Important Applications Research Articles

Synthesis and DFT investigations of novel 13-(pyrimidin-2-yl)-6H,12H-6,12-epiminodibenzo[b,f][1,5]dioxocine derivatives

Heterocyclic compounds containing nitrogen and oxygen atoms are of great importance and wide applications in the fields of biomedicine and functional materials. In this paper, novel 13-aryl-6H,12H-6,12-epiminodibenzo[b,f][1,5]dioxocines generated from cascade reactions between arylamines and 2-hydroxybenzaldehyde have been explored through both experiments and theoretical calculations. Experimental investigations showed that pyrimidin-2-amine could react with 2-hydroxybenzaldehyde in acetonitrile at 80 °C to afford the desired 13-(pyrimidin-2-yl)-6H,12H-6,12-epiminodibenzo[b,f][1,5]dioxocines with 50 mol % of p-toluenesulfonic acid as catalyst, but aniline reacted with 2-hydroxybenzaldehyde to generate aldimine instead of the target bridged heterocylic compound. Density functional theory (DFT) computations were conducted to investigate the reaction mechanisms of 2-hydroxybenzaldehyde reacting with pyrimidin-2-amine and aniline, respectively. The experimental findings can be reasonably explained by theoretical calculations. Both the experimental explorations and suggested mechanisms indicate that the reactivity of the model reaction is sensitive to the electron density on the amino group of arylamine and the aldehyde group of 2-hydroxybenzaldehyde, which can be finely tuned with the aromatic ring structure and the substituents on it.

Motif Graph Neural Network.

Graphs can model complicated interactions between entities, which naturally emerge in many important applications. These applications can often be cast into standard graph learning tasks, in which a crucial step is to learn low-dimensional graph representations. Graph neural networks (GNNs) are currently the most popular model in graph embedding approaches. However, standard GNNs in the neighborhood aggregation paradigm suffer from limited discriminative power in distinguishing high-order graph structures as opposed to low-order structures. To capture high-order structures, researchers have resorted to motifs and developed motif-based GNNs. However, the existing motif-based GNNs still often suffer from less discriminative power on high-order structures. To overcome the above limitations, we propose motif GNN (MGNN), a novel framework to better capture high-order structures, hinging on our proposed motif redundancy minimization operator and injective motif combination. First, MGNN produces a set of node representations with respect to each motif. The next phase is our proposed redundancy minimization among motifs which compares the motifs with each other and distills the features unique to each motif. Finally, MGNN performs the updating of node representations by combining multiple representations from different motifs. In particular, to enhance the discriminative power, MGNN uses an injective function to combine the representations with respect to different motifs. We further show that our proposed architecture increases the expressive power of GNNs with a theoretical analysis. We demonstrate that MGNN outperforms state-of-the-art methods on seven public benchmarks on both the node classification and graph classification tasks.

Optimization and correction of breast dynamic optical imaging projection data based on deep learning

Breast cancer poses a significant health threat to women, necessitating advancements in diagnostic technologies. Breast dynamic optical imaging (DOI) technology, recognized for its non-invasive and radiation-free properties, is extensively utilized for the early screening and quantitative analysis of breast tumors. The integration of deep learning, a robust technology for automatic image feature extraction, with breast DOI has the potential to enhance tumor detection and diagnosis significantly. This paper introduces a deep learning-enhanced image optimization approach to overcome challenges such as poor image quality and distorted projection data commonly encountered in existing DOI methods. The approach utilizes convolutional neural networks (CNNs) to extract features from raw images and employs generative adversarial networks (GANs) to enhance these images, thereby improving their quality and contrast. Additionally, a novel correction algorithm is developed to address projection data distortion, enabling the reconstruction and correction of this data for more accurate and reliable imaging results. Experimental findings confirm that the proposed method markedly enhances both image quality and projection data accuracy in breast DOI, offering a reliable foundation for clinical diagnosis. This study not only provides a new perspective and methodology for the early screening and diagnosis of breast cancer but also holds substantial clinical importance and prospective applications.

The application of nanodiscs in membrane protein drug discovery & development and drug delivery.

The phospholipid bilayer nanodiscs (LNDs), as a rapidly-developing tool in recent years, provide a natural bio-memebrane environment to maintain the native conformation and functions of membrane proteins as well as a versatile delivery vehicle for a variety of hydrophobic and hydrophilic drugs. We have seen unprecedented advantages of phospholipid bilayer nanodiscs in membrane protein structure characterization, biochemical and physiological studies of membrane proteins, membrane environment studies, drug discovery & development, and drug delivery. Many previous reviews have been mainly focused on the advantages of nanodiscs in membrane protein researches, but few have touched upon the importance and potential application of nanodiscs in pharmaceutical industries. This review will provide general description of the structural characteristics, advantages, classification, and applications of phospholipid nanodiscs, with particular focus on nanodisc-enabled membrane protein drug discovery & development as well as drug delivery.

Deciphering aroma complexity between melting flesh and stony hard peach (Prunus persica L.) fruit through integrative analysis of volatile contributions

Stony hard peach is favored in production for resistance to storage and transportation, but presents a challenge of lacking fragrance compared to traditional fragrant peach i.e., melting flesh peach. Addressing this concern necessitates material composition analysis from the perspective of volatile composition and aroma characteristics. However, the complex relationship between volatile compounds and aroma profiles in melting flesh and stony hard peach cultivars remains poorly understood. To address this gap, we examined contribution of volatiles to their aroma. The application of multiple analysis methods, including distribution, odor importance, and partial least squares discriminant analysis facilitated a comprehensive understanding of the contribution of 30 volatiles to aroma; and hierarchical clustering analysis revealed relationships among the compounds within the two types of peaches. The primary differential volatiles between them were cis-3-hexenol, pentyl acetate, prenyl acetate, trans-2-hexenal, cis-3-hexenyl acetate, dihydro-β-ionone, benzaldehyde, and a range of lactones (γ-decalactone, γ-hexalactone, γ-octalactone, δ-decalactone and γ-undecalactone). Overall, the melting flesh peach exhibited more pronounced aroma than the stony hard type. This study underscores the significance of understanding the intricate interplay between volatile compounds and aroma characteristics across cultivars with different textures, offering valuable insights into cultivar selection, flavor enhancement, and consumer preference assessment within the peach industry.

Characterization, quantitation, and natural variability of phenolics in Rhododendron arboreum leaves of western Himalayan origin using UPLC-PDA and UHPLC-ESI-Q/TOF-MS/MS

Rhododendron arboreum Sm. is very popular in the Indian Himalayan region due to its significant ethnomedicinal importance and potential commercial applications. Apart from the ornamental value derived from its vibrant red flowers, fresh flowers are used to prepare medicinal juices, and dried are utilized as spices. Despite being a rich source of phenolics, these lack identified marker chemicals and validated methods. The present work encompasses the chemical investigation of R. arboreum leaves using UHPLC-ESI-Q/TOF-MS/MS. The chemotypic variability was assessed through a reverse phase ultra-performance liquid chromatography method coupled with a photodiode array detector (RP-UPLC-PDA) chemical signatures of R. arboreum leaves from different altitudes combined with chemometric analysis. The chromatographic method was developed and validated for the quantitative analysis of three vital secondary metabolites, i.e., Isoquercetin or Quercetin-3-O-glucoside (Qg), Reynoutrin or Quercetin-3-O-xyloside (Qx), and Quercitrin or Quercetin-3-O-rhamnoside (Qr)The hyphenation of the method with ESI-Q/TOF-MS/MS has confirmed the distribution of 11 chemicals (nine known and two unknown) in R. arboreum leaves belonging to the phenolic and hydrolyzable tannins class. Samples from higher altitudes >3000 m (above mean sea level) were exceptionally rich in Quercitrin. The chemometric investigation, comprising hierarchical cluster analysis (HCA), principal component analysis (PCA), and partial least square method-discriminant analysis (PLS-DA), successfully categorized thirty-three samples from diverse geographic regions into two distinct categories. This current study provides a validated method for the chemotypic discrimination and quality assessment of the R. arboreum leaves from Uttarakhand, Western Himalayan region of India, and laid the foundation for the comprehensive utilization of these renewable and sustainable resources.

Propagation of M-shaped and W-shaped similaritons in birefringent tapered graded-index nonlinear fiber amplifiers

We study the self-similar transmission of optical beams inside an inhomogeneous birefringent tapered graded-index nonlinear fiber amplifier within the framework of generalized coupled Schrödinger equations with spatially inhomogeneous nonlinearity, group velocity dispersion, tapering and gain or loss. New kinds of similariton solutions for the governing model are constructed by means of the similarity transformation method. Especially, the M-shaped and W-shaped similariton pulses are found successfully for the first time, which do not exist in single-mode waveguide amplifier. In addition, bright–dark similaritons are found in the presence of tapering effect. It is shown that these waveforms exhibit a quadratic phase structure, which leads to chirped self-similar pulses. In addition, we determine the relationships among the tapering profile, gain or loss distribution, nonlinearity, and similariton width, which provide the required conditions for controlling the self-similar wave dynamics. Moreover, we discuss the dynamical evolution of the similariton pulses under the influence of special tapering profiles, which are of physical importance in practical applications. The results show that through selecting the appropriate tapering, dispersion and nonlinearity profiles, we can control the dynamics of similaritons effectively.

Dual-tube MEMS-based spectrophone for sub-ppb mid-IR photoacoustic gas detection

Nowadays, the scientific community and industry are increasingly pressed to provide solutions for developing compact and highly-performing trace-gas sensors for several applications of crucial importance, such as environmental monitoring or medical diagnostics. In this context, this work describes a novel configuration, making use of a mid-IR spectrophone combining the compactness of a photo-acoustic setup, a non-conventional micro-electro-mechanical (MEMS) acousto-to-voltage transducer, and the sensitivity enhancement given by a cost-effective and easy-to-build dual-tube resonator configuration. In the optimal condition of sample pressure, the system developed in this work can achieve a minimum detection limit (MDL) equal to 0.34 ppb when averaging up to 10 s. Compared with previous literature of single-pass photoacoustic-based sensors for N2O, this corresponds to a significant improvement both for the achieved normalized noise equivalent absorption coefficient (NNEA) equal to 1.41 × 10−9 cm−1WHz−1/2, and for a Noise-Equivalent-Concentration (NEC) of 1 ppb obtained at 1 s of averaging time.

Computational fluid dynamics: Its carbon footprint and role in carbon reduction

Turbulent flow physics regulates the aerodynamic properties of lifting surfaces, the thermodynamic efficiency of vapor power systems, and exchanges of natural and anthropogenic quantities between the atmosphere and ocean, to name just a few applications of contemporary importance. The space-time dynamics of turbulent flows are described via numerical integration of the non-linear Navier–Stokes equation—a procedure known as computational fluid dynamics (CFD). At the dawn of scientific computing in the late 1950s, it would be many decades before terms such as “carbon footprint” or “sustainability” entered the lexicon, and longer still before these themes attained national priority throughout advanced economies. The environmental cost associated with CFD is seldom considered. Yet, large-scale scientific computing relies on intensive cooling realized via external power generation that is primarily accomplished through the combustion of fossil fuels, which leads to carbon emissions. This paper introduces a framework designed to calculate the carbon footprint of CFD and its contribution to carbon emission reduction strategies. We will distinguish between “hero” and “routine” calculations, noting that the carbon footprint of hero calculations—which demand significant computing resources at top-tier data centers—is largely determined by the energy source mix utilized. We will also review CFD of flows where turbulence effects are modeled, thus reducing the degrees of freedom. Estimates of the carbon footprint are presented for such fully and partially resolved simulations as functions of turbulence activity and calculation year, demonstrating a reduction in carbon emissions by two to five orders of magnitude at practical conditions. Besides generating a carbon footprint, the community's effort to avoid redundant calculations via turbulence databases merits particular attention, with estimates indicating that a single database could potentially reduce CO2 emissions by approximately O(1) × 106 metric tons.

The association between smartphone addiction and thumb/wrist pain among medical students of Jazan University, Saudi Arabia, A cross-sectional study.

The past decade has witnessed a revolution in smartphones owing to their growing importance and various applications. However, excessive usage can lead to addiction and joint pain in the wrist/thumb area. Despite these negative effects, smartphones offer medical students access to the resources they need. To determine the association between smartphone addiction and thumb/wrist pain among undergraduate students of Jazan University. A cross-sectional self-administrated, online survey and online convenience sample technique was used. The collected data were analyzed using SPSS Software. This survey included 337 participants, of which two-thirds were female (66.8%) and the rest male (33.2%); 32.6% of the respondents were studying in the College of Medicine. Of the 337 participants, 32% reported experiencing pain in their wrists or at the back of their neck while using a smartphone. The study found an average Smartphone Addition Scale-Short Version (SAS-SV) score of 32.5 ± 8.0 out of 50 and a total Patient-Rated Wrist Evaluation score of 48.8 ± 14.7, with a pain score of 13.3 ± 11.0 and a function score of 35.5 ± 11.7. The study also revealed a significant association between smartphone addiction and thumb/wrist pain (P value = 0.029), with females and students of the College of Applied Medical Science associated with higher SAS-SV scores, thereby indicating higher smartphone addiction. Significant correlation exists between smartphone addiction and thumb/wirst pain among medical students of Jazan University.

Transition Metals Catalyzed Direct C-H Chalcogenation of Arenes and Heteroarenes

Abstract: Transition metals catalyzed C-H bond activation reactions have appeared as an emerg-ing field to introduce different functional groups in the inactivated saturated and unsaturated C-H bonds. C-S and C-Se bond constructions in aromatic scaffolds are very interesting due to the im-portant applications of organochalcogen reagents in pharmaceutical chemistry and the material world. The introduction of sulphur or selenium moiety to an inert C-H functionality of an arene under transition metal catalysis has become one of the prime challenges and targets in recent years. In this perspective, various transition metals such as Cu, Ni, Co, Pd, Rh, Ru etc. have been extensively studied. Aromatic arenes owning bearing suitable directing groups appeared as the most promising coupling partners to selectively synthesize differently substituted aryl sulfones and aryl sulfides/selenides. The synthetic strategies were highly convenient owing to the regiose-lectivity of products, broad substrate scope, mild reaction conditions and excellent functional group tolerance. The current review article comprehensively summarizes the extent of C-S/Se bond formation via transition metal-catalyzed C-H bond activation with the assistance of directing groups to govern the site selectivity.

The Dairy Matrix: Its Importance, Definition, and Current Application in the Context of Nutrition and Health.

Nutrition research has shifted from single nutrients to examining the association of foods and dietary patterns with health. This includes recognizing that food is more than the sum of the individual nutrients and relates to the concept of the food matrix. Like other foods, dairy foods are characterized by their unique matrices and associated health effects. Although the concepts of the food matrix and/or dairy matrix are receiving increasing attention in the nutrition and health literature, there are different terms and definitions that refer to it. This article aims to provide insights into the application of the concepts of the food matrix and dairy matrix and to provide a current overview of the definitions and terminology surrounding the food matrix and dairy matrix. By analysing these aspects, we aim to illustrate the practical implications of the food matrix and dairy matrix on nutrition and health outcomes and evaluate their roles in shaping evidence-based policies for the benefit of public health. There is a need for harmonized definitions within the literature. Therefore, the International Dairy Federation put forward harmonized terms to be internationally applicable: the "dairy matrix" describes the unique structure of a dairy food, its components (e.g., nutrients and non-nutrients), and how they interact; "dairy matrix health effects" refers to the impact of a dairy food on health that extend beyond its individual components.

Nonconjugated amylopectin-grafted copolymers with dual fluorescence/low-temperature phosphorescence emission and superior processability

Nonconjugated fluorescent polymers devoid of large π–π conjugated structures have received considerable attention due to their significant academic importance and broad application potentials in various fields. Herein, we report an effective strategy to fabricate multifunctional fluorescent amylopectin derivatives and reveal their unique aspects of aggregation-induced emission (AIE), cryogenic long-persistent phosphorescence (~6 s) and excellent processabilities characteristics, which are extremely different from traditional luminogens. These amylopectin-graft-poly(n-butyl acrylate-co-1-vinylimidazole) copolymers (Amylopectin-BVs) prepared by the grafting-from method employing RAFT and experienced subsequently with metal-ligand cross-linking. Specifically, clustering-triggered fluorescent emission or cryogenic long-persistent phosphorescence of amylopectin could be achieved by the aggregation of oxygen and nitrogen atoms along with conformation rigidification, which shows great promise in optoelectronic and biological applications.

Finite element analysis of the electro-mechanical coupling effects in piezoelectric laminated structures

This study utilized the COMSOL Multiphysics software to conduct finite element analysis on the piezoelectric coupling effect of the composite layer structure. The article begins by introducing the basic principles and history of the piezoelectric effect, emphasizing the importance and widespread application of piezoelectric materials in the field of smart materials. Subsequently, it defines in detail the models of bimorph piezoelectric beams and multilayer composite piezoelectric plates, and conducts potential verification. The bimorph piezoelectric beam model uses PVDF material, and the potential distribution is monitored by boundary probes, confirming the influence of the quantity and distribution of sensor electrodes on voltage distribution. The multilayer composite piezoelectric plate model combines a Si non-piezoelectric semiconductor layer with a PZT-4 piezoelectric material layer, demonstrating the in-plane distribution of potential under lateral mechanical loads. This study not only verifies the reliability of COMSOL software in piezoelectric coupling analysis but also provides an in-depth understanding of the behavior and performance of piezoelectric materials in complex environments.

Importance application discipline Yāma and Nyāma for yoga trainer in Bali

Background: Application discipline Yāma and Nyāma hold roles crucial for yoga trainers in Bali, good in development personal or professional. Yāma, which includes principles of ethics like ahimsa (without violence), satya (honesty), asteya (no stealing), brahmacharya (control self), and aparigraha (no greedy), purposeful For form moral character and integrity a yoga trainer . On the other hand , Nyāma, which consists from saucha (cleanliness), santosha (satisfaction), tapas (discipline), svadhyaya (study self), and Ishvara pranidhana (surrender to God), focuses on spiritual development and well-being inner. Practicing yoga trainer second principle This in a way consistent. Not only capable of creating an environment practice that is harmonious and conducive, but also plays a role as an example for his students. Method: This qualitative study examines how yoga trainers in Bali apply Yāma and Nyāma. Data were collected through in-depth interviews with 25 trainers, participatory observations, and documentation. Thematic analysis was used to identify and interpret key themes related to the application of these principles in teaching and personal development. Findings: Discipline Yāma and Nyāma help coach in controlling emotions, increase concentration, and maintain mental balance, which in turn increase quality yoga teaching. Additionally, implementation​ discipline also supports coaches in undergoing a healthy and sustainable, which is essential in maintaining stamina and energy positive . In Bali, it is known as a spiritual and cultural center, and applications to Yāma and Nyāma become more significant. Culture local rich with spiritual values are in line with the principles of yoga, so it makes it easier to coach For integrate discipline This in life daily. Conclusion: With internalization of Yāma and Nyāma, yoga trainers in Bali can enrich their spiritual experience, enhance professionalism, and deliver a more positive impact to the yoga community. Application discipline This Not only as moral rules, but as road comprehensive, reflective life​ harmony between body, mind, and soul. Novelty/Originality in this Study: This study crosses the line between ancient tradition and modern practice, revealing how the principles of Yama and Nyama translate into the lived realities of yoga instructors in Bali. By investigating the unique intersection of yoga philosophy, Balinese culture, and the global wellness industry, this study provides valuable insights into the adaptation and relevance of spiritual teachings in a contemporary context.

Inexact Fixed-Point Proximity Algorithm for the ℓ0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _0$$\\end{document} Sparse Regularization Problem

We study inexact fixed-point proximity algorithms for solving a class of sparse regularization problems involving the ℓ0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _0$$\\end{document} norm. Specifically, the ℓ0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _0$$\\end{document} model has an objective function that is the sum of a convex fidelity term and a Moreau envelope of the ℓ0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _0$$\\end{document} norm regularization term. Such an ℓ0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _0$$\\end{document} model is non-convex. Existing exact algorithms for solving the problems require the availability of closed-form formulas for the proximity operator of convex functions involved in the objective function. When such formulas are not available, numerical computation of the proximity operator becomes inevitable. This leads to inexact iteration algorithms. We investigate in this paper how the numerical error for every step of the iteration should be controlled to ensure global convergence of the inexact algorithms. We establish a theoretical result that guarantees the sequence generated by the proposed inexact algorithm converges to a local minimizer of the optimization problem. We implement the proposed algorithms for three applications of practical importance in machine learning and image science, which include regression, classification, and image deblurring. The numerical results demonstrate the convergence of the proposed algorithm and confirm that local minimizers of the ℓ0\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _0$$\\end{document} models found by the proposed inexact algorithm outperform global minimizers of the corresponding ℓ1\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$\\ell _1$$\\end{document} models, in terms of approximation accuracy and sparsity of the solutions.

Neural Projection Filter: Learning Unknown Dynamics Driven by Noisy Observations.

In this article, we propose the novel neural stochastic differential equations (SDEs) driven by noisy sequential observations called neural projection filter (NPF) under the continuous state-space models (SSMs) framework. The contributions of this work are both theoretical and algorithmic. On the one hand, we investigate the approximation capacity of the NPF, i.e., the universal approximation theorem for NPF. More explicitly, under some natural assumptions, we prove that the solution of the SDE driven by the semimartingale can be well approximated by the solution of the NPF. In particular, the explicit estimation bound is given. On the other hand, as an important application of this result, we develop a novel data-driven filter based on NPF. Also, under certain condition, we prove the algorithm convergence; i.e., the dynamics of NPF converges to the target dynamics. At last, we systematically compare the NPF with the existing filters. We verify the convergence theorem in linear case and experimentally demonstrate that the NPF outperforms existing filters in nonlinear case with robustness and efficiency. Furthermore, NPF could handle high-dimensional systems in real-time manner, even for the 100-D cubic sensor, while the state-of-the-art (SOTA) filter fails to do it.

Utilization of Organic Waste Into Compost Fertilizer: A Wise Solution to Manage Waste in Gebanganom Village, Kendal Regency

Activity Studying Work This is real (KKN) aiming For increase understanding and application use fertilizer compost in society Village Compost chosen as focus main Because its potential in support agriculture sustainable and reduce dependence to fertilizer chemistry . Through socialization This time , the KKN Posko 3 team invited Service Agriculture and Food Kendal Regency for can give education about benefit fertilizer compost , its production , and techniques its use in practice agriculture everyday. The method used For ensure effective knowledge transfer covering consultation , demonstration live , and discussion group . The results of activity This expected can increase awareness public will importance management rubbish organic and application fertilizer compost , so that in the end contribute to improvement productivity agriculture and sustainability environment in the village Gebanganom

A Thermal–EM Concentrator for Enhancing EM Signals and Focusing Heat Fluxes Simultaneously

AbstractThe simultaneous concentration of electromagnetic (EM) waves and heat fluxes in the same target region within an on‐chip system has substantial academic research importance and practical application value. However, the existing research is primarily aimed at the design and experimentation of concentrators for individual EM waves or temperature fields. In this study, a thermal‐EM concentrator capable of simultaneously concentrating EM waves and heat fluxes is designed using transformation optics and thermodynamics and fabricated using engineered thermal‐EM metamaterials. Both the numerically simulated and experimentally measured results demonstrate the concentrating capability of the proposed thermal‐EM concentrator, which can concentrate broadband transverse magnetic (TM)‐polarized EM waves ranging from 8 to 12 GHz and heat flux to the same target region within an on‐chip operating environment. The proposed thermal‐EM concentrator can be utilized for the efficient cooling of the specified component and for simultaneously enhancing the radiation and reception efficiency of the EM antenna within an on‐chip system.

Deep learning-based virtual H& E staining from label-free autofluorescence lifetime images

Label-free autofluorescence lifetime is a unique feature of the inherent fluorescence signals emitted by natural fluorophores in biological samples. Fluorescence lifetime imaging microscopy (FLIM) can capture these signals enabling comprehensive analyses of biological samples. Despite the fundamental importance and wide application of FLIM in biomedical and clinical sciences, existing methods for analysing FLIM images often struggle to provide rapid and precise interpretations without reliable references, such as histology images, which are usually unavailable alongside FLIM images. To address this issue, we propose a deep learning (DL)-based approach for generating virtual Hematoxylin and Eosin (H&E) staining. By combining an advanced DL model with a contemporary image quality metric, we can generate clinical-grade virtual H&E-stained images from label-free FLIM images acquired on unstained tissue samples. Our experiments also show that the inclusion of lifetime information, an extra dimension beyond intensity, results in more accurate reconstructions of virtual staining when compared to using intensity-only images. This advancement allows for the instant and accurate interpretation of FLIM images at the cellular level without the complexities associated with co-registering FLIM and histology images. Consequently, we are able to identify distinct lifetime signatures of seven different cell types commonly found in the tumour microenvironment, opening up new opportunities towards biomarker-free tissue histology using FLIM across multiple cancer types.