Information transmittal, Newton’s law of gravitation, and tensor approach to general relativity

The special relativity considered in [A. Einstein, Zur Elektrodynamik der bewegte Körper. Ann. Physik, 17 (1905) 891-921] is based on the concept of finite speed of information transmittal by the available signals (rays of light). It is demonstrated that the same concept applies to Newton’s law of universal gravitation since the magnitude of distances between attracting masses can be physically defined (carried, accounted in acting forces of gravity) only by signals (physical processes) propagating at finite velocities. It follows that the speed of propagation of gravity is finite. The linear transformations of special relativity are applied to Newton’s law of gravitation to take into account the relativistic effects of information transmittal in a field of central forces of attraction. Relativistic representations of Newton’s law are obtained with respect to the center of gravity exposing illusory effects that appear at high velocities. It is verified that in atomic physics the effect of Newtonian gravitation on the motion of elementary particles at high velocities is negligible also in relativistic consideration. Computational methods are developed to measure the intensity of gravitation at a distant space-time location using a body that travels in space, emitting uniform pulses of light that are received by the observer at a different space-time location. It is demonstrated that the tensor approach to the general relativity and the united theory of space, time and gravitation in which the geometrical properties (metric) of the four-dimensional space-time continuum depend on the distribution of gravitating masses in space and their motion represent a transformed Lorentz invariant with a new type of inertia in the field of forces changing in space and time. Real physical processes evolve according to the forces represented in the tensor form by this invariant which is equivalent to the coordinate-free local invariant of relativistic dynamics that defines the field and the motion of a body whose velocities and accelerations can be measured by relativistic identification methods at a point, time and direction of interest. The results open new avenues for research in the general relativity and can be used for software development, field measurements and experimental studies in application to distant or fast moving systems.     

Ascorbic acid imprinted polymer-modified graphite electrode: A diagnostic sensor for hypovitaminosis C at ultra trace ascorbic acid level

A new kind of molecularly imprinted polymer-modified graphite electrode was fabricated by “grafting-to” approach, incorporating sol–gel technique, for the detection of acute deficiency in serum ascorbic acid level (SAAL), manifesting hypovitaminosis C. The modified electrode exhibited ascorbic acid (AA) oxidation at less positive potential (0.0 V) than the earlier reported methods, resulting in a limit of detection as low as 6.13 ng mL⁻¹ (RSD = 1.2%, S/N = 3). The diffusion coefficient (1.096 × 10⁻⁵ cm² s⁻¹), rate constant (7.308 s⁻¹), and Gibb's free energy change (−12.59 kJ mol⁻¹) due to analyte adsorption, were also calculated to explore the kinetics of AA oxidation. The proposed sensor was found to enhance sensitivity substantially so as to detect ultra trace level of AA in the presence of other biologically important compounds (dopamine, uric acid, etc.), without any cross interference and matrix complications from biological fluids and pharmaceutical samples.     

Failure mode and effects analysis using a group-based evidential reasoning approach

Failure mode and effects analysis (FMEA) is a methodology to evaluate a system, design, process or service for possible ways in which failures (problems, errors, risks and concerns) can occur. It is a group decision function and cannot be done on an individual basis. The FMEA team often demonstrates different opinions and knowledge from one team member to another and produces different types of assessment information such as complete and incomplete, precise and imprecise and known and unknown because of its cross-functional and multidisciplinary nature. These different types of information are very difficult to incorporate into the FMEA by the traditional risk priority number (RPN) model and fuzzy rule-based approximate reasoning methodologies. In this paper we present an FMEA using the evidential reasoning (ER) approach, a newly developed methodology for multiple attribute decision analysis. The proposed FMEA is then illustrated with an application to a fishing vessel. As is illustrated by the numerical example, the proposed FMEA can well capture FMEA team members’ diversity opinions and prioritize failure modes under different types of uncertainties.     

The labeled maximum matching problem

Given a graph G where a label is associated with each edge, we address the problem of looking for a maximum matching of G using the minimum number of different labels, namely the labeled maximum matching problem. It is a relatively new problem whose application is related to the timetabling problem. We prove it is NP-complete and present four different mathematical formulations. Moreover, we propose an exact algorithm based on a branch-and-bound approach to solve it. We evaluate the performance of our algorithm on a wide set of instances and compare our computational times with the ones required by CPLEX to solve the proposed mathematical formulations. Test results show the effectiveness of our procedure, that hugely outperforms the solver.     

A novel hybrid immune algorithm for global optimization in design and manufacturing

This paper presents a new hybrid optimization approach based on immune algorithm and hill climbing local search algorithm. The purpose of the present research is to develop a new optimization approach for solving design and manufacturing optimization problems. This research is the first application of immune algorithm to the optimization of machining parameters in the literature. In order to evaluate the proposed optimization approach, single objective test problem, multi-objective I-beam and machine-tool optimization problems taken from the literature are solved. Finally, the hybrid approach is applied to a case study for milling operations to show its effectiveness in machining operations. The results of the hybrid approach for the case study are compared with those of genetic algorithm, the feasible direction method and handbook recommendation.     

An order-clique-based approach for mining maximal co-locations

Most algorithms for mining spatial co-locations adopt an Apriori-like approach to generate size-k prevalence co-locations after size-(k − 1) prevalence co-locations. However, generating and storing the co-locations and table instances is costly. A novel order-clique-based approach for mining maximal co-locations is proposed in this paper. The efficiency of the approach is achieved by two techniques: (1) the spatial neighbor relationships and the size-2 prevalence co-locations are compressed into extended prefix-tree structures, which allows the order-clique-based approach to mine candidate maximal co-locations and co-location instances; and (2) the co-location instances do not need to be stored after computing some characteristics of the corresponding co-location, which significantly reduces the execution time and space required for mining maximal co-locations. The performance study shows that the new method is efficient for mining both long and short co-location patterns, and is faster than some other methods (in particular the join-based method and the join-less method).     

Variational implicit surface meshing

In this paper, we propose a new algorithm to mesh implicit surfaces which produces meshes both with a good triangle aspect ratio as well as a good approximation quality. The number of vertices of the output mesh is defined by the end-user. For this goal, we perform a two-stage processing: an initialization step followed by an iterative optimization step. The initialization step consists in capturing the surface topology and allocating the vertex budget. The optimization algorithm is based on a variational vertices relaxation and triangulation update. In addition a gradation parameter can be defined to adapt the mesh sampling to the curvature of the implicit surface. We demonstrate the efficiency of the approach on synthetic models as well as real-world acquired data, and provide comparisons with previous approaches.     

A hybrid multi-criteria decision-making model for firms competence evaluation

In this paper, we present a hybrid multi-criteria decision-making (MCDM) model to evaluate the competence of the firms. According to the competence-based theory reveals that firm competencies are recognized from exclusive and unique capabilities that each firm enjoy in marketplace and are tightly intertwined within different business functions throughout the company. Therefore, competence in the firm is a composite of various attributes. Among them many intangible and tangible attributes are difficult to measure. In order to overcome the issue, we invite fuzzy set theory into the measurement of performance. In this paper first we calculate the weight of each criterion through adaptive analytic hierarchy process (AHP) approach (A³) method, and then we appraise the performance of firms via linguistic variables which are expressed as trapezoidal fuzzy numbers. In the next step we transform these fuzzy numbers into interval data by means of α-cut. Then considering different values for α we rank the firms through TOPSIS method with interval data. Since there are different ranks for different α values, we apply linear assignment method to obtain final rank for alternatives.