Abstract
The revolutionary 5G cellular systems represent a breakthrough in the communication network design to provide a single platform for enabling enhanced broadband communications, virtual ...reality, autonomous driving, and the internet of everything. However, the ongoing massive deployment of 5G networks has unveiled inherent limitations that have stimulated the demand for innovative technologies with a vision toward 6G communications. Terahertz (0.1-10 THz) technology has been identified as a critical enabler for 6G communications with the prospect of massive capacity and connectivity. Nonetheless, existing terahertz on-chip communication devices suffer from crosstalk, scattering losses, limited data speed, and insufficient tunability. Here, we demonstrate a new class of phototunable, on-chip topological terahertz devices consisting of a broadband single-channel 160 Gbit/s communication link and a silicon Valley Photonic Crystal based demultiplexer. The optically controllable demultiplexing of two different carriers modulated signals without crosstalk is enabled by the topological protection and a critically coupled high-quality (
Q
) cavity. As a proof of concept, we demultiplexed high spectral efficiency 40 Gbit/s signals and demonstrated real-time streaming of uncompressed high-definition (HD) video (1.5 Gbit/s) using the topological photonic chip. Phototunable silicon topological photonics will augment complementary metal oxide semiconductor (CMOS) compatible terahertz technologies, vital for accelerating the development of futuristic 6G and 7G communication era driving the real-time terabits per second wireless connectivity for network sensing, holographic communication, and cognitive internet of everything.
There is a widespread repeated exposure of the population to the pesticides and heavy metals of occupational and environmental origin. Such population is forced to undergo continuous stress imposed ...by combined exposure of the heavy metals and different classes of the pesticides used in agricultural as well as health practices. The existing reports from several workers have indicated that heavy metals and pesticides in combination may lead more severe impact on the human health when compared to their individual effects. Such a combination of pesticides and heavy metals may also change or influence the detection of exposure. Several studies in past have shown the synergistic toxic effects of heavy metals and pesticides. Such evaluations have revealed the synergistic interactions of various heavy metals and pesticides in animals as well as humans. The aim of the present article is to provide a synthesis of existing knowledge on the synergistic effects of heavy metal and pesticides in living systems. The information included in this article may be useful for different environment protection agencies and policy makers to consider the combined effects of heavy metals and pesticides on humans while designing strategies toward environmental protection and safety regulations about human health.
Rapid scaling of semiconductor devices has led to an increase in the number of processor cores and integrated functionalities onto a single chip to support the growing demands of high‐speed and ...large‐volume consumer electronics. To meet this burgeoning demand, an improved interconnect capacity in terms of bandwidth density and active tunability is required for enhanced throughput and energy efficiency. Low‐loss terahertz silicon interconnects with larger bandwidth offer a solution for the existing inter‐/intrachip bandwidth density and energy‐efficiency bottleneck. Here, a low‐loss terahertz topological interconnect–cavity system is presented that can actively route signals through sharp bends, by critically coupling to a topological cavity with an ultrahigh‐quality (Q) factor of 0.2 × 106. The topologically protected large Q factor cavity enables energy‐efficient optical control showing 60 dB modulation. Dynamic control is further demonstrated of the critical coupling between the topological interconnect–cavity for on‐chip active tailoring of the cavity resonance linewidth, frequency, and modulation through complete suppression of the back reflection. The silicon topological cavity is complementary metal–oxide–semiconductor (CMOS)‐compatible and highly desirable for hybrid electronic–photonic technologies for sixth (6G) generation terahertz communication devices. Ultrahigh‐Q cavity also paves the path for designing ultrasensitive topological sensors, terahertz topological integrated circuits, and nonlinear topological photonic devices.
A low‐loss terahertz (THz) silicon topological interconnect–cavity system that can actively route signals through sharp bends by critically coupling to a topological cavity with an ultrahigh‐quality (Q) factor of 0.2 × 106 is shown. On‐chip active tailoring is demonstrated of the cavity resonance linewidth, frequency, and modulation at extremely low power through complete suppression of the back reflection, essential for sixth generation (6G) communication with THz topological integrated photonic circuits.
•A new method for detection of focal EEG signals has been proposed.•The proposed method is based on the flexible analytic wavelet transform.•The developed method has been compared with the other ...existing methods.•The obtained classification accuracy by proposed method is 94.41%.
Epilepsy is a neurological disease which is difficult to diagnose accurately. An authentic detection of focal epilepsy will help the clinicians to provide proper treatment for the patients. Generally, focal electroencephalogram (EEG) signals are used to diagnose the epilepsy. In this paper, we have developed an automated system for the detection of focal EEG signals using differencing and flexible analytic wavelet transform (FAWT) methods. The differenced EEG signals are subjected to 15 levels of FAWT. Various entropies namely cross correntropy, Stein’s unbiased risk estimate (SURE) entropy, and log energy entropy are extracted from the reconstructed original signal and 16 sub-band signals. The statistically significant features are obtained from Kruskal–Wallis test based on (p < 0.05). K-nearest neighbor (KNN) and least squares support vector machine (LS-SVM) classifiers with different distances and kernels respectively are used for automated diagnosis. In the proposed methodology, we have achieved classification accuracy of 94.41% in detecting focal EEG signals using LS-SVM classifier with ten-fold cross validation strategy.
Organic long‐persistent luminescence (OLPL) is one of the most promising methods for long‐lived‐emission applications. However, present room‐temperature OLPL emitters are mainly based on a ...bimolecular exciplex system which usually needs an expensive small molecule such as 2,8‐bis(diphenyl‐phosphoryl)dibenzob,dthiophene (PPT) as the acceptor. In this study, a new thermally activated delayed fluorescence (TADF) compound, 3‐(4‐(9H‐carbazol‐9‐yl)phenyl)acenaphtho1,2‐bpyrazine‐8,9‐dicarbonitrile (CzPhAP), is designed, which also shows OLPL in many well‐known hosts such as PPT, 2,2′,2″‐(1,3,5‐benzinetriyl)‐tris(1‐phenyl‐1‐H‐benzimidazole) (TPBi), and poly(methyl methacrylate) (PMMA), without any exciplex formation, and its OLPL duration reaches more than 1 h at room temperature. Combining the low cost of PMMA manufacture and flexible designs of TADF molecules, pure organic, large‐scale, color tunable, and low‐cost room‐temperature OLPL applications become possible. Moreover, it is found that the onset of the 77 K afterglow spectra from a TADF‐emitter‐doped film is not necessarily reliable for determining the lowest triplet state energy level. This is because in some TADF‐emitter‐doped films, optical excitation can generate charges (electron and holes) that can later recombine to form singlet excitons during the phosphorescence spectrum measurement. The spectrum taken in the phosphorescence time window at low temperature may consequently consist of both singlet and triplet emission.
Organic long‐persistent luminescence from an organic thermally activated delayed fluorescence (TADF) compound is demonstrated. It is also shown that this phenomenon can occur in a number of chemically distinct host matrices. Furthermore, this work reveals the important role that photoinduced charges play in TADF‐emitter‐doped films, which has a significant impact on their photophysical properties.
Topological slow light exhibits potential to achieve stopped light by virtue of its widely known robust and non-reciprocal behaviours. Conventional approach for achieving topological slow light often ...involves flat-band engineering without disentangling the underlying physical mechanism. Here, we unveil the presence of counter-propagating waves within valley kink states as the distinctive hallmark of the slow light topological photonic waveguides. These counter-propagating waves, supported by topological vortices along glide-symmetric interface, provide significant flexibility for controlling the slowness of light. We tune the group velocity of light by changing the spatial separation between vortices adjacent to the glide-symmetric interface. We also dynamically control the group delay by introducing a non-Hermitian defect using photoexcitation to adjust the relative strength of the counter-propagating waves. This study introduces active slow light topological photonic device on a silicon chip, opening new horizons for topological photon transport through defects, topological light-matter interactions, nonlinear topological photonics, and topological quantum photonics.
Invasive plant species are considered one of the significant drivers of habitat loss, leading to biodiversity loss. They have also been observed to alter the local ecology, resulting in a decline of ...native flora. The management of invasive species is widely recognised as one of the most severe challenges to biodiversity conservation. The International Union for Conservation of Nature (IUCN) considers Lantana camara, as one of the ten worst weeds. Over time, native and indigenous species may evolve to co-exist or compete with invasive species, reducing invader fitness. It is observed that species competition fluctuates throughout environmental gradients, life phases, and abundances. Hence, competition outcome is very context-dependent. To address this challenge, we conducted a comprehensive study in three phases: we identified native species coexisting with Lantana in their natural habitats in the Doon Valley (Phase I) and documented the phenotypic traits of selected coexisting species using the Landmark BBCH (Biologische Bun-desantalt, Bundessortenamt und Chemische Industrie) scale, revealing the phenological growth patterns of selected co-existing species (Phase II). This was followed by conducting pot (Phase IIIa) and field (Phase IIIb) experiments to study the interactions between them. Notably, Justicia adhatoda, Broussonetia papyrifera, Pongamia pinnata, Urtica dioica and Bauhinia variegata demonstrated promising results in both pot and field conditions. Furthermore, after the mechanical removal of Lantana and prior to the plantation in the field experiments, four native grass species were introduced using the seed ball method. Among these, Pennisetum pedicellatum and Sorghum halpense exhibited prompt regeneration and effectively colonised the field, densely covering the cleared area. The study provides a comprehensive management plan for the restoration of Lantana affected areas through competition using native species. This study utilizes phenological assessment for native plant selection using reclamation from native grasses and proposes a management plan for combating invasive Lantana.
Cervical cancer is the leading cause of death in women, mainly in developing countries, including India. Recent advancements in technologies could allow for more rapid, cost-effective, and sensitive ...screening and treatment measures for cervical cancer. To this end, deep learning-based methods have received importance for classifying cervical cancer patients into different risk groups. Furthermore, deep learning models are now available to study the progression and treatment of cancerous cervical conditions. Undoubtedly, deep learning methods can enhance our knowledge toward a better understanding of cervical cancer progression. However, it is essential to thoroughly validate the deep learning-based models before they can be implicated in everyday clinical practice. This work reviews recent development in deep learning approaches employed in cervical cancer diagnosis and prognosis. Further, we provide an overview of recent methods and databases leveraging these new approaches for cervical cancer risk prediction and patient outcomes. Finally, we conclude the state-of-the-art approaches for future research opportunities in this domain.
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