bluuebunny/crossref_metadata_2025 huggingface dataset:
GABAA-ρ Receptors in the CNS: Their Functional, Pharmacological, and Structural Properties in Neurons and Astroglia
Authors: Rosas-Arellano Abraham , Estrada-Mondragón Argel , Martínez-Torres Ataúlfo , Reyes-Haro Daniel
Published: October 2023

Gamma-aminobutyric acid (GABA) is known as the main inhibitory transmitter in the central nervous system (CNS), where it hyperpolarizes mature neurons through activation of GABAA receptors, pentameric complexes assembled by combination of subunits (α1–6, β1–3, γ1–3, δ, ε, θ, π and ρ1–3). GABAA-ρ subunits were originally described in the retina where they generate non-desensitizing Cl- currents that are insensitive to bicuculline and baclofen. However, now is known that they are widely expressed throughout the brain including glial cells. For example, whole-cell patch-clamp recordings demonstrated the functional expression of GABAA-ρ receptors in primary cultures of cerebellar astrocytes, as well as in cerebellar ependymal cells and striatal astrocytes. In these cells GABA-currents were partially blocked by TPMPA and insensitive to barbiturates. These receptors are proposed to be involved in extrasynaptic communication and dysfunction of the signaling is accompanied by reduced expression of GABAA-ρ receptors in Huntington’s disease and autism spectrum disorders (ASD). Thus, the aim of this review is to present an overview about GABAA-ρ receptors including their structure and function, as well as their importance in the excitatory/inhibitory (E/I) balance in neurodevelopment and in disease.
Liposome-Derived Nanosystems for the Treatment of Behavioral and Neurodegenerative Diseases: The Promise of Niosomes, Transfersomes, and Ethosomes for Increased Brain Drug Bioavailability
Authors: Pires Patrícia C. , Paiva-Santos Ana Cláudia , Veiga Francisco
Published: October 2023

Psychiatric and neurodegenerative disorders are amongst the most prevalent and debilitating diseases, but current treatments either have low success rates, greatly due to the low permeability of the blood–brain barrier, and/or are connected to severe side effects. Hence, new strategies are extremely important, and here is where liposome-derived nanosystems come in. Niosomes, transfersomes, and ethosomes are nanometric vesicular structures that allow drug encapsulation, protecting them from degradation, and increasing their solubility, permeability, brain targeting, and bioavailability. This review highlighted the great potential of these nanosystems for the treatment of Alzheimer’s disease, Parkinson’s disease, schizophrenia, bipolar disorder, anxiety, and depression. Studies regarding the encapsulation of synthetic and natural-derived molecules in these systems, for intravenous, oral, transdermal, or intranasal administration, have led to an increased brain bioavailability when compared to conventional pharmaceutical forms. Moreover, the developed formulations proved to have neuroprotective, anti-inflammatory, and antioxidant effects, including brain neurotransmitter level restoration and brain oxidative status improvement, and improved locomotor activity or enhancement of recognition and working memories in animal models. Hence, albeit being relatively new technologies, niosomes, transfersomes, and ethosomes have already proven to increase the brain bioavailability of psychoactive drugs, leading to increased effectiveness and decreased side effects, showing promise as future therapeutics.
Plausible Protective Role of Encephalartos villosus Extract in Acetic-Acid-Induced Ulcerative Colitis in Rats
Authors: Alanazi Ashwag S. , Alanazi Mohammed M. , Elekhnawy Engy , Attallah Nashwah G. M. , Negm Walaa A. , El-Kadem Aya H.
Published: October 2023

Ulcerative colitis (UC) is an inflammatory ailment of the intestine associated with the upregulation of oxidative stress and pro-inflammatory cytokines. Here, we aimed to assess the consequences of Encephalartos villosus (EV) Lem extract on acetic acid (AA)-induced UC. Rats were randomly classified into five groups, as follows: control, AA, AA + mesalazine, AA + EV (50 mg/kg), and AA + EV (100 mg/kg) groups. EV (50 mg/kg and 100 mg/kg) and mesalzine (100 mg/kg) were administered orally for 14 days before the induction of UC. On the last day of the experiment, colitis was provoked via the intra-rectal delivery of 3% AA. Then, after 24 h, the rats were sacrificed and their colon tissues were isolated and inspected. Interestingly, EV pretreatment substantially (p < 0.05) reduced the elevated colon weight/length ratio and ulcer area and normalized the histological changes and immunohistochemical features. In addition, EV efficiently reduced the levels of myeloperoxidase (MPO) and increased the activity of glutathione peroxidase (GS-PX) and catalase (CAT). EV (100 mg/kg) resulted in a downregulation of toll-like receptor 4 (TLR-4) and upregulation of heme oxygenase 1 (HO-1) and occludin expression levels. Concerning the anti-inflammatory mechanisms, EV reduced the levels of tumor necrosis factor-alpha (TNF-α), interleukin-6 (IL-6), and nuclear transcription factor kappa B (NF-ĸB) and inhibited cyclooxygenase-2 (COX-2) expression levels. It also decreased caspase-3 levels. Our results indicate that the oral intake of EV improves AA-induced colitis in rats through its antioxidative effects and the modulation of pro-inflammatory cytokines, as well as the restoration of mucosal integrity. Consequently, EV may be an efficient therapeutic candidate for UC.
Antipsychotic Use: Cross-Sectional Opinion Survey of Psychiatrists in India and United Kingdom
Authors: Qadir Zina Sherzad , Kar Nilamadhab , Ball Patrick Anthony , Morrissey Hana
Published: October 2023

The aim of this survey of psychiatrists from the UK and India was to compare their opinions on antipsychotic medication choice and their experiences of such medications’ effectiveness and tolerability in patients who were newly diagnosed with acute schizophrenia. Following ethical approval, a cross-sectional online survey of psychiatrists from the UK and India was conducted. Ninety-five responses were received from each country. The most selected first-line APDs in both countries were olanzapine (47.5%), risperidone (42.8%) and aripiprazole (25.3%). A total of 60% of psychiatrists from India (60%) and 48% from the UK (48%) selected ‘medication efficacy’ as the main factor in their choice. Reassessment and consideration to switch most often took place within 4–6 weeks (53.7%) and 3–6 months (11.6%). The major reasons for switching antipsychotic medications were poor clinical efficacy (69%) and lack of tolerability (45%). Nonadherence was the most common reason for relapse (90% of UK psychiatrists and 70% of Indian psychiatrists), followed by illicit drug use (27.6%). The most commonly reported side effects that led to nonadherence were weight gain (10.8%), drowsiness (10.4%), erectile dysfunction and movement disorders (equally 8.7%). It was concluded that olanzapine, risperidone and aripiprazole are the most commonly selected as the initial treatment choice by psychiatrists from India and the UK. They are perceived as widely effective and well tolerated.
Exilic Ecologies
Authors: Marder Michael
Published: October 2023

A term of relatively recent mintage, coined by German scientist Ernst Haeckel in 1866, ecology draws on ancient Greek to establish and consolidate its meaning. Although scholars all too often overlook it, the anachronistic rise of ecology in its semantic and conceptual determinations is noteworthy. Formed by analogy with economy, the word may be translated as “the articulation of a dwelling”, the logos of oikos. Here, I argue not only that a vast majority of ecosystems on the planet are subject to environmental upheavals and ecological crises, but also that ecology as the crossroads of dwelling and articulation is in crisis, having come into its own and made explicit what was silently present in its historical enunciation. As a result, ecology needs to be deromanticized, decoupled from the bucolic and the picturesque, and dissociated from nativism and autochthony. Every organism, ecosystem, or place is affected by the forces of unsettlement and displacement; all dwellings and their articulations are shaken to the core and set in motion, rendering ecologies exilic. Ecologies today share the exilic condition, which also threatens to level the differences among them, without the chance of returning to a stable origin, itself nothing other than a theoretical fiction. In what follows, I propose to chalk out the outlines of exilic ecologies.
Key Technologies for a Beyond-100G Next-Generation Passive Optical Network
Authors: Feng Nan , Ma Mingyi , Zhang Yinsong , Tan Xiaochuan , Li Zhe , Li Shaobo
Published: October 2023

The explosive development of emerging telecommunication services has stimulated a huge growth in bandwidth demand as people seek universal access to telecommunication networks. In addition, the kinds of services of an existing optical access network are becoming more flexible. In order to provide higher capacity and meet higher transmission performance requirements, it is necessary to further explore the application of the beyond-100G passive optical network (PON). This paper offers a comprehensive review and outline of the prospects of technologies for bringing a beyond-100G PON to practical applications in the future. We review the current existing technologies, mainly in terms of the physical layer and higher media access control layer. These key technologies for the beyond-100G PON, which plays an increasingly significant role, include the advanced multiplexing technology, physical layer digital signal processing technology, infrastructure-sharing technology, security protection technology, and intelligent control management key technologies. Finally, open issues and new challenges for the next-generation PON are focused upon.
Microresonator Effective Thermal Parameters Definition via Thermal Modes Decomposition
Authors: Pavlov Vladislav I. , Kondratiev Nikita M. , Shitikov Artem E. , Lobanov Valery E.
Published: October 2023

High-Q optical microresonators are particularly efficient practical tools of modern applied optics and photonics. Using them, one inevitably faces the problem of thermal effects. Accurate determination of effective thermal parameters of high-Q microresonators (effective thermal relaxation rate and optical absorption rate) is of particular importance for developing microresonator-based devices. Our investigation looks into diverse methodologies to estimate these effective parameters for such systems, ultimately revealing a divergence between the commonly employed simplified model, the direct numerical approach, and classical analytical formulas. We introduce a novel approach to calculate effective parameters based on the decomposition of the thermal field into microresonator thermal modes, which inherently considers the intricate geometry and material anisotropy inherent in microresonators, as well as the influence of external conditions. The method for the accurate determination of the effective thermal parameters of the microresonator for corresponding thermal modes is developed. As a result of applying this method, we modified the classical approach for the simulation of thermal effects in optical microresonators for better agreement with the numerical simulations. By accounting for the complexities of microresonator shapes, material properties, and external factors, our proposed method contributes to a more accurate understanding of thermal dynamics and enhances the predictive capabilities of simulations for these systems. We demonstrated the application of this method on the example of integrated microring resonators, but it can be used to analyze thermal effects in other microresonator platforms.
Experimental Investigations and Modeling of Interference Fringe Geometry in Line-Shaped Gaussian Beam Intersections for Laser Doppler Sensors
Authors: Zhang Hao , Wang Jieping , Wang Shiji
Published: October 2023

Line-shaped beam-based Doppler distance sensors enable 3D shape measurements of rotating objects, for instance for working lathe monitoring with a simultaneous, multipoint velocity, and distance measurement. The velocity and distance estimations are fundamentally referred to the interference fringe spacing of the sensors. In this case, the fringe spacing variation-caused measurement error is significant; however, a complete, accurate model of the fringe geometry distribution for the line-shaped fringe volume is still missing and required to identify and minimize the error. Therefore, this work presents a high-accuracy 3D model for the fringe spacing evaluation of the sensors with experimental investigations. The model is derived from the phase expression of Gaussian beams introducing extension ratio, and can be universally applied to describe fringe geometry distribution throughout the intersection volume of spherical and line-shaped beams. With an experimental setup of a laser Doppler sensor, a full-field fringe spacing estimation using a high-resolution matrix camera is performed with dual-wavelength beams. The numerical modeling and experimental results show an average relative difference below 0.6%.
Robust Design and Operation of a Multistage Reactor for Methanol Synthesis from Renewable Resources
Authors: Keßler Tobias , Kienle Achim
Published: October 2023

Methanol is an important raw material widely used in the chemical industry. This article addresses the challenge of fluctuations in green hydrogen as a feed stream for methanol production from renewable feedstock. For a staged reactor design, robust operating conditions are generated through the simultaneous steady-state optimization of 50 process scenarios. The feed can be split and fed separately to the different reactor stages. However, neglected transient effects may render this design infeasible under dynamic conditions concerning carbon conversion and reactor temperature constraints. To overcome this, an additional dynamic optimization is conducted to ensure a feasible operation by an optimized feed-forward control of feed distribution and shell temperatures. In practice, this is possible because the disturbance, i.e., fluctuation, is measurable and predictable in a short time frame. The optimization yields optimal operating conditions, resulting in a reactor that is dynamically feasible for measurable fluctuating inlet conditions.
Evaluation of Polyurethane Foam Derived from the Liquefied Driftwood Approaching for Untapped Biomass
Authors: Masuda Go , Nagao Ayana , Wang Weiqian , Wang Qingyue
Published: October 2023

Nowadays, climate change has become a serious concern, and more attention has been drawn to utilizing biomass sources instead of fossil sources and how petroleum chemical plastics should be reduced or replaced with bio-based materials. In this study, the optimized condition of liquefaction of driftwood was examined. There was a concern that driftwood might have some decay and chemical change. However, according to the Organic Micro Element Analyzer (CHN analyzer) test and Klason lignin and Wise methods, the results proved that lignin content (37.5%), holocellulose content (66.9%), and CHN compositions were very similar to regular wood. The lowest residue content of bio-polyols was produced using liquefaction conditions of 150 °C, reaction time of 180 min, catalyst content of 10%w/w, and 12.5%w/w driftwood loading. Polyurethane foam (PUF) derived from the liquefaction of driftwood and bio-based cyanate was prepared. The PUF prepared from the liquefaction of the driftwood exhibited slightly decreased thermal durability but was superior in terms of 3-time faster biodegradation and 2.8-time increased water adsorption rate compared to pure petroleum-based PUF. As a result, it was shown that driftwood can be identified as a biomass resource for biodegradable PUF.
Modelling and Analysis of Dynamic Servo Error of Heavy Vertical Machining Centre Considering Nonlinear Factors
Authors: Wang Han , Li Tianjian , Sun Xizhi , Mynors Diane , Wu Tao
Published: October 2023

The dynamic servo error of heavy-duty vertical machining centres is one of the decisive factors affecting the machining accuracy of large and complex parts. Due to the characteristics of large mass, large load, and the large travel distance of the machine tool, non-linear factors such as friction, backlash, and lateral shift are more likely to cause unstable behaviours such as stick-slip and oscillation of the servo feed system of the machine tool, and reduce the performance and servo accuracy of the motion axis. In this paper, to consider the influence of non-linear factors such as friction, backlash, and lateral shift, an appropriately simplified representation of the mechanical transmission system of the ball screw has been used. According to the control structure of the Siemens 840D numerical control system, a theoretical model of the servo feed system for the heavy-duty vertical machining centre was established based on three-loop control. Then, the single-axis and double-axis closed-loop simulation models of the servo feed system were built in Simulink, and the influence pattern of control parameters and nonlinear factors on the dynamic servo error was obtained through simulation analysis. Finally, the validity of the theoretical model for the servo feed system was verified through a comprehensive comparison of simulation and experimental outcomes. This encompasses an analysis of the control system Bode plots, critical stick-slip velocity, and tracking errors in the X-axis with linear motion. The validation provides theoretical guidance for parameter design and mechanical adjustments of the servo feed system in heavy-duty vertical machining centres.
The Secondary Flows in a Cyclone Separator: A Review
Authors: Wang Chenwen , Ma Yongshan , Sui Wenxuan
Published: October 2023

A cyclone separator holds significant importance as the primary gas–solid separation apparatus in the industrial sector. Cyclone separators operate based on a fundamental principle, primarily harnessing the centrifugal force produced by the rotation of air in order to segregate solid particles from the gas stream and then collect them. In addition to the main vortex in the flow field, there are a number of secondary flows, which significantly impact the aggregation of fine particles and contribute to the heightened energy consumption. This paper provides a summary of the three secondary flows in a cyclone separator. These include the recirculation flow in the annular space, which is greatly influenced by the inlet particle concentration. Additionally, the short-circuit flow occurs beneath the vortex finder as a result of the collision between the incoming flow and the rotating flow. Furthermore, the eccentric circumfluence is defined as the deviation of the rotation center caused by the interaction between the upward and downward flows near the discharge. This paper aims to establish a theoretical framework to investigate the flow pattern tracking and the mitigation of secondary flows in order to enhance the operational efficiency of cyclone separators.
Comparative Proteomic Analysis of Two Commonly Used Laboratory Yeast Strains: W303 and BY4742
Authors: Rossio Valentina , Liu Xinyue , Paulo Joao A.
Published: October 2023

The yeast Saccharomyces cerevisiae is a powerful model system that is often used to expand our understanding of cellular processes and biological functions. Although many genetically well-characterized laboratory strains of S. cerevisiae are available, they may have different genetic backgrounds which can confound data interpretation. Here, we report a comparative whole-proteome analysis of two common laboratory yeast background strains, W303 and BY4742, in both exponential and stationary growth phases using isobaric-tag-based mass spectrometry to highlight differences in proteome complexity. We quantified over 4400 proteins, hundreds of which showed differences in abundance between strains and/or growth phases. Moreover, we used proteome-wide protein abundance to profile the mating type of the strains used in the experiment, the auxotrophic markers, and associated metabolic pathways, as well as to investigate differences in particular classes of proteins, such as the pleiotropic drug resistance (PDR) proteins. This study is a valuable resource that offers insight into mechanistic differences between two common yeast background strains and can be used as a guide to select a background that is best suited for addressing a particular biological question.
The Long-Term Deep Loessal Sediments of Northeast China: Loess or Loessal Paleosols?
Authors: Sun Zhong-Xiu , Zhang Nai-Wen , Jiang Ying-Ying , Wang Qiu-Bing , Zhang Gan-Lin
Published: October 2023

Previous research assumed deep buried loess as the initial composition of the overlying paleosol and failed to address the long-term continuous pedogenic weathering history in the deep loess-paleosol sequence, which was attributed to little understanding on the difference between loess and paleosol in the long-term deep loess-paleosol sequence. To distinguish between the loess and paleosol, in the long-term deep loess-paleosol sequence in northeast China, the morphology, dust deposition fluxes, geochemical characteristics, magnetic susceptibility, and grain size distributions were investigated. Results showed that the loess layers buried at depth could be differentiated from the paleosol by their poor pedogenic development. The presence of coarser grains in the loess as well as lesser amounts of clay and Fe–Mn coatings compared to paleosol indicated weaker weathering of the loess. Also, optical iron clay films deposited on the surface of the skeleton particles were less visible in the loesses than in the paleosols. From the loess evolution perspective, the pedogenic formation process of the newly formed loess soils should be considered as important as that of the reddish paleosol layer based on criteria of formation time, depth within profile, and morphological characteristics. The formation of the reddish or yellowish paleosol layer was constrained by pedogenic environments associated with climatic change and by the relative rates of deposition and pedogenesis. The terms “loessal paleosol” and “loessal paleosol sequence” are suggested to aid in systematically and consistently addressing the long-term pedogenic weathering evolution recorded in the complex formation of deep loess and paleosol sequences in pedology research. The long-term deep loessal sediments of Northeast China are loessal paleosols, which cannot be simply used as a reference for the overlying paleosol and be deducted from pedogenesis consideration.
Hyperspectral Image Super-Resolution via Adaptive Factor Group Sparsity Regularization-Based Subspace Representation
Authors: Peng Yidong , Li Weisheng , Luo Xiaobo , Du Jiao
Published: October 2023

Hyperspectral image (HSI) super-resolution is a vital technique that generates high spatial-resolution HSI (HR-HSI) by integrating information from low spatial-resolution HSI with high spatial-resolution multispectral image (MSI). However, existing subspace representation-based methods face challenges, including adaptive subspace dimension determination, inadequate spectral correlation capture, and expensive computation. In this paper, we propose a novel factor group sparsity regularized subspace representation (FGSSR)-based method for HSI super-resolution that can simultaneously address these issues encountered in previous methods. Specifically, by incorporating the factor group sparsity regularization into the subspace representation model, we first propose an FGSSR model to capture the spectral correlation property of the HR-HSI. The key advantage of FGSSR lies in its equivalence to the Schatten-p norm and its adaptive determination of the accurate subspace dimension, enabling it to capture spectral correlation more effectively. To preserve the spatial self-similarity prior in the HR-HSI, the tensor nuclear norm regularization on the low-dimensional coefficients is also incorporated into the proposed FGSSR-based model. Finally, an effective proximal alternating minimization-based algorithm is developed to solve the FGSSR-based model. Experimental results on the simulated and real datasets demonstrate that the proposed FGSSR-based method outperforms several state-of-the-art fusion methods with significant improvements.
The Retrieval of Forest and Grass Fractional Vegetation Coverage in Mountain Regions Based on Spatio-Temporal Transfer Learning
Authors: Huang Yuxuan , Zhou Xiang , Lv Tingting , Tao Zui , Zhang Hongming , Li Ruoxi , Zhai Mingjian , Liang Houyu
Published: October 2023

The vegetation cover of forests and grasslands in mountain regions plays a crucial role in regulating climate at both regional and global scales. Thus, it is necessary to develop accurate methods for estimating and monitoring fractional vegetation cover (FVC) in mountain areas. However, the complex topographic and climate factors pose significant challenges to accurately estimating the FVC of mountain forests and grassland. Existing remote sensing products, FVC retrieval methods, and FVC samples may fail to meet the required accuracy standards. In this study, we propose a method based on spatio-temporal transfer learning for the retrieval of FVC in mountain forests and grasslands, using the mountain region of Huzhu County, Qinghai Province, as the study area. The method combines simulated FVC samples, Sentinel-2 images, and mountain topographic factor data to pre-train LSTM and 1DCNN models and subsequently transfer the models to HJ-2A/B remote sensing images. The results of the study indicated the following: (1) The FVC samples generated by the proposed method (R2 = 0.7536, RMSE = 0.0596) are more accurate than those generated by the dichotomy method (R2 = 0.4997, RMSE = 0.1060) based on validation with ground truth data. (2) The LSTM model performed better than the 1DCNN model: the average R2 of the two models was 0.9275 and 0.8955; the average RMSE was 0.0653 and 0.0735. (3) Topographic features have a significant impact on FVC retrieval results, particularly in relatively high-altitude mountain regions (DEM > 3000 m) or non-growing seasons (May and October). Therefore, the proposed method has better potential in FVC fine spatio-temporal retrieval of high-resolution mountainous remote sensing images.
A Rapid Parallel Mosaicking Algorithm for Massive Remote Sensing Images Utilizing Read Filtering
Authors: Nie Pei , Cui Zhenqi , Wan Yaping
Published: October 2023

Mosaicking is a crucial step in the application of remote sensing images. The amount of remote sensing image data has grown rapidly, along with the expansion of observed areas and increased image resolution. As a result, traditional serial mosaicking techniques are facing significant challenges. In recent times, various studies have utilized high-performance computing to hasten image mosaicking and attain favorable outcomes. Nevertheless, the current research only accelerates mosaicking through external technology, without optimizing from the perspective of algorithm flow, which introduces unnecessary data I/O and slows down the mosaicking. This paper introduces a rapid parallel remote sensing image mosaicking algorithm utilizing read filtering. To begin with, the target images are divided into blocks and stored in a distributed file system. Subsequently, the image blocks are read and filtered based on a designated input format. Finally, the overlapping and non-overlapping areas are read and processed asynchronously, reducing the data I/O and computing overhead, thereby improving the efficiency of parallel computing. The experiments indicate that the mosaicking algorithm introduced in this paper enhances throughput and speedup by an average of 1.38 MB/S and 0.87 relative to the current techniques, respectively, concerning various datasets and cores. This study provides a theoretical foundation and novel ideas for processing remote sensing images on cluster platforms.
Aerial Drone Imaging in Alongshore Marine Ecosystems: Small-Scale Detection of a Coastal Spring System in the North-Eastern Adriatic Sea
Authors: Savonitto Gilda , Paganini Paolo , Pavan Alessandro , Busetti Martina , Giustiniani Michela , Dal Cin Michela , Comici Cinzia , Küchler Stefano , Gerin Riccardo
Published: October 2023

The eastern coastline of the Gulf of Trieste (north-eastern Adriatic Sea, Italy) is characterized by the occurrence of coastal and submarine freshwater springs of karstic origin. In one of these areas, we performed a survey with a drone with a thermal camera installed, in tandem with in situ oceanographic sampling with a CTD. Drone images revealed a small time-space scale (i.e., up to a few meters) phenomenon of freshwater plumes floating over seawater. Comparing sea surface temperature data with those acquired in situ revealed that the phenomenon was not clearly detectable by the classical oceanographic monitoring, this surface spring freshwater layer being too thin. Instead, the drone’s thermal camera detected these dynamics with great accuracy, indicating that aerial drones can be efficiently used for studying fine-scale events involving surface waters (e.g., spills/pollution). The experience gained allowed us to discuss some of the advantages and disadvantages of using drone thermal imaging for monitoring alongshore areas.
Advancing Skyborne Technologies and High-Resolution Satellites for Pasture Monitoring and Improved Management: A Review
Authors: Ogungbuyi Michael Gbenga , Mohammed Caroline , Ara Iffat , Fischer Andrew M. , Harrison Matthew Tom
Published: October 2023

The timely and accurate quantification of grassland biomass is a prerequisite for sustainable grazing management. With advances in artificial intelligence, the launch of new satellites, and perceived efficiency gains in the time and cost of the quantification of remote methods, there has been growing interest in using satellite imagery and machine learning to quantify pastures at the field scale. Here, we systematically reviewed 214 journal articles published between 1991 to 2021 to determine how vegetation indices derived from satellite imagery impacted the type and quantification of pasture indicators. We reveal that previous studies have been limited by highly spatiotemporal satellite imagery and prognostic analytics. While the number of studies on pasture classification, degradation, productivity, and management has increased exponentially over the last five years, the majority of vegetation parameters have been derived from satellite imagery using simple linear regression approaches, which, as a corollary, often result in site-specific parameterization that become spurious when extrapolated to new sites or production systems. Few studies have successfully invoked machine learning as retrievals to understand the relationship between image patterns and accurately quantify the biophysical variables, although many studies have purported to do so. Satellite imagery has contributed to the ability to quantify pasture indicators but has faced the barrier of monitoring at the paddock/field scale (20 hectares or less) due to (1) low sensor (coarse pixel) resolution, (2) infrequent satellite passes, with visibility in many locations often constrained by cloud cover, and (3) the prohibitive cost of accessing fine-resolution imagery. These issues are perhaps a reflection of historical efforts, which have been directed at the continental or global scales, rather than at the field level. Indeed, we found less than 20 studies that quantified pasture biomass at pixel resolutions of less than 50 hectares. As such, the use of remote sensing technologies by agricultural practitioners has been relatively low compared with the adoption of physical agronomic interventions (such as ‘no-till’ practices). We contend that (1) considerable opportunity for advancement may lie in fusing optical and radar imagery or hybrid imagery through the combination of optical sensors, (2) there is a greater accessibility of satellite imagery for research, teaching, and education, and (3) developers who understand the value proposition of satellite imagery to end users will collectively fast track the advancement and uptake of remote sensing applications in agriculture.
Insighting Drivers of Population Exposure to Ambient Ozone (O3) Concentrations across China Using a Spatiotemporal Causal Inference Method
Authors: Li Junming , Xue Jing , Wei Jing , Ren Zhoupeng , Yu Yiming , An Huize , Yang Xingyan , Yang Yixue
Published: October 2023

Ground-level ozone (O3) is a well-known atmospheric pollutant aside from particulate matter. China as a global populous country is facing serious surface O3 pollution. To detect the complex spatiotemporal transformation of the population exposure to ambient O3 pollution in China from 2005 to 2019, the Bayesian multi-stage spatiotemporal evolution hierarchy model was employed. To insight the drivers of the population exposure to ambient O3 pollution in China, a Bayesian spatiotemporal LASSO regression model (BST-LASSO-RM) and a spatiotemporal propensity score matching (STPSM) were firstly applied; then, a spatiotemporal causal inference method integrating the BST-LASSO-RM and STPSM was presented. The results show that the spatial pattern of the annual population-weighted ground-level O3 (PWGLO3) concentrations, representing population exposure to ambient O3, in China has transformed since 2014. Most regions (72.2%) experienced a decreasing trend in PWGLO3 pollution in the early stage, but in the late stage, most areas (79.3%) underwent an increasing trend. Some drivers on PWGLO3 concentrations have partial spatial spillover effects. The PWGLO3 concentrations in a region can be driven by this region’s surrounding areas’ economic factors, wind speed, and PWGLO3 concentrations. The major drivers with six local factors in 2005–2014 changed to five local factors and one spatial adjacent factor in 2015–2019. The driving of the traffic and green factors have no spatial spillover effects. Three traffic factors showed a negative driving effect in the early stage, but only one, bus ridership per capita (BRPC), retains the negative driving effect in the late stage. The factor with the maximum driving contribution is BRPC in the early stage, but PM2.5 pollution in the late stage, and the corresponding driving contribution is 17.57%. Green area per capita and urban green coverage rates have positive driving effects. The driving effects of the climate factors intensified from the early to the later stage.
Construction and Analysis of the EMC Evaluation Model for Vehicular Communication Systems Based on Digital Maps
Authors: Zhang Guangshuo , Lu Hongmin , Zhang Shiwei , Wu Fulin , Qin Yangzhen , Jiang Bo
Published: October 2023

With the development of vehicular communication technology, the electromagnetic compatibility requirements of vehicular communication systems are becoming more demanding. The traditional four-level electromagnetic compatibility evaluation model is widely applied in many scenarios. However, this model neglects the mutual interference of electronic devices inside a vehicle, and it cannot evaluate whether reduced radio receiver sensitivity, antenna isolation, and communication distance satisfy the system requirements for vehicular communication, thus making it unsuitable for digital communication systems. With the development of remote sensing technology, high-precision digital maps are easy to acquire and thus widely used. In this work, a modified five-level evaluation model based on digital maps is proposed, where digital maps are employed to support receiver sensitivity, antenna isolation, and communication performance evaluation. Through remote sensing technology and digital maps, a terrain profile is obtained, and a more accurate vehicle communication propagation model is established. In the experiment, an actual armored vehicular communication system example is applied to verify the performance of the proposed five-level evaluation model. Compared with the free-space propagation model, the error of the actual power received by the receiver is reduced by 0.97%, and the error of the communication distance where the sensitivity of the receiver is reduced by more than the system EMC threshold is reduced by 16.78%. The calculated antenna isolation degree is basically consistent with the actual measurement data. The model is able to evaluate the electromagnetic compatibility of an armored vehicular communication system more quickly, accurately, and comprehensively compared to previous evaluation models.
Segmentation and Connectivity Reconstruction of Urban Rivers from Sentinel-2 Multi-Spectral Imagery by the WaterSCNet Deep Learning Model
Authors: Dui Zixuan , Huang Yongjian , Wang Mingquan , Jin Jiuping , Gu Qianrong
Published: October 2023

Quick and automatic detection of the distribution and connectivity of urban rivers and their changes from satellite imagery is of great importance for urban flood control, river management, and ecological conservation. By improving the E-UNet model, this study proposed a cascaded river segmentation and connectivity reconstruction deep learning network model (WaterSCNet) to segment urban rivers from Sentinel-2 multi-spectral imagery and simultaneously reconstruct their connectivity obscured by road and bridge crossings from the segmentation results. The experimental results indicated that the WaterSCNet model could achieve better river segmentation and connectivity reconstruction results compared to the E-UNet, U-Net, SegNet, and HRNet models. Compared with the classic U-Net model, the MCC, F1, Kappa, and Recall evaluation metrics of the river segmentation results of the WaterSCNet model were improved by 3.24%, 3.10%, 3.36%, and 3.93%, respectively, and the evaluation metrics of the connectivity reconstruction results were improved by 4.25%, 4.11%, 4.37%, and 4.83%, respectively. The variance of the evaluation metrics of the five independent experiments indicated that the WaterSCNet model also had the best robustness compared to the other four models.
Spatiotemporal Analysis of Landscape Ecological Risk and Driving Factors: A Case Study in the Three Gorges Reservoir Area, China
Authors: Yan Zhiyi , Wang Yunqi , Wang Zhen , Zhang Churui , Wang Yujie , Li Yaoming
Published: October 2023

Landscape ecological risk is considered the basis for regional ecosystem management decisions. Thus, it is essential to understand the spatial and temporal evolutionary patterns and drivers of landscape ecological risk. However, existing studies lack exploration of the long-term time series and driving mechanisms of landscape ecological risk. Based on multi-type remote sensing data, this study assesses landscape pattern changes and ecological risk in the Three Gorges Reservoir Area from 1990 to 2020 and ranks the driving factors using a geographical detector. We then introduce the geographically weighted regression model to explore the local spatial contributions of driving factors. Our results show: (1) From 1990 to 2020, the agricultural land decreased, while forest and construction land expanded in the Three Gorges Reservoir Area. The overall landscape pattern shifted toward aggregation. (2) The landscape ecological risk exhibited a decreasing trend. The areas with relatively high landscape ecological risk were primarily concentrated in the main urban area in the western region of the Three Gorges Reservoir Area and along the Yangtze River, with apparent spatial aggregation. (3) Social and natural factors affected landscape ecological risk. The main driving factors were human interference, annual average temperature, population density, and annual precipitation; interactions occurred between the drivers. (4) The influence of driving factors on landscape ecological risk showed spatial heterogeneity. Spatially, the influence of social factors (human interference and population density) on landscape ecological risk was primarily positively correlated. Meanwhile, the natural factors’ (annual average temperature and annual precipitation) influence on landscape ecological risk varied widely in spatial distribution, and the driving mechanisms were more complex. This study provides a scientific basis and reference for landscape ecological risk management, land use policy formulation, and optimization of ecological security patterns.
Retrievals of Chlorophyll-a from GOCI and GOCI-II Data in Optically Complex Lakes
Authors: Guo Yuyu , Wei Xiaoqi , Huang Zehui , Li Hanhan , Ma Ronghua , Cao Zhigang , Shen Ming , Xue Kun
Published: October 2023

The chlorophyll-a (Chla) concentration is a key parameter to evaluate the eutrophication conditions of water, which is very important for monitoring algal blooms. Although Geostationary Ocean Color Imager (GOCI) has been widely used in Chla inversion, the consistency of the Rayleigh-corrected reflectance (Rrc) of GOCI and GOCI-II sensors still needs to be further evaluated, and a model suitable for lakes with complex optical properties needs to be constructed. The results show that (1) the derived Chla values of the GOCI and GOCI-II synchronous data were relatively consistent and continuous in three lakes in China. (2) The accuracy of the random forest (RF) model (R2 = 0.84, root mean square error (RMSE) =11.77 μg/L) was higher than that of the empirical model (R2 = 0.79, RMSE = 12.63 μg/L) based on the alternative floating algae index (AFAI). (3) The interannual variation trend fluctuated, with high Chla levels in Lake Chaohu in 2015 and 2019, while those in Lake Hongze were high in 2013, 2015, and 2022, and those in Lake Taihu reached their peak in 2017 and 2019. There were three types of diurnal variation patterns, namely, near-continuous increase (Class 1), near-continuous decrease (Class 2), and first an increase and then a decrease (Class 3), among which Lake Chaohu and Lake Taihu occupied the highest proportion in Class 3. The results analyzed the temporal and spatial variations of Chla in three lakes for 12 years and provided support for the use of GOCI and GOCI-II data and monitoring of Chla in optical complex inland waters.
Design, Fabrication, and Characterization of Inkjet-Printed Organic Piezoresistive Tactile Sensor on Flexible Substrate
Authors: Olowo Olalekan O. , Harris Bryan , Sills Daniel , Zhang Ruoshi , Sherehiy Andriy , Tofangchi Alireza , Wei Danming , Popa Dan O.
Published: October 2023

In this paper, we propose a novel tactile sensor with a “fingerprint” design, named due to its spiral shape and dimensions of 3.80 mm × 3.80 mm. The sensor is duplicated in a four-by-four array containing 16 tactile sensors to form a “SkinCell” pad of approximately 45 mm by 29 mm. The SkinCell was fabricated using a custom-built microfabrication platform called the NeXus which contains additive deposition tools and several robotic systems. We used the NeXus’ six-degrees-of-freedom robotic platform with two different inkjet printers to deposit a conductive silver ink sensor electrode as well as the organic piezoresistive polymer PEDOT:PSS-Poly (3,4-ethylene dioxythiophene)-poly(styrene sulfonate) of our tactile sensor. Printing deposition profiles of 100-micron- and 250-micron-thick layers were measured using microscopy. The resulting structure was sintered in an oven and laminated. The lamination consisted of two different sensor sheets placed back-to-back to create a half-Wheatstone-bridge configuration, doubling the sensitivity and accomplishing temperature compensation. The resulting sensor array was then sandwiched between two layers of silicone elastomer that had protrusions and inner cavities to concentrate stresses and strains and increase the detection resolution. Furthermore, the tactile sensor was characterized under static and dynamic force loading. Over 180,000 cycles of indentation were conducted to establish its durability and repeatability. The results demonstrate that the SkinCell has an average spatial resolution of 0.827 mm, an average sensitivity of 0.328 mΩ/Ω/N, expressed as the change in resistance per force in Newtons, an average sensitivity of 1.795 µV/N at a loading pressure of 2.365 PSI, and a dynamic response time constant of 63 ms which make it suitable for both large area skins and fingertip human–robot interaction applications.
Automatic Detection Method for Black Smoke Vehicles Considering Motion Shadows
Authors: Wang Han , Chen Ke , Li Yanfeng
Published: October 2023

Various statistical data indicate that mobile source pollutants have become a significant contributor to atmospheric environmental pollution, with vehicle tailpipe emissions being the primary contributor to these mobile source pollutants. The motion shadow generated by motor vehicles bears a visual resemblance to emitted black smoke, making this study primarily focused on the interference of motion shadows in the detection of black smoke vehicles. Initially, the YOLOv5s model is used to locate moving objects, including motor vehicles, motion shadows, and black smoke emissions. The extracted images of these moving objects are then processed using simple linear iterative clustering to obtain superpixel images of the three categories for model training. Finally, these superpixel images are fed into a lightweight MobileNetv3 network to build a black smoke vehicle detection model for recognition and classification. This study breaks away from the traditional approach of “detection first, then removal” to overcome shadow interference and instead employs a “segmentation-classification” approach, ingeniously addressing the coexistence of motion shadows and black smoke emissions. Experimental results show that the Y-MobileNetv3 model, which takes motion shadows into account, achieves an accuracy rate of 95.17%, a 4.73% improvement compared with the N-MobileNetv3 model (which does not consider motion shadows). Moreover, the average single-image inference time is only 7.3 ms. The superpixel segmentation algorithm effectively clusters similar pixels, facilitating the detection of trace amounts of black smoke emissions from motor vehicles. The Y-MobileNetv3 model not only improves the accuracy of black smoke vehicle recognition but also meets the real-time detection requirements.
Anomaly Detection in a Smart Industrial Machinery Plant Using IoT and Machine Learning
Authors: Jaramillo-Alcazar Angel , Govea Jaime , Villegas-Ch William
Published: October 2023

In an increasingly technology-driven world, the security of Internet-of-Things systems has become a top priority. This article presents a study on the implementation of security solutions in an innovative manufacturing plant using IoT and machine learning. The research was based on collecting historical data from telemetry sensors, IoT cameras, and control devices in a smart manufacturing plant. The data provided the basis for training machine learning models, which were used for real-time anomaly detection. After training the machine learning models, we achieved a 13% improvement in the anomaly detection rate and a 3% decrease in the false positive rate. These results significantly impacted plant efficiency and safety, with faster and more effective responses seen to unusual events. The results showed that there was a significant impact on the efficiency and safety of the smart manufacturing plant. Improved anomaly detection enabled faster and more effective responses to unusual events, decreasing critical incidents and improving overall security. Additionally, algorithm optimization and IoT infrastructure improved operational efficiency by reducing unscheduled downtime and increasing resource utilization. This study highlights the effectiveness of machine learning-based security solutions by comparing the results with those of previous research on IoT security and anomaly detection in industrial environments. The adaptability of these solutions makes them applicable in various industrial and commercial environments.