Issues and Constrains in Manpower Supply in Indian Hospitality Industry

By the very nature of tourism as a service industry, its efficient management and successful operation depend largely on the quality of manpower. In India, the shortage of skilled manpower poses a major threat to the overall development of tourism. In particular, the rapid expansion of hotels of an international standard in India is creating a high level of demand for skilled and experienced staff. The nature of the decisions facing hotel management is continually expanding. For their business to remain competitive, managers must be skilful in many diverse areas. Tourism statistics reveal that both domestic and foreign tourism are on a robust growth path. This growth will need to be serviced by a substantial increase in infrastructure, including air-road, rail connectivity as well as hotels and restaurants The availability of skilled and trained manpower is a crucial element in the successful long-term development and sustainability of a tourist destination. Skilled and trained human resources will ensure the delivery of efficient, high-quality service to visitors, which is a direct and visible element of a successful tourism product. High standards of service are particularly important in sustaining long-term growth, since success as a tourist destination is determined not only by price competitiveness or the range of attractions available, but also by the quality of the services provided, there by the qualified human capital. This paper elaborates the issues and constrains relating to demand and supply of manpower in hospitality industry and also suggested the recommendations to fill the gap.

MINIMIZATION OF MOBILE AD HOC NETWORKS ROUTING ATTACKS USING DS MATHEMATICAL THEORY

Mobile Ad hoc Networks (MANET) have been highly vulnerable to attacks due to the dynamic nature of its network infrastructure. Among these attacks, routing attacks have received considerable attention since it could cause the most devastating damage to MANET. Even though there exist several intrusion response techniques to mitigate such critical attacks, existing solutions typically attempt to isolate malicious nodes based on binary or naı¨ve fuzzy response decisions. However, binary responses may result in the unexpected network partition, causing additional damages to the network infrastructure, and naı¨ve fuzzy responses could lead to uncertainty in countering routing attacks in MANET. In this paper, we propose a risk-aware response mechanism to systematically cope with the identified routing attacks. Our risk-aware approach is based on an extended Dempster-Shafer mathematical theory of evidence introducing a notion of importance factors. In addition, our experiments demonstrate the effectiveness of our approach with the consideration of several performance metric

A Novel Approach for Detection of DoS / DDoS Attack in Network Environment using Ensemble Machine Learning Model

One of the most  serious threat to network security is Denial of service (DOS) attacks. Internet and computer networks are now important parts of our businesses and daily lives. Malicious actions have become more common as our reliance on computers and communication networks has grown. Network threats are a big problem in the way people communicate today. To make sure that the networks work well and that users' information is safe, the network data must be watched and analysed to find malicious activities and attacks. Flooding may be the simplest DDoS assault. Computer networks and services are vulnerable to DoS and DDoS attacks. These assaults flood target systems with malicious traffic, making them unreachable to genuine users. The work aims to enhance the resilience of network infrastructures against these attacks and ensure uninterrupted service delivery. This research develops and evaluates enhanced DoS/DDoS detection methods. DoS attacks usually stop or slow down legal computer or network use. Denial-of-service (DoS) attacks prevent genuine users from accessing and using information systems and resources. The OSI model's layers make up the computer network. Different types of DDoS strikes target different layers. The Network Layer can be broken by using ICMP Floods or Smurf Attacks. The Transport layer can be attacked using UDP Floods, TCP Connection Exhaustion, and SYN Floods. HTTP-encrypted attacks can be used to get through to the application layer. DoS/DDoS attacks are malicious attacks. Protect network data from harm. Computer network services are increasingly threatened by DoS/DDoS attacks. Machine learning may detect prior DoS/DDoS attacks. DoS/DDoS attacks proliferate online and via social media. Network security is IT's top priority. DoS and DDoS assaults include ICMP, UDP, and the more prevalent TCP flood attacks. These strikes must be identified and stopped immediately. In this work, a stacking ensemble method is suggested for detecting DoS/DDoS attacks so that our networked data doesn't get any worse. This paper used a method called "Ensemble of classifiers," in which each class uses a different way to learn. In proposed  methodology Experiment#1 , I used the Home Wifi Network Traffic Collected and generated own Dataset named it as MywifiNetwork.csv, whereas in proposed methodology Experiment#2, I used the kaggle repository “NSL-KDD benchmark dataset” to perform experiments in order to find detection accuracy of dos attack detection using python language in jupyter notebook. The system detects attack-type or legitimate-type of network traffic during detection ML classification methods are used to compare how well the suggested system works. The results show that when the ensembled stacking learning model is used, 99% of the time it is able to find the problem. In proposed methodology two Experiments are implemented for comparing detection accuracy with the existing techniques. Compared to other measuring methods, we get a big step forward in finding attacks. So, our model gives a lot of faith in securing these networks. This paper will analyse the behaviour of network traffics

Therapeutic Implications of Targeting AKT Signaling in Melanoma

Identification of key enzymes regulating melanoma progression and drug resistance has the potential to lead to the development of novel, more effective targeted agents for inhibiting this deadly form of skin cancer. The Akt3, also known as protein kinase B gamma, pathway enzymes regulate diverse cellular processes including proliferation, survival, and invasion thereby promoting the development of melanoma. Accumulating preclinical evidence demonstrates that therapeutic agents targeting these kinases alone or in combination with other pathway members could be effective for the long-term treatment of advanced-stage disease. However, currently, no selective and effective therapeutic agent targeting these kinases has been identified for clinical use. This paper provides an overview of the key enzymes of the PI3K pathway with emphasis placed on Akt3 and the negative regulator of this kinase called PTEN (phosphatase and tensin homolog deleted on chromosome 10). Mechanisms regulating these enzymes, their substrates and therapeutic implications of targeting these proteins to treat melanoma are also discussed. Finally, key issues that remain to be answered and future directions for interested researchers pertaining to this signaling cascade are highlighted

Pressure induced electronic topological transition in Sb2S3

Pressure induced electronic topological transitions in the wide band gap semiconductor Sb2S3 (Eg = 1.7-1.8 eV) with similar crystal symmetry (SG: Pnma) to its illustrious analog, Sb2Se3, has been studied using Raman spectroscopy, resistivity and the available literature on the x-ray diffraction studies. In this report, the vibrational and the transport properties of Sb2S3 have been studied up to 22 GPa and 11 GPa, respectively. We observed the softening of phonon modes Ag(2), Ag(3) and B2g and a sharp anomaly in their line widths at 4 GPa. The resistivity studies also shows an anomaly around this pressure. The changes in resistivity as well as Raman line widths can be ascribed to the changes in the topology of the Fermi surface which induces the electron-phonon and the strong phonon-phonon coupling, indicating a clear evidence of the electronic topological transition (ETT) in Sb2S3. The pressure dependence of a/c ratio plot obtained from the literature showed a minimum at ~ 5 GPa, which is consistent with our high pressure Raman and resistivity results. Finally, we give the plausible reasons for the non-existence of a non-trivial topological state in Sb2S3 at high pressures.Comment: 24 pages, 6 Figures, 2 tables submitted for publicatio

Preparation and Characterization of CuO Nanoparticles by Novel Sol-Gel Technique

Recent developments of nanosize materials of metal and metal oxide particles are intensively pursued because of their prominence in different fields of applications. Among all the transition metal oxides, CuO is a potential candidate for the application of magnetic storage devices, solar energy transfer, sensors, and super capacitors etc. Moreover CuO nanoparticles act as a good catalyst in some of the chemical reactions. CuO nanoparticles were prepared by novel sol-gel method. In this technique CuCl2.6H2O is added with acetic acid and heated to 100 °C with continuous stirring. To control the ph of the above solution, NaOH is added to the solution till ph reached desired value. The color of the solution changed from blue to black with precipitation. The black precipitation was washed 3 – 4 times with distilled water. Finally the solution was centrifuged and dried in air for one day. The CuO nanoparticles were characterized by studying their structure with X-ray diffraction and composition by energy dispersive X-ray analysis. The size of the nanoparticles is estimated by particle size analyzer and transmission electron microscopy. The optical studies were carried out with Uv-Vis spectrophotometer. When you are citing the document, use the following link http://essuir.sumdu.edu.ua/handle/123456789/2960

The Evolution of the Global Star Formation History as Measured from the Hubble Deep Field

The Hubble Deep Field (HDF) is the deepest set of multicolor optical photometric observations ever undertaken, and offers a valuable data set with which to study galaxy evolution. Combining the optical WFPC2 data with ground-based near-infrared photometry, we derive photometrically estimated redshifts for HDF galaxies with J<23.5. We demonstrate that incorporating the near-infrared data reduces the uncertainty in the estimated redshifts by approximately 40% and is required to remove systematic uncertainties within the redshift range 1<z<2. Utilizing these photometric redshifts, we determine the evolution of the comoving ultraviolet (2800 A) luminosity density (presumed to be proportional to the global star formation rate) from a redshift of z=0.5 to z=2. We find that the global star formation rate increases rapidly with redshift, rising by a factor of 12 from a redshift of zero to a peak at z~1.5. For redshifts beyond 1.5, it decreases monotonically. Our measures of the star formation rate are consistent with those found by Lilly et al. (1996) from the CFRS at z 2, and bridge the redshift gap between those two samples. The overall star formation or metal enrichment rate history is consistent with the predictions of Pei and Fall (1995) based on the evolving HI content of Lyman-alpha QSO absorption line systems.Comment: Latex format, 10 pages, 3 postscript figures. Accepted for publication in Ap J Letter

Expanding the Family of Grassmannian Kernels: An Embedding Perspective

Modeling videos and image-sets as linear subspaces has proven beneficial for many visual recognition tasks. However, it also incurs challenges arising from the fact that linear subspaces do not obey Euclidean geometry, but lie on a special type of Riemannian manifolds known as Grassmannian. To leverage the techniques developed for Euclidean spaces (e.g, support vector machines) with subspaces, several recent studies have proposed to embed the Grassmannian into a Hilbert space by making use of a positive definite kernel. Unfortunately, only two Grassmannian kernels are known, none of which -as we will show- is universal, which limits their ability to approximate a target function arbitrarily well. Here, we introduce several positive definite Grassmannian kernels, including universal ones, and demonstrate their superiority over previously-known kernels in various tasks, such as classification, clustering, sparse coding and hashing

Early-type galaxies in the SDSS. II. Correlations between observables

A magnitude limited sample of nearly 9000 early-type galaxies, in the redshift range 0.01 < z < 0.3, was selected from the Sloan Digital Sky Survey using morphological and spectral criteria. The sample was used to study how early-type galaxy observables, including luminosity L, effective radius R_o, surface brightness I_o, color, and velocity dispersion sigma, are correlated with one another. Measurement biases are understood with mock catalogs which reproduce all of the observed scaling relations and their dependences on fitting technique. At any given redshift, the intrinsic distribution of luminosities, sizes and velocity dispersions in our sample are all approximately Gaussian. A maximum likelihood analysis shows that sigma ~ L^{0.25\pm 0.012}, R_o ~ L^{0.63\pm 0.025}, and R_o ~ I^{-0.75\pm 0.02} in the r* band. In addition, the mass-to-light ratio within the effective radius scales as M_o/L ~ L^{0.14\pm 0.02} or M_o/L ~ M_o^{0.22\pm 0.05}, and galaxies with larger effective masses have smaller effective densities: Delta_o ~ M_o^{-0.52\pm 0.03}. These relations are approximately the same in the g*, i* and z* bands. Relative to the population at the median redshift in the sample, galaxies at lower and higher redshifts have evolved only little, with more evolution in the bluer bands. The luminosity function is consistent with weak passive luminosity evolution and a formation time of about 9 Gyrs ago.Comment: 29 pages, 11 figures. Accepted by AJ (scheduled for April 2003). This paper is part II of a revised version of astro-ph/011034

Sorgoleone release from sorghum roots shapes the composition of nitrifying populations, total bacteria, and archaea and determines the level of nitrification

Sorgoleone is a secondary sorghum metabolite released from roots. It has allelopathic properties and is considered to inhibit ammonia-oxidizing archaea (AOA) and bacteria (AOB) responsible for the rate-limiting step (ammonia oxidation) in nitrification. Low activity of these microorganisms in soil may contribute to slow down nitrification and reduce nitrogen loss via denitrification and NO3 − leaching. The potential nitrification rate (PNR) and the composition of microbial communities were monitored in rhizosphere soil to investigate the growth effect sorghum on biological nitrification inhibition (BNI). A greenhouse pipe experiment was conducted using sorghum lines IS20205 (highsorgoleone release ability), IS32234 (medium-sorgoleone release ability), 296B (low-sorgoleone release ability), and a control (no plants) combined with fertilization application of 0 or 120 kg N ha−1. We applied nitrogen as ammonium sulfate at 16 days (20 N), 37 days (40 N), and 54 days (60 N). We collected soil solutions at 7.5 cm depths every 3 days and measured the pH and nitrate levels. At 1 and 2.3 months, we sampled the bulk and rhizosphere soils and roots in the 0–10 cm, 10–30 cm, and 30–80 cm depths to determine NO2, mineral N, total N, total C, sorgoleone, the composition of AOA, AOB, and total bacteria and archaea. Sorgoleone was continuously released throughout the 2.3 months’ growth and was significantly higher in IS20205, followed by IS32234 then 296B, which showed shallow levels. The IS2020 5rhizosphere showed lower NO2 and nitrate levels and significant inhibition of AOA populations. However, we did not find significant differences in the abundance of AOB between plant treatments. Multivariate analysis and Spearman’s correlations revealed that sorgoleone as well as environmental factors such as soil pH, soil moisture, NO3 −-N, and NH4 +- N shape the composition of microbial communities. This study demonstrated that the release of higher amounts of sorgoleone has great potential to inhibit the abundance of AOA and soil nitrification. The breeding of sorghum lines with the ability to release higher amounts of sorgoleone could be a strategic way to improve the biological nitrification inhibition during cultivation