The sense of belonging in any organization is vital to generate a work motivation with the objective of a good organizational performance, because of this, companies usually take this point into account, ensuring that this leads to greater performance. For this reason, the objective of this article is to determine the relationship between the sense of belonging and the work motivation in the workers of a small Peruvian research company. For this purpose, a quantitative methodology was used, with a cross-sectional descriptive design. The instrument used was a survey consisting of 10 items, which were interpreted using the Likert scale. The survey was conducted and delivered to 24 workers, who were selected by non-probabilistic convenience sampling. After verifying the validity of the instrument and the study variables by means of Cronbach's Alpha statistic, we proceeded to determine the existence of correlation between the variables, which, using Spearman's Rho coefficient, obtained a 70.2% which demonstrates a moderate positive correlation, therefore it indicates that employees feel highly motivated as they feel an indispensable part of the company, therefore they feel job satisfaction by being part of the organization.
The MDA-MB-231 cell line is derived from triple-negative breast cancer (TNBC), representing one of the most aggressive forms of breast cancer. Innovative therapeutic strategies, including s targeted therapies using nanocarriers, hold significant promise, particularly for difficult-to-treat cancers such as TNBC. Nanoparticles have transformed the medical field by serving as advanced drug delivery systems for cancer treatment. They play a critical role in overcoming the drug resistance often associated with cancer therapies. When utilized as drug delivery vehicles, nanoparticles can specifically target cancer cells and effectively reduce or eliminate multidrug resistance. Among them, chitosan-coated magnetic nanoparticles (MNPs) have been widely explored for the loading and controlled release of various anticancer agents. In this study, we evaluated the effects of dexamethasone-loaded chitosan-coated MNPs on MDA-MB-231 cell lines. Fourier transform infrared spectroscopy and scanning electron microscopy were employed to verify the successful loading of dexamethasone onto the nanoparticles. To assess cytotoxicity, empty nanoparticles, free drug, and drug-loaded nanoparticles were tested on the cells. The results indicated that empty nanoparticles exhibited no toxic effects. The IC50 value of the free drug was 123 µg/mL, while the IC50 value of the drug-loaded nanoparticles was significantly lower, at 63 µg/mL. These findings confirmed the successful conjugation of dexamethasone to the chitosan-coated MNPs, demonstrating substantial cytotoxic effects on breast cancer cells. Although dexamethasone has been reported to exhibit both tumor-suppressive and pro-metastatic effects, its specific impact on TNBC warrants further investigation in future studies.
The expanding adoption of artificial intelligence systems across high-impact sectors has catalyzed concerns regarding inherent biases and discrimination, leading to calls for greater transparency and accountability. Algorithm auditing has emerged as a pivotal method to assess fairness and mitigate risks in applied machine learning models. This systematic literature review comprehensively analyzes contemporary techniques for auditing the biases of black-box AI systems beyond traditional software testing approaches. An extensive search across technology, law, and social sciences publications identified 22 recent studies exemplifying innovations in quantitative benchmarking, model inspections, adversarial evaluations, and participatory engagements situated in applied contexts like clinical predictions, lending decisions, and employment screenings. A rigorous analytical lens spotlighted considerable limitations in current approaches, including predominant technical orientations divorced from lived realities, lack of transparent value deliberations, overwhelming reliance on one-shot assessments, scarce participation of affected communities, and limited corrective actions instituted in response to audits. At the same time, directions like subsidiarity analyses, human-cent
The debate on the effect of work environment on job satisfaction is very inconclusive. Most of the existing literature has focused on either the developed economy or job satisfaction and other variables other than the dimensions of the work environment. To fill the contextual and conceptual gap this study examined the effect of dimensions of work environment on job satisfaction among public sector workers in a developing economy. The study used the quantitative method and positivist philosophical viewpoint but specifically, the explanatory design was used to guide the study. A structured questionnaire was used for data collection and data analysis was done by partial least square modelling. The study found that the three dimensions of work environment such as physical, psychological and administrative work environment had a significant relationship with job satisfaction among public workers in a developing economy. It was recommended that the management of public sector organisations should improve upon the psychological, physical and administrative work environment to ensure job satisfaction among their workers.
The rapid expansion of smart cities has led to the widespread deployment of Internet of Things (IoT) devices for real-time data collection and urban optimization. However, these interconnected systems face critical cybersecurity risks, including data tampering, unauthorized access, and privacy breaches. This paper proposes a blockchain-based framework designed to enhance the security, integrity, and resilience of IoT data in smart city environments. Leveraging a private blockchain, the system ensures decentralized, tamper-proof data storage, and transaction verification through digital signatures and a lightweight Proof of Work consensus mechanism. Smart contracts are employed to automate access control and respond to anomalies in real time. A Python-based simulation demonstrates the framework’s effectiveness in securing IoT communications. The system supports rapid transaction validation with minimal latency and enables timely detection of anomalous patterns through integrated machine learning. Evaluations show that the framework maintains consistent performance across diverse smart city components such as transportation, healthcare, and building security. These results highlight the potential of the proposed solution to enable secure, scalable, and real-time IoT ecosystems for modern urban infrastructures.
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