Concern over microplastics (MPs) in the environment is rising. Microplastics are generally known to exist in aquatic settings, but less is known about their occurrence in soil ecosystems. When plastic waste builds up in agricultural areas, it can have a negative impact on the environment and food sources, as well as have an indirect effect on all trophic levels of the food chain. This paper addresses the relationship between microplastics and the management of plastic waste, which contributes to their accumulation, and it describes the sources and primarily the movement processes of microplastics in agricultural soils as a result of natural events. Evaluating the impact of weather on coastal microplastic contamination is critical, as extreme weather events have become more frequent in recent years. This study sheds light on how weather patterns affect the dispersion of plastic waste in terrestrial habitats, including the impacts of seasonality and extreme weather. According to our data, typhoons, monsoons, rainfall, and floods all contribute significantly more microplastics to the surface sediment through surface runoff and wind transport, particle redistribution caused by agitated waves, and fragmentation under intense abrasion forces. Severe weather conditions have the potential to disperse larger and more varied kinds of microplastics.

The foundation for accurately understanding regional land-use structures and pursuing the coordination of human–land relations is the scientific identification and simulation of temporal and spatial evolution patterns of land-use spatial conflict (LUSC). Taking the Yimengshan Geopark (YG) as an example, based on the productional–living–ecological space (PLES) perspective, which constructs a land-use spatial conflict identification and intensity diagnosis model (LUCSII) using the landscape ecology index. We apply geographic information system (GIS) and other methods to achieve the spatial pattern of LUSC over the last 20 years, and we use the GMOP–Markov–PLUS model to simulate the evolution of LUSC in the future under various scenarios. From 2000 to 2020, the LUSC values in the YG were mainly stable and controllable, with mild conflict, while the areas of severe conflict were mainly concentrated in the central urban area of Mengyin County and in low and flat terrain areas such as southern Bailin Town. The LUSC in the YG showed a significant positive spatial correlation, and spatial agglomeration is gradually strengthening. The high–high clusters are found in contiguous areas at the junction of Changlu Town, Gaodu Town, and Mengyin Street, as well as in the southern hilly areas. The low–low clusters were concentrated in Yedian Town, Daigu Town inarea north of the study, and areas surrounding Yunmeng Lake Wetland Park. In the next ten years, the ecological priority scenario (EPD) and sustainable development scenario (ESD) will both be reasonable options for easing and controlling LUSC in YG. Local governments and park management bureaus should determine the three lines and three zones based on the needs of social and economic development, particularly the boundary red line for construction land growth, and plan production and living spaces to alleviate land-use conflicts and stabilize the land-use system. Regional ecological security can be maintained, and future deterioration of the park’s ecological environment avoided, by performing well in terms of ecological isolation.

The emergence of Internet of Things (IoT) devices is revolutionizing healthcare. This dynamic healthcare environment relies on Internet of Healthcare Things (IoHT) and Internet of Medical Things (IoMT) technologies. Economic, social, and technological opportunities from the IoHT are transforming healthcare systems. This article covers the history, present condition, and future prospects of IoHT, as well as its topologies, platforms, services, applications, market trends, and solution types. It examines security issues, requirements, and a threat taxonomy, studying smart healthcare privacy and security. It explores future research and the challenges IoHT-based solutions face. International health rules and regulations based on the IoT and their social and economic effects on long-term development are also examined. Integrating IoMT into healthcare infrastructures enhances scalability, efficiency, reliability, and accuracy. Through communication networks and data-processing algorithms, IoMT creates smart hardware and software platforms that enhance decision-making. IoMT devices' low resources raise privacy and security concerns. Security and privacy issues are organised by healthcare IoMT adoption in this study. We give an extensive attack taxonomy against the IoMT infrastructure, encompassing devices, security mechanisms, and their pros and cons. Furthermore, it describes the privacy and security requirements for any unique security solutions against IoMT's multiple attack vectors. This article explains the complicated difficulties, security concerns, and future research pathways needed to safeguard and ensure the long-term expansion of these game-changing technologies in the healthcare ecosystem.

This paper introduces a new routing and touring service both for outdoor and indoor places of touristic and cultural interest designed to be used in the wider area of ​​Attica, Greece. This service is the result of the work done in OPTORER project and it aspires to offer a range of innovative and thematic routes to several specified points of interest in the selected area of Attica, encouraging the combination of indoors and outdoors routes in a single tour. The aim is to optimize the user experience while promoting specific, user-centric features with safety and social welfare being a priority for every designed tour resulting in enhancing the touristic experience in the area. Using a common smartphone device, as well as common wearable devices (i.e., smartwatches), the OPTORER service will provide an end-to-end solution by developing the algorithms and end-user applications, together with an orchestration platform responsible for managing, operating and executing the service that produces and presents to the end user results derived from solving dynamically complex optimization problems.

Archaeology is often argued to provide a unique long-term perspective on humans that can be utilised for effective policy-making, for example in discussions of resilience and sustainability. However, the specific archaeological evidence for resilient/sustainable systems is rarely explored, with these terms used simply to describe a community that survived a particular shock. In this study, a set of 74 case studies of papers discussing archaeological evidence for resilience/sustainability are identified and analysed using bibliometric methods. Variables from the papers are also quantified to assess patterns and provide a review of current knowledge. A great variety of scales of analysis, case study locations, stressors, resilient/sustainable characteristics, and archaeological evidence types are present. Climate change was the most cited stressor (n=40) and strategies relating to natural resources were common across case studies, especially subsistence adaptations (n=35), other solutions to subsistence deficiencies (n=23), and water management (n=23). Resilient/sustainable characteristics were often in direct contrast to one-another, suggesting the combination of factors is more important than each factor taken individually. Further quantification of well-defined variables within a formally-produced framework is required to extract greater value from archaeological case studies of resilience/sustainability.

The lack of a uniform approach in Earth and planetary science is apparent in the current levels of inconsistency found within the research itself, the data analysis and the interpretation of results. Thus, data interpretation differ depending on whether the study refer to Earth conditions or from space. These differences are particularly pronounced for cryosphere studies, where geocentric approaches remain within ice research and its application in analogical studies. Here, the principle of spatial uniformitarianism is presented, to allow for a definitive departure from geocentrism and a proper understanding of the role of ice within both the Earth and celestial bodies. At the practical level, it may affect several geo-scientific disciplines currently inconsistent and bridging the gap among them. This rule is universal and complements the Hutton-Lyell 1795/1830 principle.

Academic stress affects students' psychological and physiological well-being. Dental undergraduate programs are known for their demanding curriculum, leading to significant stress symptoms. Objective was to determine if salivary cortisol levels were higher in students exposed to academic stress, assess the relationship between stress severity/temperament and cortisol values, and explore relaxation technique effects. Salivary cortisol was measured at two time points for all participants: before exams and during a relaxation period after summer break. A third measurement was conducted for students with high pre-test cortisol levels who received instructions on progressive muscle relaxation(PMR) before subsequent exams. Additionally, participants completed two questionnaires: Perceived Stress Scale(PSS) and Fisher's Temperament Questionnaire. The group analysis based on the PSS indicated that 39 participants reported high stress. Women demonstrated significantly higher stress than men(p=0.042054). A significant difference in stress levels was observed between Director and Builder temperament types(p=0.029276). Cortisol levels showed a significant decrease from the first measurement to the second measurement, and the third measurement after implementing PMR. The grade in the "Dermatovenereology" course correlated with stress level according to the PSS(k=0.578467). Pre-test cortisol levels correlated with the frequency of using PMR guidelines during winter test periods(k=0.416138). Stress negatively affects the immune system and poses health risks. Implementing stress reduction techniques in dental/medical education could benefit students and the healthcare system.

In this study, the causal-effect between carbon dioxide emissions and forestry production and trade was investigated in Ghana by employing a data spanning from 1961 to 2014 by using the VECM and ARDL model. Evidence of the long-run equilibrium relationship in the VECM shows that, a 1% increase in veneer sheet production reduces carbon dioxide emissions by 1.47% in the long-run. There was evidence of a bidirectional causality between carbon dioxide emissions and veneer sheet production, carbon dioxide emissions and wood charcoal production, and a unidirectional causality running from carbon dioxide emissions to wood fuel production and plywood production to carbon dioxide emissions. Evidence from the long-run equilibrium relationship in the ARDL model shows that; a 1% increase in plywood production will increase carbon dioxide emissions by 0.17% in the long-run, a 1% increase in sawnwood production will increase carbon dioxide emissions by 0.17% in the long-run, a 1% increase in wood charcoal production will increase carbon dioxide emissions by 0.36% in the long-run and a 1% increase in wood fuel production will increase carbon dioxide emissions by 0.37% in the long-run.

Aiming at the problem of small size of some spots on apple leaves and the difficulty of accurate detection of spot targets brought by the complex background of the orchard, this paper takes the alternaria leaf spot, rust, brown spot, grey spot and frog eye leaf spot on apple leaves as the research object, and proposes a high-accuracy detection model YOLOv5-Res and lightweight detection model YOLOv5-Res4 are proposed. Firstly, a multiscale feature extraction module ResBlock is designed by combining the Inception multi-branch structure and ResNet residual idea. Secondly, a lightweight feature fusion module C4 is designed to reduce the number of model parameters while improving the detection ability of small targets. Finally, a parameter streamlining strategy based on optimized model architecture is proposed. The experimental results show that the performance of YOLOv5-Res model and YOLOv5-Res4 model is significantly improved, the mAP@0.5 value is improved by 2.8% and 2.2% compared with YOLOv5s model and YOLOv5n model, respectively, and the size of YOLOv5-Res model and YOLOv5-Res4 model is only 10.8MB and 2.4MB, and the number of model parameters is reduced compared with YOLOv5s model and YOLOv5n model. counts are reduced by 22% and 38.3% compared to the YOLOv5s model and YOLOv5n model.

Cold chains are essential in providing us humans with food and medicine across the globe. However, cold transportation, like all transportation, has massive negative impacts on the environment and human well-being, which especially culminate in cities. Thus, it is important to research the ways to reduce these impacts and reinvent urban logistics infrastructure. In this article, we articulate the massive problems the road-based transportation sector imposes on the environment and on the health of humans, especially in cities. We present algorithms that show promise in optimizing the whole road transportation sector as well as some innovations and alternative ways to model cities and the whole urban transportation infrastructure. We approach the issues discussed specifically from the point of view of refrigerated transportation, but many of the solutions can be applied to all the transportation sector in general.

Based on big data and artificial intelligence technology, the judicial big data intelligent service platform provides users with efficient and accurate legal consulting services. It has many functions such as intelligent question and answer, case analysis, and law inquiry. It also analyzes the application value of the intelligent legal consulting service platform in the field of judicial big data. By building an intelligent service platform, the rapid processing and analysis of massive judicial data has been realized, and the efficiency and quality of judicial work have been significantly improved. In addition, it emphasizes the importance of data management and quality control in the application of judicial big data, and puts forward specific measures to strengthen data management and quality control. With the continuous advancement of technology and the expansion of application fields, the platform is expected to achieve deep integration with other legal services, expand new application fields, and provide users with more comprehensive and efficient legal services. At the same time, it points out the problems that need to be paid attention to in the future development, such as data security, privacy protection and so on. Through in-depth analysis and research on judicial big data, the platform can provide strong support for judicial decision-making and promote the improvement of judicial justice and efficiency.

With much more challenging scenarios encountered in engineering, traditional quadrotors couldn’t accomplish all desired missions perfectly. Task-driven based reconfigurable quadrotors have more latent capacity in environment with dense obstacles and narrow corridors. The translational reconfigurable quadrotors and rotational reconfigurable quadrotors are proposed in this presentation, assumptions, mathematical models and related motion control laws are given subsequently, in order to cope with hazard missions, model reference adaptive control laws are proposed, sufficient numerical simulations demonstrate the validity of the proposed control laws.

Tumor immunotherapy represented by immune target blocking and chimeric antigen receptor cell therapy has developed rapidly. Among them, Natural Killer Cell (NK) is gradually becoming another preferred immunotherapy method after T cell immunotherapy because of its unique killing effect in innate immunity and adaptive immunity. Although NK cell therapy has achieved promising results in clinical trials, there are still some problems, such as low efficacy in solid tumors, insufficient penetration of NK cells and high cost of treatment. Nanomaterials are expected to be an excellent tool to improve the anti-tumor immune response of NK cells due to their advantages such as chemical specificity, biocompatibility and simple synthesis. Nanomaterials can enhance the cytotoxicity of NK cells, participate in the modification and noninvasive cell-tracking patterns of NK cells，and mediate their homing and infiltration, and thus significantly improve the efficiency of NK cell immunotherapy. In this review, we will briefly introduce the methods and mechanisms of tumor immunotherapy, emphasize the research progress of NK cell-based immunotherapy and imaging, and finally summarize the direction and research progress of nanomaterials in NK cell-based immunotherapy.

The 3D reconstruction of vermiculite-based sealing material was carried out based on CT scanning experiments and Avizo software. The precision and representation of the reconstructed digital core were tested by calculating the pore structure parameters and comparing them with the experimental results of mercury compression. Avizo is a perfect interface with Fluent software to perform micro-scale seepage simulation to explore the distribution of pressure and velocity fields during fluid transport. In this paper, a new approach is proposed to study the penetration leakage of non-metallic gaskets by using equivalent pore networks.

Assessment of the genetic distinctiveness of a cultivar through morphological descriptors is an important tool for both the registration and the protection. New mango genotypes have been improved using valuable diverse germplasm resources to ensure food security. DNA fingerprinting based simple sequence repeats (SSR)-markers have been the most broadly used, effective and accurate in evaluation of genetic characterization of a cultivar. Molecular breeding is an effective source of genetic gain after improvement of fruit trees using marker assisted genomic selection. Mango (Mangifera indica L.) is an allotetraploid (2n = 4X= 40) drupe fruit and has high nutritional value belongs to genus Mangifera and family Anacardiaceae. Mango cultivars are used with worldwide acceptance to pharmacological, ethnomedical, and phytochemical industries. This study investigated the molecular evaluation of a new mango cultivar ‘Azeem Chaunsa’ using a set of the most effective 50 hyper-variable polymorphic SSR markers. Highly specific DNA fingerprints were identified in the genome of this mango cultivar, ‘Azeem Chaunsa’ compared with three standard cultivars such as Sindhri, Samar Bahisht (S.B) Chaunsa and Sufaid Chaunsa. Our results showed that SSR markers could efficiently assess genetic diversity in mango. An agglomerative hierarchical clustering method was used to construct dendrogram based on the ‘Unweighted Pair- Group Method with Arithmetic Mean’ (UPGMA). The genetic similarity coefficients were recorded between the mangos cultivars ranged from 0.49 to 0.67. Cultivar identification (CID) evaluates association among standard cultivars and Azeem Chaunsa and further concludes significant variations. CID results concluded that cultivar ‘Azeem Chaunsa’ varied significantly from the check cultivar, Sindhri (46.2%), S.B Chaunsa (45%) and Sufaid Chaunsa (46.7%). The results obtained in this study will orient cultivar identification strategies for a successful future mango breeding programmes in the context of climate change.

Enzymatic polyethylene terephthalate (PET) recycling processes are gaining interest for their low environmental impact, use of mild conditions, and specificity. Furthermore, PET hydrolase enzymes are continuously being discovered and engineered. In this work, we studied a PET hydrolase (PET2), initially characterized as an alkaline thermostable lipase. PET2 was produced in a fusion form with a 6-histidine tag in the N-terminal. The PET2 activity on aromatic terephthalate and new indole-based polyesters was evaluated using polymers in powder form. Compared with IsPETase, an enzyme derived from Ideonella sakaiensis, PET2 showed a lower PET depolymerization yield. However, interestingly, PET2 gave significantly higher polybutylene terephthalate (PBT) and polyhexylene terephthalate (PHT) depolymerization yields. A clear preference was found for aromatic indole-derived polyesters over non-aromatic ones. No activity was detected on Akestra™, an amorphous copolyester with spiroacetal structures. Docking studies suggest that a narrower and more hydrophobic active site reduces its activity on PET but favours its interaction with PBT and PHT. Understanding the enzyme preferences of polymers will contribute to their effective use to depolymerize different types of polyesters.

The current design process of a new aircraft includes several mod- eling and testing steps.The main purpose of the Ground Vibration Testing is to achieve experimental vibration data of the whole aircraft structure, in terms of frequencies, damping factors, and mode shapes to ensure that the aircraft will be free from any aeroelastic instability and safe to fly in the desired flight envelope. GVT results are also mainly used for validating and eventually im- proving FEM models of the aircraft examined. On the other hand, structural dynamic models are used to predict the flutter behavior and carefully plan the in-flight flutter tests, and lead to mitigation of the risks related to them. This paper aims to compare different aeroelastic methods outcomes achieved using two diverse commercial software: the widely used NASTRAN™ and the most recently developed Zonatech ZAERO. The flutter computation results are obtained by means of P-K, K, and G methods. The first case presented is the aeroelastic analysis of a forward-swept wing whereas the second case concerns the stabilator of a two-seater ultralight aircraft, for which an experimental campaign of vibrations test is also carried out. The structural mode shapes at each generated frequency mode are also visually presented.

The rapid development of technology has led to the implementation of data-driven systems whose performance heavily relies on the amount and type of the data itself. In the latest decades, in the fields of bioengineering data management, among others, eye-tracking data has become one of the most interesting and essential components for many medical, psychological, and engineering research applications. However, despite the large usage of eye-tracking data in many studies and applications, a strong gap is still present in the literature regarding real-time data collection and management, which led to strong constraints for the reliability and accuracy of on-time results. To address this gap, this study aims to introduce a system that enables the collection, processing, real-time streaming, and storage of eye-tracking data. The system is developed by using Java programming language, WebSocket protocol, and Representational State Transfer (REST), improving the efficiency in transferring and managing eye-tracking data. Results were computed in two test conditions, i.e., local and online scenarios, within a time window of 100 seconds. The experiments conducted for this study were carried out by comparing the time delay between two different scenarios. Even if preliminary, results showed a significantly improved performance of data management systems in managing real-time data transfer. Overall, this system can significantly benefit the research community by providing real-time data transfer and storing the data, enabling more extensive studies using eye-tracking data.

This work is related to the environmental toxicology risk assessment and evaluation of possible transformation of carbon-based nanomaterials (CNMs) after the contact with marine microalgae. The materials used in the study represent common and widely applied multiwalled carbon nanotubes (CNTs), fullerene (C60), graphene (Gr), and graphene oxide (GrO). The toxicity was evaluated as growth rate inhibition, esterase activity, membrane potential, and reactive oxygen species generation changes. The measurement was performed with flow cytometry after 3, 24, 96 h, and 7 days. Biotransformation of nanomaterials were evaluated after 7 days of microalgae cultivation with CNMs by FTIR and Raman spectroscopy. The calculated toxic level (EC50 in mg/L, 96 h) of ued CNMs reduced in the following order: CNTs (18.98) > GrO (76.77) > Gr (159.40) > C60 (414.0). Oxidative stress and membrane depolarization was the main toxic action of CNTs and GrO. At the same time, Gr and C60 decreased the toxic action with time and had no negative impact on microalgae even at concentration of 125 mg/L. Moreover, C60 and Gr after seven days of the contact with microalgae cells obtained structural deformations.

Over the centuries, honey is known for its superior usage in culinary, and for its rich nutrition and therapeutic values which are scientifically proven in the medical field. The chemical composition of honey varies depending on its botanical sources and environment. Therefore, the nutrition content in honey is highly likely to be affected by contaminants, such as heavy metals and pesticides. To ensure the quality of honey, parameters such as the heavy metal content should be within the safe range of total standard mineral and trace elements as defined by the International Food Standard (Codex Alimentarius), and pesticides should not be present at all. The high concentration of heavy metal and pesticides not only deteriorates the quality and quantity of honey, but also causes harm to the bee colony itself. In the agriculture sector, the excessive usage of pesticides and fertilizer negatively impacts the overall honey production process. Bees, a pollinating agent, bring the polluted nectar back to their beehives, eventually contaminating the honey and depreciating its value. Hence, this article will comprehensively review the activities that contribute to heavy metal and pesticide contamination, the interactions of bees as a pollinating agent, the impact of the pollutant to the colonies, and subsequently to the honey production.

In the present study, the corrosion resistance of amorphous coating and composite coatings in 3.5 wt.% NaCl, 0.5 M H₂SO₄ and 10 wt.% NaOH solution were studied. The composite coatings exhibit superior corrosion resistance. When the content of AT13 （Al₂O₃–13 wt.% TiO₂）was 15 wt.%, the composite coating has the lowest corrosion current density (1.75×10^-6 A cm^-2), which is 5.14×10^-5 A cm^-2 for Fe-based metallic glassy coating, and the highest corrosion potential (-411 mV), which is -580 mV for Fe-based metallic glassy coating. The breakdown potential of the passivation film in 3.5 wt.% NaCl solution was much higher than that of 316L.The long-time immersion corrosion tests carried out on different coatings showed that the corrosion protection effect of coating was enhanced with the increase of the amount of AT13 added.

Brain, the most complex object known in the universe, uses few watts of power. To mimic it a nuclear power plant will be required and this power house controls the human body, single handedly. Surprisingly, “On the left side, nothing is right and on the right side there is nothing left”. Typically, there are two lateral halves of the brain: Left hemisphere and right hemisphere working distinctly. Left hemisphere is inclined towards logic; Right hemisphere is the root cause of imagination added with critical thinking. In situations like the current pandemic, COVID-19, it is the right half which tends to dominate the processing. This gives birth to mental stress, anxiety thus, aggravating the existing medical condition. Considering this pattern, a survey was conducted in Durg district of Chhattisgarh, which is one of the most hard hit epicentre of the COVID-19 second wave in India. According to this survey, it was revealed that largely women of all age groups (10-25, 26-40, 40 above) were right brained i.e. dominance of right over left hemisphere. Being more imaginative and creative thinkers they are more likely to suffer from mental issues than males. The aim of this research is to improve the mental wellbeing of the citizens in such threatening conditions. To prevent this situation awareness is a must and some stress relieving games have also been created.

The goal of clustering is to identify common structures in a data set by forming groups of homogeneous objects. The observed characteristics of many economic time series motivated the development of classes of distributions that can accommodate properties such as heavy tails and skewness. Thanks to its flexibility, the Skewed Exponential Power Distribution (also called Skewed Generalized Error Distribution) ensures a unified and general framework for clustering possibly skewed and heavy tailed time series. This paper develops a clustering procedure of model-based type, assuming that the time series are generated by the same underlying probability distribution but with different parameters. Moreover, we propose to optimally combine the estimated parameters to form the clusters with an entropy weighing $k$-means approach. The usefulness of the proposal is showed by means of application to financial time series, demonstrating also how the obtained clusters can be used to form portfolio of stocks.

In this scientific research, thermally stable nickel nanoparticles were synthesized and characterized. Nickel nanoparticles were synthesized using phenol –formaldehyde by chemical method followed by calcination. The polymer metal complex was confirmed by FTIR and NMR. The spherical morphology of nickel nanoparticles confirmed by SEM. The crystallographic structure is confirmed by XRD and size of cobalt nanoparticles is 24.0 nm. The TGA analysis was performed over a range of 29-600OC. The TGA thermograph predicts mass decomposition of 11%, for nickel phenol-formaldehyde nanocomposite. The decomposition rate of composites is very low 2% weight loss per 100OC increment in temperature.

Copper-based nanoparticles were synthesized using the glycine–nitrate process (GNP) by using copper nitrate trihydrate [Cu(NO₃)₂‧3H₂O] as the main starting material and glycine [C₂H₅NO₂] as the complexing and incendiary agent. The as-prepared powders were characterized through X-ray diffraction (XRD), Brunauer–Emmett–Teller (BET) analysis, X-ray photoelectron spectroscopy, and scanning electron microscopy. Using Cu(NO₃)₂.3H₂O as the oxidizer (N) and glycine as fuel (G), we obtained CuO, mixed-valence copper oxides (CuO + Cu₂O, G/N = 0.3–0.5), and metallic Cu (G/N = 0.7). The XRD and BET results indicated that increasing the glycine concentration (G/N = 0.7) and reducing particle surface area increased the yield of metallic Cu. The effects of varying reaction parameters such as catalyst activity, catalyst dose, and H₂O₂ concentration on nonylphenol-9-polyethoxylate (NP9EO) degradation were assessed. With a copper‐based catalyst in a heterogeneous system, the NP9EO and total organic carbon removal efficiencies were 83.1% and 70.6%, respectively, under optimum operating conditions (pH, 6.0; catalyst dose, 0.3 g/L; H₂O₂ concentration, 0.05 mM). The results suggested that removal efficiency increased with an increase in H₂O₂ concentration but decreased when the H₂O₂ concentration exceeded 0.0.5 mM. Furthermore, the trend of photocatalytic activity was as follows: G/N = 0.5 > G/N = 0.7 > G/N = 0.3. The G/N = 0.5 catalysts showed the highest photocatalytic activity and resulted in 94.6% NP9EO degradation in 600 min.

G protein-coupled receptor 15 (GPR15, also known as BOB) is an extensively studied orphan GPCR involving HIV infection, colonic inflammation and smoking-related diseases. Recently, GPR15 was deorphanized and its corresponding natural ligand demonstrated an ability of inhibiting cancer cell growth. However, no study reported the potential role of GPR15 in a pan-cancer manner. Using large-scale publicly available data from the Cancer Genome Atlas (TCGA) and the Genotype-Tissue Expression (GTEx) databases, we found that GPR15 expression is significantly lower in colon adenocarcinoma (COAD) than in normal tissues. And among 33 cancer types, GPR15 expression is significantly correlated with the prognoses of COAD, neck squamous carcinoma (HNSC), lung adenocarcinoma (LUAD) positively and stomach adenocarcinoma (STAD) negatively. This study also revealed that commonly upregulated gene set in the high GPR15 expression group (stratified via median) of COAD, HNSC, LUAD and STAD are enriched in immune systems, indicating that GPR15 might be considered as a potential target for cancer immunotherapy. Furthermore, we modelled the 3D structure of GPR15 and conducted the structure-based virtual screening. The top 8 hits compounds were screened and then subjected to molecular dynamics (MD) simulation for stability analysis. Our study provided novel insights into the role of GPR15 in a pan-cancer manner and discovered a potential hit compound for GPR15 antagonists.

Carbon-based nanomaterials, such as carbon nanomaterials, play an important role in many promising nanomaterials in the field of biomedicine. Among them, carbon nanomaterials are a new kind of porous carbon nanomaterials with great application potential. Therefore, based on carbon nanomaterials, the interaction between biomaterials and cells and surface modification were analyzed. Studies have shown that the interaction between the surface of biological materials and cells is mainly the mutual molecular recognition between the surface receptors of cell membranes and the ligands on the surface of biomaterials. Therefore, biomimetic modification of the surface of biomaterials is used to enhance cell affinity and specific recognition. The carbon nanomaterial has a large specific surface area and pore volume, can provide high drug-loading capacity, has an adjustable pore structure and pores, can control the release of the drug molecules, and has a good application prospect in the field of drug delivery systems.

A method for infrared and cameras sensor fusion, applied to indoor positioning in intelligent spaces, is proposed in this work. The fused position is obtained with a maximum likelihood estimator from infrared and camera independent observations. Specific models are proposed for variance propagation from infrared and camera observations (phase shifts and image respectively) to their respective position estimates and to the final fused estimation. Model simulations are compared with real measurements in a setup designed to validate the system. The difference between theoretical prediction and real measurements is between 0.4 cm (fusion) and 2.5 cm (camera), within a 95% confidence margin. The positioning precision is in the cm level (sub-cm level can be achieved at most tested positions) in a 4x3 m locating cell with 5 infrared detectors on the ceiling and one single camera, at distances from target up to 5 m and 7 m respectively. Due to the low cost system design and the results observed, the system is expected to be feasible and scalable to large real spaces.

Butyl rubber-based composite (BRC) is one of the most popular materials for the fabrication of protective glove against chemical and mechanical risks. However, in many working places such as metal manufacturing or automotive mechanical services, its mechanical hazards usually appear together with metalworking fluids (MWFs). The presence of these contaminants, particularly at high temperature, could modify its properties due to the scission, the plasticization, the crosslinking of polymer network and thus led to severe modification of mechanical and physicochemical properties of material. This work aims to determine the effect of temperature and a metalworking fluid on mechanical behavior of butyl rubber composite dealing with crosslinking density, cohesion forces and elastic constant of BRC on the based on Mooney-Rivlin’s theory. The effect of temperature with and without MWFs on thermo dynamical properties and morphology of butyl membranes is also investigated. The prediction of service lifetime is then evaluated from extrapolation of Arrhenius plot at different temperatures.

Principle of memristor presented by Leon Chua and time reversal based non-linear elastic wave spectroscopy (TR-NEWS) which can be applied to acoustic tomography are reviewed. Correlation of acoustic or electromagnetic nonlinear waves and its time reversed wave can produce strong signal. Materials like transducer or biological skin can be assumed to be composed of a large number of small elastic unit: hysteretic elementary unit (HEU), and one can take a HEU as a memristor whose operation depends on time. Relation between TR-NEWS and solitonic wave in fluid, and relation between decomposition of time reversal operator (DORT) and chaotic oscillation are discussed.

Background: Melatonin is a potent mitochondrial, cytoprotective and antioxidant molecule with potentially strong anti-aging properties. Topical melatonin has shown to improve the clinical signs of skin aging. Melatosphere™ is a new lipid-based delivery system able to improve stability and skin penetration of melatonin when used in topical formulations. No clinical studies, using objective instrumental data, are available so far regarding the positive effect of Melatosphere™ in improving wrinkles in women with mild-to-moderate skin aging. Study Aim: We evaluate, in an open prospective, evaluator-blinded trial, the effects on skin texture of 2 months treatment with a Melatosphere™ based cream. Subjects and Methods: 15 women aged >45 years with mild to moderate facial skin aging (Glogau score ≥2) participated in the trial, after their informed consent. An ANTERA 3D computer-assisted skin analysis evaluation for the assessment of coarse and fine wrinkles of the periorbital area and melanin content was performed at baseline and after two months of treatment. An evaluator-blinded Investigator Global assessment of skin elastosis, roughness, level of dyscromia, skin dryness and presence of actinic damage was also performed at the same time points using a 4-grade score from 0 (no sign) to 3 (severe sign). Results: At baseline the mean (SD) IGA score was 8.2(1.0). After 2 months the IGA score significantly decrease to 4.2(1.4) (49% reduction) (P=0.0007). ANTERA 3D evaluations showed a significant reduction in skin coarse and fine wrinkles volume in the target area of -31% and -18%, respectively. Melanin content was reduced significantly by -17%. Conclusion: Topical melatonin carried in Melatosphere improves in the short-term signs of skin aging evaluated clinically and by ANTERA 3D device in women with mild to moderate skin aging.

A total of five protoclones were successfully cultured on PDA medium out of regenerated twenty two colonies of Termitomyces protoplast and further studied. Liquid MYG grown mycelial tissue is used for protoplast isolation by enzymatic digestionin a mixture containing Lysing enzyme 2% and Cellulase R10 2% in 0.6 M mannitol. The incubation conditions like temperature, shaking and time were standardized at 24ºC, 60 rpm and 10 hours, respectively for healthy protoplasts liberation. The purified protoplasts showed an average yield of 1.2 × 10⁷ cells/gm tissue with 31.60 ± 9.31% regeneration efficiency on specific medium and 77.12 ± 2.72% viability by FDA test. Four ISSR primers were used in this study resulting a total of 27 reproducible bands with mean value of 6.75. They showed similar banding pattern in all the lines with zero percent polymorphism ranged from 280 bp–2700 bp. The amplified rRNA-ITS gene showed ~600 bp size in gel and found a single restriction site for enzyme HaeIII in all the protoclones and parent with similar fragment size in all.

Multi-cameras system is widely applied in 3D computer vision especially when multiple cameras are distributed on both sides of the measured object. The calibration methods of multi-cameras system are critical to the accuracy of vision measurement and the key is to find an appropriate calibration target. In this paper, a high-precision camera calibration method for multi-cameras system based on transparent glass checkerboard and ray tracing is described, which is used to calibrate multiple cameras distributed on both sides of the glass checkerboard. Firstly, the intrinsic parameters of each camera is obtained by Zhang’s calibration method. Then, multiple cameras capture several images from the front and back of the glass checkerboard with different orientations, and all images contain distinct grid corners. As the cameras on one side are not affected by the refraction of glass checkerboard, extrinsic parameters can be directly calculated. However, the cameras on another side are influenced by the refraction of glass checkerboard, and the direct use of projection model will produce calibration error. A multi-cameras calibration method using refractive projection model and ray tracing is developed to eliminate this error. Furthermore, both synthetic and real data are employed to validate the proposed approach. The experimental results of refractive calibration show that the error of the 3D reconstruction is smaller than 0.2 mm, the relative errors of both rotation and translation are less than 0.014%, and the mean and standard deviation of reprojection error of 4-cameras system are 0.00007 and 0.4543 pixel. The proposed method is flexible, high accurate, and simple to carry out.

This paper presents an improved indoor localization system based on RF and ultrasonic signal which we named SNSH system. This system composes of a transmitter mounted in a mobile target, and a series of receiver nodes which are managed by a coordinator. By measuring Time Delay of Arrival (TDoA) of RF and ultrasonic signal from the transmitter, distance from target to receiver node is calculated and sent to the coordinator through CAN network, and all the information are gathered in PC to estimate 3D position of the target. Sine wave detector and dynamic threshold filter are applied to provide excellent accuracy of range measurement from TDoA result, and multilateration algorithms is realized to optimize coordinate determination accuracy. Specifically, Linear Least Square and Non-linear Least Square techniques are implemented to contrast their performances in target coordinate estimation. RF signal encoding/decoding time, time delay in CAN network and math calculating are carefully considered to ensure optimal system performance and get ready for field application. Experiments show that sine wave detector algorithm has greatly improved range measurement accuracy, with mean error at 2.2mm and maximum error at 6.7 mm, for distance below 5m. In addition, 3D position accuracy is greatly enhanced by multilateration methods, with mean error in position stay under 15mm, and 90% confident error values at 23mm for LLS, 20mm for NLS. Overall system update period has been verified in real system operation, with maximum rate at 25ms, and compared with other existing researches.

In a saline semi-isolated Lagoon on the Cape Zeleny (White Sea), the annual dynamics of the vertical hydrological structure, and the seasonal dynamics of phytoplankton were traced, including species composition, and vertical distribution, abundance, nutrition type,and biomass.293 species and supraspecific taxa of algae and cyanobacteria were found. Most of the identified species are marine, and 38 species are freshwater. Taxonomic composition changed in the Lagoon during the year. Dinoflagellates dominated in winter and early June; unidentified cocci and flagellates in July; diatoms, dinoflagellates and unidentified cells in August; dinoflagellates in September; and unidentified cocci and flagellates in October–November. The abundance of algae also changed in the Lagoon during the year. The integrated biomass in the water column varied from 0.01 g C/m2 in January to 0.78 g C/m2 in early September. According to the abiotic parameters, the water column of the Lagoon is subdivided into several zones with different conditions and corresponding phytoplankton communities. Similarity between the communities of different horizons was 32-46% in summer and 7% in winter. The chemocline layer was the most populous. Almost the entire year it contained a maximum of phytoplankton biomass, 1-2 orders of magnitude higher than that in the overlying horizons. Comparison phytoplankton in the lagoon and in the nearby sea area was carried out. Despite the connection between the Lagoon and the sea phytoplankton structure in the surface water layer in the Lagoon and in the sea differs significantly in composition, quantitative parameters, and seasonal dynamics. The similarity of the communities in the sea and the Lagoon never exceeded 50%. In terms of biomass, the Lagoon lags behind the sea until mid-summer, but, starting from August, it outnumbers it, and the phytoplankton development in the Lagoon lasts longer, until late autumn. The phytoplankton structure and dynamics in the Lagoon and the sea by 24.5% was related to the daylight, water salinity, oxygen content and pH, but did not depend on water T, underwater illuminance, and depth. Oxygen content and pH are defined by PhP activity. Salinity serves as a vector of the vertical sequence of ecological niches. The daylight length seems to be the crucial factor of the seasonal PhP dynamics in the semi-isolated coastal stratified lakes and lagoons.

Steel slag is a co-product of the steelmaking industry, this material is rarely used as a binder due to its low cementitious properties. Thus, it´s believed that the alkaline activation of steel slag can enable its use as a cementitious material. This work has the objective of making a systematic bibliographic review on the theme "slag melt as a precursor in activated alkali binders". To this end, the ProKnow-C method was used for the selection and analysis of relevant research on the topic. The portfolio resulted in 71 texts, mostly journals paper. Bibliometrics showed that the country with the most publications is China and the journal with the most publications is Construction and Building Materials. The critical analysis concluded that the XRD, XRF, and compressive strength tests are the most used for the characterization of the material. It is possible to establish that there are some knowledge gaps on the subject, such as better mechanical characterization and greater production of works with dosing by the one-part method.

Egyptian cotton is one of the most important commodities to the Egyptian economy and is renowned globally for its quality, which is currently graded by manual inspection. This has several drawbacks including significant labour requirement, low inspection efficiency, and influence from inspection conditions such as light and human subjectivity. This current work uses a low-cost colour vision system, combined with machine learning to predict the cotton lint grade of the cultivars Giza 86, 97, 90, 94 and 96. Unsupervised and supervised machine learning approaches were explored and compared. Three different supervised learning algorithms were evaluated: linear discriminant analysis, decision trees and ensemble modelling. The highest accuracy models (77.3-98.2%) used an ensemble modelling technique to classify samples within the Egyptian cotton grades: Fully Good, Good, Fully Good Fair, Good Fair and Fully Fair. The unsupervised learning technique k-means showed that human error is more likely to occur when classifying lint belonging to the higher quality grades and underlined the need for an intelligent system to replace manual inspection.

Abstract: An efficient and environmentally Pumice as a new catalyst for designing of 3,4- dihydropyrimidin-2(1H) ones / thiones via one-pot multi component condensation of aromatic aldehydes, urea/ thiourea and ethyl acetoacetate or acetyl acetone in excellent yields (96-99 %). The advantages using of this new catalyst is very cheap, available, non-toxic, stable under thermal conditions, easy work-up, improved yields, the product of reaction is very pure without using chromatographic methods and solvent free conditions.

New developments in broadcast technology, such as the ad hoc wireless network and the detection devices that work, enable daily connections to the Internet, commonly referred to as the Internet of Things (IoT). IoT saw this as a support for the development of creative and contextual services and applications. The service can respond to the user's environment and preferences. Finite state machines are recently making waves when the issue of self-controlling systems come into the picture of designs. This research focuses on the design of a fault tolerant system that controls random failures impressed on the network. The method comprises of diodes, Resistors, Optocouplers, ESP8266 WI-FI Module Webpage with button, and two Arduinos which in effect will determine iteratively who to handle a failure at hand modelled as when the button on the webpage is pressed. In this work, a Heartbeat Detection and Monitoring System will be made using Arduino that detect the electrical state using the Pulse Sensor and show the readings in Beats Per Minute on the LED connected a microcontroller. It sends the texts to the server using the Wi-Fi module ESP8266 to signals can be monitored from the monitoring zone via the internet. This is reflected by the declaration of a master state by either of two Arduinos whenever the push button is pressed as observed on the codes. The result obtained from the research shows apparently, the state of the fault tolerance system none power deny of heartbeat condition connected to the LED.

Inorganic nanoparticles based on magnetite improve the mechanical, thermal, and magnetic properties of microporous cryogel polymer composites. Here we report the synthesis of microporous cryogel based on the crosslinked sodium vinyl sulfonate (Na-VS) and 2-acrylamido-2-methylpropane sulfonic acid sodium salt (Na-AMPS). The magnetite nanoparticles were inserted into Na-VS/Na-AMPS cryogel either during its crosslinking polymerization or by in-situ technique after its crosslinking. The morphology, particle sizes, thermal stability and magnetite contents of Na-VS/Na-AMPS cryogel and its magnetite composites were investigated. The prepared Na-VS/Na-AMPS cryogel and its magnetite composites were used as adsorbents for methylene blue (MB) cationic dye using optimum conditions. The magnetite Na-VS/Na-AMPS cryogel composite prepared by in-situ technique achieved the best adsorption MB removal capacity for 7 cycles among the other adsorbents via chemical adsorption mechanism at room temperature.

(1) Background: Immunosuppressed patients, especially those receiving B-cells depleting therapies (BCDT), are at major risk to develop reduced vaccine seroconversion and contract severe acute respiratory syndrome coronavirus 2 (SARS-CoV2) infection. The aim of our study is to assess safety and efficacy of the pre-exposure prophylactic combination of Tixagevimab and Cilgavimab (TGM-CGM) in a cohort of rheumatic patients diagnosed with Autoimmune Rheumatic Diseases; (2) Methods: We performed a prospective study using the clinical medical charts of 25 patients treated with Rituximab and received a single injection of TGM/CGM. The cohort was followed for 6 months from the injection and compared to a control group of 25 immunosuppressed patients who did not receive TGM-CGM. We assessed the incidence and the severity of Covid-19 in both groups, as well as early and late adverse events; (3) Results: Despite the small sample, we noticed a downward trend in the incidence and severity of symptomatic infection in the group treated with TGM/CGM. In the experimental cohort, one patient was completely asymptomatic, four patients were oligosymptomatic infection, and just one had mild-moderate infection versus 4 oligosymptomatic and 5 mild-moderate infection in the control group. We observed also a reduction in time to nasopharyngeal swab negativization. No adverse events were reported in our data. We collected the dosage of anti-Receptor-Binding Domain (RBD) SARS-CoV2 spike protein for 21 patients, revealing adequate seroconversion in 12 patients out of 21; (4) Conclusions: Even though the study was conducted during the Omicron wave, notably known to be less responsive to monoclonal antibodies, we proved that TGM-CGM could be a risk-free additional tool to prevent SARS-Cov2 infection in rheumatic immunosuppressed patients.

A widely discussed topic in the pathophysiology of thyroid nodules is the role of obesity that leads to increased systemic inflammatory markers. Leptin plays a vital role in forming thyroid nodules and cancer through several mechanisms. Together with chronic inflammation, there is an augmentation in the secretion of tumor necrosis factor (TNF) and cytokines interleukin 6 (IL-6), contributing to cancer development, progression and metastasis. In addition, leptin exerts a modulatory action in the growth, proliferation and invasion of thyroid carcinoma cell lines via activating various signal pathways, such as Janus kinase/signal transducer and activator of transcription, mitogen-activated protein kinase (MAPK) and/or phosphoinositide 3-kinase (PI3K) /protein kinase B (Akt). Through several proposed mechanisms, aberrant endogenous estrogen levels have been suggested to play a vital role in the development of both benign and malignant nodules. Metabolic syndrome triggers the development of thyroid nodules by stimulating thyroid proliferation and angiogenesis due to hyperinsulinemia, hyperglycemia and dyslipidemia. Insulin resistance influences the distribution and structure of the thyroid blood vessels. Insulin growth factor -1 (IGF-1) and insulin affect the regulation of the expression of thyroid genes and the proliferation and differentiation of thyroid cells. TSH can promote the differentiation of pre-adipocytes to mature adipocytes, but also, in the presence of insulin, TSH possesses mitogenic properties. This review aims to summarize the underlying mechanisms explaining the role of obesity in the pathophysiology of thyroid nodules and discuss potential clinical implications.

Proteases represent common targets in combating infectious diseases including COVID-19. The 3-chymotrypsin-like protease (3CLpro) is a validated molecular target for COVID-19 and it is key for developing potent and selective inhibitors for inhibiting viral replication of SARS-CoV-2. In this review, we discuss structural relationships and diverse subsites of 3CLpro, shedding light on the pivotal role of dimerization and active site architecture in substrate recognition and catalysis. Our analysis of bioinformatics and other published studies unveil a proposal of a novel catalytic mechanism for the SARS-CoV-2 polyprotein cleavage by 3CLpro, centering on the triad mechanism involving His41-Cys145-Asp187 and its indispensable role in viral replication. Recognizing Asp187 as a crucial component in the catalytic process underscores its significance as a fundamental pharmacophoric element in drug design. Next, we provide an overview of both covalent and non-covalent inhibitors, elucidating advancements in drug development observed in preclinical and clinical trials. By highlighting various chemical classes and their pharmacokinetic profiles, our review aims to guide future research directions toward the development of highly selective inhibitors, underscore the significance of 3CLpro as a validated therapeutic target, and propel the progression of drug candidates through preclinical and clinical phases.

Preschoolers with disabilities and their family caregivers are missed out in many public health initiatives especially in low-income countries. Yet they can benefit from early intervention to promote their development from soon after birth and to provide a better quality of life for their families. In this paper, we describe how a community-based approach has been implemented with minimal funding in two areas in Zimbabwe: a township in Harare and in rural areas of Manicaland Province. Our aim in sharing this information – allied with references to research studies recently undertaken in Africa – is that it will enable similar supports to be replicated in other communities by local personnel. A logic model is used to describe the situation in which the two projects work, the various inputs they have provided to their community and the different forms of support they have offered to the children and their caregivers. The project outputs are listed in terms of the number of beneficiaries helped and activities undertaken. The outcomes achieved for the children, the family and communities are reported. The sustainability and extensions of community-based projects to address unmet needs are discussed. The main conclusion is that disadvantaged communities can be energized to address the needs of their most marginalized residents.

The energy crisis is currently a major topic in scientific and managerial discussions in the building and construction industry. While employing low-cost strategies for reducing energy usage is beneficial for principals and decision-makers, the primary purpose of this study is to recommend the use of phase change materials (PCM) to improve cooling and heating load management in buildings. In order to accomplish this, the study applies design-builder simulations to a developing country case study. The results indicate that integrating PCM in buildings can reduce energy usage by around 6 percent. Although this value may be lower than other techniques, it is nevertheless important for reaching green building goals in both developed and developing nations. The case study, which takes place in a four-season region, demonstrates the significant potential of the PCM system for both cooling and heating energy management. In addition, the reciprocal performance improvement of PCMs is a remarkable characteristic, especially when contrasted to some conventional systems that focus on a single energy management objective in buildings. Lastly, it should be underlined that the suggested platform is consistent with Sustainable Development Goal 7 (Affordable and Clean Energy Targets).

With the proliferation and development of social media platforms, social media data has become an important source for acquiring spatio-temporal information on various urban events. Providing accurate spatio-temporal information for events contributes to enhancing the capabilities of urban management and emergency response. However, existing research on mining spatio-temporal information of events often focuses solely on textual content, neglecting data from other modalities such as images and videos. Therefore, this study proposes an innovative spatio-temporal information extraction method for multi-modal social media data (MIST-SMMD), which extracts the spatio-temporal information of events from multi-modal data on Weibo at coarse and fine-grained hierarchical levels, serving as a beneficial supplement to existing urban event monitoring methods. This paper takes the "July 20th Zhengzhou Heavy Rainfall" incident as an example, to evaluate and analyze the effectiveness of the proposed method. The results indicate that in the coarse-grained spatial information extraction using only textual data, our method achieves a Spatial Precision of 87.54% within a 60m range, and reaches 100% Spatial Precision for ranges beyond 200m. For fine-grained spatial information extraction, the introduction of other modal data such as images and videos results in a significant improvement in Spatial Error. These results demonstrate the ability of the MIST-SMMD method to extract spatio-temporal information from urban events at both coarse and fine levels, and confirms the significant advantages of multi-modal data in enhancing the precision of spatial information extraction.

In the European Union, the eCall system has been mandatory since March 31, 2018. The system enabled a significant increase in safety on roads and highways, making help faster in the event of an accident. However, based on circuit-switched 2G/3G communications and an onboard device called IVS, it is generally unavailable on legacy vehicles. Some of its limitations tend to be remedied by the future NG eCall based on 4G/5G packet-switched communications. This paper discusses why IVS may be an Achilles’ heel of any future IVS-based eCall and analyzes the advantages of a smartphone-based system. The TEZE system, implemented and installed in Italian highway tunnels, is a first general-purpose safety system, and allows highly reliable smartphone-based emergency calls. It is based on a dedicated low-cost ground infrastructure that allows monitoring of the availability of emergency call service through the mobile radio networks of the operators present inside the tunnels. The system complies with the ISO/IEC 30141:2018 reference standard. Identifying functional/non-functional requirements and their verification criteria provide an overall validation of the analyzed system. The paper concludes by examining some key functionalities for vehicular services that can be implemented in an integrated system based on smartphones and heterogeneous networks.

CD44 regulates cell adhesion, proliferation, survival, and stemness and has been considered a tumor therapy target. CD44 possesses the shortest CD44 standard (CD44s) and a variety of CD44 variant (CD44v) isoforms. Since the expression of CD44v is restricted in epithelial cells and carcinomas compared to CD44s, CD44v has been considered a promising target for monoclonal antibody (mAb) therapy. We previously developed an anti‐CD44v10 mAb, C44Mab-18 (IgM, kappa) to recognize the variant exon 10-encoded region. In the present study, a mouse IgG2a version of C44Mab-18 (C44Mab-18-mG2a) was generated to evaluate the antitumor activities against CD44-positive cells compared with previously established anti-pan CD44 mAb, C44Mab-46-mG2a. C44Mab-18-mG2a exhibited higher reactivity compared with C44Mab-46-mG2a to CD44v3–10-overexpressed CHO-K1 (CHO/CD44v3–10) and oral squamous cell carcinoma cell lines (HSC-2 and SAS) in flow cytometry. C44Mab-18-mG2a exerted a superior antibody‐dependent cellular cytotoxicity (ADCC) against CD44v3–10. In contrast, C44Mab-46-mG2a showed a superior complement‐dependent cytotoxicity (CDC) against CD44v3–10. A similar tendency was observed in ADCC and CDC against HSC-2 and SAS. Furthermore, administering C44Mab-18-mG2a or C44Mab-46-mG2a significantly suppressed CD44v3–10, HSC-2, and SAS xenograft tumor growth compared with the control mouse IgG2a. These results indicate that C44Mab-18-mG2a could be a promising therapeutic regimen for CD44v10-positive tumors.

The major global pressures of rapid urbanization and urban growth are compounded by climate impacts resulting in increased vulnerability for urban dwellers, with these vulnerabilities exacerbated during COVID-19. Much of this is concentrated in urban and peri-urban areas where urban development spreads into hazard-prone areas. Often this development is dominated by poor quality homes in informal settlements or slums with poor tenure security. Lessons from a current resilience-building project shows that the fit-for-purpose (FFP) approach to land administration can provide a solution to increase the number of households with security of tenure and improve resilience outcomes as informal settlements grow. This paper discusses the influence of FFP land administration on vulnerabilities to multiple shocks related to climate change and COVID-19. This paper proposes ways the growth of human settlements can be better managed through responsible governance of land tenure rights, and effective land-use planning to improve resilience to different shocks and stresses and provide adequate access to safe land and shelter. Land administration systems can support improved resilience to the multiple stressors of climate and pandemics through improving tenure security and enhancing land use planning controls. Climate change adaptation and risk management need to be better mainstreamed into two major elements of land governance: (i) securing and safeguarding of land rights, and (ii) planning and control of land-use.

Individuals have different rumination patterns after experiencing traumatic events in different cultural backgrounds and situations. This study was aimed to explore the experience of Chinese young and middle-aged patients with acute coronary syndrome (ACS) to understand the content and nature of their rumination. Fourteen participants were selected using the purposive sampling method in XXX hospital from May 2023 to December 2023. Data were collected using semi-structured interviews and analyzed using Colaizzi’ s 7-step phenomenological method. The content of rumination in young and middle-aged patients with ACS can be summarized into 2 categories: non-adaptive rumination and adaptive rumination. Non-adaptive rumination included 3 themes: worry or anxiety, scare, and depression. Adaptive rumination included 4 themes: tracing of disease processes, enhancement of disease cognition, improvement of health awareness, and adjustment of lifestyle cognition. In conclusion, although Chinese young and middle-aged patients with ACS experience negative emotions after a traumatic cardiac event, they gradually make positive changes, and optimism and information support play important roles in this transition, which provide a fundamental understanding of rumination experiences in Chinese young and middle-aged patients with ACS and provide new data for healthcare providers when designing intervention programs to enhance post-traumatic growth in these patients.

Background/Objectives: Traditional neurosurgical training involves a steep learning curve. The introduction of advanced simulation technologies, such as virtual reality (VR), provides an alternative method for skill acquisition, allowing for repeated practice and objective assessment. This study focuses on evaluating the learning curve associated with lumbar pedicle Kirschner wire insertion using a virtual fluoroscopic simulator among neurosurgical residents. Methods: Eighteen neurosurgery residents and two interns participated in this study. Participants’ initial skill levels were assessed through a questionnaire. Performance metrics, including insertion accuracy, time, and the number of virtual X-ray scans, were recorded across multiple attempts until a “practical learning plateau” was reached. Statistical analyses were conducted to model learning curves, assess the correlation between pre-training experience and performance. Results: The average number of attempts required to stabilize performance was 5.61. Learning rates varied, with some residents showing rapid improvement while others progressed more slowly. Higher accuracy in wire placement correlated with reduced usage on X-rays over time. However, experienced residents did not necessarily learn faster, indicating potential challenges in adapting to new simulation-based methods. Post-training feedback highlighted the simulator’s utility in increasing confidence and skill levels, although some limitations in anatomical accuracy were noted. Conclusions: This study demonstrates the potential of virtual simulation to enhance neurosurgical training by providing a controlled environment for repeated practice and objective feedback. Simulation-based training can effectively complement traditional methods, though individualized approaches may be necessary to accommodate varying learning rates among residents. Further refinement of simulation tools and their integration into standard training curricula are recommended.

Drought is one of the major stress factors affecting growth and development of plants. In this context, drought-related losses of crop plant productivity impede sustainable agriculture all over the world. In general, plants responses to water deficit by multiple physiological and metabolic adaptations at the molecular, cellular and organism levels. To understand the underlying mechanisms of drought tolerance, adequate stress models and arrays of reliable stress markers are required. Therefore, in this review we comprehensively address currently available models of drought stress, based on culturing plants in soil, hydroponic or agar culture. These experimental setups give access to different aspects of plant response to drought, like decrease of tissue water potential, reduction of stomata conductance and photosynthesis efficiency, accumulation of low-molecular weight solutes (metabolic adjustment) and drought protective proteins. Till now, this pattern of markers was successfully extended to the methods of enzyme chemistry, molecular biology and omics techniques. Thus, conventional tests can be efficiently complemented by determination of phytohormone and reactive oxygen species (ROS) contents, activities of antioxidant enzymes, as well as comprehensive profiling of transcriptome, proteome and metabolome.

Offshore Wind Turbines (OWT) with increasingly higher energy output are being developed to meet energy demand, posing challenges for their foundation design. Several foundation types are used to support these turbines with monopiles (MP) accounting for 80% of the installed capacity. In this study, three-dimensional (3D) nonlinear finite element models (FEM) were employed to investigate the behaviour of monopile foundation supporting a 5MW wind turbine when subjected to lateral loading. Results indicate that the MP behavior depending on the pile length to diameter (L/D) ratio and the soil shear strength. Inspection of bending moment profiles at the lateral ultimate capacity indicated the monopiles can behave in flexible manner even with low L/D ratio. The L/D ratio affected the MP normalized lateral ultimate capacity at varying degrees and the biggest effect was for soft clays, amounting to around 5 folds increase for L/D values of 3.33 to 13.33. Lesser effects were found for stiff clays.

The genus Beauveria include important entomopathogenic and endophytic fungi, among them, Beauveria bassiana is the most studied species. However, there are few knowledge regarding their antimicrobial activity. The current research has been conducted to evaluate in vitro antagonistic activity of B. bassiana and the antimicrobial efficacy of its Exo and Endo metabolites against Bacillus cereus, B. megaterium, Clavibacter michiganensis (gram positive bacteria, G+ve), Xanthomonas campestris, Pseudomonas aeruginosa and P. fluorescence (gram negative bacteria, G-ve). In addition, Solid-phase microextraction (SPME) was coupled to Gas Chromatography-Mass Spectrometry (GC/MS) to qualitatively measure the volatile organic compounds (VOCs) metabolic profile of the most efficient studied isolate of B. bassiana. The obtained results showed that, the isolate UniB2439-3 has promising antibacterial effect against most of studied target bacteria. SPME-GC/MS analysis of VOCs revealed the presence of ethanol; butanal,2-methyl; 2,4-dimethyl-1-heptene; octane, 4-methyl and β-elemene as the main dominant bioactive compounds. The outgoing results explicated that the efficient isolate of B. bassiana can be potentially used as a biocontrol agent against several bacteria especially G+ve ones.

Diabetic retinopathy (DR), a major complication of prolonged diabetes, poses a significant risk of vision loss. Early detection is critical for effective treatment, yet traditional diagnostic methods by ophthalmologists are time-consuming, costly, and subject to variability. This study introduces a novel approach employing a hybrid Convolutional Neural Network-Radial Basis Function (CNN-RBF) classifier integrated with Multi-Scale Discriminative Robust Local Binary Pattern (MS-DRLBP) features for enhanced DR detection. We implemented advanced image preprocessing techniques, including noise reduction, morphological operations, and Otsu’s thresholding, to optimize blood vessel segmentation from retinal images. Our method demonstrates exceptional performance in screening DR, achieving an average of 96.10% precision, 95.35% sensitivity, 97.06% specificity, and 96.10% accuracy. These results significantly outperform traditional methods and offer a promising tool for remote and efficient screening of DR. Applied to publicly available datasets, this research contributes to the development of accessible, accurate diagnostic methods in ophthalmology, potentially reducing the global burden of diabetic vision loss.

Antimicrobial resistance (AMR) poses a global public health threat, with rates directly linked with consumption. The World Health Organization (WHO) AWaRе-classification aims to guide antibiotic use, which is influenced by drug availability, affordability, and economic policies. In Jordan, a high proportion of consumed antibiotics belong to the 'Watch' category. Data from the WHO-AWaRе classification, the Essential Medicines List, and the Jordan Food and Drug Administration were analyzed. Antibiotics for adults were classified and their cost per Defined Daily Dose (DDD) was determined, then affordability was assessed. In 2023, 43 injectable and 43 oral antibiotics for adults were registered in Jordan. 'Watch' antibiotics were the most common. 'Access' antibiotics had the lowest cost/DDD. 'Rеsеrvе' antibiotics were the most expensive with few generics. Injectable antibiotics had a negative correlation between cost and the number of alternatives. Affordability was higher for oral antibiotics compared to injectable ones. 'Rеsеrvе' antibiotics were generally unaffordable. This study highlights the need to promote 'Access' antibiotics over other categories by encouraging registration of missing ‘Access’ antibiotics and adjusting 'Watch' and 'Rеsеrvе' antibiotic prices. Competition among generics can lead to lower prices, increasing affordability and accessibility. We emphasize the importance of the AWaRе-classification in guiding antibiotic use in Jordan.

The aim of this study is to examine an efficient and environmental friendly process for hydrolyzing starch, an industrial-derived by-product of the potato processing industry, and to use the produced hydrolysates as substrates for methane production. A full factorial experiment was designed and the optimal concentrations of FeSO4·7H2O, H2O2 and temperature were determined obtaining a yield of 99.5%, with a total carbohydrates content equal to 28.65 g, when the input parameters were at their high level: FeSO4·7H2O 1.00g/L, H2O2 0.51 g/L and temperature 70.0oC. The hydrolysis kinetics were also examined revealing that temperature was the most important factor among the three optimization parameters. Furthermore, biodegradability tests were carried out to measure the methane production potential from the produced hydrolysates. The specific methanogenic activity was reached 0.669 g CODCH4/gVSS-day proving the effectiveness of the hydrolysis process and highlighting the potential of this food manufacturing by-product for bioenergy production. A preliminary cost analysis showed that a small investment for utilizing the starch in an existing wastewater treatment facility of a potato processing industry becomes profitable before the end of the 3rd year obtaining a NPV 37.5% higher than the NPV of the current utilization scenario.

: Sulfate presence is of great importance for anaerobic digestion as its addition can control the anaerobic microbial community. In this study, the effect of sulfuric acid addition on the perfor-mance of an UASB reactor fed with hydrolyzed starch was investigated. TOC, Fe and SO4-2 re-moval as well as the methane production were monitored under various COD/ SO4-2 ratios, HRTs and OLRs. When HRT was 16 hours and OLR equal to 2 g COD/LUASB-d the methane production was 0.24 L CH4/LUASB-day. After the sulfuric acid addition, TOC removal reached 95%. At HRT, OLR, and COD/ SO4-2 ratio equal to 16 hours, 2 g COD/ LUASB-d and 3.72 respectively, the methane production was 0.267 L CH4/LUASB-d. When the OLR was increased to 5.94 g COD/LUASB-d and COD/SO4-2 ratio was equal to 12.5, the methane production was 3 times higher (0.84 L CH4/LUASB-d) with a methane content in the produced biogas greater than 70%. 16s rRNA gene analysis demonstrated that after the sulfate addition, SRB were enriched and methane was mainly produced by Methanosaeta sp. This study indicates that appropriate sulfate concentrations in the feed in combination with the presence of iron in the UASB reactor lead to enhanced me-thane yields.

A theoretical nonlocal thermoelastic model for studying the effects of the thermal conductivity variability on a rotating nanobeam has been described in the present article. The theory of thermal stress is employed using the Euler–Bernoulli beam model and generalized heat conduction with phase lags. It is believed that the thermal conductivity of the current model varies linearly according to temperature. Due to variable harmonic heat, the considered nanobeam excited and was subjected to a time-varying exponential decay load. Using the Laplace transform process, the analytical solutions for displacement, deflection, thermodynamic temperature and bending moment of rotating nanobeams are provided in final forms and a numerical example has been taken to address the problem. A comparison of the stated results was displayed and additionally, the influences of non-local parameters and varying load were analyzed and examined. We also investigate how the linear changes in the temperature of physical properties can influence both the static and dynamic responses to the rotating nanobeam.

In this work, we report the magnetocaloric effect (MCE) in two systems of non-interactive particles, the first corresponds to the Landau problem case and the second, the case of an electron in a quantum dot subjected to a parabolic confinement potential. In the first scenario, we realize that the effect is totally different from what happens when the degeneration of a single electron confined in a magnetic field is not taken into account. In particular, when the degeneracy of the system is negligible, the magnetocaloric effect cools the system, while in the other case, when the degeneracy is strong, the system heats up. For the second case, we study the competition between the characteristic frequency of the potential trap and the cyclotron frequency to find the optimal region that maximizes the ΔT of the magnetocaloric effect, and due to the strong degeneration of this problem, the results are in coherence with those obtained for the Landau problem. Finally, we consider the case of a transition from a normal MCE to an inverse one and back to normal as a function of temperature. This is due to the competition between the diamagnetic and paramagnetic response when the electron spin in the formulation is included.

Phakopsora pachyrhizi, the causative agent of the Asian Soybean Rust, is one of the most prominent causes of yield loss in soybean production worldwide. In this study, a combination of hyperspectral imaging with advanced data analysis methodology is shown to accurately detect soybean rust symptoms in early stages and differentiate them from other factors at leaf scale. Data analysis was performed with neural networks based on training data from a small subset of the respective experiments and compared with classical machine learning methods for its accuracy and early disease detection capabilities. Results show that even with a comparably small subset of training data the analysis through neural networks can outperform classical machine learning methods in a complex dataset with high variability in the designated classes. In two separate experiments it was possible to accurately detect disease in inoculated leaves before symptoms were visible to the human eye. These results do not only show the potential of this methodology for precision farming applications, but also allow accurate and objective assessment of symptom severity and development for scientific studies and resistance breeding.

Sustainable development of our society demands recycling of metallic wastes which may contain a variety of metallic impurities. Thus, it is desirable to develop a ‘universal’ structure, such as an amorphous alloy tolerating high concentrations of metallic pollutants. We performed ab initio molecular dynamics simulations to manufacture a novel multicomponent amorphous alloy of twenty elements (20e-HEAA). Analysis reveals both topological and chemical compositional short-range ordering in the obtained 20e-HEAA and formation of B-B clusters. Electronic structure calculations reveal the metallic nature of the amorphous alloy with its Fermi level falling in a valley in the density of states. Moreover, the Zn 3d and Cd 4d electrons exhibit a localized nature and thus, should be excluded from the valence electron concentration (VEC) analysis in the study of high-entropy alloys. The information obtained here is helpful to get insight into multicomponent high entropy materials, especially multicomponent high-entropy amorphous alloys as potential circular metals.

Objective: SARS-CoV-2 remains the third most common cause of death in North America. We studied methotrexate and tumor necrosis factor inhibitor (TNFi) effects on neutralization responses post-COVID vaccination, in immune-mediated inflammatory disease(IMID). Methods: Prospective data and sera on adults with inflammatory bowel disease (IBD), rheumatoid arthritis (RA), spondyloarthritis (SpA), psoriatic arthritis (PsA) and systemic lupus (SLE) were collected at 6 academic centres in Alberta, Manitoba, Ontario, and Quebec between 2022-2023. Sera from two time points were evaluated for each subject. Neutralization studies were divided between 5 laboratories, and each lab’s results analyzed separately using multivariate generalized logit models (ordinal outcomes: absent, low, medium, and high neutralization). Odds ratios (ORs) for methotrexate and TNFi effects were adjusted for demographics, IMID, other biologics and immunosuppressives, prednisone, COVID vaccinations (number/type), and infections in the 6 months prior to sample. Adjusted ORs for methotrexate and TNFi were then pooled in random-effects meta-analyses (separately for ancestral, and Omicron BA1 and BA5 strains). Results: Of 479 individuals (958 samples), 292 (61%) were IBD, 141 (29.4%) RA, and the remainder PsA, SpA and SLE. Mean age was 57 (62.2 % female). For both individual labs and the meta-analyses, adjusted ORs suggested independent negative effects of TNFi and methotrexate on neutralization. The meta-analysis adjusted ORs for TNFi were 0.56 (95% confidence interval, CI 0.39, 0.81) for the ancestral strain and 0.56 (95% CI 0.39, 0.81) for BA5. The meta-analysis adjusted OR for methotrexate was 0.39 (95% CI 0.19, 0.76) for BA1. Conclusions: SARS-CoV-2 neutralization in vaccinated IMID was diminished independently by TNFi and methotrexate. As SARS-CoV-2 circulation continues, ongoing vigilance regarding optimized vaccination is required.

Earth’s climate cannot be ignored any longer. Policies are vital in order to mitigate the negative effects of climate change. The energy crisis created by the Russo-Ukrainian war and pandemic affected the EU and its member states. The focus is more than ever on its energy policies and independence. The EU revised the energy strategy in response to Russia’s blackmail, and it sped up all the processes for energetic independence from Russia. This benefited the climate change policies the most, as all the measures involved reducing energy consumption and increasing renewables, thus contributing to reducing greenhouse gas emissions. As a member state of the EU, Romania is committed to complying with EU regulations. With a high degree of energy independence compared with the other EU members, Romania plans to become a regional energy provider and modernize the energy infrastructure internally as a response to Russia’s unprovoked aggression. The measures that the EU and Romania implemented after the war started in 2022 have come to fruition, and the effects are becoming visible a year later. This study aims to study the energy strategy of Romania in correlation with the EU strategy in the turbulent period of pandemics and war between 2019 and 2023, with the latest available data.

Background: Low T3 syndrome occurs frequently in patients with sepsis. Type 3 deiodinase (Dio3) is present in immune cells but there is no description of its presence in this patients. Here we aimed to determine the prognostic impact of thyroid hormones levels (TH) measured on ICU admission on mortality and on evolution to chronic critical illness (CCI) and the presence of D3 in white cells. Methods: Prospective cohort study with a follow-up for 28 days or deceased. Results: Low T3 levels at admission were present in 86.5% of the patients. Dio3 was 55% induced in immune cells. The cut-off value of 60 pg/mL for T3 displayed a sensitivity and specificity of 81% and 64% for predicting death, odds ratio of 4.89. Lower T3 yielded an area under the receiver operating characteristic curve of 0.76 and 0.75 for mortality and evolution to CCI, respectively, thus displaying better performance than commonly used prognostic scores. Conclusions: We advance in the pathophysiology of low T3 showing induced D3 in immune cells during sepsis. Low T3 levels independently predicted a progression to CCI and mortality within 28 days in sepsis and septic shock.

The utilization of artificial intelligence in education can contribute to sustainable development goals in various ways. AI in education can effectively convey sustainability issues to students. Through simulations and virtual environments, learners can experience sustainable practices firsthand. On the other hand artificial intelligence can assist administrators and educators in making data-informed decisions. This allows for more effective shaping of educational policies. This study explores how teachers and artificial intelligence (AI) address ethical dilemmas in edu-cation, comparing their decisions and examining the influence of variables such as gender, years of service, and education level. A total of 141 teachers from public schools in Turkey participated, and their responses were compared to AI-generated decisions using Yin's nested multiple-case de-sign. The scenarios were analyzed through five ethical frameworks: virtue ethics, deontological ethics, utilitarianism, social justice ethics, and situation ethics. Results showed that AI provided the same answer as the majority of teachers in six out of eight dilemmas but differed in two. AI favored a more analytical, result-oriented approach, while teachers emphasized empathy and rule adherence. Gender differences revealed that males leaned towards utilitarianism, while females favored social justice and situation ethics. Less experienced teachers preferred deontological and social justice ethics, while more experienced teachers leaned towards virtue ethics and utilitarian-ism. Elementary teachers emphasized virtue and social justice ethics, while middle and high school teachers favored deontological ethics and utilitarianism. The findings provide insights into ethical decision-making in education by highlighting the differences between solutions proposed by artificial intelligence and those offered by teachers. Additionally, they emphasize the im-portance of adapting artificial intelligence to everyday life to foster a sustainable world.

In this review, our goal is to design and test quantum-like algorithms for Artificial Intelligence (AI) in open systems to structure a human-machine team to be able to reach its maximum performance. Unlike the laboratory, in open systems, teams face complexity, uncertainty and conflict. All task domains have complexity levels, some have low, and others high levels. Complexity in this new domain is affected by the environment and the task, which are both affected by uncertainty and conflict. We contrast the individual and interdependence approaches to teams. The traditional and individual approach focuses on building teams and systems by aggregating the best available information for individuals, their thoughts, behaviors and skills. Its concepts are characterized chiefly by one-to-one relations between mind and body, a summation of disembodied individual mental and physical attributes, and degrees of freedom corresponding to the number of members in a team; however, this approach is characterized by the many researchers who have invested in it for almost a century with few effects that can be generalized to human-machine interactions; by the replication crisis of today (e.g., the invalid scales for self-esteem, implicit racism, and honesty); and by its many disembodied concepts. In contrast, our approach is based on the quantum-like nature of interdependence. It allows us to theorize about the bistability of mind and body, but it proposes a measurement problem and a non-factorable nature. Bistability addresses team structure and performance; the measurement problem solves the replication crisis; and the non-factorable aspect of teams reduces the degrees of freedom and the information derivable from teammates to match findings by the National Academies of Science. We begin with a review of the science of teams and a focus on human-machine team research in the laboratory versus the open; justifications for rejecting traditional social science while supporting our approach; a full understand of the complexity of the domain, tasks in the domain, and how teams address both; the mathematics involved; a review of results from our model versus the open field; a discussion of the results; conclusions; and the path forward to successfully advance the science of interdependence and autonomy.

Background: Olecranon fractures are common injuries of the upper limb in adults. Simple dis-placed trasverse fractures are generally surgically treated with tension-band wiring (TBW) or plate fixation (PF). The purpose of this retrospective study is to compare the clinical-functional outcome, complications and reoperation rates between TBW and PF for Mayo IIA fractures. Methods: 72 patients treated with PF or TBW at our institution, completed our survey and clinical evaluation and their demographic and clinical data were recorded and analysed. The clini-cal-functional outcomes were evaluated assessing ROMs and three validated scoring systems: the Disabilities of the Arm, Shoulder, and Hand (DASH), the Mayo Elbow Performance Score (MEPS) and the Patient American Shoulder and Elbow Surgeons Standardized Elbow Assess-ment score (pASES-e). Results: 38 patients (53%) underwent TBW and 34 (47%) PF. The mean DASH, MEPS and pASES-e scores were respectively 14.5±17.2, 80.5±14.7 and 83.6±12.4 in the TBW group and 21±21.7, 75.6±15.3 and 75.1±19.2 in the PF group (p=0.16, p=0.17 and p=0.03). The mean duration of surgery and hospitalisation period were longer in the PF group (p=0.002, p=0.37) whereas the complication and reoperation rates were higher after TBW (p=0.15, p=0.24). Conclusion: According to the literature, both TBW and PF resulted comparable valid surgical op-tions for the treatment of simple isolated displaced olecranon fractures, revealing that they both provide good/excellent outcomes with no significant differences.

A simple approach to enable water-management agencies employing free data to achieve the goal of using a single set of predictive equations for water-quality retrievals with satisfactory accuracy is proposed. Multiple regression-derived equations based on surface reflectance, band ratios, and environmental factors as predictor variables for concentrations of Total Suspended Solids (TSS), Total Nitrogen (TN), and Total Phosphorus (TP) were derived using a hybrid forward-selection method that considers Variance Inflation Factor (VIF) in the forward-selection process. Landsat TM, ETM+, and OLI/TIRS images were jointly utilized with environmental factors, such as wind speed and water surface temperature, to derive the single set of equations. The coefficients of determination of the best-fitting resultant equations varied from 0.62 to 0.79. Among all chosen predictor variables, ratio of reflectance of visible red (Band 3 for Landsat TM and ETM+, or Band 4 for Landsat OLI/TIRS) to visible blue (Band 1 for Landsat TM and ETM+, or Band 2 for Landsat OLI/TIRS) has a strong influence on the predictive power for TSS retrieval. Environmental factors including wind speed, remote sensing-derived water surface temperature, solar altitude, and time difference (in days) between the image acquisition and water sampling were found important in water-quality parameter estimation.

The Zika virus (ZIKV) is a mosquito-borne Flavivirus and can be transmitted through an infected mosquito bite or through human-to-human interaction by sexual activity, blood transfusion, breastfeeding or perinatal exposure. After the 2015-2016 outbreak in Brazil, a strong link between ZIKV infection and microcephaly emerged. ZIKV specifically targets human neural progenitor cells, suggesting that proteins encoded by ZIKV bind and inactivate host cell proteins leading to microcephaly. Here, we present a systematic annotation of interactions between human proteins and the seven non-structural ZIKV proteins corresponding to a Brazilian isolate. The interaction network was generated by combining tandem-affinity purification followed by mass spectrometry with yeast two-hybrid screens. We identified 150 human proteins, involved in distinct biological processes, as interactors to ZIKV non-structural proteins. Our interacting network is composed of proteins that have been previously associated with microcephaly in human genetic disorders and/or animal models. This study builds on previously published interacting networks of ZIKV and genes related to autosomal recessive primary microcephaly to generate a catalog of human cellular targets of ZIKV proteins implicated in processes related to microcephaly in humans. Collectively, this data can be used as a resource for future characterization of ZIKV infection biology and help create a basis for the discovery of drugs which may disrupt the interaction and reduce the health damage to the fetus.

A construction for the systems of natural and real numbers is presented in Zermelo-Fraenkel Set Thoery, that allows for simple proofs of the properties of these systems, and practical and mathematical applications. A practical application is discussed, in the form of a Simple and Linear Fast Adder (Patent Pending). Applications to finite group theory and analysis are also presented. A method is illustrated for finding the automorphisms of any finite group $G$, which consists of defining a canonical block form for finite groups. Examples are given, to illustrate the procedure for finding all groups of $n$ elements along with their automorphisms. The canonical block form of the symmetry group $\Delta_4$ is provided along with its automorphisms. The construction of natural numbers is naturally generalized to provide a simple and sound construction of the continuum with order and addition properties, and where a real number is an infinite set of natural numbers. A basic outline of analysis is proposed with a fast derivative algorithm. Under this representation, a countable sequence of real numbers is represented by a single real number. Furthermore, an infinite $\infty\times\infty$ real-valued matrix is represented with a single real number. A real function is represented by a set of real numbers, and a countable sequence of real functions is also represented by a set of real numbers. In general, mathematical objects can be represented using the smallest possible data type and these representations are calculable. In the last section, mathematical objects of all types are well assigned to tree structures in a proposed type hierarchy.

We propose a novel moving median estimator specifically designed for online detection of threshold crossings in multi-channel signals, such as extracellular neural recordings. This estimator offers two key advantages: a reduced sensitivity to outliers and the elimination of memory requirements for storing arrival times. Furthermore, its design facilitates parallel implementation on FPGAs, making it ideal for real-time processing of multi-channel recordings.

Energy management systems (EMS) play a central role in improving the performance of microgrids by ensuring their efficient operation while minimizing operational costs and environmental impacts. This paper presents a comprehensive study of a MILP-based EMS developed and implemented in MATLAB using Yalmip software for the energy management of a new positive energy district in the city of Savona, Italy, as part of the Interreg Alpine Space Project ALPGRIDS. The main objective of this research is to optimize the functioning of the microgrid, focusing on cost efficiency and environmental sustainability. In pursuit of this objective, the EMS undergoes comprehensive testing and analysis, replicating actual conditions and addressing the diverse demands of end-users across typical days throughout the year considering real electricity selling and purchase price. The EMS also accounts for the reactive power capabilities of the various technologies integrated into the microgrid. The levelized cost of electricity (LCOE) serves as a metric for assessing curtailment costs, while penalties related to reactive power absorption from the distribution network are appraised in alignment with prevailing regulatory guidelines. The case study provides valuable insights into the practical implementation of EMS technology in microgrids and demonstrates its potential for sustainable energy management in complex urban energy districts. The insights and strategies developed through this research contribute to the overall goals of improving energy efficiency and reducing the environmental footprint of future energy districts.

Species dispersal from a territorial zone is a complex process. The reasons for species dispersal are determined by both natural and human factors. The purpose of this study is to develop a cost surface for a hypothetical landscape that accounts for various species dispersion features. With tigers (Panthera Tigris Tigris) as the focal species, a model for a hypothetical landscape has been built to predict the dispersion patterns of the species' individuals from one habitat patch to another. Initially, four major factors influencing tiger dispersal are explored. Following that, Game Theory assigns a score to each grid in the landscape matrix based on the landscape features in the focal landscape. Specific predefined ratings are also utilized for scenarios that are very complex and may change depending on variables, such as the interaction of the dispersing tiger with co-predators. The two scores mentioned above are combined to create a cost matrix that is shown across a landscape complex to estimate the impact of each landscape component on tiger dispersal. This approach helps wildlife managers develop conservation plans by recognizing important characteristics in the landscape. The results of the model like the one described in this work might be beneficial for a wide range of wildlife management activities such as corridor management, smart patrols, and so on. A cost surface over any focal landscape may serve as a basis for policy and purpose design based on current landscape conditions.

The objective of this paper is to analyze students’ assessments of the attainment of skills acquired by university graduates enrolled in a Training Programme (TP) to develop entrepreneurial skills and transversal employability competencies. Both of which are vital for the success of today's societies and play an important role in the sustainable development agenda. The TP was a twelve-week programme with six modules combining entrepreneurship training and traditional teaching methods. There were nine editions and 148 students worked in teams preparing business plans to present to consultants and potential investors. The Focal Group method was used to identify competencies and a survey was designed to identify the level to which skills were attained. The results suggest that the students attained both types of skills, acquiring a higher level of proficiency in entrepreneurial skills and those transversal competencies most connected to entrepreneurship. Additionally, different levels of attainment of entrepreneurial skills were found in different groups. We provide new evidence that TPs, as compulsory subjects in the core curriculum of degree programmes, will help accomplish both objectives: the acquisition of entrepreneurial skills and generic university educational competencies. The overall outcome of this research highlights the value added by the TPs.

This communication is to summarize our single center experience on applications of anti-IL-1 biologic response modifiers on autoimmune and autoinflammatory conditions in children. We present our rationale for off-label utilization of Anakinra and discuss evolving treatment paradigms that warrants further research and validation. Based on our observations and successful treatment outcomes, we advocate future multilateral efforts to liberate access to anti-IL-1 medications to control inordinate and detrimental inflammation in children.

Ensuring the accuracy of wind power prediction is paramount for the reliable and stable operation of power systems. This study introduces a novel approach aimed at enhancing the precision of wind power prediction through the development of a multiscale hybrid model. This model integrates advanced methodologies including Improved Intrinsic Mode Function with Ensemble Empirical Mode Decomposition with Adaptive Noise (ICEEMDAN), Permutation Entropy(PE), Least Squares Support Vector Regression (LSSVR), Regularized Extreme Learning Machine(RELM), Multi-head Attention (MHA), and Bidirectional Gated Recurrent Unit (BiGRU). Firstly, the ICEEMDAN technique is employed to decompose the non-stationary raw wind power data into multiple relatively stable sub-modes, while concurrently utilizing PE to assess the complexity of each sub-mode. Secondly, the dataset is reconstituted into three distinct components: high-frequency, mid-frequency, and low-frequency, to alleviate data complexity. Following this, the LSSVR, RELM, and MHA-BiGRU models are individually applied to predict the high, mid,and low-frequency components, respectively. Thirdly, the parameters of the low-frequency prediction model are optimized utilizing the Dung Betele Optimizer(DBO) algorithm. Ultimately, the predicted results of each component are aggregated to derive the final prediction. Empirical findings illustrate the exceptional predictive performance of the multiscale hybrid model incorporating LSSVR, RELM,and MHA-BiGRU. In comparison to other benchmark models, the proposed model exhibits a reduction in Root Mean Square Error(RMSE) values of over 10%, conclusively affirming its superior predictive accuracy.

Background: COVID-19, caused by SARS-CoV-2, is still a global public health concern due to the absence of effective antiviral treatment against different strains. Studies have shown that pregnant women are more susceptible to COVID-19 due to altered physiology and immunological features. Therefore, this study was designed to investigate pregnant women's knowledge, attitudes, and practice (KAP) to prevent COVID-19 and determine the factors associated with KAP. Methods: A community-based cross-sectional study was conducted among 425 pregnant women in Northern Bangladesh. The samples were obtained using a simple random sampling technique from April 5 to June 15, 2020. The data were collected by face-to-face survey with a structured and pre-tested questionnaire and analyzed using SPSS version 25. Bivariable and multivariable logistic regression analyses were performed, and p-values < 0.05 at 95% CI were considered statistically significant. Results: Overall, the score of KAP among the respondents was 47.76%, 49.41%, and 56.24%, respectively. Participants' area of residence, educational status of the husband, and antenatal care (ANC) visit were significantly associated with the level of knowledge, whereas age, educational status of the husband, number of living children, and knowledge were significant predictors of attitude. The knowledge of COVID-19 was the only predictor associated with the practice. Conclusion: Our study shows that almost half of the participants had poor knowledge, a negative attitude, and poor practices toward COVID-19. Additional health education programs by healthcare professionals and different media, coordinated and combined efforts of government and individuals' participation will be required to fight the spread of the infection.

In the modern world where research is taking a huge leap, the collaboration network between authors is also expanding, increasing the probability of different authors coming together to work on the same project, same research paper making them co-authors. In coauthorship, link prediction is used to anticipate new interactions between its members that are likely to occur in the future. Researchers have concentrated their efforts on studying and suggesting methods for providing effective reviews for authors who can collaborate on a scientific endeavor. In order to provide a precise link prediction, a graph database approach is proposed in this paper using nodes to determine most possible co-authors in future. In order to forecast the connections, we preprocessed the data set for the maximum relative contents. A supervised learning approach is used to execute the solution, which includes random forest classifier and logistic regression. The first findings of our technique reveal that the total of two author node’s research collaboration indices has the greatest influence on the performance of supervised link prediction than that of the traditional approach, which stimulates us to conduct further study on employing such a forecast.

The spread of the Coronavirus pandemic has been accompanied by an infodemic. The false information that is embedded in the infodemic affects people’s ability to have access to safety information and follow proper procedures to mitigate the risks. This research aims to target the falsehood part of the infodemic, which prominently proliferates in news articles and false medical publications. Here, we present NeoNet, a novel supervised machine learning text mining algorithm that analyzes the content of a document (news article, a medical publication) and assigns a label to it. The algorithm is trained by TFIDF bigram features which contribute a network training model. The algorithm is tested on two different real-world datasets from the CBC news network and Covid-19 publications. In five different fold comparisons, the algorithm predicted a label of an article with a precision of 97-99 %. When compared with prominent algorithms such as Neural Networks, SVM, and Random Forests NeoNet surpassed them. The analysis highlighted the promise of NeoNet in detecting disputed online contents which may contribute negatively to the COVID-19 pandemic.

Remote sensing technology provides a new way to explore the earth for geoscience applications. In the context of continuous deepening of geological research and the vigorous development of geospatial information science, 3D geological modeling technology has become a research hotspot in the intersection of earth science and information science. The traditional 3D geological modeling technology refers to underground 3D modeling using geological data. This modeling method can only display the underground scene in 3D visualization, but cannot display the surface in detail. In this study, remote sensing technology is adopted to improve the traditional 3D geological modeling method, so that geological modeling can be integrated with remote sensing image, and a new 3D geological modeling method based on remote sensing technology is developed. This method can perform integrated 3D visualization of underground and aboveground scenes, which provides a new way for disaster prevention and reduction, geological prospecting and tectonic interpretation. The new 3D geological modeling method is mainly applied in the geological field, and has made new progress in multi-source heterogeneous geological data fusion, large-scale high-precision modeling, geological grid subdivision, attribute modeling technology, remote sensing image fusion and other aspects. Taking the Ya'an area as an example, this paper makes use of the new generation of 3D geological modeling technology to carry out 3D geological modeling and visual display. The 3D visualization in Ya'an area verifies the feasibility and effectiveness of the new 3D geological modeling method based on remote sensing technology in the current data environment.

Nowadays, we live in a world in which people are facing with a lot of data that should be stored or displayed. One of the key methods to control and manage this data refers to grouping and classifying them in clusters. Today, clustering has a critical role in information retrieval methods for organizing large collections inside a few significant clusters. One of the main motivations for the use of clustering is to determine and reveal the hidden and inherent structure of a set of data. Ensemble clustering algorithms combine multiple clustering algorithms to finally reach an overall clustering system. Ensemble clustering methods by lack of information fusing utilize several primary partitions of data to find better ways. Since various clustering algorithms look at the different data points, they can produce various partitions from such data. It is possible to create a partition with high performance by combining the partitions obtained from different algorithms, even if the clusters to be very dense from each other. Most studies in this area have examined all the initial clusters. In this study, a new method is used in which the most sustainable clusters are utilized instead of all primary produced clusters. Consensus function based on co-association matrixes used to select more stable clusters. The most stable clusters selection method is done by cluster stability criterion based on F-measure. Optimization functions are used to optimize the obtained final clusters. The genetic algorithm is the optimizer used in this article to find the ultimate clusters participated in a consensus. Experimental results on several datasets show that the output of proposed method is various clusters with high stability.

The overarching aim of this paper is to investigate the lake ecosystem response to different drivers over a long term period by a paleolimnological study in Lake Narlay (46°64N, 5°91E) located in the Jura Mountains of France. It is a small, hard-water lake with a maximum water depth of 40 m and extended anoxic condition of the bottom water. Previous results on sediments analysis have documented a differential response of the lake to the environmental changes that occurred in AD 1600 when major shift in the trophic reliance on methane of the benthic food web were observed. From 1920 with intensification of modern agriculture, animal farming and the construction of a cheese making facility, the lake become eutrophic, with Oscillatoria rubescens bloom. However, the lake showed pronounced changes in an older period that remained unanswered. In this paper we aim at reconstructing in more detail the limnological conditions of this Lake over the last 1200 yrs. using combined analyses of specific algal carotenoids and subfossil diatom remains. A comparison with other proxies (chironomid, pollen, and instrumental climatic reconstruction) will be used to better identify, between the complex combination of climate and anthropogenic pressure, the driving factors that determined the ecological trajectory of Lake Narlay.

Background Diabetic patients have an increased risk of infections; however, the association between type 2 diabetes and hospital outcomes of sepsis remains controversial when the diabetes severity is not considered. We examined this association using nationwide and hospital-based databases concomitantly. Methods The first part of this study was conducted using 2 nationwide databases: the Longitudinal Cohort of Diabetes Patients and the Longitudinal Health Insurance Database 2000. The diabetic complication burden was evaluated using the adapted Diabetes Complications Severity Index score (aDCSI score). In the second part, we used the hospital-based database with laboratory data, such as initial blood glucose and HbA1c levels, to make comparisons between surviving and dead patients with type 2 diabetes and sepsis. Results The nationwide study included 19,719 type 2 diabetic sepsis patients and an equal number of non-diabetic patients. The diabetic sepsis patients had an increased odds ratio (OR) of 1.14 (95% CI 1.1-1.19) for hospital mortality. The OR for mortality increased as the complication burden increased (diabetic sepsis patients with aDCSI scores of 0, 1, 2, 3, 4, and ≥5 had ORs of 0.91, 0.87, 1.14, 1.25, 1.56, and 1.77 for mortality, respectively (all P<0.001 and P for trend <0.001)). A total of 1,054 diabetic sepsis patients were included from the hospital-based database. Initial blood glucose levels in the surviving and dead diabetic sepsis patients did not differ significantly: 273.9 ± 180.3 versus 266.1 ± 200.2 (mg/dL) (P=0.095). Moreover, the surviving diabetic sepsis patients did not have a lower HbA1c (%): 8.4 ± 2.6 versus 8.0 ± 2.5 (P=0.078). Conclusions In the case of type 2 diabetic sepsis patients, the diabetes-related complication burden is the major determinant of hospital mortality rather than the diabetes itself. Contrary to popular belief, initial blood glucose and HbA1c levels may not be as important as previously thought.

Nickel electrodeposition is a widely utilized method for creating thin films on various substrates with various desirable attributes. Recently, there has been a growing interest in developing nickel composite coatings that incorporate additional elements or particles into the nickel matrix to enhance their properties. These composite coatings offer superior wear resistance, corrosion resistance, hardness, and other functional benefits compared to pure nickel coatings. Some of the recent advancements in electrodeposited nickel composite coatings include improved wear resistance, enhanced mechanical properties and better corrosion resistance. Researchers have discovered that adding Al, Si, Zr, C, W, and Ti particles to nickel coatings can significantly enhance wear resistance. The uniform distribution of these particles within the nickel matrix acts as a barrier to wear and tear. Studies have also shown that nickel composite coatings with Al, Si, Zr, C, W, and Ti particles exhibit superior mechanical properties, including increased hardness. These particles refine the grain size of the nickel matrix and hinder defect movement, leading to greater mechanical strength. Moreover, nickel composite coatings offer improved protection against corrosion compared to pure nickel coatings. This review provides a detail discussion of nickel composite coatings with regard to their comparative advantaged compared to pure nickel coatings on different substrates.

In order to enhance the efficiency and combined performances of nitriding layer, aluminum-modified plasma nitriding (Al-PN) was developed in this research by adding some FeAl particles around the samples during plasma nitriding (PN) for 42CrMo steel. The results show that the efficiency is dramatically enhanced almost 6 times comparing with traditional PN, with the effective hardening layer increasing from 224 μm to 1246 μm at 520℃/4h. More importantly, the compound layer increased just a little bit from 11.64 μm to 14.32 μm, which remarkably reduces the ratio of the compound layer thickness to effective hardening layer thickness, thus being quite beneficial to decrease the brittleness level, making the brittleness level decreasing from Level 4 to Level 1. Meanwhile, ultra-high surface hardness and excellent wear behaviors are obtained by aluminum-modified plasma nitriding (Al-PN) due to the formation of dispersed strengthening phased of AlN and FexAl in the nitriding layer, with the surface hardness increasing from 755 HV0.025 to 1251 HV0.025, wear rate decreasing from 8.15 x10-5g N-1 m-1 to 4.07 x10-5g N-1 m-1. In other words, comparing with PN, the wear resistance has been almost doubled by Al-PN. Therefore, the research can provide comprehensive insights into the surface characteristics and combined performances of the aluminum-modified plasma nitriding layer.

Background: The different disease forms of cutaneous leishmaniasis (CL) are influenced by parasite and host factors. Malnutrition impairs immunity. It has been found to disrupt immune protection by favoring the production of anti-inflammatory prostaglandins over pro-inflammatory cytokines. In Ethiopia, data on CL patients’ nutritional status remains scarce. This study aims to assess clinical polymorphism and its association with the nutritional status of the patient. Methods: A retrospective study conducted from June to July 2024, and patient’s data was analyzed from those treated for CL. Nutritional status was assessed through MUAC, Z-score, and BMI. Results: A total of 217 CL patient’s statuses were nutritionally defined and 22% of them were found malnourished. Most of the CL problems concentrate on age <18 years and found mainly affected groups than others 23 (27%). Malnutrition explained more in MCL (30%) and Repeat/recurrent patients (38.5%) than in LCL (20%) and new CL cases (21%) respectively. In Early disease onset, the malnutrition level was higher than in later times (29%) versus (17%). Conclusions: The burden of Malnutrition is high in CL patients and might modify the clinical variability like CL forms, treatment response, and polymorphism of the disease.

The International Renewable Energy Agency (IRENA) was established in 2009 and in 2024, its membership increased to 168 members plus the European Union countries. The purpose of IRENA is to promote the adoption of renewable energy across all industries in the world. Despite the utilization of renewable energy in sectors like power generation and construction, its application in other industries remains limited. Therefore, a literature review study was conducted to investigate the cost and other spatial constraints associated with renewable energy resources, including modeling and optimization techniques that could offer viable solutions to the future challenges faced by the renewable energy production systems. This paper has summarized the result of this study with a focus on the significance of modeling and optimization techniques in energy conservation to highlight the untapped potential for leveraging renewable energy resources in industrial applications. This paper also examined a few case studies from various regions of the world, demonstrating the promising opportunities for research in the modeling and optimization of renewable energy management systems.

This study investigates the transformative impact of ChatGPT, a cutting-edge AI language model developed by OpenAI, on the financial sector. We explore ChatGPT's applications in financial analysis, focusing on its ability to perform tasks traditionally handled by human analysts. By creating a set of multi-step and advanced reasoning financial tasks, and establishing several task-specific evaluation metrics, our experimental results demonstrate that ChatGPT-4o can effectively perform both basic and some complex financial analysis tasks comparable to those executed by humans. However, our empirical evaluation reveals limitations in its performance on complex tasks that require comprehensive understanding and critical thinking. The study highlights the necessity for meticulous task formulation and robust evaluation to achieve consistent functionality. While ChatGPT enhances efficiency in financial analysis, its integration with human expertise remains crucial for effective decision-making. This study contributes to the understanding of AI's role in finance and provides insights into its potential and constraints, guiding future integration of AI technologies in financial practices.

This research investigates the fatigue behaviour and fracture mechanics of high-performance concrete (HPC), including various compositions such as HPC with basalt aggregates (HPC-B), HPC with gravel (HPC-G), and high-strength coarse mortar (CM) static and cyclic tensile loading within the special priority program SPP 2020. The study aims to integrate fracture mechanics into structural analysis to enhance design guidelines for slender cross-sections and safety-related high-performance structural components. The experimental investigations reveal HPC-B's re-markable superiority, displaying higher compressive strength, modulus of elasticity, and tensile strength compared to HPC-G and CM. A modified disk-shaped compact tension (MDCT) based on ASTM standards, aided by digital image correlation (DIC) unveils fracture behaviour, empha-sizing fracture energy as a crucial parameter. HPC-B exhibits improved crack resistance and notch sensitivity reduction attributed to crushed basalt aggregates and enhanced interfacial transition zone (ITZ). The research scrutinizes factors like material characterization, aggregate morphology, stress levels, and displacement rate on crack formation. High-cycle fatigue tests show HPC-B's superior performance, and post-fatigue analysis reveals enhanced residual fracture toughness attributed to nano-level structural changes, stress redistribution and aggregate-matrix interac-tion. 3D image analysis via Computed Tomography (CT) scans captures mesostructural crack propagation and provide quantitative insights. This study advances the understanding of HPC fatigue behaviour, emphasizing the interplay of aggregate types, morphologies, and their dy-namic response to cyclic loading, offering valuable insights for optimizing design guidelines and fostering innovation in structural engineering.

The construction industry is a resource and energy intensive industry and thus, has been criti-cized due to rising environmental concerns. As a result of that it has gained heightened interest in the concept of Circular Economy (CE) over the last decade to its ability to promote the slowing, reducing, and closing production and consumption cycles of materials and products used in the construction practices. Digital technologies hold potential to enhance the construction industry's ability to integrate the concept of CE in its practices. However, a clear understanding of digital technology (DT) related barriers that hinder practical implementation of CE, appears to be lacking within the sector. In light of this, this study aims to identify the barriers to adopting DTs to im-plement CE practices in the construction industry. To achieve this aim, a systematic literature review was conducted by reviewing twenty-three (23) relevant papers published until January 2024 in Scopus database. The VOS viewer software was used to perform a co-occurrence analysis of keywords to identify new and popular study areas in the field and content analysis was used to analyse the major barriers to adapt DTs to implement CE in the construction industry. The study identified thirty-four (34) barriers to use DTs to implement CE and these were categorized into nine areas such as: infrastructure, organizational, regulatory, standardization, investment, nature of the construction industry, technological, stakeholder and data related barriers. These findings will be beneficial for construction practitioners and policy makers to adopt DTs to integrate CE practices in the construction industry.

The transition to sustainable energy systems in the Middle East and North Africa (MENA) region addresses critical challenges of environmental protection, energy security, and socio-economic resilience. Using neo-institutional theory, this paper examines the intricate relationship between formal and informal institutions and their significant impact on the region’s energy transition. Despite the MENA region’s abundant renewable energy resources, numerous institutional barriers, such as governance inefficiencies, regulatory inconsistencies, and entrenched socio-cultural norms, significantly hinder this transition. Through a review of existing literature, this study presents a comprehensive analytical framework designed to overcome these barriers, with a focus on the Just and Sustainable Energy Transition model. This strategic model emphasizes equity, sustainability, and inclusive governance as pillars for reform. The study offers detailed recommendations for institutional change and underscores the importance of collaborative efforts among governments, industry stakeholders, and international partners to unlock the MENA region’s renewable energy potential, advance global sustainability goals, and ensure a more secure and resilient energy future.

Remote Photoplethysmography (rPPG) has emerged as a non-intrusive and promising physiological sensing capability in HCI research, gradually extending its applications in health-monitoring and clinical care contexts. With advanced machine learning models, recent datasets collected in real-world conditions have gradually enhanced the performance of rPPG methods in recovering heart-rate and heart-rate variability metrics. However, the signal quality of reference ground-truth PPG data in existing datasets is by and large neglected, while poor quality references negatively influence models. Here, this work introduces a new imaging blood volume pulse (iBVP) dataset of synchronized RGB and thermal infrared videos with PPG ground-truth signals from the ear and its high resolution signal quality labels, for the first time. Participants perform rhythmic breathing, head-movement, and stress-inducing tasks, which help reflect real-world variations in psycho-physiological states. This work conducts dense (per sample) signal quality assessment to discard noisy segments of ground-truth and corresponding video frames. We further present a novel end-to-end machine learning framework, iBVPNet that features an efficient and effective spatio-temporal feature aggregation for reliable estimation of BVP signals. Finally, this work examines the feasibility of extracting BVP signals from thermal video frames, which is underexplored. The iBVP dataset and source codes are publicly available for research use.

According to the guidelines of the European Association of Urology (EAU), it is recommended to exclude PSA testing when the expected remaining lifespan is less than 10 years. In this study, we provide a comprehensive analysis of the impact of prostate-specific antigen (PSA) testing on the quality of life of elderly men who are frail. We conducted a comprehensive literature review to identify publications that examine the use of PSA as a screening and prognostic tool for prostatic illnesses in frail men. Our search included the following keywords: Prostate-specific antigen, Frailty, Prostate cancer screening, Hypogonadism, and Benign Prostatic Obstruction (BPO). Prostate-specific antigen (PSA) testing can be used to identify prostate cancer in its early and treatable stages in fragile men with a life expectancy of over 15 years. Additionally, PSA testing allows for the evaluation of the effectiveness of various prostate cancer treatments and the monitoring of testosterone replacement therapy for hypogonadism. PSA is also indicative of the clinical development of patients with benign prostatic obstruction (BPO). Nevertheless, PSA is being extensively provided without individualised patient assessment, contributing to the economic strain on healthcare systems. Conducting a frailty evaluation on men to determine the potential advantages of PSA tests on their quality of life will help minimise avoidable expenses.

Long gone was the time when tumors were thought to be insular masses of cells, residing independently at specific sites in an organ. Now, researchers gradually realize that tumors interact with the extracellular matrix (ECM), blood vessels, connective tissues and immune cells in their environment, which is now known as the tumor microenvironment (TME). It is found that the interactions between tumors and their surrounding promote tumor growth, invasion and metastasis. The dynamics and diversity of TME cause the tumors to be heterogeneous and thus pose a challenge for cancer diagnosis, drug design and therapy. As TME is significant in enhancing tumor progression, it is vital to identify the different components in the TME. This review explores how different factors in the TME supply tumors with the required growth factors and signaling molecules to proliferate, invade and metastasis. We also examine the development of TME-targeted nanotheranostics over the recent years for cancer therapy, diagnosis and anticancer drug delivery system. This review further discusses the limitations and future perspective of nanoparticle based theranostics when used in combination with current imaging modalities like Optical Imaging, Magnetic Resonance Imaging (MRI) and Nuclear Imaging (PET and SPECT).

This paper presents two novel bio-inspired particle swarm optimisation (PSO) variants: biased eavesdropping PSO (BEPSO) and altruistic heterogeneous PSO (AHPSO). These algorithms are inspired by types of group behaviour found in nature that have not previously been exploited in search algorithms. The primary search behaviour of the BEPSO algorithm is inspired by eavesdropping behaviour observed in nature coupled with a cognitive bias mechanism that enables particles to make decisions on cooperation. The second algorithm, AHPSO, conceptualises particles in the swarm as energy-driven agents with bio-inspired altruistic behaviour which allows the formation of lending-borrowing relationships. The mechanisms underlying these algorithms provide new approaches to maintaining swarm diversity which contributes to preventing premature convergence. The new algorithms were tested on the 30, 50 and 100-dimensional CEC'13, CEC'14 and CEC'17 test suites, and various constrained real-world optimisation problems, against 13 well-known PSO variants and the CEC competition winner, differential evolution algorithm L-SHADE. The experimental results show that both algorithms, BEPSO and AHPSO, provide very competitive performance on the unconstrained test suites and the constrained real-world problems. They were significantly better than most other PSO variant on most problem sets and no other comparator algorithm was significantly better than either of them on any of the 50 and 100-d problem sets.

In this paper we describe and exploit a geometric framework for Gibbs probability densities and the associated concepts in statistical mechanics, which unifies several earlier works on the subject, including Souriau's symplectic model of statistical mechanics, its polysymplectic extension, Koszul model, and approaches developed in quantum information geometry. We emphasize the role of equivariance with respect to Lie group actions and the role of several concepts from geometric mechanics, such as momentum maps, Casimir functions, coadjoint orbits, and Lie-Poisson brackets with cocycles, as unifying structures appearing in various applications of this framework to information geometry and machine learning. For instance, we discuss the expression of the Fisher metric in presence of equivariance and we exploit the property of the entropy of the Souriau model as a Casimir function to apply a geometric model for energy preserving entropy production. We illustrate this framework with several examples including multivariate Gaussian probability densities, and the Bogoliubov-Kubo-Mori metric as a quantum version of the Fisher metric for quantum information on coadjoint orbits. We exploit this geometric setting and Lie group equivariance to present symplectic and multisymplectic variational Lie group integration schemes for some of the equations associated to Souriau symplectic and polysymplectic models, such as the Lie-Poisson equation with cocycle.

Objectives: Although the prevalence of metabolic syndrome (MetS) has increased among Koreans, the specific health benefits of the Korean Healthy Diet (KHD) score remain unexplored. This study aimed to evaluate the association between the KHD score and MetS, and to identify the optimal cutoff of the KHD score for reducing MetS prevalence. Methods: This cross-sectional study used data from 11,403 participants of the 7th and 8th Korea National Health and Nutrition Examination Survey. The KHD score was calculated based on adherence to 13 dietary components. Logistic regression analysis was used to determine the association between the KHD score and MetS, and to identify the optimal cutoff values for the KHD score. Results: The average KHD score was significantly lower in participants with MetS than in those without MetS (5.03 vs. 5.14, p=0.016). A one- and two-point increase in the KHD score was associated with reduction in MetS prevalence (one-point, odds ratio [OR]: 0.95, 95% confidence interval [CI]: 0.91–0.98; two-point, OR: 0.90, 95% CI: 0.84–0.97). The optimal cutoff for the KHD score was identified as &gt;7 points, which was related to lower odds of MetS (OR: 0.78, 95% CI: 0.65–0.93) compared to 1–4 points, particularly with hypertriglyceridemia (OR: 0.80, 95% CI: 0.69–0.94) and low high-density lipoprotein cholesterol (OR: 0.85, 95% CI: 0.74–0.98). Conclusions: The KHD score was inversely associated with MetS prevalence, and the identified optimal cutoff values can serve as a practical tool for public health interventions aimed at reducing the risk of MetS.

TmMgB5O10 spontaneous crystals were synthesized via flux-growth technique from a K2Mo3O10-based solvent. The crystal structure of the compound was solved and refined within the space group P21/n. The first principles calculations of the electronic structure reveal that TmMg-pentaborate is an insulator with an indirect energy band gap of approximately 6.37 eV. Differential scanning calorimetry measurements and powder X-ray diffraction studies of heat-treated solids show that TmMgB5O10 is an incongruent melting compound. A characteristic band of Tm3+ cation corresponding to the 3H6 → 1D2 transition is observed in the photoluminescence excitation spectra of TmMg-borate. The as-obtained crystals exhibit intense blue emission with the emission peaks centered at 455, 479, 667 and 753 nm. The most intensive band corresponds to the 1D2 3F4 transition.

Omega-3 polyunsaturated fatty acids (ω-3 PUFAs, n-3 PUFAs), including eicosapentaenoic acid (EPA), docosahexaenoic acid (DHA), and alpha-linolenic acid (ALA), are essential polyunsatu-rated fats primarily obtained from fatty fish and plant-based sources. Compelling evidence from preclinical and epidemiological studies consistently suggests beneficial effects of ω-3 PUFAs on bone health and healthy aging processes. However, clinical trials have yielded mixed results, with some failing to replicate these benefits seen in preclinical models. This contraindication is mainly due to challenges such as low bioavailability, potential adverse effects with higher doses, and susceptibility to oxidation of ω-3 fatty acids, hindering their clinical effectiveness. This review provides an in-depth analysis of recent findings, focusing on clinical perspectives and encom-passing all relevant published studies to date, including preclinical and epidemiological research, with an emphasis on the role of omega-3 fatty acids in promoting bone health and supporting healthy aging. Additionally, it explores strategies to improve ω-3 PUFA efficacy, including na-noparticle encapsulation and incorporation of specialized pro-resolving mediators (SPM) derived from DHA and EPA, to mitigate oxidation and enhance solubility, thereby improving therapeutic potential. By consolidating evidence from various studies, this review underscores current in-sights and future directions in leveraging ω-3 PUFAs for therapeutic applications.