Search Results (31)

Over the past two decades, Light Detection and Ranging (LiDAR) technology has evolved from early National Aeronautics and Space Administration (NASA)-led airborne laser altimetry into commercially mature systems that now underpin vegetation remote sensing across scales. Continuous advancements in laser engineering, signal processing, and complementary technologies—such as Inertial Measurement Units (IMU) and Global Navigation Satellite Systems (GNSS)—have yielded compact, cost-effective, and highly sophisticated LiDAR sensors. Concurrently, innovations in carrier platforms, including uncrewed aerial systems (UAS), mobile laser scanning (MLS), Simultaneous Localization and Mapping (SLAM) frameworks, have expanded LiDAR’s observational capacity from plot- to global-scale applications in forestry, precision agriculture, ecological monitoring, Above Ground Biomass (AGB) modeling, and wildfire science. This review synthesizes LiDAR’s cross-domain capabilities for the following: (a) quantifying vegetation structure, function, and compositional dynamics; (b) recent sensor developments encompassing ALS discrete-return ${(ALS}_D)$, and ALS full-waveform $\left({ALS}_{FW}\right)$, photon-counting LiDAR (PCL), emerging multispectral LiDAR (MSL), and hyperspectral LiDAR (HSL) systems; and (c) state-of-the-art data processing and fusion workflows integrating optical and radar datasets. The synthesis demonstrates that many LiDAR-derived vegetation metrics are inherently transferable across domains when interpreted within a unified structural framework. The review further highlights the growing role of artificial-intelligence (AI)-driven approaches for segmentation, classification, and multitemporal analysis, enabling scalable assessments of vegetation dynamics at unprecedented spatial and temporal extents. By consolidating historical developments, current methodological advances, and emerging research directions, this review establishes a comprehensive state-of-the-art perspective on LiDAR’s transformative role and future potential in monitoring and modeling Earth’s vegetated ecosystems. Full article

Hyperuricemia (HUA) is a metabolic disorder characterized by excessive uric acid (UA) production and impaired excretion. Goji, as a representative medicinal food, holds significant research and development value, while probiotic fermentation technology is finding increasingly widespread applications in the functional food sector. This study developed a novel goji fermented with three probiotic strains (Lactoplantibacillus plantarum CGMCC8198, Lactococcus lactis LTJ28, and Lactocaseibacillus casei YR2-2) and investigated its anti-HUA effects. Optimal fermentation conditions (7.913 material–liquid ratio, 3.92% inoculation, 7.49 h at 37 °C with 1:1:2 strain ratio) yielded a beverage with enhanced flavor profiles (19 aroma compounds) and high viable counts. In HUA cell models, the 15% fermented goji juice significantly reduced UA levels by 56% (p < 0.01). In potassium oxonate-induced HUA mice, the beverage effectively lowered serum UA, xanthine oxidase activity, and renal function markers (blood urea nitrogen and creatinine, p < 0.0001) while improving hepatic parameters (alanine aminotransferase, aspartate Aminotransferase). The goji-fermented juice significantly reduced the expression of renal UA transporters GLUT9 and URAT1 (p < 0.0001) while improving gut microbiota composition, as evidenced by increased beneficial SCFAs (acetic acid, butyric acid, p < 0.0001) and elevated Lactobacillus abundance 2.14-fold. Our findings demonstrate that this triple-probiotic-fermented goji beverage represents an effective dietary strategy for HUA management by simultaneously inhibiting UA production, enhancing excretion, and restoring gut microbiota homeostasis, providing a scientific basis for developing probiotic-based functional foods against HUA. Full article

In order to solve the problem of detecting, tracking and estimating the size of “low, slow and small” targets (such as UAVs) in the air, this paper designs a single-photon LiDAR imaging system based on Geiger-mode Avalanche Photodiode (Gm-APD). It improves the Mean-Shift algorithm and proposes an automatic tracking method that combines the weighted centroid method to realize target extraction, and the principal component analysis (PCA) method of the adaptive rotating rectangle is realized to fit the flight attitude of the target. This method uses the target intensity and distance information provided by Gm-APD LiDAR. It addresses the problem of automatic calibration and size estimation under multiple flight attitudes. The experimental results show that the improved algorithm can automatically track the targets in different flight attitudes in real time and accurately calculate their sizes. The improved algorithm is stable in the 1250-frame tracking experiment of DJI Elf 4 UAV with a flying speed of 5 m/s and a flying distance of 100 m. Among them, the fitting error of the target is always less than 2 pixels, while the size calculation error of the target is less than 2.5 cm. This shows the remarkable advantages of Gm-APD LiDAR in detecting “low, slow and small” targets. It is of practical significance to comprehensively improve the ability of UAV detection and C-UAS systems. However, the application of this technology in complex backgrounds, especially in occlusion or multi-target tracking, still faces certain challenges. In order to realize long-distance detection, further optimizing the field of view of the Gm-APD single-photon LiDAR is still a future research direction. Full article

This study illustrates the utility of a mixed-methods approach in assessing the value of an example novel technology—biosensor-integrated self-reporting arteriovenous grafts (smart AVGs). Currently in preclinical development, the device will detect arteriovenous graft stenosis (surveillance-only use case) and treat stenosis (interventional use case). The approach to value assessment adopted in this study was multifaceted, with one stage informing the next and comprised a stakeholder engagement with clinical experts to explore the device’s clinical value, a cost–utility analysis (CUA) from a US Medicare perspective to estimate pricing headroom, and an investment model estimating risk-adjusted net present value analysis (rNPVs) to determine commercial viability. The stakeholder engagement suggested that it would currently be difficult to establish the current value of the surveillance-only use case due to the lack of well-established interventions for preclinical stenosis. Based on this, the CUA focused on the interventional use case and estimated economically justifiable prices at assumed effectiveness levels. Using these prices, rNPVs were estimated over a range of scenarios. This value assessment informs early decision-making on health technology R&D by identifying the conditions (including clinical study success, potential market size and penetration, market access strategies, and assumptions associated with CUA) under which investment may be considered attractive. Full article

With the wide application of UAVs in modern intelligent warfare as well as in civil fields, the demand for C-UAS technology is increasingly urgent. Traditional detection methods have many limitations in dealing with “low, slow, and small” targets. This paper presents a pure laser automatic tracking system based on Geiger-mode avalanche photodiode (Gm-APD). Combining the target motion state prediction of the Kalman filter and the adaptive target tracking of Camshift, a Cam–Kalm algorithm is proposed to achieve high-precision and stable tracking of moving targets. The proposed system also introduces two-dimensional Gaussian fitting and edge detection algorithms to automatically determine the target’s center position and the tracking rectangular box, thereby improving the automation of target tracking. Experimental results show that the system designed in this paper can effectively track UAVs in a 70 m laboratory environment and a 3.07 km to 3.32 km long-distance scene while achieving low center positioning error and MSE. This technology provides a new solution for real-time tracking and ranging of long-distance UAVs, shows the potential of pure laser approaches in long-distancelow, slow, and small target tracking, and provides essential technical support for C-UAS technology. Full article

This study aims to address the challenges associated with rockfall assessment and monitoring, focusing on the coastal cliffs of “Navagio Shipwreck Beach” in Zakynthos. A complete time-series analysis was conducted using state-of-the-art methodologies including a 2020 survey using unmanned aerial systems (UASs) and two subsequent surveys, incorporating terrestrial laser scanning (TLS) and UAS survey techniques in 2023. Achieving high precision and accuracy in georeferencing involving direct georeferencing, the utilization of pseudo ground control points (pGCPs), and integrating post-processing kinematics (PPK) with global navigation satellite system (GNSS) permanent stations’ RINEX data is necessary for co-registering the multitemporal models effectively. For the change detection analysis, UAS surveys were utilized, employing the multiscale model-to-model cloud comparison (M3C2) algorithm, while TLS data were used in a validation methodology due to their very high-resolution model. The synergy of these advanced technologies and methodologies offers a comprehensive understanding of rockfall dynamics, aiding in effective assessment and monitoring strategies for coastal cliffs prone to rockfall risk. Full article

Given the growing threat posed by the widespread availability of unmanned aircraft systems (UASs), which can be utilised for various unlawful activities, the need for a standardised method to evaluate the effectiveness of systems capable of detecting, tracking, and identifying (DTI) these devices has become increasingly urgent. This article draws upon research conducted under the European project COURAGEOUS, where 260 existing drone detection systems were analysed, and a methodology was developed for assessing the suitability of C-UASs in relation to specific threat scenarios. The article provides an overview of the most commonly employed technologies in C-UASs, such as radars, visible light cameras, thermal imaging cameras, laser range finders (lidars), and acoustic sensors. It explores the advantages and limitations of each technology, highlighting their reliance on different physical principles, and also briefly touches upon the legal implications associated with their deployment. The article presents the research framework and provides a structural description, alongside the functional and performance requirements, as well as the defined metrics. Furthermore, the methodology for testing the usability and effectiveness of individual C-UAS technologies in addressing specific threat scenarios is elaborated. Lastly, the article offers a concise list of prospective research directions concerning the analysis and evaluation of these technologies. Full article

Injection molding is an efficient and precise manufacturing technology that is widely used in the production of plastic products. In recent years, injection molding technology has made significant progress, especially with the combination of in-mold electronics (IME) technology, which makes it possible to embed electronic components directly into the surface of a product. IME technology improves the integration and performance of a product by embedding conductive materials and functional components in the mold. Brain–computer interfaces (BCIs) are a rapidly growing field of research that aims to capture, analyze, and feedback brain signals by directly connecting the brain to external devices. The Utah array, a high-density microelectrode array, has been widely used for the recording and transmission of brain signals. However, the traditional fabrication method of the Utah array suffers from high cost and low integration, which limits its promotion in practical applications. The lines that receive EEG signals are one of the key parts of a brain–computer interface system. The optimization of injection molding parameters is particularly important in order to effectively embed these lines into thin films and to ensure the precise displacement of the line nodes and the stability of signal transmission during the injection molding process. In this study, a method based on the Kriging prediction model and sparse regression partial differential equations (PDEs) is proposed to optimize the key parameters in the injection molding process. This method can effectively predict and control the displacement of nodes in the film, ensure the stability and reliability of the line during the injection process, and improve the accuracy of EEG signal transmission and system performance. The optimal injection parameters were finally obtained: a holding pressure of 525 MPa, a holding time of 50 s, and a melting temperature of 285 °C. Under this condition, the average node displacement of UA was reduced from the initial 0.19 mm to 0.89 µm, with an optimization rate of 95.32%. Full article

The imperative to combat climate change necessitates the rapid implementation of technologically advanced, zero-emission renewable energy solutions, particularly considering the mounting energy demands and the pressing need to mitigate global warming. The proposed SOFC system, integrated with a modified Rankine Cycle and CCUS technology, offers a highly efficient, renewable system with a net-zero carbon footprint, utilising green biogas as an alternative. The fully integrated system at continuous operation does not require outside heat sources and, besides, its main electricity production can supply 231 households with hot sanitary water. A base case and sensitivity analysis of the system was conducted studying different operating parameters. The base case simulation, conducted at SOFC/reformer operating temperatures of 850 °C/650 °C and operating parameters S/C = 2.5, U_f = 0.70 U_a = 0.1806, yielded an overall efficiency of 71.64%, with a 67.70% electrical efficiency. Further simulations demonstrated that a 1.60% and 1.53% increase in the overall and electrical efficiencies of the proposed alternative, respectively, would be achieved at SOFC/reformer operating temperatures of 950 °C/650 °C. The simulated hybrid system represents a competitive installation in the renewable energy market, which offers a viable and sustainable alternative to traditional forms of energy generation. Full article

As major hubs for energy and resource consumption and carbon emissions, cities are at the forefront of the discussion on the impacts of megatrends, such as demographic changes, technological advancements, and the shift toward climate neutrality. Despite growing literature suggesting pathways for cities to cope with these challenges, the intersection between circular economy and urban agriculture for sustainable urban development has been little explored, especially concerning practical applications. To bridge this gap, this study aimed to explore the role of urban agriculture in promoting the circularity of resources at the city level. Aarhus, in Denmark, and Ås, in Norway, provide the empirical context for this discussion that uncovers the barriers that impact the successful implementation of C-E practices in the context of UA and delves into how these obstacles challenge cities in transitioning to circular and sustainable food production models. Using a case study approach and qualitative data sources, the findings suggest that while urban agriculture demonstrates potential in reducing resource consumption, it requires further evidence-based research and clear monitoring tools to assess its environmental impact and economic viability. Obstacles to urban agriculture implementation include regulatory challenges, social acceptance of waste, high investment costs, and limited recognition of its indirect impacts. Concerning recommendations, local governance and public policies were found to play a central role in fostering circular urban agriculture by promoting collaboration, fostering innovation, developing regulatory frameworks, and showcasing successful examples. Full article

Investigating the impact of disease-causing mutations, their affected pathways, and/or potential therapeutic strategies using disease modeling often requires the generation of different in vivo and in cellulo models. To date, several approaches have been established to induce transgene expression in a controlled manner in different model systems. Several rounds of subcloning are, however, required, depending on the model organism used, thus bringing labor-intensive experiments into the technical approach and analysis comparison. The GeneSwitch™ technology is an adapted version of the classical UAS-GAL4 inducible system, allowing the spatial and temporal modulation of transgene expression. It consists of three components: a plasmid encoding for the chimeric regulatory pSwitch protein, Mifepristone as an inducer, and an inducible plasmid. While the pSwitch-containing first plasmid can be used both in vivo and in cellulo, the inducible second plasmid can only be used in cellulo. This requires a specific subcloning strategy of the inducible plasmid tailored to the model organism used. To avoid this step and unify gene expression in the transgenic models generated, we replaced the backbone vector with standard pUAS-attB plasmid for both plasmids containing either the chimeric GeneSwitch™ cDNA sequence or the transgene cDNA sequence. We optimized this adapted system to regulate transgene expression in several mammalian cell lines. Moreover, we took advantage of this new system to generate unified cellular and fruit fly models for YARS1-induced Charco–Marie–Tooth neuropathy (CMT). These new models displayed the expected CMT-like phenotypes. In the N2a neuroblastoma cells expressing YARS1 transgenes, we observed the typical “teardrop” distribution of the synthetase that was perturbed when expressing the YARS1^CMT mutation. In flies, the ubiquitous expression of YARS1^CMT induced dose-dependent developmental lethality and pan-neuronal expression caused locomotor deficit, while expression of the wild-type allele was harmless. Our proof-of-concept disease modeling studies support the efficacy of the adapted transgenesis system as a powerful tool allowing the design of studies with optimal data comparability. Full article

Ribonucleic acid (RNA) viruses pose heavy burdens on public-health systems. Synthetic biology holds great potential for artificially controlling their replication, a strategy that could be used to attenuate infectious viruses but is still in the exploratory stage. Herein, we used the genetic-code expansion technique to convert Enterovirus 71 (EV71), a prototypical RNA virus, into a controllable EV71 strain carrying the unnatural amino acid (UAA) Nε-2-azidoethyloxycarbonyl-L-lysine (NAEK), which we termed an EV71-NAEK virus. After NAEK supplementation, EV71-NAEK could recapitulate an authentic NAEK time- and dose-dependent infection in vitro, which could serve as a novel method to manipulate virulent viruses in conventional laboratories. We further validated the prophylactic effect of EV71-NAEK in two mouse models. In susceptible parent mice, vaccination with EV71-NAEK elicited a strong immune response and protected their neonatal offspring from lethal challenges similar to that of commercial vaccines. Meanwhile, in transgenic mice harboring a PylRS-${\mathrm{tRNA}}_{\mathrm{CUA}}^{\mathrm{Pyl}}$ pair, substantial elements of genetic-code expansion technology, EV71-NAEK evoked an adjustable neutralizing-antibody response in a strictly external NAEK dose-dependent manner. These findings suggested that EV71-NAEK could be the basis of a feasible immunization program for populations with different levels of immunity. Moreover, we expanded the strategy to generate controllable coxsackieviruses for conceptual verification. In combination, these results could underlie a competent strategy for attenuating viruses and priming the immune system via artificial control, which might be a promising direction for the development of amenable vaccine candidates and be broadly applied to other RNA viruses. Full article

Polyion complex (PIC) materials have been widely used in biosensors due to their molecular selectivity. However, achieving both widely controllable molecular selectivity and long-term solution stability with traditional PIC materials has been challenging due to the different molecular structures of polycations (poly-C) and polyanions (poly-A). To address this issue, we propose a novel polyurethane (PU)-based PIC material in which the main chains of both poly-A and poly-C are composed of PU structures. In this study, we electrochemically detect dopamine (DA) as the analyte and L-ascorbic acid (AA) and uric acid (UA) as the interferents to evaluate the selective property of our material. The results show that AA and UA are significantly eliminated, while DA can be detected with a high sensitivity and selectivity. Moreover, we successfully tune the sensitivity and selectivity by changing the poly-A and poly-C ratios and adding nonionic polyurethane. These excellent results were employed in the development of a highly selective DA biosensor with a detection range from 500 nM to 100 μM and a 3.4 μM detection limit. Overall, our novel PIC-modified electrode has the potential to advance biosensing technologies for molecular detection. Full article

Background: Adenomyosis and congenital uterine anomalies (CUAs) can compromise reproductive potential and may coexist in the same patient, especially in cases of infertility. This review (CRD42022382850) aims to evaluate the published cases of concurrent adenomyosis and syndromic and nonsyndromic CUAs. Methods: A literature search for suitable articles published in the English language was performed using the following databases from inception to 30 November 2022: MEDLINE, EMBASE, Global Health, the Cochrane Library, Health Technology Assessment Database, and Web of Science. Articles including both CUAs and adenomyosis, with data about their potential relationship, were included. Results: The literature search retrieved 14 articles that met the purpose of this review and summarized the most recent findings regarding the concurrent diagnosis of adenomyosis and CUAs. Conclusions: Adenomyosis can be found in both syndromic and nonsyndromic CUAs, and may arise from several etiologies. The hypothesis that obstructions in CUAs increase uterine pressure and promote the development of adenomyosis remains to be further elucidated, and additional findings may also play a role. The patient’s genetic, epigenetic, and hormonal patterns, as well as normal physiological processes, such as pregnancy, may influence the growth of adenomyosis. Full article

Small uncrewed aerial systems (sUASs) have the potential to serve as ideal platforms for high spatial and temporal resolution wildfire measurements to complement aircraft and satellite observations, but typically have very limited payload capacity. Recognizing the need for improved data from wildfire management and smoke forecasting communities and the potential advantages of sUAS platforms, the Nighttime Fire Observations eXperiment (NightFOX) project was funded by the US National Oceanic and Atmospheric Administration (NOAA) to develop a suite of miniaturized, relatively low-cost scientific instruments for wildfire-related measurements that would satisfy the size, weight and power constraints of a sUAS payload. Here we report on a remote sensing system developed under the NightFOX project that consists of three optical instruments with five individual sensors for wildfire mapping and fire radiative power measurement and a GPS-aided inertial navigation system module for aircraft position and attitude determination. The first instrument consists of two scanning telescopes with infrared (IR) channels using narrow wavelength bands near 1.6 and 4 µm to make fire radiative power measurements with a blackbody equivalent temperature range of 320–1500 °C. The second instrument is a broadband shortwave (0.95–1.7 µm) IR imager for high spatial resolution fire mapping. Both instruments are custom built. The third instrument is a commercial off-the-shelf visible/thermal IR dual camera. The entire system weighs about 1500 g and consumes approximately 15 W of power. The system has been successfully operated for fire observations using a Black Swift Technologies S2 small, fixed-wing UAS for flights over a prescribed grassland burn in Colorado and onboard an NOAA Twin Otter crewed aircraft over several western US wildfires during the 2019 Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field mission. Full article