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An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
An entry from the Cambridge Structural Database, the world’s repository for small molecule crystal structures. The entry contains experimental data from a crystal diffraction study. The deposited dataset for this entry is freely available from the CCDC and typically includes 3D coordinates, cell parameters, space group, experimental conditions and quality measures.
A highly sensitive sensor based on ZnO@SiO<sub>2</sub>nanospheres has been developed for the detection of ascorbic acid. The developed sensor is very simple and has been fabricated using low cost materials.
Medical misinformation on social media has emerged as a critical challenge for public health, undermining trust in healthcare systems and promoting harmful practices. The rapid spread of unverified health information is fueled by social media algorithms prioritizing engagement over accuracy, creating echo chambers that reinforce misinformation. This phenomenon poses significant ethical dilemmas for healthcare providers, who must balance their responsibility to correct false claims with maintaining professional integrity and patient trust. Engaging with misinformation requires careful navigation of reputational risks, time constraints, and the emotional dynamics of online interactions. Healthcare providers are uniquely positioned to address misinformation by leveraging their expertise and public trust, but effective strategies require collaboration with social media platforms and systemic changes. Measures such as enhancing digital health literacy, promoting verified professional accounts, and utilizing multimedia content have proven effective in countering misinformation. Training healthcare providers in digital communication skills enables them to engage more effectively, while proactive dissemination of evidence-based content can prevent the spread of false claims. Furthermore, partnerships between healthcare organizations and technology companies play a vital role in moderating content and amplifying accurate information. Addressing misinformation also requires a focus on health literacy among the public, empowering individuals to critically evaluate online health information. Predictive tools and trend analyses can help healthcare organizations identify emerging misinformation and respond with timely, accurate content. By fostering trust, enhancing communication, and implementing multifaceted strategies, healthcare providers and organizations can mitigate the impact of medical misinformation and protect public health. The integration of these approaches underscores the need for a collective effort to navigate the ethical and practical complexities of combating misinformation in the digital age.
Federated Learning has emerged as an approach to distributed learning that utilizes artificial intelligence (AI) to protect data privacy on edge networks and devices. However, Federated Learning-based Internet of Things (IoT) edge networks can still be vulnerable to distributed denial of service (DDoS) attacks which can negatively influence the operations of Federated Learning models running on these networks. Current methods for detecting DDoS primarily focus on securing devices and data, overlooking model protection. In this paper, we utilize and adapt Federated Explainable AI (FedXAI), a Federated Learning designed with SHapley Additive exPlanations (SHAP) to enhance DDoS detection and interpretation within Federated Learning on IoT networks. FedXAI provides interpretable insights into the models that can be crucial for identifying anomalies indicative of DDoS. Our results show that FedXAI improves DDoS detection, contributing to data and model security with higher accuracy, precision, recall, and F-score than the selected baseline models.
Herein, the micellization phenomena of an amphiphilic antidepressant drug nortriptyline hydrochloride (NOT) have been studied using tensiometric technique in the absence and presence of different concentration of inorganic salts (NaCl, NaBr and KCl) at 298.15 K. NOT is employed for the relief of symptoms of depression. In presence of inorganic salt the CMC value decreases which is explained on the basis of nature and ion size. Various parameters, i.e., the maximum surface excess concentration at the air/solution interface (Γmax), minimum area per head group at the air/solution interface (Amin), free energy of micellization (ΔG m o), minimum energy of surface (G min) and standard Gibbs energy of adsorption (ΔG ads o) were evaluated and discussed in detail.
The problem of predicting crystal structures is discussed in the context of artificial intelligence systems.
