bluuebunny/crossref_metadata_2025 huggingface dataset:
Progressive thermal onset of modified hybrid nanoparticles for oscillating flow via modified fractional approach
Authors: Raza Ali , Al-Khaled Kamel , Khan Sami Ullah , Elboughdiri Noureddine , Farah Anouar , Gasmi Hatem , Helali Abdelhamid
Published: September 2022

The aim of this research is to develop a fractional supported thermal model for studying the features of modified hybrid nanofluid endorsed by uniformly accelerating plate. The novel impact of this work is observing the comparative thermal enhancement of water base fluid by utilizing four types of nanoparticles. The silver, copper, aluminum oxide and titanium oxide nanomaterials are utilized to present the comparative thermal aspect of modified hybrid nanofluid model. Moreover, the inclined direction of magnetic impact is treated. The second-grade nonlinear model is used to explore the base fluid properties. The fractional model is first attained into dimensionless form. The fractional computations with employing the Prabhakar fractional mathematical definitions are reported. The motivations for suggesting the Prabhakar algorithm are justified as this fractional algorithm contains modern definitions without any restriction of singularities. The verification of model is accomplished after simulating the comparison task with already performed studies. The physical dynamic and thermal enhancement of transportation phenomenon is performed for specific range of flow parameters like [Formula: see text] [Formula: see text] and [Formula: see text] Based on the computational model, it is concluded that the thermal transportation phenomenon is more impressive for water-based titanium oxide nanoparticles. The temperature profile rises due to factional parameter for both copper–water- and sliver — water-based hybrid nanofluid suspension.
Event horizons are tunable factories of quantum entanglement
Authors: Agullo Ivan , Brady Anthony J. , Kranas Dimitrios
Published: September 2022

That event horizons generate quantum correlations via the Hawking effect is well known. We argue, however, that the creation of entanglement can be modulated, as desired, by appropriately illuminating the horizon. We adapt techniques from quantum information theory to quantify the entanglement produced during the Hawking process and show that, while ambient thermal noise (e.g. cosmic microwave background radiation) degrades it, the use of squeezed inputs can boost the nonseparability between the interior and exterior regions in a controlled manner. We further apply our ideas to analog event horizons concocted in the laboratory and insist that the ability to tune the generation of entanglement offers a promising route towards detecting quantum signatures of the elusive Hawking effect.
Performance of Shape Memory Alloy Lead Rubber Bearing (SMA-LRB) Isolated Building Under Blast-Induced Ground Motion Considering Parametric Uncertainty
Authors: Upadhyaya Sonali , Gur Sourav
Published: September 2022

Blast-induced ground motions (BIGM) can cause significant damage to the structure, leading to loss of life and economy. Previous studies proposed the use of base isolation system to prevent such adverse effects of blast loading on the structures. This study explores the effect of parametric uncertainty on the performance of shape memory alloy-assisted lead rubber bearing (SMA-LRB) isolator for the vibration control of building, under the BIGM. Nonlinear time-history analyses are performed to estimation responses, and compared them with the LRB isolated building. The building is modeled as a linear system, and nonlinear force–displacement behavior of LRB and SMA has been modeled via Bouc–Wen model and thermo-mechanical material model. To identify the critical design parameters and study the effect of parameter uncertainty, sensitivity analysis and Monte Carlo (MC) simulation are performed. Study results reveal that, compared to LRB, SMA-LRB reduces the peak floor acceleration by 20% with an added benefit of 35% and 67% reduction in peak and residual isolator displacement. Parametric uncertainty causes noticeable increase in mean responses of LRB isolated system than deterministic case, which get diminishes for SMA-LRB isolation system. This increase in the mean value of peak floor acceleration, peak, and residual isolator displacement are 64%, 68%, and 60% less for SMA-LRB than LRB isolated building. Further, compare to the LRB, SMA-LRB reduces standard deviation by 77% for the peak floor acceleration and peak isolator displacement, and 85% for the isolator residual displacement. Thus, conjunction of SMA with the LRB isolator enhances the performance and provides robust control effect under parameter uncertainty.
Seismic-Induced Track Spectrum Characteristics of High-Speed Railway Bridges
Authors: Jiang Lizhong , Zuo Yongjian , Zhou Wangbao , Yu Jian , Peng Kang , Zheng Yali
Published: September 2022

The construction of high-speed railways continues to extend in seismic prone areas, but there is limited research on the seismic-induced track irregularities occurred in positions where high-speed railway bridges are involved. Taking a five-span high-speed railway simply supported beam bridge as the engineering example, the corresponding nonlinear system model, with CRTS II slab ballastless track, is established by using ANSYS finite element analysis software. The distribution mode of post-earthquake track residual irregularity is studied, the statistical characteristics of its power spectral density are analyzed, and a representative seismic-induced track geometric irregularity spectrum is constructed. The effects of the site categories and the epicenter distance on seismic-induced track spectrum are also discussed. The results show that after the earthquake, the alignment irregularity is significant, while the vertical, cross-level and gauge irregularities can be ignored. The amplitude of track geometric irregularity, caused by earthquake, increases significantly with the increase of ground motion intensity. The seismic-induced track spectrum obeys lognormal distribution at all frequencies. The track spectra almost coincide for all five site categories, thus, the impact of site categories on the seismic-induced track spectrum of high-speed railway bridges is considered negligible. Due to the pulse effect, the seismic-induced track spectrum of near-field earthquakes is significantly larger than that of far-field earthquakes.
Analysis on the Emergency Management Vulnerability of Two First-Class Tertiary Hospitals in Shanghai under the Perspective of Patient Satisfaction
Authors: Wang Yujia , Wang Tao , He Yeran , Zhang Jinzi , Sun Xiaonan , Yin Mei , Wu Yibo
Published: September 2022

The uncertainty, immediate hazards, severe consequences and broad impact of medical emergencies are far beyond the scope of ordinary medical treatment, calling for special attention. The importance of constructing hospital emergency management system has been increasingly reflected. The system not only needs the practical solution of hospital management at the present stage, but also the working direction of the hospital management in the future. In this paper, the emergency satisfaction survey was conducted on the patients in two first-class tertiary hospitals in Shanghai. The service satisfaction scores of a hospital with integrated traditional Chinese medicine (TCM) and Western medicine as well as a general hospital were [Formula: see text] and [Formula: see text], respectively. There was a significant difference in the service satisfaction scores between the two hospitals ([Formula: see text]). Poor network signal was a vulnerability problem in the dimension of medical auxiliary facilities in both hospitals, accounting for the highest proportion. For the dimension of medical building, the main vulnerability problems were wall cracking and uneven ground, accounting for a high proportion. In the dimension of medical process, the parking inconvenience of a hospital with integrated traditional Chinese and Western medicine was particularly serious, accounting for 81.7%. In the dimension of medical service, the vulnerability of “medical staff overwork” in a general hospital was more prominent, accounting for 39.10%, which was significantly higher than that in a hospital with integrated TCM and Western medicine. In terms of service attitude, the vulnerability of the two hospitals was represented as the poor service attitude of doctors, nurses and managers. In the medical safety dimension of the two hospitals, “lack of assistance in emergencies” and “hospital equipment failures” were the main vulnerability issues. From the perspective of patients, this paper analyzed the current vulnerability problems of the hospital, identified the actual and potential risks of hospital emergency management and put forward suggestions on adjusting the spatial layout, strengthening internal management and comprehensively improving employees’ emergency awareness and skills. This will provide a reference for the hospital’s emergency management system to make necessary adjustments to meet the practical needs.
FD-CNN: A Frequency-Domain FPGA Acceleration Scheme for CNN-Based Image-Processing Applications
Authors: Wang Xiaoyang , Zhou Zhe , Yuan Zhihang , Zhu Jingchen , Cao Yulong , Zhang Yao , Sun Kangrui , Sun Guangyu
Published: September 2022

In the emerging edge-computing scenarios, FPGAs have been widely adopted to accelerate convolutional neural network (CNN)–based image-processing applications, such as image classification, object detection, and image segmentation, and so on. A standard image-processing pipeline first decodes the collected compressed images from Internet of Things (IoTs) to RGB data, then feeds them into CNN engines to compute the results. Previous works mainly focus on optimizing the CNN inference parts. However, we notice that on the popular ZYNQ FPGA platforms, image decoding can also become the bottleneck due to the poor performance of embedded ARM CPUs. Even with a hardware accelerator, the decoding operations still incur considerable latency. Moreover, conventional RGB-based CNNs have too few input channels at the first layer, which can hardly utilize the high parallelism of CNN engines and greatly slows down the network inference. To overcome these problems, in this article, we propose FD-CNN, a novel CNN accelerator leveraging the partial-decoding technique to accelerate CNNs directly in the frequency domain. Specifically, we omit the most time-consuming IDCT (Inverse Discrete Cosine Transform) operations of image decoding and directly feed the DCT coefficients (i.e., the frequency data) into CNNs. By this means, the image decoder can be greatly simplified. Moreover, compared to the RGB data, frequency data has a narrower input resolution but has 64× more channels. Such an input shape is more hardware friendly than RGB data and can substantially reduce the CNN inference time. We then systematically discuss the algorithm, architecture, and command set design of FD-CNN. To deal with the irregularity of different CNN applications, we propose an image-decoding-aware design-space exploration (DSE) workflow to optimize the pipeline. We further propose an early stopping strategy to tackle the time-consuming progressive JPEG decoding. Comprehensive experiments demonstrate that FD-CNN achieves, on average, 3.24×, 4.29×  throughput improvement, 2.55×, 2.54×  energy reduction and 2.38×, 2.58×  lower latency on ZC-706 and ZCU-102 platforms, respectively, compared to the baseline image-processing pipelines.
QA Dataset Explosion: A Taxonomy of NLP Resources for Question Answering and Reading Comprehension
Authors: Rogers Anna , Gardner Matt , Augenstein Isabelle
Published: September 2022

Alongside huge volumes of research on deep learning models in NLP in the recent years, there has been much work on benchmark datasets needed to track modeling progress. Question answering and reading comprehension have been particularly prolific in this regard, with more than 80 new datasets appearing in the past 2 years. This study is the largest survey of the field to date. We provide an overview of the various formats and domains of the current resources, highlighting the current lacunae for future work. We further discuss the current classifications of “skills” that question answering/reading comprehension systems are supposed to acquire and propose a new taxonomy. The supplementary materials survey the current multilingual resources and monolingual resources for languages other than English, and we discuss the implications of overfocusing on English. The study is aimed at both practitioners looking for pointers to the wealth of existing data and at researchers working on new resources.
Arachne: Search-Based Repair of Deep Neural Networks
Authors: Sohn Jeongju , Kang Sungmin , Yoo Shin
Published: September 2022

The rapid and widespread adoption of Deep Neural Networks (DNNs) has called for ways to test their behaviour, and many testing approaches have successfully revealed misbehaviour of DNNs. However, it is relatively unclear what one can do to correct such behaviour after revelation, as retraining involves costly data collection and does not guarantee to fix the underlying issue. This article introduces Arachne, a novel program repair technique for DNNs, which directly repairs DNNs using their input-output pairs as a specification. Arachne localises neural weights on which it can generate effective patches and uses differential evolution to optimise the localised weights and correct the misbehaviour. An empirical study using different benchmarks shows that Arachne can fix specific misclassifications of a DNN without reducing general accuracy significantly. On average, patches generated by Arachne generalise to
61.3%
of unseen misbehaviour, whereas those by a state-of-the-art DNN repair technique generalise only to
10.2% and sometimes to none while taking tens of times more than Arachne.
We also show that Arachne can address fairness issues by debiasing a gender classification model.
Finally, we successfully apply Arachne to a text sentiment model to show that it generalises beyond convolutional neural networks.
Can Conservative Criminal Justice Reform Survive a Rise in Crime?
Authors: Rizer Arthur L.
Published: September 2022

Over the past 20 years, conservatives have often been at the forefront of criminal justice reform efforts, including to reduce mandatory minimum sentencing, lengthy prison terms, and excessive criminal fines and fees and to improve conditions in prisons and jails. Rejecting the Nixonian “law and order” impulse, criminal justice reform has increasingly become incorporated into the conservative political self-identity. But this has been an elite-driven phenomenon, and it is open to question whether the roots of that political identity are deep enough to withstand the rising salience of crime as a political issue. This review traces how criminal justice reform came to be incorporated into the conservative political identity, raises questions concerning its staying power in the face of rising crime and increasingly strident progressive demands, and proposes some principles that might ground a more lasting conservative commitment to a just, proportionate system of criminal justice.
Chemo-Mechanical Effects of Stack Pressure and Temperature on Anode-Free Lithium Metal Batteries
Authors: Chang Wesley , Xu Tongwei , Steingart Daniel
Published: September 2022

Electrochemical cells using rechargeable lithium metal anodes are sensitive to operating temperature and stack pressure. Current understanding generally assumes that temperature drives changes in lithium metal surface chemistry while stack pressure impacts the anode morphology. In this study, we provide quantifiable evidence for these assumptions and propose mechanisms to guide understanding of temperature and pressure effects on lithium metal cell dynamics. Beyond the direct coupling of pressure with mechanics and temperature with kinetics, we also explore possible effects of temperature on cell mechanics and stack pressure on cell chemistry. We investigate an electrolyte composition based on LiDFOB salt, using a range of operando and ex situ techniques. Mechanistic mapping of temperature- and pressure-dependent cell behavior will aid development of improved lithium metal batteries.
The Critical Stack Pressure to Alter Void Generation at Li/Solid-Electrolyte Interfaces during Stripping
Authors: Feng Min , Yang Chi-Ta , Qi Yue
Published: September 2022

The lithium stripping process generates vacancies, which may accumulate as voids and lead to uneven current distribution and dendrite growth in the following plating cycles. A stack pressure is typically required during stripping, but how to optimize the stack pressure is not clear. In this work, extremely lithiophilic Li/Li2O and lithiophobic Li/LiF interfaces were used to reveal the combining effect of interface interaction and stack pressure induced lithium creep on the stripping critical current density (CCD). A multiscale simulation scheme with Density Functional Theory (DFT), kinetic Monte Carlo (KMC) simulations, and an analytical model was developed. The analytical model predicted lithiophobic interfaces require a higher stack pressure than lithiophilic interfaces to reach the same CCD. The KMC simulations also showed higher stack pressure is needed at lithiophobic interfaces to accelerate Li vacancy diffusion into the bulk and maintain a flat surface. This stack pressure needs to be high enough to alter the Li forward-and-backward hopping barriers at the interface. This multiscale simulation scheme illustrates the importance to include the chemical-mechanical effects during Li stripping morphology evolution. It can be used to design ideal interlayer coating materials to maintain a flat Li surface during cycling.
Internal Short Circuit Diagnosis of Lithium-Ion Battery Based on Mechanism Model and Deep Learning
Authors: Liu Yangyang , Liao Chenglin , Zhang Wenjie , Hu Guang , Zhang Chengzhong , Wang Liye
Published: September 2022

Most safety problems of the lithium-ion battery are attributed to internal short circuits in the battery. There are many factors leading to the internal short circuit of Li-ion battery, and this paper makes the experimental repeatability and controllability of the internal short circuit of the battery better by establishing the mechanism model simulation. In the process of internal short-circuit heat generation in the battery, the battery thermal effect affects the electrochemical reaction of the battery, generating a larger short-circuit current, which releases more short-circuit heat. Therefore, a ternary battery electrochemical-thermal-internal short-circuit coupling mechanism model is established based on the characteristic connection between the three factors. Finally, a lithium-ion battery internal short-circuit diagnosis model is established by combining deep learning algorithms. Six evaluation parameters, including model training time, convergence speed, accuracy, precision, recall and F-value, are also compared, and the effectiveness of convolutional neural network (CNN) and long short-term memory neural network (LSTM) for classification and diagnosis of the severity of internal short circuit in batteries is compared: the CNN model gets better results for classification and diagnosis of internal short circuit in batteries. And lays the foundation for online diagnosis of battery internal short circuit.
Graphene Aerogel-encapsulated Silicon Nanoparticles Mechanofused on Graphite without Prelithiation for Cylindrical Ni-rich NMC811 Li-ion Batteries
Authors: Kongsawatvoragul Ketsuda , Bunyanidhi Panyawee , Jangsan Chonticha , Tejangkura Worapol , Sawangphruk Montree
Published: September 2022

Silicon (Si), one of the promising anodes, provides a high theoretical specific capacity of ca. 3500 mAh g−1 at room temperature. It experiences many drastic issues, such as cost-effectiveness, large volume expansion, and unstable thick solid–electrolyte interfaces (SEI), leading to poor cycling stability. A small amount of Si has recently been added to graphite and used as the anode for commercial Li-ion batteries. Nevertheless, the intrinsic issues of Si still occur. Herein, we encapsulated Si nanoparticles with reduced graphene oxide (RGO) aerogel and graphite to obtain Si-RGO@Graphite using a dry surface coating technique so-called mechanofusion. This technique enhances the strong binding between these materials. We also demonstrated the practical use of the as-prepared Si-RGO@Graphite (9.9:0.1:90.0 wt% of Si:RGO:Graphite) anode coupling with Ni-rich NMC811 cathode at a 18650 cylindrical cell level. In this attempt, we avoid using an expensive vacuum-required prelithiation process, which currently inhibits the practical and commercial use of the Si-based anode. We believe this new composite material may be useful for high-energy LIBs in the future.
Urolithin A attenuates severity of chronic pancreatitis associated with continued alcohol intake by inhibiting PI3K/AKT/mTOR signaling
Authors: Mehra Siddharth , Srinivasan Supriya , Singh Samara , Zhou Zhiqun , Garrido Vanessa , Silva Iago De Castro , Totiger Tulasigeri M. , Dosch Austin R. , Dai Xizi , Dawra Rajinder K. , Jala Venkatakrishna Rao , Shi Chanjuan , Datta Jashodeep , VanSaun Michael , Merchant Nipun , Nagathihalli Nagaraj
Published: September 2022

Our novel findings presented here demonstrate the utility of Uro A as an effective therapeutic agent in attenuating alcoholic chronic pancreatitis (ACP) severity with alcohol continuation after established disease, through suppression of the PI3K/AKT/mTOR signaling pathway.
Wnt5a/β-catenin axis is involved in the downregulation of AT2 lineage by PAI-1
Authors: Jain Krishan G. , Zhao Runzhen , Liu Yang , Guo Xuan , Yi Guohua , Ji Hong-Long
Published: September 2022

Failure to regenerate injured alveoli functionally and promptly causes a high incidence of fatality in coronavirus disease 2019 (COVID-19). How elevated plasminogen activator inhibitor-1 (PAI-1) regulates the lineage of alveolar type 2 (AT2) cells for re-alveolarization has not been studied. This study aimed to examine the role of PAI-1-Wnt5a-β catenin cascades in AT2 fate. Dramatic reduction in AT2 yield was observed in Serpine1Tg mice. Elevated PAI-1 level suppressed organoid number, development efficiency, and total surface area in vitro. Anti-PAI-1 neutralizing antibody restored organoid number, proliferation and differentiation of AT2 cells, and β-catenin level in organoids. Both Wnt family member 5A (Wnt5a) and Wnt5a-derived N-butyloxycarbonyl hexapeptide (Box5) altered the lineage of AT2 cells. This study demonstrates that elevated PAI-1 regulates AT2 proliferation and differentiation via the Wnt5a/β catenin cascades. PAI-1 could serve as autocrine signaling for lung injury repair.
Precision Medicine Approaches to Mental Health Care
Authors: Scala Jack J. , Ganz Ariel B. , Snyder Michael P.
Published: September 2022

Developing a more comprehensive understanding of the physiological underpinnings of mental illness, precision medicine has the potential to revolutionize psychiatric care. With recent breakthroughs in next-generation multi-omics technologies and data analytics, it is becoming more feasible to leverage multimodal biomarkers, from genetic variants to neuroimaging biomarkers, to objectify diagnostics and treatment decisions in psychiatry and improve patient outcomes. Ongoing work in precision psychiatry will parallel progress in precision oncology and cardiology to develop an expanded suite of blood- and neuroimaging-based diagnostic tests, empower monitoring of treatment efficacy over time, and reduce patient exposure to ineffective treatments. The emerging model of precision psychiatry has the potential to mitigate some of psychiatry’s most pressing issues, including improving disease classification, lengthy treatment duration, and suboptimal treatment outcomes. This narrative-style review summarizes some of the emerging breakthroughs and recurring challenges in the application of precision medicine approaches to mental health care.
[Retracted] Rehabilitation of Post‐Stroke Swallowing Dysfunction with Repeated Transcranial Magnetic Stimulation RTMS Based on Tomographic Images
Authors: Liu Jin , Zhuo Hengye , Sun Mingliang
Published: September 2022

In order to study the rehabilitation of dysphagia after stroke, this paper uses tomographic imaging technology and repeated transcranial magnetic stimulation rTMS therapy to verify the effect of this treatment method on the rehabilitation of dysphagia after stroke. In this study, the method of systematic review and meta‐analysis are used to comprehensively collect the current published literature on the application of repeated transcranial magnetic stimulation in post‐stroke dysphagia, and to quantitatively synthesize and qualitatively analyze the data and opinions. This article aims to explore the therapeutic effect and mechanism of repeated transcranial magnetic stimulation in the treatment of dysphagia after stroke, and to evaluate its effectiveness, so as to better guide the application of repetitive transcranial magnetic stimulation in the rehabilitation of dysphagia after stroke. The evaluation methods of rTMS for the clinical efficacy of PAS, FDS, VDS, DOSS, DD, and BI in patients with dysphagia after stroke are consistent. The influencing factors of rTMS on the swallowing function impairment of stroke include the choice of stimulation frequency, stimulation site, and stimulation time. Low‐frequency stimulation of the contralateral hemisphere and high‐frequency stimulation of the affected brain can reduce the excitability of the contralateral brain and enhance the excitability of the affected brain. The stimulation site is mainly in the pharyngeal cortex. Experiments show that rTMS can improve swallowing dysfunction after stroke within 2 weeks, and rTMS has no obvious side effects on swallowing dysfunction in patients with cerebral infarction.
Expressway Information Collection Processing and Control Technology Based on RFID Internet of Things
Authors: He Zhenbang
Published: September 2022

In order to solve the problems of highway traffic congestion and frequent traffic accidents, a technology method for highway information collection, processing, and control based on RFID Internet of Things is proposed. The highway information collection and processing control technology designed in this paper is based on radio frequency identification technology to realize the collection of highway vehicle information, analyze, and process the collected information, and finally get the road condition information. Through the extraction and analysis of the traffic flow information in the video surveillance, the background difference method is used to extract the relevant vehicle factors in the video, including the extraction of traffic flow information, vehicle model extraction, and vehicle speed extraction. Traffic detection provides convenience. The experimental data shows that from Monday to Friday on weekdays, the road congestion rates are 2.72%, 1.94%, 2.77%, 2.27%, and 3.47% respectively; the average vehicle speed on weekdays is 89.96 km/h, and the test results are fast and accurate. The proposed method integrates road information collection and road information service, realizes traffic detection when there are vehicles, and realizes detection and perception of large-scale road traffic systems. Travelers provide real-time traffic information and navigation services.
Deep Convolutional Asymmetric Autoencoder-Based Spatial-Spectral Clustering Network for Hyperspectral Image
Authors: Liu Baisen , Kong Weili , Wang Yan
Published: September 2022

Due to the complex properties of hyperspectral images (HSI), such as spatial-spectral structure, high dimension, and great spectral variability, HSI clustering is a challenging operation. In this paper, we propose a novel deep convolutional asymmetric autoencoder-based spatial-spectral clustering network (DCAAES2C-Net) which employs a convolutional autoencoder (CAE) and an asymmetric autoencoder to investigate spatial-spectral information. First, we use a CAE to extract spatial-spectral features. Then, we introduce an asymmetric autoencoder between the encoder and decoder of CAE to suppress some non-material-related spatial information in latten feature like shading and texture. By using a collaborative strategy to train the proposed networks, we obtain the representation features in a low dimension. Furthermore, we improve the

k

-means algorithm by using the concept of over-clustering to handle fuzzy representation which is difficult to distinguish the cluster, and utilize it to obtain the final HSI clustering result. The results of the experiments demonstrated that the proposed methodology outperforms other methods on the frequently used hyperspectral image dataset.
Systematic Identification, Fragmentation Pattern, And Metabolic Pathways of Hyperoside in Rat Plasma, Urine, And Feces by UPLC-Q-Exactive Orbitrap MS
Authors: Ji Li , Shi Wenjun , Li Yanling , He Jing , Xu Guang , Qin Ming , Guo Yuying , Ma Qun
Published: September 2022

Hyperoside is a natural flavonol glycoside, which has antioxidation, antitumor, and anticancer activities together with other healthy effects like improving cardiovascular function, protecting the liver, and regulating the immune system. It is a popular compound used in the traditional Chinese medicine and different studies on hyperoside are present in the literature. However, studies on the metabolism of hyperoside in vivo were not comprehensive. In this study, UPLC-Q-Exactive Orbitrap MS technology was used to establish a rapid and comprehensive analysis strategy to explore the metabolites and metabolic process of hyperoside in rats. The metabolites of hyperoside were systematically identified in rat plasma, urine, and feces. According to the hyperoside standard substance and relevant works of literature, a total of 33 metabolites were identified, including 16 in plasma, 31 in urine, and 14 in feces. Among them, the metabolites quercetin and dihydroquercetin were unambiguously confirmed by comparison with standard substances. In addition, 13 metabolites had not been reported in hyperoside metabolism-related articles at present. The metabolic reactions of hyperoside in vivo were further explored, including phase I metabolism (hydroxylation, dehydroxylation, glycoside hydrolysis, hydrogenation, and hydration) and phase II metabolism (methylation, acetylation, sulfation, and glucuronide conjugation). The fragment ions of hyperoside and its metabolites were usually produced by glucoside bond hydrolysis, the neutral loss of (CO + OH), COH, CO, O, and Retro-Diels Alder (RDA) cleavage. In conclusion, this study comprehensively characterized the metabolism of hyperoside in rats, providing a basis for exploring its various biological activities.
A Novel Biometric Identification Scheme Based on Zero-Knowledge Succinct Noninteractive Argument of Knowledge
Authors: Guo Chunjie , You Lin , Hu Gengran
Published: September 2022

Biometric identification is a convenient and reliable method in identity authentication. The widespread adoption of biometric identification requires strong privacy protection against possible theft or loss of biometric data. Existing techniques for privacy-preserving biometric identification mainly rely on traditional cryptographic technology such as oblivious transfer and homomorphic encryption, which will incur huge expenses to the system and cannot be applied to large-scale practical applications. For these issues, we propose a biometric identification scheme by constructing zero-knowledge succinct noninteractive argument of knowledge (zk-SNARK). Our scheme not only reduces the communication overhead, which only needs to send 8 constants to the verifier but also can protect the fingerprint template from disclosure. The time complexity of proof generation and proof verification are about O(C) and

O

x

+
log
 
 
C

, respectively, and the size of the proof is only 8 constants, where C and x represent the size of the circuit and the public input, respectively. We have implemented the proposed authentication solution on a public data set of fingerprint images and evaluated the performance and security.
Volatile Organic Compounds of Anchote Tuber and Leaf Extracted Using Simultaneous Steam Distillation and Solvent Extraction
Authors: Ayalew Yenenesh , Berhe Dargie Tsegay , Retta Nigusse , Desse Gulelat , Mohammed Ali , Kim Kyong Su
Published: September 2022

Anchote (Coccinia abyssinica (Lam.) (Cogn)) is an endemic and potentially valuable crop of Ethiopia principally categorized under root and tuber crops, and its newly growing leaves along with the tendrils are also used as nutritious vegetable served after being cooked. Leaf and tuber powders were extracted for the first time to identify volatile organic compounds by simultaneous steam distillation and solvent extraction (SDE) and characterized using gas chromatography mass spectrometry (GC-MS). VOCs having an area percentage above 0.5% were used for identification analysis. From the results, thirty volatile flavor compounds from leaves and fifteen from tubers were identified with the total fraction yield of 770.57 mg/kg and 4536.91 mg/kg, respectively, and from the 30 compounds identified from leaf 16 were distinguished in each of the tested accessions. Ethyl acetate 90.47% (697.13 mg/kg) was detected in a higher amount exhibiting >1% peak area. The rest 6.03% (46.46 mg/kg) were minor quantities (<1%) of the total (770.57 mg/kg) volatile flavor fraction. Among the 15 identified compounds in the tuber, ethyl acetate was the only major compound that accounted together for 99.15% (4498.33 mg/kg) of the total volatile flavor fraction and 0.85% (38.58 mg/kg) being reported in minor quantities (<1%). The SDE extraction and GC-MS analysis of anchote leaves and tubers successfully identified various volatile flavor compounds, which indicates that anchote was found to be a potential source of volatile flavor compounds that can be used as a food flavoring agent and in folk medicines. Thus, this study confirms that anchote leaf and tuber can be used for more specific and valuable applications in food and medicine industries.
Research on the Application of Hybrid Density Network Combined with Gaussian Model in Computer Music Choreography
Authors: Wang Li-Juan
Published: September 2022

Dance, as a unique form of expression, is usually accompanied by music and presented to the audience visually, improving people’s cultural and spiritual lives while also strengthening their creative energy. And dance choreography is usually created by a few skilled choreographers, either individually or together, with a high level of expertise and complexity. With the introduction of motion capture technology and artificial intelligence, computers can now do autonomous choreography based on music, and science and technology are changing the way artists produce art today. Computer music choreography must solve two fundamental issues: how to create realistic and creative dance moves without relying on motion capture and manual creation and how to improve music and dance synchronization utilizing appropriate music and movement elements and matching algorithms. This article employs a hybrid density network to generate dances that fit the target music in three steps, action generation, action screening, and feature matching, to address the aforementioned two concerns.
Primary Repair versus Reconstruction in Patients with Bilateral Anterior Cruciate Ligament Injuries: What Do Patients Prefer?
Authors: Vermeijden Harmen D. , Monaco Edoardo , Marzilli Fabio , Yang Xiuyi A. , van der List Jelle P. , Ferretti Andrea , DiFelice Gregory S.
Published: September 2022

Purpose. The purpose is to evaluate knee preference and functional outcomes of patients with primary anterior cruciate ligament (ACL) repair in one knee and ACL reconstruction in the contralateral side. Methods. All patients who underwent both procedures were retrospectively reviewed at minimum two-year follow-up. Patients were asked to complete questionnaires regarding their operated knees’ preferences during rehabilitation, daily activities, sports activities, and overall function. Furthermore, the Subjective International Knee Documentation Committee, Forgotten Joint Score-12, and Anterior Cruciate Ligament-Return to Sport after Injury were completed. Results. Twenty-one patients were included. All patients underwent ACL reconstruction first, which was displayed at younger age at surgery (24 vs. 33 years,

p
=
0.010

) and longer follow-up (10.2 vs. 2.3 years,

p
<
0.001

), respectively. Thirty-three percent preferred the repaired knee, 11% the reconstructed knee, and 56% had no preference; however, 78% indicated that their repaired knee was less painful during rehabilitation and 83% reported earlier range of motion (ROM) return following repair, which was similar for both knees in 17%. Eighty-three percent of patients indicated better function and progression during rehabilitation with their repaired knee and 11% with their reconstructed knees. No statistical differences were found in patient-reported outcomes between both procedures (all

p
>
0.4

). Objective laxity assessment showed mean side-to-side difference of 0.6 mm between both sides in favor of the reconstructed knee. Conclusion. This study showed that ACL repair and ACL reconstruction lead to similar functional outcomes. However, patients undergoing both procedures may have less pain, earlier ROM return, and faster rehabilitation progression following primary repair.
Halobacterium salinarum NRC-1 Sustains Voltage Production in a Dual-Chambered Closed Microbial Fuel Cell
Authors: Goncalves Rodrigo Oliveira , Salehi Ali , Publico Marlon , Nyende Jimmy , Nadarajah Nalina , Ghoreyshi Soheil , Shastri Padmaja
Published: September 2022

Sustained bioenergy production from organisms that thrive in high salinity, low oxygen, and low nutrition levels is useful in monitoring hypersaline polluted environments. Microbial fuel cell (MFC) studies utilizing single species halophiles under salt concentrations higher than 1 M and as a closed microbial system are limited. The current study aimed to establish baseline voltage, current, and power density from a dual-chambered MFC utilizing the halophile Halobacterium salinarum NRC-1. MFC performance was determined with two different electrode sizes (5 cm2 and 10 cm2), under oscillating and nonoscillating conditions, as well as in a stacked series. A closed dual-chamber MFC system of 100 mL capacity was devised with Halobacterium media (4.3 M salt concentration) as both anolyte and catholyte, with H. salinarum NRC-1 being the anodic organism. The MFC measured electrical output over 7, 14, 28, and 42 days. MFC output increased with 5 cm2 sized electrodes under nonoscillating (

p
<
0.0001

) relative to oscillating conditions. However, under oscillating conditions, doubling the electrode size increased MFC output significantly (

p
=
0.01

). The stacked series MFC, with an electrode size of 10 cm2, produced the highest power density (1.2672 mW/m2) over 14 days under oscillation. Our results highlight the potentiality of H. salinarum as a viable anodic organism to produce sustained voltage in a closed-MFC system.
Digital Transformation of Tourism Industry and Smart Tourism Recommendation Algorithm Based on 5G Background
Authors: Xia Wenting
Published: September 2022

As a hot issue of current research, tourism information service has higher and higher requirements for intelligent construction. Tourism service recommendation is the embodiment of smart tourism. However, there are still obvious deficiencies in solving the problem of Internet information load and improving user experience. Through functional analysis, architecture design, selection of relevant development frameworks, and improvement of collaborative filtering algorithms, a stable, reliable, high-performance, multi-functional intelligent travel recommendation system can be developed that can complete personalized recommendations. It can achieve the purpose of improving the efficiency and accuracy of recommendation and recommending tourism-related information to users in a targeted manner. Analyze the test plan and test the recommended algorithm module. In the case of different concurrency and database levels, the system response time is 0.9 s and 1 s, respectively. And in the case of high throughput, the system response time is 1.5 s, indicating that the system is running stably. It not only tested the storage and calculation of big data but also improved the usability of the travel information service recommendation system and the user experience of the system.
An Enhanced Drone Technology for Detecting the Human Object in the Dense Areas Using a Deep Learning Model
Authors: Refaai Mohamad Reda A. , Rinku Dhruva R. , Thamarai I. , S. Meera  , Sripada Naresh Kumar , Yishak Simon
Published: September 2022

During the recent decade, emerging technological and dramatic uses for drones were devised and accomplished, including rescue operations, monitoring, vehicle tracking, forest fire monitoring, and environmental monitoring, among others. Wildfires are one of the most significant environmental threats to wild areas and forest management. Traditional firefighting methods, which rely on ground operation inspections, have major limits and may threaten firefighters’ lives. As a result, remote sensing techniques, particularly UAV-based remotely sensed techniques, are currently among the most sought-after wildfire-fighting approaches. Current improvements in drone technology have resulted in significant breakthroughs that allow drones to perform a wide range of more sophisticated jobs. Rescue operations and forest monitoring, for example, demand a large security camera, making the drone a perfect tool for executing intricate responsibilities. Meanwhile, growing movement of the deep learning techniques in computer vision offers an interesting perspective into the project’s objective. They were used to identify forest fires in their beginning stages before they become out of control. This research describes a methodology for recognizing the presence of humans in a forest setting utilizing a deep learning framework and a human object detection method. The goal of identifying human presence in forestry areas is to prevent illicit forestry operations like illegal access into forbidden areas and illegal logging. In recent years, a lot of interest in automated wildfire identification utilizes UAV-based visual information and various deep learning techniques. This study focused on detecting wildfires at the beginning stages in forest and wilderness areas, utilizing deep learning-based computer vision algorithms that control and then mitigate massive damages to human life and forest management.
Ki-67 Index Provides Long-Term Survival Information for Early-Stage HER2-Low-Positive Breast Cancer: A Single-Institute Retrospective Analysis
Authors: Qi Wei-Xiang , Chen Lingyan , Cao Lu , Xu Cheng , Cai Gang , Chen Jiayi
Published: September 2022

Aim. It has been reported that more than half of breast cancer (BC) could be identified as HER2-low-positive, which might be a distinct subtype. But the results are controversial. We aim to compare the survival outcomes between HER2-low-positive and HER2-0 BC with Asian women based on HR status or Ki-67 index. Methods. Between January 2009 and December 2017, HER2-nonamplified BC in our single institute was identified. Patients were classified as HER2-low and HER2-0 cohort. Clinical characteristics were compared between these two groups and survival outcomes were calculated by the Kaplan–Meier method. We also performed subgroup analysis according to Ki-67 index and hormone-receptor (HR) status. Results. Of the 2,230 included patients, 536 presented with HER2-0, and 1,694 with HER2-low positive. After a median follow-up of 85 months (range: 1–152 months), the 8-year OS, BCSS, and RFS of the overall cohort were 91%, 95%, and 89%, respectively. In comparison with the HER2-0 cohort, majority of HER2-low-expression BC concurrently presented with HR positive (82.3% vs. 69%,

P
<
0.001

). There was no significant survival difference between the two groups in terms of OS, BCSS, and RFS (all

p
>
0.05

). We then performed subgroup analysis according to HR status and Ki-67 index (<14% vs. ≥14%). Our results indicated that there was no significant survival difference between HER2-low-positive and HER2-0 tumors regardless of HR status (

p
>
0.05

), while OS (

p
=
0.026

) and BCSS (

p
=
0.052

) of HER2-0 BC with high Ki-67 index were significantly poorer than that of HER2-low positive with high Ki-67, but not for RFS (

p
=
0.17

). Conclusion. Among early stage HER2-nonamplified BC, no significant survival difference could be found between HER2-low positive and HER2-0 cohort regardless of HR status. Survival outcomes of HER2-low positive with high Ki-67 seem to be poorer than that of HER2-0 tumors with high Ki-67 index.
Design and Application of e-Commerce Platform System Based on Blockchain Technology on the Internet of Things
Authors: Zhao Bin , Jiang Xue , Zhang Na , Guo Qian , Song RongRong
Published: September 2022

Focusing on the problems of platform monopoly, power rent-seeking and high cost in the current Internet e-commerce platform system, a new method and paradigm of e-commerce platform is introduced in this paper based on the blockchain, smart contract technology, tokenomics thought, and e-commerce professional theory. The interconnection of e-commerce information value chain can be realized by means of blockchain related technology and the construction of circulation system, payment system, and credit system in the core module of e-commerce platform. From the perspective of users of e-commerce platform, the optimizing of the application process and operation structure of e-commerce operation can significantly improve users’ service experience, avoid information disclosure risk, and reduce process redundancy as well. The prominent advantages of this e-commerce platform include data security, data sharing, shopping cost reduction, monopoly elimination, community construction, and commercial form innovation. The e-commerce function was originally defined as building an e-commerce platform, optimizing and promoting various search products, maintaining, refactoring and extending the e-commerce platform, and developing and monetizing online products. It has 6 operating modes: direct sales model, yellow pages model, coupon model, brokerage model, reservation model, and market model.
Physical, Chemical, and Antioxidant Characterization of Nano-Pomegranate Peel and Its Impact on Lipid Oxidation of Refrigerated Meat Ball
Authors: ElBeltagy Alaa E. , Elsayed Mahmoud , Khalil Sabry , Kishk Yasser F. M. , Abdel Fattah Abdel Fattah A. , Alharthi Salman S.
Published: September 2022

Pomegranate peel (CPP), enriched with bioactive constituents, had potent antioxidant features. Therefore, it is worth finding out functional and antioxidant features of the nanoscale pomegranate peel. The nanoscale of pomegranate peel was prepared by ultrafine grinding in a ball mill for 45 min (NPP45) and 90 min (NPP90). The physical (SEM, TEM, FTIR, and XRD) and chemical characteristics (phenolics, flavonoids, DPPH scavenging activity, FRAP, and reducing power) of nanoparticles were studied. The quality aspects of cold stored (5 ± 2°C) meatballs formulated with 0.5% (W/W) of nano-peel powder were evaluated. Similarly, FTIR spectra and XRD patterns were recorded for nano and crude pomegranate peel samples. Generally, grinding the crude peel for 45 and 90 min enhanced its scavenging activity, reducing power, FRAP, total phenolic, and flavonoid by a range of 12.58 to 20.37 and 20.57% to 35.18%, respectively. The addition of crude/nanosized peel to the meat ball diminish (p<0.05) formation of thiobarbituric acid (TBARS), peroxide (PV), and volatile nitrogen and kept the sensory attributes up to 9 days of cold storage. No significant differences were noticed in PV and TBARS of meatballs formulated with 0.5% NPP90 and 0.1% BHT, which suggests the potential use of nanoscale pomegranate peel as natural substitutes to BHT in meat products.