Slow Changes Research Articles

Memory representations during slow change blindness.

Classic change blindness is the phenomenon where seemingly obvious changes that coincide with visual disruptions (such as blinks or brief blanks) go unnoticed by an attentive observer. Some early work into the causes of classic change blindness suggested that any pre-change stimulus representation is overwritten by a representation of the altered post-change stimulus, preventing change detection. However, recent work revealed that, even when observers do maintain memory representations of both the pre- and post-change stimulus states, they can still miss the change, suggesting that change blindness can also arise from a failure to compare the stored representations. Here, we studied slow change blindness, a related phenomenon that occurs even in the absence of visual disruptions when the change occurs sufficiently slowly, to determine whether it could be explained by conclusions from classic change blindness. Across three different slow change blindness experiments we found that observers who consistently failed to notice the change had access to at least two memory representations of the changing display. One representation was precise but short lived: a detailed representation of the more recent stimulus states, but fragile. The other representation lasted longer but was fairly general: stable but too coarse to differentiate the various stages of the change. These findings suggest that, although multiple representations are formed, the failure to compare hypotheses might not explain slow change blindness; even if a comparison were made, the representations would be too sparse (longer term stores) or too fragile (short-lived stores) for such comparison to inform about the change.

The Helicopter Turboshaft Engine’s Reconfigured Dynamic Model for Functional Safety Estimation

This research substantiates the necessity for developing and implementing structural reconfiguration methods for automatic control systems in the event of a parametric sensor failure to enhance the helicopter turboshaft engine’s overall reliability and safety. The research aim is the substantiation of the helicopter turboshaft engine’s mathematically reconfigured automatic control system in the event of the failure of a standard sensor, which will ensure the helicopter turboshaft engine’s stable operation under failure conditions, minimizing the impact on engine control and performance. A theorem was developed and proven concerning the reconfiguration of the helicopter turboshaft engine’s automatic control system structure, defining the system’s new mathematical form using nonlinear thermogas-dynamic parameters. A method was proposed to determine the values of these parameters that keep the reconfigured control system stable. This method uses numerical optimization to find the best thermogas-dynamic parameters to ensure system stability. Experimental results showed that for slow changes, using parameters from the previous step works best, while for fast changes, restarting is more effective due to significant differences in the system states. The accuracy of the proposed mathematical model for the reconfigured control system was confirmed through mean square error analysis (within 0.4% and 0.77% under white noise), regression analysis (with a determination coefficient of 0.986), and cross-validation (with a metric deviation from the maximum mean square error of 3.88%).

Effect of selective preservatives on shelf-life of guava juice extracted using pectinase enzyme

The study investigated the feasibility of enzymatic extraction for guava juice and evaluated the effects of various preservatives on its shelf life. The crushed guava puree was undergone different pectinase enzyme concentrations over three incubation periods. The findings revealed that pectinase concentrations of 0.1 % and 0.2 %, when incubated for 1 and 2 h, were the most effective. Juice yields ranged from 65.24 % to 78.64 %, with Total Soluble Solids (TSS) varying from 9.12°Brix to 11.56°Brix. The physicochemical properties of the guava juice resulted 84.2 % moisture, 2.16 % protein, 0.77 % fat, 3.27 % fiber, 0.65 % acidity, 2.25 % reducing sugars, 8.27 % non-reducing sugars, 79.53 % antioxidant activity, 173.2 mg/100g of ascorbic acid, 10.52 TSS, 109.7 mg/100g of phenolic content, and a pH of 3.2. Eight juice samples were prepared as per formulation with sodium benzoate and potassium metabisulfite (KMS) at concentrations of 150 ppm, 200 ppm, and 250 ppm, in addition to one refrigerated sample and one control. The stability of these guava juice samples was monitored every 15 days over a 90-day period. Results showed that acidity, TSS, pH, reducing sugars, and non-reducing sugars changed over time. Samples with preservatives exhibited slower changes compared to the control. Phenolic compounds diminished more quickly at ambient temperature than in refrigerated or preservative-treated samples. Initially, phenolic content and antioxidant capacity were 44 mg GAE/100g and 44 %, respectively, but declined to 10–15 mg and 15–17 % by the end of the storage period. Color changes were more noticeable in samples stored at room temperature, whereas preservatives effectively reduced color degradation caused by enzymatic browning. Moreover, ascorbic acid retention was better in samples with preservatives and those stored under refrigeration. The ascorbic acid degradation rate was highest at room temperature (0.023 day^-1) and lowest with 250 ppm KMS (0.016 day^-1). Microbiological tests indicated that the juice remained safe for 40 days at room temperature, 90 days under refrigeration, and approximately 85 days with preservatives.

Experimenting With the Forecasting Power of Publicity in the Predictability of Climate Change in Africa

Motivated by the fact that the majority of news outlets in Africa have not sufficiently covered climate change issues, we innovatively construct a news-based index, drawing from the big data archive of Google Trends, as a proxy for publicity in the predictability of climate change. We show that increasing publicity about climate change has the potential to cause emission reduction and slow climate change.

Factors influencing fidelity to guideline implementation strategies for improving pain care at cancer centres: a qualitative sub-study of the Stop Cancer PAIN Trial

BackgroundThe Stop Cancer PAIN Trial was a phase III pragmatic stepped wedge cluster randomised controlled trial which compared effectiveness of screening and guidelines with or without implementation strategies for improving pain in adults with cancer attending six Australian outpatient comprehensive cancer centres (n = 688). A system for pain screening was introduced before observation of a ‘control’ phase. Implementation strategies introduced in the ‘intervention’ phase included: (1) audit of adherence to guideline recommendations, with feedback to clinical teams; (2) health professional education via an email-administered ‘spaced education’ module; and (3) a patient education booklet and self-management resource. Selection of strategies was informed by the Capability, Opportunity and Motivation Behaviour (COM-B) Model (Michie et al., 2011) and evidence for each strategy’s stand-alone effectiveness. A consultant physician at each centre supported the intervention as a ‘clinical champion’. However, fidelity to the intervention was limited, and the Trial did not demonstrate effectiveness. This paper reports a sub-study of the Trial which aimed to identify factors inhibiting or enabling fidelity to inform future guideline implementation initiatives.MethodsThe qualitative sub-study enabled in-depth exploration of factors from the perspectives of personnel at each centre. Clinical champions, clinicians and clinic receptionists were invited to participate in semi-structured interviews. Analysis used a framework method and a largely deductive approach based on the COM-B Model.ResultsTwenty-four people participated, including 15 physicians, 8 nurses and 1 clinic receptionist. Coding against the COM-B Model identified ‘capability’ to be the most influential component, with ‘opportunity’ and ‘motivation’ playing largely subsidiary roles. Findings suggest that fidelity could have been improved by: considering the readiness for change of each clinical setting; better articulating the intervention’s value proposition; defining clinician roles and responsibilities, addressing perceptions that pain care falls beyond oncology clinicians’ scopes of practice; integrating the intervention within existing systems and processes; promoting patient-clinician partnerships; investing in clinical champions among senior nursing and junior medical personnel, supported by medical leaders; and planning for slow incremental change rather than rapid uptake.ConclusionsFuture guideline implementation interventions may require a ‘meta-implementation’ approach based on complex systems theory to successfully integrate multiple strategies.Trial registrationRegistry: Australian New Zealand Clinical Trials Registry; number: ACTRN 12615000064505; data: https://www.anzctr.org.au/Trial/Registration/TrialReview.aspxid=367236&isReview=true.

Cloud Radiative Effects Slow Sea Ice Changes During Summer Arctic Dipole Anomaly

AbstractOver the past 30 years, the Arctic Dipole Anomaly (DA) has repeatedly led to record lows in summer sea ice extent, with cloud radiative effects (CRE) playing a crucial regulatory role. Here, we reveal the CRE variations between positive and negative DA events and elucidate the slowing impacts of CRE on sea ice thickness (SIT) changes. The DA triggers robust meridional winds and transpolar drift, markedly reducing SIT in the Beaufort Sea (BeS), Chukchi Sea (CS), and East Siberian Sea (ESS), while increasing it in the Greenland Sea (GS). CRE significantly slow SIT changes, contributing +14.4, +4.4, +16.4, and −26.7 cm to changes from June to August, against total changes of −55.9, −29.4, −39.8, and +42.8 cm in September over BeS, CS, ESS, and GS, respectively. This study underscores the key impacts of CRE on sea ice variation, emphasizing their significance in the polar climate system.

Quantitative calculation of rock strain concentration and corresponding damage evolution analysis

Understanding the critical strain and damage evolution process of sandstone can help engineers more accurately predict the failure modes and critical states of sandstone in actual engineering structures. Therefore, this article quantitatively analyzes the strain field data obtained through digital image correlation (DIC), and for the first time establishes a strain concentration calculation model (SCCM) to analyze the critical strain, compaction, and damage evolution process of sandstone under uniaxial compression conditions. The experimental results show that both elastic and plastic strain zones exist on the specimen surface. The proportion curve of strain concentration calculated by SCCM indicates that the strain field ε1 generally undergoes two stages: the elastic strain fluctuation stage and the plastic strain development stage. In contrast, the strain field ε2 exhibits roughly three stages: a rapid change phase, a slow change phase, and a stability phase. Microscopic strain analysis reveals an overlap between the compaction and damage processes of the specimen, and the critical strain value εmd for micro-damage in the specimen is significantly smaller than values obtained by traditional discrimination methods. Specifically, when the defect width of the specimen is 15 mm and 40 mm, the mean εmd vaules are approximately 0.006016 and 0.00539, respectively. In contrast, the mean critical strain values determined by traditional discrimination methods are 0.0180 and 0.0178, respectively. The above research results provide a new method for analyzing the strain field data of geotechnical materials, serving the optimization of engineering structure design.

Reverse Intersystem Crossing Dynamics in Vibronically Modulated Inverted Singlet-Triplet Gap System: A Wigner Phase Space Study.

We inspect the origin of the inverted singlet-triplet gap (INVEST) and slow change in the reverse intersystem crossing (rISC) rate with temperature, as recently observed. A Wigner phase space study reveals that, though INVEST is found at equilibrium geometry, variation in the exchange interaction and the doubles-excitation for other geometries in the harmonic region leads to non-INVEST behavior. This highlights the importance of nuclear degrees of freedom for the INVEST phenomenon, and in this case, geometric puckering of the studied molecule determines INVEST and the associated rISC dynamics.

Ppb-Level Ammonia Sensor for Exhaled Breath Diagnosis Based on UV-DOAS Combined with Spectral Reconstruction Fitting Neural Network.

Ammonia (NH3) in exhaled breath (EB) has been a biomarker for kidney function, and accurate measurement of NH3 is essential for early screening of kidney disease. In this work, we report an optical sensor that combines ultraviolet differential optical absorption spectroscopy (UV-DOAS) and spectral reconstruction fitting neural network (SRFNN) for detecting NH3 in EB. UV-DOAS is introduced to eliminate interference from slow change absorption in the EB spectrum while spectral reconstruction fitting is proposed for the first time to map the original spectra onto the sine function spectra by the principle of least absolute deviations. The sine function spectra are then fitted by the least-squares method to eliminate noise signals and the interference of exhaled nitric oxide. Finally, the neural network is built to enable the detection of NH3 in EB at parts per billion (ppb) level. The laboratory results show that the detection range is 9.50-12425.82 ppb, the mean absolute percentage error (MAPE) is 0.83%, and the detection accuracy is 0.42%. Experimental results prove that the sensor can detect breath NH3 and identify EB in simulated patients and healthy people. Our sensor will serve as a new and effective system for detecting breath NH3 with high accuracy and stability in the medical field.

Pathways to green personalisation: Reducing consumption through design

Humanity has reached the point where current consumer lifestyles are not sustainable, and this status quo must be disrupted if we are to slow down climate change. Personalisation is a technique which, amongst other purposes, uses machine learning algorithms and personal data harvesting to prompt people to engage in various financial and social activities that often encourage us to consume more. It creates wealth for businesses and brings satisfaction to customers. We rely on personalised messaging and allow Artificial Intelligence (AI) select information for our personal newsfeeds, contributing to the overall personalisation economy. Personalisation is most often deliberately designed to promote consumption, but that purpose is not preordained. Designers could repurpose personalisation processes to promote ‘greener’ consumption choices or even to reduce consumption all together. This concept paper coins a new term ‘green personalisation’ 1 and discusses pathways to inform new approaches to reduce consumption and the resulting environmental harm.

Early Predictor for the Onset of Critical Transitions in Networked Dynamical Systems

Numerous natural and human-made systems exhibit critical transitions whereby slow changes in environmental conditions spark abrupt shifts to a qualitatively distinct state. These shifts very often entail severe consequences; therefore, it is imperative to devise robust and informative approaches for anticipating the onset of critical transitions. Real-world complex systems can comprise hundreds or thousands of interacting entities, and implementing prevention or management strategies for critical transitions requires knowledge of the exact condition in which they will manifest. However, most research so far has focused on low-dimensional systems and small networks containing fewer than ten nodes or has not provided an estimate of the location where the transition will occur. We address these weaknesses by developing a deep-learning framework which can predict the specific location where critical transitions happen in networked systems with size up to hundreds of nodes. These predictions do not rely on the network topology, the edge weights, or the knowledge of system dynamics. We validate the effectiveness of our machine-learning-based framework by considering a diverse selection of systems representing both smooth (second-order) and explosive (first-order) transitions: the synchronization transition in coupled Kuramoto oscillators; the sharp decline in the resource biomass present in an ecosystem; and the abrupt collapse of a Wilson-Cowan neuronal system. We show that our method provides accurate predictions for the onset of critical transitions well in advance of their occurrences, is robust to noise and transient data, and relies only on observations of a small fraction of nodes. Finally, we demonstrate the applicability of our approach to real-world systems by considering empirical vegetated ecosystems in Africa. Published by the American Physical Society 2024

Message Passing Detectors for UAV-Based Uplink Grant-Free NOMA Systems

Utilizing unmanned aerial vehicles (UAVs) as mobile access points or base stations has emerged as a promising solution to address the excessive traffic demands in wireless networks. This paper investigates improving the detector performance at the unmanned aerial vehicle base stations (UAV-BSs) in an uplink grant-free non-orthogonal multiple access (GF-NOMA) system by considering the activity state (AS) temporal correlation of the different user equipments (UEs) in the time domain. The Bernoulli Gaussian-Markov chain (BG-MC) probability model is used for exploiting both the sparsity and slow change characteristic of the AS of the UE. The GAMP Bernoulli Gaussian-Markov chain (GAMP-BG-MC) algorithm is proposed to improve the detector performance, which can utilize the bidirectional message passing between the neighboring time slots to fully exploit the temporally correlated AS of the UE. Furthermore, the parameters of the BG-MC model can be updated adaptively during the estimation procedure with unknown system statistics. Simulation results show that the proposed algorithm can improve the detection accuracy compared to existing methods while keeping the same order complexity.

Brain state dynamics differ between eyes open and eyes closed rest.

The human brain exhibits spatio-temporally complex activity even in the absence of external stimuli, cycling through recurring patterns of activity known as brain states. Thus far, brain state analysis has primarily been restricted to unimodal neuroimaging data sets, resulting in a limited definition of state and a poor understanding of the spatial and temporal relationships between states identified from different modalities. Here, we applied hidden Markov model (HMM) to concurrent electroencephalography-functional magnetic resonance imaging (EEG-fMRI) eyes open (EO) and eyes closed (EC) resting-state data, training models on the EEG and fMRI data separately, and evaluated the models' ability to distinguish dynamics between the two rest conditions. Additionally, we employed a general linear model approach to identify the BOLD correlates of the EEG-defined states to investigate whether the fMRI data could be used to improve the spatial definition of the EEG states. Finally, we performed a sliding window-based analysis on the state time courses to identify slower changes in the temporal dynamics, and then correlated these time courses across modalities. We found that both models could identify expected changes during EC rest compared to EO rest, with the fMRI model identifying changes in the activity and functional connectivity of visual and attention resting-state networks, while the EEG model correctly identified the canonical increase in alpha upon eye closure. In addition, by using the fMRI data, it was possible to infer the spatial properties of the EEG states, resulting in BOLD correlation maps resembling canonical alpha-BOLD correlations. Finally, the sliding window analysis revealed unique fractional occupancy dynamics for states from both models, with a selection of states showing strong temporal correlations across modalities. Overall, this study highlights the efficacy of using HMMs for brain state analysis, confirms that multimodal data can be used to provide more in-depth definitions of state and demonstrates that states defined across different modalities show similar temporal dynamics.

Fractional neutron point kinetics model for reactivity transients of the NuScale and comparison with the classical kinetics approach

In this work, the behavior of the neutron and natural circulation parameters for Reactivity Transients in the PWR reactor called NuScale are analyzed through the novel system equations. It model includes non-integer and ordinary order differentials coupling the neutron point kinetic with the thermos-hydraulic core of the reactor. Transient processes of increase and decrease of the feed flow and the coolant inlet temperature are analyzed to identify the feedback of the thermohydraulic effects with the neutron phenomena in a non-integer order scheme. In the results, it is observed that for short times the power and reactivity have the same behavior with the classical model and the non-integer order model for all values of the anomalous diffusion coefficient simulated. However, for long times of simulation, there is more reactivity and less power, in contrast to the classical model, when reducing the value of the anomalous diffusion coefficient these counter-intuitive effects are increased. Additionally, a delay is observed between the change in reactivity and power. In conclusion, it is observed that sub-diffusive processes are relevant when there are slow changes in reactivity, which can be analyzed through the Fractional Order Neutron Density Equation.

Gender, immigration, and ethnicity

Abstract We complement analyses of both gender and ethnic inequality by examining cleavages among women of different ethnic and migrant groups, with a particular focus on labour force participation. Such differences can be shown to be shaped by gender attitudes and norms, which are relatively slow changing, transmitted intergenerationally, and socially embedded. We also examine the benefits of taking a household and individual income perspective on economic inequalities, extending existing analysis of earnings and employment. This facilitates the investigation of the economic position of women both in and out of the labour market, and how their income relates to the resources of other family members. By these means we can shed light on the ways in which gendered processes that are common across ethnic groups can nevertheless result in differentiated outcomes that may persist across generations. This can enhance our understanding of persistence and change in gendered inequalities more widely.

The Influence of the #MeToo Movement on Socio-Political Dynamics in the MENA Region

This research explains the influence of the #MeToo movement phenomenon on social and political dynamics in the MENA. Against the backdrop of slow structural change and an authoritarian social contract in the region, the #MeToo movement expanded the space for women and men to overcome cultural taboos regarding gender-based violence. With social movement theory, this research analyses this phenomenon through political opportunities, mobilisation structures, and artistic framing. The analysis results show that the #MeToo movement in MENA gives women and men a voice in fighting gender oppression, changing cultural norms, and demanding policy reform by involving social media as a critical tool to trigger a change in society's perception of sexual violence and provide the impetus for broader reform in women's rights in the region.

Nonlinear adaptive pose motion control of a servicer spacecraft in approximation with an accelerated tumbling target

Removing a limited number of large debris can significantly reduce space debris risks. These bodies are generally exposed to extreme environmental disturbance torques or consecutive accidents due to their large wet area, which causes them to experience accelerated high-rate tumbling motion. The existing literature has adequately explored the approximation operations with non-cooperative targets exhibiting 3-axis tumbling motion. However, the research gap lies in the lack of attention given to addressing this approximation for targets undergoing accelerated motion. Agile, accurate, and large-angle maneuvers are three common necessities for safely capturing such targets. Changes in the moment of inertia brought on by fuel slushing cannot be disregarded during such a maneuver. To deal with nonlinearities, adverse coupling effects, actuator saturation constraints, time-varying moment of inertia, and external disturbances that worsen during accelerated agile large-angle maneuvers, a novel adaptive control approach is developed in this paper. The controller's main advantage is its adjustable desired acceleration, which maintains its performance even when dealing with accelerated motion. The control law is directly synthesized from the nonlinear relative equations of motion, without any linearization or simplification of the system dynamics, making it robust to a variety of orbital elements and target behaviors. Adaptation laws are extracted from the Lyapunov stability theorem in a way that guarantees asymptotic stability. Moreover, control actuator roles (delay, saturation, and allocation) are accounted for in modeling and simulation. Finally, a comprehensive numerical simulation based on three different realistic and strict scenarios is carried out to demonstrate the effectiveness and performance of the proposed control approach. The controller's robustness against time-varying dynamic parameters (sharp and sudden change, smooth and slow change, and periodic change) is extensively demonstrated through simulation.

Financialization of the environment: responses presented at COP-26 to slow climate change

Financialization of the environment: responses presented at COP-26 to slow climate change

Andrographis paniculata Leaf Extract Increases Interleukin-2 in Malnutrition Rat Model

BACKGROUND: Malnutrition is a global health concern that results in changes in nutritional status, as indicated by alterations in phenotypic markers, hematological and biochemical parameters, and increased susceptibility to infection, as shown by decreased interleukin (IL)-2 levels. Andrographolide, the active component of Andrographis paniculata, stimulates the immune system and exhibits antibacterial and antiviral activity. Therefore, A. paniculata may serve as a potential adjuvant therapy for malnutrition. This study was conducted to analyze the effect of A. paniculata as an immunomodulator against malnutrition with characteristics of environmental enteric dysfunction (EED) and a low-protein diet by examining phenotypic markers, hematological, biochemical, and IL-2 levels.METHODS: Forty-five male Wistar rats were divided into seven groups. They were fed either a standard or a low-protein diet before receiving oral administration of various concentrations of A. paniculata leaf extract (APLE). APLE was administered 21 days after the initial low-protein diet. Hematological, biochemical, and phenotypic markers were assessed to determine the nutritional status of the rats. The protective effects of APLE were evaluated by measuring IL-2 levels using enzyme-linked immunosorbent assay (ELISA).RESULTS: Malnourished rats exhibited slow body growth, physical and behavioral changes, reduced leukocyte count, total protein, albumin, cholesterol, and villi length. Malnourished rats treated with APLE showed a more effective and significant increase in IL-2 levels, with higher concentrations of APLE resulting in higher IL-2 levels.CONCLUSION: APLE, in a concentration-dependent manner, can increase IL-2 levels, suggesting that APLE may have potential protective effects in a rat model of malnutrition.KEYWORDS: Andrographis paniculata, environmental enteric dysfunction, interleukin (IL)-2, low protein, malnutrition

Parvalbumin interneuron activity induces slow cerebrovascular fluctuations in awake mice.

Neuronal regulation of cerebrovasculature underlies brain imaging techniques reliant on cerebral blood flow (CBF) changes. However, interpreting these signals requires understanding their neural correlates. Parvalbumin (PV) interneurons are crucial in network activity, but their impact on CBF is not fully understood. Optogenetic studies show that stimulating cortical PV interneurons induces diverse CBF responses, including rapid increases, decreases, and slower delayed increases. To clarify this relationship, we measured hemodynamic and neural responses to optogenetic stimulation of PV interneurons expressing Channelrhodopsin-2 during evoked and ongoing resting-state activity in the somatosensory cortex of awake mice. Two-photon microscopy (2P) Ca2+ imaging showed robust activation of PV-positive (PV+) cells and inhibition of PV-negative (PV-) cells. Prolonged PV+ cell stimulation led to a delayed, slow CBF increase, resembling a secondary peak in the CBF response to whisker stimulation. 2P vessel diameter measurements revealed that PV+ cell stimulation induced rapid arterial vasodilation in superficial layers and delayed vasodilation in deeper layers. Ongoing activity recordings indicated that both PV+ and PV- cell populations modulate arterial fluctuations at rest, with PV+ cells having a greater impact. These findings show that PV interneurons generate a complex depth-dependent vascular response, dominated by slow vascular changes in deeper layers.