Over the past two decades, persistent occurrences of harmful algal blooms (HAB; Karenia brevis) have been reported in Charlotte County, southwestern Florida. We developed data-driven models that rely on spatiotemporal remote sensing and field data to identify factors controlling HAB propagation, provide a same-day distribution (nowcasting), and forecast their occurrences up to three days in advance. We constructed multivariate regression models using historical HAB occurrences (213 events reported from January 2010 to October 2017) compiled by the Florida Fish and Wildlife Conservation Commission and validated the models against a subset (20%) of the reported historical events. The models were designed to specifically capture the onset of the HABs instead of those that developed days earlier and continued thereafter. A prototype of an early warning system was developed through a threefold exercise. The first step involved the automatic downloading and processing of daily Moderate Resolution Imaging Spectroradiometer (MODIS) Aqua products using SeaDAS ocean color processing software to extract temporal and spatial variations of remote sensing-based variables over the study area. The second step involved the development of a multivariate regression model for same-day mapping of HABs and similar subsequent models for forecasting HAB occurrences one, two, and three days in advance. Eleven remote sensing variables and two non-remote sensing variables were used as inputs for the generated models. In the third and final step, model outputs (same-day and forecasted distribution of HABs) were posted automatically on a web-based GIS (http://www.esrs.wmich.edu/webmap/bloom/). Our findings include the following: (1) the variables most indicative of the timing of bloom propagation are bathymetry, euphotic depth, wind direction, SST, chlorophyll-a [OC3M] and distance from the river mouth, and (2) the model predictions were 90% successful for same-day mapping and 65%, 72% and 71% for the one-, two- and three-day advance predictions, respectively. The adopted methodologies are reliable, dependent on readily available remote sensing data sets, and cost-effective and thus could potentially be used to map and forecast algal bloom occurrences in data-scarce regions.

Developing countries like Pakistan is among those where lack of adoption to science and technology advancement is a major constraint for Satellite Remote Sensing use in crops and land use land cover digital information generation. Exponential rise in country population, increased food demand, limiting natural resources coupled with migration of rural community to urban areas had further led to skewed official statistics. This study is an attempt to demonstrate the possible use of freely available satellite data like Landsat8 under complex cropping system of Okara district of Punjab, Pakistan. An Integrated approach has been developed for the satellite data based crops and land use/cover spatial area estimation. The resultant quality was found above 96% with Kappa statistics of 0.95. Land utilization statistics provided detail information about cropping patterns as well as land use land cover status. Rice was recorded as most dominating crop in term of cultivation area of around 0.165 million ha followed by autumn maize 0.074 million ha, Fallow crop fields 0.067 million ha and Sorghum 0.047 million ha. Other minor crops observed were potato, fodder and cotton being cultivated on less than 0.010 million ha. Population settlements were observed over an area of around 0.081 million ha of land.

Impervious surface mapping with high-resolution remote sensing imagery has attracted increasing interest as it can provide detailed information for urban structure and distribution. Previous studies have suggested that the combination of LiDAR data and high-resolution imagery for impervious surface mapping performs better than using only high-resolution imagery. However, due to the high cost of the acquisition of LiDAR data, it is difficult to obtain the multi-sensor remote sensing data acquired at the same acquisition time for impervious surface mapping. Consequently, real landscape changes between multi-sensor remote sensing data at different acquisition times would lead to the error of misclassification in impervious surface mapping. This issue has mostly been neglected in previous works. Furthermore, the observation differences generated from multi-sensor data, including the problems of misregistration, missing data in LiDAR data, and shadow in high-resolution images would also challenge the final mapping result in the fusion of LiDAR data and high-resolution images. In order to conquer these problems, we propose an improved impervious surface mapping method incorporating both LiDAR data and high-resolution imagery at different acquisition times in consideration of real landscape changes and observation differences. In the proposed method, a multi-sensor change detection by supervised multivariate alteration detection is employed to obtain changed areas and misregistration areas. The no-data areas in the LiDAR data and the shadow areas in the high-resolution imagery are extracted by independent classification yielded by its corresponding single sensor data. Finally, an object-based post-classification fusion is proposed to take advantage of independent classification results with single-sensor data and the joint classification result with stacked multi-sensor data. Experiments covering the study site in Buffalo, NY, USA demonstrate that our method can accurately detect landscape changes and obviously improve the performance of impervious surface mapping.

Mitochondrial DNA copy number (mtDNAcn) is associated with mitochondrial function contributing various diseases. Understanding of the association between mtDNAcn and infant neurodevelopment, as well as the role of racial disparities, remains unknown. A cohort study was conducted with 55 preterm infants and a single blood sample was collected from each infant during their NICU stay to measure mtDNAcn. NICU Network Neurobehavioral Scale (NNNS) and Bayley Scale of Infant Developmental testing Edition III was assessed during NICU until 2 years of corrected age (CA). Linear regression models were performed to investigate the relationship between the clinical characteristics, neurobehavioral outcomes and mtDNAcn. Majority of our subjects were male, White, non-Hispanic, had C-section, and without preterm premature rupture of membrane (PPROM). Increased mtDNAcn was found associated with younger birth gestational age (GA), nonoptimal neurodevelopment. However, the opposite associations between mtDNAcn and neurodevelopmental outcomes were observed between Black and White infants up to 1 year of CA. Adverse early life experience, together with increased mtDNAcn in White infants, and decreased mtDNAcn in Black infants may be considered as significant positive predictors of poor early life neurodevelopmental outcomes in infants.

In recent years, electroencephalogram (EEG) emotion recognition has emerged as a research hotspot in the field of artificial intelligence (AI). Despite advancements in EEG-based emotion recognition, room for enhancing performance persists due to EEG signal redundancy and limitations in feature extraction, leading to inefficient models and emotional information loss. To fully utilize EEG's emotional information and improve recognition accuracy while reducing computational costs, this paper proposes a Convolutional-Recurrent Hybrid Network with a dual-stream adaptive approach and an attention mechanism (CSA-SA-CRTNN). Firstly, the model utilizes a CSAM module to assign corresponding weights to EEG channels. Then, an adaptive dual-stream convolutional-recurrent network (SA-CRNN and MHSA-CRNN) is applied to extract local spatial-temporal features. After that, the extracted local features are concatenated and fed into a temporal convolutional network with a multi-head self-attention mechanism (MHSA-TCN) to capture global information. Finally, the extracted EEG information is used for emotion classification. We conducted binary and ternary classification experiments on the DEAP dataset, achieving 99.26% and 99.15% accuracy for arousal and valence in binary classification, and 97.69% and 98.05% in ternary classification, surpassing relevant algorithms. Additionally, the model's efficiency is significantly higher than other models, achieving better accuracy with lower resource consumption.

Glycosidic linkage analysis was conducted on the unfractionated polysaccharides in the alcohol-insoluble residues (AIRs) prepared from six red seaweeds (Gracilariopsis sp., Prionitis sp., Mastocarpus papillatus, Callophyllis sp., Mazzaella splendens, and Palmaria palmata) using GC-MS/FID analysis of partially methylated alditol acetates (PMAAs). The cell walls of P. palmata primarily contained mixed-linkage xylans and small amounts of sulfated galactans and cellulose. In contrast, the unfractionated polysaccharides of the other five species were rich in galactans displaying diverse 3,6-anhydro-galactose and galactose linkages with varied sulfation patterns. Different levels of cellulose were also observed. This glycosidic linkage method offers advantages for cellulose analysis over traditional monosaccharide analysis that is known for underrepresenting glucose in crystalline cellulose. Relative linkage compositions calculated from GC-MS and GC-FID measurements showed that anhydro sugar linkages generated more response in the latter detection method. This improved linkage workflow presents a useful tool for studying polysaccharide structural variations across red seaweed species. Furthermore, for the first time, relative linkage compositions from GC-MS and GC-FID measurements, along with normalized FID and total ion current (TIC) chromatograms without peak assignments, were analyzed using principal component analysis (PCA) as a proof-of-concept demonstration of the technique's potential to differentiate various red seaweed species.

Background: Educating college students about the importance of good sleep habits could lead to the adoption of healthier sleep routines, including sufficient sleep duration and sleep quality. The current pre-post study examines the impact of completing a sleep education program called “Sleep 101” on (1) sleep hygiene practices, (2) sleep quality, and (3) weekday and weekend sleep behaviors among college students. “Sleep 101” is an interactive online course developed to educate college students about the importance of sleep by addressing basic sleep physiology, the impact of sleep on mood, academic and physical performance, the interactions between sleep and various substances, and common sleep disorders. Methods: 338 college students were recruited from a midsized public university in the US and invited to participate in “Sleep 101”, of which 25 participants completed the baseline and post-intervention questionnaire. The Sleep Hygiene Index (SHI) was utilized to evaluate sleep hygiene practices, while standard questions were employed to assess weekday and weekend sleep behaviors and overall sleep quality. Results: Those who completed the study demonstrated a significant increase in weekday sleep duration and were less likely to engage in negative prior bedtime activities. In addition, 40% of those who completed baseline and post-intervention assessments noticed improvements in their sleep quality. Conclusion: These findings suggest that a brief online program is beneficial in improving sleep behaviors among college and could be a scalable approach to addressing current sleep issues and preventing future sleep problems in college students.

The corpus luteum is a temporary endocrine gland formed in the ovary after ovulation, and it plays a critical role in reproductive processes. Tumors rely on the development of an adequate blood supply to ensure the delivery of nutrients and oxygen and the removal of waste products. While angiogenesis occurs in various physiological and pathological contexts, the corpus luteum and tumors share similarities in the signaling pathways that promote angiogenesis. In the corpus luteum and tumors, apoptosis plays a crucial role in controlling cell numbers and ensuring proper tissue development and function. Interestingly, there are similarities in the apoptotic-regulated signaling pathways involved in apoptosis between the corpus luteum and tumors. However, the regulation of apoptosis can differ due to their distinct physiological and pathological characteristics. Thus, we reviewed the biological events of the corpus luteum and tumors in similar microenvironments of angiogenesis and apoptosis for studying novel research methods.

In order to achieve its carbon peak and neutrality targets, a high proportion of distributed power sources are connected to the distribution network in China, which greatly increases the risk of distribution network operation. Aiming at the above problems, this paper proposes an active distribution network risk planning model based on multi-source data from carbon capture and power internet of things, and conducts exceeding testing of distribution network based on the stochastic load flow algorithm with semi-invariant and level expansion, calculates the semi-invariant of each order of node state vectors and branch current vectors, and then utilizes the Gram-Charlier level expansion to obtain the exceeding probability density function and probability distribution function of the node voltages and line powers of the distribution network. The probability density function and probability distribution function of the exceeding are obtained using the Gram-Charlier series expansion. Combined with multi-source data, the active distribution network with integrated energy system considering carbon capture is modeled. According to the risk scenario of the distribution network, the nonconvex constraints in the model are simplified by the second-order cone relaxation, and the optimal planning scheme of the distribution network is solved by combining the gurobi solver with the risk index as the first-level objective and the economic benefit as the second-level objective. The simulation results of a coupled network consisting of a 39-node distribution network and an 11-node transportation network verify the effectiveness of the proposed model.

Snow-covered regions are the main source of reflection of incident shortwave radiation (ISR) on Earth’s surface. The deposition of light-absorbing particles (LAPs) on these regions increases the capacity of snow to absorb ISR and decreases surface snow albedo (SSA), which intensifies the radiative forcing leading to accelerated snowmelt and modifications of the hydrologic cycle. In this work we investigate changes in SSA and radiative forcing (RF) induced by LAPs in the Upper Aconcagua River Basin (Chilean central Andes) using remote sensing satellite data (MODIS), in-situ spectral SSA measurements, and the ISR (Chilean Solar Explorer platform) during the austral-winter months (May to August) for the 2004-2016 period. To estimate the changes in SSA and RF, we define two spectral ranges: i) an enclosed range (Ecr) between 841-876 nm, which isolates effects of Black-Carbon, an important LAP derived from anthropogenic activities, and ii) a broadband range (Bbr) between 300-2500 nm. Our results show that percent variations in SSA in the Ecr are higher than in the Bbr, regardless of the total amount of radiation received, which may be attributed to the presence of LAPs as these particles have a greater impact on SSA at wavelengths in the Ecr band than in the Bbr band.

The present paper contains data on the density (ρ), sound velocity (u), specific heat capacity (c_p ) of the mixture of N,N-dimethylformamide + 1-butanol (DMF + BuOH) determined in the entire concentration range of solution and in the temperature range: (293.15–318.15) K. The analysis of thermodynamic functions such as isobaric molar expansion, isentropic and isothermal molar compression, isobaric and isochoric molar heat capacity, as well as their excess functions (〖 E〗_(p,m)^E,〖 K〗_(S,m)^E,〖 K〗_(T,m)^E,〖 C〗_"p,m" ^E,〖 C〗_"V,m" ^E) and also V_m^E was undertaken. The analysis of changes in the physicochemical quantities was based on consideration of the system in an aspect of intermolecular interactions with resulting changes in the mixture structure. The results available in the literature are confusing during the analysis and became the reason for our decision to thoroughly examine the system. What is more, for a system whose components are widely used, there is no information in the literature regarding the heat capacity of the tested mixture, which was also achieved and presented in this publication. The conclusions drawn from so many data allow us to approximate and understand the changes that occur in the structure of the system due to repeatability and consistency of the obtained results

Buyer practices have changed as individuals are figuring out how to live with the new truth of COVID-19. Take-out and conveyance orders have expanded, and our customer has added new items to their menu because of new client inclinations. With every one of the continuous changes, the customer had numerous unanswered inquiries, for example, Smartbridge has broad involvement with café innovation development Café TECHNOLOGY CAPABILITIES :Are the most famous items as yet unchanged after COVID? :Which are the most sold item blends now? :What is the acknowledgment of new things? :What are clients purchasing alongside new things? :How have liquor deals changed? The customer previously had reports that followed item deals and operational measurements, notwithstanding, there was a need to get a more profound knowledge into item examination. The customer expected to recognize what items and introductions were being sold all the more frequently, measure the acknowledgment of new items, and figure out what items clients buy together to improve advertising efforts, advancements, and deals. he E-business industry is filling immensely in the Indian market. The modest 4G web bundles in India clearly gives a push to these ventures. Thus, as Covid19 first hit in Quite a while, individuals got terrified to go out from their homes in light of the fact that, in their mind, it's a dread of Covid. They even wonder whether or not to go out to purchase fundamental (FMCG) products. Frenzy purchasing additionally has seen and to stay away from this dread of COVID-19, individuals are offering inclinations to the E-Commerce destinations to purchase fundamental products and a few clients are new which joined to purchase fundamental merchandise during this Pandemic Lockdown period. Numerous clients are moving their purchasing conduct from disconnected retail locations to online stores. This paper examines the customer buying pattern during lockdown.

Over the last decade, there has been a renewed interest in oral health workforce planning. The purpose of this review is to examine oral health workforce planning models on supply, demand and needs, mainly in respect to their data sources, modelling technique and use of skill mix. A search was carried out on PubMed, Web of Science, and Google Scholar databases for published scientific articles on oral health workforce planning models between 2010 to 2020. No restrictions were placed on the type of modelling philosophy, and all studies including supply, demand or needs based models were included. Rapid review methods guided the review process. Twenty-three studies from 15 different countries were included in the review. A majority were from high income countries (n=17). Dentists were the sole oral health workforce group modelled in 13 studies; only five studied included skill mix (allied dental personnel) considerations. The most common application of modelling was a workforce to population ratio or a needs-based demand weighted variant. Nearly all studies presented weaknesses in modelling process due to the limitations in data sources and/or non availability of necessary data to inform oral health workforce planning. Skill mix considerations in planning models were also limited to horizontal integration within oral health professionals. Planning for the future oral health workforce is heavily reliant on quality data being available for supply, demand and needs models. Integrated methodologies that expand skill mix considerations and account for uncertainty are essential for future planning exercises.

Perovskite based solar cells have achieved tremendous progress reaching record efficiency in the past 5 years. Numerous new processes and chemistry have been reported and contribute to the perovskite rapid progress. Continuous efficiency improvements are still necessary for perovskite solar cells, and an exploratory data analysis on devices performance over multiple studies could boost the technology development. Such analysis could identify patterns or provide insights that are not obvious in a single study. Here we present a high quality dataset containing only independently certified Pb-based perovskite solar cells summarizing their efficiency, relevant I-V metrics, manufacturing processes and materials used. Analysis over the dataset provides insights on how aperture size, perovskite deposition methods and materials used in each functional layer affect the final solar cell efficiency and I-V metrics. Future directions are also suggested for efficiency improvements.

This work presents an application called APPMAR 1.0 based on Python ® environment, built to perform the downloading, treatment and analysis of meteorological and marine information. This application is composed of two main modules: the first module allows the downloading of information from the database (NOAA - WW3); the second module uses the principles of statistical mathematics for the treatment of waves and wind. The importance of this simple application is based on the free and agile access to meteorological and marine information for a coastal project. The determination of representative conditions of sea states ultimately will govern the process of design of coastal and oceanic infrastructure. The analysis of historical time series of local waves and winds allows the evaluation of average regimes or operational design, the ultimate limit states or extreme design, and the storms or design by persistence. In spite that the former analysis is a common task for coastal engineers, the codes generated are seldom shared for public use. In summary, for operational purposes is useful to have a freeware that can assist in the data processing for decision making and forcing of the mathematical models that are part of the common practice of coastal, oceanic and offshore engineering. This application has been tested in the Caribbean area of Colombia where meteorological and marine information are scarce.

The severity of forest fires derived from remote sensing data for research and management has become increasingly widespread in the last decade, where these data typically quantify the pre- and post-fire spectral change between satellite images on multi-spectral sensors. However, there is an active discussion about which of the main indices (dNBR, RdNBR or RBR) is the most adequate to estimate the severity of the fire, as well about the adjustment model used in the classification of severity levels. This study proposes and evaluates a new technique for mapping severity as an alternative to regression models, based on the use of the maximum likelihood estimation (MLE) automatic learning algorithm, from GeoCBI field data and spectral indices dNBR, RdNBR and RBR applied to Landsat TM, ETM+ Images, for two fires in central Spain. We compare the severity discrimination capability on dNBR, RdNBR and RBR, through a spectral separability index (M) and then evaluated the concordance of these metrics with field data based on GeoCBI measurements. Specifically, we evaluated the correspondence (R²) between each metric and the continuous measurement of fire severity (GeoCBI) and the general precision of the regression and MLE models, for the four categorized levels of severity (Unburned, Low, Moderate, and High). The results show that the RBR has more spectral separability (average between two fires M = 2.00) that the dNBR (M = 1.82) and the RdNBR (M=1.80), additionally the GeoCBI has a better adjustment with the RBR of (R² = 0.73), than the RdNBR (R² = 0.72), and dNBR (R² = 0.71). Finally, the overall classification accuracy achieved with the MLE (Kappa = 0.65) has a better result than regression models (Kappa = 0.58) and higher accuracy of individual classes.

The use of artificial intelligence for political decision-making is in an early stage of development; however, there are several questions that arise about its current and hypothetical uses. These questions often come from areas of philosophy such as ethics, political philosophy, and logic. In this article, first, the theoretical approaches from which the current and hypothetical uses of artificial intelligence for political decision-making can be interpreted will be presented. These approaches include realistic politics, bureaucracy theory, and conflict theory. Then, the possible uses that artificial intelligence could have in politics, as well as the attempts that have already been made, will be discussed. Subsequently, the logical, ethical, and political problems that the use of artificial intelligence for political decision-making could cause will be outlined. Next, a basic experiment will be presented on what kind of political decisions artificial intelligence could suggest. Finally, the points previously discussed will be analyzed from the mentioned theories. The conclusion reached was that, at the present time, the use of artificial intelligence for political decision-making could align more with the approaches of Machiavelli, focusing primarily on achieving goals such as maintaining power, while downplaying moral dilemmas.

This paper presents the analysis of four error models that can appear in the field of adaptive systems, where adaptive laws for parameter estimation are represented as fractional order differential equations whose order can switch between an order within the range (0,1) and 1. These switched adaptive laws have been proposed in the past few years to improve the balance between control energy and system performance, which is often a challenge when using traditional integer-order or fractional-order adaptive laws in adaptive identification and control. Boundedness of the solutions is proved for all cases, together with the convergence to zero of the estimation/tracking error. Additionally, sufficient conditions for parameter convergence are presented, showing that the excitation condition required for parameter convergence in the vector case is also sufficient for parameter estimation in the matrix case.

This paper presents an acoustic emission (AE) detection method for refined oil storage tanks, enabling online detection of corrosion points and microleakage in the tank floor. The method utilizes an explosion-proof acoustic emission instrument to detect the floor of a refined oil storage tank. By calculating the time difference between the defective acoustic signal and the speed of acoustic wave transmission, a mathematical model is constructed to analyze the detected signals. A technical scheme for acoustic emission detection in storage tanks is designed, and the instru-ment is used to carry out acoustic emission testing on the product oil storage tank floor. Based on these test results, location analysis of acoustic emission sources is conducted on the bottom plate of the tank, evaluating its corrosion condition accurately. The consistency between the evaluation and subsequent open tank tests confirms that using acoustic emission technology effectively captures corrosion signals from product oil storage tank bottoms. Consequently, it significantly improves inspection efficiency by avoiding unnecessary tank-opening operations. Combining this method with open tank inspection operations such as the leakage magnetic method, one can fully grasp the status of the refined oil storage tanks.

The profile of secondary metabolites occurring in apple cuticular layer is not only characteristic for particular apple cultivar, but also dynamically reflects various external factors in growing environment. In this study, the possibility to authenticate apple samples based on analysis of their cuticular layer extracts was investigated. Ultra-high-performance liquid chromatography coupled to high resolution tandem mass spectrometry (UHPLC-HRMS/MS) was employed for obtaining metabolomic fingerprints. In total, 274 authentic apple samples, four cultivars harvested within the seasons 2020 to 2022 in the Czech Republic and Poland were analysed. The generated complex data processed by univariate and multivariate statistical methods enabled to build classification models for discrimination of apple cultivars as well as their geographical origin. The models showed very good performance in differentiation of Czech and Polish samples for three of four cultivars: ‘Gala’, ‘Golden Delicious’ and ‘Idared’, moreover, the validity of the models was verified over harvest seasons. The diagnostic markers, besides metabolites of triterpene biosynthetic pathway, mainly represented wax esters. ‘Jonagold’, which is known to be susceptible to mutations, was the only cultivar for which the unambiguous classification of geographical origin was not possible.

A 17th century wall painting representing a Virgin between two Saints in a noble Italian renaissance palace, Palazzo Gallo in Bagnaia (Viterbo, Italy), was restored in 2021 in the context of a wider restoration campaign interesting the main room of the palace built by cardinal Sansoni Riario. Diagnostic analyses done with traditional characterization techniques (optical microscopy on micro-stratigraphic sections, X-ray fluorescence spectroscopy and Fourier transform infrared spectroscopy) provided the identification of both original painting and restoration materials, while imaging investigations as ultraviolet fluorescence photography, false color images and multispectral mapping provided by hypercolorimetric multispectral imaging (HMI) technique enabled the evaluation of the state of conservation, locating restoration interventions and supporting the monitoring of the cleaning procedure. An altered protective Paraloid-based coating dating from early 2000s had to be removed due to the unpleasant glossy finishing given to the painted surface, making the scene barely readable. To pursue a restoration protocol based on environmental sustainability and green chemistry, enzyme-based gels marketed by Nasier-Brenta© and CTS© companies were tested in different protocols for the cleaning of the mash covering the painting. Although some interesting results were observed, the enzymatic cleaning had a scarce effectiveness with timing beyond a reasonable interval. Traditional chemical solvents as Dowanol PM (methoxy-propanol) and benzyl alcohol were necessary to complete the cleaning of the painting surface.

Material selection for product design is a complex task. Thus, one of the objectives of this work is to analyze, understand and promote the importance of selection of materials to conceive quality products with help of designers' that promote green self-identity in the early stage of the conception of new products. A questionnaire was sent to professional designers and engineers. 38 responses were validated, this being the sample of our study. The aspects that influence the complex selection of materials and the final quality of the products through the design and production process is presented. As a result of the answers carried out with product designers who work in the market, with students who are graduating and with some engineers, a new approach for the selection of materials was developed. Based on a collection of main ideas from traditional and non-traditional methods of material selection, seeking to group the maximum requirements of both methods, inspired by the "Canvas" model on the basic modular methodology, a new model for a project of new product is presented. Our study focuses on the selection of materials, since this aspect is one of the most relevant steps in early stage of the prototyping phase of new products, with a view to reducing CO² from the air in the atmosphere that we all breathe. The classification of materials is complex, due to the diversity of available options. The novelty of this model is that all properties of the newly designed product, such as technical, aesthetic, productive, environmental and other properties, are all grouped in a model, which serves as an innovative support. Thus, the designer has at his disposal a tool that can help him in the selection of the best material for the products he designs. This study intends to make a contribution in the field of material selection, quality and design of new products, promoting a green self-identity for designers in the initial phase of product design. Consequently, all consumers in search of a sustainable planet will profit from it.

This paper presents a comparison between rain gauges, C-band and X-band radar data over an instrumented and regulated catchment of the Paris region, as well as their respective hydrological impacts with the help of flow observations and a semi-distributed hydrological model. Both radars confirm the high spatial variability of the rainfall down to their space resolution (respectively one kilometer and 250 m) and therefore underscore limitations of semi-distributed simulations. The use of the polarimetric capacity of the Météo-France C-band radar was limited to corrections of the horizontal reflectivity and its rainfall estimates are adjusted with the help of a rain gauge network. On the contrary, neither calibration was performed for the polarimetric X-band radar of the Ecole des Ponts ParisTech (below called ENPC X-band radar), nor any optimization of its scans. In spite of that and the non-negligible fact that the catchment was much closer to the C-band radar than to the X-band radar (20 km vs. 40 km), the latter seems to perform at least as well as the former, but with a higher scale resolution. This characteristic was best highlighted with the help of a multifractal analysis of the respective radar data, which also shows that the X-band radar was able to pick up a few extremes that were smoothed out by the C-band radar.

Superior fiber quality is one of the most important objectives in cotton breeding. To detect the genetic basis underlying fiber quality, an F2 population containing 413 plants was constructed by crossing Jifeng 914 and Jifeng 173, both of which have superior fiber quality, with Jifeng 173 being better. Five fiber quality traits were investigated in the F2, F2:3, F2:4, and F2:5 populations. Quantitative trait loci (QTL) mapping was conducted based on a high-density genetic map containing 11,488 single nucleotide polymorphisms (SNPs) and spanning 4,202.12 cM in length. Transgressive segregation patterns and complex correlations in the five tested traits were observed. A total of 108 QTLs were found, including 27 for fiber length (FL), 16 for fiber strength (FS), 24 for micronaire (MC), 17 for fiber uniformity (FU), and 24 for fiber elongation rate (FE). Chromosome A7 contained 12 QTL, ranking the first. No QTL was found on chromosome D1 and D11. Each QTL contributed 1.98%–21.45% to the phenotypic variation (PV), including 13 major effect QTLs that contributed more than 10% toward PV. Two QTLs could be repeatedly detected in three populations, including qFL-D3-2 in F2, F2:4, and F2:5 with 9.18%–21.45% of PV, qFS-A11-1 in F2:3, F2:4 and F2:5 with 6.05%–10.41% of PV. Another seven stable QTLs could be detected in two populations, including four major effect QTLs: qFL-A12-3, qFS-D10-2, qMC-D6-2, and qMC-D8-1. Fourteen QTL-overlapping regions were found, which might explain the complex correlations among the five phenotypic traits. Four regions on chromosome A11, D3, D6, and D10 covered by both stable and major effect QTLs are promising for further fine mapping. The genomic regions of the two QTLs detected in three populations and the four major effect QTLs contain 810 genes. Gene functional analysis revealed that the annotated genes are mainly involved in protein binding and metabolic pathways. Fifteen candidate genes in the qFL-D3-2 region are highly expressed in fiber or ovules during fiber initiation, elongation, secondary cell wall thickening, or maturation stages. qRT-PCR revealed that Ghir_D03G005440.1 and Ghir_D03G011310.1 may play a role in promoting fiber initiation, Ghir_D03G006470.1 may be beneficial for promoting fiber elongation. This study provides more information for revealing the molecular genetic basis underlying cotton fiber quality.

Biopolymers are of growing interest, but to improve some of their poor properties and performance, it is required the formulation of bio-based blends and/or adding of nanoparticles. For this purpose, in this work, two different metal oxides, such as zinc oxide (ZnO) and titanium dioxide (TiO2), at different concentrations (0.5, 1 and 2 %wt.) have been added in polylactic acid (PLA) and polylactic acid/polyamide 11 (PLA/PA11) blends to establish their effects on solid-state properties, morphology, melt behaviour, and photo-oxidation resistance. It seems that the addition of ZnO in PLA leads to a significant reduction of its rigidity probably due to an inefficient dispersion in the melt state, while the addition of TiO2 does not penalise PLA rigidity. Interestingly, the addition of both ZnO and TiO2 in PLA/PA11 blend has a positive effect on the rigidity because of blend morphology refinement and leads to a slight increase in film hydrophobicity. The photo-oxidation resistance of neat PLA and PLA/PA11 blend is significantly reduced due to the presence of both metal oxides, and this must be considered when designing the potential applications. The last results suggest that both metal oxides could be considered as photo-sensitive degradant agents for biopolymer and biopolymer blends.

Currently there are fewer depth models applied to pepper picking detection, while the existing generalized neural networks have problems such as large model parameters, long training time, and low model accuracy.In order to solve the above problems, this paper proposes a Yolo-chili target detection algorithm for chili pepper detection. First, the classical target detection algorithm yolov5 is used as a benchmark model, and an adaptive spatial feature pyramid structure combining the attention mechanism and the idea of multi-scale prediction is introduced to improve the model's detection effect on occluded peppers and small target peppers. Secondly, a three-channel attention mechanism module is introduced to improve the algorithm's long-distance recognition ability and reduce the interference of redundant testers. Finally, the quantized pruning method is used to reduce the model parameters and realize the lightweight processing of the model. Applying the method to the homemade chili pepper dataset, the AP value of chili pepper reaches 93.11%; the accuracy rate is 93.51% and the recall rate is 92.55%.The experimental results show that yolo-chili is able to achieve accurate and real-time pepper detection under complex orchards.

The temporary immersion system bioreactor named GreenTray®[2], [3]presents advantages over the existing ones. Firstly, there is no need to use forceps to move the shoots or plantlets in or out of the recipient, neither the use of scalpel to divide them. Secondly, the basis of the shoot can remain in the GreenTray® and can sprout again in several cycles of growth. These two characteristics reduce the costs in the process of micropropagation. Besides, shoots or plantlets development is enhanced by the aeration of the recipient, thus enriching with CO2 the recipient atmosphere. The chapter describes and provides images and values of interest in (1) the micropropagation process of some commercial Prunus or Pyrus rootstocks; (2) the plantlet growth under autotrophic conditions; and (2) the inoculation of plants with pathogens that reproduce in vitro the symptoms observed in vivo, using the GreenTray® bioreactor.

The aim of this retrospective study was to compare the clinical outcomes of treating isolated surgical neck fractures of the humerus using either the intramedullary nail or the MIROS (Minimally Invasive Reduction and Osteosynthesis System®) system. The study included 42 patients who underwent surgical treatment at the Polyclinic from January 2014 to June 2020. The results indicated that both treatments were effective; however, the MIROS system exhibited a higher complication rate. Intramedullary nailing was more reliable, thus recommended as the primary treatment method. This study underscores the importance of selecting the appropriate fixation technique to minimize complications and enhance patient outcomes.

Arthrospira platensis (Ap) and Bacillus coagulans (Bc) have been successfully used to develop functional foods, but not a combination of both regarding functional implications in nutritional value and antioxidant capacity. This work aimed to develop an artisanal wheat flour pasta with egg using 5% A. platensis and 1% B. coagulans GBI 6068 (Bc+Ap). Uncooked pasta was characterized regarding nutritional value; furthermore, total phenolic content, antioxidant capacity (by ABTS and FRAP), pigment content (chlorophyll a + b, and total carotenoids), colorimetry assay, textural profile analysis, buffering capacity and probiotic viability were carried out on uncooked and cooked pasta to assess the changes induced by cooking. The Bc+Ap pasta showed enhanced nutritional value with a significant increase in protein content (30.61%). After cooking, the pasta increased phenolic content (14.22% mg GAE/g), antioxidant capacity (55.59% µmol Trolox Equivalents/g and 10.88% µmol Fe+2/g) for ABTS and FRAP respectively, pigment content (6.72 and 1.17 mg/100g), for chlorophyll a+b and total carotenoids respectively, but relative positive impacts on colorimetric parameters in contrast to control (wheat flour pasta). Furthermore, Bc+Ap showed improved firmness (59%, measured in g), buffer capacity (87.80% μmol H+(g × ΔpH)−1), and good probiotic viability (7.2 ± 0.17 log CFU/g) after the cooking process.

Excess nitrogen in agricultural drainage poses a serious threat to the water quality safety of the Yellow River basin. Utilizing aquatic plants to modify the rhizosphere microbial community structure and facilitate nitrogen transformation is a crucial strategy for mitigating regional water eutrophication. Our results revealed that the denitrification rate (DR), dissimilatory nitrate reduction to ammonium rate (DNRAR) and nitrogen fixation rate (NFR) of rhizosphere sediments exhibited a significant increase in artificially cultivated ditch. Compared with those in natural ditches, the richness of the bacterial community and the relative abundances of Bacteroidota, Verrucomicrobiota, Firmicutes, Anaeromyxobacter and Geobacter, which are involved mainly in the nitrogen cycle, in the rhizosphere sediments of artificially cultivated ditch were significantly greater due to improvements in environmental conditions. In artificially cultivated ditch, the dominant functional genes affecting DRNARs in the rhizosphere sediments of P. australis were nrfC and nrfA, while DRs were driven mainly by norB and napA, which were influenced by the nitrogen and carbon levels; and the dominant functional genes affecting NFRs in the rhizosphere sediments of T. orientalis were nifD, nifK and nifH. Our results provide a scientific basis for the use of aquatic plants for mitigating excess nitrogen levels in agricultural drainage.

Aiming at the problems of inaccurate segmentation of long object and information loss of small object in real-time semantic segmentation algorithm, this paper proposes a lightweight multi-branch real-time semantic segmentation network based on BiseNetV2. The new auxiliary branch makes full use of spatial details and context information to cover the long object in the field of view. Meanwhile, in order to ensure the inference speed of the model, the asymmetric convolution is used in each stage of the auxiliary branch to design a structure with low computational complexity. In the multi-branch fusion stage, the alignment and fusion module is designed to provide guidance information for deep and shallow feature mapping, so as to make up for the problem of feature misalignment in the fusion of information at different scales, and thus reduce the loss of small target information. In order to further improve the model’s awareness of key information, a global context module is designed to capture the most important features in the input data. The proposed network uses NVIDIA GeForce RTX 3080 Laptop GPU experiment, on the road street view data set Cityscapes and CamVid average occurring simultaneously ratio reached 77.1% and 78.4% respectively, with running speed of 127 frames/s respectively and 112 frames/s. The experimental results show that the proposed algorithm can achieve real-time segmentation and improve the accuracy significantly, showing good semantic segmentation performance

Strawberry root rot caused by Neopestalotiopsis clavispora is one of the main diseases of strawberry and significantly impacts the yield and quality of strawberry fruit. There are now no effective control techniques available except for fungicide sprays, which may directly impact consumers. Biological control is becoming an alternative method for the control of plant diseases to replace or decrease the application of traditional synthetic chemical fungicides. Trichoderma is widely used as a biological agent for controlling strawberry root rot. In order to provide resources for screening the highly effective biocontrol fungus for controlling strawberry root rot caused by Neopestalotiopsis clavispora, the biocontrol mechanism,the control effects of Trichoderma asperellum CMT10 against strawberry root rot and growth-promoting effects on strawberry seedlings were investigated by plate culture, microscopy observation and root drenching methods.The results showed that CMT10 had obvious competitive, antibiotic and hyperparasitism effects on Neopestalotiopsis clavispora CMGF3. The CMT10 could quickly occupy nutritional space, and the inhibition rates of CMT10 against CMGF3 in confrontation culture on 7 d was 65.49%. The inhibition rates of volatile metabolite and fermentation metabolite produced by CMT10 against CMGF3 were 79.67% and 69.84%, respectively. The mycelium of strain CMT10 can act as hyperparasites by making contacting, winding and penetrating hyphae of CMGF3. Pot experiment showed that the biocontrol efficacy of CMT10 on strawberry root rot caused by Neopestalotiopsis clavispora was 63.00%. CMT10 had obvious promotion effect on strawberry growth, the plant height, root length, total fresh weight, root fresh weight, stem fresh weight and root dry weight, with the promotion rates were 20.09%, 22.39%, 87.11%, 101.58%, 79.82% and 72.33%, respectively. Overall, this study showed the ability of Trichoderma asperellum CMT10 to control strawberry root rot and their potential to develop as novel biocontrol agents to replace chemical fungicides for eco-friendly, sustainable agriculture.

In this paper we study a class of impulsive delayed neural networks with parameter uncertainties and reaction-diffusion terms. Using the properties of M-matrices and some inequality techniques, some sufficient conditions are derived to guarantee the global exponential stability of the given system. The results obtained are presented by simple algebraic inequalities, which are certainly more concise than previous methods. Finally, the validity and usefulness of our results are illustrated by arithmetic examples.

Abstract: Giant Cell Arteritis (GCA) is an autoimmune/autoinflammatory disease affecting large vessels in patients over 50 years old. The disease presents as an acute inflammatory response with two phenotypes, cranial-GCA and Large Vessel Vasculitis (LV)-GCA, involving the thoracic aor-ta and its branches. 18F-fluorodeoxyglucose positron emission tomography-computed tomogra-phy (18F-FDG PET-CT) is among the imaging techniques contributing to diagnosis of patients with systemic disease. However, its association with soluble inflammatory markers is still elu-sive. This proof-of-concept study aims to identify novel soluble serum biomarkers in PET/CT pos-itive patients with LV-GCA and associate them with active (0 months) and inactive disease (6 months following treatment), in sequential samples. The Most-Disease-Segment Target-to-Background Ratio (TBRMDS) was calculated for 9 LV-GCA patients, while 12 cranial-GCA and 7 Polymyalgia Rheumatica patients with negative initial PET/CT served as disease controls. Serum macrophage-related cytokines were evaluated by Cytometric Bead Array (CBA). Finally, previously published NMR-metabolomics data acquired at the same blood sampling were associated with PET/CT findings. TBRMDS was significantly increased in active versus inactive disease (3.45 vs. 2.55, p=0.008). The analysis identified 6 serum metabolites, as more sensitive to change from the active to inactive state. Among them, choline levels were exclusively altered in the LV-GCA group, but not the disease controls. Cytokine levels were not associated with PET/CT activity. Combining with CRP, ESR, and TBRMDS a composite index was generated to depict well the differences between active and inactive systemic LV-GCA (25.45 vs 11.45, p=0.0039). These preliminary results could pave the way for more extensive studies integrating serum metabolomic parameters with PET/CT imaging data to extract sensitive composite disease indexes useful for the everyday clinical practice.

Apis mellifera, especially weak ones, are highly vulnerable to Carpoglyphus lactis mites, which can rapidly infest and consume stored pollen, leading to weakened colonies and potentially colony collapse. This study aimed to ascertain and investigate the prevalence of this mite in honeybee colonies across 9 provinces in the Republic of Korea (ROK). A total of 615 honeybee colonies were collected, from 66 apiaries during the spring and 58 apiaries during the summer of 2023. A 1,242 bp segment of the the Cytochrome c oxidase subunit 1 (COI) gene was amplified using the polymerase chain reaction method. The detection levels of C. lactis in the honeybees were compared between winter and summer. Based on the COI sequence analysis, the nucleotide sequence similarity of C. lactis mites isolated in the ROK with those from China (NC048990.1) was found to be 99.5%, and with those from the United Kingdom (KY922482.1) was 99.3%. This study is the first report of C. lactis in Korean apiaries. The findings in this study demonstrate a significantly higher detection rate in winter, which is 4.1 times greater than that in summer (p < 0.001). Furthermore, the results underscore the usefulness of molecular diagnostic techniques for detecting C. lactis mites.

Our previous studies demonstrated a tight connection between simvastatin (SIM) inhibition of B16.F10 murine melanoma cell proliferation in vivo and in vitro and strong suppression of the subunit α of hypoxia inducible factor 1 (HIF-1) (HIF-1 α) production in these cancer cells. Since the translation of HIF-1α is modulated by signaling pathways coordinated by Akt, SIM effects on the link between these two regulatory factors and cancer cell metabolism under normoxia was investigated, which to our knowledge have never been described before. To achieve the proposed aim, SIM effects on cell metabolism were analysed at mRNA (via RNA-seq and RT-qPCR) as well as protein level (via western blot and catalytic activity of the key enzymes) Our data suggested that SIM might orientate glucose transport and metabolism to ensure replenishment of tricarboxylic acid (TCA) cycle activity. Moreover, it seems that SIM enhanced biosynthetic role of TCA over its energy role to further support synthesis of lipid-derived signaling molecules. Thus, under normoxia SIM treatment orientated lipid metabolism to stimulate isoprenoid and prostaglandin synthesis which might favor melanoma cell survival and aggressiveness.

Vibration suppression in a field of frequencies and the creation of band gaps which do not allow the propagation of the waves is studied in this paper by means of microstructures designed through topology optimization. Topology optimization is formulated in frequency domain. Band gap design is based on Floquet-Bloch theory and genetic optimization. The result is appealing in view of modern 3D printing techniques.

COX II containing a dual core CuA active site is one of the three core subunits of mitochondrial Cco, which plays a significant role in the physiological process. In this report, the full-length cDNA of COXⅡ gene was cloned from Sitophilus zeamais, which had an ORF of 684 bp encoding 227 amino acids residues. The predicted COXⅡ protein had a molecular mass of 26.2 kDa with pI value of 6.37, and multiple sequence alignment and phylogenetic analysis indicated that Sitophilus zeamais COXⅡ had high sequence identity 78.51% with the COXⅡ of other insect species, especially similarity to sitophilus oryzae. This gene was subcloned into the prokaryotic expression vector pET-32a, and induced by IPTG in E.coli Transetta (DE3) expression system. Finally the COXⅡ with 6-His tag was purified using affinity chromatography with Ni²⁺-NTA agarose. WB showed the recombinant COXⅡ was about 44 kD, and the concentration of fusion protein was 50μg/mL. UV-spectrophotometer and infrared spectrometer analysis showed that recombinant COXⅡ could catalyze the oxidation of substrate Cytc, and influenced by AITC. It was found that AITC could form a hydrophobic region with COXⅡ protein via molecular docking, besides, a sulfur atom of AITC structure could form a length of 2.9 Å hydrogen bond with Leu-31. These results will provide valuable information for elucidating the role of COXⅡ in Sitophilus zeamais responses to AITC, meanwhile, it will helpful to carry out a point mutation in AITC binding sites for the future research.

Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), a novel betacoronavirus, is the causative agent of COVID-19, which has caused economic and social disruption worldwide. To date, many drugs and vaccines have been developed for the treatment and prevention of COVID-19 and have effectively controlled the global epidemic of SARS-CoV-2. However, SARS-CoV-2 is highly mutable, leading to the emergence of new variants that may counteract current therapeutic measures. Electron microscopy (EM) is a valuable technique for obtaining ultrastructural information about the intracellular process of virus replication. In particular, EM allows us to visualize the morphological and subcellular changes during virion formation, which would provide a promising avenue for the development of antiviral agents effective against new SARS-CoV-2 variants. In this review article, we present our recent findings using transmission electron microscopy (TEM) combined with electron tomography (ET) to demonstrate the morphologically distinct types of SARS-CoV-2 particles and the application of EM analysis to evaluate genetically engineered viruses.

In this paper, we extend the KdVSKR equations in (1+1)-dimension and (2+1)-dimension to fractional KdVSKR equations with modified Riemann-Liouville derivative. The (2+1)-dimensional KdVSKR equation, which is a recent extension of (1+1)-dimensional KdVSKR equation, can model the resonances of solitons in shallow water. By means of the Hirota bilinear method, finite symmetry group method and consistent Riccati expansion method, many new interaction solutions have been derived. Soliton-cnoidal interaction solution of the (1+1)-dimensional fractional KdVSKR equation has been derived for the first time. For the (2+1)-dimensional frac-tional KdVSKR equation, two-wave interaction solutions and three-wave interaction solutions, including dark-soliton-sine interaction solution, bright-soliton-elliptic interaction solution, and lump-hyperbolic-sine interaction solution, have been derived. Impact of the fractional order on the shapes of these solutions has been illustrated by figures. Three-wave interaction solutions of fractional systems have not been reported in the existing references. The research idea in this paper can be applied to other fractional differential equations.

Despite the technological advancements of 3-D city models, the 3-D shape abstraction methods for urban vegetation are recent and still limited to support spatial analysis of individual or groups of trees and forest patches. In this paper, a scheme for individual urban tree abstraction, which retains the semantic complexity of the object, is proposed. Our contribution is three-fold. First, an initial tree structure based on a new 3-D aggregation operator is proposed. Second, we developed Gestalt rules for 3-D urban vegetation mapping. Third, current state-of-the-art deep learning is adapted for the abstraction of individual urban trees. Quantitative and qualitative results show the effectiveness of our proposed approach in accurately reducing the spatial density of trees and the degree of fragmentation of the total green area, which can access information about the location, size, and spatial distribution of mapped urban trees.

Object detection often struggles with accurately identifying visually similar parts, a challenge commonly faced in industrial applications. To address this issue, we propose a clustering methodology based on the visual similarity of 3D object models. This approach is particularly effective when integrated with synthetic image generation, as both processes rely on 3D models. In a case study, we observed a 22 % increase in classification performance on a validation dataset when training an object detector on visually similar groups rather than on all classes, suggesting the potential of our method as a baseline for a multi-stage classification scheme.

A smart precipitation estimation method based on FY-4B meteorological satellite data (FY-4B_AI) is proposed in this paper. This method spatiotemporally matches 125 features derived from multi-temporal and multi-channels of FY-4B satellite data with the precipitation at stations. Then a precipitation model using the Light Gradient Boosting Machine algorithm is constructed. Comparative results between FY-4B_AI and GPM/IMERG-L products for over 450 million station cases throughout 2023 shows that: 1) FY-4B_AI is superior to GPM IMERG-L in the average absolute error, root mean square error, relative error, correlation coefficient, probability of detection and critical success index. While in the mean error and false alarm rate, FY-4B_AI is slightly inferior to GPM/IMERG-L. 2) Evaluation of strong weather event applications reveals that both FY-4B_AI and GPM/IMERG-L can accurately represent the spatial distribution characteristics of precipitation, no matter in the southeast humid region or the northwest dry region. Notably, FY-4B_AI, due to its higher spatiotemporal resolution, provides a more detailed distribution of precipitation.

Safety signal detection, an integral component of Pharmacovigilance (PhV), aims to identify new or known adverse events (AEs) resulting from the use of pharmacotherapeutic products. Post-marketing spontaneous reports from different sources are commonly utilized as a data source for detecting these signals but there are underlying challenges arising from data complexity. This paper investigates the implementation of the Apriori algorithm, a popular method in association rule mining, to identify frequently co-occurring drugs and AEs within safety data. We discuss previous applications of the Apriori algorithm for safety signal detection and conduct a detailed study of an improved method specifically tailored for this purpose. This enhanced approach refines the classical Apriori method to effectively reveal potential associations between drugs/vaccines and AEs from post-marketing safety monitoring datasets, especially when AEs are rare. Detailed comparative simulation studies across varied settings, coupled with the application of the method to vaccine safety data from the Vaccine Adverse Event Reporting System (VAERS), demonstrate the efficacy of the improved approach. In conclusion, the improved Apriori algorithm is shown to be a useful screening tool for detecting rarely occurring potential safety signals from the use of drugs/vaccines using post-marketing safety data.

Kyrgyzstan, a landlocked nation in Central Asia, is characterized by its rugged mountainous terrain, which covers approximately 90% of its land area. This unique geography poses specific challenges related to climate vulnerability. To address these challenges, we propose a comprehensive approach that involves gathering meteorological data and making it accessible to decision-makers. By leveraging LoRaWAN communication technology, which efficiently transmits sparse and low-speed data over long distances while minimizing power consumption, we can enhance climate resilience. The Internet Society Kyrgyz Chapter, in collaboration with the International Centre for Theoretical Physics (ICTP) and the Central Asia Institute for Applied Geosciences (CAIAG), has initiated the installation of meteorological sensors and disaster mitigation devices, including river water level sensors, terrain moisture sensors, and tilt detectors. These sensors collect critical data, which is stored within the country on an ad hoc server. Stakeholders can access this data according to their specific requirements. This paper outlines the criteria for selecting the deployed equipment and provides details on the installation process at pilot sites, along with the challenges encountered during project execution.

This research investigates the exposure of plant species to extreme drought events in the Brazilian Atlantic Forest, employing an extensive dataset collected from 205 automatic weather stations across the region. Meteorological indicators derived from hourly data, encompassing precipitation, maximum and minimum air temperature, were utilized to quantify past, current, and future drought conditions. The dataset, comprising 10,299,236 data points, spans a substantial temporal window and exhibits a modest percentage of missing data. Missing data were excluded from analysis, aligning with the decision to refrain from imputation methods due to potential bias. Drought quantification involved the computation of the Aridity Index, the analysis of consecutive hours without precipitation, and the classification of wet and dry days per month. Mann-Kendall trend analysis was applied to assess trends in evapotranspiration and maximum air temperature, considering their significance. The hazard assessment, incorporating environmental factors influencing tree growth dynamics, facilitated the ranking of meteorological indicators to identify regions most exposed to drought events. The results revealed consistent occurrences of extreme rainfall events, indicated by positive outliers in monthly precipitation values. However, significant trends were observed, including an increase in daily maximum temperature and consecutive hours without precipitation, coupled with a decrease in daily precipitation across the Brazilian Atlantic Forest. No significant correlation between vulnerability ranks and weather station latitudes and elevation were found, suggesting geographical location and elevation does not strongly influence observed dryness trends.

This paper deals with wh-in situ elements (i.e. wh-elements that stay in the argument position) in islands such as adverbial clauses, conditionals, relative clauses, complement clauses, etc. extracted from corpora of Italian and Spanish. I will show that there are two types of wh-in situ embedded inside an island in these languages. One type is a real genuine question. Another type is an echo question. There are several approaches that convincingly account for the insensitivity of echo questions to islands. The genuine question type is still a puzzle for semantic and syntactic approaches. It turns out that wh-in situ questions with a genuine question interpretation are only possible inside islands that are not marked for sentence type and do not contain any operator that could block the question interpretation of the wh-in situ. This way question alternatives can be passed higher up the clause until they meet a question operator in the matrix clause that interprets them. Consequently, the question is well-formed and interpreted without any problem. Real islands contain semantic operators or quantifiers that interpret question alternatives triggered by wh-in situ until they reach the question morpheme to be interpreted as question alternatives. The result is that the derivation crashes.

The challenge of missing data in scientific research prompts researchers to decide between imputing incomplete data or discarding observations, where discarding can lead to information loss. Various methods exist, from simple deletion to sophisticated approaches like Multiple Imputation (MI). However, these methods often fall short with high-dimensional datasets. Multiple Imputation by Chained Equations (MICE) and Random Forest (RF) proximity imputation offer promising alternatives. Therefore, in this paper, we propose integrating MICE with Bayesian random forest (BRF) to enhance imputation accuracy and predictive power, particularly in high-dimensional analyses. Our approach combines MICE’s efficiency with BRF’s robustness, offering a comprehensive solution to missing data challenges. By way of example, we provide empirical evaluations to validate its effectiveness using synthetic data of various missing data scenarios. The results from the simulations showed that the combination of BRF and MICE offered a promising strategy for high-dimensional analysis in the presence of missing data.

The physics of Hubble's law, motivated by the anomalous acceleration of Pioneer 10/11, suggests that the Hubble constant corresponds to a gravitational field of the Universe. The Hubble constant inferred from Pioneer 10/11 data is $\sim 69$ km/s/Mpc. The anomalous acceleration of Pioneer 10/11 can be interpreted as an observational evidence for Mach's principle. The redshift is an apparent value. Its intrinsic value has a limit resulting to $v

Cancer is a disease caused by the abnormal growth of cells in different parts of body is one of the top causes of death globally. Microarray gene expression data plays a critical role in the identification and classification of cancer tissues. Due to recent advancements in Machine Learning (ML) techniques, researchers are analyzing gene expression data using a variety of such techniques to model the progression rate & treatment of cancer patients with great effect. But high dimensionality alongside the presence of highly correlated columns in gene expression datasets leads to computational difficulties. This paper aims to propose the use of ML classification techniques- Linear Discriminant Analysis (LDA) & Random Forest (RF) for classifying five types of cancer (breast cancer, kidney cancer, colon cancer, lung cancer and prostate cancer) based on high dimensional microarray gene expression data. Principal component analysis (PCA) was used for dimensionality reduction, and principal component scores of the raw data for classification. Six distinct categorization performance measures were used to evaluate these approaches; RF method provided us with higher accuracy than LDA method. The method and results of this article should be helpful to researchers who are dealing with many genes in microarray data.

Most works related to the identification of mathematical nonlinear systems make one suppose that such approaches can always be directly applied to any nonlinear system. This misconception produces a great deal of discouragement when the obtained results are not the expected ones. Thus, the current work hypothesizes that the most information one has about the mathematical structure of the model, the most precise the identification result should be. Therefore, a variant of the Sparse Identification of Nonlinear Dynamics (SINDY) approach is presented to obtain the full mathematical nonlinear model of a high-order system with coupled dynamics, namely, a commercial quadcopter. Furthermore, due to its high sensitivity to inputs, a control system is devised using the identified model to stabilize the quadcopter. This illustrates the effectiveness of the proposed identification method.

We construct hypergraphs to analyze functional brain connectivity, leveraging event-related coherence in magnetoencephalography (MEG) data during the visual perception of a flickering image. Principal network characteristics are computed for delta, theta, alpha, beta, and gamma frequency ranges. Employing a coherence measure, a statistical estimate of correlation between signal pairs across frequencies, we generate edge time series, depicting how an edge evolves over time. This forms the basis for constructing an edge-to-edge functional connectivity network. Emphasizing hyperedges as connected components in an absolute-valued functional connectivity network, we focus on exploring these hyperedges in the context of individual variability. Our coherence-based hypergraph construction specifically addresses functional connectivity among four brain lobes: frontal, parietal, temporal, and occipital. This approach enables a nuanced exploration of individual differences within diverse frequency bands, providing insights into the dynamical nature of brain connectivity during visual perception tasks. The results furnish compelling evidence supporting the hypothesis of cortico-cortical interactions occurring across varying scales. The derived hypergraph illustrates robust activation patterns in specific brain regions, indicative of their engagement across diverse cognitive contexts and different frequency bands. Our findings suggest potential integration or multifunctionality within the examined lobes, contributing valuable perspectives to our understanding of brain dynamics during visual perception.

This study addressed the need for efficient data collection in micro-mobility using low-end hardware, focusing on developing a system capable of handling large real-time data streams. We proposed a novel data collection system that leverages incremental learning techniques, enabling AI-based services for micro-mobility passengers through smartphones. Our approach utilizes 3-axis acceleration sensors integrated into micro-mobility devices, collecting motion data in real-time. This data is processed through an incremental learning pre-processing server, employing the Broad Learning System algorithm, to ensure robust performance in noisy, real-world environments. The use of incremental learning is pivotal, allowing our system to adapt continuously to new data, enhancing the accuracy and relevance of the AI services provided to passengers. This research not only advances the technical capabilities in micro-mobility data handling but also has the potential to enhance the overall passenger experience through improved AI-based services.

The aim of this paper is to study a semi-functional partial linear regression model (SFPLR) for spatial data with responses missing at random. The estimators are constructed by the kernel method, and some asymptotic properties such as probability convergence rates of the nonparametric component and asymptotic distribution of the parametric and nonparametric components are established under certain conditions. Next, the performances and the superiority of these estimators are presented and examined using a study on simulated data and on real data by carrying out a comparison between our semi-functional partially linear model with MAR estimator (SFPLRM), the semi-functional partially linear model with the full-case estimator (SFPLRC) and the nonparametric functional model estimator with MAR (FNPM). The results show that the proposed estimators outperform existing estimators as the number of random missing data increases.

Human-to-human communication via the computer is mainly done using a keyboard or microphone. In the field of Virtual Reality (VR), where the most immersive experience possible is desired, the use of a keyboard contradicts this goal, while the use of a microphone is not always desirable (e.g. silent commands during task force training) or simply not possible (e.g. if the user has a hearing loss). Data gloves help to increase immersion within the VR as they correspond to our natural interaction. At the same time, they offer the possibility to accurately capture hand shapes, such as those used in non-verbal communication (e.g. thumbs up, okay gesture, ...) and in sign language. In this paper, we present a hand shape recognition system using Manus Prime X data gloves, including data acquisition, data preprocessing, and data classification to enable nonverbal communication within VR. We investigate the impact on accuracy and classification time of using an Outlier Detection and a Feature Selection approach in our data preprocessing. To obtain a more generalized approach, we also studied the impact of artificial Data Augmentation, i.e., we create new artificial data from the recorded and filtered data to augment the training dataset. With our approach, 56 different hand shapes could be distinguished with an accuracy of up to 93.28%. With a reduced number of 27 hand shapes, an accuracy of up to 95.55% could be achieved. Voting Meta-Classifier (VL2) has proven to be the most accurate, albeit slowest, classifier. A good alternative is Random Forest (RF), which was even able to achieve better accuracy values in a few cases and was generally somewhat faster. Outlier Detection has proven to be a effective approach, especially in improving classification time. Overall, we have shown that our hand shape recognition system using data gloves is suitable for communication within VR.

The emergence of the Internet of Things (IoT) has led to the deployment of various types of sensors in many application fields, including environment monitoring, smart cities, health, industries, and others. The increasing number of connected devices has led to the creation of massive quantities of data that need to be analyzed. Typically, this data is ordered by time, as a time series. In this context, this paper presents a time series prediction model based on Recurrent Neural Networks in order to predict one step ahead. Result obtained through five Internet of Things monitoring datasets, showed that the Recurrent Neural Network obtained better performance that the prediction methods, ARIMA and SVM.

Data mining techniques have garnered significant attention within the realm of education. However, the procurement of ample student data poses a formidable challenge. In response to this challenge, we present a student dataset characterized by its size and distinctive attributes. This dataset encompasses various task-related topics interconnected through a learning pathway, thereby enabling researchers to delve into the data from novel perspectives. Moreover, it encompasses extensive longitudinal student behavioral data, a rarity that adds substantial value. Spanning the years from 2010 to 2021, our dataset comprises a cohort of 7,933 students, 64,344 test scores, and 183,390 behavior records, solidifying its status as a valuable resource for educational research. In our experiments, we achieved successful predictions of students' test outcomes based on behavioral learning data. The strengths of our dataset render it apt for analyzing the nexus between student conduct and academic performance, crafting personalized learning recommendations, and pursuing various other research pursuits.

Accurate climate data at fine spatial resolution are essential for scientific research and the development and planning of crucial social systems, such as energy and agriculture. Among them, sea surface temperature plays a critical role as the associated El Niño-Southern Oscillation (ENSO) is considered a significant signal of global interannual climate system. In this paper, we propose an implicit neural representation-based interpolation method with temporal information (T_INRI) to reconstruct climate data of high spatial resolution, with sea surface temperature as the research object. Traditional deep learning models for generating high-resolution climate data are only applicable to fixed resolution enhancement scales. In contrast, the proposed T_INRI method is not limited to the enhancement scale provided during the training process and its results indicate that it can enhance low-resolution input by arbitrary scale. Additionally, we discuss the impact of temporal information on the generation of high-resolution climate data, specifically, which month the low-resolution sea surface temperature data is from. Our experimental results indicate that T_INRI is advantageous over traditional interpolation methods under different enhancement scales, and the temporal information can improve T_INRI performance for a different calendar month. We also examined the potential capability of T_INRI in recovering missing grid value. These results demonstrate that the proposed T_INRI is a promising method for generating high-resolution climate data and has significant implications for climate research and related applications.

A stable driver state is essential in the process of manually transition control, which inevitably occurs in Level 3 automated driving situations. To this end, this paper proposed a CNN algorithm-based driver state monitoring system that uses multi-sensor data such as driver's face image, biometric information, and vehicle behavior information as input. This system calculates the probability of drowsiness for each of the four time periods using a convolutional neural network (CNN) based on ToF camera-based eye blinking, ECG information (pulse rate) embedded in the steering wheel, and vehicle information (steering angle data). In order to build a reliable and high-quality training dataset (Ground Truth) for the CNN algorithm, a baseline was established by matching the driver's face image with the electrocardiogram (ECG) and electroencephalogram (EEG) changes in the drowsy and normal states. In a simulation test of the proposed CNN algorithm using more than 20,000 driver image data acquired using a driving simulator, the TNR was 94.8% and the accuracy was 94.2%. Our proposed method is expected to minimize human errors that may occur when switching control by monitoring inappropriate driver state (drowsiness) in real time.

Purpose: Therapeutic targeting of RAF1 is promising, but it requires further investigation for the relation between clinical features and RAF1 aberrations regarding the MAPK signaling pathway in various solid tumors to realize the precision medicine. Methods: Between October 2019 and June 2023, Samsung Medical Center, 3,895 patients with metastatic cancer patients received a next-generation sequencing (NGS) using TruSight Oncology 500 (TSO500) assay as a routine clinical practice. We surveyed the incidence of RAF1 aberration including mutation (single-nucleotide variant [SNV]), amplification (copy-number variation), and fusion. Results: In 3,895 metastatic cancer patients, 77 (2.0%) had RAF1 aberrations in their tumor specimen. Of 77 patients, 44 (1.1%) had RAF1 mutations (SNV), 25 (0.6%) had RAF1 amplification and 10 (0.3%) had RAF1 fusions. Among 10 patients with RAF1 fusion, concurrent RAF1 amplification and RAF1 mutation were detected in one patient each. The most common tumor types were bladder cancer (11.5%), followed by ampulla of Vater (AoV) cancer (5.3%), melanoma (3.0%), gallbladder (GB) cancer (2.6%), and gastric cancer (2.3%). Microsatellite instability high (MSI-H) tumors were found in 5 out of 76 patients (6.6%) with RAF1 aberration, while MSI-H tumors were found only in 2.1% of patients with wild-type RAF1 cancer (p < 0.0001). Conclusion: We showed that when patients with metastatic solid cancer receive NGS test, approximately 2.0% have RAF1 aberrations in their tumor specimen.

Low nasal nitric oxide (nNO) is a typical feature in Primary Ciliary Dyskinesia (PCD). nNO is part of the PCD diagnostic algorithm due to its discriminative power against other lung diseases such cystic fibrosis (CF). However, the underlying pathomechanisms are elusive. To better understand NO dysregulation in PCD, the L-arginine/NO (Arg/NO) pathway in patients with PCD (pwPCD) and CF (pwCF) and in healthy control (HC) subjects was investigated. In a prospective, controlled study, we measured in 24 pwPCD, 25 age-matched pwCF, and 14 HC the concentrations of the NO precursors Arg and homoarginine (hArg), the arginase metabolite ornithine (Orn), the NO inhibitor asymmetric dimethylarginine (ADMA), and the major NO metabolites (nitrate, nitrite) in sputum, plasma and urine by validated methods. In comparison to HC, the sputum contents (in µmol/mg) of L-Arg (PCD 18.43 vs. CF 329.46 vs. HC 9.86, p &lt; 0.001) and of ADMA (PCD 0.055 vs. CF 0.015 vs. HC 0.010, p &lt; 0.001) were higher. In contrast, the sputum contents (in µmol/mg) of nitrate and nitrite were lower in PCD compared to HC (nitrite 4.54 vs. 9.26, p = 0.023; nitrate 12.86 vs. 40.33, p = 0.008), but higher in CF (nitrite 16.28, p &lt;0.001; nitrate 56.83, p = 0.002). The metabolite concentrations in urine and plasma were similar in all groups. The results of our study indicate that PCD, unlike CF, is associated with impaired NO synthesis in the lung, presumably due to mechano-chemical uncoupling.

This paper proposes an optimal trading algorithm based on a novel application of the conventional Control Engineering (CE). We consider a fundamental CE concept, namely, the feedback control and apply it to the Algorithmic Trading (AT). The concrete feedback control strategy is designed in a form of the celebrated Proportional-Integral-Derivative (PID) model. The highly fluctuating nature of the modern financial markets has led to the adoption of a model-free realisation of the generic PID framework. The control theoretical methodology we propose is combined with the advanced statistics for the historical market data. We obtain a specific log-normal probability distribution function (pdf) associated with the specific quantities associated with the available stock data. The empirical log-normal pdf mentioned above makes it possible the necessary PID gains optimization. For this aim, we apply the data-driven optimization approaches and consider the corresponding Monte Carlo solution procedure. The optimized PID trading algorithm we propose is also studied in the Fourier analysis framework. This equivalent frequency domain representation involves a new concept in the financial engineering, namely, the "stock market energy" concept. For the evaluation, we implement the proposed PID optimal trading algorithm and develop a Python based prototype software. We finally apply the corresponding prototype software to a dataset from the Binance BTC / USDT stock market. The experimental result illustrates the implementability of the proposed optimal PID trading scheme and also shows the effectiveness of the proposed CE methods in the modern AT.

Two algorithms of data processing are proposed to shorten the unmeasurable dead-zone close to the zero-position of measurement, i.e., the minimum working distance of a dispersive interferometer using optical frequency comb, which is a critical issue in millimeter-order short-range absolute distance measurement. After demonstrating the limitation of the conventional data processing algorithm, the principles of the proposed algorithms, namely the spectral fringe algorithm, and the combined algorithm that combines the spectral fringe algorithm with the excess fraction method, are presented, together with simulation results for demonstrating the possibility of the proposed algorithms for shortening the dead-zone with a high accuracy. An experimental setup of dispersive interferometer is also constructed for implementing the proposed data processing algorithms over spectral interference signals. Experimental results demonstrate that the dead-zone by the proposed algorithms can be as small as half of that by the conventional algorithm while the measurement accuracy can be further improved with the combined algorithm.

The digital era offers many opportunities to the wind energy industry and research community. Digitalisation is one of the key drivers for reducing costs and risks over the whole wind energy project life cycle. One of the largest challenges in successfully implementing digitalisation is the lack of data sharing and collaboration between organisations in the sector. In order to overcome this challenge, a new collaboration method called WeDoWind was developed in recent work. The main innovation of this method is the way it creates tangible incentives to motivate and empower different types of people from all over the world to actually share data and knowledge in practice. In this present paper, the challenges related to comparing and evaluating different SCADA data based wind turbine fault detection models are investigated by carrying out a new case study, the "WinJi Gearbox Fault Detection Challenge", based on the WeDoWind Method. Six new solutions were submitted to the challenge, and a comparison and evaluation of the results show that, in general, some of the approaches (Particle Swarm Optimisation algorithm for constructing health indicators, performance monitoring using Deep Neural Networks, Combined Ward Hierarchical Clustering and Novelty Detection with Local Outlier Factor and Time-to-failure prediction using Random Forest Regression) appear to have a high potential to reach the goals of the Challenge. However, there are a number of concrete things that would have to have been done by the Challenge providers and the Challenge moderators in order to ensure success. This includes enabling access to more details of the different failure types, access to multiple data sets from more wind turbines experiencing gearbox failure, provision of a model or rule relating fault detection times or a remaining useful lifetime to the estimated costs for repairs, replacements and inspections, provision of a clear strategy for training and test periods in advance, as well as provision of a pre-defined template or requirements for the results. These learning outcomes are used directly to define a set of best practice data sharing guidelines for wind turbine fault detection model evaluation. They can be used by the sector in order to improve model evaluation and data sharing in the future.

Abstract This essay presents a real-world demonstration of the evidence-based science communication process, showing how it can be used to create scientific data visualizations for public audiences. Visualizing research data can be an important science communication tool. Maximizing its effectiveness has the potential to benefit millions of viewers. As with many forms of science communication, creators of such data visualizations typically rely on their own judgments and the views of the scientists providing the data to inform their science communication decision-making. But that leaves out a critical stakeholder in the communications pipeline: the intended audience. Here, we show the practical steps that our team - the Advanced Visualization Lab at the University of Illinois at Urbana-Champaign - has taken to shift towards more evidence-based practice to enhance our science communication impact. We do this using concrete examples from our work on two scientific documentary films, one on the theme of ‘solar superstorms’ and the other focusing on the black hole at the center of the Milky Way galaxy. We used audience research with each of these films to inform our strategies and designs. We describe how such research evidence informed our understanding of ‘what works and why’ with cinematic-style data visualizations for the public. We close the essay with our key ‘take home’ messages from this evidence-based science communication process.

Satellite measurements are one of the main sources of data on the state of the marine environment. To obtain information about the sea brightness coefficient , it is needed to correctly carry out atmospheric correction. In the presence of dust aerosol over the Black Sea, physically incorrect values of the spectral brightness coefficient often occur, and specifically negative values in the IR region of the spectrum. The main objective of the study is to evaluate the influence of dust aerosol on the spectral dependence of sea brightness, based on analytical calculations from the transfer theory using the principle of plane-parallel layers and results of validation of AERONET-OC field and remote sensing data. The work analyzes spectral dependence of the first error eigenvector of the standard atmospheric correction in the presence of dust aerosol. As result it is given that with an absorbing aerosol, the atmospheric correction error is described by the spectral course of molecular scattering, i.e. close to

Current commercial, pre-commercial, and experimental in ovo techniques for the sex determination of fertilised eggs employ either minimally invasive biomolecular assays (extracting fluid via a small laser-drilled window in the eggshell, for detection of genetic or hormonal biomarkers), analysis of volatile compounds emitted from the eggshell, visible imaging, and reflectance or transmission spectroscopic analysis exploiting molecular optical fluorescence, polarisation, and scattering phenomena, including various combinations of these modalities. , to date no endeavour employing the NIR and FTIR based spectroscopic techniques has resulted in a commercially sustainable solution to the egg sexing problem. Besides achieving only subpar performance in overall accuracy, specificity, and sensitivity, the least invasive of the current state-of-the-art optical methods still requires, creating a transmission window (fenestration) of 12–15 mm diameter through to the mammillae layer of the shell, proximal to the external shell membrane, which can affect the incubation or post-hatch development viability of up to 10% of incubated eggs. Multimodal solution combining Raman spectroscopy and hyperspectral imaging has strong prospects to overcome the hard barriers existing before the perfection of a non-invasive in-line process for high reliability and rapid throughput for sex determination of eggs within 3 days of incubation. The method for sexing of chicken embryos needs to take a multipronged approach in collecting and analyzing spectral data that points to biomarkers using the machine learning approaches to look for nanomolar to picomolar concentrations of these in the fluid.

Nowadays, the Internet of Things (IoT) devices and applications have rapidly expanded worldwide due to their benefits in improving the business environment, industrial environment, and people's daily lives. However, the IoT devices are not immune to malicious network traffic, which causes potential negative consequences and sabotages IoT operating devices. Therefore, developing a method for screening network traffic is necessary to detect and classify malicious activity to mitigate its negative impacts. Therefore, this research proposes a predictive machine learning model to detect and classify network activity in an IoT system. Specifically, our model distinguishes between normal and anomaly network activity. Furthermore, it classifies network traffic into five categories, normal, Mirai attack, denial of service (DoS) attack, Scan attack, and man-in-the-middle (MITM) attack. Five supervised learning models were implemented to characterize their performance in detecting and classifying network activities for IoT systems. This includes models shallow neural networks (SNN), decision trees (DT), bagging trees (BT), support vector machine (SVM), and k-nearest neighbor (kNN). The learning models were evaluated on a new and broad dataset for IoT attacks, the IoTID20 dataset. Besides, a deep feature engineering process was applied to the dataset to improve the accuracy of the learning models. Our experimental evaluation exhibited an accuracy of 100% recorded for the detection using all implemented models and an accuracy of 99.4%-99.9% recorded for the classification process.

We present a differentially private extension of the block coordinate descent based on objective perturbation. The algorithm iteratively performs linear regression in a federated setting on vertically partitioned data. In addition to a privacy guarantee, the algorithm also offers a utility guarantee; a tolerance parameter indicates how much the differentially private regression may deviate from an analysis without differential privacy. The algorithm’s performance is compared with the standard block coordinate descent algorithm and the trade-off between utility and privacy is studied. The performance is studied using both artificial test data and the forest fires data set. We find that the algorithm is fast and able to generate practical predictions with single-digit privacy budgets, albeit with some accuracy loss.

The X-ray emission from a supernova remnant (SNR) is a powerful diagnostic of the state of the shocked plasma, and, given a model, can be used to determine the energy of the explosion, the age of the SNR and the density of the surrounding medium. Observed properties are shock radius, electron temperature (kTe) and emission measure (EM) of the shocked-gas. The standard and XSPEC definitions have an important difference. The XSPEC definition is superior for SNRs, which have components with low hydrogen abundance. SNR model calculations are based on hydrodynamic solutions for fluid variables of density, pressure and velocity. The relations between fluid variables and kTe or EM depend on composition, ionization state and electron-ion temperature ratio (Te/TI). Here the effects of composition, ionization and Te/TI on standard and on XSPEC versions of kTe and EM are investigated.

Stage of cancer is a discrete ordinal response that indicates aggressiveness of disease and is often used by physicians to determine the type and intensity of treatment to be administered. For example, the FIGO stage in cervical cancer is based on the size and depth of the tumor as well as the level of spread. It may be of clinical relevance to identify molecular features from high-throughput genomic assays that are associated with stage of cervical cancer, to elucidate pathways related to tumor aggressiveness, identify improved molecular features that may be useful for staging, and identify therapeutic targets. High-throughput RNA-Seq data and corresponding clinical data (including stage) for cervical cancer patients has been made available through The Cancer Genome Atlas Project (TCGA). We recently described penalized Bayesian ordinal response models that can be used for variable selection for over-parameterized datasets such as the TCGA-CESC dataset. Herein, we describe our ordinalbayes R package, available from the Comprehensive R Archive Network (CRAN), which is capable of fitting cumulative logit models when the outcome is ordinal and the number of predictors exceeds the sample size, P>N, such as for TCGA data. We demonstrate use of this package through application to TCGA cervical cancer dataset. Our ordinalbayes package can be used to fit models to high-dimensional dataset and effectively performs variable selection.

We have used Mass Multivariate Method on structural, resting state and task related fMRI data from two groups of patients with schizophrenia and depression, respectively, in order to define several regions of significant relevance to the differential diagnosis between those conditions. The regions included the left Planum polare, Left opercular part of the inferior frontal gyrus (OpIFG), Medial orbital gyrus (MOrG), Posterior Insula (PIns), and Parahippocampal gyrus (PHG). This study delivers evidence that multimodal neuroimaging approach can potentially enhance the validity of psychiatric diagnosis. Either structural, or resting state or task related functional MRI modality cannot provide independent biomarkers. Further studies need to consider and implement a model of incremental validity to combine clinical measures with different neuroimaging modalities to discriminate depressive disorders from schizophrenia. Biological signatures of disease on the level of neuroimaging are more likely to underpin broader nosological entities in psychiatry.

Object detection is one of the most critical tasks in the field of Computer vision. This task comprises identifying and localizing an object in the image. Architectural floor plans represent the layout of buildings and apartments. The floor plans consist of walls, windows, stairs, and other furniture objects. While recognizing floor plan objects is straightforward for humans, automatically processing floor plans and recognizing objects is a challenging problem. In this work, we investigate the performance of the recently introduced Cascade Mask R-CNN network to solve object detection in floor plan images. Furthermore, we experimentally establish that deformable convolution works better than conventional convolutions in the proposed framework. Identifying objects in floor plan images is also challenging due to the variety of floor plans and different objects. We faced a problem in training our network because of the lack of publicly available datasets. Currently, available public datasets do not have enough images to train deep neural networks efficiently. We introduce SFPI, a novel synthetic floor plan dataset consisting of 10000 images to address this issue. Our proposed method conveniently surpasses the previous state-of-the-art results on the SESYD dataset and sets impressive baseline results on the proposed SFPI dataset. The dataset can be downloaded from SFPI Dataset Link. We believe that the novel dataset enables the researcher to enhance the research in this domain further.

The aerosol optical depth is an important indicator of aerosol particle properties and associated radiative impacts. AOD determination is therefore very important to achieve relevant climate modeling. Most remote sensing techniques to retrieve aerosol optical depth are applicable to daytime given the high level of light available. The night represents half of the time but in such conditions only a few remote sensing techniques are available. Among these techniques, the most reliable are moon photometers and star photometers. In this paper, we attempt to fill gaps in the aerosol detection performed with the aforementioned techniques using night sky brightness measurements during moonless nights with the novel CoSQM: a portable, low cost and open-source multispectral photometer. In this paper, we present an innovative method for estimating the aerosol optical depth by using an empirical relationship between the zenith night sky brightness measured at night with the CoSQM and the aerosol optical depth retrieved at daytime from the AErosol Robotic NETwork. Such a method is especially suited to light-polluted regions with light pollution sources located within a few kilometers of the observation site. A coherent day-to-night aerosol optical depth and Ångström Exponent evolution in a set of 354 days and nights from August 2019 to February 2021 was verified at the location of Santa Cruz de Tenerife on the island of Tenerife, Spain. The preliminary uncertainty of this technique was evaluated using the variance under stable day-to-night conditions, set at 0.02 for aerosol optical depth and 0.75 for Ångström Exponent. These results indicate the set of CoSQM and the proposed methodology appear to be a promising tool to add new information on the aerosol optical properties at night, which could be of key importance to improve climate predictions.

Diverse forms of artificial intelligence (AI in further text) are at the forefront of triggering digital security innovations, based on the threats that are arising in this post COVID world. On the one hand, companies are experiencing difficulty in dealing with security challenges with regard to a variety of issues ranging from system openness, decision making, quality control and web domain, just to mention a few. On the other hand, in the last decade, research has focused on security capabilities based on tools such as platform complacency, intelligent trees, modeling methods and outage management systems, in an effort to understanding the interplay between AI and those issues. The dependence on the emergence of AI in running industries and shaping the education, transports and health sectors is now well known in literature. AI is increasingly employed in managing data security across economic sectors. Thus, a literature review of AI and system secu-rity within the current digital society is opportune. This paper aims at identifying research trends in the field through a Systematic Bibliometric Literature Review (LRSB) of research on AI and system security. The review entails 77 articles published in Scopus® database, presenting up-to-date knowledge on the topic. The LRSB results were synthesized across current research subthemes. Findings are presented. The originality of the paper relies on its LRSB method, together with extant review of articles that have not been categorized so far. Implications for future re-search are suggested.

This paper presents two designs of high-efficiency polarizer reflectarray antennas able to generate a collimated beam in dual-circular polarization using a linearly polarized feed, with application to high-gain antennas for data transmission links from a Cubesat. First, an 18 cm x 18 cm polarizer reflectarray operating in the 17.2 - 22.7 GHz band has been designed, fabricated and tested. The measurements of the prototype show an aperture efficiency of 52.7% for right-handed circular polarization (RHCP) and 57.3% for left-handed circular polarization (LHCP), both values higher than those previously reported in related works. Then, a dual-band polarizer reflectarray is presented for the first time, which operates in dual-CP in the frequency bands of 20 and 30 GHz. The proposed antenna technology enables a reduction of the complexity and cost of the feed chain to operate in dual-CP, as a linear-to-circular polarizer is no longer required. This property, combined with the lightweight, flat profile and low fabrication cost of printed reflectarrays, makes the proposed antennas good candidates for Cubesat applications.

Abstract Background The number of immune-related endocrine dysfunctions (irEDs) has concurrently increased with the widespread use of immunotherapy in clinical practice and further expansion of the approved indications for immune checkpoint inhibitor (ICI) combinations using different modalities of anti-cancer treatment. Method A retrospective analysis was conducted on consecutive patients >18 years of age with advanced solid malignancies who had received at least one dose of anti-programmed cell death protein 1 (anti-PD-1) and/or anti-CTLA4 antibodies between January 2014 and December 2019 at a Hong Kong university hospital. Patients were reviewed for up to two months after the last administration of an ICI. The types, onset times and grades of irEDs, including hypothyroidism, hyperthyroidism, adrenal insufficiency and immune-related diabetes mellitus, were recorded. Factors associated with irEDs were identified using multivariate analysis. Result A total of 953 patients (male: 603, 64.0%; median age: 62.0 years) received ICIs during the study period. Of these, 580 patients (60.9%) used ICI-alone, 132 (13.9%) used dual-ICI, 187 (19.6%) used an ICI combined with chemotherapy (chemo+ICI), and 54 (5.70%) used immunotherapy with a targeted agent (targeted+ICI). A significantly higher proportion of patients using targeted+ICI had irEDs and hypothyroidism; in contrast, a higher proportion of patients using dual-ICI had adrenal insufficiency. There was no significant difference in the incidence of irED between the younger (<65 years) and older (>65 years) patients. Using logistic regression, only treatment type was significantly associated with irEDs. Notably, older patients had a higher risk of having immune-related diabetes mellitus. Conclusions This large, real-world cohort demonstrates that combining ICI with targeted therapy has a higher risk of overall irED and hypothyroidism. Immunotherapy is safe and well-tolerated regardless of age, but close monitoring of fasting glucose is needed in older populations.

Mitochondrial Encephalohepatopathy (MEH) is an autosomal recessive neurodevelopmental disorder usually accompanied by microcephaly, white matter changes, cardiac and hepatic failure. Here, we applied the whole-exome sequencing (WES) framework on a trio family data with unaffected non-consanguineous parents and proband (neonate girl) with this inherited disorder. A total of 2,928,402 variants were observed with 2,613,746 SNPs, 112,336 multiple nucleotide polymorphisms (MNPs), 72,610 insertions, 113,207 deletions and 16,503 mixed variants. These variations are responsible for 82,813,631 effects on various genomic regions. Our pipeline uncovered candidate gene mutations from these variants and retained a handful of 5,277 variants harboring 3,598 genes, out of which, 8 genes codes for non-coding RNA while 178 genes are those with high impact severity. Among these 178 variants, 125 are de-novo variants that are not previously reported in the ClinVar database. Consistent to previous studies, the leftover high impact severity genes are involved in encephalopathy, Leigh syndrome, Charcot–Marie–Tooth disease, global developmental disorder, seizures, spastic paraplegia, premature ovarian failure, mitochondrial myopathy-cerebellar, ataxia-pigmentary, retinopathy syndrome, ocular and retinal degeneration, deafness, intellectual disability, cardiofacioneurodevelopmental syndrome etc. All these clinical features were also observed in the patient studied. The current analysis highlights and expands the genetic architecture of the MEH phenotype. Furthermore, this pipeline on trio family data significantly broadens the concept of its usefulness as a first-tier diagnostic method in the detection of complex multisystem phenotypic disorders.

Nowadays, and with the mechanization of life, speech processing has become so crucial for the interaction between humans and machines. Deep neural networks require a database with enough data for training. The more features are extracted from the speech signal, the more samples are needed to train these networks. Adequate training of these networks can be ensured when there is access to sufficient and varied data in each class. If there is not enough data; it is possible to use data augmentation methods to obtain a database with enough samples. One of the obstacles to developing speech emotion recognition systems is the Data sparsity problem in each class for neural network training. The current study has focused on making a cycle generative adversarial network for data augmentation in a system for speech emotion recognition. For each of the five emotions employed, an adversarial generating network is designed to generate data that is very similar to the main data in that class, as well as differentiate the emotions of the other classes. These networks are taught in an adversarial way to produce feature vectors like each class in the space of the main feature, and then they add to the training sets existing in the database to train the classifier network. Instead of using the common cross-entropy error to train generative adversarial networks and to remove the vanishing gradient problem, Wasserstein Divergence has been used to produce high-quality artificial samples. The suggested network has been tested to be applied for speech emotion recognition using EMODB as training, testing, and evaluating sets, and the quality of artificial data evaluated using two Support Vector Machine (SVM) and Deep Neural Network (DNN) classifiers. Moreover, it has been revealed that extracting and reproducing high-level features from acoustic features, speech emotion recognition with separating five primary emotions has been done with acceptable accuracy.

This paper presents an unsupervised feature selection method for multi-dimensional histogram-valued data. We define a multi-role measure, called the compactness, based on the concept size of given objects and/or clusters described by a fixed number of equal probability bin-rectangles. In each step of clustering, we agglomerate objects and/or clusters so as to minimize the compactness for the generated cluster. This means that the compactness plays the role of a similarity measure between objects and/or clusters to be merged. To minimize the compactness is equivalent to maximize the dis-similarity of the generated cluster, i.e., concept, against the whole concept in each step. In this sense, the compactness plays the role of cluster quality. We also show that the average compactness of each feature with respect to objects and/or clusters in several clustering steps is useful as feature effectiveness criterion. Features having small average compactness are mutually covariate, and are able to detect geometrically thin structure embedded in the given multi-dimensional histogram-valued data. We obtain thorough understandings of the given data by the visualization using dendrograms and scatter diagrams with respect to the selected informative features. We illustrate the effectiveness of the proposed method by using an artificial data set and real histogram-valued data sets.

Knowledge processing is an important feature of intelligence in general and artificial intelligence in particular. To develop computing systems working with knowledge, it is necessary to elaborate means of working with knowledge representations (as opposed to data) because knowledge is an abstract structure. There are different forms of knowledge representations derived from data. One of the basic forms is called a schema. The goal of this paper is the development of theoretical and practical tools for processing schemas. To achieve this goal, we use schema representations elaborated in the mathematical theory of schemas and use structural machine as a powerful theoretical tool for modeling parallel and concurrent computational processes. We describe the schema of autopoietic machines as physical realizations of structural machines. An autopoietic Machine is a technical system capable of regenerating, reproducing and maintaining itself by production, transformation and destruction of its components and the networks of processes downstream contained in them. We present the theory and practice of designing and implementing autopoietic machines as information processing structures integrating both symbolic computing and neural networks. Autopoietic machines use knowledge structures containing the behavioral evolution of the system and its interactions with the environment to maintain stability by counteracting fluctuations.

In the last two decades, there have been massive advancements in high throughput technologies, which resulted in the exponential growth of public repositories of gene expression datasets for various phenotypes. It is possible to unravel biomarkers by comparing the gene expression levels under different conditions, such as disease vs. control, treated vs. not treated, drug A vs. drug B, etc. This problem refers to a well-studied problem in the machine learning domain, i.e., the feature selection problem. In biological data analysis, most of the computational feature selection methodologies were taken from other fields, without considering the nature of the biological data. For gene expression data analysis, most of the existing feature selection methods rely on expression values alone to select the genes; and biological knowledge is integrated at the end of the analysis in order to gain biological insights or to support the initial findings. Thus, integrative approaches that utilize the biological knowledge while performing feature selection are necessary for this kind of data. The main idea behind the integrative gene selection process is to generate a ranked list of genes considering both the statistical metrics that are applied to the gene expression data, and the biological background information which is provided as external datasets. Since the integrative approach attracted attention in the gene expression domain, lately the gene selection process shifted from being purely data-centric to more incorporative analysis with additional biological knowledge.

Social media data is heavily used to analyze and evaluate situations in times of disasters, and derive decisions for action from it. A cruicial part of the analysis is to avoid unnecessary data retention during that process, in order to prevent subsequent abuse, theft or public exposure of collected datasets and thus, protect the privacy of social media users. There are a number of technical approaches out to face the problem. One of them is using a cardinality estimation algorithm called HyperLogLog to store data in a privacy-aware structure, that can not be used for purposes other than the originally intended. In this case study, we developed and conducted a focus group discussion with teams of social media analysts, in which we identified challenges and opportunities of working with such a privacy-enhanced social media data structure in place of conventional techniques. Our findings show that, with the exception of training scenarios, deploying HyperLogLog in the data acquisiton process will not distract the data analysis process. Instead, it will improve working with huge datasets due to the improved characteristics of the resulting data structure.

As the Autonomous Vehicle (AV) industry is rapidly advancing, classification of non-motorized (vulnerable) road users (VRUs) becomes essential to ensure their safety and to smooth operation of road applications. The typical practice of non-motorized road users’ classification usually takes numerous training time and ignores the temporal evolution and behavior of the signal. In this research effort, we attempt to detect VRUs with high accuracy be proposing a novel framework that includes using Deep Transfer Learning, which saves training time and cost, to classify images constructed from Recurrence Quantification Analysis (RQA) that reflect the temporal dynamics and behavior of the signal. Recurrence Plots (RPs) were constructed from low-power smartphone sensors without using GPS data. The resulted RPs were used as inputs for different pre-trained Convolutional Neural Network (CNN) classifiers including constructing 227×227 images to be used for AlexNet and SqueezeNet; and constructing 224×224 images to be used for VGG16 and VGG19. Results show that the classification accuracy of Convolutional Neural Network Transfer Learning (CNN-TL) reaches 98.70%, 98.62%, 98.71%, and 98.71% for AlexNet, SqueezeNet, VGG16, and VGG19, respectively. The results of the proposed framework outperform other results in the literature (to the best of our knowledge) and show that using CNN-TL is promising for VRUs classification. Because of its relative straightforwardness, ability to be generalized and transferred, and potential high accuracy, we anticipate that this framework might be able to solve various problems related to signal classification.

The Public Health Alliance for Genomic Epidemiology (PHA4GE) (https://pha4ge.org) is a global coalition that is actively working to establish consensus standards, document and share best practices, improve the availability of critical bioinformatic tools and resources, and advocate for greater openness, interoperability, accessibility and reproducibility in public health microbial bioinformatics. In the face of the current pandemic, PHA4GE has identified a clear and present need for a fit-for-purpose, open source SARS-CoV-2 contextual data standard. As such, we have developed an extension to the INSDC pathogen package, providing a SARS-CoV-2 contextual data specification based on harmonisable, publicly available, community standards. The specification is implementable via a collection template, as well as an array of protocols and tools to support the harmonisation and submission of sequence data and contextual information to public repositories. Well-structured, rich contextual data adds value, promotes reuse, and enables aggregation and integration of disparate data sets. Adoption of the proposed standard and practices will better enable interoperability between datasets and systems, improve the consistency and utility of generated data, and ultimately facilitate novel insights and discoveries in SARS-CoV-2 and COVID-19.

Increased smart devices in various industries is creating numerous sensors in each of the equipment prompting the need for methods and models for sensor data. Current research proposes a systematic approach to analyze the data generated from sensors attached to industrial equipment. The methodology involves data cleaning, preprocessing, basics statistics, outlier, and anomaly detection. Present study presents the prediction of RUL by using various Machine Learning models like Regression, Polynomial Regression, Random Forest, Decision Tree, XG Boost. Hyper Parameter Optimization is performed to find the optimal parameters for each variable. In each of the model for RUL prediction RMSE, MAE are compared. Outcome of the RUL prediction should be useful for decision maker to drive the business decision; hence Binary classification is performed, and business case analysis is performed. Business case analysis includes the cost of maintenance and cost of non-maintaining a particular asset. Current research is aimed at integrating the machine intelligence and business intelligence so that the industrial operations optimized both in resource and profit.

The Independent Component Analysis (ICA) has been recently introduced as a reliable alternative to identify canonical scattering mechanisms within Polarimetric Synthetic Aperture Radar (PolSAR) images. This manuscript addresses two important aspects when applying such methods on real data, namely speckle filtering and statistical classification with ICA. A novel PolSAR data processing framework is introduced by adjusting the Lee's sigma filter to the particular nature of the Touzi's polarimetric decomposition. In its current form, it allows the use of the ICA mixing matrix in the derived speckle filter. An extension of the Fromont at al. iterative segmentation is introduced, equally. This proposed framework is tested using P band airborne PolSAR data acquired for the ESA campaign TropiSAR campaign.

The interaction of human immunodeficiency virus with human cells is responsible for all stages of the viral life cycle, from the infection of CD4+ cells to reverse transcription, integration, and the assembly of new viral particles. To date, a large amount of OMICs data as well as information from functional genomics screenings regarding the HIV-1-host interaction has been accumulated in the literature and in public databases. We processed databases containing HIV-host interactions and found 2910 HIV-1-human protein-protein interactions, mostly related to viral group M subtype B, 137 interactions between human and HIV-1 coding and non-coding RNAs, essential for viral lifecycle and cell defense mechanisms, 232 transcriptomics, 27 proteomics, and 34 epigenomics HIV-related experiments. Numerous studies regarding network-based analysis of corresponding OMICs data have been published in recent years. We overview various types of molecular networks, which can be created using OMICs data, including HIV-human protein-protein interaction networks, co-expression networks, gene regulatory and signaling networks, and approaches for the analysis of their topology and dynamics. The network-based analysis can be used to determine the critical pathways and key proteins involved in the HIV life cycle, cellular and immune responses to infection, viral escape from host defense mechanisms, and mechanisms mediating different susceptibility of humans to infection. The proteins and pathways identified in these studies may represent a basis for developing new anti-HIV therapeutic strategies such as new small-molecule drugs preventing infection of CD4+ cells and viral replication, effective vaccines, "shock and kill" and "block and lock" approaches to cure latent infection.

Data science is facing the following major challenges: (1) developing scalable cross-disciplinary capabilities, (2) dealing with the increasing data volumes and their inherent complexity, (3) building tools that help to build trust, (4) creating mechanisms to efficiently operate in the domain of scientific assertions, (5) turning data into actionable knowledge units and (6) promoting data interoperability. As a way to overcome these challenges, we further develop the proposals by early Internet pioneers for Digital Objects as encapsulations of data and metadata made accessible by persistent identifiers. In the past decade, this concept was revisited by various groups within the Research Data Alliance and put in the context of the FAIR Guiding Principles for findable, accessible, interoperable and reusable data. The basic components of a FAIR Digital Object (FDO) as a self-contained, typed, machine-actionable data package are explained. A survey of use cases has indicated the growing interest of research communities in FDO solutions. We conclude that the FDO concept has the potential to act as the interoperable federative core of a hyperinfrastructure initiative such as the European Open Science Cloud (EOSC).

In an era of rapid global change, our ability to understand and predict Earth's natural systems is lagging behind our ability to monitor and measure changes in the biosphere. Bottlenecks in our ability to process information have reduced our capacity to fully exploit the growing volume and variety of data. Here, we take a critical look at the information infrastructure that connects modeling and measurement efforts, and propose a roadmap that accelerates production of new knowledge. We propose that community cyberinfrastructure tools can help mend the divisions between empirical research and modeling, and accelerate the pace of discovery. A new era of data-model integration requires investment in accessible, scalable, transparent tools that integrate the expertise of the whole community, not just a clique of ‘modelers’. This roadmap focuses on five key opportunities for community tools: the underlying backbone to community cyberinfrastructure; data ingest; calibration of models to data; model-data benchmarking; and data assimilation and ecological forecasting. This community-driven approach is key to meeting the pressing needs of science and society in the 21st century.

In this study a new approach for planning Micro-Catchment Water Harvesting (M.C.W.H.) systems for irrigation in semi-arid regions such as the Aegean islands, is presented. M.C.W.H. is a cheap solution for constructing irrigation infrastructure with zero energy cost in regions where water is scarce. The proposed approach introduces simple linear relationships for estimating the annual volume of water V_s collected mainly from the CA (Contributing Area), stored in the root zone (Infiltration Basin, IB), according to the annual rainfall and runoff depths, after having determined the ratio of areas of micro-catchment (MC) components i.e. λ= Α_CA/Α_ΙΒ and its whole area A_MC This procedure was applied in Paros island of the Cyclades complex in the middle of the Aegean sea in east Mediterranean. Besides, income-cost analysis was performed via NPV method for almond, peach and apricot trees. The new approach was proved versatile and easy to use. Besides, the investment turned out to be advantageous two years after the MCs construction.

VHF Data Exchange System (VDES) is considered as an important component of the future maritime communication system by the International Maritime Organization (IMO). On the basis of existing Automatic Identification System (AIS), VDES adds the other two higher capacity subsystems: Application Specific Message (ASM) and VHF Data Exchange (VDE). The Random Access Channel (RACH) of VDE was first introduced in the International Telecommunication Union (ITU) Recommendation M.2092-0. As the slot planning of RACH in VDE is interval, which is significantly different from the continuous slot map for access algorithms in AIS, the existing slot access algorithms cannot meet the requirements of VDE. The simulation results show that the VDE slot map can reduce the normalized throughput of the existing algorithm by 39%. A novel random access algorithm called Adaptive Traffic Load Contention Resolution Diversity Slotted ALOHA (ATL-CRDSA) is proposed in this paper. The algorithm combines the load control strategy and contention resolution scheme to overcome the challenges of the new RACH of VDE. Simulation results show that ATL-CRDSA has remarkable improvement on RACH, and makes RACH very efficient and provides low latency of the packets. The insights gained from this study may be of assistance to the Media Access Control (MAC) layer design for upcoming versions of VDES standard.

A coupled stratospheric chemistry-meteorology model was developed by combining the Canadian operational weather prediction model Global Environmental Multiscale (GEM) with a comprehensive stratospheric photochemistry model from the Belgian Assimilation System for Chemical ObsErvations (BASCOE). The coupled model was called GEM-BACH for GEM-Belgian Atmospheric CHemistry. The coupling was made across a chemical interface that preserves time splitting while being modular, allowing GEM to run with or without chemistry. An evaluation of the coupling was performed by comparing the coupled model, refreshed by meteorological analyses every 6 hours, against the standard offline chemical transport model (CTM) approach. Results show that the dynamical meteorological consistency between meteorological analysis times far outweighs the error created by the jump resulting from the meteorological analysis increments at regular time intervals, irrespective whether a 3D-Var or 4D-Var meteorological analysis is used. GEM-BACH forecast refreshed by meteorological analyses every 6 hours were compared against independent measurements of temperature, long-lived species, ozone and water vapor. The comparison showed a relatively good agreement throughout the stratosphere except for an upper-level warm temperature bias and an ozone deficit of nearly 15%. Arguments in favor of using the same horizontal resolution for chemistry, meteorology, and meteorological analysis increments are also presented. In particular, the coupled model simulation during an ozone hole event gives better ozone concentrations than a 4D-Var chemical assimilation at a lower resolution.

Modeling brain dynamics to better understand and control complex behaviors underlying various cognitive brain functions are of interests to engineers, mathematicians, and physicists from the last several decades. With a motivation of developing computationally efficient models of brain dynamics to use in designing control-theoretic neurostimulation strategies, we have developed a novel data-driven approach in a long short-term memory (LSTM) neural network architecture to predict the temporal dynamics of complex systems over an extended long time-horizon in future. In contrast to recent LSTM-based dynamical modeling approaches that make use of multi-layer perceptrons or linear combination layers as output layers, our architecture uses a single fully connected output layer and reversed-order sequence-to-sequence mapping to improve short time-horizon prediction accuracy and to make multi-timestep predictions of dynamical behaviors. We demonstrate the efficacy of our approach in reconstructing the regular spiking to bursting dynamics exhibited by an experimentally-validated 9-dimensional Hodgkin-Huxley model of hippocampal CA1 pyramidal neurons. Through simulations, we show that our LSTM neural network can predict the multi-time scale temporal dynamics underlying various spiking patterns with reasonable accuracy. Moreover, our results show that the predictions improve with increasing predictive time-horizon in the multi-timestep deep LSTM neural network.

This data descriptor summarizes the process applied to identify, screen, select and gather data from the content of 142 peer-reviewed papers/sources that report on the sources and impacts of recreational disturbance on coastal avifauna. While populations of resident and migratory coastal avifauna are under threat and diminishing rapidly across the planet, and particularly in association with Asian flyways, many governments are leveraging booming global demand for coastal recreation and tourism in order to deliver economic development to regional communities. The summary data shared via this data description was extracted from papers collected in a systematic literature review that was designed to explore the global literature on the recreational disturbance of coastal avifauna in order to elucidate the state of the global knowledge regarding this issue and to identify management strategies that could be applied at tropical Asian destinations to minimize the impacts of recreational disturbance and thus enhance the ecological sustainability of coastal recreation and tourism across the region. The data shared via the Excel worksheet associated with this data descriptor was extracted from peer-reviewed articles published in English between 1 January 2000 and the 31 December 2018 with the full text of the article available online. These articles were found by searching several online indexing several databases including Scopus, Web of Science, ProQuest and Google Scholar.

Rapid technological advancements in information communication technologies have enabled water resource data collection at greater spatial and temporal scale. However, this water data is often limited to the purposes of its primary collection, and limits decisions made by stakeholders towards sustainable urban water management. This empirically focused research paper examines how water practitioners involved in strategic planning can capture additional values from integrating different water data. Furthermore, the perception of 22 urban water practitioners across Australia are presented, regarding the importance of and difficulty in using water data for strategic planning, and the necessary steps for achieving integrated water management practices. Interviewees perception revealed gaps in available water resource data (i.e. water quality, ground water, stormwater, and urban water use), and limitations of industry guidelines for operating within existing governance frameworks. Overall, the research highlights the Australian urban water sector’s perception of water data’s crucial role in representing stakeholders interest; however, changes made in water data’s collection are required for an integrated water management approach. Implications for future open water data standard are discussed.

Crop breeding is as ancient as the invention of cultivation. In essence, the objective of crop breeding is to improve plant fitness under human cultivation conditions, making crops more productive while maintaining consistency in life cycle and quality. The applications of predictive breeding has been gaining momentum in agricultural industry and public breeding programs for the last decade, in the aftermath of genomic selection being recognized and widely applied for accelerating genetic gain in breeding programs. The massive amounts of data that has been generated by industry and farmers year after year through several decades has finally been recognized as an asset. A wide range of analytical methods such as machine learning, deep learning and artificial intelligence that were initially developed for diverse quantitative disciplines are now being adopted to crop breeding decision making processes. New technologies are currently being developed that would enable integration of data from various domains such as geospatial variables and a multitude of phenotypic responses as well as genetic information, in order to identify, develop and improve crop faster via partial or full automation of the decisions that pertain to variety development. Here we will discuss and summarize efforts from public and private domains for predictive analytics, and its applications to crop breeding and agricultural product development, and provide suggestions for future research.

Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis’ sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al.). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub.

In this paper, sea surface wind speed (SSWS) retrieval from Gaofen-3 (GF-3) quad-polarization stripmap (QPS) data in vertical-vertical (VV), horizontal-horizontal (HH) and vertical-horizontal (VH) polarizations is investigated in detail based on 3,170 scenes acquired from October 2016 to May 2018. The radiometric calibration factor of the VV polarization data is examined first. This calibration factor generally meets the requirement of SSWS retrieval accuracy with an absolute bias of less than 0.5 m/s but shows highly dispersed characteristics. These results lead to SSWS retrievals with a small bias of 0.18 m/s but a rather high root mean square error (RMSE) of 2.36 m/s compared with the ERA-Interim reanalysis model data. Two refitted polarization ratio (PR) models for the QPS HH polarization data are presented. Based on a combination of the incidence angle- and azimuth angle-dependent PR model and CMOD5.N, the SSWS derived from the QPS HH data shows a bias of 0.07 m/s and an RMSE of 2.26 m/s relative to the ERA-Interim reanalysis model wind speed. A linear function relating SSWS and the normalized radar cross section (NRCS) of QPS VH data is derived. The SSWS data retrieved from the QPS VH data show good agreement with the WindSat SSWS data, with a bias of 0.1 m/s and an RMSE of 2.02 m/s. We also apply the linear function to the GF-3 Wide ScanSAR data acquired for the typhoon SOULIK, which surprisingly yields a very good agreement with the model results. A comparison of SSWS retrievals among three different polarization datasets is also presented. The current study and our previous work demonstrate that the general accuracy of the SSWS retrieval based on GF-3 QPS data has an absolute bias of less than 0.3 m/s and an RMSE of 2.0 ±0.2 m/s relative to various datasets. Further improvement will depend on dedicated radiometric calibration efforts.

The study focuses on the application of R programming language towards marine geological research with a case study of Mariana Trench. Due to its logical and straightforward syntax, multi-functional standard libraries, R is especially attractive to the geologists for the scientific computing. Using R libraries, the unevenness of various factors affecting Mariana Trench geomorphic structure has been studied. These include sediment thickness, slope steepness, angle aspect, depth at the basement and magmatism of the nearby areas. Methods includes using following R libraries: {ggplot2} for regression analysis, Kernel density curves, compositional charts; {ggalt} for Dumbbell charts for data comparison by tectonic plates, ranking dot plots for correlation analysis; {vcd} for mosaic plots, silhouette plots for compositional similarities among the bathymetric profiles, association plots; {car} for ANOVA. Bathymetric GIS data processing was dome in QGIS and LaTeX. The innovativeness of the work consists in the multi-disciplinary approach combining GIS analysis and statistical methods of R which contributes towards studies of ocean trenches, aimed at geospatial analysis of big data.