12 research outputs found
Psychological Contract Breach and Its Consequences on Employee Turnover Intentions, Job Satisfaction, and Organizational Commitment: Insights from Human Resource Management and Workplace Psychology
No full textThis study investigated the impact of psychological contract breach on employee outcomes, focusing on turnover intentions, job satisfaction, and organizational commitment. Psychological contracts, which encompass the unwritten expectations between employees and employers, play a central role in maintaining workplace trust and stability. When these implicit agreements are perceived as violated, employees often experience reduced satisfaction, weakened organizational commitment, and a stronger inclination to leave their jobs. Using a quantitative research design, data were collected from employees across multiple organizational contexts, and regression analysis was applied to determine the relationships among variables. The findings revealed that psychological contract breach significantly increased turnover intentions while exerting a negative effect on both job satisfaction and organizational commitment. These results underscored the importance of organizational transparency, fair HR practices, and effective communication in mitigating contract breaches. The study contributed to the growing body of knowledge on employee-employer relations by emphasizing the critical role of psychological contracts in shaping workplace attitudes and behaviours. Moreover, it highlighted the need for organizations to address breaches proactively to retain talent and sustain long-term performance. Future research should explore the role of mediating factors, such as organizational justice or leadership style, and examine psychological contract dynamics across industries and cultures.
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Improved numerical solutions for chaotic-cancer-model
No full textIn biological sciences, dynamical system of cancer
model is well
known due to its sensitivity and chaoticity. Present work provides detailed computational
study of cancer
model by
counterbalancing its sensitive dependency on initial conditions and parameter values.
Cancer chaotic
model is
discretized into a system of nonlinear equations that are solved using the well-known
Successive-Over-Relaxation (SOR) method with a proven convergence. This technique enables
to solve large systems and provides more accurate approximation which is illustrated
through tables, time history maps and phase portraits with detailed analysis
EFFECT OF STATE MINDFULNESS ON EMPLOYEE JOB SATISFACTION AND WELL-BEING: ROLE OF WORK-FAMILY BALANCE
No full textThis study aims to know how; committed and satisfied employee is an asset to an organization. How’ Scan help the organization to compete and grow aggressively in a dynamic global environment. Therefore, state m unless is a factor that increases employee job satisfaction and well-being by ensuring work-life balance as a mediator which is vital for organizational productivity and profitability. Quantitative data from 400 respondents of the telecom and banking sector of Pakistan has been collected through self-admin red questionnaires for analysis. The collected data has been analyzed through IBM SPSS 23 to explore the relationship among independent and dependent variables and the mediating effect of work-life balance has also been substantiated. Testing of mode confirms the positive impact of state mindfulness on employee job satisfaction and well-being by ensuring work-life balance as a mediator. This research is providing a theoretical and empirical foundation to link state mindfulness with employee job satisfaction and wellbeing of employees. This study also adds to the limited stick of knowledge, providing a mediating link of work-family balance between state mindfulness and job satisfaction and employee well-being. This study also broadens previous research findings
A Review on Secure Authentication Mechanisms for Mobile Security
No full textCybersecurity, complimenting authentication, has become the backbone of the Internet of Things. In the authentication process, the word authentication is of the utmost importance, as it is the door through which both Mr. Right Guy and Mr. Wrong Guy can pass. It is the key to opening the most important and secure accounts worldwide. When authentication is complete, surely there will be passwords. Passwords are a brain-confusing option for the user to choose when making an account during the registration/sign-up process. Providing reliable, effective, and privacy-preserving authentication for individuals in mobile networks is challenging due to user mobility, many attack vectors, and resource-constrained devices. This review paper explores the transformation and modern mobile authentication schemes, categorizing them into password, graphical, behavioral, keystroke, biometric, touchscreen, color, and gaze-based methodologies. It aims to examine the strengths and limitations focused on challenges like security and usability. Standard datasets and performance evaluation measures are also discussed. Finally, research gaps and future directions in this essential and emerging area of research are discussed
Synthesis, characterization, DNA-Binding, enzyme inhibition and antioxidant studies of new N -methylated derivatives of pyridinium amine
No full textSpam Email Detection Using Long Short-Term Memory and Gated Recurrent Unit
No full textIn today’s business environment, emails are essential across all sectors, including finance and academia. There are two main types of emails: ham (legitimate) and spam (unsolicited). Spam wastes consumers’ time and resources and poses risks to sensitive data, with volumes doubling daily. Current spam identification methods, such as Blocklist approaches and content-based techniques, have limitations, highlighting the need for more effective solutions. These constraints call for detailed and more accurate approaches, such as machine learning (ML) and deep learning (DL), for realistic detection of new scams. Emphasis has since been placed on the possibility that ML and DL technologies are present in detecting email spam. In this work, we have succeeded in developing a hybrid deep learning model, where Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU) are applied distinctly to identify spam email. Despite the fact that the other models have been applied independently (CNNs, LSTM, GRU, or ensemble machine learning classifier) in previous studies, the given research has provided a contribution to the existing body of literature since it has managed to combine the advantage of LSTM in capturing the long-term dependency and the effectiveness of GRU in terms of computational efficiency. In this hybridization, we have addressed key issues such as the vanishing gradient problem and outrageous resource consumption that are usually encountered in applying standalone deep learning. Moreover, our proposed model is superior regarding the detection accuracy (90%) and AUC (98.99%). Though Transformer-based models are significantly lighter and can be used in real-time applications, they require extensive computation resources. The proposed work presents a substantive and scalable foundation to spam detection that is technically and practically dissimilar to the familiar approaches due to the powerful preprocessing steps, including particular stop-word removal, TF-IDF vectorization, and model testing on large, real-world size dataset (Enron-Spam). Additionally, delays in the feature comparison technique within the model minimize false positives and false negatives
Association of serum levels of carbohydrate antigen 19-9 (CA19-9) and carcinoembryonic antigen (CEA) with diabetes mellitus (DM): a systematic review and meta-analysis
No full textMagnetic Levitation System Isolates and Purifies Airborne Viruses
No full textDetection
of viable viruses in the air is critical in
order to
determine the level of risk associated with the airborne diffusion
of viruses. Different methods have been developed for the isolation,
purification, and detection of viable airborne viruses, but they require
an extensive processing time and often present limitations including
low physical efficiency (i.e., the amount of collected viruses), low
biological efficiency (i.e., the number of viable viruses), or a combination
of all. To mitigate such limitations, we have employed an efficient
technique based on the magnetic levitation (Maglev) technique with
a paramagnetic solution and successfully identified distinct variations
in levitation and density characteristics among bacteria (Escherichia coli), phages (MS2), and human viruses
(SARS-CoV-2 and influenza H1N1). Notably, the Maglev approach enabled
a significant enrichment of viable airborne viruses in air samples.
Furthermore, the enriched viruses obtained through Maglev exhibited
high purity, rendering them suitable for direct utilization in subsequent
analyses such as reverse transcription-polymerase chain reaction (RT-PCR)
or colorimetric assays. The system is portable, easy to use, and cost-efficient
and can potentially provide proactive surveillance data for monitoring
future outbreaks of airborne infectious diseases and allow for the
induction of various preventative and mitigative measures
