Miracle-dz/UniFAR-FTUs huggingface dataset:
AN ADAPTIVE SUPERPIXELS FOR VEGETATION DETECTION ON HIGH RESOLUTION IMAGES BASED ON MLP

Abstract. Vegetation detection aims to find the area which should be attributed with the labels of vegetation on the captured images, such as forest, grass land etc., and nowadays it is a key research topic in the field of remote sensing information processing and application. Over the last few years, the deep learning method based on convolutional neural network (CNN) has become the mainstream method for vegetation detection. However, due to the peculiarities of the underlying encoding and decoding structures, it is common for some CNN methods to loss some boundary details of vegetation when employing high-resolution images with rich details and clear boundaries. In order to improve the boundary localization capability of vegetation, this paper proposes a hybrid solution, i.e., an MLP (MultiLayer Perceptron)-based high-resolution image adaptive superpixels vegetation detection method. Compared with the traditional watershed transform algorithm, this method adopts the two-step boundary marching criterion to generate superpixels with more adherent boundary and compact regularity which contains adaptive neighborhood information by design. Based on the generated superpixels with boundary detail information, this paper applies MLP for binary predictions, i.e., vegetation or non-vegetation. The experimental results show that our method has more precise vegetation boundary localization and higher accuracy compared with several state-of-the-art methods on the UAV image data set and ISPRS data set.
Automated modal parameter-based anomaly detection under varying wind excitation

Wind-induced operational variability is one of the major challenges for structural health monitoring of slender engineering structures like aircraft wings or wind turbine blades. Damage sensitive features often show an even bigger sensitivity to operational variability. In this study a composite cantilever was subjected to multiple mass configurations, velocities and angles of attack in a controlled wind tunnel environment. A small-scale impact damage was introduced to the specimen and the structural response measurements were repeated. The proposed damage detection methodology is based on automated operational modal analysis. A novel baseline preparation procedure is described that reduces the amount of user interaction to the provision of a single consistency threshold. The procedure starts with an indeterminate number of operational modal analysis identifications from a large number of datasets and returns a complete baseline matrix of natural frequencies and damping ratios that is suitable for subsequent anomaly detection. Mahalanobis distance-based anomaly detection is then applied to successfully detect the damage under varying severities of operational variability and with various degrees of knowledge about the present operational conditions. The damage detection capabilities of the proposed methodology were found to be excellent under varying velocities and angles of attack. Damage detection was less successful under joint mass and wind variability but could be significantly improved through the provision of the currently encountered operational conditions.
Photogrammetry Basic Principles And General Survey

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Molecular detection of Vibrio cholerae and Vibrio parahaemolyticus from healthy broilers and backyard chickens for the first time in Bangladesh- A preliminary study

Many of the Vibrio spp. are major public health concerns globally. Vibrio cholerae and Vibrio parahaemolyticus are the etiology of pandemic and epidemic diarrhea and foodborne illness, respectively. Poultry has the potential to harbor pathogenic Vibrio spp. , which can have a detrimental impact on public health if they are transmitted to humans. We, therefore, screened 54 cloacal swab samples from healthy chickens (broiler=27, backyard= 27) to detect V. cholerae and V. parahaemolyticus. Vibrio spp. were isolated and identified by culturing, biochemical tests, PCR, and antibiogram profiles were determined by disk diffusion method. By PCR, 29.63% (16/54; 95% CI: 19.14-42.83%) samples were positive for Vibrio spp. , where backyard chickens had a significantly higher (p< 0.05) occurrence (44.44%; 27.59-62.69%) than broilers (14.82%; 95% CI: 5.92-32.48%). V. parahaemolyticus was found in 22.22% (6/27; 95% CI: 10.61-40.76%) of backyard chicken samples, which was significantly dominant (p< 0.05) than in broilers (0/27, 0%, 95% CI: 0.00-12.46%). In addition, V. cholerae was positive in 7.41% (2/27; 95% CI: 1.32-23.37%) of broiler, and 14.82% (4/27; 95% CI: 5.92-32.48%) of backyard chicken samples. The toxR gene was found in all V. cholerae isolates, suggesting the presence of other virulence genes, whereas no isolates of V. parahaemolyticus contained the tdh gene. Isolated Vibrio spp. had high to moderate resistance to tetracycline, azithromycin, erythromycin, and streptomycin. The occurrence of antibiotic-resistant V. cholerae and V. parahaemolyticus in broiler and backyard chickens is of public health concern because of the possibility of food chain contamination
The Effect of Treatment With Clear Aligners Versus Fixed Appliances on Oral Health-Related Quality of Life in Patients With Severe Crowding: A One-Year Follow-Up Randomized Controlled Clinical Trial

Objective To compare the level of oral health-related quality of life (OHRQoL) between patients receiving clear aligners or fixed appliances within one year of follow-up using Oral Health Impact Profile 14 (OHIP-14), a validated self-administered questionnaire. Materials and methods A single-centered, two-arm parallel-group randomized controlled clinical trial was conducted on 36 adult patients (19 females, 17 males; age range: 18 to 25 years) who had severe crowding and require orthodontic treatment with first premolars extraction. The patients were equally and randomly divided into two groups: The clear aligners (CA) group and the fixed appliances (FA) group. OHRQoL was assessed using the OHIP-14 tool at various times during comprehensive orthodontic therapy: baseline (T0), one week (T1), two weeks (T2), one month (T3), 6 months (T4), and 12 months (T5) after starting the active orthodontic treatment. Mann-Whitney U test or Friedman test were used to detect significant differences. The level of significance was set at 5%. Results All of the selected patients entered the statistical analysis stage. There were no significant differences between the CA and FA groups for the psychological discomfort, psychological disability, social disability, and handicap (P˃0.05) at almost all assessment times. For the functional limitation, physical pain, physical disability, and the overall score, there were significant differences between the studied groups (P˂0.05), with the FA group having higher mean scores than the CA group in all of the assessment times. Conclusion Patients' treatment with clear aligners has less impact on OHRQoL than those treated using conventional fixed appliances during the first year of treatment.
Energy-Efficient Computation Offloading in Collaborative Edge Computing

Edge computing is an indispensable technology that overcomes delay limitations of cloud computing. In edge computing, computational resources are deployed at the network edge, and computational tasks and data of end terminals can be efficiently processed by edge nodes. Considering the computational resource limitations of edge nodes, collaborative edge computing integrates computational resources of edge nodes and provides more efficient computing services for end terminals. This article considers a computation offloading problem in collaborative edge computing networks, where computation offloading and resource allocation are optimized by means of a collaborative load shedding approach: a terminal can offload a computing task to an edge node, which either can process the task with its computing resource or further offload the task to other edge nodes. Long-term objectives and long-term constraints are considered, and Lyapunov optimization is applied to convert the original nonconvex computation offloading problem into a second problem that approximate the original problem and it is still nonconvex but has a special structure, which gives rise to a new distributed algorithm that optimally solves the second problem. Finally, the performance and provable bound of the distributed algorithm is theoretically analyzed. Numerical results demonstrate that the distributed algorithm can achieve a guaranteed long-term performance, and also demonstrate the improvement in performance achieved over the case of computation offloading without collaborating edge nodes.
Unilateral Transient Enhanced SEP during Integrated Multiparameter Neurophysiological Monitoring in a Newborn with Symptomatic Seizure

During Integrated Multiparametric Neurophysiological Monitoring (IMNA), a newborn with suspected hypoxia at birth and microhaemorrhagic and ischaemic lesions presented some clonic-tonic episodes with specific EEG patterns characterized by rolandic and temporal spikes and the appearance of a unilateral enhanced Somatosensory Evoked Potential (SEP) (10.45 µv). Since the literature does not seem to describe cases of giant SEP in newborns, in this case report, we will discuss the hypotheses underlying this potential. It could be assumed that the ischaemic and haemorrhagic lesions presented by the newborn may have developed as a result of neurotransmitter balance failure. This may be the origin of the EEG picture, which, consequently, could have triggered a potential with high amplitude.
Informing Future Paralympic Media Approaches: The Perspective of Canadian Paralympic Athletes

Media coverage of the Paralympic Games can affect how athletes with impairment and disability sport are perceived by the public. Researchers investigating media representations of disability sport have focused on how Paralympic athletes and disability sport are represented by the media. Limited research, however, has examined how Paralympic athletes perceive these representations of themselves and the meanings they attribute to such representations. The purpose of this study was to examine how Paralympic athletes make meaning of discourses of disability within Paralympic coverage. This involved semi-structured photo-elicitation interviews with eight Canadian Paralympic athletes. A reflexive thematic analysis (RTA) was used to analyze the data utilizing Foucault’s notions of discourse, power, and technologies of the self. The findings demonstrate that Paralympic athletes made meaning of the discourses of disability within Paralympic media coverage by drawing on their lived and media experiences. Athletes with more media experience articulated problematizations of dominant discourses of disability in Paralympic media coverage and engagement in technologies of the self. Knowledge generated from this study offers media personnel an informed understanding of how Paralympic athletes understand representations of disability and disability sport. This knowledge may offer insight and inform future media approaches of disability sport and the Paralympic Games.
Coral responses to climate change exposure

A brief historical narrative of coral responses to climate change exposures is followed by a review of evidence. I trace the history of investigations and summarize the findings from 112 multiple-site field studies that examined environmental exposure variables and coral bleaching and mortality response relationships. A total of 59 environmental variables in six topic areas were studied of which excess thermal exposure was the most common topic and variable. Investigations were broadly classified into two categories; those focused on either excess thermal stress thresholds (TM) or on continuous variables (VM). The TM investigations considered a total of 28 variables, but only 1.7 ± 1.3 (SD) variables per publication, and only 11% completed a variable selection process that competed variables for fit or parsimony. The 65 VM publications considered 59 variables, more variables per publication (4.1 ± 4.3), and 43% of the studies followed a variable selection procedure. TM investigations received more citation and were most frequently used to identify future climate change impacts and sanctuaries. VM investigations often report excess heat threshold variables as weak single predictors of coral bleaching and mortality. Coral responses to exposure favors mechanisms of causation that are additive and interactive; specifically, the interactions between chronic and acute stresses within the geographic and habitat contexts of local environmental and coral genetic histories. Some of the potentially most important variables for predicting coral responses to exposure have seldom been studied or modeled. The implication is that the future status and health of coral reefs will be better than predicted by TMs. Moreover, impacts and sanctuaries are expected to be patchy and influenced by space, time, genetics, and taxa heterogeneity that will reflect a mix of avoidance, resistance, and recovery processes and their associated sanctuary locations.
Next Generation Ingredients Based on Winemaking By-Products and an Approaching to Antiviral Properties

Management of waste and use of winemaking by-products plays an important role in the development of new ingredients, especially with antiviral properties. Although the richness of bioactive compounds from wine waste is known, less is known about potential antiviral action. Bioactive compounds and health-enhancing effects of winery by-products make them potential candidates for use in antiviral ingredients. The design of new formulations by using nano-microencapsulation techniques will be necessary to successfully control diseases produced by viruses. Outcomes about the use of winery by-products, bioactive compounds found in winery wastes, green extraction techniques to concentrate these compounds, and development of formulations to obtain new ingredients were extracted from research around the world to be discussed and updated in this manuscript. The evidence collected in this review aims to encourage transfer of in vitro and in vivo knowledge to a new step for the development of antiviral and treatments.
Engineering Inspiration: Enhancing Scientific Creativity through Image Flows

This chapter proposes a computerized tool to promote inspiration in a specific, but very important, kind of scientific creativity, for significant scientific breakthroughs are often enabled by conceptual revolutions. The creative process is often divided into four phases: preparation, incubation, inspiration, and verification/elaboration. The proposed tool enhances the incubation phase of scientific creativity, with the goal of inspiring fruitful reconceptualization of a problem. It accomplishes this by exposing the scientist-user to continuous sequences of images designed to engage innate, unconscious cognitive structures. The sequence is not fixed, but may vary either randomly or under user direction. When this image flow seems relevant to the problem, users can record their position in it and their own ideas with a variety of low-interference recording techniques. Several simple image flows are described, along with the computational engine for generating them.
Hardware-assisted Machine Learning in Resource-constrained IoT Environments for Security: Review and Future Prospective

As the Internet of Things (IoT) technology advances, billions of multidisciplinary smart devices act in concert, rarely requiring human intervention, posing significant challenges in supporting trusted computing and user privacy, as well as protecting against attacks such as spoofing, denial of service (DoS), jamming, and eavesdropping. To tackle attacks on the IoT and cyber-physical ecosystem, many intrusion detection and security approaches have been presented in the literature. Machine learning (ML) based intrusion and anomaly detection has lately gained traction due to its capacity to cope with encrypted and rapidly developing threat techniques. This work investigates into machine learning (ML) and deep learning (DL) methodologies for IoT device security and examine the benefits, drawbacks, and potential. To protect an IoT infrastructure, various solutions look into hardware-based methods for ML-based IoT authentication, access control, secure offloading, and malware detection schemes. This review aims to illuminate the value of various approaches for addressing IoT security in a truly effective, flexible, and seamless manner, as well as to provide answers to questions about tradeoffs in integrating accelerators and customizing embedded device architectures for effective use of ML-based methods.
Identification DNA Methylation Change of ABCC8 Gene in Type 2 Diabetes Mellitus as Predictive Biomarkers

Type 2 diabetes mellitus is the most common chronic endocrine disorder that affecting 5%–10% of adults globally. Recently, the disease has rapidly spread throughout the Kurdistan Region. This study investigates DNA methylation status in the ABCC8 gene among the study population, and it possibly used as a biomarker. One hundred and thirteen individuals were included in this study, and they were divided into three categories (47 diabetes, 36 prediabetic, and 30 controls). Blood samples were collected to investigate DNA methylation status in patients who attended private clinical sectors in Koya city, Kurdistan Region of Iraq, between August and December 2021. Methylation-specific PCR (MSP) uses paired primers for each methylated and unmethylated region. In addition, the X2 Kruskal–Wallis statistical and Wilcoxon signed-rank tests were run with a significance level of p 0.05. In comparison to the healthy group, hypermethylation of DNA is detected in the promoter region of diabetes and prediabetes. In addition, age, gender, BMI, alcohol use, family history, and physical activity all influence the degree of DNA methylation in people who have had coronavirus illness. The abovementioned findings suggest that DNA methylation alterations in the ABCC8 promoter region might be exploited as a possible predictive biomarker for type 2 diabetes mellitus diagnosis.
Determination of salt content in traditional and industrial Moroccan white bread by inductively coupled plasma mass spectrometry

Introduction evaluating the sodium content of staple foods is essential for implementing a salt reduction strategy. In Morocco, bread is a major contributor to sodium intake. However, currently few studies have been carried out to assess the salt content in bread. Our study aimed to estimate the sodium and salt content of white bread available in artisanal and industrial bakeries in the twelve regions of Morocco. Methods it is a cross-sectional study of the sodium content of white bread available for sale in artisanal and industrial bakeries in Morocco (N=120). Inductively coupled plasma mass spectrometry (ICP-MS) was used to quantify the sodium content of the bread. The percentage of samples meeting the recommendations and bread contribution to the daily salt intake was calculated. Results the results of our study show that the mean levels of sodium and salt added to bread samples were 5.7 ± 1.5 g/Kg and 14.5 ± 3.7 g/Kg, respectively. With an average of 4.4 ± 0.5 g/Kg and 11.2 ± 1.2 g/Kg for artisanal bread and an average of 7.0 ± 0.8 g/Kg and 17.8 ± 2.1 g/Kg for industrial bread, respectively. Daily salt intake from bread consumption (500 g/d/person) is estimated at 5.6 g/d (52.8% of total salt intake) for artisanal bread and 8.9 g/d (84% of total salt intake) for industrial bread. Conclusion bread salt content in Morocco exceeds the recommended threshold of the national federation of bakery and pastry and health authorities. Further efforts are necessary to increase knowledge and awareness of bakers and to teach them how to reduce salt content without affecting the flavor and the quality of their products.
MALDI Mass Spectrometry Imaging for the Distinction of Adenocarcinomas of the Pancreas and Biliary Tree

Pancreatic ductal adenocarcinoma and cholangiocarcinoma constitute two aggressive tumor types that originate from the epithelial lining of the excretory ducts of the pancreatobiliary tract. Given their close histomorphological resemblance, a correct diagnosis can be challenging and almost impossible without clinical information. In this study, we investigated whether mass spectrometric peptide features could be employed to distinguish pancreatic ductal adenocarcinoma from cholangiocarcinoma. Three tissue microarrays of formalin-fixed and paraffin-embedded material (FFPE) comprising 41 cases of pancreatic ductal adenocarcinoma and 41 cases of cholangiocarcinoma were analyzed by matrix-assisted laser desorption/ionization mass spectrometry imaging (MALDI-MSI). The derived peptide features and respective intensities were used to build different supervised classification algorithms: gradient boosting (GB), support vector machine (SVM), and k-nearest neighbors (KNN). On a pixel-by-pixel level, a classification accuracy of up to 95% could be achieved. The tentative identification of discriminative tryptic peptide signatures revealed proteins that are involved in the epigenetic regulation of the genome and tumor microenvironment. Despite their histomorphological similarities, mass spectrometry imaging represents an efficient and reliable approach for the distinction of PDAC from CC, offering a promising complementary or alternative approach to the existing tools used in diagnostics such as immunohistochemistry.
TILT CORRECTION OF ONBOARD DRONE IRRADIANCE MEASUREMENTS – EVALUATION OF HYPERSPECTRAL METHODS

Abstract. An accurate onboard irradiance measurement on drones is a requirement direct reflectance transformation of aerial images and remote sensing data without use of on-ground reference targets. One of the major error sources in onboard irradiance measurement is the effect of sensor tilt to the observed irradiances. In this paper/presentation, we will present an intercomparison of two methods: the FGI AIRS method and the Köppl method. Both methods require an irradiance sensor with good cosine response and IMU system providing accurate tilt angle for each irradiance measurement. Additionally to this, the AIRS method requires special hardware with multiple tilted irradiance sensors, which effectively allow interpolation of a virtual horizontal sensor. The Köppl method requires the irradiance sensor to be a spectral sensor, which allows the direct and diffuse fractions of irradiance spectrum to be deducted from shape of the measured spectrum using the spectral unmixing between direct and diffuse spectrum shapes. We present a field experiment where the AIRS sensor was flown in a typical mapping pattern flight and evaluate the tilt correction methods during it. During a fully sunny period of the flight, the standard deviations of the irradiances was ±6.4% for the uncorrected tilted irradiances, ±1.8% for the AIRS method, and ±2.0% for the Köppl method. The data shows that an accurate irradiance measurement onboard a drone is feasible using a well-calibrated irradiance sensor system and both methods are capable to produce quite similar results.