Enhanced Molecular Spectroscopy via Localized Surface Plasmon Resonance

Numerous novel spectroscopy techniques have been developed to perform detection and characterization at molecular level. Nevertheless, the resolution of spectroscopy remains to be the bottleneck, and local electric field is involved to solve this issue. Localized surface plasmon resonance (LSPR) occurred at the surface of noble metal nanoparticles is a major source of enhanced local electric field which provide notable enhancement factor of spectroscopy applying fluorescence and the Raman scattering. In this chapter, we will firstly present the physics of localized surface plasmon resonance to gain a basic understanding. Several current techniques to prepare a wide variety of nanoparticles and localized surface plasmon resonance detector are subsequently introduced. We further illustrate two examples taking advantage of experiments and modeling to elaborate the effect of localized surface plasmon resonance on spectroscopy under different circumstances. The combination of experimental and theoretical approaches elucidates the influence of each factor and promotes the design of localized surface plasmon resonance detector used in spectroscopy

A GPT-Based Approach for Scientometric Analysis: Exploring the Landscape of Artificial Intelligence Research

This study presents a comprehensive approach that addresses the challenges of scientometric analysis in the rapidly evolving field of Artificial Intelligence (AI). By combining search terms related to AI with the advanced language processing capabilities of generative pre-trained transformers (GPT), we developed a highly accurate method for identifying and analyzing AI-related articles in the Web of Science (WoS) database. Our multi-step approach included filtering articles based on WoS citation topics, category, keyword screening, and GPT classification. We evaluated the effectiveness of our method through precision and recall calculations, finding that our combined approach captured around 94% of AI-related articles in the entire WoS corpus with a precision of 90%. Following this, we analyzed the publication volume trends, revealing a continuous growth pattern from 2013 to 2022 and an increasing degree of interdisciplinarity. We conducted citation analysis on the top countries and institutions and identified common research themes using keyword analysis and GPT. This study demonstrates the potential of our approach to facilitate accurate scientometric analysis, by providing insights into the growth, interdisciplinary nature, and key players in the field.Comment: 29 pages, 10 figures, 5 table

IMPLIKASI ARTIFICIAL INTELLIGENCE TERHADAP PELAYANAN BANTUAN HUKUM BAGI PENYANDANG DISABILITAS

Bangsa Indonesia saat ini berada dalam krisis penegakan hukum. Dalam praktiknya, aparat penegak hukum cenderung mengabaikan hingga menganut ketidakpedulian terhadap keadilan hukum yang  menimbulkan penurunan kepercayaan masyarakat terhadap aparat penegak hukum. Adapun kelompok yang paling rentan mengalami ketidakadilan akibat krisis penegakan hukum adalah masyarakat penyandang disabilitas. Pada dasarnya ketidaksempurnaan itu tidak boleh menjadi penyebab hilangnya harkat dan martabat masyarakat penyandang disabilitas. Adapun terdapat urgensi penelitian ini yakni dikarenakan adanya terdapat krisis penegakan hukum sehinggadalam mencegah terjadinya pengabaian hak yang mengarah kepada diskriminatif bagi masyarakat penyandang disabilitas didalam praktik penegakan hukum diperlukan pembahasan mengenai pemanfaatan Artificial Intelligence (AI) bagi  masyarakat penyandang disabilitas menjadi hal yang penting dan aktual untuk dilakukan pengkajian lebih lanjut. Penelitian ini berfokus kepada pemanfaatan Artificial Intelligence (AI) sebagai alat bantu pemberian bantuan hukum bagi masyarakat penyandang disabilitas agar tercapainya penegakan hukum yang memenuhi nilai keadilan, nilai kepastian dan nilai kemanfaatan. Spesifikasi dalam penelitian ini menggunakan jenis metode penelitian yuridis normatif dengan pendekatan perundang-undangan (statute approach) dan pendekatan konseptual (conceptual approach). Data yang dianalisis yakni data sekunder yang diperoleh secara tidak langsung melalui teknik studi kepustakaan.Bangsa Indonesia saat ini berada dalam krisis penegakan hukum. Dalam praktiknya, aparat penegak hukum cenderung mengabaikan hingga menganut ketidakpedulian terhadap keadilan hukum yang  menimbulkan penurunan kepercayaan masyarakat terhadap aparat penegak hukum. Adapun kelompok yang paling rentan mengalami ketidakadilan akibat krisis penegakan hukum adalah masyarakat penyandang disabilitas. Pada dasarnya ketidaksempurnaan itu tidak boleh menjadi penyebab hilangnya harkat dan martabat masyarakat penyandang disabilitas. Adapun terdapat urgensi penelitian ini yakni dikarenakan adanya terdapat krisis penegakan hukum sehinggadalam mencegah terjadinya pengabaian hak yang mengarah kepada diskriminatif bagi masyarakat penyandang disabilitas didalam praktik penegakan hukum diperlukan pembahasan mengenai pemanfaatan Artificial Intelligence (AI) bagi  masyarakat penyandang disabilitas menjadi hal yang penting dan aktual untuk dilakukan pengkajian lebih lanjut. Penelitian ini berfokus kepada pemanfaatan Artificial Intelligence (AI) sebagai alat bantu pemberian bantuan hukum bagi masyarakat penyandang disabilitas agar tercapainya penegakan hukum yang memenuhi nilai keadilan, nilai kepastian dan nilai kemanfaatan. Spesifikasi dalam penelitian ini menggunakan jenis metode penelitian yuridis normatif dengan pendekatan perundang-undangan (statute approach) dan pendekatan konseptual (conceptual approach). Data yang dianalisis yakni data sekunder yang diperoleh secara tidak langsung melalui teknik studi kepustakaan

Generalised grid-forming VSC control for grid connection and island network

This study proposes a generalised grid-forming VSC control for grid connection and island network for distributed generations. The proposed control scheme is based on grid-forming direct voltage control to establish the AC voltage and frequency for island network, while it works as a controlled AC voltage source to regulate the active and reactive power flowing to the local load and AC grid during grid-connected operation. Strategy for fault current limiting is also proposed to overcome the overcurrent problem brought by the grid-forming direct voltage control. Simulation results during three-phase AC fault and transition from grid-connected operation to islanding operation are presented to confirm the feasibility and effectiveness of the proposed control strategy

High Quality Audio Coding with MDCTNet

We propose a neural audio generative model, MDCTNet, operating in the perceptually weighted domain of an adaptive modified discrete cosine transform (MDCT). The architecture of the model captures correlations in both time and frequency directions with recurrent layers (RNNs). An audio coding system is obtained by training MDCTNet on a diverse set of fullband monophonic audio signals at 48 kHz sampling, conditioned by a perceptual audio encoder. In a subjective listening test with ten excerpts chosen to be balanced across content types, yet stressful for both codecs, the mean performance of the proposed system for 24 kb/s variable bitrate (VBR) is similar to that of Opus at twice the bitrate.Comment: Five pages, five figure

FM-DBEM Simulation of 3D Microvoid and Microcrack Graphite Models

The graphite is porous medium, and the geometry and size distribution of its structural deficiencies such as microcracks and microvoids at different oxidation degrees have a great influence on the overall performance. In this paper, we adopt the FM-DBEM to study 3D models which contain spheroidal microvoids and circular microcracks. The accuracy of this method is tested by a comparison to the theoretical solution to the problem of 2D microcrack and microvoid interaction problem. Two simulations are conducted: the simulation of graphite model containing a large number of randomly distributed microcracks and microvoids and the simulation of graphite model containing microcracks and growing microvoids. The simulations investigate the effective moduli versus the two microstructures’ density and the effect of microvoid’s growth on the SIF of microcrack

Automatic Mood Detection from Acoustic Music Data

Music mood describes the inherent emotional meaning of a music clip. It is helpful in music understanding and music search and some music-related applications. In this paper, a hierarchical framework is presented to automate the task of mood detection from acoustic music data, by following some music psychological theories in western cultures. Three feature sets, intensity, timbre and rhythm, are extracted to represent the characteristics of a music clip. Moreover, a mood tracking approach is also presented for a whole piece of music. Experimental evaluations indicate that the proposed algorithms produce satisfactory results