Mobile application (app) use is increasingly becoming an essential part of our daily lives. Due to their significant usefulness, people rely on them to perform multiple tasks seamlessly in almost all aspects of everyday life. Similarly, there has been immense progress in artificial intelligence (AI) technology, especially deep learning, computer vision, natural language processing, and robotics. These technologies are now actively being implemented in smartphone apps and healthcare, providing multiple healthcare services. However, several factors affect the usefulness of mobile healthcare apps, and usability is an important one. There are various healthcare apps developed for each specific task, and the success of these apps depends on their performance. This study presents a systematic review of the existing apps and discusses their usability attributes. It highlights the usability models, outlines, and guidelines proposed in previous research for designing apps with improved usability characteristics. Thirty-nine research articles were reviewed and examined to identify the usability attributes, framework, and app design conducted. The results showed that satisfaction, efficiency, and learnability are the most important usability attributes to consider when designing eHealth mobile apps. Surprisingly, other significant attributes for healthcare apps, such as privacy and security, were not among the most indicated attributes in the studies.

Medication errors related to medication administration done by both doctors and nurses can be considered a vital issue around the world. It is believed that systematisation and the introduction of main documents are done manually, which might increase the opportunities to have inaccuracies and errors because of unexpected wrong actions done by medical practitioners. Experts stated that the lack of pharmacological knowledge is one of the key factors, which play an important role in causing such errors. Doctors and nurses may face problems when they move from one unit to another and the medication administration list has changed. However, promoting public health activities and recent AI-enabled applications can provide general information about medication that helps both doctors and nurses administer the right medication. However, such an application can require a lot of time and effort to search and then find a medication. Therefore, this article aims to investigate whether AI-enabled applications can help avoid or at least minimize medication error rates.

In this work, the performance of coded continuous phase modulation (CPM) transceivers for orthogonal frequency-division multiplexing (OFDM) in underwater acoustic channels is evaluated. The proposed technique employs the Bose–Chaudhuri–Hocquenghem (BCH) class of cyclic codes, and a CPM-based mapper is being used in place of a traditional OFDM mapper. Bit-error-rates (BERs) for various distances are evaluated. We use the Bellhop acoustic channel simulator that is considered to be very close to a real underwater channel. The performance of the proposed system is evaluated for 23 values of the CPM modulation index $h$ as a function of the distance between the transmitter (Tx) and receiver (Rx). Based upon the error performance, we identified several best-performing CPM indices. We also observed that for Tx–Rx distances of 100 and 250 m, the proposed system gives acceptable performance without the need for equalization. We also compared the out-of-band radiation of the proposed system with PSK-OFDM and observed that the uncoded CPM-OFDM has a better out-of-band (OOB) performance than the traditional OFDM. Moreover, a comparison of BERs with the traditional PSK-OFDM system shows that the proposed system outperforms the traditional OFDM system by a wide margin.

Understanding how media narratives frame the Sustainable Development Goals (SDGs) is essential for global sustainability governance. This study presents a novel, data-driven analysis of 135,000 news articles mapped to SDGs 1–17 across 100 countries. Using polarity-based sentiment aggregation and principal component analysis (PCA), we reduce high-dimensional SDG sentiment profiles into a two-dimensional space and identify emergent clusters of countries using K-means. To contextualize these clusters, we integrate national-level indicators like Human Development Index (HDI), GDP per capita, CO₂ emissions, and press freedom scores, revealing robust correlations between sentiment structure and developmental attributes. Countries with higher HDI and freer media environments produce more optimistic and diverse SDG narratives, while lower-HDI countries tend toward more polarized or crisis-framed coverage. Our findings offer a typology of SDG discourse that reflects geopolitical, environmental, and informational asymmetries, providing new insights to support international policy coordination and sustainability communication. This work contributes a scalable methodology for monitoring global sustainability sentiment and underscores the importance of narrative equity in achieving Agenda 2030.

Accurate segmentation and classification of adipose tissue are critical tasks in the field of medical imaging, as they contribute to enhanced medical diagnostics. However, manual segmentation and classification are time-consuming and prone to variability. These limitations drive the development of automated and efficient artificial intelligence (AI) methods, including machine learning (ML) and deep learning (DL). A wide range of ML and DL techniques now exist, offering diverse strategies for medical image analysis and early disease detection. Given this growing body of work, a systematic literature review is necessary to synthesize evidence, compare methodologies, and identify research gaps. Existing literature reviews on adipose segmentation and classification often focus on specific imaging modalities or limited disease applications. Nevertheless, none provide a systematic quantitative evaluation of ML and DL methods. Such a systematic quantitative evaluation is essential for a fair comparison of methodologies and guiding the development of more robust AI models. Moreover, existing reviews overlook key challenges, including limited annotated datasets, feature extraction, and predictive modeling for disease risk. This systematic literature review investigates ML and DL techniques for adipose tissue segmentation, classification, and disease risk prediction. An extensive search across four major scientific databases is conducted, followed by rigorous selection criteria that yield forty high-quality studies. The selected studies are classified into two types: ML-based segmentation and classification, covering fourteen studies, and DL-based segmentation and classification, covering twenty-six studies. Within the ML-based approaches, further analysis focuses on four major radiomic features, providing deeper insights into feature extraction techniques. In contrast, among the twenty-six DL-based studies, nine specifically address limited annotated data, with a focus on semi-supervised learning strategies and data augmentation methods. The review also presents a critical analysis of dataset constraints as well as the inherent limitations of both ML and DL architectures. To provide a more detailed perspective, ML and DL techniques are further examined across applications in ten different disease types. In addition, the role of adipose tissue biomarkers in disease risk prediction is analyzed. Despite these advancements, the review identifies challenges related to dataset limitations, annotation constraints, feature extraction, model interpretability, prediction reliability in disease risk assessment, and integration into clinical practice. Consequently, future research directions should focus on improving imaging modalities, expanding standardized datasets, advancing DL architectures and hybrid models, enhancing generalization and interpretability, and ensuring seamless clinical workflow integration.