Urban wind energy has emerged as an attractive source of distributed generation in cities to achieve sustainable development goals. The advance in technologies for the use of urban wind energy has turned this source into an attractive alternative for the decarbonization of cities and the energy transition. The objectives of this work are (1) to identify the potential of wind energy through numerical weather prediction (NWP) data tools and (2) to identify the roles and responsibilities of the stakeholders involved in the decision-making process. A methodology was developed in two phases and applied to a case study in the Dominican Republic. The first phase consisted of estimating the wind energy potential for the 32 provinces at a height of 10 m using open-access geographic reference systems tools provided by NASA. In the second phase, 28 stakeholders were identified through snowball sampling. The Responsible, Accountable, Consult, and Informed (RACI) matrix tool was applied to identify the roles of the 28 institutions addressed at the country level as relevant in the decision-making process for the energy sector. The annual average wind speed and energy potential for each province were determined. It was found 24 provinces have poor potential, below <4.5 m/s. In the northwest and east is where there is the greatest potential, between 4.83 - 6.63 m/s. The population density was established and was observed that the provinces with greater potential are less densely populated. Through 59 interviews 28 institutions were identified and evaluated due to their relevance in decision-making for the implementation of energy projects. The MEM has strongly categorized as “A”, electricity distribution companies as “R”, energy associations and universities as “C”, and educational and justice institutions as “I”.

Large-scale farms are pivotal sites for the development of the agricultural industry, and the quality of the ecological environment is a significant factor affecting the growth of cereal crops. Consequently, assessing the ecological quality of large-scale farms is of great importance. This paper leverages the advantages of remote sensing imagery for long-term, quantitative, and dynamic monitoring of ecological quality over extensive areas. It develops an ecological assessment procedure suitable for agricultural regions based on an Improved Remote Sensing Ecological Index (IRSEI), which considers the coupling of ecosystem component elements. This procedure introduces a Pan-Salinity Index (PSI) tailored to the characteristics of soil salinization in farming areas and incorporates an ecological greenness index based on the Normalized Difference Vegetation Index (NDVI), an ecological humidity index derived from the K-T transform (WET), an ecological dryness index synthesized from the bare soil index and the built-up index (NDBSI), and an ecological heat index represented by the Land Surface Temperature (LST) calculated from atmospheric parameter models. These five indices are integrated into the ecological assessment system. The experimental results visually demonstrate the development and changes of ecological impact factors in the farm research area over the past decade, as well as the spatiotemporal variations in the quality of the farm's ecological environment. The findings indicate that between 2010 and 2019, the overall trend of IRSEI first declined, then rose, and subsequently experienced a slight decrease. Through differential analysis, regions and areas with declining ecological quality were identified, providing a data reference for the formulation and implementation of future ecological protection and management measures for the farm. The proposed IRSEI method offers a rapid and effective new monitoring approach for agricultural planting areas with a tendency towards soil salinization.

One of the current challenges in Higher Education is the development of students’ emotional competencies, and consequently, how these competencies can be trained. Moreover, this must be addressed within the UN framework of inclusive, equitable, quality education (United Nations Agenda 2030) (SDG 4). To address this challenge, the present study set the following objectives: (1) to analyze pre-service teachers’ level of interest in EI and their perceived level of general EI knowledge, (2) to examine their self-perception of certain variables of emotional knowledge, according to their degree program and year of studies, (3) to analyze the relationship between these emotional knowledge variables and perceived emotional intelligence (EI); and (4) to study the predictive capacity of the study variables on future teachers' emotional knowledge variables. The study sample contained a total of 434 university students from three degree programs at two universities in Spain, of which 82.70% were women, and the mean age was 20.91 years. Two instruments were used: an ad hoc questionnaire, which assessed perceived emotional knowledge according to three variables (General knowledge of EI, where level of training in EI is considered; Intrapersonal knowledge of EI; and Theoretical-practical knowledge related to teaching), and the Trait Meta-Mood Scale (TMMS-24), which assesses metaknowledge of emotional states across three variables (Attention, Clarity, and Repair). Results of the study suggest that there are differences in perceived EI knowledge according to students' year of studies and according to their degree, and indicate that students present high interest, while their perceived level of knowledge is low. In addition, emotional clarity is the EI variable that was identified as being important in the emotional knowledge of future teachers. This study shows evidence of the importance of social-emotional competencies in initial teacher training.

In the rapidly evolving landscape of genetic engineering, the advent of CRISPR-Cas technologies has catalyzed a paradigm shift, empowering scientists to manipulate the genetic code with unprece-dented accuracy and efficiency. Despite the remarkable capabilities inherent to CRISPR-Cas sys-tems, recent advancements have witnessed the integration of small molecule to augment their functionality, introducing new dimensions to the precision and versatility of gene editing applica-tions. This review delves into the synergy between CRISPR-Cas technologies and small molecule drugs, elucidating the pivotal role of chemicals in optimizing target specificity and editing efficiency. By examining a diverse array of applications, ranging from therapeutic interventions to agricultural advancements, we explore how the judicious use of chemicals enhances the precision of CRISPR-Cas-mediated genetic modifications. In this review we emphasize the significance of small molecule drugs in fine-tuning the CRISPR-Cas machinery, which allows researchers to exert meticulous con-trol over the editing process. We delve into the mechanisms through which these chemicals bolster target specificity, mitigate off-target effects, and contribute to the overall refinement of gene edit-ing outcomes. Additionally, we discuss the potential of chemical integration in expanding the scope of CRISPR-Cas technologies, enabling tailored solutions for diverse genetic manipulation challenges. As CRISPR-Cas technologies continue to evolve, the integration of small molecule drugs emerges as a crucial avenue for advancing the precision and applicability of gene editing techniques. This re-view not only synthesizes current knowledge but also highlights future prospects, paving the way for a deeper understanding of the synergistic interplay between CRISPR-Cas systems and chemical modulators in the pursuit of more controlled and efficient genetic modifications.

Small island developing states (SIDS) traditionally face a set of challenges like the narrow economic structure, environmental issues, and dependence on a few ranges of economic activities forcing them to open the economy to the exterior. Therefore, their development model, like in Cape Verde depends on migration, remittances, dependence on aid, tourism, and state employment. The current research offers an insight about the nature of Cape Verde’s economy as a SIDS and the degree to which its economy depends on tourism and government services, both of which are supported through aid programs. Understanding Cape Verde’s development model is important to clarify the challenges the country faces, and its development needs to gather a long-term resilience and to understand if it is changing from a MIRAB (Migrations, Remittances, Aid and Bureaucracy) model into another one.

The variability of the material properties of steel from different suppliers causes problems in achieving the required surface quality after turning. Therefore, the manufacturer needs to be able to estimate the resulting quality before starting production, especially if it is an expensive, small-batch production from stainless steel. Predictive models will make it possible to estimate the surface roughness from the mechanical properties of steel in the Mill Test Certificate (MTC) and thus support decision-making about the supplier selection or the acceptance of a material supply. Multivariate second-order polynomial model and feedforward back-propagation Artificial Neural Network (ANN) model were used to enhance the methodological robustness in formulating the decision if the predicted surface roughness is outside the required range, even before accepting the delivery. Both models can accurately predict surface roughness, while the ANN model is more accurate than the polynomial model; however, the predictive model is sensitive to the accuracy of the input data, and the model’s prediction is valid only under precisely defined conditions. The prediction model is used in a step-by-step decision-making procedure, which enables the trained staff to make a quick decision.

Plant diseases and pests reduce crop yields, accounting for global crop losses of 30% to 50%. In conventional agricultural production systems, these losses are typically controlled by applying chemical pesticides. However, public pressure is mounting to curtail agrochemical use. In this context, employing beneficial endophytic microorganisms is an increasingly attractive alternative to the use of conventional chemical pesticides in agriculture. A multitude of fungal endophytes are naturally present in plants, producing enzymes, small peptides and secondary metabolites due to their bioactivity, can protect hosts from pathogens, pests and abiotic stresses. The use of beneficial endophytic microorganisms in agriculture is an increasingly attractive alternative to conventional pesticides. The aim of this study was to characterize fungal endophytes isolated from apparently healthy, feral wine grapes in eastern Canada that have grown without agrochemical inputs for decades. Host plants ranged from unknown seedlings to long-lost cultivars not widely propagated since the 1800s. HPLC-MS was used to identify unique endophyte-derived chemical compounds in the host plants, while dual-culture competition assays showed a range in endophytes’ ability to suppress the mycelial growth of Botrytis, which is typically controlled in viticulture with pesticides. Twelve of the most promising fungal endophytes isolated were identified using multilocus sequencing and morphology, while DNA barcoding was employed to identify some of their host vines. These fungal endophyte isolates, which consisted of both known and putative novel strains, belonged to seven genera in six families and five orders of Ascomycota. Exploring the fungal endophytes in these specimens may yield clues to the vines’ survival and lead to the discovery of novel biocontrol agents.

The EU’s energy targets are to achieve at least 32% renewables in the energy mix by 2030. Part of the solution is strengthening consumer rights by empowering individuals to generate their own electricity. The study aimed to identify the most suitable energy system for electricity generation of up to 50 kW in the EU residential sector. The Multi-Criteria Decision Analysis was used to compare four systems: proton exchange membrane fuel cell with photovoltaic panels, photovoltaic panels, biomass powered Stirling engine, and solar powered Stirling engine. Based on the results, the most beneficial system for household electricity generation is the Biomass-fueled Stirling engine system due to its affordability, reliability, and low environmental impact. Governments and businesses can use these findings to improve information for the residential sector and enable the transition to renewable energy.

Abstract Background: Small Cajal body-specific RNAs (scaRNAs) are a specific subset of small nucleolar RNAs (snoRNAs) that have recently been emerged as pivotal contributors in diverse physiological and pathological processes. However, their defined roles in carcinogenesis remain largely elusive. This study aims to explore the potential function and mechanism of SCARNA12 in bladder cancer (BLCA) and to provide a theoretical basis for further investigations into the biological functionalities of scaRNAs. Materials and Methods: TCGA, GEO and GTEx data sets were used to analyze the expression of SCARNA12 and its clinicopathological significance in BLCA. Quantitative real-time PCR (qPCR) and in situ hybridization were applied to validate the expression of SCARNA12 in both BLCA cell lines and tissues. RNA sequencing (RNA-seq) combined with bioinformatics analyses were conducted to reveal the changes of gene expression patterns and functional pathways in BLCA patients with different expression of SCARNA12 and T24 cell lines upon SCARNA12 knockdown. Single-cell mass cytometry (CyTOF) was then used to evaluate the tumor-related cell cluster affected by SCARNA12. Moreover, SCARNA12 was stably knocked down in T24 and UMUC3 cell lines by lentivirus-mediated CRISPR/Cas9 approach. The biological effects of SCARNA12 on the proliferation, clonogenic, migration, invasion, cell apoptosis, cell cycle and tumor growth were assessed by in vitro MTT, colony formation, wound healing, transwell, flow cytometry assays and in vivo nude mice xenograft models, respectively. Finally, chromatin isolation by RNA purification (ChIRP) experiment were further conducted to delineate the potential mechanisms of SCARNA12 in BLCA. Results: The expression of SCARNA12 was significantly up-regulated in both BLCA tissues and cell lines. RNA-seq data elucidated that SCARAN12 may play a potential role in cell adhesion and extracellular matrix (ECM) related signaling pathways. CyTOF results further showed that an ECM-related cell cluster with vimentin+, CD13+, CD44+, CD47+ was enriched in BLCA patients with high SCARNA12 expression. Additionally, SCARNA12 knockdown significantly inhibited the proliferation, colony formation, migration, and invasion abilities in T24 and UMUC3 cell lines. SCARNA12 knockdown prompted cell arrest in the G0/G1 and G2/M phase, and promoted the apoptosis in T24 and UMUC3 cell lines. Furthermore, SCARNA12 knockdown could suppress the in vivo tumor growth in nude mice. ChIRP experiment further suggested that SCARNA12 may combine transcription factors H2AFZ to modulate transcription program and then affect BLCA progression. Conclusion: Our study is the first to propose aberrant alteration of SCARNA12 and elucidate its potential oncogenic roles in BLCA via the modulation of ECM signaling. The interaction of SCARNA12 with the transcriptional factor H2AFZ emerges as a key contributor to the carcinogenesis and progression of BLCA. These findings suggest SCARNA12 may serve as a diagnostic biomarker and potential therapeutic target for the treatment of BLCA.

This study presents a method for the rapid and accurate generation of two-dimensional (2D) orthomosaic maps using selected image data collected by drone-captured video. The focus is on developing a real-time method capable of creating maps more quickly than image selection. The scale-invariant feature transform (SIFT) algorithm is applied to drone images to extract features in various scale regions. For feature point matching, a matching method based on the fast library for approximate nearest neighbors (FLANN) was adopted. A comparison of the computational speed of the FLANN with that of the k-nearest neighbors (KNN) and brute force matcher during the matching process revealed FLANN's superior capability for real-time data processing. The random sample consensus (RANSAC) algorithm was employed to enhance the accuracy of the matching by removing outliers, effectively identifying and eliminating mismatches, and reinforcing the reliability of feature point matching. The combination of SIFT, FLANN, and RANSAC algorithms demonstrates the capacity to process drone-captured image data in real time, facilitating the generation of precise 2D orthomosaic maps. The proposed method was assessed and validated using imagery obtained as the drone executed curvilinear and straight flight paths, confirming its accuracy and operational efficiency concurrently with the image capture process.

Posttraumatic stress disorder (PTSD) and/or specific PTSD symptoms may evoke disordered eating and unhealthy lifestyles, resulting in adverse cardiometabolic events (e.g., hypertension and obesity) in certain groups, which may implicate the treatment of this complex condition. The diagnostic criteria of PTSD have lately expanded beyond the three common symptoms (intrusion, avoidance, and hyperarousal). Simultaneously, four-, five-, and six-dimensional structures of the Impact of Event Scale-Revised (IES-R), a popular PTSD measure, seem to be more robust than the original three-dimension structure. Within the context of COVID-19, this instrumental study used a convenience sample of 58 dental healthcare workers (HCWs) from Russia (mean age = 44.1±12.2 years, 82.8% females) to examine the criterion and predictive validity of two IES-R structures: the IES-R3 and the IES-R6 (with the added symptoms of numbing, sleep disturbance, and irritability). The subscales of the two IES-R structures expressed good internal consistency, strong correlations with the PTSD Symptom Scale (PSS), hypertension diagnosis, and high body mass index (BMI). In receiver-operating characteristic (ROC) curve analysis, all IES-R models perfectly predicted the PSS (all area under the curve (AUC) >0.9, p values <0.001). The IES-R, both hyperarousal subscales, and the IES-R3 intrusion subscale significantly predicted high BMI. Both avoidance subscales and the IES-R3 intrusion subscale, not the IES-R, significantly predicted hypertension. In conclusion, both IES-R structures can reliably measure PTSD symptoms. The IES-R, hyperarousal, and intrusion may be credible criterion variables for predicting high BMI within PTSD while the intrusion and avoidance subscales may reflect the cardiovascular consequences associated with PTSD more reliably than the IES-R itself. The IES-R subscales may predict cardiometabolic adversities in PTSD, signifying a need for proper assessment of lifestyle and the application of dietary and exercise interventions in order to lower physical morbidity in PTSD. Replicating the study in larger samples, which comprise different physical and mental conditions from heterogenous cultural contexts is pivotal to validate the results (e.g., in specific groups such as those with confirmed traumatic exposure and comorbid mood dysfunction).

Semiconductors play a very important role in modern society. The improvement in performance of semiconductors is what has enabled the world to grow at such a fast pace. For decades scaling has been done through Moore’s law i.e performance improvements have been obtained due to transistor scaling where the number of transistors per unit area increase is what has contributed to the increase in performance. However, with transistor scaling reaching its limitations we need to find alternate methods to enhance silicon design to suit the computational need of modern society. To address this problem we will be looking into a few methods that enable performance scaling with a focus on analysing the most popular implementation of each in the industry.

Wool quality and yield are important economic traits of livestock. In this study, the skin tissues of the upper scapula of Super Merino sheep (SM) and Small-Tailed Han sheep (STH) during the growing period were studied. Differentially expressed (DE) long non-coding RNAs (lncRNAs) and messenger RNAs (mRNA) were identified via histopathological observation and high-throughput RNA sequencing. Gene ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were used to analyze differentially expressed genes. Reverse transcription quantitative Polymerase Chain Reaction (RT-qPCR) was used to verify nine selected DE lncRNAs and five DE mRNAs. Significant differences were found in the expression levels of LOC114113396 (KRTAP15-1), CRABP1, KRT10, KRTAP1-3, KRTAP4.3, KRT2, FGF1, IGF-1, and KIT in SM and STH skin tissues. These results indicate that these mRNAs may play an important role in the growth and development of hair follicles and the regulation of hair fiber traits. Differentially expressed lncRNA target genes and mRNAs were mainly enriched in biological processes and pathways related to skin development and fiber traits, which is consistent with previous studies. LncRNA MSTRG.9225.1, MSTRG.124320.1, MSTRG.96951.1, MSTRG.91685.1, MSTRG.122229.1, MSTRG.89255.1, MSTRG.98769.1, and MSTRG.84658.1 may indirectly participate in the growth and development of sheep hair follicles and the regulation of hair fiber diameter by regulating the expression of their respective target genes. These candidate lncRNAs and mRNAs may provide a basis for the study of sheep hair follicle growth and development and hair fiber traits.

A significant amount of data is available for bench-scale fire assessments. However, there is little surrounding large-scale tests and even less so for bench-to-large scale comparisons. Large-scale tests require more material, time and preparation, making them more expensive than bench-scale assessments. However, large-scale testing is an essential component to bench-scale fire testing. The bench-scale tests must be representative of both large-scale and real fire behaviour to provide an insight into how a material or product will behave in the event of a fire during its end use application. The few existing studies reviewed in this study show more guidance and data is needed, especially on a large-scale. Unfortunately, the data that is presented is done so in an inconsistent manner using various means of presentation, statistical analysis, and modelling that doesn’t show clear comparisons between bench and large scale. Currently, no bench-scale method shows good agreement with large-scale fire behaviour. Overall, there is a need for more large-scale testing and data for direct comparisons to be made.

The small-molecule iododiflunisal (IDIF) is a transthyretin (TTR) tetramer stabilizer and acts as a chaperone of the TTR-Amyloid beta interaction. Oral administration of IDIF improves Alzheimer’s Disease (AD)-like pathology in mice, although mechanism of action and pharmacokinetics remain unknown. Radiolabeling IDIF with positron or gamma emitters may aid in the in vivo evaluation of IDIF using non-invasive nuclear imaging techniques. In this work, we report isotopic exchange reaction to obtain IDIF radiolabeled with 18F. [19F/ 18F]exchange reaction over IDIF in dimethylsulfoxide at 160 °C resulted in the formation of [18F]IDIF in 7±3% radiochemical yield in a 20 minutes reaction time, with a final radiochemical purity of >99%. Biodistribution studies after intravenous administration of [18F]IDIF in wild-type mice using positron emission tomography (PET) imaging showed capacity to cross the blood-brain barrier (ca. 1% of injected dose per gram of tissue in the brain at t&gt;10 min post administration), rapid accumulation in the liver, long circulation time and progressive elimination via urine. Our results open opportunities for future studies in larger animal species or human subjects.

Small-scale fisheries are significant sources of nutrition and livelihood globally. However, increasing anthropogenic activities in coastal areas of developing countries have threatened the sustainability of artisanal fisheries and species. Fisheries of Sardinella maderensis (Lowe, 1838), to which Nigeria contributes 9% of the global stock and is a significant livelihood source in the coastal communities of Ibeju-Lekki, Lagos, face sustainability threats. This research investigated the effects of anthropogenic activities on S. maderensis fishery in the coastal areas of Ibeju-Lekki, Lagos, Nigeria. The study adopted a mixed-method approach involving qualitative and quantitative research methods. These include species identification, water quality analysis, land use change analysis, field surveys, focus group discussions and interviews. Results show genetic analysis of the fish samples from six stations revealed that the species had a mean of 98% similarity to Sardinella maderensis in NCBI. Multiple linear regression indicates that anthropogenic variables explained approximately 4.62% of the variance in the fish catch (p &lt; .001, R2 = .05). Water samples showed heavy metal levels were above international limits, high total petroleum hydrocarbon (TPH) pollution in all stations (27.56 mg/L- 3985.40 mg/L). Physiochemical analysis of water samples indicated TDS higher than acceptable limits (Mean = 24971.1 mg/L) and inadequate chlorophyll-a level (Mean = 0.01 µg/L). Hence, urgent strategies are required to mitigate anthropogenic threats through inclusive coastal management policies supporting resilient artisanal fisheries and marine spatial planning.

The authors of the article analyze the existing methods and models of technological preparation of machine-building industries. The structure of a three-level simulation model with network-centric control, the structures of individual elements of the simulation model and the process of simulation modeling are described. The criteria for choosing a rational option for the processing technological route have been determined. During the research, a simulation program was implemented in C++. It allows you to select the optimal scenario for the operation of a production site based on two criteria: time and cost. The volume of implementation is about 2x103 lines of code. A diagram of the modeling algorithm for the implemented program, a description of the classes and their interactions are given in the article. The developed simulation model was tested at a machine-building enterprise using the example of the “Pusher” part, manufactured under single-unit production condi-tions. The technological equipment used for the manufacture of this part was formed in the form of input data of the simulation model. The results of simulation modeling for the selected part are described. For each variant of the technological processing route, the values of variable costs and the duration of the production cycle were determined.

Micro and small wood-processing enterprises represent the heart of the European forest-based industries, being among the key drivers of economic growth in rural, mountainous, and poor regions. Their economic efficiency is of fundamental importance for their existence and the pro-vision of income for the local population in rural areas. Data Envelopment Analysis (DEA) is nonparametric, linear-programming-based approach, commonly used to analyse the efficiency of organizational units. This method allows estimating the economic efficiency of a certain eco-nomic system without assumptions about the functional form between resources and products. Furthermore, DEA determines the efficiency frontier and gives results of whether an enterprise, i.e., a Decision Making Unit (DMU) is efficient or not. The main objective of this study was to investigate and evaluate the economic efficiency of micro and small wood-processing enterpris-es in the EU countries and reveal the hidden inputs that facilitate efficiency generation. The eco-nomic efficiency evaluation was carried out on the basis of the official statistical data for the mi-cro and small wood-processing companies in the EU member states for the period 2015-2020 by performing a two-stage DEA analysis. The data used were standardized by value per employee. In addition to the first stage of DEA, fractional regression probit and logit models with four contextual variables were used to reveal the influence of the hidden inputs in the model. The results showed that the micro and small wood-processing enterprises can be regarded as more scale-efficient than technically-efficient entities. The only contextual variable affecting the eco-nomic efficiency was Investments per Person Employed, improving the efficiency by 2% per 1% increase of the investments.

As smoke toxicity is unregulated outside of the mass transport industry, there is no set methodology for assessing smoke toxicity of construction products, like OSB, on a large-scale. This research aims to assess the novel design of a modified ISO 9705 room corner test designed for smoke toxicity quantification. Three tests conditions will be assessed to determine if restriction of ventilation is enough to force a fire to transition to under-ventilated flaming, or if the fuel loading is much more important in affecting the fire severity and fire condition. The research found that fuel loading and test geometry has more significant impact on the fire condition than restricting the ventilation. While imposing restrictions on the test rooms ventilation did increase the equivalence ratio, it was not sufficient enough to force the transition into under-ventilated flaming. The addition of a door to the test room did not force the fire to transition to under-ventilated flaming. When doubling the fuel loading, there was sufficient fuel for the fire to transition to under-ventilated flaming. This research has provided the experimental methodologies to assess smoke toxicity at a range of ventilation conditions on a large-scale.

The effects of rare earth Y content on the microstructure and high-temperature oxidation properties of 47Fe-36Ni-15Cr-1.5Mn alloy at 1000°C were investigated. The results show that 47Fe-36Ni-15Cr-1.5Mn alloy oxidizes at 1000°C under laboratory atmospheric pressure to form an oxide film layer dominated by Cr2O3 and (Fe, Ni, Mn) Cr2O4 spinel, and the oxidation curve of the alloy follows the parabolic law. After the addition of rare earth Y, rare earth phases precipitated along the grain boundaries in the alloy, and the more Y content, the more precipitated phases in the grain boundaries. The alloy with added rare earths was oxidized at a high temperature of 1000 °C for 2160 min, the oxidation law still followed the parabolic law, while the rare earth phase at the grain boundary reacted with the oxygen atoms infiltrated from the outside to generate a rare earth Y-rich oxide residing at the boundary between the grain boundary and the precipitated phase of the alloy. Add 0.5 wt.% rare earth Y 47Fe-36Ni-15Cr-1.5Mn-0.5Y alloy 1000 ° C high temperature oxidation of the formation of spinel oxide crystalline particles, dense and complete oxide film layer, the surface of the addition of a small amount of (0.5 wt.%) rare earth Y after the refinement of the oxide particles, and in the alloy grain boundaries in the form of oxides reside in the alloy to inhibit the cations in the matrix and the oxygen negative ions. and oxygen-negative ions in the alloy matrix, improving the antioxidant performance of the alloy. With the further increase of rare earth Y, during the oxidation of the alloy at high temperature, the precipitation phase at the grain boundary promotes the inward diffusion of oxygen, resulting in the deterioration of the antioxidant property of the alloy.

Airworthiness certification requires proof of structure strength, which is performed generally through building block approach. To achieve this, representative intermediate scale experiments generated by test benches are in general needed, additionally to material characterization at coupons scale and structure testing at large scale. The VERTEX test bench can generate combined loading of tension/compression-shear-pressure on structural elements and was modelled with Finite Elements to perform virtual testing representative of its intermediate-scale specificity. The numerous bolted joints of the bench were modelled and their behavior identified in previous tests, so the model could quantitatively estimate the transfer function of the bench, which is the relation between the displacements imposed by the jacks and the resulting loads on a given sample. The VERTEX model was identified to represent load shapes and amplitudes on a training set, and was later confronted to a validation set of tests of tension and shear. A model with ideal boundary conditions was also developed for comparison but it failed to predict some load shape specificities and did not give any indication of loading amplitude. Application cases of the developed model are shown to assess a range of virtual testing possibilities.

Permeability is an important transport property of porous materials, and its accurate evaluation is relevant in studying applied tasks such as CO2 injection into reservoirs and investigating groundwater quality issues. This study examines the dependence of permeability on total and connected porosity, hydraulic tortuosity, specific surface area, and mean pore radius based on the data of 408 cubic sub-volumes extracted from heterogeneous and naturally fractured cylindrical carbonate samples before and after injection of HCl solutions into them. These parameters were computed using pore-scale modeling of fluid flow. It has been shown that permeability correlates well with porosity and mean pore radius with correlation coefficients of R2≈0.65-0.79 for heterogeneous samples. It was found that the presence of natural fractures significantly influenced the relationship between permeability and other parameters. The relationship between permeability k, tortuosity τ, and specific surface area S is described by power laws k~τ-α and k~S-β, with coefficients α and β substantially exceeding those in the Kozeny-Carman equation. It was also found that there is a parabolic relationship between connected and total porosities both before and after rock dissolution with R2≈0.96-0.99. This allowed for estimation of percolation threshold porosity that are in good agreement with the literature data.

The contemporary landscape of education is witnessing a paradigm shift towards innovative instructional methods, with the flipped learning approach gaining considerable attention. The purpose of this research was to investigate the impact of the flipped learning approach on students' perception and acceptance throughout an entire semester in the 'Introduction to Programming with Java' course, using a research design with a quantitative approach, ultimately aiming to inform educational practice and advance our knowledge of innovative teaching methods in higher education. This study was conducted at a university with 174 students involved, divided into two groups, 87 students in the experimental group and 87 students in the control group. The data collected through the scales were analyzed by using descriptive and inferential statistical analysis techniques in statistical software. At the end of the measurements, the technology acceptance level and self-directed learning perceptions of engineering students who received education with flipped learning were high. The results suggest that educators should consider students' readiness for self-directed learning when implementing the flipped learning approach and focus on creating an environment that supports their autonomy and engagement. This research offers valuable guidance for instructors, curriculum designers, and educational policymakers seeking to enhance the effectiveness of flipped learning in higher education courses.

Different prognostic scores have been applied to identify patients with non-small cell lung cancer who have a higher probability of poor outcome. In this study we evaluated whether Naples Prognostic Score, a novel index which considers both inflammatory and nutritional values, was associated with long-term survival. The study is a retrospective propensity score matching analysis of patients who underwent curative surgery for non-small cell lung cancer from January 2016 to December 2021. The score considered four pre-operative parameters: neutrophil to lymphocyte ratio, lymphocyte to monocyte ratio, serum albumin and total cholesterol. Kaplan-Meier method and Cox regression analysis were performed to evaluate the relation between the score and disease-free survival, overall survival and cancer-related survival. A total of 260 patients were selected for the study, reduced to 154 after propensity score matching. Post-propensity Kaplan-Meier analysis showed a significant correlation between Naples Prognostic Score and overall survival (p=0.018) and cancer-related survival (p=0.007). Multivariate Cox regression analysis further validated the score as an independent prognostic indicator for both these survivals (p=0.007 and p=0.010 respectively). Naples Prognostic Score proved to be an easily achievable prognostic factor of long-term survival in patients with non-small cell lung cancer after surgical treatment.

Capsizing accidents are regarded as marine accidents with a high rate of casualties per accident. Approximately 89% of all such accidents occur in small ships (vessels with gross tonnage less than 10 tons). Stability calculations are critical for assessing the risk of capsizing incidents and evaluating a ship's seaworthiness. Despite the high frequency of capsizing accidents involving small ships, they are generally exempt from adhering to stability regulations, thus remaining systemically exposed to the risk of capsizing. Moreover, the absence of essential design documents complicates direct ship stability calculations. This study utilizes hull form feature data—obtained from the general arrangement of small ships—as input for a deep learning model. The model is structured as a multilayer neural network and aims to infer hydrostatic curves, which is required data for stability calculations.

Several studies in computer vision have examined specular removal, which is crucial for object detection and recognition. This research has traditionally been divided into two tasks: specular highlight removal, which focuses on removing specular highlights on object surfaces, and reflection removal, which deals with specular reflections occurring on glass surfaces. In reality, however, both types of specular effects often coexist, making it a fundamental challenge that has not been adequately addressed. Recognizing the necessity of integrating specular components handled in both tasks, we constructed a Specular-Light (S-Light) DB for training single-image-based deep learning models. Moreover, considering the absence of benchmark datasets for quantitative evaluation, the Multi-Scale Normalized Cross Correlation (MS-NCC) metric, which considers the correlation between specular and diffuse components, was introduced to assess the learning outcomes.

In the evolving landscape of pharmaceutical manufacturing, a comprehensive continuous production process is being crafted for small-scale active pharmaceutical ingredient production. This study focuses on continuous crystallization with separate nucleation and crystal growth units, as well as continuous downstream processing, encompassing filtration, washing, and drying until the formation of free-flowing particles. We introduce a novel continuous nucleator designed based on solubility data and produced via 3D printing, enabling a fast and precise small-scale manufacturing of a nucleator meeting the requirements for nucleation and for the further growth processes. The nucleator was evaluated with regard to its suitability for continuous long-term operation across various coupled crystallizers. As a practical application example it is connected to a slug flow crystallizer to enable high-quality continuous crystallization. Additionally, the full integration of downstream processes using the continuous vacuum screw filter to achieve free-flowing product particles is realized. Even under non-optimized process conditions, with the help of in-situ generation of nuclei free-flowing product particles were successfully obtained. This is particularly useful during drug development when no material is available for seed addition and quickly obtain product for further characterization.

Radiation therapy (RT) is one of the main approaches of cancer therapy, including non-small cell lung cancer (NSCLC). Radiation-induced DNA double-stranded breaks (DSBs), single-stranded DNA breaks (SSBs) and oxidized nucleobases cause replication fork stalling, chromosomal instability or point mutagenesis and can lead to cell death. Translesion DNA synthesis (TLS) is one of the DNA damage tolerance (DDT) pathways promoting replication of damaged DNA. Here, we explored the effects of the knockout of primase-polymerase PRIMPOL (PRIMPOL −/−) and TLS polymerase POLI (POLI −/−) genes in two different monoclonal lines derived from human NSCLC A549 cells. Our study aimed to investigate the impact of their absence on the susceptibility of these cells to ionizing radiation (IR)-induced stress. Without IR, cells that do not have PRIMPOL or POLI experience a considerable rise in apoptosis by day 7 of continuous cultivation and a noteworthy decline in cell migration under spatial confinement when compared to the parental wild-type cells. Despite this, we have found that PRIMPOL −/− exhibited higher clonogenic survival compared to wild-type cell lines in response to IR stress. Both of these cell lines demonstrated remarkable resilience to radiation stress, surpassing the survival rate of the original A549 cells. POLI deficiency reduces cellular γH2AX foci accumulation, while the presence of highest number of γH2AX foci in PRIMPOL−/− cells was ATM-independent. We report for the first time that increased post-irradiation clonogenic survival of PRIMPOL −/− A549 cells might be associated with the increase of CD133+ but not CD44+ populations of cancer stem-like cells.

The residual content of the only carbon source increases at the end of the exponential growth phase when P. putida BS3701 cells are cultured under nitrogen deficiency. Moreover, the mRNA quantity of some genes involved in carbon catabolism decreases. There are no available data on regulators capable of stimulating the target mRNA degradation in pseudomonads under nitrogen deficiency in the literature. We have applied both comparative genomics methods and a number of biomolecular ones to identify new small P. putida BS3701 RNAs. This is due to the fact that the formation of ncRNA-mRNA duplexes, recognized by RNases, is one of the main mechanisms for reducing the number of transcripts in a cell. We have used the following selection criteria: 1) the presence of the interest gene of the NtrC binding site and a sigma54-dependent promoter in the upstream area, 2) the conservative location of the interest gene in the P. putida and P. aeruginosa genomes, 3) increase in the product quantity of the interest gene in a cell under nitrogen deficiency on two different carbon sources (succinate and naphthalene), 4) the product detection of the interest gene in a fraction enriched with small RNAs. The expression of two new small RNAs has increased twice or more times under nitrogen deficiency on both succinate and naphthalene: expression of ng171 and ng379. ng379 contains narK RNA motif.

Anastomosing rivers are wetland systems with multiple channels, and there are many constraints limiting in-situ observations of their hydrodynamics. Therefore, there are few reports on the hydrodynamic data of such rivers. In order to obtain the hydrodynamic parameters of the river channels without hydrological observations in the anastomosing rivers, this study proposes an expression method namely expression of channel morphological parameters (ECMP) for hydrodynamic calculations. The calculation formula of the ECMP method based on the shape factor, scale factor, and gradient factor of the flow cross-section as independent variables. This method can be used to calculate the average velocity, discharge, specific stream power, gross stream power and other hydrodynamic parameters of the flow cross-section at different water levels, only requiring the measurement of morphological parameters such as the average depth, width-depth ratio, and gradient of the flow (channel) cross-section. The applicability of the ECMP method was verified using measured hydrological data. The results show that the ECMP method is a practical calculation method with high computational accuracy and convenient application for calculating river hydrodynamic parameters. It has great application value in study on fluvial geomorphology and hydrodynamics, as well as in the reconstruction of ancient river hydrology and hydrodynamics.

Small cell lung cancer (SCLC) and large cell neuroendocrine carcinoma (LCNEC) have recently been grouped as lung neuroendocrine carcinomas (NECs). Because these lung NECs are clinically malignant and their treatment strategies differ from those of non-SCLC, the quality of diagnosis has a significant prognostic impact. The diagnosis of LCNEC requires positive immunohistochemical staining with chromogranin A, synaptophysin, and CD56, along with morphological diagnosis, and insulinoma-associated protein 1 (INSM1) has been proposed as an additional marker but sometimes experience difficulty in making the diagnosis. We investigated Musashi-1 as a novel immunohistochemical marker in 42 patients with SCLCs and 44 with LCNECs who underwent lung resection between 1998 and 2020 at our institution. We found Musashi-1 expression in 41 (98%) of 42 SCLCs and 40 (90%) of 44 LCNECs. These findings were similar to CD56 expression and superior to synaptophysin, chromogranin A, and INSM1. Musashi-1 also tended to show more diffuse and intense staining, especially in LCNEC, with more cases staining &gt;10% than any other existing markers (Musashi-1, 77%; INSM1, 45%; chromogranin A, 34%; synaptophysin, 41%; and CD56, 66%). In conclusion, we identified Musashi-1 as a novel immunohistochemical staining marker to aid in the diagnosis of lung NEC.

In this observational study, we compared continuous physiological signals during an active stand test in adults aged 50 years and over, characterized as frail by three different criteria, using data from The Irish Longitudinal Study on Ageing (TILDA). Four independent groups were identified: those characterized as frail only by one of the frailty tools used (the physical Frailty Phenotype (FP), the 32-item Frailty Index (FI), or the Clinical Frailty Scale (CFS) classification tree), and a fourth group where participants were not characterized as frail by any of these tools. Continuous non-invasive physiological signals were collected during an active stand test: systolic (sBP) and diastolic (dBP) blood pressure, as well as heart rate (HR), using digital artery photoplethysmography. Additionally, frontal lobe cerebral oxygenation (Oxy), deoxygenation (Deoxy), and tissue saturation index (TSI) were also non-invasively measured using near-infrared spectroscopy (NIRS). The signals were visualized across frailty groups and statistically compared using one-dimensional statistical parametric mapping (SPM). A total of 1,124 participants (mean age 63.5 years, 50.2% women) were included: 23 were characterized as frail only by FP, 97 by FI, 38 by CFS, and 966 by none of these criteria. SPM analyses revealed that only the group characterized as frail by FI had significantly different signals (p<0.05) compared to the non-frail group. Specifically, they exhibited a lesser increase in HR between 5-15 seconds post-stand and larger deficits in sBP and dBP between 15-40 seconds post-stand, and possibly a lower Delta TSI at 20 seconds post-stand. In this comparison, the FI proved more adept at capturing distinct physiological responses to standing, likely due to its direct inclusion of cardiovascular morbidities in its definition.

In Bangladesh, family businesses play a significant role in the country's business landscape because so few private companies are set up as large-scale, non-family ownership firms. Thus, it is vital to comprehend family businesses in order to comprehend Bangladeshi company dynamics. In this paper, authors attempted to survey the existing research in the area of family business in Bangladesh. It was found that the area as a research is totally an open field and a lot of new research can be done in the said area. Although a good number of studies can be classified as focused on the SME (small and medium enterprise) domain, a major shortcoming of those SME studies is that the SMEs themselves vary greatly in terms of size, scope, and technology. Whereas most of the family businesses can be predominantly put in the SME category, the particular issue of family business and family dynamics is not very common in those studies. This paper suggests that a good amount of research or studies can be done focusing on family business constitution, family members' participation norm, recruitment and promotion norm, family business succession rules and succession planning, training for family business leadership and management, training for board and directorial supervision, and so on.

Sensation seeking (SS) is a psychobiological personality trait characterized by an individual’s propensity to engage in various forms of risk-taking behavior. The Brief Sensation Seeking Scale (BSSS-8) is a widely used instrument for assessing SS that has been translated into several languages. However, only outdated and non-validated questionnaires have been used to measure SS in the Slovenian population. The aim of this study was to translate and psychometrically validate the Slovenian version of the BSSS-8. A total of 363 participants aged between 14 and 65 years completed the translated BSSS-8 and the questionnaire on drug abuse. The scale demonstrated good reliability (Cronbach’s α=0.81) and a unidimensional factorial structure, as revealed by confirmatory factor analysis (CFA). The multigroup CFA showed gender-specific measurement invariance. In the nomological network, SS was positively associated with drug-related variables. The Slovenian version of the BSSS-8 scale is a short and simple instrument to assess SS for research and epidemiological purposes.

Constraint-based metabolic modeling approaches have enhanced our knowledge and understanding of the metabolism of prokaryotes and eukaryotes. This approach highly depends on the reconstruction process of genome-scale metabolic models (Mmodels). M-models can guide effective experimental design and yield new insights into the function and control of biological systems. Despite the recent advances in the automated generation of draft metabolic network reconstructions, the manual curation of these networks remains a labor-intensive and challenging task. Thus, these ten quick tips for the manual curation process are essential for optimizing high-quality metabolic model generation in less time. This collection of tips describes in great detail the resources and methods to ensure successful reconstruction. Furthermore, it increases the scope of other protocols of metabolic modeling by including resources to reconstruct eukaryotic organisms. Thus, all tips are applicable to a wide range of eukaryotic organisms. We believe this manuscript will interest a broad audience and researchers from different disciplines, spanning from microbiology and systems biology to biotechnology.

When describing the tribological behaviour of technical surfaces, the need for full length scale microtopographic characterization often arises. The self-affine of surfaces and the characterization of self-affine by a fractal dimension and its implantation into tribological models are commonly used. The goal of our present work was to determine the frequency range of fractal behaviour of surfaces by analysing the microtopographic measurements of a brake plunger. We also wanted to know if the bifractal and multifractal behaviour can be detected in real machine parts. As a result, we developed a new methodology for determining the fractal range boundaries to separate the nano- and micro-roughness. In order to reach our goals, we used atomic force microscope (AFM) and stylus instrument to provide measurements in wide frequency range (19 nm - 3 mm). As a result of the power spectral density (PSD) based fractal evaluation, it was found that the examined surface could not be characterized by a single fractal dimension and that we developed a methodology for stopping the margin of validity of each fractal dimension, and we also developed a methodology for determining the limits of validity of certain fractal dimensions.

Background. Chronic Obstructive Pulmonary Disease (COPD) is a heterogeneous systemic syn-drome that often coexists with multiple comorbidities. In high-complex COPD patients the role of the Cumulative Illness Rating Scale (CIRS) as a risk predictor of COPD exacerbation is not known. Objective. To determine the effectiveness of the CIRS score in detecting the association of comorbidities and disease severity with the risk of acute exacerbations in COPD patients. Methods. 105 adults with COPD were included in this prospective study. All participants at baseline had at least 2 moderate exacerbations or 1 leading to hospitalization. The primary outcome was a composite of moderate or severe COPD exacerbation during 12-months follow-up. Results. CIRS indices (CIRS total score, Severity Index and Comorbidity Index) showed a positive correlation with mMRC, CAT and a negative correlation with FEV1, FVC, FEV1/FVC. The three CIRS indices were able to predict the 12-month rate of moderate or severe exacerbation (CIRS-Total Score: Hazard Ratio (HR) = 1.12 (95% CI: 1.08-1.21); CIRS-Severity Index: HR = 1.21 (95% CI: 1.12-1-31); CIRS-Comorbidity Index = 1.58 (95% CI: 1.33-1.89)). Conclusions. Among patients with COPD, comorbidity number and severity, assessed by the CIRS score, influence the risk in moderate to severe exacerbations. The CIRS score also correlates with the severity of respiratory symptoms and lung function

(1) Background: Thrombophilia is a tendency towards hypercoagulability, and it is increased by pregnancy, and peaking around delivery.; (2) Methods: 80 Eighty thrombophilia patients admitted for delivery between 1/10/2017 and 1/12/2021 were prospectively studied, together with 80 eighty age- and para-matched control patients. Patients were admitted for delivery at term by means of cesarean section. The inflammation indexes from the complete blood count values analysis, in during both the 24 hours before and the 24 hours after labor—, extracted from the hospital’s medical records—, were calculated, and then correlated with uterine involution.; (3) Results: In pregnant patients treated for thrombophilia patients, the patients who did not manage to have close their uterine cavity closed with in the first 24-48 hours had significantly higher SII and AISI inflammation indexes SII and AISI , than the onesthose who did manage to have their uterine cavity closed their uterine cavity. In pregnant patients treated for thrombophilia patients, MLR is higher in patients without the Rh factor than in those with the Rh factor. In non-thrombophilia patients, PLR was significantly higher as compared to treated thrombophilia patients. In non-thrombophilia patients, MCVL increased with age and, before labor, while SII and AISI increased with age, postpartum. ; (4) Conclusions: There may be an underlying inflammation state that persisted despite treatment in some pregnant thrombophilia pregnant patients.

Cable-supported façades represent a novel approach in the design and technology of double skin façades (DSF). This type of systems not only offer flexibility in terms of exterior finishes, but also regulates the access of solar radiation, thereby transforming the appearance of the building in response to varying daylight conditions. However, the structural performance of these façades under wind, impact and seismic loads remains an active area of research. The study is a groundbreaking work that experimentally evaluates the wind and seismic behaviour of these type of façades. The methodology used for the evaluation of flexible masonry facades includes laboratory tests analysing the individual capacity of the connections and materials of the system under standardized and non-standardized procedures. A full-scale experimental sub-assembly specimen of a representative module of the façade is also subjected to uniformly distributed pressures of wind load tests as well as hard body and soft body impact tests. The setup considered the border conditions, tension loads, and actual materials. Furthermore, the earthquake assessment includes tests of full-scale specimens subjected to these demands. Research findings allowed the refinement and redesign of the system's components granting the validation of the DSF used as a case study.

A novel approach was developed that combined LP-based row generation with optimization-based sorting to tackle computational challenges posed by budget allocation problems with combinatorial constraints. The proposed approach dynamically generated constraints using row generation and prioritized them using optimization-based sorting to ensure a high-quality solution. Computational experiments and case studies revealed that as the problem size increased, the proposed approach outperformed simplex solutions in terms of solution search time. Specifically, for a problem with 50 projects (N = 50) and 2,251,799,813,685,250 constraints, the proposed approach found a solution in just 1.4 seconds, while LP failed due to the problem size. The proposed approach demonstrated enhanced computational efficiency and solution quality compared to traditional LP methods.

Abstract: Epigenetic approaches in direct correlation with assessment of critical genetic mutations in non-small cell lung cancer (NSCLC) are currently very intensive, as the epigenetic components in NSCLC development and progression have attained high recognition. In this level of research, established human NSCLC cell lines as well as experimental animals are widely used to detect novel biomarkers and pharmacological targets to treat NSCLC. The epigenetic background of NSCLC is highly complex and requires precise definition as these phenomena are variable between NSCLC subtypes and systems origin. We engaged an in-depth characterization of non-coding (nc)RNAs prevalent in human KRAS-mutant NSCLC cell lines A549 and H460 and mouse KRAS-mutant NSCLC tissue by Next Generation Sequencing (NGS) and quantitative Real Time PCRs (qPCRs). Also, the effect of transcription factor (TF) LRF, a known epigenetic silencer, was examined on the epigenome modulation. Finally, interacting networks underlying epigenetic variations in NSCLC subtypes were created. Data derived from our study highlights the divergent epigenetic profiles of NSCLC of human and mouse origin, as well as the significant contribution of 12qf1: 109,709,060-109,747,960 mouse chromosomal region to micro-RNA upregulated species. Fur-thermore, the novel epigenetic miR-148b-3p/PVT1/ZBTB7A axis was identified, which differentiates lung adenocarcinoma from large cell lung carcinoma, two characteristic NSCLC subtypes. The detailed recording of epigenetic events in NSCLC and combinational studies including networking between ncRNAs and TFs validate the identification of significant epigenetic features, prevailing in NSCLC subtypes and among experimental models. Our results enrich knowledge in the field and empower research on the epigenetic prognostic biomarkers of the disease and patient-tailored treatments.

The semantic segmentation of high-resolution remote sensing images (HR-RSI) is crucial for a wide range of applications, such as precision agriculture, urban planning, natural resource assessment, and ecological monitoring. However, accurately classifying pixels in HR-RSI faces challenges due to densely distributed small objects and scale variations. Existing techniques, including Convolutional Neural Networks (CNNs) and other methods for hierarchical feature extraction and fusion of remote sensing image, often do not achieve the desired accuracy. In this paper, we propose a novel approach called the Hierarchical Rich-scale Fusion Network (HRFNet) to address these challenges. HRFNet utilizes advanced information rating and image partition techniques to extract rich-scale features within image layers. This allows for the adaptive exploration of both local and global contextual information. Moreover, we introduce a structured intra-layer to inter-layer feature aggregation module, which enables the adaptive extraction of fine-grained details and high-level semantic information from multi-layer feature maps in a highly flexible manner. Extensive experimentation has been conducted to validate the effectiveness of our proposed method. Our results demonstrate that HRFNet outperforms existing techniques, achieving state-of-the-art (SOTA) results on benchmark datasets, specifically the ISPRS Potsdam and Vaihingen datasets.

To conduct a precise impact assessment of marine diesel engines, a 200t floating impact platform was utilized to simulate realistic testing conditions. The testing generated the acceleration time curve and the impact response spectrum for the diesel engine. According to the applicable standards, the spectral velocity was chosen as the evaluation index, and an evaluation of the longitudinal, transverse, and vertical impact environment of the diesel engine was conducted. The shock factor interpolation method was corrected using the confidence interval based on normal distribution, and the interpolated confidence interval of the shock factor was determined. The findings reveal that: The 200t-class floating shock platform is capable of providing a reliable shock environment for diesel engine tests. The shock wave serves as the primary external force during the experiment, and the impact of bubble pulsation can be disregarded when assessing using the floating shock platform. To more accurately describe the experimental shock intensity when presented with dispersed data, the confidence interval can be utilized, and the resulting prediction formula provides a higher level of impact safety. The obtained conclusions can serve as a reference for predicting the shock environment of large shipboard equipment using the shock platform.

The Quantum-Cosmos Nexus Theory delves into the intricate web of relationships that tie together fundamental constants, cosmological properties, and the boundaries of our comprehension in the realm of physics. It unveils a profound interconnection between the gravitational constant (G), the speed of light (c), and the parameters of the observable universe. It introduces a unification equation that hints at a connection between the fundamental constants. It also introduces a new constant, , which is similar to the Planck constant but, on a large scale, signifies the ultimate unit of action or energy on a cosmic scale. With this new constant, new units similar to Planck units have been introduced. They are the equivalence of Planck units in the sense that they represent the limits of our current understanding of physics on the largest scale. The theory also shows that most of the fundamental constants of nature, including the gravitational constant (G), the speed of light (c), vacuum permittivity (ε₀) vacuum permeability (u₀), the Boltzmann constant (K_B), and the fine-structure constant (α), can be derived from these new units and Planck units. The first derived equation suggests a potential weakening of the gravitational constant over time, assuming the density remains relatively constant. Although this is a matter of speculation, it has the potential to explain observations related to dark matter. The theory also makes use of the Planck force, defining it as the maximum force on the quantum and cosmic scales. It also emphasizes that the Planck scale is not the ultimate limit and reveals our observational constraints, offering a unique perspective on the universe's fundamental workings. The Quantum-Cosmos Nexus Theory reveals how fundamental constants, cosmic dynamics, and the limits of our understanding converge, opening the door to fresh perspectives on the underlying principles of the universe.

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

Detecting rotational objects in remote sensing imagery is a significant challenge. These images typically encompass a broad field of view, featuring diverse and intricate backgrounds, with ground objects of various sizes densely scattered. As a result, identifying objects of interest within these images is a daunting task. While the integration of Convolutional Neural Networks (CNN) and Transformer networks leads to some advancements in rotational object detection, there is still room for improvement, particularly in enhancing the extraction and utilization of information related to smaller objects. To address this, our paper presents a multi-scale feature fusion module and a global feature context aggregation module. Initially, we fuse original, shallow, and deep features to reduce the loss of shallow feature information, thereby improving the detection performance of small objects in complex backgrounds. Subsequently, we compute the correlation of contextual information within feature maps to extract valuable insights. We name the newly proposed model the "Multiscale Feature Context Aggregation Module" (MFCA). We evaluate our proposed methodology on three challenging remote sensing datasets: DIOR-R, HRSC, and MAR20. Comprehensive experimental results show that our approach surpasses baseline models by 2.07\% mAP, 1.02\% mAP, and 1.98\% mAP on the DIOR-R, HRSC2016, and MAR20 datasets, respectively.

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

Repairing of small and isolated delamination area in carbon fiber reinforced polymer (CFRP) without significantly altering the composite structure is studied in this research. A small drilled hole was introduced at the center of the delamination section of CFRP, providing the channel for epoxy infill of sharp delamination cracks. The pressureless infill repair was achieved through the capillary action of an acetone-diluted resin solution (without hardener) with CNT reinforcement, e.g., 89 m/m% of acetone, 10 m/m% of resin and 1m/m% of CNT. Such an acetone-rich resin pre-coating (RPC) solution is easy to prepare and apply to the drilled hole area. Curing of CNT-toughened resin infill was induced by filling the small-drilled hole with a resin-hardener mixture toughened by CNT/Aramid pulp. The effectiveness of delamination repair with curing periods of two weeks and three months were compared. Flexural strength measurements indicated that 77% (potentially 100%) of the restoration was achievable.

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

Small bowel non-Meckelian diverticulosis is a rare condition with only few published cases despite being described over 200 years ago. In the midst of the COVID-19 pandemic, studies suggested that a lot of patients may experience gastrointestinal manifestations. Intestinal symptoms could worsen the inflammation and infection associated with small bowel diverticulitis. Here, we present two cases: one with inflammation and rupture in a COVID-19 patient and the other as an asymptomatic detection. Furthermore, we provide a mini-review of the literature to emphasize the importance of considering this entity in the differential diagnosis of an acute abdomen.

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

Serine/threonine kinase (AKT) signaling regulates diverse cellular processes and is one of the most important aberrant cell survival mechanisms associated with tumorigenesis, metastasis, and chemoresistance. Targeting AKT has become an effective therapeutic strategy for the treatment of many cancers. AKT3 (PKBγ), the least studied isoform of the AKT family, has emerged as a major contributor to malignancy. AKT3 is frequently overexpressed in human cancers, and many regulatory oncogenic or tumor suppressor small non-coding RNAs (ncRNAs), including microRNAs (miRNAs), have recently been identified to be involved in regulating AKT3 expression. Therefore, a better understanding of regulatory miRNA/AKT3 networks may reveal novel biomarkers for diagnosis of patients with cancer and provide invaluable information for developing more effective therapeutic strategies. The aim of this review is to summarize current research progress in the isoform-specific functions of AKT3 in human cancers and the roles of dysregulated miRNA/AKT3 in specific types of human cancers.

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

The antimicrobial properties of the new strains of microorganisms isolated from natural sources of various ecological niches in the Moscow Region and the Republic of Tatarstan were studied. An antifungal activity of isolates was established in a test culture of toxin-producing microscopic fungi that cause various plant diseases: Fusarium oxysporum, Aspergillus flavus, Penicillium spp. and Candida albicans. Of the 46 studied microorganisms, 4 isolates (Bacillus subtilis, Propionibacterium freudenreichii, Lactobacillus plantarum and Streptomyces spp.) showed an ability to produce biologically active metabolites with a pronounced antimicrobial potential. This is promising in the growth and protection of the Scots pine, pedunculate oak and small-leaved linden forest crops from diseases caused by fungi Lophodermium pinastri Chev. and Microsphaera alphitoides stimulation. Based on selected isolates, a Bacterial product LRV composition has been developed. A Scots pine, pedunculate oak and small-leaved linden seedlings single and double foliar treatment with Bacterial product LRV at a concentration of 10 ml/l led to an increase in the growth of an above-ground part by 31.8, 51.9 and 25.4 %, respectively, and an underground part – by 25.0, 37.2 and 25.7 %, respectively, compared with the control. The weight of seedling at the end of the study exceeded the control variant by an average of 26.0, 44.0 and 78.0 %, respectively. A use of a plant protection Bacterial product LRV did not have a significant effect on a powdery mildew and Schütte disease damage to forest crops. A Biogeosystem Technique methodology has been developed to improve a long-term forest growing.

Outcome of redo-mitral valve replacement in the small mitral annulus with the use of the chimney technology is not well documented. From 2019 to 2020, 77 consecutive patients underwent redo-mitral valve replacement with the use of the chimney technology because of a small mitral annulus. To evaluate heart structural integrity and clinical outcomes, postoperative clinical data and echocardiograms were examined. Mean age was 56.7 ± 15.98 years. All patients underwent mitral valve surgery of which 62 were mitral valve replacements, 7 mitral valve repairs, and 8 double valve replacements. Preoperative Mitral valve mean gradient was 18.07 ± 9.40 mmHg. Postoperative mitral prosthesis size 28.51 ± 1.22 mm. The median increment of mitral size enlargement was 4 (0, 6) valve sizes. Mean mitral gradient coming out of the operating room was 10.34 ± 2.12 mmHg and at follow-up echocardiogram performed at 3 years after the procedure was 10.36 ± 1.70 mmHg. One-year survival was 93.3%, while 4-year survival was 89.3% with no reoperation. Use of the chimney technology in small mitral valve re-mitral valve replacement results in larger valve sizes. Moreover, the mean gradients over the mitral valve are acceptable intraoperatively and over time.

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

Mathematical models of non-small cell lung cancer are powerful tools that use clinical and experimental data to describe various aspects of tumorigenesis. The developed algorithms capture phenotypic changes in the tumor and predict changes in tumor behavior, drug resistance, and clinical outcomes of anti-cancer therapy. The aim of this study was to propose a mathematical model that predicts the changes in the cellular composition of patient-derived tumor organoids over time with a perspective of translation of these results to the parental tumor, and therefore to possible clinical course and outcomes for the patient. Using the data on specific biomarkers of cancer cells (PD-L1), tumor-associated macrophages (CD206), natural killer cells (CD8), and fibroblasts (αSMA) as input, we proposed a model that accurately predicts the cellular composition of patient-derived tumor organoids at a desired time point. Combining the obtained results with “omics” approaches will improve our understanding of the nature of NSCLC. Moreover, their implementation into clinical practice will facilitate a decision-making process on treatment strategy and develop a new personalized approach in anti-cancer therapy.

In this study, we aimed to identify blood lymphocytes as a prognostic factor for survival in patients with locally advanced stage III non-small cell lung cancer (NSCLC) treated with concurrent chemoradiotherapy (CCRT). We conducted a secondary analysis of 196 patients enrolled in the Korean Radiation Oncology Group 0903 phase III clinical trial to evaluate the prognostic significance of circulating blood lymphocyte levels. The median total lymphocyte count (TLC) reduction ratio during CCRT was 0.74 (range: 0.29 – 0.97). In multivariate analysis, patient age (p = 0.014) and gross tumor volume (GTV, p = 0.031) were significant factors associated with overall survival, while TLC reduction (p = 0.018) and pretreatment neutrophil-to-lymphocyte ratio (NLR; p = 0.010) were associated with progression-free survival (PFS). In multivariate logistic regression analysis, pretreatment NLR, GTV, and heart V20 were significantly associated with TLC reduction. TLC reduction during CCRT is a significant prognostic factor for PFS, and heart V20 is significantly associated with TLC reduction. Thus, constraining the volume of the radiation dose to the whole heart must be prioritized to reduce TLC.

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

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

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

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

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

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

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

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

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

Ship detection in large-scene offshore synthetic aperture radar (SAR) images is crucial in civil and military fields, such as maritime management and wartime reconnaissance. However, the problems of low detection rates, high false alarm rates, and high missed detection rates of offshore ship targets in large-scene SAR images are due to the occlusion of objects or mutual occlusion among targets, especially for small ship targets. To solve this problem, this study proposes a target detection model (TAC_CSAC_Net) that incorporates a multi-attention mechanism for detecting marine vessels in large-scene SAR images. Experiments were conducted on two public datasets, the SAR-Ship-Dataset and high-resolution SAR image dataset (HRSID), with multi-scene and multi-size, and the results showed that the proposed TAC_CSAC_Net model achieves good performance for both small and occluded target detection. Experiments were conducted on a real large-scene dataset, LS-SSDD, to obtain the detection results of subgraphs of the same scene. Quantitative comparisons were made with classical and recently developed deep learning models, and the experiments demonstrated that the proposed model outperformed other models for large-scene SAR image target detection.

A recent study has suggested that the age profiles for deaths due to COVID-19 variants differs between variants and shows male/female specificity. This study demonstrates the age and sex dependency is common among human pathogens. The often-reported higher susceptibility to infections among the young and elderly is true in general but does not apply to individual pathogens. Even among different types of pneumonia there are subtle differences in the age profile. The gender ratio between pathogens likewise shows wide variation from as low as 10% female admissions for leptospirosis to around 90% for gonococcal admissions. The observed age/sex variation observed for mortality due to COVID-19 variants is an expression of a far wider phenomenon with implications to the age/sex response to vaccines. We propose that such differences are likely to be controlled by small noncoding RNAs which act as potent regulators of gene expression leading to altered cell morphology, metabolism, immune function, and response to vaccines.

Self-selecting the intensity of aerobic training is a perspective that allows practitioners to develop autonomy (choice of pace), improve physical fitness and achieve weight loss. To compare the effect of four aerobic training protocols on cardiometabolic and psycho-affective responses in physically inactive adult men and women. The sample consisted of 11 individuals aged 56 ± 10 years and BMI 29.2 ± 2.4 kg/m2 performed four different treadmill aerobic training protocols, including: 1-individual with self-selected intensity (ISS), 2-individual with prescribed intensity (IPI) (64-76% of HR max), 3-small group with self-select. selected intensity (SGS) and 4-small group prescribed intensity (SGP) (64-76% of HR max). Heart rate (HR), lactate (LACT), perceived exertion (RPE), affective response (FS), activation scale (FAS), enjoyment (PACES) and intention to repeat the session (INT) were analyzed. Statistical analysis used ANOVA with Tukey's post hoc test (p&lt;0.05). HR, LACT and RPE analyzes did not show significant differences between protocols. The FS showed a smaller decline in the SI protocol (p&lt;0.05), and the intention to repeat the training session was greater in the SI protocol (p&lt;0.05). Conclusion: The data allow us to conclude that the SI training protocol produced superior results in affective response, pleasure and intention to repeat the session.

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

Proposed study aims to investigate the digital transformation of Greek small and medium enterprises (SMEs), with a particular focus on their digital maturity and the strategic and organizational factors contributing to digital transformation. The research issue of digital transformation is attracting considerable interest among academics and business practicians since COVID-19 accelerated the procedure of implementing Industry’s 4.0 principles all over global economies. Quantitative research on 147 Greek SMEs revealed most important issues on how these businesses implement digital transformation, factors accelerating or decelerating the process, barriers and expected outcomes. Research enlights how SMEs face the transition to digital business and reveals the factors that can facilitate the whole process. Results can be valuable for stakeholders enabled to digital transformation process from both business and academic point of view, while there exist aspects that can contribute to policy makers / motivation developers in state’s level as well.

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

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

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

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.

Besides the increase in global energy demand, access to clean energy, reduction in greenhouse gas emissions caused by conventional power generation sources, energy security, and availability of electricity in remote villages in emerging nations are some of the factors that foster the use of renewable energy sources (RESs) in generating electricity. One of the aims of initiating microgrids (MGs) is to maximize the benefits of RES and alleviate the associated grid integration issues. Microgrids are made up of RES connected to electrical loads within clearly delineated electrical limits that operate as individual controllable units on the electrical network. It can operate in-dependently as well as in connection with the grid. The paper presents an overview of microgrids and investigates the system's performance when connected to and disconnected from the grid. Furthermore, in both modes of operation, the functioning and behavior of system components such as the bidirectional DC-DC converter and energy storage system (ESS) were evaluated. The architecture of the proposed microgrid consists of a small hydropower plant, a wind farm, and a battery energy storage system (BESS). The microgrid under investigation is modeled and simu-lated using MATLAB and Simulink.

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

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

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

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

Recent advances in embedded antenna and sensor technologies for 5G communications have galvanized a response toward the investigation of their electromagnetic (EM) performance for urban context and civil engineering applications. This article quantitatively investigates the effects of material loading on an evolved antecedent-based design of a hexagonal-stubbed Complementary Split-Ring Resonator (CSRR)-loaded antenna through simulation and experimentation. The optimized antenna design is first conceptualized within a simulation environment to achieve EM resonance at 3.50 GHz before delving into the analysis of operational performance characteristics. As a proof-of-concept, a physical antenna prototype is fabricated on a printed circuit board for the in-situ evaluation of S11 parameter plot. Subsequently, a simulation-based parametric study is conducted on antenna prototypes embedded into Ordinary Portland Cement pastes with varying weight percentages of iron(III) oxide. Simulation-derived and experimental results are mutually verified, achieving a systemic downward shift in resonant frequency and corresponding variations in impedance matching induced by changes in the loading reactance. Finally, an inversion modeling procedure is employed using perturbation theory to extrapolate the relative permittivity of the dielectric embedding materials. Our proposed analysis contributes to optimizing concrete-embedded 5G antenna sensor designs and establishes a foundational framework for estimating unknown EM parameters of cement-based composites.

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

Background: Lipid disorders are one of risk factors for cardiovascular diseases. The aim of the study was to determine the lipid profile in early childhood in the group of Polish children born small for gestational age (SGA). Materials and Methods: The study included 140 children (93 SGA and 47 controls) aged 5 to 11 years. All children underwent a physical examination and blood tests for the glucose and lipid profile. SGA group was divided in subgroups: symmetrical and asymmetric intrauterine growth restriction (IUGR). Results: Blood sample analysis revealed higher levels of total cholesterol levels (SGA group 190.61 +/- 24.66 mg/dl vs control 143.23 +/- 23.90; p&lt;0.001). The analysis of particular fractions of cholesterol showed significantly higher mean values of triglycerides, LDL-cholesterol as well as lower mean values of HDL-cholesterol in children born as SGA. Children in both groups did not differ significantly according to weight and body mass index. A statistically significantly higher glucose concentration was obtained in the group of children with IUGR with symmetrical type of IUGR. Conclusions: Children born SGA even in their early childhood, without overweight or obesity, have abnormal lipid profile, which may be a risk factor of cardiovascular diseases in adulthood.

Concatemeric viral DNA is packaged into Bacteriophage P22 procapsids via a headful packaging mechanism mediated by a molecular machine consisting of small (gp3) and large (gp2) terminase subunits. Although a negative stain reconstruction exists for the terminase holoenzyme, it is not clear how this complex binds dodecameric portal protein located at a 5-fold mismatch vertex. Herein, we describe new assemblies for the holoenzyme. Both native mass spectrometry and transmission electron microscopy reveal that the P22 terminase complex adopts three main assemblies, which include a nonameric S-terminase bound to two L-terminase 1(gp3)9:2(gp2), two nonameric S-terminase bound to five L-terminase 2(gp3)9:5(gp2), and three nonameric S-terminase bound to seven L-terminase 3(gp3)9:7(gp2). Native agarose gel electrophoresis shows that the terminase complex interacts with procapsid with mild crosslinking. These results herein illustrate the P22 terminase complex can adopt a variety of confirmations and assembly states.

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

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

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

EGFR tyrosine kinase inhibitors (TKIs) are the preferred initial treatment for non-small cell lung cancer (NSCLC) patients harboring EGFR mutations. However, remission is transient, and no further effective treatment options are available for EGFR-TKI-advanced EGFR-mutant NSCLCs. Immunotherapy with immune checkpoint inhibitors (ICIs) induces sustained cancer remission in a subset of NSCLCs. However, ICI therapy exhibits limited activity in most EGFR-mutant NSCLCs. Mechanistically, the strong oncogenic EGFR signaling in EGFR-mutant NSCLCs contributes to the non-inflamed tumor immune microenvironment (TIME), characterized by a limited number of CD8+ T cell infiltration, a high number of regulatory CD4+ T cells and a high number of inactivated infiltrated T cells. Besides, EGFR-mutant NSCLC patients are generally non-smokers with low levels of PD-L1 expression and tumor mutation burden. However, current understanding only partially explains why a small population of EGFR-mutant NSCLCs durably respond to ICI therapy, resulting in many researchers actively working in this field. This review reviews the hope seen from pre-clinical studies and clinical trials which may be adopted to improve the outcome of ICI therapy in EGFR-mutant NSCLCs. Besides, the underlying mechanisms leading to the inferior clinical outcome of ICI therapy in EGFR-mutant NSCLCs are discussed.

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

The biodiversity of the State National Natural Park (SNNP) "Buiratau" has not been studied, as it is young in Kazakhstan. The purpose of our study was an independent botanical and geographical study of the vegetation cover of the SNNP "Buiratau". This study have been carried out using the method route-reconnaissance and identifying plants, according to plant guide “Flora of Kazakhstan” (1999), “Key to Plants of Central Asia” (1974). The results showed that depending on the geographical location (Kazakh highland, steppe zone) of the SNNP "Buiratau" territory this natural complex has a very diverse botanical feature. In these types of vegetation of this territory, there are both Red Data Book and rare and endemic plants, such as black alder and birch forests, the peculiarity of which is that some species (Alnus glutinosa (L) Gaertn, Betula Kirghisorum Sav. - Rysz.) are listed in the Red book and very few. Medicinal, plant species and their resource potential are an important component of the flora. The analysis showed that the SNNP "Buiratau" is a unique natural environment, which is characterized by 10 species of plants listed in the Red Book, and 26 species of rare and endangered species.

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

The pathogenesis of Cerebral Small Vessel Disease (CSVD) is largely unknown. Endothelial disfunction has been suggested as the turning point in CSVD development. In this study we tested the effect of plasma from CSVD patients on human cerebral microvascular endothelial cells with the aim of describing the pattern of endothelial activation. Plasma samples from 3 groups of young subjects have been tested: PTs (subjects affected by early stage CSVD); CTRLs (control subjects without abnormalities at MRI scanning); BDs (blood donors). Human Brain Endothelial Cells 5i (HBEC5i) were treated with plasma and total RNA was extracted. RNAs were pooled to reduce gene expression-based variability and NGS analysis was performed. Differentially expressed genes were highlighted comparing PTs, CTRLs and BDs with HBEC5i untreated cells. No significantly altered pathway was evaluated in BD related treatment. Regulation of p38 MAPK cascade (GO:1900744) was the solely pathway altered in CTRLs related treatment. Indeed, 36 different biological processes turned out to be deregulated after PTs treatment of HBEC5i, i.e., the cytokine-mediated signaling pathway (GO:0019221). Endothelial cells activate inflammatory pathways in response to stimuli from CSVD patients’ plasma, suggesting the pathogenetic role of neuroinflammation from the early asymptomatic phases of cerebrovascular disease.

Craters are the most prominent geomorphological features on the surface of celestial bodies, which is playing a crucial role in studying the formation and evolution of celestial bodies, as well as in landing and planning for surface exploration. Currently, the main automatic crater detection models and datasets focus on the detection of large and medium craters. In this paper, we created 23 small lunar crater datasets for model training based on the Chang’E-2 (CE-2) DOM, DEM, Slope, and integrated data with 7 kinds of visualization stretching methods. And then, we proposed the YOLO-Crater model for Lunar and Martian small crater detection by replacing EioU and VariFocal loss to solve the crater samples imbalance problem and introducing a CBAM attention mechanism to mitigate interference from the complex extraterrestrial environment. The results show that the accuracy (P = 87.86%, R = 66.04%, and F1 = 75.41%) of the Lunar YOLO-Crater model based on the DOM-MMS (Maximum-Minimum Stretching) dataset is the highest and better than that of YOLOX model. And the Martian YOLO-Crater, trained by the Martian dataset from the 2022 GeoAI Martian Challenge, achieves good performance with P = 88.37%, R = 69.25%, and F1 = 77.65%. It indicates that the YOLO-Crater model has a strong transferability and generalization capability, which can be applied to detect small craters on the Moon and other celestial bodies.

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

Still, the most common in vitro model for absorption, distribution, metabolism and excretion (ADME) purposes is the Caco-2 cell line. However, clear differences in gene- and protein expression towards the small intestine and an at best fair prediction accuracy of intestinal drug absorption restrict the usefulness as model for intestinal epithelial cells. To overcome these limitations, we evaluated a panel of low-passaged patient-derived colorectal cancer cell lines of the HROC collection concerning similarities to small intestinal epithelial cells and their potential to predict intestinal drug absorption. After initial screen of a larger panel, ten cell lines with confluent outgrowth and long-lasting barrier forming potential were further characterized in close detail. Tight junctional complexes and microvilli structures were detected in all lines, differentiated epithelial cells were abundant in 7/10. All lines expressed multiple transporter molecules, with expression levels in three lines being close to those of small intestinal epithelial cells. Compared to the Caco-2 model, three HROC lines demonstrated both higher similarity to jejunal epithelial tissue cells and higher regulatory potential of relevant drug transporters. In sum, these lines would be better suited human small intestinal epithelium models for basic and translational research; especially for ADME studies.

Zoonotic abortive diseases represent a significant health and economic risk for national public health. This cross-sectional survey was conducted from April to October 2021 among 200 selected small ruminant farmers in the three northern regions of Cameroon. Data collection was done through questionnaires administered by exchange with the herder and responses were coded and recorded on an Excel spreadsheet. The data were then analyzed with R software, version 2.13.0. An ANOVA test was used to assess significant differences in mean of Knowledge, Attitudes, Practices and Perception of zoonotic risks (KAPP) scores between regions. Pearson correlations were used to calculate the association between KAPP scores within regions. Small ruminant farmers surveyed had low mean scores for knowledge of abortive Toxoplasmosis and Chla-mydophilosis (0.1 ± 0.2), desirable attitude (0.32 ± 0.07), appropriate practice (0.36 ± 0.13) in managing abortion, and positive perception of zoonotic risks of Toxoplasmosis and Chlamydo-philosis in small ruminants (0.12 ± 0.33), respectively. KAPP was significantly (p<0.01) and posi-tively associated with knowledge (r=0.98) and risk perception (r=0.99). However, attitudes (r=0.06), practices (r=0.05) and risk perception of toxoplasmosis (r=0.07) and Chlamydophilosis (r=0.08) were not associated with farmers' knowledge. This study revealed significant knowledge gaps, low levels of desired attitudes, and high-risk behavioral practices. These results therefore call for capacity building of health professionals and farmers to better integrate the One Health concept in the management of neglected zoonotic diseases.

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

Postprandial hyperlipidemia showing postprandial increase in serum triglyceride (TG) is atherogenic and associated with the development of coronary artery disease (CAD). To diagnose postprandial hyperlipidemia, the oral fat loading test (OFLT) should be performed, however, this test is very time-consuming and is difficult to perform. Ele-vated serum TG levels reflect an increase in TG-rich lipoproteins (TRL) such chylomi-crons (CM), very low-density lipoproteins (VLDL) and their remnants (CM remnant [CMR] and VLDL remnant [VLDLR]). Understanding of elevation in CMR and/or VLDLR can lead us to understand the existence of postprandial hyperlipidemia. The measurements of apo B48 which is a constituent of CM and CMR, non-fasting TG which includes TG content in all lipoproteins including CM and CMR, non-high-density lipoprotein-cholesterol (non-HDL-C) which includes TRL and low-density lipoprotein, and remnant cholesterol, are useful to suspect the existence of postprandial hyperlipidemia. Postprandial hyperlipidemia is observed in patients with familial type III hyperlipoproteinemia, familial combined hyperlipidemia, chronic kidney disease, metabolic syndrome and type 2 diabetes. Postprandial hyperlipidemia is closely related to postprandial hyperglycemia, and insulin resistance may be an in-ducing and enhancing factor for both postprandial hyperlipidemia and postprandial hyperglycemia. Remnant lipoproteins and metabolic disorders associated with post-prandial hyperlipidemia have various atherogenic properties such as induction of in-flammation and endothelial dysfunction. A healthy diet, calorie restriction, weight loss and exercise, positively impact postprandial hyperlipidemia. Anti-hyperlipidemic drugs such pemafibrate, bezafibrate, ezetimibe and eicosapentaenoic acid have shown to improve postprandial hyperlipidemia. Anti-diabetic drugs including metformin, alpha-glucosidase inhibitors, pioglitazone, dipeptidyl-peptidase-4 inhibitors and glu-cagon like peptide 1 analogues, have shown to ameliorate postprandial hyperlipidemia. Although sodium glucose cotransporter-2 inhibitors have not been proven to reduce postprandial hyperlipidemia, they reduced fasting apo B48 and remnant lipoprotein cholesterol. In conclusion, it is important to appropriately understand the existence of postprandial hyperlipidemia and to connect it to optimal treatments.

Matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI–TOF MS) is a commonly used technique for analyzing large biomolecules. However, the utilization of organic matrices limits the small-molecule analysis because of the interferences in the low-mass region and reproducibility issues. To overcome these limitations, a surface-assisted laser desorption/ionization (SALDI), which utilizes nanostructured metallic surfaces, has been developed. Herein, a novel approach for SALDI–MS was proposed using nanoengineered gold shell with nanogaps on the silica core (SiO2@Au NGS), which is an emerging material due to its excellent heat-generating capabilities. The gold shell thickness was controlled by adjusting the concentration of gold precursor for the growth of gold nanoparticles. SALDI-MS measurements were performed on a layer formed by drop-casting a mixture of SiO2@Au NGS and analytes. At the optimized process, the gold shell thickness was observed to be 17.2 nm, which showed the highest absorbance. The ion desorption efficiency was confirmed with a survival yield upon fragmentation. Based on the enhanced SALDI capability, SiO2@Au NGS was utilized to detect various small molecules including amino acids, sugars, and flavonoids. The limits of detection, reproducibility, and salt tolerance of SiO2@Au NGS demonstrate its potential as an effective and reliable SALDI material for small-molecule analyses.