Search Results (912)

This study conducts a preliminary FE simulation of screw piercing and screw rolling processes for producing bimetallic pipes with variable inner and outer positioning and thickness of the corrosion-resistant steel CL (13Cr and 18Cr10Ni grades) as a rational first step before experimental testing. The results demonstrate that a favorable stress–strain state is formed in both processes under the selected deformation parameters (there are no high tensile stresses in the area of high strains and low temperatures). Shape change analysis confirmed that the pipe geometric dimensions according to simulation are sufficiently close to the target values, with only minor deviations in wall thickness and ovality. The change in CL thickness during piercing ranges from 34% to 51% and increases with the elongation ratio. In the rolling process, it reaches approximately 55–56%. The CL position, its thickness and the material choice significantly influence the deformation heating intensity within the bonding of base and clad materials, as well as the magnitude of the forces acting on the tool in contact with the CL. The obtained results can serve as a methodology that lays the groundwork for experimental verification and the further technology implementation, while minimizing risks and costs. Full article

This review synthesizes recent advances in WHO-endorsed NAATs for TB diagnosis and drug resistance detection. We examine the principles, genetic targets, diagnostic performance, implementation settings, and programmatic role of assays across the spectrum of technological complexity, from cartridge-based platforms to line probe assays and sequencing-based technologies. In addition, we highlight emerging molecular tools that show promise for future WHO endorsement and may further strengthen decentralized and near-patient testing. Low- and moderate-complexity assays such as Xpert^® MTB/RIF, Xpert^® Ultra, and Truenat^TM MTB/MTB Plus have emerged as essential frontline tools for TB diagnosis and rifampicin resistance detection, especially in decentralized settings. LPAs, including GenoType^® MTBDRplus and MTBDRsl, extend resistance profiling to isoniazid, fluoroquinolones, and second-line injectables, and remain valuable in intermediate and central laboratories. More recent developments, including Xpert^® XDR, Deeplex^® Myc-TB, AmPORE-TB^®, and TBseq^®, enable broader resistance detection and, in the case of targeted sequencing assays, comprehensive characterization of multidrug-resistant and extensively drug-resistant TB (MDR/XDR-TB). Emerging diagnostic innovations—such as CRISPR-based detection systems, streamlined isothermal amplification assays, and portable sequencing technologies—further expand the landscape and may complement existing WHO-endorsed platforms. Importantly, these technologies reduce delays in regimen selection, improve patient outcomes, and provide critical data for surveillance. Nevertheless, performance gaps for rare mutations, limited sensitivity in paucibacillary or extrapulmonary disease, infrastructure requirements, and cost remain barriers to universal adoption. The evolution of TB molecular diagnostics demonstrates a clear shift toward more rapid, accurate, and comprehensive resistance detection. No single assay is universally optimal, yet the combined portfolio, spanning rapid cartridge-based NAATs, LPAs, and next-generation sequencing, forms a complementary framework for improving diagnosis, optimizing treatment, and supporting global TB elimination strategies. Full article

Digital terrain models (DTMs) are essential elevation datasets that represent the morphology of the Earth’s surface and play a critical role in applications, such as urban planning, civil engineering, infrastructure design, and environmental assessment. However, the excessive cost remains the major challenge in obtaining accurate terrain models. Recent advancements in low-cost inertial navigation and motion-sensing technologies offer significant potential to enhance the cost-effectiveness of surveying projects. This study investigates the vertical accuracy and operational usability of a handheld inertial measurement unit (IMU) device (Moasure 2) for DTM generation in urban environments through the comparison with traditional total station and digital levels procedures. It also assesses the device compliance with The American Society for Photogrammetry and Remote Sensing (ASPRS) Positional Accuracy Standards. For this purpose, a comprehensive field survey was conducted in a small urban area characterized by varied terrain morphology. The vertical accuracy of the Moasure 2 was acceptable for many urban mapping applications based on a rigorous analysis of checkpoint data and error patterns, which were quantitatively assessed relative to reference surfaces. Profile-based validation showed that the elevation differences between similar terrain types were mainly within ±25 cm, with minimal bias and symmetric error distributions. The findings indicate that Moasure 2 can be a viable alternative tool for fast DTM generation in low-cost urban projects. It offers significant advantages in terms of portability, ease of use, and reduced fieldwork time compared to conventional methodologies. Furthermore, this study addresses the critical gap in the validation of the new IMU-based surveying technology and provides evidence for choosing appropriate equipment for urban terrain modeling. Full article

Vehicles with electrified powertrains carry the promise of significant reductions in greenhouse gas (GHG) emissions from a lifecycle analysis (LCA) standpoint compared to conventional internal combustion engine (CICE) vehicles. However, trade-offs exist between different types of electrified powertrains in terms of cost, consumer acceptance, and GHG reduction efficacy for different operating conditions. The open-source tool CarGHG was developed with an aim to enable the exploration of a plethora of parametric study scenarios, including the cost of electrification technologies, different driving patterns and charging habits, and the cost and carbon intensity of electricity and fuel blends. This paper introduces the framework of CarGHG, then showcases total cost of ownership (TCO) and LCA GHG results for select models of light-duty vehicles. Another capability of CarGHG, which is the ability to estimate the performance of “virtual” vehicle models (perceived vehicle design specifications not yet on the market), is utilized to explore future scenarios of electrification and low-carbon fuel blends for Small Sports Utility Vehicles (SUVs), a popular light-duty vehicle segment in North America. With opportunities, but also uncertainties, in future scenarios, it is likely wise to continue pursuing multiple ways towards the reduction of LCA GHG. Full article

Non-invasive Electrical Impedance Tomography (EIT) and Electrical Impedance Spectroscopy (EIS) are emerging as promising techniques for real-time monitoring and quality assessment in food processing and agri-food applications. This study reviews recent advances in impedance-based sensing for fruit characterization and investigates the experimental implementation of multi-electrode impedance measurements for tomographic imaging. Particular attention is devoted to electrode configurations, electrode polarization effects, and equivalent circuit modeling. Experimental measurements were performed on yellow honeydew melon samples using a four-electrode configuration and a impedance analyzer Keysight E4990 (Keysight Technologies, Santa Rosa, USA) over the frequency range from 20 Hz to 1 MHz. The impedance spectra were validated through Kramers–Kronig consistency tests and interpolated using several fractional-order equivalent circuit models, including single-Cole, double-Cole, and Hayden-based models. The results show that four-electrode measurements are less sensitive to electrode-sample interface artifacts than conventional two-electrode approaches, thereby providing a more reliable estimate of the sample impedance, particularly at low frequencies. Among the tested models, the double-Cole model provided the best interpolation accuracy, while the fractional Hayden models effectively described the temporal evolution of extracellular resistance and membrane-related parameters. Preliminary EIT reconstructions further demonstrate the feasibility of non-destructive tomographic imaging for fruit monitoring. These findings support the potential of EIS and EIT as low-cost, portable, and non-invasive tools for smart food quality assessment and precision agriculture applications. Full article

Asthma is a prevalent, long-term inflammatory airway condition that is difficult to diagnose and treat because there is no single reliable diagnostic test. Misdiagnosis is therefore common, with rates as high as 73% in juvenile groups and up to 35% in adult populations. This ultimately exacerbates their illness by postponing therapy for some people and administering needless medication to others. Although well-known biomarkers such as blood eosinophils and fractional exhaled nitric oxide, as well as conventional diagnostic techniques such as spirometry, have improved clinical assessment, they are nevertheless constrained in many healthcare settings by limited availability, high cost, and inconsistent use. Furthermore, these indicators primarily reflect type-2 inflammation and are less useful for non-type-2 asthma, highlighting the need for more comprehensive, readily accessible diagnostic techniques. Identifying novel biomarkers of oxidative stress, metabolic alterations, and airway inflammation, including volatile organic compounds and redox-related chemicals, has been the focus of recent studies. These biomarkers offer opportunities for improved disease phenotyping and non-invasive detection. Simultaneously, advances in biosensor technology have enabled highly sensitive platforms to rapidly detect these biomarkers at low concentrations. In particular, optical biosensors are becoming more and more popular due to their ability to do real-time detection without the need for labels and their ease of miniaturization for point-of-care devices. This work summarizes traditional diagnostic tools alongside existing information on asthma phenotypes and clinically important biomarkers, and discusses advanced biosensors ranging from electrochemical to optical systems, including recent developments in nanomaterial-enhanced optical biosensing techniques. The importance of artificial intelligence and smartphone-integrated hardware is also covered, along with the main challenges that need to be overcome for these technologies to become useful clinical tools for asthma diagnosis and monitoring. Full article

Monitoring ultraviolet (UV) radiation dose distribution is crucial in many fields, like medicine and materials science, but traditional point-of-care methods limit the ability to fully assess the spatial extent of the irradiated surface. This paper presents the characterisation of a two-dimensional (2D) dosimetry system based on Pluronic F-127 hydrogel matrix doped with 2,3,5-triphenyltetrazolium chloride (TTC) with respect to exposition to UVA radiation. The hydrogel matrix (25% w/w) provides both high transparency and mechanical stability, while TTC (0.1% w/w) functions as a colour precursor that undergoes irreversible reduction to form water-insoluble red formazan upon UVA exposure. The insolubility of TTC formazan ensures that the resulting colour changes remain spatially stable within the dosimeter. The study included sample preparation in flat PMMA containers and analysis of the effect of radiation field uniformity in a UVP CL-1000 exposure chamber. It was supported by application of Kodak X-Omat 100 NIF UV Film dosimetry. The actual dose distribution in the chamber was shown to be significantly heterogeneous (CV coefficient of variation of approximately 18%), which emphasises the need for 2D dosimeters for precise validation of irradiation devices. The use of flatbed scanning and dedicated image analysis software allowed obtaining precise 2D dose distribution maps. The dosimeter was characterised in the dose range of 0–5000 mJ/cm², showing a reproducible response (R² = 0.9967). A resolution test was conducted to assess the precision of geometric representation. In the final stage of the study, the suitability of the developed dosimetry system was verified under conditions simulating heterogeneous UV radiation dose distribution using patterns printed with Computer-to-Film (CtF) technology. The results showed that optical effects in printed films significantly affect UV transmission, limiting accurate dose recording for black coverage above approximately 40–50%. The results obtained confirm that the TTC-Pluronic F-127 system is an effective, simple and low-cost tool for 2D monitoring of UVA radiation, with potential applications in cosmetology, dermatology, and material ageing tests. Full article

Heavy metal ion pollution has emerged as a global issue. These contaminants are not only present in water sources but are also commonly detected in air, soil, food, and consumer products, posing serious risks to ecosystems and human health. Even at very low concentrations, heavy metal ions can exhibit substantial toxicity. Traditional methods for the detection of heavy metal ions typically require complex laboratory equipment and specialized technicians, making them inadequate for rapid on-site monitoring. Microfluidic technology, as an innovative platform capable of precisely controlling and manipulating minute volumes of fluid, has demonstrated enormous potential in analytical chemistry, biomedicine, and environmental monitoring. In the rapidly developing field of microfluidics, paper-based microfluidic platforms have become prominent due to their low cost, straightforward fabrication, and eco-friendly nature, offering powerful tools for the detection of heavy metal ions in diverse samples. This survey consolidates the major advances reported from 2015 to 2025 in utilizing paper-based microfluidic systems for identifying heavy metal ion pollutants in diverse sample types, including air, explosive residues, water sources, herbal supplements, skin-whitening cosmetics, environmental aerosols, urine, soil, gunshot residues, cucumber plants, and food. The review analyzes in detail the principles and applications of detection strategies based on colorimetric methods, fluorescent methods, electrochemical methods, dual-detection systems, and other methods, as well as the role of nanomaterials and selective recognition elements in improving detection sensitivity and specificity. These portable, low-cost, and easy-to-operate detection systems provide viable solutions for environmental and public health monitoring, particularly suitable for resource-limited regions and scenarios requiring rapid detection. Full article

Importance: Most couples who turn to assisted reproductive technology (ART) treatment do so, usually, after giving up emotionally on the chances of conceiving naturally. Others undergo ovulation induction with intrauterine insemination (IUI) and turn to ART after the latter has failed. Spontaneous conception after having experienced the exhausting process of ART, whether it was successful or not, could be very surprising and confusing for many couples. Objective: Review all the scenarios within which an unexpected spontaneous conception can occur and the likelihood of its occurrence. These are four such scenarios: (1) after being referred to ART but before the actual initiation of ART; (2) between ART cycles; (3) after a successful ART pregnancy; (4) after giving up on treatment. We have found only a systematic review on #3, but not the other three. Evidence Review: We collected all PubMed citations for the terms “spontaneous conception” and ART or IVF. Thereafter, we realized that no AI tool can filter only the relevant literature. Hence, we exhausted all possible cross-references by manual search to ensure the completeness of the search. Findings: In each of the four scenarios, spontaneous conceptions occur. Before treatment, a critical element is the length of the waiting time, as is the gap between treatments when already treated, with the cost of treatment being a critical determinant. After the conclusion of treatment, whether successful or failed, the main determinants of the chance for spontaneous conception are age, length of infertility, and the leading etiology for infertility. Overall, the chances range from as little as 2% and up to 25%, with severe male factor and a woman’s age being the most notable for low rates. Conclusion and Relevance: Each couple entering ART treatment should be informed of the chances for spontaneous conception, whether as an aid to the decision to enter or the decision to leave after a failure, and on the more cheerful side, to be aware of the chances for unplanned pregnancy after a successful treatment. Full article

Low-cost optical sensors have emerged as promising tools for in situ freshwater quality monitoring, offering the potential to expand spatial and temporal data coverage, particularly in community-based monitoring projects. However, despite rapid technological development of low-cost optical sensors, analytical validation practices of these devices remain poorly studied. This study aims to systematically and critically assess analytical validation practices applied to low-cost optical sensors based on absorbance, fluorescence, colorimetry, and light scattering, potentially designed for community-based freshwater monitoring. A total of 40 studies were analysed to evaluate how key analytical performance parameters, including sensitivity, accuracy, precision, and repeatability, as well as comparison with reference methods or benchtop instruments, were assessed and reported in relation to established validation guidelines. The analysis revealed substantial heterogeneity and critical gaps in validation approaches. While most studies report sensitivity metrics such as limits of detection and quantification, comprehensive evaluation of key analytical parameters such as accuracy, precision, and reproducibility was often limited. The reliance on single calibration experiments and high determination coefficients ($R^2$) frequently overestimates sensor performance. The lack of open-source materials further limits reproducibility and deployment: essential information such as design files, calibration procedures, and open-source resources is often incomplete or unavailable. To address these limitations, we propose a structured framework for validation and reporting that integrates established analytical guidelines with the practicalities of low-cost sensor development. Adoption of this approach would enable more consistent performance evaluation, improving reproducibility and facilitating comparison across studies and devices. Overall, strengthening analytical validation and reporting practices is essential to support the transition of low-cost optical sensors from proof-of-concept systems to reliable analytical devices for freshwater quality monitoring. Full article

Background: The rapid rise in antimicrobial resistance (AMR) represents one of the most pressing global health challenges of the 21st century, threatening antibiotic effectiveness, compromising clinical outcomes, and undermining healthcare systems. Understanding how resistant pathogens emerge and spread across human, animal, and environmental sectors is essential for effective global response. Main body: This review evaluates traditional and advanced AMR detection methodologies, including phenotypic assays, molecular diagnostics, whole-genome sequencing (WGS), metagenomics, and biosensor-based technologies. It also highlights the role of bioinformatics tools, surveillance databases, and integrated platforms that support real-time analysis. Genomic surveillance provides unparalleled resolution for characterizing resistance mechanisms, transmission patterns, and evolutionary trajectories of multidrug-resistant organisms. Techniques such as WGS and metagenomics allow timely and precise identification of resistance genes, improving outbreak detection and strengthening antimicrobial stewardship. Despite these advantages, the adoption of genomic surveillance faces barriers in low- and middle-income countries, including high costs, limited infrastructure, insufficient technical expertise, and the lack of standardized data frameworks. Conclusions: Genomic surveillance is a transformative tool for combating AMR and strengthening global health systems. Effective implementation requires sustained investment, capacity-building, coordinated cross-sector collaboration, and commitment to the One Health approach to ensure equitable access and long-term global impact. Full article

Driven by artificial intelligence, multi-source sensing, agricultural robots and big data technologies, global agriculture is rapidly upgrading from precision agriculture and agriculture 4.0 to agriculture 5.0. Artificial intelligence has evolved from a single diagnostic tool to an intelligent system that integrates the “perception-decision-execution” process throughout. It is widely applied in crop phenotype analysis, remote sensing monitoring, yield prediction, and autonomous operation of intelligent equipment, etc. This article takes the framework of “intelligent perception-cognitive decision-autonomous execution” to systematically review the core technologies, typical applications, and frontier directions of agricultural artificial intelligence. It focuses on introducing the progress of key technologies such as three-dimensional phenotype, hyperspectral remote sensing, multimodal fusion, and causal machine learning, as well as their value in improving resource utilization efficiency, enhancing climate resilience, and supporting field precision management. At the same time, it points out that current agricultural AI still faces practical bottlenecks such as insufficient generalization ability of models, scarce data and high annotation costs, difficulties in edge deployment, barriers in multi-source data integration, and weak interpretability and engineering reliability. Future research will focus on the construction of closed-loop autonomous farms, the collaboration of agricultural large models and intelligent agents, the construction of data centers and AI and data infrastructure, and the development of green and low-cost AI research. This will provide support for the technological innovation and industrialization implementation of agricultural artificial intelligence. Full article

Background: Cataract diagnosis and management pose a significant global health challenge, contributing to 17 million cases of blindness and over 83 million cases of vision impairment worldwide in 2020. This issue is particularly acute in regions lacking adequate ophthalmological services, where a shortage of eye care clinicians and specialized equipment like slit-lamp cameras leads to late diagnoses. To address this accessibility gap, we developed a computer-assisted cataract grading system using smartphone-acquired external eye photographs. This approach utilizes image processing and deep learning on a standard, hardware-free smartphone, offering a low-cost and portable alternative to traditional equipment. Methods: The study introduces a new advanced algorithm to stratify cataract severity into three distinct stages: normal, pre-mature, and mature. The methodology was developed using a combined dataset of 799 images sourced from the Cataract v01 Computer Vision Project and the Indian Institute of Technology, Delhi. A key step is isolating the iris and lens using a region of interest (ROI) extraction procedure powered by the open-source MediaPipe framework. Key to the algorithm’s efficacy is the use of transfer learning, adapting four customized ResNet architectures (ResNet-18, ResNet-34, ResNet-50, and ResNet-101) to address medical image analysis intricacies. These models were fine-tuned with specific modifications, including dropout layers and the Adam optimizer, for analyzing the digital periocular images. Results: Evaluation of the models shows varied performance across the various architectures when classifying cataract stages. While the simpler ResNet-18 model exhibited the lowest performance, the deeper models showed significant improvement. The ResNet-50 architecture achieved the highest accuracy of 94%. This model also demonstrated excellent precision (94%), recall (95%), and an F1-score of 95% in multi-class classification, outperforming the other tested models. Its depth enables precise cataract classification, positioning it as a robust and reliable tool for potential medical diagnostic deployment. Conclusions: Deep learning-based analysis of smartphone-acquired external eye images demonstrated feasibility for cataract detection in this study. This method could be a scalable and easy-to-use addition to screening, especially in places where resources are limited. Further work is needed to expand the dataset and to validate the algorithm against established clinical grading systems before broader clinical implementation. Full article

Ground-Penetrating Radar (GPR) serves as an essential non-destructive tool for subsurface exploration, and its antenna system largely determines the performance of the overall system. This paper presents a comprehensive review of advanced GPR antenna technologies, examining six major types: Vivaldi, bowtie, tapered, dipole, envelope, and spiral. This analysis shows that trade-offs among these antennas are unavoidable. High-frequency wideband antennas deliver high gain, but their penetration depth is limited to very shallow targets. Some wideband designs achieve wide bandwidth and reasonable gain with compact footprints, while others are suited for detecting embedded metallic objects. By comparison, low-frequency designs operating in the VHF and UHF bands enable very deep penetration, making them suitable for detecting deeply buried targets in lossy media and subsurface utilities. However, deep penetration often comes at the cost of lower gain or larger physical size. Ultimately, no universal antenna exists; the optimal choice depends on whether depth, resolution, or adaptability to attenuating environments is prioritized. Emerging metasurface-integrated and frequency-selective surface (FSS)-backed antennas represent a promising frontier, enabling better bandwidth, gain, and compactness. Ongoing challenges include miniaturization without compromising performance, reliable operation in heterogeneous and lossy soils, and the development of robust, manufacturable designs for field deployment. This review offers researchers and practitioners a structured reference, guiding the development of next-generation GPR systems that balance deeper penetration, higher resolution, and operational versatility. Full article

Biofouling presents numerous challenges across various sectors, including aquaculture, agriculture, infrastructure, and medicine. The development of anti-biofouling techniques remains a significant challenge. In the water industry, biofouling on monitoring sensors substantially compromises the accuracy of measurements by interfering with the sensors’ measuring ability. Biofouling also significantly increases the running costs by increasing the frequency of maintenance needed to keep sensors clean and accurate. Consequently, anti-biofouling techniques are widely employed to clean in situ optical sensors, ensuring accurate measurements while minimizing overall system costs. The conventional approach for preventing biofouling from in situ sensors typically involves the application of coatings, mechanical brushes, ultraviolet radiation, and ultrasonic waves, which possess distinct advantages and disadvantages contingent upon their application. The challenges associated with protecting the small windows of water quality sensors from biofouling over extended periods using current methods are either expensive or adversely affect the integrity of monitoring data. This study introduces a low-cost centimeter-scale high-frequency surface acoustic wave (SAW) device to protect the small windows of in situ water quality sensors continuously from biofouling, functioning as an auxiliary anti-biofouling mechanism. This study found that this 16 MHz SAW device can mitigate the formation of biofilms by adhesive diatom strains CS-1664, CS-1665, and by planktonic algae CS-327 by approximately 98% in comparison to control conditions, functioning effectively as an anti-biofouling tool for itself and surrounding surfaces without adversely affecting aquatic organisms. The dimension and resonance frequency (RF) of the SAW device are also capable of being fabricated according to the area requiring cleaning. A miniaturized 16 MHz SAW device can sustain operation for prolonged periods up to a couple of months without maintenance, at a low cost and power consumption, providing a new anti-biofouling technology. This methodology aims to assist the Australian inland and coastal water quality monitoring system by reducing maintenance costs while simultaneously enhancing the longevity of sensors submerged in water for extended periods. Full article