Operational Criteria Research Articles

Experimental study on how the settings of plasma affect its effectiveness of suppression of thermoacoustic oscillations in a Rijke tube

Thermoacoustic instabilities, frequent in a wide range of combustors, arise from the intricate coupling between the flame’s unsteady heat release and the combustor acoustic. The application of plasma, characterized by its substantial local energy addition and radical generation, has demonstrated potential in disrupting this coupling and thereby mitigating thermoacoustic oscillations. Notably, open-loop control using discharge plasma has been successfully implemented to suppress self-excited oscillations in a Rijke tube setup. Considering the broader context of a complex system that integrates both thermoacoustic systems and discharge plasma, experiments were done to see its performance in stability under different arrangement and discharge repetition rates of discharge plasma. These experimental investigations identified critical operating conditions essential for complete suppression: (A) the optimal location of discharge actuations, and (B) the minimum energy required for complete suppression. Moreover, the impact of discharge plasma, particularly when tuned to the eigenfrequencies of oscillations within the thermoacoustic system, was thoroughly examined. The insights gained from these successful suppression trials are instrumental in guiding the strategic design of both physical arrangement and electrical configurations for the control of combustion instabilities via discharge plasma. Furthermore, through the photographing of the swinging arcs and their thermal disturbances by high-speed camera and high-speed schlieren, the characteristics of arcs’ thermal disturbances and the reasons affecting its suppression effectiveness were determined.

Design and performance study of a kilowatt-class PEMFC stack with metal fiber felts as flow fields

The study presents a comprehensive design and evaluation of a kilowatt-class proton exchange membrane fuel cell (PEMFC) stack, wherein stainless steel fiber felts are innovatively utilized as flow fields. The impacts of critical operating parameters, namely operating temperature, pressure, and reactant gas relative humidity, on the performance of the 22-cell stack with an active area of 83 cm2 were thoroughly investigated. An analysis of the voltage consistency among individual cells was conducted across varying operating conditions and output currents. The total power output of the PEMFC stack reaches 1183W under the fundamental operating conditions, where each cell achieves an average voltage of 0.6V. As the operating temperature and pressure are incremented, the stack’s output power exhibits a noticeable increase. The optimal range for the reactant gas relative humidity is found to be 60% to 80%. As the output current increases, the voltages of individual cells gradually decrease and the voltage consistencies tend to deteriorate. The optimal operating conditions for achieving the best voltage consistency vary with current density.

Comprehensive data security and compliance framework for SMEs

Small and Medium-sized Enterprises (SMEs) are increasingly relying on cloud platforms to support critical business operations, making effective disaster recovery (DR) strategies essential for ensuring business continuity. This review proposes a robust disaster recovery framework tailored for SMEs, designed to minimize downtime and data loss in the event of a system failure, cyberattack, or natural disaster. The framework integrates advanced cloud technologies to create a cost-effective, scalable solution that aligns with the resource constraints of SMEs while providing enterprise-grade resilience. Key components of the disaster recovery framework include cloud-based data replication, automated backup solutions, and geo-redundant storage to ensure that data is continuously available and recoverable. This model employs real-time data synchronization and incremental backups to minimize Recovery Point Objectives (RPO), ensuring that critical data is not lost during an unexpected outage. Additionally, the framework leverages automated failover mechanisms to achieve low Recovery Time Objectives (RTO), allowing businesses to restore operations quickly after an interruption. Cloud orchestration tools such as AWS Elastic Disaster Recovery or Azure Site Recovery are utilized to automate disaster recovery processes, reducing manual intervention and improving the speed of recovery. The framework also incorporates regular testing of disaster recovery plans, using simulation tools to identify weaknesses and optimize response times. For SMEs, cost-effectiveness and ease of management are crucial. The framework emphasizes a pay-as-you-go model for cloud resources, allowing businesses to scale their disaster recovery solutions as they grow without incurring excessive upfront costs. By providing continuous monitoring and proactive threat detection, this disaster recovery framework ensures that SMEs can maintain uninterrupted business operations on cloud platforms, thereby enhancing resilience and mitigating the financial and operational risks associated with data loss and system downtime.

A Study on the Critical Transmission Operating Constraint Forecasting(COCF) of Transmission Systems with High Renewable Resources Penetrations

A Study on the Critical Transmission Operating Constraint Forecasting(COCF) of Transmission Systems with High Renewable Resources Penetrations

Two Adaptive Sliding Mode Control Algorithms for Vehicle Stability Enhancement

This paper discusses two adaptive sliding mode control (ASMC) algorithms for enhancing vehicle stability through direct yaw moment control (DYC). DYC is based on a logic process derived from the understeering and oversteering behavior of a cornering vehicle. Furthermore, to achieve DYC in a four-wheeled passenger vehicle, the reference yaw moment computed by the proposed algorihms is converted into a braking pressure that is to be applied to the four wheels. The objective is to minimize the yaw rate error and constrain the sideslip angle to an acceptable range. According to Lyapunov theory, complete stability analysis guarantees the closed-loop stability and robustness of a control system. Simulation results were used to compare the two ASMC algorithms, and both algorithms showed high performance even under critical operating conditions.

Efficient Quantum Circuit Simulation by Tensor Network Methods on Modern GPUs

Efficient simulation of quantum circuits has become indispensable with the rapid development of quantum hardware. The primary simulation methods are based on state vectors and tensor networks. As the number of qubits and quantum gates grows larger in current quantum devices, traditional state-vector based quantum circuit simulation methods prove inadequate due to the overwhelming size of the Hilbert space and extensive entanglement. Consequently, brutal force tensor network simulation algorithms become the only viable solution in such scenarios. The two main challenges faced in tensor network simulation algorithms are optimal contraction path finding and efficient execution on modern computing devices, with the latter determines the actual efficiency. In this study, we investigate the optimization of such tensor network simulations on modern GPUs and propose general optimization strategies from two aspects: computational efficiency and accuracy. Firstly, we propose to transform critical Einstein summation operations into GEMM operations, leveraging the specific features of tensor network simulations to amplify the efficiency of GPUs. Secondly, by analyzing the data characteristics of quantum circuits, we employ extended precision to ensure the accuracy of simulation results and mixed precision to fully exploit the potential of GPUs, resulting in faster and more precise simulations. Our numerical experiments demonstrate that our approach can achieve a 3.96x reduction in verification time for random quantum circuit samples in the 18-cycle case of Sycamore, with sustained performance exceeding 21 TFLOPS on one A100. This method can be easily extended to the 20-cycle case, maintaining the same performance, accelerating by 12.5x compared to the state-of-the-art CPU-based results and 4.48-6.78x compared to the state-of-the-art GPU-based results reported in the literature. Furthermore, the strategies presented in this article hold general applicability for accelerating problems that can be tackled by contracting tensor networks such as graphical models and combinatorial optimizations.

Spiking representation learning for associative memories.

Networks of interconnected neurons communicating through spiking signals offer the bedrock of neural computations. Our brain's spiking neural networks have the computational capacity to achieve complex pattern recognition and cognitive functions effortlessly. However, solving real-world problems with artificial spiking neural networks (SNNs) has proved to be difficult for a variety of reasons. Crucially, scaling SNNs to large networks and processing large-scale real-world datasets have been challenging, especially when compared to their non-spiking deep learning counterparts. The critical operation that is needed of SNNs is the ability to learn distributed representations from data and use these representations for perceptual, cognitive and memory operations. In this work, we introduce a novel SNN that performs unsupervised representation learning and associative memory operations leveraging Hebbian synaptic and activity-dependent structural plasticity coupled with neuron-units modelled as Poisson spike generators with sparse firing (~1 Hz mean and ~100 Hz maximum firing rate). Crucially, the architecture of our model derives from the neocortical columnar organization and combines feedforward projections for learning hidden representations and recurrent projections for forming associative memories. We evaluated the model on properties relevant for attractor-based associative memories such as pattern completion, perceptual rivalry, distortion resistance, and prototype extraction.

Definition and diagnosis of Parkinson’s disease: guideline “Parkinson’s disease” of the German Society of Neurology

Abstract Background Accurate definition and operational criteria for diagnosing Parkinson’s disease (PD) are crucial for evidence-based, patient-centered care. Objective To offer evidence-based recommendations for defining and diagnosing PD, incorporating contemporary clinical, imaging, biomarker, and genetic insights. Methods The guideline development began with the steering committee establishing key PICO (patient, intervention, comparison, outcome) questions, which were refined by the coauthors. Systematic literature searches identified relevant studies, reviews, and meta-analyses. Recommendations were drafted, evaluated, optimized, and voted upon by the German Parkinson’s Guideline Group. Results Parkinson’s disease (PD) is now understood to encompass a broader spectrum of etiologies than previously recognized. Advances in molecular pathogenesis, neuroimaging, and early clinical phenotypes suggest that PD is not a uniform disease entity and is often not idiopathic. This necessitates an updated framework for PD definition and diagnosis. The German Society for Neurology now endorses a broader concept of PD, incorporating both idiopathic and hereditary forms, as opposed to the previously narrower concept of “idiopathic Parkinson syndrome.” The revised guidelines recommend using the 2015 Movement Disorders Society diagnostic criteria, emphasize the importance of long-term clinical follow-up for improved diagnostic accuracy, and highlight the significance of non-motor symptoms in clinical diagnosis. Specific recommendations are provided for the use of imaging and fluid biomarkers and genetic testing to support the clinical diagnosis. Conclusion The updated guidelines from the German Society for Neurology enhance diagnostic accuracy for PD, promoting optimized clinical care.

System for the Prevention of Critical Operating Modes of a Hydraulic Unit Based on the Phase-Chronometric Method

System for the Prevention of Critical Operating Modes of a Hydraulic Unit Based on the Phase-Chronometric Method

Experimental Determination of Influences of Static Eccentricities on the Structural Dynamic Behavior of a Permanent Magnet Synchronous Machine

In electrified vehicles, the masking noise behavior of internal combustion engines is absent, making the tonal excitation of the electric machine particularly noticeable in vehicle acoustics, which is perceived as disturbing by consumers. Due to manufacturing tolerances, the tonal NVH characteristics of the electric machine are significantly influenced at wide frequency ranges. This paper presents a systematic exploration of the influence of static eccentricity as one manufacturing tolerance on the NVH behavior of Permanent Magnet Synchronous Machines (PMSMs). The study utilizes a novel test bench setup enabling isolated variations in static eccentricity of up to 0.2 mm in one PMSM. Comparative analysis of acceleration signals reveals significant variations in the dominance of excitation orders with different eccentricity states, impacting critical operating points and dominant frequency rages of the electric machine. Despite experimentation, no linear correlation is observed between increased eccentricity and changes in acceleration behavior. Manufacturing eccentricity and deviations in rotor magnetization are discussed as potential contributors to the observed effects. The findings emphasize static eccentricity as a critical parameter in NVH optimization, particularly in electrified powertrains. However, the results indicate that further investigations are needed to explore the influence of eccentricities and magnetization deviations on NVH behavior comprehensively.

Socioeconomic inequalities in patients undergoing abdominal wall reconstruction in the North-West of England, UK: a three-centre retrospective cohort study

PurposePatients from deprived areas are more likely to experience longer waiting times for elective surgery, be multimorbid, and have inferior outcomes from elective and emergency surgery. This study aims to investigate how surgical outcomes vary by deprivation for patients undergoing elective abdominal wall reconstruction.MethodsA three-centre retrospective cohort study was conducted across three hospitals in North-West England, including patients with complex ventral hernias undergoing abdominal wall reconstruction between 2013 and 2021. Demographic data, comorbidities, and index of multiple deprivation quintiles were recorded.Results234 patients (49.6% female), age 57 (SD 13) years, underwent elective abdominal wall reconstruction. Significantly higher unemployment rates were found in the most deprived quintiles (Q1 and Q2). There were more smokers in Q1 and Q2, but no significant deprivation related differences in BMI, diabetes, chronic kidney disease or ischaemic heart disease. There were also higher rates of Clavien-Dindo 1–2 complications in Q1 and Q5, but no difference in the Clavien-Dindo 3–4 outcomes. Patients in Q1 and Q5 had a significantly greater hospital length of stay.ConclusionThe association between deprivation and greater unemployment and smoking rates highlights the potential need for equitable support in patient optimisation. The lack of differences in patient co-morbidities and hernia characteristics could represent the application of standardised operative criteria and thresholds. Further research is needed to better understand the relationship between socioeconomic status, complications, and prolonged hospital length of stay.

Predicting unparalleled degradation progression in PV module using simulation of shunt resistance experimental data

Datasets of various levels of solar cell shunt resistances were available in the literature, resulting in a linear correlation to forecast shunt resistance (Rsh) degradation and failures in photovoltaic (PV) cells. However, some PV failures initiate mildly with one or few cells in a PV module and then distribute to other cells which leads to a major power reduction as well as critical safe operation. Simulating these experimental datasets gives the potential to catch these unparalleled degradations in PV modules at early stages, preventing them from turning critical. In response, SPICE simulation was used to study the effect of PV cells with low shunt resistance in a PV module. The simulation follows four steps, focusing on the electrical output of Current-Voltage (I-V) curve of the solar cells: (a) simulation of the experimental healthy I-V curve of a PV cell; (b) simulation of experimental eleven I-V curves at reduced levels of shunt resistance; (c) simulation of a PV module by using the simulation parameters obtained from the former steps; and (d) lastly, simulation of four different scenarios depending on the number of faulty cells in a PV module. The effect of these scenarios on the I-V curve parameters is compared. Results revealed it is feasible to detect unparalleled degradations, spotting the failed cells (reduced Rsh) in a PV module by implementing a linear model correlating open-circuit voltage (VOC) with short-circuit-current (ISC) (acting as a proxy of Irradiance) at different shunt resistance levels. This model can be simulated as a robust indicator of PV abnormalities and is appropriate for employment in online PV monitoring systems.

Incidence of university sustainability on university performance: An exploratory analysis from five private Latin American universities

Sustainability has become a generalized concern for society, specifically businesses, governments, and academia. In the specific case of universities, sustainability has been approached from different perspectives, some viewing it from environmental practices, management initiatives, operational criteria, green buildings, and even education for sustainable development. This research focuses on sustainability as a managerial practice and investigates how it affects the performance of five private universities in Medellin, Colombia. For this purpose, a literature review using a mixed sequential approach, including bibliometric and content analysis, was initially conducted. In the s second phase, more than 5000 responses from students, professors, and employees of the five mentioned private universities were collected. A previously validated instrument for both sustainability and performance was applied in the quantitative phase, and a novel dimensionality of the constructs was proposed by conducting an exploratory factor analysis using the SPSS software. Results were then processed through a structural equation analysis with the Smart PLS software. The impact of sustainability on university performance is verified, making some managerial recommendations.

Assessing Critical Entities: Risk Management for IoT Devices in Ports

Integrating Internet of Things (IoT) devices into port operations has brought substantial improvements in efficiency, automation, and connectivity. However, this technological advancement has also introduced new operational risks, particularly in terms of cybersecurity vulnerabilities and potential disruptions. The primary objective of this scientific article is to comprehensively analyze and identify the primary security threats and vulnerabilities that IoT devices face when deployed in port environments. This includes examining potential risks, such as unauthorized access, cyberattacks, malware, etc., that could disrupt critical port operations and compromise sensitive information. This research aims to assess the critical entities associated with IoT devices in port environments and develop a comprehensive risk-management framework tailored to these settings. It also aims to explore and propose strategic measures and best practices to mitigate these risks. For this research, a risk-management framework grounded in the principles of ORM, which includes risk avoidance, reduction, sharing, and retention strategies, was developed. The primary outcome of this research is the development of a comprehensive risk-management framework specifically tailored for IoT devices in port environments, utilizing Operational Risk-Management (ORM) methodology. This framework will systematically identify and categorize critical vulnerabilities and potential threats for IoT devices. By addressing these objectives, the article seeks to provide actionable insights and guidelines that can be adopted by port authorities and stakeholders to safeguard their IoT infrastructure and maintain operational stability in the face of emerging threats.

Optimization of Biodiesel Production from Jatropha Oil and its Impact on Engine Performance and Emissions Using Response Surface Methodology

This study aims to improve biodiesel production by assessing the effects of biodiesel-diesel fuel blends on engine performance and emissions using response surface methodology (RSM). The biodiesel was produced by the transesterification process. Here we see how the molar ratio (A), catalyst quantity (B), reaction temperature (C), and reaction time (D) affect the biodiesel conversion rate. During optimization, a Box-Behnken design (BBD) based on RSM was employed. Ideal conditions for achieving a biodiesel yield of 98.2069% were a B of 0.811601 wt%, a C of 75.8837°C, a D of 98.2069 min, and A of 10935:1. Adjusting parameters like engine speed and biodiesel fuel mix ratio enhanced engine behavior and condensed exhaust emissions. The trials were structured utilizing the central composite design (CCD) technique grounded on RSM. The optimum operating criteria for the engine were evaluated to be a biodiesel ratio of 12.5845% and speed of engine is 2011.24 rpm. Under these conditions, the power output was 50.0817kW, torque was 254.757 Nm, smoke opacity was 6.48966%, CO emissions were 270.009 ppm, and NOx emissions were 819.573 ppm.

Wafer-Scale Replication of Plasmonic Nanostructures via Microbubbles for Nanophotonics.

Quasi-3D plasmonic nanostructures are in high demand for their ability to manipulate and enhance light-matter interactions at subwavelength scales, making them promising building blocks for diverse nanophotonic devices. Despite their potential, the integration of these nanostructures with optical sensors and imaging systems on a large scale poses challenges. Here, a robust technique for the rapid, scalable, and seamless replication of quasi-3D plasmonic nanostructures is presented straight from their production wafers using a microbubble process. This approach not only simplifies the integration of quasi-3D plasmonic nanostructures into a wide range of standard and custom optical imaging devices and sensors but also significantly enhances their imaging and sensing performance beyond the limits of conventional methods. This study encompasses experimental, computational, and theoretical investigations, and it fully elucidates the operational mechanism. Additionally, it explores a versatile set of options for outfitting nanophotonic devices with custom-designed plasmonic nanostructures, thereby fulfilling specific operational criteria.

A linear programming aggregation method based on generalized Zhenyuan integral in q-ROFN environment and the application of talent recruitment in universities

The reasonable ranking of binary pairs that characterize fuzzy information in many fuzzy decision problems is very important. To overcome some defects of the existing score functions for the q-rung orthopair fuzzy numbers (q-ROFNs), a novel score function and ranking criterion are proposed by the q-compression transformation and hesitation factor. The main motivation is to introduce the generalized Zhenyuan (GZ)-integral into the q-ROFN environment, and cleverly transform the aggregation operations into a linear programming problem through the arithmetic operations of q-ROFNs. The main contribution is to solve the aggregation problem of q-rung orthopair fuzzy generalized Zhenyuan integral ordered weighted average (q-ROFGZIOWA) operator through the optimization technique of linear programming, and a new decision making method is established by using the q-ROFGZIOWA operator and ranking criterion. The main innovation is to map all q-ROFNs to the unit triangle in the first quadrant (converted into intuitionistic fuzzy numbers, IFNs) according to the q-compression transformation in geometric significance, and the novel score function and its ranking criterion are proposed by combining hesitation factor, and then the aggregation operation based on generalized Z-integral is converted to an optimization problem in linear programming. Finally, the superiority of the proposed method are verified by comparing the aggregation results of two integral operators through an example, and apply the proposed method to the optimal decision-making of talent recruitment in universities. The proposed method can not only correct some flaws in the ranking of existing q-ROFNs, but also overcomes some defects of existing Choquet integral average (geometric) operators in a q-ROFN environment. These results are of great significance for further research on the widespread application of q-ROFNs.

Principles of Construction System for Monitoring the Psychoemotional State and Health of Critical Infrastructure Operators

Principles of Construction System for Monitoring the Psychoemotional State and Health of Critical Infrastructure Operators

Development of a Disc Blade Cutting System for Sesame Harvesting: Determination of Operating Criteria and Energy Requirements

This study aimed to explore the use of rotary disc blades in cutting, since the mowing units used in sesame harvesting contain many parts, require frequent lubrication, and have vibration problems. An experimental setup with a disc blade working on a cut-mowing system based from harvesting methods of sesame plants was designed and fabricated. The most suitable cutting criteria which are blade edge types (30°-30°, 30°-Flat, 45°-45°, 45°-Flat) and disc blade diameters (150, 180, and 210 mm) were determined for shearing and disc cutting action. The lowest cutting energy was consumed by the disc blade with a 30°-wedge angle at all stem thicknesses and by the disc blade with a diameter of 180 mm for large stem thicknesses. In all pairs of edge, as the disc diameter increased, the energy spent for cutting the stems decreased. There was a significant difference between the two methods in terms of energy consumption only when cutting stems with a diameter of 10 mm. These results show that disc cutting can be recommended as a method for sesame harvesting.

Optimizing Energy Usage in Hospitals and Medical Centers with AI/ML: Ensuring Continuous Critical Operations while Reducing Costs and Carbon Footprint

Optimizing Energy Usage in Hospitals and Medical Centers with AI/ML: Ensuring Continuous Critical Operations while Reducing Costs and Carbon Footprint