Penggunaan Karakter Kontrol ASCII Untuk Integrasi Data Pada Hasil Enkripsi Algoritma Caesar Cipher

Cryptography is a technique or method for securing data from other unauthorized parties. The substitution algorithm is the simplest algorithm and is classified as a classic in the field of cryptography, for example the Caesar cipher algorithm. ASCII code (American Standard Code for Information Interchange) is a code that contains characters that can be processed by a computer. By type, not all ASCII characters can be printed by the computer. Some characters are not printed or illegible as usual. These unreadable characters are called control characters. The control character can be used to improve the performance of the Caesar Cipher algorithm, because it focuses on processing text data. The application of control characters uses a simple method but has a complex solution. The results of this study are in the form of a new method with the Caesar cipher algorithm as a classical cryptographic method or technique and ASCII characters as the basis for the development of the ciphertext performance resulting from the encryption process. In testing this method, the success rate reaches 100% in securing the contents of the document with a sample of 500 letters. While the possibility of solving the ciphertext results is classified as difficult because the control characters of the ciphertext results that are illegible will make the decryption result multiple interpretations.

Ranking of decision making units using the imperialist competitive algorithm in DEA

One of the challenging and important subjects in Data Envelopment Analysis (DEA) is the ranking of Decision Making Units (DMUs). In this paper, a new method for ranking the efficient DMUs is firstly proposed by utilizing the DEA technique and also developing a capable metaheuristic, imperialist competitive algorithm, derived from social, political, and cultural phenomena. Efficient DMUs are known as colonizers, and the virtual units, which are within their regions of exclusive domination, are considered as colonies. Efficient units are ranked by utilizing the factor of competition among imperialists to attract each other’s colonies. One advantage of proposed method is that, without solving any mathematical, and complex solution approaches, all extreme and non-extreme units are ranked only by comparing the pairs.

Complex solution on solving problems of estimating plant resources by GIS and climate model methods

The paper considers the applied aspects of the use of modern information technologies for an accurateassessment of plant resources using GIS and climate model methods. For the most effective achievement of the goals ofintegrated monitoring and assessment of plant resources, the authors discussed and proposed a number of requirementsfor the initial data, factors affecting the change in the area and the results of the assessment of plant resources. As anavailable free analogue of the method for correcting the spatial unevenness of the points of registration of species inSDMtoolbox (ArcGIS), we proposed the DBSCAN clustering method, which is implemented in the Python library sklearn.

Competitive Ligand Binding and Photosensitivity Studies of Iron(III)-thiocyanate in presence of Glutamate ion

Addition of a chelating ligand (glutamate ion) to [Fe(SCN)]2+ solution leads to change in the colour. On increasing the glutamate ion concentration in iron thiocyanate complex solution, the colour of [Fe(SCN)]2+ disappears with the emergence of a new peak at lower wavelength due to the formation of [Fe(Glu)]2+complex. The conductance of [Fe(SCN)]2+ complex ion in solution is high while on addition of different concentrations of glutamate ion to iron thiocyanate complex, their conductance value decreases due to formation of [Fe(Glu)]2+. Photosensitivity studies of a series of solutions prepared by the addition of glutamate ion of varying concentrations to ferric chloride-ammonium thiocyanate/potassium thiocyanate solution in the short UV region demonstrate the better stability of [Fe(Glu)]2+compared to [Fe(SCN)]2+ and the rate kinetics of decomposition has been reported.

Methods for optimizing the delivery of goods to consumers by several vehicles

Freight transport is one of the most important sectors of the national economy. The increase in the efficiency of the logistics company is ensured by the accurate organization of supplies, as well as the timely delivery of goods to the points of the route. That is why there is a need to optimize the delivery of goods. A delivery task is a transport task of delivering oversized cargo from a distribution center to a multitude of recipients located in the area of operation of a transport company. It is a generalization of the well-known traveling salesman problem, from which it differs in the condition of the limited carrying capacity of the vehicle used and, as a consequence, the need to repeatedly return to the base to replenish the transported cargo. This article proposes to supplement the traditional formulation of the problem with the requirement to distribute customers among several simultaneously operating vehicles (TC) so that the maximum lead time is minimal. This takes into account not only the interests of the delivery contractor, but also the customers. The solution to the problem consists of two stages. On the first one, in some known way, rational ring routes for each vehicle are determined, minimizing the total mileage. Based on the results of the stage, the time for passing each route is calculated. At the second stage, the problem of reducing the maximum travel time of routes is solved by using several vehicles delivering at the same time. To do this, it is necessary to optimally distribute vehicles along individual routes. It is proposed to use the algorithm for solving the well-known problem "Packing items into containers" to solve this problem. This problem belongs to the class of NP-hard problems in the strong sense and does not have an exact solution algorithm for arbitrary input data. This paper proposes a complex solution method combining the well-known First fitted (FF) algorithm, the original First fitted with reordering (FFR) algorithm, and S. Martello's lower bounds and P. Thoth to control the optimality of the obtained solution. Test calculations have shown the effectiveness of this approach for a moderate dimension of the problem.