Miracle-dz/UniFAR-FTUs huggingface dataset:
Security Control of Sampled-Data T–S Fuzzy Systems Subject to Cyberattacks and Successive Packet Losses

This article investigates the security control problem of sampled-data Takagi–Sugeno (T–S) fuzzy systems subject to cyberattacks and successive packet losses. The cyberattack considered in this article is a stochastic deception attack. The packet losses under consideration occur in the sensor-to-controller and controller-to-actor channels, both of which are modeled as two independent Bernoulli processes. On this basis, a novel successive packet loss modeling method is proposed with some basic assumptions. By the exact discrete-time model approach, a discretized sampled-data T–S fuzzy model is first established by considering the probability characteristic of the deception attacks and successive packet losses in a unified framework. Based on the established mode, the sufficient conditions to guarantee the security degree are derived by the Lyapunov function approach. Furthermore, the fuzzy security controller is designed in view of linear matrix inequalities. Finally, a benchmark example is given to show the validity of the proposed approach.
Development of the Software Engineering Education Virtual Classroom Prototype: An Experience Report

COVID-19 pandemic forced educational institutions to reinvent themselves by using digital information and communication technologies to support the continuity of distance learning. Google Meet and Zoom were one of the tools adopted as solution. However, the learning process presented challenges, mainly related to the lack of engagement and growing demotivation of students. Immersive Learning (iL) is a research topic that has the potential to improve learning outcomes. Through immersive environments, students have access to new ways of viewing and interacting, providing more engagement in learning. Therefore, the objective of this work is to present the development of a Virtual Classroom prototype, a virtual environment to support Software Engineering Education (SEE). An initial evaluation with a specialist was carried out and it was found that the usability of the environment is satisfactory, but aspects of performance must be improved. As future work, apart from adjusting the indicated points for improvement, a user experience evaluation with 3 to 5 extended reality specialists and another evaluation with students in order to verify the influence of the Virtual Classrooms on learning outcomes will be carried out.
Comportamento predatório de operárias de Odontomachus bauri (Hymenoptera: Formicidae) sobre Hermetia illucens (Diptera: Stratiomyidae)

O elemento mais característico da morfologia de Odontomachus são as mandíbulas de fechamento rápido, adaptações para capturar presas eficientemente. Embora facilmente encontrada, existem poucos estudos sobre o comportamento predatório de O. bauri. As presas utilizadas neste experimento foram larvas e adultos de H. illucens. Este trabalho comparou a frequência dos comportamentos de predação de O. bauri sobre larvas e adultos de H. illucens e analisou a sequência em que ocorrem. Foram coletados dois ninhos de O. bauri e as formigas foram transferidas para ninhos artificiais. Na arena de forrageamento, ao lado do ninho, foram oferecidas as presas vivas. Os comportamentos foram filmados e as suas frequências foram calculadas para montagem de um etograma e descrição da sequência comportamental. Os cálculos foram feitos separadamente para cada tipo de presa. Registrou-se 81 atos comportamentais agrupados em 14 categorias, sendo “inspecionar” a mais comum. Na predação sobre adultos houve maior variação comportamental do que sobre larvas. Repertório e sequência comportamental de predação de O. bauri não diferem muito de outras espécies do gênero. Operárias tiveram maior interesse em predar adultos, enquanto as larvas não foram utilizadas como alimento. O fechamento rápido das mandíbulas foi utilizado na mutilação de partes do corpo da presa, algo ignorado em estudos anteriores.
Phytochemical and physicochemical evaluation, and photoprotection, antioxidant, antifungal, and antibacterial activities of the floral extract of Schubertia grandiflora Mart. & Zucc. (Apocynaceae)

Schubertia grandiflora is a species belonging to the floral of the Cerrado domain, presented annually with aromatic flowers. The study aimed to evaluate the hydroethanolic floral extract of S. grandiflora for phytochemistry, physicochemical and photoprotection, antioxidant, antifungal and antibacterial activities. Fresh flowers were collected and the floral extract produced by maceration. Qualitative phytochemical tests were carried out for the main phytochemical classes, organoleptic physico-chemical parameters, extract mass, moisture content, pH, relative density, refractive index, phenolic and total flavonoid contents, color density, Hue tint, and tests biological factors for protection factor, DPPH free radical reduction activities, antifungal and antibacterial. Phytochemistry has demonstrated the presence of several groups of important metabolites mainly in pharmaceutical use, visual color of the citrus yellow floral extract, aromatic and homogeneous, extract mass = 12.57%, moisture content = 6.21%, pH = 5.85, refractive index = 1.4217 nD, relative density 0.9044 g mL-1 20 °C, total phenolics = 238.83 mg GAE 100 g-1 dried extract, total flavonoids = 17.93 mg QC 100 g-1 dried extract, color density = 1.05, Hue color = 8.24, protection factor UVA and UVC, antioxidant activity with CI50 = 9.44 µg mL-1. Antifungal activity demonstrated inhibition zone only for Candida tropicalis between 10-6 mm and for Candida krusei between 18-5 mm, and antibacterial for Escherichia coli between 13-7 mm, Pseudomonas aeruginosa between 21-5 mm and Enterococcus faecalis between 32-17 mm. The hydroethanolic floral extract of Schubertia grandiflora demonstrated potential biological activities, characterizing this natural product for the development of pharmaceutical, biological and agricultural formulations.
Impossible-Differential Attacks on block-cipher based Hash and Compression Functions using 3D and Whirlpool

In this paper, we analyse block-cipher-based hash functions, which means hash functions that use block ciphers as compression functions in a mode of operation, such as Davies-Meyer (DM), Matyas-Meyer-Oseas (MMO) and Miyaguchi-Preneel (MP), for instance. We use impossible differentials (ID) to distinguish the compression (or hash) function from an ideal primitive (a random oracle) by detecting a nonrandom behavior. We applied an ID analysis to an 8-round variant of the 3D block cipher used in MMO mode, as a compression function of a hypothetical hash function. This attack effectively improves upon the previously known distinguishing ID attacks on reduced-round 3D. We can also attack a hash function using 3D as compression function in DM mode. Finally, we attacked the compression function in Whirlpool with a 5-round W cipher in MP mode with 2100 time and 264 memory.
Relationship between Bruises on Carcasses of Beef Cattle and Transport-Related Factors

Simple Summary The use of monitoring schemes to determine the health and welfare status of animals at the abattoir level provides useful feedbacks to farmers and to all those involved in the activities carried out before slaughtering. Bruises are considered welfare indicators in cattle carcasses, as they represent a sign of improper handling or management executed during the pre-slaughter phases. The aims of this study were to assess the prevalence of bruises in beef cattle at the slaughterhouse and to determine their correlation with factors linked to animal transport. The 21.6% of the animals included in the study had bruises, which were mainly characterized by being red, small and located in one region. Steers had less probability of presenting bruises compared to the other categories, while animals transported in the condition of a high space allowance had more chance of presenting bruises. Even though more studies are needed, the results of this study have shown that the assessment of bruises on cattle carcasses could reflect some welfare issues occurring during pre-slaughter activities. These data can be used to improve specific procedures and to boost cattle welfare. Abstract The assessment of bruises on carcasses at the slaughterhouse has been lately indicated as a valid method to evaluate cattle welfare. However, little is known about the prevalence and the causes of bruises of cattle slaughtered in Italy. The aim of this study was to collect information concerning the prevalence of bruises on the carcasses of beef cattle slaughtered in an Italian abattoir and to determine a relationship between fresh bruises and transport-related factors. In total, 1265 animals were included in this study, with 21.6% of them being positive for at least one bruise, either fresh or old. In most cases, the bruising was mild, with lesions exclusively located in one area of the carcass. Most of the bruised animals (63%) showed at least one red lesion. Occurrence of such red, fresh bruises varied significantly depending on the body parts (p < 0.0001), with the flank being the most affected area (39.5%), followed by the butt (36.0%) and the front (23.8%). The probability of fresh bruising varied significantly depending on the category of each animal (p < 0.0001), with steers showing fewer red bruises than both heifers and veal. Finally, animals transported in conditions of a high density had a lower probability of bruising (p = 0.0003). These findings support the use of a monitoring scheme based on the presence of bruises to assess cattle welfare at the abattoir level in order to provide feedback to farmers and to implement procedures carried out during transport.
A comparative study on the effect of YAG: Er and 089 nm Diode laser irradiation on the attachment of osteoblast cells on the surface of dental implant fixtures

Background and Aim: Nowadays, lasers are the focus of attention as a new treatment in dentistry. One of the applications of lasers is their use in implant dentistry. The present study aimed to compare the effect of YAG: Er and 089 nm Diode laser irradiation on the attachment of osteoblast cells on the surface of dental implant felxtures. Materials and Methods: In the present study, 36 titanium implants were divided into three groups of 12. Twelve implants were irradiated with Er: YAG laser and 12 implants were irradiated with a diode laser, and 12 implants were not irradiated as a control group. Osteoblast cells were cultured on the samples, and the attachment and viability rate of these cells were examined by SEM electron microscopy and MTT test. Results: There was no significant difference between laser irradiated implants groups and the control group in terms of cell viability rate based on the MTT test.
Does global value chain participation induce economic growth? Evidence from panel threshold regression

ABSTRACT We investigate the threshold effects of GVC participation and its disaggregated level (forward and backward participation) on economic growth for 62 economies for the period 2000–2018. Our paper is one of the first to analyse the multiple regime effects of GVC participation on economic growth. We find that GVC participation positively impacts economic growth in a country with higher economic growth in both the aggregate and disaggregate analysis. Contemporaneously, it is negatively associated with economic growth in countries with lower economic growth. The coefficient value of forward participation for lower growing economies is higher in the first and second regime than the backward participation, which implies that forward participation has more deleterious effects on economic growth in the less developed and developing economies. The growth effects of forward and backward participation indicate that both have different effects on economic growth in the moderate growing economies, which implies that the direction of GVC participation does matter in determining economic growth in the moderate growing economies.
Virtual Reality and Its Application in Military

In recent years, the virtual reality technology has been greatly developed and applied to many fields, its application extends to product design, education and training, military and aerospace, entertainment and leisure fields. This article summarizes the current developments in hardware and software of virtual reality technology and its application in the military field, and then analyzes the future development trends of the application of virtual reality in military.
Thin head atomic force microscope for integration with optical microscope.

We present a novel thin head atomic force microscope (AFM) that can be easily integrated with an upright optical microscope (OM). The optical beam detection unit in the AFM used an obliquely incident laser beam onto the cantilever, reducing the AFM head's effective thickness to 7.3 mm. That allows an open space above the cantilever probe to accommodate the objective lens up to 0.6 numerical aperture (N.A.) without obstruction. A multi-function digital controller was developed to control the AFM and reserved interfaces to communicate with the OM. To assess the performance of the developed AFM, we first measured the noise level and bandwidths of the AFM system. Then, the imaging quality of the AFM was evaluated by both calibration grids and two-dimensional materials. Finally, the thin head AFM was integrated into a homemade white light interferometer as a demonstration of combined use with an advanced optical system. The experimental results demonstrated that our developed AFM is suitable for integration under upright OM and brings AFM high-resolution advantages to the existing OM system.
MIGAN: Mutual-Interaction Graph Attention Network for Collaborative Filtering

Many web platforms now include recommender systems. Network representation learning has been a successful approach for building these efficient recommender systems. However, learning the mutual influence of nodes in the network is challenging. Indeed, it carries collaborative signals accounting for complex user-item interactions on user decisions. For this purpose, in this paper, we develop a Mutual Interaction Graph Attention Network “MIGAN”, a new algorithm based on self-supervised representation learning on a large-scale bipartite graph (BGNN). Experimental investigation with real-world data demonstrates that MIGAN compares favorably with the baselines in terms of prediction accuracy and recommendation efficiency.
Patients’ Assessment of Chronic Illness Care (PACIC): Validation and Evaluation of PACIC Scale among Patients with Type 2 Diabetes in Hungary

Introduction: The aims of this study were to evaluate the psychometric properties of the Hungarian translation of the PACIC in a sample of patients with type 2 diabetes and to reveal the associations between the mean PACIC scores and the number of chronic diseases, or visits to GPs, and specialist. An exploratory factor analysis (EFA) has also been performed to test the structural validity of the PACIC scale. Methods: The Hungarian version of PACIC was validated using randomly selected patients with type 2 diabetes (N = 684) from licensed GP practices. Results: Floor (1.6%–30.2%) and ceiling effects (11.3–33.6%) were similar of the PACIC scale. The internal consistency of the total scale (Cronbach’s alpha 0.93) was excellent and subscales were good (between 0.73–0.9). The mean scores of each PACIC subscale group were between 2.99–3.53. There was a weak significant correlation between the mean PACIC scores of subscales and the number of GP visits (p < 0.001), and specialist visits (p < 0.001). The EFA identified four factors on the sample (KMO = 0.931). Gender and education showed correlation with some new factors. Conclusion: The psychometric properties of the Hungarian version of PACIC questionnaire showed a reasonable level of validity among patients with type 2 diabetes. Now, this instrument is ready to assess the chronic care of diabetic patients in Hungary.
Semi-Supervised Clustering via Cannot Link Relationship for Multiview Data

Due to the diversity of data modalities, the research interest of multi-view clustering is gradually increasing, in the field of large-data analytics, particularly in clustering. However, the greater part of current multi-view clustering methods is mainly in view of unsupervised learning, which leads to unpredictable results and algorithmic instability. Besides, they ignore the diversity of graphs, which is not desirable in practical applications, because the characteristic properties of each view are different. To solve these problems, inspired by the outstanding performance of semi-supervised learning in machine learning, we propose a valid semi-supervised multi-view spectral clustering algorithm. We use the pre-set labels as prior knowledge to obtain the overall distribution of the remaining unlabeled data. Tensor minimization Schatten <inline-formula> <tex-math notation="LaTeX">$p$ </tex-math></inline-formula>-norm is utilized to mine the mutual information hidden in multiple views. Meanwhile, we also use the cannot-link as another semi-supervised constraint to update the graph. Our proposed algorithm is generally 5%-10% better than the comparison algorithms in view of the experimental results on five datasets, and our algorithm is relatively fast with the computational complexity of <inline-formula> <tex-math notation="LaTeX">$\mathcal {O}({T({n^{2}}\log (n) + {n^{2}} + {u^{2}}l + ulc + uc\log (c))})$ </tex-math></inline-formula>, where <inline-formula> <tex-math notation="LaTeX">$T$ </tex-math></inline-formula> denotes the number of iterations and <inline-formula> <tex-math notation="LaTeX">$n$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$l$ </tex-math></inline-formula>, <inline-formula> <tex-math notation="LaTeX">$u$ </tex-math></inline-formula> represent the number of samples, the number of labeled and unlabeled samples, respectively, which shows that our proposed method has broad application prospects.
An Efficient Framework for Constructing Speech Emotion Corpus Based on Integrated Active Learning Strategies

Speech emotion recognition has been developed rapidly in recent decades because of the appearance of machine learning. Nevertheless, lack of corpus remains a significant issue. For actual speech emotion corpus construction, many professional actors are required to perform voices with various emotions in specific scenes. In the process of data labelling, since the number of samples of different emotion categories is extremely imbalanced, it is difficult to efficiently label the samples. Hence, we proposed an integrated active learning sampling strategy and designed an efficient framework for constructing speech emotion corpora in order to address the problems presented above. Comparing experiments with other active learning algorithms on 13 datasets, our method was shown to improve sampling efficiency. In addition, it is able to select small category samples to be labelled with preference in imbalanced datasets. During the actual corpus construction experiments, our method can prioritize selecting small class emotion samples. As even when the amount of labelled data is less than 50%, the accuracy rate still can reach 90%. This greatly enhances the efficiency of constructing the speech emotion corpus and fills in the gaps.
Application of Linear Programming for Decision Making to Business in Whao Beverages Nig. Ltd

: Linear Programming is a scientific method used for obtaining and determine the most optimal solution for a problem with given constraints. This technique is useful for guidance of a quantitative decision-making in different fields such as business planning, industrial engineering, production firm and to the extent of social and physical sciences. This research examines the application of linear programming to determine the product(s) that maximize(s) the profit of WHAO Beverages Nig. Ltd. The data used for this research is a secondary data obtained from the company. The method used to analyze the data is a revised simplex method of a linear programming. The findings in this research was able to formulate mathematical model for the research problem; determine the product that will yield the maximum profit for the company; and know the quantity of this product that will give this profit. Data obtained from the company undergoes two iterations, whose analysis shows that the squash drinks maximize the profit of the company at the second iteration. The quantity of SQUASH Drinks that gave the maximum profit of 264 units produced and the maximum profit of the company (WHAO Beverages Nig. Ltd.) was #2200.83.
VLA-SMILES: Variable-Length-Array SMILES Descriptors in Neural Network-Based QSAR Modeling

Machine learning represents a milestone in data-driven research, including material informatics, robotics, and computer-aided drug discovery. With the continuously growing virtual and synthetically available chemical space, efficient and robust quantitative structure–activity relationship (QSAR) methods are required to uncover molecules with desired properties. Herein, we propose variable-length-array SMILES-based (VLA-SMILES) structural descriptors that expand conventional SMILES descriptors widely used in machine learning. This structural representation extends the family of numerically coded SMILES, particularly binary SMILES, to expedite the discovery of new deep learning QSAR models with high predictive ability. VLA-SMILES descriptors were shown to speed up the training of QSAR models based on multilayer perceptron (MLP) with optimized backpropagation (ATransformedBP), resilient propagation (iRPROP‒), and Adam optimization learning algorithms featuring rational train–test splitting, while improving the predictive ability toward the more compute-intensive binary SMILES representation format. All the tested MLPs under the same length-array-based SMILES descriptors showed similar predictive ability and convergence rate of training in combination with the considered learning procedures. Validation with the Kennard–Stone train–test splitting based on the structural descriptor similarity metrics was found more effective than the partitioning with the ranking by activity based on biological activity values metrics for the entire set of VLA-SMILES featured QSAR. Robustness and the predictive ability of MLP models based on VLA-SMILES were assessed via the method of QSAR parametric model validation. In addition, the method of the statistical H0 hypothesis testing of the linear regression between real and observed activities based on the F2,n−2 -criteria was used for predictability estimation among VLA-SMILES featured QSAR-MLPs (with n being the volume of the testing set). Both approaches of QSAR parametric model validation and statistical hypothesis testing were found to correlate when used for the quantitative evaluation of predictabilities of the designed QSAR models with VLA-SMILES descriptors.