TThe following three objectives were tested in this study: (1) investigate the utility of low-altitude remote sensing using UAS technology to compare the effects of different N application systems in rice production; (2) use spatial extrapolation to scale up plot-level generated to farmer field rice yield data based on crop spectral signatures, and (3) predict and map out rice productivity as a function of N placement systems. Images were captured on a UAV platform at midseason of the rice crop. Orthomosaics were developed for selected fields in rice-producing zones. Grain yields were assessed from low, medium, and high crop health plots delineated based on NDVI values. On the plot scale, UDP outyielded non-UDP by 0.84%. Individual plot yield data were scaled up to the farmer field level through Jenks natural breaks classification and es-tablishing an empirical relationship between OSAVI and plot yields. Assessment of the scaled-up field levelfield-level data also confirmed the superiority of UDP N man-agement over the non-UDP systems in promoting rice yields. Scaling up plot scale da-ta to whole field levels also facilitated generating and mapping expected yield maps for individual farmer fields in the three zones studied. This study has established a tangible simple but tangible protocol protocol for predicting and mapping rice yields in small-scale farmer fields using UAS data.

Image segmentation is the process of partitioning an image into multiple segments where the goal is to simplify the representation of the image and make the image more meaningful and easier to analyze. In particular, semantic segmentation is an approach of detecting the classes of objects, based on each pixel. In the past, most semantic segmentation models were for only one single style, such as urban street views, medical images, or even Manga. In this paper, we proposed a semantic segmentation model called Dual-Pyramid Wide Residual Network (DPWRN) to solve the segmentation for cross-style datasets, which is suitable for diverse segmentation applications. The DPWRN integrated the Pyramid of Kernel paralleled with Dilation (PKD) and Multi-Feature Fusion (MFF) to improve the accuracy of segmentation. To evaluate the generalization of the DPWRN and its superiority over most state-of-the-art models, three datasets with completely different styles are tested in the experiments. Finally, the DPWRN verified its generalization and also showed its superiority over most state-of-the-art models.

By the end of 2023, biowaste must be completely separated or recycled at source based on the EU legislation. Separate biowaste collection and valorisation towards biofuels could play an essential role in the biobased circular economy. In this context, the principal goal of this paper was to demonstrate in pilot scale the technological solution of bioethanol production via the utilisation of urban source-separated biowaste within the city context of Athens, Greece. More specifically, the main aim was the demonstration of a pilot system in continuous operation (over 10 operating cycles) with real feedstock – wet separately collected biowaste. From the 11 pilot trials performed with wet feedstock, the mean starch and cellulose degradation of the pilot trials amounted up to 80.69±16.27% and 79.41±10.37% respectively, while the bioethanol yield was 74.05±6.82%. The latter was comparable to that of more intensive pretreatment methods. Homogenization and shredding that was applied in this study, stand as promising and cost-effective pretreatment methods for bioethanol production from wet feedstock. Further research is needed to optimize conditions and evaluate scalability. Nevertheless, pilot scale testing is a crucial step in the development and deployment of this technology since it serves as a bridge between laboratory research and full-scale implementation, offering a practical and controlled environment to validate and optimize the technology while minimizing risks and uncertainties.

This paper deals with the analysis of the cognitive functions of the cerebral cortex or cortex in the evolutionary context. The triune brain model is presented given its notorious popularity in fields that are not strictly neuroscientific, and its limitations and inaccuracies are also shown. Although it is an outdated theory, this one represented the passage to a new era, because it really was the model of the movement towards understanding the brain in evolutionary terms. Next, the paper illustrates the different areas of the cortex and their functions, as well as the evolution of the brain and the cortex in vertebrates. To date, the expansion of the cortex with the amniotes was placed 300 million years ago, although recent studies place it in the lampreys more than 500 million years ago. Evolutionary changes in mammalian brain and cortex size are discussed, showing the evolution from three to six cell layers and trends in different groups of mammals. Finally, the most recent contributions in the field of research are illustrated, such as the role of neurogenesis and the evolution of the connectivity of the cortex.

In recent decades, porous scaffolds for bone tissue engineering (BTE) have gained significant attention. Considerable research efforts have been dedicated to investigating the impact of scaffold pore size on a diverse array of biological processes, including cell-scaffold interactions, substance transportation, and vascularization. However, the multi-scale hierarchical porosity, a key structural characteristic of natural bone, has rarely been replicated in BTE scaffolds due to the challenges of controlling the scaffold structure across multiple length scales. With the advancement of manufacturing technology, there has been increasing research on biomimetic multi-scale hierarchical materials, which are also gaining favor in the field of BTE. Therefore, there is an urgent need to review multi-scale hierarchical porous BTE scaffolds. This paper aims to review the role and fabrication methods of multi-scale porous BTE scaffolds at different length scales, the design and manufacturing of new BTE scaffolds for bone regeneration.

Based on the high-precision quantitative measurement ability, the S-band subsystem of China's high- resolution aviation system carried out the agricultural application test for the first time in Jilin elm, China. The experiment is mainly carried out in combination with vegetation growth, soil moisture content inversion, wheat straw returning and ground feature classification. Through the design of quantitative measurement scheme, the results of different straw coverage and observation at different times are compared, which shows the prospect of S-band full polarization synthetic aperture radar system in the field of agricultural application.

: Medial temporal lobe atrophy (MTA) remains an important imaging biomarker in brain magnetic resonance imaging (MRI) for diagnosing and evaluating Alzheimer's disease (AD). We compared the detection performance for determining significant MTA and the correlation with Scheltens' scale between two automatic volumetry software, NeuroQuant® and Aqua®, in non-AD and AD groups. Overall, 127 (non-AD, 54; AD, 73) individuals who underwent three-dimensional T1-weighted volumetric MRI in our hospital between January and August 2011 were enrolled and retrospectively analyzed using NeuroQuant® and Aqua®. The accuracy, sensitivity, and specificity for detecting significant MTA and the correlation for Scheltens' scale with the mean hippocampal volume of each software were evaluated. The study sample’s mean age was 78 (range, 59–97) years. Regarding accuracy and specificity, Aqua® was non-inferior to NeuroQuant®. Aqua® had significantly higher overall specificity than NeuroQuant® for detecting significant MTA. Additionally, the calculated mean hippocampal volume of Aqua® was more correlated with Scheltens' scale (Kendall's τ, - 0.48) than that of NeuroQuant® (Kendall's τ, - 0.20). The accuracy and specificity of Aqua® in detecting MTA are not inferior to those of NeuroQuant®. Additionally, Aqua® has a stronger correlation of mean hippocampal volume with Scheltens' scale than NeuroQuant®.

The duality is one of the most interesting properties of the Laplace and Fourier transforms associated to the integer order derivative. Here, we will generalize it for fractional derivatives and extend the results to the Mellin, Z and discrete-time Fourier transforms. The scale and nabla derivatives are used.

COVID-19 has rapidly spread worldwide since December 2019. During the pandemic, SARS-CoV-2 repeatedly mutated from Alpha to Omicron. The severity and mortality are related to age, sex, and underlying diseases (respiratory, cerebrovascular, cardiovascular, metabolic, cancer, and immune diseases). We retrospectively studied 44 hospitalized stroke patients infected with the SARS-CoV-2 Omicron variant. The mortality rate of hospitalized stroke patients with COVID-19 was 6 (13.6%). The cause of death was coagulopathy in 3 cases of ischemic stroke. High modified Rankin Scale score（mRS）of stroke patient was correlated with severity of COVID-19. The timing of vaccination is inversely correlated with COVID-19 severity. In conclusion, stroke patients with COVID-19 have high mortality rates attributed to coagulopathy. Stroke patients with high mRS scores are more likely to develop severe COVID-19. In high mRS scores, therapy for severe COVID-19 should be administered.

Underwater robotics equipped with visual detection systems have the function of detecting un-derwater artifacts, which is of great significance to deep-sea archaeology. Underwater artifacts are located in complex environments with poor imaging conditions, and the targets have chal-lenging problems such as breakage, stacking, and sediment burial, which causes the marine cul-tural target detection failures. To solve these problems, this paper proposes an underwater cul-tural target detection algorithm based on the deformable deep aggregation network model for underwater robotics exploration. To fully extract the target feature information of underwater targets in complex environments, this paper designs a multi-scale deep aggregation network with deformable convolutional layers. Besides, the BAM attention module is designed for fea-ture optimization, which enhances the potential feature information of the target while weak-ening the background interference information. Finally, the target prediction is achieved through feature fusion at different scales. The proposed algorithm has been extensively validat-ed and analyzed on the collected underwater artifact datasets, and the precision, recall, and mAP of the algorithm have reached 93.1%, 91.4%, and 92.8%, respectively. In addition, the present al-gorithm has been practically deployed on the underwater robotics. In the deep-sea tests, the ar-tifact detection frame rate reaches up to 19 fps, which satisfies the real-time target detection。

Small-scale facilities play a significant role in the landscape of nuclear physics research in Europe. They address a wide range of fundamental questions and are essential for teaching and training personnel in accelerator technology and science, providing them with diverse skill sets, complementary to large projects. The current status and perspectives of nuclear physics research at small-scale facilities in Europe will be given.

Women's empowerment represents a process in which they are recognized as subjects of rights. Despite advances in women's rights, inequalities persist in various areas. This study aims to validate a psychometric scale of 31 items in seven factors: Participatory Empowerment, Temerity, External Influences, Independence, Social Satisfaction and Equality. The objective is to measure the empowerment of women in the university context. The research was carried out in a transversal and non-experimental manner, with the participants' voluntary collaboration, including the participation of 1,478 university students from four Higher Education Institutions in Zone 4 of Ecuador. The data was analyzed through inferential statistics, including exploratory and confirmatory factor analysis. The results indicated that the instrument for measuring empowerment can be made up of 24 items grouped into six factors, and this model showed a good fit.

The grinding process by friction at the micro-scale in a mill with stones is considered a variable combination of contacts: with two-body (the asperities of lower millstone in direct contact with the asperities of upper millstone) and the three-body (micro-particles of ground seeds trapped between the asperities of lower and the upper stones of the mill). Three elements are described: (1) the mechanic contact of the asperities of the lower and upper millstone, to predict pressures on asperities, by modeling; (2) tests on a millstone sample covered with grinding particles, and (3) tests on a wafer sample formed by the millstones with the grinding particles between them. The paper highlights the combined effects of the micro-scale friction by individual measurements and to sum these effects an analytical model was used, and to validate the model, several experiments were performed. A suitable grind by friction assumes the grain’s movement and the interaction between the seeds and solid surfaces and is highlighted through theoretical and experimental studies. Topography analysis of the surface of the millstones revealed the model of microscopic frictional force. Endpoint measurements (the traces of the surface topography evolution), enable model verification in the grinding process.

Background: Patients with allergic rhinitis (AR) have bothersome symptoms that may assess using the visual analog scale (VAS). Asthma frequently is associated with AR. The present study investigated the role of VAS in children with newly-diagnosed AR. Methods: Parameters were VAS for nasal and asthma symptoms, and doctor’s perception of these diseases, medication use, and asthma comorbidity. Results: 67% of AR children had asthma. Severity of perceived nasal symptoms was associated with more intense asthma symptoms and doctor’s perception of disease severity (p<0.001 and p=0.02, re-spectively. Conclusions: Allergic rhinitis frequently is associated with asthma in child-hood. Symptom perception assessment using VAS is a simple tool for assessing their intensity. Moreover, the severity of nasal symptoms is associated with asthma symptom severity. Therefore, asthma should be carefully considered in AR children and require appropriate symptom perception assessment.

Simple weir irrigation was recently introduced in rural areas of Zambia. This irrigation technique is based on gravity diversion of the river flow using simple weirs. This study was conducted between November 2022 to January 2023 in 3 northern region provinces (Luapula, Copperbelt, and North-western) of Zambia to perform a diagnostic assessment on simple weirs during the dry and the wet seasons. In this study, 15 single-line simple weirs were selected. The objective of the study is to assess the physical status, identify problems and extent, and estimate water diversion potential by each single-line weir. The findings of this study showed that 67% of weirs were in excellent status (recently maintained and repaired), and 26% of the weirs were partially damaged (leakages, decayed thatch grass). The study revealed that single-line weirs were able to raise a flood height of 0.1m at low flow and 0.5m at medium flow. It was found that single-line weirs in excellent condition diverted more than 90% of the river flow while poorly maintained weirs diverted below 45% of the river discharge. Based on the findings, Single-line weirs have the potential to assist small-scale farmers in accessing river water for irrigation purposes through diversion.

Keywords: bariatric surgery, sleeve gastrectomy, Roux-en-y gastric bypass, obesity, microbiota, gut-brain axis, beck scale, depression, diet, probiotics

Background: Pro-golfers are increasingly trained scientifically, but swing technique characteristics have not yet been conclusively determined for golf performance. We investigated the relationship between the player's thorax, pelvis and club based on a wireless biofeedback system. We also determined whether thoracic and pelvic changes during different swing phases have different effects on ball striking. Methods: Eight male professionals performed 10 successful full-swing shots using a 5-iron and applied the K-vest wireless biofeedback system for analysis. Results: Peak angular velocities between the pelvis, torso, and clubs were all significantly different (p&lt;0.05) in the men's professional players. All players deviated more from the PGA players range metrics during the preparation stance. Excessive lateral pelvic bending angle, over-rotation, and hyperextension of the trunk were prevalent at the top of the backswing. Conclusions: Most players have an irrational sequence of pelvic, trunk and club rotation times, which is not conducive to improving club head speed and distance. Excessive pelvic rotation in the closed direction at the apex of the backswing is not conducive to maximizing the X-factor. At the moment of striking the ball, maintain a moderate forward trunk lean and full trunk rotation to avoid sports injuries.

In recent years, the high-frequency abnormal price fluctuations of small-scale agricultural products have been detrimental to market stability. The traditional method based on statistical data of production factors can no longer accurately predict abnormal price fluctuations. Taking scallions as a case, the paper identifies price influencing factors from massive network information, and analyzes the causes and changing characteristics of price fluctuations from four perspectives: market supply, economic environment and market sentiment, and attention. Initially, the study employs an LDA topic model to extract factors from online sentiment data over the past four years, including market supply and demand, economic conditions, related agricultural prices, and market attention. Subsequently, using the SO-PMI algorithm to expand domain-specific lexicons and employing semantic and sentiment analysis with syntactic parsing, the study aims to improve the accuracy of sentiment quantification in text analysis. Furthermore, cointegration and Granger causality tests validate the significant impact of these factors on scallion price fluctuations, mitigating spurious regression issues. Finally, by employing a TVP-VAR model, the study compares the impulse responses and shock magnitudes of various factors in different time and spatial dimensions. It also delves into the mechanisms of heterogeneous impacts and trends in scallion prices, considering the corresponding socio-economic context and public sentiment events within specific time windows, thereby providing valuable decision-making insights for the healthy development of agricultural markets.

Deep networks-based hashing has gained significant popularity in recent years, particularly in the field of image retrieval. However, most existing methods only focus on extracting semantic information from the final layer, disregarding valuable structural information that contains important semantic details crucial for effective hash learning. To address this limitation and improve image retrieval accuracy, we propose a novel deep hashing method called Deep Supervised Hashing by Fusing Multiscale Deep Features (DSHFMDF). Our approach involves extracting multiscale features from multiple convolutional layers and fusing them to generate more robust representations for efficient image retrieval. Experimental results on CIFAR10 and NUS-WIDE datasets demonstrate that our method surpasses the performance of state-of-the-art hashing techniques.

We studied Ni2FeSi, Co2FeSi and Co2MnSi -based full Heusler alloy glass-coated microwires with the same geometric aspect ratio ρ ≈ 0.50 ± 0.01 prepared using Taylor-Ulitovsky method. The fabrication of X2YZ; (X = Co &amp; Ni, Y = Fe &amp; Mn and Z = Si) based glass coated microwires with fixed aspect ratio is a quite challenging due to the different samples preparation conditions. The XRD analysis shows nanocrystalline microstructure for all the samples. Space group Fm3¯m (FCC) with disordered B2 and A2-types for Ni2FeSi and Co2FeSi and ordered L21-type for Co2MnSi are observed. The change in the positions of Ni, Co and Mn, Fe in X2YSi resulted in a variation in the lattice cell parameters and the average grain size of the sample. Room temperature magnetic behavior shows a dramatic change depending on the chemical composition, where Ni2FeSi-MWs shows the highest coercivity (Hc) compared to Co2FeSi-MWS and Co2MnSi-MWs. The Hc –value of Ni2FeSi-MWs is 16 times higher than that of Co2MnSi-MWs and 3 times higher than that of Co2FeSi-MWS. Meanwhile, the highest reduced remanence is reported for Co2FeSi-MWS (Mr =0.92), , being about 0.33 and 0.22 for Ni2FeSi-MWs and Co2MnSi-MWs, respectively. From the analysis of the temperature dependence of the magnetic properties (Hc and Mr) of X2YZ-MWs we deduced that Hc shows a stable tendency for Co2MnSi-MWs and Co2FeSi-MWs, meanwhile two flipped points are observed for Ni2FeSi-MWs where the behavior of Hc changes with temperature. For Mr monotonic increase by decreasing the temperature is observed for Co2FeSi-MWs and Ni2FeSi-MWs and roughly stable for Co2MnSi-MWs. The thermomagnetic curves at low magnetic field show irreversible magnetic behavior for Co2MnSi-MWs and Co2FeSi-MWs and regular ferromagnetic behavior for Ni2FeSi-MWs. The current result illustrates the ability to tailor the structure and magnetic behavior of X2YZ-MWs at a fixed internal stresses, i.e., with fixed aspect ratio. Additionally, a different behavior was revealed in X2YZ-MWs depending on the degree of ordering and elements distribution. The tunability of magnetic properties of X2YZ-MWs makes them suitable for sensing applications.

Digital Image Correlation (DIC) is an optical technique used to measure surface displacements and strains in materials and structures. This technique has demonstrated significant utility in structural examination and monitoring. This manuscript offers a comprehensive review of contemporary research and applications that have leveraged the DIC technique in laboratory-based structural tests. The reviewed works encompass a broad spectrum of structural components, such as concrete beams, columns, pillars, masonry walls, infills, composite materials, structural joints, steel beams, slabs, and other structural elements. These investigations have underscored the efficacy of DIC as a metrological instrument for precise quantification of surface deformation and strain in these structural components. Moreover, the constraints of the DIC technique have been highlighted, especially in scenarios involving extensive or complex test configurations. Notwithstanding these constraints, the DIC methodology has validated its effectiveness as a strain measurement instrument, offering numerous benefits such as non-invasive operation, full-field measurement capability, high precision, real-time surveillance, and compatibility with integration into other measurement instruments and methodologies.

Background: The potential impact of the pandemic on mental health should not be overlooked, especially among vulnerable populations such as women who gave birth during the COVID-19 pandemic. The aim of the study was to assess the mental health status of women who gave birth during a pandemic and to examine the possible links between the onset of depressive disorder symptoms in close proximity to the time of delivery and the socio-demographic conditions, health status and obstetric circumstances of the patients. Materials and Methods: The study is a cross-sectional survey conducted from 01.03.2020 to 01.03.2023, during the period of SARS-CoV-2 (COVID-19) pandemic, based on a retrospective evaluation of 860 postpartum women. The screening tool used to assess symptoms of postpartum depression was the Edinburgh Postnatal Depression Rating Scale (EPDS) questionnaire. The questionnaire was completed both in the Obstetrics and Gynaecology Clinical Sections I and II of the "Pius Brînzeu" County Emergency Hospital from Timisoara, Romania and online using Google Forms. Results: The highest severity of postpartum depression symptoms was observed during the COVID-19 pandemic. The results of the study conducted during the period of SARS-CoV-2 pandemic (COVID-19) showed that the prevalence of major postpartum depressive disorder (EPDS ≥ 13) was 54.2% (466 patients), while 15.6% (134) had minor depressive disorder (10 &lt; EPDS ≤ 12) in the first year after delivery. Comparing these results with those obtained in research conducted before the onset of the pandemic period shows an alarming increase in the prevalence of postpartum depression. Risk factors associated with postpartum depression included type of delivery, level of education, socio-economic conditions, health status, age, background, personal obstetric history (number of abortions on demand, parity). Conclusions: The effects of the pandemic on mental health are of particular concern for women in the first year after childbirth. Observing these challenges and developing effective measures to prepare our health system early can help and facilitate effective mental health screening for postpartum women, promoting maternal and child health.

Recent advances in understanding the Planck scale have led to a new, straightforward method for quantizing the general theory of relativity. This results in a theory of gravity that predicts the same outcomes as general relativity with greater fundamental comprehension connected directly to the Planck scale.

Background: Ultrasonography (US) represents the gold standard imaging method for the assessment of testicular lesions (TL). The gray-scale (GSUS) and color-Doppler (CDUS) ultrasound examination allow sonographers to investigate size, margins, echotexture and vascular features of TL, with the effort to differentiate benign from malignant lesions. Recently, the use of contrast-enhanced US (CEUS) and sonoelastography (SE) led to further improvements in the differential diagnosis of TL. Although GSUS and CDUS are often sufficient to suggest the benign or malignant nature of TL, CEUS can be decisive in the differential diagnosis of unclear findings, while SE can help to strengthen the diagnosis. The contemporary combination of GSUS, CDUS, CEUS and SE led to a new diagnostic paradigm named multiparametric US (mp-US), able to provide a more detailed characterization of TL than the single techniques alone. This narrative and pictorial review is aimed to describe the mp-US appearance of several TL. Methods: An extensive Medline search was performed identifying studies in the English language focusing on mp-US evaluation of TL. Results: a practical mp-US “identity card” and iconographic characterization of several benign and malignant TL is provided herein. Conclusions: the mp-US characterization of TL reported herein can be useful in daily clinical practice.

The work presents a mathematical model of a thermogravimetric curve for the growth and simultaneous sublimation of scale on a metal or alloy surface. Reduction of the reaction area as a result of the formation of oxides of alloying elements (for obtaining the basic oxide) is considered for the alloy. For metals, the case where this area is increased as a result of powder metal crushing is also considered. The equations that were obtained are employed to explain the kinetic curves of the mass change of the samples.

This paper presents a two-stage hierarchical neural network using image classification and object detection algorithms as key building blocks for a system that automatically detects a potential design right infringement. This neural network is trained to return the Top-N original design right records that highly resemble the input image of a counterfeit. Design rights specify the unique aesthetic characteristics of a product. Due to the rapid change of trends, new design rights are continuously generated. This work proposes an Ensemble Neural Network (ENN), an artificial neural network model that aims to deal with a large amount of counterfeit data and design right records that are frequently added and deleted. At first, we performed image classification and objection detection learning per design right using the existing models with a proven track record of high accuracy. The distributed models form the backbone of the ENN and yield intermediate results aggregated at a master neural network. This master neural network is a deep residual network paired with a fully connected network. This ensemble layer is trained to determine the sub-models that return the best result for a given input image of a product. In the final stage, the ENN model multiples the inferred similarity coefficients to the weighted input vectors produced by the individual sub-models to assess the similarity between the test input image and the existing product design rights to see any sign of violation. Given 84 design rights and the sample product images taken meticulously under various conditions, our ENN model achieved average Top-1 and Top-3 accuracies of 98.409% and 99.460%, respectively. Upon introducing new design rights data, a partial update of the inference model was done an order of magnitude faster than the single model. ENN maintained a high level of accuracy as it scaled out to handle more design rights. Therefore, the ENN model is expected to offer practical help to the inspectors in the field, such as the customs at the border that deal with a swarm of products.

Taiwan is highly susceptible to global warming, experiencing a 1.4°C increase in air temperatures from 1911-2005, which is twice the average for the Northern Hemisphere. This has led to higher rates of respiratory and cardiovascular mortality. Accurately predicting maximum temperatures during the summer season is crucial, but numerical weather models become less accurate and more uncertain beyond five days. To improve forecast reliability, statistical post-processing is needed to address systematic errors. In September 2020, NOAA NCEP implemented the Global Ensemble Forecast System version 12 (GEFSv12) to help manage climate risks. This study developed a Hybrid statistical post-processing method that combines Artificial Neural Networks (ANN) and Quantile mapping (QQ) approaches to predict daily maximum temperatures and extremes in Taiwan during the summer season. The Hybrid technique, utilizing deep learning techniques, was applied to the GEFSv12 reforecast data and evaluated against ERA5 reanalysis. The Hybrid technique was the most effective among the three techniques tested. It had the lowest bias, RMSE, and highest correlation coefficient. It successfully reduced the warm bias and overestimation of Tmax extreme days. This led to improved prediction skills for all forecast lead times. Compared to ANN and QQ, the Hybrid method was more effective in predicting summer daily Tmax and its extremes on an extended-range time scale deterministic and ensemble probabilistic forecasts over Taiwan.

The performance of bearings plays a pivotal role in determining the dependability and security of rotating machinery. In intricate systems demanding exceptional reliability and safety, the ability to accurately forecast fault occurrences during operation holds profound significance. Such predictions serve as invaluable guides for crafting well-considered reliability strategies and executing maintenance practices aimed at enhancing reliability. In order to ensure the reliability of bearing operation, this article investigates the application of three advanced techniques—Maximum Correlation Kurtosis Deconvolution (MCKD), Multi-Scale Permutation Entropy (MPE), and Long Short-Term Memory (LSTM) recurrent neural networks—for the prediction of the remaining useful life (RUL) of rolling bearings. Each technique's principles, methodologies, and applications are comprehensively reviewed, offering insights into their respective strengths and limitations. Case studies and experimental evaluations are presented to assess their performance in RUL prediction. Findings reveal that MCKD enhances fault signatures, MPE captures complexity, and LSTM excels in modeling temporal patterns. The root mean square error of the prediction results is 0.007. The fusion of these techniques offers a comprehensive approach to RUL prediction, leveraging their unique attributes for more accurate and reliable predictions.

The sustainability of agroecological systems, biodiversity protection, animal welfare and consumer demand for higher-quality products from alternative and extensive farming methods, have reinforced interest in local breeds that are well adapted to low-input environments. However, food safety needs to be safeguarded to reinforce consumer confidence. The aim of this study was to conduct a preliminary investigation on the occurrence of Salmonella spp. in eggshells, hen’s cloaca and litter materials from autochthonous Portuguese laying hens raised in a semi-extensive system for small-scale production. A total of 279 samples were obtained, with 63 samples from the “Preta Lusitânica” breed, and 72 samples each from the remaining autochthonous breeds, namely “Branca”, “Amarela” and “Pedrês Portuguesa”. None (0%) of the samples analyzed were positive for Salmonella spp.. To the best of our knowledge, these are the first results of Salmonella evaluation from hen’s cloaca, eggshells, and litter materials in autochthonous Portuguese chickens, suggesting that a semi-extensive production system can contribute to better food security and a lower risk to public health and the environment.

The 3D/3D+ multidimensional geriatric assessment tool provides an optimal model of emergency care for patients aged 75 and over who attend the Emergency Department (ED). The baseline, or static, component (3D) stratifies the degree of frailty prior to the acute illness, while the current, or dynamic, component (3D+) assesses the multidimensional impact caused by the acute illness and helps to guide the choice of care facility for patients upon their discharge from the ED. The objective of this study was to evaluate the prognostic value of the 3D/3D+ to predict short- and long-term adverse outcomes in ED patients aged 75 years and older. Multivariable logistic regression models were used to identify the predictors of mortality 30 days after 3D/3D+ assessment. Two hundred and seventy-eight patients (59.7% women) with a median age of 86 years (interquartile range: 83-90) were analysed. According to the baseline component (3D), 83.1% (95%CI: 78.2-87.3) presented some degree of frailty. The current component (3D+) presented alterations in 60.1% (95%CI: 54.1-65.9). The choice of care facility at ED discharge indicated by the 3D/3D+ was considered appropriate in 96.4% (95%CI: 93.0-98.0). Thirty-day all-cause mortality was 19.4%. Delirium and functional decline were the dimensions on the 3D/3D+ that were independently associated with 30-day mortality. These two dimensions had an area under receiver operating characteristic of 0.80 (95%CI: 0.73-0.86) for predicting 30-day mortality. 3D/3D+ enhances the provision of comprehensive care by ED professionals, guides them in the choice of patients’ discharge destination, and has a prognostic validity that serves to establish future therapeutic objectives.

Revealing the multidimensional value of cultivated land resources, improving calculation methods, and exploring their spatial distribution characteristics and influencing factors are crucial for the enhancement of natural resource accounting system and the protection of cultivated land resources. This study take Guangxi, China as an example and construct a comprehensive multidimensional value evaluation system for cultivated land resources at the county scale. The income capitalization method, substitution market method, and value equivalent correction method were applied to calculate the economic, social, and ecological values of cultivated land resources in 111 counties. Furthermore, spatial autocorrelation analysis was employed to study their distribution characteristics and influencing factors. The results show that: (1) The total value of cultivated land resources in Guangxi reach 19,729.2 billion yuan, and the average cultivated land value is 5.3511 million yuan/hm2, of which the ratio of economic, social and ecological values is about 6:1:2; (2) The value of cultivated land has a certain distribution pattern in the global space, and the average economic and social value show the spatial distribution patterns of "low in the west and high in the east" and "low in the northwest and high in the southeast" respectively."(3) There is a significant spatial clustering effect of all values of cultivated land resources in local space, with "high-high" and "low-low" clustering. (4) Economic development level is significantly negatively correlated with the ecological value of cultivated land, while has a significant positive correlation with social value; (5) Natural conditions, economic development and agricultural development approaches are the key factors affecting the value of cultivated land resources.

We carry out in a thin heterogeneous porous layer, the multiscale analysis of Smoluchowski's discrete diffusion-coagulation equations describing the evolution density of diffusing particles that are subject to coagulate in pairs. Assuming that the thin heterogeneous layer is made of microstructures that are uniformly distributed inside, we obtain in the limit an upscaled model in lower space dimension. We also prove a corrector-type result very useful in numerical computations. In view of the thin structure of the domain, we appeal to a concept of two-scale convergence adapted to thin heterogeneous media to achieve our goal.

Mindfulness interventions have been implemented in many countries. This study aimed to examine the psychometric properties of English and Thai-version of the 15-item mindfulness attention awareness scale (MAAS) across five samples. A descriptive correlation research was conducted in the Philippines and Thailand, and five data sets were generated using self-reported and online questionnaires. Exploratory factor analyses were used to explore the factorial structure of the MAAS. Confirmatory factor analyses were performed to test measurement invariance across samples. Concurrent validity was tested by examining relationships between the MAAS and psychological well-being (PWB). A unidimensional structure of the MAAS was documented across the five samples. The MAAS demonstrated measurement invariance across samples for both English and Thai versions. Concurrent validity was displayed through significant correlations with PWB. This study produced evidence to support the construct validity, concurrent validity, and reliability of the English and Thai versions of MAAS. This study was not registered.

A maxillary totally edentulism was rehabilitated with one-piece implants starting from pre-existing totally removable denture. A 3D digital scan of the removable denture and upper and lower arches was performed. On this basis, a prototype with optimal esthetic and functional outcomes was re-alized and turned into a custom-made radiological stent with markers. The superimposition of STL and DICOM files allowed the virtual planning of one-piece implants ideal position. The stackable templates composed of a fixed component, namely base template, and removable components, were realized. The fixed component, once secured with anchor pins to the bone, was no longer removed. The removable components, screwed to the fixed component, were used to perform the implant surgery and the immediate prosthetic loading. A base template allowed for maintaining a fixed reference during the entire workflow, improving the transition between the digital project, the surgical procedure, and the prosthetic rehabilitation.

This study updates previous research showing that 22 all-cause mortality in 31 European countries increased over time the higher the 21 COVID-19 full vaccination uptake. The update illuminates that a one percentage point increase in 21 full vaccination uptake initially decreased all-cause mortality from Jan to Mar 22 by –0.423 percent (95% CI –0.577, –0.270), but the following 14 months, a one percentage point increase in 21 booster vaccination uptake oppositely increased mortality by 0.366 percent (95% CI 0.250, 0.482). The findings indicate that full vaccination initially prevented mortality, but subsequently, booster vaccination, in particular, detrimentally and consistently induced higher mortality. The effects remained robust when controlling for alternative explanations. Studies have argued that heat waves caused mortality in the 22 summer and energy prices caused mortality in the 22-23 winter. However, the update shows that booster vaccination consistently induced higher mortality when neither heat waves nor energy prices were likely explanations.

In the context of digital empowerment, the digital literacy level of tea farmers has a significant impact on the intelligent development and transformation of the tea industry. This study extends the original model of UTAUT theory by introducing Personal innovativeness theory and Self-efficacy theory, and constructs a new model to explore the influencing factors of moderate scale tea farmers' digital literacy improvement behavior. There are a total of 22 research hypotheses. Using structural equation modeling and collecting questionnaire data for analysis, the following research results were obtained: (1) Performance expectancy, social influence, effort expectancy, personal innovativeness and self-efficacy all significantly positively affect the willingness of tea farmers to improve their digital literacy, and then according to the path coefficient, in descending order, social influence (0.226)>self-efficacy (0.224)>effort expectancy (0.178)>performance expectancy (0.157)> personal innovativeness (0.155); (2) Facilitating condition and willingness to improve digital literacy have a significant positive impact on tea farmers' digital literacy improvement behavior，and then from the size of the path coefficient, the willingness to improve (0.271)>facilitating condition (0.106); (3) The willingness of tea farmers to improve their digital literacy plays a complete mediating role between personal innovativeness and self-efficacy on their digital literacy improvement behavior, and partially mediates between performance expectancy, social influence, and effort expectancy on their digital literacy improvement behavior, and according to the proportion of indirect effects, the order is effort expectancy (27%), performance expectancy (47%), and social influence (49%); (4) The gender and age of tea farmers have a significant positive moderating effect on the impact of performance expectancy on the willingness to improve digital literacy; age and experience have a significant positive moderating effect on the impact of effort expectancy on the willingness to improve digital literacy; the age of tea farmers has a significant positive moderating effect on the improvement of digital literacy behavior through facilitating condition. This study extends the applicability of the UTAUT theoretical model and proposes strategies to improve the digital literacy of tea farmers, providing practical guidance for tea farmers to improve their digital literacy and providing reference for research related to farmers' digital literacy.

Grazing exclusion (GE) and rest grazing (RG) are important management to restore grassland ecosystems. In order to evaluate the effects and mechanisms of different grazing management on species diversity, vegetation community indices and soil variables were determined in the plots of Qilian Mountains in Gansu Province. The results showed that diversity effects and their regulating mechanisms had space-scale dependence under different grazing management. Species richness and species diversity indices of RG in grassland were significantly higher than that of GE at the regional scale. Additionally, three grazing management in mountain meadows had only a significant effect on species richness, but different management in mountain meadows and temperate steppes had a significantly different effect on species diversity indices. Meanwhile, soil variables only influenced species diversity at the regional scale. Most of community and soil variables at each scale had positive effects on species diversity, except that the direction of biodiversity effects was negative for species coverage, mean plant height, soil porosity (SK) and bulk density (BD) under two contrasting grazing management. In conclusion, choosing RG at the regional scale and select grazing management according to different grassland types at the local scale to restore degraded grassland vegetation.

Thanks to its valuable assessment possibilities (subjective complaints and changes in nasal patency in the course of the examination), nasal provocation test may serve as an alternative tool for oral food challenges in the future. However, this test requires successive attempts to regulate its methodology in order to develop a standardized lyophilisate form and determine the threshold dose for a positive result. The study objective was to present the methodological foundation for nasal food allergen provocation tests induced by freeze-dried powdered chicken egg white. A control group of 25 individuals with no history of allergy to chicken eggs or any other allergy was included in the study. Optical rhinometry and visual analog scale were used to assess the response of nasal mucosa to local allergen challenge. Minor variations in nasal flows as measured by optical rhinometry were observed in the provocation tests. The mean optical density measurements (as measured regardless of the allergen dose used) varied from positive to negative values and vice versa; e.g., amounting to 0.018 OD (standard deviation 0,095) at 15 minutes and −0.011 OD (standard deviation 0,090) at 30 minutes. No significant differences were observed with regard to the perceived nasal discomfort using the visual analog scale. Due to the absence of nasal mucosal reactivity, nasal challenge is an excellent methodological tool for implementing food allergen tests.

Pain is a complex multidimensional feeling combined with sensorial and emotional features. The majority of patients undergoing orthodontic treatment reported various degrees of pain, which is perceived as widely variable between individuals, even when the stimulus is the same. Orthodontic pain is considered the main cause of poor-quality outcomes, patients' dissatisfaction, and lack of collaboration up to the interruption of the therapy. A deep understanding of pain and how it influences the patient’s daily life is fundamental to establishing proper therapeutic procedures and obtaining the correct collaboration. Because of its multifaced and subjective nature, pain is a difficult dimension to measure. The use of questionnaires and their relative rating scales is actually considered the gold standard for pain assessment. Depending on the patient’s age and cognitive abilities is possible to choose the most appropriate instrument for self-reported pain records. Although several scales have been proposed and a lot of them are applied, it remains uncertain which of these tools represents the standard and performs the most precise, universal, and predictable task. This review aims to give an overview of the aspects which describe pain, specifically the pain experienced during orthodontic treatment, the main tool to assess self-perceived pain in a better and more efficient way, the different indications for each of them, and their correlated advantages or disadvantages.

A growing number of clinical data suggests that the use of antiplatelets and/or statins pre-stroke are associated with reduced severity and better functional outcomes for ischemic stroke. Studies have shown that the long-term use of ACEI/ARB pre-stroke is beneficial in reducing severity at onset and improving functional outcomes. We aimed to compare stroke severity and early functional outcome between patients taking ACEI/ARB and those who were not, and to identify any additional effects to taking ACEI/ARB with antiplatelets, statins, or both. In this retrospective case-control study, we assessed 218 patients who presented with ischemic stroke to the stroke unit in Padeh-Poriyah Hospital between 2019-2020. Of these patients, 119 took ACEI/ARB pre-stroke. A significantly higher percentage of ACEI/ARB-treated patients had a history of hypertension (HTN) and hyperlipidemia and were taking antiplatelets and/or statins, compared to the non-ACEI/ARB group. We observed that pre-stroke treatment with ACEI/ARB is associated with worsened stroke severity and reduced early functional outcomes. No significant benefit to taking ACEI/ARB with antiplatelets and/or statins pre-stroke was observed. Due to the nature of our study, the data should be interpreted as suggestive. Thus, prospective studies of these medications on ischemic stroke severity and early outcomes are warranted.

Alfalfa is an important forage crop around the world. LncRNAs are considered to be a class of functional biomacromolecules, while little is known about lncRNAs in alfalfa. In this study, RNAs from different tissues of alfalfa were sequenced and anlyzed with full-length transcriptome sequencing technology, yielding the full-length transcripts dataset of alfalfa. Based on sequencing and public RNA-seq data, lncRNAs of alfalfa were predicted genome-wide by CPC2 and PLEK. The results showed most lncRNAs shared low sequence conservation with those in other plant species, part of which seems originate from plastid genome. We also identified 88563 lncRNAs, approximately 99.8% of total lncRNAs, with possibility of coding small ORFs using two prediction tools. Our research generated the biggest sequence set of alfalfa lncRNAs, and revealed some plastid originated lncRNAs with high sequence conservation.

Keywords: glycerin; Ni-based catalyst; lanthanum; semi-industrial scale; hydrogen production; biorefinery; monitoring.

This paper examines the Business Model (BM) from a socioeconomic system perspective to discern key factors, and understand its interactions resulting in the Scaling of Social Impact (SSI) in Social Entrepreneurship (SE). Previously, studies have explained the importance of BM in relation to SE. However, there is a lack of empirical studies on how BM’s transitions through participation of various actors result in SSI. This research applies qualitative analysis on a single case study of a Japanese social startup, “mymizu”, the first water refill application platform in Japan. The findings show that collaboration amongst different stakeholders on the initial phase of the BM could increase awareness of responsible consumption, convert into actual users for sustainability, and change their behavior. Secondly, members of the society could take on dual roles, both as users and collaborators in the BM, which results in an exponential scaling effect of the social impact. This paper contributes towards adding a participatory stakeholder (PS) to the ecosystem of SSI and building a “regenerative” BM that is relevant in SE towards sustainability.

Jabulile Ndlovu1 and Kebogile Mokwena1, 2 1 Department of Public Health, Sefako Makgatho Health Sciences University, Pretoria, South Africa 2 Research Chair in Substance abuse and population Mental Health, Sefako Makgatho Health Sciences University, Pretoria, South Africa Corresponding author: jabulilendlovu5@gmail.com Abstract An estimated 6% of the world population has serious mental illness, with one in four families having a member with some form of psychiatric disorder, who are mostly cared for by their relatives within a family setting. Although caregiving in a home setting is reported to be associated with significant mental distress, the burden of such distress is rarely measured. The purpose of this study was to quantify the burden of care among family caregivers of relatives with serious mental disorders, as well as to explore possible association between the caregiver burden of care and a range of caregiver and Mental Health Care User (MHCU) variables in a rural district in Kwa-Zulu Natal, South Africa. The Zarit Burden Interview (ZBI) scale was used to collect data from 357 caregivers, and STATA 14 was used to analyze data. The ages of the sample ranged from 18 to 65, with a mean of 50.29, and the majority (86%) were female and unemployed (83%). The ZBI scores ranged from 8 to 85, with a mean of 41.59. The majority (91%) tested positive for caregiver burden of care, which ranged from mild to severe. Using the Pearson Chi-square test of association (p=0.05), variables that were significantly associated with the burden of care were clinically related (caregiver self-reported depression, MHCU diagnosis, recent relapse of the MHCU), socio-economic (caregiver family monthly income, MHCU disability grant status and MHCU employment status) and socio-demographic (MHCU gender and MHCU level of education). The prevalence of burden of care is high and severe, and scarcity of resources in families and communities contribute to the high burden of care in these rural communities.

Innovation and technology are important tools of minerals sector efficiency and productivity im-provement. The uptake of technologies has proven important levelrs to improve the productivity of mining sector. This paper provides a comprehensive analysis of mine-level productivity by using a global data of copper, gold, and platinum from 1991-2020. Various drivers of proioductivity have been analysed to draw policy insights. Empirical findings reveal significant disparities in technical efficiency and productivity across mines and regions. A further decomposition of TFP into its different components suggest that the adoption of innovative practices and investment in technology adoption could improve the overall productivity of these commodities. The findings also suggest that an appropriate input mix and optimal scale of production could boost platinum mining productivity. Regional disparities in productivity in different commodities (e.g., South Africa vs. Zimbabwe) give policymakers with insights into how to support production scale and productivity through appropriate input mixtures.

Accurate evaluation of thermo-fluid dynamic characteristics in tank are critically important for designing liquid hydrogen tank of small-scale hydrogen liquefier to minimize heat in-leak into the liquid and ullage. Due to the huge cost, most future liquid hydrogen storage tank designs will have to rely on predictive computational models for storage tank pressurization and heat-leak minimization. Thus, reliable on predictive numerical models to aid design process of tank design have been required. Therefore, in this study, in order to improve the storage efficiency of the small-scale hydrogen liquefier, a three-dimensional CFD model that can predict the boil-off rate and the thermo-fluid characteristics due to heat penetration has been developed. The prediction performance and accuracy of the CFD model is validated on the basis of the comparisons between its results and previous experimental data and a good agreement is obtained. To evaluate insulation performance of polyurethane foam according to thickness, the pressure change and thermo-fluid characteristics in a partially liquid hydrogen tank, subject to fixed ambient temperature and wind velocity, has been investigated numerically. Three different insulation thickness were considered to investigate not only interfacial characteristics between liquid and ullage but thermo-fluid dynamics in hydrogen storage tank for various heat-leak situation. The results indicate that as the insulation thickness becomes thinner and more heat penetration exists, natural convection by buoyancy develops stronger, so strong vortices are created near the interfacial zone by upward flow driven by buoyancy. In particular, as the amount of heat in-leak increased, wider and stronger heat accumulation was found in the top dished head area of the ullage area. In addition, it was confirmed that the CFD model developed in this study could well describe the phenomenon in which the temperature of the tank wall and the insulation mat increased at a faster rate due to faster heat transfer from the vapor and interface. In the future, the numerical model developed in this study will be used for optimizing insulation system of storage tank for small-scale hydrogen which is cost-effective and highly efficient.

In recent years, the emergence of spatiotemporal big data has made the transition of functional identification from the physical dimension to socioeconomic or human activities becoming more common. In the identification of urban functional areas, most studies considered only a single data source and a single division scale, the research results have problems such as low update frequency or incomplete information in a single data set, and overfitting or underfitting in a single spatial resolution. Using taxi trajectory data and point of interest (POI) data as the main data source, this study proposes a multi-scale recursive identification method for urban functional areas based on cross-validation. First, used the dynamic time warping (DTW) algorithm generates a time series similarity matrix, the CA-RFM model combines the clustering algorithm and random forest model is constructed, the model uses a clustering algorithm (K-MEDOIDS) to extract sig-nificant feature regions as input, which are imported into the random forest model for UFZ identification. Then, to overcome the shortcomings of single scale in expressing urban structural characteristics, a recursive model of different levels of urban road networks is established to classify multi-scale functional areas. Finally, cross-validation using the CA-RFM model and POI quantitative identification method, obtains the final identification results of urban functional areas. This paper selects Shenzhen as the study area for the case study, the results show that the com-bination of clustering algorithm and random forest model greatly reduces the error of manual selection of training samples. In addition, the research shows the superiority of the multi-scale recursive identification method that fuses multi-source data and performs cross-validation from two aspects, that is, the division speed of urban functional area identification results is accelerated and the accuracy is improved.

Silver nanoparticles from Origanum vulgare (Oregano) have been synthesized in the past. However, no investigation has been performed on the combined effects of independent factors that affect the synthesis. Silver nitrate and Oregano concentrations, incubation temperature and time, as well as pH can play crucial roles in synthesis. In this regard Full Factorial Design was applied. A Voigt function was fitted on the measured UV – Vis spectra. The fitting parameters of the Voigt function (peak wavelength, area, and Full Width at Half Maximum) were used as the responses. A quadratic model was fitted for the peak wavelength and area, and the pH proved the dominant factor on nanoparticle synthesis.

The reagents and raw materials used, as well as the products obtained (bio-oil, reaction condensates, polyols and sugar phases) were characterized by elemental analysis, infrared spectroscopy, thermogravimetry and high performance liquid chromatography with mass spectrometry. The heating value of the bio-oils is higher than that of the original biomass (higher heating value of Eucalyptus sawdust bio-oil 29 MJ/kg versus 19.5 MJ/kg of the original Eucalyptus sawdust). The analyses of the bio-oils allowed to identify the presence of high added-value compounds, such as levulinic acid and furfural. Finally, a study of the accelerated aging of the liquefied biomass showed that the biofuel density increases with the storage time due to the occurrence of repolymerization reactions.

During two decades back, the growth of the fisheries sector in Indonesia was showing increasing trend, however, behind the rapid development of the sector, small-scale fisheries role is often neglected as one of the main actors to support the whole sector. They remain poor and doing fishing activities traditionally. Therefore, this study aims to describe the real situation of small-scale fishers, analyzing fishers’ ownership of the boat, analyzing their income level to reveal their poverty condition, to analyze the factors affecting their income. The regression analysis results indicate that fishing days, boat category, sea fish catch, fish selling price, variable cost, and secondary job have significant affect to fishers’ net income.

Combinations of Dilwet, an organosilicone surfactant, and ethephon, a hormonal compound often applied to fruit trees to thin the crop, were applied at full bloom to prevent fruit development in two young vineyards differing in elevation by 600 m. Each vineyard was planted with the same 19 red and 10 white grape cultivars from a range of countries of origin. Vineyard elevation did not affect response to treatment. A combined spray of 0.5% Dilwet with 0.04% ethephon reduced cluster number by an average of 85%, while weight of the remaining clusters was reduced by 63%, compared to unsprayed controls. Increasing the concentration of Dilwet to 1% reduced cluster number by 93% and cluster weight by 76%. There were significant differences in the response of cultivars to the treatments, depending on country of origin, with grapes originating in France responding better to higher concentrations of Dilwet. White grapes were more responsive than red grapes. These results highlight the need to test different cultivars and genetic origins of crops when examining the effects of new agricultural chemicals.

Interspecific competition between herbivorous insects is a major selection pressure affecting the distribution, abundance, and structure of their populations. Facilitator-mediated interactions, such as mutualism, can influence competition. Furthermore, the temporal dynamics of competitive relationships affect the interaction’s outcome. Here, we re-evaluated the data on the competition for space between two herbivorous insects commonly known as scales (Toumeyella martinezae and Opuntiaspis philococcus) in either the presence or absence of Liometopum apiculatum (a mutualistic species of T. martinezae) and its variations over time. We selected 27 Myrtillocactus geometrizans plants on which the studied insects were present; the plants were classified into one of five different conditions: either of the scale species were present on the plant, without its competitor; T. martinezae with neither its mutualistic species nor the competitor; and both scale species competing in either presence or absence of the mutualistic species. We kept a photographic record of each condition, measured the size of (as an indicator of the development stage) and area occupied by the individual scales, estimated the total coverage of each scale species, and assessed their relative occupation of space and their competitive intensity. We found temporal variations in competitive intensity. T. martinezae occupied more space during the first months, whereas O. philococcus did so towards the end of the study period. The population structure changed over time and between species, affecting the competitive interactions. In conclusion, the dynamics of competition change over time, and the mutualistic species has a positive effect on T. martinezae when the scales are in competition. However, temporal variations resulting from changes in the life cycle of the scales allow the two competitors to coexist in the same place at the same time.

Background: Asthma is a very prevalent disease with special characteristics during pregnancy, however little is known about its relation to the psychological wellbeing of women in this period; We aimed to know whether depression and anxiety symptoms are more frequent in asthmatic pregnant women. (2) Methods: Family Apgar (week 20), Edinburgh (weeks 20 and 32) and STAI (week 32) tests were administered to 738 pregnant women (81 asthmatics) in the NELA birth cohort; (3) Results: There were no significant differences between asthmatic and non-asthmatic pregnant women in any of the different tests at any of the time points. The mean? scores for the different tests and timepoints between asthmatic and non-asthmatic pregnant women were: Apgar 20, 17.9±2.2 vs. 10.0±2.2; Edinburgh 20, 6.7±4.2 vs. 6.9±4.3; Edinburgh 32, 5.9±4.4 vs. 5.6±4.3; and STAI 32, 16.7±8.4 vs. 15.8±8.3. The proportion of pregnant women out of the normal range score for any of the tests and time points was also similar in both populations. (4) Conclusions: Asthma is not associated with the psychological wellbeing among pregnant women.

The papaya mealybug Paracoccus marginatus is an invasive, polyphagous pest and poses a quarantine threat to tropical and subtropical countries. Infested commodity internationally should be undergoing phytosanitary treatment, while irradiation is recommended to replace methyl bromide fumigation. Dose-response tests were conducted on the 2-, 4-, and 6-d-old eggs and gravid females of P. marginatus using the X-ray radiation doses of 15–105 Gy with an interval of 15 Gy, respectively. Radiotolerance was compared using fiducial overlapping and lethal dose ratio (LDR) test, resulting in insignificant difference except for LDR test at LD90 (leading 90% mortality at 95% confidence level (CL)); the estimated dose for 99.9968% mortality at 95% CL was 176.5–185.2 Gy, which was validated in the confirmatory tests using gamma irradiation because gammy and X-rays have equal biological effect on pests. Therefore, a total of estimating 60,386 gravid females of papaya mealybug irradiated with 146.8–185.0 Gy resulted in no F1 nymphs developing. Eggs can therefore be used as an alternative to gravid females for dose-response tests; the recommended minimum dose for phytosanitary treatment of P. marginatus on infested commodity is 185 Gy, with treatment efficacy ≥ 99.9950% at 95% CL.

Mercury emissions from small-scale coal-fired boilers are still a serious environmental problem in Poland and the Czech Republic. The publication presents the results of mercury emission tests conducted using five different small-scale coal-fired boilers and five different coal fuels. The research was carried out under laboratory conditions, but also using residential users' heating devices. They covered a wide range of operational parameters, both energy and emission. It was shown that more than 94% of the mercury contained in coal fuel undergoing combustion is emitted into the atmosphere with the volatile products of combustion (including dust), and the amount of emissions is strongly dependent on the original mercury content of the fuel. One solution to the problem of mercury emissions from small-scale coal-fired boilers may be to replace traditional coal with low-emission carbon fuels. The paper shows that mercury emissions from burning such fuels are many times lower than from burning coal. The widespread use of low-emission carbon fuels in individual heating systems in Poland and the Czech Republic would reduce mercury emissions to the atmosphere by up to 90% compared to the current level of emissions generated by individual heating systems in these countries.

Background: The adoption of self-care behaviors among patients with arterial hypertension (AH) plays an important role in the management of their health condition. However, a lack of scales assessing self-care is observed. We aimed to develop and validate the Hippocratic hypertension self-care scale. Methods: From a pool of questions derived from a literature review, 18 items were included in the scale and reviewed by a committee of experts. Participants indicate the frequency at which they follow the self-behavior prescribed in each statement on a five-point Likert scale. Data were collected between April 2019 and December 2019. Re-sults: 202 consecutive adult patients with AH were enrolled in the study. The internal consistency of the scale was found to be 0.807, using Cronbach’s alpha coefficient. An exploratory factor analysis identified two do-mains that accounted for 92.94% of the variance of the scale items; however, each sub-scale could not be used as an independent scale. Finally, the test- retest of the scale showed a significant strong correlation (r=0.0095, p<0.001). Conclusion: Our data indicate that the scale is a reliable and valid tool for assessing self-behaviors in patients with AH. Health professionals can use it in their clinical practice to improve the management of patients’ health condition.

Background - Mandibular flexion (MF) is a complex biomechanical phenomenon, which involves a deformation of the mandible, due mainly to the contraction of the masticatory muscles, and can have numerous clinical effects. The main aim of the current systematic review is to highlight the different factors that can increase MF, to help clinicians, identify patients to whom they should pay more attention. As a secondary outcome, we wanted to analyze the preventive measures and suitable techniques to be adopted to minimize the negative effects of this phenomenon on oral fixed rehabilitations. Methods - The review, which was carried out in accordance with the “Preferred Reporting Items for Systematic reviews and Meta-Analyses” (PRISMA) flowchart, was recorded in the “International Prospective Register of Systematic Reviews” (PROSPERO). As research questions, “Patient/Population, Intervention, Comparison and Outcomes” (PICO) questions were employed. Using the ROBINS-I technique, the risk of bias in non-randomized clinical studies was evaluated. Results - The initial electronic search identified over 1300 potential articles, of which 54 studies were included in this systematic review. Information regarding the relationship between MF and individual factors, mandibular movements, impression-taking and fixed rehabilitations were obtained. Conclusions – The studies included in this systematic review showed that MF is greater during protrusive movements, in the posterior areas of the lower jaw, and in patients with brachial facial type, greater jaw length, small gonial angle and less density, length and bone surface of the symphysis. The biomechanical effects of mandibular flexion on fixed restorations are debated. Prospective clinical and radiological observational studies should be conducted to evaluate the potential short-, medium- and long-term consequences of MF.

To address the challenges of climate change and food security, the establishment of smart farm complexes is necessary. While there have been numerous studies on the productivity and environmental control of individual greenhouses, research on greenhouse complexes is considerably limited. Conducting environmental studies during the design phase of these complexes poses financial constraints and practical limitations in terms of on-site experiments. To identify potential issues that may arise when developing large-scale greenhouse complexes, it is possible to utilize modeling techniques using Computational Fluid Dynamics (CFD) to assess environmental concerns and location issues before constructing the facilities. Consequently, simulating large-scale CFD models that incorporate multiple greenhouses and atmospheric conditions simultaneously presents significant numerical challenges. The objective of this study was to develop a guideline for verifying CFD models for a large-scale Venlo greenhouse, where acquiring field data before construction is not feasible for designing a greenhouse complex. The verification processes of the CFD models were conducted using 2D and 3D iterative simulations of a 2-hectare greenhouse model, using the improved Grid Independence Test (GIT) and wall Y+ approaches. Subsequently, the aerodynamic characteristics were analyzed in a 3D greenhouse model to access its performance when the wind direction was 90° in summer season. The findings revealed that a grid resolution of 0.8 meters and a first layer height of 0.04 meters were suitable for developing large-scale greenhouse models, resulting in a low Root Mean Square Error (RMSE) of 3.9% and a high coefficient of determination (R2) of 0.968. This process led to a significant reduction of 38% in the number of grid cells. These results will serve as design standards for large-scale greenhouses.

A new pterygosomatid mite species, Neopterygosoma robertmertensi sp. n. (Acariformes: Pterygosomatidae) was collected from two specimens of Liolaemus robertmertensi (Liolaemidae) from Argentina. This new species is described based on active stages: adults (female and male) and juveniles (deutonymphs, protonymphs, larvae), and quiescent stages (nymphchrysalis, deutochrysalis and imagochrysalis). The changes in morphological characters during the ontogeny of N. robertmertensi have been analyzed in detail. A difference in larval sex morphology was observed for the first time in the family Pterygosomatidae (female larvae differ from male larvae by the shape and size of the idiosoma and position of the genital area). This new mite species is most similar to N. cyanogasteri but can be distinguished by the presence of different leg chaetotaxy patterns of genua IV and femora IV, four to six genital setae, three to five dorsomedial setae and two to three ventromedial setae. Phylogenetic analysis was conducted based on 120 morphological characters of all Neopterygosoma spp. and four outgroup species using the maximum parsimony approach. The results indicated that this species is nested within mites of the chilensis group of Neopterygosoma associated with host species of the section chiliensis of Liolaemus s. str. An updated diagnosis of the chilensis group of Neopterygosoma and an identification key for all species of this genus has been provided.

Nitrile butadiene rubber (NBR) and its various composites are extensively studied as matrix materials for oil-free lubricated mechanical friction surfaces. While numerous macroscopic friction experiments can compare the frictional performance of different NBR composites, the complex material preparation and low experimental efficiency pose challenges. Nanoscale molecular dynamics (MD) simulations can help develop materials with improved friction properties, but they cannot directly provide the friction coefficients of actual macroscopic friction surfaces. Therefore, it is important to integrate the advantages of macroscopic and nanoscale friction studies and perform a synergistic analysis to modify the friction properties of composite materials. In this study, a multi-scale approach is proposed to simulate the frictional characteristics of carbon nanotube (CNT)-reinforced NBR by combining the MD friction simulation， micromechanical bridging model, and macroscopic finite element analysis (FEA) methods. The results of the multi-scale friction simulation of copper (Cu)-CNT/NBR composites show that the addition of CNTs significantly improves the frictional properties of NBR, and the extent of the improvement is related to the mass fraction of CNTs. As the mass fraction of CNTs increases (0%, 1.25%, 2.5%, 5%), the coefficient of friction decreases from 0.50 to 0.38. The results are validated by ring-block friction experiments at the same scale as the simulations.

Background: Functional restoration of abdominal wall defects represent one of the fundamental challenges of reconstructive surgery. Synthetic grafts or crosslinked animal-derived biological grafts are characterized by significant adverse reactions, which are mostly observed after their implantation. The aim of this study was to evaluate the efficacy of the decellularization protocol to produce a completely acellular full-thickness abdominal wall scaffold. Methods: Full-thickness abdominal wall samples were harvested from Wistar rats and submitted to a 3 cylce decellularization process. Histological, biochemical and DNA quantification analyses were applied to evaluate the effect of the decellularization protocol. Mechanical testing and immunogenicity assessment were also performed. Results: Histological, biochemical and DNA analysis results showed the efficient decellularization abdominal wall samples after the 3rd cycle. Decellularized abdominal wall scaffolds were characterized by good biochemical and mechanical properties Conclusion: The data presented herein confirm the effective production of rat-derived full-thickness abdominal wall scaffold. Expanding this approach will allow the exploitation of the capacity of the proposed decellularization protocol in producing acellular abdominal wall scaffolds from larger animal models or human cadaveric donors. In this way, the utility of biological scaffolds with preserved in vivo remodelling properties may be one step closer to its application in clinical studies.

At the highest level of endurance sports, the differences in finishing times are small, so equipment is constantly being constantly improved to enhance the athletes' performance. For instance, one dominant resistive force in connection with cross-country skiing is the friction between the skis and snow and since the 1930s, research designed to understand and reduce this friction has been ongoing. The mechanisms involved in ski-snow friction include compaction, micro-ploughing, adhesion, viscosity, and water-bridging. Of these, adhesive and viscous friction have been most studied, while much less is presently known about compaction, micro-ploughing, and water-bridging. At the macro-scale, the ski-camber profile plays a key role in determining whether, with respect to friction, a particular cross-country ski is suitable for use under a given set of conditions. Through its influence on where ski-snow contact occurs, this ski-camber profile has a profound impact on adhesive and viscous friction, micro-ploughing, the formation of water bridges, and the rate of compaction. Different contact zones with different apparent areas of contact and pressure have been found to be most suitable for different conditions. In addition, the micro-scale texture of the ski base is also an important determinant of ski-snow friction. Topographical measurements of this texture can be achieved with white-light interferometer and recent characterisation has focused on the mechanical properties of contact. In numerous tribological studies the interactions between features at different scales are modelled. In most tribological systems, features on several scales influence friction and ski-snow contact is certainly no exception. Accordingly, the current investigation was designed to evaluate the multi-scale properties of different combinations of two skis with two different base textures. The real area of contact and the average interfacial separation and total average reciprocal interfacial separation between the ski and snow were taken into consideration. We found that different macro- and micro-scale properties of the ski favour different mechanisms of friction. Both the profile of the ski camber and texture of the base play decisive roles in determining viscous friction. At the same time, the texture of the ski base exerts a greater impact on the average real contact pressure, real contact area and minimal average interfacial separation between the ski and snow than does the ski-camber profile.

Introduction: Lack of certainty or knowledge in health professions generates uncertainty, affecting clinical practice in decision-making (diagnostic or therapeutic), intervention design and therapeutic communication. In nursing, the lack of information to predict prognosis and outcome of interventions generates uncertainty: recognizing and managing it is essential for individualized, safe, and effective care. Objective: to assess uncertainty and the perception of a debriefing within the same shift ("intrathoracic pass") as a proposal for improvement. Method: ad-hoc survey of nursing professionals and Auxiliary Nursing Care Technicians (ANC). Variables: age, work experience, PRU scale (Physisicians' Reactions to Uncertainty) and additional question on intrathoracic pass. Results: Sample: 61 nurses and 10 TCAE (90.14 % female, 9.86 % male; mean age 35.18, standard deviation SD=13.04). Mean professional experience 11.74 years (SD=12.21); 56.33% had ≤4 years of experience. Origin: 49.30% Intensive Care Units (ICU), 11.27% Hospitalization, 7.04% Emergency, 1.41% Primary Care and 30.99% Other Services. The mean level of uncertainty was 27.99 points (max. possible 75; SD=7.50), with stress (mean=25.55, max. possible: 40; SD=5.24) and anxiety (mean=15.80, max. possible: 25; SD=3.05) as major manifestations. The association uncertainty-age (r=0.339; p=0.004) and uncertainty-experience (r=0.391; p=0.001) correlated negatively (the older and more experienced, the less uncertainty), with no significant differences between degrees or services. 91.55% stated that the intra-day pass was useful to reduce uncertainty during the working day. Conclusion: Uncertainty is frequent in nursing, and implementing the intrashift pass is perceived as a competent strategy.

The coastal zone, situated at the sensitive interface between land and sea, serves as a pivotal area of extensive human economic activities. As one of China's economic special zones, Xiamen exemplifies the comprehensive trajectory of coastal governance in China. This research adopts an interdisciplinary approach grounded in historical geography and political ecology, employing data collection methods encompassing historical literature, field surveys, and interviews. The research findings indicate that:(1) The year 1994 witnessed the pivotal role played by the United Nations Convention on the Law of the Sea (UNCLOS) in connecting Jimei Peninsula from a local context to the international arena, thus signifying a momentous turning point in the process of scale reconstruction. However, the subsequent implementation of the Coastal Exclusion Policy in 2002 had detrimental consequences, leading to the disintegration of the local scale, the erosion of maritime culture, and the disregard for the livelihood security of displaced coastal populations.(2)The Coastal Exclusion Policy serves as an instrumental tool for implementing scale politics, while its functional realignment has had profound implications for the coastal zone, encompassing economic transformation, ecological degradation, and the marginalization of the socio-cultural fabric that intertwines humans and the coastal environment.

Human pose estimation is a complex detection task in which the network needs to capture the rich information contained in the images. In this paper, we propose MSTPose (Multi-Scale Transformer for human Pose estimation). Specifically, MSTPose leverages a high-resolution Convolution neural network (CNN) to extract texture information from images. For the feature maps from three different scales produced by the backbone network, each branch performs the coordinate attention operations. The feature maps are then spatially and channel-wise flattened, combined with keypoint tokens generated through random initialization, and fed into a parallel Transformer structure to learn spatial dependencies between features. As the Transformer outputs one-dimensional sequential features, the mainstream two-dimensional heatmap method is abandoned in favor of one-dimensional coordinate vector regression. The experiments show that MSTPose outperforms other CNN-based pose estimation models and demonstrates clear advantages over CNN + Transformer networks of similar types.

To prevent CO2 leakage and ensure the safety of long-term CO2 storage, it is essential to investigate the flow mechanism of CO2 in complex pore structures at the pore scale. This study focuses on reviewing the experimental, theoretical, and numerical simulation studies on the microscopic flow of CO2 in complex pore structures during the last decade. For example, advanced imaging techniques, such as X-ray computed tomography (CT) and nuclear magnetic resonance (NMR), are used to reconstruct the complex pore structures of rocks. Mathematical methods, such as Darcy's law, Young–Laplace’s law, and the Navier-Stokes equation, are used to describe the microscopic flow of CO2. Numerical methods, such as the lattice Boltzmann method (LBM) and pore network (PN) model, are used for numerical simulation. The application of these experimental and theoretical models and numerical simulation studies is discussed, considering the effect of complex pore structures. Finally, future research is suggested to focus on: (1) Conducting real-time CT scanning experiments of CO2 displacement combined with the developed real-time CT scanning clamping device to realize real-time visualization and provide quantitative description of the flow behavior of CO2 in complex pore structures; (2) The effect of pore structures change on the CO2 flow mechanism caused by the chemical reaction between CO2 and the pore surface, the flow theory of CO2 considering wettability and damage theory in a complex pore structures; (3) The flow mechanism of multi-phase CO2 in complex pore structures; (4) The flow mechanism of CO2 in the pore structures at multiscale and the scale upgrade from microscopic to mesoscopic to macroscopic. Generally, this study focuses on reviewing the research progress of CO2 flow mechanisms in complex pore structures at the pore scale and affords an overview of potential advanced developments to enhance the current understanding of CO2 microscopic flow mechanisms.

When there are multiple key strata in the overburden of deep coal seam and the surface subsid-ence coefficient after mining is small, it indicates that the overlying key strata fail to break com-pletely after mining. On this occasion, the stress concentration on the working face occurs easily, which in turn leads to the occurrence of dynamic disasters such as rock burst. This study adopted a comprehensive analysis method of field monitoring and numerical simulation to explore the in-fluence of key stratum on the evolution law of mining-induced stress in the working face. Dis-tributed optical fiber sensor (DOFS) and surface subsidence GNSS monitoring system were re-spectively arranged inside and at the mouth of the ground observation borehole. According to the monitoring results of strain obtained from DOFS, the height of broken stratum inside the overlying strata was obtained; according to the monitoring results of surface subsidence, the sur-face subsidence coefficient was proved to be less than 0.1, indicating that the high key stratum does not break completely, but enters a state of bending subsidence instead. In order to reveal the influence of key stratum on the mining-induced stress of working face, two 3DEC numerical models with and without key stratum were established for comparative analysis. As the numeri-cal simulation results show, when there are multiple key strata in the overburden, the stress in-fluence range and stress concentration coefficient of coal seam after mining are relatively large. The study revealed the working mechanism of rock burst accidents after large-scale mining and predicted the potential area of rock burst risk after the mining of the working face, which has been verified by field investigation. The research results are of great guiding significance for the revelation of the working mechanism of rock burst in deep mining condition and its prevention and control.

A retrospective cohort study was performed to determine the likelihood of malignant stroke after mechanical thrombectomy via ASPECTS-PASS score(A-P score), a novel scoring system using ASPECT score and number of arterial-passes. This prediction score is designed to help clinicians take early measures. Malignant stroke is defined as a large MCA infarct with mass effect that develops during the first 5 days following presentation and is associated with approximately 80% mortality. Previous studies have shown an association of low ASPECTS and high number of passes with poor outcome.We performed a retrospective cohort analysis on 78 patients with anterior circulation stroke who underwent mechanical thrombectomy at our hospital. We analyzed the likelihood of developing malignant stroke post thrombectomy in the first 5 days following successful recanalization (TICI ≽ 2b). Malignant stroke is defined as severe symptoms secondary to edema as evidenced by midline shift, cistern engorgement, and hemorrhagic conversion with edema. Patients with ASPECTS of 0-5 were excluded. We used two variables to predict the outcome: ASPECT score and number-of-passes. We used the equation ASPECT score minus number-of-passes (A-P) to arrive at a single number to predict the risk of malignant stroke. A-P scoring scale ranged from 0-9. Our study showed a statistically significant inverse relationship of A-P score and risk of malignant stroke. Approximately 90% of patients with A-P score < 5 developed malignant stroke in our study. A-P score can help predict malignant stroke post thrombectomy even after successful recanalization. Thus, A-P score of ≤ 5 suggests very high risk. (A-P) Score can assist clinicians to take early measures, and thereby prevent worse outcomes.

The Principle of Indifference (“PI”: the simplest non-informative prior in Bayesian probability) has been shown to lead to paradoxes since Bertrand (1889). Von Mises (1928) introduced the “Wine/Water Paradox” as a resonant example of a “Bertrand paradox”, and which has been presented as demonstrating that the PI must be rejected. We now resolve these paradoxes by a Maximum Entropy (MaxEnt) treatment of the PI that also includes information provided by Benford’s “Law of Anomalous Numbers” (1938). We show that the PI should be understood to represent a family of informationally-identical MaxEnt solutions; each solution being identified with its own explicitly justified boundary condition. In particular, our solution of the Wine/Water Paradox exploits Benford’s Law to construct a non-uniform distribution representing the universal constraint of scale invariance, which is a physical consequence of the Second Law of Thermodynamics.

Zoom dysmorphia (ZD) is a facial dysmorphia triggered or aggravated by frequent virtual meetings. The frequent use of videoconferencing platforms has been linked to a distorted perception of facial images as individuals have an increased awareness of their appearance, given constant video feedback. As a result, dysmorphic concerns can develop. It is crucial to identify ZD as the condition interferes with the individual’s life and can trigger or aggravate body dysmorphic disorder (BDD). A standardized approach for screening ZD in non-psychiatric settings has yet to be defined. We discuss the features of ZD and the challenges of screening for ZD in a virtual setting. To facilitate the recognition of ZD in telehealth consultations, we propose a comprehensive ZD screening questionnaire that includes questions related to typical ZD features and a BDD-focused question. The questionnaire is concise and allows the identification of individuals with a potential ZD. A BDD assessment in such individuals should follow.

Auricular keloids pose significant aesthetic and functional challenges, and traditional treatments often fall short in ad-dressing these. Our study presents an innovative combined approach of ablative CO2 and dye laser therapy for improved keloid management. This treatment protocol was applied to 15 patients with auricular keloids after an initial multispec-tral analysis to assess keloid composition. The laser sequence was tailored per patient based on this analysis. Evalua-tions using the Vancouver Scar Scale and Patient and Observer Scar Assessment Scale were carried out at baseline and at 3-week intervals post-treatment. The results showed a significant reduction in these scores at the final follow-up (p<0.05), suggesting improvements in keloid color, texture, and pliability, with minimal adverse events. Additionally, no recur-rence of keloids was observed. Our findings indicate that this novel methodology of multispectral analysis followed by tailored laser therapy may offer a safe and effective solution for auricular keloids, promising enhanced keloid treatment and prevention of recurrence. However, further investigations, including randomized controlled trials, are needed to confirm and optimize this treatment protocol.

This paper summaries the main findings from the full-scale CFD analyses conducted at SINTEF Ocean on the case of MV REGAL, which is one of the benchmark vessels studied in the ongoing joint industry project JoRes. The numerical approach is described in detail, and comparative results are presented regarding the propeller open water characteristics, ship towing resistance, and ship self-propulsion performance. The focus of numerical investigations is on the assessment of the existing simulation best practices applied to a ship scale case in a blind simulation exercise, and performance of thereof with different turbulence modelling methods. The results are compared directly with full-scale performance predictions based on model tests conducted at SINTEF Ocean and sea trails data obtained in the JoRes project.

Although the Geriatric Depression Scale (GDS) is a well-established instrument for the assessment of depressive symptoms in older adults, this has not been validated specifically for Portuguese cognitively impaired persons. The objective of this study was to analyze the psychometric properties of two Portuguese versions of the GDS (GDS-27 and GDS-15) in a Portuguese mild-to-moderate cognitively impaired sample. Clinicians assessed for major depressive disorder and cognitive functioning in 117 participants with mild to moderate cognitive decline (76.9% female, Mage = 83.66 years). The internal consistency of GDS-27 and GDS-15 were 0.874 and 0.812, respectively. There was a significant correlation between GDS-27 and GDS-15 with Beck Depression Inventory-II GDS-27: rho = 0.738, p < 0.001; GDS-15: rho = 0.760, p < 0.001), suggesting good validity. A cutoff point of 15/16 in GDS-27 and 8/9 in GDS-15 resulted in identification of persons with depression (GDS-27: sensitivity 100%, specificity 63%; GDS-15: sensitivity 90%, specificity 62%). Overall, the GDS-27 and GDS-15 are reliable and valid instruments for the assessment of depression in Portuguese-speaking cognitively impaired persons.

In response to reducing the energy cost of unmanned surface vehicles (USVs) while overcoming the low accuracy problem in surface target detection, a lightweight detection algorithm with multi-scale feature fusion is proposed. Based on the popular one-stage lightweight Yolov7-tiny target detection model, a lightweight extraction module is designed first by introducing the multiscale residual module to reduce the number of parameters and computational complexity while improving accuracy. The Mish and SiLU activation functions are used to enhance network feature extraction. Second, the path aggregation network employs coordinate convolution to strengthen spatial information perception. Finally, the dynamic head, which is based on the at-tention mechanism, improves the representation ability of object detection heads without any computational overhead. According to the experimental findings, the proposed model has 22.1% fewer parameters than the original model, 15% fewer GFLOPs, a 6.2% improvement in mAP@0.5, a 4.3% rise in mAP@0.5:0.95, and it satisfies the real-time criteria. According to the research, the suggested lightweight water surface detection approach includes a lighter model, a simpler computational architecture, more accuracy, and a wide range of generalizability. It performs bet-ter in a variety of difficult water surface circumstances.

Blast charge initiation procedures have a significant impact on both mining safety and production rates. In this study, the inventory benefit of an electric initiation system was investigated to assess its influence on both fragmentation and blast-induced damages. The Wipfrag software's was used to examine the size distribution and productivity of 12 small-scale blasts initiated by both non-electric and electric detonators. All blast rounds was initiated with plain type of electric and NONEL detonators. The average burden, spacing, stemming length, and charge weight were, respectively, 0.85 m, 1.10 m, 0.66m, and 1.1kg. The results showed that the mesh through which 80% of the blast fragments will pass for the electric blast is smaller than the mesh through which the material products from the NONEL blast will pass. The results also demonstrate that the generated blast-induced ground vibration (PPV) from all blast rounds for electric blast varies from 0.4–1.2 mm/s and 80–105dB, while that for non-electric blast ranges from 0.05–0.2mm/s and 72–95dB. As a result, the electric blast initiation technique was found to produce good fragmentation, with a higher percentage of optimum fragment sizes on spec than non-electrically initiated blasts.

When undertaking wind assessment around buildings using large eddy simulation (LES), the implementation of the integral length scale at the inlet for inflow generation is controversial, as real atmospheric length scales require huge computational domains. While length scales significantly influence inflow generation in the domain, their effect on the downstream flow field has not yet, been investigated. In this paper, we validate the effectiveness and accuracy of implementing a reduced turbulence integral length scale for inflow generation in LES results at the rooftop of low-rise buildings and develop a technique to estimate the real local length scales using simulation results. We measure the wind locally and calculate the turbulence length scales from the energy spectrum of the wind data and simulation data. According to these results, there is an excellent agreement between the length scale from simulation and measurement when they are scaled with their corresponding freestream/inlet value. These results indicate that a reduced integral length scale can be safely used for LES to provide a reliable prediction of the energy spectrum as well as the length scales around complex geometries. The simulation results were confidently employed to obtain the best location for a wind turbine installation on low-rise buildings.

In this work,the wave behaviour propagation of single-walled fluid conveying carbon nanotubes (SWCNT) under longitudinal magnetic fields and elastic foundations is studied, based on the nonlocal strain gradients theory(NSGT). With considertion of surface effect, the governing differential equation are obtained utilising Timoshenko beam theory and Hamilton's variational principle. The influence of small-scale parameters, fluid density, magnetic flux, surrounding elastic medium and surface effects on wave behaviour characteristics of carbon nanotubes are discussed in detail. The numerical results illustrated that with the magnetic flux increase，the phase velocities of the carbon nanotube will increase. When the fluid effects inside the carbon nanotube, the wave frequencies of the system reduces with increase in the non-local coefficient, while promotes with increase in the strain gradient coefficient. In addition, fluid density, surrounding elastic medium and surface effects have meaningful influence on the phase velocity of the system.

Background: The novel coronavirus (COVID-19) infection is primarily transmitted through respiratory droplets from an infected person and contact with tainted surfaces or items. Social distancing became the primary measure of preventing the spread of COVID-19, with the principal aim being to reduce the probability of contact with an already infected person. Social distancing can be distressing, as it is against the community's way of life. Saudi Arabia imposed a curfew and suspended all religious, entertainment, sporting, and mass congregations. The daily updates on COVID-19 infection and mortality are upsetting and further, aggravate depression and anxiety. With this background, this study was planned with the following objectives. To determine the sociodemographic profile of the study participants, to find the prevalence of psychological morbidity, and to measure the association between the sociodemographic details, COVID-19 associated factors, and psychological effects among Abha citizens during the COVID-19 pandemic. Methodology: A descriptive cross-sectional community-based study was conducted in the Abha city population from August 2021 to July 22. This study followed snowball sampling, the data was collected using an online Google form the link was shared in WhatsApp groups and through emails to contacts, asking friends to share it with their friends, until reaching the adequate sample size the responses were accepted. Results: A total of 530 participated, and their age ranged from 19 -70 years. Many of the participants (34.5%, n=183) belonged to 36-45 years old. Most of the responders were 69.8% married and Females (69.2%, n=367). Half of the participants had been quarantined (49.6%), and 51.5% were scared from fear of COVID-19. 64.8% of the participants had depression; 14.2%, 20.8%, 13.6%, and 16.2% had mild, moderate, severe, and extremely severe depression, respectively. Around 58.2% of participants suffered from anxiety; 5.8% experienced mild anxiety, 21.7% moderate anxiety, and 30.7% severe anxiety. The proportion of stress among the participants was 45.0%; 10.8% had mild stress, 9.4% had moderate stress, and 24.5% had severe to extremely severe stress. Conclusions: COVID-19 poses mental health problems among individuals and communities. There is a need to find the more susceptible individuals for mental health issues or other psychological disorders. Mental health and psychological problems can be resolved with health education, counseling, and appropriate interventions to curb the negative health impacts of COVID-19.

In recent years, intelligent identification and prediction of ore deposits based on deep learning algorithm and image processing technology has gradually become one of the research frontiers in the field of geological and metallogenic prediction. However, this method also has many problems that need to be solved. For example, (1) very few trainable image samples containing mineral point labels; (2) The geological image features are small and irregular, and the image similarity is high; (3) It is difficult to calculate the influence of different geological prospecting factors on ore mineralization. Based on this, this paper constructs a deep learning network model Multiscale Feature Attention Framework (MFAF) based on geo-image data. The results show that MFCA-Net module in MFAF model can solve the problem of scarce mine label images to a certain extent. In addition, the channel attention mechanism SE-Net module can quantify the difference of influence of different source factors on mineralization. The prediction map is obtained by applying the MFAF model in the study of deposit identification and prediction in the research area of the southern section of Qin-hang metallogenic Belt. In the forecast map, many regions have high metallogenic potential. Through observation, it is found that the above predicted area covers 100% of the known ore deposits at present, which has a good prediction effect. Later, we can find out the specific reasons through field verification. The multi-scale feature fusion and attention mechanism MFA Framework in this paper can provide a new way of thinking for geologists in mineral exploration. The research of this paper also provides resource guarantee and technical support for the sustainable exploitation of mineral resources and the sustainable growth of society and economy.

The DNS of film boiling requires strong computational resources that are difficult to obtain for a daily CFD use by the practitioners of the industrial R&amp;D experts. On the other hand, the film boiling experiments are associated with the usage of the expensive and highly sophisticated apparatus, and the research to this end is found to be relatively difficult due to high heat flow rates that are present in the process itself. When combined with a transient heat conduction in a solid, the problem becomes significantly difficult. Therefore, a novel method in computation of conjugate heat transfer during film boiling in a quiescent liquid has been proposed in this paper. The method relies on the solution of mass, momentum and energy conservation equations in a two-fluid framework, supplemented with the appropriate closures. Furthermore, the turbulent flow has been found as an important parameter in obtaining the accurate solution of the temperature field evolution in a solid specimen, via the proper modeling of turbulent kinetic energy (TKE) value, that has been imposed as a constant value, i.e., the frozen turbulence approach. It was found, in addition, that the appropriate TKE value can be obtained by use of Kelvin-Helmholtz instability theory in conjunction with the boundary layer theory. The obtained results show excellent agreement with the experimental data within the first 15 s of the experiment, i.e., the first ca. 10 % of the total duration of the film boiling mode of heat transfer. Furthermore, the heat transfer coefficient has matched the error bands prescribed by the authors of the paper that has presented the correlations, whilst the averaged values are far beyond this band, i.e., are slightly more than 30 % higher. The further inspection has revealed a measure of similarity between the computational result of the volume fraction field distribution and the experiment, thus confirming the capability of the method to obtain realistic interface evolution in time. The method has shown full capability for further pursuing the industrial-scale film boiling problems that involve turbulent flow and the conjugate heat transfer approach.

Evaluating the performance of water indices and mapping the spatial distribution of water-related ecosystems are important for monitoring surface water resources. This is particularly the case for Ethiopia since there is limited information available on water resources development over time despite its relevance for the people and ecosystems. To address this problem, this paper evaluates the performance of seven water indices for country-scale surface water detection based on high spatial and multi-temporal resolution Sentinel-2 data, processed using the Google Earth Engine cloud computing system. Results show that the water index (WI) and automatic water extraction index with shadow (AWEIsh) are the most accurate ones to extract surface water. Comparisons are based on qualitative visual inspections and quantitative accuracy indicators. For the latter, WI and AWEIsh obtained kappa coefficients of 0.96 and 0.95, respectively, and an overall accuracy of 0.98 each. Both indices accounted for similar spatial coverages of surface waters with 82,650 km2 (WI) and 86,530 km2 (AWEIsh) for the whole of Ethiopia.

Considering the differences between the European and African continents concerning the management of the mining production sector, we decided to carry out this study with the main objective of demonstrating that, in Africa, mining can positively change the quality of life of the populations where it develops and, at the same time, it is possible to respect the environment, which is our main wealth. To achieve these objectives, it is necessary to present the mining activity of the continent, emphasizing both the negative aspects and its strong points. The most important thing is to make a good diagnosis of the situation, which will allow us to cure our "patient", that is, African mining production.

Target detection and segmentation in synthetic aperture radar (SAR) images are vital steps for many remote sensing applications. In the era of data-driven deep learning, this task is extremely challenging due to the limited labeled data. Few-shot Learning has the ability to learn quickly from few samples with supervised information. Inspired by this, a few-shot learning framework named MSG-FN is proposed to solve the segmentation of ship targets in heterologous SAR images with few annotated samples. The proposed MSG-FN adopts a dual-branch network consisting of a support branch and a query branch. The support branch is used to extract features with an encoder, and the query branch uses a U-shaped encoder-decoder structure to segment the target in the query image. The encoder of each branch is composed of well-designed residual blocks combined with filter response normalization to capture robust and domain-independent features. A multi-scale similarity guidance module is proposed to improve the scale adaptability of detection by applying hand-on-hand guidance of support features to query features of various scales. In addition, a SAR dataset named SARShip-4i is built to evaluate the proposed MSG-FN and the experimental results show that the proposed method achieves superior segmentation results compared with the state-of-the-arts.

Unmanned Aerial Vehicles (UAVs) image object detection has great application value in military and civilian fields. However, the objects in the captured images from UAVs have problems of large scale variation, complex backgrounds, and a large proportion of small objects. To resolve these problems, a multi-scale object detector based on coordinate and global information aggregation is proposed, named CGMDet. Firstly, a Coordinate and Global Information Aggregation Module (CGAM) is designed by aggregating local, coordinate, and global information, which can obtain features with richer context information. Secondly, a Multi-Feature Fusion Pyramid Network (MF-FPN) is proposed, which can better fuse features of different scales and obtain features containing more context information through repeated use of feature maps, to better detect multi-scale targets. Moreover, more location information of low-level feature maps is integrated to improve the detection results of small targets. Furthermore, we modified the bounding box regression loss of the model to make the model more accurately regress the bounding box and faster convergence. Finally, the proposed CGMDet was tested on VisDrone and UAVDT datasets and mAP0.5 of 50.9% and 48% was obtained, respectively. At the same time, our detector achieved the best results compared to other detectors.

Cerebrovascular and airway structures are tubular structures used for transporting blood and gases, respectively, providing essential support for the normal activities of the human body. Accurately segmenting these tubular structures is the basis of morphology research and patho-logical detection. Nevertheless, accurately segmenting these structures from images presents great challenges due to their complex morphological and topological characteristics. To address this challenge, this paper proposes a framework UARAI based on the U-Net multi-scale reverse at-tention network and sparse convolution network. The framework utilizes a multi-scale structure to effectively extract the global and deep detail features of vessels and airways. Further, it enhances the extraction ability of fine-edge features by a joint reverse attention module. In addition, the sparse convolution structure is introduced to improve the features' expression ability without increasing the model's complexity. Finally, the proposed training sample cropping strategy re-duces the influence of block boundaries on the accuracy of tubular structure segmentation. The experimental findings demonstrate that the UARAI-based metrics, namely Dice and IoU, achieve impressive scores of 90.31% and 82.33% for cerebrovascular segmentation and 93.34% and 87.51% for airway segmentation, respectively. Compared to commonly employed segmentation tech-niques, the proposed method exhibits remarkable accuracy and robustness in delineating tubular structures such as cerebrovascular and airway. These results hold significant promise in facilitating medical image analysis and clinical diagnosis, offering invaluable support to healthcare profes-sionals.

Background: This study aimed to evaluate survival rate, marginal bone levels, and full arch prosthetic success on short implants when placed in areas of severely resorbed and edentulous mandibles. Methods: This is a systematic review of all randomized controlled trials of at least 10 patients with a control group in which bone augmentations were performed that were published between January 2010 and February 2023. Only 3 relevant studies met the inclusion criteria. Results: It obtained demonstrate that short-term dental implant survival rates ranged from 94.2% to 97.4% with a 5-year follow-up and the prosthetic success rate varied by 62% during the same follow-up. The mean marginal bone level values ​​of the affected short implants ranged from 0.2 mm to 0.6 mm. Conclusions: The data obtained demonstrated that short dental implants positioned with criterion and precision as a full-arch fixed support are a valid therapeutic choice for the medium-long term rehabilitation of severe edentulous mandibular atrophy.

This study investigates the efficiency of a biogas-powered cooling system through the utilization of energy and exergy calculations. Biogas, which can be generated and stored in small-scale plants as needed, serves as a viable fuel source for absorption cooling systems. The present research focuses on the biogas consumption of a triple-effect absorption cooling system, specifically designed to supply a fixed cooling load of 100 kW under varying operational conditions. The study highlights the COP (Coefficient of Performance) and ECOP (Exergetic Coefficient of Performance) values of the system, along with the exergy destruction rates of its individual components, at the optimal temperatures of operation. Furthermore, to determine the necessary biogas consumption, the study explores the establishment of dedicated farms for various animal species, ensuring an adequate number of animals for biogas production. The findings reveal a COP of 1.78 and an ECOP of 35.4% at the optimized operating temperatures. The minimum mass flow rate of biogas is determined to be 0.0034 kg/s, facilitating the operation of the boiler with a methane content of 65%. The study concludes that a total of 290 head of cattle is required to generate the annual biogas consumption necessary for the cooling system. Also, number of the cattle is enough to establish 284 biogas plants in Bursa province in Türkiye.

Knowledge about ecological conditions and processes in centers of endemism (CoEs) is still limited with respect to various systematic groups of organisms, ecosystem types, ecological conditions and ecosystem functions. Endemics and CoEs play a prominent role in nature conservation monitoring and management, and in the organization of zoos, aquaria and botanical gardens. We review the meaning of different groups of organisms and indicators for the characterization of endemic-rich regions, the importance of heterogeneity in space, continuity in time, isolation and ex-situ management for the survival of endemic and threatened species. Currently, conversion of land cover and land use change are the most important causes of biodiversity decline and extinction risk of endemic and endangered species concentrated in CoEs. These are followed by climate change, including severe weather, and then natural processes such as volcanism, landslides or tsunamis. For conservation purposes the management of zoos, aquaria, botanical gardens, of regional land use, and social aspects of the diversity of endemics and CoEs have to be taken into account as well. We need better answers to questions about the relationship between ecoregions, CoEs and regional awareness of biodiversity, which is linked with human culture including the management of zoos, aquaria and botanical gardens, aesthetics, wellbeing, health and trade.

MCrAlYHf bond coats are employed in jet and aircraft engines, stationary gas turbines, and power plants, which require strong resistance to oxidation at high temperatures. The oxidation behavior of a free-standing CoNiCrAlYHf coating was investigated in this study. Isothermal oxidation tests were performed in air at 1050 °C on the specimens with various levels of surface roughness to investigate the oxidation kinetics. X-ray diffraction, focused ion beam, scanning electron microscopy (SEM), and scanning transmission electron microscopy were used to characterize the surface oxides. The surface roughness was examined using a contact profilometer and SEM. An empirical model was proposed to elucidate the impact of surface roughness on the oxidation behavior based on the correlation between the surface roughness level and oxidation rates.

Despite the development of biorefinery processes, so far this area does not take into account the possibility of coupling the “conventional” composting process with the production of biochemicals. However, net carbon monoxide (CO) production has been observed during bio-waste composting. So far, O2 concentration and temperature have been identified as main variables influencing CO formation. This study aimed to investigate the CO net production during bio-waste composting under controlled laboratory conditions, depending on different aeration rates and temperatures. A series of composting processes were carried out in conditions ranging from ~psychrophilic to thermophilic (T=35, 45, 55, and 65°C) and an aeration rate of 2.7, 3.4, 4.8, and 7.8 L‧h-1. The highest concentrations of CO in each thermal variant was achieved with oxygen deficit (aeration rate 2.7 L‧h-1); additionally, CO levels increased with temperature, reaching ~300 ppm at 65°C. Production of CO in mesophilic and thermophilic conditions draws attention to the biological CO formation by microorganisms capable of producing the CODH enzyme. Further research on the CO production efficiency in these thermal ranges is necessary with the characterization of the microbial community and analysis of the ability of the identified bacteria to produce the CODH enzyme and convert CO from CO2.

Number of studies claim that tobacco control (TC) regulations are associated with reductions in smoking-related hospitalisation rates, however very few have estimated the impact of TC laws (TCL) at both countrywide and regional levels, and none of them - in relation to compliance with TC regulations. This study evaluates the effects of Russian TCL on hospital admission (HA) rates for pneumonia countrywide and in 10 Russian regions and the extent of these effects in connection with the compliance to TCL. Methods: HA rates for pneumonia during 2005–2019 were analysed to compare the periods before and after the adoption of TCL in 2013. An interrupted time series design and a Poisson regression model were used to estimate the immediate and long-term effects of TCL on pneumonia annual hospitalisation rates after the TCL adoption, compared with the pre-law period. The 10 Russian regions were compared using the TCL Implementation Scale (TCIS) developed on the basis of the results of Russian TC policy evaluation survey; Spearman’s rank correlation and linear regression models were employed. Results showed a 14.3% reduction in HA rates for pneumonia (RR 0.88; p=0.01) after the adoption of TCL in Russia with significant long-term effect after 2013 (RR 0.86; p=0.006). Regions with better enforcement of TCL exhibited greater reductions in pneumonia HA rates (rsp=−0.55; p=0.04); (=−4.21; p=0.02). Conclusions: TCL resulted in a sustained reduction in pneumonia hospitalisation rates, but these effects, varying by region, may depend on the scale of the TCL enforcement.

Sign Language Recognition (SLR) aims to bridge speech-impaired and general communities by recognizing signs from given videos. Researchers still face challenges developing efficient SLR systems because of the video’s complex background, light illumination, and subject structures. Recently many researchers developed a skeleton-based sign language recognition system to overcome the subject and background variation of hand gesture signs. However, skeleton-based SLR is still under exploration due to the lack of information and annotations on hand key points. More recently, researchers included body and face information with the hand gesture for the SLR, but their performance and efficiency are unsatisfactory. We proposed a Multi-Stream Graph-based Deep Neural Network (SL-GDN) for a skeleton-based SLR system to overcome the problems. The main purpose of the proposed SL-GDN approach is to improve the efficiency and performance of the SLR system with a low computational cost based on the human body pose in the form of 2D landmark locations. In the procedure, firstly, we constructed a skeleton graph based on the selected 27 whole-body key points among 67 key points to solve the inefficiency problems. Then we proposed multi-stream SL-GDN to extract features from the whole-body skeleton graph for four streams. Finally, we concatenated the four different features and applied a classification module to refine the feature and recognize corresponding sign classes. Our data-driven and graph construction method increases the system’s flexibility and brings high generability to adapt various data samples. We used three large-scale benchmark SLR datasets to evaluate the proposed model: WLASL, AUTSL and CSL. The demonstrated performance accuracy table proved the superiority of the proposed model, and we believe this will be considered a great invention in the SLR domain.

Land cover data are important basic data for earth system science and other fields. Multi-source remote sensing images have become the main data source for land cover classification. There are still many uncertainties in the scale effect of image spatial resolution on land cover classification. Since it is difficult to obtain multiple spatial resolution remote sensing images of the same area at the same time, the main current method to study the scale effect of land cover classification is to use the same image resampled to different resolutions, however errors in the resampling process lead to uncertainty in the accuracy of land cover classification. To study the land cover classification scale effect of different spatial resolutions of multi-source remote sensing data, we selected 1 m and 4 m of GF-2, 6 m of SPOT-6, 10 m of Sentinel-2 and 30 m of Landsat-8 multi-sensor data, and explored the scale effect of image spatial resolution on land cover classification from two aspects of mixed image element decomposition and spatial heterogeneity. For the study area, we compared the classification obtained from GF-2, SPOT-6, Sentinel-2, and Landsat-8 images at different spatial resolutions based on GBDT and RF. The results show that (1) GF-2 and SPOT-6 had the best classification results, and the optimal scale based on this classification accuracy was 4–6 m; (2) the optimal scale based on linear decomposition depended on the study area; (3) the optimal scale of land cover was related to spatial heterogeneity, i.e., the more fragmented and complex was the space, the smaller the scale needed; and (4) the resampled images were not sensitive to scale and increase the uncertainty of the classification. These findings have implications for land cover classification and optimal scale selection, scale effects and landscape ecology uncertainty studies.

The sustainable metabolic engineering (SME) concept was defined by Stalidzans and Dace as an approach for the selection of most sustainable metabolic engineering designs taking the economic, environmental and social components of sustainability into account. In the centre of sustainability calculations is a genome scale metabolic model that provides full balance of all incoming and outgoing metabolic fluxes at steady state. Therefore, sustainability indicators are assigned for each exchange reaction enabling calculation of sustainability features of consumption or production of each metabolite. The further development of the SME concept depends on its implementation at the computational level to acquire applicable results – sustainable production strain designs. This study proposes for the first time a workflow and tools of SME implementation using constraint based stoichiometric modelling, genome scale metabolic models and growth coupled product synthesis approach. For SME application demonstration purposes, a relatively simple engineering task has been carried out. The most sustainable designs have been identified using Escherichia coli as chassis organism, glucose as a substrate and gene deletions as metabolic engineering tool. A growth coupled production design tool has been used to reduce the variability of sustainability. The 10 000 most sustainable designs were producing succinate as the main product with the number of deleted genes ranging from two to ten. A big number of similar designs has been identified due to the combinatorial explosion of different alternative combinations of gene deletion sets that result in interruption of the same metabolic pathways with the same impact on the metabolism.

Calibration of flow devices is important in several areas of pharmaceutical, flow chemistry and microfluidic applications where dosage of process liquids or accurate measurement of the flow rate is important. The process-oriented liquid itself might influence the performance of the flow device and the simultaneous determination of the dynamic viscosity under flow conditions might be a valuable information for the process parameters. To offer the simultaneous calibration of the dynamic viscosity of the process-oriented liquid at the corresponding flowrate, METAS has built a pipe viscometer for the traceable in-line measurement of the dynamic viscosity in the current flow facilities for low flow rates from 1 L/min to 150 mL/min and pressure drops up to 10 bar. The traceability of all the measuring quantities as well as the geometrical dimensions of the micro tube guarantee the traceability of the pipe viscometer to SI units. The most challenging part is the traceable determination of the inner diameter of the micro tube. This can be achieved by measuring the pressure drop as a function of flow rate with the pipe viscometer and applying the law of Hagen-Poiseuille with a traceable dynamic viscosity of a reference liquid (water) or perform the measurements with the micro-CT at METAS, which determines the inner diameter by x-ray diffraction. The validation of the stated measurement uncertainty of the pipe viscometer has been performed by means of calibration of the dynamic viscosity of several reference liquids with traceable density and kinematic viscosity. The setup of the facility, the traceability as well as the uncertainty calculation of the pipe viscometer for the in-line measurement of the dynamic viscosity are discussed in this paper.

Solid asperity interactions are common and inevitable under severe loading conditions for any lubricated contact. Heavy-duty machine components (gear, bearings etc.) generally operate under Mixed Lubrication, where uneven surface features contact each other when generated fluid pressure is not enough to support the external load. The Reynolds equation is commonly used to simulate smooth lubricated contacts numerically. In rough lubricated interfaces where opposite surface asperities make contact, the Reynolds equation alone cannot accurately predict pressure using the traditional numerical simulation method. In this paper, Lubrication-Contact interface conditions (LCICs) have been implemented and extended to solve the multiple asperity contact problem using the full-multigrid approach. The developed novel algorithm has significantly accelerated the solution process and improved the accuracy and efficiency of pressure calculation for fluid-solid sub-interactions that can occur under ML regions. The Finite Difference Method (FDM) results have been compared with Computational Fluid Dynamics (CFD) simulation to validate the newly developed model. Hence, the proposed optimized solution method will provide valuable insight to researchers and industry engineers interested in simulating the ML problem where the effect of the fluid-solid interface can be captured effectively to improve reliability in the calculation of the life expectancy of the lubricated parts.

Alcohol and substance addiction is a type of brain disease that damages many areas of life of the individual and has psychological components. Since questioning alcohol use alone is not sufficient in the diagnostic evaluation process, self-awareness and the individual's long-term psychological well-being are important in the rehabilitation process. Since investigating alcohol use alone is not sufficient in the diagnostic evaluation process, self-awareness and the individual’s long-term psychological well-being are important in the rehabilitation process. Primary prevention is used to prevent disease in healthy people, and secondary prevention is used for early diagnosis to people at risk. Tertiary prevention is important to prevent recurrence of the disease in the treated person. In addiction, which is a chronic problem, a need has emerged for tertiary prevention, that is, rehabilitation standards. If people are uncertain about the harm perception and the natural result of their behavior, they easily slip into the substance. Therefore, as a positive psychotherapy technique, harm perception and result awareness levels should be clarified. For this reason, scales were developed. The inclusion of new psychology perspectives and therapy techniques, such as positive psychology, in the treatment process requires setting new standards. In this study, it was aimed to develop result awareness and harm perception scales to determine the awareness levels and rehabilitation standards of alcohol and substance addicted individuals, and to compare awareness in normal and addicted groups. The study group consisted of 1134 participants, 41 of whom were addicts. Among the two scales developed in the study, the Uskudar Result Awareness Scale (USRAS) consisting of 25 items and 6 factor, explained 58.4% of the total variance. The Uskudar Harm Perception Scale (USHPS), consisting of 36 items and 10 factors, explained 56.3% of the total variance. Confirmatory factor analysis of the two scales resulted in acceptable goodness-of-fit values. (X2/df<3; RMSEA<.08; NFI>.90; NNFI>.95; CFI>.95; GFI>.90; AGFI>.85). In the comparisons made, the result awareness of the normal group was moderate (X=3,81); whereas the addicted group had a low result awareness (X=3,20); The effect size of the difference between the two groups was found to be high (d=1,45; >0,8). In the other measure, the harm perception of the normal group was found in the low-risk group (X=3,78); the harm perception of the addicted group was found in the moderate-risk group (X=3,43). According to Cohen d calculations, the effect size of the difference between the two groups is high (d=1,43; >0,8). It has been concluded that the two scales that emerged in the research are valid and safe, can be included in the rehabilitation studies and the creation of self-awareness, and their effectiveness should be tested.

The purpose of this study was to examine the problem of violent extremism among young people. Additionally, a youth violent extremism scale (YVES) was developed that takes into account the differences between and within Arab cultures. The goal was to produce a scale that could be used and tested within an Arab country or between Arab countries. The scale was developed in three stages: generation, refinement, and validation. A literature review and nomological network were utilized to show the scale's dimensions, correlations to those dimensions, and other relevant variables (such as the LSC, LSE, and criminality scale). A sample of 6726 young students from fifteen Arab countries and the authorities involved were selected. Findings showed that six factors were obtained through principal component factor analysis using Varimax and Kaiser normalization (29 items). A significant positive association was found (r=.651, p = 0.000), supporting the scale's validity. Using Cronbach's alpha, the scale reliability was strong and assessed at 0.98. The scale's construct validity was estimated by assessing the correlation between the Youth Violent Extremism scale and low self-control. A significant positive correlation (r=.651, p = 0.000) indicates the scale's validity. Findings showed significant differences between males and females in youth violent extremism (F = 13.678, α ≤0.000). Descriptive results showed, however, that females have a slightly higher mean of violent extremism than males (M =85.2 vs. 87.6), with a close variation (26 vs. 27) for males and females, respectively. In this study, a measurement tool was provided that can be used as a knowledge base for security strategies. The scale can be applied to identify legal, social, and educational policies and applications of violent extremism. Future research is needed to test the scale on different professions like teachers, police officers, and parents. Also, a need to test the scale on different age groups, settings, and cultures. A need for atheoretical and empirical framework for understanding and preventing youth violent extremism through promoting a more cohesive and resilient youth society.

We work within a Winterberg framework where space, i.e., the vacuum, consists of a two component superfluid/super-solid made up of a vast assembly (sea) of positive and negative mass Planck particles, called planckions. These material particles interact indirectly, and have very strong restoring forces keeping them a finite distance apart from each other within their respective species. Because of their mass compensating effect, the vacuum appears massless, charge-less, without pressure, net energy density or entropy. In addition, we consider two variable models, where, , is Newton’s constant, and, , increases with an increase in cosmological time. We argue that there are at least two competing models for the quantum vacuum within such a framework. The first follows a strict extension of Winterberg’s model. This leads to nonsensible results, if increases, going back in cosmological time, as the length scale inherent in such a model will not scale properly. The second model introduces a different length scale, which does scale properly, but keeps the mass of the Planck particle as, the Planck mass. Moreover we establish a connection between ordinary matter, dark matter, and dark energy, where all three mass densities within the Friedman equation must be interpreted as residual vacuum energies, which only surface, once aggregate matter has formed, at relatively low temperatures. The symmetry of the vacuum will be shown to be broken, because of the different scaling laws, beginning with the formation of elementary particles. Much like waves on an ocean where positive and negative planckion mass densities effectively cancel each other out and form a zero vacuum energy density/ zero vacuum pressure surface, these positive mass densities are very small perturbations (anomalies) about the mean. This greatly alleviates, i.e., minimizes the cosmological constant problem, a long standing problem associated with the vacuum.

We present a new interpretation of the Higgs field as a composite particle made up of a positive, with, a negative mass Planck particle. According to the Winterberg hypothesis, space, i.e., the vacuum, consists of both positive and negative physical massive particles, which he called planckions, interacting through strong superfluid forces. In our composite model for the Higgs boson, there is an intrinsic length scale associated with the vacuum, different from the one introduced by Winterberg, where, when the vacuum is in a perfectly balanced state, the number density of positive Planck particles equals the number density of negative Planck particles. Due to the mass compensating effect, the vacuum thus appears massless, charge-less, without pressure, energy density, or entropy. However, a situation can arise where there is an effective mass density imbalance due to the two species of Planck particle not matching in terms of populations, within their respective excited energy states. This does not require the physical addition or removal of either positive or negative Planck particles, within a given region of space, as originally thought. Ordinary matter, dark matter, and dark energy can thus be given a new interpretation as residual vacuum energies within the context of a greater vacuum, where the populations of the positive and negative energy states exactly balance. In the present epoch, it is estimated that the dark energy number density imbalance amounts to, , per cubic meter, when cosmic distance scales in excess of, , are considered. Compared to a strictly balanced vacuum, where we estimate that the positive, and the negative Planck number density, is of the order, particles per cubic meter, the above is a very small perturbation. This slight imbalance, we argue, would dramatically alleviate, if not altogether eliminate, the long standing cosmological constant problem.