Miracle-dz/UniFAR-FTUs huggingface dataset:
Post-Stack Seismic Data Enhancement of Thrust-Belt Area, Sabah Basin

In this paper, an integrated post-stack seismic data processing and interpretation for a complex thrust-belt area was proposed. The sequence was suggested due to poor seismic data quality of the Sabah basin area that was obtained after a pre-stack data processing sequences. This basin consists of a complex geological setting such as thrust-belt with steep dip reflector which is the main features of the region. In this paper, we outlined several methods used in the seismic data processing and interpretation such as amplitude recovery and frequency filtering for enhancing seismic data quality, and relative acoustic impedance, structural smoothing and wavelet coherency were used for attribute analysis. The outcome from this research aims at illuminating the hidden structures such as proper beds termination and faults systems that was heavily affected by low signal-to-noise ratio.
On a Neurocomputing based predictive model forecasting the Winter Shower in India

The development of a neurocomputing technique to forecast the average winter shower in India has been modeled from 48 years of records (1950 to 1998). The complexities in the rainfall-sea surface temperature relationships have been statistically analyzed along with the collinearity diagnostics. Presence of multicollinearity has been revealed and a variable selection has been executed accordingly. Absence of persistence is also exhibited. For this reason, Artificial Neural Net Model as predictive tool for the said meteorological event in the form of Multiple Layer Perceptron has been generated with sea surface temperature anomaly and monthly average winter shower data over India during the above period. After proper training and testing, a Neural Net model with small prediction error is developed and supremacy of Artificial Neural Net over conventional statistical predictive procedure has been established statistically.
Water-Body Type Classification in Dual PolSAR Imagery Using a Two-Step Deep-Learning Method

Water-body type problems classification plays a vital role in ecological conservation, water resource management, and urban planning. Accurate classification can aid decision-makers in understanding the functions of different water-body types, providing key information for urban planning and promoting harmony between human activities and the natural environment. Despite extensive research in the field of water-body segmentation, exploration in the water-body type classification community is not as widespread. Therefore, this article proposes a novel water-body type classification method based on a two-step deep-learning model, decomposing water-body type classification into water-body segmentation and water-body type identification. Especially, this method constructs a unique data strategy by organically integrating backscatter features, polarimetric features, and DEM features, providing the model with rich and comprehensive information. In the first step, the segmentation network uses the fused feature to extract all water-body from synthetic aperture radar images. Subsequently, the extracted water-body are combined with the input data, forming a multifeature input for the identification network to distinguish between natural and artificial water-body. During this process, a swarm intelligence optimization algorithm is employed to explore the optimal hyperparameters of the network, including those of the segmentation and identification networks. Finally, the proposed method is assessed using extensive experiments on water-body segmentation tasks, water-body type identification tasks, and joint water-body type classification tasks. This article not only provides a new perspective in the field of water-body type classification but also demonstrates the immense potential of deep-learning network hyperparameter optimization and feature fusion in solving such.
Energy-Efficient Joint User and Power Allocation in 5G Millimeter Wave Networks: A Genetic Algorithm-Based Approach

Reducing power consumption is a pivotal challenge in 5G millimeter wave (mmWave) networks due to the density of the base stations (BSs) in these networks. In this paper, we focus on the joint user and power allocation problem in 5G mmWave networks, aiming to minimize power consumption while maintaining the user Quality of Service (QoS), considering the BSs switching on/off strategy. Initially, we formulate the problem as an Integer Linear Program (ILP), aiming to obtain the optimal solution. Due to the NP-hardness of the problem, we propose a Genetic Algorithm (GA)-based heuristic strategy. Extensive simulations are conducted to evaluate the performance of the GA. The obtained results demonstrate the efficiency of the proposed GA in providing close to optimal solutions, even in scenarios with a large number of users, when compared to the ILP. Additionally, the proposed GA outperforms the benchmark algorithms in terms of network power consumption, network throughput, energy efficiency (EE), and the number of switched-off BSs. Moreover, when comparing the running time of the different methods, the proposed GA shows a significantly reduced time compared to the optimal solution obtained by the ILP method. At the same time, it requires a running time close to the running times of the benchmark solutions. Further insights from power consumption patterns in residential and office areas affirm the consistent energy savings achieved by the proposed GA, emphasizing its applicability in real-world scenarios. In our case, the proposed GA yields energy savings up to 22.34% and 33.12% in residential and office areas, respectively. These findings underscore the practicality and efficiency of our proposed GA in optimizing power consumption and enhancing EE in mmWave networks.
Using Digital Health Technologies to Monitor Pain, Medication Adherence and Physical Activity in Young People with Juvenile Idiopathic Arthritis: A Feasibility Study

Juvenile idiopathic arthritis can be influenced by pain, medication adherence, and physical activity. A new digital health intervention, InteractiveClinics, aims to monitor these modifiable risk factors. Twelve children, aged 10 to 18 years, received daily notifications on a smartwatch to record their pain levels and take their medications, using a customised mobile app synchronised to a secure web-based platform. Daily physical activity levels were automatically recorded by wearing a smartwatch. Using a quantitative descriptive research design, feasibility and user adoption were evaluated. The web-based data revealed the following: Pain: mean app usage: 68% (SD 30, range: 28.6% to 100%); pain score: 2.9 out of 10 (SD 1.8, range: 0.3 to 6.2 out of 10). Medication adherence: mean app usage: 20.7% (SD, range: 0% to 71.4%), recording 39% (71/182) of the expected daily and 37.5% (3/8) of the weekly medications. Pro-re-nata (PRN) medication monitoring: 33.3% (4/12), one to six additional medications (mean 3.5, SD 2.4) for 2–6 days. Physical activity: watch wearing behaviour: 69.7% (439/630), recording low levels of moderate-to-vigorous physical activity (mean: 11.8, SD: 13.5 min, range: 0–47 min). To conclude, remote monitoring of real-time data is feasible. However, further research is needed to increase adoption rates among children.
RF Chain-Wise Clustering Schemes for Millimeter Wave Cell-Free Massive MIMO With Centralized Hybrid Beamforming

In this paper, we propose clustering schemes appropriate for a cell-free massive multi-input multi-output (CF mMIMO) system with centralized hybrid beamforming (BF) in the millimeter wave (mmWave) band, with the aim of reducing computational complexity and achieving scalability at the central processing unit (CPU) while ensuring system performance. Conventional access point (AP)-wise clustering schemes have been proposed for a decentralized architecture that mitigates inter-user interference at each AP through local digital BF and analog BF of each radio frequency (RF) chain to efficiently improve performance. However, in a centralized architecture, in which the digital BF weights is designed at the CPU while considering all RF chains of all APs together. In cases where each AP employs multiple RF chains forming different analog beams, the coupling loss (the sum of the path loss and BF gain of the analog beam) of each RF chain may differ even between the same AP and UE due to differences in the analog BF gain. AP-wise clustering is not well suited because RF chains with a high coupling loss (the sum of the path loss and BF gain of the analog beam) may be included among the APs of the cluster assigned to each UE. In the method proposed in this paper, clusters are formed on a per-RF-chain basis (RF chain-wise clustering). In this way, RF chains with lower coupling loss can be selected for each UE regardless of to which AP an RF chain belongs when forming a cluster for each UE. Moreover, the existing clustering schemes do not account for the amount of inter-cluster interference. Accordingly, we propose a cluster recombination scheme to effectively mitigate inter-cluster interference. Then, by combining RF chain-wise clustering and cluster recombination, a higher received signal power can be achieved while mitigating inter-cluster interference. Through simulation-based evaluations, we show that the proposed RF chain-wise clustering and cluster recombination schemes can achieve superior spectral efficiency while effectively reducing the complexity of centralized digital BF.
An Inexpensive Device for Multiple Depth Sampling of Groundwater Quality in Cohesionless Aquifers 1

1. This document is CIR943, one of a series of the Agricultural and Biological Engineering Department, Florida Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida. Original publication date May 1991. Reviewed July 2002. Visit the EDIS Web Site at http://edis.ifas. ufl.edu. 2. Wendy D. Graham, assistant professor, Department of Agricultural and Biological Engineering Department, Cooperative Extension Service, Institute of Food and Agricultural Sciences, University of Florida, Gainesville, 32611.
Deep-Learning-Based Analysis Reveals a Social Behavior Deficit in Mice Exposed Prenatally to Nicotine

Cigarette smoking during pregnancy is known to be associated with the incidence of attention-deficit/hyperactive disorder (ADHD). Recent developments in deep learning algorithms enable us to assess the behavioral phenotypes of animal models without cognitive bias during manual analysis. In this study, we established prenatal nicotine exposure (PNE) mice and evaluated their behavioral phenotypes using DeepLabCut and SimBA. We optimized the training parameters of DeepLabCut for pose estimation and succeeded in labeling a single-mouse or two-mouse model with high fidelity during free-moving behavior. We applied the trained network to analyze the behavior of the mice and found that PNE mice exhibited impulsivity and a lessened working memory, which are characteristics of ADHD. PNE mice also showed elevated anxiety and deficits in social interaction, reminiscent of autism spectrum disorder (ASD). We further examined PNE mice by evaluating adult neurogenesis in the hippocampus, which is a pathological hallmark of ASD, and demonstrated that newborn neurons were decreased, specifically in the ventral part of the hippocampus, which is reported to be related to emotional and social behaviors. These results support the hypothesis that PNE is a risk factor for comorbidity with ADHD and ASD in mice.
Ageing, Childhood and Social Identity in the Early Neolithic of Central Europe

In this paper, osteological and archaeological data are brought together to further our understanding of childhood in the early Neolithic Linearbandkeramik culture (LBK; c. 5500–5000 cal BC). In many characterizations of LBK society, fixed representations of sex or identities based on subsistence strategies pervade, with children rarely considered and then only as a specialized and separate topic of study. As a challenge to this view, a summary of the current models of childhood in the LBK culture is presented and debated with reference to the burial rites of children. A period of ‘middle’ childhood is proposed for the LBK culture. The osteological evidence suggests that childhood could be a time of dietary stress, perhaps with sex-based differences from childhood, and examples of the diseases and traumas suffered are discussed. Finally, the possibility that the children were actively contributing to acts of personal violence is raised. While the recognition of identity making as a continuous process remains a powerful exploratory route to investigating prehistoric societies, we argue that this should not discourage us from seeing identity as formed over the entire lifecourse.
ETNOGRAFIA VIRTUAL

The present work aims to show the research carried out in online environments, showing relevant information for the construction of scientifi c knowledge. The virtual environment is improving and progressing every day to provide a more facilitative resource mode for our society. Having Ethnography as a methodological support, using Youtube as a locus of data production. Taking into account the various social networks that we currently have, the video platform was selected as a place to search for the empirical material of the research. related to shares, likes, comments and views. In order to be able to track the most relevant information about events related to the negative impacts of the pandemic on the environment. made with the broadcasting of news and information regarding the knowledge of these impacts, to the audience that most frequents the video platform. As we know, the situation started in China and spread worldwide. And with that, one of the means used to suppress the advance of the virus as a form of combat attributed to society was isolation and social distance. What can be seen is that the pandemic had a very impacting factor on our environment, such as isolation and social distance, caused by restrictions due to the pandemic, had a signifi cant increase in hospital waste worldwide, as well as plastics discarded in domestic environments.
Resistome, mobilome, and virulome explored in clinical isolates derived from acne patients in Egypt: unveiling unique traits of an emerging coagulase-negative Staphylococcus pathogen

Coagulase-negative staphylococci (CoNS) are a group of gram-positive staphylococcal species that naturally inhabit the healthy human skin and mucosa. The clinical impact of CoNS-associated infections has recently been regarded as a challenge for diagnosis and therapeutic options. CoNS-associated infections are primarily caused by bacterial resistance to antibiotics and biofilm formation. As antibiotics are still the most used treatment, this problem will likely persist in the future. The present study aimed to investigate the resistance and virulence of CoNS recovered from various acne lesions and explore their genetic basis. Skin swab samples were collected from participants with acne and healthy skin. All samples underwent conventional culture for the isolation of CoNS, MALDI-TOF confirmation, antibiotic susceptibility, and biofilm formation testing. A total of 85 CoNS isolates were recovered from the samples and preliminarily identified as Staphylococcus epidermidis. Isolates from the acne group (n = 60) showed the highest rates of resistance to penicillin (73%), cefoxitin (63%), clindamycin (53.3%), and erythromycin (48%), followed by levofloxacin (36.7%) and gentamycin (31.7%). The lowest rates of resistance were observed against tetracycline (28.3%), doxycycline (11.7%), and minocycline (8.3%). CoNS isolated from mild, moderate acne and healthy isolates did not show strong biofilm formation, whereas the isolates from the severe cases of the acne group showed strong biofilm formation (76.6%). Four extensively drug-resistant and strong biofilm-forming staphylococcal isolates recovered from patients with severe acne were selected for whole-genome sequencing (WGS), and their genomes were investigated using bioinformatics tools. Three of the sequenced genomes were identified as S. epidermidis; however, isolate 29AM was identified as Staphylococcus warneri, which is a newly emerging pathogen that is not commonly associated with acne and was not detected by MALDI-TOF. All the sequenced strains were multidrug-resistant and carried multiple resistance genes, including blaZ, mecA, tet(K), erm(C), lnuA, vgaA, dfrC, fusB, fosBx1, norA, and vanT, which were found to be located on plasmids and chromosomes. Virulence features were detected in all genomes in the presence of genes involved in adherence and biofilm formation (icaA, icaB, icaC, sdrG, sdrH, atl, ebh, and ebp). Only the S. warneri isolate 29AM contained immune evasion genes (capB, capC, acpXL, and manA), an anti-phagocytosis gene (cdsA), and other unique features. As a result of their potential pathogenicity and antibiotic resistance, CoNS must be monitored as an emerging pathogen associated with acne infections. To the best of our knowledge, this is the first report to isolate, identify, and correlate S. warneri with severe acne infections among Egyptian patients using WGS and bioinformatic analysis.
Species diversity in the Phanerozoic: species-area effects

Raup's (1976a) data on Phanerozoic species numbers are examined for species-area relationships, using published estimates of areas of continental seas. By means of multiple regression, species numbers are regressed on both estimated areas of seas and amounts of available rock for sampling, as measured by outcrop area and rock volume. Although the sampling effects apparently have the strongest influence on fossil species diversity, areas of seas substantially increase the total correlation, suggesting that Phanerozoic species numbers were in equilibrium with habitat area. This is further supported by the fact that estimated parameters in the regressions are fairly consistent with established island biogeographic theory. Much of the remaining residual variation can be explained by periods of disequilibrium.
Bacteriological Profile of Patients Suffering from Acute Versus Chronic Port Site Infection at a Tertiary Care Hospital

Background: Surgical (port) site infections can complicate the course of recovery in a surgical patient. It is important to know the spectrum of organisms and their antibiotic sensitivity pattern for targeted treatment of SSI. Objective: To determine bacteriological profile of patients suffering from acute and chronic surgical (port) site infection (SSI). Methods: This descriptive cross-sectional study was held at CMH, Rawalpindi from May-Dec 2022 which included the patients who had elective laparoscopic surgery and eventually developed acute or chronic surgical-site infection. Bacteriologic profile of both acute and chronic surgical-site infections was assessed along with their sensitivity to various commonly used antibiotics. Data was analyzed by statistical package for social sciences 21. Results: A total of 526 patients had elective laparoscopic surgical procedures during study period amongst which 60 patients (11.40%) developed SSI which were included in the study. Amongst these 60 patients, 39 (65.00%) were male and 21 (35.00%) were females with a mean age of 34.31 ± 8.93years. Acute SSI occurred in 38 (63.30%) while 22 (36.70%) were found to have chronic SSI. In cases of acute SSI, Staphylococcus aureus while in chronic SSI cases, Mycobacterium fortuitum was common. Pseudomonas aeruginosa was found in both types of SSI. Conclusion: Overall frequency of SSI was 11.40%. In acute SSI, Staphylococcus aureus was the main culprit while in chronic SSI, Mycobacterium fortuitum was found to be the major pathogen.
Post-Anesthesia Cognitive Dysfunction in Mice Is Associated with an Age-Related Increase in Neuronal Intracellular [Ca2+]—Neuroprotective Effect of Reducing Intracellular [Ca2+]: In Vivo and In Vitro Studies

Background: Postoperative cognitive dysfunction (POCD) is a common disorder after general anesthesia in elderly patients, the precise mechanisms of which remain unclear. Methods: We investigated the effect of isoflurane with or without dantrolene pretreatment on intracellular calcium concentration ([Ca2+]i), reactive oxygen species (ROS) production, cellular lactate dehydrogenase (LDH) leak, calpain activity, and cognitive function using the Morris water maze test of young (3 months), middle-aged (12–13 months), and aged (24–25 months) C57BL6/J mice. Results: Aged cortical and hippocampal neurons showed chronically elevated [Ca2+]i compared to young neurons. Furthermore, aged hippocampal neurons exhibited higher ROS production, increased LDH leak, and elevated calpain activity. Exposure to isoflurane exacerbated these markers in aged neurons, contributing to increased cognitive deficits in aged mice. Dantrolene pretreatment reduced [Ca2+]i for all age groups and prevented or significantly mitigated the effects of isoflurane on [Ca2+]i, ROS production, LDH leak, and calpain activity in aged neurons. Dantrolene also normalized or improved age-associated cognitive deficits and mitigated the cognitive deficits caused by isoflurane. Conclusions: These findings suggest that isoflurane-induced cytotoxicity and cognitive decline in aging are linked to disruptions in neuronal intracellular processes, highlighting the reduction of [Ca2+]i as a potential therapeutic intervention.
Landsat Image Analysis of Tree Mortality in the Southern Sierra NevadaRegion of California during the 2013-2015 Drought

The United States Forest Service (USFS) observed marked increases in tree mortality in the southern Sierra Nevada foothills and mountains of California using aerial monitoring surveys in April 2015. Aircraft flew over 4 million acres of forested land and found that about 20 percent of forest stands had detectable mortality, totaling 10.45 million dead trees. In this study, Landsat satellite imagery was analyzed at over 90% of these high tree mortality sites in the southern Sierra to understand how the three consecutive years (2013-2015) of extreme drought conditions compared to changes in forest stand growth rates dating to the mid-1980s across this region. Results showed that changes in Landsat droughtsensitive indices from the years 2011 to 2015 closely matched patterns of tree mortality across USFS April 2015 aerial survey locations in the southern Sierra Nevada. The historically low snow year of 2015 could have essentially reset the average forest canopy density for many forests in the region to late 1980s levels, due to drought-related mortality, combined with numerous large stand-replacing wildfires. The corresponding patterns and trends in Landast droughtsensitive indices with observed tree mortality rates can better inform region-wide assessments of forest growth trends and enable long-term drought impact monitoring.