The rapid decline in water availability for irrigation on the Texas High Plains (THP) is a significant problem affecting crop production and the viability of a large regional economy worth approximately USD 7 billion annually. This region is the largest continuous cotton-producing area in the United States, and the timely delivery and efficient use of irrigation water are critical to the sustainability and profitability of cotton production in this region. Current irrigation scheduling must be improved to reduce water consumption without compromising crop production. Presently, irrigation scheduling based on reference evapotranspiration (ETo) is limited due to the lack of reliable and readily available in-field weather data and updated crop coefficients. Additionally, in-field variability in crop water demand is often overlooked, leading to lower irrigation efficiency. To address these challenges, we explored the potential use of an unmanned aerial vehicle (UAV)-based crop monitoring system to support irrigation management decisions. This study was conducted in Lubbock, Texas, in 2022, where high temporal and spatial resolution images were acquired using a UAV from a cotton field experiment with four irrigation levels. Soil moisture and canopy temperature sensors were deployed to monitor crop response to irrigation and rainfall. The results indicated a significant effect of water stress on crop growth (revealed by UAV-based canopy cover (CC) measurements), yield, and fiber quality. Strong correlations between multi-temporal CC and lint yield (R² = 0.68 to 0.88) emphasized a clear trend: rainfed treatments with lower yields exhibited reduced CC, while irrigated plots with higher CC displayed increased yields. Furthermore, irrigated plots produced more mature and uniform fibers. This study also explored various evapotranspiration calculation approaches indicating that site-specific CC measurements obtained from a UAV could significantly reduce irrigation application. A regression model linking evapotranspiration to canopy cover demonstrated promising potential for estimating water demand in crops with an R² as high as 0.68. The findings highlight the efficacy of UAV-based canopy features in assessing drought effects and managing irrigation water in water-limited production regions like the THP. Full article

Adolescents and young adults (AYAs; 15–39 years) diagnosed with cancer face disparities in outcomes and survival. Patient advocacy organizations can play a pivotal role in advancing outcomes for underserved health conditions, such as AYA cancer. In 2018 a group of AYA patient advocates founded AYA Canada (later renamed to “AYA Can—Canadian Cancer Advocacy”), a peer-led national organization aimed at improving the experiences and outcomes of Canadian AYAs affected by cancer. The aim of this article is to describe the development and impact of AYA Can. AYA Can was incorporated as a not-for-profit organization in 2021 and became a registered charity in 2023. Since 2018, AYA Can has established a thriving community of practice comprising nearly 300 patients, healthcare providers, researchers, and charitable organizations with an interest in advocacy for AYA cancer. Other activities have included advocacy at academic conferences and on scientific committees, collaboration with scientists to advance AYA cancer research, training the next generation of AYA patient advocates through a “patient ambassador program,” and developing a national resource hub to centralize knowledge and information on AYA cancer. Through its work to foster collaboration and amplify patient priorities on a national scale, AYA Can has become a leading voice for AYA cancer advocacy in Canada. Full article

Introduction: The management of aortic dissection has evolved significantly over the decades, with the frozen elephant trunk (FET) procedure emerging as a key technique for treating complex aortic pathologies. Recent practices involve deploying the FET prosthesis more proximally in the aorta (Zone 0) to reduce complications, leading to questions about its impact on long-term aortic remodeling compared to traditional Zone 2 deployment. Methods: This retrospective analysis utilized 3D segmentation software to assess the volumetric changes in aortic remodeling after acute Type A aortic dissections, comparing FET stent graft deployment in Zone 0 and Zone 2. The study included 27 patients operated on between 2020 and 2022, with volumetric measurements taken from postoperative and 6-month follow-up CT scans. Statistical analyses were performed to evaluate the differences in the aortic true lumen (TL) and the perfused false lumen (PFL) between the two groups. Results: Both Zone 0 and Zone 2 deployments resulted in significant true lumen (TL) increases (Z0 p = 0.001, Z2 p < 0.001) and perfused false lumen (PFL) decreases (Z0 p = 0.02, Z2 p = 0.04), with no significant differences in volumetric changes between the groups (p = 0.7 post op and p = 0.9 after 6 months). The distal anastomosis in Zone 0 did not compromise the aortic remodeling outcomes and was associated with reduced distal ischemia and cerebral perfusion times (p = 0.041). The angle measurements in Zone 0 did not show any significant changes after the 6-month control (p = 0.2). However, in Zone 2, a significant change was detected. (p = 0.022). The part comparison analyses did not indicate significant differences in aortic deviation between the groups (p = 0.62), suggesting comparable effectiveness in aortic remodeling. Conclusions: Performing the distal anastomosis more proximally in Zone 0 offers technical advantages without compromising the effectiveness of aortic remodeling compared to the traditional Zone 2 deployment. This finding supports the continued recommendation of Zone 0 deployment in the management of acute Type A aortic dissections, with ongoing studies being needed to confirm the long-term outcomes and survival benefits. Full article

Traditional roadway lighting is intended to provide safe guidance for drivers and pedestrians, but the large-scale application of roadway lighting has resulted in significant energy consumption and light pollution. However, road markings prepared by luminous coating are a kind of multi-functional road marking that can meet the needs of highway lighting at night and save energy. Here, CaAl₂O₄:Eu²⁺,Nd³⁺,Gd³⁺ blue long-afterglow phosphor is obtained by the high-temperature solid-state method, and the blue luminescent coating is synthesized by the blending method. The phase composition, microscopic morphology, luminescence properties and water resistance of the phosphor and luminescent coatings are characterized. The best components and processes of the luminescent coating are explored to meet the application of an expressway. Considering the afterglow’s performance, the optimal calcination temperature of the phosphor is determined to be 1300 °C. The afterglow of the phosphor can be over 8 h after 2 h of daylight excitation. The addition of 1.25% SiO₂ to the luminescent coating improves the uniformity of the components, and the incorporation of 3.5% CaCO₃ improves the denseness of the coating. When the coating thickness is 0.8mm, the luminescent coating can achieve the best luminous effect. After 120 h of immersion in water, the afterglow intensity of the luminescent coating reduced to 70% of the original, which has excellent water resistance. The blue luminescent coating with the addition of appropriate amounts of CaCO₃ and SiO₂ improves the dispersion as well as the densification of the components in the coating to achieve the best luminescent effect. In the Shenyang area, different weather conditions (cloudy, sunny, rainy) have no significant effect on the afterglow performance of the luminescent coatings, all of which can achieve over 5 h of afterglow and are suitable for expressways. Full article

Considering the rapid evolution of lithium silicate-based glass ceramics (LSCs) in dentistry, this review paper aims to present an updated overview of the recently introduced commercial novel LSCs. The clinical and in vitro English-language literature relating to the microstructure, manufacturing, strengthening, properties, surface treatments and clinical performance of LSC materials was obtained through an electronic search. Findings from relevant articles were extracted and summarised for this manuscript. There is considerable evidence supporting the mechanical and aesthetic competency of LSC variants, namely zirconia-reinforced lithium silicates and lithium–aluminium disilicates. Nonetheless, the literature assessing the biocompatibility and cytotoxicity of novel LSCs is scarce. An exploration of the chemical, mechanical and chemo-mechanical intaglio surface treatments—alternative to hydrofluoric acid etching—revealed promising adhesion performance for acid neutralisation and plasma treatment. The subtractive manufacturing methods of partially crystallised and fully crystallised LSC blocks and the additive manufacturing modalities pertaining to the fabrication of LSC dental restorations are addressed, wherein that challenges that could be encountered upon implementing novel additive manufacturing approaches using LSC print materials are highlighted. Furthermore, the short-term clinical performance of zirconia-reinforced lithium silicates and lithium–aluminium disilicates is demonstrated to be comparable to that of lithium disilicate ceramics and reveals promising potential for their long-term clinical performance. Full article

Modern society increasingly recognizes brain fatigue as a critical factor affecting human health and productivity. This study introduces a novel, portable, cost-effective, and user-friendly system for real-time collection, monitoring, and analysis of physiological signals aimed at enhancing the precision and efficiency of brain fatigue recognition and broadening its application scope. Utilizing raw physiological data, this study constructed a compact dataset that incorporated EEG and ECG data from 20 subjects to index fatigue characteristics. By employing a Bayesian-optimized multi-granularity cascade forest (Bayes-gcForest) for fatigue state recognition, this study achieved recognition rates of 95.71% and 96.13% on the DROZY public dataset and constructed dataset, respectively. These results highlight the effectiveness of the multi-modal feature fusion model in brain fatigue recognition, providing a viable solution for cost-effective and efficient fatigue monitoring. Furthermore, this approach offers theoretical support for designing rest systems for researchers. Full article

Artificial intelligence (AI) has been implemented in multiple fields of medicine to assist in the diagnosis and treatment of patients. AI implementation in radiology, more specifically for breast imaging, has advanced considerably. Breast cancer is one of the most important causes of cancer mortality among women, and there has been increased attention towards creating more efficacious methods for breast cancer detection utilizing AI to improve radiologist accuracy and efficiency to meet the increasing demand of our patients. AI can be applied to imaging studies to improve image quality, increase interpretation accuracy, and improve time efficiency and cost efficiency. AI applied to mammography, ultrasound, and MRI allows for improved cancer detection and diagnosis while decreasing intra- and interobserver variability. The synergistic effect between a radiologist and AI has the potential to improve patient care in underserved populations with the intention of providing quality and equitable care for all. Additionally, AI has allowed for improved risk stratification. Further, AI application can have treatment implications as well by identifying upstage risk of ductal carcinoma in situ (DCIS) to invasive carcinoma and by better predicting individualized patient response to neoadjuvant chemotherapy. AI has potential for advancement in pre-operative 3-dimensional models of the breast as well as improved viability of reconstructive grafts. Full article

The concept of symmetry has played a major role in Hilbert space setting owing to the structure of a complete inner product space. Subsequently, different studies pertaining to symmetry, including symmetric operators, have investigated real Hilbert spaces. In this paper, we study the solutions to multiple-set split feasibility problems for a pair of finite families of $\beta$-enriched, strictly pseudocontractive mappings in the setup of a real Hilbert space. In view of this, we constructed an iterative scheme that properly included these two mappings into the formula. Under this iterative scheme, an appropriate condition for the existence of solutions and strong and weak convergent results are presented. No sum condition is imposed on the countably finite family of the iteration parameters in obtaining our results unlike for several other results in this direction. In addition, we prove that a slight modification of our iterative scheme could be applied in studying hierarchical variational inequality problems in a real Hilbert space. Our results improve, extend and generalize several results currently existing in the literature. Full article

In brain tumor surgery, maximal tumor resection is typically desired. This is complicated by infiltrative tumor cells which cannot be visually distinguished from healthy brain tissue. Optical methods are an emerging field that can potentially revolutionize brain tumor surgery through intraoperative differentiation between healthy and tumor tissues. This study aimed to systematically explore and summarize the existing literature on the use of Raman Spectroscopy (RS), Hyperspectral Imaging (HSI), Optical Coherence Tomography (OCT), and Diffuse Reflectance Spectroscopy (DRS) for brain tumor detection. MEDLINE, Embase, and Web of Science were searched for studies evaluating the accuracy of these systems for brain tumor detection. Outcome measures included accuracy, sensitivity, and specificity. In total, 44 studies were included, covering a range of tumor types and technologies. Accuracy metrics in the studies ranged between 54 and 100% for RS, 69 and 99% for HSI, 82 and 99% for OCT, and 42 and 100% for DRS. This review provides insightful evidence on the use of optical methods in distinguishing tumor from healthy brain tissue. Full article

The advancement of novel water treatment technologies requires the implementation of both accurate data measurement and recording processes. These procedures are essential for acquiring results and conducting thorough analyses to enhance operational efficiency. In addition, accurate sensor data facilitate precise control over chemical treatment dosages, ensuring optimal water quality and corrosion inhibition while minimizing chemical usage and associated costs. Under this framework, this paper describes the sensoring and monitoring solution for a hybrid system based on a cooling tower (CT) connected to mechanical vapor compression (MVC) equipment for desalination and brine concentration purposes. Sensors connected to the data commercial logger solution, Almemo 2890-9, are also discussed in detail such as temperature, relative humidity, pressure, flow rate, etc. The monitoring system allows remote control of the MVC based on a server, GateManager, and TightVNC. In this way, the proposed solution provides remote access to the hybrid system, being able to visualize gathered data in real time. A case study located in Cartagena (Spain) is used to assess the proposed solution. Collected data from temperature transmitters, pneumatic valves, level sensors, and power demand are included and discussed in the paper. These variables allow a subsequent forecasting process to estimate brine concentration values. Different sample times are included in this paper to minimize the collected data from the hybrid system within suitable operation conditions. This solution is suitable to be applied to other desalination processes and locations. Full article

Based on 208 city-level data in China, this paper empirically analyzes the impact of ESG rating on carbon emissions through the SDM spatial metrology model, identifies the direct and indirect consequences and spatial spillover effects of ESG rating on carbon emissions, and compares the regional heterogeneity and city-size heterogeneity of such impacts. This paper draws three conclusions: (1) Empirical evidence shows that the ESG rating performance of enterprises has a significant inhibition effect on carbon dioxide emissions. Specifically, when the ESG rating performance increases by 1%, carbon emissions will decrease by 0.076; among other control variables, the effect of FDI on carbon emission reduction is that when ESG score performance increases by 1%, carbon emission decreases by 0.022. (2) In the decomposition of the total effects, indirect effects and direct effects have the same impact on carbon emissions, and the total effect is −0.393. (3) The inhibition effect is more significant in the Eastern Region and in megacities, where the effect of −0.096 in the Eastern Region is more obvious than that of −0.078 at the national level, and the effect of carbon reduction in megacities is significantly greater than 0.013 in big cities. This suggests regional heterogeneity in regards to the role of ESG ratings in reducing CO2 emissions. This paper reveals the specific effects and internal logic of the impact of ESG performance on CO2 emissions, which has certain implications for various regions to further promote the construction of an ESG system, according to local conditions, and to encourage enterprises to focus on emission reduction and high-quality development. Full article

Concept mapping is a valuable method to represent a domain of knowledge, also with the aim of supporting educational needs. Students are called upon to construct their own knowledge through a meaningful learning process, linking new concepts to concepts they have already learned, i.e., connecting new knowledge to knowledge they already possess. Moreover, the particular graphic form of a concept map makes it easy for the teacher to construct and interpret both. Consequently, for an educator, the ability to assess concept maps offered by students, facilitated by an automated system, can prove invaluable. This becomes even more apparent in educational settings where there is a large number of students, such as in Massive Open Online Courses. Here, we propose two new measures devised to evaluate the similarity between concept maps based on two deep-learning embedding models: InferSent and Universal Sentence Encoder. An experimental evaluation with a sample of teachers confirms the validity of one such deep-learning model as the baseline of the new similarity measure. Subsequently, we present a proof-of-concept dashboard where the measures are used to encode a concept map in a 2D space point, with the aim of helping teachers monitor students’ concept-mapping activity. Full article

This article presents an original method for minimising the energy expenditure of electric vehicles used in municipal service undertakings, taking into account the uncertainty in the functioning of their charging points. The uncertainty of the charging points’ operation was presented as the probability of the occurrence of an emergency situation hindering a point’s operation, e.g., a breakdown or lack of energy supply. The problem is how to calculate the driving routes of electric vehicles so that they will arrive at charging points at times at which there is a minimal probability of breakdowns. The second aspect of this problem to be solved is that the designated routes are supposed to ensure the minimum energy expenditure that is needed for the vehicles to complete the tasks assigned. The developed method is based on two heuristic algorithms, i.e., the ant algorithm and genetic algorithms. These algorithms work in a hybrid combination, i.e., the ant algorithm generates the initial population for the genetic algorithm. An important element of this method is the decision-making model for defining the driving routes of electric vehicles with various restrictions, e.g., their battery capacity or the permissible risk of charging point breakdown along the routes of the vehicles. The criterion function of the model was defined as the minimisation of the energy expenditure needed by the vehicles to perform their transport tasks. The method was verified against real-life data, and its effectiveness was confirmed. The authors presented a method of calibrating the developed optimisation algorithms. Theoretical distributions of the probability of charging point failure were determined based on the Statistica 13 program, while a graphical implementation of the method was carried out using the PTV Visum 23 software. Full article

Novel therapies for the treatment of familial dilated cardiomyopathy (DCM) are lacking. Shaping research directions to clinical needs is critical. Triggers for the progression of the disorder commonly occur due to specific gene variants that affect the production of sarcomeric/cytoskeletal proteins. Generally, these variants cause a decrease in tension by the myofilaments, resulting in signaling abnormalities within the micro-environment, which over time result in structural and functional maladaptations, leading to heart failure (HF). Current concepts support the hypothesis that the mutant sarcomere proteins induce a causal depression in the tension-time integral (TTI) of linear preparations of cardiac muscle. However, molecular mechanisms underlying tension generation particularly concerning mutant proteins and their impact on sarcomere molecular signaling are currently controversial. Thus, there is a need for clarification as to how mutant proteins affect sarcomere molecular signaling in the etiology and progression of DCM. A main topic in this controversy is the control of the number of tension-generating myosin heads reacting with the thin filament. One line of investigation proposes that this number is determined by changes in the ratio of myosin heads in a sequestered super-relaxed state (SRX) or in a disordered relaxed state (DRX) poised for force generation upon the Ca²⁺ activation of the thin filament. Contrasting evidence from nanometer–micrometer-scale X-ray diffraction in intact trabeculae indicates that the SRX/DRX states may have a lesser role. Instead, the proposal is that myosin heads are in a basal OFF state in relaxation then transfer to an ON state through a mechano-sensing mechanism induced during early thin filament activation and increasing thick filament strain. Recent evidence about the modulation of these mechanisms by protein phosphorylation has also introduced a need for reconsidering the control of tension. We discuss these mechanisms that lead to different ideas related to how tension is disturbed by levels of mutant sarcomere proteins linked to the expression of gene variants in the complex landscape of DCM. Resolving the various mechanisms and incorporating them into a unified concept is crucial for gaining a comprehensive understanding of DCM. This deeper understanding is not only important for diagnosis and treatment strategies with small molecules, but also for understanding the reciprocal signaling processes that occur between cardiac myocytes and their micro-environment. By unraveling these complexities, we can pave the way for improved therapeutic interventions for managing DCM. Full article

Molecular biomarkers are central to personalised medicine for human cancer patients. It is gaining traction as part of standard veterinary clinical practice for dogs and cats with cancer. Molecular biomarkers can be somatic or germline genomic alterations and can be ascertained from tissues or body fluids using various techniques. This review discusses how these genomic alterations can be determined and the findings used in clinical settings as diagnostic, prognostic, predictive, and screening biomarkers. We showcase the somatic and germline genomic alterations currently available to date for testing dogs and cats in a clinical setting, discussing their utility in each biomarker class. We also look at some emerging molecular biomarkers that are promising for clinical use. Finally, we discuss the hurdles that need to be overcome in going ‘bench to bedside’, i.e., the translation from discovery of genomic alterations to adoption by veterinary clinicians. As we understand more of the genomics underlying canine and feline tumours, molecular biomarkers will undoubtedly become a mainstay in delivering precision veterinary care to dogs and cats with cancer. Full article

Mesenchymal stem cells (MSCs) have garnered significant interest in the field of regenerative medicine for their ability to potentially treat various diseases, especially neurodegenerative disorders [...] Full article

The layered double hydroxides (LDHs) of transition metals are of great interest as building blocks for the creation of composite photocatalytic materials for hydrogen production, environmental remediation and other applications. However, the synthesis of most LDHs is reported only by the conventional coprecipitation method, which makes it difficult to control the catalyst’s crystallinity. In the present study, ZnCr- and NiCr-LDHs have been successfully prepared using a facile hydrothermal approach. Varying the hydrothermal synthesis conditions allowed us to obtain target products with a controllable crystallite size in the range of 2–26 nm and a specific surface area of 45–83 m²∙g⁻¹. The LDHs synthesized were investigated as photocatalysts of hydrogen generation from aqueous methanol. It was revealed that the photocatalytic activity of ZnCr-LDH samples grows monotonically with the increase in their average crystallite size, while that of NiCr-LDH ones reaches a maximum with intermediate-sized crystallites and then decreases due to the specific surface area reduction. The concentration dependence of the hydrogen evolution activity is generally consistent with the standard Langmuir–Hinshelwood model for heterogeneous catalysis. At a methanol content of 50 mol. %, the rate of hydrogen generation over ZnCr- and NiCr-LDHs reaches 88 and 41 μmol∙h⁻¹∙g⁻¹, respectively. The hydrothermally synthesized LDHs with enhanced crystallinity may be of interest for further fabrication of their nanosheets being promising components of new composite photocatalysts. Full article

Water availability is a local issue of growing importance in Mediterranean areas where water scarcity linked to climate change and population growth is already leading to increased competition for this resource. This study is aimed at the following: (i) assessing the water-related environmental impacts (water use, freshwater ecotoxicity and eutrophication, marine eutrophication, acidification, human toxicity, and ionizing radiation) along the production chain of cow milk in Catalonia, northeastern Spain; and (ii) addressing the issues encountered (modelling choices, data gaps and inconsistencies) which t can affect the quality of results when performing a water-footprint comprehensive assessment, focusing on water use and associated water scarcity impacts. The scope included the process from the extraction of raw materials up to the distribution of the packaged fat- and protein-corrected milk to the distribution centres of the supermarket chains and markets. Results showed the farm stage to be determinant (contributing to over 60% of the impact), due to the impact of feed production. Impact results were in the range of the European benchmark given by the Product Environmental Footprint Category Rules for dairy products, except for the water scarcity footprint which was one order of magnitude larger than the reference value, due to water scarcity in Spain. Considering compound feed ingredients with a lower water scarcity footprint, and research into slurry treatment for its use as irrigation and cleaning water (without compromising safety and health) could help reduce this impact. Water accounting and traceability along the production chain could support the dairy industry to take responsibility for the consequences of their production choices. Full article

The optimization of the affinity of monoclonal antibodies is crucial for the development of drug candidates, as it can impact the efficacy of the drug and, thus, the dose and dosing regimen, limit adverse effects, and reduce therapy costs. Here, we present the affinity maturation of an EGFR×PD-L1 Two-in-One antibody for EGFR binding utilizing site-directed mutagenesis and yeast surface display. The isolated antibody variants target EGFR with a 60-fold-improved affinity due to the replacement of a single amino acid in the CDR3 region of the light chain. The binding properties of the Two-in-One variants were confirmed using various methods, including BLI measurements, real-time antigen binding measurements on surfaces with a mixture of both recombinant proteins and cellular binding experiments using flow cytometry as well as real-time interaction cytometry. An AlphaFold-based model predicted that the amino acid exchange of tyrosine to glutamic acid enables the formation of a salt bridge to an arginine at EGFR position 165. This easily adaptable approach provides a strategy for the affinity maturation of bispecific antibodies with respect to the binding of one of the two antigens. Full article

We define the digital economy as all economic activities conducted through the use of information technologies, including activities to create new markets, expand old ones, and produce digital goods and services. The digital economy has become a major driver of sustainable development and the transition towards a greener economy. However, studies show an unequal level of progress among cities, regions, and countries. Consequently, this paper explores the associations between socio-cultural factors (such as attitudes towards marriage/raising a family) and components of the digital economy (such as expenses/investment in ICT and the proportion of personal computers and the Internet in households). This study specifically examines twenty-two ethnic minority regions in Russia with data covering a five-year period. Using the Pearson correlation coefficient, the results show that socio-cultural factors including a large urban population and high divorce rates were positively associated with the digital economy, whereas a large rural population, higher birth rates, and higher natural population growth were negatively correlated with the digital economy. Full article