bluuebunny/crossref_metadata_2025 huggingface dataset:
Transcriptome and Proteome Association Analysis to Screen Candidate Genes Related to Salt Tolerance in Reaumuria soongorica Leaves under Salt Stress
Authors: Liu Hanghang , Chong Peifang , Yan Shipeng , Liu Zehua , Bao Xinguang , Tan Bingbing
Published: October 2023

This work aims at studying the molecular mechanisms underlying the response of Reaumuria soongorica to salt stress. We used RNA sequencing (RNA-Seq) and Tandem Mass Tag (TMT) techniques to identify differentially expressed genes (DEGs) and differentially expressed proteins (DEPs) in R. soongorica leaves treated with 0, 200, and 500 mM NaCl for 72 h. The results indicated that compared with the 0 mM NaCl treatment group, 2391 and 6400 DEGs were identified in the 200 and 500 mM NaCl treatment groups, respectively, while 47 and 177 DEPs were also identified. Transcriptome and proteome association analysis was further performed on R. soongorica leaves in the 0/500 mM NaCl treatment group, and 32 genes with consistent mRNA and protein expression trends were identified. SYP71, CS, PCC13-62, PASN, ZIFL1, CHS2, and other differential genes are involved in photosynthesis, vesicle transport, auxin transport, and other functions of plants, and might play a key role in the salt tolerance of R. soongorica. In this study, transcriptome and proteome association techniques were used to screen candidate genes associated with salt tolerance in R. soongorica, which provides an important theoretical basis for understanding the molecular mechanism of salt tolerance in R. soongorica and breeding high-quality germplasm resources.
Soil Water Deficit Reduced Root Hydraulic Conductivity of Common Reed (Phragmites australis)
Authors: Wang Ruiqing , Zhang Zhenming , Wang Haoyue , Chen Yinglong , Zhang Mingxiang
Published: October 2023

Alterations in root hydraulics in response to varying moisture conditions remain a subject of debate. In our investigation, we subjected common reeds (Phragmites australis) to a 45-day treatment with four distinct soil moisture levels. The findings unveiled that, in response to drought stress, the total root length, surface area, volume, and average diameter exhibited varying degrees of reduction. Anatomically, drought caused a reduction in root diameter (RD), cortex thickness (CT), vessel diameter (VD), and root cross-sectional area (RCA). A decrease in soil moisture significantly reduced both whole- and single-root hydraulic conductivity (Lpwr, Lpsr). The total length, surface area, volume, and average diameter of the reed root system were significantly correlated with Lpwr, while RD, CT, and RCA were significantly correlated with Lpsr. A decrease in soil moisture content significantly influenced root morphological and anatomical characteristics, which, in turn, altered Lpr, and the transcriptome results suggest that this may be associated with the variation in the expression of abscisic acid (ABA) and aquaporins (AQPs) genes. Our initial findings address a gap in our understanding of reed hydraulics, offering fresh theoretical insights into how herbaceous plants respond to external stressors.
Assessment of Growth, Yield, and Nutrient Uptake of Mediterranean Tomato Landraces in Response to Salinity Stress
Authors: Ntanasi Theodora , Karavidas Ioannis , Zioviris Georgios , Ziogas Ioannis , Karaolani Melini , Fortis Dimitrios , Conesa Miquel À. , Schubert Andrea , Savvas Dimitrios , Ntatsi Georgia
Published: October 2023

Salinity is a major stress factor that compromises vegetable production in semi-arid climates such as the Mediterranean. The accumulation of salts in the soil can be attributed to limited water availability, which can be exacerbated by changes in rainfall patterns and rising temperatures. These factors can alter soil moisture levels and evaporation rates, ultimately leading to an increase in soil salinity, and, concomitantly, the extent to which crop yield is affected by salinity stress is considered cultivar-dependent. In contrast to tomato hybrids, tomato landraces often exhibit greater genetic diversity and resilience to environmental stresses, constituting valuable resources for breeding programs seeking to introduce new tolerance mechanisms. Therefore, in the present study, we investigated the effects of mild salinity stress on the growth, yield, and nutritional status of sixteen Mediterranean tomato landraces of all size types that had been pre-selected as salinity tolerant in previous screening trials. The experiment was carried out in the greenhouse facilities of the Laboratory of Vegetable Production at the Agricultural University of Athens. To induce salinity stress, plants were grown hydroponically and irrigated with a nutrient solution containing NaCl at a concentration that could maintain the NaCl level in the root zone at 30 mM, while the non-salt-treated plants were irrigated with a nutrient solution containing 0.5 mM NaCl. Various plant growth parameters, including dry matter content and fruit yield (measured by the number and weight of fruits per plant), were evaluated to assess the impact of salinity stress. In addition, the nutritional status of the plants was assessed by determining the concentrations of macro- and micronutrients in the leaves, roots, and fruit of the plants. The key results of this study reveal that cherry-type tomato landraces exhibit the highest tolerance to salinity stress, as the landraces ‘Cherry-INRAE (1)’, ‘Cherry-INRAE (3)’, and ‘Cherry-INRAE (4)’ did not experience a decrease in yield when exposed to salinity stress. However, larger landraces such as ‘de Ramellet’ also exhibit mechanisms conferring tolerance to salinity, as their yield was not compromised by the stress applied. The identified tolerant and resistant varieties could potentially be used in breeding programs to develop new varieties and hybrids that are better adapted to salinity-affected environments. The identification and utilization of tomato varieties that are adapted to salinity stress is an important strategy for promoting agriculture sustainability, particularly in semi-arid regions where salinity stress is a major challenge.
The Genus Pinnularia Ehrenberg (Bacillariophyta) from the Transbaikal Area (Russia, Siberia): Description of Seven New Species on the Basis of Morphology and Molecular Data with Discussion of the Phylogenetic Position of Caloneis
Authors: Kulikovskiy Maxim , Glushchenko Anton , Kezlya Elena , Kuznetsova Irina , Kociolek John Patrick , Maltsev Yevhen
Published: October 2023

Seven Pinnularia species from the Transbaikal area, Russia, are described as new for science. These are P. baicalgenkalii, P. baicalflexuosa, P. microfrauenbergiana, P. pergrunowii, P. siberiosinistra, P. baicalodivergens, and P. baicalislandica. All species are described by original LM and SEM microphotographs and molecular phylogeny. We provide comparisons between the taxa and document variability in the features found in the species. The number of Pinnularia species in the Transbaikal area is the largest number of species of the genus anywhere in the world.
End-Point Predictors of Water Quality in Tropical Rivers
Authors: Shahady Thomas , Montero-Ramírez José Joaquín
Published: October 2023

End-point evaluation of stream health is essential for the quantification of water quality. To this end, many Multi-Metric Indices (MMIs) have been developed to quantify water quality. The most extensive work has occurred in North America and Europe, while other areas of the world are in development. In this study, we compared the use of relevant physical, chemical and biological parameters in MMIs to various other stream health indicators to assess water quality throughout a three-river corridor along the north central Pacific slope of Costa Rica. Analysis of the data suggested MMIs were the best indicators of water quality and, more specifically, insect MMIs were the most predicative. MMIs were also best at pinpointing anthropomorphic impact throughout the corridor. Further, less complex insect MMIs such as compilations of family diversity using Ephemeroptera, Plecoptera and Trichoptera (EPT) orders were equally as predictive as the more complex models. With a need to better understand and use citizen monitors to predict water quality in these tropical environments, less complex insect MMIs show promise as a solution.
Investigation of Structural Energy Absorption Performance in 3D-Printed Polymer (Tough 1500 Resin) Materials with Novel Multilayer Thin-Walled Sandwich Structures Inspired by Peano Space-Filling Curves
Authors: Lin Peng , Zhang Zhiqiang , Chen Yun , Hu Dayong
Published: October 2023

Inspired by Peano space-filling curves (PSCs), this study introduced the space-filling structure design concept to novel thin-walled sandwich structures and fabricated polymer samples by 3D printing technology. The crushing behaviors and energy absorption performance of the PSC multilayer thin-walled sandwich structures and the traditional serpentine space-filling curve (SSC) multilayer thin-walled sandwich structures were investigated using quasi-static compression experiments and numerical analysis. Taking the initial peak crushing force (IPF), specific energy absorption (SEA), and crushing force efficiency (CFE) as evaluation criteria, the effects of geometric parameters, including the curve order, layer height, septa thickness, and wall thickness, on energy absorption performance were comprehensively examined. The results indicated that the energy absorption capacity of the PSC structure was significantly enhanced due to its complex hierarchy. Specifically, the second-order PSC structure demonstrated a 53.2% increase in energy absorption compared to the second-order SSC structure, while the third-order PSC structure showed more than a six-fold increase in energy absorption compared to the third-order SSC structure. Furthermore, a multi-objective optimization method based on the response surface method and the NSGA-II algorithm were employed to optimize the wall thickness and layer height of the proposed novel PSC structures. The optimal solutions suggested that a reasonable wall thickness and layer height were two important factors for designing PSC structures with better energy absorption performance. The findings of this study provide an effective guide for using the space-filling concept with Peano curves for the design of a novel polymer thin-walled energy absorber with high energy absorption efficiency.
Assessment of Denitrification and Nitrification Processes during Landfill Leachate Treatment
Authors: Schneider Irina , Yotinov Ivaylo , Dinova Nora , Geneva Bogidara , Daskalova Elmira , Lincheva Stilyana , Topalova Yana
Published: October 2023

Landfill leachate is generated from waste degradation in landfill sites, and its treatment includes biological or combined treatment with physico-chemical methods. A frequently applied technology in the biological stage of landfill leachate treatment plants is based on denitrification and nitrification. Nevertheless, with the availability of a huge number of scientific reports, the management of denitrification and nitrification (D/N) processes in the frame of real wastewater treatment plants (WWTPs) is rather difficult and always remains a critical technological problem. This study aims to perform a comparative assessment between denitrification and nitrification processes during landfill leachate treatment in three different situations in the WWTP of the Municipal Enterprise for Waste Treatment in Sofia City, Bulgaria. The comparative assessment is based on the biotransformation effectiveness of ammonium ions in the course of nitrification and of nitrates in the course of denitrification. Mixed samples (wastewater and activated sludge) were taken from the sequencing batch reactors operating in the mode of denitrification and nitrification. All physico-chemical (pH, dissolved oxygen, organics concentration, BOD5:COD, nitrogen and phosphorous) and biological (sludge volume index, sludge biotic index, quantity of aerobic heterotrophs, denitrifiers and nitrifiers, total dehydrogenase activity) indicators were investigated in the samples from the biobasins in the mode of denitrification and nitrification. The conditions for the implementation of the two processes were compared, along with the state of the activated sludge in the different reactors. The obtained results showed that denitrification was the critical process in the technology on the base of D/N. A major factor that managed the denitrification was the lower concentration of biodegradable organic matter which led to a deformation of the activated sludge structure, a decrease in the count of heterotrophic microorganisms, a decrease in the total activity of the activated sludge and an inhibition of the nitrate reductase activity. The nitrification processes were accomplished with high intensity. The results confirmed that D/N could be optimized and controlled using specific wastewater treatment technology parameters and purposely applied indicators.
Exploring Tannery Solid Wastes as a Source of Animal Feed
Authors: Flores Tapia Nelly Esther , Brito Moina Hannibal
Published: October 2023

This review delves into the processing of tannery solid waste, emphasizing fleshings and chromium-tanned leather waste. This paper centers on fat recovery, chromium elimination, and protein preservation, aiming to make them apt for animal consumption. This paper also assesses the potential of introducing such recycled products to the global market. Drawing on the literature from the past two decades, sourced comprehensively from Scopus and Web of Science, 36 articles were selected because of their significant contributions from leather production powerhouses such as India and Brazil. Fleshings have shown immense potential as animal feed, and the extraction of tallow and collagen from rawhide trimmings yields up to 98% and 93%, respectively. Fermented tannery fleshings, notably with Enterococcus faecium HAB01, also demonstrate strong antioxidant capabilities. The overarching consensus emphasizes the need for rigorous purification when dealing with chromium-containing wastes, addressing concerns tied to Cr (III) and Cr (VI). Furthermore, raw tannery fleshings stand out as a sustainable, cost-effective, and globally marketable solution for animal feed production.
Archaeology and Hauntology: An Ongoing, Stalled Conversation
Authors: Dickinson Colby
Published: October 2023

It is certainly possible that we might learn to better acknowledge the spirits of our ancestors who came before us, as well as to recognize them in such ways that we also learn to embrace the ‘woven density’ of our own lives, our histories and our communities. By doing so, we might begin to discover that the spirits we had thought were removed from our modern, secularized world never fully left us, just as the irrationality of our humanity cannot be fully tamed via a reductive, rational and scientific outlook on life. There are, as Bruno Latour had frequently argued, many modes of human existence that interact in complex networks of relations. To fall back on any one particular mode as if it could dominate over others would only grant us a mistaken impression of our own humanity, even though this is what has been practiced for centuries in order to legitimate a particular, sovereign configuration of power. It is to the credit of archaeologists and hauntologists alike that we are more able than ever to take account of the complexity of ourselves in ways that we had previously ignored or repressed.
A Tropical Cyclone or Typhoon as an Element of the Earth–Atmosphere–Ionosphere–Magnetosphere System: Theory, Simulations, and Observations
Authors: Chernogor Leonid F.
Published: October 2023

The premise has been validated that a tropical cyclone (TC, typhoon, hurricane), one of the most powerful large-scale formations systematically arising in the atmosphere, is an element of the ocean–atmosphere–ionosphere–magnetosphere system. The TC plays a crucial role with regard to a global-scale mass and energy exchange in this system. The study of this system encompasses a broad spectrum of physical phenomena occurring and processes operating within the system components, as well as the mechanisms of their interactions. The problem under discussion pertains to interdisciplinary science. Its scope ranges from different Earth sciences to geospace sciences, which comprise the physics of the ocean, meteorology, the physics of the Earth’s atmospheric and space environment, etc. Observations of the ionospheric response to the impact of a number of unique typhoons made using multifrequency multiple path oblique incidence ionospheric sounding have confirmed the definitive role that the internal gravity waves and infrasound play in producing atmospheric–ionospheric disturbances. It has been demonstrated that these disturbances are capable of significantly affecting the characteristics of high-frequency radio waves.
Adaptive Beamforming with Sidelobe Level Control for Multiband Sparse Linear Array
Authors: Li Hongtao , Ran Longyao , He Cheng , Ding Zhoupeng , Chen Shengyao
Published: October 2023

Multiband antenna arrays have the capability of effectively working in multiple frequency bands and thus significantly simplify the antenna system. To further reduce the system overhead, this paper discusses the joint design of antenna selection and adaptive beamforming for multiband antenna arrays, where the sidelobe level is also controlled so as to alleviate the effect of unknown sporadic interference. Based on the maximum signal-to-interference-plus-noise ratio (SINR) criterion and sidelobe level constraints, the proposed multiband sparse array design is formulated into a nonconvex constrained nonlinear optimization problem with an l0,2-mixed norm regularization. This problem ensures that the same antenna positions are selected at all operating frequencies while the beamformer weights of each frequency are optimized independently. By exploiting the semi-definite relaxation and the reweighted l1,∞-norm approximation, the problem is converted into a series of convex subproblems and is then effectively solved by the proposed iterative reweighted method. Numerical results show that the proposed multiband sparse array significantly reduces the sidelobe levels in all operating frequencies while maintaining the maximum SINR, so it provides superior performance of interference suppression.
Utilizing Hyperspectral Reflectance and Machine Learning Algorithms for Non-Destructive Estimation of Chlorophyll Content in Citrus Leaves
Authors: Li Dasui , Hu Qingqing , Ruan Siqi , Liu Jun , Zhang Jinzhi , Hu Chungen , Liu Yongzhong , Dian Yuanyong , Zhou Jingjing
Published: October 2023

To address the demands of precision agriculture and the measurement of plant photosynthetic response and nitrogen status, it is necessary to employ advanced methods for estimating chlorophyll content quickly and non-destructively at a large scale. Therefore, we explored the utilization of both linear regression and machine learning methodology to improve the prediction of leaf chlorophyll content (LCC) in citrus trees through the analysis of hyperspectral reflectance data in a field experiment. And the relationship between phenology and LCC estimation was also tested in this study. The LCC of citrus tree leaves at five growth seasons (May, June, August, October, and December) were measured alongside measurements of leaf hyperspectral reflectance. The measured LCC data and spectral parameters were used for evaluating LCC using univariate linear regression (ULR), multivariate linear regression (MLR), random forest regression (RFR), K-nearest neighbor regression (KNNR), and support vector regression (SVR). The results revealed the following: the MLR and machine learning models (RFR, KNNR, SVR), in both October and December, performed well in LCC estimation with a coefficient of determination (R2) greater than 0.70. In August, the ULR model performed the best, achieving an R2 of 0.69 and root mean square error (RMSE) of 8.92. However, the RFR model demonstrated the highest predictive power for estimating LCC in May, June, October, and December. Furthermore, the prediction accuracy was the best with the RFR model with parameters VOG2 and Carte4 in October, achieving an R2 of 0.83 and RMSE of 6.67. Our findings revealed that using just a few spectral parameters can efficiently estimate LCC in citrus trees, showing substantial promise for implementation in large-scale orchards.
Large-Scale Surface Deformation Monitoring Using SBAS-InSAR and Intelligent Prediction in Typical Cities of Yangtze River Delta
Authors: Wang Rong , Feng Yongjiu , Tong Xiaohua , Li Pengshuo , Wang Jiafeng , Tang Panli , Tang Xiaoyan , Xi Mengrong , Zhou Yi
Published: October 2023

Large-scale short-term monitoring and prediction of surface deformation are of great significance for the prevention and control of geohazards in rapidly urbanizing developing cities. Most studies focus on individual cities, but it would be more meaningful to address large urban agglomerations and consider the relevance of the regions within them. In addition, the commonly used linear fitting prediction methods cannot accurately capture the dynamic mechanisms of deformation. In this study, we proposed an automatic PS extraction method (named PS-SBAS-InSAR) that improves SBAS-InSAR to extract surface deformation and an Informer-based short-term surface deformation prediction method for case studies in 16 typical cities of the Yangtze River Delta (YRD). The results show that PS-SBAS-InSAR successfully extracted accurate surface deformation sequences of the YRD. During the period from January 2019 to January 2021, the YRD experienced a slight deformation with an average deformation rate within [−4, 4] mm/year. Geographically neighboring cities may have associated deformation distributions and similar deformation trends, as indicated by average deformation rate maps and landscape metrics. Both types of deformation (i.e., subsidence/uplift) tend to occur simultaneously, with specific areas of subsidence/uplift occurring in close proximity to areas of concentrated deformation. The Informer model effectively captured the time-series variation in surface deformation, suggesting a slowdown of deformation over the next two months (February 2021–March 2021). Our work contributes to a better understanding of changes and trends in large-scale surface deformation and provides useful methods for monitoring and predicting surface deformation in coastal areas.
A Wireless 2-Channel Layered EMG/NIRS Sensor System for Local Muscular Activity Evaluation
Authors: Kimoto Akira , Oishi Yuya , Machida Masanao
Published: October 2023

A wireless 2-channel layered sensor system that enables electromyography (EMG) and near-infrared spectroscopy (NIRS) measurements at two local positions was developed. The layered sensor consists of a thin silver electrode and a photosensor consisting of a photoemitting diode (LED) or photodiode (PD). The EMG and NIRS signals were simultaneously measured using a pair of electrodes and photosensors for the LED and PD, respectively. Two local muscular activities are presented in detail using layered sensors. In the experiments, EMG and NIRS signals were measured for isometric constant and ramp contractions at each forearm using layered sensors. The results showed that local muscle activity analysis is possible using simultaneous EMG and NIRS signals at each local position.
Focusing Coherent Light through Volume Scattering Phantoms via Wavefront Shaping
Authors: Fritzsche Niklas , Ott Felix , Pink Karsten , Kienle Alwin
Published: October 2023

Manipulating the wavefront of coherent light incident on scattering media to enhance the imaging depth, sensitivity, and resolution is a common technique in biomedical applications. Local phase variations cause changes in the interference and can be used to create a focus inside or behind a scattering medium. We use wavefront shaping (WFS) to force constructive interference at an arbitrary location. The amount of light transmitted into a given region strongly depends on the scattering and absorption characteristics. These are described by their respective coefficients μs and μa and the scattering phase function. Controlling the scattering and absorption coefficients, we study the behavior of wavefront shaping and the achievable intensity enhancement behind volume scattering media with well-defined optical properties. The phantoms designed in this publication are made of epoxy resin. Into these epoxy matrices, specific amounts of scattering and absorbing particles, such as titanium dioxide pigments and molecular dyes, are mixed. The mixture obtained is filled into 3D-printed frames of various thicknesses. After a precise fabrication procedure, an integrating sphere-based setup characterizes the phantoms experimentally. It detects the total hemispherical transmission and reflection. Further theoretical characterization is performed with a newly developed hybrid PN method. This method senses the flux of light into a particular angular range at the lower boundary of a slab. The calculations are performed without suffering from ringing and fulfill the exact boundary conditions there. A decoupled two-path detection system allows for fast optimization as well as sensitive detection. The measurements yield results that agree well with the theoretically expected behavior.
AFTR: A Robustness Multi-Sensor Fusion Model for 3D Object Detection Based on Adaptive Fusion Transformer
Authors: Zhang Yan , Liu Kang , Bao Hong , Qian Xu , Wang Zihan , Ye Shiqing , Wang Weicen
Published: October 2023

Multi-modal sensors are the key to ensuring the robust and accurate operation of autonomous driving systems, where LiDAR and cameras are important on-board sensors. However, current fusion methods face challenges due to inconsistent multi-sensor data representations and the misalignment of dynamic scenes. Specifically, current fusion methods either explicitly correlate multi-sensor data features by calibrating parameters, ignoring the feature blurring problems caused by misalignment, or find correlated features between multi-sensor data through global attention, causing rapidly escalating computational costs. On this basis, we propose a transformer-based end-to-end multi-sensor fusion framework named the adaptive fusion transformer (AFTR). The proposed AFTR consists of the adaptive spatial cross-attention (ASCA) mechanism and the spatial temporal self-attention (STSA) mechanism. Specifically, ASCA adaptively associates and interacts with multi-sensor data features in 3D space through learnable local attention, alleviating the problem of the misalignment of geometric information and reducing computational costs, and STSA interacts with cross-temporal information using learnable offsets in deformable attention, mitigating displacements due to dynamic scenes. We show through numerous experiments that the AFTR obtains SOTA performance in the nuScenes 3D object detection task (74.9% NDS and 73.2% mAP) and demonstrates strong robustness to misalignment (only a 0.2% NDS drop with slight noise). At the same time, we demonstrate the effectiveness of the AFTR components through ablation studies. In summary, the proposed AFTR is an accurate, efficient, and robust multi-sensor data fusion framework.
Enhanced Near-Infrared Ultra-Narrow Absorber Based on a Dielectric Nano-Resonant Ring for Refractive Index Sensing
Authors: Li Xingyu , Liu Dingquan , Su Junli , Sun Leihao , Luo Haihan , Chen Gang , Ma Chong , Zhang Qiuyu
Published: October 2023

In this paper, a plasmon resonance-enhanced narrow-band absorber based on the nano-resonant ring array of transparent conductive oxides (TCOs) is proposed and verified numerically. Due to the unique properties of TCOs, the structure achieves an ultra-narrowband perfect absorption by exhibiting a near-field enhancement effect. Consequently, we achieve a peak absorption rate of 99.94% at 792.2 nm. The simulation results indicate that the Full Width Half Maximum (FWHM) can be limited to within 8.8 nm. As a refractive index sensor, the device reaches a sensitivity S of 300 nm/RIU and a Figure of Merit (FOM) value of 34.1 1/RIU. By analyzing the distribution characteristics of the electromagnetic field at the 792.2 nm, we find high absorption with a narrow FWHM of the ITO nano-resonant ring (INRR) owing to plasmon resonance excited by the free carriers at the interface between the metal and the interior of the ITO. Additionally, the device exhibits polarization independence and maintains absorption rates above 90% even when the incident formed by the axis perpendicular to the film is greater than 13°. This study opens a new prospective channel for research into TCOs, which will increase the potential of compact photoelectric devices, such as optical sensing, narrowband filtering, non-radiative data transmission and biomolecular manipulation.
Multi-Objective NSGA-II Optimization for Broadband Beamforming with Spherical Harmonic Domain Assistance
Authors: Liu Zhenghong , Zhou Haocheng , Song Xiyu , Wang Mei , Weng Liuqing
Published: October 2023

Sidelobe suppression is a major challenge in wideband beamforming for acoustic research, especially in high noise and reverberation environments. In this paper, we propose a multi-objective NSGA-II wideband beamforming method based on a spherical harmonic domain for spherical microphone arrays topology. The method takes white noise gain, directional index and maximum sidelobe level as the optimization objectives of broadband beamforming, adopts the NSGA-II optimization strategy with constraints to estimate the Pareto optimal solution, and provides three-dimensional broadband beamforming capability. Our method provides superior sidelobe suppression across different spherical harmonic orders compared to commonly used multi-constrained single-objective optimal beamforming methods. We also validate the effectiveness of our proposed method in a conference room setting. The proposed method achieves a white noise gain of 8.28 dB and a maximum sidelobe level of −23.42 dB at low frequency, while at high frequency it yields comparable directivity index results to both DolphChebyshev and SOCP methods, but outperforms them in terms of white noise gain and maximum sidelobe level, measuring 16.14 dB and −25.18 dB, respectively.
Hybrid CNN–Transformer Network for Electricity Theft Detection in Smart Grids
Authors: Bai Yu , Sun Haitong , Zhang Lili , Wu Haoqi
Published: October 2023

Illicitly obtaining electricity, commonly referred to as electricity theft, is a prominent contributor to power loss. In recent years, there has been growing recognition of the significance of neural network models in electrical theft detection (ETD). Nevertheless, the existing approaches have a restricted capacity to acquire profound characteristics, posing a persistent challenge in reliably and effectively detecting anomalies in power consumption data. Hence, the present study puts forth a hybrid model that amalgamates a convolutional neural network (CNN) and a transformer network as a means to tackle this concern. The CNN model with a dual-scale dual-branch (DSDB) structure incorporates inter- and intra-periodic convolutional blocks to conduct shallow feature extraction of sequences from varying dimensions. This enables the model to capture multi-scale features in a local-to-global fashion. The transformer module with Gaussian weighting (GWT) effectively captures the overall temporal dependencies present in the electricity consumption data, enabling the extraction of sequence features at a deep level. Numerous studies have demonstrated that the proposed method exhibits enhanced efficiency in feature extraction, yielding high F1 scores and AUC values, while also exhibiting notable robustness.
Enhanced 3D Pose Estimation in Multi-Person, Multi-View Scenarios through Unsupervised Domain Adaptation with Dropout Discriminator
Authors: Deng Junli , Yao Haoyuan , Shi Ping
Published: October 2023

Data-driven pose estimation methods often assume equal distributions between training and test data. However, in reality, this assumption does not always hold true, leading to significant performance degradation due to distribution mismatches. In this study, our objective is to enhance the cross-domain robustness of multi-view, multi-person 3D pose estimation. We tackle the domain shift challenge through three key approaches: (1) A domain adaptation component is introduced to improve estimation accuracy for specific target domains. (2) By incorporating a dropout mechanism, we train a more reliable model tailored to the target domain. (3) Transferable Parameter Learning is employed to retain crucial parameters for learning domain-invariant data. The foundation for these approaches lies in the H-divergence theory and the lottery ticket hypothesis, which are realized through adversarial training by learning domain classifiers. Our proposed methodology is evaluated using three datasets: Panoptic, Shelf, and Campus, allowing us to assess its efficacy in addressing domain shifts in multi-view, multi-person pose estimation. Both qualitative and quantitative experiments demonstrate that our algorithm performs well in two different domain shift scenarios.
DOA Estimation for Coherent Sources Based on Uniformly Distributed Two Concentric Rings Array
Authors: Han Chuang , Guo Shenghong , Yan Ning , Dong Jingwei , Xing Bowen
Published: October 2023

The direction estimation of the coherent source in a uniform circular array is an essential part of the signal processing area of the array, but the traditional uniform circular array algorithm has a low localization accuracy and a poor localization effect on the coherent source. To solve this problem, this paper proposes a two-dimensional direction of arrival (DOA) estimation for the coherent source in broadband. Firstly, the central frequency of the coherent sound source is estimated using the frequency estimation method of the delayed data, and a real-valued beamformer is constructed using the concept of the multiloop phase mode. Then, the cost function in the beam space is obtained. Finally, the cost function is searched in two dimensions to locate the sound source. In this paper, we simulate the DOA of the sound source at different frequencies and signal-to-noise ratios and analyze the resolution of the circular array. The simulation results show that the proposed algorithm can estimate the direction of arrival with high precision and achieve the desired results.
Comparing Inertial Measurement Units to Markerless Video Analysis for Movement Symmetry in Quarter Horses
Authors: Pfau Thilo , Landsbergen Kiki , Davis Brittany L. , Kenny Olivia , Kernot Nicole , Rochard Nina , Porte-Proust Marion , Sparks Holly , Takahashi Yuji , Toth Kasara , Scott W. Michael
Published: October 2023

Background: With an increasing number of systems for quantifying lameness-related movement asymmetry, between-system comparisons under non-laboratory conditions are important for multi-centre or referral-level studies. This study compares an artificial intelligence video app to a validated inertial measurement unit (IMU) gait analysis system in a specific group of horses. Methods: Twenty-two reining Quarter horses were equipped with nine body-mounted IMUs while being videoed with a smartphone app. Both systems quantified head and pelvic movement symmetry during in-hand trot (hard/soft ground) and on the lunge (left/right rein, soft ground). Proportional limits of agreement (pLoA) were established. Results: Widths of pLoA were larger for head movement (29% to 50% in-hand; 22% to 38% on lunge) than for pelvic movement (13% to 24% in-hand; 14% to 24% on lunge). Conclusion: The between-system pLoAs exceed current “lameness thresholds” aimed at identifying the affected limb(s) in lame horses. They also exceed published limits of agreement for stride-matched data but are similar to repeatability values and “lameness thresholds” from “non-lame” horses. This is encouraging for multi-centre studies and referral-level veterinary practice. The narrower pLoA values for pelvic movement asymmetry are particularly encouraging, given the difficulty of grading hind limb lameness “by eye”.
A Comprehensive Eco-Driving Strategy for CAVs with Microscopic Traffic Simulation Testing Evaluation
Authors: Kavas-Torris Ozgenur , Guvenc Levent
Published: October 2023

In this paper, a comprehensive deterministic Eco-Driving strategy for Connected and Autonomous Vehicles (CAVs) is presented. In this setup, multiple driving modes calculate speed profiles that are ideal for their own set of constraints simultaneously to save fuel as much as possible, while a High-Level (HL) controller ensures smooth and safe transitions between the driving modes for Eco-Driving. This Eco-Driving deterministic controller for an ego CAV was equipped with Vehicle-to-Infrastructure (V2I) and Vehicle-to-Vehicle (V2V) algorithms. This comprehensive Eco-Driving strategy and its individual components were tested by using simulations to quantify the fuel economy performance. Simulation results are used to show that the HL controller ensures significant fuel economy improvement as compared to baseline driving modes with no collisions between the ego CAV and traffic vehicles, while the driving mode of the ego CAV was set correctly under changing constraints. For the microscopic traffic simulations, a 6.41% fuel economy improvement was observed for the CAV that was controlled by this comprehensive Eco-Driving strategy.
Resistive Sensing of Seed Cotton Moisture Regain Based on Pressure Compensation
Authors: Fang Liang , Zhang Ruoyu , Duan Hongwei , Chang Jinqiang , Zeng Zhaoquan , Qian Yifu , Hong Mianzhe
Published: October 2023

The measurement of seed cotton moisture regain (MR) during harvesting operations is an open and challenging problem. In this study, a new method for resistive sensing of seed cotton MR measurement based on pressure compensation is proposed. First, an experimental platform was designed. After that, the change of cotton bale parameters during the cotton picker packaging process was simulated through the experimental platform, and the correlations among the compression volume, compression density, contact pressure, and conductivity of seed cotton were analyzed. Then, support vector regression (SVR), random forest (RF), and a backpropagation neural network (BPNN) were employed to build seed cotton MR prediction models. Finally, the performance of the method was evaluated through the experimental platform test. The results showed that there was a weak correlation between contact pressure and compression volume, while there was a significant correlation (p < 0.01) between contact pressure and compression density. Moreover, the nonlinear mathematical models exhibited better fitting performance than the linear mathematical models in describing the relationships among compression density, contact pressure, and conductivity. The comparative analysis results of the three MR prediction models showed that the BPNN algorithm had the highest prediction accuracy, with a coefficient of determination (R2) of 0.986 and a root mean square error (RMSE) of 0.204%. The mean RMSE and mean coefficient of variation (CV) of the performance evaluation test results were 0.20% and 2.22%, respectively. Therefore, the method proposed in this study is reliable. In addition, the study will provide a technical reference for the accurate and rapid measurement of seed cotton MR during harvesting operations.
Single-Frame Infrared Image Non-Uniformity Correction Based on Wavelet Domain Noise Separation
Authors: Li Mingqing , Wang Yuqing , Sun Haijiang
Published: October 2023

In the context of non-uniformity correction (NUC) within infrared imaging systems, current methods frequently concentrate solely on high-frequency stripe non-uniformity noise, neglecting the impact of global low-frequency non-uniformity on image quality, and are susceptible to ghosting artifacts from neighboring frames. In response to such challenges, we propose a method for the correction of non-uniformity in single-frame infrared images based on noise separation in the wavelet domain. More specifically, we commence by decomposing the noisy image into distinct frequency components through wavelet transformation. Subsequently, we employ a clustering algorithm to extract high-frequency noise from the vertical components within the wavelet domain, concurrently employing a method of surface fitting to capture low-frequency noise from the approximate components within the wavelet domain. Ultimately, the restored image is obtained by subtracting the combined noise components. The experimental results demonstrate that the proposed method, when applied to simulated noisy images, achieves the optimal levels among seven compared methods in terms of MSE, PSNR, and SSIM metrics. After correction on three sets of real-world test image sequences, the average non-uniformity index is reduced by 75.54%. Moreover, our method does not impose significant computational overhead in the elimination of superimposed noise, which is particularly suitable for applications necessitating stringent requirements in both image quality and processing speed.
Optimization of Gradient-Echo Echo-Planar Imaging for T2* Contrast in the Brain at 0.5 T
Authors: Halder Arjama , Harris Chad T. , Wiens Curtis N. , Soddu Andrea , Chronik Blaine A.
Published: October 2023

Gradient-recalled echo (GRE) echo-planar imaging (EPI) is an efficient MRI pulse sequence that is commonly used for several enticing applications, including functional MRI (fMRI), susceptibility-weighted imaging (SWI), and proton resonance frequency (PRF) thermometry. These applications are typically not performed in the mid-field (<1 T) as longer T2* and lower polarization present significant challenges. However, recent developments of mid-field scanners equipped with high-performance gradient sets offer the possibility to re-evaluate the feasibility of these applications. The paper introduces a metric “T2* contrast efficiency” for this evaluation, which minimizes dead time in the EPI sequence while maximizing T2* contrast so that the temporal and pseudo signal-to-noise ratios (SNRs) can be attained, which could be used to quantify experimental parameters for future fMRI experiments in the mid-field. To guide the optimization, T2* measurements of the cortical gray matter are conducted, focusing on specific regions of interest (ROIs). Temporal and pseudo SNR are calculated with the measured time-series EPI data to observe the echo times at which the maximum T2* contrast efficiency is achieved. T2* for a specific cortical ROI is reported at 0.5 T. The results suggest the optimized echo time for the EPI protocols is shorter than the effective T2* of that region. The effective reduction of dead time prior to the echo train is feasible with an optimized EPI protocol, which will increase the overall scan efficiency for several EPI-based applications at 0.5 T.
RBF-Based Camera Model Based on a Ray Constraint to Compensate for Refraction Error
Authors: Kim Jaehyun , Kim Chanyoung , Yoon Seongwook , Choi Taehyeon , Sull Sanghoon
Published: October 2023

A camera equipped with a transparent shield can be modeled using the pinhole camera model and residual error vectors defined by the difference between the estimated ray from the pinhole camera model and the actual three-dimensional (3D) point. To calculate the residual error vectors, we employ sparse calibration data consisting of 3D points and their corresponding 2D points on the image. However, the observation noise and sparsity of the 3D calibration points pose challenges in determining the residual error vectors. To address this, we first fit Gaussian Process Regression (GPR) operating robustly against data noise to the observed residual error vectors from the sparse calibration data to obtain dense residual error vectors. Subsequently, to improve performance in unobserved areas due to data sparsity, we use an additional constraint; the 3D points on the estimated ray should be projected to one 2D image point, called the ray constraint. Finally, we optimize the radial basis function (RBF)-based regression model to reduce the residual error vector differences with GPR at the predetermined dense set of 3D points while reflecting the ray constraint. The proposed RBF-based camera model reduces the error of the estimated rays by 6% on average and the reprojection error by 26% on average.
Qualitative Research in Digital Era: Innovations, Methodologies and Collaborations
Authors: Bryda Grzegorz , Costa António Pedro
Published: October 2023

The differentiation of contemporary approaches to qualitative data analysis can seem daunting even for experienced social science researchers. Especially when they move forward in the data analysis process from general analytical strategies used in qualitative research to more specific approaches for different types of qualitative data, including interviews, text, audio, images, videos, and so-called virtual data, by discovering the domain ontology of the qualitative research field, we see that there are more than twice as many different classes of data analysis methods as qualitative research methods. This article critically reflects on qualitative research and the qualitative computer data analysis process, emphasising its significance in harnessing digital opportunities and shaping collaborative work. Using our extensive analytical and research project experience, the last research results, and a literature review, we try to show the impact of new technologies and digital possibilities on our thinking. We also try to do the qualitative data analysis. The essence of this procedure is a dialectical interplay between the new world of digital technology and the classic methodology. The use of digital possibilities in qualitative research practices shapes the researcher’s identity and their analytical and research workshop. Moreover, it teaches collaborative thinking and teamwork and fosters the development of new analytical, digital, and Information Technology (IT) skills. Imagining contemporary qualitative research and data analysis in the humanities and social sciences is difficult. Opening to modern technologies in computer-based qualitative data analysis shapes our interpretation frameworks and changes the optics and perception of research problems.
Advancing Sustainable Bio-Waste Management through Law and Policy: How Co-Creation Can Help Pursue Fair Environmental Public Policies in the European Context
Authors: Mendes Ireneu , Rocha Pedro , Aragão Alexandra
Published: October 2023

Alongside production and consumption, bio-waste management is central to the food systems debate. To achieve sustainable food systems—an essential component of the Sustainable Development Goals and the world they envision—public authorities must address the shortage of current bio-waste-management policies and strive towards a new paradigm of bio-waste management, where environmental justice primarily informs policy design and decision making. In order to achieve fair environmental policies, particularly in the context of food systems and bio-waste management, it is essential to understand what drives public policy in these matters. In the present review, we seek to contribute by closing a gap in the literature by proposing a set of bio-waste-management drivers in the European context. Moreover, we focus on the “policy and legislation” driver, hoping to examine its main components and understand both their limitations and the opportunities they provide. Finally, we explore the role that co-creation can play as a facilitator of a public-governance paradigm that promotes sustainable development.