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 Intellectual Property (IP) chapter of the Comprehensive and Progressive Agreement for Trans-Pacific Partnership (CPTPP) is recognized for its extensive coverage, encompassing a broad range of innovation areas such as patents, trademarks, geographical indications, and copyright. This chapter sets a new global benchmark for IP protection, posing significant challenges to the existing legal frameworks of member countries and necessitating rapid adaptation, particularly for developing members like Vietnam, Malaysia, and Mexico. These nations have undertaken comprehensive revisions to their IP laws to align with the international standards established by the CPTPP. Despite their unique national contexts, the legal amendments reflect distinct strategies and methodologies in meeting international standards. This paper conducts a qualitative analysis of Vietnam, Malaysia, and Mexico, comparing their law amendment strategies, contents, and techniques across three dimensions. It highlights the distinctive characteristics and impacts of their legal revisions, offering valuable insights for other prospective developing members within the CPTPP framework on the practice of IP law reform.