Miracle-dz/UniFAR-FTUs huggingface dataset:
A Comparative Study of IDS-Based Deep Learning Models for IoT Network

The proliferation of connected devices within Internet of Things (IoT) networks has underscored the critical necessity of developing efficient Intrusion Detection Systems (IDS) that can adeptly identify and counteract threats. Deep learning algorithms are better than classical machine learning at automatically learning patterns and representations from raw data, enabling them to detect complex attacks. This research aims to conduct a comparative study of IDSs implemented through three deep learning models: Convolutional Neural Networks (CNN), Deep Neural Networks (DNN), and Recurrent Neural Networks (RNN). The study employs three datasets (NF-UNSW-NB15, NF-BoT-IoT, and NF-ToN-IoT) to assess model performance in both binary and multiclass classification scenarios. The findings indicate that for binary traffic classification, DNN outperforms the other two models with an accuracy bout (98%) for all datasets. In the context of multiclass traffic classification, the DNN model surpasses the performance of the other model except for NF-ToN-IoT a CNN model score (69.08%).
A Temporal Differential Dynamic Logic Formal Embedding

Differential temporal dynamic logic dTL2 is a logic to specify and verify temporal properties of hybrid systems. It extends differential dynamic logic (dL) with temporal operators that enable reasoning on intermediate states in both discrete and continuous dynamics. This paper presents an embedding of dTL2 in the Prototype Verification System (PVS). The embedding includes the formalization of a trace semantics as well as the logic and proof calculus of dTL, which have been enhanced to support the verification of universally quantified reachability properties. The embedding is fully functional and can be used to interactively verify hybrid programs in PVS using a combination of PVS proof commands and specialized proof strategies.
Lean Formalization of Extended Regular Expression Matching with Lookarounds

We present a formalization of a matching algorithm for extended regular expression matching based on locations and symbolic derivatives which supports intersection, complement and lookarounds and whose implementation mirrors an extension of the recent . NET NonBacktracking regular expression engine. The formalization of the algorithm and its semantics uses the Lean 4 proof assistant. The proof of its correctness is with respect to standard matching semantics.
C2f-Enhanced YOLOv5 for Lightweight Concrete Surface Crack Detection

In recent years, there have been significant breakthroughs in the field of detection using deep learning technology. What used to be challenging defects for traditional visual methods can now be addressed with the help of deep learning techniques. This paper employs the YOLOv5 network architecture to achieve rapid and precise detection of concrete surface cracks. Additionally, it integrates the C2f module to overcome issues like gradient vanishing and information bottlenecks, which can limit the performance of the traditional YOLOv5 network. The C2f module enhances feature propagation and utilization by introducing strategies such as feature reuse, cross-stage partial connections, and attention mechanisms, thereby improving feature representation and information flow. Various training techniques are also applied to enhance training speed and detection accuracy, including weighted residual connections (WRC), cross-stage partial connections (SCP), cross mini-batch normalization (CmBN), self-adaptive training (SAT), and mish activation function. As a result, the system achieves a detection accuracy of 96.91% on a concrete crack detection dataset.
Measuring the added value of virtual communities of practice for developing the educator role of critical care professionals

In healthcare settings, workplace learning is often supported by clinicians who strive to combine service provision and educator roles. We evaluated an international 12-month programme that supports widely distributed critical care health professional educators (HPEs) through a virtual community of practice (vCoP). Specifically, we evaluate if and how the vCoP approach affects learning experiences using an innovative evaluation framework in medical education—the value-creation framework (VCF). We used a mixed-methods approach to evaluation, including an anonymous survey and semistructured interviews. Themes from data sources were identified using the VCF as the common thread. Themes discussed by at least two-thirds of interview participants were analysed using narrative inquiry. 27 of 66 participants responded to the survey, and 15 participated in interviews. Positive and negative indicators of value creation were extracted and organised according to the framework’s eight value cycles. Framework analysis made value-creation and potential flow-on effects in one value-creation cycle to another visible, offering insight into relationships. Themes from narrative inquiry elaborated on the results of the framework analysis. Using the VCF to evaluate the Incubator programme brings to bear the complexity of boundary-crossing HPE faculty development for critical care educators. The framework can be a valuable tool for evaluating a vCoP associated with faculty development programmes.
An Improved YOLOv5-based Network on Pine Wilt Disease Detection Using UAV Image

Pine is one of the important forest tree species in China, with a wide distribution and significant economic value, accounting for over 70% of China's artificial forest area. However, due to the rapid spread of pine wilt disease, a large number of pine resources have been damaged, seriously affecting ecological civilization. The most effective method currently is to promptly detect and accurately locate pine trees infected with pine wilt disease (diseased trees). Manual survey and traditional detection method based on satellite remote sensing image have limitations in monitoring complex forest areas. With the advantages of real-time monitoring, low cost, and high spatial resolution, unmanned aerial vehicle (UAV) image has been widely used for agriculture and forestry. Currently, UAV images are mostly used for monitoring diseased trees. To further improve the accuracy of identifying diseased trees, this paper proposes a diseased tree detection method based on improved YOLOv5s network, which combines the YOLOv5s algorithm with Bi-directional Feature Pyramid Network (BiFPN) and Shuffle Attention (SA). In order to demonstrate the role and mutual influence of various modules in the improved YOLOv5s network, the detection results of the improved YOLOv5s algorithm and other algorithms on the same pine wilt disease dataset are compared. The experimental results show that our improved algorithm increased 2.1% in mAP and 6.1% in recall while the quantity of parameter and GFLOPs have only increased by 3.2% and 2.5%, respectively.
A Survey of Magnetic Field Line Curvature in Jovian Dawn Magnetodisc

The Jovian magnetosphere is highly dynamic, influenced by both solar wind and internal processes associated with the rapid planetary rotation and Io's volcanic activities. Accompanying the mass and energy circulations driven by the magnetospheric dynamics, the magnetic configuration also changes dramatically. One of the crucial parameters to characterize the magnetic configuration is magnetic field line curvature (FLC), which generally describes how stretched the field line is. The curvature is pivotal to influence particle behaviors, for example, pitch angle scattering which may lead to auroral particle precipitation. In this work, a method is proposed to investigate the real‐time magnetic FLC in Jovian current sheet using the magnetic field data from the Juno spacecraft. The results indicate that the FLC scattering of ions and relativistic electrons are common in Jovian magnetosphere, providing a crucial insight to understand the particle behaviors.
Thermodynamic Characterization of LC3/GABARAP Ligands by Isothermal Titration Calorimetry

Isothermal titration calorimetry (ITC) is a widely used technique for the characterization of protein-protein and protein-ligand interactions. It provides information on the stoichiometry, affinity, and the thermodynamic driving forces of interactions. This chapter exemplifies the use of ITC to investigate interactions between human autophagy modifiers (LC3/GABARAP proteins) and their interaction partners, the LIR motif containing sequences. The purpose of this report is to present a detailed protocol for the production of LC3/GABARAP-interacting LIR peptides using E. coli expression systems. In addition, we outline the design of ITC experiments using the LC3/GABARAP:peptide interactions as an example. Comprehensive troubleshooting notes are provided to facilitate the adaptation of these protocols to different ligand-receptor systems. The methodology outlined for studying protein-ligand interactions will help to avoid common errors and misinterpretations of experimental results.
MATE-Pred: Multimodal Attention-based TCR-Epitope interaction Predictor

An accurate binding affinity prediction between T-cell receptors and epitopes contributes decisively to develop successful immunotherapy strategies. Some state-of-the-art computational methods implement deep learning techniques by integrating evolutionary features to convert the amino acid residues of cell receptors and epitope sequences into numerical values, while some other methods employ pre-trained language models to summarize the embedding vectors at the amino acid residue level to obtain sequence-wise representations. Here, we propose a highly reliable novel method, MATE-Pred, that performs multi-modal attention-based prediction of T-cell receptors and epitopes binding affinity. The MATE-Pred is compared and benchmarked with other deep learning models that leverage multi-modal representations of T-cell receptors and epitopes. In the proposed method, the textual representation of proteins is embedded with a pre-trained bi-directional encoder model and combined with two additional modalities: a) a comprehensive set of selected physicochemical properties; b) predicted contact maps that estimate the 3D distances between amino acid residues in the sequences. The MATE-Pred demonstrates the potential of multi-modal model in achieving state-of-the-art performance (+8.4% MCC, +5.5% AUC compared to baselines) and efficiently capturing contextual, physicochemical, and structural information from amino acid residues. The performance of MATE-Pred projects its potential application in various drug discovery regimes.
La capacidad de absorción del conocimiento y sus dimensiones en Pymes Acuícolas: el caso Sonora, México

El presente articulo ofrece un panorama sobre la capacidad de absorcion del conocimiento en las pymes Acuicolas de Sonora integrando tres dimensiones claves: (a) Adquisicion , entendida como la capacidad de la empresa para localizar, identificar, valorar y adquirir conocimiento externo; (b) Asimilacion , que abarca los procesos y rutinas que permitiran analizar, procesar, interpretar, comprender, interiorizar y clasificar en nuevo conocimiento; (c) Transformacion y explotacion de conocimientos dimension donde se refina, perfecciona, amplia y aprovecha rutinas existentes o innovadoras, procesos, competencias y conocimiento, asumiendo distintas formas y fuentes de conocimiento (internas y externas a la organizacion). El trabajo esta organizado en dos secciones. La primera seccion se introduce la importancia de la capacidad de absorcion del conocimiento en las pymes acuicolas y presenta sus dimensiones. La segunda ofrece consideraciones finales e implicancias en base a instrumento aplicado a dieciseis pymes acuicolas de Sonora, Mexico. Absorptive capacity and knowledge dimensions SMEs Aquaculture: the case Sonora, Mexico This article provides an overview of the absorptive capacity of knowledge in aquaculture SMEs Sonora integrating three key dimensions: (a) Acquisition, understood as the ability of the company to locate, identify, assess and acquire external knowledge; (B) Assimilation, covering processes and routines that allow you to analyze, process, interpret, understand, internalize and classified into new knowledge; (C) Processing and exploitation of knowledge dimension which is refined, perfect, wide and leverages existing and innovative routines, processes, skills and knowledge, taking on different forms and sources of knowledge (internal and external to the organization). The paper is organized into two sections. The first section of the importance of the absorptive capacity of knowledge in aquaculture SMEs is introduced and presented its dimensions. The second offers concluding remarks and implications based on instrument applied to sixteen aquaculture SMEs Sonora, Mexico.
Gaze in Interspecies Human–Pet Interaction: Some Exploratory Analyses

ABSTRACT This article examines video-recorded naturally occurring human–pet interactions during which the animal’s gaze is treated by the human as a turn-allocation device. Gaze exchange has been extensively studied as the social phenomenon par excellence, especially by scientific paradigms interested in defining sociality as mutual orientation. Recently, studies in ethology have shown the relevance of doing sequential analyses of gaze exchanges in animal interactions, providing important information on the “monitoring function” of gaze. Less is known about the “regulatory function” (i.e., the effect of animal gaze on the sequential organization of action) in human–animal interspecies interactions. This article aims to fill this gap, by investigating the role of domestic dogs’, cats’, and horses’ gazes on human conversation and courses of action. Findings demonstrate a systematic format: an animal-initiated gaze followed by a verbal turn of the human participant, which evidences the turn-allocational function of the animal’s gaze on human participation. Data are in French.
Immunomodulatory and cytotoxic effects of bergenin and cinchonain Ia isolated from Peltophorum pterocarpum

Abstract This paper reports for the first time the immunomodulatory and cytotoxic effects of two previously isolated compounds from the stem of Peltophorum pterocarpum and characterized as bergenin (1) and cinchonain Ia (2). The immunomodulatory capacity of these two compounds was assessed based on their ability to inhibit IL-2 release from RAW264.7 cells. The results demonstrated that cinchonain Ia (2) possesses distinctly immunomodulatory effects in vitro since this compound strongly inhibited IL-2 cytokine level (at concentrations of 4.0 and 0.8 μg/mL) (P<0.01 and P<0.05), while it was increased up to 8.84 time at the 100 μg/mL concentration. Additionally, compound 2 showed a weak cytotoxic effect against 5 human cancer cells (A549, Huh-7, T24, 8505, and SNU-1) with the IC50 values between 39.19 g/mL and 60.44 μg/mL. GRAPHICAL ABSTRACT
Operational-based decarbonization of container ports: The case of Ningbo-Zhoushan Port

Abstract In recent years, the shipping industry has witnessed significant growth in its operations and is considered to be one of the fastest-growing sectors regarding energy consumption and carbon dioxide equivalent (CO2-e). Due to an increasing mandate for the reduction of carbon footprint, ports are developing policies to aid this initiative by upgrading outdated facilities, enhancing operational processes, and incorporating sustainable technologies. This study analyses the impact of the decarbonization strategies adopted in Ningbo-Zhoushan Port in an operation-based scenario and accounts for both the energy saved and for CO2 emissions. Incremental annual energy savings of 2.6, 3.0, and 3.7 (× 103 tce) in 2013, 2014, and 2015 respectively were achieved. From 2013, a 14% reduction in total CO2 emissions was achieved. However, considering annual energy consumption, an estimated reduction from 2.26 tonnes CO2-e/tce to 1.77 tonnes CO2-e/tce (approximately 22%) was obtained.
Recycling of Paleotethyan oceanic crust: Geochemical record from postcollisional mafic igneous rocks in the Tongbai-Hong’an orogens

Plate tectonics can develop from oceanic subduction to continental collision, with disappearance of presubducted oceanic crust in continental subduction zones. This is typical for collisional orogens where Tethyan oceanic crust was consumed during continental convergence, with recycling of the Tethyan oceanic crust into the mantle. However, it is difficult to trace the geochemical signature of the previously subducted oceanic crust in collisional orogens. Postcollisional mafic igneous rocks in the Tongbai-Hong’an orogens provide a geochemical record of crust-mantle interaction during Paleotethyan oceanic subduction preceding the Triassic continental collision. These mafic igneous rocks were emplaced in the Cretaceous after continental collision and exhibit oceanic-island basalt (OIB)−like trace-element distribution patterns, with enrichment of large ion lithophile elements (LILEs) and light rare earth elements (LREEs) but no depletion of high field strength elements (HFSEs). They also show relatively depleted whole-rock Sr-Nd-Hf isotope compositions, with initial 87 Sr/ 86 Sr ratios of 0.7043−0.7050, e Nd ( t ) values of −1.8−4.5, and e Hf ( t ) values of 4.7−10.3. Such geochemical features indicate their origin from a juvenile mantle source that contained subducted oceanic crust−derived material. Thus, we suggest that the mantle source was generated by a melt-peridotite reaction at the slab-mantle interface in the Paleotethyan oceanic subduction channel during continental collision. The original peridotite would have been characterized by depletion of melt-mobile incompatible trace elements and radiogenic Sr-Nd-Hf isotopes, corresponding to the juvenile subarc lithospheric mantle. In contrast, the subducted crust−derived material would have been characterized by enrichment of LILEs and LREEs but no depletion of HFSEs, corresponding to felsic melts that were derived from partial melting of the subducting Paleotethyan oceanic basalt and sediment outside the rutile stability field. As such, the melt-peridotite reaction would generate pyroxenite-rich ultramafic metasomatites in the lower part of the mantle wedge, with relative enrichment of LILEs and LREEs but no depletion of HFSEs. In this model, partial melting of the metasomatites gave rise to the postcollisional mafic igneous rocks in the Tongbai-Hong’an orogens. Therefore, the postcollisional mafic igneous rocks record the recycling of Paleotethyan oceanic crust in the collisional orogen.
Health and Fitness Apps: An Analysis of Gamification Elements in Austria

Gamification has been recognized as an important factor driving user engagement and satisfaction. However, few studies have conducted an in-depth analysis of gamification elements in health and fitness apps. The fundamental objective of this research study was to fill this gap by providing insights into the prevalence of gamification in health and fitness apps. To achieve this, a list of 24 gamification elements has been determined based on a systematic review of the literature and 38 health and fitness apps from the Austrian App Store have been analysed for their implementation of gamification. The results show that 37 of the reviewed apps in Austria include some form of gamification, however the elements considered crucial by research for ensuring high customer satisfaction and retention were found to be sparingly integrated. This research incentivizes app providers to make more use of gamification in apps and tailor its implementation to better meet customers’ needs.
Relationship between emotion recognition and cognition in multiple sclerosis: a meta-analysis protocol

Introduction Multiple sclerosis (MS) is a chronic inflammatory and neurodegenerative disease of the central nervous system characterised by a broad and unpredictable range of symptoms, including cognitive and socio-cognitive dysfunction. Alongside the well-known deficits in information processing speed (IPS), executive functioning and episodic memory, recent evidence also highlighted socio-cognitive impairments in MS, such as emotion-recognition deficits. Recently, several studies investigated the association between emotion-recognition and cognitive impairment to assess whether social cognition is parallel to (or even dependent on) general cognitive dysfunction. Yet, there have been inconsistent findings, raising the need for a meta-analysis of the literature. Objectives The aim of the present paper is to outline the protocol for an upcoming meta-analysis we designed to clarify these conclusions. Methods and analysis We plan to estimate combined effect sizes for the association between emotion-recognition and cognitive impairment in MS across three cognitive domains (IPS, executive functions and episodic memory) and 7 emotion scores of interests (total and by 6-basic emotions subscores). Further, we plan to investigate whether identified variables are the cause for heterogeneity in any combined association. To that end, we will conduct additional meta-regression analyses to explore whether overall correlations differ according to clinical characteristics of MS patients (ie, disease duration, MS-phenotype, severity of depression and disability). Ultimately, this study will provide support either for an association of these disorders (in which emotion-recognition deficits might result from more fundamental cognitive dysfunction), or for two distinct sets of symptoms which may occur independently, for targeted patient profiles.
Obsessive-compulsive disorder following severe traumatic brain injury

Obsessive-compulsive disorder (OCD) as a consequence of severe traumatic brain injury (TBI) has been reported in a limited number of case studies. Informed by CARE guidelines, we present the case of a boy in his late adolescence who sustained a severe TBI from a motor vehicle crash. His injuries required a prolonged stay in the hospital, including 3 weeks in the intensive care unit and a craniotomy to evacuate a large subdural haematoma. Obsessive-compulsive behaviours were first observed on discharge from the hospital and became worse over time. Compulsive behaviours were considered in light of a neuropsychological examination, and a diagnosis of OCD was attained. Sertraline was prescribed and effectively reduced the severity of OCD symptoms. Given the challenges comorbid conditions can pose to neurorehabilitation, a better understanding of patterns in OCD symptoms and brain lesions among reported cases will help guide the diagnosis of OCD among individuals with severe TBI.