System Performance Assessment Research Articles

An Open-label Exploratory Study Investigating BDNF Essentials® on Cognition in Healthy Adults with Self-reported Memory Complaints

Background:: An open-label, exploratory study has been conducted to investigate the ef-ficacy of BDNF Essentials® for cognitive function and neurological health. Methods:: Twenty healthy adults ≥45 years of age, with a score ≥24 on the Mini-Mental State Ex-amination (MMSE-2) and self-reported memory complaints were supplemented with BDNF Es-sentials® for 84 days. Computerized Mental Performance Assessment System (COMPASS) tests for memory, attention, and processing speed were conducted, and change in plasma brain-derived neurotrophic factor (BDNF), Profile of Mood States (POMS), the Healthy People Sleep Quality Index (HPSQI), perceived stress scale (PSS), and salivary cortisol, interleukin (IL)-6, and tumour necrosis factor-α (TNF-α) were assessed at baseline and days 28, 56, and 84. Results:: Supplementation with BDNF Essentials® significantly increased COMPASS tests for memory, attention, and processing speed (all P<0.05). Salivary cortisol was found to be signifi-cantly decreased at days 56 and 84, and IL-6 at day 84 (P<0.05). A reduction in depression-dejection at day 56 and in confusion-bewilderment at day 84, and an improvement in sleep satis-faction at day 56 (all p<0.05) were reported. Conclusion:: BDNF Essentials® improved reaction time in measures of working memory, episodic memory, and attention, reduced biomarkers of stress and inflammation, and improved mood and sleep. BDNF Essentials® has been found to be safe and well-tolerated in adults with self-reported memory complaints.

Looking beyond individual failures: A system-wide assessment of water infrastructure resilience to extreme events

Water infrastructure systems throughout the United States face compounding challenges due to aging infrastructure and increasingly extreme and more frequent weather events. Identifying vulnerabilities and assessing systems’ ability to withstand and recover from such events is a critical component of water infrastructure planning and management. Pipe failures and system structure can substantially impact water infrastructure resilience, but these two factors are often assessed separately. Here, we present a holistic assessment of system performance that considers pipe failures, active water storage, and system structure utilizing data collected during normal and emergency operating conditions. The four-step framework evaluates pipe failures spatially and temporally to assess overall system performance, as measured by active storage, and identifies targeted areas for improvements. Employing control charts, resilience curves, and spatial clustering analysis, our results simultaneously reveal an agreement between trends in pipe failures during normal and emergency operating conditions as well as unexpected discrepancies between pipe failures and system performance due to the water system structure. Our results offer insights to utilities seeking to assess system vulnerabilities to increase water infrastructure resilience to extreme events.

Exploring the Utility of the Rorschach Test in Predicting Weight Loss After Bariatric Surgery

Abstract: Bariatric surgery (BS) is an effective approach to treat severe obesity, which should include multidisciplinary care. Prior to surgery, candidates for BS usually undergo a psychological assessment based mostly on self-report of clinical symptoms. However, the results of this assessment are not always sufficient to predict outcomes. Therefore, this study examined the potential of a multimethod approach to assessing patients seeking BS using both self-report and performance-based measures of cognitive and affective functioning. A sample of 50 women who had applied for surgery at a public facility in Brazil were assessed with the Difficulties in Emotion Regulation Scale (DERS), the Rorschach Performance Assessment System (R-PAS), and the Trail Making Test (TMT). Patients who dropped out before treatment ( n = 27) had higher initial body mass index and poorer performance on cognitive flexibility at baseline. In the remaining 23 patients who underwent surgery, indicators of emotional and cognitive functioning from all measures were associated with weight loss (WL) at 6 and 12 months after surgery. However, only R-PAS variables regarding psychological resources were associated with WL 18 months after surgery. We discuss the usefulness of the Rorschach as a source of information for patients seeking BS and the implications for current assessment practices in the field.

Scenario Analysis of Offshore Wind-Power Systems under Uncertainty

Wind-energy systems are strongly affected by uncertainty and variability. Therefore, uncertainty sources should be considered during the economic evaluation of this type of system. In the literature, a framework for the economic performance assessment of wind-power systems has been proposed. Furthermore, in another contribution, the random discontinuities of political and regulatory scenarios have been included by using scenario analysis. However, the implemented models neglected the uncertainty related to disruptive events and the effect of climate change on the wind resource. To fill this gap, in this paper, climate change and disruptive events are included in a new model for evaluating the economic performance of wind turbine systems using scenario analysis. Analysis of a numerical example has been carried out to show the framework’s capabilities and to evaluate the effects of the added issues. The main results confirm previous findings on the necessity of including regulatory and political risks to achieve a proper economic evaluation. Additionally, they show that disruptive events increase the variability of the expected value of the Net Present Value (NPV). Therefore, even though climate change is expected to increase wind producibility in the numerical example location, the inclusion of disruptive events constrains the NPV growth.

A Proposed Performance Assessment System for Sustainable Recruitment Processes for Experienced Cabin Attendants: The PAS Model

Among airlines, the continuous increase in the transfer of cabin crews is noteworthy. This phenomenon can be attributed to the perception held by most airlines that experienced cabin attendants play a pivotal role in gaining a competitive advantage. Hence, airlines seek to transfer in experienced cabin attendants who enhance service quality, align with industry standards, and support ongoing improvement initiatives. This study aims to formulate a standardized performance assessment system for airlines to address challenges encountered by transferred cabin crews stemming from the difficulty of moving their past performance data to the receiving airlines. In this research, the triangulation technique was adopted to develop recommendations for the Turkish airline industry that can address the challenges airlines face during transfers of experienced cabin crew. Within the framework of the acquired information, a proposed performance assessment system for sustainable recruitment processes for experienced cabin attendants, termed the PAS model, is suggested. It is recommended that the proposed model undergo thorough investigation and implementation across various airlines, each adhering to different business models, through the International Air Transport Association (IATA). This would then contribute to sustainable professional career management of cabin attendants and enhance decision-making processes of managers, particularly in strategic human resources management, such as procurement and recruitment activities.

Safeguarding Transactional Data: A Comprehensive Investigation of VPN Integration in Point-of-Sale Systems for Enhanced Security and Connectivity

The integration of Virtual Private Network (VPN) technology into Point-of-Sale (POS) systems has emerged as a solution for securing transactional data in businesses. This study investigates the implementation of a VPN server to address security and connectivity challenges faced by the University of St. La Salle (USLS) Bookstore. The research delves into VPN policy frameworks, assessing the scope and binding, authentication methods, and VPN credentials and security protocols. The system design employs a combination of conceptual models and activity diagrams to illustrate the process of user authentication and data encryption. Furthermore, the study evaluates the system's performance in terms of scalability, efficiency, and effectiveness in ensuring authentication and security. The deployment of the VPN infrastructure involves the installation of requisite software and hardware components, along with rigorous testing procedures to assess system performance and reliability. The findings highlight the significance of VPN in safeguarding sensitive transaction data and enhancing the efficiency of POS operations. The study's outcomes offer valuable insights into the effective implementation and management of VPN technology for securing business operations.

Effects of Covid-19 Emergency and Associated Lockdown Policies on the Normative Expectations for the Personality Assessment Inventory (PAI) and the Rorschach Performance Assessment System (R-PAS)

Standardized personality tests compare the test taker’s scores to those of a large sample of individuals representing normative expectations. However, what is psychologically normal in one historical context may not be similarly normal in another, so the recent spread of a new coronavirus, SARS-Cov-2 (COVID-19), may have implications for what should normally be expected of a nonclinical person taking a personality test shortly after this dramatic event. To address this research question, we administered the Personality Assessment Inventory (PAI) and the Rorschach Performance Assessment System (R-PAS) to 60 nonclinical volunteers from Italy and compared their scores with the official normative reference values of the two tests, which had been established before COVID-19. The results of a series of two-sample t-tests indicated that our newly collected sample appeared somewhat less psychologically healthy compared with normative expectations, and these discrepancies were more pronounced on the PAI than on the R-PAS. Implications and future perspectives are discussed.

Optimal sizing of photovoltaic-battery system for peak demand reduction using statistical models

Due to increasing environmental concerns and demand for clean energy resources, photovoltaic (PV) systems are becoming more prevalent. Considering that in several instances, customers pay for both energy and power, PV installations not only must reduce the customers’ energy purchases but also lower their peak demand for maximum financial benefits. However, in many cases, the peak demand does not coincide with the peak of photovoltaic generation. To address this issue, excess energy generated during low-demand periods can be stored in a battery, which can then be used to meet peak demand. Determining the optimal size of photovoltaic and battery components while ensuring system performance and financial benefits is significantly challenging. This study proposes a novel statistical methodology for optimizing PV-battery system size. In the proposed method, the PV-battery system must meet peak demand thresholds with a specific probability. Further, cost and benefit functions are used for financial evaluation. Finally, Monte Carlo simulations, developed using time series clustering and a Bayesian model are utilized to assess system performance and financial feasibility.

A Complete Water Balance of a Rain Garden

AbstractA bioinfiltration rain garden was retrofitted from an existing traffic island at Villanova University in 2001. It has been monitored continuously since 2003 at a 5‐min timeseries resolution and with instrumentation that would enable a water balance calculation. This 20‐year data set allows for an in‐depth analysis of the hydrologic pathways and management in the rain garden. Using physical equations and modeled data (based on real‐time measurements), a balance of all influent, stored, and effluent water within the rain garden was constructed. Analysis shows the rain garden captures 73.5% of runoff, resulting in a post‐implementation management of 86.2% of all rainfall in its watershed. In comparison to the hydrology of other land covers, implementing the rain garden resulted in the management of 37.6% more rainfall than pre‐implementation, producing a hydrological signature similar to that of cultivated land or low development levels (e.g., 30% impervious). Additionally, with the long data record, several statistical techniques were applied to determine the amount of monitoring needed for a certain level of precision in system performance assessment. For 5% uncertainty, approximately 3 years of continuous data is needed to assess performance. This analysis not only facilitates understanding the function of rain garden systems, but also provides conclusions and methodology for understanding the uncertainty associated with the extent of monitoring performed on these green stormwater infrastructure systems. These findings provide practical knowledge as monitoring of stormwater management infrastructures is becoming a more standard part of their operation.

Robotic Catheter Ablation: An Evaluation and Prototyping Platform

Cardiac arrhythmia affects a large portion of the population, particularly the elderly. As the population ages, the number of patients suffering from this condition is expected to rise, underlining the importance of more efficient treatment methods. Radio-frequency (RF) catheter ablation is the treatment of choice for many cardiac arrhythmias. To recover a normal heartbeat, the abnormal pacemakers inside the heart are first identified and then isolated or destroyed by applying RF energy through the use of an ablation catheter. While the catheter’s design and precise navigation are topics of significant interest, there is currently no standardized evaluation setup to compare different catheter ablation systems. This paper addresses this problem by introducing an evaluation platform including an in vitro anatomical model, a catheter tracking system, and a dedicated graphical user interface. This platform enables performance assessment of catheter ablation systems using various metrics, such as procedure duration, contact stability, ablation angle and user satisfaction. A simulation environment is also presented, allowing for rapid assessment of usability-related parameters. We demonstrate the efficacy of the evaluation platform through a proof-of-concept study, and the efficacy of the simulation environment through evaluating different control gains.

A Kriging-based probabilistic framework for multi-hazard performance assessment of transmission tower-line systems under coupled wind and rain loads

A Kriging-based probabilistic framework for multi-hazard performance assessment of transmission tower-line systems under coupled wind and rain loads

Physical modeling and simulation of electro-mechanical actuator-based TVC systems for reusable launch vehicles

This paper presents the high-fidelity physical modeling of a Thrust Vector Control (TVC) system operated by Electro-Mechanical Actuators (EMAs) for Reusable Launch Vehicles (RLVs). In contrast to simplified, often linear, models, high-fidelity physical models enable a better assessment of the actual system performance and a deeper verification of the on-board software robustness against disturbances, unmodeled dynamics or degradation. This aspect is essential to enable the reusability concept as driven by long-term reliability requirements. Moreover, critical systems like Fault Detection, Isolation and Recovery (FDIR) logic, whose robustness and efficacy are often difficult to prove, can be thoroughly tested without requiring hardware experiments with eventual invasive modifications for fault injections. This work captures the whole dynamics of the EMA and TVC components, including the power drive electronics, the electrical motor, and the mechanical transmission for the EMA, as well as the mechanical properties of the engine nozzle acting as a load. The resulting differential algebraic equation system is modeled in the Modelica acausal object-oriented modeling language. An ad-hoc framework is implemented to guarantee flexibility and modularity, which allows models with different fidelity levels to be easily exchanged to achieve the simulation objectives and needed accuracy level. The impact of the different models on the closed-loop TVC dynamics is analyzed in both the frequency and time domain, and then benchmarked with a realistic RLV mission. The results show that the high-fidelity physical models provide a better understanding of the more complex effects governing the TVC dynamics and can, in turn, effectively improve its requirement definition process.

Electrical parameters identification for three diode photovoltaic based on the manta ray foraging optimization with dynamic fitness distance balance

This study focuses on the critical task of accurately simulating and operating solar cells and photovoltaic modules by acquiring essential model parameters through experimental data. These parameters play a pivotal role in assessing system performance under varying environmental conditions. However, due to the inherent nonlinearity of solar photovoltaic systems, a dependable algorithm is imperative for precise modeling. Introducing the MRFO-dFDB algorithm (Manta ray foraging optimization with Dynamic fitness distance balance), designed to address the intricate challenges associated with modeling solar photovoltaic systems. This algorithm harnesses fitness distance balance to maintain a harmonious balance between exploration and exploitation within the search space. Its adaptability to shifts in the search landscape, achieved through dynamic adjustments to distance and fitness metrics, enhances its versatility and effectiveness. Our extensive evaluation encompasses a comparative analysis of the MRFO-dFDB algorithm against six other optimization methods across diverse solar module types, including STP6–120/36, Photowatt-PWP201, XKD_50W, and XHYG-10W. The outcomes underscore the MRFO-dFDB algorithm's remarkable equilibrium between precision and computational efficiency. Numerical findings provide concrete evidence of the MRFO-dFDB algorithm's capabilities. For example, it achieves an RMSE of 0.002087 A and an NRMSE of 0.0015639 for the Photowatt-PWP201 module, underscoring its proficiency in parameter extraction. Similarly, for the STP6–120/36 module, it records an RMSE of 0.015194 A, highlighting its effectiveness in modeling.

The Involvement of School Administrative Staff in Harnessing Information Technology: A Systematic Literature Review

This research aims to elucidate the challenges confronted by school administrative staff in the execution of their responsibilities and to identify avenues for enhancing their performance through the application of information technology. The chosen research methodology is a Systematic Literature Review (SLR) designed to recognize, evaluate, and interpret prior research findings. Out of 465 articles initially screened, 43 were meticulously reviewed for their relevance to the study's scope, with 18 articles ultimately included in this systematic literature review. The research findings underscore the pivotal role of information technology in providing student services by school administrative staff. Nevertheless, several issues surfaced post-implementation, including increased workloads, challenges related to coordination and communication, adaptations to policies and regulations, the utilization of technology and information systems, and issues concerning service quality and user satisfaction. Resolving these challenges necessitates identifying the staff's needs and competencies, offering suitable training and development opportunities, instituting a performance assessment system, enhancing accessibility and utilization of information technology, and sustaining ongoing monitoring and support.

The telltale fluorescence fingerprints of sewer flows for interpreting the low influent concentration in wastewater treatment plant

Low degradability of wastewater treatment plant (WWTP) influents negatively affects its ability to effectively remove pollutants through wastewater treatment processes. Proactive assessment of urban sewer system performance is highly valued in the selection of targeted countermeasures for this occurrence. In this study, a fluorescence spectrum interpretation approach was developed to identify the causes of low biodegradability of WWTP influent by using parallel factor analysis (PARAFAC) and fluorescence regional integration (FRI) of excitation-emission matrix spectroscopy. Statistical analysis was also used to further interpret the PARAFAC- and FRI-derived data. The urban sewer catchment served by a WWTP in Wuhan City, China, was used as the test site to demonstrate the effectiveness of this approach. The results showed that electronics manufacturing industrial wastewater and groundwater input into the urban sewer would significantly decrease the biodegradability of the WWTP influents, and these sources were characterized by much lower fluorescence peak intensities, especially for protein-like substances, including tryptophan-like T and tyrosine-like B1 and B2. The potential conversion of high freshness T into low freshness B2 within the sewer may also contribute to this undesirable scenario. The ratio of peak T to peak B2 and the ratio of the FRI fraction of region I to that of region II can be used together to determine the predominance of industrial wastewater and groundwater. T/B2 < 1.3 indicates the entry of industrial wastewater or groundwater into urban sewers, and I/II > 0.5 further confirms the input of industrial wastewater. Accordingly, the low biodegradability of the WWTP influents in our study site is mostly due to the inflow of industrial wastewater rather than groundwater infiltration into the urban sewers. Therefore, actions should be focused on the surveillance of industrial wastewater rather than widespread sewer inspection and repairs. In this way, this methodology is cost-effective in aiding targeted countermeasures to improve the urban sewer system performance.

Assessing midwifery services inIran via the balanced scorecard framework.

This study investigates the impact of intra-organizational information, midwife job satisfaction and performance assessment on the quality of midwife services. The questions are empirically tested with survey data obtained from 276 midwives, specialist doctors and nurses, and mothers who recently gave birth in a cross-section of Iranian public healthcare organizations. The results from a structural equation model suggest that an improved performance assessment system leads to higher quality midwife services. In addition, the results indicate that midwife job satisfaction and intra-organizational information increases the quality of midwife services, both directly and indirectly, through the mediating effect of a performance assessment system. Our study contributes to the growing research exploring the interface between accounting and health issues by recognizing the importance of a performance assessment system of midwifery services via the balanced scorecard framework for understanding the quality of midwife services.

A quality of experience model for live video in first-person-view drone control in cellular networks

Several upcoming 5G and 6G services will rely on unmanned aerial vehicles (UAV) sending live information to remote terminals. Thus, understanding the traffic flows that might influence end-user experience in these services is key for cellular network operators. One of these UAV-based services is first person view (FPV) drone control, consisting on the remote control of the UAV in Beyond Visual Line of Sight scenarios using only the live video visualized in a ground control station. This work focuses on the networking aspects of this service by presenting the assembly, integration and evaluation methodology of an UAV quadrotor teleoperated via FPV through a Long Term Evolution (LTE) network or WiFi radio access link. To assess system performance, three different connectivity schemes between UAV and ground control station are tested, namely server-based connection via LTE, direct LTE, and peer-to-peer WiFi connection. Then, several experiments are carried out in the testbed to characterize telemetry, control and video traffic for FPV service in the above schemes. Later, a methodology is defined to estimate Quality of Experience (QoE) for FPV service based on image quality and video latency measurements collected at network and application level. Results show that the QoE model for live video introduced in this work can be the basis of more sophisticated models for cellular FPV services.

Performance assessment of surgical tracking systems based on statistical process control and longitudinal QA

A system for performance assessment and quality assurance (QA) of surgical trackers is reported based on principles of geometric accuracy and statistical process control (SPC) for routine longitudinal testing. A simple QA test phantom was designed, where the number and distribution of registration fiducials was determined drawing from analytical models for target registration error (TRE). A tracker testbed was configured with open-source software for measurement of a TRE-based accuracy metric ε and Jitter ( J ). Six trackers were tested: 2 electromagnetic (EM – Aurora); and 4 infrared (IR − 1 Spectra, 1 Vega, and 2 Vicra) – all NDI (Waterloo, ON). Phase I SPC analysis of Shewhart mean ( x ¯ ) and standard deviation ( s ) determined system control limits. Phase II involved weekly QA of each system for up to 32 weeks and identified Pass, Note, Alert, and Failure action rules. The process permitted QA in <1 min. Phase I control limits were established for all trackers: EM trackers exhibited higher upper control limits than IR trackers in ε (EM: x ¯ ε ∼ 2.8–3.3 mm, IR: x ¯ ε ∼ 1.6–2.0 mm) and Jitter (EM: x ¯ jitter ∼ 0.30–0.33 mm, IR: x ¯ jitter ∼ 0.08–0.10 mm), and older trackers showed evidence of degradation – e.g. higher Jitter for the older Vicra (p-value < .05). Phase II longitudinal tests yielded 676 outcomes in which a total of 4 Failures were noted − 3 resolved by intervention (metal interference for EM trackers) – and 1 owing to restrictive control limits for a new system (Vega). Weekly tests also yielded 40 Notes and 16 Alerts – each spontaneously resolved in subsequent monitoring.

Optimising fleet sizing and management of shared automated vehicle (SAV) services: A mixed-integer programming approach integrating endogenous demand, congestion effects, and accept/reject mechanism impacts

Shared automated vehicles (SAV) are expected to benefit the sustainable development of urban regions and alleviate the negative impacts brought by the increasing number of private cars. In this paper, we envision a future scenario where non-pooled SAVs replace private cars and provide public on-demand mobility services to satisfy the mobility needs of a city’s residents. To help service providers make profitable fleet sizing and management decisions, we develop a mixed-integer non-linear programming model that considers the congestion effects and the mode choice of urban travellers in different income classes, between SAVs and bicycles. Our model optimises both strategic decisions (fleet size, initial fleet distribution, and service quality level) and operational decisions (trip assignment, vehicle routing, parking, and relocation). Travellers’ preference for both transport modes is described through a binary logit model and congestion effects are described by dynamically varying travel times with respect to traffic flow in a non-linear fashion. In addition, we investigate two types of accept/reject mechanisms (mandatory vs. non-mandatory acceptance) which lead to an endogenously determined acceptance rate that can affect travellers’ willingness to use SAV services. The computational challenge posed by the non-linear and non-convex nature of the model is addressed through reformulation and the use of outer-inner approximation methods combined with a breakpoint generation algorithm. We demonstrate the effectiveness of our proposed method in a case study of the city of Delft in The Netherlands, as well as a scaling analysis on three toy networks with various sizes and demand profiles. A sensitivity analysis of key parameters is carried out to assess system performance. Computational results indicate that fleet sizing decisions are influenced not only by the population’s geographical distribution and land use patterns but also by the pricing strategy, unit operating costs of the SAV fleet, network congestion level, and traveller behaviour. When the price rate of using SAVs is low, the fleet sizing decisions can also be influenced by the trip accept/reject mechanism and the travellers’ sensitivity to the service quality level. In addition, a low price of SAV service will attract more users but may not necessarily bring a higher profit because of the increased traffic congestion.

Best Practices for Artificial Intelligence and Machine Learning for Computer-Aided Diagnosis in Medical Imaging

The American Association of Physicists in Medicine (AAPM) Task Group (TG) 273 has been charged with developing recommendations on best practices for the development and performance assessment of computer-aided decision support systems. The TG report [1] addresses broad issues common to the development of most, if not all, CAD-AI applications and their translation from the bench to the clinic. The goal was to bring attention to issues such as proper data collection and training and validation methods for ML algorithms, aiming to improve generalizability and reliability and thus accelerate the adoption of CAD-AI systems for clinical decision support. The report focuses on several developmental stages of CAD-AI: data collection, reference standards, model development, performance assessment, and translation to the clinic.