Miracle-dz/UniFAR-FTUs huggingface dataset:
ITrust News Certificate: A Blockchain-Based Solution for News Verification and Reputation Management

The proliferation of fake news and misinformation in the digital era poses a significant challenge to news organizations and content creators. In this paper, we introduce the iTrust News Certificate, the architecture of an online blockchain-based solution designed to combat fake news, enhance news verification, and maintain reputation within the media ecosystem. Unlike previous attempts, iTrust News Certificate focuses on user friendly features while ensuring transparency and reliability. By leveraging blockchain technology, iTrust News Certificate establishes a decentralized and immutable ledger for storing news-related metadata. This ledger ensures the integrity and traceability of news articles, making it extremely difficult for malicious actors to tamper with or propagate false information.
Assessment of data fusion oriented on data mining approaches to enhance precision agriculture practices aimed at increase of Durum Wheat (Triticum turgidum L. var. durum) yield

In 2050, world population will reach a total of 9 billion inhabitants and their food demand have to be satisfied. Durum wheat (Triticum turgidum L. var. durum) is one of the most important food crop and its consumption is increasing worldwide. Productivity growth in agriculture and profitable returns are strongly influenced by investment in research and development, where Precision Agriculture (PA) represents an innovative way to manage farms by introducing the Information and Communication Technology (ICT) into the production process. It is known that farms activities produce large amounts of data. Today ICT allows, with electronic and software systems, to collect and transfer automatically these data, thus increasing yields and profits. In this direction significant data are processed from agricultural production, and retrieved to extract useful information important to increase the knowledge base. Data from multiple data sources can be processed by a Data Fusion (DF) approach able to combine multiple data sources into an unique database system. Raw data are transformed into useful information, thus DF improves pattern recognition, analysis of growth factors, and relationship between crops and environments. Data Fusion is synonym of Data Integration, Sensor Fusion, and Image Fusion. By means of Data Mining (DM) it is possible to extract useful information from data of the production processes thus providing new outputs concerning product quality and product “health status”. The following literature take into account the DF and DM techniques applied to Precision Agriculture (PA) and to cultivation inputs (water, nitrogen, etc.) management. We report also last advances of DF and DM in modern agriculture and in precision durum wheat production.
Translational and Rotational Postural Aberrations Are Related to Pulmonary Functions and Skill-Related Physical Fitness Components in Collegiate Athletes

This study assessed the relationship between body posture displacements, cardiopulmonary exercise testing (CPET), and skill-related physical fitness tests. One hundred male (60%) and female collegiate athletes (22.2 ± 4 yrs) with normal body mass indexes (BMI up to 24.9) were assessed via the PostureScreen Mobile® app to quantify postural displacements such as head, thorax, and pelvis rotations and translations. CPET and physical performance tests, including the agility t-test, vertical jump test, stork static balance test (SSBT), and dynamic Y-balance test (YBT), were performed. Spearman correlation (r) and p-values are reported. The postural parameters were found to have moderate-to-high associations with the CPET and agility test, moderate correlations with the vertical jump test and SSBT (head and pelvic postures only), and weak correlations with the YBT. As the postural parameters were more asymmetric, both the CPET and performance skills scores were worse. For example: (1) a medium positive correlation was found between cranio-vertebral angle (CVA) and the vertical jump test (r = 0.54; p-value < 0.001) and SSBT (r = 0.57; p-value < 0.001), while a strong negative correlation was found between CVA and the agility test (r = −0.86; p-value < 0.001). (2) A strong positive correlation was found between CVA and oxygen uptake efficiency slope, load watts VO2 at VT, VO2/kg, and load watts at the respiratory compensation point (RCP) (r = 0.65 and r = 0.71; p < 0.001). Conversely, a significant negative correlation was found between CVA and VE/VO2 at VT (r = −0.61; p < 0.001). Postural rotations and translations of the head, thorax, and pelvis were statistically correlated with the physical performance skills and CPET in the young collegiate athletes. There were moderate-to-high associations with cardiopulmonary functions and the agility tests, moderate correlations with the vertical jump test, and weak correlations with the YBT. Postural alignment may be important for optimal physical performance and optimal cardiopulmonary function. Further research is necessary to elucidate the reasons for these correlations found in our sample of young and healthy athletes.
Should oncologists trust cannabinoids?

Cannabis enjoyed a “golden age” as a medicinal product in the late 19th, early 20th century, but the increased risk of overdose and abuse led to its criminalization. However, the 21st century have witnessed a resurgence of interest and a large body of literature regarding the benefits of cannabinoids have emerged. As legalization and decriminalization have spread around the world, cancer patients are increasingly interested in the potential utility of cannabinoids. Although eager to discuss cannabis use with their oncologist, patients often find them to be reluctant, mainly because clinicians are still not convinced by the existing evidence-based data to guide their treatment plans. Physicians should prescribe cannabis only if a careful explanation can be provided and follow up response evaluation ensured, making it mandatory for them to be up to date with the positive and also negative aspects of the cannabis in the case of cancer patients. Consequently, this article aims to bring some clarifications to clinicians regarding the sometimes-confusing various nomenclature under which this plant is mentioned, current legislation and the existing evidence (both preclinical and clinical) for the utility of cannabinoids in cancer patients, for either palliation of the associated symptoms or even the potential antitumor effects that cannabinoids may have.
Quality Attributes and Sensory Acceptance of Different Botanical Coffee Co-Products

Coffee processing is a major contributor to the creation of food and product waste. Using coffee co-products can play an essential role in addressing environmental problems and issues with nutritionally unbalanced foods, population growth, and food-related diseases. This research aimed to determine the quality and sensory parameters (aw, pH, dry matter, TAC, TPC, fat, fatty acids profile, fiber, caffeine, chlorogenic acids, color, and sensory analysis) of different botanical origins of cascara (coffee husks) and silverskin (thin layer). The results of this study show that silverskin and cascara are a good source of TAC (1S 58.17 ± 1.28%, 2S 46.65 ± 1.20%, 1C 36.54 ± 1.84%, 2C 41.12 ± 2.11%). Cascara showed the presence of polyphenols (2C 49.135 g GAE·kg−1). Coffee co-products are good sources of fiber. Silverskin had higher values of caffeine than cascara. Palmitic, stearic, oleic, linoleic, and arachidic acids were the most represented acids in the samples. Given the obtained results, cascara can be considered “low-fat” (1C 4.240 g·kg−1 and 2C 5.4 g·kg−1). Based on the sensory evaluation, no sample reached the acceptable index value of 70%. Understanding the link between the character, identification properties, and composition of coffee co-products of different botanical origins can enable their application in the food industry.
The Impact of Multicomponent Exercise Protocols Order on the Maximum Voluntary Contraction of Older Women

The aim of this study was to evaluate the impact of exercise order in multicomponent training (MCT) on the maximum voluntary contraction (MVC) of older women. A total of 91 older women, ranging in age from 60 to 85 years, were randomly assigned to either Group A or Group B. Group A performed a warm up followed by aerobic training and resistance training, whereas Group B followed a warm up followed by resistance training and aerobic training. A control group (CG) did not engage in any exercise interventions. Statistical analysis was conducted using one-way ANOVA for between-group comparisons, and ANOVA was used for repeated measures. The results revealed that Group A demonstrated significant increases in MVC for knee extensors (KEs) between M1 and M3 (p < 0.001) and between M2 and M3 (p < 0.001). Similarly, Group A exhibited significant increases in MVC for knee flexors (KFs) between M1 and M3 (p = 0.001) and between M2 and M3 (p < 0.001). Both Group A and Group B demonstrated significant increases in MVC for elbow flexors (EFs) between M1 and M3 (p < 0.001). Furthermore, Group B showed a significant increase in hand grip strength (HGS) between M1 and M3 (p < 0.001). Overall, the findings suggest that initiating MCT with aerobic training followed by resistance training is the most effective approach for improving muscle strength in older women.
Antimicrobial evaluation of plant essential oils against pathogenic microorganisms: In vitro study of oregano oil combined with conventional food preservatives

Essential oils (EO) are promising natural antimicrobial additives to control microbial pathogens. This study aims to investigate the antimicrobial activities of plant essential oils and to study the antimicrobial effect of oregano oil (OrO) in combination with food preservatives. The antimicrobial screening showed that Escherichia coli and Salmonella enterica subsp. enterica serovar Typhimurium (Salmonella ser. Typhimurium) appeared to be less susceptible to EO, whereas Staphylococcus aureus and Candida albicans were more affected. The Minimum Inhibitory Concentration (MIC) and Minimum Lethal Concentration (MLC) for laurel, cumin, oregano and rosemary oils showed values ranging from 0.078% to 1.25% (v/v). Also, synergic and viability effects of OrO combined with acetic acid (AcA) showed an additive effect against E. coli and C. albicans, while combination OrO + ascorbic acid (Asc) exhibited the same effect over Salmonella ser. Typhimurium and C. albicans. Therefore, oregano oil in combination with preservatives could be used to control the growth of pathogenic microorganisms for food preservation.
Estimation de l'ordre d'une chaîne de Markov cachée à émissions de la famille exponentielle

Nous cherchons a estimer l'ordre, i.e. le nombre d'etats caches, d'un modele de Markov cache (HMM), quand aucune borne superieure sur cet ordre n'est connue. Nous nous interessons aux HMM dont la distribution des etats observables appartient a la famille exponentielle. Deux estimateurs pour l'ordre sont presentes : l'un est base sur l'estimateur du maximum de vraisemblance, et l'autre sur un melange bayesien. Tous deux sont penalises. Nous prouvons la consistance de ces estimateurs, et detaillons les penalites pour des distributions de la famille exponentielle.
Adaptive Output Feedback Control for Nonholonomic Chained Systems with Integral Input State Stability Inverse Dynamics

This paper investigates a class of nonholonomic chained systems with integral input-to-state stable (iISS) inverse dynamics subject to unknown virtual control directions and parameter uncertainty included in drift terms. First, the system is divided into two interconnected subsystems according to the system’s structure. Second, one controller is designed using a switch strategy for state finite escape. Then, another controller and adaptive law are designed by combining a reduced-order state observer and backstepping method after input-state scaling. Finally, simulation results validate the feasibility of the proposed control algorithm.
Supervised Copy Mechanism for Grammatical Error Correction

AI has introduced a new reform direction for traditional education, such as automating Grammatical Error Correction (GEC) to reduce teachers’ workload and improve efficiency. However, current GEC models still have flaws because human language is very variable, and the available labeled datasets are often too small to learn everything automatically. One of the key principles of GEC is to preserve correct parts of the input text while correcting grammatical errors. However, previous sequence-to-sequence (Seq2Seq) models may be prone to over-correction as they generate corrections from scratch. Over-correction is a phenomenon where a grammatically correct sentence is incorrectly flagged as containing errors that require correction, leading to incorrect corrections that can change the meaning or structure of the original sentence. This can significantly reduce the accuracy and usefulness of GEC systems, highlighting the need for improved approaches that can reduce over-correction and ensure more accurate and natural corrections. Recently, sequence tagging-based models have been used to mitigate this issue by only predicting edit operations that convert the source sentence to a corrected one. Despite their good performance on datasets with minimal edits, they struggle to restore texts with drastic changes. This issue artificially restricts the type of changes that can be made to a sentence and does not reflect those required for native speakers to find sentences fluent or natural sounding. Moreover, sequence tagging-based models are usually conditioned on human-designed language-specific tagging labels, hindering generalization and the real error distribution generated by diverse learners from different nationalities. In this work, we introduce a novel Seq2Seq-based approach that can handle a wide variety of grammatical errors on a low-fluency dataset. Our approach enhances the Seq2Seq architecture with a novel copy mechanism based on a supervised attention approach. Instead of merely predicting the next token in context, the model predicts additional correctness-related information for each token. This auxiliary objective propagates into the weights of the model during training without requiring extra labels at testing time. Experimental results on benchmark datasets show that our model achieves competitive performance compared to state-of-the-art(SOTA) models.
Impact of Reduced Enoxaparin Prophylactic Dosing on Bleeding and Thrombotic Risk for Low Body Weight Post-Operative Colorectal Surgery Patients

Introduction: Colorectal surgery (CORS) patients are at the highest risk of any general surgery population for the development of post-operative venous thromboembolism (VTE) and are also at an increased risk for post-operative bleeding, especially with procedures related to early cancer stages. Low molecular weight heparin (LMWH) is recommended for VTE prophylaxis in this surgical population. Current guidelines and primary literature along with institutional protocols support dosage adjustments for obesity and impaired renal function, but literature providing optimal VTE prophylactic LMWH dosing is extremely limited for underweight, surgical patients. Objectives: The aim of this study was to evaluate both bleeding and VTE rates for hospitalized underweight colorectal surgery patients receiving post-operative VTE prophylaxis with standard enoxaparin 40 mg subcutaneously (SQ) every 24 hours versus reduced dose enoxaparin 30 mg SQ every 24 hours. Methods: This trial was a multicenter, retrospective chart review with data collected on post-operative CORS patients over a 10-year period. Patients were included if aged 18 years and older, weighed less than 50 kg, and received at least 1 dose of enoxaparin 40 mg or 30 mg post-operatively. The primary endpoint was a descriptive comparison of post-operative bleeding with use of standard enoxaparin 40 mg SQ every 24 hours compared to reduced dose enoxaparin 30 mg SQ every 24 hours for post-operative VTE prophylaxis. The secondary endpoint was the descriptive comparison of post-operative thrombotic event rates in the same population. Results: The total population investigated included 146 patients. One hundred twenty-nine (88.4%) received standard enoxaparin and 17 (11.6%) received reduced enoxaparin. Overall, 9 bleeding events (7.0%) and 2 major VTE events (1.6%) occurred, all within the standard enoxaparin group. Conclusion: Standard dose enoxaparin for post-operative VTE prophylaxis demonstrated increased bleeding and thrombotic events requiring medical and surgical intervention in comparison to reduced dose enoxaparin for low body weight CORS patients. The results of this study are hypothesis generating and continued research involving more robust systematic reviews are needed to evaluate both safety and efficacy of reduced VTE prophylaxis with LMWH in the underweight, surgical patient population.
Design and Simulation of a 3.5 GHz Pyramidal Horn Antenna for IoT and High Speed 5G Wireless Technology Mobile Networks

The wireless communication system is an imperative component of Internet of Things (IoT) framework, serving as a bridge for bidirectional connection for data gathering and control message conveyance for effective data application hence, wireless techniques has short range of IoT band and consume a lot of power. This study, design and simulation of a pyramidal horn antenna for IoT and fifth generation (5G) applications operating at 3.5 GHz is presented. The antenna is designed and simulated using MATLAB 2020a produced by MathWorks Inc The gain, bandwidth, and maximum directivity of the single element antenna is 14.1 dB, 460 MHz and 15 dBi respectively. A low simulated return loss (-15.8 dB) is obtained. Hence, the antenna has a very good efficiency. The performance after simulation of this antenna that is combined with a 3.5 GHz radio frequency module and IoT sensors. The distance of the radiating element is 5 cm, and its overall waveguide length 15 cm is obtained at a resonance frequency 3.5 GHz. the simulated antenna has the axial lengths 𝐿 𝐻 = 𝐿 𝐸 = 4 𝑐𝑚 that suitable for short range IoT applications. All mutual-coupling (S21 to S44) characteristics between the elements lie within -27 and -84 dB, which follows the beam steering requirements perfectly. Therefore, 5G and other applications are possible with this antenna.
Running Guidance for Visually Impaired People Using Sensory Augmentation Technology Based Robotic System

Participating in sports is of great significance to people's physical and mental well-being. While physical activity is commonplace for healthy individuals, it presents challenges for those with visual impairments, as they can not rely on visual cues to perceive essential information related to sports participation, such as their surroundings. Many related studies including our previous work for assisting users in doing sports using sensory augmentation technology, which couples haptic feedback with people's desired movements, are proposed for this challenge. On the basis of these studies, we propose a system for guiding visually impaired users running outdoors using a drone-based robotic system to locate a user and a track, calculate desired moving directions, and provide haptic feedback to the user. We conduct an experiment to explore how accurately people can recognize the directions conveyed by the proposed guidance method. Subjects were asked to select their felt directions on a tablet while running on a treadmill at 6.5 km/h and 7.5 km/h. The results show subjects could recognize the cued directions with an average resolution of $\mathbf {19.8^{\circ }}$ and $\mathbf {19.6^{\circ }}$ at different speeds, respectively, and there is no significant difference exist between the two speeds. In addition, we guide users in realistic running scenarios on sports tracks. Subjects in this experiment wore an eye mask to simulate the visually impaired. They were instructed to run by following the perceived directions conveyed by haptic feedback. According to the results, they could run within a specific track 81% of the time with the proposed system.
The influence of socially responsible human resource management on green behaviours in the aviation industry

Many organizations are facing environmental issues and learning how to improve employee green behavior. However, few researchers have addressed the problem of GFHRM policies from the viewpoint of aviation industry employees. The purpose of this study is to examine the influence of general CSR facilitation human resource management (GFHRM) on green behaviour in the aviation industry and to improve the competitiveness of organisations. Using the social exchange theory, this study looks at the relationships between GFHRM and green behaviour and the mediation of an employee’s green work engagement while moderating roles of environmental leadership. The data were gathered from 397 respondents in the aviation industry in Vietnam through the use of the questionnaires on Google Docs. The time-lag technique is used to collect data to avoid common method bias. The results show that GFHRM has a positive relationship with employees’ in-role green behavior. Furthermore, GFHRM impacts positively on employees’ in-role green behaviors through employee work engagement. Moreover, environmental leadership has a positive moderating influence on the link between GFHRM and employees' in-role green behaviors. The study focuses on CSR practices that put employees first, which have been looked at by a number of aviation industry companies. The importance of leaders in achieving environmental sustainability is shown by our research. This study notably highlights the interaction between GFHRM and environmental leadership on staff's green behaviour while performing their job duties. Employee green work engagement may be best placed to moderate the association between GFHRM and in-role employee green behavior.
Image histogram as a tool for a coal stream homogeneity evaluation

The paper presents selected results of the preliminary laboratory research on the application of histogram analysis to the problem of coal stream homogeneity evaluation. This is an important problem of industrial coal mine output stream quality testing. Image acquisition is fast and cheap, but needs complex analysis techniques. Histogram analysis can be considered to be one of them. There has been proposed a distance measure, and there have been presented analysis results which proved an ability to detect gangue, rock, schist, and even wooden block contamination of the homogenous coal stream. The method needs further improvement and comprehensive analysis of other available options of the RGB-to-grayscale conversion and of histogram comparison to make it more selective. These further research directions have also been described in the paper.
Resolvent Estimates and Semigroup Expansions for Non-self-adjoint Schrodinger Operators

Author(s): Bellis, Ben | Advisor(s): Khitrik, Mikhail | Abstract: In this work we examine aspects of spectral theory for semiclassical non-self-adjoint Schrodinger operators. First, we show a subelliptic resolvent estimate for spectral parameters in an unbounded cubic neighborhood of the imaginary axis for a broad class of semiclassical Schrodinger operators with complex potentials of at most quadratic growth. We then generalize this result by showing that the same type of resolvent estimate also holds for non-self-adjoint magnetic Schrodinger operators under suitable growth conditions on the magnetic potential. Lastly, we show how this resolvent estimate can be applied to yield a large time expansion for the semigroup generated by such an operator in terms of spectral data near the origin.