Human resource management´s practices and organizational change: the role of high-performance human resource management practices in enhancing employees´ readiness for change. Evidence from Jordanian banking sector

Using a sample of bank employees in Jordan, this study seeks to analyse how high performance human resource management practices and organizational commitment impact employees readiness for change. We also seek to study the role of readiness for change in improving employee performance. The results demonstrate a positive association between some high performance Human resource management practices and both affective commitment and readiness for change. Results also show a positive relationship between affective commitment and readiness for change. We have also found that readiness for change positively influences employees individual performance. Hierarchy culture moderates the relation of high performance HRM practices with affective commitment

Screening of Irish Fruit and Vegetable Germplasm for Novel Anti-tumour and Pesticidal Compounds

Conference paperPhytochemicals are a rich source of novel therapeutic and insecticidal agents (McLaughlin and Chang, 1999). Considerable research effort has been directed at screening exotic and medicinal plants in the search for novel products. However, plants which have traditional food uses have been little explored. In addition the range, type and level of individual bioactive compounds can vary significantly between different species, different cultivars of the same species and different tissue types of the plant (Reilly, in press) Therefore, the objective of this study was to screen a range of fruits and vegetables which can be grown in Ireland for novel bioactive compounds for use in food production and as bio-pesticides.The author wishes to acknowledge the financial support from the Dublin Institute of Technology through an ABBEST fellowshi

Automatic Identity Recognition Using Speech Biometric

Biometric technology refers to the automatic identification of a person using physical or behavioral traits associated with him/her. This technology can be an excellent candidate for developing intelligent systems such as speaker identification, facial recognition, signature verification...etc. Biometric technology can be used to design and develop automatic identity recognition systems, which are highly demanded and can be used in banking systems, employee identification, immigration, e-commerce…etc. The first phase of this research emphasizes on the development of automatic identity recognizer using speech biometric technology based on Artificial Intelligence (AI) techniques provided in MATLAB. For our phase one, speech data is collected from 20 (10 male and 10 female) participants in order to develop the recognizer. The speech data include utterances recorded for the English language digits (0 to 9), where each participant recorded each digit 3 times, which resulted in a total of 600 utterances for all participants. For our phase two, speech data is collected from 100 (50 male and 50 female) participants in order to develop the recognizer. The speech data is divided into text-dependent and text-independent data, whereby each participant selected his/her full name and recorded it 30 times, which makes up the text-independent data. On the other hand, the text-dependent data is represented by a short Arabic language story that contains 16 sentences, whereby every sentence was recorded by every participant 5 times. As a result, this new corpus contains 3000 (30 utterances * 100 speakers) sound files that represent the text-independent data using their full names and 8000 (16 sentences * 5 utterances * 100 speakers) sound files that represent the text-dependent data using the short story. For the purpose of our phase one of developing the automatic identity recognizer using speech, the 600 utterances have undergone the feature extraction and feature classification phases. The speech-based automatic identity recognition system is based on the most dominating feature extraction technique, which is known as the Mel-Frequency Cepstral Coefficient (MFCC). For feature classification phase, the system is based on the Vector Quantization (VQ) algorithm. Based on our experimental results, the highest accuracy achieved is 76%. The experimental results have shown acceptable performance, but can be improved further in our phase two using larger speech data size and better performance classification techniques such as the Hidden Markov Model (HMM)

Uncovering the genetic architecture of spike related traits in bread wheat: a viable alternative to increase yield potential

Non-Peer ReviewedBased on the projected demand, further improvements in wheat grain yield are required. In this sense, exploring the genetic diversity associated with yield related traits is critical to derive superior progenies from crossing and selection. However, the possible presence of trade-off between traits must be considered to determine their relevance for improving yield potential. In this study, we determined the phenotypic and genetic relationships between twelve spike related traits and their genetic basis through an association mapping study using a 15K Infinium SNP array, characterized in a bread wheat panel. To identify potential candidate genes, regions of interest were positioned onto the newly released wheat reference genome sequence by blasting their peaking marker sequences against the IWGSC RefSeq v1.0. From all the analyzed traits, grain number per fertile spikelet (GFS) showed the highest correlation with grain number per spike (GNS), whereas there was no relationship with thousand kernel weight (TKW). As a result, significant increases in grain weight per spike (GWS) associated with higher GFS was observed. Interestingly, GFS was mostly explained by spikelet weight (SW), indicating that improvements in yield potential could be achieved through partition improving within the spike. In addition, the genetic analysis showed independent genetic control between GFS and both, GNS and TKW, suggesting the potential value of GFS as selection criterion to increase yield potential in wheat breeding programs. A total of 54 significant marker-trait associations were detected for spike related traits, including two genomic regions on 1B and 7A linked to GFS and 6 genomic regions located on 1A, 1B, 2B, 3A, 5A and 7B associated to SW. The potential candidate genes for these regions included several sugar transporter and carbohydrate-binding protein. The markers linked to GFS and SW are really promising, especially considering that due to the destructive phenotypic determination, their improvement in early breeding generations can only be made by marker-assisted selection