Search Results (852)

Robotics has emerged as a transformative discipline at the intersection of the engineering, computer science, and cognitive sciences. This state-of-the-art review explores the current trends, methodologies, and challenges in both robotics research and education. This paper presents a comprehensive review of the evolution of robotics, tracing its development from early automation to intelligent, autonomous systems. Key enabling technologies, such as Artificial Intelligence (AI), soft robotics, the Internet of Things (IoT), and swarm intelligence, are examined along with real-world applications in healthcare, manufacturing, agriculture, and sustainable smart cities. A central focus is placed on robotics education, where hands-on, interdisciplinary learning is reshaping curricula from K–12 to postgraduate levels. This paper analyzes instructional models including project-based learning, laboratory work, capstone design courses, and robotics competitions, highlighting their effectiveness in developing both technical and creative competencies. Widely adopted platforms such as the Robot Operating System (ROS) are briefly discussed in the context of their educational value and real-world alignment. Through case studies, institutional insights, and synthesis of academic and industry practices, this review underscores the vital role of robotics education in fostering innovation, systems thinking, and workforce readiness. The paper concludes by identifying the key challenges and future directions to guide researchers, educators, industry stakeholders, and policymakers in advancing robotics as both technological and educational frontiers. Full article

The dimensional accuracy of 3D-printed edentulous jaw models is critical for successful prosthetic treatment outcomes. This study investigated the accuracy of 3D-printed working models of a completely edentulous jaw through comparative analysis of digital images generated by three laboratory scanners. A reference plaster model of a mandibular edentulous arch was digitized and used to produce ten resin models via digital light processing (DLP) technology. Each model was scanned using three different laboratory scanners: AutoScan-DS-EX, AutoScan-DS-EX Pro(H), and Optical 3D Scanner Vinyl. Digital comparison was performed using specialized software, evaluating the root mean square (RMS) deviation and percentage of values within an acceptable deviation range ±0.05 mm. All printed models showed significant deviations from the reference model (p < 0.05), with RMS values ranging from 109.2–139.7 µm and acceptable deviation percentages ranging from 29.34 to 32.31%. The mean precision RMS value was 66.37 µm. The mean intraclass correlation coefficient of 0.544 indicated moderate precision. Optical 3D Scanner Vinyl demonstrated the highest consistency, while AutoScan-DS-EX Pro(H) showed maximum variability. No statistically significant differences were found between scanners (p = 0.075). While the investigated scanners demonstrated reliable performance and sufficient accuracy, the additive manufacturing process introduced clinically significant deviations, highlighting the importance of verification between printed models and their digital originals before proceeding with clinical stages. Clinical practice should prioritize scanning systems with advanced software algorithms over those with superior hardware specifications alone. Full article

Microbiological contamination of food remains a critical global public health concern, contributing to millions of foodborne illness cases each year. Traditional diagnostic methods, particularly culture-based techniques, have been widely employed but are often limited by low sensitivity, insufficient specificity, and lengthy turnaround times. Recent advances in molecular biology, biosensor technology, and analytical chemistry have enabled the development of more rapid and precise diagnostic tools. Among these, Matrix-Assisted Laser Desorption/Ionization Time-of-Flight Mass Spectrometry (MALDI-TOF MS) has emerged as a transformative method for microbial identification. This review provides a comprehensive overview of the current applications of MALDI-TOF MS in the diagnosis of microbiological contamination in food. The method offers rapid, accurate, and cost-effective identification of microorganisms and is increasingly used in food safety laboratories for the detection of foodborne pathogens, ensuring the safety and quality of food products. We highlight the fundamental principles of MALDI-TOF MS, discuss its methodologies, and examine its advantages, limitations, and future prospects in food microbiology and quality assurance. Full article

The analysis of wastewater is essential in environmental chemistry, particularly for monitoring emerging contaminants and assessing ecological impacts. In this context, hyphenated chromatographic techniques are widely used, with liquid chromatography being one of the most common. However, gas chromatography coupled with mass spectrometry (GC-MS) remains a valuable tool in this field due to its sensitivity, selectivity, and widespread availability in most laboratories. This review examines the application of validated methods for wastewater analysis using GC-MS (MS), highlighting its relevance in identifying micropollutants such as pharmaceuticals, drugs of abuse, pesticides, hormones, and industrial by-products. The validation of analytical methods is crucial to ensuring the reliability and reproducibility of data and the accurate monitoring of contaminants. Key parameters, including sample volume, recovery efficiency, and detection and quantification limits, are discussed, evaluating different approaches to optimising the identification of different classes of contaminants. Additionally, this study explores advances in sample preparation techniques, such as solid-phase microextraction (SPME), dispersive liquid–liquid microextraction (DLLME), and solid-phase extraction (SPE), which enhance efficiency and minimise interferences in the analysis. Finally, future perspectives are discussed, including the integration of emerging technologies such as high-resolution mass spectrometry, the miniaturisation of GC systems, and the development of faster and more sustainable analytical methods. Full article

Mycotoxins are responsible for a multitude of diseases in both humans and animals, resulting in significant medical and economic burdens worldwide. Conventional detection methods, such as enzyme-linked immunosorbent assay (ELISA), high-performance liquid chromatography (HPLC), and liquid chromatography-tandem mass spectrometry (LC-MS/MS), are highly effective, but they are generally confined to laboratory settings. Consequently, there is a growing demand for point-of-care testing (POCT) solutions that are rapid, sensitive, portable, and cost-effective. Lateral flow assays (LFAs) are a pivotal technology in POCT due to their simplicity, rapidity, and ease of use. This review synthesizes data from 78 peer-reviewed studies published between 2015 and 2024, evaluating advances in nanoparticle-based LFAs for detection of singular or multiplex mycotoxin types. Gold nanoparticles (AuNPs) remain the most widely used, due to their favorable optical and surface chemistry; however, significant progress has also been made with silver nanoparticles (AgNPs), magnetic nanoparticles, quantum dots (QDs), nanozymes, and hybrid nanostructures. The integration of multifunctional nanomaterials has enhanced assay sensitivity, specificity, and operational usability, with innovations including smartphone-based readers, signal amplification strategies, and supplementary technologies such as surface-enhanced Raman spectroscopy (SERS). While most singular LFAs achieved moderate sensitivity (0.001–1 ng/mL), only 6% reached ultra-sensitive detection (<0.001 ng/mL), and no significant improvement was evident over time (ρ = −0.162, p = 0.261). In contrast, multiplex assays demonstrated clear performance gains post-2022 (ρ = −0.357, p = 0.0008), largely driven by system-level optimization and advanced nanomaterials. Importantly, the type of sample matrix (e.g., cereals, dairy, feed) did not significantly influence the analytical sensitivity of singular or multiplex lateral LFAs (Kruskal–Wallis p > 0.05), confirming the matrix-independence of these optimized platforms. While analytical challenges remain for complex targets like fumonisins and deoxynivalenol (DON), ongoing innovations in signal amplification, biorecognition chemistry, and assay standardization are driving LFAs toward becoming reliable, ultra-sensitive, and field-deployable platforms for high-throughput mycotoxin screening in global food safety surveillance. Full article

The COVID-19 pandemic and recent viral outbreaks have highlighted the need for viral diagnostics that balance accuracy with accessibility. While traditional laboratory methods remain essential, point-of-care solutions are critical for decentralized testing at the population level. However, a gap persists between academic proof-of-concept studies and clinically viable tools, with novel technologies remaining inaccessible to clinics due to cost, complexity, training, and logistical constraints. Recent advances in surface functionalization, assay simplification, multiplexing, and performance in complex media have improved the feasibility of both optical and non-optical sensing techniques. These innovations, coupled with scalable manufacturing methods such as 3D printing and streamlined hardware production, pave the way for practical deployment in real-world settings. Additionally, software-assisted data interpretation, through simplified readouts, smartphone integration, and machine learning, enables the broader use of diagnostics once limited to experts. This review explores improvements in viral diagnostic approaches, including colorimetric, optical, and electrochemical assays, showcasing their potential for democratization efforts targeting the clinic. We also examine trends such as open-source hardware, modular assay design, and standardized reporting, which collectively reduce barriers to clinical adoption and the public dissemination of information. By analyzing these interdisciplinary advances, we demonstrate how emerging technologies can mature into accessible, low-cost diagnostic tools for widespread testing. Full article

Nitrogen is a vital nutrient and plays a pivotal role in maintaining ecosystem equilibrium. Owing to human activities, particularly industrial production, vehicle emissions, fossil fuel combustion, and the improper use of chemical fertilizers, nitrogen pollution has emerged as a pressing global environmental issue. It exacerbates air pollution, water eutrophication, and soil acidification, all of which pose profound risks to both ecosystems and human health. This review conducts a holistic analysis of nitrogen sources and the current status of nitrogen pollution, with a particular focus on the treatment of nitrogen-laden wastewater. It assesses various nitrogen pollution remediation technologies, including biological and physicochemical methods. In recent years, the application of novel metal–phenolic networks (MNPs) has garnered considerable scholarly attention. As innovative materials, it has been demonstrated that MNPs have great potential in nitrogen removal. For example, studies have demonstrated that iron–tanninate has the capacity to remove over 95% of ammonium nitrogen. Despite the progress made with current remediation methods, each approach has inherent limitations, such as long treatment durations, high energy demands, and poor selectivity for diverse nitrogen pollutants. Therefore, sustained research endeavors and technological innovation are indispensable for advancing nitrogen pollution control technologies. It is against this backdrop that we conducted this review. This study summarizes and analyzes the current status of nitrogen pollution and nitrogen removal technologies, and provides an overview of novel nitrogen removal MNPs. MNPs are promising and innovative materials with great potential, although current research is still at the laboratory stage and is ongoing. Full article

Background: Multiple myeloma (MM) is a heterogeneous plasma cell malignancy with non-specific symptoms and disease heterogeneity at clinical and biological levels. This non-specific set of symptoms, including bone pain, anemia, renal failure, hypercalcemia, and neuropathy, can mislead diagnosis as chronic or benign conditions, resulting in a delay in diagnosis. Timely identification is paramount to prevent organ damage and reduce morbidity. Methods: In this review, we present an overview of recent literature concerning the factors leading to the delayed diagnosis of MM and the impact of delayed diagnosis. This includes factors relevant to physicians and systems, diagnostic processes, primary healthcare services, and laboratory and imaging data access and interpretation. Other emerging technologies to diagnose NCIs include AI-based decision support systems and biomarker-focused strategies. Findings: Delayed diagnosis can lead to presentation at advanced disease stages associated with life-threatening complications and shorter progression-free survival. Patients are often seen by many physicians before they are referred to hematology. Understanding of clinical red flags for MM in primary care is inadequate. Our findings indicate that limited access to diagnostic tests, inconsistent follow-up of MGUS/SMM patients, and a lack of interdepartmental coordination delay the diagnostic process. Conclusions: Multimodal tools for early diagnosis of MM. Educational campaigns to raise awareness of the disease, algorithms dedicated to routine care and novel technologies, including AI and big data analytics, and new biomarkers may serve this purpose, as well as genomic approaches to the premalignant MGUS stage. Full article

This paper examines the relationship between investment in water education and economic performance, focusing on the context of widening countries (EU Member States and Associated Countries with lower research and innovation performance). Through time-series data and panel regression analysis, the study investigates whether increased spending on education correlates with Gross Domestic Product (GDP) growth. While the initial static model indicates a positive but statistically insignificant association, a dynamic model with lagged GDP significantly improves explanatory power, suggesting that educational investments may influence growth with a temporal delay. Complementing the macroeconomic data, the paper analyses how targeted investments in educational innovation, especially in digital technologies such as virtual reality (VR) applications, enhance teaching quality and student engagement. Examples from partner universities involved in the WATERLINE project (Horizon Europe, 101071306) show how custom-built VR modules, aligned with existing hydraulic labs, contribute to advanced water-related skills. The paper also presents a cost-benefit analysis of VR applications in water education, highlighting their economic efficiency compared to traditional laboratory equipment. Additionally, it explores how micro-level innovations in education can generate macroeconomic benefits through widespread adoption and systemic impact. Ultimately, the research highlights the long-term value of education and innovation in strengthening both economic and human capital across diverse regions. Full article

The rapid advancement of flexible sensor technology has profoundly transformed neural interface research, enabling multimodal information acquisition, real-time neurochemical and electrophysiological signal monitoring, and adaptive closed-loop regulation. This review systematically summarizes recent developments in flexible materials and microstructural designs optimized for enhanced biocompatibility, mechanical compliance, and sensing performance. We highlight the progress in integrated sensing systems capable of simultaneously capturing electrophysiological, mechanical, and neurochemical signals. The integration of carbon-based nanomaterials, metallic composites, and conductive polymers with innovative structural engineering is analyzed, emphasizing their potential in overcoming traditional rigid interface limitations. Furthermore, strategies for multimodal signal fusion, including electrochemical, optical, and mechanical co-sensing, are discussed in depth. Finally, we explore future perspectives involving the convergence of machine learning, miniaturized power systems, and intelligent responsive materials, aiming at the translation of flexible neural interfaces from laboratory research to practical clinical interventions and therapeutic applications. Full article

Yeast genetic improvement is entering a transformative phase, driven by the integration of artificial intelligence (AI), big data analytics, and synthetic microbial communities with conventional methods such as sexual breeding and random mutagenesis. These advancements have substantially expanded the potential for innovative re-engineering of yeast, ranging from single-strain cultures to complex polymicrobial consortia. This review compares traditional genetic manipulation techniques with cutting-edge approaches, highlighting recent breakthroughs in their application to beer and wine fermentation. Among the innovative strategies, adaptive laboratory evolution (ALE) stands out as a non-GMO method capable of rewiring complex fitness-related phenotypes through iterative selection. In contrast, GMO-based synthetic biology approaches, including the most recent developments in CRISPR/Cas9 technologies, enable efficient and scalable genome editing, including multiplexed modifications. These innovations are expected to accelerate product development, reduce costs, and enhance the environmental sustainability of brewing and winemaking. However, despite their technological potential, GMO-based strategies continue to face significant regulatory and market challenges, which limit their widespread adoption in the fermentation industry. Full article

Advancements in biosensing technologies have introduced opportunities for non-invasive, real-time monitoring of salivary biomarkers, enabling progress in fields ranging from personalized medicine to public health. Identifying and prioritizing the most critical analytes to measure in saliva is essential for estimating physiological status and forecasting performance in applied contexts. This study examined the value of 12 salivary analytes, including hormones, metabolites, and enzymes, for predicting cognitive and physical performance outcomes in military personnel (N = 115) engaged in stressful laboratory and field tasks. We calculated a series of features to quantify time-series analyte data and applied multiple regression techniques, including Elastic Net, Partial Least Squares, and Random Forest regression, to evaluate their predictive utility for five outcomes of interest: the ability to move, shoot, communicate, navigate, and sustain performance under stress. Predictive performance was poor across all models, with R-squared values near zero and limited evidence that salivary analytes provided stable or meaningful performance predictions. While certain features (e.g., post-peak slopes and variance metrics) appeared more frequently than others, no individual analyte emerged as a reliable predictor. These results suggest that salivary biomarkers alone are unlikely to provide robust insights into cognitive and physical performance outcomes. Future research may benefit from combining salivary and other biosensor data with contextual variables to improve predictive accuracy in real-world settings. Full article

Bioinspired morphing offers a powerful route to higher aerodynamic and hydrodynamic efficiency. Birds reposition feathers, bats extend compliant membrane wings, and fish modulate fin stiffness, tailoring lift, drag, and thrust in real time. To capture these advantages, engineers are developing airfoils, rotor blades, and hydrofoils that actively change shape, reducing drag, improving maneuverability, and harvesting energy from unsteady flows. This review surveys over 296 studies, with primary emphasis on literature published between 2015 and 2025, distilling four biological archetypes—avian wing morphing, bat-wing elasticity, fish-fin compliance, and tubercled marine flippers—and tracing their translation into morphing aircraft, ornithopters, rotorcraft, unmanned aerial vehicles, and tidal or wave-energy converters. We compare experimental demonstrations and numerical simulations, identify consensus performance gains (up to 30% increase in lift-to-drag ratio, 4 dB noise reduction, and 15% boost in propulsive or power-capture efficiency), and analyze materials, actuation, control strategies, certification, and durability as the main barriers to deployment. Advances in multifunctional composites, electroactive polymers, and model-based adaptive control have moved prototypes from laboratory proof-of-concept toward field testing. Continued collaboration among biology, materials science, control engineering, and fluid dynamics is essential to unlock robust, scalable morphing technologies that meet future efficiency and sustainability targets. Full article

Spectral flow cytometry has revolutionized traditional single-cell profiling to a new era of high-dimensional analysis, allowing for unprecedented deep phenotyping and more precise cell characterization, thereby significantly enhancing our multiplexing capability. The recent application of this technology in clinical settings has been redefining the landscape of clinical diagnostic panels and immune monitoring, particularly for hematologic malignancies, immunological disorders, and drug discovery. Emerging technologies like ghost cytometry, LASE, and imaging flow cytometry are advancing cytometry by improving sensitivity, throughput, and spatial resolution. In this review, we discuss the requirements, challenges, and considerations for spectral applications in clinical diagnostic laboratories and pharmaceutical/contract research organization (CRO) settings. We discuss how these recent innovations are set to push the boundaries of diagnostic accuracy and analytical power, heralding a new frontier in clinical cytometry with the potential to dramatically enhance patient care and treatment outcomes. Full article

Traditional coal quality assessment methods rely exclusively on the laboratory testing of physical samples, which impedes detailed stratigraphic evaluation and limits the integration of intelligent precision mining technologies. To resolve this challenge, this study investigates geophysical logging as an innovative method for coal quality prediction. By integrating scanning electron microscopy (SEM), X-ray analysis, and optical microscopy with interdisciplinary methodologies spanning mathematics, mineralogy, and applied geophysics, this research analyzes the coal quality and mineral composition of the Shanxi Formation coal seams in northern Jiangsu, China. A predictive model linking geophysical logging responses to coal quality parameters was established to delineate relationships between subsurface geophysical data and material properties. The results demonstrate that the Shanxi Formation coals are gas coal (a medium-metamorphic bituminous subclass) characterized by low sulfur content, low ash yield, low fixed carbon, high volatile matter, and high calorific value. Mineralogical analysis identifies calcite, pyrite, and clay minerals as the dominant constituents. Pyrite occurs in diverse microscopic forms, including euhedral and semi-euhedral fine grains, fissure-filling aggregates, irregular blocky structures, framboidal clusters, and disseminated particles. Systematic relationships were observed between logging parameters and coal quality: moisture, ash content, and volatile matter exhibit an initial decrease, followed by an increase with rising apparent resistivity (LLD) and bulk density (DEN). Conversely, fixed carbon and calorific value display an inverse trend, peaking at intermediate LLD/DEN values before declining. Total sulfur increases with density up to a threshold before decreasing, while showing a concave upward relationship with resistivity. Negative correlations exist between moisture, fixed carbon, calorific value lateral resistivity (LLS), natural gamma (GR), short-spaced gamma-gamma (SSGG), and acoustic transit time (AC). In contrast, ash yield, volatile matter, and total sulfur correlate positively with these logging parameters. These trends are governed by coalification processes, lithotype composition, reservoir physical properties, and the types and mass fractions of minerals. Validation through independent two-sample t-tests confirms the feasibility of the neural network model for predicting coal quality parameters from geophysical logging data. The predictive model provides technical and theoretical support for advancing intelligent coal mining practices and optimizing efficiency in coal chemical industries, enabling real-time subsurface characterization to facilitate precision resource extraction. Full article