The terpolymers of N-vinylpyrrolidone (VP) with acrylic acid and triethylene glycol methacrylate were synthesized with more than 90% yield by radical copolymerization in ethanol from monomeric mixtures of different composition (98 : 2 : 2, 95 : 5: 2 and 98 : 2: 5). Their physical and chemical characteristics such as monomer composition, absolute molecular masses and hy-drodynamic radii in aqueous media at different temperature were determined. The temperature effect on the behavior of terpolymers in water and aqueous saline buffer solution with pH 7.4 has been studied and the ability of a copolymer enriched with dimethacrylates units was discovered to respond to a temperature change in these media. On the basis of terpolymers and linear polyvi-nylpyrrolidone (PVP), polymer compositions of hydrophobic dye methyl pheophorbide a, MPP, that is fluorescent in the red region of the spectrum, were obtained; the sizes of the polymer nanostructures of MPP in water were determined. The structures based on terpolymers were demonstrated to be more stable than MPP-PVP ones. It was carried out the quantum-chemical modelling of the possible structures of MPP-copolymer that formed as a result of intermolecular interaction between MPP and terpolymer’s chains; the possibility of the formation of hydrogen bonds between the donors of the electrons of terpolymer units and the proton donors of the MPP molecule was shown. Using the MTT test, it was established that copolymers of various compositions are low toxic for non-tumor Vero cells and HeLa tumor cells. It was shown that the cytotoxicity of polymer particles loaded with MPP for FetMSC and HeLa cells increased. Using fluorescence microscopy, the accumulation and distribution of polymer particles in non-tumor (FetMSC) and tumor (HeLa) cells was studied and an increase in the accumulation of MPP with both types of particles was shown.

Keywords: pelvic ultrasound, follicle number per ovary, FNPO, ovarian volume, PCOM, upper normal limits, referral population

Ultrafast pump-probe spectroscopic studies allow for deep insights into the mechanisms and timescales of photophysical and photochemical processes. Extracting valuable information from these studies, such as reactive intermediates' lifetimes and coherent oscillation frequencies, is an example of the inverse problems of chemical kinetics. This article describes a consistent approach for solving this inverse problem that avoids the common obstacles of simple least-squares fitting that can lead to unreliable results. The presented approach is based on the regularized Markov Chain Monte Carlo sampling for the strongly nonlinear parameters, allowing for a straightforward solution of the ill-posed nonlinear inverse problem. The software to implement the described fitting routine is introduced and the numerical examples of its application are given. We will also touch on critical experimental parameters, such as the temporal overlap of pulses and cross-correlation time and their connection to the minimal reachable time resolution.

Classical methods for inverse problems are mainly based on regularization theory. In particular those which are based on optimization of a criterion with two parts: a data-model matching and a regularization term. Different choices for these two terms and great number of optimization algorithms have been proposed. When these two terms are distance or divergence measures, they can have a Bayesian Maximum A Posteriori (MAP) interpretation where these two terms correspond, respectively, to the likelihood and prior probability models.

This article proposes application of a new mathematical model of spots for solving inverse problems using a learning method, which is similar to using the deep learning. In general, the spots represent vague figures in abstract “information spaces” or crisp figures with lack of in-formation about their shapes and are adequate for representation human mental images and reasoning in Artificial Intelligence (AI). However, crisp figures are regarded as a special and limiting case of spots. A basic mathematical apparatus, basing on L4 numbers, has been developed for the representation and processing of qualitative information of elementary spatial relations between spots. Also, we defined L4 vectors, L4 matrices, and mathematical operations on them. Developed apparatus can be used in AI, in particular, for knowledge representation and for modeling qualitative reasoning and learning. Another application area is the solution of inverse problems by learning. For example, this can be applied to image reconstruction using ultrasound, X-ray, magnetic resonance, or radar scan data. The introduced apparatus was verified by solving problems of reconstruction of images, utilizing only qualitative data of its elementary relations with some scanning figures. This article also demonstrates application of spot-based inverse Radon algorithm for binary image reconstruction.

The spread of high-performance personal computers, frequently equipped with powerful Graphic Processing Units (GPU), raised the interest on a set of techniques able to extract models of electromagnetic phenomena (and devices) directly from available examples of the desired behavior. Such approaches are collectively referred to as Machine Learning (ML). A typical representative ML approach is the so called “Neural Network” (NN). Using such data-driven models allows evaluating the output in a much shorter time when a theoretical model is available, or allows predicting the behavior of the systems and devices when no theoretical model is available. With reference to a simple yet representative benchmark electromagnetic problem, some of the possibilities and the pitfalls of the use of NN for the interpretation of measurements (inverse problem) or to obtain the required measurements (optimal design problem) are discussed. The investigated aspects include the choice of the NN model; the generation of the dataset(s); the selection of hyperparameters (hidden layers, training paradigm). Finally, the capabilities in the handling of ill-posed problems are critically revised.

This paper delves into the challenge of controlling the multiple synchronizations in mechatronic vibration machines featuring a duo of unbalanced rotors. Its primary aim is to not only attain specific rotation speeds but also to ensure the desired phase shift between these rotors. To tackle this issue, the paper employs a control law integrating cross-couplings. It conducts an analysis of the dynamics of a simplified linearized system, showcasing the robustness of the control system across varying plant parameters. Additionally, the paper outlines experimental findings from a multi-resonance vibrational laboratory setup. These findings effectively demonstrate the effectiveness of the proposed approach and highlight its potential applications. Crucially, the paper provides experimental evidence illustrating the capability to control vibration fields on the operational platform, a vital aspect pertinent to vibration transportation and bulk material mixing processes.

This paper investigates an inverse problem of option pricing in the extended Black--Scholes model. We identify the model coefficients from the measured data and attempt to find arbitrage opportunities in financial markets using a Bayesian inference approach. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by Markov Chain Monte Carlo (MCMC), which explores the posterior state space. The efficient sampling strategy of MCMC enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown drift and volatility coefficients from the measured data.

This paper investigates the inverse option problems (IOP) in the extended Black--Scholes model arising in financial markets. We identify the volatility and the drift coefficient from the measured data in financial markets using a Bayesian inference approach, which is presented as an IOP solution. The posterior probability density function of the parameters is computed from the measured data. The statistics of the unknown parameters are estimated by a Markov Chain Monte Carlo (MCMC) algorithm, which exploits the posterior state space. The efficient sampling strategy of the MCMC algorithm enables us to solve inverse problems by the Bayesian inference technique. Our numerical results indicate that the Bayesian inference approach can simultaneously estimate the unknown trend and volatility coefficients from the measured data.

In the last decades the primordial black holes ({\bf pbhs}) have attracted much attention of cosmologists and astrophysicists. This is associated with origination of such black holes in the early Universe as a result of a gravitational collapse of the high-density matter, making them natural ''detectors'' of the processes involved. In inflationary cosmology of particular importance are {\bf pbhs} originated during the pre-inflationary period. And, since they are small and generated at the energies close to the Planck energies, for them we should take into consideration the quantum-gravitational corrections ({\bf qgcs}). In turn, these corrections change (shift) the inflationary parameters. The paper presents a study of the above-mentioned shifts with regard to these corrections for different scenarios. It is shown that probabilities of occurrence of the {\bf pbhs} under study with due regard for the given {\bf qgcs} are rising as compared to the semiclassical consideration. Besides, high-energy deformations of Friedmann Equations created on the basis of these corrections have been derived for different patterns. Conclusion contains the general remarks concerning the above-mentioned {\bf qgcs} for cosmological parameters and perturbations due to inflation; the steps for their investigation are outlined and the key problems of such a study are formulated.

The design is described of a readily useable technology for routine paddock-scale soil porosity estimation. The method is non-contact (proximal) and typically from sensors mounted on a small farm vehicle around 1 m above the soil surface. Challenges arise from the need to have a compact low-power rigid structure and to allow for pasture cover and surface roughness. The high-frequency regime for acoustic reflections from a porous material is a function of the porosity , the tortuosity , and the angle of incidence . There is no dependence on frequency, so measurements must be conducted at two or more angles of incidence in order to obtain two or more equations in the unknown soil properties and . A system requiring a single transmitter/receiver pair to be moved from one angle to another is not viable for rapid sampling. Therefore, the design includes at least two transmitter/reflector pairs placed at identical distances from the ground so that they would respond identically to power reflected from a perfectly reflecting surface. A single 25 kHz frequency is a compromise which allows for the frequency-dependent signal loss from a natural rough agricultural soil surface. Multiple-transmitter and multiple-microphone arrays are described which give a good signal to noise ratio while maintaining a compact system design. The resulting arrays have a diameter of 100 mm. Pulsed ultrasound is used so that the reflected sound can be separated from sound travelling directly through the air horizontally from transmitter to receiver. The average porosity estimated for soil samples in the laboratory and in the field is found to be within around 0.04 of the porosity measured independently. This level of variation is consistent with uncertainties in setting the angle of incidence, although assumptions made in modelling the interaction of ultrasound with the rough surface no doubt also contribute.

The Physics-Informed Neural Networks (PINNs) improve the efficiency of data utilization by combining physical principles with neural network algorithms and ensure that the predictions are consistent and stable with the physical laws. PINNs opens up a new approach to address inverse problems in fluid mechanics. Based on the single-relaxation-time lattice Boltzmann method (SRT-LBM) with the Bhatnagar-Gross-Krook (BGK) collision operator, the PINN-SRT-LBM model is proposed in this paper for solving the inverse problem in fluid mechanics. The PINN-SRT-LBM model consists of three components. The first component involves a deep neural network that predicts the equilibrium control equations in different discrete velocity directions within SRT-LBM. The second component employs another deep neural network to predict non-equilibrium control equations, enabling inference of the fluid's non-equilibrium characteristics. The third component, a physics informed function translates the outputs of the first two networks into physical infor-mation. By minimizing the residuals of the physical partial differential equations (PDEs), the physics informed function infers relevant macroscopic quantities of the flow. The model evolves two sub-models applicable to different dimensions, named PINN-SRT-LBM-I and PINN-SRT-LBM-II models according to the construction of the physical informed function. The innovation of this work is the introduction of SRT-LBM and discrete velocity models as physical drivers into the neural network through the interpretation function. Therefore, PINN-SRT-LBM allows the neural network to handle inverse problems of various dimensions and focus on problem-specific solving. Experimental results confirm the accurate prediction of flow infor-mation at different Reynolds numbers within the computational domain. Relying on the PINN-SRT-LBM models, inverse problems in fluid mechanics can be solved efficiently.

We introduce a new generalized inverse which is a natural generalization of (pseudo) right core inverse in a Banach *-algebra. We characterize this generalized inverse by using right core and quasi-nilpotent decomposition. We then present its polar-like characterization and investigate related algebraic properties. Finally, the right core-EP inverse is characterized by certain new ways.

Here, using the theory of electrical-quantum modelling of the neutron and proton as a three-phase alternating current electrical generator, we will analyse the proton decay and the inverse neutron decay and determine the following subatomic particles, Dproton, Protoniu, Dneutron and Neutroniumd.

In this paper, we introduce the notion of the generalized $w$-group inverse in a Banach algebra with proper involution. This is a natural generalization of generalized (weak) group inverse in Banach *-algebras. Many elementary properties of such new weighted generalized inverse are presented. We further establish the equivalent relations between generalized weighted group inverse and weighted generalized Drazin inverse. Related properties of generalized (weak) group inverse are extended to the wider cases.

We introduce a full binary directed tree structure to represent the set of natural numbers, further categorizing them into three distinct subsets: pure odd numbers, pure even numbers, and mixed numbers. We adopt a binary string representation for natural numbers and elaborate on the composite methodology encompassing odd- and even-number functions. Our analysis focuses on examining the iteration sequence (or composition) of the Collatz function and its reduced variant, which serves as an analog to the inverse function, to scrutinize the validity of the Collatz conjecture. To substantiate this conjecture, we incorporate binary strings into an algebraic formula that captures the essence of the Collatz sequence. By this means, we transform discrete powers of 2 into continuous counterparts, ultimately culminating in the smallest natural number, 1. Consequently, the sequence generated through infinite iterations of the Collatz function emerges as an eventually periodic sequence, thereby validating an enduring 87-year-old conjecture.

In this paper, we provide alternative proofs to some results proposed in the article "New inequalities involving circular, inverse circular, hyperbolic, inverse hyperbolic and exponential functions" authored by Yogesh J. Bagul.

The spread of multidrug-resistant mycobacterium strains requires the development of new approaches to combat diseases caused by these pathogens. For that, photodynamic inactivation (PDI) is a promising approach. In this study, a tricarbocyanine (TCC) is used for the first time as a near-infrared (740 nm) activatable PDI photosensitizer to kill mycobacteria with deeper light penetration. For better targeting, a novel tricarbocyanine dye functionalized with two trehalose units (TCC2Tre) is developed. The photodynamic effect of the conjugate against mycobacteria, including Mycobacterium tuberculosis, is evaluated. Under irradiation, TCC2Tre causes more effective killing of mycobacteria compared to the photosensitizer without trehalose conjugation, with 99.99% dead vegetative cells of M. tuberculosis and M. smegmatis. In addition, effective photoinactivation of dormant forms of M. smegmatis is observed after incubation with TCC2Tre. Mycobacteria treated with TCC2Tre are more sensitive to 740 nm light than the Gram-positive Micrococcus luteus and the Gram-negative Escherichia coli. For the first time, this study demonstrates the possibility of in vitro PDI of mycobacteria including the fast-growing M. smegmatis and the slow-growing M. tuberculosis using near-infrared activatable photosensitizers. These findings are useful for the development of new efficient alternatives to antibiotic therapy.

The inverse system identification toolbox named INVSID 1.0 for MATLAB, which is used to identify the inversion of single-input single-output systems, is developed. The complete process of developing the toolbox is demonstrated. Afterwards, a numerical example is illustrated to describe how the toolbox can be used to solve inverse identification problems. Simulation results demonstrate the effectiveness of the toolbox.

The poly lactic acid (PLA) is one of the most used bio-derived thermoplastic polymers in 3D and 4D printing applications. The determination of PLA surface properties is of capital importance in 3D/4D printing technology. (1) Background: The surface thermodynamic properties of PLA polymer were determined by using the inverse gas chromatography (IGC) technique at infinite dilution. The determination of the retention volume of polar and non-polar molecules adsorbed on the PLA particles filling the column allowed us to obtain the dispersive, polar, and Lewis’s acid-base surface properties at different temperatures from 40 °C to 100 °C. (2) Methods: the applied surface method was based on our recent model that used the London dispersion equation and the new chromatographic parameter function of the deformation polarizability and the harmonic mean of the ionization energies of the PLA polymer and organic molecules. (3) Results: The application of this new method led to the determination of the dispersive and polar free surface energy of adsorption of molecules on the polymeric material, as well as the glass transition and the Lewis acid-base constants. (4) Conclusions: four interval temperatures were distinguished showing four zones of the variations of the surface properties of PLA as a function of the temperature before and after the glass transition.

In this note we generalize the regularity concept for semigroups in two ways simultaneously: to higher regularity and to higher arity. We show that the one-relational and multi-relational formulations of higher regularity do not coincide, and each element has several inverses. The higher idempotents are introduced, and their commutation leads to unique inverses in the multi-relational formulation, and then further to the higher inverse semigroups. For polyadic semigroups we introduce several types of higher regularity which satisfy the arity invariance principle as introduced: the expressions should not depend of the numerical arity values, which allows us to provide natural and correct binary limits. In the first definition no idempotents can be defined, analogously to the binary semigroups, and therefore the uniqueness of inverses can be governed by shifts. In the second definition called sandwich higher regularity, we are able to introduce the higher polyadic idempotents, but their commutation does not provide uniqueness of inverses, because of the middle terms in the higher polyadic regularity conditions.

This study investigated the content of macro- and microelements in the lower reaches of the Ob River (Western Siberia). Seasonal sampling was performed over a four-year period (2020-2023) during the main hydrological seasons (winter low water, spring-summer floods, and early fall low water) at three river stations. Suspended and dissolved fractions were separated by filtration through 0.45-micron nitrocellulose filters. The results revealed significant seasonal variations in the elemental content of the Ob River water, associated with changes in catchment inputs, physical and chemical conditions of the aquatic environment, and the amount and composition of incoming suspended sediment. During high water flow events in the Ob River, the concentration of suspended solids increased substantially. These included significant quantities of sparingly soluble hydrolysates such as Al, Ti, Zr, Ga. During the winter period when the Ob River was ice-covered, a 2- to 3-fold rise was observed in the concentration of Na, Mg, Ca, K, Si, Mn. Having accounted for these seasonal variations in water chemistry, we were able to refine our estimates of elemental export to the Arctic Ocean. Compared to estimates from previous studies, we observed with 2.3-fold higher dissolved loads of Mn, 2.1-fold for Zn, 1.6-fold for Fe, and 1.4-fold for Pb.

This article introduces the Theory of Inverse Discrete Dynamical Systems (TIDDS), a novel methodology for modeling and analyzing discrete dynamical systems via inverse algebraic models. Key concepts such as inverse modeling, structural analysis of inverse algebraic trees, and the establishment of topological equivalences for property transfer between a system and its inverse are elucidated. Central theorems on homeomorphic invariance and topological transport validate the transfer of cardinal attributes between dynamic representations, offering a fresh perspective on complex system analysis. A significant application presented is an alternative proof of the Collatz Conjecture, achieved by constructing an associated inverse model and leveraging analytical property transfers within the inverted tree structure. This work not only demonstrates the theory's capability to address and solve open problems in discrete dynamics but also suggests vast implications for expanding our understanding of such systems.

This paper addresses the Collatz Conjecture, an open question in mathematics that postulates all positive integers will eventually reach one when a pair of specific operations are repeatedly applied. Despite its apparent simplicity, the conjecture lacks a formal proof. To tackle this enigma, we introduce Algebraic Inverse Trees (AITs), data structures that trace inverse operations of the Collatz sequence. This new approach not only elaborates our unique methodology but also sheds light on the underlying complexities of the Collatz Conjecture.

In this paper, we consider a class of bilevel programming problems (abbreviated as, BLPP). Exploiting the generalized approximate convexity assumptions, we investigate the relations among the solutions of approximate Minty (respectively, Stampacchia) type variational inequalities (abbreviated as, AMTVI (respectively, ASTVI)), and the local ϵ-quasi solutions of the BLPP. Moreover, by employing the generalized Knaster–Kuratowski–Mazurkiewicz (abbreviated as, KKM)-Fan’s lemma, we derive some existence results for the solutions of approximate variational inequalities (abbreviated as, AVI), namely, AMTVI and ASTVI. A non-trivial example is given to highlight the importance of the established results. To the best of our knowledge, there is no research paper available in the literature that establishes relationships between the AVI and the BLPP under the assumptions of generalized approximate convexity in terms of limiting subdifferentials.

In this paper, we prove the existence of three solutions for a $p$-Kirchhoff problem with $\psi$-Hilfer fractional derivative. To be more precise, we use the variational method and we prove that the associated functional energy admits a critical point in each of the three constructed sets, these critical points are weak solutions for a studied problem. Moreover, by definition of these sets, one of these solutions is positive, the second is negative, and the third one change sign. At the end of this work, we present an example to validate our main results.

The surface thermodynamic properties of polymers and copolymers modified by supramolecular structures are very used in several industrial processes, such as selective adsorption, paints, coatings, colloids, and adhesion applications. Background: Inverse gas chromatography at infinite dilution was proved to be the best technique to determine the surface properties of solid surfaces by studying the adsorption of some model polar and non-polar organic molecules adsorbed on solid surfaces by varying the temperature. Methods: The retention volume of adsorbed solvents is a precious parameter to obtain the London dispersive and polar free energies and the London dispersive surface energy of styrene–divinylbenzene copolymer modified by supramolecular structure of melamine using the Hamieh thermal model and our new methodology consisting in the separation of the two polar and dispersive free energy of interaction. This led to the determination of the polar acid and base surface energy, and the Lewis’s acid-base constants of the various solid materials. Results: all surface energetic properties of styrene–divinylbenzene copolymer modified by melamine at different percentages were determined as a function of temperature, following our new methodology. Conclusions: It was observed that the styrene–divinylbenzene copolymer exhibited the highest London dispersive surface energy which decreased when the melamine percentage increased. All materials presented higher Lewis’s basicity and this Lewis’s basicity increased with the percentage of melamine.

There is no method of predicting the price of an option other than hedging strategies such as the binomial hedging strategy, the Black-Scholes hedging strategy and others. The price of an option is defined to be equal to the amount the seller needs to implement a hedging strategy. We will study these two basic hedging strategies in terms of their feasibility and we will see that the Black-Scholes hedging strategy is not feasible because this strategy demands instantaneously rebuilding the replicating portfolio. Consequently, the real world prices of the options are not relevant at all with the Black-Scholes hedging strategy! We will suitably redefine the binomial hedging strategy so that it will be practically useful and present other feasible and generally more effective hedging strategies with some of them are practically useful for options with no tradable underlying assets. Finally, we will mention some open questions related to the above.

This article presents a new methodology for modeling and analyzing discrete dynamic systems through the construction of inverse algebraic models, giving rise to the Theory of Inverse Discrete Dynamical Systems. Key concepts such as inverse modeling, structural analysis in inverse algebraic trees, and the establishment of topological equivalences for the analytical transfer of properties between the canonical system and its inverted counterpart are introduced. Central theorems on homeomorphic invariance and topological transport are demonstrated, consolidating the validity of transferring cardinal attributes between both equivalent dynamic representations. As a concrete application, an alternative proof of the historic Collatz Conjecture is presented through the rigorous construction of the associated inverse model and the analytical transfer of properties exhibited in the inverted tree structure. Furthermore, evidence has been presented on Conway's Conjecture in the Game of Life through meticulous inverse modeling in bounded cases, overcoming initial limitations of the theory in combinatorial explosions. Although a complete proof of the conjecture is not achieved, this positively expands the modeling and analysis capacity of the methodology. The impact of properly applying this novel theory to expand understanding and solve open problems in discrete dynamical systems seems vast and profound.

The study is aimed at developing a resilient benchmarking system for small hotels in Ukraine, designed to ensure their survival and growth amid global disruptions and local crises. Given the severe challenges associated with the COVID-19 pandemic and military actions, the resilience of the regional tourism business becomes particularly relevant. The methods used in the study, including factor and cluster analysis, the method of taxonomy, and dendrograms, have enabled the development of development programs for two clusters of hotels: those located in areas with increased military risk and those in relatively safe territories. The taxonomic analysis revealed significant differences in managerial practices and operational efficiency, largely determined by the geographic location of the hotels. Hotels in active combat zones experience a 40% reduction in tourist flow and financial instability, while hotels in safe areas demonstrate a 30% higher level of customer satisfaction. The application of advanced security systems and modern marketing techniques has led to a 40% reduction in incidents.

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

Let BT4l denotes a subclass of bounded turning functions connected with a four-leaf-type domain. The goal of the study is to probe into coefficients of |b6|, |b7|, |b8|, the bounds of the logarithmic coefficients and the third-order determinants of H3,1, H3,2, H3,3 for functions in this class.

Background: Medication errors often stem from incomplete or incorrect transfer of drug information during reviews. To enhance medication review processes, a collaborative healthcare team, including a pharmacist in the surgical ward, is essential. This study aimed to identify the frequency and the kind of intentional and unintentional discrepancies related to the prescriptions within two surgical wards. Methods: A prospective observational study was conducted among adult patients admitted to two surgical settings. The patients were selected between May 30th and November 30th, 2021. The study followed a three-phase structure: patient selection, medication review to identify discrepancies and DRPs, and the detection of any such issues in the discharge letter. Results: The most frequent types of discrepancies collected were substitutions (60.9%) for the Endocrinological Surgery and omissions for General Surgery (53.4%). In Endocrinological Surgery, 45.9% of cases exhibited class C drug interactions. In General Surgery, class D drug interactions were predominantly observed (54.1%), with 93.3% attributed to drugs carrying an elevated risk of prolonging the QT interval. Conclusions: Frequently, the medication review process is superficially carried out, introducing errors that may adversely affect patient health. This study explores the potential positive impact of involving the clinical pharmacist in identifying and managing discrepancies and DRPs.

The thermal surface properties of graphenes and carbon materials are of crucial importance in chemistry of materials, chemical engineering, and many industrial processes. (1) Background: The determination of these surface properties is carried out using inverse gas chromatography at infinite dilution which leads to the retention volume of organic solvents adsorbed on solid surfaces. This experimental and fundamental parameter actually reflects the surface thermodynamic interactions between injected probes and solid substrates. (2) Methods: The London dispersion equation and the Hamieh thermal model were used to quantify the London dispersive and polar surface energy of graphenes and carbon fibers as well their Lewis acid-base constants by introducing the coupling amphoteric constant of materials. (3) Results: The London dispersive and polar acid-base surface energies, the free energy of adsorption, the polar enthalpy and entropy, and the Lewis acid-base constants of graphenes and carbon materials were determined. (4) Conclusions: it was showed that graphene exhibited the highest values of London dispersive surface energy, polar surface energy, and Lewis’s acid-base constants. These highest characteristics of graphene justify its great potentiality and uses in many industrial applications.

This research aimed to study and compare prevalence of work-related musculoskeletal (MSK) problem and perceived discomfort among port crane operators consisting of quay and rubber tyred gantry (RTG) cranes working in the Laem Chabang Deep-sea Port, Thailand. Direct interviews for a cross-sectional analysis was selected for this unique job with a small amount of sample size. A standardized modified version of the Nordic Musculoskeletal Questionnaire (NMQ) was lunched for 26 male participants, consisting of 11 quay and 15 RTG, where a priori power analysis using G*Power suggested the minimum as 24 for the hypothesis test. Socio-demographic characteristics showed no significant difference among them. The NMQ revealed lower back as the highest prevalent body part (76.9%) followed by neck, shoulders and upper back at the same percentage (23.1%) for all port crane group, with up to 96.2% reporting MSK problem over the past 12 months. And Borg scale rating, assessing the perceived discomfort, revealed lower back with the highest score (2.38) followed by upper back (0.65), shoulders (0.54) and neck (0.5). Remarkably, the comparation between the quey and RTG crane operators on the MSK prevalence (χ2 test) and perceived discomfort levels (Kruskal-Wallis test), regarding different 9 body parts, revealed a significant difference between the two on upper back with quay crane having more problem than the RTG at p < 0.05. while the rest of them were mainly in the same direction. MSK contributors such as: working awkward posture, mental stress, height, trolley distance and vibration are considerably the main causes of this. However, the larger in scale of exposure to those for the quey crane compared to the RTG might have led to the distinguishing MSK problem found in this study which require thoroughly deliberation for the particular solutions.

The temperature effect plays an important role for an accurate determination of the polar free energy, polar thermodynamic parameters, and enthalpic and entropic Lewis’s acid base properties of oxides, polymers and composites polymers/oxides. The adsorption of polymers on other solid surfaces is crucial in many industrial applications, coatings, paints, catalysis, colloids and adhesion processes. (1) Background: Inverse gas chromatography at infinite was used for the physico-chemical characterization of solid substrates by determining the thermodynamic parameters of adsorption of model organic solvents on solid surfaces such as polymers, metallic oxides, carbon fibers, pharmaceutical or 3D/4D printing substrates. (2) Methods: A new method based on the London dispersion equation was applied with a new parameter associating the deformation po-larizability and the harmonic mean of the ionization energies of solvent. More accurate values of the dispersive and polar interaction energies of the various organic solvents adsorbed on PMMA in bulk phase and PMMA/silica at different recovery fractions were obtained as well as the Lewis acid-base parameters and the transition temperatures of the different composites. (3) Results: This new methodology gave very interesting results on the behavior of PMMA adsorbed on silica. An important effect of the temperature and the recovery fraction on the various physicochemical and thermodynamic properties was highlighted. The variations of all interaction parameters showed the presence of three transition temperatures for the different PMMA adsorbed on silica with the various coverage rates with a shift of these temperatures for a recovery fraction of 31% (4) Con-clusions: An important variation of the polar enthalpy and entropy of adsorption, the Lewis ac-id-base parameters and the intermolecular separation distance as a function of the temperature and the recovery fraction of PMMA on silica.

Using geometric considerations, we provide a clear derivation of the integral representation for the error function, known as the Craig formula. We calculate the corresponding power series expansion and prove the convergence. The same geometric means finally help to systematically derive handy formulas that approximate the inverse error function. Our approach can be used for applications in e.g. high-speed Monte Carlo simulations where this function is used extensively.

Seismic velocity inversion is one of the most critical issues in the field of seismic exploration and has long been the focus of numerous experts and scholars. In recent years, the advancement of machine learning technologies has infused new vitality into the research of seismic velocity inversion and yielded a wealth of research outcomes. Typically, seismic velocity inversion based on machine learning lacks control over physical processes and interpretability. Starting from wave theory and the physical processes of seismic data acquisition, this paper proposes a method for seismic velocity model inversion based on Physical Embedding Recurrent Neural Networks. Firstly, the wave equation is a mathematical representation of the physical process of acoustic waves propagating through a medium, and the finite difference method is an effective approach to solving the wave equation. With this in mind, we introduce the architecture of recurrent neural networks to describe the finite difference solution of the wave equation, realizing the embedding of physical processes into machine learning. Secondly, in seismic data acquisition, the propagation of acoustic waves from multiple sources through the medium represents a high-dimensional causal time series (wavefield snapshots), where the influential variable is the velocity model, and the received signals are the observations of the wavefield. This forms a forward modeling process as the forward simulation of the wavefield equation, and the use of error back-propagation between observations and calculations as the velocity inversion process. Through time-lapse inversion and incorporating the causal information of wavefield propagation, the non-uniqueness issue in velocity inversion is mitigated. Through mathematical derivations and theoretical model analyses, the effectiveness and rationality of the method are demonstrated. In conjunction with simulation results for complex models, the method proposed in this paper can achieve velocity inversion in complex geological structures.

In this paper, we introduce the notion of weighted generalized group inverse in a Banach algebra with proper involution. This is a natural generalization of weighted weak group inverse for a complex matrix and Hilbert space operator. We present several characterizations and representations of this generalized inverse. In addition, a new partial order on elements in a Banach *-algebra is investigated by using the weighted generalized group inverse and some known results are thus generalized.

Schrödinger dynamics is a nonlocal process. Not only does local perturbation affect instantaneously the entire space, but the effect decays slowly. When the wavefunction is spectrally bounded, the Schrödinger equation can be written as a universal set of ordinary differential equations, with universal coupling between them, which is related to Euler’s formula. Since every variable represents a different local value of the wave equation, the coupling represents the dynamics’ nonlocality. It is shown that the nonlocal coefficient is inversely proportional to the distance between the centers of these local areas. As far as we know, this is the first time that this inverse square law was formulated.

Inverse gas chromatography at infinite dilution was used to determine the surface thermodynamic properties of silica particles and PMMA adsorbed on silica, and more particularly, to quantify the London dispersive energy γsd, the Lewis acid γs+ and base γs- polar surface energies of PMMA/silica composites as a function of the temperature and the recovery fraction θ of PMMA. The polar acid–base surface energy γsAB and the total surface energy of the different composites were then deduced as a function of the temperature. In this paper, the Hamieh thermal model was used to quantify the surface thermodynamic energy of poly methyl methacrylate (PMMA) adsorbed on silica particles at different recovery fractions. A comparison of the new results was carried out with those obtained by applying other molecular models of the surface areas of organic molecules adsorbed on the different solid substrates. An important deviation of these molecular models from the thermal model was proved. The determination of γsd, γs+, γs-, and γsAB of PMMA in both bulk and adsorbed phases showed an important non-linearity variation of these surface parameters as a function of the temperature. The presence of maxima in the curves of γsd(T) highlighted the second order transition temperatures in PMMA showing beta-relaxation, glass transition and liquid-liquid temperatures. These three transition temperatures depended on the adsorption rate of PMMA on silica. The proposed method gave a new relation between the recovery fraction of PMMA and its London dispersive energy showing an important effect of the temperature on the surface energy parameters of adsorption of PMMA on silica. A universal equation relating γsd(T,θ) of the systems PMMA/silica to the recovery fraction and the temperature was proposed.

This research was devoted to study the effect of the tacticity of PMMA on the surface physicochemical properties such as the London dispersive component of the surface energy of the polymer, the polar and dispersive free energies, the Lewis’s acid-base surface parameters, and the Lewis acid and base surface energies of atactic, isotactic and syndiotactic PMMAs as a function of the temperature. Inverse gas chromatography (IGC) at infinite dilution was used to determine the surface thermodynamic properties of PMMA at different tacticities. The experimental values of the net retention time and volume of model organic molecules adsorbed on the atactic, isotactic and syndiotactic PMMAs, were obtained by IGC technique at infinite dilution. The London dispersion interaction energy was used for the separation of the polar and dispersive energy of the different n-alkanes and polar solvents adsorbed on the different PMMA surfaces. The London dispersive surface energy γ_s^d (T) of PMMAs was determined by applying the Hamieh thermal model that took into account the effect of the temperature on the surface area of adsorbed molecules. The obtained results showed non-linear variations of γ_s^d (T) of atactic, isotactic, and syndiotactic PMMAs with three maxima characterizing the three transition temperatures of PMMAs, T_g, T_g and T_(liq-liq) with a shift of these temperatures with the isotactic and syndiotactic PMMAs. It was showed that the polar interaction free energy ∆G_a^p (T) also showed the presence of the three above transition temperatures of the polymers. The study showed an important non-linearity variation of the different acid-base parameters in the various polymer surfaces proving the highest effect of the temperature and the tacticity on these parameters. The determination of the enthalpic and entropic Lewis’s acid-base parameters showed an amphoteric character for the different PMMAs. The basicity of the atactic PMMA was about four times larger than its acidity outside the neighborhood of the transition temperatures of the polymer. Whereas, the isotactic PMMA was about 2 to 2.5 times more basic than acid, while the syndiotactic PMMA exhibited the highest basicity by showing a surface 5 to 8 times more basic than acid. However, the Lewis acid-base parameters strongly depended on the temperature and sudden variations of these surface parameters were observed around the transition temperatures largely depending on the PMMA tacticity. The determination of the polar acid and base surface energies of the three PMMAs led to the same previous transition temperatures with an important effect of the tacticity of PMMA on the values of these acid-base surface energies.

The correct selection of the value of p is a complex and iterative procedure that requires experience in the interpretation of the obtained interpolated maps. Inverse Distance Weighting is a method applied to the porosities of the K and L hydrocarbon reservoirs, discovered in the Neogene (Lower Pontian) subsurface sandstones in the northern Croatia (Pannonian Basin System). They represent small and large data samples. Also a standard statistical analysis of the data was made, followed by the qualitative-quantitative analysis of the maps, based on the selection of different values of the power distance exponent (p-value) for the K and L reservoirs maps. According to the qualitative analysis, for a small data set, p-value could be set on 1 or 2 giving the most optimal result, while for a large data set, a p value of 3 and 4 could be applied. By quantitative analysis, for the case of small data p=2 is recommended, resulting in a root mean square error value of 0.03458, a mean absolute error of 0.02013 and a median absolute deviation of 0.00546. Oppositely, a p-value of 3 and 4 is selected as appropriate for a large data set, with root mean square errors of 0.02435 and 0.02437, mean square errors of 0.01582 and 0.01509 and median absolute deviations 0.00896 and 0.00444. Eventually for a small data set, it is recommended to use a p-value of 2, and for a large data set, a p-value of 3 or 4.

Aortic valve stenosis and malignancy frequently coexist and share the same risk factors as atherosclerotic disease. Data reporting the prognosis of patients with severe aortic stenosis and cancer are limited. Tailoring the correct and most optimal care for cancer patients with severe aortic stenosis is complex. Cancer patients may be further disadvantaged by aortic stenosis if it interferes with their treatment by increasing the risk associated with oncologic surgery and compounding the risks associated with cardiotoxicity and heart failure (HF). Nowadays, several therapeutic options are available for aortic valve stenosis (surgical valve replacement , transcatheter valve implantation, balloon valvuloplasty, or medical therapy), but in presence of malignancy, the use of one of these versus the others should be decided on a case-by-case approach, depending on cancer stage and associated treatment, expected outcome, comorbidities and after an accurate physical examination and Doppler-echocardiography which are the key tools for diagnosing and evaluating aortic stenosis. The current review considers the available data on the association between aortic stenosis and cancer, and therapeutic options.

The description of the microscopic world in quantum mechanics is very different from that in classical physics, and there are some points of view that are contrary to intuition and logic. The first is the loss of reality, the behavior of micro particles shows randomness and hopping. The second is the loss of certainty, the conjugate physical variables of a system cannot be determined synchronously, they satisfy the Heisenberg uncertainty principle. The third is the non-local correlation. The measurement of one particle in the quantum entanglement pair will change the state of the other entangled particle simultaneously. In this paper, some concepts related to quantum entanglement, such as EPR correlation, quantum entanglement correlation function, Bell's inequality and so on, are analyzed in detail. Analysis shows that the mystery and confusion in quantum theory may be caused by the logical problems in its basic framework. Bell's inequality is only a mathematical theorem, but its physical meaning is actually unclear. The Bell state of quantum entangled pair may not satisfy the dynamic equation of quantum theory, so it cannot describe the true state of microscopic particles. In this paper, the correct correlation functions of spin entanglement pair and photonic entanglement pair are strictly derived according to normal logic. Quantum theory is a more fundamental theory than classical mechanics, and they are not parallel relation in logic. However, there are still some unreasonable contents in the framework of quantum theory, which need to be improved. In order to disclose the real relationship between quantum theory and classical mechanics, we propose some experiments which show the wave-particle duality simultaneously and provide intuitionistic teaching materials for the new interpretation of quantum theory.

Inverse Sumudu transform multiple shifting properties are used to design methodology for solving ordinary differential equations. Then algorithm applied to solve Whittaker and Zettl equations to get their new exact solutions and profiles which shown through Maple complex graphicals. Table of inverse Sumudu transforms for elementary functions given for supporting the differential equations solving using inverse Sumudu transform.

Brachycephalic dogs are not only affected by brachycephalic obstructive airway syndrome (BOAS), but also comprise up to 30% of canine patients seen by veterinary dermatologists, with English Bulldogs and Pugs particularly overrepresented. Some skin diseases are associated with the abnormal anatomic conformation of brachycephalic dogs, while for others there is a underlying genetic basis or a general predisposition. Anatomic alterations associated with brachycephaly, leading to fold formation of the skin and stenosis of the ear canal, together with primary immunodeficiencies described in some breeds, favor the development of pyoderma, Malassezia dermatitis and otitis externa/media. Frequently neglected but often lifelong dermatological problems of brachycephalic dogs are an important consideration when discussing genetic and medical conditions affecting the welfare of those dogs. Here we review the current state of knowledge concerning dermatological problems of brachycephalic dogs, and combine it with clinical experience in the management of these challenging disorders.

In August 4 2011, United Nations Environmental Program (UNEP) submitted an unprecedented, scientific, groundbreaking Environmental Assessment Report (EAR) of Ogoniland, to the Nigerian government. This was the outcome of a 14–month intensive evaluation of the extent of pollution. It was intended that UNEP’s recommendations would be implemented to restore the devastated environment, on the one hand, and on the other, counteract the numerous environmental health issues that have for decades, plagued Ogoniland. However, five years post EAR, and, despite the seriousness of the situation, no significant resolution has occurred, both on the part of the government, and on the part of Shell Petroleum Development Company (SPDC) or Shell. To date, millions of Niger Delta residents, particularly those living in the oil-bearing communities, continue to suffer severe consequences. Although, the assessment was conducted in Ogoniland, other communities in the Niger Delta are also affected. This article explores prevailing issues, using Ogoniland (a microcosm of the Niger Delta) as an example. A multidisciplinary approach for sustainable mitigation of environmental health risks in the Niger Delta is paramount, and Environmental Management tools offer valuable strategies. Adopting UNEP’s recommendations for addressing environmental health problems requires implementing the Environmental Management/Environmental Management System (EM/EMS) model.

In this paper, under the symmetric entropy and the scale squared error loss functions, we consider the maximum likelihood (ML) estimation and Bayesian estimation of the Shannon entropy and Rényi entropy of the two-parameter inverse Weibull distribution. In the ML estimation, the dichotomy is used to solve likelihood equation. In addition, the approximation confidence interval is given by the Delta method. Because the form of estimation results is more complex in the Bayesian estimation, the Lindley approximation method is used to achieve the numerical calculation. Finally, Monte Carlo simulations and a real data set are used to illustrate the results derived. By comparing the mean square error between the estimated value and the real value, it can be found that the performance of ML estimation of Shannon entropy is better than that of Bayesian estimation, and there is no significant difference between the performance of ML estimation of Rényi entropy and that of Bayesian estimation.

The canonical decomposition of a bipartite graph is a new decompositionmethod that involves three operators, parallel, series and K+S. The class of weak bisblit graphs is totally decomposable with respect of these operators, and the class of bicographs is totally decomposable with respect of parallel and series operators. We prove in this paper that the class of bipartite (P_6,C_6)-free graphs is exactly the class of bipartite graphs that are totally decomposable with respect of parallel and K+S operators. We simplify the recognition algorithm of weak-bisplit graphs to adepte with bipartite (P_6,C_6)-free graphs. As a result of this adapted algorithm, we prsent efficient solutions in this class of graphs for two optimization graph problems, the first is the maximm balanced bi-clique problem and the the second is the maximum independent set problem.

We consider a Cauchy problem for differential equations in a Hilbert space $X$. The problem is stated in a time interval $I$, which can be finite or infinite. Weuse a fixed point argument for history-dependent operators to prove the unique solvability of the problem. Then, we state and prove convergence criteria for both a general fixed point problem and thecorresponding Cauchy problem. These criteria provide necessary and sufficient conditions on a sequence $\{u_n\}$ which guarantee its convergence to the solution of the corresponding problem, in the space of both continuous and continuously differentiable functions. We then specify our results in the study of a particular differential equation governed by two nonlinear operators. Finally, we provide an application in viscoelasticity and give a mechanical interpretation of the corresponding convergence result.

This article is some review of results that were obtained at 2007-2021 years development of “The Information as Absolute” concept and the informational physical model, which is based on the concept; including a number of fundamental physical problems are briefly considered in framework of the conception and the model. Recently in physics there are several publications, that present lists of the problems. However, those lists are essentially incomplete, for at least two reasons. Firsts of all, a number of phenomena are studied traditionally by philosophy, and so corresponding problems are usually considered to be “metaphysical”. However, they relate also to some concrete physical phenomena. For example, physics evidently studies Matter, and so the metaphysical problems “what is ontology of Matter”, “what is “Space”, “Time” and a few other physical phenomena and notions as well, are really a Meta-physical problems “what does physics study?” There are other fundamental physical problems, which are not considered as such in physics, and are absent in the “fundamental problems lists”. Those include the problems, which really exist, yet are incorporated into standard physical theories, and so are fundamental “implicitly”, which in physics are “solved by default” ‒ and mostly erroneously. Note, though, that a number of “Meta-physical”, and concrete fundamental, problems more in detail are considered in the paper “The Informational Conception and Basic Physics”, https://arxiv.org/abs/0707.4657, v5 (2021), so this paper is, in certain sense, an expanded conclusion of this paper, which includes, correspondingly, more in detail consideration of some more general physical problems; and, besides, in this article, the problem “what are Gravity and Electric Forces” is essentially clarified comparing with the arXiv 2021 paper version above. Besides, the concrete problem “What is Life”, and the rational cosmological model, where a few vague points in standard cosmology rather probably are rationally clarified, while the fundamental problem “matter – antimatter asymmetry” in Matter is solved practically for sure, are considered, and one of recently published rather complete “lists of fundamental problems” is commented in Appendix.

In this paper, our goal is to establish the relationship between solutions of local sharp vector variational type inequality and sharp efficient solutions of vector optimization problems, also Minty local sharp vector variational type inequality and sharp efficient solutions of vector optimization problems, under generalized approximate $\eta$-convexity conditions for locally Lipschitzian functions

The main foundations of the standard $\Lambda $CDM model of cosmology are that: 1) The redshifts of the galaxies are due to the expansion of the Universe plus peculiar motions; 2) The cosmic microwave background radiation and its anisotropies derive from the high energy primordial Universe when matter and radiation became decoupled; 3) The abundance pattern of the light elements is explained in terms of primordial nucleosynthesis; and 4) The formation and evolution of galaxies can be explained only in terms of gravitation within a inflation+dark matter+dark energy scenario. Numerous tests have been carried out on these ideas and, although the standard model works pretty well in fitting many observations, there are also many data that present apparent caveats to be understood with it. In this paper, I offer a review of these tests and problems, as well as some examples of alternative models.

This study addresses the formidable challenge of solving large-scale Max-Cut problems (MCP). We introduce a rapid computational procedure utilizing a hybrid 1-flip/r-flip local search heuristic. This innovative strategy significantly reduces the computational time required for MCP problems while consistently generating solutions of exceptional quality. The paper presents substantial computational insights, showcasing the effectiveness of our approach on large-scale Max-Cut instances with varying densities. Our proposed heuristic is rigorously evaluated by comparing its performance against a quantum annealing solver, leveraging a multi-start Tabu Search framework. The results underscore the potency of this unique combination as an efficient and effective solution for large-scale QUBO problems. Notably, our hybrid heuristic consistently delivers high-quality solutions within the stringent CPU time limits of 600 seconds, demonstrating its efficacy across Max-Cut instances ranging from 10,000 to 40,000 variables. This research contributes to advancing the state-of-the-art in large-scale QUBO problem-solving, offering a powerful and time-efficient approach with broad applicability.

Cobalt oxide nanoparticles are promising candidates for various kinds of practical application. The interest of researchers and scientists in these nanoparticles is due to their unique physical and chemical properties such as density, melting point, magnetic characteristics and band gap. The need to develop high performance and low-cost technologies for obtaining Co3O4 nanoparticles is a major reason in research in this area. The widespread commercial use of Co3O4 nanoparticles will open doors for numerous novel cost effective technologies, which are mostly dependent on the type of synthesis method and raw materials. A possible solution to this problem is to use Cobalt nitrates as a cost-effective precursor using the solution combustion method. Solution combustion synthesis method is one of the best candidate methods that have the potential for high performance and is also cost-effective. This review examines recent progress in the synthesis of Co3O4 nanoparticles by solution combustion method and underscores their significance across various fields, highlighting the need for further research and exploration of their multifaceted applications. In addition, the most recent advancements in the applications of Co3O4 nanoparticles in sensitive sensing, catalysis, supercapacitors/batteries and water treatment/pollutants removal are reviewed and described, focusing on current trends, challenges, and future outlooks.

Inverse optimal control is a method for recovering the cost function used in an optimal control problem in expert demonstrations. Most studies on inverse optimal control have focused on building the unknown cost function through the linear combination of given features with unknown cost weights, which are generally considered to be constant. However, in many real-world applications, the cost weights may vary over time. In this study, we propose an adaptive online inverse optimal control approach based on a neural-network approximation to address the challenge of recovering time-varying cost weights. We conduct a well-posedness analysis of the problem and suggest a condition for the adaptive goal, under which the weights of the neural network generated to achieve this adaptive goal are unique to the corresponding inverse optimal control problem. Furthermore, we propose an updating law for the weights of the neural network to ensure the stability of the convergence of the solutions. Finally, simulation results for an example linear system are presented to demonstrate the effectiveness of the proposed strategy. The proposed method is applicable to a wide range of problems requiring real-time inverse optimal control calculations

As many if not most ligands at G protein-coupled receptor antagonists are inverse agonists, we have systematically reviewed inverse agonism at the nine adrenoceptor subtypes. Except for β3-adrenoceptors, inverse agonism has been reported for each of the adrenoceptor subtypes, most often for β2-adrenoceptors, including endogenously expressed receptors in human tissues. As with other receptors, detection and degree of inverse agonism depends on the cells and tissues under investigation, i.e. is greatest when the model has a high intrinsic tone/constitutive activity for the response being studied. Accordingly, it may differ between parts of a tissue, for instance atria vs. ventricles of the heart, and within a cell type between cellular responses. The basal tone of endogenously expressed receptors often is low, leading to less consistent detection and smaller extent of observed inverse agonism. The extent inverse agonism depends on specific molecular properties of a compound but clusters by chemical class. While inverse agonism is a fascinating facet in attempts to mechanistically understand observed drug effects, we are skeptical whether an a priori definition of extent of inverse agonism in the target product profile of a developmental candidate is a meaningful option in drug discovery and development.

We present an improved algorithm for fast calculation of the inverse square root for single-precision floating-point numbers. The algorithm is much more accurate than the famous fast inverse square root algorithm and has a similar computational cost. The presented modification concern Newton-Raphson corrections and can be applied when the distribution of these corrections is not symmetric (for instance, in our case they are always negative).

In this paper, we prove that the sub convexlikeness introduced by V. Jeyakumar [1], and the subconvexlikeness defined in V. Jeyakumar [2] are equivalent in loccallly convex topological spaces. And then, we work with set-valued vector optimization problems and obtain some vector saddle-point theorems and vector Lagrangian theorems.

The usual equation for both motions of a single planet around the sun and electrons in the deterministic Rutherford-Bohr atomic model is conservative with a singular potential at the origin. When a dissipation is added, new phenomena appear which were investigated thoroughly by R. Ortega and his co-authors between 2014 and 2017, in particular all solutions are bounded and tend to $0$ for $t$ large, some of them with asymptotically spiraling exponentially fast convergence to the center. We provide explicit estimates for the bounds in the general case that we refine under specific restrictions on the initial state, and we give a formal calculation which could be used to determine practically some special asymptotically spiraling orbits. Besides, a related model with exponentially damped central charge or mass gives some explicit exponentially decaying solutions which might help future investigations. An atomic contraction hypothesis related to the asymptotic dying off of solutions proven for the dissipative model might give a solution to some intriguing phenomena observed in paleontology, familiar electrical devices and high scale cosmology

In this paper, we generalize the problems of finding simple polygons with the minimum area, maximum perimeter and maximum number of vertices so that they contain a given set of points and their angles are bounded by $\alpha+\pi$ where $\alpha$ ($0\leq\alpha\leq \pi$) is a parameter. We also consider the maximum angle of each possible simple polygon crossing a given set of points, and derive an upper bound for the minimum of these angles. The correspondence between the problems of finding simple polygons with the minimum area and maximum number of vertices is investigated from a theoretical perspective. We formulate the three generalized problems as nonlinear programming models, and then present a Genetic Algorithm to solve them. Finally, the computed solutions are evaluated on several datasets and the results are compared with those from the optimal approach.

Introduction: Depending on the kind of cancer and neurological disorders, there might be a direct or inverse link between neurodegeneration and cancer. The inverse interaction between cancer and Alzheimer's pathology has garnered substantial interest in medical research. While this inverse interaction between Alzheimer's pathology and cancer presents a fascinating scientific puzzle, there is still limited information and studies on the potential mechanisms involved in this inverse rela-tionship. Aim: This study aims to identify potential genes from Alzheimer's disease that may protect against cancer. Method: The Gene Expression Omnibus (GEO) database provided the Alzheimer's pathology dataset, and GEO 2R was utilized to examine genes that were differentially expressed. The expression of upregulated Alzheimer's pathology genes in breast, colorectal, and prostate cancer was compared using VENN 2.1, colorectal, and prostate cancer was compared using VENN 2.1, and colorectal and prostate cancers were compared using VENN 2.1. Results: Among the upregulated genes associated with Alzheimer's disease, 25 genes are shared among breast cancer, colorectal cancer, and prostate cancer, while 28 of these genes were exclusive to Alzheimer's dis-ease. Gene expression profiling interactive analysis among the shared genes revealed that AQP1, C4A, CSRP2, CRYAB, MACF1, HSPB2, and NEAT1 were downregulated in breast, colorectal, and prostate cancers. These genes have varying roles in cancer progression and suppression. Functional analysis of Alzheimer's disease-related upregulated genes revealed that C1orf56 and HIST1H2AC control cell proliferation, with HIST1H2AC playing a role in necroptosis. Additionally, C1orf56 worked as an oncogenic modifier to support DNMT3B's tumor suppressor properties. Conclusion: Given the role of HIST1H2AC and C1orf56 in the negative control of cell proliferation and their upregulation in Alzheimer's disease, there is a need to further investigate HIST1H2AC and C1orf56 as potential tumor suppressors responsible for Alzheimer's disease and cancer inverse association.

In this paper, at first, a color image of a car is taken. Then the image is transformed into a grayscale image. After that, the motion blurring effect is applied to that image according to the image degradation model described in equation 3. The blurring effect can be controlled by a and b components of the model. Then random noise is added in the image via Matlab programming. Many methods can restore the noisy and motion blurred image; particularly in this paper Inverse filtering as well as Wiener filtering are implemented for the restoration purpose. Consequently, both motion blurred and noisy motion blurred images are restored via Inverse filtering as well as Wiener filtering techniques and the comparison is made among them.

We recently formulated Modular Dvoretzky, Type-Cotype, Khinchin-Kahane and Grothendieck Inequality problems in the Appendix of \textit{[arXiv:2302.03718v1]}. For the sake of wide accessibility we give a separate treatment of them.

Considering the actual world economical trends, one of the most important questions is now and in the future: how to reduce power consumption of electronic systems. Since the invention of computers, the electrical energy consumption step by step increased. Now when not only computers, but electric vehicles, robots, automation, and unmanned aerial vehicles play a very important role of our life, the main problem of system designers is how to reduce energy consumption in these systems. But also the existing already working systems must be revised in order to decrease their electric power consumption. The importance of this subject (energy control) shows that a huge number of research publications and survey papers deal with it. Just focusing on the last one or two years (2021 and 2022) the search hit 221000 titles (103000 hits only in 2022). Analyzing all the research areas is almost impossible, but focusing on some important research subjects, where one of the main topic is “energy saving methods” can give an overview about the subject. The paper focuses on the area of industrial robotic systems, electric vehicles, and embedded systems.

In recent years, several brands have received much negative press coverage when trying to market their food and drink products specifically at women. This is, in part, because the taste preferences/sensitivities of men and women are actually quite similar. In fact, perhaps the one and only area where consumers are willing to accept (or should that be swallow) ingested products explicitly targeted at women or men is in the case of nutritional foods/supplements. Such products are not really sold on the basis of their taste/flavour anyway. Many consumers are also sensitive to the so-called pink tax, when near-identical products cost more when sold to women rather than to men (e.g., as in the case of female razors). As the four recent examples discussed in this review make clear, it can be difficult to roll-out a new food or beverage product, or else extend a pre-existing product line, that is especially for women without coming across as sexist/condescending. As such, marketers need to tread carefully, otherwise they may end-up generating unwanted negative publicity. Ultimately, therefore, adopting an implicit approach to gender-based marketing, should that be the direction that a brand wants to take, will likely have more chance of avoiding negative publicity than the explicit targeting of food/beverage-related products in what is undoubtedly a highly-politicized area.

The direct measurements of the magnetocaloric effect were performed in Heusler Ni44.4Mn36.2Sn14.9Cu4.5 alloy at cryogenic temperatures in magnetic fields up to 10 T. The maximum value of the inverse magnetocaloric effect in a field of 10 T was observed ∆Tad = –2.7 K in the vicinity of the 1st order magnetostructural phase transition at T0 = 117 K. The ab initio and Monte Carlo calculations were performed to discuss the effect Cu doping into Ni-Mn-Sn compound on the ground state structural and magnetic properties. It is shown that with increasing Cu content the martensitic transition temperature decreases and the Curie temperature of austenite slightly increases. In general, calculated transition temperatures, magnetization values are correlated well with the experimental ones.

After proposing a natural metric for the space in which particles spin which implements the principle of maximum frequency, E=hf is generalised and its inverse Fourier transform is calculated. As a necessary result, a metric is found for the space in which quantum particles spin, hence the possibility of explanation of correlation of spacelike-separated particles is opened up.

Giant plasmonic surface enhancement has been observed in gold coated micron sized inverse pyramids entrapping a gold nanoparticle. The amplification of both surface enhanced Raman and photoluminescence signals was found to be dependent on the diameter of trapped gold nanoparticle and around 50-fold enhancement was detected for 250nm diameter sample relatively to the 50nm one. Finite differential time domain simulations, performed to determine the near-field distribution in the structure, showed that when the nanoparticle protrudes into the hotspot zone of the void, coupling of electromagnetic field occurs and the plasmon-related near-field enhancement is concentrated into the close vicinity of the nanoparticle, mainly into the close gaps around the tangential points of the curved sphere and the flat pyramid surface. This results in a more than 15 times increase of the near-field intensity, compared to the empty void.

This paper describes a new approach that can be used to determine the mechanical properties of unknown materials and complex material systems. The approach uses inverse finite element modelling (FEM) accompanied with a designed algorithm to obtain the modulus of elasticity, yield stress and strain hardening material constants of an isotropic hardening material model, as well as the material constants of the Drucker-Prager material model (modulus of elasticity, cap yield stress and angle of friction). The algorithm automatically feeds the input material properties data to finite element software and automatically runs simulations to establish a convergence between the numerical loading-unloading curve and the target data obtained from continuous indentation tests using common indenter geometries. A further module was developed to optimise convergence using an inverse FEM analysis interfaced with a non-linear MATLAB algorithm. A sensitivity analysis determined that the dual Spherical and Berkovich (S&B) approach delivered better results than other dual indentation methods such as Berkovich and Vickers (B&V) and Vickers and Spherical (V&S). It was found that better convergence values can be achieved despite a large variation in the starting parameter values and / or material constitutive model and such behaviour reflects the uniqueness of the dual S&B indentation in predicting complex material systems. The study has shown that a robust optimization method based on a non-linear least-squares curve fitting function (LSQNONLIN) within MATLAB and ABAQUS can be used to accurately predict a unique set of elastic plastic material properties and Drucker-Prager material properties. This is of benefit to the scientific investigation of properties of new materials or obtaining the material properties at different location of a part which might be not be similar due to manufacturing processes (e.g. different heating and cooling rates at different locations).

In laser-assisted milling, higher temperature in shear zone softens the material potentially resulting in a shift of mean residual stress, which significantly affects the damage tolerance and fatigue performance of product. In order to guide the selection of laser and cutting parameters based on the preferred mean residual stress, inverse analysis is conducted by predicting residual stress based on guessed process parameters, which is defined as the forward problem, and applying iterative gradient search to find process parameters for next iteration, which is defined as the inverse problem. An analytical inverse analysis is therefore proposed for the mean residual stress in laser-assisted milling. The forward problem is solved by analytical prediction of mean residual stress after laser-assisted milling. The residual stress profile is predicted through the calculation of thermal stress, by treating laser beam as heat source, and plastic stress by first assuming pure elastic stress in loading process, then obtaining true stress with kinematic hardening followed by the stress relaxation. The variance-based recursive method is applied to solve inverse problem by updating process parameters to match the measured mean residual stress. Three cutting parameters including depth of cut, feed per tooth, and cutting speed, and two laser parameters including laser-tool distance and laser power, are updated with respected to the minimization of resulting residual stress and measurement in each iteration. Experimental measurements are referred on the laser-assisted milling of Ti-6Al-4V grade 5 and ELI. The percentage difference between experiments and predictions is less than 5% for both materials, and the selection is completed within 50 loops.

Ocean surface waves have been utilized as fundamental information in various fields of oceanic research. In this paper, we suggest a simulation and modelling technique for generating an ocean surface wave using an inverse Fast Fourier Transform (iFFT), and we subsequently verify the wave’s accuracy. The conventional method, linear superposition, requires recursive calculation because of the double summation and the time variable; to circumvent this issue, the new algorithm is presented. The Joint North Sea Wave Project (JONSWAP) spectrum is utilized for the ocean surface wave simulation example, and the parameters are the significant wave height (HS) and the zero-crossing wave period (TZ). A coordinate transform for the wavenumber domain was used to apply the inverse FFT algorithm. To verify the accuracy of the simulation result, the relative error between the input condition and the analysis result was calculated. The result for TZ is below 4% relative error, and the maximum relative error for HS is 7%. To avoid the Nyquist frequency for wave-field analysis and simulation, the minimum grid size was calculated by twice applying the maximum wavenumber.

We investigate the use of amplitude amplification on the gate-based model of quantum computing as a means for solving combinatorial optimization problems. This study focuses primarily on QUBO (quadratic unconstrained binary optimization) problems, which are well-suited for qubit superposition states. Specifically, we demonstrate circuit designs which encode QUBOs as ‘cost oracle’ operations UC, which when combined with the standard Grover diffusion operator Us lead to high probabilities of measurement for states corresponding to the optimal and near optimal solutions. In order to achieve these probabilities, a single scalar parameter ps is required, which we show can be found through a variational quantum-classical hybrid approach.

In this paper, we consider the development of algorithms for the solution of first order initial value problems whose solution exhibits exponential behaviour. Method of interpolation and collocation of basis function to give system of nonlinear equations which is solved for the unknown parameters to give a continuous scheme. The discrete methods recovered from the continuous scheme are implemented in block form. The stability properties of the method is verified and numerical experiments show that our method is efficient in handling these problems

The paper deals with the Compound Inverse Rayleigh distribution, shown to constitute a proper model for the characterization of the probability distribution of extreme values of wind-speed, a topic which is gaining growing interest in the field of renewable generation assessment, both in view of wind power production evaluation and the wind-tower mechanical reliability and safety. The first part of the paper illustrates such model starting from its origin as a generalization of the Inverse Rayleigh model - already proven to be a valid model for extreme wind-speeds - by means of a continuous mixture generated by a Gamma distribution on the scale parameter, which gives rise to its name. Moreover, its validity to interpret different field data is illustrated, also by means of numerous numerical examples based upon real wind speed measurements. Then, a novel Bayes approach for the estimation of such extreme wind-speed model is proposed. The method relies upon the assessment of prior information in a practical way, that should be easily available to system engineers. In practice, the method allows to express one’s prior beliefs both in terms of parameters, as customary, and/or in terms of probabilities. The results of a large set of numerical simulations – using typical values of wind-speed parameters - are reported to illustrate the efficiency and the accuracy of the proposed method. The validity of the approach is also verified in terms of its robustness with respect to significant differences compared to the assumed prior information.

Our work uses Iterative Boltzmann Inversion (IBI) to study the coarse-grained interaction between 20 amino acids and the representative carbon nanotube CNT55L3. IBI is a multi-scale simulation method that has attracted the attention of many researchers in recent years. It can effectively modify the coarse-grained model derived from the Potential of Mean Force (PMF). IBI is based on the distribution result obtained by All-Atom molecular dynamics simulation, that is, the target distribution function, the PMF potential energy is extracted, and then the initial potential energy extracted by the PMF is used to perform simulation iterations using IBI. Our research results have gone through more than 100 iterations, and finally, the distribution obtained by coarse-grained molecular simulation (CGMD) can effectively overlap with the results of all-atom molecular dynamics simulation (AAMD). In addition, our work lays the foundation for the study of force fields for the simulation of the coarse-graining of super-large proteins and other important nanoparticles.

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.

It is shown that the 5D geodetic equations and 5D Ricci identities give us a way to create a new viewpoint on some problems of Modern Physics, Astrophysics, and Cosmology. Specifically, the application of the 5D geodetic equations in (4+1) and (3+1+1) splintered forms obtained with the help of the monad and dyad methods made it possible to introduce a new, effective generalized concept of the rest mass of the elementary particle. The latter leads one to novel connections between the General Relativity and quantum field theories, as well as all of that, including the (4+1) splitting of the 5D Ricci identities, brings about a better understanding of the magnetic monopole problem and the vital difference in the origins of the Maxwell equations and gives rise to surprising connections between them. The obtained results also provide new insight into the mechanism of the 4D Universe’s expansion and its following acceleration.

Second-level hospitals face peculiarities that hinder the implementation of the hearing rescreening protocol, which are not uncommon in other settings. This study analyzes the hearing rescreening process in this kind of hospital. A total of 1130 individuals were included. In this cohort, 61.07% were newborns in the hospital who failed their first otoemission test after birth (n=679) or were unable to have the test performed (n=11), then being referred to outpatient clinic. The remaining 38.93% were individuals who were born in another hospital with their first test conducted in the outpatient clinic (n=440). A high amount of rescreenings were made outside the recommended time frame, mainly in children referred from another hospital. There was a high rate of lost to follow-up especially with otolaryngologist referrals. Neonatal Hearing screening in second level hospitals is difficult because of the staffing and time constraints. This results in longer than recommended turnaround times and interferes with the timely detection of hearing loss. This is particularly serious in outpatients. Referral of children with impaired screening to out-of-town centers leads to unacceptable loss of follow-up. A legislative support for all these rescreening issues is necessary. We discuss these findings and propose some solutions.

Since 1974, when Heinz von Foerster made the distinction between “the cybernetics of observed systems” as first-order cybernetics (1oC) and “the cybernetics of observing systems” as second-order cybernetics (2oC), cybernetics has been dominated by this observer-centric paradigm that he claimed cannot be extended meaningfully to a third-order. Rather than attempting to extend his paradigm, we derive an alternative, model-centric paradigm from first principles of regulation that naturally extends to three orders, where the third order is ethical regulation. We thus consider a type of regulator that requires a third model and a third observer: If the third model is a model of acceptable (ethical) situations, then a third observer is a necessary element of the system’s “conscience” that prevents or detects any violations of the model of ethical situations. In this paradigm, the cybernetics of systems that are designed to exhibit ethical behaviour can be characterized as third-order cybernetics (3oC). By being able to extend the paradigm to include ethical systems, the model-centric paradigm brings clarity and utility that is not possible using the philosopher-friendly observer-centric paradigm and its under-specified (abstract) observers. Finally new definitions for cybernetics are proposed that clearly differentiate between the science and the philosophy, of cybernetics.

Parts of shotgun cartridges are a significant source of plastic litter in the marine environment. Empty cartridge shells may not be picked up by the hunter who fired them, and plastic wads that serve to separate the propellant from the shot load, are lost down-range when a shot is fired. Such litter items constitute a cosmetic and aesthetic problem on coastlines and may cause harm to marine animals and in the later stages of decomposition break down into harmful micro plastic particles. There exists no reliable estimate of the global exposure of marine ecosystems to this plastic source. However, in some countries it has been subject to closer examination, including for example, Denmark where the annual contribution of plastic wads into marine systems was estimated to 600,000 pieces (c2 tonnes), and the accumulated density of washed-up items (both wads and shells) was 3.7 items per 100 m coastline. Increasing awareness of this problem has caused scientists, hunters’ communities and governments to suggest altered practice including transition to the use of biodegradable cartridge components, first and foremost wads as this item is invariable lost during hunting. Several manufacturers provide shotgun cartridges containing biodegradable wads based on different types of materials, including fibers and various types of plastics, for example PVAL (poly(vinyl alcohol)) and PHA (polyhydroxyalkanoate). In this paper, we review the most recent literature on the amounts and related environmental hazards of plastic dispersed from hunting ammunition into marine ecosystems. We summarize the market availability of shotgun cartridges with biodegradable wads and discuss chemical, technical, economical and legal aspects of a transition to the use of such products.

To deal with the uncertainties in modeling offshore wind turbines, we proposed a parameter inversion method for the pile-soil interaction model based on structural health monitoring results and the numerical model. The proposed parameter inversion method has a numerical model, an objective function selected using both the numerical and identified results, and an inverse optimization using a random search algorithm in the assumed parameter space. The parameter results in the minimum optimization objective function are identified as the in-situ parameter. The proposed method is confirmed to converge after some iterations, whatever the initial parameter values are. However, different initial parameter cases may converge to slightly different optimal parameters, implying the pile results are sensitive to geological parameters. Moreover, a comparison with the original design results shows design redundancy or risks. Though the proposed method has several flaws, it can shed some light on the influence of uncertainties in offshore wind turbines, such as soil parameters in geological surveys.

An inverse aeroacoustic problem for a helicopter rotor combined with aerodynamic constraint is proposed based on Ffowcs Williams and Hawkings equation in subsonic. The rotor noise includes thickness noise and loading noise when quadrupole noise is neglected. Thickness noise is related to geometry and motion conditions. Loading noise is related to the pressure on the wall. Therefore, the equation between pressure on the wall and far-field noise can be established, thus the pressure on the wall can be obtained by solving this equation. Since this equation is an ill-posed, the singular value decomposition combined with the regulation method is applied and the aerodynamic constraint is taken into account. The direct noise prediction is verify firstly and then the inverse problem is solved. The reconstruction pressure is compared to the input data. The result is in good agreement with the input value. At the same time, the influence of interference noise is also considered. Under low signal-to-noise ratio, the reconstruction result is also reasonable.

The article proposes asymptotic methods for calculating Sommerfeld integrals, which enable us to calculate the integral using the expansion of a function into an infinite power series at the saddle point, where the role of a rapidly oscillating function under the integral can be fulfilled either by an exponential or by its product by the Hankel function. The proposed types of Sommerfeld integrals are generalized on the basis of integral representations of the Hertz radiator fields in the form of the inverse Hankel transform with the subsequent replacement of the Bessel function by the Hankel function. It is shown that the numerical values of the saddle point are complex. During integration, auxiliary or so-called standard integrals, which contain the main features of the integrand function, were used. As a demonstration of the accuracy of the technique, a previously known asymptotic formula for the Hankel functions was obtained in the form of an infinite series. The proposed method for calculating Sommerfeld integrals can be useful in solving the half-space Sommerfeld problem. An example is the expression in the form of an infinite series for the magnetic field of reflected waves, obtained directly through the Sommerfeld integral (SI).

We propose a novel adaptive importance sampling scheme for Bayesian inversion problems where the inference of the variables of interest and the power of the data noise is split. More specifically, we consider a Bayesian analysis for the variables of interest (i.e., the parameters of the model to invert), whereas we employ a maximum likelihood approach for the estimation of the noise power. The whole technique is implemented by means of an iterative procedure, alternating sampling and optimization steps. Moreover, the noise power is also used as a tempered parameter for the posterior distribution of the the variables of interest. Therefore, a sequence of tempered posterior densities is generated, where the tempered parameter is automatically selected according to the actual estimation of the noise power. A complete Bayesian study over the model parameters and the scale parameter can be also performed. Numerical experiments show the benefits of the proposed approach.

In this study, we investigate spectral structure of conformable Sturm-Liouville problems and with this end, we obtain representation of solutions under different initial conditions and asymptotic formulas for eigenfunctions, eigenvalues, norming constants and normalized eigenfunctions. Consequently, we prove the existence of infinitely many eigenvalues. Also, we compare the solutions with graphics with different orders, different eigenvalues, different potentials and so, we observe the behaviors of eigenfunctions. We give an application to the α-orthogonality of eigenfunctions and reality of eigenvalues for conformable Sturm-Liouville problems defined by [15] in the last section.

Today, mechanical tracking systems have been downsized to allow them to be used in the field of airborne laser communications and in the military domain. Risley systems are used for this purpose, which work by directing a beam of light to a given target point, this procedure is commonly known as the inverse problem. In this paper, an analytical method, the geometric method, has been designed and developed to determine the beam steering in a Risley system and solve the inverse problem. The method focuses on different geometric shapes, like circumference or ellipse, that are described when the beam passes through the second prism. The accuracy and efficiency of the geometric method has been analysed and found to be faster than the two-step method. Furthermore, the geometric method has been implemented in an iterative process and an accuracy of up to 1 pm has been achieved. This high accuracy would allow the geometric method to be applied in fields such as lithography, stereolithography or 3D printers.

Inverse problems of recovering surface fluxes on the boundary of a domain from pointwise observations are considered. Sharp conditions on the data ensuring existence and uniqueness of solutions in Sobolev classes are exposed. They are smoothness conditions on the data, geometric conditions on the location of measurement points, and the boundary of a domain. The proof relies on asymptotics of fundamental solutions to the corresponding elliptic problems and the Laplace transform. The problem is reduced to a linear algebraic system with a nondegerate matrix.

The application of instrumented indentation to assess material properties like Young’s modulus and micro-hardness has become a standard method. In recent developments, indentation experiments and simulations have been combined to inverse methods, from which further material parameters as yield strength, work hardening rate, and tensile strength can be determined. In this work, an inverse method is introduced by which material parameters for cyclic plasticity, i.e. kinematic hardening parameters, can be determined. To accomplish this, cyclic Vickers indentation experiments are combined with finite element simulations of the indentation with unknown material properties, which are then determined by inverse analysis. To validate the proposed method, these parameters are subsequently applied to predict the uniaxial stress-strain response of a material with success. The method has been validated successfully for a quenched and tempered martensitic steel and for technically pure copper, where an excellent agreement between measured and predicted cyclic stress-strain-curves has been achieved. Hence, the proposed inverse method based on cyclic nanoindentation, as a quasi-non-destructive method, could complement or even substitute the resource-intensive conventional fatigue testing in the future for some applications.

A novel means of applying radiotherapy in cancer treatment is the application of the radiation dose at a very high intensity for a very short time, FLASH radiotherapy (FLASH-RT). This technique involves exposure of tumors to >40 Gy/s, usually for less than one second. Studies conducted in cell and preclinical models suggest that FLASH-RT seems less damaging to normal tissues from adverse effects, relative to the same overall dose of radiation administered in conventional therapy (CONV-RT) which involves administration of lower levels of radiation repeated intermittently over a protracted period. In contrast, the susceptibility of tumor tissues to FLASH-RT is not diminished relative to CONV-RT. Within solid tumors both modes of dispensation of radiation produce an equivalent degree of cell damage. This differential between normal and malignant material has been found in isolated tissues, animal studies, and more recently in clinical trials. However, the classical radiation concept is that high energy linear transfer radiation (LET) is more damaging than the equivalent total dose of low LET. Thus, the susceptibility of cells should be greater to short-term exposure to high LET. This article discusses the potential reasons that may account for this discrepancy. While the relative protection given to untransformed tissues by FLASH-RT relative to tumor tissue is a major step forward in radiation therapy of cancer, the processes that lie behind this phenomenon are incompletely understood and are considered here.

This work presents a historical-epistemological analysis of the foundations of quantum mechanics. From a philosophical point of view, the relationships between quantum mechanics and scientific realism are highlighted, as well as the influence of the neo-positivist philosophy of the first decades of the 20th century. The great significance of measurement in the context of quantum mechanics is underlined, as an essential element in providing a coherent description of reality moving from the microcosm to the macrocosm, as well as the role that the measurement apparatus and the consciousness of the observer assume during the act of measurement, the ontological problem, the problem of the objectification of the wave function and the problem of the definition of discernibility. This is followed by an overview of the most important alternative proposals to the standard interpretation (Copenhagen interpretation), with particular reference to hidden variable theories, and other alternatives to standard quantum mechanics that profoundly modify the vision of reality, such as the Everett’s many-worlds theory, the QBism, the unified holistic approach.

The standard approach to integrable nonlinear evolution equations (NLEE) usually uses the following steps: 1) Lax representation $[L,M]=0$; 2)~construction of fundamental analytic solutions (FAS); 3) reducing the inverse scattering problem (ISP) to a Riemann-Hilbert problem (RHP) $\xi^+(x,t,\lambda) =\xi^-(x,t,\lambda) G(x,t\lambda)$ on a contour $\Gamma$ with sewing function $G(x,t,\lambda)$; 4) soliton solutions and possible applications.Step 1) involves several assumptions: the choice of the Lie algebra $\mathfrak{g}$ underlying $L$, as well as its dependence on the spectral parameter, typically linear or quadratic in $\lambda$. In the present paper we propose another approach which substantially extends the classes of integrable NLEE.Its first advantage is that one can effectively use any polynomial dependence in both $L$ and $M$. We use the following steps: A) Start with canonically normalized RHP with predefined contour $\Gamma$; B) Specify the $x$ and $t$ dependence of the sewing function defined on $\Gamma$; C) Introduce convenient parametrization for the solutions $ \xi^\pm(x,t,\lambda)$ of the RHP and formulate the Lax pair and the nonlinear evolution equations (NLEE); D) use Zakharov-Shabat dressing method to derive their soliton solutions. This needs correctly taking into account the symmetries of the RHP. E) Define the resolvent of the Lax operator and use it analyze its spectral properties.

The municipal water supply, related mainly to the cities of Albanian, began to develop in the second half of the 19th century and much intensively after 1945. Today the reported mean water production for the cities, on average, is about 300 l/capita/d including drinking and industrial water supply. The territory of Albania consists of uneven distribution of very heterogeneous aquifers conditioning the reach variety of the municipal water supply solutions. In this article are analyzed and classified the hydrogeological aspects of water supply sources of the settlements, which are summarized in five groups: (a) Wells in alluvial intergranular aquifers; (b) karst springs; (c) wells in karst aquifers; (d) springs in fissured rocks, and (e) mixed water sources. For each group of the water supply sources the main concerns regarding the quantity and quality problems are analyzed facilitated by the description of a variety of representative examples of different situations. Based on the gained experience important recommendation are given for the better understanding of hydrogeological aspects of water supply systems emphasizing the problematics along the river water recharge areas and on the seawater intrusion in coastal aquifers, as well as on transboundary aquifers. However, the main problems of public water supply of Albania remain the poor management of water supply systems which is reflected in the e high water losses, as well as the low public awareness of request for sustainable use.

The current design process of a new aircraft includes several mod- eling and testing steps.The main purpose of the Ground Vibration Testing is to achieve experimental vibration data of the whole aircraft structure, in terms of frequencies, damping factors, and mode shapes to ensure that the aircraft will be free from any aeroelastic instability and safe to fly in the desired flight envelope. GVT results are also mainly used for validating and eventually im- proving FEM models of the aircraft examined. On the other hand, structural dynamic models are used to predict the flutter behavior and carefully plan the in-flight flutter tests, and lead to mitigation of the risks related to them. This paper aims to compare different aeroelastic methods outcomes achieved using two diverse commercial software: the widely used NASTRAN™ and the most recently developed Zonatech ZAERO. The flutter computation results are obtained by means of P-K, K, and G methods. The first case presented is the aeroelastic analysis of a forward-swept wing whereas the second case concerns the stabilator of a two-seater ultralight aircraft, for which an experimental campaign of vibrations test is also carried out. The structural mode shapes at each generated frequency mode are also visually presented.

A review on several classical, algebraic models in nuclear structure physics, which use symmetries as an important tool, are presented. After a conceptual introduction to group theory, a selection of models is chosen to illustrate the methods and the power of the usage of symmetries. It enables to describe very involved systems in a greatly simplified manner. Some problems are also discussed, when ignoring basic principles of nature, as the Pauli Exclusion principle. We also show that, nevertheless, occasionally one can rescue these omissions. In a couple of representative models, applications of symmetries are explicitly applied, in order to illustrate how extremely complicate systems can be treated. This contribution is meant bas a review on the use of algebraic models in nuclear physics, leading to a better understanding of the articles in the same special volume.

In this work, meshless methods are applied for the solution of two-dimensional steady-state heat conduction problems with nonlocal multi-point boundary conditions (NMBC). These meshless procedures are based on multiquadric radial basis function (MQ RBF) and its modified version (i.e. integrated MQ RBF). The proposed meshless methods which were recently published in \cite{Reutskiy2016} is compared with standard collocation method (i.e. Kansa's method). Three different sorts of solution domain are considered in which Dirichlet boundary condition is specified on some part of the boundary and is related to the unknown function values at a discrete set of interior points. The influence of NMBC on the accuracy and condition number of the system matrix associated to the proposed methods is investigated. The sensitivity of the shape parameter is also analyzed in the proposed methods. Performance of the proposed approaches in terms of accuracy and efficiency is confirmed on the benchmark problems.

Recently, the conformable derivative and its properties have been introduced. In this paper, we propose and prove some new results on conformable Boundary Value Problems. First, we introduce a conformable version of classical Sturm´s separation, and comparison theorems. For a conformable Sturm-Liouville problem, Green's function is constructed, and its properties are also studied. In addition, we propose the applicability of the Green´s Function in solving conformable inhomogeneous linear differential equations with homogeneous boundary conditions, whose associated homogeneous boundary value problem has only trivial solution. Finally, we prove the generalized Hyers-Ulam stability of the conformable inhomogeneous boundary value problem.

Modeling the reaction time required for the removal of chemical oxygen demand (COD) from wastewater is important because can be an indicator of harmful pollutants that may pose risks to human health.. Here, we evaluate the use of an inverse artificial neural network (ANNi) to optimize the removal of COD from aqueous herbicide solutions using sonophotocatalysis as the treatment process.. This algorithm takes the operating conditions as input and estimates the optimal reaction time to reach the required effluent COD. This model was validated by comparison with both experimental measurements and simulated analysis, resulting in low error (0.5%), high Pearson correlation (R2 = 0.9956), and short computing time (less than one minute). The results for the proposed methodology (ANNi-GAs) were efficient and attractive in a reasonable way providing a satisfactory picture of the relationships between the optimal input parameters. Such parameters control the degradation process of herbicides online and can be used to predict the behavior of COD removal, thus taking actions to reduce the impact of aqueous treatment with alazine and gesaprim herbicides on aquatic, soil biota, and human health. Therefore, this technique constitutes a promising framework for predicting the environmental impact of herbicides within a tolerable degree of error.

There are many problems based on solving non-autonomous differential equations of the form x¨(t)+ω2(t)x(t)=0, where x(t) represents the coordinate of a material point and ω is the angular frequency. The inverse problem involves finding the bounded coefficient ω. Continuity of the function ω(t) is not required. The trajectory x(t) is unknown, but the initial and final values of the phase variables are given. The variation principle of minimum time for the entire dynamic process allows for the determination of the optimal solution {x(t),ω(t)}. Thus, the inverse problem is an optimal control problem. No simplifying assumptions were made.