artificial intelligence

Modern weather forecasting: mathematical foundations, tools and current state of the art

Abstract Modern weather forecasting relies on the integration of observational systems, numerical modeling, data assimilation, high-performance computing, and increasingly artificial intelligence techniques. This paper reviews the scientific and technological foundations of contemporary weather prediction, with particular emphasis on the mathematical and computational tools that support operational forecasting. Weather prediction is based on the numerical solution of the nonlinear partial differential equations governing atmospheric dynamics. The accuracy of these forecasts critically depends on the availability of observations collected from heterogeneous sources, including ground-based networks, weather radars, satellites, aircraft, and other remote sensing platforms. These data are combined with model forecasts through advanced data assimilation techniques, which provide dynamically consistent estimates of the atmospheric state and improve forecast quality. Special attention is devoted to nowcasting, one of the most challenging areas of modern meteorology. At lead times of a few minutes to several hours, rapidly evolving phenomena such as severe thunderstorms, hailstorms, and flash floods require the integration of high-frequency observations, radar extrapolation methods, ensemble prediction techniques, and machine learning approaches. Artificial intelligence is increasingly used to enhance the detection, tracking, and short-term prediction of convective systems, complementing traditional physics-based methodologies. The paper also discusses the requirements of a modern national forecasting system and presents the high-resolution ICON-2I modeling framework operational at the ItaliaMeteo Agency. Particular emphasis is placed on the role of high-performance computing infrastructures, ensemble forecasting systems, and kilometer-scale data assimilation methods. Finally, current challenges and future developments are examined, including convective-scale prediction, uncertainty quantification, exascale computing, digital twins of the atmosphere, and the growing integration of artificial intelligence with numerical weather prediction.

Mapping Gender Bias and Workplace Inclusion in Artificial Intelligence-Based Recruitment: A Bibliometric Co-Occurrence

This study aims to systematically map the research landscape of gender bias and inclusion in Artificial Intelligence (AI)-based recruitment by identifying dominant themes, conceptual relationships, and existing research gaps. It addresses the fragmentation of prior studies that often examine AI, bias, and inclusion as separate domains. Using a bibliometric approach with keyword co-occurrence analysis, data were collected from Scopus-indexed journal articles published between 2016 and 2025 and analyzed using VOSviewer to visualize keyword networks, identify thematic clusters, and examine research trend evolution. The results indicate a strong conceptual relationship between AI, gender bias, recruitment decision-making, and inclusion, where gender bias acts as a mediating mechanism influencing recruitment decisions and subsequently determining workplace inclusion outcomes. This study contributes theoretically by proposing an integrated conceptual framework that positions AI-based recruitment as a socio-technical system connecting technological processes, algorithmic bias, and inclusion outcomes.

Automated Blood Group Detection Using Fingerprint Biometrics and Deep Learning

Blood group identification is an important requirement in transfusion medicine, emergency care, surgery, and healthcare record management. Conventional ABO and Rh typing is highly established and normally relies on a biological blood sample and serological reactions. This paper presents an artificial-intelligence-based research prototype that investigates whether fingerprint images can be used as a non-invasive input for predicting the eight common ABO/Rh classes: A+, A−, B+, B−, AB+, AB−, O+, and O−. The proposed pipeline accepts a fingerprint image, performs image quality enhancement and normalization, extracts discriminative ridge features using a convolutional neural network (CNN), and assigns the image to a blood-group class. The system is designed as a screening and decision-support prototype rather than a replacement for clinical blood typing. Recent studies have reported promising classification performance on fingerprint datasets, while other clinical and dermatoglyphic investigations have found inconsistent or statistically weak relationships between fingerprints and blood groups. Therefore, this work emphasizes reproducible image processing, supervised learning. KEYWORDS Fingerprint Analysis, Blood Group Prediction, Machine Learning, Image Processing, Deep Learning, Classification

Practical Applications of Artificial Intelligence in Education

AI-Driven Governance Process Reengineering (GPR) in Sri Lanka: A Strategic Review of Barriers and Opportunities for Public Sector Reforms

The Sri Lankan public sector has continued to struggle with operational inefficiencies, bureaucracy, low public trust and weak service responsiveness to citizens. This hinders effective governance. These issues necessitate structural transformations rather than haphazard technological upgrades. This study examines how Artificial Intelligence (AI) can enable Governance Process Reengineering (GPR) in Sri Lanka’s government sector. This research utilises a Systematic Literature Review, following the PRISMA 2020 approach, to select literature from 60 peer-reviewed articles across multiple databases, published between 2015 and 2025. The analysis is grounded in the integrated Technology-Organisation-Environment (TOE) framework and Technology Affordance and Constraints (TACT) Theory to provide a multidimensional assessment of AI adoption. The study discovered that challenges to AI adoption include outdated ICT infrastructure, legacy systems, poor data quality, limited specialised talent, organisational resistance, fragmented regulations, socio-ethical concerns, and weak citizen trust. AI has the potential to improve accountability, transparency, and efficiency; enhance service delivery; simplify administrative processes; create public value through redesign; and enhance data-driven decision-making and proactive governance. AI-driven GPR redesigns government processes to enhance public value through accountability, transparency, and public centricity. A proposed 10-year roadmap, tailored to Sri Lanka, adds value by eliminating fragmented and ineffective government structures and translating them into effective governance reforms.

Deepfakes, Electoral Integrity, and Constitutional Democracy: Rethinking Freedom of Expression in the Digital Era

The rapid advancement of artificial intelligence has enabled the creation and dissemination of highly realistic deepfake content, creating significant challenges for electoral integrity and constitutional democracy. This paper critically examines the impact of deepfakes on democratic elections, with particular emphasis on the constitutional tension between safeguarding electoral processes and protecting freedom of expression. Adopting a qualitative, interpretivist and inductive approach, the research relies on secondary data comprising academic literature, legislation, judicial decisions, governmental reports and policy documents, analysed through thematic analysis. The paper finds that deepfakes can facilitate political misinformation, voter manipulation, identity impersonation and declining public trust, while existing legal frameworks remain insufficiently specific to address election-related synthetic media. A comparative assessment of India, the European Union, the United Kingdom and the United States demonstrates significant differences in legislative clarity, platform accountability and election-specific regulation. The research further establishes that excessive regulation may suppress legitimate political expression, whereas inadequate regulation may undermine informed democratic participation. It therefore advocates a proportionate constitutional approach based on clear and dedicated deepfake election legislation, stronger platform accountability and transparency, technological detection, digital literacy, judicial oversight and international regulatory cooperation. Such measures can strengthen electoral integrity while preserving the fundamental democratic value of freedom of expression in the digital era. Keywords: political misinformation; Deepfake Technology; Electoral Integrity; Constitutional Democracy; Freedom of Expression

Assessing the Efficacy of AI-Powered Writing Prompts in Enhancing Students’ Writing Skills

The quantitative study examines the effectiveness of artificial intelligence writing prompts in teaching English for Specific Purposes (ESP) to 221 students from technical disciplines. The survey findings indicated that between 60% and 70% of respondents found the prompts readable and helpful for improving their writing and for receiving feedback. However, only 54.8% of participants strongly supported the diversity of prompts. Structured activities were perceived as the most beneficial, specifically narrative reviews (68.8%), slang exercises (68.3%), and essay completions (65.1%). Sixty-one percent to seventy-five percent of students reported perceived improvements in their capabilities, with 74.2% indicating that their confidence and attitudes towards writing were enhanced. The research confirms that AI is an effective tool for structured writing activities in line with the principles of Vygotskian education. However, AI's potential to encourage creative activities is limited, underscoring the importance of further human-centered instruction. These results provide important insights into how ESP curricula should be developed and suggest that further research is needed in non-technical areas.

Figure S13 from Counterfactual Diffusion Models Provide Interpretable Explanations of Artificial Intelligence Models in Pathology

Counterfactual results for liver and breast cancer types

AI-driven optimisation of drug delivery systems for therapeutic enhanced efficacy

By guaranteeing that medicinal materials reach the intended spot in the body with maximum efficacy and few side effects, drug delivery systems (DDS) are essential to modern medicine. However, time-consuming and expensive trial-and-error methods are frequently used in traditional drug delivery systems. Predictive modelling, formulation optimisation, and tailored therapy have all been made possible by the development of artificial intelligence (AI), especially machine learning and deep learning, which have drastically changed pharmaceutical research. This study examines how AI-assisted optimisation affects therapeutic results in drug delivery systems. Advanced drug carrier design, pharmacokinetic and pharmacodynamic behaviour prediction, and real-time drug release monitoring are all made possible by AI technologies. Additionally, specialized delivery methods like nanoparticle-based carriers, which increase drug bioavailability and lower toxicity, are made possible by AI-driven models. AI has a lot of promise, but there are still issues with data quality, regulations, and model interpretability. Future medication delivery is anticipated to be revolutionized by the integration of AI with digital health platforms, nanotechnology, and precision medicine. This study highlights important technologies, applications, difficulties, and opportunities while examining the function of AI-assisted optimization in drug delivery systems and assessing its potential to enhance therapeutic outcomes.

Artificial Intelligence in Counseling and Counseling Psychology: A Counselor-Led Framework for Ethical Practice & Training

As generative artificial intelligence (AI) tools like ChatGPT become increasingly accessible, counseling psychologists are exploring their potential use in clinical practice, often without clear guidance or ethical frameworks. This paper presents a counselor-led model for integrating generative AI tools into counseling practice. Grounded in ethical principles, the model emphasizes human oversight, transparency, and client-centered care. A case study illustrates the application of this model with a veteran receiving counseling services through the Veterans Health Administration. The case demonstrates how generative AI can support foundational career counseling tasks (i.e., career exploration and career planning), while maintaining full counselor oversight. Key ethical considerations are discussed, including transparency and informed consent, data privacy and security, accuracy and bias, and human oversight and professional judgment. This work contributes to the emerging literature on AI-assisted counseling by offering an ethically grounded and practice-oriented model for counseling professionals interested in engaging with these technologies responsibly.

Exploring student and faculty experiences and perceptions of generative artificial intelligence in medical education

The potential and challenges of using Generative artificial intelligence (GAI) in medical education are widely discussed, yet its use by medical students and faculty remains under-researched in the UK context. This exploratory sequential mixed-methods pilot study empirically investigates and compares student and faculty experiences and perceptions of using GAI in medical education. A questionnaire was developed based on focus groups and administered to undergraduate medical students and faculty within a UK medical school. Descriptive analysis and comparative analysis were used to quantify and compare student (n = 29) and faculty (n = 32) experiences and perceptions of GAI. Reflexive thematic analysis of open-ended question responses was undertaken to complement quantitative results. We found that GAI is being used for a variety of purposes by students and faculty in learning/teaching/assessment. However, both of them generally have relatively low self-confidence in GAI-related knowledge and skills. Faculty members tend to have stronger concerns of GAI limitations and its ethical challenges. Males are more confident in using GAI and have more positive attitudes towards GAI than females. The findings also provide preliminary evidence for the importance of open communication of GAI between students and faculty and the need for schools’, universities’, and national regulators’ strategic support to ensure faculty and student ethical and effective use of GAI and equitable access to GAI technology between subgroups. Our findings and the pilot tools can inform future research investigating the use of GAI in medical education in other institutional context or at a larger scale.

Borrowing Knowledge Across Tasks: A Critical Survey Review of Transfer Learning and the Pretraining Paradigm

This article presents a narrative review of Transfer Learning and the Pretraining Paradigm in the context of Artificial Intelligence. The literature on this topic has expanded substantially over recent decades, yet it remains fragmented across subfields, methods, and national research traditions. Drawing on an interpretive synthesis of representative contributions, the review reconstructs the historical development of the area, examines the conceptual foundations and definitional disputes that organize its debates, and maps the contemporary landscape of research, including the methodological shift toward data-intensive approaches and the institutional pressures that shape publication practice. Particular attention is given to the role of transfer learning and pretraining as organizing themes, and to the conditions under which findings from different research traditions can be brought into productive comparison. The review identifies three synthetic conclusions: the literature is cumulatively strong but organizationally weak; methodological pluralism is better understood as a resource than as a defect; and the growing practical salience of the topic raises the stakes of its unresolved conceptual questions. An agenda for future work is proposed, emphasizing integrative research designs, transparent synthesis practices, and the protection of definitional and infrastructural work on which cumulative progress depends. The article is intended as both a reference map for newcomers and a provocation for specialists in Artificial Intelligence.