This paper presents an exact solution to the Timoshenko beam theory (TBT) for first-order analysis, second-order analysis, and stability. The TBT covers cases associated with small deflections based on shear deformation considerations, whereas the Euler–Bernoulli beam theory (EBBT) neglects shear deformations. Thus, the Euler–Bernoulli beam is a special case of the Timoshenko beam. The moment-curvature relationship is one of the governing equations of the EBBT, and closed-form expressions of efforts and deformations are available in the literature. However, neither an equivalent to the moment-curvature relationship of EBBT nor closed-form expressions of efforts and deformations can be found in the TBT. In this paper, a moment-shear force-curvature relationship, the equivalent in TBT of the moment-curvature relationship of EBBT, was presented. Based on this relationship, first-order and second-order analyses were conducted, and closed-form expressions of efforts and deformations were derived for various load cases. Furthermore, beam stability was analyzed and buckling loads were calculated. Finally, first-order and second-order element stiffness matrices were determined.

Mobile devices have become tools we spend our free time where we carry them with us every moment, they allow us to interact with the environment, we immortalize the moment when necessary. These devices which we spend most of our daily life become very common in recent years and even there are unique business areas emerged. It was announced that the number of people using smartphones is over than 2.5 billion in the first quarter of 2016. As people become more addicted to mobile technology, they become the target of malevolent people. A huge increase in the number of mobile malware is observed as the number of the users increase. Billions of users at risk day by day due to the development of the methods. We have addressed the recent methods used and the types of malware that target mobile devices in our study. We have mentioned the proxy server and reverse proxy server operation logic. We discuss the method of turning mobile devices into reverse proxy servers, risks involved and protection methods.

We suggest that despite McLuhan’s claim not to have a theory of communication that in fact the body of his work does indeed constitute a theory of media and their effects which I have called his General Theory of Media (GToM) that also includes his Laws of Media (LoM). Both McLuhan’s GToM and his LoM are described. A comparison is made of three notions of law: i. McLuhan’s notion of law as used in his Laws of Media; ii. the notions of the Law in the legal sense and iii. the notion of law as formulated in scientific laws. McLuhan’s understanding of media is used to analyze some of the negative effects of social media suggesting that laws need to be formulated to prevent the misuse of social media that are antithetical to democracy and the invasion of the privacy of the individual users of these apps. McLuhan’s Laws of Media are then used to provide insights into the nature of scientific laws, the Law in the legal sense and his own Laws of Media.

Malaysia implemented a movement control order (MCO) to curb the spread of the SARS-CoV-2 virus in March 2020. Despite healthcare services remaining operational, rising COVID-19 infections and MCO restrictions have forced healthcare professionals to seek a compromise between service capacity and standard operating procedure compliance. A cross-sectional survey was conducted among public and private sector doctors, dentists and pharmacists to determine how lockdown measures impacted healthcare services. Responses from 413 doctors, 193 dentists, and 163 pharmacists were analysed. Pharmacy services remained mostly unaffected throughout the MCO. In contrast, operational capacities for surgical and dental services were severely affected during the MCO. All service sectors reported restricting patients or accompanying individuals at the workplace, reductions in staffing capacity, and shortened patient contact time at various degrees. Many pharmacy and medical participants reported supplying extended medication supplies to patients. Adoption of virtual healthcare services was generally poor. All private service sectors suffered financial losses ranging between 59 to 75%. Periodic assessments of the healthcare system throughout the pandemic are required to identify which patients have their treatment compromised so that healthcare managers and policy makers can plan and implement appropriate interventions that help alleviate pressure within the health system.

In the paper, the authors consider the generating functions of the Hermite polynomials and their squares, present explicit formulas for higher order derivatives of the generating functions of the Hermite polynomials and their squares, which can be viewed as ordinary differential equations or derivative polynomials, find differential equations that the generating functions of the Hermite polynomials and their squares satisfy, and derive explicit formulas and recurrence relations for the Hermite polynomials and their squares.

We monitored the water quality and hydrological conditions of a horizontal subsurface constructed wetland (HSSF-CW) in Beijing, China, for 2 years. We simulated the area rate constant and the temperature coefficient with the first-order kinetic model. We examined the relationships between the nitrogen (N) removal rate, N load, seasonal variations in the N removal rate, and environmental factors, such as the area rate constant, temperature, and dissolved oxygen (DO). The effluent ammonia (NH₄⁺-N) and nitrate (NO₃⁻-N) concentrations were significantly lower than the influent concentrations (p<0.01, n=38). The NO₃⁻-N load was significantly correlated with the removal rate (R²=0.9566, p<0.01), but the NH^₄+-N load was not correlated with the removal rate (R²=0.0187, p>0.01). The area rate constants of NO₃⁻-N and NH₄⁺-N at 20 °C were 27.01±26.49 and 16.63±10.58 m∙yr⁻¹, respectively. The temperature coefficients for NO₃⁻-N and NH₄⁺-N were estimated at 1.0042 and 0.9604, respectively. The area rate constants for NO₃⁻-N and NH₄⁺-N were not correlated with temperature (p>0.01). The NO₃⁻-N area rate constant was correlated with the corresponding load (R²=0.9625, p<0.01). The NH₄⁺-N area rate was correlated with DO (R²=0.6922, p<0.01), suggesting that the factors that influenced the N removal rate in this wetland met Liebig's law of the minimum.

Proper management of water is a challenge for every individual but especially for companies. Nowadays also legislation obliges companies to clean the wastewater before being discharged into municipal public sewer especially if they use some chemicals or oily elements in their production process. Construction of the wastewater cleaner depends directly on the way of pollution, the amount of contaminated water and the energy demand of the cleaning process. The paper deals with the construction of the wastewater cleaner, which is based on the technology of electro-flotation for the treatment of water contaminated with disperse colorants. The experimental work as well as the modelling using the statistical methods proved the suitability of the chosen technology. Also, each colour combination requires a specific time period for the water treatment. The authors determined the time interval for cleaning the wastewater that was polluted with yellow colour to 33 minutes. Finally, the wastewater cleaner that is based on the electro-flotation technology was included in the company’s reverse logistics system.

This paper presents an approach to the Euler-Bernoulli beam theory (EBBT) using the finite difference method (FDM). The EBBT covers the case of small deflections, and shear deformations are not considered. The FDM is an approximate method for solving problems described with differential equations. The FDM does not involve solving differential equations; equations are formulated with values at selected points of the structure. Generally, the finite difference approximations are derived based on fourth-order polynomial hypothesis (FOPH) and second-order polynomial hypothesis (SOPH) for the deflection curve; the FOPH is made for the fourth and third derivative of the deflection curve while the SOPH is made for its second and first derivative. In addition, the boundary conditions and not the governing equations are applied at the beam’s ends. In this paper, the FOPH was made for all of the derivatives of the deflection curve, and additional points were introduced at the beam’s ends and positions of discontinuity (concentrated loads or moments, supports, hinges, springs, etc.). The introduction of additional points allowed us to apply the governing equations at the beam’s ends and to satisfy the boundary and continuity conditions. Moreover, grid points with variable spacing were also considered, the grid being uniform within beam segments. First-order analysis, second-order analysis, and vibration analysis of structures were conducted with this model. Furthermore, tapered beams were analyzed (element stiffness matrix, second-order analysis). Finally, a direct time integration method (DTIM) was presented. The FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. The results obtained in this paper showed good agreement with those of other studies, and the accuracy was increased through a grid refinement. Especially in the first-order analysis of uniform beams, the results were exact for uniformly distributed and concentrated loads regardless of the grid. Further research will be needed to investigate polynomial refinements (higher-order polynomials such as fifth-order, sixth-order…) of the deflection curve; the polynomial refinements aimed to increase the accuracy, whereby non-centered finite difference approximations at beam’s ends and positions of discontinuity would be used.

Cell-penetrating peptides (CPPs), a group of small peptides capable of promoting the transport of molecular cargo across the plasma membrane, have become important tools in promoting the cellular uptake of exogenously delivered macromolecules. GV1001, a peptide derived from a reverse-transcriptase subunit of telomerase (hTERT) and developed as a vaccine against various cancers, reportedly has unexpected CPP properties. Unlike typical CPPs, such as the HIV-1 TAT peptide, GV1001 enabled the cytosolic delivery of macromolecules such as proteins, DNA and siRNA via extracellular heat shock protein 90 (eHSP90) and 70 (eHSP70) complexes. The eHSP-GV1001 interaction may have biological effects in addition to its cytosolic delivery function. GV1001 was originally designed as a MHC class II-binding cancer epitope, but its CPP properties may contribute to its strong anti-cancer immune response relative to other telomerase peptide-based vaccines. Cell signaling via eHSP-GV1001 binding may lead to unexpected biological effects, such as direct anticancer or antiviral effects. In this review, we focus on the CPP effects of GV1001 bound to eHSP90 and ehsp70.

RO membrane fouling is one of the main challenges that membrane manufactures, the scientific community and industry professionals have to deal with. The consequences of this inevitable phenomenon have a negative effect on the performance of the desalination system. Predicting fouling in RO systems is key to evaluating the long-term operating conditions and costs. Much research has been done on fouling indices, methods, techniques and prediction models to estimate the influence of fouling on the performance of RO systems. This paper offers a critical review evaluating the state of industry knowledge in the development of fouling indices and models in membrane systems for desalination in terms of use and applicability. Despite major efforts in this field, there are gaps in terms of effective methods and models for the estimation of fouling in full-scale RO desalination plants. In existing models applied to full-scale RO desalination plants, neither the spacer geometry of membranes nor the efficiency and frequency of chemical cleanings - which play an important role in the performance of this process - are considered.

Low-cost RGB-D cameras are increasingly used in several research fields including human-machine interaction, safety, robotics, biomedical engineering and even Reverse Engineering applications. Among the plethora of commercial devices, the Intel RealSense cameras proved to be among the best suitable devices, providing a good compromise between cost, ease of use, compactness and precision. Released on the market in January 2018, the new Intel model RealSense D415 has a wide acquisition range (i.e. ~160-10000 mm) and a narrow field of view to capture objects in rapid motion. Given the unexplored potential of this new device, especially when used as a 3D scanner, the present work aims to characterize and to provide metrological considerations on the RealSense D415. In particular, tests are carried out to assess the device performances in the near range (i.e. 100-1000 mm). Characterization is performed by integrating the guidelines of the existing standard (i.e. the German VDI/VDE 2634 part 2 normative) with a number of literature-based strategies. Performance analysis is finally compared against latest close-range sensors, thus providing a useful guidance for researchers and practitioners aiming to use RGB-D cameras in Reverse Engineering applications.

The present study aims to develop an efficient numerical method for computing the diffraction and radiation of water waves with horizontal long cylindrical structures, such as floating breakwaters. A higher-order scheme is used to discretize geometry of the structure as well as the relevant physical quantities. As the kernel of this method, Wehausen’s free-surface Green function is calculated by a newly-developed Gauss-Kronrod adaptive quadrature algorithm after elimination of its Cauchy-type singularities. To improve computational efficiency, a Chebyshev approximation approach is applied to a fast calculation of the Green function that needs evaluation thousands of times. In addition, OpenMP parallel technique is used to the formation of influence coefficient matrix, which significantly reduces CPU time. Finally, computations are performed on wave exciting forces and hydrodynamic coefficients for the long cylindrical structures, either floating or submerged. Comparison with other numerical and analytical methods demonstrates good performance of the present method.

This paper presents an overview of the principal structural and dynamics characteristics of reverse micelles (RMs) in order to highlight their structural flexibility and versatility, along with the possibility to modulate their parameters in a controlled-manner. The multifunctionality in a large range of different scientific fields is exemplified in two distinct directions: a theoretical model for mimicry of biological microenvironment and practical application in the field of nanotechnology and nano-based sensors. RMs represents a convenient experimental approach that limits the drawbacks of the conventionally biological studies in vitro, while the particular structure confers them the status of simplified mimics of cells by reproducing a complex supramolecular organization in an artificial system. The biological relevance of RMs is discussed in some particular cases referring to the confinement and crowding environment, molecular dynamics of water and cell membrane structure. The use of RMs in different range of applications seems to be more promising due to their structural and compositional flexibility, a high efficiency and selectivity being achieved. The advance in nanotechnology is based on developing new methods of nanomaterials synthesis and deposition. This review highlighting the advantages of using RMs in synthesis of nanoparticles with specific properties and in nano (bio)sensors design.

Fast neutron (FN) radiation mediated mutagenesis is a unique approach among the several induced mutagenesis methods being used in plant science in terms of impacted mutations. The FN mutagenesis usually creates deletions from a few bases to several million bases (Mb). A library of random deletion generated using FN mutagenesis lines can provide indispensable resources for the reverse genetic approaches. In this review, information from several efforts made using FN mutagenesis has been compiled to understand the type of induced mutations, frequency, and genetic stability. Concerns regarding the utilization of FN mutagenesis technique for a plant with different level of ploidy and genome complexity are discussed. We have highlighted the utility of next-generation sequencing techniques which can be efficiently utilized for the characterization of mutant lines as well as for the mapping of causal mutations. Pros and cons of Mut-map, MutChromSeq, exon capture, whole genome sequencing, MutRen-Seq, and different tilling approaches can be used for the detection of FN-induced mutation has also been discussed. Genomic resources developed using the FN mutagenesis have been catalogued wooing to meaningful utilization of the available resources. The information provided here will be helpful for the efficient exploration for the crop improvement programs and for better understanding of genetic regulations.

This study presents an exact solution to the free vibration analysis of a uniform Timoshenko beam, with a harmonic vibration being assumed. The Timoshenko beam theory covers cases associated with small deflections based on shear deformation and rotary inertia considerations. In this paper, a moment-shear force-circular frequency-curvature relationship was presented. The complete study was based on this relationship and closed-form expressions of efforts and deformations were derived. The free vibration response of single-span systems and spring-mass systems was analyzed; closed-form formulations of matrices expressing the boundary conditions were presented and the natural frequencies were determined by solving the eigenvalue problem. Systems with intermediate mass, spring, or spring-mass system were also analyzed. Furthermore, first-order dynamic stiffness matrices in local coordinates were derived. Finally, a second-order analysis of beams resting on an elastic Winkler foundation was conducted. The results obtained in this paper were in good agreement with those of other studies.

This paper presents an approach to the Timoshenko beam theory (TBT) using the finite difference method (FDM). The Timoshenko beam theory covers cases associated with small deflections based on shear deformation and rotary inertia considerations. The FDM is an approximate method for solving problems described with differential equations. It does not involve solving differential equations; equations are formulated with values at selected points of the structure. In addition, the boundary conditions and not the governing equations are applied at the beam’s ends. The model developed in this paper consisted of formulating differential equations with finite differences and introducing additional points at the beam’s ends and at positions of discontinuity (concentrated loads or moments, supports, hinges, springs, brutal change of stiffness, spring-mass system, etc.). The introduction of additional points allowed us to apply the governing equations at the beam’s ends. Moreover, grid points with variable spacing were considered, the grid being uniform within beam segments. First-order, second-order, and vibration analyses of structures were conducted with this model. Furthermore, tapered beams were analyzed (element stiffness matrix, second-order analysis, vibration analysis). Finally, a direct time integration method (DTIM) was presented; the FDM-based DTIM enabled the analysis of forced vibration of structures, with damping taken into account. The results obtained in this paper showed good agreement with those of other studies, and the accuracy was increased through a grid refinement. Especially in the first-order analysis of uniform beams, the results were exact for uniformly distributed and concentrated loads regardless of the grid.

In this paper, we consider a fourth-order suspension bridge equation with nonlinear damping term |u_t|^m-2u_t and source term |u|^p-2u. We give necessary and sufficient condition for global existence and energy decay results without considering the relation between m and p. Moreover, when p>m, we give sufficient condition for finite time blow-up of solutions. The lower bound of the blow-up time T_max is also established. It worth to mention that our obtained results extend the recent results of Wang (J. Math. Anal. Appl., 2014) to the nonlinear damping case.

In the paper, the authors establish two identities, which can be regarded as nonlinear differential equations, for the generating function of Eulerian polynomials, find two identities for the Stirling numbers of the second kind, and present two identities for Eulerian polynomials and higher order Eulerian polynomials, pose two open problems about summability of two finite sums involving the Stirling numbers of the second kind. Some of these conclusions meaningfully and significantly simplify several known results.

This study presents a prediction model based on Logistic Growth Curve to evaluate the effectiveness of Movement Control Order (MCO) on COVID-19 pandemic spread. The evaluation assesses and predicts the growth models. The estimated model is a forecast-based model that depended on partial data from the COVID-19 cases in Malaysia. The model is then studied together with the effectiveness of the three phases of MCO implemented in Malaysia. Evidence from this study suggests that results of the LGC prediction model match with the progress and effectiveness of the MCO to flatten the curve, thus helped to control the spike in number of active COVID-19 cases and spread of COVID-19 infection growth.

A critical discussion on the computation of the dispersion diagram in periodic one-dimensional guiding structures is carried out. In particular, an analysis is presented of the pros and cons of combined methods that make use of full-wave simulations done with commercial software packages with further analytical post-processing based on simplifications brought by an equivalent circuit model of the structure. Some of the most common methods reported in the literature are reviewed and their advantanges and limitations highlighted. Our discussion is complemented with several selected numerical examples in order to discuss the most relevant aspects that a potential user of these methods should be aware of. Special attention is paid to the relevant role played by the high-order coupling between the two halves of a symmetric unit cell of the periodic structure.

Chivalry is a concept that is both familiar and distant to many Japanese. There is no other social class in the world that resembles knights as much as the samurai in Japan. However, why is it that there is such a gap between knights and warriors, even though they are both warriors with the same social status and honor? This paper reviews the legal debates on the deposed sovereigns’ rights have emerged since 20th century. Among them, the right to appoint knights by heads of deposed royal families is one of the focal points. The author begins with a comprehensive review of legal debates on the subject. Six principles on the appointment are extracted from the review. Then, a comparison between chivalry and Bushido, the Japanese Samurai code, was carried out.

Load transfer through orthopaedic joint implants is poorly understood. The longer-term outcomes of these implants are just starting to be studied, making it imperative to monitor contact loads across the entire joint implant interface to elucidate the force transmission and distribution mechanisms exhibited by these implants in service. This study proposes and demonstrates the design, implementation, and characterization of a 3D-printed smart polymer sensor array using conductive polyaniline (PANI) structures embedded within a polymeric parent phase. The piezoresistive characteristics of PANI were studied to characterize the sensing behaviours inherent to these embedded pressure sensor arrays. PANI's stable response to a continuous load, its stability throughout loading and unloading cycles, and its repeatable and linear response to incremental loading cycles together with the accuracy of these measurements were investigated. It is demonstrated that this specially developed multi-material additive manufacturing process for polyaniline is an attractive approach for the fabrication of implant components having embedded smart-polymer sensors for the measurement and analysis of joint loads in orthopaedic implants.

The capacity and the mechanism of the adsorption of aqueous barium (Ba), cobalt (Co), strontium (Sr) and zinc (Zn) by Ecuadorian (NatAllo) and synthetic (SynAllo-1 and SynAllo-2) allophanes were studied as a function of contact time, pH and metal ion concentration using kinetic and equilibrium experiments. The mineralogy, nano-structure and chemical composition of the allophanes were characterized by X-ray diffraction, Fourier transform infrared spectroscopy, transmission electron microscopy and specific surface area analyses. The evolution of adsorption fitted to a pseudo-first-order reaction kinetics, where equilibrium between aqueous metal ions and allophane was reached within < 10 min. The metal ion removal efficiencies varied from 0.7 to 99.7 % at pH 4.0 to 8.5. At equilibrium, the adsorption behavior is better described by the Langmuir model than by the Dubinin-Radushkevich model, yielding sorption capacities of 10.6, 17.2 and 38.6 mg/g for Ba^(2+), 12.4, 19.3 and 29.0 mg/g for HCoO_2^-, 7.2, 15.9 and 34.4 mg/g for Sr^(2+) and 20.9, 26.9 and 36.9 mg/g for Zn^(2+), respectively, by NatAllo, SynAllo-2 and SynAllo-1. The uptake mechanism is based on a physical adsorption process. Allophane holds great potential to remove aqueous metal ions and could be used instead of zeolites, montmorillonite, carbonates and phosphates for wastewater treatment.

The magnetic properties of RCo3B2 compounds, with heavy rare earths, are investigated by using magnetic measurements and electronic structure calculations. The cobalt sublattice magnetizations are very small and antiparallel oriented to rare earths moments. Contributions of cobalt moments to the Curie constants were also shown. The cobalt magnetic behavior has been analyzed in spin fluctuations model. The presence of short-range order above the Curie temperatures in RCo3B2 compounds with R=Tb, Dy, and Ho has been attributed to reminiscent R5d-Co3d interactions.

Materials design for processing and application requires fundamental understanding of their properties based interatomic interaction. The use of the novel concept of total bond order density (TBOD) as a single quantum mechanical metric to characterize the internal cohesion of a crystal and correlate with the calculated physical properties is particularly appealing. This requires detailed first-principles calculation of the electronic structure, interatomic bonding and related properties. In this article, we use this new concept and apply it to chalcogenide crystals based on data obtained from 25 crystals: Ag₂S, Ag₂Se, Ag₂Te, As₂S₃, As₂Se₃, As₂Te₃, As₄Se₄, Cu₂S, Cu₂Se, Cu₂Te, Cu₄GeS₄, Cu₂SnS₃, Cu₂SnSe₃, GeS₂, GeSe₂, Ge₄Se₉, Sb₂S₃, Sb₂Se₃, Sb₂Te₃, SnS, SnSe, CdSe, CdTe, ZnSe, and ZnTe. Together with the calculated optical and mechanical properties, we demonstrate the efficacy of using this novel approach for materials design that could facilitate the exploration and development of new chalcogenide crystals and glasses. Moreover, the TBOD and its partial components (PBOD) could be the key descriptors in machine learning protocol for broader scale design when a large database is available.

I deal with perception and action (e.g. movements) using results from Synergetics, a comprehensive mathematical theory of the selforganized formation ("emergence“) of spatial, temporal, or functional structures in complex systems. I illustrate basic concepts such as order parameters (ops), enslavement, complexity reduction, circular causality first by examples of well known collective, spontaneous modes of human behavior such as rhythmic clapping of hands etc., and then by face recognition. The role played by ops depends on context. In the case of face (or pattern) recognition an op represents the concept of an individual face (action of mind) and it enslaves the action (firing rates) of neurons (body). This insight allows me to interpret syndroms as order parameters playing their mind/body double role. I present criteria for the identification of ops and discuss their general properties including error correction and "remedy“ of deficiencies. Contact is made with a recent paper by Sabine Koch on embodied aesthetics. My approach includes the saturation of attention at various time scales (ambiguous figures/fashion). Adopting a psychological perspective, I discuss some ingredients of beauty such as proportionality and symmetry, but also the importance of irregularities.

In the paper, by virtue of the Faà di Bruno formula, some properties of the Bell polynomials of the second kind, and an inversion formula for the Stirling numbers of the first and second kinds, the authors establish meaningfully and significantly two identities which simplify coefficients in a family of ordinary differential equations associated with higher order Bernoulli numbers of the second kind.

This paper first discusses a fractional-order Liu system of order as low as 2.7 and shows its chaotic characteristics by carrying out numerical simulations such as Lyapunov exponents, bifurcation diagrams and phase portraits. Then, by using the topological horseshoe theory and computer-assisted proof, the existence of chaos in the system is verified theoretically. Finally, the fractional-order system is implemented on a Field Programmable Gate Array (FPGA) and the results obtained show that the fractional-order Liu system is indeed chaotic.

Permanent magnet synchronous motor (PMSM) AC servo system shows the characteristics of uncertainty, time-varying and non-linearity, which makes it difficult for traditional PID control to achieve ideal control effect. Fuzzy control has strong adaptability to the problems of parameter variation, non-linearity and model inaccuracy of the controlled object. Vector control strategy is used to study its control principle and the realization of SVPWM. Because the motor object has certain fractional order characteristics, the fuzzy parameter self-tuning PIλ control is chosen as the position regulator of servo motor. It combines the accuracy of fractional order PI control and the adaptability of fuzzy control. A simulation model of PMSM three-closed-loop system is built in MATLAB/Simulink environment. The results show that the control method is effective and can satisfy the trajectory tracking of servo control.

Despite the successes earned in cataloguing and finding the role of the most of molecular components in living matter, the “biochemical and molecular” perspective, popular in biology, medicine and genetics, is unable to give account for crucial topics as the faculty of living systems to “feel”, to “perceive” what a given stimulus implies (means, indeed) for their survival. Condensed matter physics too, if bounded to a local, short-range, and perturbative approach, fails dramatically. This is also due to the role commonly assigned to water – actually the main constituent of living matter – deemed for long time to be merely chemical (as “solvent” or a reactant/product). Nonetheless, today many evidences show how living matter can be right conceived as a super-structured coherent water-based matrix, suggesting that the characterization of bio(electro)chemical and physical processes undertaken at molecular level in living matter, would let us unable to answer a question like this: what allows an amoeba, moreover without any neurons, to “know” to get closer to a nutrient or escape away from a toxin? I propose that to pursue such a fundamental inquiry it’s necessary an essentially relational approach, that is: to consider the living being at its grounding as the outcome of a physical history of relationships where symmetry-breakings, dissipation and coherence yield the emergence of the living state of matter, conceivable only as a time-dependent open process, and not as a portraited “body”. The effective tools to build up such an approach may be retrieved in far-from-equilibrium thermodynamics (TD), symmetry-breakings and gauge-fields theory, science of complexity, within the framework of a Quantum Field Theory. Indeed, within a field-view of matter, and of water especially, as it has been developed by a Quantum Electrodynamic (QED) description of condensed matter, it’s possible to give account for a physical basis too such an epistemologically elusive, though crucial, feature of living systems (i.e.: perception and meaning). The emerging landscape allows some important meditations about adaptation, evolution, ecodynamics, and about different conceptions of complexity and “information” in living realm. Furthermore, some neuroscientific themes like consciousness, qualia and their links to artificial intelligence could be supplied with due insights.

In this paper, a novel voltage control strategy of stand-alone operation brushless doubly fed induction generator for variable speed constant frequency wind energy conversion systems was presented and discussed particularly. Based on the model of the generator power system, the proposed direct flux control strategy employs a nonlinear reduced-order generalized integrator based resonant sliding-mode control scheme to directly calculate and regulated the output value of converter which control winding stator required so as to eliminate the instantaneous errors of power winding stator flux, and no involving any synchronous rotating coordinate transformations. The stability, robustness and convergence capability of the proposed control strategy were described and analyzed. Owing to no extra current control loops involved, therefore simplifying the system configuration design and enhancing the transient performance. Constant converter switching frequency was achieved by using space vector pulse width modulation, which reduce the harmonic of generator terminal voltage. In addition, experimental results prove the feasibility and validity of the proposed scheme, and excellent steady and dynamic state performance is achieved.

All life on Earth uses three integrated molecular systems in which genetic information contained in DNA base sequences is transmitted to ribosomes by RNA and a genetic code, then translated into the amino acid sequences of structural and catalytic proteins. Therefore, the most important point for understanding the origin of life is to determine how such systems could emerge from random processes on the early Earth. In this review, two alternatives are compared: the RNA world hypothesis and the [GADV]-protein world hypothesis. [GADV] refers to four amino acids, Gly [G], Ala [A], Asp [D] and Val [V] that are conserved in the amino acid sequences of many common proteins. Here I will argue that the origins of the three primary processes required for life to begin can be better explained by the GADV hypothesis than the RNA world hypothesis. The GADV hypothesis also incorporates a conversion process by which random polymers can evolve into proteins with ordered sequences.

In the paper, by virtue of the Fa`a di Bruno formula, some properties of the Bell polynomials of the second kind, and the inversion formulas of binomial numbers and the Stirling numbers of the first and second kinds, the authors simplify meaningfully and significantly coefficients in two families of ordinary differential equations associated with higher order Frobenius–Euler numbers.

This paper discusses the concept of fractional derivative with complex order from the application point of view. It is shown that a fractional derivative is hermitian, if and only if the order is real. The hermitian part of complex order derivatives behave like allpass filters with almost logarithmic phase.

The demand of high data rate and ubiquitous coverage in heterogeneous cellular (HetNets) is increasing progressively. In order to meet this demand, sophisticated model having applied interference reduction scheme and cell association technique is needed. The small base station (sBS)are deployed inside the broadcasting area of macro base station (mBS), in heterogeneous cellular networks (HetNets). Since mBS has high transmission power therefore a large number of users get connected with mBS. This causes disproportion of load distribution across the HetNets. For load balancing users from high power mBS are migrated to low power sBS to increase network capacity and to decrease the load from mBS. This results in interference in the communication signal because of strong mBS Interference. Hence, we need interference management technique to mitigate interference and user association and to efficiently use sBSs’ resources. Inter-cell interference (ICI) limit the HetNets’ performance. Additionally, there exist deliberate jamming interference which depends on jammers transmission power and its proximity with the target, which notably degrades the network performance. In this paper, we employ reverse frequency allocation scheme (RFA) to reduce inter cell interference, deliberate jamming interference and to accomplish load balancing. The proposed setup is analyzed inquisitively and with the help of simulations. The result shows reduction in interferences as well as balance of load distribution in the network achieved by employing RFA scheme together with cell association.

Estimation of core and surface temperature is one of the crucial functionalities of the lithium-ion Battery Management System (BMS) towards providing effective thermal management, fault detection and operational safety. While, it is impractical to measure core temperature using physical sensors, implementing a complex estimation strategy in on-board low-cost BMS is challenging due to high computational cost and the cost of implementation. Typically, a temperature estimation scheme consists of a heat generation model and a heat transfer model. Several researchers have already proposed ranges of thermal models having different levels of accuracy and complexity. Broadly, there are first-order and second-order heat capacitor-resistor-based thermal models of lithium-ion batteries (LIBs) for core and surface temperature estimation. This paper deals with a detailed comparative study between these two models using extensive laboratory test data and simulation study to access suitability in online prediction and onboard BMS. The aim is to guide whether it’s worth investing towards developing a second-order model instead of a first-order model with respect to prediction accuracy considering modelling complexity, experiments required and the computational cost. Both the thermal models along with the parameter estimation scheme are modelled and simulated using MATLAB/Simulink environment. Models are validated using laboratory test data of a cylindrical 18650 LIB cell. Further, a Kalman Filter with appropriate process and measurement noise levels are used to estimate the core temperature in terms of measured surface and ambient temperatures. Results from the first-order model and second-order models are analyzed for comparison purposes.

In the paper, the author introduces the notions "multi-order logarithmic numbers" and "multi-order logarithmic polynomials", establishes an explicit formula, an identity, and two recurrence relations by virtue of the Faa di Bruno formula and two identities of the Bell polynomials of the second kind in terms of the Stirling numbers of the first and second kinds, and constructs some determinantal inequalities, product inequalities, logarithmic convexity for multi-order logarithmic numbers and polynomials by virtue of some properties of completely monotonic functions.

Using a nonclassical symmetry of nonlinear reaction-diffusion equations, some exact multi-dimensional time-dependent solutions are constructed for a fourth-order Allen-Cahn-Hilliard equation. This models a phase field that gives a phenomenological description of a two-phase system near the critical temperature. Solutions are given for the changing phase of a cylindrical or spherical inclusion, allowing for a 'mushy zone' with mixed state that is controlled by imposing a pure state at the boundary. The diffusion coefficients for transport of one phase through the mixture, depend on the phase field value, since the physical structure of the mixture depends on the relative proportions of the two phases. A source term promotes stability of both of the pure phases but this tendency may be controlled or even reversed through the boundary conditions.

Collatz conjecture (3x+1 problem) is a natural phenomenon in set theory that may be reconstructed using known combinatorics and order theory. Construction begins by selecting a specific order isomorphism with a bijectional order-embedding. Mapping by 3x+1 induces a unique property to only members of the mapped embedding. Under selective congruence from recursive division by two, that complex controls sequence behavior. This demonstration uses an order isomorphism consisting of two linear orders: the (1) odd positive integers and the always odd (2) Rule 50 Jacobsthal numbers, as the embedding. The argument proceeds by cardinality. When the order isomorphism is mapped under 3x+1, all Rule 50 Jacobsthal numbers are mapped to all the powers of four. This one-to-one correspondence guarantees that every odd integer is assigned to a Rule 50 Jacobsthal number, and subsequently, to a power of four. To align this construction with the conjecture, expand the set to all positive integers by showing that: (1) the powers of four embed the powers of two, in a natural way, and (2) the unique factorization of any positive even integer, not congruent to a power of two, is simply an odd integer, once the factorized power of two is divided out. In other words, if the initial choice for a positive integer is not congruent to a power of two, then recursion continues until a Rule 50 Jacobsthal number (guaranteed by cardinality) is attained. Since this value mapped by 3x+1 is always a power of four, repeated division by two will always send the sequence to one. Because this same process occurs for any choice of positive integer, Collatz conjecture is true.

In the present study, nonlinear vibration of a nanobeam resting on fractional order viscoelastic Winkler-Pasternak foundaion is studied using nonlocal elasticity theory. D'Alembert principle is used to derive the governing equation and the associated boundary conditions. The approximate analytical solution is obtained by applying the multiple scales method. Detailled parametric study is conducted, the effects of variation in different parameters belonging to the application problems on the system are calculated numerically and depicted. We remark that the order and the coefficient of the fractional derivative have significant effect on the natural frequency and the amplitude of vibrations

A probabilistic description is essential for understanding the dynamics of stochastic systems far from equilibrium, given uncertainty inherent in the systems. To compare different Probability Density Functions (PDFs), it is extremely useful to quantify the difference among different PDFs by assigning an appropriate metric to probability such that the distance increases with the difference between the two PDFs. This metric structure then provides a key link between stochastic systems and information geometry. For a non-equilibrium process, we define an infinitesimal distance at any time by comparing two PDFs at times infinitesimally apart and sum these distances in time. The total distance along the trajectory of the system quantifies the total number of different states that the system undergoes in time, and is called the information length. By using this concept, we investigate the information geometry of non-equilibrium processes involved in disorder-order transitions between the critical and subcritical states in a bistable system. Specifically, we compute time-dependent PDFs, information length, the rate of change in information length, entropy change and Fisher information in disorder-to-order and order-to-disorder transitions, and discuss similarities and disparities between the two transitions. In particular, we show that the total information length in order-to-disorder transition is much larger than that in disorder-to-order transition, and elucidate the link to the drastically different evolution of entropy in both transitions. We also provide the comparison of the results with those in the case of the transition between the subcritical and supercritical states and discuss implications for fitness.

Based on the fractional order of nonlinear system for love model with a periodic function as an external force, analyzed the characteristics of the chaotic dynamic in this study. The relationship between the chaotic dynamic of the fractional-love model with the external force and the fractional-order system was analyzed when the parameters are fixed. Further, we also studied the relationship between the chaotic systemic dynamic and the parameters when the fractional-order system is fixed. The results show that when the parameters are fixed, the fractional-order system exhibited segmented chaotic states for the different fractional orders of the system. When fixed the fractional-order system, the system exhibited the periodic and chaotic states as parameter changes.

Below T_CO=157 K the quasi-one-dimensional charge-transfer salt (TMTTF)_2SbF_6 undergoes a pronounced phase transition to a charge-ordered ground state. We have explored the non-linear and photoconductive behavior as a function of applied voltage, laser pulse energy and temperature. Besides a decay of the photoconductive signal in a double exponential fashion in the millisecond range, we discover current oscillations in the kHz range induced by the application of short laser pulses. While the resonance frequencies do not depend on voltage or laser intensity and vary only slightly with temperature, the amplitude changes linearly with the laser intensity and voltage. The findings are discussed and compared to comparable phenomena in other low-dimensional electron systems.

The coronavirus disease 2019 (COVID-19) pandemic, caused by the severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), has spread rapidly to most parts of the world, causing high numbers of deaths and significant social and economic impacts. SARS-CoV-2 is a novel coronavirus with a suggested zoonotic origin and with the potential for cross-species transmission among animals. Antarctica can be considered the only continent free of SARS-CoV-2 although at the end of the 2019-2020 tourist season, at least one SARS-CoV-2 positive tourist visited the Antarctic Peninsula. Therefore, concerns have been expressed regarding the potential human introduction of this virus to the continent through the activities of research or tourism with potential effects including those related to human health, but also the potential for virus transmission to Antarctic wildlife. This reverse-zoonotic transmission risk to Antarctic wildlife is assessed considering the available information on host susceptibility, dynamics of the infection in humans, and contact interactions between humans and Antarctic wildlife. Measures to reduce the risk are proposed as well as the identification of knowledge gaps related to this issue.

In the paper, the author (1) presents an explicit formula and its inversion formula for higher order derivatives of generating functions of the Bell polynomials, with the help of the Faà di Bruno formula, properties of the Bell polynomials of the second kind, and the inversion theorem for the Stirling numbers of the first and second kinds; (2) recovers an explicit formula and its inversion formula for the Bell polynomials in terms of the Stirling numbers of the first and second kinds, with the aid of the above explicit formula and its inversion formula for higher order derivatives of generating functions of the Bell polynomials; (3) constructs some determinantal and product inequalities and deduces the logarithmic convexity of the Bell polynomials, with the assistance of the complete monotonicity of generating functions of the Bell polynomials. These inequalities are main results of the paper.

We introduce poly-symplectic extension of Souriau Lie groups Thermodynamics based on higher-order model of statistical physics introduced by R.S. Ingarden. This extended model could be used for small data analytics and Machine Learning on Lie groups. Souriau Geometric Theory of Heat is well adapted to describe density of probability (Maximum Entropy Gibbs density) of data living on groups or on homogeneous manifolds. For Small Data Analytics (Rarified Gases , sparse statistical survey,…), density of maximum entropy should consider Higher Order Moments constraints (Gibbs density is not only defined by first moment but fluctuations request 2nd order and higher moments) as introduced by R.S. Ingarden. We use Poly-sympletic model introduced by Christian Günther, replacing the symplectic form by a vector-valued form. The poly-symplectic approach generalizes the Noether theorem, the existence of momentum mappings, the Lie algebra structure of the space of currents, the (non-)equivariant cohomology and the classification of G-homogeneous systems. The formalism is covariant, i.e. no special coordinates or coordinate systems on the parameter space are used to construct the Hamiltonian equations. We underline the contextures of these models, and the process to build these generic structures.

In this paper, a two-stage stochastic programming modelling is proposed to design a multi-period, multistage, and single-commodity integrated forward/reverse logistics network design problem under uncertainty. The problem involves both strategic and tactical decision levels. The first stage deals with strategic decisions, which are the number, capacity, and location of forward and reverse facilities. At the second stage tactical decisions such as base stock level as an inventory policy is determined. The generic introduced model consists of suppliers, manufactures, and distribution centers in forward logistic and collection centers, remanufactures, redistribution, and disposal centers in reverse logistic. The strength of proposed model is its applicability to various industries. The problem is formulated as a mixed-integer linear programming model and is solved by using Benders’ Decomposition (BD) approach. In order to accelerate the Benders’ decomposition, a number of valid inequalities are added to the master problem. The proposed accelerated BD is evaluated through small-, medium-, and large-sized test problems. Numerical results reveal that proposed solution algorithm increases convergence of lower bound and upper bound of BD and is able to reach an acceptable optimality gap in a convenient CPU time.

The high dimensions and governing non linear dynamics in wind farm systems make the design of numerical optimal controllers computationally expensive. A possible pathway to circumvent this challenge lies in finding reduced order models which can accurately embed the existing non linearities. The work here presented applies the ideas motivated by non linear dynamical systems theory - the Koopman Operator - to an innovative algorithm in the context of wind farm systems - Input Output Dynamic Mode Decomposition - to improve on the ability to model the aerodynamic interaction between wind turbines in a wind farm and uncover insights into the existing dynamics. It is shown that a reduced order linear state space model can reproduce the downstream turbine generator power dynamics and reconstruct the upstream turbine wake. It is further shown that the fit can be improved by judiciously choosing the Koopman observables used in the IODMD algorithm without jeopardizing the models ability to rebuild the turbine wake. The extensions to the IODMD algorithm provide an important step towards the design of linear reduced order models which can accurately reproduce the dynamics in a wind farm.

This paper combines the Good Regulator Theorem with the Law of Requisite Variety and seven other requisites that are necessary and sufficient for a cybernetic regulator to be effective and ethical. The resulting Ethical Regulator Theorem provides a basis for systematically evaluating and improving the adequacy of existing or proposed designs for systems that make decisions that can have ethical consequences; regardless of whether the regulators are human, machines, cyberanthropic hybrids, organizations, corporations, or government institutions. The theorem is then used to define an ethical design process that has potentially far-reaching implications for society. A six-level framework is proposed for classifying cybernetic and superintelligent systems, which highlights the existence of a possibility-space bifurcation in our future time-line. The implementation of “super-ethical” systems is identified as an urgent imperative for humanity to avoid the danger that superintelligent machines might lead to a technological dystopia. Third-order cybernetics is defined as the cybernetics of ethical systems. Concrete actions, a grand challenge, and a vision of a super-ethical society are proposed to help steer the future of the human race and our wonderful planet towards a realistically achievable minimum viable cyberanthropic utopia.

The article is dedicated to the role of polychrome solutions of the architectonic order in the concept of the Baroque façade. The ancient principles of designing architectural structures, inherited from the Renaissance were subjected to reinterpretations in order to impart different expressive values. The arrangements of façades, initially balanced or even horizontal, were replaced by ambiguous bivalent compositions. Vertical layouts began to dominate in the Baroque. Appropriately selected polychrome of the elements of the order could emphasize the compositional expression. The relationship between the layout of the polychrome in a given architectural order and the expression of a work of art has been established for quite a long time. However, the generally available data on color schemes of architectural structures in baroque buildings are still not fully organized. The paper analyses examples of Baroque façades preserved in their original state and revalorized in recent years after thorough conservator’s research in the field of architecture and color. The examples are mainly designed in the so-called great order, i.e. pertaining largely to church façades. In the Baroque, the vertical direction of the composition was strongly emphasized by multiplying or applying perspective arrangements of supports, and finally by embattled entablatures. The decisive field of change became the shaping of the coloristic decoration of the entablature – decisions regarding the material and color separation of elements of the frieze above the supports. The uniform color of all vertical elements of the façade structure guaranteed an unambiguous verticality of the composition.

Mixing is considered as a critical process parameter (CPP) during process development due to its significant influence on reaction selectivity and process safety. Nevertheless, mixing issues are difficult to identify and solve owing to their complexity and dependence on knowledge of kinetics and hydrodynamics. In this paper, we proposed an optimization methodology using Computational Fluid Dynamics (CFD) based compartmental modelling to improve mixing and reaction selectivity. More importantly, we have demonstrated that through the implementation of surrogate-based optimization, the proposed methodology can be used as a computationally non-intensive way for rapid process development of reaction unit operations. For illustration purpose, reaction selectivity of a process with Bourne competitive reaction network is discussed. Results demonstrate that we can improve reaction selectivity by dynamically controlling rates and locations of feeding in the reactor. The proposed methodology incorporates mechanistic understanding of the reaction kinetics together with an efficient optimization algorithm to determine the optimal process operation and thus can serve as a tool for quality-by-design (QbD) during product development stage.

Desalination accounts for 1% of the total global water consumption and is an energy-intensive process, with the majority of operational expenses attributed to energy consumption. Moreover, at present, a significant portion of the power comes from traditional fossil fuel-fired power plants and the greenhouse gas emissions associated with power production along with concentrated brine discharge from the process, pose a severe threat to the environment. Due to the dramatic impact of climate change, there is a major opportunity to develop sustainable desalination processes to combat the issues of brine discharge, greenhouse gas emissions along with a reduction in energy consumption per unit of freshwater produced. Nanotechnology can play a vital role to achieve specific energy consumption reduction as nanofluids application increases the overall heat transfer coefficient enabling the production of more water for the same size desalination plant. Furthermore, concentrated brine discharge harms the marine ecosystems, and hence, this problem must also be solved to support the objective of sustainable desalination. Several studies have been carried out in the past several years in the field of nanotechnology applications for desalination, brine treatment and the role of renewable energy in desalination. This paper aims to review the major advances in this field of nanotechnology for desalination. Furthermore, a hypothesis for developing an integrated solar thermal and nanofluid sustainable desalination system, based on the cyclic economy model is proposed.

This article aims to solve the problem of robust supervision of a reverse osmosis desalination system (RO-DS) with two models, external model and bond graph model. The structure of an industrial system from the point of view of the external model operates according to several modes of operation (degraded and normal). For this external model, we cannot locate the faults since we are talking about a global operation of the system. The possible solution for the development of research was to use the multidisciplinary model called bond graph. This model by its graphic nature and using a unified language makes it possible to model the industrial system element by element from where it helps the user not only to detect the faults but also to locate them when they appear in the system. The results suggest that the use of the bond graph model for alarm 02 is a reverse osmosis error (RO1); these phenomena are readable on the bond graph model and can be quantified by equations. The equation of the model of this reverse osmosis (RO1) is found in the residual equations (r5, r6 and r7), so that these residues will be sensitive to this rupture. The possible solution during research development was to use the bond graph model to supplement information with physical knowledge and to locate faults. This article also describes the operating safety (by minimizing false alarms and non-detections as well as delays in fault detection) of the desalination system by using the bond graph model described in Linear Fractional Transformation (LFT) form for enable it to manage the robust supervision of a desalination system. An approach based on (LFT-BG) is developed to monitor tank leakage (Cu) and reverse osmosis (RO1 and RO2) faults or valve level (V1 and V2) closures and membrane clogging reverse osmosis (Rm1 and Rm2) that can occur in the reverse osmosis desalination system (RO-DS).

Vaccination has been one of the most successful and the most significant scientific advances in human health and life expectancy all around the globe. The World Health Organization considers that immunization should be recognized as the main component of human health right, due to the fact that vaccination prevents 2.5 million deaths annually (World Health Organization, 2011). The most successful vaccines have been developed using conventional methods that follow the paradigm established by Pasteur: "to isolate, inactivate and inject" the pathogen microorganism and mimic a natural infection. Recently, metagenomics have played an important role in the discovery of new immunogens for vaccine design and the selection of antigens based on genomic information. The main approach that has used this strategy has been called "reverse vaccinology". This promising and arising field allows the screening of the entire potential antigenic repertoire of an organism using predictive bioinformatic tools. Once the antigenic protein or proteins have been selected, they are expressed and purified using molecular cloning and in vitro expression techniques. Following the in vitro production step, they are probed in animal models to evaluate the in vivo protective strength of the immune response. The main aim of this in vivo approach is to evaluate the ability of the immune response to eliminate or neutralize the pathogen at the time of infection. Those antigens capable of generate a specific immune response with neutralizing activity for natural infections are the best candidate vaccines. In this review we summarize the evolution of vaccinology since its inception, with special emphasis on the development of VLPs as vaccine platforms and their future in preventive medicine and we introduce a new recombinant platform for antigen presentation based on Junin virus VLPs (JUNV-VLPs).

DGJ (Daftardar-Gejii-Jafaris) method is used to obtain numerical solution of the third order fractional differential equation. Providing the DGJ method converges, the approximate solution is a good and effective numerical result which is close to the exact solution or the exact solution. For this,the examples of the explaning the method are presented. The proposed method is implemented for the approximation solution of the third order nonlinear fractional partial differential equations. The method was shown to be unsuitable and inconsistent for an example of a nonlinear fractional partial differential equation depend on initial-boundary value conditions. The fact that these numerical results are not consistent can be explained by the fact that the method is not convergent.

We construct a family of two-step optimal fourth order iterative methods for finding single root of non-linear equations. We generalize these methods to simultaneous iterative methods for determining all the distinct as well as multiple roots of single variable non-linear equations. Convergence analysis is present for both cases to show that the order of convergence is four in case of single root finding method and is twelve for simultaneous determination of all roots of non-linear equation. The computational cost, Basin of attraction, efficiency, log of residual and numerical test examples shows, the newly constructed methods are more efficient as compared to the existing methods in literature.

Multiobjective optimization plays an increasingly important role in modern applications, where several criteria are often of equal importance. The task in multiobjective optimization and multiobjective optimal control is therefore to compute the set of optimal compromises (the Pareto set) between the conflicting objectives. The advances in algorithms and the increasing interest in Pareto optimal solutions have led to a wide range of new applications related to optimal and feedback control which results in new challenges such as expensive models or real-time applicability. Since the Pareto set generally consists of an infinite number of solutions, the computational effort can quickly become challenging which is particularly problematic when the objectives are costly to evaluate or when a solution has to be presented very quickly. This article gives an overview over recent developments in accelerating multiobjective optimal control for complex problems where either PDE constraints are present or where a feedback behavior has to be achieved. In the first case, surrogate models yield significant speed-ups. Besides classical meta-modeling techniques for multiobjective optimization, a promising alternative for control problems is to introduce a surrogate model for the system dynamics. In the case of real-time requirements, various promising model predictive control approaches have been proposed, using either fast online solvers or offline-online decomposition. We also briefly comment on dimension reduction in many-objective optimization problems as another technique for reducing the numerical effort.

In this effort we propose a data-driven learning framework for reduced order modeling of fluid dynamics. Designing accurate and efficient reduced order models for nonlinear fluid dynamic problems is challenging for many practical engineering applications. Classical projection-based model reduction methods generate reduced systems by projecting full-order differential operators into low-dimensional subspaces. However, these techniques usually lead to severe instabilities in the presence of highly nonlinear dynamics, which dramatically deteriorates the accuracy of the reduced-order models. In contrast, our new framework exploits linear multistep networks, based on implicit Adams-Moulton schemes, to construct the reduced system. The advantage is that the method optimally approximates the full order model in the low-dimensional space with a given supervised learning task. Moreover, our approach is non-intrusive, such that it can be applied to other complex nonlinear dynamical systems with sophisticated legacy codes. We demonstrate the performance of our method through the numerical simulation of a two-dimensional flow past a circular cylinder with Reynolds number Re = 100. The results reveal that the new data-driven model is significantly more accurate than standard projection-based approaches

District heating networks have become widespread due to their ability to distribute thermal energy efficiently, which leads to reduced carbon emissions and improved air quality. The characteristics of these networks vary remarkably depending on the urban layout and system amplitude. Moreover, extensive data about the energy distribution and thermal capacity of different areas are seldom available. Design, optimization and control of these systems are enabled by the availability of fast and scalable models of district heating networks. This work addresses this issue by proposing a novel method to develop a scale-free model of large-scale district heating networks. Starting from coarse data available at the main substations, a physics-based model of the system aggregated regions is developed by identifying the heat capacity and heat loss coefficients. The model validation on the network of Västerås, Sweden, shows compatibility with literature data and can therefore be exploited for system design, optimization and control-oriented applications. In particular, the possibility to estimate the heat storage potential of network regions allows new smart management strategies to be investigated.

Creating self-learning algorithms, developing deep neural networks and improving other methods that "learn" for various areas of human activity is the main goal of the theory of machine learning. It helps to replace the human with a machine, aiming to increase the quality of production. The theory of artificial neural networks, which already have replaced the humans in problems of detection of moving objects, recognition of images or sounds, time series prediction, big data analysis and numerical methods remains the most dispersed branch of the theory of machine learning. Certainly, for each area of human activity it is necessary to select appropriate neural network architectures, methods of data processing and some novel tools from applied mathematics. But the universal problem for all these neural networks with specific data is the achieving the highest accuracy in short time. Such problem can be resolved by increasing sizes of architectures and improving data preprocessing, where the accuracy rises with the training time. But there is a possibility to increase the accuracy without time growing, applying optimization methods. In this survey we demonstrate existing optimization algorithms of all types, which can be used in neural networks. There are presented modifications of basic optimization algorithms, such as stochastic gradient descent, adaptive moment estimation, Newton and quasi-Newton optimization methods. But the most recent optimization algorithms are related to information geometry, for Fisher-Rao and Bregman metrics. This approach in optimization extended the theory of classic neural networks to quantum and complex-valued neural networks, due to geometric and probabilistic tools. There are provided applications of all introduced optimization algorithms, what delighted many kinds of neural networks, which can be improved by including any advanced approaches in minimization of the loss function. Afterwards, we demonstrated ways of developing optimization algorithms in further researches, engaging neural networks with progressive architectures. Classical gradient based optimizers can be replaced by fractional order, bilevel and, even, gradient free optimization methods. There is a possibility to add such analogues in graph, spiking, complex-valued, quantum and wavelet neural networks. Besides the usual problems of image recognition, time series prediction, object detection, there are many are other tasks for modern theory of machine learning, such as solving problem of quantum computations, partial differential and integro-differential equations, stochastic processes and Brownian motion, making decisions and computer algebra.

In this paper, we introduce the evolve-then-filter (EF) regularization method for reduced order modeling of convection-dominated stochastic systems. The standard Galerkin projection reduced order model (G-ROM) yield numerical oscillations in a convection-dominated regime. The evolve-then-filter reduced order model (EF-ROM) aims at the numerical stabilization of the standard G-ROM, which uses explicit ROM spatial filter to regularize various terms in the reduced order model (ROM). Our numerical results based on a stochastic Burgers equation with linear multiplicative noise. It shows that the EF-ROM is significantly better results than G-ROM.

Results from a behavioral economic laboratory experiment are used to enhance our understanding of public health decisions made during the COVID-19 pandemic. The identification of systematic biases from optimal decision theory found in controlled experiments could help inform public policy design for future public health crises. The laboratory and the shelter-in-place decisions made during COVID-19 included elements of risk, uncertainty and ambiguity. The lab findings found individuals adopt different decision rules depending on both personal attributes and on the context and environment in which the decision task is conducted. Key observations to consider in the context of the COVID-19 decision environment include the importance of past experience, the ability to understand and calculate the odds of each action, the size and differences in economic payoffs given the choice, the value of information received, and how past statistical independent outcomes influence future decisions. The academic space encompassing both public health and behavioral economics is small, yet important, particularly in the current crisis. The objective of continued research in this area would be to develop a more representative model of decision-making processes, particularly during crisis, that would serve to enhance future public health policy design.

The optical conductivity in the charge order phase is calculated in the extended Hubbard model describing an organic Dirac electron system α-(BEDT-TTF)₂I₃ using the mean field theory and the Nakano-Kubo formula. A peak structure due to interband excitation being characteristic in two-dimensional Dirac electron system is found above the charge order gap. It is shown that the peak structure originates from the Van Hove singularities of the conduction and valence bands, where those singularities are located at a saddle point between two Dirac cones in momentum space. The frequency of the peak structure exhibits drastic change in the vicinity of the charge order transition.

Ofloxacin is a highly efficient and widely used antibiotic drug. It is classified as a refractory pollutant due to its poor biodegradability. Consequently, it is commonly found in water sources, requiring efficient methods for its removal. Advanced Oxidation Processes (AOPs) offer efficient alternatives since those yield complete degradation not achieved in adsorption or membrane processes. Previous studies suggest ofloxacin degradation follows a pseudo-first or -second order processes, whereas for full removal of refractory pollutants – lower pseudo-orders are required. Monitoring the actual “pseudo-order” degradation kinetics of ofloxacin is needed to evaluate any proposed AOP process. This study presents a simple procedure to evaluate pseudo-orders of AOPs. Photolysis of 20 mM ofloxacin solutions follow pseudo-zero order kinetics, with half-life times (t_1/2) of approx. 60 min. TiO₂ heterogenous catalyst show to have no influence at low concentration (0.2 mg L^-1) but a significant reduction of half-life time (t_1/2 = 20 min) and increase in pseudo-order (0.8) is measured at 2.0 mg L^-1. Similar results are obtained with homogenous catalysis by 2.0 mg L^-1 H₂O₂. The combination of H₂O₂ and TiO₂ catalysts shows additional reduction in half-time life with increase in the pseudo-order to 1.2. The conclusions are (1) heterogenous and homogenous photocatalysis can effectively degrade ofloxacin, (2) combined photocatalysis yields higher pseudo-order, being less prone to achieve full removal, (3) analysis of specific pseudo-orders in AOPs of refractory pollutants helps to further elucidate the efficiency of the processes.

The active layer thickness (ALT) is affected by local soil material and surface vegetation coverage in Tibetan plateau permafrost region. The human activities and engineering construction along the Qinghai-Tibet Highway (QTH) destroyed the surface vegetation coverage, which have seriously influenced the ALT and heat exchange balance. Meanwhile, the changes of the detailed internal structure in the active layer will affect the stability of engineering infrastructure. Ground-penetrating radar (GPR) was used to detect the ALT in the permafrost region. However, researches on the detailed structure of the active layer and spatial variation information of ALT are rare, the main reason given for this is that the lack of advanced imaging techniques to enhance the GPR images. In this paper, the researchers focus on solving this problem by applying the reverse time migration (RTM) method to GPR field data acquired in the Beiluhe region. Analyses of the results led to conclusions as follows: a) The RTM algorithm is proved to be accurate for imaging the characteristics of the active layer through the dielectric constant model obtained by common midpoint and GPR data velocity analysis. b) The result of RTM profiles show the loose structure and graben-like fine internal structure. Combining with drill data and soil materials information, the researchers deduce the graben-like structure might be coarse gravel layer, which may be related to subgrade settlements in some local area. c) The 2D contour maps not only show the spatial variation feature of ALT at two sides of the highway, but also can compensate the defect of the research for single 2D survey line. The ALT is about 0.6~0.8 m in most areas, and about 0.8~0.9 m near the highway at west side of the QTH on the 2D contour maps. From the RTM interpretation profile, the closer to the plash, the deeper thawed active layer. The detailed internal structure of the active layer and 2D contour maps of ALT have great significance for the study of fine internal structure and the characteristic of ALT spatial variation in the Tibetan plateau under the background of climatic change.

As freshwater sources of drinking water become limited cities and urban areas must consider higher-salinity waters as potential sources of drinking water. The Salton Sea in the Imperial Valley of California has a very high salinity (43 ppt), total dissolved solids (70,000 mg/L) and color (1440 CU). Proposals to desalinate the Salton Sea are expected to lower the equilibrium salinity from 45 ppt to 3 ppt yielding significant benefits for ecological restoration. High salinity eutrophic waters such as the Salton Sea are difficult to treat yet more desirable sources of drinking water are not always available. Jar tests were performed to evaluate the treatability of Salton Sea water for potential urban water use by coagulation using aluminum chlorohydrate, ferric chloride and alum. Coagulation-sedimentation proved to be relatively ineffective for lowering turbidity with no clear optimum dose for any of the coagulants tested. Alum was most effective for color removal (28 percent) at a dose of 40 mg/L. Turbidity was removed effectively with 0.45 m and 0.1 m microfiltration. Bench tests of Salton Sea water using Sea Water Reverse Osmosis (SWRO) achieved rejections of 99 percent salinity, 97.7 percent conductivity, 98.6 percent total dissolved solids, 98.7 percent chloride, 65 percent sulfate, and 99.3 percent turbidity.

Two major uses of linear perspective are in planar paintings - the flat canvas is incongruent with the painted 3-D scene - and in forced perspectives, such as theater stages that are concave truncated pyramids, where the physical geometry and the depicted scene are congruent. Patrick Hughes pioneered a third major art form, the reverse perspective, where the depicted scene opposes the physical geometry. Reverse perspectives comprise solid forms (truncated pyramids and prisms) jutting toward the viewer, thus forming concave spaces between the solids. The solids are painted in reverse perspective: as an example, the left and right trapezoids of a truncated pyramid are painted as rows of houses; the bottom trapezoid is painted as the road between them and the top forms the sky. This elicits the percept of a street receding away, even though it physically juts toward the viewer. Under this illusion, the concave void spaces between the solids are transformed into convex volumes. This depth-inversion creates a concomitant motion illusion: when a viewer moves in front of the art piece, the scene appears to move vividly. Two additional contributions by the artist are discussed, in which he combines reverse perspective parts with forced and planar perspective parts on the same art piece. The effect is spectacular, creating objects on the same planar surface that move in different directions, thus “breaking” the surface apart, demonstrating the superiority of objects over surfaces. We conclude with a discussion on the value of these art pieces in vision science.

Change orders have received considerable attention from researchers thus far, but none have considered pricing strategies of change orders through the interaction between general contractors and subcontractors. Previous studies found that contractors’ opportunistic bidding considering beyond-contractual reward (BCR) in the execution stage can be reduced by improving the construction management system and strengthening the supervision of contractors’ performance. However, the BCR remains in ecology of construction engineering. This study proposes an integrated evolutionary game theory-system dynamics model (ET-SD model) and simulates the pricing strategy of change orders between general contractors and subcontractors to explore the root cause of BCR phenomenon. Sensitivity analysis on the evolutionary dynamics of payoff is explored. Results reveal that change orders with BCR maintain Nash equilibrium and evolutionary stable strategy (ESS) unless changing the payoff structure between general and subcontractors’ pricing strategies. This study presents important managerial insights from the evolutionary game perspectives, nature of change orders, and payoff of the alternative.

In this paper we introduce the hypo-q-operator norm and hypo-q-numerical radius on a Cartesian product of algebras of bounded linear operators on Banach spaces. A representation of these norms in terms of semi-inner products, the equivalence with the q-norms on a Cartesian product and some reverse inequalities obtained via the scalar reverses of Cauchy-Buniakowski-Schwarz inequality are also given.

The reverse transcription quantitative polymerase chain reaction (RT-qPCR) is an established tool for the diagnosis of RNA pathogens. Its potential for automation has caused it to be used as a presence/absence diagnostic tool even when RNA quantification is not required. This technology has been pushed to the forefront of public awareness by the COVID-19 pandemic, as its global application has enabled rapid and analytically sensitive mass testing, with the first test targeting three viral genes published within days of the publication of the SARS-CoV-2 genomic sequence. One of those, targeting the RNA-dependent RNA polymerase gene, has been heavily criticised for supposed scientific flaws at the molecular and methodological level and this criticism has been extrapolated to doubts about the validity of RT-qPCR for COVID-19 testing in general. We have analysed this assay in detail and our findings reveal some limitations, but also highlight the robustness of the RT-qPCR methodology for SARS-CoV-2 detection. Whilst our data show that some errors can be tolerated, it is always prudent to confirm that primer and probe sequences complement their intended target, since when errors do occur, they may result in a reduction in the analytical sensitivity. However, in this case it is unlikely that a mismatch will result in poor specificity or significant number of false positive SARS-CoV-2 diagnoses, especially as this is routinely checked by diagnostic laboratories as part of their quality assurance.

A classical origin for the Bohmian quantum potential, as that potential term arises in the quantum mechanical treatment of black holes and Einstein-Rosen (ER) bridges, can be based on 4th-order extensions of Einstein's equations. In Bohm's ontological interpretation, black hole radiation, and the analogous tunneling process of quantum transmission through an ER bridge, are classically allowed if the dynamics are modified to include such a quantum potential. The 4th-order extension of general relativity required to generate the quantum potential is given by adding quadratic curvature terms with coefficients that maintain a fixed ratio, as their magnitudes approach zero. Quantum transmission through the classically non-traversable bridge is replaced by classical transmission through a traversable wormhole. If entangled particles are connected by a Planck-width ER bridge, as conjectured by Maldacena and Susskind, then the classical wormhole transmission effect gives the ontological nonlocal connection between the particles posited in Bohm's interpretation of their entanglement. It is hypothesized that higher-derivative extensions of classical gravity can account for the nonlocal part of the quantum potential generally.

This paper describes a proposed fractional filter-based multi-scale underwater and hazy image enhancement algorithm. The proposed system combines a modified global contrast operator with fractional order-based multi-scale filters used to generate several images, which are fused based on entropy and standard deviation. The multi-scale-global enhancement technique enables fully adaptive and controlled colour correction and contrast enhancement without over exposure of highlights when processing hazy and underwater images. This in addition to illumination/reflectance estimation coupled with global and local contrast enhancement. The proposed algorithm is also compared with the most recent available state-of-the-art multi-scale fusion de-hazing algorithm. Experimental comparisons indicate that the proposed approach yields better edge and contrast enhancement results without halo effect, colour degradation and is faster and more adaptive than all other algorithms from the literature.

The search for new materials that could improve the energy density of Li-ion batteries (LIB) is one of today’s most challenging issues. Recently, cation-disordered lithium-excess metal oxides have emerged as a promising new class of cathode materials for LIB, due to their high reversible capacities and nice structural stability. However, a full structural model of the Li-transition metal (TM) sharing sublattice and the origin of short range ordering (SRO) of cation ions requires further investigation. In this work, we put forward a Monte Carlo strategy of building a cation-disordered rocksalt material supercell model. The cation ions of Li_1.0Ti_0.5Ni_0.5O₂ (LTNO) are placed at the FCC sublattice sites with the constraint of Pauling’s electroneutrality rule, instead of a random way. This constraint causes the Li-Ti and Ni-Ni clustering (the cation short range ordering). Based on this model, we discussed the relationship between the short range ordering, the local distorting, the theoretic capacity and the order-disorder strengths. A unified understanding of these factors in cation-disordered materials may enable a better design of disordered-electrode materials with high capacity and high energy density.

This study investigated the removal characteristics of N-nitrosamines and their precursors at three pilot-scale water reclamation plants. These plants applies different integrated membrane systems: (1) microfiltration (MF) / nanofiltration (NF) / RO; (2) sand filtration/three-step RO; and (3) ultrafiltration (UF) / NF and UF / RO. Variable removal of N-nitrosodimethylamine (NDMA) by the RO membrane processes could be attributable to membrane fouling and feed water temperature. The effect of membrane fouling on N-nitrosamines removal was extensively evaluated at one of the plants by conducting one month of operation and chemical cleaning of the RO membrane element. Membrane fouling enhanced N-nitrosamines removal by the pilot-scale RO membrane process. This finding contributes to better understanding of variable removal of NDMA by RO membrane processes. This study also investigated the removal characteristics of N-nitrosamines precursors. The NF and RO membrane processes greatly reduced NDMA formation potential (FP), but the UF process had little effect. The contributions of MF, NF, and RO membrane processes for reducing FPs of NDMA, N-nitrosopyrrolidine (NPYR) and N-nitrosodiethylamine (NDEA) were different, suggesting different size distributions of their precursors.

Consumption of coffee by women early in their pregnancy has been viewed as potentially increasing the risk of miscarriage, low birth weight, and childhood leukemias. Many of these reports of epidemiologic studies have not acknowledged the potential biases inherent in studying the relationship between early-pregnancy-coffee consumption and subsequent events. I discuss five of these biases, recall bias, misclassification, residual confounding, reverse causation, and publication bias. Each might account for claims that attribute adversities to early-pregnancy-coffee consumption. To what extent these biases can be avoided remains to be determined. At a minimum, they need to be acknowledged wherever they might account for what is reported.

We discuss the role of the opposing principles of order and disorder in physical and biological systems in determining stability, growth and evolution and bring forth the potential role of a cosmic ordering agency. We analyze its role in decreasing entropy by coarse-graining and hence in determining the initial low entropy state of the big bang universe. Since all physical and biological systems have either cycles of order and disorder alternating, or may have chaotic evolution with non-linear laws, the same is expected of the dynamics of the whole universe as well. The entropy of the initial state of the universe could be low because of the reduction of degrees of freedom (DoF) as one moves from physical encoding to neural encoding and then on to psychic encoding of information in a nested manner by coarse-graining. It is by such encoding that this cosmic agency enables the universe to pass through the big crunch phase and then rolls it out as the big bang universe from the initial state of low entropy.

Humans are incapable of judging the temporal order of visual events at brief temporal separations with perfect accuracy. Their performance---which is of much interest in visual cognition and attention research---can be measured with the temporal-order judgment task, which typically produces S-shaped psychometric functions. Occasionally, researchers reported plateaus within these functions, and some theories predict such deviation from the basic S shape. However, the centers of the psychometric functions result from the weakest performance at the most difficult presentations and therefore fluctuate strongly, leaving existence and exact shapes of plateaus unclear. This study set out to investigate whether plateaus disappear if the data accuracy is enhanced, or if we are ``stuck on a plateau'', or rather with it. For this purpose, highly accurate data were assessed by model-based analysis. The existence of plateaus is confidently confirmed and two plausible mechanisms derived from very different models are presented. Neither model, however, performs well in the presence of a strong attention manipulation, and model comparison remains unclear on the question which of the models describes the data best. Nevertheless, the present study includes the highest accuracy in visual TOJ data and the most explicit models of plateaus in TOJ studied so far.

A new approach is presented to defining the amount of information, in which information is understood as the data about a finite set as a whole, whereas the average length of an integrative code of elements serves as a measure of information. We obtain a formula for the syntropy of reflection, that is, the information that two intersecting finite sets reflect about each other. The specificities of reflection of discrete systems through the combination of their parts are considered, and it is shown that the additive syntropy and entropy of reflection are measures of structural order and chaos. Three information laws have been established: the law of conservation of the sum of chaos and order; information law of reflection; the law of conservation and transformation of information. An assessment of the structural organization and the level of development of discrete systems is presented. It is shown that various measures of information are structural characteristics of integrative codes of elements of discrete systems. The conclusion is made that, from the informational-genetic positions, the synergetic theory of information is primary in relation to the Hartley-Shannon information theory. In the appendix we consider the asymmetry of the mutual reflection of finite sets and the proportionality of arbitrary quantities.

To quantify the time-varying reproduction number (Rt) for Malaysia using the COVID-19 incidence data., we used data the from the Johns Hopkins University Center for Systems Science and Engineering (JHU CCSE) Coronavirus repository. Day 1 was taken from the first assumed local transmission of COVID-19. Data was split into four intervals: a) Interval 1: from Day 1 to Day 10 MCO 1, b) Interval 2: from Day 1 to Day 10 MCO 2, c) Interval 3: from Day 1 to Day 10 MCO 3 and d) Interval 4: from Day 1 to Day 10 MCO 4. We estimated the Rt using the EpiEstim package. The means for Rt at Day 1, Day 5 and Day 10 for all MCOs ranged between 0.665 to 1.147. The average Rt gradually decreased in MCO 1 and MCO 2. However, Rt increased in MCO 3 before stabilized around 0.8 in MCO 4. MCO 1 and MCO 2 which were stricter coincide with the gradual reduction of Rt. However, the more relaxed MCO 3 and MCO 4 correspond to a slight increase in the Rt before it stabilized.

Considering the fault "N-1" checksum and the power flow, the single-phase power flow model is further transformed into a three-phase power flow model, and the asymmetry of the three-phase power flow is measured by the three-phase unbalance factor. The calculation model is linearized by the second-order cone relaxation and the Big-M method. At the same time, the load response and distribution network reconstruction are used to improve the reliability of the power supply network to cope with the power failure. The relationship between power supply capability and power flow constraints, main transformer capacity and distributed power parameters is analyzed by IEEE 33-node three-phase power distribution system. The feasibility of the proposed model and the accuracy of the second-order cone relaxation are verified by numerical examples, which provides a technical reference for distribution network planning.

Firstly, a new set of Quasi-Cubic Trigonometric Bernstein basis with two tension shape parameters is constructed, and we prove that it is an optimal normalized totally basis in the framework of Quasi Extended Chebyshev space. And the Quasi-Cubic Trigonometric Bézier curve is generated by the basis function and the cutting algorithm of the curve are given, the shape features (cusp, inflection point, loop and convexity) of the Quasi-Cubic Trigonometric Bézier curve are analyzed by using envelope theory and topological mapping; Next we construct the non-uniform Quasi-Cubic Trigonometric B-spline basis by assuming the linear combination of the optimal normalized totally positive basis have partition of unity and continuity, and its expression is obtained. And the non-uniform B-spline basis is proved to have totally positive and high-order continuity. Finally, the non-uniform Quasi Cubic Trigonometric B-spline curve and surface are defined, the shape features of the non-uniform Quasi-Cubic Trigonometric B-spline curve are discussed, and the curve and surface are proved to be continuous.

Base on the sequence of S genes, which encode spike proteins, we previously identified three different types (North American, S INDEL and S large-DEL types) of porcine epidemic diarrhea virus (PEDV) that have re-emerged in Japan since 2013. Base on experimental infections with the North American and S large-DEL types, we also hypothesized that PEDV virulence may be linked to the S1 subunit of the S protein. To test this hypothesis, we have now assayed in gnotobiotic piglets various recombinant PEDVs generated by reverse genetics. Piglets inoculated with CV777 maintained in National Institute of Animal Health, along with piglets infected with a recombinant form of the same virus, developed subclinical to mild diarrhea. In contrast, severe watery diarrhea, dehydration, weight loss, astasia, and high mortality were observed in piglets inoculated with recombinant strains in which the S gene was partially or fully replaced with corresponding sequences from the highly virulent Japanese PEDV isolate OKN-1/JPN/2013. Indeed, symptoms resembled those in piglets inoculated with the OKN-1/JPN/2013, and were especially pronounced in younger piglets. Collectively, the data demonstrate that the S1 subunit of the S protein is an important determinant of PEDV virulence, and advance development of new vaccine candidate.

Abstract The last mile has to deliver the goods at the right time and at the right cost for business logistics services to enhance the customer service experience. The objective of this paper is to examine the challenges of retail omni-channel distribution influencing the effective last mile fulfilment operations processes in the apparel group (organisation phase). It further establishes the extent of how the last mile for demand-driven store replenishment influences the level of retailers’ order fulfilment (in full and on time) (environment phase). This paper, persistently assesses the contribution of integrated information systems on brick and mortar and virtual omni-channel distribution networks to improve timeousness, order accuracy and fill rate (technology phase). The mixed method has triangulated the methods from both quantitative methodology using frequencies and binomial testing and subjective thematic analysis of qualitative data from interviewees. The sample of 175 questionnaires were received with a response rate is 85 per cent (148 responses). The main findings of the paper revealed that the current order fulfilment process aligned with the last mile is effective on the basis of the fill rate, lead time and order condition of orders delivered via store economy and store express.

We investigated the radioclinical outcomes of naviculectomy and limited/tailored soft-tissue releases in a short series of ambulatory children with complex/intractable congenital vertical talus subsets namely neglected, multiple operated and recurrent patients. We postulated that this technique will yield satisfactory radioclinical outcomes and minimal complications because it avoids extensive surgical release/trauma that is otherwise classically recommended for complex congenital vertical talus (CVT). The cohort consisted of five -four boys, one girl- complex CVT children with neglected, multiple operated and/or recurrent subsets. Patients were included if manipulative casting techniques were deemed unlikely to produce a plantigrade foot. Patients underwent naviculectomy with variable on-demand limited soft tissue releases. Two patients had bilateral affection and two had a non-idiopathic aetiology. The mean age was 5.2 years (4 to 6.25) and mean follow-up was 2.3 years (1 to 3). We reported satisfactory outcomes as per foot posture, function, overall parent satisfaction including pain and radiography per lateral views of talar-axis-first metatarsal base angle on the short/intermediate term. Whereas manipulative casting is unlikely to yield lasting outcomes in ambulatory children with complex subsets of CVT, extensive surgical soft-tissue releases have unfavourable long-term complications. As a substitute, naviculectomy as a form of resection arthroplasty created a practical and affordable third way between manipulative casting with minimally invasive surgery and the extensive surgical soft tissue releases on the short/intermediate term.

When a rotary machine is running, from which the acquired vibro-acoustic signals enable to reveal its operation status and health condition. The study proposed a DSP-based adaptive angular-velocity Vold-Kalman filtering order tracking (AV2KF_OT) algorithm with an online real-time nature for signal interpretation and machine condition monitoring. Theoretical derivation and numerical implementation of computation schemes are briefly introduced. An online real-time monitoring system based on the AV2KF_OT algorithm, which was implemented through both a digital signal processor and a user interface coded by using LabVIEW, was developed. Two experimental tasks were applied to justify the proposed technique, including (i) the detection of startup on the fluid-induced whirl performed through a journal-bearing rotor rig, and (ii) the separation of close orders from the measured signals of a multifunction transmission-element ball-bearing bench.

This paper focuses on parametric model order reduction (PMOR) of guided ultrasonic wave propagation and its interaction with damage in a fiber metal laminate (FML). Structural health monitoring in FML seeks to detect, localize and characterize the damage with high accuracy and minimal use of sensors. This can be achieved by the inverse problem analysis approach which employs the signal measurement data recorded by the embedded sensors in the structure. The inverse analysis requires to solve the forward simulation of the underlying system several thousand times. These simulations are often exorbitantly expensive and triggered the need for improving their computational efficiency. A PMOR approach hinged on the proper orthogonal decomposition method is presented in this paper. An adaptive parameter sampling technique is established with the aid of a surrogate model to efficiently update the reduced-order basis in a greedy fashion. A numerical experiment is conducted to illustrate the parametric training of the reduced-order model. The results show that the reduced-order solution based on the PMOR approach is accurately complying with that of the high fidelity solution.

Sustainability in material purchasing is a growing area of research. Goods purchasing decisions strongly affect transportation path flows, vehicle consolidation, inventory levels and related obsolescence costs. Within a global sourcing context, companies experience the need of new decision making approaches capable to consider a large variety of factors, also linked with society and environment. Environmental impact assessment has become a key requirement for materials purchasing and transportation decisions since global warming is a rising concern both in academic and industrial researches. In fact, it is well known that the freight transport industry is responsible for large amounts of carbon emissions contributing to global warming. In this paper, we initially analyse and compare the environmental economic policies established by the International Governments in relation to the carbon trading systems adopted. Then, we develop a multi-objective lot sizing approach useful in practice to define the sustainable quantity to purchase when a Cap and Trade mitigation policy is present. We further analyse the model behaviour according to different carbon price values by demonstrating that carbon prices are still far too low to motivate managers towards sustainable purchasing choices.

In this paper, we compare the performance between systems of ordinary and (Caputo) fractional differential equations depicting the susceptible-exposed-infectious-recovered (SEIR) models of diseases. In order to understand the origins of both approaches as mean-field approximations of integer and fractional stochastic processes, we introduce the fractional differential equations as approximations of some type of fractional nonlinear birth--death processes. Then, we examine validity of the two approaches against empirical courses of epidemics; we fit both of them to case counts of three measles epidemics that occurred during the pre-vaccination era in three different locations. While FDEs appear more flexible in fitting empirical data, our ODEs offered better fits to two out of three data sets. Important differences in transient dynamics between these modeling approaches are discussed.

The semi-logarithmic plot of the cumulative number of cases of epidemics resembles the response of a first-order systems for a step load. This similarity was utilized to develop a first order model that can be used for extracting information about the dynamics of infectious disease epidemics. The developed model was validated using COVID-19 data of China. It was also heuristically fitted to other 13 countries. Obtained results indicated that the model can reliably forecasts the number of infected person, epidemic growth speed towards steady-state condition (process time constant, T), and time to reach steady-state condition (4T). The developed model will help public health authorities in developing more effective control strategies of epidemics.

We shall discuss three methods of inverse Laplace transforms. A Sinc-Thiele approxi- mation, a pure Sinc, and a Sinc-Gaussian based method. The two last Sinc related methods are exact methods of inverse Laplace transforms which allow us a numerical approximation using Sinc methods. The inverse Laplace transform converges exponentially and does not use Bromwich contours for computations. We apply the three methods to Mittag-Leffler functions incorporating one, two, and three parameters. The three parameter Mittag-Leffler function represents Prabhakar’s function. The exact Sinc methods are used to solve fractional differential equations of constant and variable differentiation order.

Most of the existing influence maximization algorithms are not suitable for large-scale social networks due to their high time complexity or limited influence propagation range. Therefore, a D-RIS influence maximization algorithm is proposed based on the independent cascade model and combined with the reverse reachable set sampling. Under the premise that the influence propagation function satisfies monotonicity and submodularity, the D-RIS algorithm uses automatic debugging method to determine the critical value of the number of reverse reachable sets, which not only obtains a better influence propagation range, and greatly reduce the time complexity. The experimental results on the two real data sets of Slashdot and Epinions show that D-RIS algorithm is close to the CELF algorithm and higher than RIS algorithm, HighDegree algorithm, LIR algorithm and pBmH algorithm in influence propagation range. At the same time, it is significantly better than the CELF algorithm and RIS algorithm in running time, which indicates that D-RIS algorithm is more suitable for large scale social network.

Pseudomonas aeruginosa is a critical healthcare challenge due to its ability to cause persistent infections and the acquisition of antibiotic resistance mechanisms. Lack of preventive vaccines and rampant drug resistance phenomenon has rendered patients vulnerable. As new antimicrobials are in the preclinical stages of development, mining for the unexploited drug targets is also crucial. Here, we designed a chimeric vaccine against P. aeruginosa using a subtractive proteomics approach and identified nine unique enzymes as novel drug targets in PAO1 proteome. A total of five unique proteins were selected as potential vaccine candidates based on essentiality, extracellular localization, virulence, antigenicity, pathway association, protein-protein interaction analysis, hydrophilicity, and low molecular weight. These include two outer membrane porins OprF (P13794) and OprD (P32722), a protein activator precursor pra (G3XDA9), a probable outer membrane protein precursor PA1288 (Q9I456), and a conserved hypothetical protein PA4874 (Q9HUT9). These proteins were further analyzed using a reverse vaccinology approach to identify immunogenic and antigenic T cell and B cell epitopes. The best scoring epitopes qualifying for all set criteria were then further subjected to the construction of a polypeptide multi-epitope vaccine construct with cholera toxin B (CtxB) subunit as an adjuvant. The identified drug targets qualifying the screening criteria were: UDP-2-acetamido-2-deoxy-d-glucuronic acid 3-dehydrogenase WbpB (G3XD23), aspartate semialdehyde dehydrogenase (Q51344), 2-amino-4-hydroxy-6-hydroxymethyldihydropteridine pyrophosphokinase (Q9HV71), 3-deoxy-D-manno-octulosonic-acid transferase (Q9HUH7), glycyl-tRNA synthetase alpha chain (Q9I7B7), riboflavin kinase/FAD synthase (Q9HVM3), aconitate hydratase 2 (Q9I2V5), probable glycosyltransferase WbpH (G3XD85) and UDP-3-O-[3-hydroxylauroyl] glucosamine N-acyltransferase (Q9HXY6). For druggability and pocketome analysis crystal and homology structures of these proteins were retrieved and developed. A sequence-based search was performed in different databases (ChEMBL, Drug Bank, PubChem and Pseudomonas database) for the availability of reported ligands and tested drugs for the screened targets. These predicted targets may provide a basis for the development of reliable antibacterial preventive and therapeutic options against P. aeruginosa.

The high mutation rate of human immunodeficiency virus type 1 (HIV-1) plays a major role in treatment resistance from the development of vaccines to long-lasting drugs. In addressing the crux of the issue, various attempts to estimate the mutation rate of HIV-1 resulted in a large range of 10-5 - 10-3 errors/bp/cycle due to the use of different types of investigation methods. In this review, we discuss the different assay methods, their findings on the mutation rates of HIV-1 and how the location of these mutations can be further analyzed for their potential allosteric effects to reveal potentially new inhibitors with different pharmacodynamics that can be used to circumvent fast occurring HIV drug resistance. Given that HIV is one of the fastest mutating viruses, it is a good model for comprehensive study of its mutations that can give rise to much horizontal understanding towards overall viral drug resistance as well as emerging viral diseases.

In this paper, we investigate the new degenerate Daehee polynomials and numbers which are called the degenerate Daehee polynomials of the second kind, and derive some new and interesting identities and properties of those polynomials.

Soft-Core architecture for Analogue to Digital Converter (ADC) sampling is useful for mixed signal applications. Soft-core architecture for cutting edge odd or even ADC sampling with interface to block RAM memory has not been found. Soft-core architecture as a concept has become popular due to the advantage of customization for different applications as compared to general-core architecture suited for single application. The latest generation of piece wise sampling is odd sampling and was introduced in the second decade of the 20th century. Odd and even order sampling techniques are analogue in nature driven by a tuned (tuned for odd or even) mixer. This paper proposes a third-generation piece wise sampling with soft-core architecture that enables an option to select both odd and even while interfacing to memory mapping. The proposed odd/even has superior SNR performance of 6 dB as compared to existing architecture such as Mod-∆ which recorded worst performance of 18 dB. Advances in soft-core technology have allowed a niche odd/even switching field to be identified and studied, the study has also been extended to include memory architecture.

This work is intended to lead a study of necessary and sufficient optimality conditions for scalar optimization problems on Hadamard manifolds. In the context of this geometry, we obtain and present new function types characterized by the property of having all their second-order stationary points to be global minimums. In order to do so, we extend the concept convexity in Euclidean space to a more general notion of invexity on Hadamard manifolds. This is done employing notions of second-order directional derivative, second-order pseudoinvexity functions and the second-order Karush-Kuhn-Tucker-pseudoinvexity problem. Thus, we prove that every second-order stationary point is a global minimum if and only if the problem is either second-order pseudoinvex or second-order KKT-pseudoinvex depending on whether the problem regards unconstrained or constrained scalar optimization respectively. This result has not been presented in the literature before. Finally, examples of these new characterizations are provided in the context of \textit{"Higgs Boson like"} potentials among others.

In this work we present the results obtained with an artificial neural network (ANN) which we trained to predict the expected output of high-order harmonic generation (HHG) process, while exploring a multi-dimensional parameter space. We argue on the utility and efficiency of the ANN model and demonstrate its ability to predict the outcome of HHG simulations. In this case study we present the results for a loose focusing HHG beamline, where the changing parameters are: the laser pulse energy, gas pressure, gas cell position relative to focus and gas cell length. The physical quantity which we predict here using ANN is directly related to the total harmonic yield in a specified spectral domain (20-40 eV). We discuss the versatility and adaptability of the presented method.

Pyrolysis is a thermochemical conversion process designed for biomass decomposition in an oxygen-free environment. It typically operates within a temperature window of 200 to 800°C and generates various byproducts, including gases, liquid fractions (both aqueous and organic) such as bio-oil, and solids like char. Bio-oil is a significant product of pyrolysis. It is characterised by a complex blend of organic compounds. The acidic nature of bio-oil primarily originates from volatile acids, such as acetic acid. The recovery of acetic acid from bio-oil facilitates the use of the extracted substance as a precious resource, hence enhancing its significance in the framework of a circular bioeconomy. Acetic acid finds widespread application in manufacturing commercially important chemicals like vinyl acetate monomers and formulating phytosanitary products due to its inherent herbicidal and antifungal properties. However, to be used, acetic acid has to be separate from the raw bio-oil. Separation methodologies, including nanofiltration and reverse osmosis, demonstrate potential in recovering acidic constituents from bio-oil due to their high selectivity and effective system management. This article offers a concise review about acetic production from lignocellulosic biomass from pyrolysis and its recovery through various membrane separation processes.

Phosphorus is essential and non-substitutable chemical element required for the cellular processes of all living organisms. The main source of phosphorus in the biosphere is phosphate rock. With more than 700 Mt P2O5, Estonia holds the largest sedimentary phosphate rock deposits in European Union. Estonian phosphate rock is principally sandstone that holds abundant remains of phosphatic brachiopod shells and compared to other sedimentary rocks, is particularly outstanding by its remarkably low content of hazardous heavy metals such as Cd and saturated by valuable elements present in the rock such as rear earth elements (REEs).

In this paper, inspired by recent articles of G. Anatriello and G. Vincenzi (see \cite{Ana}), and G. Cerda-Morales (see \cite{Cer1,Cer2}), we will introduce the third-order $\overline{h}$-Jacobsthal and third-order $\overline{h}$-Jacobsthal--Lucas sequences and their associated quaternions. The new results that we have obtained extend most of those obtained in \cite{Cer2}.