Input Stimuli Research Articles

Roy's Adaptation Model in Reducing Academic Anxiety in Midwifery Students facing Midwifery Clinical Practice

Roy's adaptation model is designed to form an interrelated cycle consisting of input (stimulus), control process (coping mechanism), and output (response or behavior). The purpose of this study was to reduce academic anxiety in nursing students in facing midwifery clinical practice using Roy's model. The research design used a quasi experiment with a population of undergraduate nursing students with a sample size of 100 students. This study analyzed data using the T-test. From the demographic results, it was found that out of 100 respondents aged 20 years as many as 28 (28%), 58 (58%) men, 88 (54.2%) women. The results of data analysis using Shapiro Wilk obtained a probability value of 0.710> 0.05 so it can be concluded that the data is normally distributed. The mean ± SD value of the Academic Anxiety variable before the application of the Roy Adaptation Model is 43.99 ± 5. The mean ± SD value of the Academic Anxiety variable after the application of the Roy Adaptation Model is 192, while after the application of the Roy Adaptation Model is 45.59 ± 5.888. The results of the Paired Sample Statistics T-Test test obtained a value of 4.222 and a p value = 0.001 <0.05, thus the alternative hypothesis is accepted. For future researchers, it is expected to use stronger experimental designs such as randomized controlled experiments, conduct long-term measurements, analyze more complex data, include additional variables such as moderation or mediation and develop academic anxiety instruments.

Dynamic Convolutional Neural Networks Based on Adaptive 2D Memristors

AbstractConvolutional Neural Networks (CNNs) are pivotal in modern digital computing, particularly for tasks like image classification, inspired by the receptive fields of the human brain. Nevertheless, CNNs implemented on conventional digital computers face significant limitations due to inflexible kernels that cannot adjust to dynamic inputs, and the von Neumann architecture, which leads to inefficient data transfer between memory and processing units. This research presents a hardware‐software co‐designed solution, a Dynamic Convolutional Neural Network (dCNN), empowered by three‐terminal adaptive two‐dimensional (2D) memristors. These memristors consist of a vertical heterostructure integrating silver, an atomically thin insulator (CrPS4), and graphene as a semimetal. This configuration allows for the dynamic tuning of conductive filament properties, emulating the heterosynaptic plasticity observed in biological neural systems. The three‐terminal memristor design permits the dCNN to actively adjust kernel weights in its attention layer according to the input stimuli. The empirical tests demonstrate that image classification accuracy using our adaptive 2D memristor‐enhanced dVGG reaches up to 94% on the CIFAR‐10 dataset, which exceeds the performance of static VGG. Furthermore, the energy efficiency of our dVGG significantly outperforms that of GPUs, aligning more closely with the energy dynamics of the human brain in terms of both consumption and classification accuracy.

A core set of neural states underlying memory reactivation of naturalistic events in the posterior medial cortex.

In sensory and mid-level regions of the brain, stimulus information is often topographically organized; functional responses are arranged in maps according to features such as retinal coordinates, auditory pitch, and object animacy or size. However, such organization is typically measured during stimulus input, e.g., when subjects are viewing gratings or images. Much less is known about the possible spatial organization of function during episodic recall of real-world events, which seems to drive higher-order cortical regions in the default mode network, particularly in posterior midline areas. Prior studies have shown that when multiple people remember a common experience, event-specific activity patterns in the posterior medial cortex are similar across individuals. This indicates that spatially organized functional responses underlying episodic recall do exist. In this paper we leverage fMRI data collected during recall of naturalistic movies to identify a core set of neural states in the posterior medial cortex. These states are stimulus-locked, reactivated during recall, and have a shared spatial organization across brains. We show that a surprisingly small number of these states (16 states across hemispheres) is sufficient to achieve the same levels of reactivation in the posterior medial cortex as when using the standard methods of the field. Furthermore, these states are significantly related to actions and social-affective features of events in the movies. Together, these findings elucidate the properties of a spatially organized code within the posterior default mode network which appears during natural recollection of memories.

An investigation of the effect of vibration duration on the evaluation of subjective responses to horizontal vibration

AbstractA laboratory experiment was conducted to investigate an evaluation method for building habitability based on subjective responses to vibrations caused by external sources. Twenty‐five individuals participated in the experiment, which involved 76 input stimulus conditions, including vibrations based on field records and sinusoidal vibrations in the horizontal direction. Subjective responses were measured in terms of “magnitude,” “concern,” and “discomfort,” The study compared several domestic and international evaluation methods for environmental vibrations with subjective responses, revealing that the vibration dose value, which accounts for the effect of vibration duration raised to the fourth power, was the most reasonable method.

Haptic In‐Sensor Computing Device Based on CNT/PDMS Nanocomposite Physical Reservoir

The importance of haptic in‐sensor computing devices has been increasing. Herein, a haptic sensor with a hierarchical structure is successfully fabricated via the sacrificial template method, using carbon nanotube‐polydimethylsiloxane (CNT‐PDMS) nanocomposites for in‐sensor computing applications. The CNT‐PDMS nanocomposite sensors, with different sensitivities, are obtained by varying the amount of CNTs. The input stimuli are transformed into higher‐dimensional information, enabling a new path for the CNT‐PDMS nanocomposite application, which is implemented on a robotic hand as an in‐sensor computing device by applying a reservoir computing paradigm. The nonlinear output data obtained from the sensors are trained using linear regression and used to classify nine different objects used in everyday life with an object recognition accuracy of >80% for each object. This approach can enable tactile sensation in robots while reducing the computational cost.

Fingerprinting Magnetic States in Interconnected Nanoring Arrays via Spin Wave Spectra

Arrays of interconnected magnetic nanorings show emergent dynamics arising from stochastic interactions at junctions between rings. These global emergent properties have already shown potential as a platform for reservoir computing. However, their experimental computational performance has been limited by the use of a single-dimensional global measurement, their magnetoresistance, as an output, masking much of the underlying state information. Here, we show in both simulation and experiment the potential of using the spin wave spectra of such arrays of nanorings to produce a fingerprint of the number and orientation of the internal magnetic states, providing a route towards a multi-dimensional output with which to perform computation. We show that the spectra fulfill the two criteria for reservoir computing: a nonlinear response to an input stimulus, and a fading memory.

Beyond-local neural information processing in neuronal networks

While there is much knowledge about local neuronal circuitry, considerably less is known about how neuronal input is integrated and combined across neuronal networks to encode higher order brain functions. One challenge lies in the large number of complex neural interactions. Neural networks use oscillating activity for information exchange between distributed nodes. To better understand building principles underlying the observation of synchronized oscillatory activity in a large-scale network, we developed a reductionistic neuronal network model. Fundamental building principles are laterally and temporally interconnected virtual nodes (microcircuits), wherein each node was modeled as a local oscillator. By this building principle, the neuronal network model can integrate information in time and space. The simulation gives rise to a wave interference pattern that spreads over all simulated columns in form of a travelling wave. The model design stabilizes states of efficient information processing across all participating neuronal equivalents. Model-specific oscillatory patterns, generated by complex input stimuli, were similar to electrophysiological high-frequency signals that we could confirm in the primate visual cortex during a visual perception task. Important oscillatory model pre-runners, limitations and strength of our reductionistic model are discussed. Our simple scalable model shows unique integration properties and successfully reproduces a variety of biological phenomena such as harmonics, coherence patterns, frequency-speed relationships, and oscillatory activities. We suggest that our scalable model simulates aspects of a basic building principle underlying oscillatory, large-scale integration of information in small and large brains.

Tuning the performance of a TphR-based terephthalate biosensor with a design of experiments approach

Transcription factor-based biosensors are genetic tools that aim to predictability link the presence of a specific input stimuli to a tailored gene expression output. The performance characteristics of a biosensor fundamentally determines its potential applications. However, current methods to engineer and optimise tailored biosensor responses are highly nonintuitive, and struggle to investigate multidimensional sequence/design space efficiently. In this study we employ a design of experiments (DoE) approach to build a framework for efficiently engineering activator-based biosensors with tailored performances, and we apply the framework for the development of biosensors for the polyethylene terephthalate (PET) plastic degradation monomer terephthalate (TPA). We simultaneously engineer the core promoter and operator regions of the responsive promoter, and by employing a dual refactoring approach, we were able to explore an enhanced biosensor design space and assign their causative performance effects. The approach employed here serves as a foundational framework for engineering transcriptional biosensors and enabled development of tailored biosensors with enhanced dynamic range and diverse signal output, sensitivity, and steepness. We further demonstrate its applicability on the development of tailored biosensors for primary screening of PET hydrolases and enzyme condition screening, demonstrating the potential of statistical modelling in optimising biosensors for tailored industrial and environmental applications.

Heterogeneity in Slow Synaptic Transmission Diversifies Purkinje Cell Timing.

The cerebellum plays an important role in diverse brain functions, ranging from motor learning to cognition. Recent studies have suggested that molecular and cellular heterogeneity within cerebellar lobules contributes to functional differences across the cerebellum. However, the specific relationship between molecular and cellular heterogeneity and diverse functional outputs of different regions of the cerebellum remains unclear. Here, we describe a previously unappreciated form of synaptic heterogeneity at parallel fiber synapses to Purkinje cells in the mouse cerebellum (both sexes). In contrast to uniform fast synaptic transmission, we found that the properties of slow synaptic transmission varied by up to threefold across different lobules of the mouse cerebellum, resulting in surprising heterogeneity. Depending on the location of a Purkinje cell, the time of peak of slow synaptic currents varied by hundreds of milliseconds. The duration and decay time of these currents also spanned hundreds of milliseconds, based on lobule. We found that, as a consequence of the heterogeneous synaptic dynamics, the same brief input stimulus was transformed into prolonged firing patterns over a range of timescales that depended on Purkinje cell location.

Rejuvenating liquid crystal elastomers for self-growth

To date, only one polymer can self-grow to an extended length beyond its original size at room temperature without external stimuli or energy input. This breakthrough paves the way for significant advancements in untethered autonomous soft robotics, eliminating the need for the energy input or external stimuli required by all existing soft robotics systems. However, only freshly prepared samples in an initial state can self-grow, while non-fresh ones cannot. The necessity of synthesizing from monomers for each use imposes significant limitations on practical applications. Here, we propose a strategy to rejuvenate non-fresh samples to their initial state for on-demand self-growth through the synergistic effects of solvents and dynamic covalent bonds during swelling. The solvent used for swelling physically transforms the non-fresh LCEs from the liquid crystal phase to the isotropic phase. Simultaneously, the introduction of the transesterification catalyst through swelling facilitates topological rearrangements through exchange reactions of dynamic covalent bonds. The rejuvenation process can also erase growth history, be repeated several times, and be regulated by selective swelling. This strategy provides a post-modulation method for the rejuvenation and reuse of self-growing LCEs, promising to offer high-performance materials for cutting-edge soft growing robotics.

A Mathematical Model for Postimplant Collagen Remodeling in an Autologous Engineered Pulmonary Arterial Conduit.

This study was undertaken to develop a mathematical model of the long-term in vivo remodeling processes in postimplanted pulmonary artery (PA) conduits. Experimental results from two extant ovine in vivo studies, wherein polyglycolic-acid (PGA)/poly-L-lactic acid tubular conduits were constructed, cell seeded, incubated for 4 weeks, and then implanted in mature sheep to obtain the remodeling data for up to two years. Explanted conduit analysis included detailed novel structural and mechanical studies. Results in both studies indicated that the in vivo conduits remained dimensionally stable up to 80 weeks, so that the conduits maintained a constant in vivo stress and deformation state. In contrast, continued remodeling of the constituent collagen fiber network as evidenced by an increase in effective tissue uniaxial tangent modulus, which then stabilized by one year postimplant. A mesostructural constitute model was then applied to extant planar biaxial mechanical data and revealed several interesting features, including an initial pronounced increase in effective collagen fiber modulus, paralleled by a simultaneous shift toward longer, more uniformly length-distributed collagen fibers. Thus, while the conduit remained dimensionally stable, its internal collagen fibrous structure and resultant mechanical behaviors underwent continued remodeling that stabilized by one year. A time-evolving structural mixture-based mathematical model specialized for this unique form of tissue remodeling was developed, with a focus on time-evolving collagen fiber stiffness as the driver for tissue-level remodeling. The remodeling model was able to fully reproduce (1) the observed tissue-level increases in stiffness by time-evolving simultaneous increases in collagen fiber modulus and lengths, (2) maintenance of the constant collagen fiber angular dispersion, and (3) stabilization of the remodeling processes at one year. Collagen fiber remodeling geometry was directly verified experimentally by histological analysis of the time-evolving collagen fiber crimp, which matches model predictions very closely. Interestingly, the remodeling model indicated that the basis for tissue homeostasis was maintenance of the collagen fiber ensemble stress for all orientations, and not individual collagen fiber stresses. Unlike other growth and remodeling models that traditionally treat changes in the external boundary conditions (e.g., changes in blood pressure) as the primary input stimuli, the driver herein is changes to the internal constituent collagen fiber themselves due to cellular mediated cross-linking.

Design of the non‐associative learning memristive circuit and the application to brightness adaptation

AbstractNon‐associative learning plays an important role in the biological and cognitive systems and is one of the important pathways for unsupervised learning in biology. The proposed non‐associative learning memristive circuit is composed of a synapse module, a memristive neuron module, and a response boosting module. The circuit can simulate the characteristics of habituation and sensitization, which attenuates the output response when receiving repeated input stimuli and augments the output response when receiving strong input stimuli, and the circuit also has a certain anti‐interference ability. Additionally, the designed circuit is suitable for adaptive replication experiments in visual systems and automatic exposure compensation systems, demonstrating the practicality of the circuit.

Highly stable, low voltage, flexible photodetector in UV-visible region from self-assembled organic small molecule

The fabrication of a high-performance, low voltage operated flexible organic photodetector (OPD) based on solution self-assembled crystalline thin film of a small molecule, viz, 6,13 bis(tri-isopropylsilylethynyl) pentacene (Tips pentacene) is reported. The OPD demonstrated excellent photodetection ability under range of visible light as well as under UV light, with low illumination intensity. Under 590 nm illumination (5.6 µW/cm2), the device showcased photo to dark current ratio (Iphoto/Idark), ca. 25, photoresponsivity (P) of 89 mA/W, detectivity of 1.81×1010 jones at 1 V bias and response times of 0.04/0.2 sec. Photocurrent modulation of the OPD has been realized by varying the channel width, incident wavelength, applied bias and the illumination intensity. The OPD exhibited excellent photodetection ability under different tensile bending, with roughly 55 % decline in Iphoto/Idark value and 30 % decline in R value under bending radius of 7.5 mm. The OPD could withstand thousands of repeated bending/folding cycles, and regain its initial photoresponse characteristics, if put at rest for 2 hours. The excellent device performances with its precise tuning via various input stimuli suggests that present OPD have great potential for future low cost, low energy and wearable optoelectronics.

A high-sensitivity and multi-response magnetic nanofiber-aerogel sensor with directionally aligned porous structure based on triple network for interactive human–machine interfaces

Flexible multimodal sensors with a porous structure have attracted tremendous attentions due to their low density, high specific surface area, a wide detection range and satisfied deformability. However, pores disorder and maldistribution issues of these multimodal sensors are major concerns during their sensing response, which often cause poor linearity and unstable sensing characteristics. Herein, a facile and rapid approach was proposed to fabricate a multifunctional magnetic sensor with directionally aligned porous structure based on triple network by combining electrospun nanofibers, bidisperse magnetic particles and sodium alginate/chitosan foam. The electrospun nanofibers were employed as three-dimensional skeletons for the crystallization and vertical growth of ice crystals. Benefiting from the remarkable structure orientation, the homogenized nanofiber-aerogel scaffold possesses excellent flexibility, superior deformation recoverability and biocompatibility. The assembled sensors not only exhibit a high sensitivity (0.40 T−1), rapid response/recovery time and long stability under magnetic stimuli, but also displays satisfied cross-sensitivity, quick response and reliable durability (8000 cycles) in response to external mechanical functions. Importantly, the respective input stimuli could be clearly discriminated via outputting the electrical signals of opposite or different trends. Furthermore, the sensor could be employed as wearable skins to monitor various physiological signals and realize information translation by Morse mode for individuals with disabilities. Additionally, a wearable gesture recognition system with assistance of the deep learning algorithm was testified, with high average recognition accuracy of 99 %. The simple fabrication process and prominent multifunctional characteristics of the proposed sensor endow it with an extensive application prospect in the field of interactive human–machine interfaces.

Calibration method of strapdown inertial measurement unit based on two-axis turntable

Abstract This paper analyzes the causes and effects of error in the Strapdown Micro-Electro-Mechanical System Inertial Measurement Unit (MEMS-IMU), utilizing a sensor calibration method based on a two-axis turntable. By considering deterministic errors such as sensor bias, scale factor, and non-orthogonality of the sensitive axis, an error model was established to assess their impact on measurement accuracy. An expression was provided to represent the relationship between sensor data collection and external input stimuli, storing the error parameters of the accelerometer and the gyroscope separately in parameter matrices. The error parameter matrix between input and output quantities was fitted using the least squares method, and error compensation was completed in conjunction with the error model. By comparing the original data with the error-compensated data, the calibration results were obtained. This improved the consistency of the measurements from the sensitive axes to the stimulus source, reduced the zero bias error of each sensitive axis, and enhanced the positioning accuracy of the Strapdown MEMS-IMU.

An attention mechanism network based on the winner-take-all

In the field of neurology, neurons compete for brain attention in winner-take-all (WTA) competitions. Inspired by this, we propose a new WTA-based attention network (called ANW), which can be extended to general neural networks. The ANW network simulates WTA behavior in biological neurons; that is, the winner neuron itself becomes excited and sends its own spike signal. Furthermore, the winner will output an inhibition signal, which will be input into the cell synapses of other neurons. The ANW network regards the update of neuron weights after backpropagation as a stimulus input to neurons. Combined with the WTA function, the neural network will continue to update until it reaches a stable state. In this way, the ANW network can find the most active neurons in the network and generate the winners among these neurons to improve the ability of the neural network to extract features. As the ANW is a small and universal network, it can be seamlessly inserted into any neural network architecture, with negligible overhead, and can be trained together with a basic CNN. We verified the neural network with ANW by using the ImageNet-100, CIFAR-100 and MS COCO detection datasets for the experiments. The experimental results show that, compared with those of the baseline network, the top-1 err gains of 0.63% and 3.85% can be achieved for ANW, with classical ResNet-50 as the backbone network, respectively, demonstrating that the method can improve the performance of the model in image classification and target detection with wide applicability. The proposed method provides an effective, and almost consumption-neutral solution, for performance improvement in the field of computer vision target detection.

In Silico Electrophysiological Investigation of Transient Receptor Potential Melastatin-4 Ion Channel Biophysics to Study Detrusor Overactivity.

Enhanced electrical activity in detrusor smooth muscle (DSM) cells is a key factor in detrusor overactivity which causes overactive bladder pathological disorders. Transient receptor potential melastatin-4 (TRPM4) channels, which are calcium-activated cation channels, play a role in regulating DSM electrical activities. These channels likely contribute to depolarizing the DSM cell membrane, leading to bladder overactivity. Our research focuses on understanding TRPM4 channel function in the DSM cells of mice, using computational modeling. We aimed to create a detailed computational model of the TRPM4 channel based on existing electrophysiological data. We employed a modified Hodgkin-Huxley model with an incorporated TRP-like current to simulate action potential firing in response to current and synaptic stimulus inputs. Validation against experimental data showed close agreement with our simulations. Our model is the first to analyze the TRPM4 channel's role in DSM electrical activity, potentially revealing insights into bladder overactivity. In conclusion, TRPM4 channels are pivotal in regulating human DSM function, and TRPM4 channel inhibitors could be promising targets for treating overactive bladder.

Speeded classification of visual events is sensitive to crossmodal intensity correspondence.

Crossmodal correspondences refer to systematic associations between stimulus attributes encountered in different sensory modalities. These correspondences can be probed in the speeded classification task where they tend to produce congruency effects. This study aimed to replicate and extend previous work conducted by Marks (1987, Experiment 3, Journal of Experimental Psychology: Human Perception and Performance, Vol. 13, No. 3, 384-394) which demonstrated a crossmodal correspondence between auditory and visual intensity attributes. Experiment 1 successfully replicates Marks' original finding that performance in a brightness classification task is affected by whether the loudness of a concurrently presented auditory distractor matches the brightness of the visual target. Furthermore, in line with the original study, we found that this effect was absent in a lightness classification task. In Experiment 2, we demonstrate that loudness-brightness correspondence is robust even when the exact stimulus input changes. This finding suggests that there is a context-dependent mapping between loudness and brightness levels, rather than an absolute mapping between any particular intensity levels. Finally, exploratory analysis using the diffusion model for conflict tasks indicated that evidence from the task-irrelevant modality generates a burst of weak, short-lived automatic activation that can bias decision-making in difficult tasks, but not in easy tasks. Our results provide further evidence for the existence of a flexible crossmodal correspondence between brightness and loudness, which might be helpful in determining one's distance to a stimulus source during the early stages of multisensory integration. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Pineal gland volume loss in females with multiple sclerosis.

Multiple sclerosis has a complex pathophysiology, and numerous risk factors can contribute to its development, like exposure to sunlight that is associated with serum levels of melatonin. The aim of this study was to determine whether the volume of the pineal gland, assessed by magnetic resonance imaging (MRI), correlated with the presence of multiple sclerosis. This retrospective study included a total of 394 patients. Subjects were divided into two groups: the first group consisted of 188 patients with a definite diagnosis of multiple sclerosis (based on revised McDonald criteria) and the second group consisted of 206 healthy controls. To examine the influence of age on pineal gland volume, we stratified the whole sample into three age groups: first involved patients under 20 years, second patients between 20 and 40 years, and third group included patients over 40 years. The maximum length (L) and height (H) of the pineal gland were measured on the T1-weighted sagittal images, and the width (W) was measured on the T2-weighted coronal or axial images. The volume of the gland was calculated as an approximation to an ellipse, according to the formula V = (L × H × W)/2. Pineal gland volume of female multiple sclerosis (MS) patients (N = 129) was significantly lower than in healthy females (N = 123) (p = 0.013; p < 0.05), unlike in males where there is not such difference. Also, pineal gland volume is not age-dependent, and the observed smaller pineal gland in MS patients can reliably be attributed to the disease itself. Additionally, large pineal gland size, especially over 62.83 mm3 when compared to pineal gland volume below 31.85 mm3 is associated with more than double reduced risk of multiple sclerosis (OR 0.42; p = 0.003). Our results suggest that women with multiple sclerosis have smaller pineal glands that can theoretically be explained by a lack of input stimuli and the resultant decrease in gland volume. Additionally, the risk of multiple sclerosis is reduced in larger pineal gland volumes.

Positive Competitive Networks for Sparse Reconstruction.

We propose and analyze a continuous-time firing-rate neural network, the positive firing-rate competitive network (PFCN), to tackle sparse reconstruction problems with nonnegativity constraints. These problems, which involve approximating a given input stimulus from a dictionary using a set of sparse (active) neurons, play a key role in a wide range of domains, including, for example, neuroscience, signal processing, and machine learning. First, by leveraging the theory of proximal operators, we relate the equilibria of a family of continuous-time firing-rate neural networks to the optimal solutions of sparse reconstruction problems. Then we prove that the PFCN is a positive system and give rigorous conditions for the convergence to the equilibrium. Specifically, we show that the convergence depends only on a property of the dictionary and is linear-exponential in the sense that initially, the convergence rate is at worst linear and then, after a transient, becomes exponential. We also prove a number of technical results to assess the contractivity properties of the neural dynamics of interest. Our analysis leverages contraction theory to characterize the behavior of a family of firing-rate competitive networks for sparse reconstruction with and without nonnegativity constraints. Finally, we validate the effectiveness of our approach using a numerical example.