Numerical Simulation Analysis of Two-Terminal Monolithic Perovskite-CIGS Tandem Solar Cell for Enhanced Photovoltaic Performance Using SCAPS-1d

: This research designs and simulates a highefficiency tandem solar cell (TSC) using SCAPS-1D (3.3.12), exploiting tandem perovskite technology for enhanced performance. The agenda of our work is here to minimize the two largest losses associated with single-junction solar cells like thermalization and transmission losses by absorbing a broader spectrum of sunlight using CsGeI3/CIGS tandem solar cell technology. To ensure the accuracy of the simulated results, the authors first calibrate both the top and bottom solar cells using experimental data and compare the simulated results with experimental findings. This study investigates the impact of thickness, parasitic resistance, temperature, quantum efficiency, band diagram, absorption coefficients, and two-diode mod el equivalent circuit parameters on solar cell performance. This work optimizes lead-free, wide bandgap (1.6 eV) CsGeI3 perovskite and narrow bandgap (1.1 eV) CIGS solar cells individually and then proposes a tandem solar cell structure using a filtered spectrum approach. The proposed CsGeI3/CIGS tandem solar cell device structure is studied in detail and simulated using SCAPS 1D. A tandem configuration, with a thickness of a 273 nm top cell (simulated under AM1.5G) and a 1000 nm bottom cell, achieved conversion efficiencies of 16.93% and 16.49%, respectively, with respective JSC values of 19.31 mA cm−2/19.32 mA cm−2. By adding the voltages at same current points to make the tandem J-V curve, this design yielded

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Management of Hypertension in Adults

(Abstract not found)

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Porous Transport Photoelectrodes Fabricated on Felt Substrates and Applications to Polymer Electrolyte Photoelectrochemistry

(Abstract not found)

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Highly Comfortable and Durable Single-Layer Knitted Textile-Based Triboelectric Nanogenerator for Smart Wearable Applications

: Amidst the global quest for sustainable energy solutions, there have been endeavors to revolutionize energy-harvesting methods, opening pathways to innovative approaches that tap into the inherent potential of natural sources. The integrated textilebased triboelectric nanogenerator (T-TENG) offers a viable solution to harness human mechanical energy through the clothing. T-TENG has shown several advantages, including flexibility, lightweight, and conformability, for wearable applications. Herein, knitting engineering is utilized for the precise positioning of triboelectric (nylon and polypropylene (PP)) and electrode materials (copper-blended yarn) within the fabric structure for the fabrication of T-TENG. A combination of rib, single jersey, and derivatives of rib knitting techniques was employed to create different tribo structures, namely, 1R1C, pocket, plating, and ridge. The ridge and plated structures showed the highest performance with a peak power density of 110 and 45 μW/m2, respectively. Both structures demonstrated excellent air and water vapor permeability and a stable output up to 12,000 cycles of contact separation with excellent long-term durability. The study strongly affirms that the use of commodity textile materials and modifying the structural features of the knitted fabric have significant potential to produce renewable power sources for wearable electronic gadgets. KEYWORDS: textile triboelectric nanogenerator, knitting, textile structure, renewable

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Design, Synthesis, and Biological Evaluation of Selective CDK4/9 Inhibitors

: Simultaneous inhibition of cell cycle CDKs and transcriptional CDKs may provide a novel strategy for cancer therapy. Starting from a pan-CDK inhibitor, a series of novel 2-((4-substitutedphenyl)amino)-pyrrolo[2,3-d]pyrimidine derivatives were synthesized and evaluated for their inhibition effects on cellular proliferation and CDK enzymatic activity. Several new derivatives exhibited significantly improved profiles in terms of in vitro antitumor potency, metabolic stability, and kinase selectivity. Further biological and in vivo pharmacokinetic evaluation confirmed that derivative 6m (LS-Q2) is a novel, orally bioavailable, and highly selective CDK4/9 inhibitor with potent antiproliferative activity against various tumor cells. Moreover, LS-Q2 exhibited significant synergistic antitumor eff ects when combined with the BET and Bcl-2 inhibitors. The discovery of LS-Q2 provides promising nextgeneration CDK inhibitor leads for the treatment of malignant solid tumors beyond breast cancer and highlights the potential of orally available and selective CDK4/9 inhibitors in cancer treatment. KEYWORDS: pyrrolo[2,3-d]pyrimidine derivatives, CDK4/9 inhibitors, synthesis, antiproliferation, structure−activity relationship T he global cancer burden remains heavy, despite breakthrough advances in antitumor therapy in recent decades. Treatment options for patients with advanced or unresectable tumors continue to be limited, especially for cancers with hidden onset and advanced diagnosis.

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Medical News What to Know About the New Lipid Guidelines

(Abstract not found)

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HandSpeak: A CNN-Based Deep Learning Framework for Sign Language Recognition

In the modern era, sign language plays a vital role in facilitating communication for hearing and speech-impaired individuals. However, a communication barrier still exists between sign language users and the general population. In this work, we propose a real-time sign language recognition system HandspeakNet, a deep learning framework using a convolutional neural network (CNN) architecture. The objective is to classify hand gestures corresponding to sign language alphabets with high accuracy using image data. The system is trained and tested on American sign language datasets, demonstrating significant potential for use in assistive technologies and human-computer interaction. The proposed system achieves a ~ 5% improvement in recognition accuracy over current state-of-the-art methods.

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Association Between Adverse Childhood Experiences and Academic Performance Among Children and Adolescents: A Global Meta-Analysis

This study was conducted to quantify the association of adverse childhood experiences (ACEs) and the academic performance of children and adolescents. The literature was systematically searched in six electronic databases, and a meta-analysis was conducted. Twenty studies with a total of 1,196,631 children and adolescents from five countries were included. Meta-analysis showed that ACE score was positively associated with poor academic achievement, grade repetition, and special education support. Compared with children and adolescents without any ACE, those with one or more ACE had a significantly higher risk of poor academic achievement (pooled odds ratio [OR]: 1.45, 95% confidence interval [CI] [1.13, 1.85], I2 = 82.6%) and grade repetition (pooled OR: 1.36, 95% CI [1.29, 1.43], I2 = 71.0%). Moreover, all types of ACEs were positively associated with poor academic achievement and grade repetition. In addition, there was a significant dose-response relationship between the ACE score and the risk of poor academic achievement. This study supported that ACE had a significant impact on the academic performance of children and adolescents. Based on these findings, we recommend that early screening of ACEs for children and adolescent is critical and appropriate support and prevention in education should be developed for those with ACEs. Further studies are needed to further explore the long-term effect of ACEs on education and its gender differences. Keywords adverse

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Sustainable reverse logistics network design: a case of waste electrical and electronic equipment management

In this study, we develop a reverse logistics network design model to recover value from waste electrical and electronic equipment (WEEE) under uncertainty. We propose a multi-objective, multi-period, and multiproduct optimization model under uncertainty, focusing on economic, environmental, and social aspects for the WEEE management organization in India. The uncertainty associated with the price of reprocessed items, as well as the quantity and quality of product returns, is captured using the scenario generation technique. The augmented ε-constraint method is employed to address the multi-objective optimization problem to generate Pareto-optimal solutions. Our model incorporates the modular capacity expansion and inventory management aspects that accommodate the resilience of the reverse logistics network. Our results show that strategic inventory management can yield better outcomes from a sustainability perspective in the presence of fluctuating prices of reprocessed items. Furthermore, we observe that when the quality of the product returns decreases, the profit and social benefits decrease. Keywords: sustainable operations; reverse logistics network design; multi-objective optimization; stochastic optimization 1. Introduction In recent years, the usage of electrical and electronic equipment has been growing rapidly, generating a stream of waste containing both hazardous and useful materials, known as waste electrical and electronic equipment (WEEE) or e-waste. In 202

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=?UTF-8?B?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?=

(Abstract not found)

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Adaptive feature mode decomposition for fault diagnosis of rotating machinery

In industrial scenarios, decomposition methods play a pivotal role in extracting fault information from intense background noise, which is essential for mitigating operational downtime risks. However, the performance of such decomposition methods is highly dependent on critical parameters, and inappropriate parameter tuning can significantly degrade diagnostic accuracy in practical applications. Feature mode decomposition (FMD) is widely used in fault diagnosis due to its adaptive decomposition capability; nevertheless, the unreasonable parameters setting in FMD method, lead to a wrong decomposition result. This article introduces an innovative adaptive FMD fault diagnosis approach, designed to eliminate the need for manual parameter tuning. By leveraging spectral difference preprocessing and precise parameter estimation, the method effectively achieves fault feature extraction. Simulation and experimental validation results demonstrate that the method outperforms up-to-date decomposition methods in terms of noise suppression and fault feature extraction. This adaptive FMD approach provides a robust solution for mechanical health monitoring in complex industrial environments, contributing to improved operational reliability and reduced maintenance costs. Keywords Feature mode decomposition, spectrum subtraction, health signals, parameter estimation, Fault diagnosis Introduction Rolling bearings play a critical role in the operation of rotating machinery, often enduring h

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Laser Interstitial Thermal Therapy for the Treatment of Mesial Temporal Lobe Epilepsy in Children

Objective: Few studies have explored the efficacy of laser interstitial thermal therapy in pediatric epilepsy surgery. This study aims to evaluate seizure-free outcomes in children and adolescents with mesial temporal lobe epilepsy who underwent laser interstitial thermal therapy. Methods: This was a retrospective cohort study performed at a level 4 epilepsy center. All patients had comprehensive presurgical epilepsy evaluations with a consensus treatment decision made by a multidisciplinary team. Brain magnetic resonance imaging (MRI) data were used to determine lesional vs nonlesional groups. All laser interstitial thermal therapy procedures were performed using Visualase laser ablation systems by the neurosurgical team. Seizure-free outcomes were measured acco rding to the Engel surgical outcome scale. Results: This study included 19 patients (12 girls, 7 boys). Age of epilepsy onset ranged from 2 to 17 years (mean 9.9 years), and age at time of surgery ranged from 8 to 20 years (mean 15.1 years). Ten patients (52.5%) had signs of hippocampal sclerosis on MRI (lesional group), and 9 patients (47.5%) had a normal brain MRI (nonlesional group). Engel 1 score was achieved by 14 of 19 patients (73.5%): 9 of 10 patients (90%) in the lesional group and 5 of 9 patients (55.5%) in the nonlesional group. Younger age of seizure onset was a predictor of better postsurgical outcome, but no other outcome predictors could be established. Conclusion: Laser interstitial thermal therapy

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TBQ3jk

Software defect prediction (SDP) is an essential task in software engineering for identifying defective modules early in the development process, thereby improving software quality and reducing maintenance costs. Existing SDP models often face a number of difficulties despite notable improvements, such as imbalanced datasets and the inability to capture the complex relationships within the code. In this manuscript, a software defect prediction using edge feature with self-attention-based cycle-consistent generative adversarial network optimized by the pelican optimization algorithm (SDP-ESA-CycleGAN-POA) is proposed to enhance the defect prediction by capturing semantic features within the software code. The proposed methodutilizesabstractsyntaxtree(AS-Tree)tokensanddensevectortransformationthrough word embedding to improve prediction accuracy. Then, e dge feature and self-attention-based cycle-consistent generative adversarial network (ESA-CycleGAN) predicts the data as buggy and clean. Finally, the pelican optimization algorithm (POA) is employed to enhance the weight parameters of the ESA-CycleGAN, leading to improved defect prediction accuracy. This research evaluates the ESA-CycleGAN model using the PROMISE dataset. The proposed SDP-ESA-CycleGAN-POA method achieves 21.57%, 23.41%, 16.10% and 18.73% higher accuracy compared with the existing models: a new method to SDP accuracy with machine learning (SDP-RF-LR), SDP using optimum trained convolutional neural network (SDP-OT-CNN), SDP utilizing intelligent ensemble-based method (SDP-RF-SVM-ANN) and SDP through neural network and feature selections (SDP-RBFNN-FS) respectively.

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Effect of Matrix Stiffness on the Osteogenic Differentiation of Human Periodontal Ligament Stem Cells in a Three-Dimensional Culture Hydrogel: A Preliminary Study

: Two-dimensional (2D) models fail to mimic a three-dimensional (3D) environment in studying mechanosensing of stem cells. Here, we present a 3D culture model to investigate how 3D matrix stiffness influences YAP activation and osteogenic differentiation of human periodontal ligament stem cells (hPDLSCs) and evaluate the osteogenic potential of hydrogels in vivo. In this study, a 3D culture model with an adjustable matrix stiffness was established. In the in vitro study, first the osteogenic differentiation of hPDLSCs by the expression of OCN, ALP, COL-1, and RUNX-2 was assessed using qRT-PCR, accompanied by ALP staining, and then YAP expression was evaluated by immunofluorescence. In the in vivo study, hPDLSCs, together with gelatin methacrylate (GelMA) hydrogels of different stiffnesses, were implanted into a rat alveolar bone defect model. As matrix stiffness increased, hPDLSCs showed reduced spreading and significantly decreased expression of OCN, ALP, COL-1, RUNX-2, and YAP activation. Specifically, COL-1 expression in the low-stiffness group was 4.3-fold higher than that in the high-stiffness group, and the YAP nuclear/cytoplasmic ratio under low stiffness was 5.5-fold greater than that under high stiffness at day 7. In vivo, the soft-matrix-cell-laden group exhibited more new bone (86.04%) and collagen formation (74.43%) in the defect area than other groups at week 6. Reduced matrix stiffness likely promotes hPDLSC proliferation, spreading, and osteogenic differentiati

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Discovery of a Potent and Selective Tyrosine Kinase 2 Inhibitor: TAK- 279

: TYK2 is a key mediator of IL12, IL23, and type I interferon signaling, and these cytokines have been implicated in the pathogenesis of multiple inflammatory and autoimmune diseases such as psoriasis, rheumatoid arthritis, lupus, and inflammatory bowel diseases. Supported by compelling data from human genome-wide association studies and clinical results, TYK2 inhibition through small molecules is an attractive therapeutic strategy to treat these diseases. Herein, we report the discovery of a series of highly selective pseudokinase (Janus homology 2, JH2) domain inhibitors of TYK2 enzymatic activity. A computationally enabled design strategy, including the use of FEP+, was instrumental in identifying a pyrazolo-pyrimidine core. We highlight the utility of computational physics-based predict ions used to optimize this series of molecules to identify the development candidate 30, a potent, exquisitely selective cellular TYK2 inhibitor that is currently in Phase 2 clinical trials for the treatment of psoriasis and psoriatic arthritis. ■INTRODUCTION Tyrosine kinase 2 (TYK2) is a highly validated therapeutic target being pursued for the treatment of autoimmune and inflammatory diseases.1−3 TYK2 propagates the downstream intracellular signaling of the pro-inflammatory cytokines IL12, IL23, and type I interferon. These cytokines each play a critical role in the function of Th1 and Th17 cells and consequently play a key role in a range of autoimmune and chronic inflammatory diseases.

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Recent Progress in Prussian Blue Analog Nanomaterials: Structural Engineering and Functional Applications

Nanoheterostructures that integrate Prussian blue (PB) or Prussian blue analogs (PBAs) coordination polymers combined with other inorganic substances integrated into a unified nanosystem represent an extremely demanding yet rapidly developing category of distinct hybrid multifunctional nanostructures. In these structures, chemical and physical properties can function either independently or synergistically to produce unique effects. The conventional PB, distinguished by its flawless structure and highly integrated lattice, demonstrates exceptional structural stability while harboring a diverse range of vacancy networks. And so forth, providing the basis for all kinds of physicochemical reactions. In this review, we comprehensively summarize the key advancements in this category of nanomaterials, focusing on their compositions, structures, and various applications. The characteristics of different structures and their applications in battery electrocatalysis are introduced, and the role and contribution of nanomaterials are highlighted. 1 | Introduction In recent years, the escalating energy shortage and worsening environmental degradation have compelled the global scientific community to emphasize the advancement of efficient and sustainable functional materials. Within this context, Prussian blue (PB) and its derivatives, known as PB analogs (PBAs), have emerged as focal points of interest. These coordination compounds display distinctive 3D open-framework architectur

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Analysis of image forgery detection using convolutional neural network

Prior to the age of cameras, if someone wanted to see/verify any incident or document, then one must go to that place and verify. The fact is that no one ever questions once someone has verified something with their own eyes. Nowadays, with the rapid development of new technologies, one cannot be sure of an image, which one is a copy of the sight or not a sight itself. Such types of verifications are not possible in the current time due to the development of varieties of advanced image editing tools like Corel draw, Photoshop, GIMP, etc. These are low cost and open-source tools for the users and frequently used to make memes on social media websites. This paper presents an image forgery detection using convolutional neural networks (CNNs/ConvNet). The error level analysis (ELA) method is discus sed in detail for image forgery detection. The binary decision of CNN-based model helps in declaration of an image aptness for official uses. The CNN model has been trained for the Kaggle dataset and detailed simulations have been carried out to validate the accuracy and precision of the proposed model.

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Hybrid HVAC–HVDC Grids: Review on Techno-economic, Societal, and Regulatory Aspects

Nowadays, there is a growing trend towards converting existing transmission infrastructure to hybrid high-voltage alternating current (HVAC) and high-voltage direct current (HVDC) systems, driven primarily by the need for increased power transmission capability and lower operational costs. However, as HVAC grids are becoming more complex with the integration of advanced energy storage systems and power electronics, and as HVDC systems are increasingly deployed to efficiently transmit power from both onshore and offshore renewable sources over long distances or to facilitate interconnections across countries and regions, the implementation of such hybrid grids faces substantial technical, regulatory, and economic challenges. In this context, the primary objective of this work is to r eview the existing technical and regulatory aspects of HVAC–HVDC systems and to identify gaps that may hinder their effective implementation. Finally, it aims to highlight the benefits of such grids, as well as to examine the key needs and barriers associated with their deployment.

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Predictors of migraine interictal burden: The hidden role of subjective memory complaints

Background: The "interictal burden of migraine" (MIB) is a new concept that encompasses the overall impact of the disease between migraine episodes. However, the factors that contribute to this interictal burden are still unclear. Objective: This study aimed to identify explanatory factors of interictal burden in patients with migraine. Methods: This prospective cross-­sectional observational including 200 patients with migraine (92% [n = 184] female, with a mean [standard deviation] age of 44.8 [12] years, 53% [n = 106] with chronic migraine) completed a clinical and questionnaire assessment targeting MIB, migraine impact, and depressive and cognitive complaints. Results: More than three-­fourths (76% [n = 152]) of patients had moderate-­to-­severe interictal burden. Higher interictal burden (MIB Scale ≥2) was associated with higher headache frequency (eight vs. 14, p = 0.001) and intensity (headache index score 17.0 vs. 30.0, p = 0.002), higher headache impact (six-­item Headache Impact Test score 59.2 vs. 63.9, p = 0.001), and more subjective memory complaints (Subjective Memory Complaints Questionnaire [SMC] score 9.0 vs. 4.5, p = 0.001), as well as anxiety (Hospital Anxiety and Depression Scale (HADS)-­Anxiety score 5 vs. 10, p < 0.001) and depression symptoms (HADS-­Depression score 5 vs. 8, p < 0.001). Once accounted for these potential explanatory variables, subjective memory complaints and impact of headache during ictal phase remained as individual

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Organizational Greenwashing and Work Engagement: Evidence From the Hospitality Industry

Current research on organizational greenwashing primarily focuses on its impact on customers, while its effects on employees—crucial internal stakeholders—remain underexplored. Drawing on Affective Event Theory, we investigate the relationship between organizational greenwashing and the work engagement of hospitality employees. Utilizing a 2-­week time-­lagged survey (Study 1, N = 324) and an online experiment (Study 2, N = 226), we provide corroborating evidence that greenwashing behaviors by hospitality firms lead to employees' contempt for the organization, which subsequently decreases their work engagement. Additionally, family motivation negatively moderates the relationship between contempt for the organization and work engagement, indicating that the negati ve impact of contempt on work engagement is attenuated when employees have a high level of family motivation. This research enhances the theoretical understanding of how organizational greenwashing affects employees' job-­related behaviors and offers practical implications for hospitality firms to prevent and manage greenwashing practices. 1 | Introduction The hospitality sector plays a significant role in the global economy, contributing approximately 10.4% to total global GDP (Majeed and Kim 2023). However, its environmental impact is also noteworthy. Reports indicate that the annual carbon dioxide consumption per guest per night in hotels is 55.7 metric tons (Majeed and Kim 2023). Additionall

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High-Efficiency Sb 2 (S,Se) 3 Solar Cells with New Hole Transport Layer-Free Back Architecture via 2D Titanium- Carbide Mxene

(Abstract not found)

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(Abstract not found)

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Industry Trade Exposure and the Quality of Employment in India’s Manufacturing Sector

Globalization, trade and labour institutions have implications on the labour market affecting the regular nature of jobs and increasing dependence on contract labour. Using worker-level data for India's manufacturing sector, this paper evaluates the influence of outward orientation of the worker's industry on the informal nature of employment. We find that higher import penetration and export orientation of the manufacturing industries of workers promote employment of regular nature. However, the regular jobs come tied up with lack of job contracts in industries facing higher import competition. Export-oriented industries increase the chances of regular jobs with a written job contract. Keywords Trade, informal jobs, manufacturing sector, India, labour legislations Introduct ion In recent years, 'flexibilization' of labour has been a prominent feature of labour markets across developed as well as developing economies. The economic pressures of globalization and trade liberalization have translated into an increase in volatility in the labour market, higher labour demand uncertainty, flexibility of jobs and rise in job insecurity (Mazumdar, 2000; Rodrik, 1996). The informal employment arrangements and ambiguities with respect to terms and conditions of employment are, in fact, more widespread in the developing world. Conventionally, in developing countries, the major forms of informal employment encompass the self-employed and the casual workers, and contributing f

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Tramadol/Acetaminophen for the Treatment of Acute Migraine Pain: Findings of a Randomized, Placebo-Controlled Trial

(Abstract not found)

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Development of high harmonic generation spectroscopy of organic molecules and biomolecules

In this review we will discuss the topic of high order harmonic generation (HHG) from samples of organic and bio-molecules. The possibility to extract useful dynamical and structural information from the measurement of the HHG emission, a technique termed high harmonic generation spectroscopy (HHGS), will be the special focus of our discussions. We will begin by introducing the salient facts of HHG from atoms and simple molecules and explaining the principles behind HHGS. Next the technical difficulties associated with HHG from samples of organic molecules and biomolecules, principally the low sample density and the low ionization potential, will be examined. Then we will present some recent experiments where HHG spectra from samples of these molecules have been measured and discuss what has been learned from these measurements. Finally we will look at the future prospects for HHG spectroscopy of organic molecules, discussing some of the technical and in principle limits of the technique and methods that may ameliorate these limits. Keywords: high harmonic generation, strong field physics, molecules, ultrafast, structural dynamics (Some figures may appear in colour only in the online journal) 1. Introduction We begin with a brief overview of the process of high harmonic generation (HHG) and the essential attributes that allow it to be used for structural and ultrafast pump–probe studies. To do this we will try to establish a physical picture of the salient features of HHG by

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JIkyyM

(Abstract not found)

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Theoretical Investigation of Optoelectronic

Pentacene derivatives have recently emerged as a potential material for organic electronics such as OLEDs, OFETs, and OPVs. In this paper, we report a thorough investigation of a series of pentacene derivatives substituted at the 6,13 position with aryl groups, using density functional theory (DFT) and Marcus formalism. Dispersion corrected Austin-Frisch-Petersson (APFD) functional is used to explore the electronic, optical and charge transport properties. The computed energy gap between the HOMO and LUMO levels of the compounds lie in the visible range between 2.54 eV to 2.80 eV. The viability of these materials as a transport layer in OLED is evaluated by assessing their charge transport characteristics. The large transfer integral and low reorganization energy for electron transport are indicative of a reasonably hig h electron transfer rate. We find that the 6,13dithien-2-yl pentacene show higher electron transfer rate and may act as an electron transport material. The substitution with 6,13-bis(5-methoxythien-2-yl) in the pentacene backbone causes a red shift of the optical absorption spectrum. Our results suggest that aryl substitution tunes the charge transport properties along with optical absorption energies of pentacene derivatives.

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The solid-state transformer (SST), which has been regarded as one of the 10 most emerging technologies by Massachusetts Institute of Technology (MIT) Technology Review in 2010, has gained increasing importance in the future power distribution system. This paper presents a systematical technology review essential for the development and application of SST in the distribution system. The state-of-the-art technologies of four critical areas are reviewed, including high-voltage power devices, high-power and high-frequency transformers, ac/ac converter topologies, and applications of SST in the distribution system. In addition, future research directions are presented. It is concluded that the SST is an emerging technology for the future distribution system.

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Severity-aware Radiology Report Generation:

Radiology report generation aims to provide comprehensive clinical descriptions and ease radiologists' workloads. Previous research has explored using knowledge graphs and auxiliary classification tasks to enhance the model's ability to generate accurate reports. However, due to the lack of information in the knowledge graphs or insufficient class label information, these methods fail to provide models with clinical severity information about the same disease at different stages of development, resulting in less accurate reports. To address this issue, we propose a Severity-Guided Radiology Report Generation method (SR2Gen), which guides the model in identifying internal severity variations of the disease from both explicit and implicit dimensions. Specifically, SR2Gen includes two innovative modules: a Knowledge Enhancement Module (KEM) and a Disease SeverityAware Module (DSAM). First, KEM explicitly guides the report generation model by constructing a knowledge graph containing disease severity information as prior knowledge. Secondly, DSAM enhances the severity-aware classifier using pseudo-labels generated through momentum distillation and further incorporates an adaptive disease severity learning method, implicitly guiding the model to learn disease progression. Extensive experiments and analyses on IU X-Ray and MIMIC-CXR datasets demonstrate that SR2Gen outperforms previous state-of-the-art methods.

Download PDF: https://rasmiv.eu.org/VNRpAV

Less is more: A minimalist approach to robust GAN-generated face detection

(Abstract not found)

Download PDF: https://tirna.eu.org/9UDPIK

Molecular Docking as a Promising Predictive Model for Silver Nanoparticle-Mediated Inhibition of Cytochrome P450 Enzymes

: Cytochrome P450 (CYP) enzymes are responsible for oxidative metabolisms of a large number of xenobiotics. In this study, we investigated interactions of silver nanoparticles (AgNPs) and silver ions (Ag+) with six CYP isoforms, namely, CYP1A2, CYP2C9, CYP2C19, CYP2D6, CYP2E1, and CYP3A4, within CYP-specific inhibitor-binding pockets by molecular docking and quantum mechanical (QM) calculations. The docking results revealed that the Ag3 cluster, not Ag+, interacted with key amino acids of CYP2C9, CYP2C19, and CYP2D6 within a distance of about 3 Å. Moreover, the QM analysis confirmed that the amino acid residues of these CYP enzymes strongly interacted with the Ag3 cluster, providing more insight into the mechanism of the potential inhibition of CYP enzyme ac tivities. Interestingly, these results are consistent with previous in vitro data indicating that AgNPs inhibited activities of CYP2C and CYP2D in rat liver microsomes. It is suggested that the Ag3 cluster is a minimal unit of AgNPs for in silico modeling. In summary, we demonstrated that molecular docking, together with QM analysis, is a promising tool to predict AgNP-mediated CYP inhibition. These methods are useful for deeper understanding of reaction mechanisms and could be used for other nanomaterials. ■INTRODUCTION Silver nanoparticles (AgNPs) are being incorporated into consumer products at an ever increasing pace.1 Because of their potent antimicrobial properties and other capabilities of their modified functions, AgN

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An Evaluation of Channel Estimation Method Using Deep Learning for OFDM System

In the time- and frequency-variant mobile radio channel such as the Fifth- and Forth-Generation mobile communications systems (5G, 4G), it is very important to estimate the channel coefficients (gains). We propose an estimation method of channel coefficients using a deep learning. The high accuracy of estimation can be evaluated using the deep learning method for super-resolution (SR) Network compared with the conventional method. The estimation of channel gains using orthogonal frequencydivision multiplexing (OFDM) can be replaced with the SR problem and applied to the SR Network method. We show that the accuracy of the proposed method is higher than the conventional methods.

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Operative Neurosurgery 24:242 – 247, 2023 https://doi.org/10.1227/ons.0000000000000498 P

(Abstract not found)

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Patellar resurfacing in total knee arthroplasty leads to better isokinetic performance

(Abstract not found)

Download PDF: https://soalf.eu.org/AIq4mo