Miracle-dz/UniFAR-FTUs huggingface dataset:
Una nuova correlazione fra energia solare e parametri atmosferici

Analyse approchee du bilan energetique d'un systeme constitue d'une couche d'air atmospherique et d'une couche superficielle de terrain adjacent. Etablissement de l'existence d'une fonction de correlation entre le rayonnement solaire sur la surface du terrain et la temperature de bulbe humide de la couche atmospherique. Etablissement d'une relation explicite de cette fonction. Verification de la correlation obtenue par analyse de regression lineaire des donnees relevees dans 7 stations meteorologiques italiennes
The Formation of Oscillation Patterns Based on the Planetary Gravitational Field and Their Suitability for Earthquake Prediction

The fluctuating planetary gravitational field influences not only activities on the Sun but also on the Earth. A special correlation function describes the harmonics of these fluctuations. Groups of earthquakes form oscillation patterns that differ significantly from randomly chosen control groups. These patterns are suitable as an element of an AI for the probability of earthquakes. In a study of the nonlinear interaction of the fluctuating planetary gravitational field with the lithosphere suggests that not only the directly acting gravitational forces are of influence, but mainly higher harmonics of the celestial bodies considered as oscillators on large scales [1,11,12]. In the meantime, resonances caused by fluctuating gravitation can also be detected on small scales in the laboratory [2]
Android Cloud Antivirus Based on Static Analysis

Smart Phones are quickly becoming necessities for many people. Smartphones are now used for more than just making and receiving phone calls. They can manage social networks, send emails, send money online, do online shopping, book tickets online, watch videos, do research, and a lot of other services. Unfortunately, they also serve as prime locations for malware to be spread by hackers. Because data movement has increased in our modern time, security is crucial. Running computationally demanding antivirus algorithms on smartphones is hard because they require too much storage, processing, and communication overhead. We developed an Android antivirus application that used static analysis to detect if any application contains malware. The antivirus uses a TF-IDF feature extractor and extra trees classifier. We conducted two experiments. In the first experiment, antivirus is downloaded on Android devices and execution of the antivirus is entirely on the Android devices. In the second experiment, we run the malware detection process on a server to achieve the cloud computing concept. Cloud Computing is using a remote server network rather than a local server or a personal smartphone to store, manage, and process data. We compare detection time in each experiment. The results show that using cloud computing saves time and overhead. Cloud antivirus is a fast and lite way to scan an Android application.
Species Coexistence and Extinction Resulting from Higher-Order Lotka-Volterra Two-Faction Competition

It is known that the effect of species' density on species' growth is non-additive in real ecological systems. This challenges the conventional Lotka-Volterra model, where the interactions are always pairwise and their effects are additive. To address this challenge, we introduce HOIs (Higher-Order Interactions) and are able to capture, for example, the indirect effect of one species on a second one correlating to a third species. Towards this end, we propose a purely cooperative higher-order Lotka-Volterra model and a higher-order Lotka-Volterra two-faction competition model. By utilizing the theory of monotone systems, we provide stability conditions for both models. The stability analysis further shows that small HOIs usually promote the coexistence of all species, while the extinction of some species is usually caused by a huge difference among the higher-order competitive terms. Finally, illustrative numerical examples are provided to highlight our contributions.
Construction for ontology of stroke in Traditional Chinese Medicine

Objective: Using ontology technology to construct a TCM ontology for stroke, transform natural language into a machine-readable language, and realize knowledge sharing and reasoning in the field of TCM for stroke. Methods: Under the guidance of Stanford University's "seven-step approach", we integrated and analyzed information related to the field of stroke disease, sorted out the basic knowledge framework of the disease, and used Protege 5.5. 0 to build a Chinese medicine ontology of stroke to complete the knowledge representation and knowledge storage of the field. Results: Finally, 69 classes, 456 instances, 22 object properties, 3 data properties, and 2219 axioms were constructed, and a more comprehensive formal representation of knowledge in the field of stroke and Chinese medicine was achieved. Conclusion: The TCM knowledge of stroke has been integrated and relevant domain ontologies have been constructed to provide knowledge support and reasoning basis for knowledge sharing, reuse, knowledge base and construction of clinical aid decision system.
A Review on Privacy and Anonymity in Blockchain Security

The advent of blockchain technology has revolutionized various industries, including finance, supply chain management, and healthcare. However, concerns regarding privacy and anonymity in blockchain security have emerged as significant challenges. This paper aims to investigate the current state of privacy and anonymity in blockchain security by gathering insights from a diverse group of recent research. we explore the current understanding, practices, and challenges related to privacy and anonymity in blockchain security, to identify the importance of them, assess the effectiveness of existing privacy-enhancing techniques, and uncover potential areas for improvement.
The Paleoceanographic and Paleoclimatic Signature of the Cretaceous/Paleogene Boundary in the Antarctic: Stable Isotopic Results from ODP Leg 113

Stable isotopic records across the Cretaceous/Paleogene (K/P) boundary in Maud Rise Holes 689B and 690C indi­ cate that significant climatic changes occurred during the latest Cretaceous, beginning approximately 500 k.y. prior to the mass extinction event and the enrichment of iridium at the K/P boundary (66.4 Ma). An oxygen isotopic decrease of ~ 0.7%o~ 1.0%o is recorded in the Late Cretaceous planktonic and benthic foraminifers between 66.9 and 66.6 Ma. The negative isotope excursion was followed by a positive excursion of similar magnitude between 66.6 Ma (latest Creta­ ceous) and —66.3 Ma (earliest Paleocene). No other isotopic excursions of this magnitude are recorded in the plank­ tonic and benthic microfossil records 1.0 m.y prior to, and for 2.0 m.y following the mass extinction event at the K/P boundary. The magnitude and duration of these isotopic excursions were similar to those at the Paleocene/Eocene and Eocene/Oligocene boundaries. A major 5C excursion occurred 200 k.y. prior to the boundary, involving a positive shift in planktonic and benthic oC of ~0.5%o-0.75%o. Similar changes observed in other deep-sea sequences indicate that this reflected a global change in 5C of the oceanic total dissolved carbon (TDC) reservoir. The magnitude of this inferred carbon reservoir change and its association with high latitude surface-water temperature changes recorded in the 60 records implies that it was linked to global climate change through feedback loops in the carbon cycle. At the K/P boundary, the surface-to-deep water 6C gradient is reduced by approximately 0.6%o-~0.2%o. However, unlike sequences elsewhere, the planktonic-benthic 6C gradient (A5C) was not eliminated in the Antarctic. The sur­ face-to-deep water gradient was re-established gradually during the 400 k.y. following the mass extinction. Full recovery of the A6C occurred by —60.0 Ma. In addition to the reduced vertical 5C gradient across the K/P boundary, there was a negative excursion in both planktonic and benthic 6C beginning approximately 100 k.y. after the boundary (66.3 Ma). This excursion resulted in benthic 5C values in the early Paleogene that were similar to those in the pre-K/P boundary intervals. This negative shift appears to reflect a change in the 5C of the oceanic TDC reservoir shift that may have resulted from reduced carbon burial and/or increased carbon flux to the oceans. Any model that attempts to explain the demise of the oceanic plankton at the end of the Cretaceous should consider the oceanic environmental changes that were occurring prior to the massive extinction event.
Investigating the Complex Signal Kurtosis for SAR Ship Classification

This paper explores the application of the complex signal kurtosis (CSK) for ship classification in single-channel synthetic aperture radar (SAR) images. Preliminary results based on the OpenSARShip dataset reveal varying trends among oil tankers, bulk carriers, container ships and fishing vessels. In addition, high-resolution samples from Capella Space were used to examine the contribution of different ship sections, which showed that the deckhouse has the strongest influence on the CSK in the majority of cases. Nonetheless, it is important to note that limitations in either the spatial resolution or the number of available samples restrict the generalisability of the results. Consequently, this paper emphasises the necessity for a large, open dataset consisting of complex-valued, high-resolution samples to comprehensively investigate the role of the CSK, as well as phase information in general, for ship classification.
An Intrusion Detection System for Smart Autonomous E-Bikes

As the world continues to face environmental challenges, cost-effective ecological mobility solutions are sought. In this context, E-bikes have been gaining popularity. In this context, a Smart Autonomous E-Bike prototype has been implemented at our institution. This Bike can be seen as an IoT (Internet of Things) network of synchronized embedded devices and sensors/actuators that communicate and exchange data. With multiple connected modules that must function safely, securing a Smart Bike becomes crucial to avoid breaches that can cause severe consequences, such as accidents or disclosure of sensitive information. Hence, this paper presents an effective Smart Autonomous Bike Intrusion Detection System (IDS) specifically designed for smart autonomous bikes to address the aforementioned concerns. This IDS is capable of detecting and mitigating various types of cyber threats targeting different modules within the smart bike architecture, including path planning, platooning and Advanced Rider Assistance Systems (ARAS) mechanisms. The proposed system is capable of detecting both network attacks and physical tampering on the hardware components of the bike. An open-source Network-Based IDS (Snort) is first used to detect network intrusions, and an anomaly detection system is used for physical attacks. As an enhancement, the proposed IDS employs an additional hybrid Machine Learning (ML) techniques for anomaly and signature classification to analyse incoming external network traffic to detect intrusion attempts, anomalous activities, and potential security breaches. The enhanced module of the proposed IDS is trained using a comprehensive dataset (Canadian Institute for Cybersecurity Intrusion Detection System (CICIDS)-2017) and assessed using different evaluation parameters.
Variational Autoencoder with Temporal Condition for Effective Shape-based Calcium Imaging Neuron Registration

Thanks to recent advances in optical imaging techniques, calcium imaging can now record the activities of thousands of neurons simultaneously, through several sessions and over long periods. Neuron registration assumes a vital role in this process, enabling the monitoring of neurons across multiple movies and extended timeframes by aligning their spatial patterns. Previous approaches often relied on clusters or probabilistic models based on simple distance metrics like overlapping pixels or center-to-center distances, neglecting crucial neuron shape and spatial relationship details. In this paper, we introduce a novel technique for cell registration. Our investigation revealed that a neuron’s shape is influenced by its temporal behaviors, leading us to suggest a temporal-conditional variational autoencoder (tcVAE) for precise shape modeling. A comprehensive evaluation demonstrates that incorporating shape-related details can significantly enhance the quality of neuron registration.
Feasibility of Comparing Constructive Spatial Processes in Real and Virtual Spaces Among School-Age Children: A Randomized Pilot Study

Nowadays new information and communication technologies (IT) directly influence teaching methods, practices and resources. The use of virtual reality (VR) technology in education enables new approaches that can enhance, guide, and stimulate students' interest in learning while allowing them to experience the subject and acquire sensory and intuitive cognition in a more personal and realistic environment. This research aims to answer the question: can constructive spatial activity with real materials in real space be replaced by virtual space and digital tools in terms of assessing stability and knowledge acquisition? This current pilot study aims to examine the feasibility of our research setup and analysis methods while children are asked to perform an intuitive creative process to construct a structure in real and digital space. We involved twelve school-age (8,47 years average) from an elementary school, and we randomly allocated them to group 1 (real space construction) and to group 2 (tablet application construction). We created a behavioral coding scheme for the widely used and validated Noldus Observer XT (version 15) video behavior analyzer software. The video analyses showed that the creation phases defined in the construction process appeared on both real space and in a virtual setting, confirming the assumption that the acquisition of knowledge and skills related to stability can also be phased. Based on our pilot research on a small sample, the chosen observation and research methodology is feasible. We predict that the real-space constructive task applied in the measurement can be replaced by a digital interface to experience stability.
Numerical Studies for Magneto-Acousto-Electrical Tomography with Magnetic Field Measurement Using Barker Coded Excitation

Magneto-acousto-electrical tomography (MAET) is an imaging technique that utilizes the measurement of electrical currents induced by Lorentz force. This paper investigates the feasibility of applying the pulse compression method to MAET with magnetic field measurements through numerical studies. A numerical breast model with three tumor inhomogeneities of different lengths and at different depths from the transducer surface is employed. The ultrasound transducer is excited with 7-bit and 13-bit Barker codes for pulse compression. Additive white Gaussian noise is introduced to the MAET signal sensed by the receiver coils. The results demonstrate that pulse compression method leads to improvements of 14.77 dB and 17.41 dB in the signal-to-noise ratio (SNR), compared to the conventional single-cycle excitation, for 7-bit and 13-bit Barker codes, respectively. A tumor as small as 4 mm x 2 mm can be detected at an SNR of 8.96 dB with the usage of 13-bit Barker code excitation, achieving an axial resolution of 2 mm. The selected time duration of the transmitted code in the applied Barker code excitation allows for attaining the same axial resolution as the single-cycle excitation.
2.4 GHz Ultra-Low Power Direct Digital-to-RF CMOS Transmitter for Biosensing Applications

This paper presents a low-power and low-complexity direct digital-to-RF transmitter architecture, suitable for biosensing applications. The RF front end of the transmitter is based on a ring oscillator, whose output phase is modulated through the charge-to-phase mechanism using a charge injection block. Hence, the phase shift keying (PSK) modulation can be performed directly in the RF domain. Post-layout simulation results show that the transmitter is able to collect, process, and transmit sensed data with the maximum data rate of 20 Mbps and an error vector magnitude (EVM) of smaller than 3.5%, while dissipating the DC power smaller than 0.5 mW. The results demonstrate that the proposed transmitter architecture is effective for wireless biosensing applications.
Influences of agricultural management practices on Arbuscular Mycorrhiza Fungal symbioses in Kenyan agro-ecosystems

Conservation agriculture (CA) and integrated soil fertility management (ISFM) practices are receiving increased attention as pathways to sustainable high-production agriculture in sub-Saharan Africa. However, little is known about the effects of these practices on arbuscular mycorrhizal fungi (AMF). The study aimed at understanding the long-term effects of (i) ISFM and CA on AMF communities and functioning, and on glomalin concentrations. The study also aimed at understanding the (ii) role of AMF in soil aggregation, plant nutrition and crop yield under field conditions and (iii) combined effect of AMF and earthworms on soil aggregation, plant nutrition and crop yield under greenhouse conditions. The study was conducted in two long-term field trials. The ISFM trial was in Kabete (central Kenya) and compared fertilization (nitrogen and phosphorus) and organic amendments (farmyard manure, crop residue) for 32 years, while the CA trial was in Nyabeda (western Kenya) and compared effect of tillage (conventional versus no-tillage), residue application, cropping system (monocropping versus rotation) and N-fertilization for 5 years. Long-term use of mineral fertilizer and organic amendments, as well as tillage and N fertilization altered AMF species composition, but the changes were relatively minor. Organic amendments alone or in combination with NP fertilization increased AMF incidence, whereas no-tillage in the presence of residue increased spore abundance and root colonization. N fertilization increased root colonization but had a negative effect on spore abundance and species richness. Crop rotation had no effect on AMF. Glomalin was also sensitive to management, but the response was site-specific. Glomalin responded more to CA in Nyabeda than ISFM in Kabete. N fertilization and residue increased glomalin, especially under conventional tillage. Path analysis indicated that AMF symbiosis and glomalin enhanced soil aggregation and crop nutrition and yield in both sites. The positive role of AMF on crop nutrition was stronger in Kabete than Nyabeda. However, yield and nutrient use efficiency were (very) low in Kabete. There was no interaction between AMF and earthworms on soil aggregation, but AMF enhanced soil aggregation. AMF interacted positively with the epigeic earthworm to enhance nutrient uptake and biomass production, but the endogeic earthworm negatively affected AMF symbiosis and function. The study highlights the potential of ISFM and CA practices in enhancing AMF diversity and activity, and indicates factors limiting AMF functioning under ISFM and CA systems. While AMF are important for agro-ecosystem functioning, remedying the non-responsive character of soils, especially Kabete, through judicious management of nitrogen and organic amendments remains a first priority.
Group Attention-based Multi-wave Graph Learning for Dementia Diagnosis

Dementia is a syndrome caused by various diseases and injuries to the brain. Considering the differences in brain activity among different dementias, Electroencephalogram (EEG) has been used for dementia diagnosis. EEG can be divided into different brain waves according to frequency, each of which is associated with a distinct brain function. However, current EEG-based methods rarely consider the correlation between different brain waves. To address this challenge, we propose a group attention-based multi-wave graph learning (GAMGL) framework for dementia diagnosis. In order to effectively capture the hidden complementary information between different waves, GAMGL introduces the multi-scale grouped representation module and the group attention module, which extract multi-scale features from various brain waves and adaptively fuse the multi-wave and multi-scale features by using the group attention mechanism. Then, to reveal the hidden connections among subjects, a population graph with fused features as graph nodes is constructed, and efficient dementia diagnosis is performed by graph convolution network. GAMGL is evaluated on an in-house EEG dataset containing three types of dementia, namely Alzheimer’s disease, Vascular cognitive impairment and Frontotemporal dementia. Experimental results show that GAMGL outperforms state-of-the-art methods and can be an efficient and accurate framework for dementia diagnosis.
Safe Digital Stabilization of Nonlinear Systems with an Application to Glucose Control

In this paper, we propose a framework for the semiglobal practical safe stabilization of nonlinear continuous-time systems based on limited amount of information. This approach requires the availability of a continuous-time control law ensuring global asymptotic stability and a robust safety condition. Following an emulation-based approach, we introduce time sampling and quantizations on the input and state signals. We show that sufficiently high sampling frequency and small quantization error guarantee safety preservation and practical state stabilization to an arbitrarily small neighborhood of the origin while keeping the state within a safe region during the whole system evolution, which is essential in safety-critical applications. Numerical simulations on a glucose control problem in a non-ideal setting show the effectiveness of the approach.
Multitask learning approach for lung nodule segmentation and classification in CT images

Amongst the different types of cancer, lung cancer is the one with the highest mortality rate and consequently, there is an urgent need to develop early detection methods to improve the survival probabilities of the patients. Due to the millions of deaths that are caused annually by cancer, there is large interest int the scientific community to developed deep learning models that can be employed in computer aided diagnostic tools.Currently, in the literature, there are several works in the Radiomics field that try to develop new solutions by employing learning models for lung nodule classification. However, in these types of application, it is usually required to extract the lung nodule from the input images, while using a segmentation mask made by a radiologist. This means that in a clinical scenario, to be able to employ the developed learning models, it is required first to manually segment the lung nodule. Considering the fact that several patients are attended daily in the hospital with suspicion of lung cancer, the segmentation of each lung nodule would become a tiresome task. Furthermore, the available algorithms for automatic lung nodule segmentation are not efficient enough to be used in a real application.In response to the current limitations of the state of the art, the proposed work attempts to evaluate a multitasking approach where both the segmentation and the classification task are executed in parallel. As a baseline, we also study a sequential approach where first we employ DL models to segment the lung nodule, corp the lung nodule from the input image and then finally, we classify the cropped nodule. Our results show that the multitasking approach is better than to sequentially execute the segmentation and classification task for lung nodule classification. For instances, while the multitasking approach was able to achieve an AUC of 84.49% in the classification task, the sequential approach was only able to achieve an AUC of 72.43%. These results show that the proposed multitasking approach can become a viable alternative to the classification and segmentation of lung nodules.