Miracle-dz/UniFAR-FTUs huggingface dataset:
Modeling the effect of wave‐vegetation interaction on wave setup

Aquatic vegetation in the coastal zone attenuates wave energy and reduces the risk of coastal hazards, e.g., flooding. Besides the attenuation of sea-swell waves, vegetation may also affect infragravity-band (IG) waves and wave setup. To date, knowledge on the effect of vegetation on IG waves and wave setup is lacking, while they are potentially important parameters for coastal risk assessment. In this study, the storm impact model XBeach is extended with formulations for attenuation of sea-swell and IG waves, and wave setup effects in two modes: the sea-swell wave phase-resolving (nonhydrostatic) and the phase-averaged (surfbeat) mode. In surfbeat mode, a wave shape model is implemented to capture the effect of nonlinear wave-vegetation interaction processes on wave setup. Both modeling modes are verified using data from two flume experiments with mimic vegetation and show good skill in computing the sea-swell and IG wave transformation, and wave setup. In surfbeat mode, the wave setup prediction greatly improves when using the wave shape model, while in nonhydrostatic mode (nonlinear) intrawave effects are directly accounted for. Subsequently, the model is used for a range of coastal geomorphological configurations by varying bed slope and vegetation extent. The results indicate that the effect of wave-vegetation interaction on wave setup may be relevant for a range of typical coastal geomorphological configurations (e.g., relatively steep to gentle slope coasts fronted by vegetation).
Isolated renal and urinary tract aspergillosis: a systematic review

Background: Aspergillosis localized to the kidneys and the urinary tract is uncommon. We conducted a comprehensive systematic review to evaluate risk factors and clinical outcomes of patients with isolated renal and genito-urinary tract aspergillosis. Methods: We systematically searched Medline, CINAHL, Embase, African Journal Online, Google Scholar, and the Cochrane Library, covering the period from inception to August 2023 using the key terms ‘renal’ OR ‘kidney*’ OR ‘prostate’ OR ‘urinary bladder’ OR ‘urinary tract*AND ‘aspergillosis’ OR ‘aspergillus’ OR ‘aspergilloma’ OR ‘mycetoma’. We included single case reports or case series. Review articles, guidelines, meta-analyses, animal studies, protocols, and cases of genitourinary and /or renal aspergillosis occurring as a part of disseminated disease were excluded. Results: We identified 91 renal and urinary aspergillosis cases extracted from 76 publications spanning 1925–2023. Among the participants, 79 (86.8%) were male, with a median age of 46 years. Predominantly, presentations consisted of isolated renal infections (74 instances, 81.3%), followed by prostate (5 cases, 5.5%), and bladder (7 cases, 7.7%) involvement. Aspergillus fumigatus (42.9%), Aspergillus flavus (9.9%), and Aspergillus niger/glaucus (1.1% each) were isolated. Underlying risk factors included diabetes mellitus (29.7%), HIV (12.1%), haematological malignancies (11%), and liver cirrhosis (8.8%), while common symptoms encompassed flank pain (36.3%), fever (33%), and lower urinary tract symptoms (20.9%). An autopsy was conducted in 8.8% of cases. Diagnostic work-up involved histopathology (70.5%), renal CT scans and urine microscopy and culture (52.6% each), and abdominal ultrasound (17.9%). Treatments included amphotericin B (34 cases, 37.4%) and azole-based regimens (29 cases, 31.9%). Nephrectomy was performed in 16 of 78 renal cases (20.5%). All-cause mortality was 24.4% (19 cases). No significant mortality rate difference was observed among antifungal regimens (p = 0.739) or nephrectomy status (p = 0.8). Conclusion: Renal and urinary aspergillosis is an important cause of morbidity and mortality, particularly in immunocompromised and people with diabetes mellitus. While varied treatment strategies were observed, mortality rates showed no significant differences based on treatments or nephrectomy status. Further research is needed to refine diagnostics, optimize treatments, and enhance awareness among clinicians for early detection and management. PROSPERO registration number: CRD42023430959.
A Habitat Analysis of Estuarine Fishes and Invertebrates, with Observations on the Effects of Habitat-Factor Resolution

vi Chapter 1 Literature Review and a Brief History of Multivariate Community Analysis . 1 Applications to Estuarine Community Structure . 11 References . 18 Chapter 2 The Influence of Habitat Metrics on Community and Abundance . 26 Introduction . 26 Materials and Methods . 29 Study area . 29 Data collection . 32 Types of data analysis . 34 Community analysis . 34 Rationale for using distance-to-GOM as a covariate. 35 Rationale for separating spring-fed and surface-fed estuaries . 36 Species abundance analysis . 37 Reduction in spatial resolution . 42 Results . 43 dbRDA . 43 Partial dbRDA . 47 Indicator values . 52 Partial multiple regression . 55
Encapsulated Laser-Induced Graphene Flexible Piezo-resistive Sensors for Speech Recognition

Traditional sound detection systems comprised of capacitive microphones and PZT transducers are costly for impermanent healthcare monitoring. In recent times, an increasing amount of scholarly interest focuses on the utilisation of laser-induced graphene (LIG) materials due to their capacity to freely customise sensor patterns from carbon substrates. In order to reduce the cost of the device and facilitate its integration with wearable electronics, we present a promising LIG sensor design with nontoxic medical-grade adhesive encapsulation for wearable sound recognition. After encapsulation, the LIG sensor achieves a sheet resistance of approximately 300 Ω/sq. The LIG sensor with encapsulation has a fractional change in resistance within 20% after 1000 bending cycles. The LIG-based throat sound detection design shows notable advantages including one-step fabrication, high sensitivity, good flexibility, and low cost, revealing a great deal of promise for assisting the disabled and integrating with the future wearable healthcare sensing platform.
Climate Change Drives Outbreaks of Emerging Infectious Disease and Phenological Shifts

ix CHAPTER ONE: INTRODUCTION . 1 Main Text . 1 Figures . 6 References . 11 CHAPTER TWO: SPATIAL SCALE MODULATES THE STRENGTH OF ECOLOGICAL PROCESSES DRIVING DISEASE DISTRIBUTIONS . 16 Abstract . 17 Introduction . 18 Methods . 22 Predictor data . 22 Creation of rasters at multiple scales . 23 Parasite data . 24 GLS Models . 25 Multi-model inference . 25 Randomization tests . 26 Univariate models . 26 Spatial correlograms . 27 Results and Discussion . 27 Acknowledgements . 31 Figures . 32
COMPARISON OF CARBON DIOXIDE EFFLUX IN DIFFERENT LAND USE TYPOLOGIES

Management systems are crucial for soils functioning as a drain or source of greenhouse gas emissions. In this sense, among the alternatives for reducing the precursors of the greenhouse effect are integrated agricultural production systems, particularly Agroforestry Systems. These integrated systems maximize the use of biological cycles, soil, and animal waste, reducing agrochemicals and improving the condition of the rural populations involved. The present study aimed to evaluate the CO2 efflux in different land use types. The study was conducted in four agroecosystems: Forest (FO), Agroforestry Systems (AFS), Mandala (MD), and Degraded area (DA). To quantify the CO2 emanating from the soil, 10 mL of 0.5 N KOH aqueous solution is used and titrated using 0.1 N HCl, phenolphthalein, and 1% methyl orange as an indicator. The CO2 concentration from 5:00 am to 5:00 pm was lower. The lowest CO2 production occurred in FO (82.7 CO2 mg m-2 h-1), statistically differing from the other areas that obtained a CO2 rate higher than 140 mg m-2 h-1. As for kinetics, it was observed in all areas that the greatest release of CO2 during the day occurred at 7:00 am, and this release decreased as the soil temperature increased. The greatest release of CO2 and the highest temperatures occurred in DA, showing that these systems with anthropic modifications can be considered CO2 emitters however, systems closer to the natural ecosystem, such as the AFS, will store carbon in the soil, functioning as a carbon store. Therefore, to reduce CO2 emissions from agricultural activities, it is necessary to use systems that imitate natural ecosystems.
A New Intelligent Approach for Automatic Stress Level Assessment Based on Multiple Physiological Parameters Monitoring

Stress is a natural feeling of not being able to cope with specific demands and events, and it may even worsen a person’s health, especially in chronic disease patients. Stress questionnaires are inefficient and time-consuming. Several models for stress estimation are based on facial analysis, voice recognition, thermography, electrocardiography (ECG), and photoplethysmography (PPG), but they are not practical for patients. More robust systems with multiple parameters use devices that are incompatible in the same ecosystem. Machine learning techniques can also be used, but most studies only detect stress, few classify it, and none quantify it. The latest developments in health state monitoring present PPG as the leading solution. Since it is noninvasive and can be integrated into wearable devices, it is more user-friendly and could be used in smart environments. Since it is noninvasive and can be integrated into wearable devices, it is more user-friendly and could be used in smart environments. The proposed work introduces novelty regarding PPG signal processing algorithms to extract multiple physiological parameters simultaneously. In terms of innovations, a multichannel detection system with a distributed computing platform is considered, which, besides containing the algorithms, also includes the introduction of new physiological parameters and the proposal of a model for estimating stress levels based on fuzzy logic, classifying stress into five levels. To validate the results, experimental protocols were created to induce thermal stress in volunteers, which yielded excellent system efficiency and accuracy indicators. The health status monitoring results and estimations are presented using a mobile application that was also developed.
Machine Learning to Examine The Foraging Periods of Bees

This research project on Machine Learning to Examine the Foraging Periods of Bees aims to: 1 ) Study the number of honey bee foragers and data preprocessing for analysis, 2 ) Analyze the relationship between the number of honey bee foragers during different time intervals. The research process begins by defining periods from 5:00 a.m. to 4:00 p.m. for counting the number of honey bee foragers, with intervals of 30 minutes. Data preprocessing techniques are applied, and a suitable data schema is designed for data science purposes. In the subsequent steps, the Polynomial Regression algorithm is employed as part of the Machine Learning process to create a model that fits well with data exhibiting polynomial relationships. The relationship between the independent and dependent variables is considered in the form of a polynomial equation with a maximum degree of positivity. This research sets the polynomial degree to 7, yielding results with significant correlation values. The model is capable of efficiently examining the data’s variability daily. This is evident from the R-squared and adjusted R-squared values, which approach 1. The insights derived from the analysis are valuable and can be integrated into the next research phase concerning bees.
Spatial and seasonal variability in fine mineral dust and coarse aerosol mass at remote sites across the United States

Understanding the spatial and temporal variability in fine mineral dust (FD, mineral aerosols with diameters less than 2.5 µm) and coarse aerosol mass (CM, mass of aerosols with diameters between 2.5 and 10 µm) is important for accurately characterizing and perhaps mitigating their environmental and climate impacts. The spatial and seasonal variability of ambient FD and CM was characterized at rural and remote sites across the United States for 2011–2014 using concentration and elemental chemistry data from the Interagency Monitoring of Protected Visual Environments (IMPROVE) aerosol monitoring network. FD concentrations were highest (and had ≥50% contributions to PM2.5 mass) in the southwestern United States in spring and across the central and southeastern United States in summer (20–30% of PM2.5 mass). CM was highest across the Southwest and southern Great Plains in spring and central United States in spring, summer, and fall (≥70% contributions to PM10 mass). Similar FD and CM seasonal variability was observed near source regions in the Southwest, but a seasonal decoupling was observed in most other regions, suggesting the contribution of nonlocal sources of FD or perhaps non‐dust‐related CM. The seasonal and spatial variability in FD elemental ratios (calcium, iron, and aluminum) was fairly uniform across the West; however, in the eastern United States a shift in summer elemental composition indicated contributions from nonlocal source regions (e.g., North Africa). Finally, long‐term trend analyses (2000–2014) indicated increased FD concentrations during spring at sites across the Southwest and during summer and fall in the southeastern and central United States.
INJECTION OF AUTOLOGOUS WHOLE BLOOD VERSUS CORTICOSTEROID FOR LATERAL EPICONDYLITIS: A COMPARATIVE STUDY

Objective: The objective is to compare the efficacy, in terms of pain relief and functional improvement, of autologous whole blood and corticosteroid injection in managing lateral epicondylitis (Tennis elbow). Methods: This was a comparative study conducted in Department of Orthopedics, National Institute of Medical Sciences Jaipur, Rajasthan, India. Eighty patients diagnosed to be having lateral epicondylitis were included in this study on the basis of predefined inclusion and exclusion criteria. The patients were divided into 2 groups on the basis of whether they were given autologous whole blood (Group B) or corticosteroid injection (Group S). Efficacy of both the treatment modality was compared in terms of Pain relief (as assessed by reduction in visual analogue score [VAS] score) and functional improvement (as assessed by improvement in Mayo Elbow performance score). SSPS 22.0 software was used for statistical analysis and p<0.05 was taken as statistically significant. Results: A total of 80 patients were included in this study out of which there were 37 (46.25%) males and 43 (53.75%) females with a M: F ratio of 1:1.16. The mean age of patients in group B and group S was found to be found to be 52.36±9.98 years and 48.46±10.12 years, respectively. The mean age of patients in both the groups was found to be comparable with no statistically significant difference (p=0.0866). At the time of 4 weeks, 8 weeks, and 12 weeks follow–up, the mean VAS score in Group B was less as compared to group S and the difference was found to be statistically highly significant (p<0.0001). At the time of 4 weeks, 8 weeks, and 12 weeks’ follow-up, the mean MEPS in Group B was more as compared to group S, and the difference was found to be statistically significant (p<0.05). Conclusion: For lateral epicondylitis, whole blood injection demonstrated superior efficacy in terms of pain relief and functional improvement as compared to injection of corticosteroid.
Cryptographic methods and development stages used throughout history

Throughout history the need for secrecy has been important. This need for secrecy brought about the invention and the art of concealment, coding and code making [1]. As early as 1900 B.C., Egyptian scribes used hieroglyphs in a non-standard fashion, presumably to hide the meaning from those who did not know the meaning [2]. Cryptography is the effect of designing privacy requirements systems in communication [3]. From the past to the present, many classical encryption methods such as Caesar, Vigenere, and Enigma have been replaced by more powerful modern new encryption methods such as DES, RSA, PGP, AES, and SHA-3.This study aims to present chronologically together about major cryptography methods and developmental stages in the history of cryptology.Throughout history the need for secrecy has been important. This need for secrecy brought about the invention and the art of concealment, coding and code making [1]. As early as 1900 B.C., Egyptian scribes used hieroglyphs in a non-standard fashion, presumably to hide the meaning from those who did not know the meaning [2]. Cryptography is the effect of designing privacy requirements systems in communication [3]. From the past to the present, many classical encryption methods such as Caesar, Vigenere, and Enigma have been replaced by more powerful modern new encryption methods such as DES, RSA, PGP, AES, and SHA-3.This study aims to present chronologically together about major cryptography methods and developmental stages in the history of cryptology.
Active Heat Flow Sensing for Robust Material Identification

Thermal properties are significant for recognizing an object’s material but cannot be determined via visual and stiffness (or tactile)–based recognition techniques. Most studies have used temperature as a complementary part of multimodal sensing; however, the thermal signal is an unexplored capability that can be beneficial for recognizing target objects. Since changes in thermal responses can result from both material properties and initial temperature, realizing robust and high-accuracy recognition in different environments is a challenging issue. To tackle the issue, this paper proposes a novel strategy for material identification that can actively measure heat flow by heating and cooling a robot gripper, enabling the extraction of the thermal properties of contact materials regardless of the object’s initial temperature variation (referred to as “active heat flow sensing”). We use a robotic task as an example of one possible application of the proposed strategy. For this, we developed a gripper pad embedded in a temperature control system and heat flow sensor to monitor the thermal exchange during contact with a target object. The paper conducted some experiments divided into two scenarios. The first experimental results show that active heat flow sensing is realized within 0.4 sec from first contact for 100% classification of four heated materials. The second experimental results show that the three materials, whose thermal properties are largely different, can be classified within 0.7 sec from first contact using different initial temperatures of the training and test data. These results suggest robustness against environmental change, which has been difficult using conventional temperature-based methods.
Enhancing Crop Health Monitoring: A Hybrid CNN-RF Model for Early Detection and Fine-Grained Classification of Cotton Bacterial Blight

The destructive disease known as cotton leaf bacterial blight is a major danger to cotton crops all over the world. To effectively manage crops and maintain yields, early and precise diagnosis of disease severity levels is essential. To identify and categorize cotton bacterial blight illness, we suggest a hybrid strategy in this study that combines a Convolutional Neural Network (CNN) with a Random Forest (RF) model. We used a dataset of 10,000 photos of cotton leaves showing various stages of illness to train our model. The robustness of the model was improved by doing extensive data preparation, which included shrinking, normalizing, class balancing, and augmenting the data. CNN layers were used for feature extraction, while an RF classifier was used for classification in the hybrid model architecture. Our model has an overall accuracy of 94.58% on a different test set. This degree of precision suggests that the hybrid method is capable of properly differentiating between disease severities caused by bacterial blight. Our findings improve upon existing methods for detecting cotton leaf bacterial blight, which is a significant contribution to the area of agricultural disease identification. The combination of a CNN with an RF model provides a powerful tool to aid in crop management practices, allowing for prompt intervention and tailored treatments to lessen the impact of the illness on cotton harvests.
Websites Use as Learning Resources in Developing Intercultural Communicative Competence in English Language Teaching Classroom

This classroom action research (CAR) aims at developing students’ intercultural competence (ICC) and exploring English language learning models integrated to ICC on the basis of websites use as the learning resource. This research was conducted in Intensive Course online class involving 24 students of English study program in Universitas Lancang Kuning. This CAR was conducted in one cycle consisting of 6 meetings and 2 evaluations. The data was collected from (1) observation fieldnotes, (2) observation checklist, (3) collecting documents (students’ comments and accessed websites, and (4) the scores taken from the 5 groups’ discussion. This research reveals the students’ ICC dimensions development exhibited in their class discussions with the increasing percentage from 46.15% to 92.30%). Some language learning models integrated to ICC on the basis of websites use were identified: (1) Accessing websites related to ICC learning topics, and (2) accessing websites that provide authentic materials (texts, pictures, audio-videos, and (3) motivating students to communicate and collaborate with others via online learning media. This CAR concludes that “students’ ICC could be developed through language learning on the basis of websites use which contains ICC dimensions”.
What a mix! Volatile organic compounds and worker exposure in small business beauty salons in Tucson, Arizona

Introduction Small business beauty salons have volatile organic compounds (VOCs) in their workplace air. VOCs are present as ingredients in beauty or hair products. They may also form because of chemical reactions, where thermal-styling elements accelerate the volatilization of these compounds. Uncertainties remain about the relationship between air pollutant concentrations and the variety of beauty salon activities in a work shift. Investigating these associations can help determine high-risk services, associated products, and at-risk workers. Methods In this exploratory study, female community health workers recruited beauty salons from target zip codes in predominately Latino neighborhoods, including primarily Spanish-speaking small businesses. We collected salon chemical inventories, business characteristics, and participant activity logs to understand how chemicals and activities influence the total and specific VOC concentrations. We sampled personal total VOCs and specific VOCs from the same shop during the participant work shift. We also measured personal total VOCs for four work shifts per shop. Results A linear mixed effects model of log VOCs on the fixed effect of activity and the random effects of salon and shift within the salon showed that the variance between salons explains over half (55%) of the total variance and is 4.1 times bigger than for shifts within salons. Summa canisters detected 31 specific VOCs, and hazard scores ranged between 0 and 4.3. 2-Propanol (isopropyl alcohol) was the only VOC detected in all shifts of all salons. Discussion In this study, differences in VOC measurements were primarily between salons. These differences may result from differences in ventilation, services rendered, and product lines applied.