ABSTRACT: Waterlogging is a common abiotic stress in global maize production. Maize flowering stage (from tasseling to silking) is more fragile to environmental stresses among different growth stages. The effects of waterlogging at flowering stage on the grain yield and starch quality of waxy maize were studied using Suyunuo5 (SYN5) and Yunuo7 (YN7). The grain yield and starch concentration of both hybrids were reduced by waterlogging. The size of starch granules and proportion of small-molecule amylopectin increased in SYN5 and decreased in YN7 when plants suffered waterlogging at flowering, in contrast to the change trend of relative crystallinity. The pasting viscosities and gelatinization temperatures were reduced, whereas the gelatinization enthalpy was unaffected by waterlogging in both hybrids. Our study indicated that waterlogging at flowering reduced the grain yield, restricted starch deposition, and deteriorated the pasting property of waxy maize. Between the two hybrids, YN7 and high pasting viscosities and low retrogradation percentage than SYN5, indicated its advantages on produce starch for viscous and less retrograde food. Results provide information for utilization of waxy maize grain in food production.

In order to study the collapse failure of shaft wall of high depth inclined ore-pass under ore impact wear behavior, 25 ore-passes in a deep mine in Yunnan, China were investigated. Considering the ore-pass structural characteristics and the impact wear effect of ore on shaft wall, the shaft wall damage model of high depth inclined ore-pass is constructed based on the contact mechanics theory. Furthermore, No. 1#, 2#, 3# and 9# ore-passes of the mine are taken as examples for analysis, and the three results of theoretical analysis, numerical simulation and engineering case are used for comparison and verification. The shaft wall damage degree is predicted, and the damage mechanism between total shaft wall damage volume Qtol and ore-pass depth H, ore-pass dip angle θ, inclined angle of chute α, shaft diameter D and ore block size P is quantitatively investigated. The results show that the rationality of the proposed model is verified by numerical analysis and field engineering situations. Moreover, the deeper ore-pass depth H, the more shaft wall damage volume Qtol with the increase of collision frequency. Qtol increases exponentially with P and increases gradually with D. With the increase of θ or α, Qtol increases generally first and then decreases. Therefore, Qtol can be effectively reduced by selecting the appropriate structure parameters of ore-pass and adopting the technology of drilling straight ore-pass. This work provides technical support for long period safe operation of high depth inclined ore-pass.

This mixed-methods research article explores the influence of ChatGPT, a state-of-the-art AI language model, on the development of maladaptive learning habits in users. The study combines quantitative and qualitative data to provide a comprehensive understanding of how engaging with ChatGPT may inadvertently foster detrimental learning behaviors. Findings suggest that while ChatGPT can be a valuable learning tool, it has the potential to exacerbate or create bad learning habits if not used mindfully.

In contemporary society, an increasing number of individuals are identified as the "Indoor Generation". Prolonged periods spent indoors can have detrimental effects on our overall well-being. Consequently, the examination of biophilic indoor environments and their impact on occupant well-being and comfort has emerged as a significant area of study. Besides the construction of new biophilic buildings, numerous existing building stocks retain substantial social, economic, and environmental value. The need for stock renovation is considerable, revitalizing existing structures rather than embarking on new constructions. Swift feedback is crucial during the initial stages of renovation design, fostering consensus among all stakeholders. A comprehensive understanding of proposed renovation plans contributes to improved indoor environment design. Initially, generative adversarial networks (GANs) produce interior renovation drawings based on user preferences and designer styles. Subsequently, the implementation of Mixed Reality (MR) and Diminished Reality (DR) provides users with an immersive perspective, transforming 2D drawings into interactive MR experiences, and facilitating the presentation of interior renovation proposals. This paper outlines the development of a real-time system for architectural designers that seamlessly integrates MR, DR, and GANs results, with the aim of enhancing feedback efficiency during the renovation design process, enabling stakeholders to evaluate and understand renovation proposals more comprehensively. Furthermore, we incorporate several full-reference image quality assessment (FR-IQA) methods to evaluate the quality of the GANs-generated images. The evaluation results indicate that the majority of images fall within the moderate range of quality.

This paper presents a comprehensive study based on multiphase seepage and wellbore multiphase flow theories. It establishes a gas intrusion rate calculation model that considers various factors including formation pore permeability, bottomhole pressure difference, drilling fluid rheology, and surface tension. Experiments were conducted to investigate the gas intrusion mechanism under shut-in conditions, and the experimental results were employed to validate the reliability of the proposed gas intrusion rate calculation method. Furthermore, the research explores the transportation rates of single bubbles and bubble clusters in drilling fluid under shut-in conditions. Meanwhile, empirical expressions were derived for the drag coefficient for single bubbles and bubble clusters in the wellbore. These expressions can be used to calculate gas transportation rates for various equivalent radii of single bubbles and bubble clusters. Additionally, a method was developed for calculating the rising velocity of bubble clusters in water based on experimental results. The study reveals that the average bubble size in the bubble cluster is significantly smaller than the size of the single bubble generated from the orifice. When the viscosity of the drilling fluid is low, the bubble cluster transportation velocity exhibits a positive correlation with the average bubble diameter. When the average bubble diameter exceeds 1 mm, the bubble velocity no longer varies with the change in the bubble cluster diameter. The initial bubble size of intrusive gas, the transportation speed of intrusive gas in the wellbore, the gas intrusion rate, and variations of the wellbore pressure after gas intrusion were analyzed. The research results provide theoretical support for wellbore pressure prediction and pressure control under shutdown conditions.

This study focuses on the formulation and analysis of a numerical method to compute singularly perturbed functional differential equations (SP-FDEs) involving large mixed shifts. SP-FDEs are a class of differential equations with both shifts and small perturbation parameter. Such equations arise in various scientific fields to model phenomena with memory effects and perturbed dynamics. In this particular study, the considered SP-FDEs involve large negative shift (delay) and large positive shift (advance). Dealing with large mixed shifts in SP-FDEs gives rise to challenges in terms of stability analysis, numerical methods, and convergence of a solution. In this paper, the influence of the large mixed shifts is treated by choosing a uniform mesh discretized in such a way that the term with the shifts lie on the nodal points. Then, using the Numerov’s finite difference method, the continuous problem is transformed into a finite difference scheme and the influence of the perturbation parameter is handled by introducing a fitting factor in the difference scheme. Stability estimate and convergence analysis of the developed scheme are investigated and proved. Numerical experiments are carried out to demonstrate the validity and applicability of the developed scheme. It is shown that the numerical scheme obtained in this work is parameter-robust of second order convergence rate.

Food insecurity is an economic and social condition involving limited or uncertain access to food. The problem of food insecurity in communities is influenced by economic conditions, food deserts, and barriers to accessing healthy food. Individuals experiencing food insecurity often endure concurrent problems of financial instability, hunger, and poor mental and physical health. Public and non-profit services in the U.S., such as the federally supported Supplemental Nutrition Assistance Program (SNAP) and community food banks, provide food-related assistance to individuals who are at a high risk of experiencing food insecurity. Unfortunately, many individuals who qualify for these services still experience food insecurity due to barriers preventing them from accessing food. Effective approaches for removing barriers that prevent individuals from accessing food are needed to mitigate the increased risk of chronic disease among vulnerable populations. This study tested a novel food insecurity intervention called FINDING-Food, which stands for Frequent Informational Nudges Directing Individuals to Needed Goods. Informational nudges in the intervention promoted food security through the elimination of information barriers to accessing food. The intervention used in this mixed-methods feasibility study consisted of informational nudges in the form of weekly text messages that were sent to food pantry clients experiencing food insecurity. The study aims were to test the efficacy and acceptability of the intervention by examining whether the informational nudges could enhance food pantry utilization, increase SNAP registration, and promote food security. Quantitative study results showed a lower prevalence of food insecurity in the intervention group than the control group at the end of the study. Qualitative findings revealed how the intervention group found the text messages to be helpful and informative. These study findings can enhance future food insecurity interventions aiming to eliminate barriers that prevent individuals who are food insecure from accessing healthy food.

Peptide-based opioid ligands are important candidates for the development of novel, safer, and more effective analgesics to treat pain. To develop peptide-based safer analgesics, we synthesized a mixture-based cyclic pentapeptide library containing a total of 24,624 pentapeptides and screened the mixture-based library samples using a 55 °C warm-water tail-withdrawal assay. Using this phenotypic screening approach, we deconvoluted the mixture-based samples to identify a novel cyclic peptide Tyr-[D-Lys-Dap(Ant)-Thr-Gly] (CycloAnt), which produced dose- and time-dependent antinociception with an ED50 (and 95% confidence interval) of 0.70 (0.52-0.97) mg/kg i.p. mediated by the mu-opioid receptor (MOR). Additionally, higher doses (≥ 3 mg/kg, i.p.) of CycloAnt antagonized delta-opioid receptors (DOR) at least 3 hours. Pharmacological characterization of CycloAnt showed the cyclic peptide did not reduce breathing rate in mice at doses up to 15 times the analgesic ED50 value, and dramatically less hyperlocomotion than the MOR agonist, morphine. While chronic administration of CycloAnt resulted in antinociceptive tolerance, it was without opioid-induced hyperalgesia and significantly reduced signs of naloxone-precipitated withdrawal suggestive of reduced physical dependence as compared to morphine. Collectively, the results suggest this dual MOR/DOR multifunctional ligand is an excellent lead for the development of peptide-based safer analgesics.

Complex-structured polymeric microparticles hold significant promise as advance in next-generation medicine mostly due to demand from developing targeted drug delivery. However, the conventional methods for producing these microparticles of defined size, shape and sophisticated composition often face challenges in scalability, reliance on specialized components such as micro-patterned templates, or have limited control over particle size distribution and cargo release kinetics. In this study, we introduce a novel and reliably scalable approach for manufacturing microparticles of defined structures and sizes with variable parameters. The concept behind this method involves the deposition of a specific number of polymer layers on a substrate with low surface energy. Each layer can serve as either the carrier for cargo or a programmable shell-former with predefined permeability. Subsequently, this layered structure is precisely cut into desired-sized blanks using a laser. The manufacturing process is completed by applying of heat to the substrate what results on sealing the edges of the blanks. The combination of the high surface tension of the molten polymer and the low surface energy of the substrate enables the formation of discrete particles, each possessing semi-spherical or other designed geometries determined by their internal composition. Such anisotropic microparticles are envisaged versatile applications.

Art organizations are increasingly focusing on enhancing the visitor experience to attract a wider audience. The aim objective of this study was to explore the key factors that influence the visitor experience of offline art exhibitions and to use a mixed methods to data collection and analysis. The research process was divided into three stages. In the first stage, we conducted semi-structured interviews with 12 respondents by dividing the exhibition experience into three time periods: pre, during, and post-visit. We extracted seven factors affecting visitor experience through qualitative content analysis including marketing, motivation, physical environment, exhibit quality, participation, technology and service facilities; in the second stage, we developed an assessment scale and pretested it; in the third stage, we collected 725 questionnaires to validate the factors of visitor experience in art exhibitions. The results of linear regression analyses showed that all seven key factors had a significant impact on visitor experience. This study constructed a scale to assess the factors of art exhibition experience, which provides theoretical sup-port for in-depth study of art exhibition experience and useful guidance for planning and design-ing art exhibitions to improve the quality of visitors' experience of art exhibitions.

In this paper, the effects on the rheological properties, particle size distribution, and fluorescence properties of red wine were investigated under microwave irradiation, and the mechanism of the effects of microwave irradiation on the sensory properties of red wine was discussed. The results showed that the effect of microwave on the rheological properties in red wine could be fitted by the Power-law model and Carson model, through the analysis of the rheological constants, yield stress, and viscosity coefficient of wine, it was found that microwave treatment could improve leg phenomenon and thickening effect by the change of rheological properties; the particle size distribution in wine indicated that microwave irradiation did change the particle size distribution through friction effect and oxidative polymerization, resulting in the wine’s visual effect and mouthfeel; the wine quality could be improved by enhancing the fluorescence intensity under different microwave conditions, which meant that microwave technology could speed up the formation of fluorescent substances, such as the polymerization of flavan-3-ols. In conclusion, microwave treatment could modify the sensory characteristics of wine, and further provide the theoretical basis for the application of microwave in winemaking.

The main aim of this study is to predict whether the type of breast cancer is “benign” or “malignant” by classical generalized linear model (GLM) approach and an extended family of GLM called generalized linear mixed model (GLMM) approach for binomially distributed response variable with binary link functions. In this study, an advanced statistical modeling approach based on the GLMM to the traditional statistical modeling approach based on the GLM for binomially distributed response variable with various binary link functions is proposed to investigate the relationships between the “malignant or benign diagnosis of the BC in patients” and “nine attributes” of 699 BC diagnosed patients. This study also focuses on the statistical significance of the accurate classification of the BC diagnosed patients in cancer studies in medicine in “benign” or “malignant” type based on the WBC dataset. In this study, the superiority of the GLMM approach over the GLM approach for the binary response variable especially belonging to the WBC dataset is emphasized in the field of cancer diagnosis in medicine. Also the importance and the power of the IC and performance metrics as the goodness-of-fit test statistics are strongly emphasized for accurate statistical inferences from the “best” fitted model. In this study, from the main findings, the best fitted model among the GLM and GLMM approaches for the binary response variable is determined as the GLMM under “logit” link function with “id” random effect with the most statistically significant odds of the occurance of the BC being “malignant” as 7.9104, 5.6888, 5.6643, 4.9842, 4.1212, 2.0679, 1.8755, and 1.3970 times more than being “benign” for every one-unit increase in the quantities of “clump thickness”, “bland chromatin”, “mitoses”, “bare nuclei”, “cell shape”, “marginal adhesion”, “epithelial cell size”, and “cell size”, respectively.

Abstract: A biomaterial is proposed for closing extensive bone defects in the maxillofacial region. The composition of the biomaterial includes high-molecular chitosan, chondroitin sulfate, hyaluronate, heparin, alginate and inorganic nanostructured hydroxyapatite. The purpose of the study is to demonstrate morphological and histological early signs of reconstruction of a bone cavity of critical size. The studies were carried out on 84 white female rats weighing 200-250 g. The study group consisted of 84 subjects in total, 40 in the experimental group and 44 in the control group. In all animals, three-walled bone defects measuring 0.5 x 04 x 05 cm were applied subperiosteally in the region of the angle of the lower jaw and filled in experimental group using lyophilized gel mass of chitosan-alginate-hydroxyapatite (CH-SA-HA). In control animals, the bone cavities were filled with an auto-blood clot after bone trepanation and bleeding. The observation periods are 3.5.7 days, 2.3.4.6.8 and 10 weeks. The control of bone regeneration was carried out using multiple morphological and histological analyses. Results showed that following implantation the chitosan construct actively replaced early-stage defects with the formation of a full-fledged new bone tissue as compared to the control group. Already, by the 7th day morphological analysis showed that formation of spongy bone tissue could be seen. After 2 weeks there was a pronounced increase in bone volume (P<0.01), and at 6 weeks after surgical intervention the closure of the defect was 70-80%, after 8 weeks - 100% without violation of bone morphology with a high degree of mineralization. Thus, the use of modified chitosan after filling eliminates bone defects of a critical size in the maxillofacial region, reveals early signs of bone regeneration, and serves as a promising material in reconstructive dentistry.

A major challenge of augmented and mixed reality applications is identifying the context and semantics of the real environment. Studies on object and action recognition were developed based on the improvement of machine learning techniques, allowing them to be annotated and recognized. This study aims to characterize current knowledge on the use of machine learning for recognizing objects and actions in augmented and mixed reality environments, increasing context awareness. Therefore, a systematic literature review of works related to these topics was made, using the Scopus and Web of Science knowledge bases. We searched articles and conference reviews or papers published between 2018 and 2022 and selected fifteen studies to be reviewed. The results indicate that there is a great demand for using machine learning to immersive technologies in factories, engineering, entertainment, education, health, among other application domains. However, these real-time interactive systems still have challenges and limitations to be solved, involving network communication, prediction time and the creation of a model that recognize objects and actions in broad contexts. Furthermore, additional research is needed to investigate how object and action recognition can increase context awareness in augmented reality applications.

Severe COVID-19 alters the biochemical and morphological characteristics of blood cells in a wide variety of ways. To date, however, the vast majority of research has been devoted to the study of leukocytes, while erythrocyte morphological changes have received significantly less attention. The purpose of this research was to identify erythrocyte types that were unique to COVID-19, compare the number of different poikilocyte types, and measure erythrocyte sizes to provide data on size dispersion. Red blood cells obtained from 6 control donors (800-2200 cells for each donor) and 5 COVID-19 patients (800-1900 cells for each patient) were examined using low voltage scanning electron microscopy. We did not discover any forms of poikilocytes that would be unique to COVID-19. Among COVID-19 patients, we observed an increase in the number of acanthocytes (p=0.01) and a decrease in the number of spherocytes (p=0.03). In addition, our research demonstrates that COVID-19 causes an increase in the median (p=0.004) and interquartile range (p=0.009) when assessing erythrocyte size.

Scientists, politicians, and practitioners are debating the current structure of pig farms in Lithuania, as medium and small farms have almost disappeared over the past decade. The debated problem is whether the revitalization of medium and small pig farms would sustainably contribute to self-sufficiency in pork? Therefore, this research aims to determine which farms in terms of size could be the most perspective. In order to achieve this aim the multi-criteria evaluation method TOPSIS was used. The economic, production and environmental indicators of Lithuanian pig farms and pig farming sector in Lithuania and selected EU countries: Latvia, Estonia, Poland, Germany, and Denmark were analyzed in this research. The multi-criteria evaluation led to the conclusion that Danish pig farms were the best managed. Germany occupied the second place. The industrial large farms were found as dominated in those countries. Large pig farms (approximately two thousand sows) appeared most perspective in Lithuania: they took the first place in the years examined (2016–2021). The criterion estimate of their assessed indicators was much higher than of the medium (100 sows) and small (20 sows) farms. The main reasons: significantly higher labour productivity, lower cost, lower price, and better production indicators.

Network neuroscience delves into comprehending the intricate arrangement and interactions within the neural networks of the brain. By merging insights from both neuroscience and network theory, it investigates the brain's structure and function through the lens of interconnected nodes (symbolizing brain regions) and edges (symbolizing the connections among these regions) forming a dynamic network. Graph metrics play a pivotal role in neuroscience by providing quantifiable insights into the intricate connectivity patterns within the brain's neural networks. Frequently, nodes are extracted from functional or structural atlases, which can lead to diverse shapes and sizes. Nevertheless, our understanding of how these differing node characteristics and definitions impact the computed graph metrics in neuroimaging data remains limited. This study adopts a data-driven methodology to delineate functional nodes, subsequently examining the influence of their sizes on resultant graph metrics. By employing the Neuromark framework, an entirely automated independent component analysis (ICA) is applied to resting state fMRI data. Through this, functional network connectivity (FNC) matrices are computed—capturing Pearson correlations between component-time courses—while employing a proportional threshold to streamline node connections based on these correlations. Subsequently, various global and local graph metrics are computed, offering insights into network characteristics. Global metrics provide an overarching summary of the network structure, while local measures delve into structural intricacies at the individual component level. Node sizes are computed based on voxel counts surpassing a designated threshold. Next, we calculated the Pearson correlation between the obtained node sizes and the graph metrics, which we term 'node-metric coupling' (NMC). Our findings revealed consistent and noteworthy correlations between the values of graph metrics and the dimensions of brain nodes. To delve deeper into the implications of this correlation, we examined the node-metric coupling within a dataset comprising Alzheimer's disease, mild cognitive impairment, and control subjects. The disparities observed in node-metric coupling among these groups underscore the necessity of accounting for this factor during analysis. The two principal outcomes of this study are as follows: Firstly, the substantial interplay between varying node sizes within a given atlas and the resultant graph metrics; and secondly, the potential utility of node-metric coupling as a viable biomarker for brain disorders. These discoveries hold significant ramifications. While comparing studies employing diverse atlases is already a challenge, our work highlights an additional, critical source of variability: accounting for node sizes. Consequently, the association between intra-atlas node size and graph metric should be thoughtfully acknowledged in future neuroimaging investigations.

Yijing possesses inherent principles and logic, and it has achieved consensus as an auxiliary decision-making function. Research on Yijing has been validated in the field of superpsychology, primarily focusing on individuals. However, the universal value of Yijing extends beyond individual divination or telepsychological induction, and there are untapped scientific potential values associated with it. Consequently, many scholars studying Yijing, including those in Chinese medicine, attempt to decipher its internal scientific mechanism through quantitative methods. In summary, scientific quantitative analysis of Yijing can be categorized into several approaches. These include quantification based on Yijing itself (binary and hexagram order), as well as quantification based on mathematical statistics, probability, and calculus. Another approach involves quantification based on expert knowledge, as well as the quantification of timewave curves and Yi-globle. Significant progress has been made in all of these quantitative research areas within the scientific paradigm. Nevertheless, future quantification of Yijing may explore rule-based evolutionary analysis, incorporating artificial intelligence technology, and conduct quantitative analysis based on the evolutionary process and results. There is even potential to deepen this research with tools such as ChatGPT. Additionally, the focus of Yijing divination may shift from individual events to group events, accompanied by space-time modeling and analysis through the construction of a new coordinate system.

Inbreeding depression is expected to be more pronounced in fitness-related traits, such as pig litter size. Recent studies have suggested that the genetic determinism of inbreeding depression may be heterogeneous across the genome. Therefore, the objective of this study is to conduct a genomic scan across the pig autosomal genome to detect the genomic regions that control inbreeding depression for litter size in two varieties of Iberian pigs (Entrepelado and Retinto). The datasets consist of 2,069 (338 sows) and 2,028 (327 sows) records for litter size (Total Number Born and Number Born Alive) for the Entrepelado and Retinto varieties. All sows were genotyped using the Geneseek GGP PorcineHD 70 K. We employed the Unfavorable Haplotype Finder software to extract runs of homozygosity (ROHs) and conducted a mixed model analysis to identify highly significant differences between homozygous and heterozygous sows for each specific ROH. A total of 8 genomic regions located on SSC2, SSC5, SSC7, SSC8, and SSC13, were significantly associated with inbreeding depression, housing some relevant genes such as FSHR, LHCGR, CORIN, AQP6, and CEP120.

Morphological study of lakes defines the sensitivity of their watershed. Moreover, it plays important role in safeguarding the lake’s ecosystem services through proper planning and utilization of the lakes’ resources. In the last few decades, the morphology of lakes in Ethiopian and Kenyan Rift (EKR) valley are experiencing contradicting morphological changes. Being in the same system, some are shrinking and others are expanding beyond their natural boundaries. The objective of this review is to shed light on the trends in spatio-temporal water level fluctuation and their contributing factors for lakes to the academia and decision makers. Peer reviewed article journals, conference proceedings, thesis, and book chapters collected from online sources and supplied by authors on request formed a database of 346 documents where 226 are critically assessed. The review indicated that lakes in Ethiopian rift are shrinking and expanding, while Kenyan lakes are expanding. The en-dorheic lakes suffered the most fluctuations due to anthropogenic factors at the expense of loss of forest and woodlands in their watershed. While the lakes’ water quality related to the morphological changes are given less attention, the predicted 12 m drop of Lake Turkana due to projects in the Omo catchment did not materialize.

For equivalent modeling of mixed wind farms (WFs), existing clustering indicators cannot consider the complex coupling characteristics between different types of wind turbines (WTs). This paper proposes a refined equivalent modeling method for mixed WFs based on artificial intelligence technology. Firstly, the electromechanical transient performance of mixed WFs is analyzed. The WT type, wind speed and direction, and voltage dip are considered the main factors affecting the external dynamic response of mixed WFs. Secondly, the equivalent node model is established, including the selection of independent and dependent variables. Then, the multiple artificial neural networks (ANNs) are trained one by one based on sample data, to explore the nonlinear relationship between the dependent variables and the independent variables. Finally, the electromechanical transient simulations of the power systems with a mixed WF is carried out by using the MATLAB platform. Simulation results show that the proposed model can reflect the external characteristics of the mixed WF in different wind scenarios and voltage dips.

The interactions of metal ions with fulvic acids resulted in changes in the particle size and charge. Particle size distribution was trimodal for fulvic acids, bimodal for fulvic complexes with calcium and magnesium, and copper-fulvic complexes had only one size fraction. The compensation of negative charge of carboxylic and phenolic functional groups by positive charged metal ions resulted in the increase in zeta potential which was getting closer to zero in the case of copper-fulvic complexes. However, all metal humic complexes behaved as colloidally unstable which resulted in visually observable sedimentation. Calorimetric measurements provided positive values of changes in enthalpy which indicated endothermic processes. In contrast quantum chemical calculations as well as experiments with model compounds provided negative values indicated exothermic processes. Changes in Gibbs energy were determined as negative and changes in entropy as positive which meant spontaneous processes.

The Yellow River Basin (YRB) is a crucial ecological zone and an environmentally vulnerable re-gion in China. Understanding the temporal and spatial trends of terraced-field areas (TRAs) and the factors underlying them in the YRB is essential for improving land use, conserving water re-sources, promoting biodiversity, and preserving cultural heritage. In this study, we employed ma-chine learning on the Google Earth Engine (GEE) platform to obtain spatial distribution images of TRAs from 1990 to 2020 using Landsat 5 (1990－2010) and Landsat 8 (2015－2020) remote sens-ing data. The GeoDa software platform was used for spatial autocorrelation analysis, revealing distinct spatial clustering patterns. Mixed linear and random forest models were constructed to identify the driving force factors behind TRA changes. The research findings reveal that TRAs were primarily concentrated in the upper and middle reaches of the YRB, encompassing provinc-es such as Shaanxi, Shanxi, Qinghai, and Gansu, with areas exceeding 40,000 km2, whereas other provinces had TRAs of less than 30,000 km2 in total. The TRAs exhibited a relatively stable trend, with provinces such as Gansu, Qinghai, and Shaanxi showing an overall upward trajectory. Conversely, Shanxi and Inner Mongolia demonstrated an overall declining trend. When com-pared with other provinces, the variations in TRAs in Ningxia, Shandong, Sichuan, and Henan appeared to be more stable. The linear mixed model (LMM) revealed that farmland, shrubs, and grassland had significant positive effects on the TRA, explaining 41.6% of the variance. The ran-dom forest model also indicated positive effects for these factors, with high R² values of 0.983 and 0.86 for the training and testing sets, respectively, thus outperforming the LMM. The findings of this study can contribute to the restoration of the YRB's ecosystem and support sustainable devel-opment. The insights gained will be valuable for policymaking and decision support in soil and water conservation, agricultural planning, and environmental protection in the region.

This cross-country four-year longitudinal study investigated the development of processing speed throughout primary school education. The analyses were conducted on data collected from 441 schoolchildren in Grades 1, 2, 3 and 4 (aged 6.42 to 11.85 years) in the Kyrgyzstan and Russia. Mixed effects growth modeling was applied to estimate average and individual growth trajectories for processing speed at two cross-country samples. Latent class growth modeling was conducted to identify different types of growth trajectories for processing speed and to compare the distribution of the types within the analyzed samples. According to the results, processing speed significantly increases across primary school years. The trajectory is characterized by nonlinear changes with more intensive growth between Grades 1 and 2, which later slows down until Grade 4. There were no significant cross-country differences in the initial score of processing speed or developmental changes in processing speed across primary school years. The development of processing speed is described by a model with three quadratic growth types but minimally differs. It is concluded that in both samples, the development of processing speed may be characterized by homogeneity, with the most intensive growth from Grade 1 to Grade 2 and subsequent linear improvement until Grade 4.

The production of anatase titanium dioxide particles plays a crucial role in the sulfate process used for manufacturing white pigment. A key factor in improving the quality of white pigments is enhancing the thermal mixing process within the precipitation tank. This improvement ensures the uniform dispersion of seed particles instead of their agglomeration, leading to the formation of particles with uniform sizes. The objective of this study was to enhance three-phase CFD simulations involving the mixing process of H₂SO₄ solution, steam as a gas phase, and solid seed particles. By analyzing the trajectories of the seed particles using CFD, the optimal injection position for the seed particles within the mixing process was determined. Subsequently, lab scale test and real field test were conducted based on the insights gained from the CFD simulations. The particle size distribution of two different types of seed inlets was analyzed and compared using Transmission Electron Microscopy (TEM). The findings of this study demonstrate that the developed multi-phase CFD simulation can be effectively utilized to enhance the precipitation process for the production of anatase titanium dioxide particles.

Crossbreeding in dairy cattle with exotic breeds continues to be an appealing practice to the dairy farmers of Bangladesh. However, there is limited knowledge regarding the impact of crossbreeding on both the physical attributes and milk quality traits of crossbred cattle in Bangladesh. Therefore, the primary objective of this study was to evaluate the impact of crossbreeding Bangladeshi local cattle with the exotic Holstein breed on their body characteristics and milk quality. To achieve the goal, data pertaining to body traits and milk samples were gathered from a total of 981 cows from 19 dairy farms located in the northwestern region of Bangladesh. A trained evaluator measured body condition score (BCS), udder score, locomotion score, and body conformation traits. Milk yield information was acquired from official records, while milk composition details were determined through milk analysis. The average measurements for body traits were as follows: wither height (WH) at 138 cm, body length (BL) at 155 cm, and heart girth (HG) at 203 cm. Notably, crossbred cows (Holstein x Local cattle) exhibited greater values for WH, BL, HG, BCS, and udder score than their Bangladeshi local counterparts. Furthermore, the average daily milk yield of sampled cows was recorded at 8.7 kg, with fat and protein contents of 3.78% and 3.67%, respectively. It is worth noting that crossbred cows produced a higher volume of milk compared to Bangladeshi local cattle. However, milk from crossbred cows displayed lower fat and protein content, although their somatic cell score (SCS) remained similar. Additionally, milk from crossbred cows exhibited a longer coagulation time when compared to that of Bangladeshi local cattle. A more comprehensive assessment of the crossbreeding scheme, particularly in comparison to Holstein, would necessitate further research, including an examination of its impact on cheese yield traits, health, fertility, and longevity.

In this work, we report on the first attempt using size-controlled ZnO nanoparticles (NPs) to analyze role of thioacetamide (TAA) in forming ZnS nanostructures from ZnO. The size-controlled B(blue)_, G(green)_, Y(yellow)_ZnO QDs, and NC (nanocrystalline)_ZnO NPs were synthesized by sol-gel process as well as hydrothermal method, respectively and then reacted with an ethanolic TAA solution as sulfur source. After reacting with TAA, the formation of ZnS QDs was observed over time. The chemical reactions were identified using X-ray diffraction (XRD), Transmission Electron Microscope (TEM), UV-vis spectroscopy, and Photoluminescence (PL). All results indicate that ZnS formation is due to direct crystal growth and/or chemical conversion of ZnO to ZnS. Our results enable a broader understanding of the synthetic mechanisms involved in the use of TAA as a sulfur source in forming ZnS nanostructures from ZnO.

This study aims to explore the differences between loess and landslide deposits, focusing on as-pects such as particle distribution, consolidation characteristics, and dynamic shear modulus. Through a series of experiments, the research reveals the similarities and differences between these two entities, yielding several key findings. Firstly, the process of landsliding disrupts the original structure of the loess, resulting in a reduction in porosity and a densification of the soil. This alteration in structural properties leads to significant disparities in physical attributes be-tween landslide deposits and undisturbed loess. Additionally, the movement and sorting of par-ticles during landslides cause variations in particle size distribution across different sections of the landslide deposits. Secondly, the landslide process not only alters the soil's structure but also changes the particle sizes within the loess. Particle wear and sieving result in the transformation of larger particles into smaller ones, leading to a more uniform particle size distribution. This shift in structure and particle size directly impacts the consolidation characteristics of landslide deposits, resulting in a substantial reduction in compression coefficient. Despite undergoing consolidation for decades, the middle and lower sections of landslide deposits still exhibit un-der-consolidation. Although the differences in the maximum dynamic shear modulus between loess and landslide deposits at varying depths are relatively minor, differences in porosity and consolidation characteristics lead to faster decay rates of the dynamic shear modulus for the lat-ter. The study also highlights a reduction in the water sensitivity of the maximum dynamic shear modulus within landslide deposits. Based on experimental results, a predictive model is pro-posed, utilizing A and m values to estimate the maximum dynamic shear modulus of both loess and landslide deposits. In conclusion, this research uncovers the impact of landslide processes on the structure and properties of loess, providing insightful understanding into the disparities between these two entities.

Earnings management and financial distress are two critical facets of financial management in corporations that have lately been the focus of thorough research from both scholars and practitioners. The purpose of the research is to evaluate the influence of financial distress on the earnings management of companies listed at the NSE during the pre- and post-COVID eras. Multiple linear regression was applied in this study to examine the causal association between earnings management and financial distress. To assess the effect of financial distress on earnings management, the study analyzed data from 37 listed companies during the pre-COVID and post-COVID periods. The methodology also takes into account control factors like firm size, GDP, and inflation. The pre-COVID research indicates a significant positive correlation between financial strain and earnings management. In contrast, the correlation between financial distress and earnings management is not statistically significant in the post-COVID analysis. However, research shows that firm size has a statistically significant negative influence on managing earnings. The results of this study show that smaller firms are more inclined to engage in earnings management during the post-COVID era than bigger ones.

Background: Accurate estimation of dietary intake is challenging. But whilst some progress has been made in high-income countries, low- and middle-income countries (LMICs) remain behind, contributing to critical nutritional data gaps. This study aimed to validate an objective, passive image-based dietary intake assessment method against weighed food records in London, UK for onward deployment to LMICs. Methods: Wearable camera devices were used to capture food intake of eating occasions in 18 adults and 17 children of Ghanaian and Kenyan origin living in London. Participants were provided pre-weighed meals of Ghanaian and Kenyan cuisine and camera devices to automatically capture images of the eating occasions. Food images were assessed for portion size, energy, and nutrient intake, and relative validity of the method compared to the weighed food records. Results: Pearson and Intra-class correlation coefficient of estimates of intakes of food, energy and 19 nutrients ranged from 0.60 to 0.95 and 0.67 to 0.90, respectively. Bland-Altman analysis showed good agreement between the image-based method and weighed food record. Under-estimation of dietary intake by the image-based method ranged from 4 to 23%. Conclusions: Passive food image capture and analysis provides an objective assessment of dietary intake comparable to weighed food records.

Debris flows have generated major disasters worldwide due to their great destructive capacity, which is associated with their high energy levels and short response times. To achieve an adequate risk management of these events, it is necessary to define as accurately as possible the different hazard levels to which the territory is exposed. This article develops a new methodology to estimate this hazard based on the hydrodynamic characteristics of the flow and the granulometry of the sediments that can be mobilized by the flow. The hydrodynamic characteristics of the flow are determined through a mathematical modeling that considers the rheology of non-Newtonian flows and the different volumes of sediments that could be transported during events corresponding to different return periods. The proposed methodology was implemented in the Jamundí River basin (Colombia). The results obtained indicate that in the upper part of this basin there is a low hazard level, while in the lower part of the basin approximately 15% of the affected territory has a medium hazard level and the remaining 85% has a low hazard level. The methodology developed is simple to implement but technically rigorous since it considers all relevant aspects in the generation of debris flows.

Raindrop size distribution (RSD) is an index for reflecting precipitation characteristics. Analyzing the differences of RSD plays an important role in understanding the precipitation microphysical processes and improving quantitative precipitation prediction of radar. In this paper, the RSD data of Dafang (57708) station, Majang (57828) station, and Luodian (57916) station (with an altitude of 1722.7m, 985.0m, and 441.5m, respectively) in Guizhou are analyzed according to the precipitation microphysical characteristics. First,Particles with particle size less than 1mm contributes the highest value to the density of particle number , and decrease with the descending altitude. Second, the GAMMA distribution fit shows a better fit compare to M-P distribution fit, and GAMMA distribution fit increases with the ascending altitude. Third, the mass-weighted average diameter is not sensitive enough to the change of precipitation intensity with correlation coefficients of 54.84%, but there is a obvious relationship between the average volume diameter and the precipitation intensity with correlation coefficients of 69.15%. Then, the precipitation prediction model is established using LSTM neural network after fusing the representative microphysical characteristics of RSD with radar and rain gauge data. The precipitation prediction model is applied to the Dafang (57708) site to predict precipitation for time range of 0-180 minutes, and it is found that for the prediction of convective cloud and stratiform cloud precipitation, the 60-minute prediction results are the most consistent with the actual precipitation, where the correlation coefficients are 92.87% and 92.57%, respectively. Conclusively, the results demonstrate that combining with the RSD base data could improve the reliability of precipitation forecasting.

The COVID-19 pandemic caused many enduring changes in everyday life of families with negative effects on parents’ and children well-being. However, literature lacks of studies exploring emotional reactions and coping strategies of both mothers and children of different ages. Furthermore, most studies used only self-reports. This study aimed to identify the emotions and coping strategies of children, adolescents and their mothers and to verify the association between maternal and children well-being. A mixed-method design using interviews and questionnaires was applied to collect information on well-being (emotional reactions, behavioral/emotional problems) and coping strategies of both mothers (n=65), and their children (n=35, 8-10 year; n=30, 11-13 year) during the second wave of the pandemic (December 2020). No differences between the groups emerged concerning the emotional reactions reported. In contrast, mothers and children of different ages reported different self-regulation and other-regulation strategies. Moreover, maternal strategies had different effects on children well-being. The integration of qualitative and quantitative results was informative to understand how families adapted to the radical changes of everyday life caused by the pandemic. Implications for developing interventions in such similar stressful situations to promote family well-being are discussed.

Quality control is a critical component in industrial manufacturing, directly influencing efficiency, product reliability, and ultimately, customer satisfaction. In the dynamic environment of industrial manufacturing, traditional methods of inspection may not adequately meet the evolving complexity, necessitating innovative approaches to bolster precision and productivity. In this study, we explore the application of mixed reality (MR) technology for real-time quality control in the assembly process. Our methodology involved the integration of smart glasses with a server-based image recognition system, designed to conduct real-time component analysis. The innovative aspect of our study lies in the harmonization of MR and computer vision algorithms, providing immediate visual feedback to inspectors and thereby improving the speed and accuracy of defect detection. YOLOv8 have been adopted in this study for detection object model. The project implementation occurred in a controlled environment to enable a comprehensive evaluation of the system functionality, the identification of possible problems and improvements in the system performance. The results indicated the viability of mixed reality as a powerful tool for enhancing traditional inspection processes. The fusion of MR and computer vision offers possibilities for future advancements in industrial quality control, paving the way for more efficient and reliable manufacturing ecosystems.

Nanoparticles are of great importance for various scientific and technological applications, drug discovery, ecology, molecular biology, etc. Size is a basic characteristic of any particle, and dynamic light scattering is beyond competition when measuring particle size in dispersions ranging from nanometers to micrometers. Unfortunately, this method has a serious problem in processing and interpreting experimental data. Instrument noise and polydispersity of the sample lead to the situation when one initial autocorrelation function corresponds to several different particle size distributions. Sometimes these different solutions of the inverse task can be found by changing parameters in the software. Sometimes the scientist cannot be suspicious of possible other solutions, having one result from the program. In this article I demonstrate the problem on model and experimental data using three known programs: CONTIN, the Malvern Zetasizer and DynaLS. I also present my own free program Autocor, which allows to test our own hypotheses about the modality and shape of the particle size distribution by optimizing the parameters of our own models with a small number of degrees of freedom.

In this paper, we consider the one-dimensional Ising model (shortly, 1D-MSIM) having mixed spin-(s,(2t−1)/2) with nearest neighbors and the external magnetic field. We establish the partition function of the model by means of the transfer matrix. Under a null boundary condition, we compute certain thermodynamic quantities for the 1D-MSIM. We find some precise formulas to determine the model’s free energy, entropy, magnetization, and susceptibility. By examining the model’s associated iterative equations, we use the cavity approach to investigate the phase transition problem. We numerically determine the model’s periodicity.

It is known that complexes based on natural polysaccharides are able to eliminate bone defects. Prolonged hyperglycemia means low bone regeneration and a chronic inflammatory response. The purpose of the study was to increase the efficiency of early bone formation in a cavity of critical size in diabetes mellitus in the experiment. The polyelectrolyte complex contains high molecular ascorbate of chitosan, chondroitin sulfate, sodium hyaluronate, heparin, adgelon serum growth factor, sodium alginate and amorphous nanohydroxyapatite (CH-SA-HA). Studies were conducted on 5 groups of white female Wistar rats: group 1 - regeneration of a bone defect in healthy animals under a blood clot; group 2 - regeneration of a bone defect under a blood clot in animals with diabetes mellitus; group 3 - bone regeneration in animals with diabetes mellitus after filling the bone cavity with a collagen sponge; group 4 - filling of a bone defect with a CH-SA-HA construct in healthy animals; Group 5 - filling of a bone defect CH-SA-HA in animals with diabetes mellitus. Implantation of the CH-SA-HA construct into bone cavities in type I diabetic rats can accelerate the rate of bone tissue repair. The inclusion of modifying polysaccharides and apatite agents in the construction may be a prospect for further improvement of the properties of implants.

This paper presents a theoretical evaluation of the prices of mixed briquettes produced from co-conut shells (CCS), banana peels (BNP), rattan waste (RWT), and sugarcane bagasse (SGC), and, on the other hand, an analysis of the economic viability of their use as a replacement for conventional household fuels (Liquefied Petroleum Gas, fuelwood, and wood charcoal) in house-holds in Cameroon. The investigation was carried out using the Life Cycle Cost method on a typical household over a ten-year period with annual cooking energy requirements of 950 kWhth. The SGC-CCS and SGC-RWT mixed briquettes with a ratio higher than 7.75% and 11.1%, respectively, have prices lower than 0.063 €/kWhth. The Present Value of the Net Benefit is posi-tive for the use of SGC-CCS and SGC-RWT mixed briquettes. The results show that by making the right mixes of residues, it is possible to obtain biomass briquettes that are less expensive than conventional fuels.

Google Trends data can be informative for infectious disease incidences, including Lyme disease. However, the use of Google Trends for predictive purposes is underutilized. In this study, we tested the ability of Google Trends search data to predict monthly state-level Lyme disease case counts in the United States. We requested Lyme disease data for the years 2010-2021. We downloaded Google Trends search data on terms for Lyme disease, symptoms of Lyme disease, and diseases with similar symptoms as Lyme disease. We built mixed negative binomial models based on a training dataset (2010-2016) and tested the models on a test dataset (2017-2021). A model was built for each search term and monthly lags of search terms were included as predictors. The highest performing models had high predictive ability, indicated by low Root Mean Squared Errors (RMSEs) and close association between observed and predicted case counts. The highest performing model was for the search term “Summer Flu”, which indicates low specificity of some of the terms. We outline challenges of using Google Trends data, including data availability and a mismatch between geographic units. We discuss opportunities for Google Trends data, including prediction of additional zoonotic diseases and incorporating environmental and companion animal data.

This paper focuses on hyperglycemic hyperosmolar syndrome (HHS), a unique hyperglycemic state requiring divergent diagnosis and treatment approaches from diabetic ketoacidosis (DKA), despite some shared characteristics. We introduce the mnemonic DI-FF-ER-EN-CE-S to encapsulate unique HHS management and complications. 'DI' emphasizes the need to delay and reduce initial insulin therapy until serum glucose decline is managed by fluid resuscitation alone. 'FF' and 'ER' stress the importance of fluid and electrolyte replacement due to severe dehydration and imbalances. 'EN' denotes the potential for encephalopathy and the requirement for a controlled serum osmolality reduction. 'CE' indicates that cerebral edema is rare but necessitates aggressive fluid therapy. 'S' signifies systemic multiorgan failure. We categorize the associated risks into three mnemonic groups: the 3Rs (renal failure, respiratory distress, rhabdomyolysis), the 3Hs (heart failure, hypercoagulation, hyperthermia), and AP (arrhythmias, pancreatitis) to facilitate awareness and screening of HHS.

Soybean are vulnerable to drought and temperature increases potentially induced by climate change. The purpose of this study was to determine if the use of cover crops (CC) can influence the gas exchange potential of glyphosate tolerant soybean when the vapor pressure deficit (Vpd) increases. This two years study was conducted in an open experimental field comprising Direct Seeding plots without CC (DS) or with CC (DSCC). Stomatal conductance (Gs) was measured five times on the same identified leaves following GBH application. These leaves were then collected in order to observe the stomata (size, density and stomatal index) with a scanning electron microscope and to perform measurements on foliar traits (venation density, width of midrib). The Vpd was calculated concomitantly to Gs measurements at the leaf surface. The results suggest that the use of CC promotes phenotypic change in soybean leaves (more elaborate venation and a higher stomatal density), which in turn may enhance their tolerance to drier conditions. In 2019, Gs could be up to 29% higher in DSCC plots compared to DS ones along with similar Vpd values. This study shows that the benefits of using CC can be observed through the morphological development strategies of the crop plants and their higher tolerance to drought.

Despite the proliferation of Blockchain Metaverse projects, the inclusion of physically disabled individuals in the Metaverse remains distant, with limited standards and regulations in place. However, the article proposes a concept of the Metaverse that leverages emerging technologies, such as Virtual and Augmented Reality, and the Internet of Things, to enable greater engagement of disabled creatives. This approach aims to enhance inclusiveness in the Metaverse landscape. Based on the findings, the paper concludes that the active involvement of physically disabled individuals in the design and development of Metaverse platforms is crucial for promoting inclusivity. The proposed framework for accessibility and inclusiveness in Virtual, Augmented, and Mixed realities of decentralised Metaverses provides a basis for the meaningful participation of disabled creatives. The article emphasises the importance of addressing the mechanisms for art production by individuals with disabilities in the emerging Metaverse landscape. Additionally, it highlights the need for further research and collaboration to establish standards and regulations that facilitate the inclusion of physically disabled individuals in Metaverse projects.

The study on graphs emerging from different algebraic structures like groups, rings, fields, vector spaces, etc. is a prominent area of research in mathematics, as algebra and graph theory are two mathematical fields that focuses on creating and analysing structures. There are numerous studies linking algebraic structures and graphs, which began with the introduction of Cayley graphs of groups. Several algebraic graphs have been defined on rings, which have huge-growing literature. In this article, we systematically review the literature on some variants of Cayley graphs that are defined on rings, to understand the research in this area.

Electrical energy demand increase does evolve rapidly due to several socioeconomic factors such as industrialization, population growth, urbanization and of course the evolution of modern technologies in this 4th industrial revolution era. Such rapid increase in energy demand introduces a huge challenge in power system. Such has paved way for network operators to seek for alternative energy resources other than the conventional fossil fuel system. Hence, the penetration of renewable energy into the electricity supply mix has evolved rapidly in the past three decades. However, the grid system has to be well planned ahead to accommodate such increase in energy demand in the long run. Transmission Network Expansion Planning (TNEP) is a well ordered and profitable expansion of power facilities that meets the expected electric energy demand with an allowable degree of reliability. This paper proposes a TNEP model that minimises the network reinforcements, operational costs and costs of renewable energy penetrations, while satisfying the increase in demand. The problem is formulated as a mixed integer linear programming (MILP) problem. The developed model has been tested in several IEEE test systems in multi-period scenarios. The paper also carried out a detailed derivation of the new non-negative variables in terms of the power flow magnitudes, the bus voltage phase angles and the lines’ phase angles for proper mixed integer variables’ decomposition techniques. Moreover, this paper tends to provide additional recommendation in terms of which particular year (within 20 years of planning period) can the network operators install new line(s), new corridor(s) and/or additional generation capacity to the respective existing power networks. Such is achieved by running incremental periods simulations from base year through the planning horizon. The results show the efficacy of the developed model in solving the TNEP problem with a reduced and acceptable computation time even for large power grid system.

The cutter structure and layout scheme of PDC (Polycrystalline Diamond Compact) bits are important factors in improving efficiency. To further improve the drilling efficiency of PDC bits, axe, triangular prism, and cylindrical PDC cutters were used as research objects. Based on the measured granite data, a finite element model of non-homogeneous granite was established and verified by uniaxial compression simulation. The rock-breaking process of different cutter combination schemes was compared using the finite element method, and the parameters in the best scheme were optimized using the Box-Behnken response surface method. The results show that the constructed model of non-homogeneous granite is consistent with the stress-strain relationship of real granite and is reliable. The axe PDC cutter is more aggressive than the other two cutters and is more suitable for the front row of the bit blade tooth arrangement, while the triangular prism cutter is the second most aggressive and is suitable for the rear row of tooth arrangement, and the best combination scheme is the front row of axe cutter and the rear row of triangular prism cutter arranged alternately with the axe cutter. The optimal transverse and longitudinal distances of the optimized triangular prism cutter from the front axe cutter are 10mm and 7mm, and the optimal transverse and longitudinal distances of the rear axe cutter from the front cutter are 10.06mm and 7mm. The drilling speed is more stable during drilling and the PDC bit with mixed tooth arrangement has 16.8% and 16.6% higher rate of penetration(ROP) compared with the bit with single axe cutter and triangular prism cutter, and the drilling speed is more stable during working, which can effectively improve the rock breaking efficiency of the PDC bit. The field application proves that the bit with mixed cutter arrangement is easier to break the complex formations, with more stable ROP and better efficiency. The study can provide theoretical support for the cutter layout of the PDC bit.

Why do the eyes remain comparatively large whereas the cornea is reduced in most troglobiont mysids? Are there any important organ size-dependent functions served by non-visual eye rudiments? This issue was approached by measuring eye structures in five troglobiont species of Mysida and two Stygiomysida compared with 14 troglophile and 49 trogloxene Mysida species. The Organ of Bellonci (OB) was found in all Mysida and as first records also in Stygiomysida. The length of OBs increased with individual body length and eye length in four examined species: from postnauplioid larvae to adult stage in a troglophile and a trogloxene mysid species examined; and from juveniles to adults in a troglobiont mysid and a troglobiont stygiomysid. At the interspecific level, eye length was on average 26% shorter at a given body length while the OB was 40% longer at a given body length and 54% longer at a given eye length, respectively, in adult troglobionts compared with trogloxene mysids. The OB is clearly proliferating while the cornea is reduced in troglobionts. This points to sensory functions (possibly together with other functions). The sensory pore organ was found in all 15 Mysida species whose eyes were mounted on slides, and a first record of this organ was also found in Stygiomysida.

Polyaniline or PANI is a conductive polymer (CPs) with flexibility in turning into conducting properties. The drawback is mainly in terms of limited mechanical properties improvement which attracted the interest in fabrication of PANI with other polymeric materials. PANI-PVL composites film was synthesized and fabricated in this work with chronological steps according to the objectives aligned in two different phases. The first phase determines the optimum parameters to synthesize nanosized PANI. The ideal initial molar ratio, r was 1, stirring speed of 600 rpm, purified by filtration, synthesis temperature of 25 oC, washed by dopant acid, acetone and distilled water. The usage of non-ionic surfactant, Triton X-100 (TX100) with 0.1 % concentration favours the formation of smaller particle size of PANI and good dispersibility compared to the usage of anionic sodium dodecyl sulphate (SDS). The synthesis of PANI in nanosized dimension has the potential to be applied for antimicrobial applications such as gloves and antimicrobial coating of frequent contact points in the near future.

This article explores matter collineations (MCs) of static plane-symmetric spacetimes, considering the stress-energy tensor in its contravariant and mixed forms. We solve the MC equations in two cases: when the energy-momentum tensor is nondegenerate and degenerate. For the case of degenerate energy-momentum tensor, we employ a direct integration technique to solve the MC equations, which leads to an infinite-dimensional Lie algebra. On the other hand, when considering the nondegenerate energy-momentum tensor, the contravariant form results in a finite-dimensional Lie algebra with dimensions of either 4 or 10. However, in the case of the mixed form of the energy-momentum tensor, the dimension of the Lie algebra is infinite. Moreover, the obtained MCs are compared with those already found for covariant stress-energy.

This article presents the main results of the Kids in Action (KIA) project, which aimed to promote physical activity and sport among children aged 3 to 11, by linking the education, sports and family sectors. It applies the concepts of proximity consumption and the 15-minute city to sustainable and healthy habits. This study aims to evaluate the application of the concept of proximity to the provision of physical sports activities from an early age, to reduce the drop-out rate of PA, through collaboration between schools, local government and the sports sector. A mixed methodology was used, applying a survey and interviews with the stakeholders to identify good practices in the promotion of physical activity at an early age. A total of 147 children and 10 adults (3 family members, 4 school managers or teachers and 3 sports managers) participated. The main results included the importance of introducing sport and providing adequate facilities, especially through guided workshops, as this not only influences the actual participation in sport but also the intention to continue practising it. It is observed that children with less affinity towards conventional sports benefit particularly from initiatives such as the KIA programme. On the other hand, the lack of available resources and the influence of the sports culture in families are identified as the main barriers, factors that harm the quality of participation.

When developing bone implants Young's modulus is one of the primary characteristics of the material that should be considered. The study focuses on the possibility of regulating the modulus of the alloy Ti-50.8 at % Ni by varying its initial structure as well as aging-induced microstructure in a wide range. Microstructure observations were performed using TEM and SEM observations. The calorimetric studies of martensitic transformations were carried out using "Mettler Toledo 822e". Tensile tests were performed in a test temperature range of ‒196≤T≤+100°C using an «IN-STRON 5966». The grain/subgrain size of B2-austenite strongly affects the modulus magnitude. This effect is ambiguous for a material with a grain size of 0.13–3 µm and depends on the test temperature. The effectiveness of aging on the modulus reduction depends on the initial structure: it is most pronounced in an alloy with a relatively coarse grain size of 9 µm and brings a decrease by 3.8 times at a temperature of 37°C. Bone-like elastic modulus (10–30 GPa) at a human body temperature of 37°C is realized in an aged fine-grained NiTi alloy. An ultrafine-grained sub-structure exhibits he same values of Young's modulus in the temperature range from -100°С to 25°С.

The total tree height (h) and diameter at breast height (dbh) relationship is an essential tool in forest management and planning. The height—diameter (h-dbh) relationship had been studied with several approaches and for several species worldwide. The nonlinear mixed effect modeling (NLMEM) has been extensively used and lately the resilient backpropagation artificial neural network (RBPANN) approach has been a trend topic for modeling this relationship. The artificial neural network (ANN) is a computing system based in artificial intelligence and inspired in biological neural network for supervised learning. In this study the NLMEN and RBPANN approaches were used for modeling the h—dbh relationship for Durango pine species (Pinus durangensis Martínez) in mixed-species forest from Mexico. The total dataset considered 1,000 (11,472 measured trees) randomly selected from 14,390 temporary forest inventory plots and the dataset was randomly divided into two parts; 50% for training and 50% for testing. An unsupervised clustering analysis was used to grouped the dataset into 10 clusters based on k-means clustering method and plot-variables like density, basal area, mean dbh, mean h, quadratic mean diameter, altitude and aspect. The RBPANN was performed for tangent hyperbolicus (RBPANN-tanh), softplus (RBPANN-softplus), and logistic (RBPANN-logistic) activation functions for functions in cross product of the covariate or neurons and the weights for the ANN analysis. For both training and testing, 10 classical statistics (e.g., RMSE, R2, AIC, BIC, logLik) were computed for the residual values and assess the approaches for h—dbh relationship. For training and testing, the ANNs approach outperformed the NLMEM approach, and the RBPANN-tanh has the best performance in both training and testing phases.

Repetitive elements are a major component of DNA sequences due to their ability to propagate through the genome. Characterization of Metazoan repetitive profiles is picking up, however, current pipelines are failing to identify a significant proportion of divergent repeats in non-model organisms. The order Decapoda, for which repeat content analyses are largely lacking, is characterized by extremely variable genome sizes that suggest an important presence of repetitive elements. Here we developed a new standardized pipeline to annotate repetitive elements in non-model organisms, which we applied on 20 Decapoda and 6 other crustacean genomes. With our new tool we identify 10% more repetitive elements than standard pipelines. Repetitive elements were more abundant in Decapoda species than in other crustaceans, with an incredibly large number of highly repeated satDNA families. Moreover, we demonstrated a high correlation between assembly size and transposable elements and a different repeat dynamics between Dendrobranchiata and Reptantia. The patterns of repetitive elements largely reflect the phylogenetic relationships of Decapoda and the distinct evolutionary trajectories within Crustacea. In summary, our results highlight the impact of repetitive elements on genome evolution in Decapoda and the value of our novel annotation pipeline, which will provide a baseline for future comparative analyses.

The mobile payment has been emerging as an alternative to cash and credit cards and is rapidly evolving around the world. This study integrated the general model of innovation diffusion theory (IDT) with the technology acceptance model (TAM) to examine the impact of near-field-communication (NFC) mobile payment environment on the restaurant operating performance (ROP). This paper used convenience sampling to distribute questionnaires to restaurant owners and managers in order to investigate the impact of NFC mobile payment environment on ROP. A total of 279 valid questionnaires were collected. The empirical results show that sales growth, cost saving, flexibility, accessibility and trust &amp; safety had significantly positive impacts on ROP. After considering the restaurant size as a moderator for analysis, there are only two constructs- accessibility and trust &amp; safety had significant impacts on ROP. This empirical finding could provide restaurateurs with a reference to improve ROP by increasing the mobile payment environment.

Nowadays, there are implemented devices whose purpose is to perform massive computations by saving resources at the time they reduce the latency of arithmetic operations. These devices are usually GPUs, FPGAs and other specialised devices such as "Coral". Neural networks, digital filters and numerical simulators take advantage of the massively parallel operations of such devices. One way to reduce the amount of resources used is to limit the size of the registers that store data. This has led to the proliferation of numeric formats with a length of less than 32 bits, known as short floating point or SFP. We have developed several SFP’s for use in our neural network accelerator design, allowing for different levels of accuracy. We use a 16-bit format for data transfer and different formats can be used simultaneously for internal operations. The internal operations can be performed in 16-bit, 20-bit and 24-bit. The use of registers larger than 16-bit allows the preservation of fractional information while increasing precision. By leveraging some of the FPGA’s arithmetic resources, our design outperforms designs implemented from scratch and is competitive with specialized arithmetic circuits already implemented in the FPGA.

Since the 1990’s, studies and pilot tests have been conducted to reduce traffic, accidents, and pollution due to urban freight transport (UFT). These ended up in several policies, regulations, and restrictions for UFT, such as low emission zones, delivery time windows, and vehicle size and weight restrictions. However, issues in UFT under regulatory measures still persevere. This study introduces an optimization framework for deriving an optimal combination of various types of vehicles with different capacities for vehicle replacement in UFT. This framework allows an understanding of how an urban freight company with a limited budget efficiently satisfies its freight demand within an urban area in the presence of regulatory measures by urban administrators. The introduced formulation, which is a mixed-integer linear programming, will assist the operator in choosing the best investment strategy for introducing new vehicles of certain types and sizes, for operation in different zones, into its fleet while gaining economic benefits and having a positive impact on the liveability of the urban area. Furthermore, a sensitivity analysis is performed to consider the effects of specific uncertain parameters on the total cost. The numerical results show that the share of electric vehicles in the fleet increases, and they are more competitive than diesel vehicles.

This paper investigates the physical/virtual dynamics when constructing an immersive environ-ment for medieval courtyard houses in Historic Cairo. Using a VR Lab at a university campus, the paper analyzes a courtyard that symbolizes social resilience and community bonding at the newly rehabilitated Bayt Yakan. The courtyard is reconstructed as a Mixed Object through 3D documentation and scanning the space to develop a photogrammetry and VR experience. The results show that the sense of presence is perceived through a combination of factors and juxta-positions of the real and the virtual including: 1) architectural aesthetics, 2) intangible elements and 3) socio-cultural activities in space. Based on the pilot case study, the paper concludes with proposing a “Geospatial Framework for Immersive Environments and Social Sustainability” that is transdisciplinary, pluralistic and expansive in nature to encompass the multiple narratives of the contested past, and overlay the material and immaterial sustainable features embedded in historical contexts, where the Mixed Reality paradigm becomes an enabler for multiple voices, tools and mediums.

Health services research is a multidisciplinary discipline that aims to improve population health by studying healthcare services' organization, delivery, and financing. Over time, the field has worked to define its boundaries and establish a set of core research topics and methods addressing population wellbeing, access, quality, and cost. Although the influence of health technology is significant, our literature survey on health services research showed that SCs in general—containing their management, cost, and policy—had received little attention. Importantly, our assessment also showed that the system’s readiness to handle supply policy and supply device deficiencies like those encountered during pandemic was hardly ever mentioned. Reduced open areas and water bodies, deteriorating infrastructure, and changes in the biological morphology all affect cities’ uncontrollable and unplanned growth. Urban amenities, facilities, and healthcare services were unevenly distributed due to this unchecked urban population development. In light of this, this research suggests two reliable models for site selection issues for one of Hong Kong’s main hospitals. Levels of uncertainty, infeasibility tolerance, and reliability are taken into consideration. Then, two categories of uncertainty—symmetric and bound-ed—were examined. As a result, the challenge of scheduling in uncertainty has been considered, and potential solutions have been presented using a specified probability distribution function. The paper concludes by introducing the specific justice and health problems and outlining four typical strategies. In order to ensure that medications and other healthcare supplies are produced, distributed, and given to patients, a vast network of systems, components, and procedures must be effectively managed. This research contributes in this regard. Also, its grantee fairness in health systems and the populations health locally, nationally, and globally. It highlights health inequity, advances the measurement of health inequity, and promotes public dialogues on health inequity.

The production of fuels and other industrial products from renewable sources has intensified the search for new substrates or for the expansion of the use of substrates already in use, as well as the search for microorganisms with different metabolic capacities. In the present work, we isolated and tested a yeast from the soil of sugarcane irrigated with vinasse, that is, with high mineral content and acidic pH. The strain of Meyerozyma caribbica URM 8365 was able to ferment glucose, but the use of xylose occurred when some oxygenation was provided. However, some fermentation of xylose to ethanol in oxygen limitation also occurs if glucose is present. This strain was able to produce ethanol from molasses substrate with 76% efficiency, showing its tolerance to possible inhibitors. High ethanol production efficiencies were also observed in acidic hydrolysates of each bagasse, sorghum, and cactus pear biomass. Mixtures of these substrates were tested and the best composition was found for the use of excess plant biomass in supplementation of primary substrates. It was also possible to verify the production of xylitol from xylose when the acetic acid concentration is reduced. Finally, the proposed metabolic model allowed calculating how much of the xylose carbon can be directed to the production of ethanol and/or xylitol in the presence of glucose. With this, it is possible to design an industrial plant that combines the production of ethanol and/or xylitol using combinations of primary substrates with hydrolysates of their biomass.

Loess is a special soil with high water sensitivity, which covers a large area in northwest China. Cracks are prone to generate in loess under the arid and semi-arid climate, which will provide the preferential channel for water and reduce the mechanical properties of soils. It is of great significance to understand the evolution characteristics and mechanisms of the cracks in loess areas. At present, the researches on cracks in soils are mainly concentrated in the characteristics of different cracking patterns. However, the spacing cracks are mostly discussed in materials like rock and concrete, rather than soils. The cracking characteristics and mechanisms of spacing cracks in loess is still inadequate. In this research, the drying tests of loess are carried out with different sizes of specimens. The parameters of spacing cracks and local strain distributions of the loess samples are obtained via PCAS and DIC methods during desiccation, respectively. The cracking modes, space cracking laws like insertion and saturation of spacing cracks are revealed. Finally, the size effect on the characteristics of spacing cracks is verified with a discrete element software MatDEM.

The major goal of this work is to present a novel fractional temperature-dependent boundary element model (BEM) for solving thermoelastic wave propagation problems in smart nanomaterials. The computing performance of the suggested methodology was demonstrated by using stable communication avoiding S-step – generalized minimal residual method (SCAS-GMRES) to solve discretized linear BEM systems. The benefits of SCAS-GMRES are investigated and compared to those of other iterative techniques. The numerical results are graphed to demonstrate the influence of fractional, piezoelectric, and length scale characteristics on total force-stresses. These findings also demonstrate that the BEM methodology is practical, feasible, effective, and has superiority over domain methods. The results of the present paper help to develop the industrial uses of smart nanomaterials.

Public Health Officers' (PHO) experiences in reaction to the COVID-19 pandemic can be based on whether the PHO is passive or active. Therefore, the study’s objectives are to identify the types of experiences and analyse the predictors of proactive practical experiences in solving COVID-19 among PHOs in the upper southern region of Thailand. Methods: An explanatory mixed-methods approach was used to collect data, through questionnaires and online in-depth interviews. The study was conducted from August 4, 2020, to August 3, 2021. Results: The results included 60 PHOs from 60 PCUs in 6 provinces, with 41 (68.3%) females and an average age of 35.57 years (SD = 11.61). The PHOs’ knowledge, understanding, and participation experience aspects were mostly proactive rather than passive. Factors that significantly predicted proactive practical experiences included sex (ORadj = 1.52, 95% CI = 1.04–2.21), age (ORadj = 1.69, 95% CI = 1.16-2.48), married status (ORadj = 1.69, 95% CI = 1.16-2.48), education level (ORadj = 1.50, 95% CI = 1.02–2.20), and position for work (ORadj = 1.69, 95% CI = 1.16-2.48). Conclusions: PHOs’ knowledge, understanding, opinion, and participation experiences were significant predictors of practical experience. Primary health care systems should promote proactive experiences in all four aspects to increase proactive practical experiences.

This paper aims at trying to answer the question of whether a low cost drone equipped with a regular camera can serve as a simple device to estimate the size of objects. Ad-ditionally, it assesses if the accuracy of measurement is good enough for using a drone for measurements, relying solely on GPS position and camera. A review of the literature pertaining to ground object measurements was conducted. The availability of such in-struments was evaluated in terms of purchase and rental costs, the number of instru-ments on the market, and the time to obtain them. The most common measurement methods were considered and their unit costs were reviewed. The cost of professional surveying and measuring equipment was compared to the possibility of using low-cost, non-commercial drones to measure objects and surfaces. The quality of the images ob-tained was compared with high-resolution satellite systems. Is an ordinary drone with a standard camera suitable for length measurement without professional measuring equipment? Should the results be considered only in the category of size estimation?

Being able to complete the production of the product project on time is the prerequisite to win more tasks, many enterprises have begun to study intelligent scheduling algorithms to achieve dynamic control of the total project duration. However, the production operation time of products is highly volatile, and it is impossible to predict the possible operation time of future products. In the actual production environment, we often encounter extreme situations such as mixed production lines, multiple varieties, variable batches, etc. A dynamic optimization CPM model in the ERP/MES system with the attention mechanism (EMA-DCPM) will collect, extract and predict the operation time through artificial intelligence technology, and it can accurately predict the exact operation time of a batch of products that have not yet been produced. Through experiments, our algorithm can realize the production scheduling and get more than 200% better precision than former algorithms and realize dynamic optimization of complex projects.

Tree growth is under the combined influence of abiotic and biotic factors. Trees with different sizes may respond differently to these factors, implying that tree heterogeneity in tree size may also modulate the overall growth trend. To test this hypothesis, we focused on the radial growth trends of natural subalpine forests on the Tibetan Plateau. We first extended the iterative growth model (IGM) to the tree-ring scale (IGMR) to test the applicability of the generalized metabolic growth theory to tree growth. As predicted by the IGMR, the radial growth of trees at the aggregate scale is constrained by a unimodal pattern. Using the IGMR, we reconstructed the historical best growth trajectory (HBGT) of trees within the same community based on the tree with the largest radius and/or longest age in the community. From the average difference between the HBGT and the current radial growth rate of trees with different sizes, we constructed an indicator that can measure the overall variation in tree radial growth. Based on this indicator, we found a negative effect of tree size heterogeneity on the overall variability of tree growth across elevations. Further analysis also revealed that the radial growth rate of trees on the Tibetan Plateau has increased significantly compared to the past, where growing season average temperature and annual minimum temperature were negatively and positively correlated with tree growth below and above the treeline, respectively. Our study not only confirmed that the overall variability of tree growth depends on tree size heterogeneity but also proposed an indicator that reveals net changes in tree radial growth rate relative to the past. These theoretical advances facilitate the understanding of changes in the extent of subalpine forests.

The binding affinity of trastuzumab and pertuzumab to HER2 has been studied using both experimental and in-silico methods. The experiments were conducted using the antibodies in their complete IgG form, as used in clinical therapy, and the extracellular domain of the HER2 protein in solution. This approach provides a precise, reproducible, and reliable view of the interaction between them in physiological conditions. Dynamic light scattering and size exclusion chromatography coupled with tetra detection were utilized to characterize the protein complexes, measure their concentrations, and calculate the equilibrium free binding energy, ΔGbind. In addition, PRODIGY, a QSAR-like model with excellent predictive ability, was employed to obtain in-silico ΔGbind estimations. The results obtained indicate that pertuzumab exhibits a slightly higher binding affinity to HER2 than trastuzumab. The difference in binding affinity was explained based on the contribution of the different interfacial contact (IC) descriptors to the ΔGbind value estimated by the PRODIGY model. Furthermore, experiments revealed that the pertuzumab IgG antibody binds preferentially to two HER2 proteins, one per Fab fragment, while trastuzumab mainly forms a monovalent complex. This finding was interpreted based on a geometrical model that identified steric crowding in the trastuzumab-HER2 complex as compared to the pertuzumab-HER2 complex.

The size of seeds is particularly important for agricultural development, as it is a key trait that determines yield. It is controlled by the coordinated development of the integument, endosperm, and embryo. Large seeds are one of the important ways to improve the ultimate “sink strength” of crops, providing more nutrients for early plant growth and showing certain tolerance to abiotic stresses. There are several pathways for regulating plant seed size, including the HAIKU (IKU) pathway, ubiquitin-proteasome pathway, G (Guanosine triphosphate) protein regulatory pathway, mitogen-activated protein kinase (MAPK) pathway, transcriptional regulators pathway, phytohormone regulatory pathways including auxin, brassinosteroid (BR), gibberellin (GA), jasmonic acid (JA), cytokinin (CK), Abscisic acid (ABA), and MicroRNAs (miRNAs) regulatory pathway. This article summarized the seed size regulatory network and prospected ways to improve yield. We expect to provide valuable reference to researcher in the related field.

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.

This study examines the effects of particle size and heat pipe angle on the thermal effectiveness of a cylindrical screen mesh heat pipe using silver nanoparticles as the test substance. The experiment investigates three different particle sizes (30nm, 50nm, and 80nm) and four different heat pipe angles (0°, 45°, 60°, and 90°) on the heat transmission characteristics of the heat pipe. The results show that the thermal conductivity of the heat pipe increased with an increase in heat pipe angle for all particle sizes, with the highest thermal conductivity attained at a 90° heat pipe angle. Furthermore, the thermal resistance of the heat pipe decreased as the particle size decreased for all heat pipe angles. The thermal conductivity measurements of the particle sizes - 30, 50, and 80 nm - were 250 W/mK, 200 W/mK, and 150 W/mK, respectively. The heat transfer coefficient values for particle sizes 30nm, 50nm, and 80nm were 5500 W/m2K, 4500 W/m2K, and 3500 W/m2K, respectively. The study also found that the heat transfer coefficient increased with increased heat pipe angle for all particle sizes, with the highest heat transfer coefficient obtained at a 90° heat pipe angle.

Background and Aims: Metabolic Syndrome (MetS), a global problem, predisposes to an increased risk for Type 2 diabetes and premature cardiovascular disease. While MetS is associated with central obesity, there is scanty data on adipocyte hypertrophy, increased fat cell size (FCS), in MetS. The aim of this study was to investigate FCS status in gluteal adipose tissue biopsy of patients with nascent MetS without the confounding of diabetes, cardiovascular disease, smoking or lipid therapy . Methods and Results: Fasting blood and subcutaneous gluteal adipose tissue (SAT) biopsy were obtained in MetS (n=20) and controls (n=19). Cardio metabolic features, FFA levels, hsCRP and HOMA-IR were significantly increased in patients with MetS. Waist-circumference adjusted-FCS was significantly increased in patients with MetS and increased with increasing severity of MetS. Furthermore, there were significant correlations between FCS with glucose, HDL-C and the ratio of TG: HDL-C. There were significant correlations between FCS and FFA, endotoxin and monocyte TLR4 abundance. Also, FCS correlated with readouts of NLRP3 Inflammasome activity. Most importantly, FCS correlated with markers of fibrosis and angiogenesis. Conclusions: In conclusion, in patients with nascent MetS, we demonstrate WC-adjusted increase in FCS from gluteal AT that correlated with cellular inflammation, fibrosis and angiogenesis. While these preliminary observations were in gluteal fat, future studies are warranted to confirm these findings in visceral and other fat depots.

There is an overall scientific consensus that public space and, mainly, space with a high ecological index – most notably experienced in green areas – positively affects individual and collective well-being and urban dwellers’ physical and mental health. However, the ‘reality check’ indicates difficulties in translating the assumed benefits of green space into implementable interventions in urban environments. To examine such an ‘implementation gap’ on the case of Zurich seems valid, given that the ‘green urban agenda’ debate has been embedded in numerous Swiss policies (from the federal to the municipal levels). In narrow terms, the research first focuses on Zurich’s district 9 consisting of two neighborhoods (Altstetten and Albisrieden) – the area under ongoing densification yet with a variety of green spaces, to then elucidate four green space clusters and their 400-meter catchment areas in the mentioned district. The mixed-method approach has been applied at three analytical levels: 1) documentary analysis of multi-scale national policy toolkits, to identify the critical tenets associated with the ‘green urban agenda’, 2) assessment of green spaces in Zurich’s district 9 based on online available quantitative data and on-site observation, to determine their provision, types, size and mutual connectivity, and 3) analysis of four green space clusters within the mentioned district through on-site observation, to identify green space attributes and main activities. Through critical mutual examination of the identified ‘green urban agenda’ principles in the national policies, on the one hand, and green space attributes, on the other, the research findings reveal the extent of the convergence and/or divergence between the ‘green urban agenda’ policy trends and their implementation in practice.

Nowadays, cyber-physical systems (CPSs) are composed of more and more agents and the demand for designers to develop ever larger multi-agent systems is a fact. When the number of agents increases, several challenges related to control or communication problems arise due to the lack of scalability of existing solutions. It is important to develop tools that allow control strategies evaluation of large-scale systems. In this paper, it is considered that a CPS is a heterogeneous robot multi-agent system that cooperatively performs a formation task through a wireless network. The goal of this research is to evaluate the system's performance when the number of agents increases. To this end, two different mixed reality frameworks developed with the open-source tools Gazebo and Webots are used. These frameworks enable combining both real and virtual agents in a realistic scenario allowing scalability experiences. They also reduce the costs required when a significant number of robots operate in a real environment, as experiences can be conducted with a few real robots and a higher number of virtual robots by mimicking the real ones. Currently, the frameworks include several types of robots being the aerial robot Crazyflie 2.1 and differential mobile robots Khepera IV those used in this work. To illustrate the usage and performance of the frameworks, an event-based control strategy for rigid formations varying the number of agents is analyzed. The agents should achieve a formation defined by a set of desired Euclidean distances to their neighbors. To compare the scalability of the system in the two different tools, the following metrics have been used: formation error, CPU usage percentage, and the ratio between the real-time and the simulation time. The results show the feasibility of using Robot Operating System (ROS) 2 in distributed architectures for multi-agent systems in mixed reality experiences regardless of the number of agents and their nature. However, the two tools under study present different behaviors when the number of virtual agents grows in some of the parameters, and such discrepancies are analyzed.

The COVID-19 pandemic had a wide-ranging impact, resulting in a global recession due to weakened purchasing power. This circumstance necessitates business organizations adapting to developments and being more conscious of the risk of financial statement fraud. The intention of this research is to investigate the way corporate governance affected financial statement fraud during the COVID-19 pandemic. To acquire empirical data for examining corporate governance variables on financial statement fraud, the research was examined using quantitative methods. The study takes advantage of secondary data acquired from annual reports of companies under special monitoring listed on the Indonesia Stock Exchange of 2020-2021. The logistic regression method was used to evaluate 134 data sets, and financial statement fraud was measured using the Z-Score and F-Score models. The results indicate that using the Z-score, only the board size has a negative effect on financial statement fraud during the COVID- 19 pandemic. Meanwhile, using the F-Score, the corporate governance variables studied are not proven to have an influence on financial statement fraud during the COVID 19 pandemic.

We derive a quantum speed limit for unitary evolution for the case of mixed quantum states using the stronger uncertainty relation for mixed quantum states. This bound can be optimized over different choices of Hermitian operators for a better bound. We illustrate this with some examples and show its better performance with respect to three existing bounds for mixed quantum states.

Variable Angle Tow (VAT) laminates offer a promising alternative to classical straight fiber composites in terms of design and performance. However, analyzing these structures can be more complex due to the introduction of new design variables. Carrera's Unified Formulation (CUF) has been successful in previous works for buckling, vibrational, and stress analysis of VAT plates. Typically, one-dimensional (1D) and two-dimensional (2D) CUF models are used, with a linear law describing the fiber orientation variation in the main plane of the structure. The objective of this article is to expand the CUF 2D plate finite elements family to perform free vibration analysis of composite laminated plate structures with curvilinear fibers. The primary contribution is the application of Reissner's Mixed Variational Theorem (RMVT) to a CUF finite element model. The Principle of Virtual Displacements (PVD) and RMVT are both used as variational statements for the study of monolayer and multilayer VAT plates dynamic behavior. The proposed approach is compared to Abaqus three-dimensional (3D) reference solutions, classical theories and literature results to investigate the effectiveness of the developed models. Results demonstrate that mixed theories provide the best approximation of the reference solution in all cases.

This study describes a comparative in vitro study of the toxicity behavior of zinc oxide (ZnO) nanoparticles and micro-sized particles. The study aimed to understand the impact of particle size on ZnO toxicity by characterizing the particles in different media, including cell culture media, human plasma, and protein solutions (bovine serum albumin and fibrinogen). The study used various techniques such as atomic force microscopy (AFM), transmission electron microscopy (TEM), and dynamic light scattering (DLS) to characterize the particles and their interactions with proteins. Hemolytic activity, coagulation time, and cell viability assays were used to assess ZnO toxicity. The results highlight the complex interactions between ZnO NPs and biological systems, including their aggregation behavior, hemolytic activity, protein corona formation, coagulation effects, and cytotoxicity. Moreover, the study indicates that ZnO nanoparticles are not more toxic than micro-sized particles, and the 50 nm particles resulted in general the least toxic. Furthermore, the study found that at low concentrations, no acute toxicity was observed. Overall, this study provides important insights into the toxicity behavior of ZnO particles and highlights that no direct relationship between nanometer size and toxicity can be directly attributed.

E-commerce is a rapidly evolving global trend that is having a significant impact on consumer behavior and business strategies. Despite its growing impact, e-commerce adoption by Moroccan firms remains low and research on this topic in this context is scarce. This study aims to fill this gap by investigating the main determinants influencing e-commerce adoption by Moroccan firms. We use logit, probit, and conditional mixed process-probit models to identify the critical factors driving e-commerce adoption. Our results reveal five key findings. First, newer firms that are more open to innovation and change are more likely to adopt e-commerce. Second, firms with a higher proportion of highly educated employees are more likely to adopt e-commerce. Third, the digital skills of new employees do not directly influence the likelihood of e-commerce adoption. Fourth, being listed on digital platforms increases the likelihood of e-commerce adoption. Finally, there is a positive relationship between firms engaged in innovation activities and e-commerce adoption. These findings highlight the need for additional investment in promoting modern organizational practices, reskilling workers, and implementing advanced technologies to facilitate effective e-commerce integration among Moroccan SMEs. By addressing these challenges, Moroccan firms can harness the full potential of e-commerce and contribute to the country’s economic growth and digital transformation.

Our previous studies showed pure blue (B) spectra from LEDs can promote shade avoidance responses (SARs). However, it is unknown whether SARs vary among different peak wavelengths (λpeak) of B or how they compare with other photomorphologically-important wavebands, such as ultraviolet-A and far-red. To answer these questions, mustard (Brassica juncea, ‘Ruby Streaks’) and arugula (Brassica eruca, ‘Rocket’) seedlings, grown to the cotyledon unfolding stage under the following narrowband spectrum treatments: UVA (λpeak = 385 nm), B1 (λpeak = 404 nm), B2 (λpeak = 440 nm), B3 (λpeak = 455 nm), and FR (λpeak = 730 nm). Both red (R, λpeak = 660 nm) and dark (D) were used as control treatments. The spectrum treatments were provided at 50 μmol m−2 s−1 on a continuous basis. There were no differences among the B treatments except for reduced mustard fresh weight (FWt) in B3. Compared with R, the B treatments promoted hypocotyl elongation, reduced cotyledon size, and increased petiole length in arugula and B1 increased petiole length in mustard. Compared with the B treatments, UVA inhibited hypocotyl and petiole elongation, similar to or greater than R in both species. Compared with the other LED treatments, seedlings grown under FR generally had the lowest hypocotyl and petiole elongation and the smallest cotyledons. Compared to the LED treatments, D substantially promoted hypocotyl elongation and reduced cotyledon size, except compared to FR in arugula. Among the spectrum treatments, the three B treatments had the greatest SAR promotion effects in both species. Despite having the lowest phytochrome activity, FR inhibited SARs normally associated with high FR environments. Legacy parameters used to estimate SAR-promoting effects of spectrum treatments may not be appropriate for characterizing narrowband spectra from LEDs – new approaches must be developed.

The review analyzes the studies published mainly in the last 10-15 years, on the synthesis, structure, and sensor properties of semiconductor nanocomposites. Particular attention is paid to the interaction between nanoparticles of the sensitive layer and its effect on the structure, sensitivity, and selectivity of semiconductor sensor systems. Various mechanisms are considered of interaction between nanoparticles in metal oxide composites including incorporation of metal ions of one component into the structure of another, heterocontacts between different nanoparticles, and core-shell systems, as well as their influence on the characteristics of gas sensors. The experimental data and studies on the modeling of charge distribution in semiconductor nanoparticles, which determine the conductivity and sensor effect in one- and two-component systems, are also discussed. It is shown that the model which considers the interactions of nanoparticles best describes the experimental results. Some mechanisms of detection selectivity are considered in the conclusion.

When the majority of museums and other cultural institutions were shut down due to the pandemic, mixed museum visits became a hot issue. After the epidemic, mixed visits, in the opinion of many experts, would become the new norm for experiencing cultural content. Diverse types of merging online and onsite visits are already being investigated by researchers, not only to avoid the spread of a disease but also to enable visits of people who were previously excluded, such as persons in remote geographic areas or people with mobility challenges. The current work takes into account the Contextual Model of Museum Learning to define potential use scenarios for visits from people of different cultural backgrounds and offers an evaluation of current practices.

In current study, the production of microparticles from fifteen commercial sugarcane pulp (SCP) lunch-boxes into different types of food under different conditions was investigated, where we used DI water, 4% HAc, and 95% EtOH to simulate aqueous, acidic, and fatty foods. Results showed that compared with DI water and 95% EtOH, 4% HAc caused the degradation of sugarcane fibers, resulting in the highest number of microparticles released, which was significantly greater than the specified limit value. It was estimated that consumers might intake a total number of 36,400 ~ 231,700 items per meal per lunch-box, of which the proportion of microparticles with particle size between 100 ~ 500 μm was the largest, with a number of 12,376 ~ 130,795 items. Moreover, of all SCP lunch-box except S-11, Al and Fe are the main metals, ranging between 35.16 ~ 1244.04 and 44.71 ~ 398.52 mg/kg, respectively, followed by Pb, Ti, and Sr. Interestingly, unlike other lunch-boxes, the content of Pb in S-11 was the highest, with a number of 136 mg/kg. This study provides important information that the safeness including both the production of microparticles and the metallic elements should be considered for SCP lunch-boxes when in contact with food.

Wave buoy measurements were carried out near the north-eastern Black Sea coast at the natural reserve Utrish in 2020-2021. In total, about 11 months of data records were collected during two stages of the experiment at 600 and 1500 meters offshore and depths of 18 and 42 meters. The measured waves propagate almost exclusively from the seaward directions. Generally, the waves do not follow the local wind directions, thus, implying a mixed sea state. Nevertheless, dimensionless wave heights and periods appears to be quite close to the previously established empirical laws for the wind-driven seas. The results of the wave turbulence theory are applied for estimates of spectral energy fluxes and their correspondence to the energy flux from the turbulent wind pulsations. These estimates are consistent with today's understanding of wind-wave interaction. It is shown that the main fraction of the wind energy flux goes to the direct Kolmogorov-Zakharov cascade to high wave frequencies and then to dissipation in small scales. Less than 1% of the wind energy flux is directed to low frequency band (the so-called inverse Kolmogorov-Zakharov cascade), thus, providing wave energy growth.

Micro-nano-plastics (MNPs) are an important constituent of atmospheric aerosol. However, there is still no standard procedure for their sampling and size fractionation, which is an obstacle to the aggregation and critical analysis of results obtained by different research groups. This review focuses on the sampling and fractionation methodologies used for MNPs. Moreover, a straightforward optimized methodology for the sampling and fractionation is proposed.

Using flexible AC transmission system (FACTS) devices in power systems while adhering to some equality and inequality constraints, researchers around the world sought to address this issue with the objectives of improving the voltage profile, reducing power losses in transmission lines, and increasing system reliability and safety. The recent development of FACTS controllers opens up new perspectives for safer and more efficient operation of electrical power networks by continuous and rapid action on power systems parameters, such as phase angle shifting, voltage injection and line impedance compensation. Thus, an improvement on voltage profile and enhancement of power transfer capability can be obtained. It is for that, the idea behind the FACTS concept is to enable the transmission system to be an active element in increasing the flexibility of power transfer requirements and in securing stability of integrated power system. It may also be effective in transient stability improvement, power oscillations damping and balancing power flow in parallel lines. The primary issue that has significantly piqued the interest of a number of researchers working in this field is the FACTS optimization problem, which involves determining the optimal type, location, and size of FACTS devices in electrical power systems. For solving this mixed integer, nonlinear and non-convex optimization problem, this paper provides an in-depth and comprehensive review of the various optimization techniques covered in published works in the field. In this review, a classification of optimization techniques in five main groups that are widely used, such as classical optimization techniques or conventional optimization approaches, Meta heuristic methods, analytic methods or sensitive index methods and mixed or hybrid methods, is summarized. In addition, a performance descriptions and comparison of these different optimization techniques are discussed in this study. Finally, some advice is offered for future research in this field.

Family support is offered to Australian parents of young children using a mix of targeted and universal child and family health services. A feature of the universal services is the ability for nurses to work in partnership with families and to offer flexibility depending on need. This model of progressive universalism relies on the voluntary engagement of families, including families with complex needs. In this study, the capacity to engage and retain families, including those at risk for child maltreatment and family violence, was examined. Child and Family Health Nurses (n=129) participated in a pragmatic, multilevel mixed-methods study using the McCurdy and Daro (2001) Integrated Theory of Parent Involvement. A questionnaire was used in the first phase of the study to collect the quantitative data. Focus groups were then held with 27 participants recruited from phase one. Both homogeneous and heterogenous practices identified from the questionnaire were the focus of the discussions. Three phases of practice were identified and described: enrolment, retention and conclusion of the nurse-parent relationship and are presented and discussed in this paper. The retention of families with complex needs relies on flexible, advanced, and multidimensional nursing practices.

X-band dual-polarization phased array radar (XPAR-D) possesses high temporal-spatial resolutions and plays a significant role in detecting meso- and micro-scale convective systems. However, the precipitation attenuation it endures necessitates an effective correction method. This study selected radar data from XPAR-D at the peak of Maofeng Mountain in Guangzhou during May 16-17, 2020 from three precipitation stages after quality control. Attenuation coefficients are calculated for different precipitation types through scattering simulations of raindrop size distribution (RSD) data. Drawing upon this, an attenuation correction algorithm (MZH-KDP method) is proposed for radar reflectivity factor (ZH) according to different raindrop types, and is compared to the ZH-KDP method currently in use. The results indicate that the attenuation amount of XPAR-D echoes depends on the attenuation path and echo intensity. When the attenuation path is shorter and the echo intensity is weaker, the amount of attenuation and correction is smaller. Difficulties arise when there are noticeable deviations in such a situation, which are challenging to solve via attenuation correction methods. Longer attenuation paths and stronger echoes highlight the advantages of the MZH-KDP method, while the ZH-KDP method tends to overcorrect the bias. The MZH-KDP method outperforms the ZH-KDP method for different precipitation types. The superior correction capability of the MZH-KDP method provides a significant advantage in improving the performance of XPAR-D for the detection of extreme weather.

The paper presents the first retrievals of clean snow properties using spaceborne hyperspectral The Environmental Mapping and Analysis Program (EnMAP) observations. The location close to the Dome C in Antarctica has been selected. At this location the atmospheric effects except molecular light scattering and absorption are weak and the simplified atmospheric correction scheme can be applied. The ice grain size, snow specific surface area and snow spectral and broadband albedos have been retrieved using single view EnMAP measurements. In addition, we propose the technique to retrieve trace gas concentrations (e.g., water vapor, ozone) from EnMAP observations over the snow surfaces. Close correspondence of satellite and ground measured parameters has been found.

As two commonly-used non-selective herbicides, glyphosate (GP) and diquat (DQ) are easily co-resided in lateritic paddy soil due to the rich iron/aluminum oxides, nevertheless there is limited information on their co-impact on microbial diversity and community structure in this type soil. In this study, the short-term effects of combined GP and DQ on soil microbial diversity and community structure shifts were investigated in lateritic paddy soil from a tropical agricultural region (Hainan, China) based on 16S rRNA and ITS high-throughput sequencing technology. The results showed that mixed herbicides promoted the abundance of Streptomyces in bacteria (0.45-1.84%) and Curvularia in fungi (0.01-5.85%), while GP and DQ had inhibitory effects on the abundance of Streptomyces (0.13-2.21%) and Curvularia (0.03-1.13%), which were significantly different with their single exposure (p < 0.05); the combined application of the two herbicides aggravate the adverse effect on the diversity of soil fungal community (p < 0.05), although their mixture did not have a greater impact on the soil bacteria abundance/diversity and fungi abundance (p > 0.05). Results suggested that the combined application of GP and DQ affected the fungal diversity although they did not cause other significant negative effects on soil microorganisms, hinting that more attention should be paid to the mixed effect caused by GP and DQ on specific fungal populations in lateritic paddy soil.

Comminution is a very important and result-determining step in mineral processing. This is because further downstream processes in the beneficiation chain depend entirely on it. Comminution includes the blasting, crushing, and grinding of ore to liberate valuable mineral particles. It consumes most of the energy used in mineral processing plants with grinding using up to 50% of the provided energy. Tumbling mills are an old technology that is still commonly used for grinding. One of the most used tumbling mills is the ball mill. This paper reviews different types of grinding media that have been developed and improved over the years and their properties. Also reviewed are the effects of grinding media on various aspects such as energy consumption, mill efficiency, minerals liberation and grinding rate. A comparison of the grinding media is given together with the knowledge gaps that still need to be bridged to improve the quality and performance of grinding media used in ball mills. Despite various research conducted on ball mills, the grinding process is still a very energy inefficient process. This creates a great opportunity for further research to improve the process because just a slight improvement in the process efficiency significantly reduces the production costs and provides a lot of environmental benefits.

The history of agricultural terraces dates back to agricultural society; and throughout history, they have enabled agricultural activities to be carried out on lands with irregular relief. They have also served as an essential element of rural landscapes due to their architectural features, building materials, and techniques, as well as the landscapes they create. Furthermore, terraces also contribute to soil conservation by preventing erosion, adding organic matter to the soil, and harvesting rainwater. The significance of agricultural terraces in the cultural landscape has triggered the need to define and classify them. In this regard, agricultural terraces across many different locations have found their place on the List of Globally Important Agricultural Heritage Sites (GIAHS) and UNESCO cultural heritage lists. In the present study, the agricultural terraces of the Erikli neighborhood of the Uzundere district were mapped through surveys, drone shots, and orthophotos, and analyzed based on the size and density indices previously used in the classification of Alpine terraces. The study is important in terms of demonstrating the use of drone technology in mapping agricultural terraces. The results of the study revealed that 51.6% of the agricultural terraces in the study area had very steep slopes and 22195 meters of total stone wall length, the average density index was 523.29 m/ha, and 76.4% of the terraces were meso and macro in character. Moreover the study shows that the terraces in the region are more susceptible to deterioration.

GaN-based micro-size light-emitting diodes (µLEDs) have a number of appealing and distinctive benefits for display, visible-light communication (VLC), and other novel applications. The smaller size of LEDs affords them the benefits of enhanced current expansion, less self-heating effects, and higher current density bearing capacity. Low external quantum efficiency (EQE) resulting from non-radiative recombination and quantum confined stark effect (QCSE) is the serious barrier for applications of µLEDs. In this work, the reasons for the poor EQE of µLEDs are reviewed, as well as the optimization techniques for improving the EQE of µLEDs.

Features of nanosecond discharge development in a non-uniform electric field are studied experimentally. High spatial resolution imaging showed that thin luminous tracks of great length with a cross-section of a few microns are observed against the background of discharge glow in air and argon. It has been established that the detected tracks are adjacent to brightly luminous white spots on the electrodes or to the vicinity of these spots, and are associated with the flight of small particles. It is shown that the traces have various shapes and change from pulse to pulse. The particle tracks may look like curvy or straight lines. In some photos, they can change the direction of movement to the opposite. The particle trace was found to abruptly cut off and a bright flash is visible at the trail break point. The color of the tracks differs from that of the spark leaders, while the bands of the second positive nitrogen system dominate in the gap spectra during the existence of a diffuse discharge. Areas of blue light are evident near the electrodes, as well. Development of glow in the gap during its breakdown is revealed using an ICCD camera. Physical reasons for the observed phenomena are discussed.

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.

Remanufacturing is a way for product recovery initiative and maintaining sufficient production flow to satisfy customer demand through circular economy. Recently, manufacturers have been progressively incorporating remanufacturing processes, making their supply chain vulnerable to disruptions. Among the disruptions that usually occur is the shortage in spare parts supply, which causes unexpected delay in production and possible loss of sales. In the face of potential disruption, it is essential for remanufacturing facilities to manage their supply chains in an optimum manner as to decrease the adverse impact of disruptions to their business. In this study, a two-stage production-inventory system is analysed by developing a cost-minimisation model focusing on the recovery schedule after a disruption occurrence in sourcing spare parts for a remanufacturer’s production cycle. The developed model was solved using the branch and bound algorithm. Through numerical experiments, the results indicate that the optimal recovery schedule and the number of recovery cycles are significantly dependant on disruption time, lost sales and backorder costs. Moreover, a sensitivity analysis shows that the lost sales option seems to be more effective than the backorder option in optimising the system’s overall cost. The proposed model could assist managers in deciding the optimal production strategy while providing interesting managerial insights into vital spare parts recovery issues when disruption strikes.

Wear usually occurs in the loaded part of hydrodynamic bearings and leads the bearing geometry imperfections. This paper investigates the effects of wear-induced geometric imperfections of tilting pad journal bearings (TPJBs) on the dynamic behavior of the system. Furthermore, the effect of wear on the journal-bearing rub-induced contact pressure severity is investigated. A novel tribo-dynamic model is proposed for a flexible rotor-worn TPJB which integrates a mixed elastohydrodynamic model with a rotor-worn TPJB thermal and dynamic model to assess the effects of the bearing wear progression on the rotor-TPJB behavior. Based on the results, wear changes the temperature distribution of the pads and oil film as well as the dynamic behavior of the system. Dynamic simulations reveal a higher vibration level and contact pressure for the worn TPJBs near the system's critical speed and service speed. Finally, thermal and dynamic condition indicators are suggested to detect TPJB wear severity at its early stages.

Establishment of mixed-forests has gained increasing attention as a way to optimize forest production, to improve ecological benefits and as a safety net for impacts of future climate uncertainties. However, practical knowledge about which species and what proportion of them should be mixed is still lacking. Thus, this study was conducted with the aim of identify suitable species for mixture with Acacia cincinnata. The mixture tested in the present study was A. cincinnata + Eucalyptus robusta (6:4), A. cincinnata + Acacia mangium (3:1) and monospecifc plantation of A. cincinnata established in 2014. After 7 years of growth, we analyzed the effects of species mixture on growth of tree species, understory vegetation and soil physico-chemical properties as well as bacterial community structure and diversity. The results showed that species mixture had no significant effect on growth characteristics, such as diameter and singletree volume of A. cincinnata. However, mixed-species planting increased the total stocking volume compered to monospecific plantation of A. cincinnata. Furthermore, stand mixture significantly increased species diversity, biomass and nutrient stocks in the understory vegetation. The soil of mixed stand of A. cincinnata and A. mangium had the highest C and N contents, whereas the soil of pure A. cincinnata stand had the highest P content. The diversity of soil bacterial community were the highest in the mixed stand of A. cincinnata and E- robusta, followed by pure A. cincinnata stand and A. cincinnata + A. mangium stand. The relative abundance of Proteobacteria and Actinobacteria was higher in soils of mixed stands. Furthermore, the relative abundance of Firmicutes was high in the soil of A. cincinnata + A. mangium while the relative abundance of Verrucomicrobia was high in A. cincinnata + E. robusta stand. As a whole, the study demonstrated that establishing mixed-species plantation enhance the diversity and composition of understory vegetation, soil physico-chemical and soils bacterial community; thereby increasing biodiversity, nutrient cycling and carbon sequestration in the biomass and soil. From the viewpoints of forest productivity and ecological benefits, it is advisable to establish a mixed plantation of A. cincinnata and A- mangium in southern China. As a whole, our work revealed that the sustainability of mixed-species plantation relies on the interactions between soil attributes, vegetation, and bacterial community.

Traditional camera sensors rely on human eyes for observation. However, the human eye 1 is prone to fatigue when observing targets of different sizes for a long time in complex scenes, and 2 human cognition is limited, which often leads to judgment errors and greatly reduces the efficiency. 3 Target recognition technology is an important technology to judge the target category in camera 4 sensor. In order to solve this problem, a small size target detection algorithm for special scenarios was 5 proposed by this paper. Its advantage is that this algorithm not only has higher precision for small 6 size target detection, but also can ensure that the detection accuracy of each size is not lower than the 7 existing algorithm. In this paper, a new down-sampling method was proposed, which could better 8 preserve the context feature information. The feature fusion network was improved to effectively 9 combine shallow information and deep information. A new network structure was proposed to 10 effectively improve the detection accuracy of the model. In terms of accuracy, it is better than: YOLOX, 11 YOLOXR, YOLOv3, scaled YOLOv5, YOLOv7-Tiny and YOLOv8.Three authoritative public data sets 12 were used in this experiment: a) On Visdron data sets (small size targets), DC-YOLOv8 is 2.5% more 13 accurate than YOLOv8. b) On Tinyperson data sets (minimal size targets), DC-YOLOv8 is 1% more 14 accurate than YOLOv8. c) On PASCAL VOC2007 data sets (Normal size target), DC-YOLOv8 is 0.5% 15 more accurate than YOLOv8.

The effect of biotic (e.g., growth characteristics) and a-biotic (e.g., crown closure) factors on cone production, fertility variation and gene diversity was evaluated in two consecutive years in 130 year old natural populations of Taurus cedar also called cedar of Lebanon (Cedrus libani A. Rich.). The a-biotic factors viz., altitude, temperature, aspect and rainfall varied; while some of them can be managed by cultural operations like management of crown and stand density. Impact of crown closure on cone production, fertility variation and related parameters were observed in Taurus cedar’s natural populations sampled from southern Turkey. Maximum cone production of 29 and 40/tree was recorded with full crown closure in two consecutive years by large differences among individuals within crown closure class. For instance, cone productions were between 10 and 67 in full crown closure of first year. The sibling coefficient, which is a measure of the fertility variation in a stand, was the highest (1.16) for full crown closure in the first (meaning 86% fertile trees) and the second year (1.55, 65%), while it was the lowest for medium crown closure (1.09, 92%) in both years. Gene diversity decreased from a degraded patch (0.987) to that with full crown closure (0.984). Results of variance analysis showed significant (p≤0.05) difference between crown closure classes for cone production within second year, and between years. Significant positive (p0.05) correlations were noted between years for cone production (r=0.22), and between cone production and crown closure in first and second years (r=0.29). However, growth characteristics had no effective on cone productions in individual crown closure classes, while there were significant (p0.05) correlations between diameter at breast height and cone production for both years (r= 0.15 and 0.17) in pooled populations.

This review focuses on developments that quantify grain quality with a range of spectral sensors in an on-farm setting. If the application of sensor technologies were expanded and adopted on-farm, growers could identify the impact and manage the harvesting operation to meet a range of quality targets and provide an economic advantage to the farming enterprise.

The interference-fit size has a significant effect on CFRP/Al-alloy laminates riveted lap joints, the requirements for the interference-fit size are different because of the strengthening of heterogeneous materials. However, in the process of the riveted CFRP/Al-alloy, the heterogeneous laminates lead to poor structural strength because of the different requirements of interference-fit size. Therefore, the different assembly types of riveting mold are designed to acquire a novel interference-fit size, and the tensile test is adopted to evaluate the tensile property. In addition, the fracture failure of CFRP/Al-alloy laminates riveted lap joint is observed by the ultra-depth of field microscope. Finally, the best assembly type is the trapezoid riveting mold combined with an arc riveting die, and the sidewall intersection angle of the trapezoid riveting mold is 66° which could achieve a suitable interference-fit size and a better mechanical performance.

Gauss quadrature integral approximation is extended to include integrals with a measure consisting of a continuous as well as a discrete component. That is, we give an approximation for the integral of a function plus its sum over a discrete weighted set.