Key Subcomponents Research Articles

What makes an event significant: an fMRI study on self-defining memories.

Self-defining memories are highly significant personal memories that contribute to an individual's life story and identity. Previous research has identified 4 key subcomponents of self-defining memories: content, affect, specificity, and self-reflection. However, these components were not tested under functional neuroimaging. In this study, we first explored how self-defining memories distinguish themselves from everyday memories (non-self-defining) through their associated brain activity. Next, we evaluated the different self-defining memory subcomponents through their activity in the underlying brain system. Participants recalled both self-defining and non-self-defining memories under functional MRI and evaluated the 4 subcomponents for each memory. Multivoxel pattern analysis uncovered a brain system closely related to the default mode network to discriminate between self-defining and non-self-defining memories. Representational similarity analysis revealed the neural coding of each subcomponent. Self-reflection was coded mainly in the precuneus, middle and inferior frontal gyri, and cingulate, lateral occipital, and insular cortices. To a much lesser extent, content coding was primarily in the left angular gyrus and fusiform gyrus. No region was found to represent information on affect and specificity. Our findings highlight the marked difference in brain processing between significant and non-significant memories, and underscore self-reflection as a predominant factor in the formation and maintenance of self-defining memories, inviting a reassessment of what constitutes significant memories.

PROFESSIONAL DEVELOPMENT OF HUMAN RESOURCES AT NON-MANAGERIAL AND MANAGERIAL POSITIONS - THE IMPACT ON ORGANISATIONAL DEVELOPMENT

The aim of this paper is to analyze the interdependence between the professional development of human resources in organizations, both in non-managerial and managerial positions at various levels of management. Professional development of human resources is one of the key factors that contribute to organizational development, and also one of the dominant factors of organizational success. The interdependence between the organizational and the individual perspective of organizational development is a subject of permanent scientific research. It is impossible to express organizational development as a sum of individual contributions, due to the complexity of this category which depends of numerous variables in the comprehensive model of organizational development. Professional development of human resources is only one of the variables of the above-mentioned model of organizational development, and the objective of this paper is to analyse their specific nature, roles and interdependence. Specifically, the research will be directed to the identification of the variables of the comprehensive models of organizational development, and moreover, a detailed elaboration of the impact of human resources development in this context will be presented. A specific approach would be implemented in terms of modeling the professional development of human resources, the processes, strategies etc. Individual development will be also taken in consideration as a key sub-component of professional development, and, consequently, as an implicit variable in comprehensive models of organizational development. Methodologically, appart from the theoretical elaboration, a blended methodological approach will be implemented, based mainly on qualitative research methods, such as case study; also supported by case-specific quantitative methods, that would contribute for better explanation of the relations and interdependence between the above-mentioned variables. Finally, research conclusions and recommendations will be presented.

Environmental exposure disparities in ultrafine particles and PM2.5 by urbanicity and socio-demographics in New York state, 2013–2020

BackgroundThe spatiotemporal and demographic disparities in exposure to ultrafine particles (UFP; number concentrations of particulate matter (PM) with diameter ≤0.1 μm), a key subcomponent of fine aerosols (PM2.5; mass concentrations of PM ≤ 2.5 μm), have not been well studied. ObjectiveTo quantify and compare the aerosol pollutant exposure disparities for UFP and PM2.5 by socio-demographic factors in New York State (NYS). MethodsAmbient atmospheric UFP and PM2.5 were quantified using a global three-dimensional model of chemical transport with state-of-the-science aerosol microphysical processes validated extensively with observations. We matched these to U.S. census demographic data for varied spatial scales (state, county, county subdivision) and derived population-weighted aerosol exposure estimates. Aerosol exposure disparities for each demographic and socioeconomic (SES) indicator, with a focus on race-ethnicity and income, were quantified for the period 2013–2020. ResultsThe average NYS resident was exposed to 4451 #·cm−3 UFP and 7.87 μg·m−3 PM2.5 in 2013–2020, but minority race-ethnicity groups were invariably exposed to greater daily aerosol pollution (UFP: +75.0% & PM2.5: +16.2%). UFP has increased since 2017 and is temporally and seasonally out-of-phase with PM2.5. Race-ethnicity exposure disparities for PM2.5 have declined over time; by −6% from 2013 to 2017 and plateaued thereafter despite its decreasing concentrations. In contrast, these disparities have increased (+12.5–13.5%) for UFP. The aerosol pollution exposure disparities were the highest for low-income minorities and were more amplified for UFP than PM2.5. DiscussionWe identified large disparities in aerosol pollution exposure by urbanization level and socio-demographics in NYS residents. Jurisdictions with higher proportions of race-ethnicity minorities, low-income residents, and greater urbanization were disproportionately exposed to higher concentrations of UFP and PM2.5 than other NYS residents. These race-ethnicity exposure disparities were much larger, more disproportionate, and unabating over time for UFP compared to PM2.5 across various income strata and levels of urbanicity.

Development of the test method for wind turbine blade subcomponent under combined bending and torsion loads

The conventional full-scale wind turbine blade testing method is unable to study the detailed structural response of blades' key subcomponents under complex loads. Therefore, developing an efficient and reliable blade subcomponent testing method is of great significance for investigating the structural characteristics of blades. This paper develops an innovative blade subcomponent test method framework, which is capable of studying the structural response under combined bending and torsion load conditions. A high-fidelity finite element model corresponding to the framework that considers nonlinear factors is established. This study further investigated the influence of boundary conditions on the results. The results showed that the test method proposed in this paper is reasonable, and the experiment based on this method is successfully conducted. The contact nonlinearity and size effects will affect the accuracy of the test results, which can be avoided by adjusting the size of the clamp.

A subcomponent-guided deep learning method for interpretable cancer drug response prediction.

Accurate prediction of cancer drug response (CDR) is a longstanding challenge in modern oncology that underpins personalized treatment. Current computational methods implement CDR prediction by modeling responses between entire drugs and cell lines, without the consideration that response outcomes may primarily attribute to a few finer-level 'subcomponents', such as privileged substructures of the drug or gene signatures of the cancer cell, thus producing predictions that are hard to explain. Herein, we present SubCDR, a subcomponent-guided deep learning method for interpretable CDR prediction, to recognize the most relevant subcomponents driving response outcomes. Technically, SubCDR is built upon a line of deep neural networks that enables a set of functional subcomponents to be extracted from each drug and cell line profile, and breaks the CDR prediction down to identifying pairwise interactions between subcomponents. Such a subcomponent interaction form can offer a traceable path to explicitly indicate which subcomponents contribute more to the response outcome. We verify the superiority of SubCDR over state-of-the-art CDR prediction methods through extensive computational experiments on the GDSC dataset. Crucially, we found many predicted cases that demonstrate the strength of SubCDR in finding the key subcomponents driving responses and exploiting these subcomponents to discover new therapeutic drugs. These results suggest that SubCDR will be highly useful for biomedical researchers, particularly in anti-cancer drug design.

The Effect of Efavirenz on Reward Processing in Asymptomatic People Living with HIV: A Randomized Controlled Trial.

Functional magnetic resonance imaging (fMRI) studies have demonstrated that HIV-infection affects the fronto-striatal network. It has not been examined what impact efavirenz (EFV), an antiretroviral drug notorious for its neurocognitive effects, has on the reward system: a key subcomponent involved in depressive and apathy symptoms. Therefore, this study aims to investigate the effect of EFV on reward processing using a monetary incentive delay (MID) task. In this multicenter randomized controlled trial, asymptomatic adult participants stable on emtricitabine/tenofovirdisoproxil fumarate (FTC/TDF)/EFV were randomly assigned in a 2:1 ratio to switch to FTC/TDF/rilpivirine (RPV) (n = 30) or continue taking FTC/TDF/EFV (n = 13). At baseline and 12 weeks after therapy switch, both groups performed an MID task. Behavior and functional brain activity related to reward anticipation and reward outcome were assessed with blood-oxygen-level-dependent fMRI. Both groups were matched for age, education level, and time since HIV diagnosis and on EFV. At the behavioral level, both groups had faster response times and better response accuracy during rewarding versus nonrewarding trials, with no improvement resulting from switching FTC/TDF/EFV to FTC/TDF/RPV. No significant change in activation related to reward anticipation in the ventral striatum was found after switching therapy. Both groups had significantly higher activation levels over time, consistent with a potential learning effect. Similar activity related to reward outcome in the orbitofrontal cortex was found. Discontinuing FTC/TDF/EFV was not found to improve activity related to reward anticipation in asymptomatic people living with HIV, with similar cortical functioning during reward outcome processing. It is therefore likely that EFV does not affect motivational control. Further research is needed to determine whether EFV affects motivational control in HIV populations with different characteristics.

Water allocation using system dynamic modelling in the aquaculture integrated with small-scale irrigation systems in Malawi

The agricultural sector is faced with numerous challenges including climate change and water scarcity in many developing countries. In order to address scarcity and improve water use efficiency for rural farmers, fish farming is being integrated with small-scale irrigation. However, there are challenges in how to allocate water between the two farming enterprises. This study explored the capabilities of system dynamics to allocate water between a fish pond and a crop field in Chingale, Malawi using a system dynamic software, Vensim™ PLE. For soil water and pond water, a simple water balance structure was built and connected to the crop growth structure. Simulations run for 125 days corresponding to the maize growth period. Model results are similar to the actual yield (about 3.5 ton/ha for hybrid) and biomass production (about 7 ton/ha) in the area. Results also show it was possible to maintain pond water depth at recommended depths for raising fish: fish stocking (1 m), operation of the pond (1.5–2.0 m) and harvesting of the fish (less than 1.2 m) throughout the maize growing period. While the study did not comprehensively build and simulate fish growth, the use of such simple tools would benefit rural farmers with few resources. Based on the promising capabilities and the results of the tool it is recommended that further comprehensive analysis to fully incorporate all key sub-components affecting crop and fish growth be carried out.

Socioeconomic disparities in aerosol pollutant exposure may be amplified by ultrafine particles despite declining PM2.5

BACKGROUND AND AIM: No study has investigated the spatiotemporal race-ethnicity and income disparities in ultrafine particles (UFP; ≤0.1µm) exposure, which is increasingly identified as a key subcomponent of PM₂.₅-related health effects. This study aims to establish the importance of distinguishing UFP from PM₂.₅ and quantify their race-ethnicity and income disparities in New York State (NYS). METHODS: Exposure disparities among seven race-ethnicity groups (Hispanics of any race, non-Hispanic Asian, Black, Native, Pacific& White alone, and Other/Mixed) and by household income across spatial scales (state, county, county subdivision, NCHS urban levels) during the period 2013–2020 for UFP and PM₂.₅, were quantified using Census data. A global three-dimensional chemical transport model with state-of-the-science aerosol microphysical processes were used to estimate UFP/PM₂.₅ that have been validated extensively with observations. RESULTS: The average New Yorker was exposed to 5171#·cm⁻³ UFP and 8.2µg·m⁻³ PM₂.₅ in 2013–2020. Minority race-ethnicity groups were invariably exposed to greater daily aerosol pollution in NYS (+51–104% UFP & +1.9–32% PM₂.₅). Race-ethnicity exposure disparities for PM₂.₅ exposure have declined over time; by −6% from 2013–2017 and plateauing thereafter despite increasing PM₂.₅. Crucially and in contrast, these disparities have persisted (+10%) for UFP exposure and even widened in periods of declining UFP. Economic status (median household income) by itself does not reveal significant exposure disparities. However, in tandem with race-ethnicity it uncovers these disparities as disproportionately magnified for socioeconomically disadvantaged subgroups. CONCLUSIONS: PM₂.₅ decline was associated with decline in race-ethnicity exposure disparities. which continued despite a post-2018 reversal in PM₂.₅ trends. However, race-ethnicity UFP exposure disparities were much larger, more disproportionate, and increased or remained constant across various income strata and levels of urbanicity. The need to distinguish ultrafine (UFP) from fine (PM₂.₅) aerosols is established through microphysical, geostatistical, and exposure assessment perspectives. KEYWORDS: air pollution, ultrafine particles, particulate matter, environmental justice, socioeconomic status

Assessing Educating for Gross National Happiness Applying the Theory of Practice Architectures

Gross National Happiness (GNH) is a central policy, symbolic of Bhutan, which gave rise to Educating for GNH (EGNH). In Bhutan, the school Performance Management System is the sole instrument used by the Ministry of Education to evaluate the performance of schools. To test the utility of the Performance Management System in fulfilling the intended goals of EGNH, two of its key subcomponents—the School Self-Assessment and School Performance Scorecards—were assessed through the lens of the theory of practice architectures. The theory describes what comprises social practices and how they are shaped by the social, cultural and political contexts in which they occur. Content analysis was carried out using Leximancer text-mining software. The findings showed that the sayings and relatings and their corresponding cultural-discursive and social-political arrangements were not as evident in the School Self-Assessment indicators as the doings and material-economic arrangements. The findings also indicated that both the School Self-Assessment’s and the School Performance Scorecards’ indicators focus more on assessing leadership than teaching. The authors present several suggestions to ensure a closer alignment with the goals of EGNH, to demonstrate a balanced focus on assessing teaching and leadership, and to represent all practice architectures proportionately. Implications of this study should inform the policy and practices of Educating for GNH values in Bhutanese schools. The methods of the study could be adapted to examine educational practices beyond those of Bhutan.

What matters for consumer sentiment in the euro area? World crude oil price or retail gasoline price?

We investigate the link between crude oil prices, retail gasoline prices and consumer sentiment in the euro area. Our results reveal that consumer sentiment and its key components deteriorate notably as a response to positive shocks to real gasoline prices at the pump. On the contrary, positive oil-specific demand shocks do not trigger such a strong deterioration of consumer sentiment. The analysis is further refined to analyse the effects of these shocks to six key sub-components of consumer sentiment, which include household finance, conditions for buying durable products and the macroeconomy.

Integrated lithium niobate electro-optic modulators: when performance meets scalability

Electro-optic modulators (EOMs) convert signals from the electrical to the optical domain. They are at the heart of optical communication, microwave signal processing, sensing, and quantum technologies. Next-generation EOMs require high-density integration, low cost, and high performance simultaneously, which are difficult to achieve with established integrated photonics platforms. Thin-film lithium niobate (LN) has recently emerged as a strong contender owing to its high intrinsic electro-optic (EO) efficiency, industry-proven performance, robustness, and, importantly, the rapid development of scalable fabrication techniques. The thin-film LN platform inherits nearly all the material advantages from the legacy bulk LN devices and amplifies them with a smaller footprint, wider bandwidths, and lower power consumption. Since the first adoption of commercial thin-film LN wafers only a few years ago, the overall performance of thin-film LN modulators is already comparable with, if not exceeding, the performance of the best alternatives based on mature platforms such as silicon and indium phosphide, which have benefited from many decades of research and development. In this mini-review, we explain the principles and technical advances that have enabled state-of-the-art LN modulator demonstrations. We discuss several approaches, their advantages and challenges. We also outline the paths to follow if LN modulators are to improve further, and we provide a perspective on what we believe their performance could become in the future. Finally, as the integrated LN modulator is a key subcomponent of more complex photonic functionalities, we look forward to exciting opportunities for larger-scale LN EO circuits beyond single components.

Psychological flexibility profiles, college adjustment, and subjective well-being among college students in China: A latent profile analysis

This study used latent profile analysis to analyze the subgroups of psychological flexibility (PF) profiles based on key subcomponents of PF in college students, and how the subgroups were related to college adjustment and subjective well-being. A sample of 644 participants from a Chinese vocational college completed the questionnaires online. We found three distinct profiles of PF: high psychological flexibility (HPF), moderate psychological flexibility (MPF), and low psychological flexibility (LPF). College adjustment and subjective well-being were significantly different across the three PF profiles, with HPF individuals adjusting the best to college life and having the highest well-being, whereas LPF individuals adjusted the worst to college life and had the lowest well-being. Moreover, students from rural areas and students with siblings were characterized by LPF. The results of this study provide a new vision for understanding PF in college students using a person-oriented approach.

Multi-terrain Quadrupedal-wheeled Robot Mechanism: Design, Modeling, and Analysis

For a robot to navigate in terrains of rough and uneven topographies, its drives and controllers must generate and control large mechanical power with great precision. This paper is aimed at developing an autonomous robot with active-suspensions in form of a hybrid quadrupedal-wheel drive mechanism. This involves a computational approach to optimizing the development cost without compromising the system’s performance. Using the Solidworks CAD tool, auxiliary components were designed and integrated with the bed structure to form an actively suspended robot drive mechanism. Also, using the S-Math Computing tool, the robot’s suspension system was optimized, employing a four-bar mechanism. To enhance the compatibility of this design with the intended controller, some mathematical equations and numerical validations were formulated and solved. These included the modeling of tip-over stability and skid steering, the trendline equations for computing the angular positions of the suspension servomotors, and the computation of R2– values for determining the accuracy of these trendline equations. Using finite element analysis (FEA), we simulated the structural integrity of key sub-components of the final structure. The results show that our mechanical design is appropriate for developing an actively suspended robot that can efficiently navigate in different terrestrial sites and topographies.

Multi-terrain Quadrupedal-wheeled Robot Mechanism: Design, Modeling, and Analysis

For a robot to navigate in terrains of rough and uneven topographies, its drives and controllers must generate and control large mechanical power with great precision. This paper is aimed at developing an autonomous robot with active-suspensions in form of a hybrid quadrupedal-wheel drive mechanism. This involves a computational approach to optimizing the development cost without compromising the system’s performance. Using the Solidworks CAD tool, auxiliary components were designed and integrated with the bed structure to form an actively suspended robot drive mechanism. Also, using the S-Math Computing tool, the robot’s suspension system was optimized, employing a four-bar mechanism. To enhance the compatibility of this design with the intended controller, some mathematical equations and numerical validations were formulated and solved. These included the modeling of tip-over stability and skid steering, the trendline equations for computing the angular positions of the suspension servomotors, and the computation of R2– values for determining the accuracy of these trendline equations. Using finite element analysis (FEA), we simulated the structural integrity of key sub-components of the final structure. The results show that our mechanical design is appropriate for developing an actively suspended robot that can efficiently navigate in different terrestrial sites and topographies.

Investigation of the Design and Fault Prediction Method for an Abrasive Particle Sensor Used in Wind Turbine Gearbox

The gearbox is a key sub-component of a wind power generation system with high failure rate leading to shutdowns. By monitoring the abrasive particles in the lubricating oil when the gearbox is running, any abnormal condition of the gearbox can be found in advance. This information may be used to improve the operational safety of the wind turbine and reduce losses because of shutdowns and maintenance. In this paper, a three-coil induction abrasive particle sensor is designed based on the application of high-power wind turbine gearbox. The performance of the sensor and the design method of the detection circuit are described in detail, and the sensor operation performance used in the 2 MW wind turbine is verified. The results show that the sensor has superior performance in identifying ferromagnetic abrasive particles above 200 μm and plays a good role in status monitoring and fault prediction for the gearbox.

Study on Electrical Tests of Superconducting Busbar Protection Insulation of ITER Feeder PF4 CFT

Poloidal field coil no. 4 (PF4) cryostat feedthrough (CFT) is a key subcomponent of ITER feeder system. The superconducting busbars of PF4 CFT have insulation protection layer, which consists of several half-overlapped pre-preg and glass Kapton for main structure. Base on the special busbar structure, how to do the electrical tests for production final acceptance was studied. In this paper, three kinds of electrical tests for PF4 CFT busbar insulation acceptance were introduced, include direct current high-voltage test, alternating current partial discharge test, and Paschen test. On-site environment control, electrodes connection configuration, and some detail processes of these electrical tests were introduced. Some test results of leakage current were showed in this paper to guarantee the high quality of PF4 CFT superconducting busbar protection insulation.

Indicators of Effective Followership for Teachers under the Local Administrative Organization, Thailand: The Structural Relationship Model

This study aims to examine the consistency of the structural relationship model developed from both related theories and previous studies. Key components, sub-components and related indicators were examined by descriptive method. Sample sizes were controlled by the ratio between sample units and number of parameters as 20:1. A total of 31,026 samples were collected. All samples were teachers who were teaching at schools under the jurisdiction of local administration in Thailand. The questionnaire was used as a 5-level rating scale with 0.979 reliability. Results were based on related hypothesis, WIE model, participatory measurement model (PAR), and critical measurement model (CRT), respectively. The Measurement of Integrity (INT) and FOLL (Good User-Conduct Modeling) models were consistent with those previous studies. The key components, sub-components and indicators were also loaded according to the criteria.

Chronotype and Mental Health: Recent Advances.

Evening chronotype is increasingly recognized as a correlate of, and perhaps a contributor to, mental illness. The current review evaluates recent evidence for the association between chronotype and mental illness and putative mechanisms underlying the association, while highlighting methodological advances and areas of research that are relatively under-examined in the literature. While evening chronotype is most consistently associated with severity of mood disorder symptoms, emerging evidence implicates evening chronotype as a transdiagnostic correlate of substance use severity, anxiety symptoms, attentional difficulties, and maladaptive behaviors such as aggression. Longitudinal studies point to the possibility that evening chronotype precedes problematic substance use, depression, and anxiety. Neural processes related to reward and affective regulation may underlie associations between evening chronotype and illness. The literature on chronotype and mental illness has evolved to (1) include associations with a broader range of psychiatric symptom profiles; (2) explore underlying mechanisms; and (3) expand on earlier research using objective measures and more sophisticated study designs. In addition to further mechanistic research, additional work is needed to examine the stability and key subcomponents of the chronotype construct, as well as more attention to pediatric and special populations. This research is needed to clarify the chronotype-mental health relationship, and to identify how, when, and what aspects of chronotype can be targeted via therapeutic interventions.

A Hybrid Electric and Thermal Solar Receiver

Summary Solar energy offers a promising renewable energy source; however, it is expensive to store electricity from photovoltaics (PV), the most widely deployed solar electricity technology. Solar thermal energy technologies can be paired with inexpensive thermal storage, but are more expensive overall. We have developed a solar receiver that combines PV and solar thermal systems to efficiently convert solar radiation to electricity (to be used immediately) and thermal energy (to be stored and converted to electricity on demand). This paper describes the Hybrid Electric And Thermal Solar (HEATS) receiver and models its performance. An idealized model predicts high solar-to-electricity efficiency (35.2%) with high dispatchability (44.2% of electricity from thermal energy) at an operating temperature of 775 K. Modeling using measured performance values for HEATS subcomponents predicts 26.8% efficiency and 81% dispatchability with silicon PV and 28.5% efficiency and 76% dispatchability with gallium arsenide PV, both operating at 700 K.

Diagnosis and Prognosis of Fuel Injectors based on Control Adaptation

Spark Ignition Direct Injection (SIDI) technology enables better fuel economy and tail pipe emissions in vehicles equipped with gasoline engines. The SIDI technology depends on the ability of the system to deliver fuel at high pressure directly into the combustion chamber, hence making thefuel injectors key subcomponents. Reliable performance of fuel injectors is vital as it directly relates to the operability of the vehicle, and hence customer satisfaction in case of failure. It, therefore, becomes very important to devise a scheme that can effectively diagnose and prognose such a component. In this article, algorithm development for diagnosis and a pathway to prognosis of fuel injectors is presented. We do not propose any additional sensing capability, and make use of what is available in most of the production vehicles today across the industry. In particular, the control adaptation of fuel control and the associated diagnostics that are mandated by regulators are employed to generate schemes for fault detection, fault isolation, and fault prediction. Results are presented from vehicle test data that allow development of such a scheme for fuel injectors.