This research investigates a model for the COVID-19 outbreak in Indonesia by analyzing comorbid diseases, self-quarantine, government-provided quarantine, and immunization regimens. Some model parameters are derived from academic journals, others are extrapolated from data collected by the Indonesian Ministry of Health in 2020, and others are assumed. Evaluation of the model reveals non-endemic equilibrium points, endemic equilibrium points, and the basic reproduction number (BRN). The contact rate and infected probability factors impact the increase in the number of COVID-19-infected individuals with comorbid disease within a group. According to the sensitivity analysis of the BRN, the key parameters impacting the spread of COVID-19 are the rate of susceptible recruitment, the rate of contact, the COVID-19 infection death rate, and the likelihood of infection. In addition, sensitivity analyses are provided to examine the effect of parameter changes on subpopulations. We discovered that the natural death rate is the most sensitive parameter based on the sensitivity index after reaching equilibrium. Symmetry aspects appear in some of the visualization of the model’s solution and the BRN and parameters’ sensitivity.

Olive picking is one of the most common social agricultural activities in many regions of Andalusia where the predominant crop is the traditional olive grove. The machinery used includes shakers, blowers and an essential low-cost type: hand-rake sweepers. The latter are generally used by the women of the squads to sweep the olives that fall from the trees. This article is focused on the design and optimisation of a hand-rake sweeper, in terms of durability and cost, for the picking of olives and other fruits, such as almonds, which are currently the main alternative to non-perennial crops in Andalusia. A parametric design of a hand-rake sweeper was created for this application using the design software CATIA, and its most vulnerable points were analysed in terms of effectiveness with varying design parameters, conducting usage simulations with ANSYS for a light material such as polypropylene. The maximum von Mises stress of the whole structure was 155.81 MPa. Using ANSYS, the dimension parameters of the hand-rake sweeper structure were optimised. The modified design was analysed again, showing a reduction of maximum tensions of 10.06%, as well as a decrease in its maximum elongations (0.0181 mm).

In this research, an attempt was made to reduce the dimension of wavelet-ANFIS/ANN (artificial neural network/adaptive neuro-fuzzy inference system) models toward reliable forecasts as well as to decrease computational cost. In this regard, the principal component analysis was performed on the input time series decomposed by a discrete wavelet transform to feed the ANN/ANFIS models. The models were applied for dissolved oxygen (DO) forecasting in rivers which is an important variable affecting aquatic life and water quality. The current values of DO, water surface temperature, salinity, and turbidity have been considered as the input variable to forecast DO in a three-time step further. The results of the study revealed that PCA can be employed as a powerful for dimension reduction of input variables and also to detect inter-correlation of input variables. Results of the PCA-Wavelet-ANN models are compared with those obtained from Wavelet-ANN models while the earlier one has the advantage of less computational time than the later models. Dealing with ANFIS models, PCA is more beneficial to avoid Wavelet-ANFIS models creating too many rules which deteriorate the efficiency of the ANFIS models. Moreover, manipulating the Wavelet-ANFIS models utilizing PCA leads to a significant decreasing in computational time. Finally, it was found that the PCA-Wavelet-ANN/ANFIS models can provide reliable forecasts of dissolved oxygen as an important water quality indicators in rivers.

This paper deals with bistatic subsurface probing of a horizontally layered dielectric half-space by means of ultra-wideband electromagnetic pulses. A receiver collects reflections from the air-ground interface and from the gradients of dielectric permittivity in the half-space. This scenario is of interest for ground penetrating radar (GPR) applications. For the analytical description of the received signal, we developed and implemented a novel time-domain version of the coupled-wave Wentzel–Kramers–Brillouin approximation. Our solution is in very good agreement with finite-difference time-domain results, radically accelerates calculations, and effectively accounts for the protracted return signals observed in the lower part of the GPR spectrum. The paper includes results showing the application of the proposed technique to two case studies: in particular, the method was employed for the post-processing of experimental radargrams collected on Lake Chebarkul, in Russia, and to simulate GPR probing of the Moon surface, to detect smooth gradients of the dielectric permittivity in lunar regolith.

A heat pump with thermal storage system is a system that operates a heat pump during night-time using inexpensive electricity; during this time, the generated thermal energy is stored in a thermal storage tank. The stored thermal energy is used by the heat pump during daytime. Based on a model of a dual latent heat storage tank and a heat pump, this study conducts control simulations using both conventional and advanced methods for heating in a building. Conventional methods include the thermal storage priority method and the heat pump priority method, while advanced approaches include the region control method and the dynamic programming method. The heating load required for an office building is identified using TRNSYS, used for simulations of various control methods. The thermal storage priority method shows a low coefficient of performance (COP), while the heat pump priority method leads to high electricity costs due to the low use of thermal storage. In contrast, electricity costs are lower for the region control method, which operates using the optimal part load ratio of the heat pump, and for dynamic programming, which operates the system by following the minimum cost path. According to simulation results for the winter season, the electricity costs using the dynamic programming method are 17% and 9% lower than those of the heat pump priority and thermal storage priority methods, respectively. The region control method shows results similar to the dynamic programming method with respect to electricity costs. In conclusion, advanced control methods are proven to have advantages over conventional methods in terms of power consumption and electricity costs.

Rice is the most important food crop in Cambodia and its production is the most organized food production system in the country. The main objective of this study is to measure technical efficiency (TE) of Cambodian rice production and also trying to identify core influencing factors of rice TE at both national and household level, for explaining the possibilities of increasing productivity and profitability of rice, by using translog production function through Stochastic Frontier Analysis (SFA) model. Four-years dataset (2012-2015) generated from the government documents was utilized for the national analysis, while at household-level, the primary three-years data (2013-2015) collected from 301 rice farmers in three selected districts of Battambang province by structured questionnaires was applied. The results indicate that level of rice output varied according to the different level of capital investment in agricultural machineries, total actual harvested area, and technically fertilizers application within provinces, while level of household rice output varied according to the differences in efficiency of production processes, techniques, total annual harvested land, and technically application of fertilizers and pesticides of farmers. The overall mean TE was estimated at 78.4% (national-level) and 34% (household-level), indicates that rice output has the potential of being increased further by 21.6% (national production) and 66% (household) at the same level of inputs and technology if farmers had been technically efficient. The TE also recorded -7% decreasing rate at the national-level and -14.3% at household-level due to highly affected of natural disasters and various environmental and social factors during the study periods.

In this article, I estimate the value of “Individual Using Internet”-IUI in the context of Environmental, Social and Governance-ESG database of the World Bank. I use data from 193 countries for the period 2011-2020. I found that among others the value of IUI is positively associated to “Methane Emissions” and “People Using Safely Managed Sanitation Services” and negatively associated among others to “Fossil Fuel Energy Consumption” and “Renewable Energy Consumption”. I apply the k-Means algorithm for the clusterization optimized with the Elbow Method and we find the presence of three clusters. Finally, I confront eight machine-learning algorithms to predict the future value of IUI. I found that the best predictive algorithm is Linear Regression and that the value of IUI is expected to decrease on average of 0.30% for the analysed countries.

The analysis of social media content plays a crucial role in uncovering intricate user behaviors and trends across diverse digital platforms. Social media is inherently multimodal, incorporating texts, images, audios, and videos, each offering unique insights into user engagement and preferences. Traditional classification methods often focus narrowly on the most prominent modality, typically neglecting the synergistic potential of integrating multiple data types. To address this gap, we introduce the Unified Multimodal Classifier (UMC), a suite of streamlined models that adeptly harness and integrate these varied modalities. UMC leverages a novel architecture that combines a pooling layer with auxiliary learning tasks, facilitating the formation of a robust and shared feature space. This integration allows UMC to not only accommodate but also capitalize on the inherent diversity of social media data. The models are designed to be inherently flexible, adjusting to the availability of data modalities and maintaining high classification accuracy under varied conditions. In emotion classification scenarios, UMC has shown exceptional performance, significantly outperforming traditional methods by effectively synthesizing information across modalities. Its robustness is highlighted in its ability to deliver consistent results, even in the absence of one or more modalities. The simplicity and efficiency of UMC make it a potent tool for social media analytics, enabling it to achieve higher accuracies and maintain effectiveness even with limited data inputs. This adaptive capability ensures that UMC can be deployed in real-world applications where data incompleteness is common, thus broadening the scope of its applicability in various analytical contexts.

Building energy modelling tools play a crucial role in quantifying and comprehending the energy performance of buildings. These tools demand substantial amounts of data, which can be challenging to acquire and are often associated with significant uncertainties. The incorporation of Sensitivity Analysis represents a relevant step towards developing reliable models, as it identifies the most critical parameters that require a meticulous characterization. In this study, a Sensitivity Analysis based on Morris method was conducted to assess the relevance of 14 input parameters affecting thermal loads across four dwelling typologies modelled in EnergyPlus. Different number of Morris trajectories and levels were considered to analyse the impact of the user-defined values of r and p when employing Morris method. Convergence was achieved from r=200 and p=12, which are higher than the typically employed values (r=10 and p=4). Setpoint temperatures, orientation, roof solar absorptivity and roof conductance rank among the most relevant parameters affecting thermal loads, however variations are observed for the four case studies.

In this work, we introduce the TriFusion Network, an innovative deep learning framework designed for the simultaneous analysis of auditory, visual, and textual data to accurately assess emotional states. The architecture of the TriFusion Network is uniquely structured, featuring both independent processing pathways for each modality and integrated layers that harness the combined strengths of these modalities to enhance emotion recognition capabilities. Our approach addresses the complexities inherent in multimodal data integration, with a focus on optimizing the interplay between modality-specific features and their joint representation. Extensive experimental evaluations on the challenging AVEC Sentiment Analysis in the Wild dataset highlight the TriFusion Network's robust performance. It significantly outperforms traditional models that rely on simple feature-level concatenation or complex score-level fusion techniques. Notably, the TriFusion Network achieves Concordance Correlation Coefficients (CCC) of 0.606, 0.534, and 0.170 for the arousal, valence, and liking dimensions respectively, demonstrating substantial improvements over existing methods. These results not only confirm the effectiveness of the TriFusion Network in capturing and interpreting complex emotional cues but also underscore its potential as a versatile tool in real-world applications where accurate emotion recognition is critical.

A corporate head office for large construction firms, holds more of a staffing role and facilitates, controls and manages subsidiaries. According to studies, little awareness of how to manage affairs, the unfavorable business environment, the lack of necessary rules and the structure of capital, form the most critical management areas to be thoughtful of. In dynamic systems, complex subject, refers to a problem in which the number of factors and relations in between are very high and sometimes unknown. In managing large construction firms, a similar situation prevails. Controlling and evaluating organization and management based on the correct information is something that is difficult to accomplish. This research seeks to provide a comprehensive review on construction firms performance measurements methods by using qualitative methods and necessary software tools. For this purpose, a summary is made of studies on areas such as construction companies, performance assessment solutions and content analysis methods. Then, considering the research gap, the research methodology will be developed, which will provide the optimal result for gathering different assessment methods suitable for construction firms in study. This article offers a step-by-step description of how qualitative data analysis software can be used for quantifying data and decision making. As illustrated in this research, using Nvivo shows how software tools can facilitate analytical flexibility and how they can enhance effectiveness of the qualitative research process. The article, aside from proposing the optimum performance assessment method for companies in study, concludes that while qualitative data analysis software will not do the analysis for the researcher, it can make the analytical process more flexible, transparent and ultimately more trustworthy.

While there are software tools available for assisting to conduct the Life Cycle Assessment (LCA), such as OpenLCA, these tools lack integration with process design, simulation and optimization software. As LCA has critical role in sustainable product design, it is presented in this work a platform called EMSO_OLCA, which integrates the LCA provided by OpenLCA into the Environment for Modeling, Simulation, and Optimization (EMSO). EMSO_OLCA incorporates a database of environmental impact assessment methodologies from OpenLCA and aligns to the principles of LCA outlined in ISO 14040 and ISO 14044. Validation tests were conducted to compare the results obtained by the LCA of sugarcane ethanol using OpenLCA and EMSO_OLCA, revealing a high level of agreement. The average relative error was 0.045%, indicating a negligible discrepancy between the tools. Besides, it took only 0.3 seconds for calculation, which is desirable for using with process systems engineering tools. A second case study was applied to a steam and bioelectricity production from the combustion of sugarcane bagasse and straw in a combined heat and power system. The results show the integration of LCA with simulation and sensitivity analysis tools, thus contributing for supporting sustainable decision-making processes. EMSO_OLCA bridges the gap between LCA and process engineering, enabling a holistic approach to sustainability, design and implementation of environmentally friendly solutions.

This review examines the quality of capital investment. It explores the essential components and analysis involved in financially assessing an engineering project, including an assessment of various associated risks. Additionally, the review delves into the historical progression of current capital budgeting practices and surveys academic research in the field. The evaluation of investments has been a recognized challenge for the past thirty years, but it has recently gained renewed attention from both management and scholars. Investments constitute a significant and growing portion of organizations’ capital expenditures. However, assessing the impact of investments on pursued goals is challenging. Consequently, there is a strong demand for methods and techniques that can aid in evaluating investments during the proposal stage. This paper explores several investment strategies and Examines the criteria for investment.

Recent advancements in machine learning have ushered in innovative techniques for augmenting datasets, particularly through contrastive learning in the computer vision domain. This study pioneers the application of contrastive learning for sentiment analysis, introducing a novel approach termed EmoConLearn. By fine-tuning contrastive learning embeddings, EmoConLearn significantly surpasses BERT-based embeddings in sentiment analysis accuracy, as evidenced by our evaluations on the DynaSent dataset. This research further delves into the efficacy of EmoConLearn across various domain-specific datasets, highlighting its versatility. Additionally, we investigate upsampling strategies to mitigate class imbalance, further enhancing EmoConLearn's performance in sentiment analysis benchmarks.

The exploration and understanding of sentiments and emotions through textual analysis are foundational to advancements in natural language processing (NLP). While sentiment analysis, which involves discerning the polarity of text, has been extensively explored, the nuanced detection of complex emotions within textual content is still an emerging area of study. In this paper, we introduce EmoLeverage, a model leveraging Transformer architecture and enhanced with a novel Fusion Adapter module. This model aims to deepen emotion detection capabilities by drawing insights from fundamental sentiment analysis tasks, focusing exclusively on textual data. EmoLeverage sets a new standard in the field by demonstrating that deep, nuanced understanding of emotions is achievable through text alone, challenging and surpassing existing methodologies in emotion identification on key datasets. This is a significant step forward, considering the complexity of human emotions and the subtleties involved in their expression through language. By concentrating on textual input, EmoLeverage offers a promising avenue for applications where audio or visual data is not available, highlighting the versatility and potential of text-based emotional analysis. Our approach illustrates the untapped potential of leveraging sophisticated NLP techniques to enhance the detection and comprehension of a wide array of emotions from text. The success of EmoLeverage in outperforming conventional models in emotion recognition tasks underscores the model's effectiveness in parsing and understanding the intricate expressions of human emotions through linguistic cues alone. This achievement paves the way for more accurate, efficient, and nuanced emotion detection tools, potentially transforming various sectors by providing deeper insights into human emotional states through text.

Analyzing horse behavior is crucial for assessing training quality, particularly in accurately identifying actions like standing, walking, and running. Research has been conducted both domestically and internationally in this regard; however, challenges persist, including reliance on single sensors, poor real-time performance, and low identification accuracy. In view of these challenges, this study investigated real-time identification of horse behavior based on wearable devices. The system, centered around a microcontroller and utilizing a 4G network as a carrier, employs multi-axis IMU sensors as input sources to perceive horse posture. The study proposed a behavior classification method based on analysis of acceleration thresholds. The method consists primarily of two sequential stages: first, the resultant acceleration interval counting method, which employs a nonlinear segmentation approach for initial behavior classification; and second, the statistical analysis of variance parameters between segments, which when coupled with multi-level threshold processing, achieves a refined classification. Experimental results indicated that the interval counting method alone achieved an accuracy of 87.55%, while for the variance analysis method alone the accuracy was 90.87%. The proposed method, comprising the two stages, reached a classification accuracy of 91.57%, underpinning its usefulness in supporting future equine research.

The study also found that older age was a protective factor in prospective studies. The consistency of results across different study designs and sensitivity analyses further supported the robustness of the findings. It is important to note that the existing literature on revictimization in women facing intimate partner violence is limited and exhibits significant heterogeneity in terms of methodology and conceptual frameworks.

Contemporary advanced models for aspect-targeted opinion word extraction (ATOWE), which predominantly utilize BERT-based encoders at a word level, have shown limited advancements when integrated with graph convolutional networks (GCNs) for syntactic tree assimilation. Recognizing the prowess of BERT subwords in encapsulating rare or context-poor words, this study pivots from syntactic trees to BERT subwords, omitting GCNs from the structural framework. Our approach, named Aspect-Enhanced Wordpiece Extraction Model (AEWEM), focuses on refining aspect representation during encoding. We propose an input format of paired sentence-aspect, diverging from traditional single-sentence inputs. AEWEM demonstrates superior performance on benchmark datasets, establishing a robust foundation for future explorations in this domain.

This paper presents the Advanced Syntactic-Graph Convolutional Model (ASGCM), a pioneering approach in Aspect-Based Sentiment Analysis (ABSA) that integrates syntactic dependency features within a graph convolution framework. ASGCM stands out for its novel use of dependency edge encoding and tag-based graph convolutions, providing a fine-grained analysis of sentiments associated with specific aspects in text. This model meticulously captures the intricacies of syntactic structures, thereby offering enhanced precision in sentiment analysis. Notably, ASGCM incorporates a dual-layer graph convolution system: one layer processes syntactic dependencies (edges), while the other interprets semantic roles (tags), ensuring a comprehensive understanding of both structural and contextual elements in text. We rigorously tested ASGCM on multiple datasets, including both English and Chinese languages, and our findings reveal a significant improvement in sentiment classification accuracy compared to existing models. The versatility of ASGCM makes it a robust tool for diverse linguistic environments, setting a new standard for ABSA methodologies.

The goal of our study was to identify signatures of selection in the Turopolje pigs and other commercial pig breeds. We conducted a comprehensive analysis of five datasets, including one local pig breed (Turopolje) and four commercial pig breeds (Large White, Landrace, Pietrain, and Duroc), using strict quality control measures. Our final dataset consisted of 485 individuals and 54,075 single nucleotide polymorphisms (SNPs). To detect selection signatures within these pig breeds, we utilized the XP-EHH and XP-nSL methodologies, which allowed us to identify candidate genes that have been subject to positive selection. Our analysis consistently highlighted the PTBP2 and DPYD genes as commonly targeted by selection across all studied breeds. Both of these genes are associated with muscular development in pigs and other species. Furthermore, in the Large White breed a number of genes were detected by the two methods, such as ATP1A1, CASQ2, CD2, IGSF3, MAB21L3, NHLH2, SLC22A15, VANGL1. In Duroc breed a different set of genes was detected, such as ARSB, BHMT, BHMT2, DMGDH, JMY. The function of these genes was related to body weight, production efficiency and meat quality, average daily gain and other similar traits. Overall, our results have identified a number of genomic regionsthat are under selective pressure between local and commercial pig breeds. This information can help to improve our understanding of the mechanisms underlying pig breeding, and ultimately contribute to the development of more efficient and sustainable pig production practices. Our study highlights the power of using multiple genomic methodologies to detect genetic signatures of selection, and provides important insights into the genetic diversity of pig breeds.

Cervical cancer (CC) continues to be a significant global public health concern, even with preventive measures in place. In women with early-stage CC, the status of lymph nodes is of paramount importance, not only for final prognosis but also for determining the best therapeutic strategy. According to main international guidelines, pelvic full lymphadenectomy (PLND) is recommended for lymph node staging. However, in these early stages of CC, Sentinel lymph node biopsy (SLNB) has emerged as a precise technique for evaluating lymph node involvement, improving its morbidity profile. We performed a literature review through PubMed articles about progress on the application of SLNB in women with early-stage CC focusing on the comparison with PET/CT and PLND in terms of oncological outcomes and diagnostic accuracy. While the superiority of SLNB is clear compared to radiologic modalities, it demonstrates no clear oncologic inferiority over PLND, given the higher detection rate of positive lymph nodes and predominance of no lymph node recurrences. However, due to a lack of prospective evidence, particularly concerning long-term oncological safety, SLNB is not the current gold standard. With careful patient selection and adherence to straightforward protocols, a low false negative rate can be ensured. The aim of the ongoing prospective trials is to address these issues.

Our natural language approach concerns syntactic analysis using a dedicated Javascript library - wink-nlp - and semantic analysis based on Prolog programming language, facilitated by another Javascript library - tau-prolog - that allows defining logical programs, declaring rules and checking for goals inside Javascript language. Firstly, our program splits the original text into sentences, than into tokens and identifies each part of the sentence, dynamically maps entities into Prolog rules, then check the spelling accordingly to the Definite Clause Grammar (DCG) by querying the pre-defined program for initial goals (the sentence itself). Basically, we let the parser infer its own rules from the syntactic point of view, then check the grammar from a semantic perspective against the DCG inside the same work flow or pipeline of steps.The provided article combine the usage of wink-nlp and tau-prolog packages for natural language processing (NLP) and understanding (NLU).

Objective The aim of this study was to explore the prognosis and influencing factors of recurrent ependymoma in children. Methods PubMed, Embase, Cochrane Library, and Web of Science were searched to collect studies on survival outcomes and influencing factors of recurrent ependymoma in children. The search time frame was from the establishment of the database to September 2023. Two evaluators independently screened the literature, extracted data, and evaluated the quality of the included studies. Meta-analysis was performed using RevMan 5.4 software. Results A total of 11 studies involving 1120 patients were included. The integrated results of mOS from the 11 studies showed significant heterogeneity (I2 = 92%). Therefore, a random-effects model was used for merging, and the results showed statistically significant differences (P < 0.00001). In all the studies, the pooled estimate of median OS from the time of recurrence was 15.54 months (95% CI 8.80-27.45; P < 0.00001), and the combined median progression-free survival (PFS) from the time of first recurrence was 6.7 months (95% CI 5.59-7.64; P < 0.0001). The median OS for patients who underwent surgery at the time of recurrence was 20.7 months (95% CI 12.40–34.72; P < 0.00001), while the mOS for patients who received radiation therapy was 29.5 months (95% CI 18.97–46.00; P < 0.0001), and for patients who received chemotherapy mOS was 18.0 months (95% CI 8.62–37.75; P < 0.00001). The mOS for patients under 3 years old at the time of recurrence was 20.1 months (95% CI 1.98-204.50; P < 0.00001), while for patients over 3 years old at the time of recurrence mOS was 16.6 months (95% CI 9.40-29.35; P < 0.00001). Conclusion The results of the study show poor prognosis for children with recurrent ependymoma, and there are significant differences in these results. The influencing factors include patient age, tumor recurrence location, and treatment methods. These findings can further guide clinical research on new treatment methods and strategies to improve the prognosis of this population.

Fish feeding behavior analysis plays a critical role in aquaculture, marine fisheries, and ecological monitoring. At present, the accuracy of current fish feeding behavior analysis is easily affected by the environment, and the hypoxia state in water and high temperature have a huge impact on behavior. In addition, the poor stability, and the speed is affected by the calculation amount of the model, so it cannot be applied to aquaculture ponds. In order to solve the above problems, we propose an improved RepVGG-based method for fish feeding behavior analysis to identify nine behavioral states of fish populations, including hypoxia, hot, and normal states of none, medium, and strong feeding. Firstly, our method adds the identity and residual branches to the block of the VGG network, focusing on the acceleration operation to ensure accuracy. At the same time, the Efficient Channel Attention (ECA) module is added to reduce dimensionality to balance speed and accuracy. To evaluate the effectiveness of the method, it is validated on a constructed fish feeding behavior dataset and compared with a Convolutional Neural Network (CNN) including AlexNet, VGG16, ResNet34, MobileNet V3, and RepVGG. The experimental results show that after 15 epochs of training, the average precision of proposed method reached 97%. Compared with the basic classification algorithm, our method can increase by at least 3% while the inference speed exceeds 85 Frames Per Second (FPS). This study can be integrated into the aquaculture vision system to guide users to plan feeding strategies, and provide new ideas and methods for water quality monitoring.

The way in which a person walks, known as human gait, is a significant indicator of overall health and well-being. Abnormalities in gait can indicate the presence of metabolic disorders, such as diabetes or obesity. However, detecting these disorders can be challenging using traditional methods, which often involve subjective assessments or invasive procedures. In this study, a novel methodology known as Criticality Analysis (CA) was proposed for the detection and monitoring of human gait in people with metabolic disorders taking part in an intervention to increase activity and reduce weight. The CA approach utilised inertial measurement unit gait data, alongside clinical health measures. This allows for the control of nonlinear growth in the system, resulting in lower dimensional, nonlinear, free-scale, stable, controlled, and organised trajectories. These trajectories were then analysed using a Support Vector Machine (SVM) algorithm, which is well-suited for this task due to its ability to handle nonlinear and dynamic data. The combination of the CA approach and the SVM algorithm demonstrated high accuracy and non-invasiveness in detecting metabolic disorders, yielding an average accuracy within the range of 78.2% to 90%. Additionally, the classification technique accuracy, at a group level was observed to reduce during period of the intervention (e.g., from week 2 to week 3) alongside changes in fitness and health, which indicates the potential of using the approach to measure and monitor biological systems. As such, this novel methodology has the potential to be a valuable tool for healthcare professionals in detecting and monitoring metabolic disorders, as well as other unknown diseases associated with the human biological system.

Vibrio splendidus is the main pathogen infect a wide range of hosts, especially sea cucumber Apostichopus japonicus. Our previous studies had shown that the level of L-glutamic acid (L-Glu) was significantly increased under heat stress and it was one of the best carbon sources that could be used by V. splendidus AJ01. In this study, the effects of exogenous L-Glu on the coelomocyte viability, tissue status and individual mortality of sea cucumbers were analyzed. The results showed that 10 mM L-Glu decreased coelomocyte viability and increased mortality, with tissue rupture and pyknosis, while 0.1 mM L-Glu slightly affected the survival of sea cucumbers without obvious damages at both cellular and tissue levels. Transcriptomic analysis showed that there were 343 upregulated and 206 downregulated genes regulated by exogenous L-Glu. GO analysis showed that differentially expressed genes (DEGs) were mainly enriched in signaling and membrane formation, while KEGG analysis showed that DEGs were significantly enriched in the upregulated endocytosis and downregulated lysosomal pathways. The coelomocyte viability further decreased 20% in the simultaneous presence of exogenous L-Glu and V. splendidus AJ01, compared to that of V. splendidus AJ01 infection alone. Consequently, a higher mortality of sea cucumber was also observed in the presence of exogenous L-Glu challenged by V. splendidus AJ01. Real time reverse transcriptase PCR showed that L-Glu specifically upregulated the expression of the fliC gene, conding the subunit protein of flagellar filament, with promoted swimming motility activity of V. splendidus. Our result indicated that L-Glu should be kept in a state of equilibrium, and the excess L-Glu in the host-pathogen interface would prompt the virulence of V. splendidus via increasing bacterial motility.

In the recent decades, per- and polyfluoroalkyl substances (PFAS) have garnered widespread public attention due to their persistence in the environment and detrimental effects on the health of living organisms, spurring the generation of several transcriptome-centered investigations to understand the biological basis of their mechanism. In this study, we collected 2144 publicly available samples from 7 distinct animal species to examine the molecular responses to PFAS exposure and to determine if there are conserved responses. Our comparative transcriptional analysis revealed that exposure to PFAS is conserved across different tissues, molecules and species. We identified and reported several genes exhibiting consistent and evolutionarily conserved transcriptional response to PFAS, such as ESR1, HADHA and ID1, as well as several pathways including lipid metabolism, immune response and hormone pathways. This study provides the first evidence that distinct PFAS molecules induce comparable transcriptional changes and affect the same metabolic processes across inter-species borders. Our findings have significant implications for understanding the impact of PFAS exposure on living organisms and the environment. We believe that this study offers a novel perspective on the molecular responses to PFAS exposure and provides a foundation for future research into developing strategies for mitigating the detrimental effects of these substances in the ecosystem.

Using SNA techniques, the study examined H2020 forestry projects. An adjacency matrix was created using the CORDIS data collection, and it was then utilized to depict the network of project members. Then, different network indicators were computed. Several statistical techniques (maximum likelihood, Kolmogorov-Smirnov test, moments, bootstrapping) were employed to do a goodness-of-fit analysis on the frequencies of the degrees to confirm scale-freedom or randomness in the search for significant distributions in network research. Additionally, the small-world aspect was investigated. The findings demonstrate that while the number of project participations by project participants follows a power distribution, the distribution of project participants’ degrees reflects various effects. As a result, the scale-freedom that has been emphasized in many scientific investigations is not evident. The network indicators demonstrate that the network is not clearly small-world.

Decision makers have to work on resolving multiple forms of problems within their organizations. The problems can be structured, semi-structured, or unstructured. Handling these issues requires intensive time dedication and resource allocation. Looking at the perspective of strategic decision making in Saudi Arabia, current operations are lacking in various sectors. This indulges an immediate need to introduce proper systems and highlight the loopholes that would allow the leaders make informed decisions aided by a support system. For this reason, the researchers set out to work on two research questions. The first question focused on how decision makers ensured accuracy of the decisions they are making. It would allow to identify why such system is important that should also guarantee consistency and accuracy. The second question asked about the proper process that would govern and control the outcome of decision. Considering the above questions, this research aimed at identifying the ideal framework, and whether the proposal developed would fit into the Saudi organizations. A case study in health sector has been reviewed containing various models across the world. Following this, interviews with five decision makers in their organization were conducted to perform the qualitative research. The researcher identified that most organizations lacked systems to ensure their decisions were accurate. The research also concluded that the tools and elements presented in the proposed informed decision framework would fit into most Saudi organizations to eliminate the identified problem, and especially due to the inclusion of the non-digital sources of information as part to the decision process. Furthermore, the analysis was conducted and discussion was determined stating validations of the study.

Lassa fever, commonly known as Lassa hemorrhagic fever (LHF), is one of the progressive illnesses invading a large population of two to three million individuals in West Africa. The infection transmitted through the rodents severely impacts the local population and the medical professionals in surrounding areas, which were also the primary target of LHF. In epidemic areas, Lassa fever causes a public health threat since it poses a significant morbidity and fatality Case rate (CFR) ≥ 50%). The disease is widespread in West Africa and has developed into one of the most common and life-threatening viral hemorrhagic fevers. Monitoring and preventing persistent disease outbreaks has been challenging in affected regions due to insufficient healthcare facilities, diagnostics labs, care centers, and low socioeconomic conditions. An absence of public awareness and the emergence of an ecological niche is advantageous for the survival and multiplication of the mouse (Mastomys natalensis) inhabiting the Lassa virus serving as the disease's natural host and reservoir. The current review focuses on early diagnosis and appropriate treatment, highlighting the immediate requirement of clinically approved vaccines for LHF, causing preventative and control actions more difficult in the present era.

The aim of the presents study is to further expand the model of translational cross-validation by investigating the brain circuits or networks which underpin the same tasks by means of group independent component analysis for FMRI toolbox (GIFT). We hypothesized that there will be neural network patterns of activation and deactivation, which correspond to real time performance on clinical self-evaluation scales. We recruited 42 subjects: 20 healthy controls and 22 patients with major depressive episode. All subjects underwent functional MRI scanning using paradigm comprised of diagnostic clinical self-assessment depression scale contrasted to neutral scale. The data were processed with group independent component analysis for functional MRI toolbox and statistical parametric mapping. The results have demonstrated that there exist positively or negatively modulated brain networks during processing of diagnostic specific task questions for major depressive disorder. There have also been confirmed differences in the networks processing diagnostic versus off blocks between patients and controls in anterior cingulate cortex and middle frontal gyrus. Diagnostic conditions (depression scale) when contrasted to neutral conditions demonstrate differential activity of right superior frontal gyrus and right middle cingulate cortex in the comparison of patients with healthy controls. It is for the first time when potential neuroimaging state dependent biomarker has been directly linked with clinical assessment self-evaluation scale, administered as stimuli simultaneously with the fMRI acquisition. It may be regarded as further evidence in support of the ability of both methods to concordantly distinguish groups by means of incremental translational cross-validation.

Transformation studies have been leaning towards the more practical aspects of change processes and have not yet dealt sufficiently with their personal and political dimensions. They are arguably constrained in doing so if they are either overly focussed on systems and how to control them or on individualistic values and behaviours. In this study we show how the actually-occurring societal transformations that people face can be usefully approached through the lenses of dialogical sense-making and critical phenomenology. While distinct, these approaches share a concern with aspects missed when transformation is abstracted and alienated from people’s lives; namely people’s lived experiences during times of change, and the conditions of possibility for these experiences. Dialogical sense-making explores how people create meaning around transformations, through interactions with other people, with different lines of arguments, and as part of broader public discourse. Critical phenomenology engages with subjectivity and lived experience, and with the role of foundational as well as socio-culturally dominant yet contingent structures in shaping our ways of perceiving, experiencing and knowing the world. Through a discussion of insights from these approaches, we show how they offer tools that enable new questions about transformative change as it is experienced and made sense of. Situating analyses of tranformation from within a focus on experience brings us closer to understanding the significance of change processes in people’s lives, and allows for an inquiry into the conditions of experience, including transformative experiences.

Crop output is directly impacted by plant infections (with fungi as the major pathogen), making accurate diagnosis of these threats crucial. Developing technology and multidisciplinary approaches are turning to genomic analyses in addition to traditional culture methods in diagnosing fungal plant diseases. The metagenomic next-generation sequencing (mNGS) method is preferred for genotyping identification of organisms, identification at the species level, illumination of metabolic pathways, and determination of microbiota. Moreover, the data obtained so far show that this new approach shows promise as an emerging new trend in fungal disease detection. Another approach covered by mNGS technologies, known as metabarcoding, enables use of specific markers specific to a genetic region and allows for genotypic identification by facilitating the sequencing of certain regions. Although the core concept of mNGS remains constant across applications, the specific sequencing methods and bioinformatics tools used to analyze the data do differ. In this review, we focus on how mNGS technology, including metabarcoding, is applied in fungal pathogenesis and its promising developments for the future.

Purpose: Pitch plays an important role in auditory perception of music and language. This study provides a systematic review with meta-analysis to investigate whether individuals with autism spectrum disorder (ASD) have enhanced pitch processing ability and identify the potential factors associated with processing differences between ASD and neurotypicals. Method: We conducted a systematic search through six major electronic databases focusing on the studies that used nonspeech stimuli to provide a qualitative and quantitative assessment across existing studies on pitch perception in autism. We identified potential participant- and methodology-related moderators and conducted meta-regression analyses using mixed-effects models. Results: On the basis of 22 studies with a total of 464 participants with ASD, we obtained a small-to-medium positive effect size (0.26) in support of enhanced pitch perception in ASD.Moreover, the mean age and non-verbal IQ of participants were found to significantly moderate the between-studies heterogeneity. Conclusion: Our study provides the first meta-analysis on auditory pitch perception in ASD and demonstrates the existence of different developmental trajectories between individuals with ASD and neurotypicals. Non-verbal ability can be a significant contributor to the lower-level/ local processing bias in ASD. We highlight the need for further investigation of pitch perception in ASD under challenging listening conditions. Future neurophysiological and brain imaging research studies with a longitudinal design are also needed to better understand the nature of the atypical processing in ASD to obtain new insights into the underlying neural mechanisms and to help guide auditory-based interventions for improving language and social functioning.

Introduction: This study aimed to validate the psychometric properties of the German eHealth Impact Questionnaire (eHIQ-G), which is divided into two independently administered and scored parts. Methods: 162 people with MS browsed one of two possible websites containing information on MS and completed an online survey. Internal consistency was assessed by Cronbach’s alpha and structural validity by Confirmatory Factor Analysis. Construct validity was examined by assessing correlations with the eHealth Literacy Questionnaire and the General Self-Efficacy Scale. Moreover, the mean difference of the eHIQ-G score between the two websites was investigated. Results: Cronbach’s alpha for the eHIQ-G subscales ranged from .833 to .885. The eHIQ-G part 1 achieved acceptable levels of goodness-of-fit indices, whereas the fit for the eHIQ-G part 2 was poor and likewise for the alternative modified models. The correlations with the reference instruments were 0.08 – 0.62 and as expected. Older age was related with lower eHIQ-G part 1 score, whereas no significant effect was found for education. Although not significant, the website ‘AMSEL’ reached higher mean scores on eHIQ part 2. Conclusion: The eHIQ-G has good internal consistency, and sufficient structural and construct validity. It will facilitate the measurement of the impact of websites providing health information.

The development of Imaging Atmospheric Cherenkov Telescopes (IACTs) unveiled the sky in the teraelectronvolt regime, initiating the so-called “TeV revolution” at the beginning of the new millennium. This revolution was also facilitated by the implementation and adaptation of statistical tools for analyzing the shower images collected by these telescopes and inferring the properties of the astrophysical sources that produce such events. The image reconstruction techniques, the background discrimination and the signal-detection analyses are just a few of the pioneering studies applied in the last decades in the analysis of IACTs data. This (succinct) review has the intent of summarizing the most common statistical tools that are used for analyzing data collected with IACTs, focusing on their application in the full analysis chain, giving references to the past literature for a deepened examination.

Several disruptive attacks against companies in the maritime industry have led experts to consider the increased risk imposed by cyber threats as a major obstacle to undergoing digitization. The industry is heading toward increased automation and connectivity, leading to reduced human involvement in the different navigational functions and increased reliance on sensor data and software for more autonomous modes of operations. To meet the objectives of increased automation under the threat of cyber attacks, the different software modules that are expected to be involved in different navigational functions need to be prepared to detect such attacks utilizing suitable detection techniques. Therefore, we propose a systematic approach for analyzing the navigational NMEA messages carrying the data of the different sensors, their possible anomalies, malicious causes of such anomalies as well as the appropriate detection algorithms. The proposed approach is evaluated through two use cases, traditional Integrated Navigation System (INS) and Autonomous Passenger Ship (APS). The results reflect the utility of specification and frequency-based detection in detecting the identified anomalies with high confidence. Also, the analysis is found to facilitate the communication of threats through indicating the possible impact of the identified anomalies against the navigational operations. Moreover, we have developed a testing environment that facilitates conducting the analysis. The environment includes a developed tool, NMEA−Manipulator that enables the invocation of the identified anomalies through a group of cyber attacks on sensor data. Our work paves the way for future work in the analysis of NMEA anomalies toward the development of an NMEA intrusion detection system.

Since the works of Zohar, the concept of spiritual intelligence has been at the center of scientific debates, but exegetical and philological, and other studies have revealed that this concept is not new (Zinsstag, 2010; Moleka, 2021a). The Bible speaks about it with Hebrew words (hokhmah, binah) and Greek words (phronesis, sunesis, noûs, and sophia). In this essay, we examine spiritual intelligence from a philosophical standpoint and discuss some ramifications, such as ethos.

Evidence regarding the effect of the onlay preparation design for different CAD/CAM restorative materials considering the preservation of cusps is lacking. Molars were 3D modeled in four preparation designs for onlay restoration: traditional design with functional cusp coverage (TFC), non-retentive design with functional cusp coverage (NFC), traditional design with non-functional cusp coverage (TNFC), non-retentive design with non-functional cusp coverage (NNFC). Restorations were simulated with two CAD/CAM restorative materials: LD – lithium disilicate (IPS e.max CAD) and RC - resin composite (GrandioBloc). A 100 N axial load was applied to the occlusal surface simulating the centric contact point. Von Mises (VM) and maximum principal (Pmax) stresses were evaluated for restorations, cement layer and dental substrate. The non-retentive preparation design reduced the stress concentration in the tooth structure in comparison to the conventional retentive design. For LD onlays, the stress distribution on the restoration intaglio surface showed that the preparation design as well as the prepared cusp, influenced the stress magnitude. The non-retentive preparation design provided better load distribution in both restorative materials and more advantageous for tooth structure. The resin composite restoration on non-functional cusp is recommended when functional cusp is preserved, in order to associate conservative dentistry and low stress magnitude.

The focus of this research is to analyze the results of encrypting audio using various authenticated encryption algorithms implemented in the Python cryptography library for ensuring authenticity and confidentiality of the original contents. The Advanced Encryption Standard (AES) is used as the underlying cryptographic primitive in conjunction with various modes including Galois Counter Mode (GCM), Counter with Cipher Block Chaining Message Authentication Code (CCM), and Cipher Block Chaining (CBC) with Keyed-Hashing for encrypting a relatively small audio file. The resulting encrypted audio shows similarity in the variance when encrypting using AES-GCM and AES-CCM. There is a noticeable reduction in variance of the performed encodings and an increase in the amount of time it takes to encrypt and decrypt the same audio file using AES-CBC with Keyed-Hashing. In addition, the corresponding encrypted using this mode audio spans a longer duration. As a result, AES should either have GCM or CCM for an efficient and reliable authenticated encryption integration within a workflow.

Breakthrough cancer pain (BTcP) is a temporary exacerbation of pain that "breaks through" a phase of adequate pain control by an opioid-based therapy. The non-predictable BTcP (NP-BTcP) is a subtype of BTcP that occurs in the absence of any specific activity. Since NP-BTcP has an important clinical impact, this analysis is aimed at characterizing the NP-BTcP phenomenon through a multidimensional statistical technique. This is a secondary analysis based on the Italian Oncologic Pain multiSetting - Multicentric Survey (IOPS-MS) . A correlation analysis was performed to characterize NP-BTcP profile about its intensity, number of episodes per day, and type. The Multidimensional Correspondence Analysis (MCA) determined the identification of 4 groups (Phenotypes). A univariate analysis was performed to assess differences between the 4 Phenotypes and selected covariates. The four phenotypes represent the hierarchical classification according to the status of NP-BTcP: from the best (Phenotype 1) to the worst (Phenotype 4). The univariate analysis found a significant association between the onset time &gt;10 min in the Phenotype 1 (37.3%) vs. the onset ≤ 10 min in Phenotype 4 (74.2%) (p&lt;0.001). The Phenotype 1 was characterized by gastrointestinal type of cancer (26.4%) respect to Phenotype 4 where the most frequent cancer affected the lung (28.8%) (p&lt;0.001). Phenotype 4 was mainly managed with rapid onset opioids, while in Phenotype 1 many patients were treated with oral, subcutaneous, or intravenous morphine (56.4% and 44.4%, respectively; p=0.008). The ability to characterize NP-BTcP can offer enormous benefits for the management of this serious aspect of cancer pain. This strategy can provide many indications for identifying the diagnostic and therapeutic gaps on NP-BTcP management.

This paper deals with the structural decomposition input-output for the economic analysis of agriculture, forestry and fishing in six EU-28 countries (Austria, Belgium, France, Germany, Italy and Spain) in the 2010-2015 period. The objective is to determine the causes of changes in production in these sectors with a particular focus on disaggregation technological change by distribution factors associated with a specific normalisation of the Leontief inverse. In calculating the net multipliers, an attempt was made to exclude sectors' own consumption appropriately. However, the treatment of own consumption upon introducing a time factor requires further investigation to avoid questionable quantifications. In general, typical characteristics of primary sectors include the accumulation of a significant amount of their own consumption, facilitated by symmetric accounting. Therefore, attention is drawn to these sectors so as to reveal possible analysis techniques that will provide nuance or validate existing techniques.

The genetic development of commercial broiler led to body misconfiguration and consequent walking disabilities, mainly at the slaughter age. The present study aimed to identify broiler locomotion ability using image analysis automatically. A total of 40 broiler 40 d-old were placed to walk on a specially built runway, and their locomotion was recorded. An image segmentation algorithm was developed, and the coordinates of the bird's center of mass were extracted from the segmented images for each frame analyzed, and the Unrest Index (UI) was applied. We calculated the center of mass's movement of the broiler walking's lateral images, therefore, capturing the bird's displacement speed in the onward direction. Results indicated that broiler speed on the runway tends to decrease with the increase of the gait score. The locomotion did not differ between males or females. The proposed algorithm was efficient if predicting the broiler gait score based on their displacement speed.

Cinematic texts provide narrative insights into lived experiences. This paper uses a qualitative, content analysis of New Jack City to explore how conservative partisanship affected the African American diaspora during the Reagan Era. I explore how the characterizations and contexts of marginalized personae reflect social scripts and invoke judicial tropes to convey hegemonic institutions. Given the prevalent conditions respective to sociology and psyche, policy is embodied through micro- and macro-phenomena. While the former relates to individual narratives of personal capacity and agency, the latter concerns historical events and precedents.

This paper is about energy as viewed through an integrated model that links energy with environment, technology and urbanisation as related areas. Our goal is to empirically investigate the (in)efficient energy use across 30 developed OECD member states during the period from 2001 to 2018. For that purpose, we set up an output-oriented BCC data envelopment analysis that employs a set of input variables with non-negative values to calculate the efficiency scores on minimising energy use and losses as well as environmental emissions. We develop a couple of baseline models for primary energy and secondary energy (electricity) in which we find that countries have mean inefficiency margins of 16.1 per cent for primary energy and from 10.8 to 13.5 per cent for electricity. Then, we extend the baseline models by adding environment as an important closely related concept and confirm the consistency of the baseline findings. In the context of this analysis, however, the inefficiency scores, on the one hand, point out to a mismatch in the utilisation of the inputs to produce efficiency but, on the other hand, they uncover a hidden potential to increasy efficiency through re-allocation under constant inputs.

Islamic banks (IBs) have been criticized as not being genuinely Islamic, and the methods in measuring their performances have been debatable. While the literature on IBs performance has been emerging, such studies precisely assess its recent development remains absent. Therefore, we aim to evaluate the development of scholarly articles that measure IBs’ performances. We employ bibliometric analysis and sample related articles from the Scopus database. We find that the development of IBs performance literature may be understood by 111 peer-reviewed journal articles, 4 conference papers, 1 book, and 1 book series. We analyze these materials based on publication sources, country and institution affiliation, keywords association, and cluster dendrogram. Our model that quantifies the keywords association and cluster dendrogram provides a novelty in assessing IBs performance literature development. Future studies may replicate our model to cluster and identify the keyword associations from the unstructured data sources.

The many strains of Cannabis spp. are associated with many effects on users and contain many different potentially psychoactive metabolites, but the links between metabolite profiles and user effects are unclear. Here we take a statistical approach to linking cause (i.e. metabolites) to effects in Cannabis spp. through the prism of strains, using quantitative data for metabolite composition and user effects. We find that species (indica vs. sativa) explains <2% of the variability in metabolite profiles, while strain explains 1/3 of variability, indicating species is nonindicative of metabolite composition, while strain is approximately indicative. Using random forests we generate a table of potential metabolite-effect links. We also find that effect-weighted metabolite composition can effectively be described in terms of four values representing the concentrations of pairs or triplets of particular compounds.

Postpartum depression (PPD), a condition that affects up to the 15% of mothers in high-income countries, reduces attention toward the needs of the child and it is among the first causes of infanticide. PPD is usually identified using self-report measures and therefore the diagnosis may not always be valid. Previous studies highlighted the presence of significant differences in the acoustical properties of the vocalizations of children of depressed and healthy mothers. In this study, cry episodes of infants of depressed and non-depressed mothers are analyzed to investigate the possibility that a machine learning model can identify PPD in mothers from the acoustical properties of infants' vocalizations. Acoustic features (F0, F1-4, Intensity) are first extracted from recordings of crying infants, then novel cloud-based artificial intelligence models are employed to identify maternal depression versus non depression from estimated features. Trained model shows that commonly adopted acoustical features can be successfully used to individuate Post-Partum Depressed mothers with very high accuracy (89.5%).

Flaxseed (Linum usitatissinum L.) oil is an important source of α-linolenic (C18:3 ω-3), this polyunsaturated fatty acid is well known for its nutritional role in human and animal diet. Understanding storage lipid biosynthesis in developing flaxseed embryos can lead to an increase in seed yield. While a tremendous amount of work has been done on different plant species to highlight their metabolism during embryos development, flaxseed metabolic flux analysis is still lacking. In this context, we have developed an in vitro cultured developing embryos of flaxseed and determined net fluxes by performing three complementary parallel labeling experiments with 13C-labeled glucose and glutamine. Metabolic fluxes were estimated by computer- aided modeling of the central metabolic network including 11 cofactors of 118 reactions of the central metabolism, 12 pseudo fluxes. A focus on lipid storage biosynthesis and the associated pathways was done in comparison with rapeseed, arabidopsis, maize and sunflower embryos. In our conditions, glucose was the main source of carbone of flaxseed embryos, leading to the conversion of phosphoenolpyruvate to pyruvate. The oxidative pentose phosphate pathway (OPPP) was identified as the producer of NADPH for fatty acid biosynthesis. Overall, the use of 13C-metabolic flux analysis provided new insight into flaxseed embryos metabolic processes involved in storage lipids biosynthesis. The elucidation of the metabolic network of this important crop plant reinforces the relevance of the application of this technique to the analysis of complex plant metabolic systems.

Here we describe ARSA-16S, a tool and accompanying reference database for the analysis of bacterial 16S amplicons. Among other features, ARSA-16S is based on a new model, approach, and algorithm for sequence-level assignment of reads understood as probability distributions, assigns reads individually, and is designed with non-overlapping amplicons covering two non-contiguous regions. A new set of primers for the amplification and sequencing of the V4 and V6 regions is also provided.

Optical Character Recognition (OCR) on historical printings is a challenging task mainly due to the complexity of the layout and the highly variant typography. Nevertheless, in the last few years great progress has been made in the area of historical OCR, resulting in several powerful open-source tools for preprocessing, layout recognition and segmentation, character recognition and post-processing. The drawback of these tools often is their limited applicability by non-technical users like humanist scholars and in particular the combined use of several tools in a workflow. In this paper we present an open-source OCR software called OCR4all, which combines state-of-the-art OCR components and continuous model training into a comprehensive workflow. A comfortable GUI allows error corrections not only in the final output, but already in early stages to minimize error propagations. Further on, extensive configuration capabilities are provided to set the degree of automation of the workflow and to make adaptations to the carefully selected default parameters for specific printings, if necessary. Experiments showed that users with minimal or no experience were able to capture the text of even the earliest printed books with manageable effort and great quality, achieving excellent character error rates (CERs) below 0.5%. The fully automated application on 19th century novels showed that OCR4all can considerably outperform the commercial state-of-the-art tool ABBYY Finereader on moderate layouts if suitably pretrained mixed OCR models are available. The architecture of OCR4all allows the easy integration (or substitution) of newly developed tools for its main components by standardized interfaces like PageXML, thus aiming at continual higher automation for historical printings.

The aim of this paper is to analyse commenting activity and sentiment (polarity and subjectivity) in interactions in response to videos by Spain’s most-subscribed YouTubers. An exploratory study was conducted on the content of the comments, their relationship with other social media actions, subjectivity and polarity, as well as from the perspective of the participatory culture. The results show that commenting is a potential option for interaction that is underused by the communities of users. Replies to comments are found to be limited to the user-user level, while YouTubers themselves and the moderators that YouTube allows them to designate rarely comment or reply on social networks. However, creators do monitor comments and provide feedback to a limited selection thereof in subsequent videos. There thus appears to be a strategic, exploitative use of comments, marked by a delayed response aimed at attracting audiences to new content.

Since it was proposed, the Robot Operating System (ROS) has fostered solutions for various problems in robotics in the form of ROS packages. One of these problems is Simultaneous Localization and Mapping (SLAM), a problem solved by computing the robot pose and a map of its environment of operation at the same time. The increasingly availability of robot kits ready to be programmed and also of RGB-D sensors often pose the question of which SLAM package should be used given the application requirements. When the SLAM subsystem must deliver estimates for robot navigation, as is the case of applications involving autonomous navigation, this question is even more relevant. This work introduces an experimental analysis of GMapping and RTAB-Map, two ROS compatible SLAM packages, regarding their SLAM accuracy, quality of produced maps and use of produced maps in navigation tasks. Our analysis aims ground robots equipped with RGB-D sensors for indoor environments and is supported by experiments conducted on datasets from simulation, benchmarks and from our own robot.

Despite the proposal of minimum reporting guidelines for metabolomics over a decade ago, reporting on the data analysis step in metabolomics studies has been shown to be unclear and incomplete. Major omissions and a lack of logical flow render the data analysis’ sections in metabolomics studies impossible to follow, and therefore replicate or even imitate. Here, we propose possible reasons why the original reporting guidelines have had poor adherence and present an approach to improve their uptake. We present in this paper an R markdown reporting template file that guides the production of text and generates workflow diagrams based on user input. This R Markdown template contains, as an example in this instance, a set of minimum information requirements specifically for the data pre-treatment and data analysis section of biomarker discovery metabolomics studies, (gleaned directly from the original proposed guidelines by Goodacre at al.). These minimum requirements are presented in the format of a questionnaire checklist in an R markdown template file. The R Markdown reporting template proposed here can be presented as a starting point to encourage the data analysis section of a metabolomics manuscript to have a more logical presentation and to contain enough information to be understandable and reusable. The idea is that these guidelines would be open to user feedback, modification and updating by the metabolomics community via GitHub.

The current work investigates how adding a battery of optimal capacity to a grid-connected photovoltaic (PV) system can improve its economic feasibility. Also, the effect of different parameters on the feasibility of the PV system was evaluated. The OBC was determined for different saving targets of the annual electricity consumption of the chosen building. For this aim, real electricity consumption data of a residential building in Landskrona, Sweden, was used as energy consumption profile. Solar World SW325XL, which is a monocrystalline solar panel, was selected as PV panels. The calculations were performed under the metrological and economic conditions of southern Sweden. Different working parameters (WP)were considered (prices of the battery, feed-in tariffs, and saving targets). The performed calculations show that the optimal battery capacity (OBC), in which the payback time (PBT) of the system is maximized, strongly depends on the WP. The proper selection of the battery can considerably increase the economic feasibility of the PV system in southern Sweden. However, in some cases, using battery can have a negative impact on the PBT of the system. The results show that the electricity price, the module price, the inverter price, and the inverter lifetime have the highest effect on the PBT.

Evacuation is the primary and most important preparedness initiative to save people from the devastating effects of a cyclone. For proper evacuation, a route is specifically chosen that will play a role during cyclones. In this study, Geographic information system has been applied for evacuation route planning for Mirzaganj Union, Patuakhali, Bangladesh. The study is based on questionnaire surveys; direct observation and literature review on the existing sheltering system and develops an evacuation route using the shortest distance analysis. This study provides us an idea how we can manage our population to find out suitable nearby shelters and the limitations that we should focused on. The result shows that only 12% of the total population gets shelter opportunity with the existing official shelter capacity by identifying evacuation route with the shortest distance and the shortest time needed. Shortest distance and time have been calculated for each cyclone shelters. If we consider the low vulnerable infrastructures as shelter, 75% of the total population can be accommodated. This study demonstrates the utilization of an ideal evacuation route planning in the coastal parts of Bangladesh.

The presence of wireless communication grows undeniably more prevalent each year. Since the introduction of the IEEE 802.11 standard for Wireless Local Area Networks (WLAN) in 1997, technologies have progressed to provide wireless accessibility to industries and consumers with growing ease and convenience. As the usage of personal devices, such as phones and watches, that connect to the Internet through Wi-Fi increases, wireless attacks on users are becoming more critical. This paper provides a novel attack model to offer an organized and comprehensive view of the possible attacks on WiFi latest security standards. All exiting attacks will be investigated, with emphasis on more recent attacks, such as the KRACK and PMKID Dictionary attacks. This paper will then analyze the technology offered in the new Wi-Fi Protected Access III (WPA3) security scheme and provide a comprehensive security analysis and discussion to determine whether it has addressed the vulnerabilities of its predecessor. An interesting finding of this paper is that WPA3 still lacks to address all the issues existed in WPA2 and explore other mitigations for future research.

Theoretical studies propose an association between family violence and low self-control in adolescence, yet empirical findings of this association are inconclusive. The aim of the present research was to systematically summarize available findings on the relation between family violence and self-control across adolescence. We included 27 studies with 143 effect sizes, representing more than 25,000 participants of eight countries from early to late adolescence. Applying a multi-level meta-analyses, taking dependency between effect sizes into account while retaining statistical power, we examined the magnitude and direction of the overall effect size. Additionally, we investigated whether theoretical moderators (e.g., age, gender, country), and methodological moderators (cross-sectional/longitudinal, informant) influenced the magnitude of the association between family violence and self-control. Our results revealed that family violence and self-control have a small to moderate significant negative association (r = -.191). This association did not vary across gender, country, and informants. The strength of the association, however, decreased with age and in longitudinal studies. This finding provides evidence that researchers and clinicians may expect low self-control in the wake of family violence, especially in early adolescence. Recommendations for future research in the area are discussed.

In the context of long-term screening for viruses on Western Palaearctic bats, we tested for the presence of adenovirus 1.392 oropharyngeal swabs and 325 stool samples taken from 27 bat species. Adenoviruses were detected in 12 species of the Vespertilionidae and the Rhinolophidae families. Fifty positive respiratory and 26 positive stool samples were studied. Phylogenetic analyses of partial hexon protein and partial DNA-dependent DNA polymerase genes, indicate all these bat adenoviruses belong to the genus Mastadenovirus but without constituting a monophyletic cluster. According to genetic identities, the new groups are distinct to the previously described Bat mastadenovirus A and B species, and contribute with potentially new members. Our data support that diversity of Bat mastadenovirus is host-dependent and increase the knowledge of potentially pathogenic virus from bats. For human concerns this knowledge is an important Public Health issue due to the active role of bats as viral reservoirs.

Recent changes in lung cancer care, including new approvals in first line and the introduction of high throughput molecular technologies in routine testing led us to question ourselves on how deeper molecular testing may be helpful for the optimal use of targeted drugs. In this article we review recent results in the scope of personalized medicine in lung cancer. We discuss biomarkers that have a therapeutic predictive value in lung cancer with a focus on recent changes and on the clinical value of large scale sequencing strategies. We review the use of second and third generation EGFR and ALK inhibitors with a focus on secondary resistance alterations. We discuss anti-BRAF and anti-MEK combo, emerging biomarkers as NRG1 and NTRKs fusions and immunotherapy. Finally we discuss the different technical issues of comprehensive molecular profiling and show how large screenings might refine the prediction value of individual markers. Based on a review of recent publications (2012–2018), we address promising approaches for the treatment of patients with lung cancers and the technical challenges associated to the identification of new predictive markers.

This paper delves into the comprehensive analysis and investigation of thermodynamic enhancement and exergy optimization of turbofan engines through the integration of fuzzy logic and newest meta-heuristic optimizers, like the Artificial Bee Colony (ABC) and Particle Swarm Optimization (PSO). In contemporary engineering practices, exergy analysis stands out as a crucial and indispensable tool utilized by numerous engineers and researchers in the design, operation, and performance evaluation of energy systems. Its multifaceted advantages include the precise determination of location, types, and quantification of exergy losses and production. By augmenting exergy efficiency while concurrently curbing exergy destruction, substantial enhancements in engine performance and cost reduction can be achieved. The study focuses on the CF6-80A and CFM56 turbofan engines, scrutinizing individual components such as the fan, low-pressure compressor, high-pressure compressor, combustion chamber, high-pressure turbine, and low-pressure turbine. Key parameters for engine evaluation encompass exergy efficiency, recovery potential, relative exergy destruction, fuel depletion rate, and inefficiency. To conduct a rigorous exergy analysis, engine modeling is imperative, particularly during the cruise phase, which predominantly characterizes engine operation. Subsequently, exergy analysis is executed in accordance with established thermodynamic principles, leveraging equations governing mass conservation, energy conservation, and exergy conservation for each engine component. Further optimization of exergy destruction is achieved through the application of meta-heuristic optimizers and fuzzy logic, facilitating informed decision-making processes.

Smartphone-based mobility apps enable users to make informed transportation decisions, offering instant access to transport-related information. This development has created a smartphone-enabled ecosystem of mobility services in developed countries while it is slowly picking up pace in the global south, which can contribute towards the decarbonization of urban transport. Work on this has already started in India, and there is considerable evidence indicating the profound impact of these apps on the perceived utility and usage of transport modes, with far-reaching implications for sustainable development goals (SDGs). However, for most users, the use of smartphone apps is a novel trend, and the knowledge of the impacts of usage of existing apps on the usage pattern of transport modes by various user groups is essential for positioning new consolidated app-based services soon. Against this backdrop, the present study uses latent class cluster analysis to empirically investigate the impacts of mobility apps on transport mode usage patterns in Delhi by classifying users into latent classes based on socioeconomic characteristics, attitudes/preferences, smartphone app usage, and mode usage pattern. The characteristics of the latent class and factors affecting the individual’s probability of being classified to these cluster have been discussed, along with some measures to encourage app-based mobility for each cluster.

The different vegetable oils used in canned fish as filling medium have a preserving effect and contribute to the palatability of the product. In this study, the colour of European eels and the filling medium (sunflower oil, olive oil or spicy olive oil) was measured at different steps of the canning process. The sensorial characteristics of canned eels packed in the different oils were also evaluated. Colour scores (CieLab values) were higher in canned eels packed in sunflower and spicy olive oil than in canned eels packed in olive oil. The changes in colour parameters depended on the type of oil, the stage of the process and the storage time. Colour changes in canned eels packed in olive oil were highest during the sterilization process. Spicy olive oil was the filling medium in which the colour change was greatest, probably due to the migration of some of the spice components into the oil. Organoleptic properties were directly related to the type of oil used as the filling medium. The canned eels packed in sunflower oil were those awarded the highest scores in consumer tests, although the preferences varied depending on the age and gender of the consumers.

To keep its business reliable, Google is concerned to ensure quality of apps on the store. One crucial aspect concerning quality is security. Security is achieved through Google Play protect and anti-malware solutions. However, they are not totally efficient since they rely on application features and application execution threads. Google provides additional elements to enable consumers to collectively evaluate applications providing their experiences via reviews or showing their satisfaction through rating. The latter is more informal and hides details of rating whereas the former is textually expressive but requires further processing to understand opinions behind. Literature lacks approaches which mine reviews through sentiment analysis to extract useful information to improve security aspects of provided applications. This work goes in this direction and in a fine-grained way, investigates in terms of confidentiality, integrity, availability and authentication (CIAA). While assuming that reviews are reliable and not fake, the proposed approach determines review polarities based on CIAA-related keywords. We rely on the popular classifier Naive Bayes to classify reviews into positive, negative and neutral sentiment. We then provide an aggregation model to fusion different polarities to obtain application global and CIAA reputations. Quantitative experiments have been conducted on 13 applications including e-banking, live messaging and anti-malware apps with a total of 1050 security-related reviews and 7.835.322 functionality-related reviews. Results show that 23% of applications (03 apps) have a reputation greater than 0.5 with an accent on integrity, authentication and availability, while the remaining 77% has a polarity under 0.5. Developers should make lot of efforts in security while developing codes and that more efforts should be made to improve confidentiality reputation. Results also show that applications with good functionality-related reputation generally offer bad security-related reputation. This situation means that even if the number of security reviews is low, it does not mean that security aspect is not a consumer preoccupation. Unlike, developers put much more time to test whether applications works without errors even if they include possible security vulnerabilities. A quantitative comparison against well-known rating systems reveals effectiveness and robustness of CIAA-RepDroid to repute apps in terms of security. CIAA-RepDroid can be associated to existing rating solutions to recommend developers exact CIAA aspects to improve within source codes.

Four models of Ce(III) complexes with four ligands based on a 1,2,3-triazole derivatives were studied at four DFT levels. The effect of different ligands on the atomic charge value on the cerium ion was analyzed. Several relationships were clearly established between this atomic charge and that of the surrounding atoms, as well as with several geometric parameters of the ligand and with the molecular properties of the Ce(III) complex. The experimental IR and Raman spectra of the newly obtained Ce(III) complex with the (pyrrolidin-1-yl)-2H-1,2,3-triazole-4-carboxylate ligand were interpreted based on their comparison to the theoretical scaled ones using the scaling equations determined by two procedures and four DFT levels. Therefore, the structure predicted for the synthesized Ce(III) complex was clearly characterized and confirmed. The potential antioxidant action of Ce(III) complex was elucidated and compared with other Ln(III) complexes in order to find out the differences in their biological activity profiles.

Morphological features and composition of ISSR DNA fragments were studied in species of the genus Tulipa L. A comparative analysis of the data obtained was carried out and an attempt was made to clarify the species belonging of plants. Plants obtained from various sources were studied. It was found that representatives of the genus Tulipa L. from the two subgenera Tulipa and Eriostemones are well separated not only morphologically, but also by the composition of ISSR fragments. At the intra- and interspecific level, the results for morphological traits and molecular data differed. Interspecific and intraspecific differences were more clearly traced in the complex analysis of morphological features and ISSR PCR data. All samples obtained in the form of bulbs and renewed vegetatively had an identical set of ISSR fragments. Plants grown from seeds were characterized by a significant variety of molecular markers and had both species-specific and in-dividual genetic variability. It was found that the samples obtained and registered as the Tulipa urumiensis Stapf are the yellow-flowered form of the Tulipa tarda Stapf, and a sample received as the Tulipa turkestanika Regel is the Tulipa bifloriformis Vved. Thus, a comprehensive study of both the data of fragmentary DNA analysis and morphological features, provided a sufficient number of DNA samples, makes it possible to clarify the species of tulip plants, and also allows us to assess the genetic diversity of the genus Tulipa.

This paper identifies a criterion for choosing the largest set of rejected hypotheses in high-dimensional data analysis where Multiple Hypothesis testing is used in exploratory research to identify significant associations among many variables. The method neither requires predetermined thresholds for level of significance, nor uses presumed thresholds for false discovery rate. The upper limit for number of rejected hypotheses is determined by finding maximum difference between expected true hypotheses and false hypotheses among all possible sets of rejected hypotheses. Methods of choosing a reasonable number of rejected hypotheses and application to non-parametric analysis of ordinal survey data are presented.

A rigorous perturbation analysis of the singular value decomposition of a real matrix of full column rank is presented. It is shown that the SVD perturbation problem is well posed only in case of distinct singular values. The analysis involves the solution of coupled systems of linear equations and produces asymptotic (local) componentwise perturbation bounds of the entries of the orthogonal matrices participating in the decomposition of the given matrix and of its singular values. Local bounds are derived for the sensitivity of the singular subspaces measured by the angles between the unperturbed and perturbed subspaces. An iterative scheme is described to find global bounds on the respective perturbations. The analysis implements the same methodology used previously to determine componentwise perturbation bounds of the Schur form and the QR decomposition of a matrix.

Although the need to assess hydration is well recognized, laboratory tests and clinical impressions are impractical and lack sensitivity to be clinically meaningful. Different approaches use bioelectrical impedance measurements to overcome some of these limitations and aid in classification of hydration status. One indirect approach utilizes single or multiple frequency bioimpedance in regression equations and theoretical models, respectively, with anthropometric measurements to predict fluid volumes (bioelectrical impedance spectroscopy; BIS) and estimate fluid overload based on deviation of calculated to reference extracellular fluid volume. Alternatively, bioimpedance vector analysis (BIVA) uses direct phase-sensitive measurements of resistance and reactance, measured at 50 kHz, normalized for height, then plotted on a bivariate graph, resulting in a vector with length related to fluid content normalized by standing height, and direction with phase angle that indexes hydration status. Comparison with healthy population norms enables BIVA to classify (normal, under- and over-) and rank (change relative to pre-treatment) hydration independent of body weight. Each approach has wide-ranging uses in evaluation and management of clinical groups with overhydration with an evolving emphasis on prognosis. This review discusses the advantages and limitations of BIS and BIVA for hydration assessment with comments on future applications.

A scientific investigation into visual preferences of rural recreation path landscapes is crucial for enhancing visual qualities and visitors' experience. Taking Baofeng, a representative traditional village in Yunlong, as an example, this study analyzed the visual preference of three typical linear recreational paths with different landscape attributes. We used eye-tracking technology to collect eye movement data and conducted a subjective semantic differential evaluation. Our findings manifest a general inclination amongst individuals to favor closer views and paths with clear trajectories and rich elevation changes while walking, despite preference differences on different types of paths. Regarding group diversity, more inclusive design should be made to meet the sensory needs of different groups. Moreover, despite the limited level of correlation between subjective and objective evaluation methods, combining these two approaches remains indispensable. This synthesis advances prior quantitative landscape evaluation with a specific focus on rural path landscapes and upholds the integrity of results by combining objectivity with subjectivity. It also provides relevant design suggestions for further rural renewal and path planning of Baofeng village.

The current study aims to develop a methodology for obtaining topology-optimal structures made of short fiber-reinforced polymers, which include the fluid-structure interaction. Accounting for the molding process during the internal iterations of topology optimization makes it possible to enhance the weight efficiency of structures—a crucial aspect, especially in aerospace. Anisotropy is considered through the fiber orientation tensor, which is modeled by solving the plastic molding equations for non-Newtonian fluids, and then introduced as a variable of the stiffness matrix during the structural analysis. The materials used were non-linearly modeled using an exponential-and-linear hardening law. The evaluation of weight efficiency in structures composed of short-reinforced composite materials using a dimensionless criterion is addressed. Experimental verification was performed to confirm the validity of the developed methodology. The evidence illustrates that considering anisotropy leads to stiffer structures and structural elements should be oriented in the direction of maximal stiffness. The load-carrying factor can be expressed in terms of failure criteria. The presented multidisciplinary methodology can be used to improve the quality of the design of structures made of short fiber-reinforced composites (SFRC) where high stiffness, high strength, and minimum mass are the primary required structural characteristics.

The carbonation of industrial calcium-rich byproducts such as steel slag demonstrates significant potential for CO2 sequestration. This technique aids in reducing carbon emissions while also promoting waste recycling. Despite its advantages, gaps remain in understanding how steel slag characteristics and operational parameters influence the carbonation process, as well as the underlying mechanism of direct aqueous carbonation. We evaluated the carbonation performance of three types of steel slag at temperatures below 100°C using a gas–liquid–solid reaction system. The slag with the highest CO2 sequestration capacity was chosen for a systematic evaluation of the effects of operating conditions on carbonation efficiency. Thermodynamic analysis indicated that the reactivity of CaO and Ca(OH)2 with CO2 exceeded that of CaO•SiO2 and 2CaO•SiO2. Under conditions of 85°C, a particle size less than 75 μm, an initial CO2 pressure of 0.5 MPa, a liquid-to-solid ratio of 5 mL/g, and a stirring speed of 200 rpm, the steel slag achieved a sequestration capacity (K) of 283.5 gCO2/kg and a carbonation efficiency (ζCa) of 51.61%. Characterization of the slag before and after carbonation using X-ray diffraction, SEM‒EDS, thermogravimetric analysis, and Fourier transform infrared spectrometry confirmed the formation of new carbonates. Mechanistic analysis revealed that the rate-limiting step initially involved the mass transfer of CO2, transitioning to Ca2+ mass transfer as time progressed. Our research provides a viable technique for CO2 capture and a beneficial approach for reutilizing waste steel slag. Furthermore, solid residues after capturing CO2 have the potential for conversion into carbon-negative building materials, offering a sustainable strategy for steel companies and other enterprises with high carbon emissions.

The steel grid structure with bolted ball joints is gaining popularity in offshore platforms and aquaculture cages, but the corrosion resistance of these joints is a critical safety concern. This article introduces an innovative method to enhance the sealing performance of bolted ball joints, preventing seawater ingress and corrosion. The approach involves creating a sealed surface within the contact gap between the sleeve and connecting components. Circular grooves and sealing washers are added to both ends of the sleeve. A two-dimensional finite element analysis model of the bolted ball joint with the sealing structure was created using SOLIDWORKS and ANSYS. The study analyzes the sealing gasket's contact pressure at various compression levels and evaluates its performance with bubble tests for air tightness. Research results show a linear relationship between contact pressure and compression rate, with a sealing pressure of 5.95MPa achieved at a 14% compression rate. This simple, cost-effective sealing method effectively prevents corrosion from seawater, making it ideal for marine environments.

The purpose of this study was to investigate the functional load capacity of the periodontal ligament (PDL) in a full arch maxilla and mandible model using numerical simulation. The goal of was to determine the functional load pattern in multi- and single rooted teeth with full and reduced periodontal support. CBCT data were used to create 3D-models of a maxilla and mandible. The DICOM dataset was used to create a CAD-model. For a precise description of the surfaces of each structure (enamel, dentin, cementum, pulp, PDL, gingiva, bone), each tooth was segmented separately, and the biomechanical characteristics were considered. A Finite-Element-Analysis (FEA) software computed the biomechanical behavior of stepwise increased force of 700N in cranial and 350N in ventral direction of the muscle approach of the Masseter muscle. The periodontal attachment (cementum-PDL-bone contact) was subsequently reduced in 1mm increments and the simulation repeated. Quantitative (pressure, tension, and deformation) and qualitative (color-coded images) data were recorded and descriptively analyzed. The teeth with the highest load capacities were the upper and lower molars (0.4-0.6MPa), followed by the premolars (0.4-0.5MPa) and canines (0.3-0.4MPa) when vertically loaded. Qualitative data showed that the area with the highest stress in the PDL were for single rooted teeth the cervical and apical area and for molars additionally the furcation roof. In both, single- and multi rooted teeth the gradual reduction of the bone levels caused an increase of the load on the remaining PDL. Cervical and apical areas as well as the furcation roof are the zones with the highest functional stress. The more bone loss, the higher the mechanical load on the residual periodontal supporting structures.

In the last decade and a half, the world has experienced the outbreak of a range of viruses such as COVID-19, H1N1, flu, Ebola, Zika Virus, Middle East Respiratory Syndrome (MERS), Measles, and West Nile Virus, just to name a few. During these virus outbreaks, the usage and effectiveness of social media platforms increased significantly as such platforms served as virtual communities, enabling their users to share and exchange information, news, perspectives, opinions, ideas, and comments related to the outbreaks. Analysis of this Big Data of conversations related to virus outbreaks using concepts of Natural Language Processing such as Topic Modeling has attracted the attention of researchers from different disciplines such as Healthcare, Epidemiology, Data Science, Medicine, and Computer Science. The recent outbreak of the MPox virus has resulted in a tremendous increase in the usage of Twitter. Prior works in this field have primarily focused on the sentiment analysis and content analysis of these Tweets, and the few works that have focused on topic modeling have multiple limitations. This paper aims to address this research gap and makes two scientific contributions to this field. First, it presents the results of performing Topic Modeling on 601,432 Tweets about the 2022 Mpox outbreak, which were posted on Twitter between May 7, 2022, and March 3, 2023. The results indicate that the conversations on Twitter related to Mpox during this time range may be broadly categorized into four distinct themes - Views and Perspectives about MPox, Updates on Cases and Investigations about Mpox, MPox and the LGBTQIA+ Community, and MPox and COVID-19. Second, the paper presents the findings from the analysis of these Tweets. The results show that the theme that was most popular on Twitter (in terms of the number of Tweets posted) during this time range was - Views and Perspectives about MPox. It is followed by the theme of MPox and the LGBTQIA+ Community, which is followed by the themes of MPox and COVID-19 and Updates on Cases and Investigations about Mpox, respectively. Finally, a comparison with prior works in this field is also presented to highlight the novelty and significance of this research work.

Sowing accuracy is a critical factor in evaluating the precision of sowing equipment, and the sowing rate of the sowing equipment developed at home and abroad cannot reach 100 %, so this paper designs a seed reseeding device, aiming to improve the sowing rate secondly. Firstly, the structural composition of the seed reseeding device is introduced; secondly, according to the production object selected in this paper, kale seed (Jingfeng No.1), the best combination of factors and levels of the suction nozzle is 8 m/s inlet speed, 1.8 mm aperture, 18 mm lead and V-shaped nozzle by using orthogonal experiment; then, the EDEM software was used to conduct the vibration test, and it was found that the seed group “boils” steadily at about 15.13 mm (the depth of the reseeding tray is 23 mm), which can effectively improve the suction rate of the suction nozzle; finally, by combining the simulation results of the flow field using Fluent/ANSYS software and the spatial distribution of seeds, the number of seeds in the effective adsorption range of a single suction nozzle is about 4. The determined number of seeds, approximately 4, accounts for only 0.04% of the total number of seeds generated by the simulation. This percentage ensures high suction precision and a single seed rate, particularly under working conditions where a 72-hole tray is being sowed. In general, domestic sowing accuracy ranges from 90% to 95%. With this configuration, the purpose of improving the sowing rate twofold can be achieved.

The biodiversity loss in urban areas has attracted public concern, which is one of the urgent global environmental issues. This study used bibliometric methods to analyze 3351 publications from 1995-2021 that retrieved from the Web of Science and visually represented the state-of-the-art of researches in city biodiversity field. The prolific authors, journals(sources), institutions, and countries are clearly identified. The most cited and influential paper proposed a conceptual framework of associations between urban green space, and ecosystem and human health, and then concluded that green infrastructure could physically and psychologically benefits people by ecosystem services it provides, and make a better socio-economic benefit. The theme hotspots are urbanization, urban ecology, ecosystem services, urban planning, green infrastructure, urban forest and urban park et al. Ingo Kowarik is the most productive author in terms of number of publications. Michael L. McKinney is the most cited authors by the publications in analyzed corpus, who identified how urbanization harms native ecosystems, however, a well ecologically educated population could greatly enhance species richness in all ecosystems. Urban park, garden, fragmented green spaces and green corridor networks could help enhance city biodiversity. In general, the city biodiversity research presents a trend that involves intensive global cooperation, and become more comprehensive.

The surface defects of shield subway tunnel can significantly affect the serviceability of the tunnel structure and may compromise the operation safety. To effectively detect the multiple surface defects, this research employs a tunnel inspection trolley (TIT) based on the mobile laser scanning technique. By conducting the inspection of the shield tunnel on a metro line section, various surface defects are identified by the TIT, including water leakage defect, dislocation, spalling, cross-section deformation, etc. To explore the root causes of the surface defects, the association rules between different defects are calculated via an improved Apriori algorithm. Results show that: i) there are significant differences in different association rules of various surface defects of the shield tunnel; ii) the average confidence of the association rule “dislocation &amp; spalling → water leakage” is as high as 57.78%, indicating that most of the water leakage defects are caused by dislocation and spalling of the shield tunnel in the sections being inspected; iii) the weakest rule appears at “water leakage → spalling”, with the average confidence of 13%. The association analysis can be used in predicting the critical defects influencing the structural reliability and operation safety, such as water leakage, and optimizing the construction and maintenance work for the shield subway tunnel.

A progressive FEA fatigue degradation model for composites was developed and implemented using a UMAT user material subroutine in Abaqus. Numerical results were compared to experimental strain field data from high frequency Digital Image Correlation (DIC) of split disk fatigue testing of pressure vessel cut outs with holes. The model correctly predicted the onset and evolution of damage in the matrix as well as the onset of fiber failure. The model use progressive failure analysis based on the maximum strain failure criterion, the cycle jump method and Miner sum damage accumulation rule. A parameter study on matrix properties was needed to capture the scatter in strain fields observed experimentally by DIC. S-N curve for the matrix material had to be lowered by 0% to 60% to capture the experimental scatter. The onset of local fiber failure had to be described by local S-N curves measured by DIC having 2.5 times greater strain than that of S-N curves found from standard coupon testing.

Bacterial adhesion to the surface of the adhesive material is an important step in the formation of plaque and enamel demineralization. In order to correlate the material composition to the specific surface roughness of the resin and to the probable more favourable adhesion of bacteria, scanning electron microscopy, combined with focus ion bean micromachining, together with stylus profilometry analysis have been in vitro performed to reveal the structural nature of three orthodontic adhesive resins used for bracket bonding and, above all, to understand how compositional factors can influence specific pivotal properties such as material’s surface roughness and robustness. In particular, we speculated about the morphological features that determine an increase in the bacterial adhesion and we proposed focused ion beam technique as a valuable tool to compare the internal structures of the polymers and to determine the peculiar mechanical properties of the examined adhesive resins.

In many image processing and computer vision applications, the main aim is to describe image contents. So, different visual properties such as color, texture and shape are extracted to make aim. In this respect, texture information play important role in image description and visual pattern classification. Texture is referred to a specific local distribution of intensities that is repeated throughout the image. Since now different operations or descriptors have been proposed to analysis texture characteristics. In the multi object images specific texture operators usually doesn’t provide accurate results. So, in many cases, combination of texture operators are used to achieve more discriminant features. In this paper, some combination methods are survived to analysis effect of combinational texture features in image content description. Also, in the result part, different related methods are compared in terms of accuracy and computational complexity.

Opium poppy (Papaver somniferum L.) is an ancient medicinal plant producing pharmaceutically important benzylisoquinoline alkaloids. In the present work we focused on the study of enzyme lipoxygenase (LOX, EC 1.13.11.12) from opium poppy cultures. LOX is involved in lipid peroxidation and lipoxygenase oxidation products of polyunsaturated fatty acids have a significant role in regulation of growth, development and plant defence responses to biotic or abiotic stress. The purpose of this study was to isolate and characterize LOX enzyme from opium poppy callus cultures. LOX was purified by ammonium sulphate precipitation followed by hydrophobic chromatography using Phenyl-Sepharose CL-4B and hydroxyapatite chromatography using HA Ultrogel sorbent. SDS-PAGE analysis and immunoblotting revealed that LOX from opium poppy cultures was a single monomeric protein showing the relative molecular weight of 83 kDa. To investigate the positional specificity of the LOX reaction, purified LOX was incubated with linoleic acid and the products were analysed by high-performance liquid chromatography. LOX converted linoleic acid primarily to 13-hydroperoxy-(9Z,11E)-octadecadienoic acids (78%) and to a lesser extent to 9-hydroperoxy-(10E,12Z)-octadecadienoic acids (22%). Characterization of LOX from opium poppy cultures provided valuable information in understanding of LOX involvement in regulation of signalling pathways leading to biosynthesis of secondary metabolites with significant biological activity.

In this work, unmodified screen-printed electrode (bare SPE) and Sb-film modified SPE (SbFSPE) sensors were employed for the analysis of trace amounts of Pb(II) in non-deaerated water solutions. The modified electrode was performed in situ in 0.5 mg/L Sb(III) and 0.01 M HCl. The methodology was validated for an accumulation potential of –1.1 V vs. Ag/AgCl and an accumulation time of 60 s. A comparative analysis of bare SPE and SbFSPE showed that the detection and quantification limits decrease for the bare SPE. The method with the bare SPE showed a linear response in the 69.8–368.4 µg/L concentration range, whereas linearity for the SbFSPE was in the 24.0–319.1 µg/L concentration range. This work also reports the reason why the multiple standard addition method instead of a linear calibration curve for Pb(II) analysis should be employed. Furthermore, the analytical method employing SbFSPE was found to be more accurate and precise compared to the use of bare SPE when sensors were employed for the first time, however this performance changed significantly when these sensors were reused in the same manner. Furthermore, electrochemical impedance spectroscopy was used for the first time to analyse the electrochemical response of sensors after being used for multiple successive analyses. Surface characterisation before and after multiple successive uses of bare SPE and SbFSPE sensors, with atomic force microscopy and field emission scanning electron microscopy, showed sensor degradation. The interference effect of Cd(II), Zn(II), As(III), Fe(II), Na(I), K(I), Ca(II), Mg(II), NO₃^– Bi(III), Cu(II), Sn(II), and Hg(II) on the Pb(II) stripping signal was also studied. Finally, the application of SbFSPE was tested on a real water sample (from a local river), which showed high precision (RSD = 8.1%, n = 5) and accurate results.

Estimating the vehicle crashworthiness parameters experimentally is expensive and time consuming. For these reasons different modelling approaches are utilized to predict the vehicle behaviour and reduce the need for full-scale crash testing. The earlier numerical methods used for vehicle crashworthiness analysis were based on the use of lumped parameters models (LPM), a combination of masses and nonlinear springs interconnected in various configurations. Nowadays, the explicit nonlinear finite element analysis (FEA) is probably the most widely recognized modelling technique. Although informative, finite element models (FEM) of vehicle crash are expensive both in terms of man-hours put into assembling the model and related computational costs. A simpler analytical tool for early analysis of vehicle crashworthiness could greatly assist the modelling and save time. In this paper a simple piecewise LPM composed of a mass-spring-damper system, is used to estimate the vehicle crashworthiness parameters, focusing on the dynamic crush and the acceleration severity index (ASI). The model is first calibrated against a full-scale crash test and a FEM, post-processed with the LS-DYNA software, at an impact velocity of 56 km/h. The genetic algorithm is used to calibrate the model by estimating the piecewise lumped parameters (stiffness and damping of the front structure of the vehicle). After calibration, the LPM is applied to a range of velocities (40, 48, 64 and 72 km/h). The predictions for crashworthiness parameters from the LPM were compared with the predictions from the FEA and the results are much similar. It is shown that the LPM can assist in crash analysis, since LPM has some predictive capabilities and requires less computation time in comparison with the explicit nonlinear FEA.

This paper aims to investigate carbon monoxide (CO) concentrations on roadways of Karachi, potential blood levels carboxy-hemoglobin (COHb) in Karachi. Geographical information system (GIS) was used for spatial analysis of diseases potentiality while an interpolation technique has been applied for surface generation with town boundaries and later evaluates risk areas.The higher concentration of carbon monoxide in the ambient is mainly due to automobile emissions. The City center and CBD areas are more perilous.

Clinical rehabilitation and sports performance analysis both require the objectification of movement. Kinovea^© is a free 2D motion analysis software that enables the establishment of kinematics parameters. This low-cost technology has been used in sports sciences, as well as clinical field and research work. Although it has been validated as a tool with which to assess time-related variables, this is not yet the case regarding angular and distance variables. The main objective of this study was to determine the validity and reliability of the Kinovea software in obtaining angular and distance data at different perspectives of 90°, 75°, 60° and 45°. For this purpose, a figure with 29 points was designed (in AutoCAD) and 24 frames analysed. Each frame was examined by three observers who each made two attempts. For each export data item, 20 angles and 20 distance variables were calculated, with intra- and inter-observer reliability also analysed. To evaluate Kinovea reliability and validity a multiple approach was applied involving the following analysis: -systematic error with a two-way ANOVA 2x4; -relative reliability with ICC and CV (95% confidence interval); -absolute reliability with Standard Error. The results thus obtained indicate that the Kinovea software is a valid and reliable tool that is able to measure accurately at distances up to 5 m from the object and at an angle range of 90°–45°. Nevertheless, for optimum results an angle of 90° is suggested.

Impact characterization of linear resonant actuator (LRA) is studied experimentally by newly developed drop tester, which can control various experimental uncertainty such as rotational moment, air resistance, secondary impact and so on. The feasibility of this test apparatus was verified by comparison with free fall test. By utilizing a high-speed camera and measuring the vibrational displacement of spring material, the impact behavior was captured and the damping ratio of the system was defined. Based on the above processes, the finite element model was established and the experimental and analytical results were successfully correlated. Finally, the damage of the system from impact loading can be expected by developed model and as a result, this research can improve the impact reliability of LRA.

The technologies influencing alternative ways of transportation are augmenting in recent years as the need for transportation is increasing rapidly due to urbanization and motorization. In this paper, a solar powered electric auto-rickshaw (SPEA) is designed and developed for Indian conditions. The developed vehicle is comprehensively analyzed techno-economically for its viability in the Indian market. The performance analysis of SPEA results in an optimal charging rate of 2 kWh per day with an average solar irradiance of 325 W/m2. The discharging characteristics are studied based on different loading conditions. The vehicle achieved a maximum speed of 21.69 km/h with battery discharge rate of 296W at 90kg load and also reached a maximum discharge rate of 540W at 390kg loading with a maximum speed of 12.11 km/h. The environmental analysis of SPEA displayed yearly CO2 emissions of 1,777 kg, 1,987 kg and 1,938 kg using Compressed Natural Gas, Liquefied Petroleum Gas and gasoline engines respectively can be mitigated using SPEA. The results of financial analysis of SPEA were welcoming as the investor gets 24.44% lesser payback duration compared to gasoline run vehicle. Socio-Economic analysis of SPEA discussed its significant advantages and showed 18.73% and 3.9% increase in yearly income over gasoline driven and battery driven vehicles.

Over recent years, the swine industry has witnessed the withdrawal of antibiotics and continuous regulation of zinc and copper oxides in the early-life nutrition of piglets. Due to this development, alternative ingredients or additives from plant sources have been tremendously explored. Therefore, this study's objective was to evaluate the effect of dietary supplementation with tannins on weaned piglets’ growth performance, serum antioxidant capacity, and immune status using a meta-analysis approach. With only the inclusion of non-challenged weaned piglets, a total of 16 studies with parameters of interest were deemed eligible after a two-step screening process following a comprehensive literature search in the scientific databases of Web of Science, Scopus, ScienceDirect, PubMed, and Google Scholar. Two main categories of data, including the study’s characteristics and response variables (growth performance, serum antioxidant, and immune indices), were extracted. Applying the random-effects models, Hedges’ g effect size of supplementation with tannins was calculated using the R software to determine the standardized mean difference (SMD) at a 95% confidence interval. Sub-group analysis and meta-regression further explored heterogeneity (PSMD 0.05, I2 50%, n ≥10). Supplementation with tannin reduced average daily gain (p = 0.20) and feed conversion ratio (p 0.01); however, it increased average daily feed intake (p = 0.07) and final body weight (p 0.01). Meta-regression models indicated that tannin dosage and supplementation duration were directly associated with tannins' effectiveness on productive performance. In the serum, the concentration of glutathione peroxidase, superoxide dismutase, and total antioxidant capacity were elevated (p 0.01) in response to tannins supplementation, while malondialdehydes was reduced (p 0.01). Likewise, decreased immunoglobin A (p = 0.47) and increased immunoglobin M and G levels (p 0.01) were detected. In conclusion, dietary supplementation with tannins, particularly with chestnut and grape seed proanthocyanidins, increases the productivity of weaned piglets. At the same time, it is a possible nutritional strategy to mitigate oxidative stress and stimulate gut health. Thus, supplementing chestnut and grape seed proanthocyanidin tannins in the early phase of swine production could be used to alleviate the incidence of diarrhea.

There is evidence of constitutional rank in Chile, and not only a sectoral rank, to estimate that the regulation of digital media is in an initial and insufficient phase to properly protect information pluralism as political pluralism. The study aims to investigate the forms of concentration, such as the communicational flow of digital media, to determine the opportunities and information defects of these media due to regulatory deficiencies in this sector. Data collection will be carried out through a qualitative and quantitative methodology. The prospect of the imminent constituent process in Chile provides the opportunity to the evaluate the possibilities to propose changes not only at the legislative level but also at the constitutional level, which are likely to benefit freedom of expression with greater guarantees to such as the pluralism of digital media. The latter also means assessing the relevance of enshrining the right to communication in the future.

As an important research content in cultural geography, the exploration and analysis of the laws of regional cultural differences has great significance for the discovery of distinctive cultures, protection of regional cultures and in-depth understanding of cultural differences. In recent years, with the "spatial turn" of sociology, scholars are paying more and more attention to the implicit spatial information in social media data and the various social phenomena and laws they reflect. One important aspect is to grasp the social cultural phenomena and its spatial distribution characteristics through the text. Using machine learning methods such as the popular natural language processing (NLP), this paper can not only extract hotspot cultural elements from text data but also accurately detect the spatial interaction pattern of some specific cultures and the characteristics of emotions towards non-native cultures. Taking the 6,128 answers to the question “what are the differences between South and North China that you never know” on the Zhihu Q&A Platform as an example, with the help of NLP, this paper has explored the cultural differences between South and North China in people’s mind. This paper probes into people’s feeling and cognition of the cultural differences between South and North China from three aspects, including spatial interaction patterns of hotspot cultural elements, components of hotspot culture and emotional characteristics under the influence of cultural differences between North and South. The study reveals that 1) people from North and South China have great differences in recognizing each other’s culture. 2) Food culture is the most popular among many cultural differences. 3) People tend to show negative attitude towards the food cultures different from their own. All these findings shed light upon the understanding of regional cultural differences and addressing cultural conflicts. In addition, this paper also provides an effective solution to the study from a macro perspective, which have been difficult for new cultural geography.

The recycling, incineration, and final disposal rate of municipal solid waste (MSW) are calculated based on the total amount of waste input to each facility in many countries. These statistic data have serious limitation in setting the national goal and policy for effective waste management because it is not considering the amount of foreign objectives in the process of each life-cycle stage. This case study is to estimate the actual rates of recycling, incineration, and final disposal by material flow analysis (MFA) after the collection of MSW in Korea. The actual rates of recycling, incineration and final disposal for MSW in 2016 were 49.9%, 32.9% and 23.1% respectively, indicating that the recycling rate was lower by 10.1%, while the incineration and final disposal rates were raised by 7.6% and 8.4% respectively, compared with the statistics for current MSW. In addition, the changed actual rates of recycling, incineration treatment, and final landfill, and variation of waste treatment charge according to treated amounts per treatment method was analyzed. This results of this study will contribute to establish national level of plan on effective waste management.

In this article we estimate the level of Government Effectiveness-GE in 193 countries in the period 2011-2020 using data of the ESG World Bank Database. Different econometric techniques are used i.e. Panel Data with Random Effects, Panel Data with Fixed Effects, and Pooled OLS. Results show that GE is positively related among others to “Control of Corruption”, “Political Stability and Absence of Violence/Terrorism”, and negatively associated with “Percentage Annual GDP Growth”. We perform a cluster analysis with the k-Means algorithm optimized with the Elbow Method and we find the presence of four clusters. Finally, we confront eight machine learning algorithms for the prediction of GE. Results show that the Polynomial Regression is the best predictive algorithm. The value of GE is expected to growth on average by 15.97%.

Dynamic variables in the non-equilibrium systems of life are determined by catalytic activities. These relate to the expression of the genome. The extent to which such a variable depends on the catalytic activity defined by a gene has become more and more important in view of the possibilities to modulate gene expression or intervene with enzyme function through the use of medicinal drugs. With all the complexity of cellular systems biology, there are still some very simple principles that guide the control of variables such as fluxes, concentrations, and half-times. Using time-unit invariance we here derive a multitude of laws governing the sums of the control coefficients that quantify the control of multiple variables by all the catalytic activities. We show that the sum of the control coefficients of any dynamic variable over all catalytic activities is determined by the control of the same property by time. When the variable is at a maximum, minimum or steady, this limits the sums to simple integers like 0, -1, 1, and -2, depending on the variable under consideration. Some of the implications for biological control are discussed as is the dependence of these results on the precise definition of control.

Chronic diseases are still known as incurable diseases, and we suspect that the medical research model is unfit for characterizing chronic diseases. In this study, we examined accuracy and reliability required for characterizing chronic diseases, reviewed implied presumptions in clinical trials and assumptions used in statistical analysis, examined sources of variances normally encountered in clinical trials, and conducted numeric simulations by using hypothetical data for several theoretical and hypothetical models. We found that the sources of variances attributable to personal differences in clinical trials can distort hypothesis test outcomes, that clinical trials introduce too many errors and too much inaccuracies that tend to hide weak and slow effects of treatments, and that the means of treatments used in statistical analysis have little or no relevance to specific patients. We further found that a large number of uncontrolled co-causal or interfering factors normally seen in human subjects can greatly enlarge the means and the variances of the experimental errors, and the use of high rejection criteria (e.g., low p values) further raises the chances of failing to find treatment effects. As a whole, we concluded that the research model using clinical trials is wrong on multiple grounds, under any of our realistic theoretical and hypothetical models, and that misuse of statistical analysis is most probably responsible for failure to identify treatment effects for chronic diseases and to detect harmful effects of toxic substances in the environment. We proposed alternative experimental models involving the use of single-person or mini optimization trials for studying low-risk weak treatments.

In this paper, we introduce a new formulation based on Floquet (Fourier) spectral analysis combined with a spectral modulation technique (and its spatial form) to study strongly coupled sublattices predefined in the infinite and large finite extent of almost periodic antenna arrays (e.g metasurfaces). This analysis is very relevant for dense massive MIMO, intelligent surfaces, 5G, and 6G applications (used for very small areas with a large number of elements such as millimeter and terahertz waves applications). The numerical method that is adopted to model the structure is the method of moments simplified by equivalent circuits MoM GEC. Other numerical methods (as the ASM array scanning method and windowing Fourier method) used this analysis in their kernel that to treat periodic and pseudo-periodic (or quasi-periodic) arrays.

Introduction: Patients’ satisfaction was extensively researched over the last decades, given its role in building loyalty, compliance to treatment, prevention, and eventually higher levels of wellbeing and improved health status. Patients’ feedback on the perceived quality of health services can be incorporated into practice; therefore, understanding factors and mechanisms responsible for patients’ satisfaction allows providers to tailor targeted interventions. Method: A questionnaire assessing patients’ perception of the quality of health services was administered to a country-representative sample of 1500 Romanian patients. Using a partial least squares—path modeling approach (PLS-PM), with cross-sectional data, we developed a variance-based structural model, emphasizing the mediating role of trust and satisfaction with various categories of health services. Results: We confirmed the mediating role of trust in shaping the relationship between the procedural accuracy of health professionals, along with the perceived intensity of their interaction with patients, and patients’ experienced quality of the health services. We confirmed the mediating role of satisfaction by the categories of services in the relationship between waiting time on the premises, attention received, and the perceived reliability of the information received, as predictors, and the experienced quality of the health services. In addition, indirect assessment of patients’ satisfaction is a good predictor for direct assessment, thereby affirming the idea that the results of the two types of evaluations converge. Discussions: One of the most efficient solutions to increase both patients’ satisfaction and their compliance is to empower the communication dimension between patients and health practitioners. Given the non-linear relationships among variables, we advocate that, unless the nature of the relationships between satisfaction and its predictors is understood, practical interventions could fail. The most relevant variable for intervention is the degree of attention patients perceive they received. We suggest three methods to turn waiting time into attention given to patients.

(1) Background: There is a need to assess the availability of essential antimicrobials as the availability of an antimicrobial is a critical element of its rational use. We aimed to assess the availability of antimicrobials listed in National List of Essential Medicines 2015, India (primary list) and a selected (secondary) list comprising of agents indicated for commonly encountered infectious illnesses in various healthcare settings and to identify the reasons for their non-availability. (2) Methods: A cross-sectional survey of 25 public, private, and other sector pharmacies was carried out in Rohtak, a district of North Indian state of Haryana, from April to June 2022. (3) Results: Most of the antimicrobials surveyed were optimally available in various sector pharmacies with the exception of benzathine benzyl penicillin, benzyl penicillin, cloxacillin, cefazolin, cefuroxime, cefadroxil, amphotericin B and antimalarials. Most frequent reasons for limited availability were low demand, no prescriptions and non-listing of drugs in state essential medicine list. (4) Conclusions: Enough evidence needs to be generated with respect to status of availability of essential antimicrobials from different regions of India as well as other lower-middle income countries to devise measures for ascertaining better availability of these agents especially antibiotics at regional, national and global scales.

Driven by industrialization and urbanization, urban air pollution can increase respiratory, heart, and cerebrovascular diseases, and thus mortality rates; as such, it is necessary to improve air quality through the consideration of individual pollutants and emissions sources. In South Korea, national and local governments have installed urban and roadside air quality monitoring systems. However, stations are lacking outside metropolitan regions, and roadside stations are sparsely distributed, limiting comparisons of pollutant concentrations with vehicle traffic and floating population levels. Local governments have begun using mobile laboratories (MLs) to supplement the fixed measurement network and investigate road pollution source characteristics based on their spatiotemporal distribution; however, the collected data cannot be used effectively if they are not visualized. Here, we propose a method to collect and visualize global information system (GIS)-based air quality data overlayed with environmental variables to support air quality management measures. Spatiotemporal analyses of ML-derived data from Bucheon, Korea, confirmed that particulate and gaseous pollutant concentrations were high during typical commuting hours, at intersections, and at a specially managed road. During commuting hours, the maximum PM10 concentration reached 200.7 µg/m³ in the Nae-dong, Gyeongin-ro, and Ojeong-dong ready-mix concrete complex areas, and the maximum PM_2.5 concentration was 161.7 µg/m³. The maximum NOx, NO₂, and NO levels of 1.34 ppm, 0.18 ppm, and 1.18 ppm, respectively, were also detected during commuting hours. These findings support the need for targeted management of air pollution in this region, and highlight the benefit of comprehensively comparing road levels, driving speed, and traffic levels when identifying hotspots of air pollution. Such analyses will contribute to the development of air quality management measures customized to regional characteristics.

Chicken infectious anemia (CIA) poses a significant threat to the chicken industry in China. De-spite its non-specific symptoms, the disease is often overlooked. This study aimed to conduct a comprehensive analysis of the etiology and pathology of CIA in Guangxi Province, China. Three strains of the chicken infectious anemia virus (CIAV) were isolated from liver samples of diseased 20-week-old chickens. The complete genomes of these strains were sequenced, and experiments on specific pathogen-free (SPF) chicks revealed that the GX21121 strain exhibited high virulence. Histopathological examination of the deceased chicks showed liver cell necrosis, fibrous-serous exudation, inflammatory cell infiltration, hemorrhage in liver tissues, as well as congestion in lung and renal tissues. Phylogenetic analysis of the genome revealed that the three strains had a close genetic relationship to the Heilongjiang wild-type (GenBank KY486144). The genetic evolu-tion of their VP1 genes indicated that all three CIAV isolates belonged to genotype IIIc. In sum-mary, this study demonstrated the genomic diversity of three CIAV strains in adult layer hens. The isolation and characterization of the GX21121 strain as a highly virulent isolate provide val-uable information for further investigations into the etiology, molecular epidemiology, and viral evolution of CIAV.