System Reliance Research Articles

Temperature control research for laser-induction hybrid strengthening process

Temperature control research for laser-induction hybrid strengthening process

Crop species diversity: A key strategy for sustainable food system transformation and climate resilience

AbstractThe global food system's reliance on a few species threatens food and nutritional security. Species diversification, including indigenous species, is a viable option to address this issue. Diversity enhances food systems' resilience against climatic and economic shocks. It offers resources for improved breeds and allows farmers to mitigate risks. However, successful diversification demands collaboration among farmers, researchers, academics, professionals, retailers, consumers, and policymakers. This review analyzes the role of crop species diversity in food system transformation, focusing on monoculture vulnerabilities, diversification benefits, indigenous species' role in nutrition and food security, and the importance of integrated policies and multi‐stakeholder collaborations. We advocate for interdisciplinary research, participatory approaches, and supportive policies to foster diverse, resilient food systems that ensure food security, biodiversity conservation, and enhanced social well‐being amidst global challenges. While acknowledging the importance of diversity in animal species for food security, the focus of this review is on crop species diversity and its potential to transform food systems.

ОЦЕНКА СИСТЕМЫ ЭПИДЕМИОЛОГИЧЕСКОГО НАДЗОРА ЗА КОРЬЮ В Г. ДУШАНБЕ, 2023 Г.

Objective: To thoroughly evaluate the current measles epidemiological surveillance (ES) system in Dushanbe, Republic of Tajikistan (RT), to uncover its strong points and areas for improvement. Methods: The ES system evaluation for measles was conducted from January to May 2023 using updated guidelines from the Centers for Disease Control (CDC), USA, for ES system evaluation. Results: Upon analysis, it was found that the ES system for measles in Dushanbe functions within an overcomplicated framework that does not have proper integration with medical data systems. The current system experiences delays in sharing data at all levels; it is based on passive case detection via patient self-referral. 32.2% of patients visit urban health centers (UHC), while 67.8% go to infectious diseases hospitals. Shortcomings have been found in disseminating knowledge and implementing approved methodological recommendations by the Ministry of Health and Social Protection of the Population of the Republic of Tajikistan (MHSPP RT) in the State Sanitary Epidemiological Surveillance Service (SSESS) centers. This deficiency is due to the absence of guidelines on the Ministry's official website, the lack of a centralized repository of guidelines, and the unavailability of training platforms for epidemiologists. Although about 80% of patients have access to measles laboratory diagnostics, the system's reliance on external funding raises issues regarding its effectiveness. Insufficient human and financial resources have been identified as barriers to timely decision-making, as illustrated by the delays in rolling out mass supplementary childhood vaccinations in September 2022 following a measles outbreak detected in January 2022. Conclusion: ing out mass supplementary childhood vaccinations in September 2022 following a measles outbreak detected in January 2022. Conclusion: To achieve the country's objective of eradicating measles, the measles ES system in Dushanbe requires additional funding for emergency outbreak response. Establishing a robust training and information-sharing platform is essential to address the existing deficiencies in disseminating and implementing guidelines Keywords: Measles, epidemiological surveillance system, assessment, Dushanbe

Coordinated Control of DC Distribution System Based on Three‐Level Energy Interaction

AbstractWhen compared to the AC distribution system, the DC distribution system can better adapt to the integration of large‐scale distributed generations (DGs), but it also has some challenges and limitations due to its complex control. This paper investigates the coordinated control of a DC distribution system containing multiple DC microgrids and proposes a coordinated control strategy for energy interaction at the microgrid, cluster, and main grid‐levels to address this problem. To begin, the interaction at the microgrid‐level employs a decentralized control strategy that only requires local voltage information and adjusts the operation modes of each unit within the microgrid based on the bus voltage information. Second, a new power equalization optimization control is added to the cluster‐level interaction based on the decentralized control strategy to achieve power equalization among microgrids with different line impedances. Furthermore, at the main grid‐level, the interaction uses one‐way communication of time‐of‐use (TOU) price to optimize system economy, ensuring that even if communication fails, the system's operation reliability is not compromised. Finally, simulation results demonstrate that the proposed three‐level energy interaction and switching meet the system's reliability requirements. Furthermore, the proposed coordinated control strategy reduces the system's reliance on communication while maintaining economic efficiency. © 2023 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

ChatGPT's Potential in Enhancing Physician Efficiency: A Japanese Case Study.

Artificial intelligence (AI), particularly ChatGPT, developed by OpenAI (San Francisco, CA, USA), is making significant strides in the medical field. In a simulated case study, a 66-year-old Japanese female patient's dialogue with a physician was transcribed and inputted into ChatGPT to assess its efficacy in drafting medical records, formulating differential diagnoses, and establishing treatment plans. The results showed a high similarity between the medical summaries generated by ChatGPT and those of the attending physician. This suggests that ChatGPT has the potential to assist physicians in clinical reasoning and reduce the administrative burden, allowing them to spend more time with patients. However, there are limitations, such as the system's reliance on linguistic data and occasional inaccuracies. Despite its potential, the ethical implications of using patient data and the risk of AI replacing clinicians emphasize the need for continuous evaluation, rigorous oversight, and the establishment of comprehensive guidelines. As AI continues to integrate into healthcare, it is crucial for physicians to ensure that technology complements, rather than replaces, human expertise, with the primary focus remaining on delivering high-quality patient care.

Towards more efficient fish farming systems in the Sistan region of Iran: An emergy-based analysis

The environmental and economic inputs of three different fish farming methods, (semi-natural, concrete, and galvanised ponds) were investigated in a region with a hot and dry climate and a severe water shortage. The data were collected in 2020 through direct interviews with fish farmers, as well as regional weather information. The inputs and outputs of each system, both environmental and purchased, as well as the water quality parameters and fish growth rates, were also recorded. Emergy analysis was applied to assess the economic, and environmental aspects of these systems and to compare their performance and sustainability. According to the emergy efficiency indicators (Tr and SpE), concrete ponds had a higher efficiency for fish farming than the other two systems. Semi-natural fish farming was sustainable, as shown by the emergy yield ratio, which measures the system's reliance on environmental inputs. Semi-natural ponds had an economic advantage (O/I, BN, O/IEmV and BNEmV) over concrete and galvanised fish farming, because they used environmental inputs more effectively for fish rearing. Environmental efficiency can be improved by taking suitable management actions, such as increasing the size of fingerlings, while economic efficiency can be enhanced by enlarging the scale of fish farms. At the same time, using economic inputs more efficiently in concrete and galvanised ponds can boost both their sustainability and economic efficiency.

Understanding the Impact of COVID-19 on Chronic Lymphocytic Leukemia (CLL) Caregiving and Related Resource Needs.

Chronic lymphocytic leukemia (CLL) caregivers play a central role in disease management-a role that has been heightened during the COVID-19 pandemic given the healthcare system's reliance on frontline family caregivers and CLL patients' increased risk of infection and mortality. Using a mixed-method design, we investigated the impact of the pandemic on CLL caregivers (Aim 1) and their perceived resource needs (Aim 2): 575 CLL caregivers responded to an online survey; 12 spousal CLL caregivers were interviewed. Two open-ended survey items were thematically analyzed and compared with interview findings. Aim 1 results showed that two years into the pandemic, CLL caregivers continue to struggle with coping with distress, living in isolation, and losing in-person care opportunities. Caregivers described experiencing increasing caregiving burden, realizing the vaccine may not work or didn't work for their loved one with CLL, feeling cautiously hopeful about EVUSHELD, and dealing with unsupportive/skeptical individuals. Aim 2 results indicate that CLL caregivers needed reliable, ongoing information about COVID-19 risk, information about and access to vaccination, safety/precautionary measures, and monoclonal infusions. Findings illustrate ongoing challenges facing CLL caregivers and provide an agenda to better support the caregivers of this vulnerable population during the COVID-19 pandemic.

Optimal scheduling of combined cooling, heating, and power system-based microgrid coupled with carbon capture storage system

The paramount role of microgrids (MGs) in strengthening the energy network and supply of many users in more cost-effective, safe, and sustainable manner cannot be overlooked. Energy carriers' interdependence on each other, as well as the rapid rise of tri-generation technologies, provide several obstacles to ensuring the network's best performance. A combined-cooling heat and power (CCHP) system based on various renewable-based resources and MGs is capable of supplying electricity, thermal, and cooling loads, in which the operation of CHP-based MG (CBM) is heavily influenced by the interaction between these carriers. On this basis, this paper is proposed a comprehensive analysis of CBM operation, which is coupled with electrical and absorption chillers (ACs), energy storage, and renewable-based units like solar and wind power under a multi-objective optimization (MOO) technique. In addition, a Power-to-Gas (PtG) technology is utilized in the proposed system to enhance the overall operation, reduce CO2 emission, and to contribute to the cooling and heat supply in the presence of various uncertainties. The suggested MOO methodology evaluates the operation cost of the system as the first objective and the reduction of CO2 emission as the second objective. An epsilon-constrained approach and Pareto solution are considered to assess the methodology. As a result, this study examines the system's reliance on individual carriers as well as the interdependence of those carriers. The results reveal the effectiveness of the proposed model to reduce CO2 emission and operational costs.

Multi-Level Fuzzy Inference System Based Handover Decision Model for Unmanned Vehicles

In coming years, mobile communications systems will have a significant role for Unmanned Aerial Vehicles (UAVs), that will either function as mobile users or as mobile base stations in the sky. No doubt UAVs or drones provide a number of benefits for mobile communications networks and other non-communication services, but they also have a number of drawbacks, particularly when it comes to managing handover (HO). A number of approaches have already been suggested by various authors for making the HO easy and efficient, but only few parameters were considered for making the HO decision. However, with the advancement in technology and continuous rise in users, the handover system's reliance factors continue to grow, which could make the system more complex. As a result, the requirement for creating and implementing a system that can effectively manage system complexity arises. In order to reduce the complexity of the system, a novel method is proposed in this paper in which multi-level fuzzy system is used. The main goal of using the fuzzy system at different levels is to minimize the rule complexity of fuzzy systems at different levels which in turn enhances the performance of handover systems. In addition to this, number of parameters like coverage, speed limit, cost, connection time, security and power consumption were taken into consideration while designing the handover system. The model works in three levels, at the first level coverage, speed limit and cost parameters are processed to generate the first probability output. At the second level, connection time, security and power consumption parameters are considered to get the second probability output. The two outputs obtained thus serves as the input to third level where again they are processed to get the final output as estimation level. The efficacy of the suggested multi-level fuzzy system is analyzed in the MATLAB tool. The experimental results are obtained and compared with the traditional handover systems in terms of various dependency factors to prove its efficiency.

Prison Policy from the “Bottom Up”

Prison Policy from the “Bottom Up”

Non‐linearchanges in phase inversion temperature for oil and water emulsions of nonionic surfactant mixtures

AbstractHydrophilic–lipophilic deviation (HLD) is a semiempirical framework for surfactant‐oil–water (SOW) formulation that has effectively assimilated SOW properties such as phase inversion temperature (PIT) and Winsor Type I ↔ II ↔ III ↔ IV microemulsion behavior. The HLD system's reliance on surfactant and oil parameters necessitates their experimental determination when relevant and accurate values are not available. In this study, experiments have been of the type commonly used to generate test surfactant HLD parameters based on perturbation of a well‐defined SOW reference system and assumption of linear mixing behavior for PIT versus component mole fractions. The reference SOW compositions comprisedn‐octane, 0.01 M aqueous NaCl, and puren‐decyl tetraethylene glycol ether (C10E4) reference surfactant. Test surfactants were primary alcohol ethoxylates and nonylphenol ethoxylates comprising disperse PEG (polyethylene glycol) chain lengths that are inherent to commercial production of nonionic ethoxylate surfactants. We observe non‐linear behavior of PIT versus test surfactant mole fraction, even when accounting for surfactant concentration effects, so the desired linear correlation that could otherwise be used to assign an HLD parameter has not been obtained. Since an objective of this study has been to improve the predictive utility of the HLD framework for commercial nonionic surfactants, relevant terms in the HLD equation have been assessed in terms of their potential contributions to non‐linear behavior. One current working‐explanation relates substantial non‐linear PIT contribution to oil‐like surfactant components in disperse commercial ethoxylates.

Ultrasound Probe Pose Classification for Task Recognition in Central Venous Catheterization.

Central Line Tutor is a system that facilitates real-time feedback during training for central venous catheterization. One limitation of Central Line Tutor is its reliance on expensive, cumbersome electromagnetic tracking to facilitate various training aids, including ultrasound task identification and segmentation of neck vasculature. The purpose of this study is to validate deep learning methods for vessel segmentation and ultrasound pose classification in order to mitigate the system's reliance on electromagnetic tracking. A large dataset of segmented and classified ultrasound images was generated from participant data captured using Central Line Tutor. A U-Net architecture was used to perform vessel segmentation, while a shallow Convolutional Neural Network (CNN) architecture was designed to classify the pose of the ultrasound probe. A second classifier architecture was also tested that used the U-Net output as the CNN input. The mean testing set Intersect over Union score for U-Net cross-validation was 0.746 ± 0.052. The mean test set classification accuracy for the CNN was 92.0% ± 3.0, while the U-Net + CNN achieved 92.7% ± 2.1%. This study highlights the potential for deep learning on ultrasound images to replace the current electromagnetic tracking-based methods for vessel segmentation and ultrasound pose classification, and represents an important step towards removing the electromagnetic tracker altogether. Removing the need for an external tracking system would significantly reduce the cost of Central Line Tutor and make it far more accessible to the medical trainees that would benefit from it most.

Detecting intrusive transactions in databases using partially-ordered sequential rule mining and fractional-distance based anomaly detection

Illegitimate access to databases may compromise their integrity and confidentiality, resulting in legal and financial ramifications for organisations. We propose a database intrusion detection system (DIDS) called fractional distance based anomaly detection with partially-ordered dependency analysis (FADPDA) to identify malicious transactions issued to databases. To weed out such transactions, our DIDS combines data dependency analysis using security sensitive partially-ordered sequential rules (POSRs) with fractional distance based anomaly detection. Unlike most prior work, FADPDA can seamlessly run on both RBAC administered and non-RBAC databases. Detailed experiments on two databases- a TPC-C benchmark and a synthetic database, revealed that POSRs effectively and efficiently represent data dependencies. Furthermore, combining data dependency analysis and anomaly detection reduces our system's reliance on hyper-parameters such as support and confidence thresholds, and enhances its intrusion detection capabilities. We also show that our approach FADPDA outperforms major existing DIDS in terms of precision and recall values.

What the Doctors Don't See: Physicians as Gatekeepers, Injured Latino Immigrants, and Workers' Compensation System

AbstractWorkers' compensation systems require injured employees to overcome cumbersome requirements in order to obtain health‐care and salary‐replacement benefits. The structural vulnerabilities of Latino immigrants put them at greater risk for workplace injuries and further complicate their search for redress. As a medico‐legal system, workers' compensation relies on medical professionals to interrogate the bodies of injured low‐wage immigrant workers. This article aims to clarify how workers' trajectories are impacted by interactions with health‐care system. Utilizing activist research methods, I collaborated with Fuerza Laboral, a worker center in Rhode Island, from the fall of 2013 to the end of 2014, to survey and interview injured Latino workers, physicians, and legal professionals. I bring together worker narratives and physician reflections to identify how the compensation system's reliance on biomedicine contributes to poor outcomes for injured immigrant workers. Considering the medical “gaze,” prejudice against injured workers, racial and ethnic bias in medicine, and the pitfalls of cultural competency training, I outline the mechanics of how the medical‐legal system corrodes the patient‐physician relationship. Evaluating the risks of positioning physicians as gatekeepers, I advocate for uncoupling medical evaluations from compensation cases, the importance of healthcare system reform, and medical curricula that emphasize structural analysis. Such reforms may liberalize access to health care for injured workers as well as stymie bias in medical education.

Insect-inspired acoustic micro-sensors.

Micro-Electro Mechanical System (MEMS) microphones inspired by the remarkable phonotactic capability of Ormia ochracea offer the promise of microscale directional microphones with a greatly reduced need for post-processing of signals. Gravid O. ochracea females can locate their host cricket's 5 kHz mating calls to an accuracy of less than 2° despite having a distance of approximately 500 μm between the ears. MEMS devices base on the principles of operation of O. ochracea's hearing system have been well studied, however commercial implementation has proven challenging due to the system's reliance on carefully tailored ratios of stiffness and damping, which are difficult to realize in standard MEMS fabrication processes, necessitating a trade-off between wide-band operation and sensitivity. A survey of the variety of strategies that have been followed to address these inherent challenges is presented.

Homeostasis-altering molecular processes as mechanisms of inflammasome activation

The innate immune system uses a distinct set of germline-encoded pattern recognition receptors (PRRs) to initiate downstream inflammatory cascades. This recognition system is in stark contrast to the adaptive immune system, which relies on highly variable, randomly generated antigen receptors. A key limitation of the innate immune system's reliance on fixed PRRs is its inflexibility in responding to rapidly evolving pathogens. Recent advances in our understanding of inflammasome activation suggest that the innate immune system also has sophisticated mechanisms for responding to pathogens for which there is no fixed PRR. This includes the recognition of debris from dying cells, known as danger-associated molecular patterns (DAMPs), which can directly activate PRRs in a similar manner to pathogen-associated molecular patterns (PAMPs). Distinct from this, emerging data for the inflammasome components NLRP3 (NOD-, LRR- and pyrin domain-containing 3) and pyrin suggest that they do not directly detect molecular patterns, but instead act as signal integrators that are capable of detecting perturbations in cytoplasmic homeostasis, for example, as initiated by infection. Monitoring these perturbations, which we term 'homeostasis-altering molecular processes' (HAMPs), provides potent flexibility in the capacity of the innate immune system to detect evolutionarily novel infections; however, HAMP sensing may also underlie the sterile inflammation that drives chronic inflammatory diseases.

Data-driven and calibration-free Lamb wave source localization with sparse sensor arrays.

Most Lamb wave localization techniques require that we know the wave's velocity characteristics; yet, in many practical scenarios, velocity estimates can be challenging to acquire, are unavailable, or are unreliable because of the complexity of Lamb waves. As a result, there is a significant need for new methods that can reduce a system's reliance on a priori velocity information. This paper addresses this challenge through two novel source localization methods designed for sparse sensor arrays in isotropic media. Both methods exploit the fundamental sparse structure of a Lamb wave's frequency-wavenumber representation. The first method uses sparse recovery techniques to extract velocities from calibration data. The second method uses kurtosis and the support earth mover's distance to measure the sparseness of a Lamb wave's approximate frequency-wavenumber representation. These measures are then used to locate acoustic sources with no prior calibration data. We experimentally study each method with a collection of acoustic emission data measured from a 1.22 m by 1.22 m isotropic aluminum plate. We show that both methods can achieve less than 1 cm localization error and have less systematic error than traditional time-of-arrival localization methods.

Considerations on the Scaling of Bending-Active Structures

Bending-active structures are composed of curved beam or shell elements which base their geometry on the elastic deformation of an initially straight or planar configuration. In bending-active structures the moment of inertia has a direct influence on the residual stress and is therefore limited by a given minimal curvature in the system and the permissible bending stress capacities of the chosen material. These interdependencies may lead to a scaling issue that limit bending-active structures to a certain size range. This range may be widened if the system's reliance on elastic stiffness is compensated by other stiffening factors such as coupling of structural elements and stress stiffening effects. This paper will analyse the scaling effects in bending-active structures. Some basic systems are studied by means of dimensional analysis and FEM parameter studies to clarify at which power each influencing factor effects scaling. Based on these findings some more complex structures are studied for their scalability. This will offer the basis for some more general conclusions.

The effects of error augmentation on learning to walk on a narrow balance beam

Error augmentation during training has been proposed as a means to facilitate motor learning due to the human nervous system's reliance on performance errors to shape motor commands. We studied the effects of error augmentation on short-term learning of walking on a balance beam to determine whether it had beneficial effects on motor performance. Four groups of able-bodied subjects walked on a treadmill-mounted balance beam (2.5-cm wide) before and after 30min of training. During training, two groups walked on the beam with a destabilization device that augmented error (Medium and High Destabilization groups). A third group walked on a narrower beam (1.27-cm) to augment error (Narrow). The fourth group practiced walking on the 2.5-cm balance beam (Wide). Subjects in the Wide group had significantly greater improvements after training than the error augmentation groups. The High Destabilization group had significantly less performance gains than the Narrow group in spite of similar failures per minute during training. In a follow-up experiment, a fifth group of subjects (Assisted) practiced with a device that greatly reduced catastrophic errors (i.e., stepping off the beam) but maintained similar pelvic movement variability. Performance gains were significantly greater in the Wide group than the Assisted group, indicating that catastrophic errors were important for short-term learning. We conclude that increasing errors during practice via destabilization and a narrower balance beam did not improve short-term learning of beam walking. In addition, the presence of qualitatively catastrophic errors seems to improve short-term learning of walking balance.

The Mixture of Bernoulli Experts: a theory to quantify reliance on cues in dichotomous perceptual decisions.

The appearances of perceptually bistable stimuli can by definition be reported with confidence, so these stimuli may be useful to investigate how visual cues are learned and combined to construct visual appearance. However, interpreting experimental data (percent of trials seen one way or the other) requires a theoretically motivated measure of cue effectiveness. Here we describe a simple Bayesian theory for dichotomous perceptual decisions: the Mixture of Bernoulli Experts or MBE. In this theory, a cue's subjective reliability is the product of a weight and an estimate of the cue's ecological validity. The theory (1) justifies the use of probit analysis to measure the system's reliance on a cue and (2) enables hypothesis testing. To illustrate, we used apparent 3D rotation direction in perceptually ambiguous Necker cube movies to test whether the visual system relied on a newly recruited cue (position of the stimulus within the visual field) to the same extent when a long-trusted cue (binocular disparity) was present or not present in the display. For six trainees, reliance on the newly recruited cue was similar whether or not the long-trusted cue was present, suggesting that the visual system assumed the new cue to be conditionally independent.