Search Results (452)

Nowadays, there have truly been spectacular advances in surgical techniques, the preservation of organs for transplants, the optimal and efficient selection of both donors and recipients, a more efficient diagnosis and prediction of possible complications of transplants, and important progress in the advances of pharmacological immunosuppression protocols and procedures. In this sense, survival rates after transplantation of various organs have been progressively increasing, especially in the case of lung transplants, whose average survival rate is usually lower than that of other types of solid organ transplants. Thus, detecting acute and subclinical rejection and chronic allograft rejection of any implant is important. This is important in all transplants, such as heart and lung transplants. In this last type of transplant, particularly, and due to the chronic dysfunction of the lung allograft, it is key to detect rejection early and on time, since it can reach close to half of the transplant patient population. Therefore, practical diagnostic tools are needed to visualize the level of allograft damage using genomic methods such as those that measure donor-derived cell-free DNA, where its amount increases in the plasma component of the transplant after tissue injury or due to allograft infection. This biomarker has become a key element with light and hope, but with some shadows of caution due to its use as a panacea. Our research team has experience in solid organ transplantation in quantifying this parameter in the progression of the lesion of the implanted allograft, and our experience and comparison with the published literature will be presented in the following review, discussing validated and non-validated results. Full article

This work presents a low-cost and scalable method for fabricating thin-film transistors (TFTs) using a single-step, 3D-printed shadow mask approach. Room temperature growth of both aluminum-doped zinc oxide (AZO) thin film was used as the semiconductor channel, and zirconium oxide (ZrO₂) as the high-k dielectric, and the films were never exposed to any post-annealing treatment. Structural and morphological characterization confirmed smooth, compact films with stable dielectric behavior. Electrical measurements revealed a field-effect mobility of 13.1 cm²/V·s, a threshold voltage of ~4.1 V, and an on/off ratio of ~10⁴, validating effective gate modulation and drain current saturation. The off-state current, estimated from AZO conductivity measurements, was ~10⁻¹⁰ A, while the on-state current reached ~10⁻⁶ A. Benchmarking against state-of-the-art devices shows that these transistors rival ALD-processed IGZO TFTs and significantly outperform reported indium-free ZnO/AZO devices, while avoiding scarce indium and costly high-temperature or photolithographic processing. These findings establish 3D-printed shadow masks as a practical alternative to conventional lithography for oxide TFT fabrication. The method offers high device performance with simplified, indium-free, and room-temperature processing, underscoring its potential for scalable, transparent, and flexible electronics. Full article

The accurate monitoring of short-term bathymetric changes in shallow waters is essential for effective coastal management and planning. Machine Learning (ML) applied to Unmanned Aerial Vehicle (UAV)-based multispectral imagery offers a rapid and cost-effective solution for bathymetric surveys. However, models based solely on multispectral imagery are inherently limited by confounding factors such as shadow effects, poor water quality, and complex seafloor textures, which obscure the spectral–depth relationship, particularly in heterogeneous coastal environments. To address these issues, we developed a hybrid bathymetric inversion model that integrates digital surface model (DSM) data—providing high-resolution topographic information—with ML applied to UAV-based multispectral imagery. The model training was supported by multibeam sonar measurements collected from an Unmanned Surface Vehicle (USV), ensuring high accuracy and adaptability to diverse underwater terrains. The study area, located around Lazarus Island, Singapore, encompasses a sandy beach slope transitioning into seagrass meadows, coral reef communities, and a fine-sediment seabed. Incorporating DSM-derived topographic information substantially improved prediction accuracy and correlation, particularly in complex environments. Compared with linear and bio-optical models, the proposed approach achieved accuracy improvements exceeding 20% in shallow-water regions, with performance reaching an R² > 0.93. The results highlighted the effectiveness of DSM integration in disentangling spectral ambiguities caused by environmental variability and improving bathymetric prediction accuracy. By combining UAV-based remote sensing with the ML model, this study presents a scalable and high-precision approach for bathymetric mapping in complex shallow-water environments, thereby enhancing the reliability of UAV-based surveys and supporting the broader application of ML in coastal monitoring and management. Full article

After heavy snowfall in the Uinta Basin, Utah, elevated surface ozone occurs if a cold-air pool persists and traps emissions from oil and gas industry operations. Sunlight and actinic flux from a high-albedo snowpack drive ozone buildup via photolysis. Snow coverage is paramount in initiating the cold pool and driving ozone generation. Its depth is critical for predicting ozone concentrations. The Basin’s location leeward of the Wasatch Mountains provides conditions for a precipitation shadow, where sinking air suppresses snowfall. We analyzed multiple years of ground-based snow depth measurements, surface ozone data, and meteorological observations; we found that ozone levels track with snow coverage, but diagnosing a shadow effect (and any impact on ozone levels) was difficult due to sparse, noisy data. The uncertainty in linking snowfall variation to ozone levels hinders forecast quality in, e.g., machine-learning training. We highlight the importance of a better understanding of regional variation when issuing outlooks to protect the local economy and health. A wider sampling of snow depth across the Basin would benefit operational forecasters and, likely, predictive skill. Full article

Reducing emissions through the invisible hand of the market has become an important way to promote sustainable environmental development. The shadow price of carbon dioxide ($C{O}_2$) is the core element of the carbon market, and its accuracy depends on the micro level of the measurement data. In view of this, this paper innovatively uses enterprise level input-output data and combines the stochastic frontier method to obtain $C{O}_2$ shadow prices in China’s industrial sector. On this basis, the impacts of research and development (R&D) intensity, opening up level, traffic development level, population density, industrial structure, urbanization level, human resources level, degree of education, and environmental governance intensity on shadow price are discussed. In further analysis, this study introduces a Spatial Durbin Model (SDM) to evaluate the spatial spillover effects of $C{O}_2$ shadow price itself and its influencing factors. The research results indicate that market-oriented emission abatement measures across industries and regions can reduce total costs, and it is necessary to consider incorporating carbon tax into low-carbon policies to compensate for the shortcomings of the carbon Emission Trading Scheme (ETS). In addition, neighboring regions should coordinate emission abatement tasks in a unified manner to realize a sustainable reduction in $C{O}_2$ emissions. Full article

We present a Bayesian statistical analysis to evaluate the Nexus Paradigm (NP) of quantum gravity, using horizon-scale observations of supermassive black holes (SMBHs) Sagittarius A* (Sgr A*) and M87* from the Event Horizon Telescope (EHT). The NP predicts angular diameters for the dark depression, emission ring, and base diameter, which we compare to EHT measurements. Employing Gaussian likelihoods and priors informed by mass-to-distance ratio uncertainties, we compute the posterior distribution for the angular scale parameter ${\theta }_g$, achieving a combined ${\chi }^2\approx 0.0062$ (four degrees of freedom) corresponding to a 4.37$\sigma$ (99.9972%) confidence level. Individual features show deviations $<0.1 \sigma$ supporting the NP’s claim of 99th percentile agreement. Compared to General Relativity (GR), which predicts a shadow diameter inconsistent with the observed dark depression (${\chi }^2\approx 168, ~12.97 \sigma$) the NP is favored with a Bayes factor of $~{10}^{36}$. These results validate the NP’s predictions and highlight its potential as a quantum gravity framework, though refined uncertainties and broader model comparisons are recommended. Full article

Device-free localization and tracking (DFLT) has emerged as a promising technique for location-aware Internet-of-Things (IoT) applications. However, most existing DFLT systems based on narrowband sensing networks suffer from reduced accuracy in indoor environments due to the susceptibility of received signal strength (RSS) measurements to multipath interference. In this paper, we propose a real-time DFLT system leveraging ultra-wideband (UWB) sensors. The system estimates target-induced shadowing using two UWB RSS measurements, which are shown to be more resilient to multipath effects compared to their narrowband counterparts. To enable real-time tracking, we further design an efficient measurement protocol tailored for UWB networks. Field experiments conducted in both indoor and outdoor environments demonstrate that our UWB-based system significantly outperforms its traditional narrowband DFLT solutions in terms of accuracy and robustness. Full article

Governments worldwide are mandating e-invoicing and real-time VAT reporting, yet many cash-intensive service SMEs continue to under-report VAT, eroding fiscal revenues. This study investigates whether financial technology (FinTech) adoption can reduce this under-reporting among tourism SMEs in Greece—an economy with high seasonal spending and a persistent shadow economy. This is the first micro-level empirical study to examine how FinTech tools affect VAT compliance in this sector, offering novel insights into how technology interacts with behavioral factors to influence fiscal behavior. Drawing on the Technology Acceptance Model, deterrence theory, and behavioral tax compliance frameworks, we surveyed 214 hotels, guesthouses, and tour operators across Greece’s main tourism regions. A structured questionnaire measured five constructs: FinTech adoption, VAT compliance behavior, tax morale, perceived audit probability, and financial performance. Using Partial Least Squares Structural Equation Modeling and bootstrapped moderation–mediation analysis, we find that FinTech adoption significantly improves declared VAT, with compliance fully mediating its impact on financial outcomes. The effect is especially strong among businesses led by owners with high tax morale or strong perceptions of audit risk. These findings suggest that FinTech tools function both as efficiency enablers and behavioral nudges. The results support targeted policy actions such as subsidies for e-invoicing, tax compliance training, and transparent audit communication. By integrating technological and psychological dimensions, the study contributes new evidence to the digital fiscal governance literature and offers a practical framework for narrowing the VAT gap in tourism-driven economies. Full article

As industrial automation evolves towards human-centric, adaptable solutions, collaborative robots must overcome challenges in unstructured, dynamic environments. This paper extends our previous work on developing a digital shadow for industrial robots by introducing a comprehensive framework that bridges the gap between physical systems and their virtual counterparts. The proposed framework advances toward a fully functional digital twin by integrating real-time perception and intuitive human–robot interaction capabilities. The framework is applied to a hospital test lab scenario, where a YuMi robot automates the sorting of microscope slides. The system incorporates a RealSense D435i depth camera for environment perception, Isaac Sim for virtual environment synchronization, and a locally hosted large language model (Mistral 7B) for interpreting user voice commands. These components work together to achieve bi-directional synchronization between the physical and digital environments. The framework was evaluated through 20 test runs under varying conditions. A validation study measured the performance of the perception module, simulation, and language interface, with a 60% overall success rate. Additionally, synchronization accuracy between the simulated and physical robot joint movements reached 98.11%, demonstrating strong alignment between the digital and physical systems. By combining local LLM processing, real-time vision, and robot simulation, the approach enables untrained users to interact with collaborative robots in dynamic settings. The results highlight its potential for improving flexibility and usability in industrial automation. Full article

Thermal control in rolling mills motors is gaining importance as more and more hard-to-deform steel grades are rolled. The capabilities of diagnostics monitoring also expand as digital IIoT-based technologies are adopted. Electrical drives in modern rolling mills are based on synchronous motors with frequency regulation. Such motors are expensive, while their reliability impacts the metallurgical plant output. Hence, developing the on-line temperature monitoring systems for such motors is extremely urgent. This paper presents a solution applying to synchronous motors of the upper and lower rolls in the horizontal roll stand of plate mill 5000. The installed capacity of each motor is 12 MW. According to the digitalization tendency, on-line monitoring systems should be based on digital shadows (coordinate observers) that are similar to digital twins, widely introduced at metallurgical plants. Modern reliability requirements set the continuous temperature monitoring for stator and rotor windings and iron core. This article is the first to describe a method for calculating thermal loads based on the data sets created during rolling. The authors have developed a thermal state observer based on four-mass model of motor heating built using the Simscape Thermal Models library domains that is part of the MATLAB Simulink. Virtual adjustment of the observer and of the thermal model was performed using hardware-in-the-loop (HIL) simulation. The authors have validated the results by comparing the observer’s values with the actual values measured at control points. The discrete masses heating was studied during the rolling cycle. The stator and rotor winding temperature was analysed at different periods. The authors have concluded that the motors of the upper and lower rolls are in a satisfactory condition. The results of the study conducted generally develop the idea of using object-oriented digital shadows for the industrial electrical equipment. The authors have introduced technologies that improve the reliability of the rolling mills electrical drives which accounts for the innovative development in metallurgy. The authors have also provided recommendations on expanded industrial applications of the research results. Full article

Underground cast-in-place pipes (CIPP, Diameter of 2’–5’) are used to transport water for the Phoenix, AZ area. These pipes have developed leaks due to their age and changes in the environment, resulting in a significant waste of water. Currently, leaks can only be identified when water pools above ground occur and are then manually confirmed through the inside of the pipe, requiring the shutdown of the water system. However, many leaks may not develop a puddle of water, making them even harder to identify. The primary objective of this research was to develop an inspection method utilizing drone-based infrared imagery to remotely and non-invasively sense thermal signatures of abnormal soil moisture underneath urban surface treatments caused by the leakage of water pipelines during the regular operation of water transportation. During the field tests, five known leak sites were evaluated using an intensive experimental procedure that involved conducting multiple flights at each test site and a stringent filtration process for the measured temperature data. A detectable thermal signal was observed at four of the five known leak sites, and these abnormal thermal signals directly overlapped with the location of the known leaks provided by the utility company. A strong correlation between ground temperature and shading before sunset was observed in the temperature data collected at night. Thus, a shadow and solar energy model was implemented to estimate the position of shadows and energy flux at given times based on the elevation of the surrounding structures. Data fusion between the metrics of shadow time, solar energy, and the temperature profile was utilized to filter the existing points of interest further. When shadows and solar energy were considered, the final detection rate of drone-based infrared imaging was determined to be 60%. Full article

Objectives: To evaluate the reproducibility of measurements of breech progression angle (BPA) by transperineal ultrasound (US) before and after its standardization by applying an image-based checklist. Methods: Eighteen 3-dimensional (3D) volumes of transperineal US from women at 36–40 weeks of gestation with a singleton fetus in breech presentation were provided to eight operators from four maternity units in Spain and Italy. All operators measured the BPA using 3D US volume processing software, and interobserver reproducibility was evaluated using the intraclass correlation coefficient (ICC). Following an online live review of all measurements by the operators, and the identification of sources of disagreement, an image-based scoring system for BPA measurement was collaboratively developed. The checklist included the following: (1) acquisition in the midsagittal plane, avoiding the posterior shadow of the pubic ramus; (2) visualization of the complete “almond-shaped” pubic symphysis; (3) drawing a first line along the longitudinal axis of the symphysis, dividing it equally; (4) extending this line to the inferior edge of the bone; and (5) drawing a second line tangentially from the lower edge of the symphysis to the lowest recognizable fetal part. The BPA measurements were then repeated using this checklist, and reproducibility was reassessed. Results: Eighteen volumes were analyzed by the eight operators, achieving a moderate reproducibility (ICC: 0.70, 95% confidence interval (CI): 0.48 to 0.86). A score was developed to include a series of landmarks for the appropriate assessment of BPA. Subsequently, the same eighteen volumes were reassessed using the new score, resulting in improved reproducibility (ICC: 0.81, 95% CI: 0.66 to 0.92). Conclusions: The measurement of BPA is feasible and reproducible when using a standardized image-based score. Full article

Like other mega cities in China, Beijing is undergoing a large-scale urban renewal process. However, in the context of global warming and the goal of promoting human health and well-being, urban renewal should follow the principle of minimal intervention, draw inspiration from the condition of the climate and environment itself, and pursue the goal of common health and development between humans and non-human beings. This study takes the Panjiayuan Antique Market as the research object. Unlike previous studies that focused on the behavior patterns of vendors and buyers, this study focuses on the increase in users’ expectation on environmental thermal comfort when the Panjiayuan Antique Market transforms from a conventional commercial market into an urban public space. This study aimed to find a minimal intervention strategy suitable for urban public space renewal from the perspective of the microclimate, encouraging people to use outdoor public spaces more, thereby promoting physical and mental health, as well as social well-being. We used a mixed-methods approach comprising microclimate measurements, questionnaires (n = 254), and field measurements. Our results show that the mean radiant temperature (T_mrt) is the key factor that affects thermal comfort, and it is a comprehensive concept that is associated with other microclimate factors. Linking the quantitative sun-related factors, such as the solar position angle (SAA), the shadow area ratio (SAR), and direct sun hours (DSHs), we also found that the correlation between the T_mrt and physical spatial characteristics, such as the ratio of the visible sky (SVF), the aspect ratio (H/W), and orientation of the building layout, helped us to generate design strategies oriented by regulating microclimate, such as controlling thermal mass/radiant heating, solar radiation, and air convection. One of the significances of this study is its development of a design method that minimizes intervention in urban public spaces from the perspective of regulating the microclimate. In addition, this study proposes a new perspective of promoting people’s health and well-being by improving outdoor thermal comfort. Full article

The COVID-19 pandemic exposed critical deficiencies in the United States’ legal system’s handling of emergency injunctions, particularly concerning religious freedom. This article examines the challenges courts faced in balancing public health measures with constitutional rights, focusing on the use of shadow dockets and the frequent dismissal of cases due to mootness. Analyzing key Supreme Court decisions and lower court rulings, we highlight the inconsistencies and delays that arose when addressing First Amendment challenges to pandemic-related restrictions. Arguments for procedural reforms, including expedited hearings and avoiding mootness dismissals in cases of national importance, are provided to protect fundamental rights during future public health crises. Full article

Pavement markings, as a crucial component of traffic guidance and safety facilities, are subject to degradation and abrasion after a period of service. To ensure traffic safety, retroreflectivity and diffuse illumination should be above the minimum thresholds and required to undergo inspection periodically. Therefore, an onboard light detection and ranging (LiDAR) and camera deployment optimization method is proposed for pavement marking distress detection to adapt to complex traffic conditions, such as shadows and changing light. First, LiDAR and camera sensors’ detection capability was assessed based on the sensors’ built-in features. Then, the LiDAR–camera deployment problem was mathematically formulated for pavement marking distress fusion detection. Finally, an improved red fox optimization (RFO) algorithm was developed to solve the deployment optimization problem by incorporating a multi-dimensional trap mechanism and an improved prey position update strategy. The experimental results illustrate that the proposed method achieves 5217 LiDAR points, which fall on a 0.58 m pavement marking per data frame for distress fusion detection, with a relative error of less than 7% between the mathematical calculation and the field test measurements. This empirical accuracy underscores the proposed method’s robustness in real-world scenarios, effectively mitigating the challenges posed by environmental interference. Full article