bluuebunny/crossref_metadata_2025 huggingface dataset:
Consensus Statements among European Sleep Surgery Experts on Snoring and Obstructive Sleep Apnea: Part 1 Definitions and Diagnosis
Authors: Olszewska Ewa , De Vito Andrea , Baptista Peter , Heiser Clemens , O’Connor-Reina Carlos , Kotecha Bhik , Vanderveken Olivier , Vicini Claudio
Published: January 2024

Seeking consensus on definitions and diagnosis of snoring and obstructive sleep apnea (OSA) among sleep surgeons is important, particularly in this relatively new field with variability in knowledge and practices. A set of statements was developed based on the literature and circulated among eight panel members of European experts, utilizing the Delphi method. Responses in agreement and disagreement on each statement and the comments were used to assess the level of consensus and develop a revised version. The new version with the level of consensus and anonymized comments was sent to each panel member as the second round. This was repeated a total of five rounds. The total number of statements included in the initial set was 112. In the first round, of all eight panelists, the percentage of questions that had consensus among the eight, seven, and six panelists were 45%, 4.5%, and 7.1%, respectively. In the final set of statements consisting of 99, the percentage of questions that had consensus among the 8, 7, and 6 panelists went up to 66.7%, 24.2%, and 6.1%, respectively. Delphi’s method demonstrated an efficient method of interaction among experts and the establishment of consensus on a specific set of statements.
Hypertensive Heart Disease: A Narrative Review Series—Part 3: Vasculature, Biomarkers and the Matrix of Hypertensive Heart Disease
Authors: Nemtsova Valeriya , Vischer Annina S. , Burkard Thilo
Published: January 2024

Over the last few decades, research efforts have resulted in major advances in our understanding of the pathophysiology of hypertensive heart disease (HHD). This is the third part of a three-part review series. Here, we focus on the influence of high blood pressure on the micro- and macroalterations that occur in the vasculature in HHD. We also provide an overview of circulating cardiac biomarkers that may prove useful for a better understanding of the pathophysiology, development and progression of HHD, and may play a unique role in the diagnostic and prognostic evaluation of patients with HHD, taking into account their properties showing as abnormal long before the onset of the disease. In the conclusion, we propose an updated definition of HHD and a matrix for clinical classification, which we suspect will be useful in practice, allowing an individual approach to HHD patients.
Long-Term Population Genetic Features of the Rhopilema nomadica Jellyfish from the Israeli Mediterranean Coasts
Authors: Douek Jacob , Giallongo Giovanni , Harbuzov Zoya , Galil Bella S. , Rinkevich Baruch
Published: January 2024

The rhizostomatid scyphozoan Rhopilema nomadica is one of the most notorious marine invasive species established in the eastern Mediterranean Sea. Using seven microsatellite loci, here, we examined the population genetic structures on 587 individual tissue samples collected from 21 sites along the Mediterranean coast of Israel over a period of 16 years. The results indicate unique microsatellite landscapes for all samples, which belong to a single unstructured population. The >20 alleles found in most loci, low fixation index (F) values (average 0.106), and high heterozygosity (average 0.667) suggest random or assortative mating. Additionally, the low overall differentiation (Fst) values (0.043) and pairwise Fst values between the samples collected in different years indicated gene flow and random mating over the years, potentially due to the long-lasting podocytes, scyphistomae, and adults causing a population overlap between the sampled months/years. Likewise, analyses were conducted between seasons, sites, and early/intermediate/late periods of collecting years. These results support the previous analyses performed on the mitochondrial gene cytochrome oxidase subunit I (COI) sequences, altogether indicating a highly polymorphic single unstructured R. nomadica population in the Levant, possibly backed by independent introductions. The results hint to the existence of highly functional connectivity with a genetically highly diverse source population.
Communication Management and Data Compression Algorithm Design of BeiDou Transparent Transmission Terminal for Argo Buoy
Authors: Li Hongyu , Fan Yanjun , Wen Yicheng , Zou Yanchao , Ma Qingfeng , Yang Shaobo
Published: January 2024

The Argo buoy detects marine environmental data by making profile movements in the ocean and transmits the profile detection data to the shore base through the communication terminal. However, due to the large volume of data collected from profile detections and the continuous operation of the terminal, the remote communication of buoys is characterized by lengthy communication times and significant power consumption. A low-power Beidou transparent transmission terminal is designed to solve these problems in this paper. The terminal performs low-power operation management and power management for terminal remote communication. After the end of a communication process, the microcontroller turns the Beidou module power off and enters STOP mode. Before the next communication process begins, the serial port wakes up the microcontroller, which powers up the Beidou module. Before the remote communication, the microcontroller compacts the profile detection data collected by the buoy to reduce the quantity of remote communication data. In this paper, a variety of data compression methods are used to compare the compression rate, and the best compression method is selected according to the format characteristics of the data. The results show that the Beidou transparent transmission terminal of the Argo buoy can realize low power consumption for remote communication in ocean exploration. The terminal reduces power consumption by 77.282% per communication, and the average number of remote communications for each profile detection data are reduced by 55 times. The low-power Beidou transparent transmission terminal improves the battery life and is conducive to the long-term operation of the buoy.
VL-Meta: Vision-Language Models for Multimodal Meta-Learning
Authors: Ma Han , Fan Baoyu , Ng Benjamin K. , Lam Chan-Tong
Published: January 2024

Multimodal learning is a promising area in artificial intelligence (AI) that can make the model understand different kinds of data. Existing works are trying to re-train a new model based on pre-trained models that requires much data, computation power, and time. However, it is difficult to achieve in low-resource or small-sample situations. Therefore, we propose VL-Meta, Vision Language Models for Multimodal Meta Learning. It (1) presents the vision-language mapper and multimodal fusion mapper, which are light model structures, to use the existing pre-trained models to make models understand images to language feature space and save training data, computation power, and time; (2) constructs the meta-task pool that can only use a small amount of data to construct enough training data and improve the generalization of the model to learn the data knowledge and task knowledge; (3) proposes the token-level training that can align inputs with the outputs during training to improve the model performance; and (4) adopts the multi-task fusion loss to learn the different abilities for the models. It achieves a good performance on the Visual Question Answering (VQA) task, which shows the feasibility and effectiveness of the model. This solution can help blind or visually impaired individuals obtain visual information.
Screening of Secondary Metabolites Produced by Nigrospora sphaerica Associated with the Invasive Weed Cenchrus ciliaris Reveals Two New Structurally Related Compounds
Authors: Salvatore Maria Michela , Russo Maria Teresa , Meyer Susan , Tuzi Angela , Della Greca Marina , Masi Marco , Andolfi Anna
Published: January 2024

In the search for new alternative biocontrol strategies, phytopathogenic fungi could represent a new frontier for weed management. In this respect, as part of our ongoing work aiming at using fungal pathogens as an alternative to common herbicides, the foliar pathogen Nigrospora sphaerica has been evaluated to control buffelgrass (Cenchrus ciliaris). In particular, in this work, the isolation and structural elucidation of two new biosynthetically related metabolites, named nigrosphaeritriol (3-(hydroxymethyl)-2-methylpentane-1,4-diol) and nigrosphaerilactol (3-(1-hydroxyethyl)-4-methyltetrahydrofuran-2-ol), from the phytotoxic culture filtrate extract were described, along with the identification of several known metabolites. Moreover, the absolute stereochemistry of (3R,4S,5S)-nigrosphaerilactone, previously reported as (3S,4R,5R)-4-hydroxymethyl-3,5-dimethyldihydro-2-furanone, was determined for the first time by X-ray diffraction analysis. Considering their structural relationship, the determination of the absolute stereochemistry of nigrosphaerilactone allowed us to hypothesize the absolute stereochemistry of nigrosphaeritriol and nigrosphaerilactol.
The Techno-Functionality of Chia Seed and Its Fractions as Ingredients for Meat Analogs
Authors: Senna Caroline , Soares Luiza , Egea Mariana Buranelo , Fernandes Sibele Santos
Published: January 2024

Eating practices are changing due to awareness about meat consumption associated with social, ethical, environmental, and nutritional issues. Plant-based meat analogs are alternatives to conventional meat products that attempt to mimic all the inherent characteristics of meat fully. Therefore, the search for raw materials that provide these characteristics is increasing. Chia seeds have excellent potential as a functional ingredient in these products since they are a source of proteins, lipids, and fibers. Allied with this, the full use of chia through the seed and its fractions highlights the numerous beneficial characteristics of the formulation regarding nutritional characteristics and techno-functionality. Therefore, this review aims to highlight the potential of chia seed and its fractions for applications in meat-like products. Chia seeds are protein sources. Chia oil is rich in polyunsaturated fatty acids, and its application in emulsions ensures the oil’s nutritional quality and maintains its technological characteristics. Defatted chia flour has a high protein content and can be used to extract chia mucilage. Due to its high emulsification capacity, chia mucilage is an effective ingredient for meat products and, consequently, meat-like products. Therefore, this literature review demonstrates the strategic potential of using chia seeds and their fractions to develop meat analogs.
From Waste to Value: Recent Insights into Producing Vanillin from Lignin
Authors: D’Arrigo Paola , Rossato Letizia A. M. , Strini Alberto , Serra Stefano
Published: January 2024

Vanillin, one of the most widely used and appreciated flavoring agents worldwide, is the main constituent of vanilla bean extract, obtained from the seed pods of various members belonging to the Orchidaceae family. Due to the great demand in the food confectionery industry, as well as in the perfume industry, medicine, and more, the majority of vanillin used today is produced synthetically, and only less than one percent of the world’s vanilla flavoring market comes directly from the traditional natural sources. The increasing global demand for vanillin requires alternative and overall sustainable new production methods, and the recovery from biobased polymers, like lignin, is an environmentally friendly alternative to chemical synthesis. The present review provides firstly an overview of the different types of vanillin, followed by a description of the main differences between natural and synthetic vanillin, their preparation, the market of interest, and the authentication issues and the related analytical techniques. Then, the review explores the real potentialities of lignin for vanillin production, presenting firstly the well-assessed classical methods and moving towards the most recent promising approaches through chemical, biotechnological and photocatalytic methodologies, together with the challenges and the principal issues associated with each technique.
PNPLA3 Genotype and Dietary Fat Modify Concentrations of Plasma and Fecal Short Chain Fatty Acids and Plasma Branched-Chain Amino Acids
Authors: Tauriainen Milla-Maria , Csader Susanne , Lankinen Maria , Lo Kwun Kwan , Chen Congjia , Lahtinen Olli , El-Nezamy Hani , Laakso Markku , Schwab Ursula
Published: January 2024

The GG genotype of the Patatin-like phosphatase domain-containing 3 (PNPLA3), dietary fat, short-chain fatty acids (SCFA) and branched-chain amino acids (BCAA) are linked with non-alcoholic fatty liver disease. We studied the impact of the quality of dietary fat on plasma (p) and fecal (f) SCFA and p-BCAA in men homozygous for the PNPLA3 rs738409 variant (I148M). Eighty-eight randomly assigned men (age 67.8 ± 4.3 years, body mass index 27.1 ± 2.5 kg/m2) participated in a 12-week diet intervention. The recommended diet (RD) group followed the National and Nordic nutrition recommendations for fat intake. The average diet (AD) group followed the average fat intake in Finland. The intervention resulted in a decrease in total p-SCFAs and iso-butyric acid in the RD group (p = 0.041 and p = 0.002). Valeric acid (p-VA) increased in participants with the GG genotype regardless of the diet (RD, 3.6 ± 0.6 to 7.0 ± 0.6 µmol/g, p = 0.005 and AD, 3.8 ± 0.3 to 9.7 ± 8.5 µmol/g, p = 0.015). Also, genotype relation to p-VA was seen statistically significantly in the RD group (CC: 3.7 ± 0.4 to 4.2 ± 1.7 µmol/g and GG: 3.6 ± 0.6 to 7.0 ± 0.6 µmol/g, p = 0.0026 for time and p = 0.004 for time and genotype). P-VA, unlike any other SCFA, correlated positively with plasma gamma-glutamyl transferase (r = 0.240, p = 0.025). Total p-BCAAs concentration changed in the AD group comparing PNPLA3 CC and GG genotypes (CC: 612 ± 184 to 532 ± 149 µmol/g and GG: 587 ± 182 to 590 ± 130 µmol/g, p = 0.015 for time). Valine decreased in the RD group (p = 0.009), and leucine decreased in the AD group (p = 0.043). RD decreased total fecal SCFA, acetic acid (f-AA), and butyric acid (f-BA) in those with CC genotype (p = 0.006, 0.013 and 0.005, respectively). Our results suggest that the PNPLA3 genotype modifies the effect of dietary fat modification for p-VA, total f-SCFA, f-AA and f-BA, and total p-BCAA.
Nutrition at the Intersection between Gut Microbiota Eubiosis and Effective Management of Type 2 Diabetes
Authors: Hamamah Sevag , Iatcu Oana C. , Covasa Mihai
Published: January 2024

Nutrition is one of the most influential environmental factors in both taxonomical shifts in gut microbiota as well as in the development of type 2 diabetes mellitus (T2DM). Emerging evidence has shown that the effects of nutrition on both these parameters is not mutually exclusive and that changes in gut microbiota and related metabolites such as short-chain fatty acids (SCFAs) and branched-chain amino acids (BCAAs) may influence systemic inflammation and signaling pathways that contribute to pathophysiological processes associated with T2DM. With this background, our review highlights the effects of macronutrients, carbohydrates, proteins, and lipids, as well as micronutrients, vitamins, and minerals, on T2DM, specifically through their alterations in gut microbiota and the metabolites they produce. Additionally, we describe the influences of common food groups, which incorporate varying combinations of these macronutrients and micronutrients, on both microbiota and metabolic parameters in the context of diabetes mellitus. Overall, nutrition is one of the first line modifiable therapies in the management of T2DM and a better understanding of the mechanisms by which gut microbiota influence its pathophysiology provides opportunities for optimizing dietary interventions.
The Development of a Resting Metabolic Rate Prediction Equation for Professional Male Rugby Union Players
Authors: Posthumus Logan , Driller Matthew , Winwood Paul , Gill Nicholas
Published: January 2024

Determining resting metabolic rate (RMR) is an important aspect when calculating energy requirements for professional rugby union players. Prediction equations are often used for convenience to estimate RMR. However, the accuracy of current prediction equations for professional rugby union players remains unclear. The aims of this study were to examine the RMR of professional male rugby union players compared to nine commonly used prediction equations and develop and validate RMR prediction equations specific to professional male rugby union players. One hundred and eight players (body mass (BM) = 102.9 ± 13.3 kg; fat-free mass (FFM) = 84.8 ± 10.2 kg) undertook Dual-energy X-ray Absorptiometry scans to assess body composition and indirect calorimetry to determine RMR. Mean RMR values of 2585 ± 176 kcal∙day−1 were observed among the group with forwards (2706 ± 94 kcal·day−1), demonstrating significantly (p < 0.01; d = 1.93) higher RMR compared to backs (2465 ± 156 kcal·day−1), which appeared to be due to their higher BM and FFM measures. Compared to the measured RMR for the group, seven of the nine commonly used prediction equations significantly (p < 0.05) under-estimated RMR (−104–346 kcal·day−1), and one equation significantly (p < 0.01) over-estimated RMR (192 kcal·day−1). This led to the development of a new prediction equation using stepwise linear regression, which determined that the strongest predictor of RMR for this group was FFM alone (R2 = 0.70; SEE = 96.65), followed by BM alone (R2 = 0.65; SEE = 104.97). Measuring RMR within a group of professional male rugby union players is important, as current prediction equations may under- or over-estimate RMR. If direct measures of RMR cannot be obtained, we propose the newly developed prediction equations be used to estimate RMR within professional male rugby union players. Otherwise, developing team- and/or group-specific prediction equations is encouraged.
Evaluation of Antibacterial and Antibiofilm Activity of Rice Husk Extract against Staphylococcus aureus
Authors: Burlacchini Gloria , Sandri Angela , Papetti Adele , Frosi Ilaria , Boschi Federico , Lleo Maria M. , Signoretto Caterina
Published: January 2024

Infections caused by Staphylococcus aureus are particularly difficult to treat due to the high rate of antibiotic resistance. S. aureus also forms biofilms that reduce the effects of antibiotics and disinfectants. Therefore, new therapeutic approaches are increasingly required. In this scenario, plant waste products represent a source of bioactive molecules. In this study, we evaluated the antimicrobial and antibiofilm activity of the rice husk extract (RHE) on S. aureus clinical isolates. In a biofilm inhibition assay, high concentrations of RHE counteracted the formation of biofilm by S. aureus isolates, both methicillin-resistant (MRSA) and -sensitive (MSSA). The observation of the MRSA biofilm by confocal laser scanning microscopy using live/dead cell viability staining confirmed that the bacterial viability in the RHE-treated biofilm was reduced. However, the extract showed no or little biofilm disaggregation ability. An additive effect was observed when treating S. aureus with a combination of RHE and oxacillin/cefoxitin. In Galleria mellonella larvae treated with RHE, the extract showed no toxicity even at high concentrations. Our results support that the rice husk has antimicrobial and antibiofilm properties and could potentially be used in the future in topical solutions or on medical devices to prevent biofilm formation.
Declining but Pronounced State-Level Disparities in Prescription Opioid Distribution in the United States
Authors: Madera Joshua D. , Ruffino Amanda E. , Feliz Adriana , McCall Kenneth L. , Davis Corey S. , Piper Brian J.
Published: January 2024

The United States (US) opioid epidemic is a persistent and pervasive public health emergency that claims the lives of over 80,000 Americans per year as of 2021. There have been sustained efforts to reverse this crisis over the past decade, including a number of measures designed to decrease the use of prescription opioids for the treatment of pain. This study analyzed the changes in federal production quotas for prescription opioids and the distribution of prescription opioids for pain and identified state-level differences between 2010 and 2019. Data (in grams) on opioid production quotas and distribution (from manufacturer to hospitals, retail pharmacies, practitioners, and teaching institutions) of 10 prescription opioids (codeine, fentanyl, hydrocodone, hydromorphone, meperidine, methadone, morphine, oxycodone, oxymorphone, and tapentadol) for 2010 to 2019 were obtained from the US Drug Enforcement Administration. Amounts of each opioid were converted from grams to morphine milligram equivalent (MME), and the per capita distribution by state was calculated using population estimates. Total opioid production quotas increased substantially from 2010 to 2013 before decreasing by 41.5% from 2013 (87.6 MME metric tons) to 2019 (51.3). The peak year for distribution of all 10 prescription opioids was between 2010 and 2013, except for codeine (2015). The largest quantities of opioid distribution were observed in Tennessee (520.70 MME per person) and Delaware (251.45) in 2011 and 2019. There was a 52.0% overall decrease in opioid distribution per capita from 2010 to 2019, with the largest decrease in Florida (−61.6%) and the smallest in Texas (−18.6%). Southern states had the highest per capita distribution for eight of the ten opioids in 2019. The highest to lowest state ratio of total opioid distribution, corrected for population, decreased from 5.25 in 2011 to 2.78 in 2019. The mean 95th/5th ratio was relatively consistent in 2011 (4.78 ± 0.70) relative to 2019 (5.64 ± 0.98). This study found a sustained decline in the distribution of ten prescription opioids during the last five years. Distribution was non-homogeneous at the state level. Analysis of state-level differences revealed a fivefold difference in the 95th:5th percentile ratio between states, which has remained unchanged over the past decade. Production quotas did not correspond with the distribution, particularly in the 2010–2016 period. Future research, focused on identifying factors contributing to the observed regional variability in opioid distribution, could prove valuable to understanding and potentially remediating the pronounced disparities in prescription opioid-related harms in the US.
The Conversion of Pistachio and Walnut Shell Waste into Valuable Components with Subcritical Water
Authors: Čolnik Maja , Irgolič Mihael , Perva Amra , Škerget Mojca
Published: January 2024

Pistachio and walnut shells accumulate in large quantities as waste during food processing and represent a promising lignocellulosic biomass for the extraction of valuable components. Subcritical water technology was used as an environmentally friendly technique to study the extraction of active ingredients and other valuable degradation products from walnut and pistachio waste. Subcritical water extraction (SWE) was carried out under different process conditions (temperature (150–300 °C) and short reaction times (15–60 min)) and compared with conventional extraction using different organic solvents (acetone, 50% acetone and ethanol). The extracts obtained from pistachio and walnut shell waste are rich in various bioactive and valuable components. The highest contents of total phenols (127.08 mg GA/g extract at 300 °C for 15 min, from walnut shells), total flavonoids (10.18 mg QU/g extract at 200 °C for 60 min, from pistachio shells), total carbohydrates (602.14 mg TCH/g extract at 200 °C for 60 min, from walnut shells) and antioxidant activity (91% at 300 °C, for 60 min, from pistachio shells) were determined when the extracts were obtained via subcritical water. High contents of total phenols (up to 86.17 mg GA/g extract) were also determined in the conventional extracts obtained with ethanol. Using the HPLC method, sugars and their valuable derivatives were determined in the extracts, with glucose, fructose, furfurals (5-hydroxymethylfurfural (5-HMF) and furfural) and levulinic acid being the most abundant in the extracts obtained by subcritical water. The results show that subcritical water technology enables better exploitation of biowaste materials than conventional extraction methods with organic solvents, as it provides a higher yields of bioactive components such as phenolic compounds and thus extracts with high antioxidant activity, while at the same time producing degradation products that are valuable secondary raw materials.
Using Reduced Kinetic Model for the Multi-Objective Optimization of Thermal Section of the Claus Process Leading to a More Cost-Effective and Environmentally Friendly Operation
Authors: Andoglu Coskun Ecem Muge , Kaytakoglu Suleyman , Manenti Flavio , Di Pretoro Alessandro
Published: January 2024

The Claus process is a sulfur recovery unit wherein hydrogen sulfide is converted into the elemental sulfur. This study aims to model the thermal section of the Claus process, which consists of a reaction furnace and a waste heat boiler, as a configuration of two reactors, and subsequently optimize the entire section. Two different reduced kinetic schemes were provided for both units. Using the validated kinetics, mathematical models were developed. The waste heat boiler was modeled as a plug flow reactor with heat transfer, instead of a heat exchanger. The main objective was to maximize the amount of elemental sulfur at the end of the thermal section. Additionally, maximizing the amount of steam generated in the WHB was considered as a secondary objective, and the multi-objective optimization problem was solved. The sulfur production was improved 14.1% and 30% as a result of single- and multi-objective optimization studies. In addition, as an alternative, the Taguchi method was also used for optimization studies, and optimum values were determined. Using the Taguchi method, we determined that an increase in sulfur production by 24% is possible.
Groundwater Ecosystem Services: Redefining and Operationalizing the Concept
Authors: Iliopoulos Vasileios G. , Damigos Dimitris
Published: January 2024

In the last decade, the publication of research studies in the field of groundwater ecosystem services and their classification has increased dramatically. Several academic and other institutions have developed ecosystem service classification systems for use in environmental policy research. However, the existing classification systems need to be optimized in the following areas: (a) the weak correlation between concepts and the definitions assigned, which creates double counting in economic valuation, (b) the absence of correlation of the concept of use value with the ecosystem service, (c) the non-categorization of final and intermediate services, and (d) the overlapping of wetland, soil, and groundwater regulating ecosystem services. Our research aims, through the results of a literature analysis and the synthesis of the results of an expert consultation process, to develop a unified categorization system for groundwater ecosystem services. In the context of the conceptualization of groundwater ecosystem services, this research implemented an expert judging elicitation process where subject discussions and targeted interviews were performed, combined with a literature review analysis. Through the completion of a specific questionnaire and expert interviews, a new groundwater ecosystem services classification system, namely GROUNDWES, was established.
A Hybrid Model for Forecasting Realized Volatility Based on Heterogeneous Autoregressive Model and Support Vector Regression
Authors: Zhuo Yue , Morimoto Takayuki
Published: January 2024

In this study, we proposed two types of hybrid models based on the heterogeneous autoregressive (HAR) model and support vector regression (SVR) model to forecast realized volatility (RV). The first model is a residual-type model, where the RV is first predicted using the HAR model, and the residuals are used to train the SVR model. The residual component is then predicted using the SVR model, and the results from both the HAR and SVR models are combined to obtain the final prediction. The second model is a weight-based model, which is a combination of the HAR and SVR models and uses the same independent variables and dependent variables as the HAR model; we adjust the contribution of the two models to the predicted values by giving different weights to each model. In particular, four volatility models are used in RV forecasting as basic models. For empirical analysis, the RV of returns of the Tokyo stock price index and five individual stocks of TOPIX 30 is used as the dataset. The empirical results reveal that according to the model confidence set test, the weight-type model outperforms the HAR model and the residual-type HAR–SVR model.
Mapping Plant Nitrogen Concentration and Aboveground Biomass of Potato Crops from Sentinel-2 Data Using Ensemble Learning Models
Authors: Yin Hang , Li Fei , Yang Haibo , Di Yunfei , Hu Yuncai , Yu Kang
Published: January 2024

Excessive nitrogen (N) fertilization poses environmental risks at regional and global levels. Satellite remote sensing provides a novel approach for large-scale N monitoring. In this study, we evaluated the performance of different types of spectral bands and indices (SIs) coupled with ensemble learning models (ELMs) at retrieving the plant N concentration (PNC) and plant aboveground biomass (AGB) of potato from Sentinel-2 images. Cloud-free Sentinel-2 imagery was acquired during the tuber-formation to starch-accumulation stages from 2020 to 2021. Fourteen optimal SIs were selected using the successive projections algorithm (SPA) and principal component analysis (PCA). The PNC and AGB estimation models were then built using an ELMs. The results showed that the SIs based on chlorophyll absorption bands were strongly related to potato PNC and AGB. Also, the N-correlated bands were mainly concentrated in the red-edge (705 nm) and short-wave infrared (1610 and 2190 nm) regions. The ELMs successfully predicted PNC and AGB (R2PNC = 0.74; R2AGB = 0.82). Compared with the other five base models (k-nearest neighbor (KNN), partial least squares regression (PLSR), support vector regression (SVR), random forest (RF), and Gaussian process regression (GPR)), the ELMs provided higher PNC and AGB estimation accuracy and effectively reduced overfitting to training data. This study demonstrated that the promising solution of using SPA-PCA coupled with an ensemble learning model improves the estimation accuracy of potato PNC and AGB based on Sentinel-2 imagery data.
Improved Object Detection with Content and Position Separation in Transformer
Authors: Wang Yao , Ha Jong-Eun
Published: January 2024

In object detection, Transformer-based models such as DETR have exhibited state-of-the-art performance, capitalizing on the attention mechanism to handle spatial relations and feature dependencies. One inherent challenge these models face is the intertwined handling of content and positional data within their attention spans, potentially blurring the specificity of the information retrieval process. We consider object detection as a comprehensive task, and simultaneously merging content and positional information like before can exacerbate task complexity. This paper presents the Multi-Task Fusion Detector (MTFD), a novel architecture that innovatively dissects the detection process into distinct tasks, addressing content and position through separate decoders. By utilizing assumed fake queries, the MTFD framework enables each decoder to operate under a presumption of known ancillary information, ensuring more specific and enriched interactions with the feature map. Experimental results affirm that this methodical separation followed by a deliberate fusion not only simplifies the task difficulty of the detection process but also augments accuracy and clarifies the details of each component, providing a fresh perspective on object detection in Transformer-based architectures.
Extraction of Tobacco Planting Information Based on UAV High-Resolution Remote Sensing Images
Authors: He Lei , Liao Kunwei , Li Yuxia , Li Bin , Zhang Jinglin , Wang Yong , Lu Liming , Jian Sichun , Qin Rui , Fu Xinjun
Published: January 2024

Tobacco is a critical cash crop in China, so its growing status has received more and more attention. How to acquire accurate plant area, row spacing, and plant spacing at the same time have been key points for its grow status monitoring and yield prediction. However, accurately detecting small and densely arranged tobacco plants during the rosette stage poses a significant challenge. In Sichuan Province, the contours of scattered tobacco fields with different shapes are not well-extracted. Additionally, there is a lack of simultaneous methods for extracting crucial tobacco planting information, including area, row spacing, and plant spacing. In view of the above scientific problems, we proposed a method to extract the planting information of tobacco at the rosette stage with Unmanned Aerial Vehicle (UAV) remote sensing images. A detection model, YOLOv8s-EFF, was constructed for the small and weak tobacco in the rosette stage. We proposed an extraction algorithm for tobacco field area based on extended contours for different-shaped fields. Meanwhile, a planting distance extraction algorithm based on tobacco coordinates was presented. Further, four experimental areas were selected in Sichuan Province, and image processing and sample label production were carried out. Four isolated tobacco fields with different shapes in four experimental areas were used to preliminarily verify the effectiveness of the model and algorithm proposed. The results show that the precision ranges of tobacco field area, row spacing, and plant spacing were 96.51~99.04%, 90.08~99.74%, and 94.69~99.15%, respectively. And another two experimental areas, Jiange County, Guangyuan, and Dazhai County, Gulin County, and Luzhou, were selected to evaluate the accuracy of the method proposed in the research in practical application. The results indicate that the average accuracy of tobacco field area, row spacing, and plant spacing extracted by this method reached 97.99%, 97.98%, and 98.31%, respectively, which proved the extraction method of plant information is valuable.
Improving Fire Severity Analysis in Mediterranean Environments: A Comparative Study of eeMETRIC and SSEBop Landsat-Based Evapotranspiration Models
Authors: Quintano Carmen , Fernández-Manso Alfonso , Fernández-Guisuraga José Manuel , Roberts Dar A.
Published: January 2024

Wildfires represent a significant threat to both ecosystems and human assets in Mediterranean countries, where fire occurrence is frequent and often devastating. Accurate assessments of the initial fire severity are required for management and mitigation efforts of the negative impacts of fire. Evapotranspiration (ET) is a crucial hydrological process that links vegetation health and water availability, making it a valuable indicator for understanding fire dynamics and ecosystem recovery after wildfires. This study uses the Mapping Evapotranspiration at High Resolution with Internalized Calibration (eeMETRIC) and Operational Simplified Surface Energy Balance (SSEBop) ET models based on Landsat imagery to estimate fire severity in five large forest fires that occurred in Spain and Portugal in 2022 from two perspectives: uni- and bi-temporal (post/pre-fire ratio). Using-fine-spatial resolution ET is particularly relevant for heterogeneous Mediterranean landscapes with different vegetation types and water availability. ET was significantly affected by fire severity according to eeMETRIC (F > 431.35; p-value < 0.001) and SSEBop (F > 373.83; p-value < 0.001) metrics, with reductions of 61.46% and 63.92%, respectively, after the wildfire event. A Random Forest machine learning algorithm was used to predict fire severity. We achieved higher accuracy (0.60 < Kappa < 0.67) when employing both ET models (eeMETRIC and SSEBop) as predictors compared to utilizing the conventional differenced Normalized Burn Ratio (dNBR) index, which resulted in a Kappa value of 0.46. We conclude that both fine resolution ET models are valid to be used as indicators of fire severity in Mediterranean countries. This research highlights the importance of Landsat-based ET models as accurate tools to improve the initial analysis of fire severity in Mediterranean countries.
Estimating Fraction of Absorbed Photosynthetically Active Radiation of Winter Wheat Based on Simulated Sentinel-2 Data under Different Varieties and Water Stress
Authors: Sun Zheng , Sun Liang , Liu Yu , Li Yangwei , Crusiol Luís Guilherme Teixeira , Chen Ruiqing , Wuyun Deji
Published: January 2024

The fraction of absorbed photosynthetically active radiation (fPAR) is an important parameter reflecting the level of photosynthesis and growth status of vegetation, and is widely used in energy cycling, carbon cycling, and vegetation productivity estimation. In agricultural production, fPAR is often combined with the light use efficiency model to estimate crop yield. Therefore, accurate estimation of PAR is of great importance for improving the accuracy of crop yield estimation and ensuring national food security. Existing studies based on vegetation indices have not considered the effects of genetic variety, light, and water stress on fPAR estimation. This study uses ground-based reflectance data to simulate 21 common Sentinel-2 vegetation indices and compare their estimation ability for winter wheat fPAR. The stability of the vegetation index with the highest correlation in inverting fPAR under different cultivars, light, and water stress was tested, and then the model was validated at the satellite scale. Finally, a sensitivity analysis was performed. The results showed that the index model based on modified NDVI (MNDVI) had the highest correlation not only throughout the critical phenological period of winter wheat (R2 of 0.6649) but also under different varieties, observation dates, and water stress (R2 of 0.918, 0.881, and 0.830, respectively). It even performed the highest R2 of 0.8312 at the satellite scale. Moreover, through comparison, we found that considering water stress and variety differences can improve the estimation accuracy of fPAR. The study showed that using MNDVI for fPAR estimation is not only feasible but also has high accuracy and stability, providing a reference for rapid and accurate estimation of fPAR by Sentinel-2 and further exploring the potential of Sentinel-2 data for high-resolution fPAR mapping.
Louvain-Based Traffic Object Detection for Roadside 4D Millimeter-Wave Radar
Authors: Gong Bowen , Sun Jinghang , Lin Ciyun , Liu Hongchao , Sun Ganghao
Published: January 2024

Object detection is the fundamental task of vision-based sensors in environmental perception and sensing. To leverage the full potential of roadside 4D MMW radars, an innovative traffic detection method is proposed based on their distinctive data characteristics. First, velocity-based filtering and region of interest (ROI) extraction were employed to filter and associate point data by merging the point cloud frames to enhance the point relationship. Then, the Louvain algorithm was used to divide the graph into modularity by converting the point cloud data into graph structure and amplifying the differences with the Gaussian kernel function. Finally, a detection augmentation method is introduced to address the problems of over-clustering and under-clustering based on the object ID characteristics of 4D MMW radar data. The experimental results showed that the proposed method obtained the highest average precision and F1 score: 98.15% and 98.58%, respectively. In addition, the proposed method showcased the lowest over-clustering and under-clustering errors in various traffic scenarios compared with the other detection methods.
Video Classification of Cloth Simulations: Deep Learning and Position-Based Dynamics for Stiffness Prediction
Authors: Mao Makara , Va Hongly , Hong Min
Published: January 2024

In virtual reality, augmented reality, or animation, the goal is to represent the movement of deformable objects in the real world as similar as possible in the virtual world. Therefore, this paper proposed a method to automatically extract cloth stiffness values from video scenes, and then they are applied as material properties for virtual cloth simulation. We propose the use of deep learning (DL) models to tackle this issue. The Transformer model, in combination with pre-trained architectures like DenseNet121, ResNet50, VGG16, and VGG19, stands as a leading choice for video classification tasks. Position-Based Dynamics (PBD) is a computational framework widely used in computer graphics and physics-based simulations for deformable entities, notably cloth. It provides an inherently stable and efficient way to replicate complex dynamic behaviors, such as folding, stretching, and collision interactions. Our proposed model characterizes virtual cloth based on softness-to-stiffness labels and accurately categorizes videos using this labeling. The cloth movement dataset utilized in this research is derived from a meticulously designed stiffness-oriented cloth simulation. Our experimental assessment encompasses an extensive dataset of 3840 videos, contributing to a multi-label video classification dataset. Our results demonstrate that our proposed model achieves an impressive average accuracy of 99.50%. These accuracies significantly outperform alternative models such as RNN, GRU, LSTM, and Transformer.
Research on Lateral Safety Spacing for Fusion Operation Based on Unmanned and Manned Aircraft-Event Modeling
Authors: Zhou Chao , Huang Chi , Huang Longyang , Xie Chuanjiang , Zhu Xingyu , Huang Tao
Published: January 2024

With the rapid development of unmanned aerial vehicle technology and its increasing application across various fields, current airspace resources are insufficient for unmanned aerial vehicles’ needs. This paper, taking Zigong General Aviation Airport in Sichuan as a case study, explores the lateral safety spacing in a mixed operation mode of unmanned aerial vehicles and manned aircraft. Currently, there are no standardized regulations for the safe spacing of the fusion operation of unmanned and manned aircraft. Theoretical research is essential to provide a reference for actual operations. It introduces the UM-Event (unmanned and manned aircraft-event) collision risk model, an adaptation of the Event collision risk model, considering factors like communication, navigation, surveillance performance, human factors, collision avoidance equipment performance, and meteorology. Safety spacing was determined via simulation experiments and actual data analysis, adhering to the target safety level (TSL). Findings indicate that surveillance performance has a minor impact on safety spacing, while communication and navigation significantly influence it. The safety spacing, influenced solely by CNS (communication performance, navigation performance, surveillance performance) and combined factors, increased from 4.42 to 4.47 nautical miles. These results offer theoretical guidance for unmanned aerial vehicle safety in non-segregated airspace.
The Added Value of Face-to-Face Supervision to a Therapeutic Exercise-Based App in the Management of Patients with Chronic Low Back Pain: A Randomized Clinical Trial
Authors: López-Marcos José Javier , Díaz-Arribas María José , Valera-Calero Juan Antonio , Navarro-Santana Marcos José , Izquierdo-García Juan , Ortiz-Gutiérrez Rosa María , Plaza-Manzano Gustavo
Published: January 2024

Low back pain (LBP) is a significant global health challenge due to its high prevalence, and chronicity and recurrence rates, with projections suggesting an increase in the next years due to population growth and aging. The chronic and recurrent nature of LBP, responsible for a significant percentage of years lived with disability, underscores the need for effective management strategies, including self-management strategies advocated by current guidelines, to empower patients and potentially improve healthcare efficiency and clinical outcomes. Therefore, the aim of this study was to analyze the added value of face-to-face visits in patients with chronic LBP undergoing a self-management program based on therapeutic exercises on pain intensity, disability, quality of life and treatment adherence and satisfaction. A randomized clinical trial was conducted, allocating 49 patients into a experimental group with a mobile health (mHealth) app usage and face-to-face sessions and 49 patients into an active control group without face-to-face sessions. Pain intensity, disability and quality of life were assessed at baseline, 4 weeks postintervention and 12 weeks postintervention. Patients’ satisfaction and adherence were assessed at the end of the study. The multivariate general model revealed no statistically significant time × group interaction for any outcome (p > 0.0068) but mental quality of life (p = 0.006). Within-group differences revealed significant improvements for all the clinical indicators (all, p < 0.001). Patients allocated to the experimental group reported greater satisfaction and adherence (both, p < 0.001) compared to the control group. The use of mHealth apps such as Healthy Back® as part of digital health initiatives may serve as a beneficial approach to enhance the management of LBP.
Low-Noise Amplifier with Bypass for 5G New Radio Frequency n77 Band and n79 Band in Radio Frequency Silicon on Insulator Complementary Metal–Oxide Semiconductor Technology
Authors: Kim Min-Su , Yoo Sang-Sun
Published: January 2024

This paper presents the design of a low-noise amplifier (LNA) with a bypass mode for the n77/79 bands in 5G New Radio (NR). The proposed LNA integrates internal matching networks for both input and output, combining two LNAs for the n77 and n79 bands into a single chip. Additionally, a bypass mode is integrated to accommodate the flexible operation of the receiving system in response to varying input signal levels. For each frequency band, we designed a low-noise amplifier for the n77 band to expand the bandwidth to 900 MHz (3.3 GHz to 4.2 GHz) using resistive–capacitance (RC) feedback and series inductive-peaking techniques. For the n79 band, only the RC feedback technique was employed to optimize the performance of the LNA for its 600 MHz bandwidth (4.4 GHz to 5.0 GHz). Because wideband techniques can lead to a trade-off between gain and noise, causing potential degradation in noise performance, appropriate bandwidth design becomes crucial. The designed n77 band low-noise amplifier achieved a simulated gain of 22.6 dB and a noise figure of 1.7 dB. Similarly, the n79 band exhibited a gain of 21.1 dB and a noise figure of 1.5 dB with a current consumption of 10 mA at a 1.2 supply voltage. The bypass mode was designed with S21 of −3.7 dB and −5.0 dB for n77 and n79, respectively.