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The importance of poultry is undeniable in human food chain and respiratory diseases threaten not only the safety of this food source but also its sufficiency. These threats could be controlled by monitoring system helps farmers and veterinarians to detect any changes in the birds health conditions. There are different sound processing techniques to decompose the target signal and extract appropriate features for detecting these changes. In this paper, a short review of chickens’ vocalization system, related studies in sound processing field their approaches and their achievements, introduction of a three-step approach for developing suggested algorithm, and a list of challenges facing such research are presented. Regarding the destructive effects of respiratory diseases on bird’s vocal system, a correlation between audio signal features and the type and severity of avian diseases is predictable. Among sound processing approach and techniques there is good conformity between wavelet transform abilities and the abilities are needed for such health monitoring system. Despite the existence of some challenges, some of which are listed in this paper and none is incredible, sound signal processing techniques can potentially play the role of a diagnosis tool and find valuable information about poultry health condition in the near future.

The contemporary organizations are confronting the issue of managing and retaining diverse workforce. This issue of diversity management becomes more significant when employees are very pertinent about preserving their social and cultural identity. Multiple diversity management programs are used to cater the issue of heterogeneity of the employees. Our paper focused on the management and employees’ perspective on diversity management while keeping in view the social identity of employees. Our theoretical underpinning was based on the idea that success of diversity management programs is contingent on the volitional participation of employees. Qualitative research methodology was followed to explain the management and employee opinion about diversity management in context of social identity. The data was collected through semi-structured interviews of 12 representatives of management and employees. It was found that there are differing opinions of employees and management which are the main cause behind the failure of diversity management programs. This research—conducted in Pakistani fast food chain store—is completely new up to the best of knowledge. The findings of this research will contribute towards the theoretical understanding and practical applications of diversity management.

Tremendous heat energy waste in steel plant through ladle, but nobody think over this issue. A Heat Energy Solidification process are simulated for a continuous casting machine and the constructive shape of the liquid pool is predicted considering at different conditions. Heat energy and Solidification model is described for the C.C. of Steel Slabs. The Model has been established on the basis of the technical conditions of the slab caster in the C.C.

A field experiment on sugarcane was conducted at Anbil Dharma lingam Agricultural College and Research Institute, Tiruchirapalli with the view to identify sodic tolerant variety and suitable amendment for sodic soil. Four sugarcane varieties viz., Co Si (Sc) 6, Co C (Sc) 86032, Co C (Sc) 23 and Co G (Sc) 5 and three amendments viz., pressmud @ 12.5 t ha -1, gypsum @ 4.8 t ha -1 and bottom slag @ 15 t ha-1 were studied along with the unamended control in the experiment were studied. The results revealed that sugarcane varieties Co G (Sc) 5 and Co C (Sc) 23 gave higher K+: Na+ as compared to the Co Si (Sc) 6 and Co C (Sc) 86032 under sodic soil. The sugarcane varieties namely Co G (Sc) 5 and Co C (Sc) 23 were found to be superior for accumulating more K than the Na+, hence these might be considered suitable for growing under sodic soil.

In this work, experimental and theoretical study on the molecular structure, scaled quantum chemical calculations of energies and vibrational wavenumbers of 4-(dimethylamine) benzophenone (4DMBP) is presented. The vibrational frequencies of the title compound were obtained theoretically by DFT/B3LYP calculations employing the standard 6-311+G(d,p) and 6-311++G(d,p) basis sets for optimized geometry and were compared with Fourier transform infrared spectrum (FTIR) in the region of 4000 – 400 cm?1 and Fourier transform Raman spectrum in the region of 4000 – 100 cm?1. Complete vibrational assignments, analysis and correlation of the fundamental modes for the title compound were carried out. The vibrational harmonic frequencies were scaled using scale factor, yielding a good agreement between the experimentally recorded and the theoretically calculated values. The study is extended to calculate the HOMO-LUMO energy gap, NBO, mapped molecular electrostatic potential (MEP) surfaces, polarizability, Mulliken charges and thermodynamic properties of the title compound.

Most of the brain disorders are diagnosed by analyzing the EEG signals. In this paper an efficient approach for detecting the presence of epileptic seizures in EEG signals using fuzzy KNN classifier is presented. Epilepsy is a disease due to temporary alternation in brain functions due to abnormal electrical activity of a group of brain cells and is termed as seizure. The analysis is performed in three stages. In the first step the biorthogonal Discrete wavelet transform is used for decompose the EEG signal into delta, theta, alpha, beta and gamma subbands. In the second step the statistical features are extracted from each subband and finally classification of the EEG signal that is epileptic seizure exists or not has been done using fuzzy KNN clasifier. This method is applied for two different groups of EEG signals: 1) healthy (Normal) EEG dataset; 2) epileptic dataset during a seizure interval .The experimental results show that the proposed method efficiently detects the presence of epileptic seizure in EEG signals and also showed a reasonable accuracy in detection.

Without the help of measurement quality software cannot be built. Measurement is an essential aspect for achieving the basic management objectives of prediction, progress, and process improvement. Software measurement is the raw data associated with various elements of the software process and product. It acts as a quantitative basis for the development and validation of models for a software development process. The major goal of software metrics is identification and measurement of the essential parameters affecting the software development. An oft-repeated phrase by De Marco holds true; “You can’t manage what you can’t measure!” [DEM86]. All process improvement must be based on measuring where you have been, where you are now, and properly using the data to predict where you are heading. Collecting good metrics and properly using them always leads to process improvement! This paper gives an exhaustive overview of metrics used in software development in different language paradigms. Metrics are classified as procedure oriented, object oriented and web oriented metrics. The object oriented metrics are further classified into Chidamber and MOOD metrics. In an object-oriented system, traditional metrics which are generally applied to the methods comprising the operations of a class is also highlighted. Web based objects further classified as multimedia files, web building blocks, scripts and links are also described.

Motivation plays an integral role in any kind of learning in general and consequently language learning in particular. Demotivation,the other side of motivation (Dornyei, 2001a), is regarded as a very significant factor in language learning. As Dornyei (2001b) postulates in second language learning ,demotivation has a central role and more studiesare required to be undertaken in this regard(p. 5). However, there is a dearth of research in demotivation in both EFL and ESL contexts.They are categorized into three majorfields: Causes related to a) instructors, b) learning/teaching context, and c) learners.

Severe malaria continues to be one of the most common and serious life-threatening disease causing high morbidity and mortality world-wide. Multivariate statistical techniques including principal component analysis (PCA) and hierarchical agglomerative cluster analysis (HCA) were applied to the raw data of severe malaria cases. PCA results identified two varifactors which were responsible for about 53% of the total variance in the data set. Rainfall possesses the highest strong positive loading (0.718) among the variables. HCA identified three spatial clusters representing high, moderate and low incidence zones. The study suggest prompt reporting of malaria cases to modern healthcare centres for proper attention so as to reduce the mortality rate attached to the disease.

In this article we introduce different proofs to the total sum of special sequences given by muslem mathematicians during the golden age of Islamic civilization