Miracle-dz/UniFAR-FTUs huggingface dataset:
Application of Shape Moments for Cloudiness Assessment in Marine Environmental Research

The search for clouds in satellite images is a challenging subject which still attracts a lot of attention due to the amount and quality of data, which is growing at a tremendous pace, the development of satellite techniques and methods, inexpensive equipment, and automation of satellite imaging processes. This paper presents a new approach to the assessment of cloudiness based on the use of the theory of moments with invariants. The values of moments with invariants, determined on the basis of the available cloudiness maps, create a new, valuable set of data, which are the geometrical parameters of the scene representing the cloud cover. In further research, the obtained data sets will be used in machine learning methods, deep machine learning methods, etc. The method is used for different conditions, including different angular positions of the Sun and time periods. The effectiveness of the method is checked on the basis of comparing the entropy results of the input maps after subtracting clouds masked by various methods. The obtained results additionally indicate the potential of the moments method as a support for the existing methods of estimating cloudiness over the sea surface.
Effects of dexmedetomidine or tramadol continuous rate infusions on the propofol requirements and cardiorespiratory variables in propofol-ketamine-midazolam anaesthetised cats

The aim of this study was to compare the effects of midazolam-ketamine alone or in combination with dexmedetomidine or tramadol in a constant rate infusion (CRI) on the minimum infusion rate (MIR) of propofol and the cardiorespiratory function in cats undergoing an ovariohysterectomy (OH). This was a prospective, randomised, blinded clinical study. Twenty-four healthy female mixed-breed cats were premedicated with ketamine and midazolam. Propofol was used for the induction and maintenance of the anaesthesia (starting at 18 mg/kg/h). Cats were assigned to groups (n = 8) to receive one of the following intravenous treatments: midazolam-ketamine group (MKG; ketamine 0.6 mg/kg/h); midazolam-ketamine-dexmedetomidine group [MKDG; ketamine 0.6 mg/kg/h and dexmedetomidine loading dose (LD) 1 mg/kg, CRI 1 mg/kg/h)] or midazolam-ketamine-tramadol group [MKTG; ketamine 0.6 mg/kg/h and tramadol (LD 2 mg/kg, CRI 1 mg/kg/h)]. During the OH, the propofol infusion was adjusted based on the clinical signs to maintain adequate anaesthetic depth. Selected variables were measured before (T0) and after (T1) the anaesthesia induction and during six surgical time points (T2–T7). The mean arterial pressure was higher and the heart rate was lower in MKDG at T1 (than in MKG and MKTG). The mean ± SD MIR of propofol were 17.4 ± 3.2, 15.0 ± 2.8 and 12.6 ± 3.5 mg/kg/h for MKG, MKTG, and MKDG, respectively. We conclude that, compared to midazolam-ketamine alone, midazolam-ketamine-tramadol and midazolam-ketamine-dexmedetomidine reduced the MRI of propofol by 13.8% and 27.5%, respectively, without significant changes in the selected indicators.
Being Mobile: Future Wireless Technologies and Applications

Do you need to get up to speed quickly on the technologies and services that could transform the wireless world over the coming decade? Whether you work directly with wireless or in a sector where wireless solutions could be beneficial (e.g. healthcare, transport, sensor networks, location and smart metering), this concise guide provides a critical insight into future developments. For the first time, you will have a clear view of all the key technologies, including mesh networks, white space/cognitive devices, 4G/LTE and femtocells, and all the sectors or applications in which they could be used, with a comparison of the positives and negatives of each technology and sector area. You'll also see where the technologies required overlap and so could bring benefits across multiple areas, as well as how the key drivers of change in the past may impact on the future.
Effect of Exercise and Obesity on Human Physiology

Physical activity and exercise on a regular basis can help you stay healthy, energetic, and independent as you age. Exercise is essential in preventing health problems such as heart disease and stroke. Many studies have demonstrated the health benefits of regular exercise. This report examines the evidence regarding the health benefits of exercise across the board. Physical activity and exercise can help to lower stress and anxiety, enhance happy neurotransmitters, promote self-confidence, boost brain function, improve memory, and strengthen our muscles and bones. It also aids in the prevention and treatment of heart disease, obesity, blood sugar swings, cardiovascular disease, and cancer. It also aids in the prevention and treatment of heart disease, obesity, blood sugar swings, cardiovascular disease, and cancer. Regular physical activity has been shown the useful in the primary and secondary prevention of a variety of chronic diseases (e.g., cardiovascular disease, diabetes, cancer, hypertension, obesity, depression, and osteoporosis) as well as premature death.
Spectrum of Ectopic Pregnancies at a Tertiary Care Center of Northern India: A Retrospective Cross-sectional Study

Objectives: Diagnosis of ectopic pregnancy (EP) needs high index of clinical suspicion. If EP is timely diagnosed and timely intervened, i.e., before rupture, it may cause reduction of serious morbidity and mortality. We aimed to analyze the profile of patients of ectopic pregnancies and their outcome. Materials and Methods: The sample for this retrospective cross-sectional study was derived from the database from January 2017 to December 2020. Data from outdoor patient registers, case record files, discharge summaries and hospital admission/discharge registers were screened. Parameters age, parity, risk factors, clinical presentation, per-operative findings, and maternal outcome in terms of morbidity and mortality were assessed. Results: Totally 27,525 deliveries occurred during the study period of 3 years, of which 640 were ectopic pregnancies, i.e., 2.3%. Out of 640, 415 (64.8%) were acute ruptured ectopic pregnancies, 62 (9.6%) were chronic ruptured pregnancies, and 163 (25.4%) patients were unruptured ectopic pregnancies. The mean age was 28.67 years (range: 29.5–27.8). The most common site of rupture was ampullary (54%, 225/415). 14.8% (95/640) of cases were in hemorrhagic shock out of total ectopic patients, and in ruptured group, they comprised 22.8% (95/415). Success for medical management with single-dose methotrexate in our study was 90.2% (147/163). Conclusion: Pelvic inflammatory disease and history of induced abortion were found to be the most important etiological factor in ectopic pregnancies. Comprehensive clinical examination is 100% sensitive in diagnosis of EP. In ultrasound, the presence of adnexal mass is the most common finding which is additive to clinical findings and not substitute. Although multiple management options are available, best outcome is attained if management of EP is done at earliest without any delay.
Bank Direct Marketing Based on Neural Network and C5.0 Models

All bank marketing campaigns are dependent on customers' huge electronic data. The size of these data source is impossible for a human analyst to come up with interesting information that will help in the decision-making process. Data mining models are completely helping in performance of these campaigns. This paper introduces applications of recent and important models of data mining; Multilayer perceptron neural network (MLPNN) and Ross Quinlan new decision tree model (C5.0). The objective is to examine the performance of MLPNN and C5.0 models on a real-world data of bank deposit subscription. The purpose is increasing the campaign effectiveness by identifying the main characteristics that affect a success (the deposit subscribed by the client) based on MLPNN and C5.0. The experimental results demonstrate, with higher accuracies, the success of these models in predicting the best campaign contact with the clients for subscribing deposit. The performances are measured by three statistical measures; classification accuracy, sensitivity, and specificity.
Joint structural annotation of small molecules using liquid chromatography retention order and tandem mass spectrometry data

Structural annotation of small molecules in biological samples remains a key bottleneck in untargeted metabolomics, despite rapid progress in predictive methods and tools during the past decade. Liquid chromatography–tandem mass spectrometry, one of the most widely used analysis platforms, can detect thousands of molecules in a sample, the vast majority of which remain unidentified even with best-of-class methods. Here we present LC-MS 2 Struct, a machine learning framework for structural annotation of small-molecule data arising from liquid chromatography–tandem mass spectrometry (LC-MS 2 ) measurements. LC-MS 2 Struct jointly predicts the annotations for a set of mass spectrometry features in a sample, using a novel structured prediction model trained to optimally combine the output of state-of-the-art MS 2 scorers and observed retention orders. We evaluate our method on a dataset covering all publicly available reversed-phase LC-MS 2 data in the MassBank reference database, including 4,327 molecules measured using 18 different LC conditions from 16 contributors, greatly expanding the chemical analytical space covered in previous multi-MS 2 scorer evaluations. LC-MS 2 Struct obtains significantly higher annotation accuracy than earlier methods and improves the annotation accuracy of state-of-the-art MS 2 scorers by up to 106%. The use of stereochemistry-aware molecular fingerprints improves prediction performance, which highlights limitations in existing approaches and has strong implications for future computational LC-MS 2 developments. Liquid chromatography–tandem mass spectrometry (LC-MS 2 ) provides high-throughput screening of molecules with a large number of features. But these features are difficult to associate with specific molecular structures of each sample. To improve structure prediction from these features, Bach et al. propose a machine learning model trained to also take into account stereochemistry to combine the different kinds of features provided by LC-MS 2 .
Knowledge, attitude, and practices regarding human papillomavirus and its’ vaccination among the young medical professionals and students of Bangladesh

Purpose Sexually transmitted infections are a major worldwide concern, and human papillomavirus (HPV) is one of the significant risk factors. Many populations suffer from various diseases caused by HPV, and the overall death toll due to cervical carcinoma is remarkable. Despite vaccine availability, perception about vaccine safety and efficacy, its’ preventive outcome is still inferior among the health professionals and vaccine providers. So, this study aims to assess the knowledge, attitude, and practice level of HPV and its’ vaccination among doctors, dentists, and medical students. Materials and Methods This cross-sectional survey was carried out between April to August 2021, where 626 participants from all types of medical institutions of Bangladesh were interviewed using a validated and structured questionnaire that consists of four extensive areas; socio-demographic characteristics, HPV knowledge, attitude, and practices regarding vaccination. Results The knowledge and practice standards showed very poor outcomes where 43.29% of the participants showed good knowledge and only 11.82% conveyed good practices. Nevertheless, the attitude towards HPV vaccination was revealed high (75.88%). Female participants showed more positive attitudes than males. Conclusion Physicians and dentists play vital roles in raising public knowledge about HPV and awareness regarding HPV vaccination programs. The provision of medical education on HPV must be prioritized, and current training techniques must be re-evaluated. Thus, by implementing this strategy, improvement in national vaccination policy can be expected.
Development of Multiple UAV Collaborative Driving Systems for Improving Field Phenotyping

Unmanned aerial vehicle-based remote sensing technology has recently been widely applied to crop monitoring due to the rapid development of unmanned aerial vehicles, and these technologies have considerable potential in smart agriculture applications. Field phenotyping using remote sensing is mostly performed using unmanned aerial vehicles equipped with RGB cameras or multispectral cameras. For accurate field phenotyping for precision agriculture, images taken from multiple perspectives need to be simultaneously collected, and phenotypic measurement errors may occur due to the movement of the drone and plants during flight. In this study, to minimize measurement error and improve the digital surface model, we proposed a collaborative driving system that allows multiple UAVs to simultaneously acquire images from different viewpoints. An integrated navigation system based on MAVSDK is configured for the attitude control and position control of unmanned aerial vehicles. Based on the leader–follower-based swarm driving algorithm and a long-range wireless network system, the follower drone cooperates with the leader drone to maintain a constant speed, direction, and image overlap ratio, and to maintain a rank to improve their phenotyping. A collision avoidance algorithm was developed because different UAVs can collide due to external disturbance (wind) when driving in groups while maintaining a rank. To verify and optimize the flight algorithm developed in this study in a virtual environment, a GAZEBO-based simulation environment was established. Based on the algorithm that has been verified and optimized in the previous simulation environment, some unmanned aerial vehicles were flown in the same flight path in a real field, and the simulation and the real field were compared. As a result of the comparative experiment, the simulated flight accuracy (RMSE) was 0.36 m and the actual field flight accuracy was 0.46 m, showing flight accuracy like that of a commercial program.
Miniaturized band‐notched UWB MIMO antenna with high isolation

A compact multiple‐input multiple‐output (MIMO) antenna is presented for band‐notched ultra‐wideband (UWB) applications. Miniaturization of the proposed antenna is realized by employing the asymmetric coplanar strip (ACS)‐fed structures. To obtain the band‐notched function, open stubs are introduced on the radiators. In addition, an H‐shaped slot is etched on the ground to reduce the mutual coupling between the antenna elements. The designed antenna with an overall size of 28 × 22 × 0.8 mm3 was fabricated and tested. Measured results show the antenna operates over a frequency band of 2.9–11.8 GHz with reflection coefficient less than −10 dB, except at the rejection band of 4.9–6 GHz. Within the operating band, measured port isolation is better than 20 dB. Radiation characteristics and diversity performance of the antenna are also investigated through measurement. © 2016 Wiley Periodicals, Inc. Microwave Opt Technol Lett 58:878–881, 2016
Objective Perfusion Assessment in Gracilis Muscle Interposition—A Novel Software-Based Approach to Indocyanine Green Derived Near-Infrared Fluorescence in Reconstructive Surgery

Background: Gracilis muscle interposition (GMI) is an established treatment option for complex perineal fistulas and reconstruction. The outcome is limited by complications such as necrosis, impaired wound healing and fistula persistence or recurrence. Quantifiable methods of assessing muscle flap perfusion intraoperatively are lacking. This study evaluates a novel and objective software-based assessment of indocyanine green near-infrared fluorescence (ICG-NIRF) in GMI. Methods: Intraoperative ICG-NIRF visualization data of five patients with inflammatory bowel disease (IBD) undergoing GMI for perineal fistula and reconstruction were analyzed retrospectively. A new software was utilized to generate perfusion curves for the specific regions of interest (ROIs) of each GMI by depicting the fluorescence intensity over time. Additionally, a pixel-to-pixel and perfusion zone analysis were performed. The findings were correlated with the clinical outcome. Results: Four patients underwent GMI without postoperative complications within 3 months. The novel perfusion indicators identified here (shape of the perfusion curve, maximum slope value, distribution and range) indicated adequate perfusion. In one patient, GMI failed. In this case, the perfusion indicators suggested impaired perfusion. Conclusions: We present a novel, software-based approach for ICG-NIRF perfusion assessment, identifying previously unknown objective indicators of muscle flap perfusion. Ready for intraoperative real-time use, this method has considerable potential to optimize GMI surgery in the future.
Implementation of fracture test of concrete to determine fracture properties

This master thesis aims at implementing a three-point-bending test for concrete where the fracture energy can be deduced. The test will also try to record the softening curve of concrete after cracking. The Digital Image Correlation (DIC) technique will also be applied, for the most interesting case, in order to determine the strain field along the test. For this purpose, test procedures have to be studied along with acquiring detailed knowledge on the fracture energy GF. Multiple tests will be performed to assess data and to interpret results, so conclusions could be formed. The main goal of this thesis is carrying out a three-point-bending test for concrete where the fracture energy can be derived. Therefore, a state-of-the-art review concerning fracture mechanics of concrete is first needed. Correct results for the fracture energy and bilinear softening curve of concrete are what are aimed for. Although the AMADE research group has precious experience in fracture mechanics of composite materials, it was the first time this test was performed for concrete at the UdG. Like all research where little in that specific field is known, a lot can go wrong. Failure of tests, incorrect or unusable data, adaptations to test setups, unclear instructions… Solutions to all those complications must be found before reaching correct results. Whenever it is clear why these correct results cannot be obtained, and a probable solution to the problem can be presented, and using/analysing the DIC (Digital Image Correlation) technique is comprehended, the goal of this thesis is reached
Improvement of milk coagulation properties in dairy chain

In many countries it has been found that as a result of cattle breeding there has been an increase in milk production, but the milk coagulation properties (MCP) have decreased, and the number of cows in the population producing non-coagulated milk has increased. The general aim of this thesis was to gain further knowledge about improving of milk coagulation properties in Italian dairy industries. The specific aims were: to propose a method for the transformation of the values of MCP traits analyzed using different methodologies; assess the influence of chemical and technological quality of milk on cheese yield; quantify the contribution of composite β- and κ-CN genotypes on additive genetic variance of MCP traits; develop a method for calculating economic values of milk coagulation properties traits; estimate the annual genetic response of MCP in Italian Holstein Friesian. The main results were that the transformation of MCP traits analyzed with different methodologies is feasible but was more precise for rennet coagulation time (RCT) than for curd firmness (a30). In field condition for Grana Padano cheese production the milk characterized by high values of a30 resulted in higher cheese yield than milk with low values of a30. In animal phenotypic recording, heritability of RCT was still appreciable after adjustment for composite β- and κ-casein genotypes, suggesting that the recording of this trait cannot be replaced by genotyping of animals for milk protein variants. Accounting for the effect of MCP on cheese yield, the weight for MCP in a possible sub-index for milk production and quality traits ranged from 2.1 to 8.2 %. Current selection index for Italian Holstein Friesian seems not affecting significantly MCP traits. Selection criteria with the implementation of Mid-Infrared spectroscopy prediction of MCP in the current recording systems allow to reach higher genetic response for MCP traits compare direct measure of MCP. Moreover including MCP traits in the selection index can increase casein:protein ratio and could be an indirect way for decrease SCS.
Human Pose Estimation through a Novel Multi-view Scheme

This paper presents a multi-view scheme to tackle the challenging problem of the self-occlusion in human pose estimation problem. The proposed approach first obtains the human body joints of a set of images, which are captured from different views at the same time. Then, it enhances the obtained joints by using a multi-view scheme. Basically, the joints from a given view are used to enhance poorly estimated joints from another view, especially intended to tackle the self occlusions cases. A network architecture initially proposed for the monocular case is adapted to be used in the proposed multi-view scheme. Experimental results and comparisons with the state-of-the-art approaches on Human3.6m dataset are presented showing improvements in the accuracy of body joints estimations.
Gel-Based Proteomic Identification of Suprabasin as a Potential New Candidate Biomarker in Endometrial Cancer

Endometrial cancer (EC) is the most frequent gynaecologic cancer in postmenopausal women. We used 2D-DIGE and mass spectrometry to identify candidate biomarkers in endometrial cancer, analysing the serum protein contents of 10 patients versus 10 control subjects. Using gel-based proteomics, we identified 24 candidate biomarkers, considering only spots with a fold change in volume percentage ≥ 1.5 or intensity change ≤ 0.6, which were significantly different between cases and controls (p < 0.05). We used Western blotting analysis both in the serum and tissue of 43 patients for data validation. Among the identified proteins, we selected Suprabasin (SBSN), an oncogene previously associated with poor prognosis in different cancers. SBSN principal isoforms were subjected to Western blotting analysis in serum and surgery-excised tissue: both isoforms were downregulated in the tissue. However, in serum, isoform 1 was upregulated, while isoform 2 was downregulated. Data-mining on the TCGA and GTEx projects, using the GEPIA2.0 interface, indicated a diminished SBSN expression in the Uterine Corpus Endometrial Cancer (UCEC) database compared to normal tissue, confirming proteomic results. These results suggest that SBSN, specifically isoform 2, in tissue or serum, could be a potential novel biomarker in endometrial cancer.
Disaster Communication via the Information Superhighway: Data and Observations on the 1995 Hurricane Season

Although both researchers and practitioners have been using the Internet to communicate information on disasters, few systematic studies have assessed the type of information that is or should be communicated via this medium. This paper presents an exploratory, yet systematic study of the Federal Emergency Management Agency (FEMA)'s coverage of the 1995 hurricane season via e-mail and the World Wide Web. An overview analysis of the 1995 season shows that FEMA distributed 184 e-mail messages of which 138 were hurricane-related and 46 were nonhurricane-related. Following this overview analysis, a case study is presented on FEMA's coverage of Hurricane Opal, which was associated with significant impacts in Florida. The focus of analysis in the case study is a type of e-mail message called the situation report (sitrep) which is used by FEMA to communicate information on disasters. The number, timing, and content of sitreps issued by FEMA for Hurricane Opal are analyzed and the results used to discuss the agency's communication efforts via the Internet.