In the process of global economy, in the face of increasing business competition, it is more difficult than ever for brands to approach consumers and persuade them to consume. In the commercial environment, the competition between enterprises is essentially the competition of brands, and the competition of brands must first carry out the competition of brand image. Brand image carries the mission of information dissemination and value creation and plays an important role in business behavior. How to improve customer purchase intention by optimizing brand image and greatly promote the development of business through brand image is the purpose of this study. The construction and application of brand image not only covers all the characteristics of the brand, but also the focus of consumers’ attention when choosing brands and products. This paper comprehensively uses the systematic theories and methods of art design, marketing and consumer psychology and behavior as support, and adopts research methods such as literature data to explore and study the field of brand image. This study finds that customer perception of brand image directly affects customer purchase intention. At present, there are relatively few researches on how brand image can empower business. Through the study of “optimizing brand image to improve customer purchase intention”, this paper focuses on the direction of brand image empowering business, broadens the research breadth and depth in the field of brand image, and enrichis the research achievements in the field of brand image.

A large number of people of the fringe areas of Sundarban enter into the forests every year and encounter with the tigers simply for their livelihood. This study attempts to examine the extent and impact of human-animal conflicts in the Sundarban Reserve Forest (SRF) area in West Bengal, India. An intensive study of the data of the victims (both death and injury) between 1999 and 2014 reveals that, fishermen crab collector, honey collectors and woodcutters are generally victimized by the tiger attack. Pre monsoon period (April to June) and early winter period (Jan to March) are noted for the two-peak periods for casualties. Maximum casualty occurs between 8-10 am, and 2-4 pm. Jhilla (21.1%), Pirkhali (19.72 %), Chandkhali (11.72%), and Arbesi (9.35%) are the four most vulnerable forest blocks accounting more than 60 per cent occurrence of incidences. 67.24 per cent of the tiger attack victims were residents of Gosaba followed by Hingalganja (15%) and Basanti, (9.76%). The vulnerability rating puts the risk of tiger attack to 0.88 for every 10,000 residents of Gosaba block followed by 0.33 at Hingalganj Block and 0.11 at Bansanti Block. The majority of the victims (68%) were found to be males, aged between 30 and 50 years.

Artificial Intelligence (AI) in education has both positive and negative impacts, particularly in term of increasing plagiarism. This research analyzes Indonesia’s plagiarism regulations and offers solutions. It uses doctrinal methods with legislative, case, and comparative studies, revealing that plagiarism is regulated but not specifically for AI involvement. The results show that plagiarism in scientific work has actually been regulated through several regulations. On the other hand, there is no regulation governing the involvement of AI in the process of preparing scientific articles. Comparative studies show that the US, Singapore, and the EU have advanced regulations for AI in education. The US has copyright laws for AI works and state regulations, Singapore’s Ministry of Education has guidelines for AI integration and ethics, and the EU has the Artificial Intelligence Act. To tackle AI-related plagiarism in Indonesia, the study suggests enacting AI-specific laws and revising existing ones. Ministerial and Rector statutes should address technical aspects of AI use and plagiarism checks. The Ministry should issue guidelines for universities to develop Standard Procedures for Writing and Checking Scientific Work, using reliable AI-checking software. These measures aim to prevent plagiarism in Indonesia’s educational sector.

The state delivery of affordable and sustainable housing continues to be a complicated challenge in Africa, and there is a need to encourage private sector participation. As a result, this study examines the risks associated with private sector participation in affordable housing and supporting infrastructure investment and the strategies towards mitigating the risks from an Afrocentric perspective. The evidence from a systematic literature review was coupled with the opinion of an international expert panel to address the paper’s aim and provide recommendations for developing improved housing and supporting infrastructure in Sub-Saharan Africa. The review outcomes and the qualitative data from the panel discussion were analysed using thematic analysis. The results revealed that market dynamics, land supply and acquisition constraints, cost of construction materials, unsupportive policies, and technical and financial factors constitute risks to affordable housing in the region. Mitigation strategies include leveraging joint efforts, strengths, and resource bases, increasing access to land and finance for private sector participation, developing a supportive government framework to promote an enabling environment for easy access to land acquisition and development finance, local production of building materials, research and technology adoption. In line with the United Nations (UN) Agenda 2030 targets and principles, reforms are required across the housing value chain, involving the private sector and community. Application of the study’s recommendations could minimise the risks of affordable housing delivery and enhance private sector participation.

 This study employs a virtual reality (VR) game to examine the role of VR gaming in learning Saudi cultural heritage. By creating 3D (Three-dimensional) virtual heritage buildings, the game immerses players in cultural scenes, fostering a lasting appreciation for art history. Objectives include making heritage information dissemination engaging, blending learning and entertainment in a 3D environment, designing a gamified setting for active learning, and igniting interest in culture, tradition, architecture, and art history. This paper further highlights the significance of serious gaming in promoting the Saudi cultural heritage among the younger generation. The research involved immersing 59 participants into a heritage building environment using a VR game and then probing their experience of the environment through a questionnaire. Results indicate positive participant experiences, increased interest in Saudi cultural heritage and appreciation for VR technology. The study demonstrates the potential of VR games to make heritage accessible and enjoyable for the younger generation, motivating further exploration and learning. Valuable resources are provided for individuals and researchers interested in using VR gaming for cultural heritage engagement.

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.