Performance Optimization Techniques for Microservice Architectures in High-Load Scenarios

The objective of this paper is to demonstrate the effective deployment of a robotic assessment tool for the evaluation of mild traumatic brain injury (mTBI) patients in a busy, resource-constrained, urban emergency department (ED). Methods: Functional integration of new robotic technology for research in the ED presented several obstacles that required a multidisciplinary approach, including participation from electrical and computer engineers, emergency medicine clinicians, and clinical operations staff of the hospital. Our team addressed many challenges in deployment of this advanced technology including: 1) adapting the investigational device for the unique clinical environment; 2) acquisition and maintenance of appropriate testing space for point-of-care assessment; and 3) dedicated technical support and upkeep of the device. Upon successful placement of the robotic device in the ED, the clinical study required screening of all patients presenting to the ED with complaints of head injury. Eligible patients were enrolled and tested using a robot-assisted test battery. Three weeks after the injury, patients were contacted to complete follow-up assessments. Results: Adapting the existing technology to meet anticipated physical constraints of the ED was performed by engineering a mobile platform. Due to the large footprint of the device, it was frequently moved before ultimately being fully integrated into the ED. Over 14 months, 1423 patients were screened. Twenty-eight patients could not be enrolled because the device was unavailable due to operations limitations. Technical problems with the device resulted in failure to include 20 patients. A total of 66 mTBI patients were enrolled and 42 of them completed both robot-assisted testing and follow-up assessment. Successful completion of screening and enrollment demonstrated that the challenges associated with integration of investigational devices into the ED can be effectively addressed through a collaborative patient-oriented research model. Conclusion: Effective deployment and use of new robotic technology for research in an urban academic ED required significant planning, coordination, and collaboration with key personnel from multiple disciplines. Clinical Impact: A pilot clinical study on mTBI patients using the robotic device provided useful data without disrupting the ED workflow. Integration of this technology into the ED serves as an important step toward pursing active clinical research in an acute care setting.

Application of methanol and optimization of mixture design over the full operating map in an intelligent charge compression ignition (ICCI) engine

We assessed the influence ofmultisensory interactions on the exogenous orienting of spatial attention by comparing the ability of auditory, tactile, and audiotactile exogenous cues to capture visuospatial attention under conditions of no perceptual load versus high perceptual load. In Experiment 1, participants discriminated the elevation of visual targets preceded by either unimodal or bimodal cues under conditions of either a high perceptual load (involving the monitoring of a rapidly presented central stream of visual letters for occasionally presented target digits) or no perceptual load (when the central stream was replaced by a fixation point). All of the cues captured spatial attention in the no-load condition, whereas only the bimodal cues captured visuospatial attention in the high-load condition. In Experiment 2, we ruled out the possibility that the presentation of any changing stimulus at fixation (i.e., a passively monitored stream of letters) would eliminate exogenous orienting, which instead appears to be a consequence of high perceptual load conditions (Experiment 1). These results demonstrate that multisensory cues capture spatial attention more effectively than unimodal cues under conditions of concurrent perceptual load.

Perceptual load, voluntary attention, and aging: An event-related potential study

The ability to focus on a task while disregarding irrelevant information is an example of selective attention. The perceptual-load hypothesis argues that the occurrence of early or late selection mechanisms is determined by task-relevant perceptual load. Additionally, evidence shows that pupil size serves as proxy of locus coeruleus-norepinephrine (LC-NE) activity, a system associated with cognitive and attentional mediation. Here, we assessed pupil baseline (and pupil dilation) as predictors of load-related early and late selection performance. Participants were asked to search for a target in conditions of high and low perceptual load, while ignoring irrelevant stimuli. The results showed that pupil baseline size, measured prior trial onset, significantly predicted the upcoming search efficiency only in low perceptual load, when—according to the perceptual-load hypothesis—all perceptual information receives attentional resources. In addition, pupil dilation was linked to the time course of perceptual processing and predicted response times in both perceptual load conditions, an association that was enhanced in high load. Thus, this study relates attentional selection mechanisms, as defined by the perceptual-load theory, with pupil-related LC-NE activity. Because pupil baseline predicted attentional performance in low load but not in high load, this suggests that different attentional mechanisms are involved, one in which the LC-NE system plays a key role (low load) and one in which it is less relevant (high load). This suggests that the degree with which LC-NE influences behavioral performance is related to the perceptual load of the task at hand.

The present work clarifies the neural underpinning of inattentional deafness under high visual load. The findings of near-simultaneous load effects on both visual and auditory evoked responses suggest shared audiovisual processing capacity. Temporary depletion of shared capacity in perceptually demanding visual tasks leads to a momentary reduction in sensory processing of auditory stimuli, resulting in inattentional deafness. The dynamic "push-pull" pattern of load effects on visual and auditory processing furthers our understanding of both the neural mechanisms of attention and of cross-modal effects across visual and auditory processing. These results also offer an explanation for many previous failures to find cross-modal effects in experiments where the visual load effects may not have coincided directly with auditory sensory processing.

Bioanalysis Rising Star Award 2018: announcing our finalists!

This paper examines the impact of rapid changes in technology and the labor market on higher education, highlighting the need to adapt accounting curricula to new technologies, such as artificial intelligence, blockchain and big data, to prepare students for a professional environment in more and more digitized. Among the main challenges found in the specialized literature are the integration of new technologies in study programs, the development of teaching methods that correspond to the preferences of new generations, as well as the security of online data. Among the solutions proposed in this paper, we mention the modernization of teaching methods through tools such as case studies, practical projects, together with the modernization of the education program and the introduction of interdisciplinary subjects. Such learning models would prepare students with up-to-date skills and the possibility to apply theoretical knowledge in real contexts, developing their critical thinking and innovation capacity. In addition, the specialized literature emphasizes that the effective involvement of students in the learning process and the integration of new technologies in education are the key variables to make accounting education adaptable and relevant in the contemporary context.

The rapid development of Big Data has profoundly transformed database management systems, necessitating novel solutions to address the escalating complexity, volume, and variety of data. This analysis examines the principal trends, difficulties, and technological advancements impacting database administration due to the rapid increase in data volume. Significant innovations, including NoSQL databases, cloud computing, distributed architectures, and AI-driven automation, have transformed the methods by which businesses store, process, and analyze extensive datasets. Nevertheless, enduring issues persist, encompassing data security concerns, scalability constraints, and the necessity for enhanced performance. This study brings together recent research to look at how modern database systems have changed over time to deal with these problems and make them more efficient, secure, and able to process data in real time. Additionally, new technologies like blockchain and edge computing are examined for their contributions to improving security and decentralization in database administration. The results underscore the significance of hybrid database models, intelligent automation, and regulatory compliance frameworks in influencing the future of database systems. As enterprises adjust to the needs of Big Data, the integration of new technologies will be essential for achieving scalable, secure, and high-performance database management systems.

This research examined whether increased cognitive task loading decreases warning compliance behavior. Participants performed a task in which they installed an external disk drive to a computer. Inside the accompanying owner's manual were a set of specific procedures that were to be followed during the installation to avoid damaging the equipment (i.e., to turn off the computer, to touch the computer's rear metal connector to discharge static electricity, and to eject a transport disk from the disk drive). Concurrent with this task, participants in the low and high task load conditions had to speak the answers to a series of single-digit or double-digit addition problems that were presented by a tape player. A control condition lacked the addition task. The results showed reduced compliance to the discharge static electricity instruction in the high load condition compared to the no load condition. The low load condition produced intermediate compliance, but was not significantly different from the other two conditions with one exception: the high load condition produced significantly lower compliance for the discharge static electricity instruction than the low load condition. These results suggest that warning effectiveness can be reduced when the mental resources necessary to carry out compliance are being absorbed by other concurrently performed tasks. Implications for further research on task loading and warning compliance are described.

Recent studies have shown that spontaneous pre-stimulus fluctuations in brain activity affect higher-order cognitive processes, including risky decision-making, cognitive flexibility, and aesthetic judgments. However, there is currently no direct evidence to suggest that pre-choice activity influences value-based decisions that require self-control. We examined the impact of fluctuations in pre-choice activity in key regions of the reward system on self-control in food choice. In the functional magnetic resonance imaging (fMRI) scanner, 49 participants made 120 food choices that required self-control in high and low working memory load conditions. The task was designed to ensure that participants were cognitively engaged and not thinking about upcoming choices. We defined self-control success as choosing a food item that was healthier over one that was tastier. The brain regions of interest (ROIs) were the ventral tegmental area (VTA), putamen, nucleus accumbens (NAc), and caudate nucleus. For each participant and condition, we calculated the mean activity in the 3-s interval preceding the presentation of food stimuli in successful and failed self-control trials. These activities were then used as predictors of self-control success in a fixed-effects logistic regression model. The results indicate that increased pre-choice VTA activity was linked to a higher probability of self-control success in a subsequent food-choice task within the low-load condition, but not in the high-load condition. We posit that pre-choice fluctuations in VTA activity change the reference point for immediate (taste) reward evaluation, which may explain our finding. This suggests that the neural context of decisions may be a key factor influencing human behavior.

In early April 2024, optical current transformers at the 110 kV voltage level were commissioned for the first time in EMS, and consequently in the entire Serbian power system (EES), within an air-insulated substation. The advancement of modern technologies in the power sector in recent years has led to a strong focus on developing entirely new types of measuring transformers that operate on principles different from those of conventional transformers, which rely on electromagnetic induction. New technologies offer significant performance improvements but also introduce numerous challenges and requirements that need to be addressed. The advantages of optical current transformers primarily include higher accuracy, linearity of characteristics (eliminating the core saturation effect), significantly lower weight and material usage (no need for a magnetic core, copper for secondary windings, oil insulation, etc.), and reduced maintenance requirements. On the other hand, optical current transformers require coupling devices that, instead of analog signals, provide a so-called sampled values stream (SV), i.e., a digital format of the measured quantity. To utilize SV in substation automation, it is necessary to develop an appropriate interface – a process bus – and ultimately implement a digital substation. This paper presents the integration of new technology – optical current transformers – from the perspective of the practical implementation of the IEC 61850-9-2 standard in real conditions. The paper describes the system architecture, experiences gained during implementation, an analysis of results obtained under operational conditions, as well as the advantages and challenges of applying this concept.

Chapter 1 - WITHDRAWN: The issues

PurposeHydrostatic thrust bearing is a key component of the vertical CNC machining equipment, and often results in friction failure under the working condition of high speed and heavy load. The lubricating oil film becomes thin or breaks because of high speed and heavy load and it affects the high precision and stable operation of the vertical CNC machining equipment; hence, it is an effective way of avoiding friction failure for achieving the oil film shape predictionDesign/methodology/approachFor the hydrostatic thrust bearing with double rectangular cavities, researchers solve the deformation of the friction pairs in hydrostatic bearing by using the computation of hydrodynamics, elasticity theory, finite element method and fluid-thermal-mechanical coupled method. The deformation includes heat deformation and elasticity deformation, the shape of gap oil film is got according to the deformation of the friction pairs in hydrostatic bearing, and gets the shape of gap oil film, and determines the influencing factors and laws of the oil film shape, and achieves the prediction of oil film shape, and ascertains the mechanism of friction failure. An experimental verification is carried out.FindingsResults show that the deformation of the rotational workbench is upturned along its radial direction under the working condition of high speed and heavy load. However, the deformation of the base is downturned along its radial direction and the deformation law of the gap oil film along the radius direction is estimated; the outer diameter is close but the inner diameter is divergent wedge.Originality/valueThe conclusion can provide a theoretical basis for the oil film control of hydrostatic thrust bearing and improve the stability of vertical CNC machining equipment.

This paper discusses the potential of machine learning, data science, and natural language processing (NLP) in mitigating the incidence of spoofing and financial risks hinged on cyber threats. Another one is spoofing; it is the act of impersonating legitimate entities to gain unauthorized information, and it is indeed a threat to the public and companies to some extent. The research introduces two primary methodologies to combat spoofing: an email filtering system using a machine learning algorithm and an encryption and decryption system using a Caesar Cipher and Python programming language. It distinguishes between approved domains and unapproved domains by using machine learning and successfully filters out phishing emails from reaching the intended clients. This study also illustrates how to conduct email domain verification using MongoDB Atlas, which a database is containing approved vendors’ domains, to reduce spoofing. Specifically, incorporating NLP helps the system analyze raw data to categorize it and identify patterns potentially leading to a spoofing attempt, enhancing the spoofing detection and prevention of the system. The paper also presents arguments that require awareness and integration of new technologies in the security frameworks. Hence, incorporating machine learning, data science, and NLP presents robust, versatile, and cost-effective solutions to enhance cybersecurity and ultimately protect vital information and organizations’ monetary loss due to cybercrimes. The paper was first completed in 2021 and later I modified the article with latest updates till date 2024.