Miracle-dz/UniFAR-FTUs huggingface dataset:
CARACTERIZAÇÃO FLORÍSTICA E ESTRUTURAL DE UMA FLORESTA OMBRÓFILA DENSA ALTOMONTANA DO MORRO DO ANHANGAVA, QUATRO BARRAS – PR

Species composition and forest structure were studied at an Upper Montane Rain Forest, in the Southwest face of the Anhangava mountain (25o23'S and 49o00'W), at Quatro Barras, Parana, among 1.250 - 1.350 m of altitude. Species composition and forest structure were determined through a survey of twenty plots (5 x 10 m each) where all trees 10 cm or higher in girth at breast height (GBH) were measured and identified. The forest community showed high density (4.830 n/ha), low height (< 8 m) and it is composed of just one story. Only 24 species of 13 families were found denoting low diversity. Three species, at least, are endemic. From these 24 species only a few dominated the community. Myrtaceae was the more representative family, however Ilex microdonta Reissek an Aquifoliaceae presented the higher density (26%) and dominance index (46%).
Secure and Eﬀicient k-NN Queries

Given the morass of available data, ranking and best match queries are often used to ﬁnd records of interest. As such, k-NN queries, which give the k closest matches to a query point, are of particular interest, and have many applications. We study this problem in the context of the ﬁnancial sector, wherein an investment portfolio database is queried for matching portfolios. Given the sensitivity of the information involved, our key contribution is to develop a secure k-NN computation protocol that can enable the computation k-NN queries in a distributed multi-party environment while taking domain semantics into account. The experimental results show that the proposed protocols are extremely efﬁcient.
Capacity for increasing soil organic carbon stocks in dryland agricultural systems

Assessment of the potential for soil carbon sequestration based on soil type, land use, and climate scenarios is crucial for determining which agricultural regions can be used to help mitigate increasing atmospheric CO2 concentrations. In semi-arid and Mediterranean-type environments, soil organic carbon (SOC) storage capacity is rarely achieved under dryland agricultural systems. We aimed to assess both actual (measured) and attainable (modelled) SOC stock values for the dryland agricultural production zone of Western Australia. We measured actual SOC storage (0–0.3 m) and known constraints to plant growth for a range of soils types (3–27% clay) and land uses (continuous cropping, mixed cropping, annual and perennial pastures) on the Albany sand plain in Western Australia (n = 261 sites), spanning a rainfall gradient of 421–747 mm. Average actual SOC stocks for land use–soil type combinations ranged from 33 to 128 t C/ha (0–0.3 m). Up to 89% of the variability in actual SOC stock was explained by soil depth, rainfall, land use, and soil type. The scenarios modelled with Roth-C predicted that attainable SOC values of 59–140 t C/ha (0–0.3 m) could be achieved within 100 years. This indicated an additional storage capacity of 5–45% (7–27 t C/ha) depending on the specific land use–soil type combination. However, actual SOC in the surface 0–0.1 m was 95 to >100% of modelled attainable SOC values, suggesting this soil depth was ‘saturated’. Our findings highlight that additional SOC storage capacity in this region is limited to the subsoil below 0.1 m. This has implications for management strategies to increase SOC sequestration in dryland agricultural systems, as current practices tend to concentrate organic matter near the soil surface.
Contamination event detection utilizing genetic algorithm

The high cost and lack of technology for designed sensors for any given contaminant makes them unfeasible for the time being. Since a contaminant intrusion type is generally unknown it is difficult to define the water quality parameters needed to be measured and analyzed to indicate its presence. It is plausible to assume that an auxiliary substance injected into the distribution system will affect the behavior of typically measured parameters (e.g. total chlorine, pH). For this reason the surrogate/indicator approach (i.e. identifying contaminants using regularly monitored water quality and hydraulic measurements), is appealing. This study focuses on interpreting data collected from sensors measuring routine parameters for revealing outliers indicating possible contamination event intrusions. The method presented utilizes Genetic Algorithm (GA) to optimize parameters to better identify contamination events. An example application is presented and results show promise.
A long-term Arctic snow depth record from Abisko, northern Sweden, 1913–2004

A newly digitized record of snow depth from the Abisko Scientifi c Research Station in northern Sweden covers the period 1913–present. Mean snow depths were taken from paper records of measurements made on a profi le comprising 10 permanent stakes. This long-term record yields snow depths consistent with two other shorter term Abisko records: measurements made at another 10-stake profi le (1974–present) and at a single stake (1956–present). The measurement interval is variable, ranging from daily to monthly, and there are no data for about half of the winter months in the period 1930–1956. To fi ll the gaps, we use a simple snowpack model driven by concurrent temperature and precipitation measurements at Abisko. Model snow depths are similar to observed; differences between the two records are comparable to those between profi le and single stake measurements. For both model and observed snow depth records, the most statistically signifi cant trend is in winter mean snow depths, amounting to an increase of about 2 cm or 5 % of the mean per decade over the whole measurement period, and 10 % per decade since the 1930–40s, but all seasonal means of snow depth show positive trends on the longest timescales. However, the start, end, and length of the snow season do not show any statistically signifi cant long-term trends. Finally, the relation between the Arctic Oscillation index and Abisko temperature, precipitation and snow depth is positive and highly signifi cant, with the best correlations for winter.
REMM: The Riparian Ecosystem Management Model

Riparian buffer zones are effective in mitigating nonpoint source pollution and have been recommended as a best management practice (BMP). The Riparian Ecosystem Management Model (REMM) has been developed for researchers and natural resource agencies as a modeling tool that can help quantify the water quality benefits of riparian buffers under varying site conditions. Processes simulated in REMM include surface and subsurface hydrology; sediment transport and deposition; carbon, nitrogen, and phosphorus transport, removal, and cycling; and vegetation growth. Management options, such as vegetation type, size of the buffer zone, and biomass harvesting also can be simulated. REMM can be used in conjunction with upland models, empirical data, or estimated loadings to examine scenarios of buffer zone design for a hillslope. Evaluation of REMM simulations with field observations shows generally good agreement between simulated and observed data for groundwater nitrate concentrations and water table depths in a mature riparian forest buffer. Sensitivity analysis showed that changes that influenced the water balance or soil moisture storage affected the streamflow output. Parameter changes that influence either hydrology or rates of nutrient cycling affected total N transport and plant N uptake.
Consumer Nutritional Awareness, Sustainability Knowledge, and Purchase Intention of Environmentally Friendly Cookies in Croatia, France, and North Macedonia

The increasing demand for greater utilization of byproducts in the food industry has been driven by growing interest in environmental sustainability. This paper examines the market potential and consumer attitudes toward whole-grain and sustainable cookies made with food byproducts and edible films. Additionally, particular attention was given to evaluating levels of sustainability knowledge and nutritional awareness, willingness to purchase environmentally friendly cookies with food byproducts, and to appraise differences in answers between countries and generations. An online questionnaire was used to collect data from Croatian (n = 472), French (n = 166), and North Macedonian consumers (n = 119) aged between 18 and 62, predominantly women (82%) with higher education degrees. Results showed that even if chocolate-coated cookies remain very popular, North Macedonians prefer whole-grain and plain cookies, while the French prefer chocolate-coated cookies and Croats prefer both types of cookie. The majority of consumers (96%) were interested in purchasing environmentally friendly cookies. However, consumers’ interest in purchasing cookies with food byproducts was generally low, which may be related to their limited knowledge of byproducts. In conclusion, there is market potential for whole-grain cookies with food byproducts, but brand, price, and consumer education may be critical to their success.
A Scalable and Accurate Chessboard-Based AMC Algorithm With Low Computing Demands

Automatic Modulation Classification (AMC) is a technique used to identify signal modulations in applications like IoT devices, cognitive radar, software-defined radio, and electronic warfare. These applications could be applied to IoT devices. With future wide applications of IoT devices, AMC algorithms need to be more compact yet suitable for embedded devices with limited resources and remain acceptable accuracy. Although current AMC algorithms deliver high accuracy, they require substantial computing power, making them unsuitable for IoT devices. This paper introduces the novel Chessboard-based Automatic Modulation Classification (CAMC) algorithm, which has dramatically high accuracy. Test results reveal that CAMC achieves 99%* accuracy under a 3dB SNR condition and 100% above 5dB SNR. Meanwhile, this algorithm is scalable and demands less computing power. It offers better accuracy results compared to state-of-the-art AMC algorithms, classifying mainstream modulations in IoT devices like BPSK, QPSK, 8PSK, and 16QAM, but requires less computing power than existing algorithms. Additionally, CAMC is hardware-friendly due to its inherent parallelism and scalability. The novelty of this paper is to classify 4 different modulations in a low-computation-loading required and hardware-friendly way and achieve a high accuracy of over 99%* above SNR of 3dB. (* Accuracy that most of the time could reach)
Still-image Detection Multi-context Suppression andMotion-guided Propagation Model Combination Tubelet Re-scoring Proposal Combination Non-Maximum Suppression High-confidence Tracking Spatial Max-pooling Temporal Re-scoring Multi-context Suppression Motion-guided Propagation Object Proposal Proposal

The state-of-the-art performance for object detection has been significantly improved over the past two years. Besides the introduction of powerful deep neural networks such as GoogleNet [1] and VGG [2], novel object detection frameworks such as R-CNN [3] and its successors, Fast R-CNN [4] and Faster R-CNN [5], play an essential role in improving the state-of-the-art. Despite their effectiveness on still images, those frameworks are not specifically designed for object detection from videos. Temporal and contextual information of videos are not fully investigated and utilized. In this work, we propose a deep learning framework that incorporates temporal and contextual information from tubelets obtained in videos, which dramatically improves the baseline performance of existing still-image detection frameworks when they are applied to videos. It is called T-CNN, i.e. tubelets with convolutional neueral networks. The proposed framework won the recently introduced object-detection-from-video (VID) task with provided data in the ImageNet Large-Scale Visual Recognition Challenge 2015 (ILSVRC2015). Code is publicly available at https://github.com/myfavouritekk/T-CNN.
Tuning optimal traﬀic measurement parameters in virtual networks with machine learning

—With the increasing popularity of cloud networking and the widespread usage of virtualization as a way to offer ﬂexible and virtual network and computing resources, it becomes more and more complex to monitor this new virtual environment. Yet, monitoring remains crucial for network troubleshooting and analysis. Controlling the measurement footprint in the virtual network is one of the main priorities in the process of monitoring as resources are shared between the compute nodes of tenants and the measurement process itself. In this paper, ﬁrst, we assess the capability of machine learning to predict measurement impact on the ongoing trafﬁc between virtual machines; second, we propose a data-driven solution that is able to provide optimal monitoring parameters for virtual network measurement with minimum trafﬁc interference.
Independence and Unity: Unseen Domain Segmentation Based on Federated Learning

The distinct attributes of Internet of Things (IoT) devices, including the disparity between training and testing data distributions and limited availability of training data, pose challenges for deep learning models in effectively addressing unseen domain segmentation tasks. Federated learning (FL) can increase the participation of various data contributors, thus has great potential to develop a unified framework to shed light on the relationship between unseen domains and generalized domains. In this article, we proposed an FL-based unseen domain segmentation model. The architecture includes 1) an external memory module as an object feature guide to reduce the feature ambiguity of unseen domain objects; 2) a reattention activation mechanism for better completing localization of unseen domain objects, enhancing the features of potential targets and suppressing interference features; and 3) a self-supervised learning paradigm for achieving specific object feature exploration. Based on flexible splitting and combining, our model is able to capture both personalization and generalization capabilities, the client side retains a strong personalization ability, while the server side has a strong generalization ability. Moreover, taking into account the inherent limitations in computing and storage resources commonly associated with IoT devices, the introduced model leverages the concept of optional dependencies to enable efficient inference within resource-constrained client environments. Our proposed model is validated through extensive experiments. The approach proposed in this article outperforms the generalization capabilities of state-of-the-art work on several benchmarks.
Electrochemistry of Flavonoids: A Comprehensive Review

Flavonoids represent a large group of aromatic amino acids that are extensively disseminated in plants. More than six thousand different flavonoids have been isolated and identified. They are important components of the human diet, presenting a broad spectrum of health benefits, including antibacterial, antiviral, antimicrobial, antineoplastic, anti-mutagenic, anti-inflammatory, anti-allergic, immunomodulatory, vasodilatory and cardioprotective properties. They are now considered indispensable compounds in the healthcare, food, pharmaceutical, cosmetic and biotechnology industries. All flavonoids are electroactive, and a relationship between their electron-transfer properties and radical-scavenging activity has been highlighted. This review seeks to provide a comprehensive overview concerning the electron-transfer reactions in flavonoids, from the point of view of their in-vitro antioxidant mode of action. Flavonoid redox behavior is related to the oxidation of the phenolic hydroxy groups present in their structures. The fundamental principles concerning the redox behavior of flavonoids will be described, and the phenol moiety oxidation pathways and the effect of substituents and experimental conditions on flavonoid electrochemical behavior will be discussed. The final sections will focus on the electroanalysis of flavonoids in natural products and their identification in highly complex matrixes, such as fruits, vegetables, beverages, food supplements, pharmaceutical compounds and human body fluids, relevant for food quality control, nutrition, and healthcare research.
Neuroprotective Agents with Therapeutic Potential for COVID-19

COVID-19 patients can exhibit a wide range of clinical manifestations affecting various organs and systems. Neurological symptoms have been reported in COVID-19 patients, both during the acute phase of the illness and in cases of long-term COVID. Moderate symptoms include ageusia, anosmia, altered mental status, and cognitive impairment, and in more severe cases can manifest as ischemic cerebrovascular disease and encephalitis. In this narrative review, we delve into the reported neurological symptoms associated with COVID-19, as well as the underlying mechanisms contributing to them. These mechanisms include direct damage to neurons, inflammation, oxidative stress, and protein misfolding. We further investigate the potential of small molecules from natural products to offer neuroprotection in models of neurodegenerative diseases. Through our analysis, we discovered that flavonoids, alkaloids, terpenoids, and other natural compounds exhibit neuroprotective effects by modulating signaling pathways known to be impacted by COVID-19. Some of these compounds also directly target SARS-CoV-2 viral replication. Therefore, molecules of natural origin show promise as potential agents to prevent or mitigate nervous system damage in COVID-19 patients. Further research and the evaluation of different stages of the disease are warranted to explore their potential benefits.
High resolution DEM extraction from terrestrial LIDAR topometry and surface kinematics of the creeping Alpine permafrost

The most common landforms associated with the creep of Alpine permafrost are the rock glaciers, the morphology of which reflects the complex processes of the internal deformation of the ice and debris mixture. In the present study, a terrestrial LIDAR device was employed to scan, at a sub-decimeter resolution, the surface of the Laurichard active rock glacier (Southern French Alps). Two points-clouds were generated at one-year interval (Sept. 2005 and Sept. 2006) and processed in order to produce two high-resolution digital elevation models (DEMs) and to compare them. Different methods were used in order to extract small-scale topography, quantify interannual surface changes, and determine the kinematic behavior of the creeping mass. For a better understanding of rock glacier dynamics, the results have also been compared to the geodetic measurements annually over a 20-year period, and to the geophysical evidences of rock glacier internal structure.
Absence of Vowels in the English Spelling of Arabic Personal Names on Social Media

Many users of social media from different countries and speaking different languages that use non-Latin orthography such as Arabic choose to transliterate their first name and surname in Romanized script, vis English. Due to the differences between the user’s native language and English in vowels and consonants and how they are pronounced, some people make vowel and consonant errors in the English spelling of their names due to the user’s proficiency level in English and their awareness level of the grapheme-phoneme correspondences in their native as well as English language. This study aims to investigate vowel omissions in the English spelling of Arabic personal names, what kind of vowels are omitted, location of the missing vowels, why educated Arabs omit vowels in the English spelling of their names, whether vowel omissions are attributed to transfer from the native language (Arabic) or lack of competence in English spelling. Analysis of a sample of Arabic personal names with missing vowels in their English spelling showed that in the vast majority of misspelled names, one short vowel is missing. In most cases, the missing short vowel is fatha /a/ in the first syllable of the name. It seems that educated Arabs transfer the Arabic vowel system to English. The Arabic orthographic system has 3 long vowels represented by written letters ا و ي /a:, i:, u:/ and 3 short vowels that are represented by diacritics which are pronounced but not shown in the written form of Arabic words. In addition, omission of short vowels in the English spelling of Arabic names may be attributed to how the name is pronounced in the dialect of the Arabic speaker, not how it is pronounced in Standard Arabic, especially when the vowel is in the initial syllable of some surnames. Vowel deletion may also be due to insufficient proficiency in English and lack of contrastive knowledge of the Arabic and English vowel and spelling systems. Examples of transliterated personal names with missing vowels, explanations, causes, and recommendations for more accurate spelling in English are given.
Thermal Digital Twin of Power Electronics Modules for Online Thermal Parameter Identification

The assessment of the state of health of power semiconductors and the use of thermal observers rely on precise knowledge of the thermal impedance of the device, which is hard to monitor online with state-of-the-art approaches. This work proposes thermal digital twins (DTs), which create a real-time-capable digital replica of the physical thermal behavior and enable monitoring the thermal impedance online. The particle swarm optimization (PSO) algorithm and the dual extended Kalman filter (DEKF) are used to extract the thermal model for online monitoring. This is demonstrated for both approaches via a real-time simulation (RTS) where the reference chip temperature is given by a digital thermal model. A comparison of the approaches is given and the DEKF-based approach is chosen for the implementation of a multichip model with thermal cross-coupling. The convergence of the DEKF-based DTs is experimentally validated in the laboratory.
Temporal Distribution of Midwestern Precipitation during the 20th Century

Temporal fluctuations in the annual and summer precipitation across the Midwest during the 1898-2002 period are defined and described. Precipitation amounts were assessed for 15-year periods to show how patterns of precipitation have shifted spatially over the 20 Century. The early part of the century featured near average precipitation conditions, followed by predominately dry conditions from 1928 through 1957. For example, during 1928-1942, 96 percent of the Midwest had below average precipitation. After 15 years with near average conditions from 1958 to 1972, extremely wet conditions developed during 1973-2002, with 91 percent of the Midwest experiencing above average precipitation. Extreme precipitation values sampled during each 15year period revealed temporal differences with much more extreme amounts during wet and dry periods than during near average periods. Annual totals reflected the long-term variations in summer rainfall, revealing the importance of summer rainfall in determining annual amounts. Regionally, amounts were below average more frequently in the southern Midwest than in the central and northern Midwest. Values were above average more frequently in the northern Midwest than in the central and southern Midwest. The results provide information that should be useful for hydrologic and agricultural planning and assessments.