Miracle-dz/UniFAR-FTUs huggingface dataset:
Task-Oriented Satellite-UAV Networks With Mobile-Edge Computing

Networked robots have become crucial for unmanned applications since they can collaborate to complete complex tasks in remote/hazardous/depopulated areas. Due to the cost inefficiency of deploying cellular network infrastructure in these areas, hybrid satellite-UAV networks emerge as a promising solution. These networks provide seamless and on-demand connectivity for multiple robots with various task requirements, and support computation-intensive and latency-sensitive services through mobile edge computing (MEC)-based offloading. However, to complete tasks in limited times, the rapid collective movement of mobile robots may cause frequent service migration, and a large number of gathered robots may compete for limited bandwidth resources in satellite and UAV communications. As a result, offloading latency may increase significantly. To address this issue, the average completion time of multi-robot offloading in task-oriented satellite-UAV networks with MEC is formulated as an optimization problem. Unlike conventional mobility-aware MEC-based offloading schemes, joint optimization of mobility control, data offloading, and resource allocation is proposed using velocity control of multiple robots. According to Lyapunov optimization, the original optimization problem is simplified into minimizing the average completion time of offloading for all robots within UAV and satellite coverage. A multi-agent <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning algorithm, including multi-group dual-agent <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning, is proposed based on local state observation and global reward calculation. In each dual-agent <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning, one agent is responsible for velocity control and communication resource allocation, while the other is responsible for data offloading and computational resource allocation. The convergence of the proposed multi-agent <inline-formula> <tex-math notation="LaTeX">$Q$ </tex-math></inline-formula>-learning algorithm is also theoretically analyzed. Simulation results show that the proposed scheme can effectively reduce the offloading latency by up to 35% in the multi-robot environment over its conventional counterparts.
A Protective HLA Extended Haplotype Outweighs the Major COVID-19 Risk Factor Inherited From Neanderthals in the Sardinian Population

Sardinia has one of the lowest incidences of hospitalization and related mortality in Europe and yet a very high frequency of the Neanderthal risk locus variant on chromosome 3 (rs35044562), considered to be a major risk factor for a severe SARS-CoV-2 disease course. We evaluated 358 SARS-CoV-2 patients and 314 healthy Sardinian controls. One hundred and twenty patients were asymptomatic, 90 were pauci-symptomatic, 108 presented a moderate disease course and 40 were severely ill. All patients were analyzed for the Neanderthal-derived genetic variants reported as being protective (rs1156361) or causative (rs35044562) for severe illness. The β°39 C>T Thalassemia variant (rs11549407), HLA haplotypes, KIR genes, KIRs and their HLA class I ligand combinations were also investigated. Our findings revealed an increased risk for severe disease in Sardinian patients carrying the rs35044562 high risk variant [OR 5.32 (95% CI 2.53 - 12.01), p = 0.000]. Conversely, the protective effect of the HLA-A*02:01, B*18:01, DRB*03:01 three-loci extended haplotype in the Sardinian population was shown to efficiently contrast the high risk of a severe and devastating outcome of the infection predicted for carriers of the Neanderthal locus [OR 15.47 (95% CI 5.8 – 41.0), p < 0.0001]. This result suggests that the balance between risk and protective immunogenetic factors plays an important role in the evolution of COVID-19. A better understanding of these mechanisms may well turn out to be the biggest advantage in the race for the development of more efficient drugs and vaccines.
Assets and aspirations: Carbon management opportunities in remote indigenous communities

Two current pressing global challenges, climate change due to anthropogenic carbon emissions and poverty, are inextricably intertwined. In Australia these two issues are particularly pertinent. The nation is one of the highest per capita carbon emitters in the world, and despite being one of the most developed, the socio-economic disadvantage of its Indigenous peoples continues. This thesis provides a contribution to the dual fields of resilient and sustainable community development and climate change mitigation, with a sub-focus on asset-based assessment models for enabling community-directed low-carbon development in remote Indigenous communities. The remaining socio-economic disparities between Indigenous and non-Indigenous Australians has revealed a need for an alternative approach to the past policy incrementalism that focuses on issues and needs, and improved engagement with remote communities. Therefore, an asset-based model, the Resilient Community and Livelihood Asset Integration Model (ReCLAIM), with a focus on aspirations and a continuous participatory appraisal cycle was developed for application with a community Advisory Committee. The six-step decision support model was applied, via a series of workshops, with two remote Indigenous communities to assist their selection of goal-oriented carbon management strategies. The application of the model identified the existing and aspirational assets of the communities, their current carbon emission profiles, the carbon management strategies they preferred for their settlement areas, the modelled outcomes and implementation plans. The carbon profiles and strategies selected differed between the two communities highlighting the need for a community-directed approach to understanding the drivers of carbon emissions, removals and mitigation responses. Economic benefits were highlighted with expected cost savings to communities and service providers. The model could be adapted to a variety of contexts including urban municipalities or remote villages in developing countries.
Bottom‐slope‐induced net sheet‐flow sediment transport rate under sinusoidal oscillatory flows

It is generally believed that the slope of beaches can lead to a net downslope (usually offshore) sediment transport rate under shoaling waves, but very few high-quality measurements have been reported for a quantitative understanding of this phenomenon. In this study, full-scale (1:1) experiments of bottom-slope-induced net sheet-flow sediment transport rate under sinusoidal oscillatory flows are conducted using a tilting oscillatory water tunnel. The tests cover a variety of flow-sediment conditions on bottom slopes up to 2.6°. A laser-based bottom profiler system is developed for measuring net transport rate based on the principle of mass conservation. Experimental results suggest that for a given flow-sediment condition the net transport rate is in the downslope direction and increases linearly with bottom slope. A conceptual model is presented based on the idea that gravity helps bottom shear stress drive bedload transport and consequently enhances (reduces) bedload transport and suspension when the flow is in the downslope (up-slope) direction. The model predicts both the measured net sediment transport rates and the experimental linear relationship between net transport rates and bottom slope with an accuracy generally better than a factor of 2. Some measured net transport rates in this study are comparable to those due to flow skewness obtained in similar sheet-flow studies, despite that our maximum slope could be milder than the actual bottom slope in surf zones, where sheet-flow conditions usually occur. This shows that the slope effect may be as important as wave nonlinearity in producing net cross-shore sheet-flow sediment transport. This article is protected by copyright. All rights reserved.
AI-Driven Medical Imaging Platform: Advancements in Image Analysis and Healthcare Diagnosis

In the realm of healthcare, the integration of artificial intelligence (AI) has revolutionized medical imaging analysis [1, 2, 3]. This research paper presents a comprehensive medical imaging platform powered by artificial intelligence (AI) techniques for automated image analysis. The platform incorporates specialized AI models for image classification using ResNet50 [4], object detection using YOLOv5l [7] and segmentation using Res50-U-Net [10]. The automated pipeline seamlessly categorizes incoming medical images, directing them to appropriate analysis modules - MRI and X-ray images trigger object detection to identify abnormalities; MRI brain images undergo additional tumor segmentation. Through three pivotal phases focused on classification, detection and segmentation, this research demonstrates an effective framework to harness AI for enhanced efficiency and precision in medical image evaluation, paving the pathway towards improved clinical diagnostics and patient care. The platform's automated workflow for progressive image analysis sets it apart from existing systems. Outcomes indicate AI's immense potential to transform medical imaging, assisting clinicians through actionable insights while mitigating subjectivity and variability in manual evaluation.
Single-cell RNA-seq analyses inform necroptosis-associated myeloid lineages influence the immune landscape of pancreas cancer

Introduction Tumor-infiltrating myeloid cells (TIMs) are key regulators in tumor progression, but the similarity and distinction of their fundamental properties in pancreatic ductal adenocarcinoma (PDAC) remain elusive. Method In this study, we conducted scRNA-seq data analysis of cells from 12 primary tumor (PT) tissues, 4 metastatic (Met) tumor tissues, 3 adjacent normal pancreas tissues (Para), and PBMC samples across 16 PDAC patients, and revealed a heterogeneous TIMs environment in PDAC. Result Systematic comparisons between tumor and non-tumor samples of myeloid lineages identified 10 necroptosis-associated genes upregulated in PDAC tumors compared to 5 upregulated in paratumor or healthy peripheral blood. A novel RTM (resident tissue macrophages), GLUL-SQSTM1- RTM, was found to act as a positive regulator of immunity. Additionally, HSP90AA1+HSP90AB1+ mast cells exhibited pro-immune characteristics, and JAK3+TLR4+ CD16 monocytes were found to be anti-immune. The findings were validated through clinical outcomes and cytokines analyses. Lastly, intercellular network reconstruction supported the associations between the identified novel clusters, cancer cells, and immune cell populations. Conclusion Our analysis comprehensively characterized major myeloid cell lineages and identified three subsets of myeloid-derived cells associated with necroptosis. These findings not only provide a valuable resource for understanding the multi-dimensional characterization of the tumor microenvironment in PDAC but also offer valuable mechanistic insights that can guide the design of effective immuno-oncology treatment strategies.
Temporal signals classification

Nowadays, there are a lot of applications related to machine vision and hearing which tried to reproduce human capabilities on machines. These problems are mainly amenable to a temporal signals classification problem, due our interest to this subject. In fact, we were interested to two distinct problems, humain gait recognition and audio signal recognition including both environmental and music ones. In the former, we have proposed a novel method to automatically learn and select the dynamic human body-parts to tackle the problem intra-class variations contrary to state-of-art methods which relied on predefined knowledge. To achieve it a group fused lasso algorithm is applied to segment the human body into parts with coherent motion value across the subjects. In the latter, while no conventional feature representation showed its ability to tackle both environmental and music problems, we propose to model audio classification as a supervised dictionary learning problem. This is done by learning a dictionary per class and encouraging the dissimilarity between the dictionaries by penalizing their pair- wise similarities. In addition the coefficients of a signal representation over these dictionaries is sought as sparse as possible. The experimental evaluations provide performing and encouraging results.
Effectiveness of the Platelet Rich Fibrin with Suture and without Suture on Postoperative Complications Following Removal of Lower Third Molars

Background: The removal of impacted 3rd molar is a traumatic surgical procedure that may lead to some postoperative sequelae such as pain, swelling, and trismus. The aim of this study was to evaluate the effects of PRF with suture, and without suture, on post-operative complications after removal of 3rd molars.Patients and Materials: This study enrolled 60 patients with partially impacted lower third molars at three centers; Postgraduate Clinic- Department of Oral and maxillofacial Surgery/College of Dentistry/ University of Sulaimani, Private Dental and Maxillofacial Clinic, and Maxillofacial Unit of Kalar General Hospital from November of 2021 till April of 2022. They were randomly allocated into 3 groups of 20 patients each. Group Ӏ was managed by PRF with suture, group ӀӀ was managed by PRF without suture and group ӀII was left without PRF and without suture. Pain, swelling, and maximum interincisal mouth opening were evaluated after the procedure at 1st, 3rd and 7th post-operative days.Results: In terms of pain (p=0.06), trismus (p=0.71), and swelling (p=0.05), there were no statistically significant differences between the study groups and the control group. In contrast to the control group, the values of the three parameters connected to the study groups were lower. In the group of PRF with suture, Visual Analogue Scale (VAS) pain scores on the 3rd and 7th days were significantly (P=0.008 and P=0.038, respectively) less than other groups. Regarding swelling and trismus, there was no significant correlation (P>0.05) between groups.Conclusions: Application of PRF with and without suture had no significant effects on postoperative complications such as pain, swelling, and trismus following removal of partially impacted third molars.
The effect of extensive interval training at altitude on the physiological, aerobic, anaerobic and various blood parameters of athletes

In this research, it was aimed to compare the physiological performances of athletes at sea level, at high altitude and 8 days after returning back to sea level on the basis of certain blood parameters, pulse and blood pressure. 12 male athletes between the ages of 19 and 23 voluntarily participated in the research. The subjects were exposed to endurance training at high altitude and at sea level between 09.00 and 11.00 in the morning. The subjects’ erythrocyte (RBC), leucocyte (WBC), haemoglobin (Hb), haematocrit (HCT), systolic blood pressure at rest (SBPR) and diastolic blood pressure at rest (DBPR), heart rate at rest (HRR), aerobic (20m shuttle run test) and anaerobic capacity (vertical jump) levels were tested at sea level, on the 15th day at high altitude (3120m) and 8 days after returning back to sea level. Statistical analysis comprised of t-test and the significance level of the results was accepted at (P<0.05). As a result of the research the following were determined: It can be said that high altitude trainings for fifteen days included in the annual training program of athletes can improve their performance.
A Lightweight Authentication Protocol for M2M Communication in IIoT Using Physical Unclonable Functions

Machine to Machine (M2M) communication is one of the most important parts of the Industrial Internet of Things (IIoT). In this field, a large number of devices and sensors communicate with each other without human intervention. To establish secure communication, IIoT devices must perform mutual authentication. Besides, not only well-known attacks but physical attacks are also expected in these systems. On the other hand, many IIoT devices (e.g., smart sensors) have limited processing power and memory. Therefore, many of the existing authentication protocols are not appropriate for these systems. To meet this issue, in this paper, a new authentication scheme is presented for M2M communication in IIoT by using lightweight cryptographic functions, such as XOR operation, one-way hash function, and Physically Unclonable Function (PUF). The security analysis demonstrates that the scheme is resistant to both physical attacks and several well-known attacks. Also, the performance evaluation shows that the protocol is efficient in terms of storage, communication, and computation costs. Conclusively, in comparison with the existing protocols, our proposed protocol is more applicable for IIoT.
Práticas Organizacionais e Estrutura de Relações no Campo do Desenvolvimento Metropolitano Organizational Practices and Relationship Structure in the Field of Metropolitan Development

Jose Bonfim Albuquerque Filho * Doutorando em Administracao pelo CEPPAD/UFPR, Curitiba/PR, Brasil. Clovis L. Machado-da-Silva Ph.D. em Estudos Organizacionais e Estrategia pela Michigan State University, Estados Unidos. Professor do CEPPAD/UFPR e do PMDA/UP, Curitiba/PR, Brasil. * Endereco: Jose Bonfim Albuquerque Filho Av. Parana, 111/801, Cabral, Curitiba/PR, 80035-130. E-mail: mifnob@terra.com. br
Polyp Detection in Gastrointestinal Images using Faster Regional Convolutional Neural Network

Colorectal cancer is the third most frequently diagnosed malignancy in the world. To prevent this disease, polyps, the principal precursor, are removed during a colonoscopy. Automatic detection of polyps in this technique could play an important role to assist doctors for achieving an accurate diagnosis. In this work, we apply a state-of-the-art Deep Learning algorithm called Faster Regional Convolutional Neural Network to each colonoscopy frame in order to detect the presence of polyps. The proposed detection system contains two main stages: (1) processing of the colonoscopy frames for training and testing datasets generation, where artifacts are extracted and the number of images in the dataset is augmented; and (2) the Neural Network model, which performs feature extraction over the frames in order to detect polyps within the frames. After training the algorithm under different conditions, our result shows that the proposed system detection has a precision of 80.31%, a recall of 75.37%, an accuracy of 71.99% and a specificity of 65.70%.
Complex Relationships Among Watershed Land Cover and Reservoir Morphometry, Productivity, and Zooplankton Communities

COMPLEX RELATIONSHIPS AMONG WATERSHED LAND COVER AND RESERVOIR MORPHOMETRY, PRODUCTIVITY, AND ZOOPLANKTON COMMUNITIES by Elisabeth J. Hagenbuch Proper management of aquatic systems requires knowledge of the links among landscape-level anthropogenic disturbances, terrestrial subsidies, and aquatic ecosystem properties. Due to their large watershed area to surface area ratios, reservoirs often receive substantial subsidies of nutrients and sediments. To better understand relationships between environmental variables and reservoir ecosystem properties, we examined relationships among variables representing landscape-level features, reservoir morphometry, water quality parameters and zooplankton community biomass for 109 reservoirs spanning a wide productivity gradient. A principal components analysis (PCA) identified two significant environmental gradients, the first representing reservoir productivity and land use cover and the second representing reservoir morphometry. A regression tree analysis used landscape-level and morphometric parameters to classify reservoirs according to their productivity level into four groups. Significant correlations were detected between small-bodied zooplankton (rotifers and/or copepod nauplii) and several environmental parameters. Non-metric multidimensional scaling plots identified a separation between reservoirs in the highest and lowest productivity groups, but only for rotifer communities. COMPLEX RELATIONSHIPS AMONG WATERSHED LAND COVER AND RESERVOIR MORPHOMETRY, PRODUCTIVITY, AND ZOOPLANKTON ASSEMBLAGES
Dispersion in the Lee of a Headland: A Case Study of Circulation off Coffs Harbour

Efficient dispersion of pollutants discharged into coastal environments is predicated on a detailed understanding of the interactions of flow and local bathymetry. The purpose of our study was to investigate such interactions in order to assess the dispersion of effluent from the Coflfs Harbour Sewagerage Strategy. Times series and transect Acoustic Doppler Current Profiles (ADCP) were used together with local wind data, 3DD hydrodynamic modelling and CORMEX modelling to evaluate dispersion characteristics in coastal waters south of Corambirra Point just beyond the southern limit of the Solitary Islands Marine Park. Based on the good correlation between local winds and observed surface currents we found that our study period (September to November 2000) favoured southward flows compared to long-term wind data which indicated that wind driven along-shore flows were typically equally distributed northward and southward. Current meter records indicated wake effects and phase eddies (transient recirculation) in the lee of Corambirra Point. The resulting shear zone was located inshore of the 30m isobath, where surface flows were 2-3 times less than those offshore. Retarded and variable flows within this sheltered zone in the lee of Corambirra Point limit the potential for dispersion and increase potential for re-entrainment of plume waters compared to offshore waters. ADCP transects suggested that the shear zone may be relatively discrete with little evidence of increasing along-shore current gradient offshore from the 30m isobath. Offshore flows included a lower proportion of northward currents compared to inshore locations. Observations together with plume modelling (CORMEX) indicate potential benefits in discharging beyond the shear zone. These factors together with cost considerations provide a basis to optimise discharge designs and subsequent monitoring strategies.
Utilization of Artificial Intelligence Technology in Higher Education Management

: Traditional university education management has issues such as low efficiency and lack of personalization. As artificial intelligence (AI) technology develops rapidly, its application in educational management in universities is increasingly becoming a focus of attention for academics and educational institutions. To explore the application of AI technology in higher education management, this paper focused on personalized course recommendations for students. The data from the 2010 KDD Cup Education Data Mining Challenge dataset was collected and cleaned using Talend and Apache Spark tools; information features were extracted using information gain, and finally the data was trained using the C4.5 decision tree algorithm to obtain a recommendation model. After experiments, the precision of this model for students' preferences in course selection reached 94%, and the F1 value of the model reached 0.93, indicating that the model had good precision and comprehensiveness. At the same time, the highest recommended course click through rate reached 0.39, indicating that the personalized recommendation ability of the model was excellent. This model improved the efficiency of students' course selection and the utilization of educational resources, exploring new ways for university education management.