ALBERT

Description: The mathematical model of diffusion in powder oxide catalysts during the process of temperature programmed oxygen isotopic exchange is proposed. The diffusion is considered together with the homogeneous and heterogeneous oxygen isotopic exchange processes. The matrix forms of exchange rate equations of simple and complex heteroexchange, and homoexchange reactions which obtain symmetrical forms are analyzed. The quantitative values of model parameters are found from the fitting of experimental data taken from literature of temperature programmed oxygen isotopic exchange process in catalysts ZrO2 and CeO2. The fittings show a good matching of model results with experimental data. The shapes of kinetic curves registered during temperature programmed oxygen isotopic exchange process are analyzed and the influence of various process parameters such as activation energies of simple and complex heteroexchange, oxygen surface concentration of catalyst, ratio of catalysts surface and volume of reactor, diffusion activation energy is considered. The depth profiles of diffusing oxygen species in oxide catalysts powder are calculated.

Description: This paper investigates the nonidentical parallel production line scheduling problem derived from an axle housing machining workshop of an axle manufacturer. The characteristics of axle housing machining lines are analyzed, and a nonidentical parallel line scheduling model with a jumping process operation (NPPLS-JP), which considers mixed model production, machine eligibility constraints, and fuzzy due dates, is established so as to minimize the makespan and earliness/tardiness penalty cost. While the physical structures of the parallel lines in the NPPLS-JP model are symmetric, the production capacities and process capabilities are asymmetric for different models. Different from the general parallel line scheduling problem, NPPLS-JP allows for a job to transfer to another production line to complete the subsequent operations (i.e., jumping process operations), and the transfer is unidirectional. The significance of the NPPLS-JP model is that it meets the demands of multivariety mixed model production and makes full use of the capacities of parallel production lines. Aiming to solve the NPPLS-JP problem, we propose a hybrid algorithm named the multi-objective grey wolf optimizer based on decomposition (MOGWO/D). This new algorithm combines the GWO with the multi-objective evolutionary algorithm based on decomposition (MOEA/D) to balance the exploration and exploitation abilities of the original MOEA/D. Furthermore, coding and decoding rules are developed according to the features of the NPPLS-JP problem. To evaluate the effectiveness of the proposed MOGWO/D algorithm, a set of instances with different job scales, job types, and production scenarios is designed, and the results are compared with those of three other famous multi-objective optimization algorithms. The experimental results show that the proposed MOGWO/D algorithm exhibits superiority in most instances.

Description: This paper introduces a new scheme for sliding mode control using symmetry principles for a rotating inverted pendulum, with the possibility of extension of this control scheme to other dynamic systems. This was proven for swing up and stabilisation control problems via the new sliding mode control scheme using both simulations and experiments of rotary inverted pendulum (RIP) underactuated systems. According to the Lyapunov theory, a section of the pendulum was compensated with a scale error in the upright position, as the desired trajectory was followed by the pendulum arm section. As the RIP’s dynamic equations were nonlinearly complex and coupled, the complex internal dynamics made the task of controller design difficult. The system control for the pathway of the reference model of the rotational actuator with the application of the sliding mode technique for moving back and forth up the inverted pendulum’s structure, till the arm to reach the linear range round the vertical upright position, was created and tested in an existent device. The stabilisation scheme was switched on in the sliding mode as soon as the arm reached the linear range. A comparison of the stabilisation performance for the same rotating inverted pendulum as discussed by other authors revealed that the proposed controller was more flexible and reliable in terms of the swing up and stabilisation time.

Description: In this paper, we derive some new fractional extensions of Hardy’s type inequalities. The corresponding reverse relations are also obtained by using the conformable fractional calculus from which the classical integral inequalities are deduced as special cases at α=1.

Description: In this article, we introduce the exponentiated Fréchet-G family of distributions. Several models of the introduced exponentiated Fréchet-G family are presented. The proposed family is precisely more flexible and effective in modeling complex data and is instrumental in reliability analysis. It covers a wide variety of shapes, such as unimodal, reverse J, right-skewed, symmetrical, and asymmetrical shapes. Various structural mathematical properties, such as the quantile, moment, incomplete moment, entropy, and order statistics, are derived. The parameters are evaluated using a parametric estimation method. The performance and flexibility of the exponentiated Fréchet-G family are analyzed via a simulation and two applications; one deals with reliability data, and the other deals with medical data.

Description: In practical engineering, it is a cost-consuming problem to consider the time-variant reliability of both random variables and interval variables, which usually requires a lot of calculation. Therefore, a time-variant reliability analysis approach with hybrid uncertain variables is proposed in this paper. In the design period, the stochastic process is discretized into random variables. Simultaneously, the original random variables and the discrete random variables are converted into independent normal variables, and the interval variables are changed into standard variables. Then it is transformed into a hybrid reliability problem of static series system. At different times, the limited state functions are linearized at the most probable point (MPP) and at the most unfavorable point (MUP). The transformed static system reliability problem with hybrid uncertain variables can be solved effectively by introducing random variables. To solve the double-loop nested optimization in the hybrid reliability calculation, an effective iterative method is proposed. Two numerical examples and an engineering example demonstrate the validity of the present approach.

Description: Intra- and inter-molecular interactions were studied in 2,3-dichloro-5,8-dihydroxy-1,4-naphthoquinone and 1,4-dihydroxy-anthraquinone to shed more light on the molecular assembly phenomena. The electronic ground and excited states features of the compounds were investigated to find structure-property dependencies. The theoretical study was carried out on the basis of Density Functional Theory (DFT), its Time-Dependent (TD-DFT) extension, and using Car–Parrinello Molecular Dynamics (CPMD). In order to show how the environmental effects modulate the physico-chemical properties, the simulations were performed in vacuo, with the solvent reaction field (Polarizable Continuum Model (PCM) and water as a solvent) and crystalline phase. The intramolecular hydrogen bonds and the bridged proton dynamics were analyzed in detail. The aromatic rings and electronic structure changes were estimated using the Harmonic Oscillator Model of Aromaticity (HOMA) and Atoms in Molecules (AIM) theory. The Symmetry-Adapted Perturbation Theory (SAPT) was employed for interaction energy decomposition in the studied dimers and trimers. It was found that the presence of a polar solvent decreased the energy barrier for the bridged proton transfer. However, it did not significantly affect the aromaticity and electronic structure. The SAPT results showed that the mutual polarization of the monomers in the dimer was weak and that the dispersion was responsible for most of the intermolecular attraction. The intermolecular hydrogen bonds seem to be much weaker than the intramolecular bridges. The TD-DFT results confirmed that the electronic excitations do not play any significant role in the intramolecular proton transfer. The CPMD results indicated that the protons are very labile in the hydrogen bridges. Short proton transfer and proton-sharing events were observed, and a correlation between them in the twin bridges was noticed, especially for the first investigated compound.

Description: The longitudinal axial flow threshing cylinder of the full feeding rice combine harvester is widely used in China and works with violent vibration. To explore the source of the excitation affecting the vibration and to reduce the vibration, a finite element modal analysis and multipoint input and multipoint output (MIMO) modal test were performed to solve the natural vibration characteristics. By analyzing the excitation frequency, we concluded that the main reason for the resonance was the coupling between the rotation frequency of the threshing cylinder and the first natural frequency. To avoid the influence of resonance and realize a lightweight design, we proposed a combination of size optimization and topology optimization. The second rotation orthogonal combination test was designed to analyze the first natural frequency, maximum stress, and maximum deformation of the threshing cylinder, and the threshing cylinder was reconstructed as a central symmetrical structure to balance the rotational inertia force. The field experiment results showed that the amplitudes of the optimized threshing cylinder were significantly lower than those of the original threshing cylinder. This study provides ideas for solving the vibration characteristics of rotating parts and provides an important reference for the design of vibration reduction and weight reduction of key parts in the field of agricultural machinery.

Description: In our present investigation, with the help of the basic (or q-) calculus, we first define a new domain which involves the Janowski function. We also define a new subclass of the class of q-starlike functions, which maps the open unit disk U, given by U= z:z∈C and z 

Description: Credit scoring of financially excluded persons is challenging for financial institutions because of a lack of financial data and long physical distances, which hamper data collection. The remote collection of alternative data has the potential to overcome these challenges, enabling credit access for such individuals. Whereas alternative data sources such as mobile phones have been investigated by previous researchers, this research proposes the integration of mobile-phone, satellite, and public geospatial data to improve credit evaluations where financial data are lacking. An approach to integrating these disparate data sources involving both spatial and temporal analysis methods such as spatial aggregation was employed, resulting in various data combinations. The resulting data sets were used to train classifiers of varying complexity, from logistic regression to ensemble learning. Comparisons were based on various performance metrics, including accuracy and the area under the receiver operating-characteristic curve. The combination of all three data sources performed significantly better than mobile-phone data, with the mean classifier accuracy and F1 score improving by 18% and 0.149, respectively. It is shown how these improvements can translate to cost savings for financial institutions through a reduction in misclassification errors. Alternative data combined in this manner could enhance credit provision to financially excluded persons while managing associated risks, leading to greater financial inclusion.

Description: Herein, we investigated the controllability of a semilinear multi-valued differential equation with non-instantaneous impulses of order α∈(1,2), where the linear part is a strongly continuous cosine family without compactness. We did not assume any compactness conditions on either the semi-group, the multi-valued function, or the inverse of the controllability operator, which is different from the previous literature.

Description: Nowadays, many business organizations are operating on the cloud environment in order to diminish their operating costs and to select the best service from many cloud providers. The increasing number of Cloud Services available on the market encourages the cloud consumer to be conscious in selecting the most apt Cloud Service Provider that satisfies functionality, as well as QoS parameters. Many disciplines of computer-based applications use standardized ontology to represent information in their fields that indicate the necessity of an ontology-based representation. The proposed generic model can help service consumers to identify QoS parameters interrelations in the cloud services selection ontology during run-time, and for service providers to enhance their business by interpreting the various relations. The ontology has been developed using the intended attributes of QoS from various service providers. A generic model has been developed and it is tested with the developed ontology.

Description: In this paper, we consider the class of strongly bi-close-to-convex functions of order α and bi-close-to-convex functions of order β. We obtain an upper bound estimate for the second Hankel determinant for functions belonging to these classes. The results in this article improve some earlier result obtained for the class of bi-convex functions.

Description: Imbalanced data and feature redundancies are common problems in many fields, especially in software defect prediction, data mining, machine learning, and industrial big data application. To resolve these problems, we propose an intelligent fusion algorithm, SMPSO-HS-AdaBoost, which combines particle swarm optimization based on subgroup migration and adaptive boosting based on hybrid-sampling. In this paper, we apply the proposed intelligent fusion algorithm to software defect prediction to improve the prediction efficiency and accuracy by solving the issues caused by imbalanced data and feature redundancies. The results show that the proposed algorithm resolves the coexisting problems of imbalanced data and feature redundancies, and ensures the efficiency and accuracy of software defect prediction.

Description: In the setting of fuzzy metric spaces (FMSs), a global optimization problem (GOP) obtaining the distance between two subsets of an FMS is solved by a tripled fixed-point (FP) technique here. Also, fuzzy weak tripled contractions (WTCs) for that are given. This problem was known before in metric space (MS) as a proximity point problem (PPP). The result is correct for each continuous τ—norms related to the FMS. Furthermore, a non-trivial example to illustrate the main theorem is discussed.

Description: Emerging scale-out I/O intensive applications are broadly used now, which process a large amount of data in buffer/cache for reorganization or analysis and their performances are greatly affected by the speed of the I/O system. Efficient management scheme of the limited kernel buffer plays a key role in improving I/O system performance, such as caching hinted data for reuse in future, prefetching hinted data, and expelling data not to be accessed again from a buffer, which are called proactive mechanisms in buffer management. However, most of the existing buffer management schemes cannot identify data reference regularities (i.e., sequential or looping patterns) that can benefit proactive mechanisms, and they also cannot perform in the application level for managing specified applications. In this paper, we present an A pplication Oriented I/O Optimization (AOIO) technique automatically benefiting the kernel buffer/cache by exploring the I/O regularities of applications based on program counter technique. In our design, the input/output data and the looping pattern are in strict symmetry. According to AOIO, each application can provide more appropriate predictions to operating system which achieve significantly better accuracy than other buffer management schemes. The trace-driven simulation experiment results show that the hit ratios are improved by an average of 25.9% and the execution times are reduced by as much as 20.2% compared to other schemes for the workloads we used.

Description: Motivated by the work of Saridakis (Phys. Rev. D102, 123525 (2020)), the present study reports the cosmological consequences of Barrow holographic dark energy (HDE) and its thermodynamics. The literature demonstrates that dark energy (DE) may result from electroweak symmetry breaking that triggers a phase transition from early inflation to late-time acceleration. In the present study, we incorporated viscosity in the Barrow HDE. A reconstruction scheme is presented for the parameters associated with Barrow holographic dark energy under the purview of viscous cosmology. The equation of state (EoS) parameter is reconstructed in this scenario and quintessence behaviour is observed. Considering Barrow HDE as a specific case of Nojiri–Odintsov (NO) HDE, we have observed quintom behaviour of the EoS parameter and for some values of n the EoS has been observed to be very close to −1 for the current universe. The generalised second law of thermodynamics has come out to be valid in all the scenarios under consideration. Physical viability of considering Barrow HDE as a specific case of NO HDE is demonstrated in this study. Finally, it has been observed that the model under consideration is very close to ΛCDM and cannot go beyond it.

Description: In this current manuscript, some general classes of weighted analytic function spaces in a unit disc are defined and studied. Special functions significant in both analytic T(p,q,m,s;Ψ) norms and analytic Ψ-Bloch norms serve as a framework for introducing new families of analytic classes. An application in operator theory is provided by establishing important properties of the composition-type operator Cϕ such as the boundedness and compactness with the help of the defined new classes.

Description: In this paper, we argue in favor of first-order homogeneous Lagrangians in the velocities. The relevant form of such Lagrangians is discussed and justified physically and geometrically. Such Lagrangian systems possess Reparametrization Invariance (RI) and explain the observed common Arrow of Time as related to the non-negative mass for physical particles. The extended Hamiltonian formulation, which is generally covariant and applicable to reparametrization-invariant systems, is emphasized. The connection between the explicit form of the extended Hamiltonian H and the meaning of the process parameter λ is illustrated. The corresponding extended Hamiltonian H defines the classical phase space-time of the system via the Hamiltonian constraint H=0 and guarantees that the Classical Hamiltonian H corresponds to p0—the energy of the particle when the coordinate time parametrization is chosen. The Schrödinger’s equation and the principle of superposition of quantum states emerge naturally. A connection is demonstrated between the positivity of the energy E=cp0〉0 and the normalizability of the wave function by using the extended Hamiltonian that is relevant for the proper-time parametrization.

Description: Current research on the cascading failure of coupling networks is mostly based on hierarchical network models and is limited to a single relationship. In reality, many relationships exist in a network system, and these relationships collectively affect the process and scale of the network cascading failure. In this paper, a composite network is constructed based on the multisubnet composite complex network model, and its cascading failure is proposed combined with multiple relationships. The effect of intranetwork relationships and coupling relationships on network robustness under different influencing factors is studied. It is shown that cascading failure in composite networks is different from coupling networks, and increasing the strength of the coupling relationship can significantly improve the robustness of the network.

Description: In this work, anchoring of cinchona derivatives to trifunctional cores (hub approach) was demonstrated to obtain size-enlarged organocatalysts. By modifying the cinchona skeleton in different positions, we prepared four C3-symmetric size-enlarged cinchona derivatives (hub-cinchonas), which were tested as organocatalysts and their catalytic activities were compared with the parent cinchona (hydroquinine) catalyst. We showed that in the hydroxyalkylation reaction of indole, hydroquinine provides good enantioselectivities (up to 73% ee), while the four new size-enlarged derivatives resulted in significantly lower values (up to 29% ee) in this reaction. Anchoring cinchonas to trifunctional cores was found to facilitate nanofiltration-supported catalyst recovery using the PolarClean alternative solvent. The C3-symmetric size-enlarged organocatalysts were completely rejected by all the applied membranes, whereas the separation of hydroquinine was found to be insufficient when using organic solvent nanofiltration. Furthermore, the asymmetric catalysis was successfully demonstrated in the case of the Michael reaction of 1,3-diketones and trans-β-nitrostyrene using Hub3-cinchona (up to 96% ee) as a result of the positive effect of the C3-symmetric structure using a bulkier substrate. This equates to an increased selectivity of the catalyst in comparison to hydroquinine in the latter Michael reaction.

Description: In this work, we outline a general method for calculating Auger spectra in molecules, which accounts for the underlying symmetry of the system. This theory starts from Fano’s formulation of the interaction between discrete and continuum states, and it generalizes this formalism to deal with the simultaneous presence of several intermediate quasi-bound states and several non-interacting decay channels. Our theoretical description is specifically tailored to resonant autoionization and Auger processes, and it explicitly includes the incoming wave boundary conditions for the continuum states and an accurate treatment of the Coulomb repulsion. This approach is implemented and applied to the calculation of the K−LL Auger and autoionization spectra of ozone, which is a C2v symmetric molecule, whose importance in our atmosphere to filter out radiation has been widely confirmed. We also show the effect that the molecular point group and, in particular, the localization of the core-hole in the oxygen atoms related by symmetry operations, has on the electronic structure of the Auger states and on the spectral lineshape by comparing our results with the experimental data.

Description: Industrial Control Systems are an essential part of our daily lives and can be found in industries such as oil, utilities, and manufacturing. Rapid growth in technology has introduced industrial components with network capabilities that allow them to communicate with traditional computer networks, thus increasing their exposure to cyber-attacks. Current research on Industrial Control Systems suffer from lack of technical information as these systems are part of critical infrastructures. To overcome this, researchers have employed different types of testbeds to develop their mechanisms of cyber-attack detection and prevention. This manuscript describes, implements, and evaluates physical, hybrid, and virtual application of a clean water supply system developed for cybersecurity research. The results show that physical testbeds allow an understanding of the behaviour and dynamics of control components like sensors and actuators, which might be affected by external influences such as noise, vibration, temperature, and non-ideal device behaviour. Although, hybrid testbeds reduce the cost of implementation, they ignore the physical dynamics of the system as explained above. Virtual testbeds are the cheapest option in comparison with physical and hybrid testbeds; however, they provide a limited view of the control system operation that could have negative consequences when developing a detection/prevention system.

Description: Immobilized multi-enzyme cascades are increasingly used in microfluidic devices. In particular, their application in continuous flow reactors shows great potential, utilizing the benefits of reusability and control of the reaction conditions. However, capitalizing on this potential is challenging and requires detailed knowledge of the investigated system. Here, we show the application of computational methods for optimization with multi-level reactor design (MLRD) methodology based on the underlying physical and chemical processes. We optimize a stereoselective reduction of a diketone catalyzed by ketoreductase (Gre2) and Nicotinamidadenindinukleotidphosphat (NADPH) cofactor regeneration with glucose dehydrogenase (GDH). Both enzymes are separately immobilized on magnetic beads forming a packed bed within the microreactor. We derive optimal reactor feed concentrations and enzyme ratios for enhanced performance and a basic economic model in order to maximize the techno-economic performance (TEP) for the first reduction of 5-nitrononane-2,8-dione.

Description: Atomic cascades are ubiquitous in nature and they have been explored within very different scenarios, from precision measurements to the modeling of astrophysical spectra, and up to the radiation damage in biological matter. However, up to the present, a quantitative analysis of these cascades often failed because of their inherent complexity. Apart from utilizing the rotational symmetry of atoms and a proper distinction of different physical schemes, a hierarchy of useful approaches is therefore needed in order to keep cascade computations feasible. We here suggest a classification of atomic cascades and demonstrate how they can be modeled within the framework of the Jena Atomic Calculator. As an example, we shall compute within a configuration-average approach the stepwise decay cascade of atomic magnesium, following a 1s inner-shell ionization, and simulate the corresponding (final) ion distribution. Our classification of physical scenarios (schemes) and the hierarchy of computational approaches are both flexible to further refinements as well as to complex shell structures of the atoms and ions, for which the excitation and decay dynamics need to be modeled in good detail.

Description: Objective image quality assessment (IQA) measures are playing an increasingly important role in the evaluation of digital image quality. New IQA indices are expected to be strongly correlated with subjective observer evaluations expressed by Mean Opinion Score (MOS) or Difference Mean Opinion Score (DMOS). One such recently proposed index is the SuperPixel-based SIMilarity (SPSIM) index, which uses superpixel patches instead of a rectangular pixel grid. The authors of this paper have proposed three modifications to the SPSIM index. For this purpose, the color space used by SPSIM was changed and the way SPSIM determines similarity maps was modified using methods derived from an algorithm for computing the Mean Deviation Similarity Index (MDSI). The third modification was a combination of the first two. These three new quality indices were used in the assessment process. The experimental results obtained for many color images from five image databases demonstrated the advantages of the proposed SPSIM modifications.

Description: Symmetry-based sub-connected hybrid precoding is an energy-friendly structure in wireless communications. Most of the prior work set a diagonal constraint on the analog precoder and used a randomly set matrix as the initial analog precoder, which did not match the optimal channel conditions, leading to a decrease in spectral efficiency, and some had huge complexity when calculating the digital precoder. Aiming to solve these problems, this paper proposed a low-complexity hybrid precoding algorithm based on Initial value Acceleration-based Alternating Minimization (IAAM). Leveraging the special structure of analog precoder in sub-connected scheme, we design the analog precoder through low-complexity quadratic programming and use the least square method to obtain the digital precoder. Moreover, we design a heuristic algorithm with the objective function of maximizing the effective channel gain to calculate the initial analog precoder as the starting point for alternating minimization. The simulation results show that the spectral efficiency of this algorithm is at least 17.5% higher than the existing two traditional sub-connected algorithms. Additionally, it increases energy efficiency by at least 12.8% compa with the Orthogonal Matching Pursuit (OMP) algorithm. Its algorithm convergence speed is fast, which increases with the number of RF chains.

Description: Clustering is more popular than the expert knowledge approach in Interval Fuzzy Type-2 membership function construction because it can construct membership function automatically with less time consumption. Most research proposed a two-fuzzifier fuzzy C-Means clustering method to construct Interval Fuzzy Type-2 membership function which mainly focused on producing Gaussian membership function. The other two important membership functions, triangular and trapezoidal, are constructed using the grid partitioning method. However, the method suffers a drawback of not being able to represent actual data composition in the underlying dataset. Some research proposed triangular and trapezoidal membership functions construction using readily formed Fuzzy Type-1 membership functions, which means it remains unclear how the membership functions are heuristically constructed using fuzzy C-Means outputs. The triangular and trapezoidal membership functions are important because previous works have shown that they may produce superior performance than Gaussian membership function in some applications. Therefore, this paper presents a structured literature review on generating triangular and trapezoidal Interval Fuzzy Type-2 membership functions using fuzzy C-Means. Initially, 110 related manuscripts were collected from Web of Science, Scopus, and Google Scholar. These manuscripts went through the identification, screening, eligibility, and inclusion processes, and as a result, 21 manuscripts were reviewed and discussed in this paper. To ensure that the review also covers the important components of fuzzy logic, this paper also reviews and discusses another 49 manuscripts on fuzzy calculation and operation. Furthermore, this paper also discusses the contributions of the conducted review to the body of knowledge, future research directions and challenges, with the aim to motivate the future works of constructing the methods to generate Interval Fuzzy Type-2 triangular and trapezoidal membership functions using fuzzy C-Means. The methods imply flexibility in choosing membership function type, hence increasing the effectiveness of fuzzy applications through leveraging the advantages that each of the three membership function types could provide.

Description: The formation of a submicron optical cavity on one side of a metal–insulator–metal (MIM) tunneling device induces a measurable electrical current between the two metal layers with no applied voltage. Reducing the cavity thickness increases the measured current. Eight types of tests were carried out to determine whether the output could be due to experimental artifacts. All gave negative results, supporting the conclusion that the observed electrical output is genuinely produced by the device. We interpret the results as being due to the suppression of vacuum optical modes by the optical cavity on one side of the MIM device, which upsets a balance in the injection of electrons excited by zero-point fluctuations. This interpretation is in accord with observed changes in the electrical output as other device parameters are varied. A feature of the MIM devices is their femtosecond-fast transport and scattering times for hot charge carriers. The fast capture in these devices is consistent with a model in which an energy ∆E may be accessed from zero-point fluctuations for a time ∆t, following a ∆E∆t uncertainty-principle-like relation governing the process.

Description: The non-alternant aromatic azulene, an isomer of alternant naphthalene, differs from the latter in peculiar properties. The large polarization of the π-electron system over the seven and five rings gives to azulene electrophile property a pronounced tendency to donate electrons to an acceptor, substituted at azulene 1 position. This paper presents cases in which azulene transfers electrons to a suitable acceptor as methylium ions, positive charged heteroaromatics and examples of neutral molecules that can accept electrons. The proposed product synthesis was outlined and the expected electron transfer was highlighted by analyzing the NMR, UV-Vis spectra and the pKR+ values.

Description: Current approach to space-time coupling (STC) phenomena is given together with a complementary version of the STC concept that emphasizes the finiteness of the energy of the considered pulses. Manifestations of STC are discussed in the framework of the simplest exact localized solution of Maxwell’s equations, exhibiting a “collapsing shell”. It falls onto the center, continuously deforming, and then, having reached maximum compression, expands back without losing energy. Analytical solutions describing this process enable to fully characterize the field in space-time. It allowed to express energy density in the center of collapse in the terms of total pulse energy, frequency and spectral width in the far zone. The change of the pulse shape while travelling from one point to another is important for coherent control of quantum systems. We considered the excitation of a two-level system located in the center of the collapsing EM (electromagnetic) pulse. The result is again expressed through the parameters of the incident pulse. This study showed that as it propagates, a unipolar pulse can turn into a bipolar one, and in the case of measuring the excitation efficiency, we can judge which of these two pulses we are dealing with. The obtained results have no limitation on the number of cycles in a pulse. Our work confirms the productivity of using exact solutions of EM wave equations for describing the phenomena associated with STC effects. This is facilitated by rapid progress in the search for new types of such solutions.

Description: To improve fire prediction accuracy over existing methods, a double weighted naive Bayes with compensation coefficient (DWCNB) method is proposed for fire prediction purposes. The fire characteristic attributes and attribute values are all weighted to weaken the assumption that the naive Bayes attributes are independent and equally important. A compensation coefficient was used to compensate for the prior probability, and a five-level orthogonal testing method was employed to properly design the coefficient. The proposed model was trained with data collected from the National Institute of Standards and Technology (NIST) fire database. Simulation comparisons show that the average prediction accuracy of the proposed method is 98.13%, which is 5.08% and 2.52% higher than the methods of naive Bayes (NB) and double weighted naive Bayes (DWNB), respectively. The experimental results show that the average accuracies of the DWCNB method for test fire and interference sources were 97.76% and 98.24%. Prediction accuracies were 5.06% and 3.74% higher than those of the NB and DWNB methods.

Description: I review the parametrisation of the full set of Λb→Λ*(1520) form factors in the framework of Heavy Quark Expansion, including next-to-leading-order O(αs) and, for the first time, next-to-leading-power O(1/mb) corrections. The unknown hadronic parameters are obtained by performing a fit to recent lattice QCD calculations. I investigate the compatibility of the Heavy Quark Expansion and the current lattice data, finding tension between these two approaches in the case of tensor and pseudo-tensor form factors, whose origin could come from an underestimation of the current lattice QCD uncertainties and higher order terms in the Heavy Quark Expansion.

Description: Under pure quantum evolution, for a wave packet that diffuses (like a Gaussian), scattering can cause localization. Other forms of the wave function, spreading more rapidly than a Gaussian, are unlikely to localize.

Description: The weights of criteria in multi-criteria decision-making (MCDM) problems are essential elements that can significantly affect the results. Accordingly, researchers developed and presented several methods to determine criteria weights. Weighting methods could be objective, subjective, and integrated. This study introduces a new method, called MEREC (MEthod based on the Removal Effects of Criteria), to determine criteria’ objective weights. This method uses a novel idea for weighting criteria. After systematically introducing the method, we present some computational analyses to confirm the efficiency of the MEREC. Firstly, an illustrative example demonstrates the procedure of the MEREC for calculation of the weights of criteria. Secondly, a comparative analysis is presented through an example for validation of the introduced method’s results. Additionally, we perform a simulation-based analysis to verify the reliability of MEREC and the stability of its results. The data of the MCDM problems generated for making this analysis follow a prevalent symmetric distribution (normal distribution). We compare the results of the MEREC with some other objective weighting methods in this analysis, and the analysis of means (ANOM) for variances shows the stability of its results. The conducted analyses demonstrate that the MEREC is efficient to determine objective weights of criteria.

Description: In this research study, we establish some necessary conditions to check the uniqueness-existence of solutions for a general multi-term ψ-fractional differential equation via generalized ψ-integral boundary conditions with respect to the generalized asymmetric operators. To arrive at such purpose, we utilize a procedure based on the fixed-point theory. We follow our study by suggesting two numerical algorithms called the Dafterdar-Gejji and Jafari method (DGJIM) and the Adomian decomposition method (ADM) techniques in which a series of approximate solutions converge to the exact ones of the given ψ-RLFBVP and the equivalent ψ-integral equation. To emphasize for the compatibility and the effectiveness of these numerical algorithms, we end this investigation by providing some examples showing the behavior of the exact solution of the existing ψ-RLFBVP compared with the approximate ones caused by DGJIM and ADM techniques graphically.

Description: The aim of this work is to investigate the oscillation of solutions of higher-order nonlinear differential equations with a middle term. By using the integral averaging technique, Riccati transformation technique and comparison technique, several oscillatory properties are presented that unify the results obtained in the literature. Some examples are presented to demonstrate the main results.

Description: Point and interval estimations are taken into account for a progressive first-failure censored left-truncated normal distribution in this paper. First, we derive the estimators for parameters on account of the maximum likelihood principle. Subsequently, we construct the asymptotic confidence intervals based on these estimates and the log-transformed estimates using the asymptotic normality of maximum likelihood estimators. Meanwhile, bootstrap methods are also proposed for the construction of confidence intervals. As for Bayesian estimation, we implement the Lindley approximation method to determine the Bayesian estimates under not only symmetric loss function but also asymmetric loss functions. The importance sampling procedure is applied at the same time, and the highest posterior density (HPD) credible intervals are established in this procedure. The efficiencies of classical statistical and Bayesian inference methods are evaluated through numerous simulations. We conclude that the Bayes estimates given by Lindley approximation under Linex loss function are highly recommended and HPD interval possesses the narrowest interval length among the proposed intervals. Ultimately, we introduce an authentic dataset describing the tensile strength of 50mm carbon fibers as an illustrative sample.

Description: A vehicular ad-hoc network (VANET) is the basic block in building an intelligent transportation system that improves the traffic flow and makes needed information conveniently accessible. VANET depends on a dense exchange of sensed data between vehicles and Road Side Units (RSUs). A large amount of sensed data requires a huge computation and storage capabilities, which is provided by the vehicular cloud computing (VCC). However, the security problems of data confidentiality, access control, vehicles’ authentication, and conductors’ privacy in VCC are issues that need to be solved. In this paper, we propose an efficient algorithm to ensure VCC security and privacy. We use Pseudo-ID instead of vehicles’ real ID to provide conductors’ privacy, Identifier-Based Signature mechanism is used to guarantee vehicles’ authentication, and Ciphertext-Policy Attribute-Based Encryption (CP-ABE) algorithm is used for key distribution. Our liGhtweight secURe AutheNticaTion and keY distribution scheme for vehicular cloud computing (GUARANTY) ensures a secure keys distribution to minimize the encryption and decryption computation cost. Vehicles use a symmetrical cryptography in their communication. We analyze the security of our algorithm using AVISPA tool. We use this tool to simulate insiders and outsiders attacks. We evaluate our algorithm’s performance in terms of computation delay and reception rate.

Description: Stem cell transplantation therapy may inhibit inflammation during stroke and increase the presence of healthy cells in the brain. The novelty of this work, is to introduce a new mathematical model of stem cells transplanted to treat stroke. This manuscript studies the stability of the mathematical model by using the current biological information on stem cell therapy as a possible treatment for inflammation from microglia during stroke. The model is proposed to represent the dynamics of various immune brain cells (resting microglia, pro-inflammation microglia, and anti-inflammation microglia), brain tissue damage and stem cells transplanted. This model is based on a set of five ordinary differential equations and explores the beneficial effects of stem cells transplanted at early stages of inflammation during stroke. The Runge–Kutta method is used to discuss the model analytically and solve it numerically. The results of our simulations are qualitatively consistent with those observed in experiments in vivo, suggesting that the transplanted stem cells could contribute to the increase in the rate of ant-inflammatory microglia and decrease the damage from pro-inflammatory microglia. It is found from the analysis and simulation results that stem cell transplantation can help stroke patients by modulation of the immune response during a stroke and decrease the damage on the brain. In conclusion, this approach may increase the contributions of stem cells transplanted during inflammation therapy in stroke and help to study various therapeutic strategies for stem cells to reduce stroke damage at the early stages.

Description: The paper is devoted to the development of the structure of a fast and flexible data collecting system based on the proposed approach to measure power quality indicators in three-phase medium-voltage distribution grids with an example of a Mikhailovsky mining and processing plant. The approach utilizes the properties of a space vector, obtained from grid currents and voltages with disturbed waveform, to allow faster extraction of the harmonic components compared to traditional approaches, based on the direct Fourier-transform applied to a line or phase values. During the study, the concept of a universal measurement device was introduced, which allows fast estimation of the following values at the grid node: magnitudes and phases of voltage and current harmonic components, active and reactive power of harmonics and fundamental components, positive and negative instantaneous powers. The structure of interconnected measurement and control units for the considered grid node with simultaneous operation of two active variable frequency drives with active rectifiers was proposed in accordance with a concept of the Internet of things. The benefits of the proposed solution are shown by the example of the model of the grid node with two operating draglines and nonlinear load, which was developed in MATLAB/Simulink software. The proposed approach was utilized to produce distributed references for control systems of grid inverters to compensate nonlinear currents, which allowed to significantly improve THDi of the grid node input power.

Description: With the use of increasingly complex software, software bugs are inevitable. Software developers rely on bug reports to identify and fix these issues. In this process, developers inspect suspected buggy source code files, relying heavily on a bug report. This process is often time-consuming and increases the cost of software maintenance. To resolve this problem, we propose a novel bug localization method using topic-based similar commit information. First, the method determines similar topics for a given bug report. Then, it extracts similar bug reports and similar commit information for these topics. To extract similar bug reports on a topic, a similarity measure is calculated for a given bug report. In the process, for a given bug report and source code, features shared by similar source codes are classified and extracted; combining these features improves the method’s performance. The extracted features are presented to the convolutional neural network’s long short-term memory algorithm for model training. Finally, when a bug report is submitted to the model, a suspected buggy source code file is detected and recommended. To evaluate the performance of our method, a baseline performance comparison was conducted using code from open-source projects. Our method exhibits good performance.

Description: This study has focused on the structure, bonding, and reactivity analysis of closo-borate anions [BnHn]2− (n = 5–12). Several descriptors of B–H interactions have been calculated. It has been found that the values of electron density and total energy at bond critical point are the most useful descriptors for investigation of B–H interactions. Using results from the descriptor analysis, one may conclude that orbital interactions in [BnHn]2− increase with increasing the boron cluster size. Several approaches to estimate atomic charges have been applied. Boron atoms in apical positions have more negative values of atomic charges as compared with atoms from equatorial positions. The mean values of boron and hydrogen atomic charges tend to be more positive with the increasing of boron cluster size. Global and local reactivity descriptors using conceptual density functional theory (DFT) theory have been calculated. Based on this theory, the closo-borate anions [BnHn]2− (n = 5–9) can be considered strong and moderate electrophiles, while the closo-borate anions [BnHn]2− (n = 10–12) can be considered marginal electrophiles. Fukui functions for electrophilic attack have been calculated. Fukui functions correlate well with atomic charges of the closo-borate anions. Boron atoms in apical positions have the most positive values of Fukui functions.

Description: The Augmented Lagrangian Smoothed Particle Hydrodynamics (ALSPH) method is a novel incompressible Smoothed Particle Hydrodynamics (SPH) approach that solves Navier–Stokes equations by an iterative augmented Lagrangian scheme through enforcing the divergence-free coupling of velocity and pressure fields. This study aims to systematically investigate the time step size and the number of inner iteration parameters to boost the performance of the ALSPH method. Additionally, the effects of computing spatial derivatives with two alternative schemes on the accuracy of numerical results are also scrutinized. Namely, the first scheme computes spatial derivatives on the updated particle positions at each iteration, whereas the second one employs the updated pressure and velocity fields on the initial particle positions to compute the gradients and divergences throughout the iterations. These two schemes are implemented to the solution of a flow over a circular cylinder at Reynolds numbers of 200 in two dimensions. Initially, simulations are performed in order to determine the optimum time step sizes by utilizing a maximum number of five iterations per time step. Subsequently, the optimum number of inner iterations is investigated by employing the predetermined optimum time step size under the same flow conditions. Finally, the schemes are tested on the same flow problem with different Reynolds numbers using the best performing combination of the aforementioned parameters. It is observed that the ALSPH method can enable one to increase the time step size without deteriorating the numerical accuracy as a consequence of imposing larger ALSPH penalty terms in larger time step sizes, which, overall, leads to improved computational efficiency. When considering the hydrodynamic flow characteristics, it can be stated that two spatial derivative schemes perform very similarly. However, the results indicate that the derivative operation with the updated particle positions produces slightly lower velocity divergence magnitudes at larger time step sizes.

Description: Insight is described as the sudden solution of a problem and is contrasted with an analytical, step-by-step approach. Traditionally, insight is thought to be associated with activity of the right hemisphere, whereas analytical solutions are thought to be associated with activity of the left hemisphere. However, empirical evidence as to the localization of insight-related brain activity is mixed and inconclusive. Some studies seem to confirm the traditional view, whereas others do not. Moreover, results of EEG and fMRI studies frequently contradict each other. In this study, EEG and fMRI data were recorded while subjects performed the remote association test and for each solved problem were asked to report whether the solution was reached analytically or insightfully. The data were analyzed in a 16-second fragment preceding the subject’s response. Source localization techniques were used in the analysis of EEG data. Based on EEG data, insightful as compared to analytical problem solving was accompanied by high-frequency synchronization in semantic cortical areas of the left hemisphere 10–12 s before the subject’s response. Based on fMRI data, however, insightful solutions were accompanied by increased activity in frontal and temporal regions of the right hemisphere. The results are interpreted in terms of different cognitive processes involved in insightful problem solving, which could be differently reflected in EEG and fMRI data.

Description: In this paper, we analyze the security of a group key establishment scheme proposed by López-Ramos et al. This proposal aims at allowing a group of users to agree on a common key. We present several attacks against the security of the proposed protocol. In particular, an active attack is presented, and it is also proved that the protocol does not provide forward secrecy.

Description: This paper introduces the Fourier spectral method combined with the strongly stable exponential time difference method as an attractive and easy-to-implement alternative for the integration of the multi-dimensional Allen–Cahn equation with no-flux boundary conditions. The main advantages of the proposed method are that it utilizes the discrete fast Fourier transform, which ensures efficiency, allows an extension to two and three spatial dimensions in a similar fashion as one-dimensional problems, and deals with various boundary conditions. Several numerical experiments are carried out on multi-dimensional Allen–Cahn equations including a two-dimensional Allen–Cahn equation with a radially symmetric circular interface initial condition to demonstrate the fourth-order temporal accuracy and stability of the method. The numerical results show that the proposed method is fourth-order accurate in the time direction and is able to satisfy the discrete energy law.

Description: This paper deals with the existence of ω-periodic solutions for nth-order ordinary differential equation involving fixed delay in Banach space E. Lnu(t)=f(t,u(t),u(t−τ)),t∈R, where Lnu(t):=u(n)(t)+∑i=0n−1aiu(i)(t), ai∈R, i=0,1,⋯,n−1, are constants, f(t,x,y):R×E×E⟶E is continuous and ω-periodic with respect to t, τ〉0. By applying the approach of upper and lower solutions and the monotone iterative technique, some existence and uniqueness theorems are proved under essential conditions.

Description: In this paper, we first obtain the Fourier transforms of some finite bivariate orthogonal polynomials and then by using the Parseval identity, we introduce some new families of bivariate orthogonal functions.

Description: An individual’s reaction time data to visual stimuli have usually been represented in Experimental Psychology by means of an ex-Gaussian function. In most previous works, researchers have mainly aimed at finding a meaning for the parameters of the ex-Gaussian function which are known to correlate with cognitive disorders. Based on the recent evidence of correlations between the reaction time series to visual stimuli produced by different individuals within a group, we go beyond and propose a Physics-inspired model to represent the reaction time data of a coetaneous group of individuals. In doing so, a Maxwell–Boltzmann-like distribution appeared, the same distribution as for the velocities of the molecules in an Ideal Gas model. We describe step by step the methodology we use to go from the individual reaction times to the distribution of the individuals response within the coetaneous group. In practical terms, by means of this model we also provide a simple entropy-based methodology for the classification of the individuals within the collective they belong to with no need for an external reference which can be applicable in diverse areas of social sciences.

Description: In recent years, numerous attempts have been made to enhance the living standard for old-aged people. Ambient Assisted Living (AAL) is an evolving interdisciplinary field aimed at the exploitation of knowledge and communication technology in health and tele-monitoring systems to combat the impact of the growing aging population. AAL systems are designed for customized, responsive, and predictive requirements, requiring high performance of functionality to ensure interoperability, accessibility, security, and consistency. Standardization, continuity, and assistance of system development have become an urgent necessity to meet the increasing needs for sustainable systems. In this article, we examine and address the methods of the different AAL systems. In addition, we analyzed the acceptance criteria of the AAL framework intending to define and evaluate different AAL-based symmetrical models, leveraging performance characteristics under the integrated fuzzy environment. The main goal is to provide an understanding of the current situation of the AAL-oriented setups. Our vision is to investigate and evaluate the potential symmetrical models of AAL systems and frameworks for the implementation of effective new installations.

Description: This manuscript addresses a new multivariate generalized predictive control strategy using the least squares support vector machine for parabolic distributed parameter systems. First, a set of proper orthogonal decomposition-based spatial basis functions constructed from a carefully selected set of data is used in a Galerkin projection for the building of an approximate low-dimensional lumped parameter systems. Then, the temporal autoregressive exogenous model obtained by the least squares support vector machine is applied in the design of a multivariate generalized predictive control strategy. Finally, the effectiveness of the proposed multivariate generalized predictive control strategy is verified through a numerical simulation study on a typical diffusion-reaction process in radical symmetry.

Description: In this review, we propose a new perspective to demonstrate the Gross conjecture regarding the high-energy symmetry of string theory. We review the construction of the exact string scattering amplitudes (SSAs) of three tachyons and one arbitrary string state, or the Lauricella SSA (LSSA), in the 26D open bosonic string theory. These LSSAs form an infinite dimensional representation of the SL(K+3,C) group. Moreover, we show that the SL(K+3,C) group can be used to solve all the LSSAs and express them in terms of one amplitude. As an application in the hard scattering limit, the LSSA can be used to directly prove the Gross conjecture, which was previously corrected and proved by the method of the decoupling of zero norm states (ZNS). Finally, the exact LSSA can be used to rederive the recurrence relations of SSA in the Regge scattering limit with associated SL(5,C) symmetry and the extended recurrence relations (including the mass and spin dependent string BCJ relations) in the nonrelativistic scattering limit with the associated SL(4,C) symmetry discovered recently.

Description: The improvement of power transmission efficiency (PTE) is an important issue in the design of a wireless power transfer (WPT) system. The WPT system with multiple transmitting (Tx) or receiving (Rx) coils is a way to improve the PTE. This paper deals with the estimation of the mutual inductance angle for a two-dimensional (2D) WPT system with two Tx coils and one Rx coil. The mutual inductance angle is one of the most important parameters to determine the PTE in the 2D WPT system. The condition for the maximum PTE is investigated and the mutual inductance angle is defined for the 2D WPT system. An improved estimation method of the mutual inductance angle is proposed based on the phase-locked loop (PLL) technique using the voltages and currents of the Tx coils. The simulation and experimental results are provided to validate the effectiveness of the proposed method.

Description: In this work, a Lie group reduction for a (2 + 1) dimensional fractional Kadomtsev-Petviashvili (KP) system is determined by using the Lie symmetry method with Riemann Liouville derivative. After reducing the system into a two-dimensional nonlinear fractional partial differential system (NLFPDEs), the power series (PS) method is applied to obtain the exact solution. Further the obtained power series solution is analyzed for convergence. Then, using the new conservation theorem with a generalized Noether’s operator, the conservation laws of the KP system are obtained.

Description: Recently, antenna array radiation pattern synthesis and adaptation has become an essential requirement for most wireless communication systems. Therefore, this paper proposes a new recursive sidelobe level (SLL) reduction algorithm using a sidelobe sequential damping (SSD) approach based on pattern subtraction, where the sidelobes are sequentially reduced to the optimum required levels with near-symmetrical distribution. The proposed SSD algorithm is demonstrated, and its performance is analyzed, including SLL reduction and convergence behavior, mainlobe scanning, processing speed, and performance under mutual coupling effects for uniform linear and planar arrays. In addition, the SSD performance is compared with both conventional tapering windows and optimization techniques, where the simulation results show that the proposed SSD approach has superior maximum and average SLL performances and lower processing speeds. In addition, the SSD is found to have a constant SLL convergence profile that is independent on the array size, working effectively on any uniform array geometry with interelement spacing less than one wavelength, and deep SLL levels of less than −70 dB can be achieved relative to the mainlobe level, especially for symmetrical arrays.

Description: Two distinct operational procedures are proposed for diagnosis and tracking of heart disease evolution (in particular atrial fibrillations). The first procedure, based on the application of non-linear dynamic methods (strange attractors, skewness, kurtosis, histograms, Lyapunov exponent, etc.) analyzes the electrical activity of the heart (electrocardiogram signals). The second procedure, based on multifractalization through Markovian and non-Markovian-type stochasticizations in the framework of the scale relativity theory, reconstructs any type of EKG signal by means of harmonic mappings from the usual space to the hyperbolic one. These mappings mime various scale transitions by differential geometries, in Riemann spaces with symmetries of SL(2R)-type. Then, the two operational procedures are not mutually exclusive, but rather become complementary, through their finality, which is gaining valuable information concerning fibrillation crises. As such, the author’s proposed method could be used for developing new models for medical diagnosis and evolution tracking of heart diseases (patterns dynamics, signal reconstruction, etc.).

Description: Fake or false information on social media platforms is a significant challenge that leads to deliberately misleading users due to the inclusion of rumors, propaganda, or deceptive information about a person, organization, or service. Twitter is one of the most widely used social media platforms, especially in the Arab region, where the number of users is steadily increasing, accompanied by an increase in the rate of fake news. This drew the attention of researchers to provide a safe online environment free of misleading information. This paper aims to propose a smart classification model for the early detection of fake news in Arabic tweets utilizing Natural Language Processing (NLP) techniques, Machine Learning (ML) models, and Harris Hawks Optimizer (HHO) as a wrapper-based feature selection approach. Arabic Twitter corpus composed of 1862 previously annotated tweets was utilized by this research to assess the efficiency of the proposed model. The Bag of Words (BoW) model is utilized using different term-weighting schemes for feature extraction. Eight well-known learning algorithms are investigated with varying combinations of features, including user-profile, content-based, and words-features. Reported results showed that the Logistic Regression (LR) with Term Frequency-Inverse Document Frequency (TF-IDF) model scores the best rank. Moreover, feature selection based on the binary HHO algorithm plays a vital role in reducing dimensionality, thereby enhancing the learning model’s performance for fake news detection. Interestingly, the proposed BHHO-LR model can yield a better enhancement of 5% compared with previous works on the same dataset.

Description: Amidation is an important post translational modification where a peptide ends with an amide group (–NH2) rather than carboxyl group (–COOH). These amidated peptides are less sensitive to proteolytic degradation with extended half-life in the bloodstream. Amides are used in different industries like pharmaceuticals, natural products, and biologically active compounds. The in-vivo, ex-vivo, and in-vitro identification of amidation sites is a costly and time-consuming but important task to study the physiochemical properties of amidated peptides. A less costly and efficient alternative is to supplement wet lab experiments with accurate computational models. Hence, an urgent need exists for efficient and accurate computational models to easily identify amidated sites in peptides. In this study, we present a new predictor, based on deep neural networks (DNN) and Pseudo Amino Acid Compositions (PseAAC), to learn efficient, task-specific, and effective representations for valine amidation site identification. Well-known DNN architectures are used in this contribution to learn peptide sequence representations and classify peptide chains. Of all the different DNN based predictors developed in this study, Convolutional neural network-based model showed the best performance surpassing all other DNN based models and reported literature contributions. The proposed model will supplement in-vivo methods and help scientists to determine valine amidation very efficiently and accurately, which in turn will enhance understanding of the valine amidation in different biological processes.

Description: Nano aluminum oxide was prepared by the combustion method using aluminum nitrate as the oxidizer and urea as a fuel. Characterization of synthesized materials was performed using SEM (scanning electron microscope), powder XRD (X-ray diffraction), FTIR (Fourier transform infrared spectroscopy), and TEM (transmission electron microscope). Al-Mg/Al2O3 (2, 4, 6, and 8 wt%) metal matrix nanocomposites were prepared by liquid metallurgy route-vertex technique. The homogeneous dispersion of nano Al2O3 particles in Al-Mg/Al2O3 metal matrix nanocomposites (MMNCs) was revealed from the field emission SEM analysis. The reinforcement particles present in the matrix were analyzed through energy-dispersive X-ray spectroscopy method. The properties (corrosion and mechanical) of the fabricated composites were evaluated. The mechanical and corrosion properties of the prepared nanocomposites initially increased and then decreased with the addition of nano Al2O3 particles in Al-Mg Matrix. The studies show that, the presence of 6 wt% of nano Al2O3 particles in the matrix improved the properties of other combinations of nano Al2O3 in the Al-Mg matrix material. Further, the Al-Mg/Al2O3 (6 wt%) MMNCs are joined by friction stir welding and evaluated for microstructural, mechanical, and corrosion properties. Al-Mg/Al2O3 MMNCs may find applications in the marine field. The response surface method (RSM) was used for the optimization of tensile strength, Young’s modulus, and microhardness of the synthesized material which resulted in a 95% of statistical confidence level. Artificial neural network (ANN) analysis was also carried out which perfectly predicted these two properties. The ANN model is optimized to obtain 99.9% accurate predictions by changing the number of neurons in the hidden layer.

Description: Some recent studies exposed preliminary but rather intriguing statistical evidence of in vacuo dispersion-like spectral lags for gamma-ray bursts (GRBs), a linear correlation between time of observation and energy of GRB particles, which is expected in some models of quantum geometry. Those results focused on testing in vacuo dispersion for the most energetic GRB particles, and in particular only included photons with energy at emission greater than 40 GeV. We here extend the window of the statistical analysis down to 5 GeV and find results that are consistent with what had been previously noticed at higher energies.

Description: Relation extraction aims to extract semantic relationships between two specified named entities in a sentence. Because a sentence often contains several named entity pairs, a neural network is easily bewildered when learning a relation representation without position and semantic information about the considered entity pair. In this paper, instead of learning an abstract representation from raw inputs, task-related entity indicators are designed to enable a deep neural network to concentrate on the task-relevant information. By implanting entity indicators into a relation instance, the neural network is effective for encoding syntactic and semantic information about a relation instance. Organized, structured and unified entity indicators can make the similarity between sentences that possess the same or similar entity pair and the internal symmetry of one sentence more obviously. In the experiment, a systemic analysis was conducted to evaluate the impact of entity indicators on relation extraction. This method has achieved state-of-the-art performance, exceeding the compared methods by more than 3.7%, 5.0% and 11.2% in F1 score on the ACE Chinese corpus, ACE English corpus and Chinese literature text corpus, respectively.

Description: The social distancing imposed by the novel coronavirus, SARS-CoV-2, has affected people’s everyday lives and has resulted in companies changing the way they conduct business. The airline industry has been continually adapting since the novel coronavirus appeared. A series of airlines have changed their airplane boarding and passenger seat allocation process to increase their passengers’ safety. Many suggest a minimum social distance among passengers in the aisle while boarding. Some airlines have reduced their airplanes’ capacities by keeping the middle seats empty. Recent literature indicates that the Reverse Pyramid boarding method provides favorable values for boarding time and passenger health metrics when compared to other boarding methods. This paper analyses the extent to which aisle social distancing, the quantity of carry-on luggage, and an airline’s relative preferences for different performance metrics influence the optimal number of passengers to board the airplane in each of three boarding groups when the Reverse Pyramid method is used and the middle seats are empty. We also investigate the resulting impact on the average boarding time and health risks to boarding passengers. We use an agent-based model and stochastic simulation approach to evaluate various levels of aisle social distancing among passengers and the quantity of luggage carried aboard the airplane. When minimizing boarding time is the primary objective of an airline, for a given value of aisle social distance, decreasing the carry-on luggage volumes increases the optimal number of boarding group 1 passengers and decreases the optimal number of group 2 passengers with aisle seats; for a given volume of luggage, an increase in aisle social distance is associated with more passengers in group 1 and more aisle seat passengers in group 2. When minimizing the health risk to aisle seat passengers or to window seat passengers, the optimal solution results from assigning an equal number of window seat passengers to groups 1 and 2 and an equal number of aisle seat passengers to groups 2 and 3. This solution is robust to changes in luggage volume and the magnitude of aisle social distance. Furthermore, across all luggage and aisle social distancing scenarios, the solution reduces the health risk to aisle seat passengers between 22.76% and 35.31% while increasing average boarding time by less than 3% in each scenario.

Description: Multivariate statistical analysis such as partial least square regression (PLSR) is the common data processing technique used to handle high-dimensional data space on near-infrared (NIR) spectral datasets. The PLSR is useful to tackle the multicollinearity and heteroscedasticity problem that can be commonly found in such data space. With the problem of the nonlinear structure in the original input space, the use of the classical PLSR model might not be appropriate. In addition, the contamination of multiple outliers and high leverage points (HLPs) in the dataset could further damage the model. Generally, HLPs contain both good leverage points (GLPs) and bad leverage points (BLPs); therefore, in this case, removing the BLPs seems relevant since it has a significant impact on the parameter estimates and can slow down the convergence process. On the other hand, the GLPs provide a good efficiency in the model calibration process; thus, they should not be eliminated. In this study, robust alternatives to the existing kernel partial least square (KPLS) regression, which are called the kernel partial robust GM6-estimator (KPRGM6) regression and the kernel partial robust modified GM6-estimator (KPRMGM6) regression are introduced. The nonlinear solution on PLSR was handled through kernel-based learning by nonlinearly projecting the original input data matrix into a high-dimensional feature mapping that corresponded to the reproducing kernel Hilbert spaces (RKHS). To increase the robustness, the improvements on GM6 estimators are presented with the nonlinear PLSR. Based on the investigation using several artificial dataset scenarios from Monte Carlo simulations and two sets from the near-infrared (NIR) spectral dataset, the proposed robust KPRMGM6 is found to be superior to the robust KPRGM6 and non-robust KPLS.

Description: A cheminformatics procedure for a partitioning model based on 135 natural compounds including Flavonoids, Saponins, Alkaloids, Terpenes and Triterpenes with drug-like features based on a descriptors pool was developed. The knowledge about the applicability of natural products as a unique source for the development of new candidates towards deadly infectious disease is a contemporary challenge for drug discovery. We propose a partitioning scheme for unveiling drug-likeness candidates with properties that are important for a prompt and efficient drug discovery process. In the present study, the vantage point is about the matching of descriptors to build the partitioning model applied to natural compounds with diversity in structures and complexity of action towards the severe diseases, as the actual SARS-CoV-2 virus. In the times of the de novo design techniques, such tools based on a chemometric and symmetrical effect by the implied descriptors represent another noticeable sign for the power and level of the descriptors applicability in drug discovery in establishing activity and target prediction pipeline for unknown drugs properties.

Description: (1) Background: Fluctuating asymmetry is often used as an indicator of developmental instability, and is proposed as a signal of genetic quality. The display of prominent masculine phenotypic features, which are a direct result of high androgen levels, is also believed to be a sign of genetic quality, as these hormones may act as immunosuppressants. Fluctuating asymmetry and masculinity are therefore expected to covary. However, there is lack of strong evidence in the literature regarding this hypothesis. (2) Materials and methods: In this study, we examined a large dataset of high-density 3D facial scans of 1260 adults (630 males and 630 females). We mapped a high-density 3D facial mask onto the facial scans in order to obtain a high number of quasi-landmarks on the faces. Multi-dimensional measures of fluctuating asymmetry were extracted from the landmarks using Principal Component Analysis, and masculinity/femininity scores were obtained for each face using Partial Least Squares. The possible correlation between these two qualities was then examined using Pearson’s coefficient and Canonical Correlation Analysis. (3) Results: We found no correlation between fluctuating asymmetry and masculinity in men. However, a weak but significant correlation was found between average fluctuating asymmetry and masculinity in women, in which feminine faces had higher levels of fluctuating asymmetry on average. This correlation could possibly point to genetic quality as an underlying mechanism for both asymmetry and masculinity; however, it might also be driven by other fitness or life history traits, such as fertility. (4) Conclusions: Our results question the idea that fluctuating asymmetry and masculinity should be (more strongly) correlated in men, which is in line with the recent literature. Future studies should possibly focus more on the evolutionary relevance of the observed correlation in women.

Description: We investigate the statistical distribution for ideal Bose gases with constant particle density in the 3D box of volume V=L3. By changing linear size L and imposing different boundary conditions on the system, we present a numerical analysis on the characteristic temperature and condensate fraction and find that a smaller linear size is efficient to increase the characteristic temperature and condensate fraction. Moreover, there is a singularity under the antiperiodic boundary condition.

Description: In this paper we introduce and study (2,k)-distance Fibonacci polynomials which are natural extensions of (2,k)-Fibonacci numbers. We give some properties of these polynomials—among others, a graph interpretation and matrix generators. Moreover, we present some connections of (2,k)-distance Fibonacci polynomials with Pascal’s triangle.

Description: The symmetric shape of some inequalities between two sequences of real numbers generates inequalities of the same shape in operator theory. In this paper, we study a new refinement of the Cauchy–Bunyakovsky–Schwarz inequality for Euclidean spaces and several inequalities for two bounded linear operators on a Hilbert space, where we mention Bohr’s inequality and Bergström’s inequality for operators. We present an inequality of the Cauchy–Bunyakovsky–Schwarz type for bounded linear operators, by the technique of the monotony of a sequence. We also prove a refinement of the Aczél inequality for bounded linear operators on a Hilbert space. Finally, we present several applications of some identities for Hermitian operators.

Description: In this paper, we establish new sufficient conditions for the oscillation of solutions of a class of second-order delay differential equations with a mixed neutral term, which are under the non-canonical condition. The results obtained complement and simplify some known results in the relevant literature. Example illustrating the results is included.

Description: We discuss quantum time states formed with a finite number of energy eigenstates with the purpose of obtaining a time coordinate. These time states are eigenstates of the recently introduced discrete time operator. The coordinate and momentum representations of these time eigenstates resemble classical time curves and become classical at high energies. To illustrate this behavior, we consider the simple example of the particle-in-a-box model. We can follow the quantum-classical transition of the system. Among the many existing solutions for the particle in a box, we use a set which leads to time eigenstates for use as a coordinate system.

Description: Both the theory of differential subordination and its dual, the theory of differential superordination, introduced by Professors Miller and Mocanu are based on reinterpreting certain inequalities for real-valued functions for the case of complex-valued functions. Studying subordination and superordination properties using different types of operators is a technique that is still widely used, some studies resulting in sandwich-type theorems as is the case in the present paper. The fractional integral of confluent hypergeometric function is introduced in the paper and certain subordination and superordination results are stated in theorems and corollaries, the study being completed by the statement of a sandwich-type theorem connecting the results obtained by using the two theories.

Description: This article provides a bivariate binary logit model and statistical inference procedures for parameter estimation and hypothesis testing. The bivariate binary logit (BBL) model is an extension of the binary logit model that has two correlated binary responses. The BBL model responses were formed using a 2 × 2 contingency table, which follows a multinomial distribution. The maximum likelihood and Berndt–Hall–Hall–Hausman (BHHH) methods were used to obtain the BBL model. Hypothesis testing of the BBL model contains the simultaneous test and the partial test. The test statistics of the simultaneous test and the partial test were determined using the maximum likelihood ratio test method. The likelihood ratio statistics of the simultaneous test and the partial test were approximately asymptotically chi-square distributed with 3p degrees of freedom. The BBL model was applied to a real dataset, and the BBL model with the single covariate was better than the BBL model with multiple covariates.

Description: Inventory-pooling (IP) is an effective tool to mitigate demand uncertainty and variability, to reduce operational costs, and consequently to increase the profit. The major assumptions of the previous works in literature on IP include the following: (1) Independents demand, which satisfy the typical normal independent and identically distributed (iid) random variables; (2) dependents (correlated) symmetric demands, which follows to a multivariate normal distribution. The effect of the dependent asymmetric demand is not yet studied. The aim of this paper is to consider this more realistic case. Indeed, the contribution of this paper is twofold. Firstly, it analyzes both the sensitivity of dependence structure and the levels of skewness of distributions on IP policies in terms of optimal total cost and demand satisfaction constraint. Secondly, both symmetric and asymmetric demand distributions are modeled using various beta distribution and the dependance between demands are modeled using various copulas. A newsvendor problem inspired by the literature, with two decentralized locations and two centralized locations, is considered the empirical study. For each dependance situation, three IP models are considered: inventory centralization, regular transshipments, and independent systems. The results suggest divergences in the decisions in about 9% of cases. Bad choice of marginal distributions given that the copula is appropriate can lead to divergences that vary between 2.2% and 4%, depending on whether the demand distributions are symmetric or asymmetric.

Description: Blockchain technology has been applied to logistics tracking, but it is not cost-effective. The development of smart lockers has solved the problem of repeated distribution to improve logistics efficiency, thereby becoming a solution with convenience and privacy compared to the in-store purchase and pickup alternative. This study prioritized the key factors of smart lockers using a simulated annealing–genetic algorithm by fractional factorial design (FFD-SAGA) and grey relational analysis, and investigated the main users of smart lockers by grey multiple attribute decision analysis. The results show that the Web application programming interface (API) concatenation and money flow provider are the key success factors of smart lockers, and office workers are the main users of the lockers. Hence, how to better meet the needs of office workers will be an issue of concern for service providers.

Description: The current article studied a nonlinear transmission of the nerve impulse model, the Fitzhugh–Nagumo (FN) model, in the conformable fractional form with an efficient analytical approach based on a combination of conformable Sumudu transform and the Adomian decomposition method. Convergence analysis and error analysis were also carried out based on the Banach fixed point theory. We also provided some examples to support our results. The results obtained revealed that the presented approach is very fantastic, effective, reliable, and is an easy method to handle specific problems in various fields of applied sciences and engineering. The Mathematica software carried out all the computations and graphics in this paper.

Description: Wave diffusion in the composite soil layer with the lined tunnel structure is often encountered in the field of seismic engineering. The wave function expansion method is an effective method for solving the wave diffusion problem. In this paper, the wave function expansion method is used to present a semi-analytical solution to the shear horizontal (SH) wave scattering problem of a circular lined tunnel under the covering soil layer. Considering the existence of the covering soil layer, the great arc assumption (that is, the curved boundary instead of the straight-line boundary) is used to construct the wavefield in the composite soil layer. Based on the wave field and boundary conditions, an infinite linear equation system is established by adding the application of complex variable functions. The finite term is intercepted and solved, and the accuracy of the solution is analyzed. Although truncation is inevitable, due to the Bessel function has better convergence, a smaller truncation coefficient can achieve mechanical accuracy. Based on numerical examples, the influence of SH wave incident frequency, soil parameters, and lining thickness on the dynamic stress concentration factor of lining is analyzed. Compared with the SH wave scattering problem by lining in a single medium half-space, due to the existence of the cover layer and the influence of its stiffness, the dynamic stress of the lining can be increased or inhibited. In addition, the lining thickness has obvious different effects on the dynamic stress concentration coefficient of the inner and outer walls of different materials.

Description: Asymmetric gait is associated with pain, injury, and reduced stability in patient populations. Data from side by side walking suggest that unintentional synchronization with an external cue may reduce gait asymmetry. Two types of asymmetric gait were examined here: (1) mass imbalance between limbs to simulate single limb amputation and (2) restriction of plantarflexion during toe-off to simulate reduced propulsion from neurological impairment. Twenty-five healthy participants walked normally and with simulated gait asymmetry on a custom-designed treadmill that oscillated in the vertical direction via pneumatic actuation (amplitude: 2 cm, frequency: participant’s preferred step frequency). Swing Time Asymmetry (STA) and Phase Coordination Index (PCI) both increased significantly with the application of unilateral mass and plantarflexion restriction (p 〈 0.001). However, walking with simulated asymmetry did not alter unintentional synchronization with the treadmill motion. Further, oscillation of the treadmill did not improve STA or PCI while walking with simulated asymmetry. Analysis of synchronized step clusters using the Weibull survival function revealed that synchronization with the platform persisted for longer durations when compared with data from side by side walking. These results suggest that walking on a vertically oscillating surface may not be an effective approach for improving gait asymmetry.

Description: The concepts of terms and tree languages are significant tools for the development of research works in both universal algebra and theoretical computer science. In this paper, we establish a strong connection between semigroups of terms and tree languages, which provides the tools for studying monomorphisms between terms and generalized hypersubstitutions. A novel concept of a seminearring of non-deterministic generalized hypersubstitutions is introduced and some interesting properties among subsets of its are provided. Furthermore, we prove that there are monomorphisms from the power diagonal semigroup of tree languages and the monoid of generalized hypersubstitutions to the power diagonal semigroup of non-deterministic generalized hypersubstitutions and the monoid of non-deterministic generalized hypersubstitutions, respectively. Finally, the representation of terms using the theory of n-ary functions is defined. We then present the Cayley’s theorem for Menger algebra of terms, which allows us to provide a concrete example via full transformation semigroups.

Description: In the past decade, various types of wavelet-based algorithms were proposed, leading to a key tool in the solution of a number of numerical problems. This work adopts the Chebyshev wavelets for the numerical solution of several models. A Chebyshev operational matrix is developed, for selected collocation points, using the fundamental properties. Moreover, the convergence of the expansion coefficients and an upper estimate for the truncation error are included. The obtained results for several case studies illustrate the accuracy and reliability of the proposed approach.

Description: Synchromodality is a freight transport process in which information is exchanged expeditiously in order to maximize the benefits of different modes of transport and transport nodes in terms of efficiency and environmental impact. The aim of the study is to analyze the problems of synchronized intermodal traffic management between the main port and inland transport nodes in European transport corridors and to find reliable solutions to these problems. Therefore, the main purpose of this article is to investigate the problem of the distribution of containers transported by rail between two transport terminals in a synchronous transport network. A specific optimization model is presented in this article. This optimization task is formulated as a stochastic integer programming model between the terminals located in Vilnius and Klaipeda Seaport, the essence of which is as follows: (a) to minimize the waiting time for container cargo at the location—terminal No. 1; (b) to minimize the total journey time of the train; (c) to minimize the waiting time for containerized cargo at the point of arrival—terminal No. 2.

Description: Neutrosophy, the study of neutralities, is a new branch of Philosophy that has applications in many different fields of science. Inspired by the idea of Neutrosophy, Smarandache introduced NeutroAlgebraicStructures (or NeutroAlgebras) by allowing the partiality and indeterminacy to be included in the structures’ operations and/or axioms. The aim of this paper is to combine the concept of Neutrosophy with hyperstructures theory. In this regard, we introduce NeutroSemihypergroups as well as NeutroHv-Semigroups and study their properties by providing several illustrative examples.

Description: This study aims to analyze and compare the disease activity control and quality of life of patients with rheumatoid arthritis (RA) who were treated with biological products in real-life settings. We tried to determine whether there is a symmetry in the performance of the biological molecules between each other and with the first Janus kinase (JAK) inhibitor. This is an observational, longitudinal, real-life study performed in the biggest rheumatology clinic during the period 2012–2020 comparing quality of life, cost of therapy, and disease control via different clinical measures. In all three disease activities measurement instruments, we observed an improvement for all biologic and target synthetic medicines. The disease activity score (DAS28) score decreased from 5.06 to 3.01, on average, for all INNs, suggesting that the majority of patients move away from moderate to low disease activity. The clinical disease activity index (CDAI) score decreased from 25.9 to 9.4, also indicating that patients with moderate disease activity reached a low level of activity. Similar results are reflected in the score, which fell from 27.7 to 10.3, again confirming the improvement to a low level of disease activity for patients treated with all INNs. Logically, with the successful control of disease activity, the quality of life (QoL) of the observed patients improved from 0.77 to 0.83 after a one-year follow up, as measured with the EuroQuol 5D-3L (EQ5D). Based on these results, we can consider that the observed biological INNs perform symmetrically in terms of the control of disease activity and improvement in the QoL of the observed patients. Biological therapy improves the disease control and quality of life of suitable patients with RA in real-life settings. All available biological therapies could be used interchangeably.

Description: The Shewhart X¯- and S-charts are most commonly used for monitoring the process mean and variability based on the assumption of normality. However, many process distributions may follow a positively skewed distribution, such as the lognormal distribution. In this study, we discuss the construction of three combined X¯- and S-charts for jointly monitoring the lognormal mean and the standard deviation. The simulation results show that the combined lognormal X¯- and S-charts are more effective when the lognormal distribution is more skewed. A real example is used to demonstrate how the combined lognormal X¯- and S-charts can be applied in practice.

Description: Linear molecules usually represent a special case in rotational-vibrational calculations due to a singularity of the kinetic energy operator that arises from the rotation about the a (the principal axis of least moment of inertia, becoming the molecular axis at the linear equilibrium geometry) being undefined. Assuming the standard ro-vibrational basis functions, in the 3N−6 approach, of the form ∣ν1,ν2,ν3ℓ3;J,k,m⟩, tackling the unique difficulties of linear molecules involves constraining the vibrational and rotational functions with k=ℓ3, which are the projections, in units of ℏ, of the corresponding angular momenta onto the molecular axis. These basis functions are assigned to irreducible representations (irreps) of the C2v(M) molecular symmetry group. This, in turn, necessitates purpose-built codes that specifically deal with linear molecules. In the present work, we describe an alternative scheme and introduce an (artificial) group that ensures that the condition ℓ3=k is automatically applied solely through symmetry group algebra. The advantage of such an approach is that the application of symmetry group algebra in ro-vibrational calculations is ubiquitous, and so this method can be used to enable ro-vibrational calculations of linear molecules in polyatomic codes with fairly minimal modifications. To this end, we construct a—formally infinite—artificial molecular symmetry group D∞h(AEM), which consists of one-dimensional (non-degenerate) irreducible representations and use it to classify vibrational and rotational basis functions according to ℓ and k. This extension to non-rigorous, artificial symmetry groups is based on cyclic groups of prime-order. Opposite to the usual scenario, where the form of symmetry adapted basis sets is dictated by the symmetry group the molecule belongs to, here the symmetry group D∞h(AEM) is built to satisfy properties for the convenience of the basis set construction and matrix elements calculations. We believe that the idea of purpose-built artificial symmetry groups can be useful in other applications.

Description: In spatial data analysis, the prior conditional autoregressive (CAR) model is used to express the spatial dependence on random effects from adjacent regions. This paper provides a new proposed approach regarding the development of the existing normal CAR model into a more flexible, Fernandez–Steel skew normal (FSSN) CAR model. This approach is able to capture spatial random effects that have both symmetrical and asymmetrical patterns. The FSSN CAR model is built on the basis of the normal CAR with an additional skew parameter. The FSSN distribution is able to provide good estimates for symmetry with heavy- or light-tailed and skewed-right and skewed-left data. The effects of this approach are demonstrated by establishing the FSSN distribution and FSSN CAR model in spatial data using Stan language. On the basis of the plot of the estimation results and histogram of the model error, the FSSN CAR model was shown to behave better than both models without a spatial effect and with the normal CAR model. Moreover, the smallest widely applicable information criterion (WAIC) and leave-one-out (LOO) statistical values also validate the model, as FSSN CAR is shown to be the best model used.

Description: The class of log-elliptical distributions is well used and studied in risk measurement and actuarial science. The reason is that risks are often skewed and positive when they describe pure risks, i.e., risks in which there is no possibility of profit. In practice, risk managers confront a system of mutually dependent risks, not only one risk. Thus, it is important to measure risks while capturing their dependence structure. In this short paper, we compute the multivariate risk measures, multivariate tail conditional expectation, and multivariate tail covariance measure for the family of log-elliptical distributions, which captures the dependence structure of the risks while focusing on the tail of their distributions, i.e., on extreme loss events. We then study our result and examine special cases, as well as the optimal portfolio selection using such measures. Finally, we show how the given multivariate tail moments can also be computed for log-skew elliptical models based on similar approaches given for the log-elliptical case.

Description: This paper presents a novel approach for an Intrusion Detection System (IDS) based on one kind of asymmetric optimization which use any three already well-known IDS algorithms and Triple Modular Redundancy (TMR) algorithm together. Namely, a variable threshold which indicates an attack on an observed and protected network is determined by using all three values obtained with three known IDS algorithms i.e., on previously recorded data by making a decision by majority. For these algorithms authors used algorithm of k-nearest neighbors, cumulative sum algorithm, and algorithm of exponentially weighted moving average. Using a proposed method we can get a threshold that is more precisely determined than in the case of any method individual. Practically, using TMR we obtain a dynamically threshold adjustment of IDS software, which reduces the existence of false alarms and undetected attacks, so the efficiency of such IDS software is notably higher and can get better results. Today, Denial of Service attacks (DoS) are one of the most present type of attacks and the reason for the special attention paid to them in this paper. In addition, the authors of the proposed method for IDS software used a known CIC-DDoS2019 dataset, which contains various data recordings of such attacks. Obtained results with the proposed solution showed better characteristics than each individual used algorithm in this solution. IDS software with the proposed method worked precisely and timely, which means alarms were triggered properly and efficiently.

Description: Industrial products have been highlighted in the agronomic field more for their technical and functional aspects than for their visual aesthetics. Currently, this concept has changed and has favoured the development of innovative products taking into account all the factors involved in the conception of a product. This article describes a real industrial product redesign case that was functionally innovative and a reference in the agronomic domain. Due to the large number of copies that emerged from its competitors, this design required a new image in its aesthetic appearance to differentiate it from the rest. The difficulty of this project lay in the technical limitations to develop it, so a conceptual design process based on analogies and inspired by nature was necessary to find the most appropriate shape. Based on this methodology, a symmetrical and static design was transformed into one with asymmetrical and dynamic shapes inspired by the helicoidal movement of water. This new design gave this product an innovative, symbolic, and differentiating image that allowed its industrial registration in a large number of countries.

Description: 2-(2-Fluoro-4-biphenyl) propionic acid (flurbiprofen), from the phenylalkanoic acid family of nonsteroidal anti-inflammatory drugs (NSAID’s), is currently on the pharmaceutical market as a racemate. This racemic compound was tested for its propensity to undergo the self-disproportionation of enantiomers (SDE) phenomenon by various forms of chromatography (SDEvC), such as routine gravity-driven column chromatography, medium-pressure liquid chromatography (MPLC), preparative thin-layer chromatography (PTLC), and size-exclusion chromatography (SEC), as well as by sublimation (SDEvS). Furthermore, examination by nuclear magnetic resonance (NMR) in various solvents found that flurbiprofen exhibited the phenomenon of self-induced diastereomeric anisochronism (SIDA). By measurement of the diffusion coefficient (D), the longitudinal relaxation time (T1), and the transverse relaxation time (T2) using NMR, as well as by electrospray ionization-mass spectrometry (ESI-MS) examinations, the preferred intermolecular association was found to be solvent dependent, e.g., heterochiral association was preferred in toluene, while homochiral association was preferred in more polar solvents. This study also attempted, unsuccessfully, to correlate the NMR measurements of flurbiprofen with chromatographic outcomes for the rationalization and prediction of chromatographic results based on NMR measurements. Because the intermolecular hydrogen bonding of the acid groups in flurbiprofen overwhelmingly predominates over other intermolecular interactions, flurbiprofen seemed to represent a good test case for this idea. The behavior of scalemic samples of flurbiprofen is important, as, although it is currently dispensed as a racemate, clinical applications of the R enantiomer have been investigated. SDEvC and SDEvS both have ramifications for the preparation, handling, and storage of enantioenriched flurbiprofen, and this concern applies to other chiral drugs as well.

Description: The aim of our study is to establish, for convex functions on an interval, a midpoint version of the fractional HHF type inequality. The corresponding fractional integral has a symmetric weight function composed with an increasing function as integral kernel. We also consider a midpoint identity and establish some related inequalities based on this identity. Some special cases can be considered from our main results. These results confirm the generality of our attempt.

Description: Symmetries and their associated conserved quantities are of great importance in the study of dynamic systems. In this paper, we describe nonconservative field theories on time scales—a model that brings together, in a single theory, discrete and continuous cases. After defining Hamilton’s principle for nonconservative field theories on time scales, we obtain the associated Lagrange equations. Next, based on the Hamilton’s action invariance for nonconservative field theories on time scales under the action of some infinitesimal transformations, we establish symmetric and quasi-symmetric Noether transformations, as well as generalized quasi-symmetric Noether transformations. Once the Noether symmetry selection criteria are defined, the conserved quantities for the nonconservative field theories on time scales are identified. We conclude with two examples to illustrate the applicability of the theory.

Description: This paper provides novel generalizations by considering the generalized conformable fractional integrals for reverse Copson’s type inequalities on time scales. The main results will be proved using a general algebraic inequality, chain rule, Hölder’s inequality, and integration by parts on fractional time scales. Our investigations unify and extend some continuous inequalities and their corresponding discrete analogues. In addition, when α = 1, we obtain some well-known time scale inequalities due to Hardy, Copson, Bennett, and Leindler inequalities.

Description: Strong magnetic fields impact quantum-chromodynamics (QCD) properties in several situations; examples include the early universe, magnetars, and heavy-ion collisions. These examples share a common trait—time evolution. A prominent QCD property impacted by a strong magnetic field is the quark condensate, an approximate order parameter of the QCD transition between a high-temperature quark-gluon phase and a low-temperature hadronic phase. We use the linear sigma model with quarks to address the quark condensate time evolution under a strong magnetic field. We use the closed time path formalism of nonequilibrium quantum field theory to integrate out the quarks and obtain a mean-field Langevin equation for the condensate. The Langevin equation features dissipation and noise kernels controlled by a damping coefficient. We compute the damping coefficient for magnetic field and temperature values achieved in peripheral relativistic heavy-ion collisions and solve the Langevin equation for a temperature quench scenario. The magnetic field changes the dissipation and noise pattern by increasing the damping coefficient compared to the zero-field case. An increased damping coefficient increases fluctuations and time scales controlling condensate’s short-time evolution, a feature that can impact hadron formation at the QCD transition. The formalism developed here can be extended to include other order parameters, hydrodynamic modes, and system’s expansion to address magnetic field effects in complex settings as heavy-ion collisions, the early universe, and magnetars.

Description: Shape classification and matching is an important branch of computer vision. It is widely used in image retrieval and target tracking. Shape context method, curvature scale space (CSS) operator and its improvement have been the main algorithms of shape matching and classification. The shape classification network (SCN) algorithm is proposed inspired by LeNet5 basic network structure. Then, the network structure of SCN is introduced and analyzed in detail, and the specific parameters of the network structure are explained. In the experimental part, SCN is used to perform classification tasks on three shape datasets, and the advantages and limitations of our algorithm are analyzed in detail according to the experimental results. SCN performs better than many traditional shape classification algorithms. Accordingly, a practical example is given to show that SCN can save computing resources.

Description: The algebraic as well as geometric topological constructions of manifold embeddings and homotopy offer interesting insights about spaces and symmetry. This paper proposes the construction of 2-quasinormed variants of locally dense p-normed 2-spheres within a non-uniformly scalable quasinormed topological (C, R) space. The fibered space is dense and the 2-spheres are equivalent to the category of 3-dimensional manifolds or three-manifolds with simply connected boundary surfaces. However, the disjoint and proper embeddings of covering three-manifolds within the convex subspaces generates separations of p-normed 2-spheres. The 2-quasinormed variants of p-normed 2-spheres are compact and path-connected varieties within the dense space. The path-connection is further extended by introducing the concept of bi-connectedness, preserving Urysohn separation of closed subspaces. The local fundamental groups are constructed from the discrete variety of path-homotopies, which are interior to the respective 2-spheres. The simple connected boundaries of p-normed 2-spheres generate finite and countable sets of homotopy contacts of the fundamental groups. Interestingly, a compact fibre can prepare a homotopy loop in the fundamental group within the fibered topological (C, R) space. It is shown that the holomorphic condition is a requirement in the topological (C, R) space to preserve a convex path-component. However, the topological projections of p-normed 2-spheres on the disjoint holomorphic complex subspaces retain the path-connection property irrespective of the projective points on real subspace. The local fundamental groups of discrete-loop variety support the formation of a homotopically Hausdorff (C, R) space.

Description: Keto-enol prototropic conversions for carbonyl compounds and phenols have been extensively studied, and many interesting review articles and even books appeared in the last 50 years. Quite a different situation takes place for derivatives of biologically active azulene, for which only scanty information on this phenomenon can be found in the literature. In this work, quantum-chemical studies have been undertaken for symmetrically and unsymmetrically substituted azulenols (constitutional isomers of naphthols). Stabilities of two enol (OH) rotamers and all possible keto (CH) tautomers have been analyzed in the gas phase {DFT(B3LYP)/6-311+G(d,p)} and also in aqueous solution {PCM(water)//DFT(B3LYP)/6-311+G(d,p)}. Contrary to naphthols, for which the keto forms can be neglected, at least one keto isomer (C1H, C2H, and/or C3H) contributes significantly to the tautomeric mixture of each azulenol to a higher degree in vacuo (non-polar environment) than in water (polar amphoteric solvent). The highest amounts of the CH forms have been found for 2- and 5-hydroxyazulenes, and the smallest ones for 1- and 6-hydroxy derivatives. The keto tautomer(s), together with the enol rotamers, can also participate in deprotonation reaction leading to a common anion and influence its acid-base properties. The strongest acidity in vacuo exhibits 6-hydroxyazulene, and the weakest one displays 1-hydroxyazulene, but all azulenols are stronger acids than phenol and naphthols. Bond length alternation in all DFT-optimized structures has been measured using the harmonic oscillator model of electron delocalization (HOMED) index. Generally, the HOMED values decrease for the keto tautomers, particularly for the ring containing the labile proton. Even for the keto tautomers possessing energetic parameters close to those of the enol isomers, the HOMED indices are low. However, some kind of parallelism exists for the keto forms between their relative energies and HOMEDs estimated for the entire molecules.

Description: In this article, we are interested in a new generic class of nonlocal fractional impulsive differential inclusions with linear sectorial operator and Lipschitz multivalued function in the setting of finite dimensional Banach spaces. By modifying the definition of PC-mild solutions initiated by Shu, we succeeded to determine new conditions that sufficiently guarantee the existence of the solutions. The results are obtained by combining techniques of fractional calculus and the fixed point theorem for contraction maps. We also characterize the topological structure of the set of solutions. Finally, we provide a demonstration to address the applicability of our theoretical results.

Description: A review of the mathematical and physical aspects of the Ermakov systems is presented. The main properties of Lie algebra invariants are extensively used. The two and tridimensional Ermakov systems are fully analyzed and the correspondent invariants found. Then, we go over quantization with special emphasis in the two dimensional case. An application to Nonlinear Optics is hereby developed. We also treat the so-called “one dimensional” case, which is easily solved in the classical case but offers special interest in the quantum realm, where one can find exactly the Feynman propagator. We finish with the stationary phase approximation which contains also some interesting features when compared with the exact solution. Some prospects for future research are also discussed.

Description: Cloud data storage is revolutionary because it eliminates the need for additional hardware, which is often costly, inconvenient, and requires additional space. Cloud data storage allows data owners to store large amounts of data in a flexible way and at low cost. The number of online cloud storage services and their consumers has therefore increased dramatically. However, ensuring the privacy and security of data on a digital platform is often a challenge. A cryptographic task-role-based access control (T-RBAC) approach can be used to protect data privacy. This approach ensures the accessibility of data for authorized consumers and keeps it safe from unauthorized consumers. However, this type of cryptographic approach does not address the issue of trust. In this paper, we propose a comprehensive trust model integrated with a cryptographic T-RBAC to enhance the privacy and security of data stored in cloud storage systems, and suggests that trust models involve inheritance and hierarchy in the roles and tasks of trustworthiness evaluation, where this study aims to identify the most feasible solution for the trust issue in T-RBAC approaches. Risk evaluations regarding other possible flaws of the design are also performed. The proposed design can decrease risk by providing high security for cloud storage systems and improve the quality of decisions of cloud operators and data owners.