Is the volatility of international tourism revenues affected by tourism source market structure? An empirical analysis of Turkey

This study examines the effect of tourism source market structure on the volatility of tourism revenues in Turkey, using the number of tourists according to nationality and the data on international tourism revenues. The tourism source market structure was measured using the normalized Herfindahl–Hirschman index and the relative entropy index, which is based on the number of tourists visiting Turkey from 107 source markets. The volatility of tourism revenues and the effect of tourism source market structure on this volatility were assessed using the autoregressive conditional heteroskedasticity (ARCH) method. The results show that both variables measuring tourism source market structure affect the volatility of tourism revenues. Accordingly, the concentration of the tourism source market increases the volatility of tourism revenues, whereas source market diversification decreases it.

Complexity of Magnetic-field Turbulence at Reconnection Exhausts in the Solar Wind at 1 au

Magnetic reconnection is a complex mechanism that converts magnetic energy into particle kinetic energy and plasma thermal energy in space and astrophysical plasmas. In addition, magnetic reconnection and turbulence appear to be intimately related in plasmas. We analyze the magnetic-field turbulence at the exhaust of four reconnection events detected in the solar wind using the Jensen–Shannon complexity-entropy index. The interplanetary magnetic field is decomposed into the LMN coordinates using the hybrid minimum variance technique. The first event is characterized by an extended exhaust period that allows us to obtain the scaling exponents of higher-order structure functions of magnetic-field fluctuations. By computing the complexity-entropy index we demonstrate that a higher degree of intermittency is related to lower entropy and higher complexity in the inertial subrange. We also compute the complexity-entropy index of three other reconnection exhaust events. For all four events, the B L component of the magnetic field displays a lower degree of entropy and higher degree of complexity than the B M and B N components. Our results show that coherent structures can be responsible for decreasing entropy and increasing complexity within reconnection exhausts in magnetic-field turbulence.

Assessment of Productivity and Crop Diversification Pattern in Punjab Agriculture

This study examines the structure of agriculture productivity and crop diversification across different zones in Punjab, India during 1966–1967 to 2017–2018. The composite entropy index shows that almost all zones are specialised in few crops but some of them are relatively less or some are more. Hence, we found zones are experiencing a lateral movement toward crop specialisation and crop diversification is not happening. Further, results reveals that accessibility of market and roadhave a positive influenced the level of crop diversification are accessibility of market, roads have found a positive influenced on crop diversification. Whereas more use of fertiliser, intensity of irrigation and rainfall have leads to concentration rather than crop diversification. Similarly, study also analysed the factors that are responsible of variation in productivity by regional factors such as better road, fertiliser, urbanisation, literacy and cropping intensity. As the analysis indicates that there is need to emphasise on agro-climatic regional preparation by clearly identifying the existing resource endowments and constraints of the agro-climatically homogeneous regions. JEL: C23,Q10, Q19

A Shannon Entropy-Based Methodology to Detect and Locate Cables Loss in a Cable-Stayed Bridge

As with any civil structure or mechanism, vehicular bridges can suffer structural damages which can conduct to devastating human and economic losses if they are not detected and corrected on time. In this work, a methodology based on the Shannon entropy index combined with statistical indexes and a fuzzy logic classifier to detect and locate a cable loss in cable-stayed bridges is proposed. Shannon entropy index is used to characterize the changes in the vibration signals associated with structural damage, which are integrated with statistical indexes for damage detection and damage location. On the other hand, the fuzzy logic classifier is used as a pattern recognition algorithm to detect structural damage automatically. For this study, the vibration data acquired experimentally from the Rio Papaloapan Bridge (Veracruz, Mexico) are analyzed. Results demonstrate the usefulness of the proposed method since 93.3% of effectiveness in the damage detection is obtained with a 100% of effectiveness in its location.

PENGGUNAAN METODE FUZZY C-MEANS UNTUK PENGELOMPOKAN PROVINSI DI INDONESIA BERDASARKAN INDIKATOR KESEHATAN LINGKUNGAN

Analisis Klaster (cluster analisis) memiliki tujuan untuk mengelompokkan objek-objek berdasarkan karakteristik yang dimiliki. Pengelompokan dengan analisis klaster dibagi menjadi dua metode yaitu metode berhirarki dan metode tak berhirarki. Salah satu metode tak berhirarki adalah Metode Fuzzy C-Means. Metode Fuzzy C-Means merupakan suatu metode yang mempertimbangkan derajat keanggotaan dan himpunan fuzzy sebagai dasar pembobotan. Untuk jumlah klaster optimum didapatkan dengan menggunakan kriteria indeks validitas Partition Entropy Index (PE). Pada penelitian ini Metode Fuzzy C-Means digunakan untuk mengelompokan provinsi-provinsi yang ada di Indonesia berdasarkan indikator kesehatan lingkungan. Indikator kesehatan lingkungan yang digunakan adalah STBM, tatanan kawasan sehat, air minum layak rumah tangga, TTU yang memenuhi syarat kesehatan, TPM yang memenuhi syarat kesehatan, sanitasi layak rumah tangga dan rumah tangga layak huni. Jumlah klaster optimum diperoleh sebanyak 2 klaster yang memiliki nilai partition entropy 0,4633829. Klaster 1 terdiri dari 22 provinsi dan klaster 2 terdiri dari 12 provinsi.Kata Kunci: Fuzzy C-Means, Indikator Kesehatan Lingkungan, Partition Entropy Index

Visualizations of Combinatorial Entropy Index on Whole SARS-CoV-2 Genomes

In this paper, a set of SARS-CoV-2 genomes from four countries are selected for visualizations under the C1 modules of the metagenomic analysis system MAS. Based on the variant construction and the theory of information entropy, the module makes statistics on the number of bases in SARS-CoV-2 sequences to calculate the base probability measures in segments to generate the combinatorial entropy index data from the base probability measures. Under visualization technology, the combinatorial entropy index is projected on 2D clustering genomic index maps and 1D histogram maps to provide projection results. The visual results provide intuitive and easy properties to analyze complicated clustering among genomes to support clustering analysis of SARS-CoV-2 genomes in batches, showing the distribution characteristics of SARS-CoV-2 genomes in different countries or regions conveniently.