Recently, there has been a burgeoning fascination with the influence of urban green spaces (UGS) on physical activity (PA) and health. This interest has been accompanied by a mounting body of evidence that establishes a connection between UGS and residents’ PA levels. Numerous studies have been conducted to investigate the significance of UGS and have generally agreed on their connection with health. However, there is still considerable variation in viewpoints regarding the intermediate factors contributing to this association. The primary objective of this study was to investigate the potential correlation between different qualitative factors of UGS and PA. The study involved the collection of data from four parks located in Edinburgh. Four trained observers utilised the Environmental Assessment of Public Recreational Spaces (EARPS Mini) tool to code various environmental characteristics. Additionally, the Method for Observing Physical Activity and Wellbeing (MOHAWk) observation tool was employed to code instances of on-site incivility and the characteristics and behaviours of residents engaging in UGS activities. The results of this study show that the facilities and environment, area and socioeconomic status (SES) of UGS positively affect the type of PA and the level of PA, as well as influence residents’ attentiveness to the environment and their interactions with each other. Demographics such as gender and age group are also significantly related to the level and type of PA. Significant differences in the level and type of PA, and race only differed significantly in the choice of activity type. These results suggest that the quality of the UGS environment affects the level, type, and status of PA among residents and that resident characteristics also have an impact. Future research suggests increasing data collection related to PA frequency and PA duration and considering longitudinal observations over time for refinement.

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.

Photovoltaic systems have shown significant attention in energy systems due to the recent machine learning approach to addressing photovoltaic technical failures and energy crises. A precise power production analysis is utilized for failure identification and detection. Therefore, detecting faults in photovoltaic systems produces a considerable challenge, as it needs to determine the fault type and location rapidly and economically while ensuring continuous system operation. Thus, applying an effective fault detection system becomes necessary to moderate damages caused by faulty photovoltaic devices and protect the system against possible losses. The contribution of this study is in two folds: firstly, the paper presents several categories of photovoltaic systems faults in literature, including line-to-line, degradation, partial shading effect, open/close circuits and bypass diode faults and explores fault discovery approaches with specific importance on detecting intricate faults earlier unexplored to address this issue; secondly, VOSviewer software is presented to assess and review the utilization of machine learning within the solar photovoltaic system sector. To achieve the aims, 2258 articles retrieved from Scopus, Google Scholar, and ScienceDirect were examined across different machine learning and energy-related keywords from 1990 to the most recent research papers on 14 January 2025. The results emphasise the efficiency of the established methods in attaining fault detection with a high accuracy of over 98%. It is also observed that considering their effortlessness and performance accuracy, artificial neural networks are the most promising technique in finding a central photovoltaic system fault detection. In this regard, an extensive application of machine learning to solar photovoltaic systems could thus clinch a quicker route through sustainable energy production.

The modification of the Turia River's course in the 1960s marked a pivotal transformation in Valencia's urban landscape, evolving from a flood protection measure into a hallmark of sustainable urban development. However, recent rainfalls and flooding events produced directly by the phenomenon known as DANA ((Isolated Depression at High Levels) in October 2024 have exposed vulnerabilities in the infrastructure, particularly in the rapidly urbanized southern areas, raising questions about the effectiveness of past solutions in the context of climate change and urban expansion. As a result of this fragility, more than 200 deaths have occurred, along with material losses in 87 municipalities, whose industrial infrastructure accounts for nearly one-third of the economic activity in the Province of Valencia, valued at 479.6 million euros. This paper presents, for the first time, a historical-document-based approach to evaluate the successes and shortcomings of Valencia's flood management strategies through policy and spatial planning analysis. Also, this paper remarks the ongoing challenges and potential strategies for enhancing Valencia's urban resilience, emphasizing the need for innovative water management systems, improved drainage infrastructure, and the renaturalization of flood-prone areas. The lessons learned from Valencia's experience in 1957 and 2024 can inform future urban planning efforts in similar contexts facing the dual pressures of environmental change and urbanization.

ZnO nanostructures were obtained by electrodeposition on Ni foam, where graphene was previously grown by chemical vapor deposition (CVD). The resulting heterostructures were characterized by X-ray diffraction and SEM microscopy, and their potential application as a catalyst for the photodegradation of methylene blue (MB) was evaluated. The incorporation of graphene to the Ni substrate increases the amount of deposited ZnO at low potentials in comparison to bare Ni. SEM images show homogeneous growth of ZnO on Ni/G but not on bare Ni foam. A percent removal of almost 60% of MB was achieved by the Ni/G/ZnO sample, which represents a double quantity than the other catalysts proved in this work. The synergistic effects of ZnO-graphene heterojunctions play a key role in achieving better adsorption and photocatalytic performance. The results demonstrate the ease of depositing ZnO on seedless graphene by electrodeposition. The use of the film as a photocatalyst delivers interesting and competitive removal percentages for a potentially scalable degradation process enhanced by a non-toxic compound such as graphene.

Graphene oxide can be referred to as oxidized graphene. Similar to graphene, oxidized graphene possesses remarkable structural features, advantageous properties, and technical applications. Among polymeric matrices, conducting polymers have been categorized for p conjugated backbone and semiconducting features. In this context, doping, or nano-additive inclusion, has been found to enhance the electrical conduction features of conjugated polymers. Like other carbon nanostructures (fullerene, carbon nanotube, etc.), graphene has been used to reinforce the conjugated matrices. Graphene can be further modified into several derived forms, including graphene oxide, reduced graphene oxide, and functionalized graphene. Among these, graphene oxide has been identified as an important graphene derivative and nanofiller for conducting matrices. This overview covers essential aspects and progressions in the sector of conjugated polymers and graphene oxide derived nanomaterials. Since the importance of graphene oxide derived nanocomposites, this overview has been developed aiming at conductive polymer/graphene oxide nanocomposites. The novelty of this article relies on the originality and design of the outline, the review framework, and recent literature gathering compared with previous literature reviews. To the best of our knowledge, such an all-inclusive overview of conducting polymer/graphene oxide focusing on fundamentals and essential technical developments has not been seen in the literature before. Due to advantageous structural, morphological, conducting, and other specific properties, conductive polymer/graphene oxide nanomaterials have been applied for a range of technical applications such as supercapacitors, photovoltaics, corrosion resistance, etc. Future research on these high-performance nanocomposites may overcome the design and performance-related challenges facing industrial utilization.