Search Results (767)

(1) Background: The convergence of Big Data and the Internet of Things (IoT) is transforming digital accounting from retrospective documentation into real-time operational intelligence. This systematic review examines how Industry 4.0 technologies—artificial intelligence (AI), blockchain, edge computing, and digital twins—transform accounting practices through intelligent automation, continuous compliance, and predictive decision support. (2) Methods: The study synthesizes 176 peer-reviewed sources (2015–2025) selected using explicit inclusion criteria emphasizing empirical evidence. Thematic analysis across seven domains—conceptual foundations, system evolution, financial reporting, fraud detection, audit transformation, implementation challenges, and emerging technologies—employs systematic bias-reduction mechanisms to develop evidence-based theoretical propositions. (3) Results: Key findings document fraud detection accuracy improvements from 65–75% (rule-based) to 85–92% (machine learning), audit cycle reductions of 40–60% with coverage expansion from 5–10% sampling to 100% population analysis, and reconciliation effort decreases of 70–80% through triple-entry blockchain systems. Edge computing reduces processing latency by 40–75%, enabling compliance response within hours versus 24–72 h. Four propositions are established with empirical support: IoT-enabled reporting superiority (15–25% error reduction), AI-blockchain fraud detection advantage (60–70% loss reduction), edge computing compliance responsiveness (55–75% improvement), and GDPR-blockchain adoption barriers (67% of European institutions affected). Persistent challenges include cybersecurity threats (300% incident increase, $5.9 million average breach cost), workforce deficits (70–80% insufficient training), and implementation costs ($100,000–$1,000,000). (4) Conclusions: The research contributes a four-layer technology architecture and challenge-mitigation framework bridging technical capabilities with regulatory requirements. Future research must address quantum computing applications (5–10 years), decentralized finance accounting standards (2–5 years), digital twins with 30–40% forecast improvement potential (3–7 years), and ESG analytics frameworks (1–3 years). The findings demonstrate accounting’s fundamental transformation from historical record-keeping to predictive decision support. Full article

Background/Objectives: Performing Hajj, the annual Islamic pilgrimage to Mecca and one of the world’s largest mass gatherings, involves considerable physical exertion in high temperatures and presents unique challenges for people with type 1 diabetes (PWT1D). We examined the feasibility, safety, and user experience of automated insulin delivery (AID) systems during Hajj. Methods: This mixed-methods study evaluated six PWT1D who used an AID pump (2 MiniMed 780G, 2 Medtrum, 1 OmniPod 5, and 1 Open-source AID) while performing Hajj in 2024–2025. Pump and CGM-derived metrics were compared across pre-Hajj, during Hajj, and post-Hajj periods. A structured survey captured participants’ experiences, challenges, and recommendations for AID use during Hajj. Results: The average percent time in range (TIR) remained stable from pre- to during Hajj (54.98 to 54.18, p > 0.05) and significantly increased post-Hajj (62.62, p < 0.05). The percent time above range (TAR > 180) and Glycemia Risk Index significantly decreased from pre- to post-Hajj (28.34 to 26.28 and 50.3 to 19.3, respectively, both p < 0.05). The percent time below range (TBR) remained low (<1%) across the three periods with no incidence of acute diabetes-related complications. Participants emphasized increased confidence and peace of mind with AID use and reported challenges related to heat exposure, prolonged walking, and lack of awareness regarding diabetes technology among HCPs. Conclusions: The use of AID during Hajj appeared to be safe and effective for PWT1D in our study, maintaining stable glycemic control under physically demanding conditions. As the first study to evaluate AID use during Hajj, our findings call for larger studies to explore the integration of diabetes technology into Hajj care protocols and highlight the need for structured pre-Hajj education for PWT1D and HCPs. Full article

Cloning large biosynthetic gene clusters (BGCs) is fundamental to unlocking microbial natural product potential for drug discovery and biotechnology. These clusters encode diverse bioactive compounds, but their size, high GC content, and complex architecture pose significant technical challenges. This review scrutinises recent advances in BGC cloning strategies, categorising them into three major groups: (1) direct release-and-capture methods, (2) genome-integrated preconditioning systems, and (3) CRISPR-assisted hybrid platforms. This review compares the strengths, limitations, and reported efficiencies of BGC cloning strategies, highlighting trade-offs in precision, scalability, and workflow complexity. Emerging trends, such as AI-driven genome mining, modular synthetic biology toolkits, and high-throughput automation, are reshaping the cloning landscape, enabling predictive design and streamlined assembly of clusters exceeding 100 kb. By integrating comparative analysis with future perspectives, this review provides outlines on how next-generation strategies will accelerate heterologous expression, natural product discovery, and sustainable biomanufacturing. Full article

Software maintenance and release management demand proper identification of bug dependencies since priority violations or unresolved dependent issues can often lead to a chain of failures. However, dependency annotations in bug reports are extremely sparse and imbalanced. These dependencies are often expressed implicitly through natural language descriptions rather than explicit metadata. This creates challenges for automated multilabel dependency classification systems. To tackle these drawbacks, we introduce a meta-contrastive optimization framework (MCOF). This framework integrates established learning paradigms to enhance transformer-based classification through two key mechanisms: (1) a meta-contrastive objective adapted for enhancing discriminative representation learning under few-shot supervision, particularly for rare dependency types, and (2) dependency-aware Laplacian regularization that captures relational structures among different dependency types, reducing confusion between semantically related labels. Experimental evaluation on a real-world dataset demonstrates that MCOF achieves significant improvement over strong baselines, including BM25-based clustering and standard BERT fine-tuning. The framework attains a micro-F1 score of 0.76 and macro-F1 score of 0.58, while reducing hamming loss to 0.14. Label-wise analysis shows significant performance gain on low-frequency dependency types, with improvements of up to 16% in F1 score. Runtime analysis exhibits only modest inference overhead at 15%, confirming that MCOF remains practical for deployment in CI/AT pipelines. These results demonstrate that integrating meta-contrastive learning and structural regularization is an effective approach for robust bug dependency discovery. The framework provides both practical and accurate solutions for supporting real-world software engineering workflows. Full article

Automated optical inspection (AOI) technologies are widely used in PCB and semiconductor manufacturing to improve accuracy and reduce human error during quality inspection. While existing AOI systems can perform defect detection, they often rely on pre-defined camera positions and lack flexibility for interactive inspection, especially when the operator needs to visually verify solder pad conditions or examine specific layout regions. This study focuses on the front-end optical positioning and inspection stage of the AOI workflow, providing an automated mechanism to link digitally generated layout reports from EDA layout tools with real PCB inspection tasks. The proposed system operates on component-placement reports exported by EDA layout environments and uses them to automatically guide the camera to the corresponding PCB coordinates. Since PCB design reports may vary in format and structure across EDA tools, this study proposes a vision-based extraction approach that employs Hough transform-based region detection and a CNN-based digit recognizer to recover component coordinates from visually rendered design data. A dual-axis sliding platform is driven through a hierarchical control architecture, where coarse positioning is performed via TB6600 stepper control and Bluetooth-based communication, while fine alignment is achieved through a non-contact, gesture-based interface designed for clean-room operation. A high-resolution autofocus camera subsequently displays the magnified solder pads on a large screen for operator verification. Experimental results show that the proposed platform provides accurate, repeatable, and intuitive optical positioning, improving inspection efficiency while maintaining operator ergonomics and system modularity. Rather than replacing defect-classification AOI systems, this work complements them by serving as a positioning-assisted inspection module for interactive and semi-automated PCB quality evaluation. Full article

The rapid spread of climate change misinformation across digital platforms undermines scientific literacy, public trust, and evidence-based policy action. Advances in Natural Language Processing (NLP) and Large Language Models (LLMs) create new opportunities for automating the detection and correction of misleading climate-related narratives. This study presents a multi-stage system that employs state-of-the-art large language models such as Generative Pre-trained Transformer 4 (GPT-4), Large Language Model Meta AI (LLaMA) version 3 (LLaMA-3), and RoBERTa-large (Robustly optimized BERT pretraining approach large) to identify, classify, and generate scientifically grounded corrections for climate misinformation. The system integrates several complementary techniques, including transformer-based text classification, semantic similarity scoring using Sentence-BERT, stance detection, and retrieval-augmented generation (RAG) for evidence-grounded debunking. Misinformation instances are detected through a fine-tuned RoBERTa–Multi-Genre Natural Language Inference (MNLI) classifier (RoBERTa-MNLI), grouped using BERTopic, and verified against curated climate-science knowledge sources using BM25 and dense retrieval via FAISS (Facebook AI Similarity Search). The debunking component employs RAG-enhanced GPT-4 to produce accurate and persuasive counter-messages aligned with authoritative scientific reports such as those from the Intergovernmental Panel on Climate Change (IPCC). A diverse dataset of climate misinformation categories covering denialism, cherry-picking of data, false causation narratives, and misleading comparisons is compiled for evaluation. Benchmarking experiments demonstrate that LLM-based models substantially outperform traditional machine-learning baselines such as Support Vector Machines, Logistic Regression, and Random Forests in precision, contextual understanding, and robustness to linguistic variation. Expert assessment further shows that generated debunking messages exhibit higher clarity, scientific accuracy, and persuasive effectiveness compared to conventional fact-checking text. These results highlight the potential of advanced LLM-driven pipelines to provide scalable, real-time mitigation of climate misinformation while offering guidelines for responsible deployment of AI-assisted debunking systems. Full article

Road infrastructure underpins Nigeria’s economic competitiveness, yet Public–Private Partnership (PPP) performance is constrained not by inadequate legislation but by persistent weaknesses in enforcement and governance. Transparency deficits across procurement, design management, certification, and toll-revenue reporting have produced chronic delays, cost overruns, and declining public trust. This study offers the first empirical investigation of blockchain–Building Information Modelling (BIM) integration as a transparency-enhancing mechanism within Nigeria’s PPP road sector, focusing on Lagos State. Using a qualitative design, ten semi-structured interviews with stakeholders across the PPP lifecycle were thematically analysed to diagnose systemic governance weaknesses and assess the contextual feasibility of digital innovations. Findings reveal entrenched opacity rooted in weak enforcement, discretionary decision-making, and informal communication practices—including biased bidder evaluations, undocumented design alterations, manipulated certifications, and toll-revenue inconsistencies. While respondents recognised BIM’s potential to centralise project information and blockchain’s capacity for immutable records and smart-contract automation, they consistently emphasised that technological benefits cannot be realised absent credible institutional foundations. The study advances an original theoretical contribution: the Codify–Condition–Capacitate framework, which explains the institutional preconditions under which digital governance tools can improve transparency. This framework argues that effectiveness depends on: codifying digital standards and legal recognition; conditioning enforcement mechanisms to reduce discretionary authority; and capacitating institutions through targeted training and phased pilots. The research generates significant practical implications for policymakers in Nigeria and comparable developing contexts seeking institution-aligned digital transformation. Methodological rigour was ensured through purposive sampling, thematic saturation assessment, and documented analytical trails. Full article

Paper-based rapid tests are widely used in point-of-care diagnostics due to their simplicity and low cost. However, their application in quantitative analysis remains limited. In this work, a nucleic acid lateral flow assay (NALFA) was integrated with an automated image acquisition system built on a Raspberry Pi platform for the quantitative detection of SARS-CoV-2 virus, increasing the accuracy of the test compared to subjective visual interpretation. The assay employed blue polystyrene microspheres as reporters, while automated image capturing, image processing and quantification were performed using custom Python software (version 3.12). Signal quantification was achieved by comparing the grayscale intensity of the test line with that of a simultaneously captured negative control strip, allowing correction for illumination and background variability. Calibration curves were used for the training of the algorithm. The system was applied for the analysis of a series of samples with varying DNA concentrations, yielding recoveries between 84 and 108%. The proposed approach integrates a simple biosensor with an accessible computational platform to achieve full low-cost automation. This work introduces the first Raspberry Pi-driven image processing approach for accurate quantification of NALFAs and establishes a foundation for future low-cost, portable diagnostic systems targeting diverse nucleic acids, proteins, and biomarkers. Full article

With the increasing complexity of cyber threats and the inefficiency of traditional vulnerability management, artificial intelligence has been increasingly integrated into cybersecurity. This review provides a comprehensive evaluation of AI-powered strategies including machine learning, deep learning, and large language models for identifying cybersecurity vulnerabilities and supporting automated patching. In this review, we conducted a synthesis and appraisal of 29 peer-reviewed studies published between 2019 and 2024. Our results indicate that AI methods substantially improve the precision of detection, scalability, and response speed compared with human-driven and rule-based approaches. We detail the transition from conventional ML categorization to using deep learning for source code analysis and dynamic network detection. Moreover, we identify advanced mitigation strategies such as AI-powered prioritization, neuro-symbolic AI, deep reinforcement learning and the generative abilities of LLMs which are used for automated patch suggestions. To strengthen methodological rigor, this review followed a registered protocol and PRISMA-based study selection, and it reports reproducible database searches (exact queries and search dates) and transparent screening decisions. We additionally assessed the quality and risk of bias of included studies using criteria tailored to AI-driven vulnerability research (dataset transparency, leakage control, evaluation rigor, reproducibility, and external validation), and we used these quality results to contextualize the synthesis. Our critical evaluation indicates that this area remains at an early stage and is characterized by significant gaps. The absence of standard benchmarks, limited generalizability of the models to various domains, and lack of adversarial testing are the obstacles that prevent adoption of these methods in real-world scenarios. Furthermore, the research suggests that the black-box nature of most models poses a serious problem in terms of trust. Thus, XAI is quite pertinent in this context. This paper serves as a thorough guide for the evolution of AI-driven vulnerability management and indicates that next-generation AI systems should not only be more accurate but also transparent, robust, and generalizable. Full article

High-flux neutron beams and high-efficiency detectors enable rapid neutron diffraction measurements at the Engineering Materials Diffractometer (VULCAN) at the Spallation Neutron Source (SNS), Oak Ridge National Laboratory (ORNL). To optimize beam time utilization, efficient sample exchange, alignment, and automated measurements are essential. Recent advances in artificial intelligence (AI) have expanded the capabilities of robotic systems. Here, we report the development of a Robotic Interactive Control and Handling (RICH) system for sample handling at VULCAN, designed to support high-throughput experiments and reduce overhead time. The RICH system employs a six-axis desktop robot integrated with AI-based computer vision models capable of recognizing and localizing samples in real time from instrument and depth-resolving cameras. Vision algorithms combine these detections to align samples with designated measurement positions or place them within complex sample environments such as furnaces. This integration of machine learning-assisted vision with robotic handling demonstrates the feasibility of autonomous sample detection and preparation, offering a pathway toward fully unmanned neutron scattering experiments. Full article

Background: Malaria remains a major public health concern around the world. Microscopic blood smear examination continues to be the gold standard for diagnosis; however, it requires high technical skills and expertise, limiting diagnostic accuracy in resource-poor settings. Artificial intelligence (AI) has emerged as a promising tool to support malaria detection. This systematic review provides an overview of the diagnostic performance of AI-based systems for malaria diagnosis in a clinical setting. Methods: This study followed the Preferred Reporting Items for Systematic reviews and Meta-Analyses (PRISMA) guidelines and involved articles within the last 10 years that were collected from PubMed, ScienceDirect, Cochrane, EBSCO, and Wiley Online Library. Original articles that reported AI diagnostic accuracy with external validation were involved. The quality of each study was evaluated using the Quality Assessment of Diagnostic Accuracy Studies (QUADAS-2). Results: Ten studies with 6754 patients were analyzed. Pooled results of sensitivity [87.7% (95% CI: 78.2–93.4)] and specificity [91.4% (95% CI: 77.3–97.1)] revealed how much the AI agrees with each method when that method is used as a gold standard. Additionally, AI achieved a sensitivity of 87.7% and a specificity of 91.4% compared to microscopy examination and a sensitivity of 90.7% and a specificity of 88.3% compared to polymerase chain reaction (PCR). Conclusions: AI-based systems improve malaria diagnosis by providing high accuracy, automation, and lower costs. Showing performance comparable to reference methods such as microscopy and PCR, AI is a promising complementary tool for malaria control. Full article

Reliable assessment of bridge technical condition is a key challenge in infrastructure management due to uncertainty, subjectivity, and heterogeneity inherent in inspection-based data. Traditional deterministic evaluation methods often fail to capture the gradual nature of structural deterioration and the complex interactions between bridge components. This study proposes a hybrid methodology that integrates fuzzy logic and artificial neural networks (ANNs) to quantify the overall technical condition of bridge structures using long-term inspection data. A comprehensive dataset, derived from real bridge inspection reports collected over more than 15 years across various regions of Ukraine, served as the basis for model development. Five key input parameters—substructure condition, superstructure condition, deck condition, overall structural condition, and channel and channel protection condition—were employed to compute an integrated Bridge Condition Assessment indicator using a Mamdani-type fuzzy inference system. The resulting fuzzy-based indicator was subsequently used as the target variable for training ANN models. To ensure optimal predictive performance and training stability, Bayesian Optimization was applied for systematic hyperparameter tuning. Model performance was evaluated using standard regression metrics, including MSE, MAE, MAPE, and the coefficient of determination (R²). The results demonstrate that the proposed approach enables accurate approximation of the fuzzy-based Bridge Condition Assessment indicator, with MAPE values as low as 0.2% and R² exceeding 0.982 for the best-performing model. The hybrid framework effectively combines interpretability and scalability, providing a decision-support framework based on fuzzy logic and surrogate modeling for automated fuzzy-based bridge condition assessment, maintenance prioritization, and integration into digital asset management systems. Full article

Obesity is among the top contributing factors for non-communicable chronic disease development and has attained menacing global proportions, affecting approximately one of eight adults. Phytochemicals that support energy metabolism and prevent obesity development have been the subject of intense research endeavors over the past several decades. Cycloastragenol is a natural triterpenoid compound and aglycon of astragaloside IV, known for activating telomerase and mitigating cellular aging. Here, we aim to characterize the effect of cycloastragenol on lipid metabolism in a glucose-induced obesity model in Caenorhabditis elegans. We assessed the changes in the body length, width, and area in C. elegans maintained under elevated glucose through automated WormLab system. Lipid accumulation in the presence of either cycloastragenol (100 μM) or orlistat (12 μM), used as a positive anti-obesity control drug, was quantified through Nile Red fluorescent staining. Furthermore, we evaluated the changes in key energy metabolism molecular players in GFP-reporter transgenic strains. Our results revealed that cycloastragenol treatment decreased mean body area and reduced lipid accumulation in the C. elegans glucose-induced model. The mechanistic data indicated that cycloastragenol suppresses the nuclear hormone receptor family member NHR-49 and the delta(9)-fatty-acid desaturase 7 (FAT-7) enzyme, and activates the 5′-AMP-activated protein kinase catalytic subunit alpha-2 (AAK-2) and the protein skinhead 1 (SKN-1) signaling. Collectively, our findings highlight that cycloastragenol reprograms lipid metabolism by down-regulating the insulin-like receptor (daf-2)/phosphatidylinositol 3-kinase (age-1)/NHR-49 signaling while simultaneously enhancing the activity of the AAK-2/NAD-dependent protein deacetylase (SIR-2.1) pathway. The anti-obesogenic potential of cycloastragenol rationalizes further validation in the context of metabolic diseases and obesity management. Full article

The convergence of food insecurity, water scarcity, and environmental degradation has intensified the global search for sustainable agricultural models. Integrated Microalgal–Aquaponic Systems (IAMS) have emerged as a novel multi-trophic platform that unites aquaculture, hydroponics, and microalgal cultivation into a closed-loop framework for resource-efficient food production and water recovery. This critical review synthesizes empirical findings and engineering advancements published between 2008 and 2024, evaluating IAMS performance relative to traditional agriculture and recirculating aquaculture systems (RAS). Reported under controlled laboratory and pilot-scale conditions, IAMS have achieved nitrogen and phosphorus recovery efficiencies exceeding 95% while potentially reducing water consumption by up to 90% compared to conventional farming. The integration of microalgal photobioreactors enhances nutrient retention, may contribute to internal carbon capture, and enables the generation of diversified co-products, including biofertilizers and protein-rich aquafeeds. Nevertheless, significant barriers to commercial scalability persist, including the biological complexity of maintaining multi-trophic synchrony, high initial capital expenditure (CAPEX), and regulatory ambiguity regarding the safety of waste-derived algal biomass. Technical challenges such as photobioreactor upscaling, biofouling control, and energy optimization are critically discussed. Finally, the review evaluates the alignment of IAMS with UN Sustainable Development Goals 2, 6, and 13, and outlines future research priorities in techno-economic modeling, automation, and policy development to facilitate the transition of IAMS from pilot-scale innovations to viable industrial solutions. Full article

Recent advances in deep learning have had a significant impact on biomedical applications, driving precise actions in automated diagnostic processes. However, integrating neural networks into medical devices requires meeting strict requirements regarding computing power, energy efficiency, reconfigurability, and latency, essential conditions for real-time inference. Field-Programmable Gate Array (FPGA) architectures provide a high level of flexibility, performance, and parallel execution, thus making them a suitable option for the real-world implementation of machine learning (ML) and deep learning (DL) models in systems dedicated to the analysis of physiological signals. This paper presents a review of intelligent algorithms for electrocardiogram (ECG) signal classification, including Support Vector Machines (SVMs), Artificial Neural Networks (ANNs), Recurrent Neural Networks (RNNs), Long Short-Term Memory Networks (LSTMs), and Convolutional Neural Networks (CNNs), which have been implemented on FPGA platforms. A comparative evaluation of the performances of these hardware-accelerated solutions is provided, focusing on their classification accuracy. At the same time, the FPGA families used are analyzed, along with the reported performances in terms of operating frequency, power consumption, and latency, as well as the optimization strategies applied in the design of deep learning hardware accelerators. The conclusions emphasize the popularity and efficiency of CNN architectures in the context of ECG signal classification. The study aims to offer a current overview and to support specialists in the field of FPGA design and biomedical engineering in the development of accelerators dedicated to physiological signals analysis. Full article