improving resource utilisation Research Articles (Page 1)

Abstract Introduction At University College London Hospital, over 100 patients annually undergo resection and free flap reconstruction for head and neck cancer. Effective post-operative monitoring is crucial for early detection of changes that may signal threatened reconstruction, helping to reduce complications, minimise length of hospital stay, and improve resource utilisation. This complex task is undertaken by a diverse multidisciplinary team, many of whom lack standardised training in flap monitoring creating the potential for variability in skill and practices. Methods We used QI methodology to conduct anonymous survey of 53 staff members across eight groups revealed significant gaps in confidence and education. In response to the findings we implemented a threefold intervention: a free flap observation chart, a unified escalation protocol, and an multimedia e-Learning module created using Articulate RISE360. Results Our survey highlighted 37.7% felt confident in assessing free/pedicled flaps, 60.4% in escalating concerns, and 52.8% had no formal training. Importantly, 100% supported the need for dedicated educational resources. Our targeted interventions led to improved staff confidence and competence in monitoring and escalation, demonstrated in the dedicated pre- and post e-learning feedback. Conclusions Empowering staff with standardised education, prompt and appropriate intervention can be enacted to mitigate delays in return to theatre and reduce length of stay. These outcomes directly contribute to a more sustainable healthcare service by optimising resource allocation and improving operational efficiency. This is the first NHS-based e-Learning module focused on flap monitoring, representing an innovative and scalable solution for modernising multidisciplinary education and improving patient care.

The development of an intelligent connected monorail transit system offers an effective solution to the mismatch between passenger flow and system capacity at various time intervals within urban rail networks. As the core of such a system lies the virtual coupling (VC) technology, which dynamically adjusts train configurations in response to real-time passenger demand, thereby improving resource utilization. However, during VC operations, severe communication delays between vehicles or the sudden emergence of obstacles ahead may still result in rear-end collisions among coupled vehicles, posing significant safety risks. To address these challenges, this paper focuses on the active collision avoidance control of intelligent connected monorail vehicles operating within the VC environment. At the modeling level, a control model is developed to facilitate VC between leading and following vehicles, and the dynamic characteristics of typical operational scenarios—including station approach coupling, tracking coupling, and departure decoupling—are thoroughly analyzed. Building upon this foundation, the train’s behaviors under collision avoidance during accelerated departures, decelerated arrivals, and unexpected obstacle encounters are further investigated. In terms of control strategy, a Model Predictive Control (MPC) algorithm is introduced to enable efficient coordination and proactive collision avoidance among trains. Ultimately, a simulation platform based on Chongqing Rail Transit Line 3 is established for validating the proposed model and algorithm under representative operating scenarios. The evaluation demonstrates gains in system flexibility and safety and technical foundation for the practical implementation of intelligent rail transit systems.

Effective and accurate management of dynamic workloads in wireless sensor networks (WSNs) is critical to ensure energy efficiency, network longevity, and reliable information delivery in heterogeneous environments. Traditional heterogeneous WSN solutions often suffer from unbalanced energy usage, poor node placement and load distribution optimization, leading to poor performance and high operating costs. This proposes a new energy-aware load balancing architecture for heterogeneous WSNs that incorporates a multi-level optimization approach. First, Energy Weighted Probabilistic Deployment (EWPD) takes energy efficiency into account, guaranteeing optimal node placement to maximize network coverage and extend lifetime. Second, network initialization and node classification based on Sliding Time Windows (STW) predict task execution patterns and allow adaptive allocation of nodes according to task performance. Third, Expected Time to Task Completion (ETTC) scheduling with cluster formation and path discovery can predict workload times to maximize routing selection. Fourth, workload analysis is a tool based on Spider Swarm Intelligence (SSI) that measures the computational needs of nodes and finds the most efficient allocation of workload resources. Finally, the Minimum-Maximum Priority Energy-Aware Self-Scheduling Mechanism (DMMPS-EASSM) is a dynamic operation and migration mechanism designed to balance energy consumption and provide efficient data transmission. Experimental results demonstrate that the proposed framework can significantly improve network performance, reduce energy wastage, and improve resource utilization compared with traditional approaches based on heterogeneous WSNs.

Workforce planning in home social care services is becoming increasingly complex due to increasing demand, limited budgets, and the need for personalized and geographically distributed care. An emerging strategy involves the territorial organization of care delivery, where cities are partitioned into subareas managed by local caregiver teams. Although this model fosters continuity and familiarity between caregivers and users, it introduces operational challenges, particularly in managing fluctuating demand and ensuring efficient staff allocation. This paper addresses these challenges by proposing the Home Care Islands Optimization framework, which supports tactical decision-making in home care planning. The approach integrates two allocation mechanisms: (i) exclusive assignments of caregivers to specific subareas (referred to as islands) and (ii) a flexible floating team capable of operating across different islands. The model also allows for strategic merging of islands to improve resource utilization. This work is the first to integrate tactical workforce planning and territory merging under demand variability in home care. We develop a single-period Integer Linear Programming model that determines optimal hiring strategies, contract types (full-time vs. part-time), and team distribution and allocation while minimizing both non-served demand and cost. The methodology is applied to a real-world case study based on Barcelona’s public home care system, with data provided by the City Council. Through extensive computational experiments, we explore the impact of key parameters on the quality and structure of the solution. We show that non-served demand is eliminated when the tolerance parameter γ ≥ 30 , while the budget variability stabilizes only when γ ≥ 40 . Regarding the composition of the workforce, the share of part-time contracts decreases once α ≥ 10 , and the size of the floating team never exceeds 6–7 caregivers. Moreover, under higher demand variability, the minimum γ required for system stability rises, and the model tends to rely more heavily on part-time and floating caregivers; however, setting α = 11 effectively controls this effect and promotes employment stability. The framework offers a robust and flexible planning tool for public service managers, enabling data-driven decisions that improve service continuity, workforce stability, and system adaptability in the face of uncertain and variable demand. • Optimization model for tactical workforce planning in home social care services. • Integrates fixed local teams and a flexible floating team for better adaptability. • Supports territory merging to improve staff allocation and resource use. • Applied to real data from Barcelona’s public home care system. • Reduces unmet demand and costs through data-driven planning.

The COVID-19 pandemic led to a significant decrease in endoscopic procedure volumes, resulting in a backlog of patients awaiting investigation. Our study thus aimed to develop a machine learning-based scheduling tool to improve resource utilization, enhance system efficiency, and increase patient throughput, ultimately reducing procedural delays. In the first phase, machine learning methods were applied to historical data to predict procedure duration based on patient characteristics and environmental factors. In the second phase, a scheduling module was built using a greedy heuristic and a Mixed Integer Programming (MIP) model to optimize resource utilization. We showed that among the tested models, an XGBoost regression model was selected with a mean absolute error of 5.67 minutes on the test set. The simulation results demonstrated that MIP increased the number of patients scheduled by 5.9% while reducing mean waiting time from 19.5 days to 17.3 days over a waiting list of 1,000 patients, evaluated within a 2-week period (10 working days). Simulations using real patient data showed that the MIP scheduled 8 more patients than the baseline. Numerical results confirmed higher resource utilization rates in adaptive schedules. Our study highlights the potential of a machine learning-based scheduling tool to enhance resource allocation, thus helping address backlogs in endoscopic procedures. Real-world clinical validation is now necessary to substantiate the tool's effectiveness. Future work should prioritize prospective data collection to refine the predictive model and seamlessly integrate the tool into clinical workflows, ensuring its practical utility and success.

In recent years, a smart, safe, and efficient way to run university labs has become increasingly popular. Traditional management systems rely on manual processes that are error-prone, slow, and offer limited adaptability. To address these challenges, this study proposes a Smart Laboratory Management System (AI-SLMS) that optimizes operations, improves safety, and enhances the user experience in academic labs. AI-SLMS integrates predictive maintenance, intelligent scheduling, and secure access control using machine learning and the Internet of Things (IoT). The system employs Random Forest and Logistic Regression models, trained on integrated datasets (Kaggle Predictive Maintenance and TON_IoT), to anticipate equipment failures. For resource allocation, an intelligent scheduling module utilizes genetic algorithms for optimization. The system also enforces role-based access through RFID and biometric authentication. Experimental validation over three months in a university setting demonstrated significant improvements across key metrics: a 71.2% reduction in equipment downtime, a 78.7% decrease in scheduling conflicts, a 53.5% improvement in resource utilization, and 98.3% authentication accuracy. In conclusion, AI-SLMS offers a scalable and intelligent framework that significantly enhances the efficiency, security, and responsiveness of university laboratory management systems.

In the context of macroeconomic instability, military actions, destruction of production and logistics infrastructure, and limited access to financial resources, ensuring stable profitability of agricultural enterprises acquires strategic importance. This article aims to analyze, systematize, and improve the existing theoretical and methodological foundations for ensuring the profitability of agricultural enterprises. It has been determined that the profitability of agricultural enterprises is not merely the final financial outcome of economic activity, but also a key factor in economic stability, innovative development, and the long-term competitiveness of the agricultural sector. Based on findings from leading researchers, profitability is a complex economic concept that reflects an agricultural enterprise's ability to efficiently convert available resources into positive financial results, taking into account risk, seasonality, and the natural-climatic specificity of agricultural production. The study shows that the profitability of agricultural enterprises is shaped by both internal and external factors. Internal factors include production-technological, organizational-managerial, financial-economic, personnel, and social factors that determine the endogenous formation of profitability and largely depend on the effectiveness of managerial decisions. At the same time, the efficiency of realizing an agricultural enterprise's internal potential is significantly determined by its external operating environment. External factors considered in the study include market and price conditions, natural and climatic risks, institutional and legal frameworks, and socio-ecological challenges. The profitability of agricultural enterprises is shaped by the interaction between internal managerial mechanisms and external business conditions. Ensuring sustainable profitability requires developing a comprehensive approach that integrates strategic management, improved resource utilization efficiency, human capital development, systematic risk management, and adaptation to external environmental changes.

Agile software development emphasizes flexibility, rapid iterations, and continuous delivery of high-quality software. However, managing task prioritization and resource allocation effectively remains a persistent challenge due to dynamic requirements, shifting priorities, and varying team capacities. This study explores the use of Artificial Intelligence (AI) techniques, including machine learning, predictive analytics, and natural language processing, to optimize task prioritization and resource scheduling in Agile environments. By analyzing historical project data, team performance metrics, and backlog information, the AI-driven system provides intelligent recommendations for task sequencing and resource assignment. The proposed approach enhances sprint planning accuracy, improves resource utilization, reduces task completion times, and identifies potential bottlenecks before they impact project delivery. The integration of AI into Agile workflows allows teams to make data-driven decisions while maintaining the flexibility and responsiveness that Agile methodologies demand, ultimately leading to improved productivity, higher-quality outcomes, and more predictable project timelines

Punjab, contributing over 85% of India’s seed potato demand, faces a paradox of high production area but suboptimal productivity due to imbalanced nutrient management, particularly phosphorus. A field study was conducted over two years (2019–2020) at two sites (Ludhiana and Jalandhar) to evaluate the effect of integrated phosphorus management—using organic amendments (farmyard manure and biofertilizer) and graded phosphorus levels—on the growth, yield, and seed efficiency of potato and its residual impact on subsequent spring maize. Results revealed that farmyard manure (FYM) significantly enhanced dry matter accumulation, tuber bulking rate, and seed tuber yield, with average increases in medium-sized tuber yield by 21–28% and total tuber yield by 16–22% over the unfertilized control. Phosphorus application at 125 kg P₂O₅ ha¹ further improved yield attributes, resulting in 14–19% higher total tuber yield compared to lower P levels. Seed production efficiency was maximized (up to 38.72) under FYM and high phosphorus regimes, indicating improved resource utilization. Notably, the residual phosphorus from potato treatments positively influenced spring maize, with FYM plots showing 8.2–11.5% yield enhancement over the control, highlighting sustained soil fertility benefits. The findings underscore the value of integrating organic and mineral phosphorus sources for sustainable seed potato production while maintaining productivity in succeeding maize crops.

Agricultural sector is undergoing a significant transformation to address the growing global demand for food while ensuring sustainability and minimizing environmental impact. Traditional farming methods, which rely heavily on manual processes and offer limited data insights, are increasingly inadequate for managing the complexities of modern agricultural needs. To overcome these challenges, the integration of advanced computing technologies specifically cloud, edge, and fog computing has emerged as a transformative solution in smart farming. This paper provides a comprehensive literature review on the role of these technologies in agriculture, focusing on their applications, benefits, challenges, and future directions. A proposed framework for integrating IoT, edge, fog, and cloud computing is presented, aiming to enhance productivity, resource efficiency, and environmental sustainability. The framework is evaluated through a case study conducted in the Maregaon District of Yavatmal (Maharashtra), demonstrating significant improvements in crop yields, resource utilization, and decision-making capabilities. The results highlight an accuracy of 95% in data collection and processing, a 20% increase in crop yields, and a 30% reduction in water usage. These findings underscore the potential of these technologies to revolutionize agriculture, ensuring food security and environmental sustainability in the face of growing global challenges.

Precision agriculture enables data-driven crop monitoring and improved resource utilization. Paddy cultivation requires continuous surveillance and timely fertilizer application because it is sensitive to soil nutrient dynamics, water availability, and climatic conditions. Conventional practices such as manual field inspection and heuristic fertilizer advisory methods are often labor-intensive and subjective, which can reduce decision consistency and contribute to yield variability. To address these limitations, this study proposes a Selfdom Enhanced CatBoost (SECB) framework for remote paddy growth-stage monitoring and fertilizer recommendation. Multispectral remote sensing data collected over multiple seasons are used to compute vegetation indices, including NDVI, GNDVI, RVI, GRVI, and NDRE, to characterize crop vigor and chlorophyll-related variation across growth stages. The proposed SECB improves CatBoost by integrating an Improved Osprey Optimization Algorithm (IOOA) to tune key model parameters, aiming to enhance feature interaction learning and reduce overfitting. In addition, oppositional function-based initialization is applied to improve the exploration capability of IOOA and accelerate convergence. Experimental results show that SECB achieves improved performance over baseline classifiers in terms of accuracy, precision, F1-score, specificity, and AUC. The proposed approach provides reliable growth-stage identification and supports fertilizer recommendations to promote efficient nutrient usage and improved productivity. Overall, the framework offers an automated and scalable decision-support strategy for paddy crop management.

Task offloading is crucial for the efficient management of vehicular wireless networks, requiring access to real-time information on network status and available computational resources. However, because vehicles are mobile network nodes with variable speeds and limited computational resources for near-real-time task offloading, task offloading schemes may incur high latency, rendering this goal practically infeasible. To address this issue, this paper presents a trajectory-prediction-based pre-offloading decision (TPPD) algorithm that analyzes historical vehicle trajectories to predict future coordinates, thereby enabling the allocation of computational resources in advance. We first use a Long Short-Term Memory (LSTM) network to predict vehicle trajectories with error correction. Then, based on the task requirements and predicted trajectories, a dynamic resource allocation algorithm using a Double Deep Q-Network (DDQN) is designed to enable the multi-access edge server to minimize task processing delay while ensuring effective utilization of available computational resources. Numerical results verify the effectiveness of the proposed approach, demonstrating that, compared to traditional real-time task offloading strategies, the proposed TPPD algorithm significantly reduces task processing delay while improving resource utilization.

Unplanned return to theater (URTT) following head and neck cancer reconstruction presents significant clinical and economic challenges. Identifying risk factors for URTT can aid in surgical planning and patient risk stratification. This study evaluates preoperative and intraoperative factors associated with URTT in patients undergoing free flap reconstruction after head and neck tumor resection. A retrospective cohort study was conducted on patients undergoing head and neck tumor resection with free flap reconstruction between July 2015 and January 2024. Demographic, clinical, and operative data were analyzed using univariable and multivariable logistic regression. Of 609 patients included, 199 (32.7%) experienced URTT within 90 days postoperatively. Common indications for URTT included infected collections (33.7%), hematoma (24.6%), and flap dehiscence (16.1%). On univariable analysis, preoperative PEG tube use (unadjusted odds ratio (OR) 1.78 [95% confidence interval (CI) 1.03-3.07], p = 0.04), higher frailty index scores (1.27 [1.03-1.58], p = 0.03), longer operative duration (1.15 [1.06-1.24], p < 0.001), use of bony free flaps (2.08 [1.44-3.01], p < 0.001), and tracheostomy (2.09 [1.22-3.56], p = 0.01) were associated with URTT. Yet, on multivariable analysis, only higher frailty index score (adjusted OR 1.32, 95% CI 1.03-1.70, p = 0.03) and use of bony free flaps (adjusted OR 1.92, 95% CI 1.19-3.11, p < 0.01) retained statistical significance. The findings of this study suggest that frailty and the use of bony free flaps are key risk factors for URTT among reconstructive head and neck cancer patients. These findings support patient risk stratification preoperatively and targeted interventions to reduce the risk of URTT and improve resource utilization in head and neck cancer surgery.

Industrial edge computing in time-sensitive, heterogeneous environments faces significant challenges in delivering deterministic, low-latency, and fair task offloading under mixed-criticality traffic. Existing solutions struggle with unpredictable delays, resource contention, and the joint optimization of offloading and scheduling. In this work, we propose a task queue mapping mechanism-based architecture that incorporates formal mixed-deterministic traffic modeling under time-sensitive networking, enabling unified computation and network resource management. The joint task offloading and traffic scheduling problem is rigorously formulated as a two-stage Markov decision process with embedded fairness constraints. To address the strong coupling and computational complexity, we develop a fairness-aware deterministic offloading and resource scheduling (FA-DORS) framework, a hierarchical multiagent deep deterministic policy gradient algorithm, enhanced by a contribution-based local reward mechanism to ensure adaptive resource allocation and fairness. Extensive experimental results demonstrate that in the proposed architecture, FA-DORS achieves lower task latency, higher completion rates, improved resource utilization, and enhanced transmission determinism while ensuring fairness in multitask offloading compared to baseline methods. In particular, FA-DORS achieves an 8.89%–39.15% reduction in average end-to-end delay and up to 39.73% reduction in average jitter under high load, compared with the baseline method.

The rapid digitalization of modern power systems has led to a dramatic increase in real-time data generated by smart grid infrastructures, posing significant challenges for low-latency analytics and accurate decision-making. This study proposes a cloud-edge collaborative framework designed to achieve efficient processing and intelligent task allocation for large-scale smart grid data. The framework integrates pruned lightweight neural networks at edge nodes for rapid event detection with a deep reinforcement learning–based agent in the cloud to dynamically partition computational tasks according to current grid conditions. Experimental validation on a provincial smart grid dataset shows that the proposed system reduces response latency by more than 60% while maintaining near-optimal detection accuracy compared to traditional pure cloud methods. In addition, the architecture has achieved significant improvements in system scalability and resource utilization. It is also capable of adapting to various operating environments and fluctuating loads. These results highlight the potential of the framework to enable smart management, flexibility and scalability in the next generation of smart grids.

In edge intelligence networks, various applications exist based on different users and datasets. To realize intelligence, many works design various edge learning models to process these data. However, different types of users and models have negative influence on the learning performance, even degrading the learning gain. To address these issues, we propose a learning-aware optimization model with probabilistic scheduling. First, we design users scheduling to reveal the relationships between resource allocation and learning performance. Then, we also develop a fast proximal linearized algorithm in the large-scale communication networks. Next, we derive tasks scheduling in asymptotic cases to improve resource utilization efficiency. At last, extensive experimental results corroborate that probabilistic scheduling and multi-objective learning models improve the distinct edge learning gain efficiently compared with the state-of-the-art benchmark algorithms.

The growing number of mobile devices used by terrestrial users has significantly amplified the traffic load on cellular networks. Especially in urban environments, the high traffic demand brought about by dense user populations has bottlenecked network resources. The Space-Air-Ground-Integrated Network (SAGIN) provides a new solution to cope with this demand, enhancing data transmission efficiency through a multi-layered network structure. However, the heterogeneous and dynamic nature of SAGIN also poses significant management and resource allocation challenges. In this paper, we propose a cloud-edge-SAGIN framework for multi-UAV assisted traffic offloading based on Hierarchical Federated Learning (HFL), aiming to improve the traffic offloading ratio while optimizing the offloading resource allocation. HFL is used instead of traditional Federated Learning (FL) to solve problems such as irrational resource allocation due to heterogeneity in SAGIN. Specifically, the framework applies a hierarchical federated average algorithm and sets a reward function at the ground level, aiming to obtain better model parameters, improve model accuracy at aggregation, enhance UAV traffic offloading ratio, and optimize its scheduling and resource allocation. In addition, an improved Reinforcement Learning (RL) algorithm TD3-A4C is designed in this paper to assist UAVs in realizing intelligent decision-making, reducing communication latency, and further improving resource utilization efficiency. Simulation results demonstrate that the proposed framework and algorithms display superior performance across all dimensions and offer robust support for the comprehensive investigation of intelligent traffic offloading networks.

Mature multilayer reservoirs frequently suffer from uneven zonal inflow, accelerated water breakthrough, and declining production efficiency under conventional commingled operation. This study investigates the effectiveness of simultaneous-separate production (SSP) in Field X by integrating a comparative before–after analysis with a field-based evaluation of hydraulic isolation performance. The implementation of SSP produced a distinct and stable improvement in well behavior, reflected in increased oil rates, reduced water cut, and a more favorable redistribution of drawdown toward deeper oil-saturated zones. These effects indicate that SSP successfully limits inflow from depleted, water-prone intervals while activating previously underperforming layers. To validate the robustness of the findings, the results were benchmarked against Tatneft’s large-scale SSP program, which demonstrates comparable production responses across hundreds of wells. The consistency between local and external outcomes confirms the reproducibility of SSP under varied geological settings. Overall, the study demonstrates that SSP is a technically reliable, operationally feasible, and economically justified method for managing vertical heterogeneity and enhancing recovery in mature Kazakh reservoirs. The results support broader deployment of SSP as a scalable strategy for prolonging field productivity and improving resource utilization.

The Industrial Internet of Things (IIoT) is characterized by the instantaneous communication of diverse critical data across complex, resource-limited networks. To maximize the efficiency and accuracy of task scheduling in such networks, this study introduces a novel task scheduling framework based on the Adaptive Lotus Effect Optimization Algorithm (ALEOA). ALEOA perfectly combines the self-cleaning mechanism and learning function of the Lotus effect with the global search and learning capabilities of the Particle Swarm Optimization (PSO) algorithm to establish equilibrium between local and global search. ALEOA can adaptively adjust routing and task scheduling to support critical data communication and a dynamic network. Results from experiments on typical benchmarking equations and simulations of the IIoT communication environment show that the proposed ALEOA-based model yields higher task acceptance rates, lower communication delays, and improved resource utilization than traditional metaheuristic methods. Statistical analyses using the Wilcoxon Signed Rank test confirmed the effectiveness of this enhancement.

Abstract Background Ventilator-associated pneumonia (VAP) is a frequent nosocomial infection in patients undergoing cardiac surgery, contributing to prolonged hospitalization and increased healthcare burden. This study aimed to evaluate the impact of VAP on clinical outcomes and identify independent risk factors in cardiac surgical intensive care unit (CSICU) patients, leveraging real-world data from the Medical Information Mart for Intensive Care IV (MIMIC-IV) database. Methods A retrospective cohort analysis was conducted on 5,975 patients receiving invasive mechanical ventilation in the CSICU, with VAP cases identified using ICD codes (‘99731’ and ‘J95851’), including 161 VAP cases. Propensity score matching (PSM) was applied to balance covariates. Statistical analyses included Mann–Whitney U and chi-square tests for group comparisons, followed by univariable and multivariable logistic regression to identify VAP risk factors, with model diagnostics ensuring robustness. Results The VAP incidence was 19.68 per 1,000 ventilator-days. Before PSM, VAP patients had higher in-hospital mortality (20% vs. 4%, P &lt; 0.05) and longer ICU/hospital stays. After PSM, mortality did not differ significantly (20% vs. 23%, P = 0.50), but ICU stays (21.47 vs. 13.19 days) and hospital stays (28.38 vs. 18.64 days) remained significantly prolonged ( P &lt; 0.05). Multivariable analysis identified aortic aneurysm dissection (OR = 1.61), higher Charlson Comorbidity Index (OR = 1.15), higher positive end-expiratory pressure (OR = 1.28), and higher minute volume (OR = 1.72) as independent risk factors, while higher fraction of inspired oxygen was protective (OR = 0.89). Conclusions VAP significantly extends ICU and hospital stays without increasing attributable mortality in cardiac surgery patients. Key modifiable risk factors include ventilator settings and patient-specific factors. Targeted interventions on these factors may reduce VAP incidence and improve resource utilization.