institutional constraints in the digital economy, elucidating how legal frameworks governing digital interactions and inefficient institutional arrangements can impede the development and optimization of digital business models. Notably, the absence of clear regulations on electronic payments, cybersecurity, and data protection can introduce unpredictability, jeopardizing security and trust within the digital environment. The abundance of data and information, coupled with unequal distribution among market participants, may lead to suboptimal decision-making, inefficient resource utilization, and disparities in access to digital opportunities. The article aims to analyze and propose strategies to overcome institutional limitations and information asymmetry, emphasizing the need for effective institutional structures and mechanisms. In conclusion, this article provides a comprehensive examination of the challenges and opportunities in optimizing the digital economy in Uzbekistan. By proposing strategies to address institutional constraints and information asymmetry, it offers a roadmap for fostering a thriving digital ecosystem that benefits all stakeholders in the Republic of Uzbekistan.

Host Detection and Classification using Support Vector Regression in Cloud Environment

Having the potential to provide global users with pay-per-use utility-oriented IT services across the Internet, cloud computing has become increasingly popular. These services are provided via the establishment of data centers (DCs) across the world. These data centers are growing increasingly with the growing demand for cloud, leading to massive energy consumption with energy requirement soaring by 63% and inefficient resource utilization. This paper contributes by utilizing a dynamic time series-based prediction support vector regression (SVR) model. This prediction model defines upper and lower limits, based on which the host is classified into four categories: overload, under pressure, normal, and underload. A series of migration strategies have been considered in the case of load imbalance. The proposed mechanism improves the load distribution and minimizes energy consumption and execution time by balancing the host in the data center. Also, it optimizes the execution cost and resource utilization. In the proposed framework, the energy consumption is 0.641kWh, and the execution time is 165.39sec. Experimental results show that the proposed approach outperforms other existing approaches.

Standardizing process in acute biliary disease.

The perioperative management of biliary disease (BD) is variable across institutions with suboptimal outcomes for patients and health care systems. This results in inefficient utilization of limited resources. The aim of the current study was to identify modifiable factors impacting patients' time to theater, intraoperative time, and time to discharge as the constituents of length of stay to guide creation of a perioperative management protocol to address this variability. Data were prospectively captured at Christchurch Hospital for all adult patients presenting for cholecystectomy between May 2015 and May 2022. Pre, post, and intraoperative factors were assessed for their impact on time to theater, operative time, and postoperative hours to discharge. Four thousand five hundred seventy-seven patients underwent cholecystectomy during the study period, of which 2807 (61%) were acute presentations and made up the cohort for analysis. Time to theater was significantly impacted by preoperative imaging type, while operative grade and the procedure type had the most clinically significant impact on operative time. Postoperatively time to discharge was significantly impacted by drain placement. Standardizing management of BD would likely result in significant savings for the health care system and improved outcomes for patients. The data seen here evidence the importance of appropriate imaging selection, intraoperative difficulty operative grade identification, and low suction drain selection. These data have been incorporated in a perioperative management protocol as standardization of care across the patient workflow in BD is a sensible approach for ensuring optimal use of scarce resources.

Discussion on Statistical Monitoring and Evaluation of High-Quality Development of County Economy

The county economy serves as the fundamental unit in China’s economic development and construction. There exists a significant correlation between the quality and level of county economic development and the overall economic construction level of the country. In recent years, China’s economy has experienced rapid growth, contributing to an improved living environment and substantial economic income for its citizens. However, this progress has also brought to light certain issues, such as an unbalanced industrial structure and inefficient resource utilization, leading to problems such as low efficiency and severe environmental pollution. Therefore, it becomes imperative to enhance the statistical monitoring and evaluation of high-quality county economic development. This approach aims to gather insights into the development status of the county economy and provide essential data support for decision-making in county economic development. Consequently, this paper proposes development suggestions for the statistical monitoring and evaluation of high-quality county economic development, serving as a crucial starting point for future initiatives.

Exploring the influence of sustainable regional planning on green economic growth: Evidence from Guangdong-Hong Kong- Macao Greater Bay Area and ASEAN+6

This study explores the influence of mining waste production on green economic growth in the ASEAN+6 and Guangdong-Hong Kong- Macao Greater Bay Area region spanning from 2000 to 2020. Employing ARDL (Autoregressive Distributed Lag) estimation and a comprehensive panel framework, the research reveals a notable adverse correlation: a 1% escalation in mining waste results in a 0.65% decline in green economic growth. Inefficient utilization of resources and inadequate waste management within the mining sector contribute to environmental deterioration, posing challenges to the sustainability of green industries. Furthermore, significant energy consumption from non-renewable sources during mining exacerbates the carbon footprint, obstructing advancements in green energy initiatives. The study also brings attention to a negative association between a 1% rise in inward Foreign Direct Investment (FDI) volume and a 0.39% decrease in green economic growth. To mitigate these unfavorable impacts, the research advocates for the promotion of sustainable FDI, the incentivization of green investments, and the implementation of rigorous environmental regulations.

A Resource Allocation Model for Mixed-Grid Optical Networks

Due to cost and service level agreement, upgrading network infrastructure is always challenging for network operators, particularly for backbone optical networks (ONs). Nowadays, to support several bandwidth-hungry applications, network operators adopt the migration process from fixed-grid nodes to flex-grid in backbone ONs. However, without proper planning, it may lead to inefficient resource utilization. This paper proposes optimization models for resource allocation in mixed-grid ONs, which initially identifies the set of eligible nodes for upgradation from fixed-grid to flex-grid capabilities for a given set of offline traffic and then uses them in the network to maximize the resource utilization. We formulate an integer linear programming (ILP) problem, named Opti-node, to determine the set of nodes selected for fixed-grid to flex-grid conversion in mixed-grid ONs. We then introduce an ILP, named Opti-resource, to enhance the resource utilization based on the determined set of nodes converted from fixed-grid to flex-grid using Opti-node. We prove that the decision problem of resource allocation in mixed-grid ONs is NP-Complete. Heuristic approaches based on the ILP-based approach and simulated annealing are introduced for a large problem size when Opti-resource is not tractable. We evaluate the proposed resource allocation model extensively, considering different networks, and observe that the simulated annealing-based approach performs better in terms of blocking ratio and spectrum utilization than the ILP-based heuristic approach, but it requires additional computation time.

Machine learning compliance-aware dynamic software allocation for energy, cost and resource-efficient cloud environment

With the growing number of cloud services protected by licenses, compliance management and assurance is becoming critical need to support the development of trustworthy cloud systems. In these systems, the multiplication of services and the inefficient resource utilization incurred energy consumption and costs increase despite the consolidation initiatives underway. Few works deal with resource allocation optimization at the SaaS level, which does not consider compliance aspects. Generally, the reported consolidation work does not address license management in the cloud environment as a whole, particularly from a resource management perspective, and the vast majority of consolidation work focuses on resource optimization at the infrastructure level. Thus, we propose a software license consolidation scheme based on multi-objective reinforcement learning that enables efficient use of resources and optimizes energy consumption, resource wastage, and costs while ensuring compliance with the processor-based licensing model. The experimental results show that our solution outperforms the baseline approaches in different scenarios with homogeneous and heterogeneous resources under different data center scales.

Shared style linear k nearest neighbor classification method

While previous linear k nearest neighbor methods have demonstrated strong generalization capability for ordinary datasets, they often fail to achieve comparable generalization capability when applied to stylistic datasets due to the unique characteristics of this type of datasets. Additionally, when multiple testing samples share a certain style characteristic, both the traditional k nearest neighbor method and evolved versions of the linear k nearest neighbor method tend to ignore the shared style in the testing sample set, leading to inefficient utilization of information resources. In order to address these issues, we propose a new classification method called Shared Style Linear kNearest Neighbor (SSL-KNN), which enhances the generalization capability of the linear k nearest neighbor method on stylistic datasets. The proposed method is motivated by the observation that one can better determine the class of a collection of homogeneous samples by evaluating their overall style, rather than simply aggregating individual sample judgments. The proposed method achieves this goal and two additional goals: a) we obtain the style membership vectors of the testing samples with different classes of the training samples in order to seek a guarantee of the generalization capability on the stylistic datasets; b) we introduce a Gaussian kernel distance metric constraining the linear expression weights in order to seek a guarantee of the generalization capability on the ordinary datasets. Furthermore, we propose an alternating optimization strategy implemented to optimize the proposed SSL-KNN method. Finally, we evaluate the proposed method and the comparative methods on 15 benchmark datasets containing both ordinary and stylistic datasets. The results show that the proposed SSL-KNN method guarantees good generalization capability on both ordinary and stylistic datasets.

Estimation of Technical Efficiency of Sorghum Production in Dejen District, North-west Ethiopia

Ethiopian agriculture is characterized by low productivity due to natural, social, economic, and institutional factors. Farmers with homogeneous resources produce different outputs per hectare due to inefficient utilization of limited resources. There is no due attention given to crop production efficiency issues, particularly in sorghum production. The objective of this study was to assess the level of technical efficiency and its determinant factors on sorghum production in Dejen District, North-west Ethiopia. The two-stage sampling procedure was used to determine the sample size. Data were collected from 192 households using a systematic random sampling technique. A semi-structured questionnaire was used for the data collection. Focus group discussions and key informant interviews were also conducted. Both descriptive statistics and econometric analysis were used for data analysis. The average technical efficiency of Sorghum producers was 62.8%, with a min of 23.5% and a max of 96.7%. About 37.2% of output variation from optimal production was observed in the study area. The estimated Gamma (γ) value was 73.4% indicating the presence of inefficiency. Thus, farmers have a chance to maximize their output level by making efficient use of existing resources and technology

Lightpath provisioning model considering crosstalk-derived fragmentation in spectrally-spatially elastic optical networks

Recently, multi-core fiber (MCF) has been incorporated with elastic optical networks (EONs) to enhance the fiber capacity, which forms spectrally-spatially EONs (SS-EONs). In MCF, inter-core crosstalk (XT) occurs and degrades the signal quality. Fragmentation is one of the major issues since it leads to inefficient utilization of spectrum resources. Fragmentation metric-aware spectrum resource allocation is one of the solutions to suppress fragmentation. In SS-EONs, several studies have been conducted to deal with inter-core XT and fragmentation. The conventional fragmentation-aware model considering XT calculates the fragmentation metric by distinguishing spectrum slots as vacant or not. Some vacant slots are unavailable due to inter-core XT, and the unavailable vacant slots can cause fragmentation. This paper proposes a fragmentation-aware lightpath provisioning model, which suppresses the fragmentation caused by allocating spectrum slots to lightpaths and due to inter-core XT. The proposed model classifies vacant slots into available ones and unavailable ones. An optimization problem is formulated as an integer linear programming (ILP) problem. A heuristic algorithm is introduced when the ILP problem is not tractable. Numerical results observe that the proposed model suppresses the blocking probability compared to a benchmark model based on the literature.

Abstract 18595: Predictors of Prolonged Post-Operative Stay in Patients Undergoing Coronary Artery Bypass Grafting: The Impact of Multi-Disciplinary Quality of Care Protocols, a Single Center Experience From the Middle East

Background: Prolonged postoperative stay (POS) in patients undergoing CABG is associated with increased cost, inefficient utilization of resources, and poor quality of care (QOC). We aim to identify predictors of prolonged POS and evaluate the overtime impact of multidisciplinary team (MDT) QOC protocols on POS at a newly established tertiary care center. Methods: A total of 1168 consecutive patients undergoing isolated CABG between 2015-2022 were evaluated. POS was identified as prolonged (&gt;6 days) and short (≤ 6 days). Short vs. prolonged stay groups were compared by appropriate testing methods. Predictors of prolonged POS were determined by multivariable logistic regression. Trends in prolonged POS were determined using Joinpoint Software to calculate the average annual percentage change (AAPC). Results: Overall, 44.3% (517/1168) of patients had prolonged POS. Patients in the prolonged POS group were older (59.1±10.5 vs. 56.3± 9.5, P&lt;0.001), more likely females (16.6% vs. 9.7%, P&lt;0.001), and had a higher burden of hypertension (88.1% vs. 81.9%, P=0.004), and diabetes (74.8% vs. 62.7%, P&lt;0.001). The prolonged POS group featured worse ejection fraction, higher serum creatinine, and more frequently required preoperative intra-aortic balloon pump insertion (Table) . On multivariable logistic regression, age, female gender, NYHA class ≥II, history of diabetes, preoperative creatinine level, preoperative EF, and preoperative IABP placement were independent predictors of prolonged POS (Figure A) . Between 2015-2022, more QOC protocols were initiated, with a 67% relative reduction in the proportion of patients with prolonged POS (85.7% to 28.3%), with an AAPC of -12.8, 95% CI [-16.3, -9.2], P&lt;0.001 (Figure B). Conclusion: Initiating MDT QOC protocols was associated with a reduction in POS among patients undergoing CABG. Identifying predictors of prolonged POS can help in timely planning of milestones aimed at eliminating unnecessary POS.

Identifying Trauma Patients in Need for Emergency Surgery in the Prehospital Setting: The Prehospital Prediction of In-Hospital Emergency Treatment (PROPHET) Study.

Prehospital field triage often fails to accurately identify the need for emergent surgical or non-surgical procedures, resulting in inefficient resource utilization and increased costs. This study aimed to analyze prehospital factors associated with the need for emergent procedures (such as surgery or interventional angiography) within 6 h of hospital admission. Additionally, our goal was to develop a prehospital triage tool capable of estimating the likelihood of requiring an emergent procedure following hospital admission. We conducted a retrospective observational study, analyzing both prehospital and in-hospital data obtained from the Lombardy Trauma Registry. We conducted a multivariable logistic regression analysis to identify independent predictors of emergency procedures within the first 6 h from admission. Subsequently, we developed and internally validated a triage score composed of factors associated with the probability of requiring an emergency procedure. The study included a total of 3985 patients, among whom 295 (7.4%) required an emergent procedure within 6 h. Age, penetrating injury, downfall, cardiac arrest, poor neurological status, endotracheal intubation, systolic pressure, diastolic pressure, shock index, respiratory rate and tachycardia were identified as predictors of requiring an emergency procedure. A triage score generated from these predictors showed a good predictive power (AUC of the ROC curve: 0.81) to identify patients requiring an emergent surgical or non-surgical procedure within 6 h from hospital admission. The proposed triage score might contribute to predicting the need for immediate resource availability in trauma patients.