Reputable Online Research Articles

An Analysis of the Challenges Teacher Educators Face when Conducting Teaching Practice in Kenya

To many, education is an equalizer; however, this research paper seeks to rephrase this common truism in the academic realm by emphasizing quality education. Undeniably, teachers are imperative players in the education sector in Kenya and other countries. However, practice is crucial in preparing future teachers by providing a practical classroom experience that bridges their theoretical gap and professionalism. It is from this fact that this study seeks to analyze the challenges faced by teacher educators while conducting their teaching practice in Kenya. The focus is on training trainee teachers at both primary and secondary levels. The methodology employed encompasses a desk review of relevant books, research papers, and reputable online and journal articles from renowned sources such as Google Scholar, Lexis Nexis, and other related platforms. The study’s key findings point out that logistical challenges, lack of standardization in practice, and limited institutional support and capacity building negatively affect teacher educators during teaching practice. This study shows an urgent need to institute systemic reforms in the Kenyan education sector and targeted reforms through policies and practices that will improve the situation. The implications of this study include but are not limited to providing practical solutions to policymakers and stakeholders in the education sector in Kenya to help improve the quality of teacher education and training in Kenya.

Understanding preconception health in Australia through the lens of people of reproductive age: Implications for care providers.

Limited awareness about the importance of preconception health is a recognised barrier to preparing for pregnancy. Opportunities exist to improve the health of future parents through preconception care. One of the recognised barriers to pregnancy preparation is a lack of knowledge and a lack of presentation for information and care. To explore the understanding of "preconception health" amongst people of reproductive age in Australia to inform the delivery of preconception care. A qualitative descriptive study using online interviews with people of reproductive age in Australia. Recruitment was via social media (Facebook). Interview transcripts were analysed thematically. Of the 20 women and five men we interviewed, all acknowledged the importance of preparing for pregnancy. Despite broadly understanding the concept, most participants had limited understanding of the details of preconception health. To increase their knowledge, participants' preferred sources of information included education in schools, reputable online sources, primary and maternity healthcare providers, and community members with lived experience. People in Australia are keen to learn about preparing for pregnancy and appreciate this as important. Suggested avenues to improve awareness and understanding about optimal preconception health included through school education, primary and reproductive healthcare providers, and online resources. This can improve understanding and behaviours before first and subsequent pregnancies. Preconception care requires a life-course approach, beginning with universal education through schools, enhanced by readily accessible reputable online resources, and access to trusted primary and maternity care providers. Maternity care providers can be key drivers in this process.

Comparison of Bureaucratic Neutrality in the 2019 and 2024 Elections From The Perspective of Online Mass Media in Achieving SDG 16 (Peace, Justice, and Strong Institutions)

Bureaucratic neutrality is a value born from the same concern and respect in the public sphere in realising Peace, Justice, and Strong Institutions to achieve the Sustainable Development Goals (SDGs). The purpose of this article is to examine the issue of bureaucratic neutrality in local online mass media. The target of this research is the independent online voice, a reputable online mass media outlet in Central Java. The online independent vote was chosen because it has a national scope, provides the latest news related to socio-political discussions, narrates the government's steps and plans in preparing for the election, and visualises the policies the government prepared before the election. This study uses a content analysis approach. The study results show that in the 6 months before the 2024 election, Suara Merdeka Online reported 26 news. Meanwhile, in the 2019 elections, the online independent vote reported 40 news stories. In 2024, there is an average frequency of recurring main issues, namely neutrality, neutrality, and democracy posts, as well as guard bureaucratic neutrality. Meanwhile, in 2019, the dominance of content is a commitment to neutrality, securing elections, the threat of hoaxes and cyber, and the potential for election vulnerability.

Automatic construction of generic Hausa language stop words list using term frequency-inverse document frequency

The Hausa language, spoken by a large population, is considered a low-resource language in the field of Natural Language Processing (NLP), presenting unique challenges. Despite increasing efforts to address these challenges, the quality of existing resources, particularly datasets, remains uncertain. A critical task like stop word identification is often hindered by the absence of standardized resources. This study bridges this gap by leveraging the Term Frequency-Inverse Document Frequency (TF-IDF) approach alongside manual evaluation to develop a comprehensive stop word list for Hausa. Using datasets from four reputable online Hausa news sources, comprising 4,501 articles and 1,202,822 tokens, we applied TF-IDF with a threshold of 0.001 to each dataset, identifying 91 candidate stop words by intersecting results across the datasets. After manual examination, the list was narrowed to 76 final stop words. Compared to prior study, our list increased the number of identified stop words by 6%. This standardized resource advances Hausa NLP by facilitating more effective text processing tasks, such as sentiment analysis and machine translation, and lays the groundwork for further research in low-resource languages.

Transfer Learning for Wildlife Classification: Evaluating YOLOv8 against DenseNet, ResNet, and VGGNet on a Custom Dataset

This study evaluates the performance of various deep learning models, specifically DenseNet, ResNet, VGGNet, and YOLOv8, for wildlife species classification on a custom dataset. The dataset comprises 575 images of 23 endangered species sourced from reputable online repositories. The study utilizes transfer learning to fine-tune pre-trained models on the dataset, focusing on reducing training time and enhancing classification accuracy. The results demonstrate that YOLOv8 outperforms other models, achieving a training accuracy of 97.39% and a validation F1-score of 96.50‘%. These findings suggest that YOLOv8, with its advanced architecture and efficient feature extraction capabilities, holds great promise for automating wildlife monitoring and conservation efforts.

IN THE REGION OF 2014: THE DEPICTION OF THE INDONESIAN PRESIDENTIAL ELECTION IN THE POLITICAL CARTOONS

Cartoons have long been recognized as a highly effective means of conveying social and political messages in political communication. This paper presents a critical discourse analysis (CDA) of the intricate portrayal of the 2014 Indonesian presidential election in cartoons, using qualitative methods to unravel the underlying themes and narratives. The study aims to provide a comprehensive understanding of the multifaceted dimensions of this significant electoral event, shedding light on the nuanced perspectives and representations presented in the selected cartoons. A qualitative approach analyzed a diverse collection of political cartoons from reputable Indonesian newspaper and online media outlet, i.e., Kontan.id, during the period leading up to the 2014 presidential election. The CDA framework employed in this research allows for an in-depth examination of the visual and textual elements, unraveling the symbolic meanings, metaphors, and ideological underpinnings that shape the cartoons’ overarching messages. The study reveals how these political cartoons function as a complex form of visual rhetoric, reflecting the artists’ perspectives and the broader social and political climate surrounding the electoral process. The findings expose the dominant themes and recurring symbols used in the cartoons to portray the candidates, their policies, and the broader political landscape. The research examines the potential influence of these cartoons in shaping public opinion and political discourse during this crucial election period. The outcomes of this research offer valuable insights into the dynamics of political communication in Indonesia and shed light on the interplay between media, politics, and public opinion. By illuminating the portrayal of the 2014 Indonesian Presidential Election through the lens of political cartoons, this study contributes to a deeper understanding of the role of visual media in shaping political discourse and public perceptions during electoral campaigns. The implications of this research extend beyond the specific case of Indonesia, providing a framework for investigating the use of political cartoons in other electoral contexts worldwide.

Adversarial Conditions for Autonomous Vehicle Sign Recognition: A Dataset with Original and Distorted Images

Autonomous vehicles rely on accurate sign recognition systems to navigate and respond effectively to various road conditions. However, these systems can be vulnerable to adversarial attacks, where small, often imperceptible changes to images cause significant misinterpretations by algorithms. To address this vulnerability, we introduce a dataset specifically designed to evaluate and improve the resilience of sign recognition models under adversarial conditions. This dataset includes high-quality images of standard traffic signs and corresponding distorted versions created using custom noise-generating algorithms that simulate real-world perturbations, such as added noise, visual occlusions, and other subtle alterations. The images were sourced from reputable online datasets and were resized to a uniform dimension of 32 × 32 pixels to maintain consistency for training and evaluation purposes. The dataset is structured to provide researchers and developers with a vital tool for training and testing autonomous vehicle recognition systems. By analyzing performance across both the original and altered images, researchers can enhance the robustness and reliability of these systems, making them better equipped to handle adversarial scenarios.

Titanium Dioxide Nanomaterials: Progress in Synthesis and Application in Drug Delivery.

Background: Recent developments in nanotechnology have provided efficient and promising methods for the treatment of diseases to achieve better therapeutic results and lower side effects. Titanium dioxide (TiO2) nanomaterials are emerging inorganic nanomaterials with excellent properties such as low toxicity and easy functionalization. TiO2 with special nanostructures can be used as delivery vehicles for drugs, genes and antigens for various therapeutic options. The exploration of TiO2-based drug delivery systems shows great promise for translating nanotechnology into clinical applications; Methods: Comprehensive data on titanium dioxide were collected from reputable online databases including PubMed, GreenMedical, Web of Science, Google Scholar, China National Knowledge Infrastructure Database, and National Intellectual Property Administration; Results: In this review, we discuss the synthesis pathways and functionalization strategies of TiO2. Recent advances of TiO2 as a drug delivery system, including sustained and controlled drug release delivery systems were introduced. Rigorous long-term systematic toxicity assessment is an extremely critical step in application to the clinic, and toxicity is still a problem that needs to be closely monitored; Conclusions: Despite the great progress made in TiO2-based smart systems, there is still a great potential for development. Future research may focus on developing dual-reaction delivery systems and single-reaction delivery systems like redox and enzyme reactions. Undertaking thorough in vivo investigations is necessary prior to initiating human clinical trials. The high versatility of these smart drug delivery systems will drive the development of novel nanomedicines for personalized treatment and diagnosis of many diseases with poor prognosis.

Avenues To Reduce Carbon Emission In Cement Fabrication To Acquire Carbon Credit

The cement industry accounts for roughly 8% of global carbon dioxide emission, and for every tonne of cement procured, a ton of carbon dioxide is released into the air. Cement industry is the backbone of global infrastructure development with no sign of slowing down, as such, it provides the most promising testbed for a carbon credit market to start up. Carbon credit functions as a conversion of money as payment to offset carbon dioxide emission, and can be acquired by reducing carbon emission or its greenhouse equivalents. This carbon credit can then be sold on the carbon market which provides financial incentive for companies to reduce their carbon emission. Acquiring carbon credit can be a viable capital gain strategy in developing nations due to their relatively low labor and overhead costs. This study aims to provide avenues to reduce carbon dioxide emission in cement fabrication as a way to acquire carbon credit for capital gain purposes to developing nations worldwide. This study uses literature review with a descriptive qualitative methodology, with data from relevant books and journals of renowned and reputable publishers online as validity. This study identified three promising avenues to reduce carbon dioxide emission in cement fabrication: alternative cement aggregate material fly ash and bottom ash, carbon capture and storage technology, and alternative fuel in cement fabrication. Three avenues provide pathways for players in the cement industry to adopt according to their capabilities in order to effectively reduce their carbon dioxide emission for acquiring carbon credit.

The risk of thyroid cancer and sex differences in Hashimoto’s thyroiditis, a meta-analysis

Background and objectiveThe prevalence of thyroid cancer (TC) has exhibited an upward trajectory in recent years. An accelerating amount of evidence shows a significant association between Hashimoto’s thyroiditis (HT) and TC. The present study encompasses a meticulously designed systematic review and meta-analysis with the aim of scrutinizing the risk of TC and clarifying sex disparities in HT.MethodsA comprehensive search was conducted across reputable online databases, including PubMed, Cochrane Library, EMBASE, and Web of Science. English-language publications on the correlation between HT and TC were examined without temporal restrictions. Two authors independently screened the articles and extracted pertinent data. The collected data underwent statistical analysis using the STATA software, enabling the calculation of the pooled Odds Ratio (OR) and 95% confidence intervals (CI). Additionally, a supplementary analysis was conducted on studies incorporating sex-specific data to determine the OR (female vs. male) and the sex-based prevalence of TC in HT.ResultsA total of 2,845 records were obtained, and 26 retrospective studies were included in this meta-analysis. The results indicated a significant role for HT in TC (OR: 2.22, 95% CI: 1.85–2.67). Supplementary analysis indicated that the prevalence of TC in HT patients was lower in women (0.31, 95% CI: 0.17–0.45) than in men (0.37, 95% CI: 0.21–0.53). However, the result was not statistically significant.ConclusionThis systematic review and meta-analysis provide evidence that HT is associated with increasing odds of TC. Regular review of HT patients holds positive clinical significance.

The Morphological Tapestry: A Linguistic Exploration

This article introduces morphological typology, exploring the patterns and structures underlying word formation and grammatical encoding across languages. A systematic literature review examines fundamental concepts, including distinctions between analytic and synthetic languages, features of agglutinating, fusional, and inflectional morphologies, and phenomena like suppletion and polysynthetic structures. Readers gain insights into language classification based on morphological characteristics, challenging strict categorical distinctions and emphasizing the continuum across types. The study highlights the diversity and complexity of morphological systems. Suppletion, where stems are irregularly replaced in inflectional patterns, and polysynthetic languages, encoding entire sentences within single complex words, are explored in depth. This work offers a concise overview of morphological typology by synthesizing reputable sources, including books, journals, and online resources. It is an accessible resource for language learners, linguists, and anyone interested in understanding the intricate morphological underpinnings of human language.

Pushing Boundaries: AI and Computer Science in the Era of Technological Revolution

The advent of Artificial Intelligence (AI) has heralded a transformative era in Computer Science, revolutionizing various facets of technology. This paper explores the profound impact of AI on the field of Computer Science, delving into its advancements, innovations, and the potential future trajectory of technology. The primary objective of this paper is to elucidate the significant role of AI in shaping the landscape of Computer Science. Through comprehensive research and analysis, it aims to provide insights into the evolution of AI, its applications, and the implications for future technological developments. This study employs a library research approach, gathering information from academic journals, conference proceedings, books, and reputable online sources. By synthesizing existing literature and scholarly works, it seeks to construct a comprehensive overview of the advancements and innovations in AI within the realm of Computer Science. The findings reveal a dynamic and rapidly evolving field driven by AI technologies. From machine learning algorithms to neural networks and deep learning models, AI has revolutionized data analysis, pattern recognition, and decision-making processes. Moreover, AI-driven applications such as natural language processing, computer vision, and robotics have reshaped various industries, including healthcare, finance, transportation, and manufacturing. However, alongside these advancements come ethical considerations, privacy concerns, and challenges related to algorithmic biases and societal implications. Looking ahead, the future of technology promises further integration of AI into various domains, leading to unprecedented opportunities and challenges in the realm of Computer Science

Enhanced Production of Microbial Cellulases as an Industrial Enzyme - A Short Review

Enzymes are biocatalysts that exist with the ability to degrade complex molecules. Enzymes play a major role in every individual's life and for commercial purposes. Various microbes can produce high-yield enzymes. Microbial cellulases have an integral part in our biosphere because cellulose is present in abundance on our earth. Cellulose is a major component of every plant cell, and it is responsible in plant cells to maintain processes like photosynthesis. The methodology for this review paper involved an extensive literature review to gather information on the production and industrial applications of microbial cellulases. Relevant data was collected from scientific journals, research papers, and reputable online databases and was thoroughly analyzed to identify key concepts and emerging trends. The primary motive of this study was to compile all the numerous applications of cellulases as well as to describe some strategies to modify microbial cellulases with the help of genetic engineering and other tools in brief. This study has involved describing the structure, role, and mode of action, economic importance, and strain improvement of cellulases. Cellulases have applications in various sectors such as pharma, food, brewery, paper, pulp, textile, detergent, and many more. This study mainly focuses on microbial cellulases retarding chemical cellulases with their versatile behavior.

Waẓīfah al-Lugah fī al-Kasyf ‘an Anmāṭ al-Ŝaqāfah fī Nījīriyā

This research aims to describe the main role of language in revealing the cultural patterns of Nigerian-African society. This research is qualitative with a bibliographic type. As literature research, this research uses primary data related to research articles that explain the function of language in intercultural communication in Nigerian-African communities. Data sources were accessed from reputable online e-journals. Data collection techniques are implemented with documentation. Data analysis was carried out using interpretative descriptive techniques. The research results state that language and culture are two of the most important things that determine the personality of individuals and groups and contribute to directing the thinking of the Nigerian-African community, especially in the expansion of cross-cultural speech and communication on the African continent. Language in the socio-cultural context of Nigeria-Africa is one of the impacts of colonialism, which caused Nigeria to have more than 200 regional languages. The Nigerian African community continues to make preventive efforts to preserve regional languages amid continuous disruption and change.

Higher dimensional sports statistics and real-time game prediction

The rapid expansion of comprehensive sports datasets and the successful application of data mining techniques in various domains have given rise to the emergence of sports data prediction techniques. These techniques enable the extraction of hidden knowledge that can significantly impact the sports industry, as more and more clubs are using Machine Learning (ML) and Deep Learning (DL) methods to manage athletes and training. In this research, the focusing and intriguing aspects is predicting the outcomes of a specific basketball athletes, which has garnered significant attention for research. The paper was motivated by a dual interest in college and NBA basketball matches, alongside a keen observation of the evolving strategies employed by coaches in athlete management. Additionally, the interest was further reinforced by firsthand observations of such evolving methods during a baseball game at City Field in New York. These factors collectively underpin the relevance and significance of this research endeavor, highlighting the intersection of personal interest and the evolving landscape of sports management as compelling reasons for its pursuit. In the process of data selection, we acquired data from previously published essays as well as from Kaggle, a reputable online platform. Following this, we proceeded to evaluate several prominent machine learning models, namely Linear Regression, KNN, Gradient Boosting, Elastic Net, and Lasso, to ascertain their effectiveness in predicting the performance of specific players. Through rigorous analysis and comparison, we concluded that Linear Regression and Gradient Boosting exhibited superior predictive capabilities compared to the other models considered. These two models demonstrated a higher degree of accuracy and reliability in forecasting player performance, thus establishing them as the most suitable choices for our predictive modeling purposes. This meticulous selection process, involving both data acquisition and model evaluation, forms the foundation of our research methodology and underscores the rigor and precision with which our conclusions are drawn.

Revving Up or Stalling Out? A Comprehensive SWOC Analysis of BSE listed India's Auto Sector

Purpose: The primary purpose of this research paper is to conduct a comprehensive SWOC (Strengths, Weaknesses, Opportunities, and Challenges) analysis of the Indian automobile sector, which plays a vital role in the economy by contributing to GDP, employment, and technological progress. Amidst globalization, technological changes, and evolving consumer preferences, the industry faces numerous internal and external factors affecting its performance. By systematically evaluating these factors, the research aims to provide insights into the sector's current state and future prospects, informing strategic decisions for stakeholders, policymakers, and investors. This study fills a gap in existing literature by offering a holistic assessment using the SWOC framework, uncovering strengths, weaknesses, opportunities, and challenges to support the industry's competitiveness and sustainability. Design/Methodology/Approach: The study employs a qualitative research approach, utilizing both primary and secondary data sources. Primary data includes industry reports, company publications, and expert interviews, while secondary data encompasses academic journals, government publications, and reputable online sources. SWOC analysis is conducted to identify and evaluate the internal strengths and weaknesses of the industry, as well as external opportunities and challenges it faces. Findings/Results: The SWOC analysis reveals several key insights into the Indian auto industry. Strengths such as a skilled workforce and a growing domestic market are counterbalanced by weaknesses such as infrastructure bottlenecks and regulatory challenges. Opportunities arising from technological advancements and export potential are tempered by threats such as global competition and changing consumer preferences. Originality/Value: This research contributes to the existing literature by offering a comprehensive assessment of the Indian auto industry through the SWOC framework. By synthesizing insights from various data sources, the paper provides valuable insights for industry stakeholders, policymakers, and investors seeking to navigate the complexities of the Indian automotive landscape. Paper Type: Descriptive Research

Supply Chain Management: Survey Consumer Preferences and Market Potential (Study Literature Review)

The supply chain management article: survey of consumer preferences and market potential is a scientific literature review article within the scope of operational management science. This article aims to create a hypothesis regarding the relationship between factors, which can subsequently be utilized for future research in the field of human resource management. Descriptive qualitative research methodology was employed in this study. The data utilized in this study was derived from prior research that remains pertinent to the current investigation. The data was gathered from reputable academic online platforms, including Publish or Perish, Google Scholar, digital reference books, and Sinta journals. The findings of this study are as follows: 1) Consumer preference surveys play a role in supply chain management: Surveys offer significant insights into consumer preferences, wants, and expectations pertaining to a specific product or service. These insights serve as the foundation for organizations in formulating marketing strategies, developing products, and managing inventory. By gaining a comprehensive grasp of consumer preferences, organizations have the potential to enhance customer happiness, streamline their supply chains, and attain a competitive edge in marketplaces that are becoming progressively intricate and dynamic; and 2) Market potential plays a role in supply chain management: market potential offers a comprehensive overview of the current market size, potential for growth, and consumer patterns in a specific region or demography. This data enables organizations to develop supply chain strategies that are characterized by enhanced efficiency and adaptability in response to dynamic market needs. Through a comprehensive comprehension of market potential, organizations are able to effectively identify prospective market segments, formulate suitable expansion strategies, and adapt their supply chain approaches in order to maximize profitability and foster corporate expansion.

ChatGPT Responses to Common Questions About Slipped Capital Femoral Epiphysis: A Reliable Resource for Parents?

We sought to evaluate the ability of ChatGPT, an AI-powered online chatbot, to answer frequently asked questions (FAQs) regarding slipped capital femoral epiphysis (SCFE). Seven FAQs regarding SCFE were presented to ChatGPT. Initial responses were recorded and compared with evidence-based literature and reputable online resources. Responses were subjectively rated as "excellent response requiring no further clarification," "satisfactory response requiring minimal clarification," "satisfactory response requiring moderate clarification," or "unsatisfactory response requiring substantial clarification." ChatGPT was frequently able to provide satisfactory responses that required only minimal clarification. One response received an excellent rating and required no further clarification, while only 1 response from ChatGPT was rated unsatisfactory and required substantial clarification. ChatGPT is able to frequently provide satisfactory responses to FAQs regarding SCFE while appropriately reiterating the importance of always consulting a medical professional.

Cultural preservation through technology in UAE libraries

PurposeThis study aims to explore how libraries in the United Arab Emirates use technology to preserve and digitize cultural and historical documents. It examined how these institutions use different technology models to facilitate the dissemination of UAE’s cultural traditions, practices, historical experiences and expressions to the local and global populations interested in learning about the country.Design/methodology/approachThis study relied heavily on a review of the relevant literature and case studies covering how UAE libraries use technology to preserve, document and share tangible and intangible cultural heritage. The methodology entailed gathering and synthesizing relevant information from scholarly journal articles, government and reputable institutional resources online and reports. Collectively, it led to a close analysis of the impact of technology on cultural preservation and an assessment of the specific technology models preferred for optimal outcomes in preserving and disseminating cultural heritage information of the UAE.FindingsMultiple UAE libraries rely heavily on technology to collect, record, translate and store cultural heritage information, including releasing it to users when required. The National Archives of the United Arab Emirates, the Arabian Gulf Digital Archive, Mohammed Bin Rashid Library, the UAE National Library and Archives, New York University Abu Dhabi and Khalifa University of Science and Technology and Research libraries have leveraged different technological models and tools to make UAE’s cultural heritage information available and accessible globally. Artificial intelligence and machine learning algorithms, three-dimensional imaging and scanning, electronic archiving systems, document management systems and ICT storage systems have helped the UAE libraries to promote and disseminate the nation’s tangible and intangible cultural heritage.Originality/valueBy relying on scholarly and authoritative sources of information and evidence to draw conclusions, this study contributes to the existing literature by offering insights into the innovative strategies used by UAE libraries to leverage technology for cultural preservation and promotion. In underlining the value of digital approaches to safeguard tangible and intangible cultural heritage, the research highlights the instrumentalism of technology in preserving the UAE’s cultural heritage and identity.

Geochemistry of Terrestrial Plants in the Central African Copperbelt: Implications for Sediment Hosted Copper-Cobalt Exploration

Mineral exploration has increasingly targeted areas covered by in situ or transported overburden for shallow to deep-seated orebodies. It remains critical to develop better means to detect the surficial chemical footprint of mineralized areas covered by thick regolith. In such settings, plant geochemistry could potentially be a useful exploration tool, as different plant species have varying degrees of tolerance to metal enrichment in the soil. This review provides insights into the geological and geochemical controls on metal accumulation patterns in soil–plant systems of the Central African Copperbelt. In addition, it highlights the opportunities for integrating the geochemistry of terrestrial plants in emerging exploration technologies, identifies research gaps, and suggests future directions for developing phytogeochemical sampling techniques. This review was conducted using reputable online scholarly databases targeting original research articles published between January 2005 and March 2023, from which selected articles were identified, screened, and used to explore current advances, opportunities, and future directions for the use of plant geochemistry in sediment hosted Cu–Co exploration in the Central African Copperbelt. Various plant species are recognized as ore deposit indicators through either independent phytogeochemistry or complementary approaches. In the Central African Copperbelt, the successful application of hyperaccumulator species for phytoremediation provides the basis for adopting phytogeochemistry in mineral exploration. Furthermore, current advances in remote sensing, machine learning, and deep learning techniques could enable multi-source data integration and allow for the integration of phytogeochemistry.