bluuebunny/crossref_metadata_2025 huggingface dataset:
The Evolution of the Spatial Distribution Pattern of Mosques in the Kashgar Region from 1955 to 2004
Authors: Wu Shangguang , Zhong Yexi , Bao Shuming , Wang Wenhui , Nie Tanyue
Published: February 2023

The spatial differences in the distribution of mosques reflect to a certain extent the diversity of the interaction between natural and human elements and Islamic beliefs in different geographic spaces. The Kashgar region of Xinjiang is one of the most developed regions of Islamic culture in China, its dominant religion is Islam, and the survival of Islamic culture in the region has a long history. The development of Islam in the region, after the founding of the People’s Republic of China, was influenced by the religious policy of Chinese Socialism, and the spatial distribution of mosques in the region has changed significantly. However, the distribution pattern of mosques in the spatial features of the region that had been especially indicated by the transformations in religious practice on the development of Islam impacted by geographical conditions and social factors has been less explored. Based on the Chinese Religious Digital Map dataset provided by the China Information Center at the University of Michigan, mathematical statistics and spatial analyses are used to analyze the spatial distribution pattern of mosques in the Kashgar region from 1955 to 2004, and the causes of the pattern characteristics in the context of the historical background of the study period. The results show that, during the study period, the spatial clustering of mosques occurred mainly in the northwestern and central parts of the Kashgar region. In all districts and counties, the number of mosques had increased and there was a growing gap in the number of mosques. Islam in the area had been well developed and the trend of spatially concentrated distribution of mosques had been increasing. The mosques in the region are mostly clustered in areas with gentle terrain, rivers and a dense population. In terms of the causes affecting the spatial distribution pattern of mosques in the Kashgar region, geographical conditions and population were the underlying factors that set the basic pattern for the location of mosques. In addition, the different effects of social factors, such as the improvement of productivity, the administrative system, religious management policies, and the historical background on the development of Islam in the area had led to a variation in the development of Islam, thus causing changes in the spatial distribution pattern of mosques in the area. In the period from 1976 to 1992, for example, the end of the Cultural Revolution and the shift in China’s foreign policy had a very major impact on Islam so that during this period the spatial distribution pattern of mosques varied the most in the area. This research has implications for learning about the spread of Islam in the Kashgar region after the founding of the People’s Republic of China, and the changes in the spatial distribution of mosques, and the causes of such variations.
Adherence and Wearing Time of Prescribed Footwear among People at Risk of Diabetes-Related Foot Ulcers: Which Measure to Use?
Authors: Jarl Gustav , Hulshof Chantal M. , Busch-Westbroek Tessa E. , Bus Sicco A. , van Netten Jaap J.
Published: February 2023

Adherence to prescribed footwear is essential to prevent diabetes-related foot ulcers. The aim was to compare different measures of adherence and wearing time of prescribed footwear with a reference adherence measure, among people with diabetes at high risk of foot ulceration. We followed 53 participants for 7 consecutive days. A temperature sensor measured wearing time of prescribed footwear and a triaxial accelerometer assessed weight-bearing activities. Subjective wearing time was self-reported. Reference adherence measure was proportion of weight-bearing time prescribed footwear was worn. We calculated Spearman’s correlation coefficients, kappa coefficients, and areas under the curve (AUC) for the association between the reference measure and other measures of adherence and wearing time. Proportion of daily steps with prescribed footwear worn had a very strong association (r = 0.96, Κ = 0.93; AUC: 0.96–1.00), objective wearing time had a strong association (r = 0.91, Κ = 0.85, AUC: 0.89–0.99), and subjective wearing time had a weak association (r = 0.42, Κ = 0.38, AUC: 0.67–0.81) with the reference measure. Objectively measured proportion of daily steps with prescribed footwear is a valid measure of footwear adherence. Objective wearing time is reasonably valid, and may be used in clinical practice and for long-term measurements. Subjective wearing time is not recommended to be used.
UX Framework Including Imbalanced UX Dataset Reduction Method for Analyzing Interaction Trends of Agent Systems
Authors: Gu Bonwoo , Sung Yunsick
Published: February 2023

The performance of game AI can significantly impact the purchase decisions of users. User experience (UX) technology can evaluate user satisfaction with game AI by analyzing user interaction input through a user interface (UI). Although traditional UX-based game agent systems use a UX evaluation to identify the common interaction trends of multiple users, there is a limit to evaluating UX data, i.e., creating a UX evaluation and identifying the interaction trend for each individual user. The loss of UX data features for each user should be minimized and reflected to provide a personalized game agent system for each user. This paper proposes a UX framework for game agent systems in which a UX data reduction method is applied to improve the interaction for each user. The proposed UX framework maintains non-trend data features in the UX dataset where overfitting occurs to provide a personalized game agent system for each user, achieved by minimizing the loss of UX data features for each user. The proposed UX framework is applied to a game called “Freestyle” to verify its performance. By using the proposed UX framework, the imbalanced UX dataset of the Freestyle game minimizes overfitting and becomes a UX dataset that reflects the interaction trend of each user. The UX dataset generated from the proposed UX framework is used to provide customized game agents of each user to enhanced interaction. Furthermore, the proposed UX framework is expected to contribute to the research on UX-based personalized services.
Hybrid Bag-of-Visual-Words and FeatureWiz Selection for Content-Based Visual Information Retrieval
Authors: Bakheet Samy , Al-Hamadi Ayoub , Soliman Emadeldeen , Heshmat Mohamed
Published: February 2023

Recently, content-based image retrieval (CBIR) based on bag-of-visual-words (BoVW) model has been one of the most promising and increasingly active research areas. In this paper, we propose a new CBIR framework based on the visual words fusion of multiple feature descriptors to achieve an improved retrieval performance, where interest points are separately extracted from an image using features from accelerated segment test (FAST) and speeded-up robust features (SURF). The extracted keypoints are then fused together in a single keypoint feature vector and the improved RootSIFT algorithm is applied to describe the region surrounding each keypoint. Afterward, the FeatureWiz algorithm is employed to reduce features and select the best features for the BoVW learning model. To create the codebook, K-means clustering is applied to quantize visual features into a smaller set of visual words. Finally, the feature vectors extracted from the BoVW model are fed into a support vector machines (SVMs) classifier for image retrieval. An inverted index technique based on cosine distance metric is applied to sort the retrieved images to the similarity of the query image. Experiments on three benchmark datasets (Corel-1000, Caltech-10 and Oxford Flower-17) show that the presented CBIR technique can deliver comparable results to other state-of-the-art techniques, by achieving average accuracies of 92.94%, 98.40% and 84.94% on these datasets, respectively.
Text Summarization Method Based on Gated Attention Graph Neural Network
Authors: Huang Jingui , Wu Wenya , Li Jingyi , Wang Shengchun
Published: February 2023

Text summarization is an information compression technology to extract important information from long text, which has become a challenging research direction in the field of natural language processing. At present, the text summary model based on deep learning has shown good results, but how to more effectively model the relationship between words, more accurately extract feature information and eliminate redundant information is still a problem of concern. This paper proposes a graph neural network model GA-GNN based on gated attention, which effectively improves the accuracy and readability of text summarization. First, the words are encoded using a concatenated sentence encoder to generate a deeper vector containing local and global semantic information. Secondly, the ability to extract key information features is improved by using gated attention units to eliminate local irrelevant information. Finally, the loss function is optimized from the three aspects of contrastive learning, confidence calculation of important sentences, and graph feature extraction to improve the robustness of the model. Experimental validation was conducted on a CNN/Daily Mail dataset and MR dataset, and the results showed that the model in this paper outperformed existing methods.
EEG Parameter Selection Reflecting the Characteristics of Internet Gaming Disorder While Playing League of Legends
Authors: Kim Jung-Yong , Kim Dong-Joon , Im Sung-Kyun , Kim Hea-Sol , Park Ji-Soo
Published: February 2023

Game playing is an accessible leisure activity. Recently, the World Health Organization officially included gaming disorder in the ICD-11, and studies using several bio-signals were conducted to quantitatively determine this. However, most EEG studies regarding internet gaming disorder (IGD) were conducted in the resting state, and the outcomes appeared to be too inconsistent to identify a general trend. Therefore, this study aimed to use a series of statistical processes with all the existing EEG parameters until the most effective ones to identify the difference between IGD subjects IGD and healthy subjects was determined. Thirty subjects were grouped into IGD (n = 15) and healthy (n = 15) subjects by using the Young’s internet addition test (IAT) and the compulsive internet use scale (CIUS). EEG data for 16 channels were collected while the subjects played League of Legends. For the exhaustive search of parameters, 240 parameters were tested in terms of t-test, factor analysis, Pearson correlation, and finally logistic regression analysis. After a series of statistical processes, the parameters from Alpha, sensory motor rhythm (SMR), and MidBeta ranging from the Fp1, C3, C4, and O1 channels were found to be best indicators of IGD symptoms. The accuracy of diagnosis was computed as 63.5–73.1% before cross-validation. The most interesting finding of the study was the dynamics of EEG relative power in the 10–20 Hz band. This EEG crossing phenomenon between IGD and healthy subjects may explain why previous research showed inconsistent outcomes. The outcome of this study could be the referential guide for further investigation to quantitatively assess IGD symptoms.
Effectiveness of the Relative Strength Index Signals in Timing the Cryptocurrency Market
Authors: Zatwarnicki Marek , Zatwarnicki Krzysztof , Stolarski Piotr
Published: February 2023

In 2020 and 2021, the cryptocurrency market attracted millions of new traders and investors. Lack of regulation, high liquidity, and modern exchanges significantly lowered the entry threshold for new market participants. In 2021, over 5 million Americans were regularly involved in cryptocurrency trading. At that time, the interest in market indicators and trading strategies remained low, leading to the conclusion that most investors did not use decision-support indicators. The correct and backtested use of technical analysis signals can give the trader a significant advantage over most market participants. This work introduces an algorithmic approach to examining the effectiveness of the signals generated by one of the most popular market indicators, the Relative Strength Index (RSI). A model corresponding to an actual cryptocurrency exchange was used to backtest the strategies. The results show that the RSI as a momentum indicator in the cryptocurrency market involves high risk. Using alternative RSI applications can allow traders to gain an advantage in the cryptocurrency market. Comparing the results with the traditional buy and hold strategy shows the credible potential of the indicated method and the usage of signals generated by the technical analysis indicators.
ArtiLock: Smartphone User Identification Based on Physiological and Behavioral Features of Monosyllable Articulation
Authors: Wong Aslan B. , Huang Ziqi , Chen Xia , Wu Kaishun
Published: February 2023

Although voice authentication is generally secure, voiceprint-based authentication methods have the drawback of being affected by environmental noise, long passphrases, and large registered samples. Therefore, we present a breakthrough idea for smartphone user authentication by analyzing articulation and integrating the physiology and behavior of the vocal tract, tongue position, and lip movement to expose the uniqueness of individuals while making utterances. The key idea is to leverage the smartphone speaker and microphone to simultaneously transmit and receive speech and ultrasonic signals, construct identity-related features, and determine whether a single utterance is a legitimate user or an attacker. Physiological authentication methods prevent other users from copying or reproducing passwords. Compared to other types of behavioral authentication, the system is more accurately able to recognize the user’s identity and adapt accordingly to environmental variations. The proposed system requires a smaller number of samples because single utterances are utilized, resulting in a user-friendly system that resists mimicry attacks with an average accuracy of 99% and an equal error rate of 0.5% under the three different surroundings.
Bi-Planar Trajectory Tracking with a Novel 3DOF Cable Driven Lower Limb Rehabilitation Exoskeleton (C-LREX)
Authors: Prasad Rajan , El-Rich Marwan , Awad Mohammad I. , Agrawal Sunil K. , Khalaf Kinda
Published: February 2023

Although Cable-driven rehabilitation devices (CDRDs) have several advantages over traditional link-driven devices, including their light weight, ease of reconfiguration, and remote actuation, the majority of existing lower-limb CDRDs are limited to rehabilitation in the sagittal plane. In this work, we proposed a novel three degrees of freedom (DOF) lower limb model which accommodates hip abduction/adduction motion in the frontal plane, as well as knee and hip flexion/extension in the sagittal plane. The proposed model was employed to investigate the feasibility of using bi-planar cable routing to track a bi-planar reference healthy trajectory. Various possible routings of four cable configurations were selected and studied with the 3DOF model. The optimal locations of the hip cuffs were determined using optimization. When compared with the five-cable routing configuration, the four-cable routing produced higher joint forces, which motivated the future study of other potential cable routing configurations and their ability to track bi-planar motion.
A Proof of Concept Combined Using Mixed Reality for Personalized Neurorehabilitation of Cerebellar Ataxic Patients
Authors: Franzò Michela , Pica Andrada , Pascucci Simona , Serrao Mariano , Marinozzi Franco , Bini Fabiano
Published: February 2023

Background: Guidelines for degenerative cerebellar ataxia neurorehabilitation suggest intensive coordinative training based on physiotherapeutic exercises. Scientific studies demonstrate virtual exergaming therapeutic value. However, patient-based personalization, post processing analyses and specific audio-visual feedbacks are not provided. This paper presents a wearable motion tracking system with recording and playback features. This system has been specifically designed for ataxic patients, for upper limbs coordination studies with the aim to retrain movement in a neurorehabilitation setting. Suggestions from neurologists and ataxia patients were considered to overcome the shortcomings of virtual systems and implement exergaming. Methods: The system consists of the mixed-reality headset Hololens2 and a proprietary exergaming implemented in Unity. Hololens2 can track and save upper limb parameters, head position and gaze direction in runtime. Results: Data collected from a healthy subject are reported to demonstrate features and outputs of the system. Conclusions: Although further improvements and validations are needed, the system meets the needs of a dynamic patient-based exergaming for patients with cerebellar ataxia. Compared with existing solutions, the mixed-reality system is designed to provide an effective and safe therapeutic exergaming that supports both primary and secondary goals of an exergaming: what a patient should do and how patient actions should be performed.
Design of Pyrrole-Based Gate-Controlled Molecular Junctions Optimized for Single-Molecule Aflatoxin B1 Detection
Authors: Mo Fabrizio , Spano Chiara Elfi , Ardesi Yuri , Ruo Roch Massimo , Piccinini Gianluca , Graziano Mariagrazia
Published: February 2023

Food contamination by aflatoxins is an urgent global issue due to its high level of toxicity and the difficulties in limiting the diffusion. Unfortunately, current detection techniques, which mainly use biosensing, prevent the pervasive monitoring of aflatoxins throughout the agri-food chain. In this work, we investigate, through ab initio atomistic calculations, a pyrrole-based Molecular Field Effect Transistor (MolFET) as a single-molecule sensor for the amperometric detection of aflatoxins. In particular, we theoretically explain the gate-tuned current modulation from a chemical–physical perspective, and we support our insights through simulations. In addition, this work demonstrates that, for the case under consideration, the use of a suitable gate voltage permits a considerable enhancement in the sensor performance. The gating effect raises the current modulation due to aflatoxin from 100% to more than 103÷104%. In particular, the current is diminished by two orders of magnitude from the μA range to the nA range due to the presence of aflatoxin B1. Our work motivates future research efforts in miniaturized FET electrical detection for future pervasive electrical measurement of aflatoxins.
Research on Algorithm of Airborne Dual-Antenna GNSS/MINS Integrated Navigation System
Authors: Xia Ming , Sun Pengfei , Guan Lianwu , Zhang Zhonghua
Published: February 2023

In view of the difficulties regarding that airborne navigation equipment relies on imports and the expensive domestic high-precision navigation equipment in the manufacturing field of Chinese navigable aircraft, a dual-antenna GNSS (global navigation satellite system)/MINS (micro-inertial navigation system) integrated navigation system was developed to implement high-precision and high-reliability airborne integrated navigation equipment. First, the state equation and measurement equation of the system were established based on the classical discrete Kalman filter principle. Second, according to the characteristics of the MEMS (micro-electric-mechanical system), the IMU (inertial measurement unit) is not sensitive to Earth rotation to realize self-alignment; the magnetometer, accelerometer and dual-antenna GNSS are utilized for reliable attitude initial alignment. Finally, flight status identification was implemented by the different satellite data, accelerometer and gyroscope parameters of the aircraft in different states. The test results shown that the RMS (root mean square) of the pitch angle and roll angle error of the testing system are less than 0.05° and the heading angle error RMS is less than 0.15° under the indoor static condition. A UAV flight test was carried out to test the navigation effect of the equipment upon aircraft take-off, climbing, turning, cruising and other states, and to verify the effectiveness of the system algorithm.
Parallel Factorization to Implement Group Analysis in Brain Networks Estimation
Authors: Ranieri Andrea , Pichiorri Floriana , Colamarino Emma , de Seta Valeria , Mattia Donatella , Toppi Jlenia
Published: February 2023

When dealing with complex functional brain networks, group analysis still represents an open issue. In this paper, we investigated the potential of an innovative approach based on PARAllel FActorization (PARAFAC) for the extraction of the grand average connectivity matrices from both simulated and real datasets. The PARAFAC approach was solved using three different numbers of rank-one tensors (PAR-FACT). Synthetic data were parametrized according to different levels of three parameters: network dimension (NODES), number of observations (SAMPLE-SIZE), and noise (SWAP-CON) in order to investigate the way they affect the grand average estimation. PARAFAC was then tested on a real connectivity dataset, derived from EEG data of 17 healthy subjects performing wrist extension with left and right hand separately. Findings on both synthetic and real data revealed the potential of the PARAFAC algorithm as a useful tool for grand average extraction. As expected, the best performances in terms of FPR, FNR, and AUC were achieved for great values of sample size and low noise level. A crucial role has been revealed for the PAR-FACT parameter, revealing that an increase in the number of rank-one tensors solving the PARAFAC problem leads to an increase in FPR values and, thus, to a worse grand average estimation.
LoRa-Based IoT Network Assessment in Rural and Urban Scenarios
Authors: Griva Aikaterini I. , Boursianis Achilles D. , Wan Shaohua , Sarigiannidis Panagiotis , Psannis Konstantinos E. , Karagiannidis George , Goudos Sotirios K.
Published: February 2023

The implementation of smart networks has made great progress due to the development of the Internet of Things (IoT). LoRa is one of the most prominent technologies in the Internet of Things industry, primarily due to its ability to achieve long-distance transmission while consuming less power. In this work, we modeled different environments and assessed the performances of networks by observing the effects of various factors and network parameters. The path loss model, the deployment area size, the transmission power, the spreading factor, the number of nodes and gateways, and the antenna gain have a significant effect on the main performance metrics such as the energy consumption and the data extraction rate of a LoRa network. In order to examine these parameters, we performed simulations in OMNeT++ using the open source framework FLoRa. The scenarios which were investigated in this work include the simulation of rural and urban environments and a parking area model. The results indicate that the optimization of the key parameters could have a huge impact on the deployment of smart networks.
Chemometric-Assisted Litmus Test: One Single Sensing Platform Adapted from 1–13 to Narrow pH Ranges
Authors: Magnaghi Lisa Rita , Alberti Giancarla , Zanoni Camilla , Guembe-Garcia Marta , Quadrelli Paolo , Biesuz Raffaela
Published: February 2023

A novel 3 × 4 colorimetric sensing platform, named the chemometric-assisted litmus test (CLT), has been developed by covalently anchoring commercial pH indicators to ethylene vinyl alcohol (EVOH). The proposed device can be exploited for pH determinations in a wide range from 1 to 13 and in specific narrow ranges, achieving sufficient accuracy and errors below 0.5 pH units. The experimental procedure is simple, quick and reliable; equilibration is reached in less than 2 h, CLT pictures are acquired by a camera, and data treatment is performed applying chemometric techniques such as principal component analysis (PCA) and partial least square regression (PLS) to RGB indices.
Mobility of LoRaWAN Gateways for Efficient Environmental Monitoring in Pristine Sites
Authors: Sobhi Salma , Elzanaty Ahmed , Selim Mohamed Y. , Ghuniem Atef M. , Abdelkader Mohamed F.
Published: February 2023

Environmental monitoring of delicate ecosystems or pristine sites is critical to their preservation. The communication infrastructure for such monitoring should have as little impact on the natural ecosystem as possible. Because of their wide range capabilities and independence from heavy infrastructure, low-power wide area network protocols have recently been used in remote monitoring. In this regard, we propose a mobile vehicle-mounted gateway architecture for IoT data collection in communication-network-free areas. The limits of reliable communication are investigated in terms of gateway speed, throughput, and energy consumption. We investigate the performance of various gateway arrival scenarios, focusing on the trade-off between freshness of data, data collection rate, and end-node power consumption. Then we validate our findings using both real-world experiments and simulations. In addition, we present a case study exploiting the proposed architecture to provide coverage for Wadi El-Gemal national park in Egypt. The results show that reliable communication is achieved over all spreading factors (SFs) for gateway speeds up to 150 km/h with negligible performance degradation at SFs=11,12 at speeds more than 100 km/h. The synchronized transmission model ensures the best performance in terms of throughput and power consumption at the expense of the freshness of data. Nonsynchronized transmission allows time-flexible data collection at the expense of increased power consumption. The same throughput as semisynchronized transmission is achieved using four gateways at only five times the energy consumption, while a single gateway requires seventeen times the amount of energy. Furthermore, increasing the number of gateways to ten increases the throughput to the level achieved by the synchronized scenario while consuming eight times the energy.
Self Calibration of a Sonar–Vision System for Underwater Vehicles: A New Method and a Dataset
Authors: Pecheux Nicolas , Creuze Vincent , Comby Frédéric , Tempier Olivier
Published: February 2023

Monocular cameras and multibeam imaging sonars are common sensors of Unmanned Underwater Vehicles (UUV). In this paper, we propose a new method for calibrating a hybrid sonar–vision system. This method is based on motion comparisons between both images and allows us to compute the transformation matrix between the camera and the sonar and to estimate the camera’s focal length. The main advantage of our method lies in performing the calibration without any specific calibration pattern, while most other existing methods use physical targets. In this paper, we also propose a new sonar–vision dataset and use it to prove the validity of our calibration method.
Strategy for Non-Orthogonal Multiple Access and Performance in 5G and 6G Networks
Authors: Al-Dulaimi Omer Mohammed Khodayer , Al-Dulaimi Aymen Mohammed Khodayer , Alexandra Maiduc Osiceanu , Al-Dulaimi Mohammed Khodayer Hassan
Published: February 2023

The purpose of this paper is to provide a high-level overview of the most important non-orthogonal multiple access (NOMA) protocols in 5G and 6G networks that incorporate code division within the context of 3GPP standardization. The article’s objective is also to look into and compare the various strategies that have been proposed as a solution to the issue of resource distribution to achieve high performance. Many different NOMA plans for 5G and 6G systems have been suggested by a multitude of businesses. NOMA is currently developing in two primary directions: one of them is with power division, and the other is with code division. During the process of standardization carried out by the 3GPP, the attention of the developers was concentrated in the second direction for the application of NOMA schemes in 5G and 6G systems. Hardware communication, also known as D2D communication, performs a significant role in the process of communication between devices. This will increase the efficiency with which network resources are utilized. Devices are now able to interact directly with one another, avoiding the need for transmission nodes. It also serves as one of the approaches to the problem of limited network coverage, which can be improved by utilizing D2D, and as a result fees and energy can be reduced. Increasing the size of the network is one way to achieve this goal, the explained of NOMA technology as well as its primary benefits in wireless technology. The most common variants of code division NOMA and the characteristics of those variants are discussed, as well as the opportunities and challenges associated with implementing those variants. NOMA protocols allow continuous expansion of wireless communication networks, i.e., 5G and 6G, which leads to enhanced performance of the networks.
Color Image Restoration Using Sub-Image Based Low-Rank Tensor Completion
Authors: Liu Xiaohua , Tang Guijin
Published: February 2023

Many restoration methods use the low-rank constraint of high-dimensional image signals to recover corrupted images. These signals are usually represented by tensors, which can maintain their inherent relevance. The image of this simple tensor presentation has a certain low-rank property, but does not have a strong low-rank property. In order to enhance the low-rank property, we propose a novel method called sub-image based low-rank tensor completion (SLRTC) for image restoration. We first sample a color image to obtain sub-images, and adopt these sub-images instead of the original single image to form a tensor. Then we conduct the mode permutation on this tensor. Next, we exploit the tensor nuclear norm defined based on the tensor-singular value decomposition (t-SVD) to build the low-rank completion model. Finally, we perform the tensor-singular value thresholding (t-SVT) based the standard alternating direction method of multipliers (ADMM) algorithm to solve the aforementioned model. Experimental results have shown that compared with the state-of-the-art tensor completion techniques, the proposed method can provide superior results in terms of objective and subjective assessment.
Identification and Isolation Techniques for Plant Growth Inhibitors in Rice
Authors: Anh Nguyen Thi Hai , Anh La Hoang , Mai Nguyen Phuong , Quan Nguyen Van , Xuan Tran Dang
Published: February 2023

Plant growth inhibitors (PGIs) in rice (Oryza sativa), or rice allelochemicals, are secondary metabolites that are either exudated by rice plants to cope with natural competitors or produced during the decomposition of rice by-products in the paddy fields. Of these, the major groups of rice PGIs include phenolics, flavonoids, terpenoids, alkaloids, steroids, and fatty acids, which also exhibit potential medicinal and pharmaceutical properties. Recently, the exploitation of rice PGIs has attracted considerable attention from scientists worldwide. The biosynthesis, exudation, and release of PGIs are dependent on environmental conditions, relevant gene expression, and biodiversity among rice varieties. Along with the mechanism clarification, numerous analytical methods have been improved to effectively support the identification and isolation of rice PGIs during the last few decades. This paper provides an overview of rice PGIs and techniques used for determining and extracting those compounds from rice. In particular, the features, advantages, and limitations of conventional and upgraded extraction methods are comprehensively reported and discussed. The conventional extraction methods have been gradually replaced by advanced techniques consisting of pressurized liquid extraction (PLE), microwave-assisted extraction (MAE), and solid-phase extraction (SPE). Meanwhile, thin-layer chromatography (TLC), liquid chromatography (LC), gas chromatography (GC), mass spectrometry (MS), nuclear magnetic resonance (NMR), high-resolution mass spectrometry (HR-MS), infrared spectroscopy (IR), near-infrared spectroscopy (NIRS), and X-ray crystallography are major tools for rice PGI identification and confirmation. With smart agriculture becoming more prevalent, the statistics of rice PGIs and extraction methods will help to provide useful datasets for building an autonomous model for safer weed control. Conceivably, the efficient exploitation of rice PGIs will not only help to increase the yield and economic value of rice but may also pave the way for research directions on the development of smart and sustainable rice farming methods.
β-Diketone-Driven Deep Eutectic Solvent for Ultra-Efficient Natural Stable Lithium-7 Isotope Separation
Authors: Xie Zexing , Xie Meiying , Tang Tingting , Yang Fan , Xue Liyan , Jiang Zhengming
Published: February 2023

6Li and 7Li are strategic resources. Because Li+ ions have no outermost electrons and the radii of 6Li and 7Li differ by only one neutron, the separation of the naturally stable isotopes of Li, especially by solvent extraction, is recognized as a difficult problem worldwide. Therefore, in this paper, an advanced β-diketone-driven deep eutectic solvent (DES) extraction system containing 2-thenoyltrifluoroacetone (HTTA) and tri-n-octyl phosphine oxide (TOPO) is introduced to the extraction and separation of 6Li+ and 7Li+ ions. Compared with those of reported HTTA extraction systems and crown ether extraction systems, the separation coefficient (β7Li/6Li) of the β-diketone-driven DES extraction system can reach the best value of 1.068, which is now the highest known β-value reported in the extraction system. From the intramolecular hydrogen bond of HTTA to the intermolecular hydrogen bond of DES, the bond energy increases by 47.8%. Because the active site of the proton in DES provides a higher energy barrier for the separation of 7Li, the β7Li/6Li is significantly increased. The extractions were characterized by spectrum, using 1H nuclear magnetic resonance (NMR) spectroscopy, Fourier-transform infrared (FT-IR) spectroscopy, and X-ray photoelectron spectroscopy (XPS). The mechanism was determined on the basis of the reaction kinetics and density functional theory (DFT). The DES extractant shows excellent cycle performance with regard to stripping and reusability. In conclusion, the highly efficient, economical, and stable β-diketone-driven DES extraction system can be used for the separation of naturally stable Li isotopes, which provides good industrial application prospects.
A Day Ahead Demand Schedule Strategy for Optimal Operation of Microgrid with Uncertainty
Authors: Battula Amrutha Raju , Vuddanti Sandeep , Salkuti Surender Reddy
Published: February 2023

A microgrid energy management system (EMS) with several generation and storage units is crucial in attaining stable and reliable operation. Optimal scheduling of energy resources in EMS becomes arduous due to uncertainty in the forecasting of intermittent renewable sources, electricity pricing, and load demand. However, with the demand response (DR) approaches the operational benefits in the EMS framework can be maximized. In order to improve the cost-effectiveness of the microgrid, a novel day-ahead energy management strategy is proposed for optimal energy allocation of the distributed generators with environmental consideration. An incentive load control-based demand response program is developed to improve the operational results. The forecasting uncertainties are handled using probability-based Hong’s 2 m approximation method. The suggested approach uses a metaheuristic genetic algorithm (GA) to solve the constrained convex problem in determining optimal load shifting. Incentive pricing is developed to adapt to the demand shifting for the benefit of the customers and utility operators. Two case studies with grid-connected and islanded modes are studied to assess the strategy. Results indicate that the proposed technique reduces the overall cost fitness by 12.28% and 18.91% in the two cases, respectively. The consistency in operational parameters with popular methods confirms the effectiveness and robustness of the method for day-ahead energy management.
XR Embodiment and the Changing Nature of Sexual Harassment
Authors: Ramirez Erick J. , Jennett Shelby , Tan Jocelyn , Campbell Sydney , Gupta Raghav
Published: February 2023

In this paper, we assess the impact of extended reality technologies as they relate to sexual forms of harassment. We begin with a brief history of the nature of sexual harassment itself. We then offer an account of extended reality technologies focusing specifically on psychological and hardware elements most likely to comprise what has been referred to as “the metaverse”. Although different forms of virtual spaces exist (i.e., private, semi-private, and public), we focus on public social metaverse spaces. We do this to better explain how the concept of sexual harassment must be adjusted to such spaces and how approaches aimed at mitigating harassment must be sensitive to the type of metaverse spaces users utilize. We then offer a typology of sexual harassment for the metaverse focusing on three distinct forms of sexual harassment: (1) invariant (2) mixed variance or modified and (3) unique or metaverse specific. Although existing normative and legal frameworks may function well with respect to the first and, possibly, second forms of harassment, we argue such frameworks will not helpfully address metaverse-specific harassment. Ultimately, the changing nature of privately owned public spaces (POPS) which metaverses are likely to represent pose distinct ethical and regulatory challenges.
The ESG Discourse Is Neither Timeless Nor Stable: How Danish Companies ‘Tactically’ Embrace ESG Concepts
Authors: Nielsen Henrik , Villadsen Kaspar
Published: February 2023

During the last decade, ESG has become a globally widespread doctrine of good investment principles. ESG defines investing for broader, extra-financial goals by the use of “environmental, social and governance” (ESG) factors. While commentators generally agree that ESG has become a crucial arena for defining responsible investment, research is so far scarce on the conceptual development of the ESG discourse as well as how companies articulate it in their public communication. By analysing ESG concepts, this article combines methods derived from corpus linguistics with dispositional analytics, inspired by Michel Foucault. The data material consists of 281 annual reports, which contain the self-representation of 24 Danish large-cap companies, including how they communicated their ESG policies from 2010 to 2021. The analysis displays the proliferation of specific ESG keywords as well as changes over time in their frequency, proportional to each other. We supplement the quantitative analyses with dispositional analytics, considering how the dispositives of law, discipline, and security condition Danish companies’ adoption of ESG. We also discuss how companies use ESG concepts ‘tactically’ to navigate a context, in which the dispositives ‘over-determine’ urgent environmental, social, and governance issues.
Analytical Investigation of the Heat Transfer Effects of Non-Newtonian Hybrid Nanofluid in MHD Flow Past an Upright Plate Using the Caputo Fractional Order Derivative
Authors: Lisha N. M. , Vijayakumar A. G.
Published: February 2023

The objective of this paper is to examine the augmentation of the heat transfer rate utilizing graphene (Gr) and multi-walled carbon nanotubes (MWCNTs) as nanoparticles, and water as a host fluid in magnetohydrodynamics (MHD) flow through an upright plate using Caputo fractional derivatives with a Brinkman model on the convective Casson hybrid nanofluid flow. The performance of hybrid nanofluids is examined with various shapes of nanoparticles. The Caputo fractional derivative is utilized to describe the governing fractional partial differential equations with initial and boundary conditions on the flow model. Exact solutions are obtained for flow transport, temperature distribution besides that heat transfer rate and friction drag in terms of Mittag-Leffler function by using Fourier sine and Laplace techniques as hybrid methods. Further, we provided the limiting case solutions for classic partial differential equations on obtained governing fluid flow models. The influence of various physical parameters with different fractional orders are investigated on hybrid nanofluid’s fractional momentum and energy by plotting velocity and energy curves. Few of the findings suggest that fractional parameters have significant effect on flow parameters and that blade-shaped nanoparticles have a high heat transfer rate. The graphical results reveal that the Grashof number shows a symmetry effect in the case of cooling and heating the plate. Furthermore, the performance of hybrid nanofluid is considerably more effective with the Caputo-fractional derivatives rather than in the classic derivative approach.
An Existence Result of Positive Solutions for the Bending Elastic Beam Equations
Authors: Li Yongxiang , Wang Dan
Published: February 2023

This paper is concerned with the existence of positive solutions to the fourth-order boundary value problem u(4)(x)=f(x,u(x),u″(x)) on the interval [0, 1] with the boundary condition u(0)=u(1)=u″(0)=u″(1)=0, which models a statically bending elastic beam whose two ends are simply supported. Without assuming that the nonlinearity f(x, u, v) is nonnegative, an existence result of positive solutions is obtained under the inequality conditions that |(u, v)| is small or large enough. The discussion is based on the method of lower and upper solutions.
High-Performance Artificial Intelligence Recommendation of Quality Research Papers Using Effective Collaborative Approach
Authors: Venkatesan Vinoth Kumar , Ramakrishna Mahesh Thyluru , Batyuk Anatoliy , Barna Andrii , Havrysh Bohdana
Published: February 2023

The Artificial Intelligence Recommender System has emerged as a significant research interest. It aims at helping users find things online by offering recommendations that closely fit their interests. Recommenders for research papers have appeared over the last decade to make it easier to find publications associated with the field of researchers’ interests. However, due to several issues, such as copyright constraints, these methodologies assume that the recommended articles’ contents are entirely openly accessible, which is not necessarily the case. This work demonstrates an efficient model, known as RPRSCA: Research Paper Recommendation System Using Effective Collaborative Approach, to address these uncertain systems for the recommendation of quality research papers. We make use of contextual metadata that are publicly available to gather hidden relationships between research papers in order to personalize recommendations by exploiting the advantages of collaborative filtering. The proposed system, RPRSCA, is unique and gives personalized recommendations irrespective of the research subject. Thus, a novel collaborative approach is proposed that provides better performance. Using a publicly available dataset, we found that our proposed method outperformed previous uncertain methods in terms of overall performance and the capacity to return relevant, valuable, and quality publications at the top of the recommendation list. Furthermore, our proposed strategy includes personalized suggestions and customer expertise, in addition to addressing multi-disciplinary concerns.
CRISPR Gene Therapy: A Promising One-Time Therapeutic Approach for Transfusion-Dependent β-Thalassemia—CRISPR-Cas9 Gene Editing for β-Thalassemia
Authors: Gamage Udani , Warnakulasuriya Kesari , Hansika Sonali , Silva Gayathri N.
Published: February 2023

β-Thalassemia is an inherited hematological disorder that results from genetic changes in the β-globin gene, leading to the reduced or absent synthesis of β-globin. For several decades, the only curative treatment option for β-thalassemia has been allogeneic hematopoietic cell transplantation (allo-HCT). Nonetheless, rapid progress in genome modification technologies holds great potential for treating this disease and will soon change the current standard of care for β-thalassemia. For instance, the emergence of the CRISPR/Cas9 genome editing platform has opened the door for precision gene editing and can serve as an effective molecular treatment for a multitude of genetic diseases. Investigational studies were carried out to treat β-thalassemia patients utilizing CRISPR-based CTX001 therapy targeting the fetal hemoglobin silencer BCL11A to restore γ-globin expression in place of deficient β-globin. The results of recently carried out clinical trials provide hope of CTX001 being a promising one-time therapeutic option to treat β-hemoglobinopathies. This review provides an insight into the key scientific steps that led to the development and application of novel CRISPR/Cas9–based gene therapies as a promising therapeutic platform for transfusion-dependent β-thalassemia (TDT). Despite the resulting ethical, moral, and social challenges, CRISPR provides an excellent treatment option against hemoglobin-associated genetic diseases.
A Novel Approach to Determining Tumor Progression Using a Three-Site Pilot Clinical Trial of Spectroscopic MRI-Guided Radiation Dose Escalation in Glioblastoma
Authors: Ramesh Karthik K. , Huang Vicki , Rosenthal Jeffrey , Mellon Eric A. , Goryawala Mohammed , Barker Peter B. , Gurbani Saumya S. , Trivedi Anuradha G. , Giuffrida Alexander S. , Schreibmann Eduard , Han Hui , de le Fuente Macarena , Dunbar Erin M. , Holdhoff Matthias , Kleinberg Lawrence R. , Shu Hui-Kuo G. , Shim Hyunsuk , Weinberg Brent D.
Published: February 2023

Glioblastoma (GBM) is a fatal disease, with poor prognosis exacerbated by difficulty in assessing tumor extent with imaging. Spectroscopic MRI (sMRI) is a non-contrast imaging technique measuring endogenous metabolite levels of the brain that can serve as biomarkers for tumor extension. We completed a three-site study to assess survival benefits of GBM patients when treated with escalated radiation dose guided by metabolic abnormalities in sMRI. Escalated radiation led to complex post-treatment imaging, requiring unique approaches to discern tumor progression from radiation-related treatment effect through our quantitative imaging platform. The purpose of this study is to determine true tumor recurrence timepoints for patients in our dose-escalation multisite study using novel methodology and to report on median progression-free survival (PFS). Follow-up imaging for all 30 trial patients were collected, lesion volumes segmented and graphed, and imaging uploaded to our platform for visual interpretation. Eighteen months post-enrollment, the median PFS was 16.6 months with a median time to follow-up of 20.3 months. With this new treatment paradigm, incidence rate of tumor recurrence one year from treatment is 30% compared to 60–70% failure under standard care. Based on the delayed tumor progression and improved survival, a randomized phase II trial is under development (EAF211).