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Task-switching is a fundamental cognitive ability that allows animals to update their knowledge of current rules or contexts. Detecting discrepancies between predicted and observed events is essential for this process. However, little is known about how the brain computes cognitive prediction-errors and whether neural prediction-error signals are causally related to task-switching behaviours. Here we trained mice to use a prediction-error to switch, in a single trial, between responding to the same stimuli using two distinct rules. Optogenetic silencing and un-silencing, together with widefield and two-photon calcium imaging revealed that the anterior cingulate cortex (ACC) was specifically required for this rapid task-switching, but only when it exhibited neural prediction-error signals. These prediction-error signals were projection-target dependent and were larger preceding successful behavioural transitions. An all-optical approach revealed a disinhibitory interneuron circuit required for successful prediction-error computation. These results reveal a circuit mechanism for computing prediction-errors and transitioning between distinct cognitive states. Our brains constantly detect discrepancies between predicted and observed events to update our knowledge of the world. Here the authors identify a neural signal driving this cognitive update and an inhibitory circuit involved in computing the prediction mismatch.

Smartphone-based activity recognition (SP-AR) recognizes users’ activities using the embedded accelerometer sensor. Only a small number of previous works can be classified as online systems, i.e., the whole process (pre-processing, feature extraction, and classification) is performed on the device. Most of these online systems use either a high sampling rate (SR) or long data-window (DW) to achieve high accuracy, resulting in short battery life or delayed system response, respectively. This paper introduces a real-time/online SP-AR system that solves this problem. Exploratory data analysis was performed on acceleration signals of 6 activities, collected from 30 subjects, to show that these signals are generated by an autoregressive (AR) process, and an accurate AR-model in this case can be built using a low SR (20 Hz) and a small DW (3 s). The high within class variance resulting from placing the phone at different positions was reduced using kernel discriminant analysis to achieve position-independent recognition. Neural networks were used as classifiers. Unlike previous works, true subject-independent evaluation was performed, where 10 new subjects evaluated the system at their homes for 1 week. The results show that our features outperformed three commonly used features by 40% in terms of accuracy for the given SR and DW.

The purpose of this work is to present the quantum Hermite–Hadamard inequality through the Green function approach. While doing this, we deduce some novel quantum identities. Using these identities, we establish some new inequalities in this direction. We contemplate the possibility of expanding the method, outlined herein, to recast the proofs of some known inequalities in the literature.

How learning enhances neural representations for behaviorally relevant stimuli via activity changes of cortical cell types remains unclear. We simultaneously imaged responses of pyramidal cells (PYR) along with parvalbumin (PV), somatostatin (SOM), and vasoactive intestinal peptide (VIP) inhibitory interneurons in primary visual cortex while mice learned to discriminate visual patterns. Learning increased selectivity for task-relevant stimuli of PYR, PV and SOM subsets but not VIP cells. Strikingly, PV neurons became as selective as PYR cells, and their functional interactions reorganized, leading to the emergence of stimulus-selective PYR–PV ensembles. Conversely, SOM activity became strongly decorrelated from the network, and PYR–SOM coupling before learning predicted selectivity increases in individual PYR cells. Thus, learning differentially shapes the activity and interactions of multiple cell classes: while SOM inhibition may gate selectivity changes, PV interneurons become recruited into stimulus-specific ensembles and provide more selective inhibition as the network becomes better at discriminating behaviorally relevant stimuli.

Due to their unique layered microstructure, the presence of various functional groups at the surface, earth abundance, and attractive electrical, optical, and thermal properties, MXenes are considered promising candidates for the solution of energy- and environmental-related problems. It is seen that the energy conversion and storage capacity of MXenes can be enhanced by changing the material dimensions, chemical composition, structure, and surface chemistry. Hence, it is also essential to understand how one can easily improve the structure–property relationship from an applied point of view. In the current review, we reviewed the fabrication, properties, and potential applications of MXenes. In addition, various properties of MXenes such as structural, optical, electrical, thermal, chemical, and mechanical have been discussed. Furthermore, the potential applications of MXenes in the areas of photocatalysis, electrocatalysis, nitrogen fixation, gas sensing, cancer therapy, and supercapacitors have also been outlooked. Based on the reported works, it could easily be observed that the properties and applications of MXenes can be further enhanced by applying various modification and functionalization approaches. This review also emphasizes the recent developments and future perspectives of MXenes-based composite materials, which will greatly help scientists working in the fields of academia and material science.

In this paper, we present a Hermite-Hadamard-Fejér inequality for conformable fractional integrals by using symmetric preinvex functions. We also establish an identity associated with the right hand side of Hermite-Hadamard inequality for preinvex functions; then by using this identity and preinvexity of functions and some well-known inequalities, we find several new Hermite-Hadamard type inequalities for conformal fractional integrals.

In this paper, we present a new bound for the Jensen gap with the help of a Green function. Using the bound, we deduce a converse of the Hölder inequality as well. Finally, we present some applications of the main result in information theory.

The widespread adoption of green energy resources worldwide, such as photovoltaic (PV) systems to generate green and renewable power, has prompted safety and reliability concerns. One of these concerns is fault diagnostics, which is needed to manage the reliability and output of PV systems. Severe PV faults make detecting faults challenging because of drastic weather circumstances. This research article presents a novel deep stack-based ensemble learning (DSEL) approach for diagnosing PV array faults. The DSEL approach compromises three deep-learning models, namely, deep neural network, long short-term memory, and Bi-directional long short-term memory, as base learners for diagnosing PV faults. To better analyze PV arrays, we use multinomial logistic regression as a meta-learner to combine the predictions of base learners. This study considers open circuits, short circuits, partial shading, bridge, degradation faults, and incorporation of the MPPT algorithm. The DSEL algorithm offers reliable, precise, and accurate PV-fault diagnostics for noiseless and noisy data. The proposed DSEL approach is quantitatively examined and compared to eight prior machine-learning and deep-learning-based PV-fault classification methodologies by using a simulated dataset. The findings show that the proposed approach outperforms other techniques, achieving 98.62% accuracy for fault detection with noiseless data and 94.87% accuracy with noisy data. The study revealed that the DSEL algorithm retains a strong generalization potential for detecting PV faults while enhancing prediction accuracy. Hence, the proposed DSEL algorithm detects and categorizes PV array faults more efficiently, reliably, and accurately.

Salinity stress hinders the growth potential and productivity of crop plants by influencing photosynthesis, disturbing the osmotic and ionic concentrations, producing excessive oxidants and radicals, regulating endogenous phytohormonal functions, counteracting essential metabolic pathways, and manipulating the patterns of gene expression. In response, plants adopt counter mechanistic cascades of physio-biochemical and molecular signaling to overcome salinity stress; however, continued exposure can overwhelm the defense system, resulting in cell death and the collapse of essential apparatuses. Improving plant vigor and defense responses can thus increase plant stress tolerance and productivity. Alternatively, the quasi-essential element silicon (Si)—the second-most abundant element in the Earth’s crust—is utilized by plants and applied exogenously to combat salinity stress and improve plant growth by enhancing physiological, metabolomic, and molecular responses. In the present review, we elucidate the potential role of Si in ameliorating salinity stress in crops and the possible mechanisms underlying Si-associated stress tolerance in plants. This review also underlines the need for future research to evaluate the role of Si in salinity stress in plants and the identification of gaps in the understanding of this process as a whole at a broader field level.

In this paper, we present several new and generalized Hermite-Hadamard type inequalities for s-convex as well as s-concave functions via classical and Riemann-Liouville fractional integrals. As applications, we provide new error estimations for the trapezoidal formula.