bluuebunny/crossref_metadata_2025_split huggingface dataset:
A multiple fault diagnosis approach for transformer based on double antenna RFID sensor and PSA-optimized LSTM
Authors: Wang Tao , Yang Song , Yin Ruihan , Lu Yin , Li Bing , He Yigang , Zhao Jing , Deng Conglong 
Published: September 2023

Abstract
This work aims to realize multiple mechanical fault diagnosis for transformers in the incubation period. A double antenna radio frequency identification (RFID) sensor is used to obtain vibration signals generated by a transformer. The measured raw vibration signals have prominent nonlinear characteristics; besides, they are mixed with vast noises, such as electromagnetic interference, measurement disturbance and so on, making it challenging to identify distinguishable features from the measured data. The long short-term memory (LSTM), which exhibits satisfactory performance in dealing with large-scale nonlinear time-series signals, is adopted to extract features from high-dimensional raw signals. Because the parameters of the LSTM cell and support vector machine significantly determine the accuracy of diagnosis, these parameters are determined using the chaotic quantum particle swarm optimization algorithm. Moreover, the sparse periodical attention (PSA) mechanism is used to enhance the LSTM model’s performance by focusing on global feature learning and determining the input data length. The experimental results verify that the exploited RFID sensor realizes reliable data wireless transmission within 17.5 m. Moreover, the PSA-optimized LSTM approach achieves satisfactory trade-off between diagnosis accuracy and computation complexity while dealing with multiple mechanical fault diagnosis of a transformer in early stages.
AC-Faster R-CNN: an improved detection architecture with high precision and sensitivity for abnormality in spine x-ray images
Authors: Zhong Bolin , Yi Jizheng , Jin Ze 
Published: September 2023

Abstract

Objective. In clinical medicine, localization and identification of disease on spinal radiographs are difficult and require a high level of expertise in the radiological discipline and extensive clinical experience. The model based on deep learning acquires certain disease recognition abilities through continuous training, thereby assisting clinical physicians in disease diagnosis. This study aims to develop an object detection network that accurately locates and classifies the abnormal parts in spinal x-ray photographs. Approach. This study proposes a deep learning-based automated multi-disease detection architecture called Abnormality Capture-Faster Region-based Convolutional Neural Network (AC-Faster R-CNN), which develops the feature fusion structure Deformable Convolution Feature Pyramid Network and the abnormality capture structure Abnormality Capture Head. Through the combination of dilated and deformable convolutions, the model better captures the multi-scale information of lesions. To further improve the detection performance, the contrast enhancement algorithm Contrast Limited Adaptive Histogram Equalization is used for image preprocessing. Main results. The proposed model is extensively evaluated on a testing set containing 1007 spine x-ray images and the experimental results show that the AC-Faster R-CNN architecture outperforms the baseline model and other advanced detection architectures. The mean Average Precision at Intersection over Union of 50% are 39.8%, the Precision and Sensitivity at the optimal cutoff point of Precision–Recall curve are 48.6% and 46.3%, respectively, reaching the current state-of-the-art detection level. Significance. AC-Faster R-CNN exhibits high precision and sensitivity in abnormality detection tasks of spinal x-ray images, and effectively locates and identifies abnormal areas. Additionally, this study would provide reference and comparison for the further development of medical automatic detection.
Confronting sound speed resonance with pulsar timing arrays
Authors: Jin Jia-Heng , Chen Zu-Cheng , Yi Zhu , You Zhi-Qiang , Liu Lang , Wu You 
Published: September 2023

AbstractThe stochastic signal detected by pulsar timing arrays (PTAs) has raised great interest in understanding its physical origin. Assuming the signal is a cosmological gravitational-wave background produced by overly large primordial curvature perturbations, we investigate the sound speed resonance effect with an oscillatory behavior using the combined PTA data from NANOGrav 15-yr data set, PPTA DR3, and EPTA DR2. We find that the stochastic signal can be explained by the induced gravitational waves sourced by the sound speed resonance mechanism, with the oscillation frequencyf*∈ [1.51, 4.90] × 10-7Hz and the start time of oscillation |τ0| ∈ [2.05, 106] × 107s.
Quantitative insights in tissue growth and morphogenesis with optogenetics
Authors: Mim Mayesha Sahir , Knight Caroline , Zartman Jeremiah J 
Published: September 2023

Abstract
Cells communicate with each other to jointly regulate cellular processes during cellular differentiation and tissue morphogenesis. This multiscale coordination arises through the spatiotemporal activity of morphogens to pattern cell signaling and transcriptional factor activity. This coded information controls cell mechanics, proliferation, and differentiation to shape the growth and morphogenesis of organs. While many of the molecular components and physical interactions have been identified in key model developmental systems, there are still many unresolved questions related to the dynamics involved due to challenges in precisely perturbing and quantitatively measuring signaling dynamics. Recently, a broad range of synthetic optogenetic tools have been developed and employed to quantitatively define relationships between signal transduction and downstream cellular responses. These optogenetic tools can control intracellular activities at the single cell or whole tissue scale to direct subsequent biological processes. In this brief review, we highlight a selected set of studies that develop and implement optogenetic tools to unravel quantitative biophysical mechanisms for tissue growth and morphogenesis across a broad range of biological systems through the manipulation of morphogens, signal transduction cascades, and cell mechanics. More generally, we discuss how optogenetic tools have emerged as a powerful platform for probing and controlling multicellular development.
Operating mode dependent energy transfer efficiency in a quantum well waveguide
Authors: Al-Dolaimy F , Kzar M H , Jamil N Y , Zaid M , Rasen F A , Hussain S , Al-Majdi K , Mohsen K S , Alawadi A H , Alsaalamy A 
Published: September 2023

Abstract
In this paper, we delve into the intricate interplay between optical fields with varying relative phases in a closed-loop configuration semiconductor quantum well waveguide with four distinct energy levels, and how it impacts the Fraunhofer diffraction patterns obtained via four-wave mixing. By harnessing a strong control field, a standing wave driving field, and two weak probe and signal fields, we drive the waveguide to generate these patterns with maximum efficiency. To achieve this, we consider three distinct light-matter interaction scenarios, where the system is first set up in either a lower electromagnetically induced transparency or a coherent population trapping state, followed by a final state that enables electron spin coherence (ESC) induction. Our results reveal that the efficiency of Fraunhofer diffraction in the quantum well waveguide can be enhanced significantly under specific parameter regimes via the spin coherence effect. Further investigation of the light-matter interaction in the ESC zone, where only one of the control fields is a standing wave field, demonstrates that spin coherence facilitates more efficient transfer of energy from the probe light to the third and fourth orders, highlighting its crucial role in shaping the diffraction patterns.
Spatially structured optical effects in semiconductor quantum dots via biexciton coherence
Authors: Batoo K M , Al-Dolaimy F , Zaid M , Sharif H , Rasen F A , Hussain S , Mohsen K S , Kareem S H , Alawadi A H , Alsaalamy A 
Published: September 2023

Abstract
In this paper, we study the spatially structured optical effects that occur when weak laser lights interact with coherently prepared semiconductor quantum dots (SQDs). Initially, the SQD is prepared in a coherent superposition of the lower exciton states. By utilizing two weak optical vortex fields that couple to a biexciton state, we observe spatially dependent effects of the absorption of probe fields. Using the well-established Maxwell–Bloch equations, we analyze the generation of composite optical vortex beams within this system. Our investigation revolves around the formation of different types of spatially dependent beams, exploring their properties and characteristics. Additionally, the transfer of optical vortices through the parametric generation process is examined, for the case where only one vortex beam is present at the beginning of the medium. This study provides insights into the spatially structured optical phenomena in coherently prepared SQDs and contributes to the understanding of light–matter interactions in such systems.
Quantification of titanium dioxide (TiO2) anatase and rutile polymorphs in binary mixtures by Raman spectroscopy: an interlaboratory comparison
Authors: Sacco Alessio , Mandrile Luisa , Tay Li-Lin , Itoh Nobuyasu , Raj Ankit , Moure Alberto , Del Campo Adolfo , Fernandez Jose F , Paton Keith R , Wood Sebastian , Kwon Hyuksang , Adel Tehseen , Hight Walker Angela R , Martins Ferreira Erlon Henrique , Theissmann Ralf , Koch Thomas , Giovannozzi Andrea Mario , Portesi Chiara , Rossi Andrea Mario 
Published: September 2023

Abstract
This article presents an interlaboratory comparison (ILC) on Raman spectroscopy as a technique for relative quantification of the two most common polymorphs of titanium dioxide (TiO2)—anatase and rutile—in binary mixtures. Some standard methods are currently employed internationally for the determination of TiO2 content in samples (ISO 591-1, ASTM D3720-90), but require extensive sample preparation, do not distinguish between the two polymorphs or are accurate only for small fractions of either polymorph. Raman spectroscopy is a well-suited characterization technique for measuring and differentiating TiO2 in a fast, non-invasive way, while requiring no particular reagent or sample preparation. Eleven international participants conducted the study under the framework of Versailles Project on Advanced Materials and Standards. The collected data was analyzed by means of partial least squares regression after spectral preprocessing. The resulting models all show discrepancies of lower than 2% from the nominal values in the quantitative analysis over the concentration range of 5%–95% mixture fractions, with many datasets showing substantial improvement margins on this figure. The results of this ILC provide validation of Raman spectroscopy as a reliable method for quantification of TiO2 phases.
BrainWave-Scattering Net: a lightweight network for EEG-based motor imagery recognition
Authors: Barmpas Konstantinos , Panagakis Yannis , Adamos Dimitrios A , Laskaris Nikolaos , Zafeiriou Stefanos 
Published: September 2023

Abstract

Objective. Brain–computer interfaces (BCIs) enable a direct communication of the brain with the external world, using one’s neural activity, measured by electroencephalography (EEG) signals. In recent years, convolutional neural networks (CNNs) have been widely used to perform automatic feature extraction and classification in various EEG-based tasks. However, their undeniable benefits are counterbalanced by the lack of interpretability properties as well as the inability to perform sufficiently when only limited amount of training data is available. Approach. In this work, we introduce a novel, lightweight, fully-learnable neural network architecture that relies on Gabor filters to delocalize EEG signal information into scattering decomposition paths along frequency and slow-varying temporal modulations. Main results. We utilize our network in two distinct modeling settings, for building either a generic (training across subjects) or a personalized (training within a subject) classifier. Significance. In both cases, using two different publicly available datasets and one in-house collected dataset, we demonstrate high performance for our model with considerably less number of trainable parameters as well as shorter training time compared to other state-of-the-art deep architectures. Moreover, our network demonstrates enhanced interpretability properties emerging at the level of the temporal filtering operation and enables us to train efficient personalized BCI models with limited amount of training data.
Technology to solve global problems: an emerging consensus for green industrial policy?
Authors: Victor David G , Carlton Emily K 
Published: September 2023

Abstract
Even as most mainstream policy analysts support the idea of active industrial policy to create new green industries and cut carbon pollution, important dissenting voices still question whether government intervention is possible without extreme waste. We suggest that many of today’s debates, which echo debates of the 1970s, need updating to reflect the reality that a lot has been learned about where and how government can pursue effective industrial policy. The more transformative the goals, the harder it is to know which policies, technologies and business models will work, and the greater the need for ‘experimental’ approaches to policy that put uncertainty as the centerpiece. Creating industrial transformation in the context of deep uncertainty is the central challenge for industrial policy. Solving this problem requires not just attention to policy design and industrial response but also possible reforms to the institutions that design and implement policies. Today’s policy institutions, like today’s firms, are mostly organized for the current industrial system—not necessarily the future.
Variability of spring ecosystem water use efficiency in Northeast Asia and its linkage to the Polar-Eurasia pattern
Authors: Xin Ning , Zhou Botao , Chen Haishan , Sun Shanlei 
Published: September 2023

Abstract
Given that water use efficiency (WUE) is an important indicator to measure the trade-off between carbon uptake and water consumption within the ecosystem, better understanding the variation of ecosystem WUE and related driving factors is of great interest. In this study, the variability of spring ecosystem WUE in Northeast Asia (NEA) was investigated. The results show that its primary mode exhibits a monosign variation. This mode is directly controlled by the variability of gross primary productivity. The climate conditions also play remarkable roles, featuring that warm surface air temperature (high soil moisture) favors enhanced ecosystem WUE in northern (southern) NEA. Further analysis reveals that the Polar-Eurasia (POL) pattern can significantly impact the variability of spring ecosystem WUE in NEA through changing surface air temperature and soil moisture. When the POL pattern lies in the positive phase during spring, anticyclonic circulation anomalies with an equivalent barotropic structure prevail over northern NEA, concurrent with anomalous easterlies over southern NEA and a weakening of the East Asian jet (EAJ). Accordingly, anomalous downward motion is introduced over northern NEA, resulting in higher surface air temperature which is beneficial for the increase of local ecosystem WUE. Meanwhile, the easterly anomalies help to increase water vapor transport into southern NEA and the weakened EAJ can induce anomalous ascending over southern NEA, favoring the increase of precipitation and hence soil moisture, which consequently enhances the ecosystem WUE in southern NEA.
Numerical approach for the optimal design of dewatering schemes for deep foundation pits
Authors: Si T T , Tan N N , Long T T , Đ Dậu T 
Published: September 2023

Abstract
In recent years, underground spaces have expanded widely in the urban zone. The appropriate estimation number of dewatering well with economical costs and securing soil stability at construction sites is challenging in designing pumping schemes. This paper aims to present the numerical approach for the optimal number of wells for designing a dewatering deep foundation pit. First, the estimated hydraulic conductivity was selected among the Theis, Cooper-Jacod, and reverse groundwater modeling methods. The estimated hydraulic conductivity via reverse groundwater modeling shows the lowest error with the monitoring pumping test. Second, hydraulic conductivity was input to groundwater modeling to estimate the groundwater level of four pumping schemes with the same total pumping rate (20 wells, 17 wells, 14 wells, and 13 wells). The number of wells was optimal from four random pumping schemes concerning the cost and the drawdown outside the pit. As a result, the pumping scheme with 16 wells is suitable, satisfying the cost and safety outside the drawdown of the foundation pit. The numerical approach could apply to estimate the number of wells for dewatering projects with economic costs and less impact on surrounding construction sites.
Reimagining coastal public space: The case study of Da Nang city, Viet Nam
Authors: Linh Dang Ngoc Thao , Son Truong Ngoc 
Published: September 2023

Abstract
Public space has been at the core of urban studies with the meaning of control element in any city planning. The benefit is clearly indispensable for maintaining urban productivity, social cohesion and inclusion, civic identity and quality of life. However, in the context of competing market forces and commercial interests dominating cities’ planning agendas, planning for coastal public space tends to be for economic gain rather than supporting residents’ social needs. This article examines the physical condition of coastal public space, how it is used, and what economic role it plays in city planning regarding tourism development. A comparative analysis of different coastal recreation spaces in Da Nang, Viet Nam is used to create public space design concepts emphasizing the distribution of spatial structure and to different group users. This research defines a future vision of coastal public space development in line with striking a balance between economic needs and the complexities of public space distribution in emerging development cities. Findings can contribute to creating public space design concepts emphasizing the distribution of spatial structure to different group users.
Contamination of microplastics in mangrove sediment cores from Lach Huyen area, Hai Phong city, Vietnam
Authors: Linh Le Thi Khanh , Duong Hoang Anh , Duc Truong Huu , Tue Nguyen Tai , Dung Luu Viet 
Published: September 2023

Abstract
Microplastics have been found in coastal ecosystems since the 1970s, causing many negative impacts on the environment and aquatic organisms. In the present study, we examined the microplastic concentration and sediment grain size in mangrove sediment cores near Lach Huyen Port, Hai Phong city, Vietnam. The microplastic concentration in mangrove sediments varied from 0-3150 particles/kg with an average value of 1309.1±124.8 particles/kg, much higher than in similar regions worldwide. They are mainly secondary microplastics with various colors and sizes. The microplastic types include 80% of microfibers and the rest is a mixture of microfoam, microfragment, and microfilm. Research results showed that the concentration of microplastics tends to decrease from top to bottom of the sediment cores. However, the present study did not find any relationship between sediment grain size and microplastic abundance. Future studies should assess the pollution sources, factors influencing microplastic distribution, and the effects of microplastic contamination on coastal ecosystems in this area.
Status assessment and solutions to improve the efficiency of the aerobic process in seafood wastewater treatment: Case study in Da Nang fisheries service industrial park
Authors: Thuy Phan Thi Kim , Quan Tran Ha , Quang Tran Van 
Published: September 2023

Abstract
This research presents the current status and solutions to improve the efficiency of the aerobic process in seafood wastewater treatment in the Da Nang fisheries service industrial park. The assessment included: (1) After pre-treatment, wastewater from seafood processing plants has a high concentration of organic matter and nutrients. The concentration of nutrients by N-NH4
+ in the influence of biological reactors fluctuates over a large range from 106.5-475.3mg/l with a low ratio of BOD5/N-NH4
+ (1.85-5.17). The ratio of C/N in the influence of the aerobic reactors is lower than 5, which causes the process’s instability. Therefore, it is fairly difficult to maintain the quality of treated water to meet discharge standards. (2) The environmental conditions of the aerobic process in biological reactors are not guaranteed. The concentration of operational activated sludge is 2-3 times higher than the recommended value. The aerobic process is operated with a low organic loading rate of 0.049-0.152gBOD5/gMLVSS.day; 0.073-0.183gCOD/g MLVSS.day; (3) In order to reach organic matter and nutrient values that satisfy column B-QCVN 11-MT:2015/BTNM, some suggestions are: (i) Adjusting the operating MLVSS concentration of 2.5-3.5g/l combined with ensuring environmental conditions of aerobic process in biological reactors; (ii) Operating parameters: HRT ≥ 8h; F/M: 0.25-0.35gCOD/gMLVSS.day (or 0.86-1.04gCOD/m3.day).
Air emission inventory and air quality modelling for developing countries: A case of Vinh Phuc province, Vietnam
Authors: Quynh Pham Huong , Van Do Thi Cam , Viet Vu Nguyen , Mai Chu Tuong , Anh Pham Nguyet , Oanh Le Thi Kim , Bang Ho Quoc 
Published: September 2023

Abstract
Vinh Phuc is a province in the Northern Key Economic Region with a developed industry, a high population rate, and a relatively high traffic density. Along with economic growth, environmental protection in general, and air quality improvement in particular are two challenges that Vinh Phuc authorities must identify in the coming years. To do so, total emission load data must be acquired in order to establish a pollution distribution map for the entire Vinh Phuc province. The paper’s aim is to provide: (i) air emissions inventory, and (ii) the application of the model to build a map of pollution distribution in Vinh Phuc province. Different methods are used in this study, including (i) emission inventory utilizing both bottom-up and top-down techniques [1], (ii) air quality simulation applying the Finite Volume Model - Transport and Air Pollution Model [2] (FVM-TAPOM), and (iii) other approaches such as interview method, statistical tool, geographic information system (GIS), expert method, etc. According to research findings, motorbikes account for more than 90% of the concentration of pollutants for traffic activities. Iron and steel industries has the highest emission among 15 industries and straw burning activities account for more than 70% of total area source emissions. An emissions distribution map for traffic and industry sources was developed based on air quality simulation which showed that Vinh Yen city has the highest emissions among 09 districts and cities in Vinh Phuc province. The study also identified the causes of air pollution in Vinh Phuc province, providing remedies for the management agency to release plans and activities to minimize Vinh Phuc province emissions and ensure the local community’s air quality and health.