Carbonaceous Materials Coated Carbon Fibre Reinforced Polymer Matrix Composites

Carbon fibre reinforced polymer composites have high mechanical properties that make them exemplary engineered materials to carry loads and stresses. Coupling fibre and matrix together require good understanding of not only fibre morphology but also matrix rheology. One way of having a strongly coupled fibre and matrix interface is to size the reinforcing fibres by means of micro- or nanocarbon materials coating on the fibre surface. Common coating materials used are carbon nanotubes and nanofibres and graphene, and more recently carbon black (colloidal particles of virtually pure elemental carbon) and graphite. There are several chemical, thermal, and electrochemical processes that are used for coating the carbonous materials onto a carbon fibre surface. Sizing of fibres provides higher interfacial adhesion between fibre and matrix and allows better fibre wetting by the surrounded matrix material. This review paper goes over numerous techniques that are used for engineering the interface between both fibre and matrix systems, which is eventually the key to better mechanical properties of the composite systems

Probabilistic Computation in Human Perception under Variability in Encoding Precision

A key function of the brain is to interpret noisy sensory information. To do so optimally, observers must, in many tasks, take into account knowledge of the precision with which stimuli are encoded. In an orientation change detection task, we find that encoding precision does not only depend on an experimentally controlled reliability parameter (shape), but also exhibits additional variability. In spite of variability in precision, human subjects seem to take into account precision near-optimally on a trial-to-trial and item-to-item basis. Our results offer a new conceptualization of the encoding of sensory information and highlight the brain’s remarkable ability to incorporate knowledge of uncertainty during complex perceptual decision-making

Stochastically Gating Ion Channels Enable Patterned Spike Firing through Activity-Dependent Modulation of Spike Probability

The transformation of synaptic input into patterns of spike output is a fundamental operation that is determined by the particular complement of ion channels that a neuron expresses. Although it is well established that individual ion channel proteins make stochastic transitions between conducting and non-conducting states, most models of synaptic integration are deterministic, and relatively little is known about the functional consequences of interactions between stochastically gating ion channels. Here, we show that a model of stellate neurons from layer II of the medial entorhinal cortex implemented with either stochastic or deterministically gating ion channels can reproduce the resting membrane properties of stellate neurons, but only the stochastic version of the model can fully account for perithreshold membrane potential fluctuations and clustered patterns of spike output that are recorded from stellate neurons during depolarized states. We demonstrate that the stochastic model implements an example of a general mechanism for patterning of neuronal output through activity-dependent changes in the probability of spike firing. Unlike deterministic mechanisms that generate spike patterns through slow changes in the state of model parameters, this general stochastic mechanism does not require retention of information beyond the duration of a single spike and its associated afterhyperpolarization. Instead, clustered patterns of spikes emerge in the stochastic model of stellate neurons as a result of a transient increase in firing probability driven by activation of HCN channels during recovery from the spike afterhyperpolarization. Using this model, we infer conditions in which stochastic ion channel gating may influence firing patterns in vivo and predict consequences of modifications of HCN channel function for in vivo firing patterns

Statistical Coding and Decoding of Heartbeat Intervals

The heart integrates neuroregulatory messages into specific bands of frequency, such that the overall amplitude spectrum of the cardiac output reflects the variations of the autonomic nervous system. This modulatory mechanism seems to be well adjusted to the unpredictability of the cardiac demand, maintaining a proper cardiac regulation. A longstanding theory holds that biological organisms facing an ever-changing environment are likely to evolve adaptive mechanisms to extract essential features in order to adjust their behavior. The key question, however, has been to understand how the neural circuitry self-organizes these feature detectors to select behaviorally relevant information. Previous studies in computational perception suggest that a neural population enhances information that is important for survival by minimizing the statistical redundancy of the stimuli. Herein we investigate whether the cardiac system makes use of a redundancy reduction strategy to regulate the cardiac rhythm. Based on a network of neural filters optimized to code heartbeat intervals, we learn a population code that maximizes the information across the neural ensemble. The emerging population code displays filter tuning proprieties whose characteristics explain diverse aspects of the autonomic cardiac regulation, such as the compromise between fast and slow cardiac responses. We show that the filters yield responses that are quantitatively similar to observed heart rate responses during direct sympathetic or parasympathetic nerve stimulation. Our findings suggest that the heart decodes autonomic stimuli according to information theory principles analogous to how perceptual cues are encoded by sensory systems

Postpartum psychiatric disorders

Pregnancy is a complex and vulnerable period that presents a number of challenges to women, including the development of postpartum psychiatric disorders (PPDs). These disorders can include postpartum depression and anxiety, which are relatively common, and the rare but more severe postpartum psychosis. In addition, other PPDs can include obsessive–compulsive disorder, post-traumatic stress disorder and eating disorders. The aetiology of PPDs is a complex interaction of psychological, social and biological factors, in addition to genetic and environmental factors. The goals of treating postpartum mental illness are reducing maternal symptoms and supporting maternal–child and family functioning. Women and their families should receive psychoeducation about the illness, including evidence-based discussions about the risks and benefits of each treatment option. Developing effective strategies in global settings that allow the delivery of targeted therapies to women with different clinical phenotypes and severities of PPDs is essential

Human Decision Making Based on Variations in Internal Noise: An EEG Study

Perceptual decision making is prone to errors, especially near threshold. Physiological, behavioural and modeling studies suggest this is due to the intrinsic or ‘internal’ noise in neural systems, which derives from a mixture of bottom-up and top-down sources. We show here that internal noise can form the basis of perceptual decision making when the external signal lacks the required information for the decision. We recorded electroencephalographic (EEG) activity in listeners attempting to discriminate between identical tones. Since the acoustic signal was constant, bottom-up and top-down influences were under experimental control. We found that early cortical responses to the identical stimuli varied in global field power and topography according to the perceptual decision made, and activity preceding stimulus presentation could predict both later activity and behavioural decision. Our results suggest that activity variations induced by internal noise of both sensory and cognitive origin are sufficient to drive discrimination judgments

HUBUNGAN KONFORMITAS TEMAN SEBAYA TERHADAP PERILAKU MEROKOK REMAJA DI SMA KESATRIAN 1 SEMARANG

Latar Belakang: Kecenderungan terjadinya peningkatan jumlah perokok usia remaja menjadi keprihatinan tersendiri. Banyak remaja berpikir bahwa merokok tidak akan memberikan dampak negatif pada tubuh mereka, padahal faktanya hampir 90% perokok remaja mulai merasakan dampak negatif jangka pendek dari rokok. Salah satu faktor yang dapat menyebabkan seseorang remaja merokok adalah faktor teman sebaya. Banyak remaja yang tidak bisa menolak ajakan dari teman sebayanya untuk melakukan perilaku merokok. Upaya pemerintah dalam mengatasi permasalahan merokok yaitu dengan mewajibkan produsen rokok mencantumkan peringatan tentang bahaya rokok, sehingga dapat mendorong keinginan perokok untuk berhenti merokok serta menekan jumlah pertumbuhan angka perokok di IndonesiaTujuan: Untuk mengetahui hubungan konformitas teman sebaya terhadap perilaku merokok pada remaja di SMA Kesatrian 1 Semarang.Metode: Desain penelitian kuantitatif dengan metode cross-sectional. Sampel pada penelitian ini yaitu pelajar SMA Kesatrian 1 Semarang yang merupakan perokok aktif berjumlah 46 responden dengan teknik sampel yaitu total sampling. Data hasil penelitian dianalisis menggunakan uji korelasi pearson.Hasil: Setelah dilakukan analisis data, pada penelitian ini nilai Sig. (2-tailed) hubungan konformitas teman sebaya dengan perilaku merokok yaitu p=0.000 dan p lebih kecil dari 0,05 yang berarti Ha diterima dengan nilai koefisien korelasi 0,529. Maka kesimpulannya adalah adanya hubungan yang sedang antara variabel konformitas teman sebaya terhadap perilaku merokok remaja di SMA Kesatrian 1 Semarang.Simpulan: Terdapat hubungan konformitas teman sebaya terhadap perilaku merokok remaja di SMA Kesatrian 1 Semarang. Edukasi bahaya merokok pada remaja perlu diberikan untuk mencegah peningkatan jumlah perokok usia remaja saat ini