Processing Delay Research Articles

Abstract C177: Increasing cervical cancer screening among women living with HIV (WLWH) in Jamaica

Abstract Background: Cervical cancer (CCA) is the third most common cancer and the third leading cause of cancer deaths among women in Jamaica. Although human papilloma virus (HPV), the primary risk factor for CCA, has a prevalence of 54% among sexually active Jamaican women, HPV vaccination uptake is low, and CCA is often diagnosed at advanced stages due to inadequate screening. Women comprise 50% of persons living with HIV in Jamaica and are about six times more likely to develop cervical cancer than women without HIV. However, despite national guidelines requiring annual screenings, a national audit found that fewer than one-third of eligible WLWH underwent a Pap smear in the past year. Objective: We aimed to address gaps in CCA screening among WLWH attending four large HIV treatment sites in Jamaica through programmatic and operational support integrated into HIV care. Method: Working through the UCSF Jamaica Country Programme, funded by PEPFAR, clinical advisors completed site reviews to identify gaps in the CCA screening process. Critical gaps identified at the four sites included insufficient supplies (speculums, slides, cytology brushes, fixative, Pap smear jackets, and bed rolls), limited provider knowledge, delays in sample processing, and the delayed return of results from the national public health lab. From January 2023 to May 2024, our team provided technical assistance and resources for sample collection, private laboratory testing, delivery of samples, and collection and review of results. Results: Cervical cancer modules were integrated into an ongoing clinical management training series that reached 33 clinicians across multiple sessions. With supportive supervision, the sites strengthened patient notification and sample transportation, that facilitated appointment adherence, timely sample delivery, and prompt collection of results. Clinical advisors further supported site teams to identify abnormal results and ensure timely and appropriate follow-up diagnostic tests and treatment referrals. This intervention supported 656 eligible WLWH to complete Pap smears, with 614 timely results returned to the sites by May 2024. Conclusion: Targeted programmatic interventions can increase CCA screening by improving the availability of Pap supplies, clinic workflow, and operational efficiency within HIV care clinics thereby reaching a population at high risk for CCA. Technical and financial support from donors can be expanded to explore clinic-based models for improving and integrating HPV screening and vaccine uptake among vulnerable WLWH. The impact of these interventions can be tested through quality improvement and implementation science frameworks. Citation Format: Sharlene T Beckford Jarrett, Sasha-Kaye Blackwood, Kimlin Tam Ashing, Kamelia Bryan, Tyrone O Ellis, Jeffrey Edwards, Willi McFarland, Nicola Skyers. Increasing cervical cancer screening among women living with HIV (WLWH) in Jamaica [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C177.

Application of edge computing and IoT technology in supply chain finance

This study proposes an IoT data management framework based on blockchain and edge computing and conducts detailed simulation experiment design and performance evaluation for the field of supply chain finance. Previous methods often struggled with high latency, limited scalability, and inadequate risk management, and to address these issues, the experimental platform includes traditional centralized systems, distributed systems, blockchain-based systems, and edge computing + blockchain systems. By monitoring indicators such as data processing delay, data transmission delay, data retrieval time, system throughput, and data integrity of each platform, and introducing evaluation formulas for credit risk, operational risk, and market risk, the performance of different systems in processing supply chain financial data is comprehensively analyzed. The experimental results show that the edge computing + blockchain system performs well in data processing efficiency, security, real-time, and system throughput, especially under high load conditions. Our market risk value is reduced to 0.015. This framework improves the efficiency and security of data transmission and effectively reduces credit risk and operational risk, providing an efficient and reliable solution for data management in supply chain finance.

Fish couple forecasting with feedback control to chase and capture moving prey.

Predator-prey interactions are fundamental to ecological and evolutionary dynamics. Yet, predicting the outcome of such interactions-whether predators intercept prey or fail to do so-remains a challenge. An emerging hypothesis holds that interception trajectories of diverse predator species can be described by simple feedback control laws that map sensory inputs to motor outputs. This form of feedback control is widely used in engineered systems but suffers from degraded performance in the presence of processing delays such as those found in biological brains. We tested whether delay-uncompensated feedback control could explain predator pursuit manoeuvres using a novel experimental system to present hunting fish with virtual targets that manoeuvred in ways that push the limits of this type of control. We found that predator behaviour cannot be explained by delay-uncompensated feedback control, but is instead consistent with a pursuit algorithm that combines short-term forecasting of self-motion and prey motion with feedback control. This model predicts both predator interception trajectories and whether predators capture or fail to capture prey on a trial-by-trial basis. Our results demonstrate how animals can combine short-term forecasting with feedback control to generate robust flexible behaviours in the face of significant processing delays.

The Effect of Mesenchymal Stem Cells on the Wound Infection.

Wound infection often requires a long period of care and an onerous treatment process. Also, the rich environment makes the wound an ideal niche for microbial growth. Stable structures, like biofilm, and drug-resistant strains cause a delay in the healing process, which has become one of the important challenges in wound treatment. Many studies have focused on alternative methods to deal the wound infections. One of the novel and highly potential ways is mesenchymal stromal cells (MSCs). MSCs are mesoderm-derived pluripotent adult stem cells with the capacity for self-renewal, multidirectional differentiation, and immunological control. Also, MSCs have anti-inflammatory and antiapoptotic effects. MScs, as pluripotent stromal cells, differentiate into many mature cells. Also, MSCs produce antimicrobial compounds, such as antimicrobial peptides (AMP), as well as secrete immune modulators, which are two basic features considered in wound healing. Despite the advantages, preserving the structure and activity of MSCs is considered one of the most important points in the treatment. MSCs' antimicrobial effects on microorganisms involved in wound infection have been confirmed in various studies. In this review, we aimed to discuss the antimicrobial and therapeutic applications of MSCs in the infected wound healing processes.

A repetitive pulsed electrothermal plasma jet ignition system based on capillary discharge.

Plasma ignition and combustion enhancement is a promising technology in applications of engines, industrial burners, pollutant emissions controls, etc. A new repetitive electrothermal plasma jet ignition system based on ablated capillary discharge under atmospheric pressure is presented in this paper. It consists of a capillary discharge module, a pulse current circuit, a pulse voltage circuit, a current release unit, an LC series resonant circuit, and a control system. The effects of the energy storage capacitor's voltage and resistance in the current release unit on the electrical parameters are investigated. Increasing the capacitor voltage helps to shorten the discharge delay and increase the energy deposition efficiency in the main discharge process. The increase of the resistance in the current release unit leads to a longer discharge delay and higher energy deposition efficiency in the main discharge process. Balanced parameters between the delay of discharge in 66 µs and the energy deposition efficiency in 84% are achieved through optimization, with a peak radiative heat flux of 23MWm-2 and a maximum jet length of 17cm. Repetitive capillary discharge at 20Hz under atmospheric pressure is achieved with the dispersion of energy storage capacitor charging voltage and energy deposition efficiency of 0.3% and 9.6%, respectively. Simplified circuit topology and control logic contribute to the miniaturization of the ignition system.

Using the Whatsapp application as a technological tool to improve the teaching and learning of the English language during natural disasters or health emergencies

The research is based on the use of the WhatsApp application as a technological tool to improve the teaching and learning of the English language during natural disasters or health emergencies. The objective was to determine the use of the WhatsApp application as a technological tool in the English teaching-learning process during national emergencies. He adopted a quantitative approach. The level of research was descriptive and explanatory. The techniques used were a survey of teachers and a knowledge test of students to evaluate results. When evaluating the students on what level of knowledge they achieved during the proposed exercise, the data indicated that 10% did not reach the required learning, but they are close to achieving the required learning 20%, mastered the required learning by 50% or 20% achieve the required learning. When evaluating the use of the WhatsApp application during natural disasters or health emergencies in students, it is evident that when an emergency occurs, face-to-face classes are interrupted, which produces a delay in the teaching-learning process in students.

Joint optimization of UAV aided covert edge computing via a deep reinforcement learning framework

In this work, we consider an Unmanned Aerial Vehicle (UAV) aided covert edge computing architecture, where multiple sensors are scattered with a certain distance on the ground. The sensor can implement several computation tasks. In an emergency scenario, the computational capabilities of sensors are often limited, as seen in vehicular networks or Internet of Things (IoT) networks. The UAV can be utilized to undertake parts of the computation tasks, i.e., edge computing. While various studies have advanced the performance of UAV-based edge computing systems, the security of wireless transmission in future 6G networks is becoming increasingly crucial due to its inherent broadcast nature, yet it has not received adequate attention. In this paper, we improve the covert performance in a UAV aided edge computing system. Parts of the computation tasks of multiple ground sensors are offloaded to the UAV, where the sensors offload the computing tasks to the UAV, and Willie around detects the transmissions. The transmit power of sensors, the offloading proportions of sensors and the hovering height of the UAV affect the system covert performance, we propose a deep reinforcement learning framework to jointly optimize them. The proposed algorithm minimizes the system average task processing delay while guaranteeing that the transmissions of sensors are not detected by the Willie under the covertness constraint. Extensive simulations are conducted to verify the effectiveness of the proposed algorithm to decrease the average task processing delay with comparison with other algorithms.

The Business Process for Receipt and Disbursement of Cash Through Website: Case Study of Riverstone Hotel & Cottage

This study aims to present an effective business process model to tackle issues related to the cash inflow and outflow cycle at Riverstone Hotel & Cottage. It utilizes Research and Development (R&D) as the research type and follows the ADDIE method, consisting of three stages: analysis, design, and development. The findings suggest the necessity of a website system featuring separate cash inflow and outflow menus for the administrative and front office departments, as well as for the hotel's services (rooms and halls) and expenditures. Implementing real-time management can help reduce processing delays during the calculation and input of room/hall prices across different platforms. Moreover, daily/monthly transaction reports can be conveniently accessed from the provided menu options, eliminating the need for manual recalculations.

Vocational Rehabilitation in Times of the Covid-19 Pandemic

The Covid-19 pandemic has posed new challenges to actors in vocational rehabilitation (VR) for people with disabilities. In this study, we were interested in the following: How did actors like service providers and financers of VR (here: German Federal Employment Agency (FEA)) experience the Covid-19 pandemic, which process changes and delays have occurred, and what impact will this possibly have on the future of vocational rehabilitation for people with disabilities? Between July 2020 und July 2021, a total of 29 persons from service providers of VR programs (n=16) and representatives of the FEA (n=8) were asked about their experiences during the Covid-19 pandemic in the course of 24 guided expert interviews. The interviews were recorded, transcribed, coded in MAXQDA; the analyses of the transcribed material are based on a system developed from inductive and deductive categories. VR programs had to be conducted remotely, digital possibilities had to be created. As a result, VR was also forced to evolve technologically, revealing opportunities (in the provision of programs) and limitations (e. g. acquisition of social skills or assessment of mental health). Thus, continuous additional support by the service providers was indispensable to contain psychological crises, to ensure the understanding of the program contents and to avoid dropouts. Just like schools, the employment agencies and job centers closed their doors for a long time and VR and general vocational counseling did not take place nor did assessment procedures carried out at the medical and psychological services of the FEA for determining VR needs. A decline in access to VR had been apparent for some time, but it became increasingly evident, particularly for 2021/22. Individuals from poor households were more likely to be affected because they were more difficult to keep in touch with. Even though VR has experienced a major digital boost, experience from the pandemic has shown that programs in face-to-face formats with social contact are particularly important for young people and people with mental disabilities. Nevertheless, VR must change with the working environment in order to prevent people with disabilities from being left behind even further.

Stabilization of delayed Markovian jump systems with sampled controllers

This paper mainly studies the stabilization problem of continuous-time delayed Markovian jump systems by a sampled controller. Not only the state is sampled, but also the switching signals, where the latter sampled signal makes the synthesis and analysis of delayed systems more complex and difficult. To address these issues, this paper develops an augmented system approach, resulting in a novel system model that incorporates two delayed exponential matrices. It has been demonstrated that the stability properties of original delayed system can be ensured by the constructed auxiliary system. The correlation among Markov process, sampling interval and time delay is first established, and several stabilization results are given. More special situations about the proposed sampled are further considered. The validity and superiority of the method proposed in this paper are verified through two numerical examples.

Motherhood Penalty and Labour Market Integration of Immigrant Women: A Review on Evidence from Four OECD Countries

Among several reasons preventing the effective labour market integration of immigrant women, the motherhood penalty and unpaid care responsibilities stand out prominently. In line with this, the present scoping review shows how motherhood affects the labour market integration of immigrant women in Australia, Canada, the UK, and the USA. This review shows that parenthood exacerbates the gender pay gap and limits labour market access, favouring men with children over immigrant mothers. Moreover, the effect of the motherhood penalty might be moderated by the level of education, age of the children, and the country of origin/ethnicity of immigrants. In the four countries examined, labour market outcomes for immigrant women are particularly poor. Factors contributing to this include limited language proficiency, traditional gender norms that restrict the full-time employment of certain groups of immigrant women, and institutional barriers like work-permit processing delays. To address these challenges, Australia, Canada, the UK, and the USA have implemented various policies facilitating immigrant mothers’ workforce participation. These measures include language and legal-system education, subsidised childcare, and integration programmes for both mothers and children. Additionally, some programmes in Canada and the USA provide employment assistance and financial support for childcare, while Australia and the UK offer comprehensive integration and settlement services.

Mobility-aware parallel offloading and resource allocation scheme for vehicular edge computing

Vehicle edge computing (VEC) enhances the distributed task processing capability within intelligent vehicle-infrastructure cooperative systems (i-VICS) by deploying servers at the network edge. However, the proliferation of onboard sensors and the continual emergence of new applications have exacerbated the inadequacy of wireless spectrum resources and edge server resources, while the high mobility of vehicles reduces reliability in task processing, resulting in increased communication and task processing delays. To address these challenges, we propose a mobile-aware Many-to-Many Parallel (MTMP) offloading scheme that integrates: a) millimeter-wave (mmWave) and cellular vehicle-to-everything (C-V2X) to mitigate excessive communication delays; and b) leveraging the underutilized resources of surrounding vehicles and parallel offloading to mitigate excessive task processing delays. To minimize the average completion delay of all tasks, this paper formulates the objective as a min-max optimization problem and solves it using the maximum entropy method (MEM), the Lagrange multiplier method, and an iterative algorithm. Extensive experimental results demonstrate the superior performance of the proposed scheme in comparison with other baseline algorithms. Specifically, our proposal achieves a 47 % reduction in task completion delay under optimal conditions, a 31.3 % increase in task completion rate, and a 30 % decrease in program runtime compared to the worst-performing algorithm.

Exploring Delays in Cardiac Care Processes Through Electronic Health Records.

Cardiovascular diseases are the leading cause of death globally. Timely health services are fundamental to the appropriate prevention, identification, care and rehabilitation of these diseases. This study aimed to explore the potential of using electronic health records as a data source to help identify health system -related delays in care processes of cardiac patients. This retrospective registry study is based on a sample of electronic health records of 200 cardiac patients admitted to one out of twenty wellbeing services counties in Finland during the years 2021-2022. A total of 426 health system -related delays were identified. All expressions were found in unstructured format and most of these (58.7%) were generated by nurses. These results show that the electronic health records contained a variety of information on health system -related patient care delays, and that most delays were associated with difficulties in finding a bed for the patient in a post-acute care facility (49.8%), but also in-hospital process delays were common (27.7%). These findings show great potential for exploring electronic health record data with natural language processing methods in the future for the development of tools to better identify and monitor different types of delays in care processes. Such tools may support leadership to respond to organisational procedures in need of improvement.

An Improved SPWM Strategy for Effectively Reducing Total Harmonic Distortion

In the inverter circuit, the speed at which the MOSFET is impacted by the presence of a parasitic inductor within the printed circuit board (PCB) leads to a delay in the switching process. Furthermore, the parasitic inductor within the circuit can easily form an LC oscillation with the parasitic capacitor of the MOSFET. These two issues result in an inconsistency between the actual output of the MOSFET and the driving signal waveform, leading to distortion in the sinusoidal pulse width modulation (SPWM) waveform and an increase in total harmonic distortion (THD). It is a common practice to mitigate gate oscillation by introducing a resistor at the gate of the MOSFET. However, elevating the resistance leads to deceleration in the charging process of the MOSFET’s parasitic capacitor, consequently causing an increase in the switching delay, and thereby increasing THD. Therefore, an effective strategy to reduce THD is proposed in this paper, while augmenting the gate resistance, computing the MOSFET switching delay, and applying corrective compensation. In this way, the inherent issues of the switch are addressed, resulting in inverter output waveforms that closely resemble sine waves and reduced THD. Through a combination of simulation and empirical experimentation, the efficacy of the proposed approach in significantly reducing THD in the inverter’s output waveform has been empirically substantiated.

Formal verification of timely knowledge propagation in airborne networks

Ensuring timely coordination between autonomous aircraft is a challenging problem in decentralized air traffic management (ATM) applications for urban air mobility (UAM) scenarios. This paper presents an approach for formally guaranteeing timely progress in a Two-Phase Acknowledge distributed knowledge propagation protocol by probabilistically modeling the delays using the theory of the Multicopy Two-Hop Relay protocol and the M/M/1 queue system. The guarantee states a probabilistic upper bound to the time for progress as a function of the probabilities of the total transmission and processing delays following two specific distributions. The proof uses a general library of formal theories, that can be used for the rigorous mechanical verification of autonomous aircraft coordination protocols using the Athena proof checker and assistant.

Design and development of FPGA based trigger system for automation of metallic tokamak (MT-I)

A field-programmable gate array (FPGA)-based trigger system (FTS) is designed and developed to replace the existing microcontroller-based system for the automation of metallic tokamak (MT-I). MT-I consists of three main systems: MT-I vacuum vessel and gas filling system, a pulsed power supply system, an electronic system based on diagnostics and data acquisition (DAQ) systems. The power supply system provides pulsed power input to the central solenoid (CS), poloidal field coils (PF), toroidal field coils (TF), and microwave systems using thyristors and insulated-gate bipolar transistors (IGBTs). The (DAQ) system acquires experimental data from MT-I diagnostics using national instruments (NI) DAQ cards. A concurrent processing system is required to incorporate time delay in the triggering process of central solenoid (CS), poloidal field coils (PF), toroidal field coils (TF), microwave source and DAQ systems. Therefore, an FTS is designed and developed to complement the processing capability, unlike a microcontroller. The FTS has twenty concurrent triggering channels, adjustable from time reference zero, and control from a simple graphical user interface (GUI) designed on LabVIEW. For current buffering and optical isolation against high voltages in the MT-I power supply system, a peripheral electronic system (PES) and field trigger modules (FTM) have been developed as part of FTS. The designed and developed FTS was tested successfully on MT-I.

Experiences of researchers with disabilities at academic institutions in the United States.

People with disabilities are underrepresented in higher education, facing systematic obstacles such as inaccessible communication and physical environments and difficulties obtaining accommodations. This study aims to shed light on barriers to accessibility and disability inclusion in research institutions through confidential qualitative interviews with researchers with disabilities. We recruited participants via virtual flyers. Eligibility criteria included working in the United States (U.S.) as researchers that had applied for grant funding (last five years), and self-identifying as having a disability. We offered participants (total n = 35) the option of either semi-structured one-on-one live or written interviews based on their preference. Two study team members analyzed written and live responses using thematic analysis to identify themes. Themes included identity/visibility, career trajectories, accessibility, accommodations, bias, representation, and inclusion. Some participants reported not disclosing their disabilities at work or during hiring processes due to fear of negative perceptions from peers or potential employers. Experiences around stigma and bias were noted both in professional relationships and when interacting with disability service offices, underscoring difficulties and delays in processes to secure accommodations. Respondents highlighted the lack of disability inclusion and low representation of people with disabilities in academia and elevated the importance of self-advocacy and of role models and mentors in shaping career pathways for future researchers with disabilities. Researchers with disabilities encounter systematic barriers at academic institutions, and lack of acknowledgement and research on these experiences has held back institutional and policy changes. To reduce disparities for researchers with disabilities, academic leadership must allocate resources to address ableism, create more inclusive environments, and raise standards beyond compliance with the Americans with Disabilities Act.

Deep Reinforcement Learning-Based Adaptive Scheduling for Wireless Time-Sensitive Networking.

Time-sensitive networking (TSN) technologies have garnered attention for supporting time-sensitive communication services, with recent interest extending to the wireless domain. However, adapting TSN to wireless areas faces challenges due to the competitive channel utilization in IEEE 802.11, necessitating exclusive channels for low-latency services. Additionally, traditional TSN scheduling algorithms may cause significant transmission delays due to dynamic wireless characteristics, which must be addressed. This paper proposes a wireless TSN model of IEEE 802.11 networks for the exclusive channel access and a novel time-sensitive traffic scheduler, named the wireless intelligent scheduler (WISE), based on deep reinforcement learning. We designed a deep reinforcement learning (DRL) framework to learn the repetitive transmission patterns of time-sensitive traffic and address potential latency issues from changing wireless conditions. Within this framework, we identified the most suitable DRL model, presenting the WISE algorithm with the best performance. Experimental results indicate that the proposed mechanisms meet up to 99.9% under the various wireless communication scenarios. In addition, they show that the processing delay is successfully limited within the specific time requirements and the scalability of TSN streams is guaranteed by the proposed mechanisms.

Derivation and dynamics of discrete population models with distributed delay in reproduction

We introduce a class of discrete single species models with distributed delay in the reproductive process and a cohort dependent survival function that accounts for survival pressure during that delay period. These delay recurrences track the mature population for species in which individuals reach maturity after at least τ and at most τ+τM breeding cycles. Under realistic model assumptions, we prove the existence of a critical delay threshold, τ˜c. For given delay kernel length τM, if each individual takes at least τ˜c time units to reach maturity, then the population is predicted to go extinct. We show that the positive equilibrium is decreasing in both τ and τM. In the case of a constant reproductive rate, we provide an equation to determine τ˜c for fixed τM, and similarly, provide a lower bound on the kernel length, τ˜M for fixed τ such that the population goes extinct if τM≥τ˜M. We compare these critical thresholds for different maturation distributions and show that if all else is the same, to avoid extinction it is best if all individuals in the population have the shortest delay possible. We apply the model derivation to a Beverton–Holt model and discuss its global dynamics. For this model with kernels that share the same mean delay, we show that populations with the largest variance in the time required to reach maturity have higher population levels and lower chances of extinction.

Cloud-edge collaborative high-frequency acquisition data processing for distribution network resilience improvement

To realize transparent monitoring and resilience improvement of low-voltage distribution network, both the data acquisition scope and frequency have been greatly expanded. Cloud-edge collaboration leverages the edge server’s real-time response capabilities and the cloud server’s robust data processing power to enhance the performance of high-frequency data acquisition processing. Nonetheless, it continues to confront challenges such as the entanglement of optimization variables, the presence of uncertain information, and a lack of awareness regarding acquisition frequencies. In this paper, we propose a machine learning-based cloud-edge collaborative data processing optimization algorithm to minimize the weighted sum of data processing delay and device energy consumption for distribution network resilience improvement. The joint optimization problem is decoupled into device-edge data offloading subproblem and edge-cloud data splitting subproblem, which are solved by the proposed upper confidence bound (UCB) based frequency-aware device-edge data offloading optimization algorithm and the exponential-weight algorithm for exploration and exploitation (EXP3) based edge-cloud data splitting optimization algorithm, respectively. Simulation results show that the proposed algorithm is superior to existing algorithms in performances of energy consumption and total processing delay.