Persistent issues in encryption software: A heuristic and cognitive walkthrough

The support information accompanying security software can be difficult to understand by end-users, who have little knowledge in cyber security. One mechanism for ensuring the integrity and confidentiality of information is encryption software. Unfortunately, software usability issues can hinder an end-user’s capability to properly utilise the security features effectively. To date there has been little research in investigating the usability of encryption software and proposing solutions for improving them. This research paper analysed the usability of encryption software targeting end-users. The research identified several issues that could impede the ability of a novice end-user to adequately utilise the encryption software. A set of proposed recommendations are suggested to improve encryption software which could be empirically verified through further research

Extraction and characterization of chitin and chitosan from crustacean by-products: Biological and physicochemical properties

Chitin has been extracted from two Tunisian crustacean species. The obtained chitin was transformed into the more useful soluble chitosan. These products were characterized by their biological activity as antimicrobial and antifungal properties. The tested bacterial strains were Escherichia coli American Type Cell Culture (ATCC) 25922, Pseudomonas aeruginosa ATCC 27950 and Staphylococcus aureus ATCC 25923. Four fungi strains were also tested Candida glabrata, Candida albicans, Candidaparapsilensis and Candida kreusei. Squilla chitosan showed a minimum inhibitory concentration (MIC) against the different fungi exceptionally for C. kreusei. Their antioxidant activity was investigated with 2,2-diphenyl-1-picrylhydrazyl (DPPH) radical scavenging activity and inhibition of linoleic acid peroxidation. Parapenaeus longirostris Chitosan showed the highest radical scavenging properties. Chitin and chitosan produced were also characterized with Fourier Transform Infrared Spectroscopy (FTIR).Key words: Antibacterial, antifungal, antioxidant, chitin, chitosan, crustacean

An expert system for nausea and vomiting problems in infants and children

Infants and children are suffering from a lots of nausea and vomiting problems. Doctors, usually face various difficulties dealing with these problems because of their similarities. In this paper, we present an expert system to help users in getting the correct diagnosis of problems of nausea and vomiting in infants and children (Gastro-esophageal reflux, Gastroenteritis, Systemic Infection, Bowel obstruction, Tumors, A bleeding disease, tonsillitis, and Hepatitis pharynx). Furthermore, this expert system provide information about the disease and how to deal with it. SL5 Object expert system language was used to design and implement this expert system

On the Geometry of Equiform Normal Curves in the Galilean Space G4

In our article, we establish the definition of the Equiform Normal curves in Galilean space G4. To obtain the position vector of an Equiform Normal curve in G4, we have to solve an integro-differential equation in μ2, where μ2 is the position function of a space curve γ (σ ) in the direction of third vector V3 of the Galilean space. Special cases of Equiform Normal curvatures are discussed. Finally, we prove that there is no equiform normal curve that is congruent to an Equiform Normal curve in G4

Plasma Β-Endorphin and Cortisol Profiles around Periparturient Period at Stressful Conditions in Egyptian Buffalo

The study determined the level of plasma β-endorphin and cortisol concentrations in peripheral blood circulation of buffalo cows suffering from reproductive disorders (dystocia and retained placenta) and weakness body condition score during periparturient period. Twenty multi-parous Egyptian buffalo cows at late pregnancy period were used for two months before parturition. β-endorphin concentrations were higher in buffalo suffering from reproductive disorders groups. Whereas, β-endorphin concentrations were 134.9±4.8 for retained placenta, 121.3±4.9 for dystocia, 114.2±8.4 for weakness and 113.5±6.5 pg/ml for control. In the closer period around parturition both of plasma β-endorphin and cortisol followed the same trend toward a gradually increased values during -2,-1days and zero time in all groups. A concomitant trend was noticed in β-endorphin and cortisol concentrations in postpartum period with reduce values were observed in all groups after parturition continued for month or more. Buffalo suffering from reproductive disorders were showed a high relative values in β-endorphin and cortisol concentrations. A significant differences (P<0.01) were observed between the experimental groups. Generally, buffaloes suffering reproductive disorders had a clear impact on blood plasma β-endorphin concentration around parturition process.The aim of this study was to determine the relationship between various reproductive disorders as a stress factors with plasma β–endorphin and cortisol in buffalo cows around parturition and changes in these parameters could be used as an objective measure of the stress associated labour. Stress has been hypothesized to be a cause of impaired reproductive efficiency. Stress may cause an overproduction of beta-endorphins and free radical

Dislocation dipoles and the nucleation of cracks in silicon nanopillars

To understand the brittle to ductile transtion at small scale in silicon nanopillars, plastic deformation of silicon nanopillars was investigated by atomistic simulations. Perfect dislocations were found to be nucleated from surfaces and nano cavities were evidenced resulting from dislocation dipoles annihilation. The formation of such cavities is consistent with previous atomistic calculations showing that the annihilation of dislocation vacancy dipole of perfect shuffle dislocations is associated to the formation of vacancy clusters in silicon and diamond [1]. In nanopillars such cavities contribute to the nucleation of cracks [2]. This mechanism of crack nucleation is relevant to single slip deformation and does not require any interactions between dislocations issued from intersecting glide planes as usually postulated for crack nucleation [3]. Incipient dipoles were also found nucleated on the glide plane swept by dislocations. These incipient dipoles result from bond flips and are similar to the Stone–Wales defects in graphene [4]. These defects could be similar and related to the “dislocations trails” found in the glide plane of dislocations in other deformation conditions, a long time and rather unsolved problem in silicon (see for example [5]). Under the applied stress those incipient dipoles appear to act as new nucleation centers for dislocations located in the glide plane. Those dislocations contribute to dislocation interactions in parallel slip planes and to the formation of nano cracks following the described above mechanism

Downscaling Using CDAnet Under Observational and Model Noises: The Rayleigh-Benard Convection Paradigm

Efficient downscaling of large ensembles of coarse-scale information is crucial in several applications, such as oceanic and atmospheric modeling. The determining form map is a theoretical lifting function from the low-resolution solution trajectories of a dissipative dynamical system to their corresponding fine-scale counterparts. Recently, a physics-informed deep neural network ("CDAnet") was introduced, providing a surrogate of the determining form map for efficient downscaling. CDAnet was demonstrated to efficiently downscale noise-free coarse-scale data in a deterministic setting. Herein, the performance of well-trained CDAnet models is analyzed in a stochastic setting involving (i) observational noise, (ii) model noise, and (iii) a combination of observational and model noises. The analysis is performed employing the Rayleigh-Benard convection paradigm, under three training conditions, namely, training with perfect, noisy, or downscaled data. Furthermore, the effects of noises, Rayleigh number, and spatial and temporal resolutions of the input coarse-scale information on the downscaled fields are examined. The results suggest that the expected l2-error of CDAnet behaves quadratically in terms of the standard deviations of the observational and model noises. The results also suggest that CDAnet responds to uncertainties similar to the theorized and numerically-validated CDA behavior with an additional error overhead due to CDAnet being a surrogate model of the determining form map

Data Assimilation in Chaotic Systems Using Deep Reinforcement Learning

Data assimilation (DA) plays a pivotal role in diverse applications, ranging from climate predictions and weather forecasts to trajectory planning for autonomous vehicles. A prime example is the widely used ensemble Kalman filter (EnKF), which relies on linear updates to minimize variance among the ensemble of forecast states. Recent advancements have seen the emergence of deep learning approaches in this domain, primarily within a supervised learning framework. However, the adaptability of such models to untrained scenarios remains a challenge. In this study, we introduce a novel DA strategy that utilizes reinforcement learning (RL) to apply state corrections using full or partial observations of the state variables. Our investigation focuses on demonstrating this approach to the chaotic Lorenz '63 system, where the agent's objective is to minimize the root-mean-squared error between the observations and corresponding forecast states. Consequently, the agent develops a correction strategy, enhancing model forecasts based on available system state observations. Our strategy employs a stochastic action policy, enabling a Monte Carlo-based DA framework that relies on randomly sampling the policy to generate an ensemble of assimilated realizations. Results demonstrate that the developed RL algorithm performs favorably when compared to the EnKF. Additionally, we illustrate the agent's capability to assimilate non-Gaussian data, addressing a significant limitation of the EnKF