bluuebunny/crossref_metadata_2025 huggingface dataset:
Visible light photocatalytic phosphorylation of heteroatom nucleophiles triggered by phosphorus-centered radical cations
Authors: Huang Yuanting , Tang Jinyu , Zhao Xi , Huo Yanping , Gao Yang , Li Xianwei , Chen Qian
Published: May 2023

A photocatalytic phosphorylation of heteroatom nucleophiles has been achievedviathe direct coupling of phosphorus-centered radical cations with heteroatom nucleophiles.
Rock-on-a-chip: “Seeing” the association/disassociation of an adaptive polymer in solutions flowing through porous media
Authors: Zhang Yan , Zhao Xuezhi , Han Peihui , He Tianlei , Yin Hongyao , Zhang Liyuan , Feng Yujun , Weitz David A.
Published: May 2023

The in situ association/disassociation of polymer supramolecular assemblies in pore bodies and throats, engendering a unique macroscopic flow behavior, different from that of conventional polymer solutions, has been investigated.
Ion modulation engineering toward stable lithium metal anodes
Authors: Wang Ce , Zhu Jiahao , Jin Yuhong , Liu Jingbing , Wang Hao , Zhang Qianqian
Published: May 2023

Homogeneous ion transport during Li+ plating/stripping plays a significant role in the stability of Li metal anodes (LMAs) and the electrochemical performance of Li metal batteries (LMBs).
Heterointerface promoted trifunctional electrocatalysts for all temperature high-performance rechargeable Zn–air batteries
Authors: Wagh Nayantara K. , Kim Dong-Hyung , Lee Chi Ho , Kim Sung-Hae , Um Han-Don , Kwon Joseph Sang-Il , Shinde Sambhaji S. , Lee Sang Uck , Lee Jung-Ho
Published: May 2023

Flexible solid-state Zn–air batteries with CoS1−δ/SnS2−δ and CBCs demonstrate a high energy density of 256 Wh kgcell−1 including all active and inactive components, high operating rates of 50 to 100 mA cm−2, and a temperature range of +80 to −40 °C.
A new cholesterol-naphthalimide conjugate: aggregation and sensing of CN− and CO2 under different conditions
Authors: Raza Rameez , Pati Chiranjit , Baildya Nabajyoti , Ghosh Kumaresh
Published: May 2023

A new gelator 1 which forms a gel in DMF and DMF:MeOH has been synthesized. The DMF gel selectively recognizes CN− over other anions by showing gel-to-gel color change. The F−/CN−- activated 1 either in solution or in the gel state successfully detects CO2 by naked eye.
Underwater superoleophobic PVDF membrane with robust stability for highly efficient oil-in-water emulsion separation
Authors: Zhong Qi , Sun Qing , Xiang Bin , Mu Peng , Li Jian
Published: May 2023

Using a facile method, a robust PEG-PVDF (PPG) membrane for the efficient, continuous and cyclic separation of oil-in-water emulsions was fabricated.
DNA hydrogels and nanogels for diagnostics, therapeutics, and theragnostics of various cancers
Authors: Zare Iman , Taheri-Ledari Reza , Esmailzadeh Farhad , Salehi Mohammad Mehdi , Mohammadi Adibeh , Maleki Ali , Mostafavi Ebrahim
Published: May 2023

As an efficient class of hydrogel-based therapeutic drug delivery systems, deoxyribonucleic acid (DNA) hydrogels (particularly DNA nanogels) have attracted massive attention in the last five years.
Successive redox modulation in an iron(ii) spin-crossover framework
Authors: Lyu Bang-Heng , Ruan Ze-Yu , Cui Wen , Wu Si-Guo , Ni Zhao-Ping , Tong Ming-Liang
Published: May 2023

Spin crossover behaviors were first modulated by successive redox reactions within an inverse-Hofmann-type metal–organic framework.
Synaptic plasticity realized by selective oxidation of TiS3 nanosheet for neuromorphic devices
Authors: Qin Jing-Kai , Sun Hai-Lin , Huang Pei-Yu , Li Yang , Zhen Liang , Xu Cheng-Yan
Published: May 2023

In-plane memristor fabricated based on a TiS3–TiOx–TiS3 heterostructure demonstrates excellent synaptic characteristics with tunable and multiple resistance levels, which can be integrated into a neural network for high-accuracy pattern recognition.
The proton conduction behavior of two 1D open-framework metal phosphates with similar crystal structures and different hydrogen bond networks
Authors: Zhang Kai-ming , Ji Min-fang , Zhou Xue-Yi , Xuan Fang , Duan Bo-yuan , Yuan Yuan , Liu Guang-xiang , Duan Hai-bao , Zhao Hai-rong
Published: May 2023

The difference in equilibrium cations of the two compounds results in a big distinction in the dense hydrogen grid. This distinction further leads to the disparity in the proton conductivity of both compounds.
Delafossite CuCoO2/ZnO derived from ZIF-8 heterojunctions as efficient photoelectrodes for dye-sensitized solar cells
Authors: Roudgar-Amoli Mostafa , Alizadeh Amin , Abedini Ebrahim , Shariatinia Zahra
Published: May 2023

Delafossite CuCoO2/ZIF-8-derived ZnO heterojunctions in various ratios were used as photoelectrodes of dye-sensitized solar cells. The champion device exhibited highest PCE of 6.27%, Jsc = 14.56 mA cm−2, Voc = 687.84 mV, FF = 62.67%.
Recent advances in synthesis of polymers based on palm oil and its fatty acids
Authors: Stavila Erythrina , Yuliati Frita , Adharis Azis , Laksmono Joddy Arya , Iqbal Muhammad
Published: May 2023

Triglycerides and fatty acids in palm oil and their derivatives can be utilized as bio-based monomers for synthesizing polymers.
Adsorption of the non-steroidal anti-inflammatory drug (ibuprofen) onto biochar and magnetic biochar prepared from chrysanthemum waste of the beverage industry
Authors: Ngernyen Yuvarat , Petsri Decha , Sribanthao Kamonchanok , Kongpennit Krittiya , Pinijnam Palita , Pedsakul Rinrada , Hunt Andrew J.
Published: May 2023

Biochar and magnetic biochar prepared from chrysanthemum waste of the beverage industry are effective adsorbents for the removal of the non-steroidal anti-inflammatory drug, ibuprofen (IBP), from aqueous systems.
Extracting higher-conductivity designs for solid polymer electrolytes by quantum-inspired annealing
Authors: Hatakeyama-Sato Kan , Uchima Yasuei , Kashikawa Takahiro , Kimura Koichi , Oyaizu Kenichi
Published: May 2023

A quantum-inspired annealing system with a hybrid algorithm accelerates functional material discovery, shown by high-conductivity polymer electrolytes.
Construction of highly-stable covalent organic framework with combined enol-imine and keto-enamine linkages
Authors: Jiang Jian , He Kaiyu , Cai Xue , Yu Hong , Zuo Minghui , Yun Guan , Yun Tao , Ma Yue , Zhang Zitong , Liu Yunling , Wang Zhenlu
Published: May 2023

A COF with both keto-enamine and ketimine linkages is reported for the first time, which shows high chemical and thermal stability.
Water soluble macrocyclic host for recognition of N-methylquinolinium salts in water
Authors: Tian Yuan-Hong , Qin Han , Ding Man-Hua , Tang Lin-Li , Zeng Fei
Published: May 2023

A water soluble macrocyclic arene 1 was synthesized and found that it could form a 1 : 1 complex with N-methylquinolinium salts in water. The complexation and decomplexation of the complexes could be achieved by changing the pH of the solution.
Photochemically controlled activation of STING by CAIX-targeting photocaged agonists to suppress tumor cell growth
Authors: Ding Chunyong , Du Mengyan , Xiong Zhi , Wang Xue , Li Hongji , He Ende , Li Han , Dang Yijing , Lu Qing , Li Shicong , Xiao Ruoxuan , Xu Zhiai , Jing Lili , Deng Liufu , Wang Xiyuan , Geng Meiyu , Xie Zuoquan , Zhang Ao
Published: May 2023

A photo-caged STING agonist featuring a tumor-targeting carbonic anhydrase warhead was designed and synthesized for photo-controllable activation of STING signaling.
Synergistic effects of Fe-substitutional-doping and a surface close-contact Fe2O3/CeO2 heterojunction in Fe/CeO2 for enhanced CH4 photocatalytic conversion
Authors: Tang Hailong , Wang Meiling , Ma Yongqing , Sun Xiao , Wang Min , Zheng Ganhong
Published: May 2023

1 : 3 Fe/CeO2-900/6 was prepared to achieve effective CH4 C–H activation and exhibited excellent CH4 photo-conversion performance with a high C1 product yield of 10.56 mmol gcat.−1 h−1.
Integrated multimodal microfluidic E-skin powered by synergistic tandem nanogenerators for sweat-based health monitoring and skin-temperature analysis
Authors: Han Kai , Zhang Dadong , Zhuang Wenbo , Wan Yanfen , Yang Peng
Published: May 2023

This work combines microfluidics with hydrovoltaic and moisture electricity generation, colorimetric assay and thermistors to develop a sweat electronic skin for real-time sweat monitoring, sweat-composition analysis and skin-surface temperature measurement.
Biomolecule-protected gold nanoclusters: synthesis and biomedical applications
Authors: Hao Dongzhao , Zhang Xuelin , Su Rongxin , Wang Yuefei , Qi Wei
Published: May 2023

This review summarizes the synthesis of gold nanoclusters using biomolecules as templates and their applications in bioimaging, disease treatment and drug delivery.
From 0 toN: circularly polarized luminescence generation from achiral luminophores in fibrous morphologies
Authors: Yu Xiaoxiao , Chen Linfeng , Yu Wanting , Cheng Yanhua , Zhu Meifang
Published: May 2023

Fibrous morphologies are ideal carriers for achiral fluorescent molecules to achieve CPL. Examples and underlying mechanisms of chirality transfer, amplification, and reversion at the single- and multiple-fiber levels are presented in this review.
Finite‐time cluster synchronization of heterogeneous complex dynamical networks with discontinuous interactions
Authors: Han Yujuan , Yan Lin , Liu Bo
Published: May 2023

In this paper, finite‐time cluster synchronization (FTCS) in networks of nonidentical coupled dynamical systems is addressed by introducing a discontinuous interaction rule. This interaction rule overcomes the effect of heterogeneous dynamics among nodes. It is proven that with proper coupling gain, the dynamics of the coupled system converges into the kernal space of the graph Laplacian matrix in finite time. Then sufficient conditions for FTCS are provided. In addition, the discontinuous interactions rule is applied to investigate finite‐time intra‐layer synchronization in multiplex networks.
MMRDF: An improved multitask multi‐modal rumor detection framework
Authors: Jiang Fangting , Liang Gang , Yang Jin , Chen Liangyin
Published: May 2023

As images are attractive and contained in plenty of posts, some studies show that combining multimodal information helps to represent posts effectively, while these approaches (1) simply concatenated unimodal features without considering inter‐modality relations, which failed to improve the prediction performance and generalization ability; (2) lacked careful consideration of the social network data composition structure, assuming that social network data was composed of image‐text pairs, cannot process the data composed of multi‐images, and cannot operate when one of the modalities is missing. To address these issues, a novel framework named multitask multimodal rumor detection framework (MMRDF) is proposed, comprising three sub‐networks, which is available to generate joint multimodal representation through merging different levels of features in multi‐modalities and can deal with all types of tweets (pure text, pure image, image‐text pair and text with multi‐images) flexibly. Since the framework does not take the data that causes extra time delay as inputs, such as propagation structures and response content, it can be applied as soon as the tweets are posted up and decrease the time delays of rumor debunking. Experiments on two real‐world datasets demonstrate the framework significantly outperforms the state‐of‐the‐art by the accuracy of 6.5% and 7.6%.
An open‐source tool for evaluating calibration techniques used in low‐cost air pollutant monitors
Authors: Tatsch Daniel Trevisan , Ramirez Alejandro Rafael Garcia , Campo Fernando , Hoinaski Leonardo , González‐Dalmau Evelio
Published: May 2023

Low‐cost air pollutant sensors suffer several interferences due to the variation of climatic elements. Recent studies look for calibration solutions based on different regression and classification machine learning algorithms. The present work brings together the implementation and extraction of performance metrics from these algorithms in a single open‐source tool. Both the input data and parameters for each algorithm are automatically configured. This feature makes the tool compatible with any input dataset and removes the need to interact with complex codes.
Schedulable capacity assessment method for PV and storage integrated fast charging stations with V2G
Authors: Zhang Kaiyu , Zhan Shuhe , Shi Shanshan , Fang Chen , Feng Donghan , Wang Haojing , Zhang Hua , Shen Bing , Wang Yun , Zhou Yun
Published: May 2023

An accurate estimation of schedulable capacity (SC) is especially crucial given the rapid growth of electric vehicles, their new energy charging stations, and the promotion of vehicle‐to‐grid (V2G) technology. In this study, an evaluation approach for a photovoltaic (PV) and storage‐integrated fast charging station is established. The energy relationship between the SC of electric vehicles (EVs), the SC of centralized energy storage, and the PV power generation is constructed to solve for the upward SC and downward SC of the entire charging station based on the detailed explanation of the electrical structure of the PV and storage integrated fast charging station. To facilitate the grid to improve the dispatching efficiency, a strategy for solving the daytime SC is developed. This work uses an actual charging station as the research object for case analysis, and fine‐grained modeling of its component characteristics is finished in order to test the validity and robustness of the model. Four views are used to examine the variable properties and affecting elements of the schedulable capacity: light circumstances, EV load typical scenarios, dispatching interval length, and centralized energy storage configuration.
Power control in microgrid using improved virtual impedance method
Authors: Truong Dinh‐Nhon , Pham Xuan Hoa Thi , Doan Nam Xuan , Tran Hai Van
Published: May 2023

In this article, a method is proposed to increase the accuracy of reactive power‐sharing for parallel‐connected inverters in Microgrid, this method is done by automatically adjusting the values of the virtual impedance to adjusting the output voltage of the inverters. The virtual impedances are automatically adjusted to compensate for the difference in the output voltage of the inverters due to the influence of the line impedances. The output voltage of the inverters will be adaptively adjusted according to the change of the load, this method greatly improves the accuracy in the reactive power sharing The correct power sharing for the inverters will ensure the stability of voltage and frequency in the Microgrid. The control method is simple and does not need to know the line impedance parameter. The feasibility and effectiveness of the proposed strategy are proven by simulation and experimental results.
Forecasting Wind Energy Production Using Machine Learning Techniques
Authors: Margarat G. Simi , Kumar C. Siva , Rajan Surulivel , B. Raj Mohan
Published: May 2023

Wind energy is an essential source of renewable energy that has gained popularity in recent years. Accurately forecasting wind energy production is crucial for efficient energy management and distribution. This paper proposes a machine learning-based approach using Support Vector Regression (SVR) and Random Forest Regression (RFR) to forecast wind energy production. The proposed methodology involves data collection, preprocessing, feature selection, model training, optimization, and evaluation. The performance of the models is assessed using mean squared error (MSE), root mean squared error (RMSE), and coefficient of determination (R-squared) metrics. The results indicate that the proposed SVR-RFR model outperforms individual models, achieving a higher accuracy in forecasting wind energy production.
Design and Implementation of a Machine Learning-Based Wind Turbine Control System
Authors: K. Prabhavathi , Prabhakar P. B. Edwin , M. Arunadevi , D. Shanthi
Published: May 2023

The Machine Learning-Based Wind Turbine Control System (MLBWTCS) is a new technology that uses machine learning algorithms to optimize the performance of wind turbines. The system collects data from sensors installed on the wind turbine to monitor various variables such as wind speed, blade pitch angle, generator torque, and power output. The data collected is preprocessed and fed into a machine learning model, which predicts the optimal settings for the turbine operations. The predictions are then used to control the operations of the wind turbine in real-time. The MLBWTCS has been shown to improve the efficiency and reliability of wind turbines, resulting in increased power generation and reduced maintenance costs. This paper presents a detailed description of the design and implementation of the MLBWTCS, including data collection, preprocessing, feature selection and machine learning model selection.
Optimizing Renewable Energy Management in Smart Grids Using Machine Learning
Authors: G.B. Santhi , Maheswari Duma , M. Anitha , Priyadharshini R. Indira
Published: May 2023

Renewable energy management in smart grids is a challenging problem due to the uncertainty and variability of renewable energy sources. To improve the efficiency and reliability of renewable energy utilization, various optimization techniques have been proposed. In this paper propose an approach based on the Extreme Learning Machine (ELM) algorithm with Particle Swarm Optimization (PSO) for optimizing renewable energy management in smart grids. The ELM algorithm is used to model and predict renewable energy generation, while the PSO algorithm is used to optimize the parameters of the ELM algorithm. The proposed approach is evaluated on a dataset of solar energy production and compared with other optimization techniques. The results show that the ELM-PSO approach can improve the accuracy of renewable energy predictions and reduce energy costs in smart grids. The proposed approach can be used in various renewable energy systems, such as wind turbines, solar panels, and hydroelectric power plants, to improve the efficiency and reliability of renewable energy utilization.
Analyzing of hydel, wind and fuel cell hybrid system for rural electrification with reduced cost of energy
Authors: Velusamy Jeyalakshmi , Murugan Guru Prasad , Sundararajan Dhanalakshmi , Rathnavel Rajeshwari
Published: May 2023

In this work, a Hybrid Renewable Energy System (HRES) is proposed to electrify remote areas. Hydroelectric and wind energy potential are exploited, and they could be converted into chemical energy and stored in hydrogen fuel cells. By this technique, carbon emissions is substantially reduced and helps to control climate change and global warming. In this proposed work, surplus amount of electrical power is diverted to the production of hydrogen through electrolysis, the stored hydrogen can be used as a energy reserve which could be utilized based on requirement. The hydrogen fuel cell acts as a backup to the grid. A simulation study was carried out using Hybrid Optimization of Multiple Energy Resources (HOMER) software. The simulation study reveals that the Cost of Energy has been significantly reduced.
An Experimental Study on Concrete with Partial Replacement of Cement By Rice Husk Ash and Bagasse Ash
Authors: Dhanalakshmi A. , Jeyaseela J. , Karthika S. , Margret A. Leema
Published: May 2023

Utilization of waste materials in concrete manufacture provides a satisfactory solution to some of environmental concerns and problems associated with waste management. Agro waste such as rice husk ash and bagasse ash were used as pozzolanic material for development of blended cement. The properties of concrete with partial replacement of cement by rice husk ash and bagasse ash for about 30% were investigated which includes its compressive strength, split tensile, flexural strength, durability property such as acid attack and sulphate attack. Among the various percentage replacement of cement by rice husk ash and bagasse ash, the strength test result shows that the optimum percentage replacement was about 70 : 20 : 10 (Cement : RHA : BA) which gives the optimum proportion of 30% replacement of cement with rice husk ash and bagasse ash to produce high performance concrete and contribute to sustainable construction. The cement in the concrete replaced by Bagasse Ash and Rice Husk Ash by the percentage of 5%, 10%, 15%, 20%, 25% and 30%. This optimum proportion result obtained is beneficial in improving the mechanical strength and durability property of the concrete. In this project work Conplast 340 Superplasticizer was used to improve the workability of concrete with replacement materials.
Investigation on Strength Characteristics of Self Compacting Concrete incorporated with AR Glass Fibers
Authors: Kalaimani R. , Subha C. , Reymond D. Justus , kumar C. Vignesh
Published: May 2023

This project presents the experimental study on self-compacting concrete (SCC) with replacement of cement by various percentage of Silica Fume and Fly Ash. The main objective is to determine the Flexural strength, Compressive strength and Split tensile strength of Self compacting concrete by partial replacement of cement by Silica Fume and Fly Ash. The use of fly ash in concrete today is an important subject and is of growing importance day by day. Using fly ash in concrete many provide both economic advantages and enhanced properties in the production of concrete. The addition of super plasticizer increases the workability of concrete. The work involves making seven types of SCCs mixes. For each mix preparation, twenty-one cube specimens, twenty-one-cylinder specimens and twenty-one beam specimens are cast and cured. The specimens are cured in water for 7 days, 14 days and 21 days. The results show that SCC with 5% SF and 10% gives higher values of Compressive strength, Split Tensile strength and Flexural Strength compared to other percentages of Silica Fume and Fly Ash. According to the SCC result, various percentages of AR Glass Fiber (0.1% to 0.4%) added with the optimum SCC (5% of SF+ 10% of FA) and super plasticizer is mixed with optimum SCC. After each mix proportion, specimens are cast and cured for 28 days in water and hardened properties are determined. The results show that the higher the percentage of glass fiber, the higher the values of concrete Compressive strength, Split Tensile Strength and Flexural Strength.
A Hybrid Machine Learning Model for Solar Power Forecasting
Authors: Kumar R. Dhilip , K Prakash , Sundari P. Abirama , S. Sathya
Published: May 2023

The paper presents a near investigation of different AI procedures for solar power forecasting. The objective of the research is to identify the most accurate and efficient machine learning algorithms for solar power forecasting. The paper also considers different parameters such as weather conditions, solar radiation, and time of day in the forecasting model. This paper proposes a hybrid machine learning model for solar power forecasting that consolidates the strengths of multiple algorithms, including support vector regression, random forest regression, and artificial neural network. However, the study also highlights the importance of incorporating domain knowledge and feature engineering in machine learning models for better forecasting accuracy.
Global Evidence Of Pandemic Effects On Educational Disruption
Authors: Kumari B. Krishna , Praseeda C. , Singh Anjali , Kumar Piyush , Pham Long Tam , Tyagi Mani
Published: May 2023

The article provides a vivid illustration of the challenges faced by the education sector during the pandemic. Education disruptions have increased stress and anxiety in students and their families. In addition to this, schools in rural areas and underdeveloped countries failed to provide the necessary equipment and facilities to help the students proceed with online classes. These articles have relied on secondary data and information to understand various concepts and theories. In order to combat these consequences, a worldwide initiative called REDS was formed to analyze the opinion of students and individuals connected with the education sector and remodel the system for combating the challenges posed by the pandemic. The study proceeded with the help of thematic analysis.
A Machine Learning-Based Energy Optimization System for Electric Vehicles
Authors: R. Padmavathy , K. Jeya Prakash , T. Greeta , K. Divya
Published: May 2023

The growing demand for sustainable and eco-friendly transportation has led to the widespread adoption of electric vehicles (EVs). However, the limited driving range of EVs and the need for frequent recharging remain significant challenges. To address these challenges, researchers have proposed various energy optimization techniques, including machine learning-based approaches. In this paper, proposed method of Smart EV energy optimization systems for EVs. The system uses machine learning algorithms to analyze and learn from historical driving data, such as the driving patterns, road conditions, weather, and traffic. Based on this analysis, the system predicts the energy consumption of the EV and optimizes the energy usage to minimize energy waste and extend the driving range.
An Experimental Evaluation of the Impact of Moulding Moisture Content on the Compressive Strength of Unstabilised Compressed Earth Blocks
Authors: Kandasamy A. , Rachel P. Priya
Published: May 2023

The purpose of this study is to evaluate the compressive strength and moulding moisture content (MMC) of unstabilized compressed earth blocks (USCEB) made from two locally accessible soil types, Than Mann (TM) and Karambai (KB). In accordance with IS 2720 (parts 4 and 5) and IS 383 standards, the physical characteristics of TM and KB are also determined. Using the ITGE VOTH machine, 24 sets of 3 USCEB, each measuring 230 × 190 × 100 mm, are cast using TM & KB in the following ratios: 80:20, 70:30, 60:40, and 50:50. The moisture contents (in %) of each mixture are 5, 6, 7, 8, 9, and 10 accordingly. For a minimum of 10 days, the created USCEB is stored outside in normal weather conditions. Based on the findings, it can be shown that in all of the mixes, the dry density rises as MMC increases up to a certain moisture content before beginning to fall. This is termed as “optimal moisture content” (OMC). Additionally, it has been found that the mixture corresponding to a 60:40 ratio produces the highest dry density for a given MMC. The maximum dry density for this mixture is also known as the maximum dry density. The compressive strength test for the 60:40 mixture was then conducted. The USCEB's maximum compressive strength of 9% moisture content was obtained. This study will enable us to determine how much water should be supplied to the soil mixtures in the USCEB for proper compaction during moulding. This research provides evidence for the contribution of MMC to USCEB’s dry density. It is also proved that the proportion of clay and sand in a particular soil influences the OMC at which is to be conducted.
Face Recognition using Deep Learning
Authors: Koodalsamy Banumalar , Veerayan Manikandan Bairavan , Narayanasamy Vanaja
Published: May 2023

Identifying a person primarily relies on their facial features, which even distinguish identical twins. As a result, facial recognition and identification become crucial for distinguishing individuals. Biometric authentication technology, specifically facial recognition systems, are utilized to verify one’s identity. This technology has gained popularity in modern applications, such as phone unlock systems, criminal identification systems, and home security systems. Due to its reliance on a facial image rather than external factors like a card or key, this method is considered more secure. The process of recognizing a person involves two primary steps: face detection and face identification. This article delves into the concept of developing a face recognition system utilizing Python’s OpenCV library through deep learning. Due to its exceptional accuracy, deep learning is an ideal method for facial recognition. The proposed approach involves utilizing the Haar cascade techniques for face detection, followed by the following steps for face identification. To begin with, facial features are extracted through a combination of CNN methods and the linear binary pattern histogram (LBPH) algorithm. For attendance to be marked as “present,” the check-in and check-out times of the detected face must be legitimate. If not, the face will be displayed as “unknown.”
Traffic Flow Prediction For Intelligent Transportation System Using Machine Learning
Authors: Manikandan B.V. , Nathan T.R. , Naresh R. , Prasad P.
Published: May 2023

This study attempts to develop a model that forecasts precise data on traffic flow. Everything that can impact the flow of traffic on the road is referred to as the traffic environment, including traffic signals, accidents, rallies, and even road repairs that could result in a traffic bottleneck. The driver or passenger can make an informed choice if they have prior knowledge about the vehicle crowd close to the area that will have the greatest impact on traffic. Additionally, it can be utilised in driverless vehicles, which are the automobiles of the future. Today’s traffic is increasing tremendously, and big data transportation concepts are becoming more popular. We are motivated to develop a machine learning model that forecasts traffic flow because the present prediction techniques and models are still insufficient for use in practical applications. The amount of data available to forecast traffic flow is so enormous that it is awkward and laborious. In this work, we intended to evaluate the data for the transportation with significantly less complexity using machine learning and deep learning methods. The user will be informed of the projected information and the constructed machine learning model will predict the traffic flow.
Monorchiids (Digenea, Trematoda) of fishes in the Yucatán Peninsula, Mexico, with the description of three new species based on morphological and molecular data
Authors: Andrade-Gómez Leopoldo , Ortega-Olivares Mirza Patricia , Solórzano-García Brenda , García-Varela Martín , Mendoza-Garfias Berenit , Pérez-Ponce de León Gerardo
Published: May 2023

Adult specimens of monorchiids (Digenea) were collected from the intestines of the white grunt, Haemulon plumierii Lacepède (Haemulidae), and the white mullet, Mugil curema Valenciennes (Mugilidae) from five localities off the Yucatán Peninsula and one locality in the Gulf of Mexico. Some specimens were photographed and sequenced for two molecular markers, the large subunit (LSU) of nuclear rDNA and the cytochrome c oxidase subunit 1 (cox1) of mitochondrial DNA. Other specimens were processed for morphological analyses. Newly generated sequences were aligned with other sequences available in GenBank. Bayesian inference and maximum likelihood analyses were implemented using the data sets of LSU and cox1 independently. Reciprocal monophyly evidenced through phylogenetic analyses, sequence divergence values for both molecular markers, and detailed morphological analyses, including scanning electron microscopy photomicrographs, revealed three new genetic lineages, i.e., species, as parasites of M. curema. The three new species are Sinistroporomonorchis mexicanus n. sp., Sinistroporomonorchis yucatanensis n. sp., and Sinistroporomonorchis minutus n. sp. Two additional species of monorchiids were sampled, characterised molecularly, and re-described, namely Sinistroporomonorchis glebulentus (Overstreet, 1971) from the white mullet, and Alloinfundiburictus haemuli (Overstreet, 1969), from the white grunt.
Deep Inferior Epigastric Perforators Topography for “Island Transverse Rectus Abdominis Musculocutaneous Flap” in Breast Reconstruction
Authors: Kim Tae Hyun , Jeong Seong Heum , Ahn Hee Chang
Published: May 2023

Background The Island transverse rectus abdominis musculocutaneous (TRAM) flap is well vascularized with very reliable blood flow, because all perforators of the zone I are included when it is harvested. The number of perforators, topographic mapping, and their relationship with reconstructed outcomes were investigated.

Methods Fifty patients with Island TRAM breast reconstruction from September 2021 to August 2022 were investigated. The zone I was divided into a total of eight sections. Under the loupe magnification, all perforators larger than 0.5 mm in zone I were counted with fine dissection, and photographs were taken in background of vessel loops. Complications like flap necrosis, seroma, and hematoma were also investigated.

Result There are 12 ideal perforators on average in zone I such as one perforator in section I, II, IV, V, VI, VIII, and three perforators in section III and VII. However, two perforators (M6 and L6) below arcuate line were sacrificed in the time of flap harvest to prevent hernia. Island TRAM included 10 perforators on average (5 perforators in each side) above arcuate line to be transferred to the recipient site. Only minor complications were identified.

Conclusion The Island TRAM flap includes 10 perforators to get the vigorous blood flow. The periumbilical to upper medial perforators become more dominant in the perfusion of the flap after deep inferior epigastric artery division. Well preserved perforators will guarantee the satisfactory breast reconstruction with the least complication.
Improved wavelength stability and heat dissipation of InGaN-based light-emitting diodes using a graphene interlayer on patterned sapphire substrate
Authors: Ke Wen-Cheng , Chiang Chih-Yung , Peter Lin Yi-Jiun , Liao Yu-Shun , Cheng Wei-Hsin , Chang Kuo-Jen , Lin Jia-Ching
Published: May 2023

This study presents a straightforward strategy that embeds a graphene interlayer between InGaN-based light-emitting diodes (InGaN LEDs) and patterned sapphire substrate (PSS substrate) for substantial improving device performances of wavelength stability and heat dissipation. The InGaN LEDs on the graphene interlayer/PSS substrate (Gr-LED) have lower piezoelectric fields of 1.16 MV/cm than 1.60 MV/cm for InGaN LEDs on the PSS substrate (Ref-LED). The low piezoelectric field diminishes the screen of the polarization field resulting in a stable electroluminescence peak wavelength. At 100 mA driving current, the chip temperature of Gr-LED shows a decrease in around 24.4 °C relative to Ref-LED. The thermal resistances measured in a vacuum environment are 351 and 128 °C/W for Ref-LED and Gr-LED, respectively. The low thermal resistance of Gr-LED is believed to be due to a low misfit dislocation density of the aluminum nitride nucleation layer that increases the vertical direction of heat transport to PSS. This work demonstrates that the graphene/PSS substrate is a promising substrate for high-power InGaN LEDs.
Effect of pressure on the carbon dioxide hydrate–water interfacial free energy along its dissociation line
Authors: Romero-Guzmán Cristóbal , Zerón Iván M. , Algaba Jesús , Mendiboure Bruno , Míguez José Manuel , Blas Felipe J.
Published: May 2023

We investigate the effect of pressure on the carbon dioxide (CO2) hydrate–water interfacial free energy along its dissociation line using advanced computer simulation techniques. In previous works, we have determined the interfacial energy of the hydrate at 400 bars using the TIP4P/Ice and TraPPE molecular models for water and CO2, respectively, in combination with two different extensions of the Mold Integration technique [J. Colloid Interface Sci. 623, 354 (2022) and J. Chem. Phys. 157, 134709 (2022)]. Results obtained from computer simulation, 29(2) and 30(2) mJ/m2, are found to be in excellent agreement with the only two measurements that exist in the literature, 28(6) mJ/m2 determined by Uchida et al. [J. Phys. Chem. B 106, 8202 (2002)] and 30(3) mJ/m2 determined by Anderson et al. [J. Phys. Chem. B 107, 3507 (2002)]. Since the experiments do not allow to obtain the variation of the interfacial energy along the dissociation line of the hydrate, we extend our previous studies to quantify the effect of pressure on the interfacial energy at different pressures. Our results suggest that there exists a correlation between the interfacial free energy values and the pressure, i.e., it decreases with the pressure between 100 and 1000 bars. We expect that the combination of reliable molecular models and advanced simulation techniques could help to improve our knowledge of the thermodynamic parameters that control the interfacial free energy of hydrates from a molecular perspective.
Hypersonic flow study in a pneumatically operated academic shock tunnel
Authors: Sudarshan B. , Pranav H. A. , Sanjay A. V.
Published: May 2023

A hypersonic shock tunnel is a primary tool used for basic experimental research and may be used in engineering and university courses to study compressible flows involving shock waves. In the present study, a pneumatically operated shock tunnel is demonstrated for hypersonic flow studies. The high-pressure nitrogen gas is used to drive a pneumatic cylinder, which is used to burst the thin metal diaphragms. Tunnel-free stream conditions are quantified using the measured pressure values and by applying shock tube relations. The free-stream Mach number of 5.5–7.2 is achieved by varying the bursting pressure and test gas pressure from 2.1 to 4.5 bars and 0.2 to 0.5 bar, respectively. The simulation is performed and the shock standoff distance quantified, and the stagnation pressures measured. The results demonstrate that the pneumatically operated tunnel enhanced operation capacity compared to the manually operated tunnel and well suits the academic hypersonic research and developmental activities.
The complex permittivity of PEDOT:PSS
Authors: Hossein-Babaei Faramarz , Chegini Ebrahim
Published: May 2023

High permittivity materials are required for efficient organic photovoltaic devices, and the addition of the conjugated polymer composite poly(3,4-ethylenedioxythiophen) polystyrene sulfonate (PEDOT:PSS) to dielectric polymers has been shown to significantly heighten their permittivity. The permittivity of PEDOT:PSS at the optical and microwave frequencies has been investigated, but PEDOT:PSS layers are mainly used for low-frequency device applications, where accurate dielectric property measurements are hindered by their high electrical conductivity and the problems arising from the metal-polymer interfaces. Here, we determine the complex relative permittivity (εr*=εr′−jεr″) of PEDOT:PSS layers perpendicular to the layer plane in the 10−2–106 Hz range by combining data from the reactive energy estimations and electrochemical impedance spectroscopy, and discover that: εr′ at <1 Hz is ultra-high (∼106) decreasing with frequency to ∼5 at 106 Hz; the experimental data fit the Cole-Cole dielectric relaxation model by considering multiple relaxation mechanisms; PEDOT:PSS polarizes nonlinearly and εr′ increases with the intensity of the applied external field; low frequency εr′ increases with both thickness and temperature of the layer, opposite trend of temperature-dependence prevails at >103 Hz; the dielectric properties of PEDOT:PSS are highly anisotropic and the in-plane εr′ at 1.0 kHz is three orders of magnitude higher than the vertical εr′; and that the εr′′ decreases proportional to the reciprocal of frequency (1/f). The latter finding provides an explanation for the ubiquitous pink noise accompanying signals transmitted through organic conductor links. The described methodology can be adopted for investigations on other conjugated polymers.
Improved photocarrier separation in Ga/Fe gradient (Ga, Fe)2O3 thin films
Authors: Wang Kai , Liu Bo , Liu Zhanqi , Jiang Xiao , Zhang Zemin , Han Weihua
Published: May 2023

Metal oxides are promising photoanode materials for photoelectrochemical reactions due to their chemical stability and low-cost synthesis. However, their high photocarrier recombination rate limits their commercial applications. One strategy to promote photocarrier separation efficiency in metal oxides is building an inner electric field through band engineering techniques. In this study, a gradient band structure was prepared in (Ga, Fe)2O3 thin films on F:SnO2 (FTO) coated glass substrate by varying the Ga/Fe ratio. The performance of the gradient photoanode was evaluated by measuring its photocatalytic activity with photocurrent. The forward gradient photoanode, with increasing Ga/Fe ratio from bottom substrate to photoanode surface, exhibited a higher photocurrent density (0.44 mA/cm2 at 1.23 V vs reversible hydrogen electrode (RHE) under AM 1.5G illumination) compared to the reverse gradient photoanode with decreasing the Ga/Fe ratio in the same situation. The forward gradient band structure, which facilitates photocarrier separation and transportation, was believed to play a dominant role in improving the photocatalytic performance. This work demonstrates an effective strategy to improve photocarrier separation and photocatalytic performance of metal oxide photocatalysts through band engineering.
The torsion pendulum dual oscillator for low-frequency Newtonian noise detection
Authors: Chua S. S. Y. , Holland N. A. , Forsyth P. W. F. , Kulur Ramamohan A. , Zhang Y. , Wright J. , Shaddock D. A. , McClelland D. E. , Slagmolen B. J. J.
Published: May 2023

We present a torsion pendulum dual oscillator sensor designed toward the direct detection of Newtonian noise. We discuss the sensitivity limitations of the system, experimental performance characterization results, and prospectives to improve performance. The sensor is being developed to contribute to the mitigation of Newtonian noise impacts in the sensitivities of next generation terrestrial gravitational-wave detectors.
Dose rate assessment of spot-scanning very high energy electrons
radiotherapy driven by laser plasma acceleration
Authors: Lv Jianfeng , Zhao Xingyi , Liu Jiaxin , Wu Di , Yang Gen , Kang Minglei , Yan Xueqing
Published: May 2023

Laser plasma accelerators (LPA) can produce very high-energy electrons (VHEE) with ultra-short bunch duration, which may facilitate the application of ultra-high dose rate radiotherapy (FLASH-RT) to treat deep-seated tumors. The study aims to evaluate the dose rate delivery by spot-scanning VHEE beams produced by LPA and to discuss the feasibility and beam specifications for FLASH-RT implementation. Various dose rate metrics, including averaged dose rate (ADR), dose-averaged dose rate (DADR), and dose-threshold dose rate (DTDR), are examined in the context of spot-scanning. Theoretical analysis and Monte Carlo simulations are employed to quantify the dose rate distribution for a water phantom and explore the impact of beam parameters. All the beam parameters are based on experimental results. With a lower pulse repetition rate of 5 Hz, ADR can only reach a dose rate in the order of 10−1Gy/s, while attaining the FLASH-RT dose rate of 40Gy/s necessitates the utilization of high-power lasers with a kilohertz working repetition rate. In contrast to ADR, DADR and DTDR remain independent of the scanning path and can reach the ultra-high dose rate surpassing 1014Gy/s at the phantom surface. Meanwhile, the ultrashort electron bunch can be stretched during scattering within the water, resulting in a dependence of DADR and DTDR on the penetration depth. Both the charge per shot and angular spread are important parameters in dose rate calculations. This investigation offers insights into practical beam parameters for preclinical applications and supplies guidance for designing the LPAs suitable for future spot-scanning VHEE FLASH-RT.
Influence of H-bonds on the photoionization of aromatic chromophores in water: The aniline molecule
Authors: Lamas Iker , González Jorge , Longarte Asier , Montero Raúl
Published: May 2023

We have conducted time-resolved experiments (pump–probe and pump–repump–probe) on a model aromatic chromophore, aniline, after excitation in water at 267 nm. In the initial spectra recorded, in addition to the absorption corresponding to the bright ππ* excitation, the fingerprint of a transient state with the electron located on the solvent molecule is identified. We postulate that the latter corresponds to the πσ* state along the N–H bond, whose complete relaxation with a ∼500 ps lifetime results in the formation of the fully solvated electron and cation. This ionization process occurs in parallel with the ππ* photophysical channel that yields the characteristic ∼1 ns fluorescence lifetime. The observed branched pathway is rationalized in terms of the different H-bonds that the water establishes with the amino group. The proposed mechanism could be common for aromatics in water containing N–H or O–H bonds and would allow the formation of separated charges after excitation at the threshold of their electronic absorptions.
Temperature-dependent elastic constants of thorium dioxide probed using time-domain Brillouin scattering
Authors: Khanolkar Amey , Wang Yuzhou , Dennett Cody A. , Hua Zilong , Mann J. Matthew , Khafizov Marat , Hurley David H.
Published: May 2023

We report the adiabatic elastic constants of single-crystal thorium dioxide over a temperature range of 77–350 K. Time-domain Brillouin scattering, an all-optical, non-contact picosecond ultrasonic technique, is used to generate and detect coherent acoustic phonons that propagate in the bulk perpendicular to the surface of the crystal. These coherent acoustic lattice vibrations have been monitored in two hydrothermally grown single-crystal thorium dioxide samples along the (100) and (311) crystallographic directions. The three independent elastic constants of the cubic crystal (C11, C12, and C44) are determined from the measured bulk acoustic velocities. The longitudinal wave along the (100) orientation provided a direct measurement of C11. Measurement of C44 and C12 was achieved by enhancing the intensity of quasi-shear mode in a (311) oriented crystal by adjusting the polarization angle relative to the crystal axes. We find the magnitude of softening of the three elastic constants to be ∼2.5% over the measured temperature range. Good agreement is found between the measured elastic constants with previously reported values at room temperature, and between the measured temperature-dependent bulk modulus with calculated values. We find that semi-empirical models capturing lattice anharmonicity adequately reproduce the observed trend. We also determine the acoustic Grüneisen anharmonicity parameter from the experimentally derived temperature-dependent bulk modulus and previously reported temperature-dependent values of volumetric thermal expansion coefficient and heat capacity. This work presents measurements of the temperature-dependent elasticity in single-crystal thorium dioxide at cryogenic temperature and provides a basis for testing ab initio theoretical models and evaluating the impact of anharmonicity on thermophysical properties.
Electric-dipole and magnetic absorption in TbFeO3 single crystals in the THz–IR range
Authors: Komandin Gennadiy A. , Kuzmenko Artem M. , Spektor Igor E. , Mukhin Alexander A.
Published: May 2023

Various mechanisms of absorption including both electric-dipole excitations and magnetic ones in a broadband (5–5000 cm−1) polarized transmission and reflection spectra were studied in orthoferrite TbFeO3 using a coherent submillimeter, pulsed THz, and IR Fourier transform spectroscopy. The classical oscillator model and generalized four-parameter model were used to analyze the spectra obtained, and the parameters of the models were determined. We analyzed main absorption processes, which include contributions of both phonons and multi-phonon processes as well as electron transitions in Tb3+ ions and two antiferromagnetic resonance modes in the Fe subsystem. As a result, the spectra of real and imaginary parts of permeability and permittivity were obtained. A distinctive feature of the studied electrodynamic response in TbFeO3 is a significant absorption in the THz range due to multi-phonon processes, which exceeds optical phonon contribution by an order of magnitude at room temperature.