Miracle-dz/UniFAR-FTUs huggingface dataset:
Effect of Magnetic Resonance Image Quality on Structural and Functional Brain Connectivity: The Maastricht Study

In population-based cohort studies, magnetic resonance imaging (MRI) is vital for examining brain structure and function. Advanced MRI techniques, such as diffusion-weighted MRI (dMRI) and resting-state functional MRI (rs-fMRI), provide insights into brain connectivity. However, biases in MRI data acquisition and processing can impact brain connectivity measures and their associations with demographic and clinical variables. This study, conducted with 5110 participants from The Maastricht Study, explored the relationship between brain connectivity and various image quality metrics (e.g., signal-to-noise ratio, head motion, and atlas–template mismatches) that were obtained from dMRI and rs-fMRI scans. Results revealed that in particular increased head motion (R2 up to 0.169, p < 0.001) and reduced signal-to-noise ratio (R2 up to 0.013, p < 0.001) negatively impacted structural and functional brain connectivity, respectively. These image quality metrics significantly affected associations of overall brain connectivity with age (up to −59%), sex (up to −25%), and body mass index (BMI) (up to +14%). Associations with diabetes status, educational level, history of cardiovascular disease, and white matter hyperintensities were generally less affected. This emphasizes the potential confounding effects of image quality in large population-based neuroimaging studies on brain connectivity and underscores the importance of accounting for it.
PENENTUAN KRITERIA NUTRIBN UNTUK PENILAIAN STATUS TROFIK PERAIRAN WADUK MRICA BANJARNEGARA, INDONBSIA (Determination of nutrient criteria for assessing trophic stutus of Mrica Reservoir

A nutrient criteria currently used for the assessment of trophic status of water bodies in Indonesia are based on OECD (1982), and Mason (1991) resulted.fro* lakes and reservoirs' trophic status study oJ temperate region. These criteria when used for reservoirs in Indonesia often did not reflect the actual conditions because there are dffirences in the mechanisms of eutrophication in tropical and temperate regions (Huszar et al., 2006). The purposes of this research were to study the relationship between nutrient and algae biomass in order to determine nutrient criteria properly for assessing the trophic levels oJ reservoir waters in Indonesia.This research was conducted by surveys by taking samples of water once a monthfor I (one) yearfrom March 2009 to February 2010 in eleven locations of Mrica reservoir. Research variables were total nitrogen (TN) total posfat (TP), nitrate NOs), orthophosphate (PO), ammonium NH), TN/TP, NO3/PO4 and chlorophyll. h can be concluded that The TP criteria of eutrophic phase during rainy season (TP > 1,55 mg/l) was higher than of dry season (TP > 1,33). During both rainy or dry season TN was not a limiting nutrient of algae growth even it was always needed. The very high nutrient (even tenfold higher than in temperate region) not resulted in algae bloom. This might due to the process oJ electricity generating and flushing also the climate regimes (ight and temperature) were not become a Iimiting factor for algae growth. Therefore, the nutrient criteria based on temperate region wqs not work properly if usedfor assessment of trophic status in Indonesia.
DDoS attack detection in smart grid network using reconstructive machine learning models

Network attacks pose a significant challenge for smart grid networks, mainly due to the existence of several multi-directional communication devices coupling consumers to the grid. One of the network attacks that can affect the smart grid is the distributed denial of service (DDoS), where numerous compromised communication devices/nodes of the grid flood the smart grid network with false data and requests, leading to disruptions in smart meters, data servers, and the state estimator, ultimately effecting the services for end-users. Machine learning-based strategies show distinctive benefits in resolving the challenge of securing the network from DDoS attacks. Regardless, a notable hindrance in deploying machine learning-based techniques is the requirement of model retraining whenever new attack classes arise. Practically, disrupting the normal operations of smart grid is really discouraged. To handle this challenge effectively and detect DDoS attacks without major disruptions, we propose the deployment of reconstructive deep learning techniques. A primary benefit of our proposed technique is the minimum disruption during the introduction of a new attack class, even after complete deployment. We trained several deep and shallow reconstructive models to get representations for each attack type separately, and we performed attack detection by class-specific reconstruction error-based classification. Our technique experienced rigid evaluation via multiple experiments using two well-acknowledged standard databases exclusively for DDoS attacks, including their subsets. Later, we performed a comparative estimation of our outcomes against six methods prevalent within the same domain. Our outcomes reveal that our technique attained higher accuracy, and notably eliminates the requirement of a complete model retraining in the event of the introduction of new attack classes. This method will not only boost the security of smart grid networks but also ensure the stability and reliability of normal operations, protecting the critical infrastructure from ever-evolving network attacks. As smart grid is advancing rapidly, our approach proposes a robust and adaptive way to overcome the continuous challenges posed by network attacks.
Relationship between suspended sediment concentration and turbidity at construction sites

Stormwater runoff from construction sites can transport eroded sediment to nearby water bodies, degrading water quality and impairing biotic communities. Turbidity is a relatively easy characteristic to measure and can be used to estimate sediment loads from construction sites if reliable relationships between turbidity and total suspended solids (TSS) are established. These relationships are investigated in this study for construction sites in Minnesota. Laboratory protocols have been developed herein for studying the factors that impact turbidity from construction site soils. Experimental procedures include the use of a rainfall simulator to generate runoff and turbidity values from soils carefully packed in appropriate test boxes. Turbidity characteristics of 14 different soils from construction sites were investigated using the laboratory protocols. Trends in turbidity with sediment concentrations were well represented by power functions. The exponent of these power functions was relatively constant between soils. The log-intercept, or scaling parameter, varied substantially among the different soils. Multiple soil properties were evaluated for each soil. An extensive regression analysis resulted in a model using percentage silt, interrill erodibility, and maximum abstraction that best represented the intercept term. A power value of 7/5 was chosen to represent all soils.
A Novel Review on Modular Multilevel Converter for Induction Motor Drive Applications

Variable speed drive applications in the industrial vicinity playing a vital role in nation development. The majority of the industries are engaged with AC induction machine drives due to easy, smooth and reliable drive operations for its various variable drive applications. Recent advances in power electronic technologies makes the efficient control schemes to obtain a smooth and reliable variable speed drives. Modular multilevel converter (MMC) technique plays a significant role in applications of high power rating such as HVDC. The application of these modular multilevel converter techniques now extended to various variable induction machine speed drive applications in industrial services due to its superior benefits of investment and life cycle cost. Modular multilevel converters designed in number of modules to obtain ripple and harmonic less voltage out puts of the induction machines. Different topologies and control strategies of modular multilevel converters are observed and employed by the various researchers elsewhere in the world. This paper presets a novel review on applications of modular multilevel converters used in different drive applications. Also, this paper presents a detail review on different topologies and control schemes used elsewhere in the world and made suggestion on application of efficient modular multilevel converter technique configuration to obtain effective performance of induction machine at different drive applications
An Optical Fiber Cantilever Beam Ultrasound Sensor Based on MI Structure

This article presents an optical fiber cantilever beam (FCB) sensor based on a Michelson interferometer (MI) structure for ultrasound detection. Optimized structural parameters of the proposed sensor can be obtained according to simulated analysis of the effect of structural parameters on sensor’s ultrasound performance. The experimental parameters of the prepared sensor are measured, with a length of FCB of <inline-formula> <tex-math notation="LaTeX">$2405.34~\mu \text{m}$ </tex-math></inline-formula>, a length of taper of 612.30 <inline-formula> <tex-math notation="LaTeX">$\mu \text{m}$ </tex-math></inline-formula>, a diameter of taper of <inline-formula> <tex-math notation="LaTeX">$26.80~\mu \text{m}$ </tex-math></inline-formula>, and a diameter of silica sphere of <inline-formula> <tex-math notation="LaTeX">$32.33~\mu \text{m}$ </tex-math></inline-formula>. The maximum signal-to-noise ratio (SNR) is calculated as 29.8 dB in the ultrasound response range of 20–509 kHz. The sensor utilizes multiple modes interference based on single mode fiber (SMF) only structure, a single-taper and a reflective structure of a silica sphere, resulting in a more compact and structurally stable design compared to sensors based on a Mach-Zehnder interferometer (MZI) and a Fabry-Perot interferometer (FPI). The proposed MI-based FCB ultrasound sensor in this article offers advantages, such as a compact structure, easy fabrication method, and a large measurement range.
Soft HRM bundles: a potential toolkit for future crisis management

Purpose The purpose of this paper spans twofold. Firstly, to investigate Human Resource Management practices (HRMP) adopted by organisations during the pandemic. Secondly, to bundle similar HRMP into Human Resource Management (HRM) bundles that provided unhindered organisational support to employees during the crisis. Design/methodology/approach The authors conducted 39 in-depth interviews across industries using a semi-structured interview schedule. Thereafter, the authors transcribed the interviews verbatim and analysed them thematically using MAXQDA 2021. Findings The study identifies effective practices during times of uncertainty and how soft HRM practices helped organisations survive during a crisis. When bundled together, these practices enabled organisations to continue operations during the pandemic, keeping their employees engaged and motivated. Practical implications Based on the learnings from the COVID-19 pandemic, the study provides a toolkit of HRMP bundles that organisations can adopt for future crisis management, enhancing the organisations’ absorptive capacity. Originality/value The study investigates the practices incorporated during COVID-19, leading to the identification of soft HRM bundles. The study adds value to the existing domain of HRM by including a unique set of soft HRMP bundles that have not been discussed in earlier studies and could be of high utility to organisations during the crisis.
Differential interference contrast phase edging net: an all-optical learning system for edge detection of phase objects

Edge detection for low-contrast phase objects cannot be performed directly by the spatial difference of intensity distribution. In this work, an all-optical diffractive neural network (DPENet) based on the differential interference contrast principle to detect the edges of phase objects in an all-optical manner is proposed. Edge information is encoded into an interference light field by dual Wollaston prisms without lenses and light-speed processed by the diffractive neural network to obtain the scale-adjustable edges. Simulation results show that DPENet achieves F-scores of 0.9308 (MNIST) and 0.9352 (NIST) and enables real-time edge detection of biological cells, achieving an F-score of 0.7462.
A deep learning-based method for multiorgan functional tissue units segmentation

Functional tissue region segmentation is the segmentation and example description of tissue epithelium, glandular cavity, fiber and other tissues in the image, which helps to accelerate the understanding of the relationship between cells and tissues in the world. By better understanding the relationship between cells, researchers will have a deeper understanding of cell functions that affect human health. Based on convolutional neural networks, we combine the structural advantages of UNet and EficientNet to create an organ tissue segmentation model. The model fuses the UNet structure with the EficientNet structure, and extracts features with the help of the pre-trained EficientNet optimal structure to improve the ability of feature learning. At the same time, the fusion of multi-scale features in the network is realized through the jump connection, and the segmentation accuracy of the model is improved. We compare our model with other models using the metrics of dice similarity efficiency. our Unet2.5D (ConvNext+ Se_resnet101) owns the highest DSC 0.702 among these models, which is 0.052, 0.024, 0.052 higher than Unet(ResNet50), Unet(Se_Resnet101), Unet(ResNet101) respectively.
Exploring Generative Artificial Intelligence-Assisted Medical Education: Assessing Case-Based Learning for Medical Students

The recent public release of generative artificial intelligence (GenAI) has brought fresh excitement by making access to GenAI for medical education easier than ever before. It is now incumbent upon both students and faculty to determine the optimal role of GenAI within the medical school curriculum. Given the promise and limitations of GenAI, this study aims to assess the current capabilities of a GenAI (Chat Generative Pre-trained Transformer, ChatGPT), specifically within the framework of a pre-clerkship case-based active learning curriculum. The role of GenAI is explored by evaluating its performance in generating educational materials, creating medical assessment questions, answering medical queries, and engaging in clinical reasoning by prompting it to respond to a problem-based learning scenario. Our results demonstrated that GenAI addressed epidemiology, diagnosis, and treatment questions well. However, there were still instances where it failed to provide comprehensive answers. Responses from GenAI might offer essential information, hint at the need for further inquiry, or sometimes omit critical details. GenAI struggled with generating information on complex topics, raising a significant concern when using it as a 'search engine' for medical student queries. This creates uncertainty for students regarding potentially missed critical information. With the increasing integration of GenAI into medical education, it is imperative for faculty to become well-versed in both its advantages and limitations. This awareness will enable them to educate students on using GenAI effectively in medical education.
ORIGIN, DIVERSITY AND HSP70 GENE FUNCTIONAL POLYMORPHISM OF THE HELMETED GUINEA FOWL IN KENYA

Despite the importance of helmeted Guinea fowls, little is known about their origin and genetic background. This study aimed to understand t he genet ic background of helmeted Guinea fowls in Kenya through the characterizat ion of t heir morphological t rait s, analysis of the mitochondrial DNA D - loop and identification of polymorphisms in the HSP70 gene associated with drought/heat tolerance. Ninety (90) Guinea fowls randomly selected from four domestic populations (70 individuals) and a wild population (20 individuals) sampled from Bungoma, Busia and Laikipia counties in Kenya were scored for primary phenotypic characteristics (shank length, body length, wing length, helmet width, helmet height, head size, live body weight, wattle colour, skin colour and shank colour). DNA was also extracted from blood collected from the 90 individuals. The phenotypic data collected were analyzed and the descriptive statistics obtained compared as percentages, continuous bar graphs and tables using Excel version 2013 and R Core version 3.1. 2. Two wattle colour variations were identified, red and blue. Skin colours were grey and white while shank colours were black, pink and grey. The wild population showed the highest mean values for the quantitative traits measured except live body weight and head size. The populations of Kenyan helmeted Guinea fowls showed a possible limited heterogeneity in the qualitative traits considered. There was a possible positive (0.333) and significant (p<0.05) correlation between body temperature of the birds and outside temperature. The first 700bp of the mtDNA D-loop and first 600bp of HSP70 were amplified, sequenced and edited. The 25 mitochondrial DNA haplotypes identified will be compared to those observed in other parts of Africa to determine origin and diversity. The SNPs in HSP70 discovered will also be compared with reference sequences from Genbank to identify polymorphisms associated with drought/heat tolerance. These findings present a genetic pool from which decisions on sustainable use and conservation of helmeted Guinea fowls could be made. This would help farmers, breeders and conservationists to genetically improve domestic helmeted Guinea fowls and also improve their survival in the wild. Key words: agro-ecological zones, climatic change, morphological traits, Numida meleagris, population
Isotopic Chronology of the Hutouya Skarn Lead-Zinc Polymetallic Ore District in Qimantage Area of Qinghai Province and Its Geological Significance

Located in Qimantage area of Qinghai Province,Hutouya is a typical skarn lead-zinc polymetallic ore district consisting of both ortho-contact and exocontact substyles.The basic geological characteristics and ages of petrogenesis and mineralization were studied.The ore-forming elements are quite complex,dominated by Fe,Cu,Sn,Mo,Pb and Zn.Mineralization occurs in the contact zone between the intermediate-acidic intrusive rock and the carbonate-bearing strata,the fault zone or discordant contact between strata of different epochs and the lithologic boundaries,showing obvious zonation of mineralization and alteration from the intrusive rock outwards.U-Pb dating of zircons from granodiorite and adamellite has yielded ages of(235.4±1.8) Ma and(219.2±1.4) Ma,respectively.Re-Os isotopic dating of molybdenite from the skarn Cu-Mo polymetallic ore and skarn molybdenite ore by ICP-MS has yielded isochron ages of(225.0±4.0) Ma and(230.1±4.7) Ma.These new age data,combined with data available,indicate that the ages of ores and related granites should be Middle to Late Triassic.Indosinian magmatism and mineralization occurred at the post-collisional stage of the orogen,corresponding to the late stage of the Late Paleozoic-Early Mesozoic tectonic event in eastern Kunlun orogenic belt.
The Surgical Outcome of Percutaneous Nephrolithotomy Based on Principle of Right Triangle for Renal Access with Fix C-Arm and not the Contrast

Results: Mean age of patients was 47.2 ± 10.7 years (23 69). Renal pelvis stones in 14 cases (13.73%), renal calyx stones in 15 cases (14.70%), staghorn stones in 29 cases (28.43%) and renal pelvis-calyx stones in 44 cases (43.14%). Hydronephrosis of grade I: 33 cases (32.35%), grade II: 36 cases (35.30%), grade III: 9 cases (8.82%), no hydronephosis: 24 cases (23.53%). Percutaneous renal access into inferior posterior of the kidney in 47 cases (46.08%), middle calyx 42 cases (41.17%) and superior calyx 13 cases (12.75%). Average time of puncture: 8 ± 18 seconds (fastest 3 seconds and slowest 67 seconds). Successful punture into calyx was 100%. No accidents in the technique.
The phytoremediation of heavy metal contaminated roadside soils in Libya by Eucalyptus camaldeulensis

There is a public concern over the potential accumulation of heavy metals in soils. Numerous studies have already demonstrated that areas in close proximity to vehicular traffic are marked noticeably by contamination of soil, air and water. Hence, such activities can affect humans and other living organisms. The aim of this study is to investigate the pollution of soils caused by vehicular traffic, on agricultural land in Azzawiyah, Liby with the view of assessing potential application of phytoremediation options for the remediation of contaminated soils and determine whether soil amendments would improve soil remediation. In an effort to improve the status of pollution of soils by vehicular traffic, a phytoremediation method of remediation of contaminated land has been used in this study, as it is relatively inexpensive and has the potential through the appropriate selection of plant species to be effective. This method is a soil clean up technology that uses the ability of metal accumulator plants to extract metal from contaminated soil with their roots and to concentrate these metals in above-ground plant parts. In this study, the investigation area was in Azzawiyah city where the soil samples and Doedonea viscose plant were collected from the road side. These soil samples were analysed using different experiments to determine physical and chemical properties, such as pH, OM and CEC. Heavy metals in soil and Doedonea viscose shoot and root were analysed using inductively coupled plasma atomic emission spectroscopy (ICP-AES).
Flocking Dynamics for Cooperation-Antagonism Multi-Agent Networks Subject to Limited Communication Resources

This contribution explores the flocking dynamic behavior of multi-agent networks with limited communication resources, where there are both cooperative and antagonistic relationships among agents. Taking into account the shortage of communication resources, a distributed control protocol with edge-asynchronous communications is designed, in which only part of the communication links are awakened at each time instant to perform information transmission. A product approach of super-stochastic matrices is utilized to rigorously check the convergence of the dynamic model. With this method, algebraic conditions on cooperative and antagonistic relationships among agents are established so that the entire multi-agent network emerges with flocking dynamics. At last, theoretical results are tested and verified through computer simulations.