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Background/Objectives: Artificial intelligence (AI) is increasingly transforming cardiology through advanced analytical tools capable of identifying complex patterns across cardiovascular imaging, electrophysiology, and clinical datasets. Machine learning (ML) and deep learning (DL) algorithms are being integrated into echocardiography, cardiac computed tomography (CT), cardiac magnetic resonance imaging (MRI), and electrocardiography (ECG), enabling earlier diagnosis and more personalized cardiovascular care. This narrative review summarizes current clinical and organizational applications of AI in cardiology and discusses emerging concepts related to explainable and trustworthy AI. Methods: A narrative review was conducted according to SANRA recommendations using the PubMed, MEDLINE, Web of Science, and Scopus databases, including peer-reviewed publications from 2015 to 2026 addressing clinical, organizational, and ethical applications of AI in cardiology, with particular emphasis on cardiovascular imaging, electrocardiography, heart failure, digital health, and explainable AI frameworks. Results: Substantial evidence demonstrates that AI-based tools can achieve expert-level performance in cardiovascular imaging interpretation, automated electrocardiographic analysis, and clinical risk prediction. Across multiple cardiovascular settings, AI has been associated with improved diagnostic accuracy, enhanced workflow efficiency, and earlier detection of cardiovascular disease. Predictive models support risk stratification in heart failure and ischemic heart disease, while chatbots and digital health platforms may facilitate patient engagement, remote monitoring, and continuity of care. Despite these advances, important challenges remain, including algorithmic bias, limited transparency, insufficient external validation, data heterogeneity, and barriers to routine clinical implementation. Emerging explainable AI approaches may improve model interpretability, clinician confidence, and the safe adoption of AI-driven decision support systems. Conclusions: Artificial intelligence is rapidly evolving from a research-oriented technology into a clinically relevant component of cardiovascular care. Current evidence indicates that AI can enhance diagnostic performance, improve risk prediction, streamline clinical workflows, and facilitate more personalized management across multiple cardiovascular domains. However, the successful translation of AI into routine practice will depend on robust external validation, transparent decision-making mechanisms, regulatory oversight, and clinician acceptance. The development of explainable and trustworthy AI frameworks represents a critical step toward the safe, ethical, and sustainable integration of AI into modern cardiology. Full article

Single-cell omics technologies have transformed the study of cellular heterogeneity, enabling high-resolution analysis of tissue development and complex traits. In sheep and goats, these approaches have been applied to skin, hair follicles, reproductive organs, metabolic tissues, and adipose tissue, revealing cell type-specific regulatory programs underlying traits such as wool quality, fertility, growth, and fat deposition. However, most studies rely on single-cell RNA sequencing (scRNA-seq) and are limited by incomplete genome annotation, insufficient coverage of production traits, and weak integration with population genetics, restricting their application in molecular breeding. This review summarizes advances in single-cell omics in sheep and goats, focusing on tissue development and trait formation. We further discuss emerging strategies that integrate single-cell multi-omics, spatial transcriptomics, and population genetics to resolve regulatory mechanisms in a cell type-specific and spatially informed context. Finally, we discuss CRISPR/Cas9-based validation to link genotype and phenotype, accelerating gene discovery and precision breeding in small ruminants. Full article

The growing prevalence of chronic degenerative diseases has increased interest in nutritional strategies based on natural bioactive compounds such as polyphenols. This study aimed to develop a polyphenol-fortified tomato juice using extracts from unripe green tomatoes and to evaluate its physicochemical, antioxidant, sensory, and storage properties. Polyphenolic extracts obtained from tomato by-products were characterized using spectrophotometric and HPLC analyses and incorporated into tomato juice, which was then pasteurized and stored for six months. Total polyphenol content increased from 40.97 to 82.45 mg GAE/100 g, decreasing to 71.44 mg after storage; HPLC confirmed higher levels of key phenolic compounds in fortified juice. DPPH antioxidant activity increased in fortified juice compared to control, since pasteurization had limited effects but decreased after storage, with a moderate reduction in bioactivity. Colorimetric and sensory analyses showed changes in color, aroma, and sweetness after storage, potentially affecting consumer acceptance, although overall composition remained largely stable. Overall, results demonstrate the feasibility of producing a polyphenol-enriched tomato juice from agro-industrial by-products with improved antioxidant properties and acceptable technological stability. These findings support the valorization of tomato processing waste and suggest potential applications in functional food development, human health promotion, and the sustainability of agri-food systems’ overall approach. Full article

Background: Precise intraoperative localisation of small colorectal tumours during laparoscopic surgery remains challenging due to absent tactile feedback and subserosal tumour location. Current standard methods, particularly India ink tattooing, demonstrate 15–30% failure rates for lesions less than 10 mm, leading to prolonged operative times, incomplete resections, and re-operations. Multiple emerging technologies promise improved localisation, yet comparative evidence remains fragmented. Objective: To map and characterise the current landscape of intraoperative marking and identification technologies for small colorectal tumour localisation during laparoscopic surgery, with emphasis on radiofrequency-based methods and alternative approaches, and to identify evidence gaps guiding future research. Methods: Following PRISMA-ScR guidelines, we systematically searched PubMed, Web of Science, and Scopus databases from January 2000 through December 2025 for studies evaluating tumour localisation technologies in colorectal cancer surgery, including primary tumour localisation during laparoscopic colectomy and localisation of colorectal liver metastases during hepatic surgery, or transferable anatomical applications with documented translational potential to colorectal surgery. Two independent reviewers screened all records, with discrepancies resolved through discussion and a third senior reviewer consulted for unresolved disagreements; data were extracted on technical performance, safety, feasibility, cost-effectiveness, usability, innovation potential, and evidence quality. Results: We included 89 studies comprising 18 colorectal-specific articles and 71 transferable/GI-adjacent studies. Detection success rates ranged from 71% to 100% across modalities. Near-infrared fluorescence with indocyanine green demonstrated the strongest clinical evidence with 75–100% detection across eight colorectal studies encompassing 2134 procedures and seamless workflow integration. Radiofrequency identification systems achieved 91.9–99% detection in feasibility studies with promising tissue penetration of 15–35 mm but limited colorectal validation. Electromagnetic navigation excelled in rigid organs with 85–98% success but showed degraded performance in mobile bowel at 71–75%. Critical evidence gaps included absent head-to-head comparative trials, non-standardised outcome metrics limiting cross-study comparability, and limited long-term safety data with only 14 studies providing follow-up exceeding six months. Conclusions: ICG fluorescence represents the most clinically mature technology identified, representing a priority candidate for colorectal-specific validation in challenging localisation scenarios. RFID systems demonstrate promising characteristics justifying prioritised research investment through adequately powered comparative trials. Future research must emphasise consortium-based comparative effectiveness studies, standardised outcome metrics, and integration with robotic and AI-assisted surgical platforms to accelerate clinical translation. Full article

Electroencephalography (EEG) is a core technique for brain activity monitoring. However, conventional EEG systems suffer from complicated setup and poor portability, which drives the development of ear EEG technology. Ear EEG is divided into in-ear and around-ear types, both with unique application strengths. This review mainly discusses in-ear EEG, as it features a compact structure and fits well with daily wearable use cases. Current research on in-ear EEG is limited to feasibility verification and small-sample experiments. Researchers have not yet combined personalized design with signal processing algorithms systematically, and multi-center clinical trials are still absent. These issues have become the major bottleneck hindering its clinical transformation. This paper reviews the latest advances in ear-EEG systems, focusing on structural innovation and material development to summarize key achievements in hardware design. It also summarizes its typical applications in brain-computer interfaces (BCI), covering steady-state responses, event-related potentials and motor imagery. Meanwhile, it analyzes the application of in-ear EEG in brain state monitoring, including sleep tracking, epilepsy detection, drowsiness evaluation and emotion recognition. Finally, future directions for in-ear EEG are outlined, including personalized design and intelligent signal processing. This review provides a technical framework for beginners and identifies key directions for future research. Full article

GNSS array receivers suffer tracking degradation under array nonidealities such as element-position perturbations, channel amplitude/phase errors, and slowly varying manifold mismatch. Conventional blind anti-jamming suppresses interference, but adaptive weight fluctuations can propagate into the correlator domain, increasing cross-branch correlation, causing Early/Late metric imbalance, and reducing Prompt phase consistency. Existing noncoherent combining methods mainly convert multi-branch correlator outputs into scalar energy metrics for code tracking, leaving the carrier loop’s complex Prompt input insufficiently constrained. To address this problem, we propose a blind adaptive joint code–carrier channel-combining method for nonideal arrays. After first-stage anti-jamming, the method estimates an Early/Late correlator-domain covariance matrix and reuses it as a shared statistical constraint. In the code loop, this matrix drives whitened noncoherent energy combining with closed-loop gain normalization to stabilize the DLL discriminator scale. In the carrier loop, it is combined with a Prompt-derived coherent direction to form a covariance-constrained PLL complex input. Simulations under wideband interference, static array errors, and dynamic mismatch show that the proposed J-WNCC reduces both code-phase error and carrier-phase jitter, improving joint tracking robustness in nonideal array environments. Ablation results further reveal a dominant-effect separation: DLL gain normalization mainly calibrates the whitened code-discriminator scale, whereas coherent Prompt combining mainly reconstructs the complex PLL input. Full article

Twice-exceptional students—those who are both gifted and have one or more disabilities—and gifted learners, more broadly, represent persistently underserved populations within educational systems. Gifted learners frequently encounter provision that does not adequately engage their potential, such as standardised approaches that neither recognise nor respond to their learning requirements. Traditional identification and programming approaches often rely on deficit-based approaches that pathologise neurodivergence and frequently neglect the complex, asynchronous learning profiles characteristic of twice-exceptional students. This article advances a functional alignment framework proposing that generative artificial intelligence’s processing patterns may align with the cognitive characteristics of some gifted and twice-exceptional learners. The proposed functional alignment spans five dimensions: conceptual movement, knowledge integration, topic continuity, working memory, and pacing and temporal flexibility; this positions GenAI as a potentially compatible interactive platform for personalised, strengths-based learning. The functional alignment framework is explicitly theoretical, advancing propositions rather than demonstrated effects, and requires empirical validation. Positioning GenAI as a mediating platform has the potential to disrupt longstanding barriers to evidence-informed educational provision for gifted and twice-exceptional students. Through examining the intersection of gifted education, special education, and educational technology, this theoretical work outlines a trajectory for the field, characterised by flexible, personalised, strengths-based approaches that can be responsive to the student in front of the teacher, instead of the all-too-often default to one-size-fits-all approaches. Critical considerations of equity, teacher capability, and ethical implementation are addressed, theorising that GenAI’s transformative potential may only be realised through deliberate, theoretically informed application grounded in deep understanding of learner neurodivergence and a proposed pivot from GenAI literacy to GenAI fluency. This work contributes to reconceptualising gifted education as inherently inclusive, responsive, and oriented towards actualising potential for gifted and twice-/multi-exceptional learners. Full article

Recent advances in generative Artificial Intelligence (AI) have rapidly transformed research and practice across the Cultural Heritage domain. While several studies have investigated AI applications in documentation, analysis and dissemination, a focused and critical assessment of generative AI within 3D virtual reconstruction workflows is still lacking. This paper presents a systematic review of the literature addressing the use of generative AI in 3D heritage virtual reconstructions, with particular attention to methodological implications, scientific reliability and ethical challenges. A large-scale bibliographic analysis covering publications from 2015 to 2024 was conducted using OpenAlex, complemented by targeted manual searches. From an initial corpus of over 8700 papers on 3D heritage reconstruction, only 13 directly addressed generative AI-driven reconstruction processes. The analysis highlights a significant gap between the rapid technological development of AI-based tools and their cautious, often problematic, adoption in virtual reconstruction practices. Results reveal recurring issues related to terminological ambiguity, opacity of reconstruction processes, evaluation metrics focused on visual plausibility rather than scientific transparency and the risk of interpretative bias. The paper argues that current AI-driven approaches tend to privilege speed and aesthetic outcomes over heuristic, source-based reconstruction workflows. Finally, future research directions are discussed, emphasizing the potential role of AI as an evaluative and analytical support tool rather than a fully autonomous reconstruction agent, in alignment with established charters and principles of virtual archaeology. Full article

Non-contact magnetic sensing technology is widely adopted in transmission line online monitoring scenarios including current measurement and fault location for its non-contact measurement capability, strong environmental robustness and low deployment cost. However, existing magnetic-sensing-based galloping monitoring methods suffer from two critical limitations: no theoretical guidance is provided for sensor placement, and a high false detection rate is observed under current fluctuation conditions. To address these issues, a novel transmission line galloping monitoring method based on spatial magnetic field distribution features is proposed in this paper. A conductor galloping-power frequency magnetic field coupling model is first established to derive the optimal magnetic sensor array arrangement strategy. Subsequently, a galloping detection algorithm fusing multi-node frequency-domain features and phase difference information is proposed to eliminate current fluctuation induced false detection. Simulations conducted based on actual 500 kV transmission line parameters and verification tests carried out on a scaled-down laboratory platform confirm that reliable galloping detection can be realized by the proposed method under both current low-frequency oscillation and random fluctuation scenarios. With advantages of non-contact deployment, high anti-interference performance and detection accuracy, the proposed method has promising application potential in engineering-oriented high-voltage transmission line monitoring. Full article

Background: Artificial intelligence (AI)-driven markerless motion capture (MMC) technologies are increasingly being integrated into pediatric healthcare to improve the assessment and management of movement disorders. These video-based systems enable non-invasive motion analysis without wearable sensors, facilitating more natural movement assessment in children, particularly those with neurological or developmental conditions. Objectives: We evaluated the clinical applicability of AI-based MMC tools in pediatric settings for diagnosis, monitoring of motor development, and rehabilitation. Methods: This systematic review was registered in PROSPERO (CRD42024511787) and conducted by two independent reviewers, with a third reviewer resolving disagreements. The literature published between 2018 and 2025 was systematically searched. Studies involving pediatric populations or clinically relevant pediatric applications of MMC were included. Results: Of 1521 identified studies, 52 were finally selected. The included studies evaluated populations across a wide age range. However, seven of the included articles were specifically focused on underage populations. Infant studies primarily analyzed whole-body movements, emphasizing the relevance of global motor patterns in early development. OpenPose and AlphaPose were the most frequently used frameworks in pediatric research because of their automatic full-body key point detection, whereas DeepLabCut was commonly selected for its customizable labeling capabilities. Theia3D emerged as a promising clinically applicable solution with high accuracy. Most studies evaluated kinematic parameters as objective markers of motor performance and development. However, methodological heterogeneity and limited pediatric-specific validation remain important limitations. Conclusions: AI-driven MMC technologies show considerable potential to support objective, accessible, and child-friendly movement assessment in pediatric clinical practice. Full article

Advanced oxidation processes using titanium dioxide (TiO₂) have emerged as a promising approach for the photocatalytic degradation of contaminants in water and have drawn extensive research attention despite limited translation of this technology to large-scale applications. The limitations of this technology include immobilization of the photocatalyst, scalability, and compatibility with available light sources. Using 3D printing to immobilize TiO₂-based photocatalysts, we systematically evaluated the rates of photocatalytic degradation of methylene blue (MB) with different light-emitting diode (LED) ultraviolet (UV) light sources and modified TiO₂-based photocatalytic materials. The UV LED lights successfully decreased the MB concentrations with half-lives ranging from 0.9 to 2.4 h, with relative photocatalytic performance of UVA-365 > UVA-395 > UVC-280. The photocatalytic degradation rates under UV LEDs were slower (0.9–2.4 h) than those achieved using a low-pressure mercury UV-C lamp (0.5 h) and were also lower than those observed under solar simulated lights (0.6 h). The TiO₂ modified by an alkyl silane entity and embedded in a polylactic acid polymeric system with 3D printing exhibited the fastest methylene blue (MB) removal among the three TiO₂-based structures evaluated, with a half-life of 0.6 h compared to the 1.6–17.7 h for the other materials. This research demonstrated that 3D printing enables the integration of functionalized photocatalysts, and, when paired with low-cost, low-energy UV LED lights, can achieve environmentally relevant rates of performance. Ultimately, these findings represent an incremental step toward improving the performance of 3D-printed photocatalytic materials. Full article

Construction sites generate large volumes of contaminated wastewater, yet sustainable treatment solutions remain limited. This study presents a case study focusing on the wastewater produced from washing construction equipment at an industrial site in northern Morocco. Initial characterization revealed a chemical oxygen demand of 3125 mg O₂/L, a five-day biochemical oxygen demand of 980 mg O₂/L, and a total suspended solids concentration of 676 mg/L, values that exceed national discharge standards. An electrocoagulation process using aluminum electrodes was employed, alongside electrochemical impedance spectroscopy, to investigate the treatment mechanisms. Under optimal conditions (30 min at 142.85 A/m²), the removal of chemical oxygen demand reached 88%, alongside significant reductions in dissolved solids and electrical conductivity. Analysis of the electrochemical impedance spectroscopy identified two relaxation phenomena associated with ionic migration and flocculation, with efficiency decreasing beyond 0.3 A. These results demonstrate that electrocoagulation is an effective and sustainable technology for treating construction site wastewater. This study highlights its potential for practical application in the built environment and its relevance for improving the environmental performance of the construction sector. Full article

Additive manufacturing (AM), commonly known as 3D printing, has rapidly transformed modern manufacturing, creating a growing demand for engineers with both theoretical knowledge and practical skills. Despite its increasing relevance, AM is often incorporated into engineering curricula as a supplementary tool rather than a fully integrated subject, limiting students’ understanding of fundamental material–process–performance relationships. This study presents the development, implementation, and assessment of an integrated lecture–laboratory framework for AM education at the New Jersey Institute of Technology (NJIT). Two complementary courses were developed: an undergraduate course (Introduction to 3D Printing, CHE 415) and a graduate course (Additive Manufacturing and Applications, CHE 722). The curriculum integrates instruction in AM technologies, materials, and digital workflows with hands-on design challenges, team-based projects, and structured literature reviews, enabling students to engage in the complete design-to-fabrication process. Student learning outcomes were evaluated over multiple academic years using ABET-aligned assessments, grade distributions, and student self-assessments. Results demonstrate consistently high levels of student proficiency and engagement, with strong performance in design, problem-solving, and communication skills. The courses also attracted students from diverse disciplines, underscoring the interdisciplinary nature of AM education. While limitations remain in providing hands-on exposure to a broader range of AM technologies, ongoing expansion of laboratory infrastructure is expected to address these challenges. Overall, this work demonstrates that an integrated, project-based approach effectively bridges theory and practice and provides a scalable model for incorporating AM into engineering curricula. Full article

Centrifugal compressors are critical assets in the oil and gas, petrochemical, and power generation industries, where unplanned downtime results in severe economic and safety consequences. Despite the application of digital twin technology for predictive maintenance, existing approaches struggle to combine accurate degradation modeling with formal assurance evidence that regulators and operators demand before trusting machine learning-augmented systems. This paper proposes a hybrid digital twin framework whose architecture is structured around a formal safety case template, addressing both the accuracy and the trustworthiness challenges simultaneously. The methodology couples a first-principles thermodynamic model with a neural-network residual learner, and the complete system is organized through a design-stage safety case constructed in Goal Structuring Notation. The design stage identifies the requirements for operational deployment. Validation through a simulation study on a one-year synthetic operational dataset shows that the hybrid model reduces root-mean-square prediction error by over 50% for both pressure ratio and polytropic efficiency compared to the physics-only baseline. The anomaly detection module, presented here as a proof of concept, achieves 92% recall in identifying injected faults, and a composite health index tracks the progression of fouling, erosion, and seal wear over the simulated service life. This study is purely theoretical, with no experimental measurements conducted. It demonstrates the structural viability and coherence of the proposed framework within a controlled environment, providing a solid theoretical and computational foundation for future physical validation efforts. These findings provide preliminary evidence that embedding a structured safety argument into the design of a hybrid digital twin is technically feasible and beneficial for building the confidence needed to deploy such systems in safety-critical industrial environments. Full article

The integration of online Digital Twin (DT) technologies with industrial control systems represents an important step toward real-time monitoring and synchronization of manufacturing processes within Industry 4.0 environments. However, reproducible approaches for connecting simulation environments with real industrial control hardware using standardized communication protocols remain insufficiently described in the existing literature. This study presents the development of an online Digital Twin for real-time monitoring of manufacturing processes using OPC UA communication and programmable logic controller (PLC) data exchange. The proposed approach combines discrete-event simulation with real-time industrial data acquisition to enable synchronization between a physical manufacturing system and its virtual representation. The implementation was experimentally validated in a laboratory-scale cyber–physical production system using Tecnomatix Plant Simulation, Siemens S7-1200 PLC, and KEPServerEX middleware. The developed architecture enables real-time process state monitoring, event-driven synchronization, and verification of selected control and safety functions within the simulation environment. The results demonstrate stable synchronization between the physical and digital systems with response times ranging from 50 to 200 ms, confirming the feasibility of near-real-time integration. The implemented light barrier scenario further demonstrated the capability of the online DT to reflect safety-related events occurring in the physical system. The main contribution of this study lies in the implementation and experimental verification of an OPC UA-based online Digital Twin architecture for manufacturing process monitoring in a laboratory environment. The presented approach provides a foundation for future extensions toward predictive analytics, scenario-based simulation, and advanced manufacturing optimization applications. Full article