Reliable Approach Research Articles

Present and future suitability of invasive and urban vectors through an environmentally driven mosquito reproduction number.

Temperature and water availability significantly influence mosquito population dynamics. We have developed a method, integrating experimental data with insights from mosquito and thermal biology, to calculate the basic reproduction number ([Formula: see text]) for urban mosquito species Aedes albopictus and Aedes aegypti. [Formula: see text] represents the number of female mosquitoes produced by one female during her lifespan, indicating suitability for growth. Environmental conditions, including temperature, rainfall and human density, influence [Formula: see text] by altering key mosquito life cycle traits. Validation using data from Spain and Europe confirms the approach's reliability. Our analysis suggests that temperature increases may not uniformly benefit Ae. albopictus proliferation but could boost Ae. aegypti expansion. We suggest using vector [Formula: see text] maps, leveraging climate and environmental data, to predict areas susceptible to invasive mosquito population growth. These maps aid resource allocation for intervention strategies, supporting effective vector surveillance and management efforts.

A Survey on Data-Driven Approaches for Reliability, Robustness, and Energy Efficiency in Wireless Body Area Networks

Wireless Body Area Networks (WBANs) are pivotal in health care and wearable technologies, enabling seamless communication between miniature sensors and devices on or within the human body. These biosensors capture critical physiological parameters, ranging from body temperature and blood oxygen levels to real-time electrocardiogram readings. However, WBANs face significant challenges during and after deployment, including energy conservation, security, reliability, and failure vulnerability. Sensor nodes, which are often battery-operated, expend considerable energy during sensing and transmission due to inherent spatiotemporal patterns in biomedical data streams. This paper provides a comprehensive survey of data-driven approaches that address these challenges, focusing on device placement and routing, sampling rate calibration, and the application of machine learning (ML) and statistical learning techniques to enhance network performance. Additionally, we validate three existing models (statistical, ML, and coding-based models) using two real datasets, namely the MIMIC clinical database and biomarkers collected from six subjects with a prototype biosensing device developed by our team. Our findings offer insights into strategies for optimizing energy efficiency while ensuring security and reliability in WBANs. We conclude by outlining future directions to leverage approaches to meet the evolving demands of healthcare applications.

Determination of reduced density matrices in the doubly occupied configuration interaction space: A Hellmann-Feynman theorem approach.

In this work, the Hellmann-Feynman theorem is extended within the doubly occupied configuration interaction space to enable practical calculations of reduced density matrices and expected values. This approach is straightforward, employing finite energy differences, yet remains reliable and accurate even with approximate energies from successive approximation methods. The method's validity is rigorously tested against the Richardson-Gaudin-Kitaev and reduced Bardeen-Cooper-Schrieffer models using approximate excitation energies procured from the Hermitian operator method within the same space, effectively proving the approach's reliability with median error rates for reduced density matrix calculations around 0.1%. These results highlight the procedure's potential as a practical tool for computing reduced density matrices and expected values, particularly valuable as an ad hoc method in scenarios where only system energies are easily available.

Maintaining reliability in a 100% decarbonized power sector: The interrelated role of flexible resources

Reliability will remain a key requirement for decarbonized power sectors, but also a concern with the high penetration of variable renewable energy (VRE). The traditional approach for reliability is by producing enough energy and adapting rapidly to changes in load with the dispatch of fossil fuel power plants, especially natural gas power plants. However, this will have to evolve as decarbonization progresses. Can various flexibility resources such as transmission, storage and demand response provide the same reliability benefits? This paper uses a detailed, capacity expansion and hourly operation model to investigate the possibility and the cost of reaching different levels of planning reserve margins (PRM), in the multi-regional context of the North American Northeast. We find that while it is possible to entirely avoid natural gas power plants or nuclear ones to obtain high PRM, it would come at a very high cost and would require high levels of transmission, storage and demand response. We also detail why these three flexibility resources are needed in combination. The implications for energy policies are significant: in order for power system's cost to remain reasonable, reliability must count on some carbon-neutral natural gas or nuclear generation, along with interties with neighboring systems, storage and high levels of demand response.

The state and the development of business organisations in Benin, ca. 1440–1897

Abstract The focus of this paper is on business organisations in Benin and their interplay with the state from the era of Oba Ewuare up to the fall of the Kingdom in 1897. The paper analyses economic nationalism in the kingdom and the roles in the protection and development of identified business groups during the period. Knowledge of business organisations in pre-British Benin and their relationship with the state, which aided their development between ca. 1440 and 1897 is a vacuum in the study of African business organisations and will be useful in understanding economic nationalism from an African perspective. The study adopts the historical method and requires a multidisciplinary approach for interpretation and reliability. As such, both primary and secondary sources were utilised, and the findings confirm that several business organisations in manufacturing, trading and in large scale crop farming existed. The work provides insights into the entrepreneurs, composition and operations of these businesses. The findings also reveal how trading and industrial activities were controlled and what implications this had for the development of these businesses and the state. The work shows in addition how these businesses benefited from the security and licences granted by the state as well as how the state used these businesses to mount up economic power and political stability.

Risk Management and Failure Analysis in Diagnostic X-ray Equipment: A Comprehensive Analysis and Novel Approaches for Failure Prevention and System Reliability

Risk Management and Failure Analysis in Diagnostic X-ray Equipment: A Comprehensive Analysis and Novel Approaches for Failure Prevention and System Reliability

Knowledge, attitude, and perception of University students toward COVID-19 Pandemic

Background: An increasing rate of COVID-19 cases throughout the world has increased the need to investigate the knowledge, attitude, and perception (KAP) of university students toward the COVID-19 pandemic to prevent its rapid spread as public cooperation and awareness regarding prevention is the only way to deal with this pandemic. Methods: A retrospective cross-sectional study was conducted in Pakistan in April 2020. Data was collected by Google statistics and percent reliability was calculated through SPSS. Results: The number of respondents n=678, out of which 443(65.2%) were female and 235(34.85%) were male. The perception of students regarding this disease is shown in a table in which 33.2% of students consider it very dangerous. 54.2% thought it moderately dangerous. 62.38% of students consider muscle pain and 73.59% of students consider throat pain as a clinical presentation of COVID-19. 80.7% of students thought that exposure to a suspected case of COVID-19 serves as a means of acquiring COVID-19 infection. The most effective measure of prevention comes out to be hand washing and covering face during coughing and sneezing. The main cause of the spread is a lack of public awareness and cooperation. Social media campaigns are an effective means of educating students about quarantine and controlling its rapid spread. Conclusion: Researchers concluded that It’s the need of the hour to increase awareness related to protective hygienic measures, rapid spread, and providing reliable approaches through social media campaigns to manage this pandemic. We believe that students should be involved in educating their surrounding society.

Reliability design for bearing capacity of steel plate strengthened RC beams based on the improved design value method

AbstractThe partial factor method is widely employed as a design approach for structural reliability and serves as a crucial foundation for structural design codes. However, in practical applications, this method has gradually revealed shortcomings, such as issues with generality and insufficient precision in reliability control. In this article, an improved design value method for the bearing capacity design of steel plate strengthened RC beams was established. Additionally, considering the impact of the live‐to‐dead load effect ratio on the partial safety factor of resistance, a simplified expression for the ultimate limit state design of bearing capacity was proposed. The results show that the reliability index calculated using the partial factor method for the bearing capacity of steel plate strengthened RC beams has a significantly larger relative error. In contrast, the reliability indexes calculated based on the improved design value method are all greater than the target reliability index, ensuring the requirements of reliability design and providing a higher degree of precision in reliability control.

A novel approach for techno-economic reliability oriented planning and assessment of droop-control technique for DG allocation in islanded power distribution systems

An effective planning strategy for deploying distributed generators (DGs) provides economic advantages through optimal net present cost (NPC), improved technical performance, enhanced voltage profile, and minimized line losses. Nevertheless, technical performance varies based on the control techniques implemented for power regulation in DGs. Therefore, it is crucial to incorporate DG control strategies for efficient and robust power distribution system planning. This study discusses a novel approach for techno-economic reliability-oriented planning in an IEEE-33 bus droop-controlled islanded power distribution system. The presented study evaluates the influence of incorporating control techniques on the sizing and placement of DGs along with the technical, economic and reliability parameters. The problem formulated for optimal DG allocation aims to achieve the optimum NPC, minimize line losses, improve system reliability, and ensure voltage and frequency agreement with IEEE std 1547™-2018. The problem formulation is structured as a multi-objective problem and is solved using the particle swarm optimization (PSO) technique to achieve optimal bus location and size for DG installation. The study reveals that considering the control strategy helps in achieving efficient and reliable planning structure.

Hydrogen-fueled microgrid energy management: Novel EMS approach for efficiency and reliability

Effective energy management within microgrids is crucial, especially given system uncertainties. This study presents a novel Energy Management System (EMS) designed for microgrids with diverse energy sources, notably hydrogen and fuel cells. The EMS integrates artificial intelligence algorithms to predict and adapt to rapid changes, enhancing energy resource efficiency and minimizing wastage. Responsive in nature, the EMS enhances efficiency, reliability, and cost reduction in microgrid energy, dynamically responding to varying conditions for improved stability and environmental protection. Employing a random pattern approach, the EMS focuses on operational efficiency and reliability in supplying electrical and thermal loads. Addressing uncertainties in the electricity market and renewable production, a stochastic framework with scenario generation enables operation in grid-connected and island modes. Enhanced optimization through an improved particle swarm algorithm ensures cost-effectiveness and system reliability in dynamic environmental conditions. This research pioneers innovative microgrid energy management, emphasizing hydrogen and fuel cell technologies to elevate efficiency and performance reliability.

From order to reorder: Assessment of the living environment of hydropower resettlement for just energy transition in China

Hydropower projects have contributed significantly to the world’s just energy transition, while at the cost of massive-scale resettlement and displacement. The principles of a just transition ensure that the low-carbon transition process is fair, inclusive and socially responsible. However, since hydropower resettlers are often involuntary, it incurs many socio-environmental disruptions for the affected. Whether the post-resttlement living environment of the resettlers can also be just and sustainable needs to be effectively evaluated. This paper establishes and validates an optimized deep neural network (DNN) approach for predictability and reliability. A comprehensive set of living environment development indicators is then used to assess the just transition in the living environment of resettlers, based on three typical hydropower station resettlement cases in the Wujiang River Basin in Guizhou Province, China. The results indicate that individual living condition, community development, and fairness in market and social participation have important impacts on the just transition of resettlers’ living environments. By summarizing the composite affiliation and score values of each sub-indicator, it is found that the Hongjiadu hydropower station has the lowest composite score of the resettlers’ living environment, ranging from 0.476 to 0.680, followed by the Dongfeng hydropower station, from 0.483 to 0.709. The resettlers of Qianzhong Hydropower Hub have the highest living environment status after resettlement, from 0.497 to 0.794. Although there are differences in the living environment scores of different hydropower stations, it is argued that the transition of the living environment in the stepped power stations along the Wujiang River is just and sustainable in the long term. This paper provides valuable insights into the just transition of hydropower and the living environment of the resettlers, so as to foster sustainable development policymaking.

Accurate and robust linear representation for containership stability conditions with ballast water

Stowage planning involves optimizing the placement of cargo on a ship while ensuring the ship's stability conditions are met. This optimization problem is typically formulated as a mixed-integer linear programming problem. However, a challenge arises in representing stability conditions as linear constraints since these conditions have non-linear relationships to the decision variables. Although there are methods for linearizing these stability conditions, they lack a systematic modeling approach and numerical verification of accuracy and reliability. In particular, the complex and diverse nature of ballast water contributes to significant non-linearity, posing a considerable challenge in developing an accurate linear approximation. This study proposes linear prediction methods that incorporate ballast water to address major stability conditions such as trim, drafts, shear force, bending moment, torsion, and transverse metacentric height. We introduce a comprehensive training and testing methodology for prediction, and validate the proposed method by comparing it with true values on two ships of different capacities, demonstrating its accuracy and scalability.

The performance of artificial intelligence in prostate magnetic resonance imaging screening

Prostate cancer is the predominant form of cancer observed in men worldwide. The application of magnetic resonance imaging (MRI) as a guidance tool for conducting biopsies has been established as a reliable and well-established approach in the diagnosis of prostate cancer. The diagnostic performance of MRI-guided prostate cancer diagnosis exhibits significant heterogeneity due to the intricate and multi-step nature of the diagnostic pathway. The development of artificial intelligence (AI) models, specifically through the utilization of machine learning techniques such as deep learning, is assuming an increasingly significant role in the field of radiology. In the realm of prostate MRI, a considerable body of literature has been dedicated to the development of various AI algorithms. These algorithms have been specifically designed for tasks such as prostate segmentation, lesion identification, and classification. The overarching objective of these endeavors is to enhance diagnostic performance and foster greater agreement among different observers within MRI scans for the prostate. This review article aims to provide a concise overview of the application of AI in the field of radiology, with a specific focus on its utilization in prostate MRI.

Estimating the slip rate in the North Tabriz Fault using focal mechanism data and GPS velocity field

Abstract This study delves into slip distribution on the North Tabriz Fault (NTF), a critical aspect of seismic hazard analysis due to its proximity to the Tabriz metropolis. The study operates within a uniform elastic half-space, maintaining constant values for fault geometry and regional rheological parameters throughout the research. To calculate strain boundary conditions, permanent and periodic global positioning system (GPS) data from the northwest region were utilized. The fault was constrained perpendicularly while allowing tangential movement, facilitating the determination of its annual slip rate using the boundary element method, with the Okada analytical model serving as the fundamental solution. The findings underscore the intricate relationship between the fault’s slip rate and boundary conditions, revealing a predominant right-lateral strike-slip motion. The study offers two slip rate assessments, obtained through earthquake focal mechanisms and GPS velocity data, yielding values of 5 and 5.5 mm / year {\rm{mm}}/{\rm{year}} , respectively. Importantly, the alignment of these calculated slip rates with paleo-seismological data underscores the credibility of the results generated via the boundary element method, distinguishing it as a reliable approach when compared to other numerical and analytical techniques. This research provides valuable insights into the behavior and slip dynamics of the NTF, which is pivotal for assessing seismic risks.

Continuous auction mechanism model for safety electric energy market transaction

With the rapid development of microgrid in the electric power industry, the microgrid electric energy transaction has begun to be marketized, and the research on the microgrid electric energy trusted transaction has important theoretical research value and social value. The existing blockchain-based microgrid electric energy trusted transaction models mostly focus on energy management and scheduling control between microgrids when conducting electric energy transactions, and do not fully consider the bidding problems in the market-based transaction of microgrid electric energy, resulting in trading strategies are difficult to adapt to new market changes. In response to this problem, this paper proposes a reliable transaction approach for microgrid electric energy based on a continuous two-way auction mechanism. The proposed strategy accounts for the volatility of electricity prices in the microgrid trading market and employs the continuous two-way auction mechanism to evaluate the microgrid electricity trading tactics. In the microgrid electric energy transaction, the self-adaptive learning theory is applied to adjust the quotations of both parties, so that both parties can make reasonable quotations according to the market environment. By simulating experimental data, the findings indicate that the continuous two-way auction mechanism transaction strategy enables both parties to modify their quotations based on market transaction information, thereby displaying a high degree of flexibility in microgrid electricity’s market-oriented trading.

Deep learning-based automated lesion segmentation on pediatric focal cortical dysplasia II preoperative MRI: a reliable approach

ObjectivesFocal cortical dysplasia (FCD) represents one of the most common causes of refractory epilepsy in children. Deep learning demonstrates great power in tissue discrimination by analyzing MRI data. A prediction model was built and verified using 3D full-resolution nnU-Net for automatic lesion detection and segmentation of children with FCD II.MethodsHigh-resolution brain MRI structure data from 65 patients, confirmed with FCD II by pathology, were retrospectively studied. Experienced neuroradiologists segmented and labeled the lesions as the ground truth. Also, we used 3D full-resolution nnU-Net to segment lesions automatically, generating detection maps. The algorithm was trained using fivefold cross-validation, with data partitioned into training (N = 200) and testing (N = 15). To evaluate performance, detection maps were compared to expert manual labels. The Dice-Sørensen coefficient (DSC) and sensitivity were used to assess the algorithm performance.ResultsThe 3D nnU-Net showed a good performance for FCD lesion detection at the voxel level, with a sensitivity of 0.73. The best segmentation model achieved a mean DSC score of 0.57 on the testing dataset.ConclusionThis pilot study confirmed that 3D full-resolution nnU-Net can automatically segment FCD lesions with reliable outcomes. This provides a novel approach to FCD lesion detection.Critical relevance statementOur fully automatic models could process the 3D T1-MPRAGE data and segment FCD II lesions with reliable outcomes.Key points• Simplified image processing promotes the DL model implemented in clinical practice.• The histopathological confirmed lesion masks enhance the clinical credibility of the AI model.• The voxel-level evaluation metrics benefit lesion detection and clinical decisions.Graphical

Consistent meta-modelling approach for a less-conservative reliability based design of shells against buckling

Consistent meta-modelling approach for a less-conservative reliability based design of shells against buckling

Monitoring Food Affordability: Reliability and Validity of an Online Nutritious Food Basket.

Purpose: This study aimed to assess the reliability and validity of an online approach to monitoring food affordability in Ontario using the updated Ontario Nutritious Food Basket (ONFB).Methods: The ONFB was priced online in 12 large multi-chain grocery stores to test intra-/inter-rater reliability using percent agreement and intra-class correlations (ICCs). Then, the ONFB was priced in-store and online in 28 stores to estimate food price differences using paired t-tests and Pearson's correlation for all (n =1708) and matched items (same product/brand and purchase unit) (n = 1134).Results: Intra-/inter-rater agreement was high (95.4%/81.6%; ICC = 0.972, F = 69.9, p < 0.001). On average, in-store prices were less than $0.02 lower than online prices. There were no significant differences between mean in-store and online prices for all items (t = 0.504 p = 0.614). The mean price was almost perfectly correlated between in-store and online (fully matched: R = 0.993 p < 0.001; all items: R = 0.967 p < 0.001). Online monthly ONFB estimates for a family of four were strongly correlated (R = 0.937 p < 0.001) with estimates calculated using in-store data.Conclusions: Online pricing is a reliable and valid approach to food costing in Ontario that contributes to modernizing the monitoring of food affordability in Canada and abroad.

Numerical method for a Cauchy problem for multi-dimensional Helmholtz equation

The main focus of this research is to address the Cauchy problem of the multi-dimensional Helmholtz equation with mixed boundary conditions. This problem is known to be ill-posed according to Hadamard's definition. To tackle this issue, we propose the mollification regularization method based on exponential decay. Using prior rule and posterior rule to create a regular approximation solution and convergence of the solution is also provided. Furthermore, the proposed method is shown to be robust against data disruption noise, making it a reliable approach for solving the Cauchy problem of the multi-dimensional Helmholtz equation with mixed boundary conditions.

Seed Train Optimization in Microcarrier-Based Cell Culture Post In Situ Cell Detachment through Scale-Down Hybrid Modeling.

Microcarrier-based cell culture is a commonly used method to facilitate the growth of anchorage-dependent cells like MA 104 for antigen manufacturing. However, conventionally, static cell culture is employed for cell propagation before seeding the production bioreactor with microcarriers (MCs). This study demonstrates the effective replacement of the conventional method by serial subculturing on MCs with in situ cell detachment under optimal conditions in closed culture units. This study proves that MA 104 can be subcultured at least five times on Cytodex 1 MC without the need for separating cells and MC after cell harvest. Process parameters impacting cell growth were studied post in situ cell detachment in a scaled-down model. Optimization, using augmented Design of Experiments (DoE) combined with hybrid modeling, facilitated rapid screening of the design space for critical process parameters (CPPs). Optimized conditions included an inoculation density of >16 cells/bead, 3.5-4.5 g/L of Cytodex 1, and a controlled agitation speed, starting at Njs (minimum agitation speed) for the first day with a maximum increase of 25% thereafter. With these design spaces for CPPs, a cell density of 2.6 ± 0.5 × 106 cells/mL was achieved after five days. This refined bioprocess methodology offers a reliable and efficient approach for seed training in stirred tank reactors, which is particularly beneficial for viral vaccine production.