Optimal Allocation Research Articles

An improved moth flame optimization for optimal DG and battery energy storage allocation in distribution systems

Deploying distributed generators (DGs) powered by renewable energy poses a significant challenge for effective power system operation. Optimally scheduling DGs, especially photovoltaic (PV) systems and wind turbines (WTs), is critical because of the unpredictable nature of wind speed and solar radiation. These intermittencies have posed considerable challenges to power grids, including power oscillation, increased losses, and voltage instability. To overcome these challenges, the battery energy storage (BES) system supports the PV unit, while the biomass aids the WT unit, mitigating power fluctuations and boosting supply continuity. Therefore, the main innovation of this study is presenting an improved moth flame optimization algorithm (IMFO) to capture the optimal scheduling of multiple dispatchable and non-dispatchable DGs for mitigating energy loss in power grids, considering different dynamic load characteristics. The IMFO algorithm comprises a new update position expression based on a roulette wheel selection strategy as well as Gaussian barebones (GB) and quasi-opposite-based learning (QOBL) mechanisms to enhance exploitation capability, global convergence rate, and solution precision. The IMFO algorithm's success rate and effectiveness are evaluated using 23rd benchmark functions and compared with the basic MFO algorithm and other seven competitors using rigorous statistical analysis. The developed optimizer is then adopted to study the performance of the 69-bus and 118-bus distribution grids, considering deterministic and stochastic DG's optimal planning. The findings reflect the superiority of the developed algorithm against its rivals, emphasizing the influence of load types and varying generations in DG planning. Numerically, the optimal deployment of BES + PV and biomass + WT significantly maximizes the energy loss reduction percent to 68.3471 and 98.0449 for the 69-bus's commercial load type and to 54.833 and 52.0623 for the 118-bus's commercial load type, respectively, confirming the efficacy of the developed algorithm for maximizing the performance of distribution systems in diverse situations.

Epidemiological Characteristics of Traumatic Spinal Cord Injury in Saudi Arabia: A Systematic Review.

Traumatic spinal cord injury (TSCI) is a severe condition with high mortality and disability rates. Understanding the regional TSCI epidemiology may facilitate the development of targeted preventive initiatives and the optimization of resource allocation. The primary goal of this systematic review was to gather and analyze the existing literature on the frequency and characteristics of TSCI in Saudi Arabia. A literature search of PubMed, Web of Science, and Google Scholar was conducted in January 2024 following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Observational studies reporting TSCI epidemiology in Saudi Arabia between 2010 and 2022 were included. Data on demographics, mechanisms, levels/severity, and outcomes were extracted. Methodological quality was assessed using the Newcastle-Ottawa Scale. Nine studies involving 2,356 TSCI cases were analyzed. Most patients were young males. Road traffic accidents were shown to be the predominant cause, accounting for 56.5-90.8% of cases. Thoracic (28.7-48.3%) and cervical (26.6-39%) levels were the most common. The extent of neurological deficits showed significant variation throughout the studies. This review provides a baseline understanding of TSCI epidemiology in Saudi Arabia but highlights critical gaps that future research should address. The review emphasizes the need for evidence-based interventions targeting road safety and falls, standardized cervical spine evaluation and management, and the use of validated metrics to optimize patient outcomes. Large-scale population-based studies with standardized methodologies are necessary to fully understand TSCI epidemiology, prognosis, and long-term disability burden in Saudi Arabia, leading to better prevention strategies and improved patient outcomes.

Optimal Task Scheduling and Resource Allocation for Self-Powered Sensors in Internet of Things: An Energy Efficient Approach

Optimal Task Scheduling and Resource Allocation for Self-Powered Sensors in Internet of Things: An Energy Efficient Approach

Pollination efficiency and the pollen-ovule ratio.

Pollination presents a risky journey for pollen grains. Pollen loss is sometimes thought to favour greater pollen investment to compensate for the inefficiency of transport. Sex allocation theory, to the contrary, has consistently concluded that postdispersal loss should have no selective effect on investment in either sex function. But the intuitively appealing compensation idea continues to be raised despite the lack of theoretical endorsement. We address the theoretical issue with a model that directly represents pollen loss (and ovule loss through floral demise or loss of receptivity) as rate-dependent dynamical processes. These loss rates can be varied to examine the effect of pollination efficiency on optimal sex allocation. Pollen-ovule ratios follow from the sex allocation based on the resource costs of pollen and ovule production. This model confirms conventional findings that pollen loss should have essentially no effect on sexual resource allocation in large, panmictic populations. Pollen limitation of seed set does not alter this conclusion. These results force us to rethink the empirical association of pollination efficiency with low pollen-ovule ratios. This pattern could arise if efficient pollen transport commonly results in stigmatic deposition of cohorts of related pollen. Empirical evidence of correlated paternity supports this explanation.

Optimal Time and Power Allocation for Phase-Shift Configuration and Downlink Channel Estimation in RIS-Aided Systems

Optimal Time and Power Allocation for Phase-Shift Configuration and Downlink Channel Estimation in RIS-Aided Systems

Virtual power plant optimization with demand response and multi-complementary energy planning

Abstract To effectively address the increasing complexity of system operation optimization and resource allocation caused by the continuous enrichment of supply-side resources and the flexible variability of demand-side loads in the energy system, a robust optimization model for virtual power plants considering demand response and multi-energy complementarity is proposed. Handling renewable energy and load uncertainty in virtual power plants by building a two-stage robust optimisation model. The introduction of robust coefficients allows for flexible adjustment of the conservatism of the optimized scheduling. Results indicate that considering demand response and multi-energy complementarity can effectively enhance the system’s robustness and flexibility.

Resource allocation for device-to-device communications underlaying uplink cellular networks

Underlying cellular networks, device-to-device (D2D) communications are a practical network technology that can increase power efficiency and spectrum usage for close-proximity wireless services and applications. However, D2D link interference, when sharing resources with cellular users (CUs), poses a major challenge in such distribution situations. In this research, we primarily utilize wireless channel data that exhibits slowly shifting large-scale fading to conduct spectrum sharing and power allocation. The overall ergodic capacity of all cellular user equipment (CUE) links is initially considered as the optimization target in order to maximize the overall throughput of CUE links while ensuring the reliability of each D2D link. Then, the expansion of the minimum ergodic capacity is measured to ensure a more consistent capacity performance across all CUE links. We utilized algorithms that are resilient to channel fluctuations and produce optimal resource allocation. We use MATLAB, and the computer simulation validates its intended performance.

Analisis Pembentukan Portofolio Optimal di Bursa Efek Indonesia dengan Menggunakan Model Markowitz

The purpose of this study is to utilize the Markowitz Model to build an optimal portfolio of stocks on the Indonesia Stock Exchange in 2019 to 2023, focusing on stocks in the IDX30 index. Based on information from KSEI, the public premium in capital market speculation continues to increase. On that basis, this study is important to be conducted in order to provide a reference for the public to invest. The IDX30 index was chosen as the subject of the study because it includes stocks with large market capitalization, high liquidity, and stable financial conditions. This study determines the optimal fund allocation for each stock and examines stocks in the IDX30 index from 2019 to 2023 that are worthy of consideration for inclusion in the optimal portfolio. In this test, it produces four stocks that become the optimal portfolio with the most extreme profits, namely ADRO 16.12%, BBCA 65.97%, BBRI 4.13%, and ICBP 13.76% with a return of 1.01% and a risk of 0.22%. In contrast, the minimum risk consists of ADRO of 5.29%, BBCA of 26.14%, CPIN of 13.91%, ICBP of 16.17%, INDF of 8%, KLBF of 0.48%, TLKM of 13.47%, and UNVR of 16.55%, with a risk of 0.1% and an expected portfolio return of 0.11%.

A new bi-level TOPSIS based neutrosophic programming technique for land allocation to medium farm holders

Agriculture impacts a country's social and economic growth. Crop allocation, crop combinations, and crop production processes are all necessary to achieve optimal results during various growing seasons. To maximize farm earnings, proper farm planning and resource allocations are necessary. In agriculture, land allocation problems involve several uncertainties and unpredictable variables, includes water supply, labour demands, fertility use, and food requirements. The objective of this study is to propose novel bi-level programming approaches to overcome such issues and obtain optimal land allocation for medium-sized farmers. The current study examines a bi-level, TOPSIS-based neutrosophic programming approaches in two cases, including non-interactive and interactive approaches with linear, exponential, and hyperbolic membership functions to maximize net profit and minimize the expenditure. The proposed methods are compared to other approaches, such as the Torabi & Hassini approach, the Fuzzy Optimization Technique (FOT), and the Intuitionistic Fuzzy Optimization Technique (IFOT) and are found to be more effective than the existing ones.

UAV Assisted Integrated Sensing and Communications for Internet of Things: 3D Trajectory Optimization and Resource Allocation

UAV Assisted Integrated Sensing and Communications for Internet of Things: 3D Trajectory Optimization and Resource Allocation

Demand-side energy sharing mechanism with residential energy storage in active distribution networks

Abstract With the widespread adoption of distributed new energy and residential energy storage (RES), demand-side users in active distribution networks (ADNs) are transitioning from traditional consumers to prosumers. The flexible resources of demand-side prosumers require an appropriate trading mechanism to achieve optimal resource allocation. A demand-side energy sharing mechanism based on residential energy storage is proposed. Firstly, a trading decision model for prosumer units is established by integrating the charge-discharge utility of RES devices with production-consumption characteristics. Then, a decentralized social welfare maximization energy sharing problem for ADNs is constructed using the Alternating Minimization Algorithm (AMA). Furthermore, a 10-node active distribution network is analyzed as an example. The proposed mechanism can maximize social welfare and increase the adaptation level of new energy from 64.58% to 66.83%.

Development of a hub-and-spoke durable left ventricular assist device program in Brazil, a middle-income country

ObjectivesTo describe the experience of a left ventricular assist device (LVAD) program in a middle-income country in a hub-and-spoke model. MethodsPatients fulfilling strict inclusion and exclusion criteria were referred from different centers in Brazil for LVAD implantation through a philanthropy program financed via a Brazilian Federal Government tax exemption structure. LVAD implantation was performed in hub-and-spoke model with a single implanting center. Data was collected retrospectively using hospital records and telephone contact with other centers. Patients that received LVAD implants external to the philanthropic program, either at this or other Brazilian centers, were not included. ResultsBetween January 1st, 2013, and December 31st, 2020, 20 adult patients underwent long-term continuous flow LVAD implantation with decentralization of post-implant patient care in regional centers. Patients were referred from 11 centers from 7 states in Brazil and underwent LVAD implantation through a philanthropy program. The median age was 52.5 years and 85% were INTERMACS profile 3 patients. Two patients had Chagas cardiomyopathy. The overall survival censored for competing risks at 1 year and 2 years were 90% and 84% respectively. Three patients (15%) underwent heart transplantation in the first 2 years after LVAD implantation. Twelve patients returned to their original centers and were followed remotely. ConclusionsThis study presents a successful LVAD implantation program in a hub-and-spoke model in Brazil. Centralization of LVAD implantation with decentralization of post-implant patient care in regional centers is feasible and safe, enabling optimal allocation of resources in middle-income countries.

Dynamic optimal allocation of energy storage systems integrated within photovoltaic based on a dual timescale dynamics model

Energy storage systems (ESSs) operate as independent market participants and collaborate with photovoltaic (PV) generation units to enhance the flexible power supply capabilities of PV units. However, the dynamic variations in the profitability of ESSs in the electricity market are yet to be fully understood. This study introduces a dual-timescale dynamics model that integrates a spot market clearing (SMC) model into a system dynamics (SD) model to investigate the profit-aware capacity growth of ESSs and compares the profitability of independent energy storage systems (IESSs) with that of an ESS integrated within a PV (PV-ESS). Furthermore, this study aims to ascertain the optimal allocation of the PV-ESS. First, SD and SMC models were set up. Second, the SMC model simulated on an hourly timescale was incorporated into the SD model as a subsystem, a dual-timescale model was constructed. Finally, a development simulation and profitability analysis was conducted from 2022 to 2040 to reveal the dynamic optimal range of PV-ESS allocation. Additionally, negative electricity prices were considered during clearing processes. The simulation results revealed differences in profitability and capacity growth between IESS and PV-ESS, helping grid investors and policymakers to determine the boundaries of ESSs and dynamic optimal allocation of PV-ESSs.

Research on the optimal allocation of regionally integrated energy systems including multiple energy storage

Research on the optimal allocation of regionally integrated energy systems including multiple energy storage

Patient Management System Using Fuzzy Multiple Attribute Decision Making Method with SAW at Noura Aesthetic Clinic

This study presents the development and implementation of a Patient Management System (PMS) at Noura Aesthetic Clinic using the Fuzzy Multiple Attribute Decision Making (FMADM) method with Simple Additive Weighting (SAW). The aim is to enhance the decision-making process for patient treatment prioritization and management. The PMS integrates various patient attributes, including medical history, treatment urgency, and resource availability, into a comprehensive decision-making framework. By employing the FMADM method, the system addresses the inherent uncertainties and subjectivities in patient data, ensuring more accurate and reliable prioritization. The SAW technique further refines this process by assigning weighted scores to each attribute, facilitating a straightforward and effective comparison. This combination allows for a balanced assessment of multiple factors, promoting optimal resource allocation and improving overall patient care. The implementation at Noura Aesthetic Clinic demonstrated significant improvements in operational efficiency and patient satisfaction. The system's adaptability to diverse clinical settings and its user-friendly interface make it a valuable tool for healthcare providers. This study underscores the potential of advanced decision-making methodologies in transforming patient management practices, paving the way for more informed and equitable healthcare delivery.

Bayesian hierarchical model for bias-correcting climate models

Abstract. Climate models, derived from process understanding, are essential tools in the study of climate change and its wide-ranging impacts. Hindcast and future simulations provide comprehensive spatiotemporal estimates of climatology that are frequently employed within the environmental sciences community, although the output can be afflicted with bias that impedes direct interpretation. Post-processing bias correction approaches utilise observational data to address this challenge, although they are typically criticised for not being physically justified and not considering uncertainty in the correction. This paper proposes a novel Bayesian bias correction framework that robustly propagates uncertainty and models underlying spatial covariance patterns. Shared latent Gaussian processes are assumed between the in situ observations and climate model output, with the aim of partially preserving the covariance structure from the climate model after bias correction, which is based on well-established physical laws. Results demonstrate added value in modelling shared generating processes under several simulated scenarios, with the most value added for the case of sparse in situ observations and smooth underlying bias. Additionally, the propagation of uncertainty to a simulated final bias-corrected time series is illustrated, which is of key importance to a range of stakeholders, such as climate scientists engaged in impact studies, decision-makers trying to understand the likelihood of particular scenarios and individuals involved in climate change adaption strategies where accurate risk assessment is required for optimal resource allocation. This paper focuses on one-dimensional simulated examples for clarity, although the code implementation is developed to also work on multi-dimensional input data, encouraging follow-on real-world application studies that will further validate performance and remaining limitations. The Bayesian framework supports uncertainty propagation under model adaptations required for specific applications, providing a flexible approach that increases the scope of data assimilation tasks more generally.

Development and Validation of a Risk Score for Predicting ICU Admission in Adults with New-Onset Encephalitis.

Timely intensive care unit (ICU) admission for patients with encephalitis is associated with better prognosis. Therefore, our aim was to create a risk score predicting ICU admission in adults with encephalitis, which could aid in optimal management and resource allocation. We initially identified variables that would be most predictive of ICU admission among 372 patients with encephalitis from two hospital systems in Houston, Texas (cohort 1), who met the International Encephalitis Consortium (IEC) criteria from 2005 to 2023. Subsequently, we used a binary logistic regression model to create a risk score for ICU admission, which we then validated externally using a separate cohort of patients from two hospitals in Baltimore, Maryland (cohort 2), who met the IEC criteria from 2006 to 2022. Of 634 patients with encephalitis, 255 (40%) were admitted to the ICU, including 45 of 113 (39.8%) patients with an autoimmune cause, 100 of 272 (36.7%) with an infectious cause, and 110 of 249 (44.1%) with an unknown cause (p = 0.225). After conducting a multivariate analysis in cohort 1, we found that the presence of focal neurological signs, new-onset seizure, a Full Outline of Unresponsiveness score ≤ 14, leukocytosis, and a history of chronic kidney disease at admission were associated with an increased risk of ICU admission. The resultant clinical score for predicting ICU admission had an area under the receiver operating characteristic curve (AUROC) of 0.77 (95% confidence interval [CI] 0.72-0.82, p < 0.001). Patients were classified into three risk categories for ICU admission: low risk (score 0, 12.5%), intermediate risk (scores 1-5, 49.5%), and high risk (scores 6-8, 87.5%). External validation in cohort 2 yielded an AUROC of 0.76 (95% CI 0.69-0.83, p < 0.001). ICU admission is common in patients with encephalitis, regardless of etiology. Our risk score, encompassing neurologic and systemic factors, may aid physicians in decisions regarding intensity of care for adult patients with encephalitis upon hospital admission.

Empirical Analysis of Portfolio Allocation During Market Turbulence Based on Efficient Frontier

The global financial markets have experienced considerable turmoil due to the COVID-19 pandemic, in this context, portfolio management becomes critically important. This paper employs Monte Carlo Simulation to determine Efficient Frontier of a portfolio comprising fourteen US technology stocks in the period of COVID-19 pandemic by python, which also allocates portfolios for Minimum Volatility and Maximum Sharpe Ratio, along with their key parameters. According to this study, Maximum Sharpe Ratio portfolio outperforms Minimum Volatility portfolio in terms of cumulative return. However, its important to note that Maximum Sharpe Ratio portfolio also experiences a greater maximum drawdown. Additionally, this paper visually depicts the optimal allocation of asset weights across different levels of risk aversion using a chart. Furthermore, it identifies NVDA as the optimal choice for investors who have low degree of risk-aversion due to its high volatility and expected return. Conversely, IBM and CSCO emerge as the preferred choices for risk-averse investors, as they effectively mitigate the portfolios entire risk.

Optimal allocation of wind power hybrid energy storage capacity based on ant colony optimization algorithm

Renewable energy sources, such as wind power, face challenges owing to their erratic construction and intermittent nature, leading to the emergence of energy storage strategies to achieve grid stability and reliable electricity supplies. Determination of the correct size of energy storage devices for wind power plants is complicated. In this study, the ant colony optimization (ACO) algorithm is proposed for the best distribution/sizing of wind-generated hybrid storage capacity. Ants’ foraging habits motivate the development of wind turbines with high dependability, high efficiency and low energy storage costs. A study is conducted to confirm the practicality of the proposed method, using real data from a wind power plant to calculate the contrast between wind energy production and load consumption features, to influence the appropriate quantity for energy storage organization. The study demonstrates that the ACO approach provides precise and efficient solutions for determining the optimal energy distribution in wind power plants.

Implementation of the clinical practice guidelines among family medicine doctors at primary health care facilities in Khartoum and Gezira states of Sudan

IntroductionThe health system in Sudan faces several challenges, including increasing numbers of patients, shortages of health supplies, and disparities in the distribution of health services. Guidelines implementation improves patients’ outcomes and ensures efficient use of the resources in such a resource limited country. The study aimed to assess the implementation of the clinical practice guidelines among family medicine doctors working in the primary health care centers in Khartoum and Gezira states to provide baseline data about the current practice in Sudan.MethodsDescriptive cross-sectional facility-based survey, conducted from April to December 2021, on 373 of the practicing family-medicine doctors. A total of 101 Primary health care centers were surveyed (77 centers in Khartoum state and 24 in Gezira state). The questionnaire was pilot tested on a small group of physicians to improve clarity and reduce response bias. Descriptive statistics were used to summarize the data and analyzed by frequency tables. Chi square and logistic regression tests were used to determine the association between categorized variables. P value < 0.05 was considered statistically significant.ResultsMost of the practicing family-medicine doctors (98.4%) reported implementation of the guidelines. Moreover, (68.6%) of them received training programs which were organized and funded by the Sudan Ministry of Health. The local Sudanese guidelines were difficult to access and not regularly updated. Services unavailability and inaccessibility (87.1%), health insurance factors (83.9%), and patient factors (81.2%) were the most frequent barriers to guidelines implementation. Service cost (79.9%), lack of regular training programs (79.9%), absence of local guidelines (77.2%), lack of continuity in the comprehensive care process (63.0%), and lack of time (57.1%) were also reported as barriers to guidelines implementation.ConclusionGuidelines implementation is limited by unavailability and inaccessibility of the health services and the health insurance limited coverage. Expansion of the health insurance coverage, organization of continuous training programs, encouragement of regular auditing and issuing regulations to ensure the use of updated guidelines, dissemination of the updated national guidelines along with establishing clinical governance in Sudan can be useful tools for policymakers in the optimum allocation of public health resources.