Miracle-dz/UniFAR-FTUs huggingface dataset:
Use of automated image analysis to detect changes in megafaunal densities at HAUSGARTEN (79°N west off Svalbard) between 2002 and 2004

Use of automated image analysis to detect changes in megafaunal densities at HAUSGARTEN (79°N west off Svalbard) between 2002 and 2004High latitudes are amongst the most sensitive ecosystems with respect to climate change, which prompted the launch of the first and only deep-sea long-term observatory beyond the polar circle, HAUSGARTEN (eastern Fram Strait), in 1999. An understanding of the abundance and spatial distribution of organisms is vital to assess the effects of global change. To map the distribution of megafaunal organisms deep-sea research relies strongly on the use of high-resolution cameras that are fitted to towed sledges, drop-down frames and remotely operated or autonomous underwater vehicles. Inevitably, such techniques generate large quantities of footage. The visual analysis of images is labour-intensive, time-consuming and subjective. Over recent years, the increase in computer power has facilitated advances to automate image analysis. Here, we present a novel approach for the automatic detection and classification of particular biological classes within image data. For this purpose, we applied machine-learning algorithms to two photographic transects from the HAUSGARTEN central station (2500m) taken by an ocean floor observation system in 2002 and 2004. Each transect contains some 700 photographs. The main goal is the automatic identification of important biological classes (e.g. sea cucumbers, sea lilies) to assess the densities of the most frequent organisms over time. So far, our system shows a promising performance in detecting sea cucumbers and sea lilies with sensitivities and positive predictive values between 75 - 80%. Results from manual analysis of 66 images taken at the central part of the transect indicate a significant decline in the mean density of sea cucumbers (Elpidia glacialis), sea lilies (Bathycrinus cf. carpenteri), burrow entrances and total megafaunal densities from 2002 to 2004 which concurs with a decrease in sea ice coverage, particulate flux to the sea floor, sediment-bound nutrients and pigments, microbial biomass and changes in meiofaunal community structure. Results from automated image analysis will increase the spatial resolution and statistical power of our analyses as it enables us to process larger quantities of images.
landslide hazard zonation for determination appropriate regions with AHP model in dry areas of Iran Khuzestan

Identify areas prone to landslide hazard zonation and its is the first step to assessment of enviromental hazard . many factors are involved in the occurrence of landslide,that their evalution,makes necessary the use of powerful analytical tools.the purpose of this reserch assessment and evalution each of the factors in landslide and select the appropriate model landslide hazard zonation in semi-arid watershed in Iran(khuzestan province ). Need to assess the area landslide potential and Determine the best and most favorable zones To do this. First , landslides distribution map of study area was prepared in Arc GIS using field observations and then the map of effective factors in landslide occurrence were prepared. Thus The layers provide information about the geomorphological factors include slope, Altitude, aspect, litho logy, NDVI, soil rain and temperature. The import layers software Geographic Information Systems and Preparation and completion of the initial matrix and the normal to determine the relative weight of the layers Using the software Expert Choice (11) And the final Arc GIS(9.3) and prepared Maps of the various weight categories were prepared using the AHP model weight of the layers and data integration software. Then, the map can be predicted from the model of landslide hazard zonation with the land use Map the current situation overlapping territories and calculation correlation matrix and Compare models. The results showed that regions model predicted have relatively complete overlap. Normal 0 false false false EN-US X-NONE AR-SA /* Style Definitions */ table.MsoNormalTable {mso-style-name:"Table Normal"; mso-tstyle-rowband-size:0; mso-tstyle-colband-size:0; mso-style-noshow:yes; mso-style-priority:99; mso-style-qformat:yes; mso-style-parent:""; mso-padding-alt:0cm 5.4pt 0cm 5.4pt; mso-para-margin:0cm; mso-para-margin-bottom:. 0001pt; mso-pagination:widow-orphan; font-size:11.0pt; font-family:"Calibri","sans-serif"; mso-ascii-font-family:Calibri; mso-ascii-theme-font:minor-latin; mso-fareast-font-family:"Times New Roman"; mso-fareast-theme-font:minor-fareast; mso-hansi-font-family:Calibri; mso-hansi-theme-font:minor-latin; mso-bidi-font-family:Arial; mso-bidi-theme-font:minor-bidi;}
The Morphological Transformation of the Thorax during the Eclosion of Drosophila melanogaster (Diptera: Drosophilidae)

Simple Summary The developmental process, divided into four different stages (egg, larva, pupa and adult), is the main reason for their remarkable diversification and expansion of the insect group Holometabola. Advanced morphological techniques have been used to demonstrate the 3D thoracic anatomical structures of the holometalous model organism fruit fly, Drosophila melanogaster, before and after emergence, in order to uncover the transformation process of the muscles, nerves, and gut during development. Skeletal changes affect the original positions of the muscles. The muscles vary in size, not only becoming longer and broader, but also shorter and narrower. Different muscle shapes may appear during development. The number of bundles may also vary. The soft tissues in the body may fix the free ends of the growing muscles, and a strong adult skeleton likely causes the absence of some muscles and tendons. The flight muscles appear very early, probably to achieve full functionality of these very large adult-specific muscles in time. There are some differences during the same developmental period between the two sexes. Most muscles of the larvae and adults with similar attachment positions change their functions from supporting crawling to supporting flying and walking under the control of a more complex ventral nerve cord. The midguts of the larva and the adult are nearly the same. Abstract The model organism Drosophila melanogaster, as a species of Holometabola, undergoes a series of transformations during metamorphosis. To deeply understand its development, it is crucial to study its anatomy during the key developmental stages. We describe the anatomical systems of the thorax, including the endoskeleton, musculature, nervous ganglion, and digestive system, from the late pupal stage to the adult stage, based on micro-CT and 3D visualizations. The development of the endoskeleton causes original and insertional changes in muscles. Several muscles change their shape during development in a non-uniform manner with respect to both absolute and relative size; some become longer and broader, while others shorten and become narrower. Muscular shape may vary during development. The number of muscular bundles also increases or decreases. Growing muscles are probably anchored by the tissues in the stroma. Some muscles and tendons are absent in the adult stage, possibly due to the hardened sclerites. Nearly all flight muscles are present by the third day of the pupal stage, which may be due to the presence of more myofibers with enough mitochondria to support flight power. There are sexual differences in the same developmental period. In contrast to the endodermal digestive system, the functions of most thoracic muscles change in the development from the larva to the adult in order to support more complex locomotion under the control of a more structured ventral nerve cord based on the serial homology proposed herein.
Trading Aggregate Statistics Over Private Internet of Things Data

The exponential growth of Internet of Things (IoT) data has pushed the boundaries of data analysis, but it has also raised concerns about the increasing commoditization of personal privacy. The intriguing problem of trading private IoT data is the focal point of this paper. We delve into three fundamental questions: Firstly, we determine the minimum privacy that a broker must purchase from data owners to achieve aggregate statistics with a fixed accuracy goal. Secondly, we propose an innovative arbitrage-free pricing framework that empowers brokers to set prices for aggregate statistics, maximizing utility while ensuring fairness. Lastly, we address the challenge of fairly compensating data owners for their privacy losses while providing economic guarantees. To achieve these objectives, we propose a data trading framework in this paper. Our framework considers ubiquitous data correlations and the potential involvement of untrusted brokers, ensuring that the total compensations remain within the brokers’ budget; each owner is guaranteed to receive non-negative revenues, and cunning data owners cannot exploit misreported privacy demands to gain extra compensations. Experiments on the MovieLens dataset demonstrated the robustness of our framework.
The relationship between tolerance for uncertainty and academic adjustment: the mediating role of students’ psychological flexibility during COVID-19

Introduction University students are among the groups most adversely affected by COVID-19 in terms of their psychological and academic wellbeing, particularly given the pandemic’s uncertainty. However, little is known about their psychological flexibility to deal with this uncertain nature of pandemic. This study aimed to examine the mediating role of psychological flexibility (PF) in the relationship between university students’ intolerance of uncertainty (IU) and their academic adjustment to the online learning process during COVID-19. Methods Data was collected from 388 university students from Türkiye (18–34 (Mage = 21.43, SD = 2.04) who completed five questionnaires – Intolerance of Uncertainty Scale, Acceptance and Action Questionnaire II, Academic Self Efficacy Scale, Educational Stress Scale, and Online Self-Regulatory Learning Scale. Additionally, as indicators of students’ academic adjustment, perceived academic performance – ranging 1 to 10, and their last academic grade point average before and during the pandemic were also collected. Results The results indicated that PF and IU had a significant effect on the academic adjustment of students during the pandemic. PF had a complete mediating role between the IU and academic adjustment. Results also revealed that online self-regulation, a sub-dimension of academic adjustment, did not show a significant relationship with any other variable in the model. Discussion Research findings showed that psychological flexibility is a very important strength for university students to maintain their academic adjustment in stressful times. The results were discussed in light of the relevant research, and recommendations for further research and implications were provided.
Epidemiology of Pneumococcal Pneumonia in Louisville, Kentucky, and Its Estimated Burden of Disease in the United States

Streptococcus pneumoniae remains a primary pathogen in hospitalized patients with community-acquired pneumonia (CAP). The objective of this study was to define the epidemiology of pneumococcal pneumonia in Louisville, Kentucky, and to estimate the burden of pneumococcal pneumonia in the United States (US). This study was nested in a prospective population-based cohort study of all adult residents in Louisville, Kentucky, who were hospitalized with CAP from 1 June 2014 to 31 May 2016. In hospitalized patients with CAP, urinary antigen detection of 24 S. pneumoniae serotypes (UAD-24) was performed. The annual population-based pneumococcal pneumonia incidence was calculated. The distribution of S. pneumoniae serotypes was characterized. Ecological associations between pneumococcal pneumonia and income level, race, and age were defined. Mortality was evaluated during hospitalization and at 30 days, 6 months, and 1 year after hospitalization. Among the 5402 CAP patients with a UAD-24 test performed, 708 (13%) patients had pneumococcal pneumonia. The annual cumulative incidence was 93 pneumococcal pneumonia hospitalizations per 100,000 adults (95% CI = 91–95), corresponding to an estimated 226,696 annual pneumococcal pneumonia hospitalizations in the US. The most frequent serotypes were 19A (12%), 3 (11%), and 22F (11%). Clusters of cases were found in areas with low incomes and a higher proportion of Black or African American population. Pneumococcal pneumonia mortality was 3.7% during hospitalization, 8.2% at 30 days, 17.6% at 6 months, and 25.4% at 1 year after hospitalization. The burden of pneumococcal pneumonia in the US remains significant, with an estimate of more than 225,000 adults hospitalized annually, and approximately 1 out of 4 hospitalized adult patients dies within 1 year after hospitalization.
The Relationship Between Loneliness and Self-Discovery in Teenagers Using K-Pop Roleplayer Accounts on Twitter

Teenagers fulfill social needs by communicating and relating well with other people. One of the media used to be used is Twitter. This research aims to determine the relationship between loneliness and self-disclosure in teenagers who use the K-Pop Roleplayer account on Twitter. The technique used in determining the sample was nonprobability sampling with purposive sampling. The subjects of this research were 100 K-Pop Roleplayer account users on Twitter. Data collection used a loneliness scale adapted from the Revised Loneliness Scale (UCLA Version 3), and a self-disclosure scale adapted from the Revised Self Disclosure (RSDS). The results of this research show that the correlation coefficient value is –0.181 with a significant value of 0.72>0.05. That means loneliness does not have a significant relationship with self-disclosure among teenagers using the K-Pop Roleplayer account on Twitter. This research can provide a reference for teenagers using K-Pop Roleplayer accounts on Twitter to maximize their use of Twitter according to their needs.
Effects of mercury on the dopamine transporter cell surface expression in PC12 cells

Environmental factors, such as heavy metal exposures, have been suggested to have an impact not only on neurodegenerative disease, such as Parkinson’s disease, but also on psychostimulants abuse and their toxicity. In this study, two questions were addressed: 1) effects of mercuric chloride on parkinsonian toxicant 1-methyl-4-phenylpyridinium (MPP + ) induced cytotoxicity and 2) effects of mercuric chloride on cell surface dopamine transporter. Pheochromocytorma cells (PC12) were treated with various concentrations of mercuric chloride (0.02~2.0 ppm) for 4 hours with and without 0.1 mM MPP + . Significant potentiation of toxicity was observed when there was co-treatment with 0.5 ppm HgCl 2 and 0.1mM MPP + vs. HgCl 2 alone. The potentiation of toxicity with the co-treatment of 0.5 ppm mercuric chloride and 0.1mM MPP + was not observed in PC12 cells expressing DAT mutant L368Q, which has impaired uptake activity. Cell surface expression of dopamine transporter was quantitatively seen to be transiently increased via western blot analysis which was correspondingly visualized via immunocytochemical assay. In summary, mercuric chloride enhanced MPP + toxicity in a dopamine transporter-dependent manner and increased the surface expression of dopamine transporter in PC12 cells. This toxicological effect of mercury on dopamine transporter could trigger an unpredicted toxicological/pharmacological interaction with drugs affecting the dopaminergic system.
2.5D SLAM Algorithm with Novel Data Fusion Method Between 2D-Lidar and Camera

The SLAM algorithm has been extensively researched and has gained significant relevance in our daily lives, particularly with the advancements in robot technology and autonomous driving. Currently, SLAM can be largely divided into 2D SLAM and 3D SLAM. In the case of 2D SLAM, accurate mapping and localization of the plane can be achieved with low computational requirements. However, it has the limitation of not considering 3D information. In contrast, 3D SLAM can generate maps and perform localization in complex indoor spaces with obstacles by incorporating 3D data on objects. However, it may face challenges in real-time implementation in embedded systems due to high computational demands. In this paper, we propose a 2.5D SLAM using the Life Long Feature(LLF) algorithm. First, 2.5D SLAM is a projection of 3D camera feature points that are not perceived by the 2D-Lidar onto the 2D-Lidar plane. The transformation matrix to project camera feature to 2D-Lidar plane is calculated by optimizing least-square problem of the same points recognized by the camera and 2D-Lidar. Additionally, the LLF algorithm enables the updating and maintenance of camera feature points based on the robot's current position, even when the camera is not perceiving objects. The difference in Field Of View(FOV) between the camera and the LiDAR causes the problem of being recognized as a dynamic object and removing feature points. The LLF algorithm can solve this problem even when the object is within the range of the 2D-LiDAR but not visible in the camera's FOV. In experiment, we confirmed our proposed 2.5D SLAM with LLF algorithm show better performance in detecting 3D objects and Mapping than pervious 2D SLAM.
Disease forecasting and patient monitoring: The great role of medical data analytics

the rapid advancement of new information technologies, such as mobile apps, cloud computing, and big data analytics, has significantly impacted various industries, with healthcare being a notable example. In healthcare, these technologies have become essential tools for delivering high- quality services. Specifically, the explosion of medical data sources, the integration of sensor technology, data analysis, and the application of data mining and machine learning techniques have the potential to bring about transformative changes in healthcare.In light of the exponential growth in medical data, it is evident that a transformative shift, often referred to as a revolution, is underway within the healthcare sector. This transformation underscores the imperative of harnessing emerging technologies to enhance the medical field. This paper aims to present a comprehensive architectural design that seamlessly amalgamates the power of big data analytics, machine learning, and mobile healthcare for self-monitoring. Through this exposition, we intend to underscore the paramount significance of applying predictive analytics techniques within medical platforms. The envisioned system will adeptly harness healthcare data, employing intelligent process analysis and extensive big data processing to glean invaluable insights for informed decision-making, ultimately ensuring superior real-time medical monitoring.
In-Hand Object Classification and Pose Estimation With Sim-to-Real Tactile Transfer for Robotic Manipulation

Dexterous robotic grasping gains great benefits from tactile sensation, but delicate object exploration by tactile information is challenged by difficulty in rich and efficient data production. In this letter, we propose a tactile-based in-hand object perception approach, empowered by a sim-to-real approach toward a data-efficient learning process. A high-fidelity tactile input, measured by a pair of vision-based tactile sensors, was represented as a point cloud facilitating dual functionality of object classification and the associated pose estimation. For the classification, we constructed PoinTacNet, a variation of PointNet to fit into tactile data processing. A reliable simulation methodology on tactile input was employed for the pretraining of the model, transferred to the fine-tuning process facing real tactile data. Taking inspiration from human behaviors, a re-grasping strategy was imparted by means of conditional accumulation of class likelihood distribution. The result of the framework facilitates a high object classification accuracy of 83.89$\%$ on the ten objects from McMaster-Carr's CAD models, which is significantly improved by the re-grasping. In addition, a set of benchmarks displays the computational efficiency in the sim-to-real transfer. In line with the successful classification, the posture of in-hand objects is estimated using point cloud registration algorithms, achieving an average angular and translational RMSE of 5.03$^\circ$ and 2.41 mm, respectively. The proposed approach has the potential to enable robots to attain human-like haptic exploration skills for perceiving unstructured environments.
Frequency and Severity of Temporomandibular Disorders Among Weightlifters at Fitness Centers in a Subset Population of Saudi Arabia: A Cross-Sectional Observational Study

Background Teeth clenching in weightlifters is a common finding that may result in temporomandibular disorders (TMDs). This study aimed to evaluate the severity and frequency of TMDs among weightlifters at fitness centers in Saudi Arabia. Methods A cross-sectional study was designed to collect data from weightlifters at fitness centers. A non-probability convenient sampling technique was applied using a valid and reliable Fonseca's questionnaire on a Google Sheet (Google, Mountain View, CA) to collect participant data. The survey was conducted from November 2022 to April 2023. Epi Info software (CDC, Atlanta, Georgia) was used to calculate the sample size, and a minimum sample of 278 was required. The data underwent analysis using SPSS version 20 (IBM Corp., Armonk, NY). Results Data analyzed from 375 participants revealed that 192 (51.2%) had mild signs of TMD, whereas 128 (34.13%) of the respondents had no symptoms of TMD. A significant difference was observed among female participants in all the temporomandibular joint severity categories. A statistically significant difference was observed between both genders concerning frequent headaches, earache, and nervousness. Conclusion The prevalence of TMD is high worldwide. Unsupervised athletic activity may result in the occurrence of TMD. Fonseca's questionnaire findings reveal a mild prevalence of TMD in weightlifters.
IDENTIFIKASI DAN AUTENTIKASI JAHE MERAH MENGGUNAKAN KOMBINASI SPEKTROSKOPI FTIR DAN KEMOMETRIK (Identification and Authentication of Jahe Merah Using Combination of FTIR Spectrocopy and Chemometrics)

FTIR spectroscopy combined with principal component analysis and discriminant analysis were used for identification and authentication of jahe merah from its related species namely jahe emprit and jahe gajah. FTIR spectra of all samples were recorded in the wavenumber range 4000-400 cm -1 and then subjected for preprocessing signal such as normalization and baseline correction. Combination of FTIR spectra with some chemometrics method such as principal component analysis and discriminant analysis were used to distinguish the three varieties of ginger. By using discriminant analysis, the three varieties of ginger were classified according to its variety. The developed method could be used for identification and authentication of jahe merah.
Design of Dual-Band Dual-Polarized Filtering Patch Antenna Using T—Shaped Feeds

This paper proposes a single-layer filtering patch antenna with dual-band dual-polarized radiation. It consists of a rectangular patch fed by two orthogonal T-shaped feeds to generate the dual-polarization. Each feeding structure is composed of a microstrip-line, a pair of $\lambda$/4 resonators, and a via, which is coupled in proximity to the patch for broadband and filtering characteristics. The dual-band operation is obtained by choosing the resonant lengths of the T-shaped feeds and the patch dimensions. For verification, a prototype operating at 5.4 and 7.8 GHz with horizontal and vertical polarization, respectively, is fabricated and tested. The antenna achieves an impedance bandwidth of 5.30 – 5.63 GHz / 7.22 – 8.27 GHz, a port-to-port isolation of ≥ 30 dB, peak gains of 7.5/8.0 dBi, and out-of-band suppression of ≥ 28 dB.