CT University

In recent times, materials with multifunctional properties have garnered significant interest. A single-phase multiferroic (PrFeO3)0.24-(PbZr0.52Ti0.48O3)0.76 was successfully synthesized using the conventional solid-state reaction method. X-ray diffraction confirmed the formation of a single phase with a tetragonal P4mm crystal structure. Field emission scanning electron microscopy (FESEM) revealed a dense microstructure with uniformly distributed grains. Magnetic field dependent ferroelectric measurements showed the change in the polarization with respect to the applied magnetic field. Magnetic hysteresis studies indicate canted antiferromagnetism with field-activated weak ferromagnetic interactions. Dielectric and AC conductivity investigations identified Maxwell-Wagner interfacial polarization as the dominant mechanism. At 1.2 T and 100 Hz, magneto-dielectric measurements demonstrated that the peak magnetoresistance (MDR) is 47.6%, while magnetoelectric coupling studies revealed a peak ME coefficient (alpha = 0.5676 mV/cm Oe). These results show that the designed composition exhibits coexistence of ferroelectric and magnetic orders along with clear magnetoelectric coupling, making it a promising candidate for multifunctional device applications.

Circadian rhythms orchestrate a wide array of physiological processes that critically influence drug absorption, distribution, metabolism, and excretion. Misalignment between drug administration and biological timing can compromise therapeutic efficacy, increase adverse effects, and lead to therapeutic failure in diseases with established diurnal variation. Chronotherapy seeks to align pharmacologic interventions with these endogenous rhythms; however, traditional drug delivery systems lack the ability to adapt dynamically to circadian cues. Recent advances in nanotechnology have enabled the development of spatiotemporal nanocarrier systems capable of both anatomical targeting and time-synchronized drug release. These smart nanocarriers leverage internal stimuli (e.g., pH, redox state, enzyme activity) or external triggers (e.g., light, temperature, magnetic fields) to optimize therapeutic action at specific circadian windows. This review provides a comprehensive analysis of circadian biology relevant to drug delivery, current clinical applications of chronotherapy, and emerging preclinical evidence for chrono-responsive nanosystems, enabling technologies such as wearable biosensors, organ-on-chip platforms, and artificial intelligence. We also discuss the translational barriers and future directions in realizing patient-specific, feedback-controlled chrono-nanomedicine. The convergence of circadian pharmacology and intelligent nanocarrier design represents a transformative approach toward achieving precision medicine that targets not only the right site but also the right time.

Crop diseases significantly impact global food security and agricultural productivity. Accurate and timely detection of crop diseases is vital for effective interventions. However, traditional diagnostic methods often fall short due to subjectivity and reliance on human expertise. Addressing this challenge, this study proposes a hybrid Deep Learning (DL) approach for crop disease classification. A novel Dirichlet Weighted Ensemble (DWE) model was developed, assigning adaptive weights to individual models using F1-score-driven Dirichlet priors. The ensemble included Convolutional Neural Networks (CNNs) optimized using Optuna and validated with 5-fold cross-validation. The model was trained on a curated dataset compiled from five public data sources, comprising 56,044 images from 36 disease classes across 16 crop species, integrating real-field and lab-controlled environments. The best-performing weighted ensemble-comprising InceptionV3, Xception, and the mobile variant of the Neural Architecture Search Network (NASNetMobile)-achieved an accuracy, weighted F1-score, precision, and recall of 99.52 %, and a Receiver Operating Characteristic Area Under Curve (ROC AUC) of 1.000. External validation on unseen datasets confirmed the model's generalizability. An ablation study established its effectiveness over conventional ensemble methods. Deployment feasibility was evaluated through inference time, model size, and memory usage, indicating suitability for web, mobile, and Internet of Things (IoT) platforms. Furthermore, SHapley Additive exPlanations (SHAP) were applied to a stacking meta-ensemble using Categorical Boosting (CatBoost) and qualitatively validated by a domain expert. This hybrid approach bridges advanced Artificial Intelligence (AI) techniques with agricultural engineering, enabling timely, data-driven interventions for sustainable crop health management.

Cytokine storm syndromes have become a much-invoked concept to describe severe immunopathology in infectious, inflammatory, and iatrogenic diseases, but the concept is poorly defined and often mechanistically imprecise. High levels of systemic cytokines have often been viewed as indicators of immune dysfunction, and based on this notion, therapeutic interventions focused on general cytokine inhibition are proposed. Nevertheless, a growing number of clinical and experimental data dispute the notion that hypercytokinemia is necessarily pathological. The present paper reconsiders cytokine storm syndromes in the light of an evolutionary, systems-immunology model, and suggests that most cytokine amplification conditions are context-specific host defence programmes, however, not uniform maladaptations of immune regulation. Its major objective is to redefine the cytokine storm syndromes, which are no longer understood as a single, uniformly pathological situation, but as a spectrum of context-specific immune programmes. The paper reviews disease-specific phenotypes of cytokine storms in viral infections, bacterial sepsis, autoimmune diseases, and immune-based therapies, noting considerable variability in cytokine composition, kinetics of production, cytokine-producing cell frequency, and localization in tissues. It also explains how the temporal dynamics, host-specific conditions, and failure of counter-regulatory mechanisms define the difference between cytokine amplification as an adaptation or as a pathological condition. In addition, it considers the clinical consequences of such a refined framework and argues that exploring precision strategies need to consider immune context, disease stage, and signalling thresholds, as opposed to a general use of blocking cytokines. In conclusion, this review attempts to draw a line between adaptive cytokine amplification and maladaptive immunopathology by integrating the insights gained in evolutionary biology and systems immunology.

The common phenomenon of urban traffic congestion leads to the loss of time as well as pollution, which consequently affects the environment. We introduce here a new approach, which is called Discrete Structure-based Traffic Optimization (DSBTO), which uses network algorithms and high resolution traffic information to reduce congestion. Road network is modeled as a discrete graph, and optimization strategies are used to optimize traffic flow across the graph. In order to verify our method's effectiveness, we use the Glasgow High-Resolution Traffic Flow Dataset, which contains fine-grained traffic measurements over several years for calibration and validation under real traffic conditions. Our approach employs DSBTO to identify significant traffic bottlenecks and identifies optimal flow modifications or control mechanisms that reduce congestion times without compromising the capacity of the network. During our method testing, we discovered that average travel times were reduced by about 15%, and network throughput increased compared to normal traffic. The findings demonstrate the potential of using granular transportation data as well as discrete optimization tools to have a favorable effect on urban traffic management. Theoretically, DSBTO might translate to improved traffic flow and reduced surge and stop congestion thus resulting to reduced emissions from vehicles in cities. The suggested system can easily be integrated into the existing traffic control infrastructure to offer data-based support process with the purpose of reducing congestion and increasing mobility.