Career Development an Imperative of Job Satisfaction and Career Commitment: Empirical Evidence from Pakistani Employees in Banking Sector

The idea of strengthening human capital to beginning creativeness, business soul, and advancement through preparing the careers of institutional members using HRM policies and methods to develop different skills, mindsets and expertise with the ultimate aim to provide a range of innovative goods and services is gaining attention. The overall perspective for the research study was to discover the effects and outcomes of profession growth initiatives on companies and employees. The survey is conducted to collect data from the Banking sector in Islamabad and sample selected is of five major private banks. The data is analyzed by using SPSS and Amos to authenticate the model and propositions made by the researcher. Organizations invest resources in profession growth kinds of actions for recruiting, there tends to be less investment in similar kinds of actions for worker retention. This paper examines the link between profession preparing and profession control as antecedents of profession growth and job fulfillment, and profession dedication as its outcome. There is a significant link between the factors of profession preparing and profession control, and profession growth, and in turn, with job fulfillment and profession dedication. The paper converses about the significances of these conclusions for career development

Emotion detection from handwriting and drawing samples using an attention-based transformer model

© 2024 The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY), https://creativecommons.org/licenses/by/4.0/Emotion detection (ED) involves the identification and understanding of an individual’s emotional state through various cues such as facial expressions, voice tones, physiological changes, and behavioral patterns. In this context, behavioral analysis is employed to observe actions and behaviors for emotional interpretation. This work specifically employs behavioral metrics like drawing and handwriting to determine a person’s emotional state, recognizing these actions as physical functions integrating motor and cognitive processes. The study proposes an attention-based transformer model as an innovative approach to identify emotions from handwriting and drawing samples, thereby advancing the capabilities of ED into the domains of fine motor skills and artistic expression. The initial data obtained provides a set of points that correspond to the handwriting or drawing strokes. Each stroke point is subsequently delivered to the attention-based transformer model, which embeds it into a high-dimensional vector space. The model builds a prediction about the emotional state of the person who generated the sample by integrating the most important components and patterns in the input sequence using self-attentional processes. The proposed approach possesses a distinct advantage in its enhanced capacity to capture long-range correlations compared to conventional recurrent neural networks (RNN). This characteristic makes it particularly well-suited for the precise identification of emotions from samples of handwriting and drawings, signifying a notable advancement in the field of emotion detection. The proposed method produced cutting-edge outcomes of 92.64% on the benchmark dataset known as EMOTHAW (Emotion Recognition via Handwriting and Drawing).Peer reviewe

Measuring Change Over Time: A Systematic Review of Evaluative Measures of Cognitive Functioning in Traumatic Brain Injury

Objectives: The purpose of evaluative instruments is to measure the magnitude of change in a construct of interest over time. The measurement properties of these instruments, as they relate to the instrument's ability to fulfill its purpose, determine the degree of certainty with which the results yielded can be viewed. This work systematically reviews all instruments that have been used to evaluate cognitive functioning in persons with traumatic brain injury (TBI), and critically assesses their evaluative measurement properties: construct validity, test-retest reliability, and responsiveness.Data Sources: MEDLINE, Central, EMBASE, Scopus, PsycINFO were searched from inception to December 2016 to identify longitudinal studies focused on cognitive evaluation of persons with TBI, from which instruments used for measuring cognitive functioning were abstracted. MEDLINE, instrument manuals, and citations of articles identified in the primary search were then screened for studies on measurement properties of instruments utilized at least twice within the longitudinal studies.Study Selection: All English-language, peer-reviewed studies of longitudinal design that measured cognition in adults with a TBI diagnosis over any period of time, identified in the primary search, were used to identify instruments. A secondary search was carried out to identify all studies that assessed the evaluative measurement properties of the instruments abstracted in the primary search.Data Extraction: Data on psychometric properties, cognitive domains covered and clinical utility were extracted for all instruments.Results: In total, 38 longitudinal studies from the primary search, utilizing 15 instruments, met inclusion and quality criteria. Following review of studies identified in the secondary search, it was determined that none of the instruments utilized had been assessed for all the relevant measurement properties in the TBI population. The most frequently assessed property was construct validity.Conclusions: There is insufficient evidence for the validity and reliability of instruments measuring cognitive functioning, longitudinally, in persons with TBI. Several instruments with well-defined construct validity in TBI samples warrant further assessment for test-retest reliability and responsiveness.Registration Number:www.crd.york.ac.uk/PROSPERO/, identifier CRD42017055309

ZnO Nanoparticle-Mediated Seed Priming Induces Biochemical and Antioxidant Changes in Chickpea to Alleviate Fusarium Wilt

Chickpea (Cicer arietinum L.) is one of the main pulse crops of Pakistan. The yield of chickpea is affected by a variety of biotic and abiotic factors. Due to their environmentally friendly nature, different nanoparticles are being synthesized and applied to economically important crops. In the present study, Trichoderma harzianum has been used as a stabilizing and reducing agent for the mycosynthesis of zinc oxide nanoparticles (ZnO NPs). Before their application to control Fusarium wilt of chickpea, synthesized ZnO NPs were characterized. X-ray diffraction (XRD) analysis revealed the average size (13 nm) of ZnO NPs. Scanning electron microscopy (SEM) indicated their spherical structure, and energy dispersive X-ray analysis (EDX) confirmed the oxide formation of ZnO NPs. Transmission electron microscopy (TEM) described the size and shape of nanoparticles, and Fourier transform infrared (FTIR) spectroscopy displayed the presence of reducing and stabilizing chemical compounds (alcohol, carboxylic acid, amines, and alkyl halide). Successfully characterized ZnO NPs exhibited significant mycelial growth inhibition of Fusarium oxysporum, in vitro. In a greenhouse pot experiment, the priming of chickpea seeds with ZnO NPs significantly increased the antioxidant activity of germinated plants and they displayed 90% less disease incidence than the control. Seed priming with ZnO NPs helped plants to accumulate higher quantities of sugars, phenol, total proteins, and superoxide dismutase (SOD) to create resistance against wilt pathogen. These nanofungicides were produced in powder form and they can easily be transferred and used in the field to control Fusarium wilt of chickpea

Resistance associated metabolite profiling of Aspergillus leaf spot in cotton through non-targeted metabolomics.

Aspergillus tubingensis is an important pathogen of economically important crops. Different biotic stresses strongly influence the balance of metabolites in plants. The aim of this study was to understand the function and response of resistance associated metabolites which, in turn are involved in many secondary metabolomics pathways to influence defense mechanism of cotton plant. Analysis of non-targeted metabolomics using ultra high performance liquid chromatography-mass spectrometry (UPLC-MS) revealed abundant accumulation of key metabolites including flavonoids, phenylpropanoids, terpenoids, fatty acids and carbohydrates, in response to leaf spot of cotton. The principal component analysis (PCA), orthogonal partial least squares discriminant analysis (OPLS-DA) and partial least squares discriminant analysis (PLS-DA) score plots illustrated the evidences of variation between two varieties of cotton under mock and pathogen inoculated treatments. Primary metabolism was affected by the up regulation of pyruvate and malate and by the accumulation of carbohydrates like cellobiose and inulobiose. Among 241 resistance related (RR) metabolites, 18 were identified as resistance related constitutive (RRC) and 223 as resistance related induced (RRI) metabolites. Several RRI metabolites, identified in the present study were the precursors for many secondary metabolic pathways. These included phenylpropanoids (stilbenes and furanocoumarin), flavonoids (phlorizin and kaempferol), alkaloids (indolizine and acetylcorynoline) and terpenoids (azelaic acid and oleanolic acid). Our results demonstrated that secondary metabolism, primary metabolism and energy metabolism were more active in resistant cultivar, as compared to sensitive cultivar. Differential protein and fatty acid metabolism was also depicted in both cultivars. Accumulation of these defense related metabolites in resistant cotton cultivar and their suppression in susceptible cotton cultivar revealed the reason of their respective tolerance and susceptibility against A. tubingensis

A user-driven machine learning approach for RNA-based sample discrimination and hierarchical classification

Summary: RNA-based sample discrimination and classification can be used to provide biological insights and/or distinguish between clinical groups. However, finding informative differences between sample groups can be challenging due to the multidimensional and noisy nature of sequencing data. Here, we apply a machine learning approach for hierarchical discrimination and classification of samples with high-dimensional miRNA expression data. Our protocol comprises data preprocessing, unsupervised learning, feature selection, and machine-learning-based hierarchical classification, alongside open-source MATLAB code. : Publisher’s note: Undertaking any experimental protocol requires adherence to local institutional guidelines for laboratory safety and ethics