Basic Attributes Research Articles

Bilinear Integrable soliton solutions and carbon emission rights pricing

AbstractPricing carbon emission rights and other financial assets using the soliton theory is a pioneering attempt. In this study, we investigated the pricing of carbon emission rights according to the basic attributes of solitons, whose amplitude and velocity remain unchanged after a collision. First, we showed that the price fluctuation in the sequence of carbon emission rights possesses the characteristics of a soliton, such as non-dispersion while spreading and being stable after a collision. With a variation in the time scale, the waveform and velocity of the carbon price movement did not change with its translation in the same direction. Second, we demonstrated that the carbon soliton equation passes the $Painlev\acute{e}$ test for integrability. Moreover, at the resonance point, there exists an arbitrary function ${u}_j(t)$ of $t$ in which the compatibility condition always holds. This indicates the existence of soliton solutions to the carbon soliton equation. Third, the exact solutions of single-soliton, two-soliton and three-soliton equations were obtained by using a nonlinear evolution equation constructed with a bilinear method. In the three soliton solutions, only the single-soliton solution is the central value of the carbon emission rights and its theoretical value is 13 Euro/tCO2e.

A Decision-Support System for Assessing Cross-Country Pipeline Systems: Approach Based on Evidential Reasoning and Cost–Benefit Analysis

This paper outlines a decision-support model based on Evidential Reasoning (ER) and Cost–Benefit-Analysis (CBA) to support the assessment of risk control measures for pipeline loss of containment following third party damage. The model identifies the main Risk Control Options (RCOs) as basic attributes, which are grouped into three categories: technical or technological, governmental, and managerial solutions. The CBA examines the costs associated with the loss of containment regarding human safety, the economic and environmental aspects that are required to reduce or eliminate the threat of using RCOs. ER is chosen because of its capability in dealing with Multiple Criteria Decision-Making (MCDM) problems with uncertainties, aggregation of conflicting information and the hybrid nature of the information. This corresponds with the challenges of analyzing different and often conflicting information identified in this study. The results show the attributes of each decision, their effectiveness in reducing the failure likelihood, and the estimated cost of each attribute. The study reduces the complexity of the decision-making into a simple hierarchical output and provide guidance to the infrastructure operator. This makes it possible for the operator to select one or more risk reduction attributes and immediately see a potential reduction in the failure likelihood. Furthermore, the study provides knowledge to the operator on budgetary expenditure required to implement the RCOs.

Characteristics of Clayey Soil for the Subggrade of Flexible Pavement

Clayey soil is typically firm when they are evaporated and gave its firmness as it looks right on soaked. Delicate clays are related with little compressive strength and beyond preposterous arrangement. This decrease in strength because of dampness prompts serious harm to the structures and substructures. The nature of the soil can experiment with the originator fabricating foundation intent on clayey sediments. This paper is based on the study of the engineering properties of clayey soil. This experimental study depicts the basic attributes of the clayey soil; which further leads to improving the nature or engineering properties of the clayey soil for the subgrade of the flexible pavement. Different tests are to be performed in the laboratory and conduct the desired results.

COGNITIVE AND NON-COGNITIVE PARAMETERS OF JOB SATISFACTION OFSCHOOL TEACHERS

Academic competency is the primary requirement for teaching profession, but it is true that some basic attributes are necessary to be an effective teacher and should have a good character, sound teaching attitude, accountability, empathy, sound mental health, liking for job and job satisfaction. According to Paul Spectres (1985), Job satisfaction is about liking your jobs and findings fulfilment in what you do, in combines an individual feelings and emotions about his profession and how his job affects his personal life. Job satisfaction is the function of a set of variables such as intelligence, socio-economic status, personality characteristics, social acceptance occupational commitment organizational climate etc. and moreover educational qualification, self-concept, mental health, and occupational stress are also important affecting variables for the job satisfaction of the teachers. We cannot have output-based education system unless it is based on job satisfaction of teachers. But it has been observed that only cognitive excellence is not sufficient condition for enabling job-satisfaction and others non cognitive factors to be added or to be practiced in an institution for acquiring job satisfaction to be explored in the present paper. The study was a Qualitative research approach. Qualitative analysis has been conducted for finding out Cognitive and Non-Cognitive factors. Based on Coding and Categorizing Cognitive and non-cognitive factors of Job Satisfaction have been Identified.

Formation of Professional Identity in Pediatric Residents Working in Military Hospitals

Background: Professionalism is one of the basic attributes of a doctor patient relationship. During development of professional identity, doctors interact with their community which provides opportunity for mutual negotiation of identity and they keep improving their abilities. The development of this professional identity is a difficult skill indoctors working in Military hospitals. Aim: To explore development of Professional Identity in doctors working in pediatrics department of military hospital. Study design: Qualitative study based on phenomenological design. Place and duration: The study was conducted in Combined Military Hospital Quetta from Jan 2022 to June 2022. Methodology: All doctors serving in uniform in paediatrics department were included.In depth interviews were taken, recorded, transcribed and anonymized. Data interpreted manually, common themes originating from interviews were analysed and results formulated. Results: Six themes were found from transcript. Participants discussed about their professional life during service in military and road to gain professional excellence. Conclusion: Military residents working in paediatrics department have dual roles of serving the nation during peace as well as war and natural disasters. Keywords: Professionalism, Professional Identity, Paediatrics, Military.

Outlier Detection of Crowdsourcing Trajectory Data Based on Spatial and Temporal Characterization

As an emerging type of spatio-temporal big data based on positioning technology and navigation devices, vehicle-based crowdsourcing data has become a valuable trajectory data resource. However, crowdsourcing trajectory data has been collected by non-professionals and with multiple measurement terminals, resulting in certain errors in data collection. In these cases, to minimize the impact of outliers and obtain relatively accurate trajectory data, it is crucial to detect and clean outliers. This paper proposes an efficient crowdsourcing trajectory outlier detection (CTOD) method that detects outliers from the trajectory sequence data in both spatial view and temporal view. Specifically, we first use the adaptive spatial clustering algorithm based on the Delaunay triangulation (ASCDT) algorithm to remove the location offset points in the trajectory sequence. After that, based on the most basic attributes of the trajectory points, a 6-dimensional movement feature vector is constructed for each point as an input. The feature-rich trajectory sequence data is reconstructed using the proposed temporal convolutional network autoencoder (TCN-AE), and the Squeeze-and-Excitation (SE) channel attention mechanism is introduced. Finally, the effectiveness of the CTOD method is experimentally verified.

A study of the frequency of cachexia and its associated factors in elderly patients with diabetes mellitus

To investigate the frequency of cachexia and its associated factors in elderly diabetic patients. The subjects were diabetic patients ≥65 years old attending the outpatient diabetes clinic of Ise Red Cross Hospital. Cachexia was evaluated as having three or more of the following: (1) muscle weakness, (2) fatigue, (3) anorexia, (4) decreased lean body mass, and (5) biochemical abnormalities. A logistic regression analysis was used to identify factors associated with cachexia, with the dependent variable as cachexia and explanatory variables as various variables (basic attributes, glucose-related parameters, comorbidities, and treatment). A total of 404 patients (233 males and 171 females) were included in the study. Twenty-two (9.4%) and twenty-two (12.8%) male and female patients, respectively, had cachexia. A logistic regression analysis showed that the HbA1c value (odds ratio [OR], 0.26,95% confidence interval [CI], 0.08-0.81; P=0.021) and cognitive and functional decline (OR, 11.81, 95% CI, 1.81-76.95; P = 0.010) were factors associated with cachexia. In women, type 1 diabetes (OR, 12.39, 95% CI, 2.33-65.87; P=0.003), the HbA1c value (OR, 1.71, 95% CI, 1.07-2.74; P=0.024), and insulin usage (OR, 0.14, 95% CI, 0.02-0.71; P=0.018) were cachexia-related factors. The frequency of cachexia in elderly diabetic patients and its associated factors were identified. It is important to increase awareness of the risk of cachexia in elderly diabetic patients with poor glycemic control, cognitive and functional decline, type 1 diabetes mellitus, and insulin non-use.

Gender Differences in Socio-Demographic Factors Associated with Pre-Frailty in Japanese Rural Community-Dwelling Older Adults: A Cross-Sectional Study.

To prevent frailty, it is necessary to focus on pre-frailty and consider preventive interventions that incorporate social aspects. This study aimed to explore socio-demographic associations with pre-frailty, focusing on modifiable social factors among community-dwelling older adults in a rural Japanese city. We conducted a self-administered survey on social, physical, and mental factors, and basic attributes, in September 2021. Respondents were classified as frail, pre-frail, or healthy according to their The Kihon Checklist scores. Of the 494 valid responses, 93 respondents classified as pre-frail and 110 as healthy were analyzed. The socio-demographic associations with pre-frailty were investigated by multiple logistic regression, and interactions between gender and other factors were examined. The analysis identified that for both genders, no-community participation in middle age (odds ratio [OR], 1.84; 95% confidence interval [CI], 1.16-2.92) was found to be a social factor associated with pre-frailty. Having friends who listen to one's concerns (OR, 2.54; 95% CI, 1.26-5.10) was a factor for women. This study showed that modifiable social factors associated with pre-frailty were community involvement and being able to share concerns with friends. The findings suggest the need for support that emphasizes social aspects to prevent pre-frailty.

Relationships Between Psychosocial Factors and Mental Health Among Nurses Working With COVID-19 Patients in Japan

Background Nurses who work with COVID-19 patients may suffer high levels of work-related stress, anxiety, and depressive symptoms. These impacts raise concerns about the deterioration of their mental health during this time. Objective This study examines the relationships between psychosocial factors (health, behavior, social, and economic factors) and mental health on among nurses working in a COVID-19 ward in Japan. Design A cross-sectional questionnaire-based study design was adopted. Methods We randomly selected 41 general hospitals that had over 400 beds. The participants were nurses who cared for COVID-19 patients at five hospitals. The questionnaire was completed by 56 nurses (seven men and 49 women) (response rate 46.6%). The survey asked participants about their basic attributes (gender, age, years of experience). It also included the Japanese version of the Patient Health Questionnaire-9, the Japanese version of the Generalized Anxiety Disorder-7, the Japanese version of the Impact of Event Scale-Revised, and the Maslach Burnout Inventory-General Survey to measure depression, anxiety, trauma experience, and burnout, respectively. To determine psychosocial factors, perception of damage from harmful rumors, nurses’ intention to quit their jobs, working environment changes, and changes in daily life were measured. Results Intention to quit was specifically associated with “wanting to continue nursing,” the posttraumatic stress disorder (PTSD) symptom of avoidance, depression, anxiety, and burnout. Nurses who had experienced the impact of harmful rumors in the workplace had high levels of PTSD symptoms, including intrusion, avoidance, and hyperarousal, as well as depression, anxiety disorder, and burnout cynicism. Regarding changes in the working environment, significant differences were confirmed in PTSD symptoms including intrusion, avoidance, and hyperarousal, as well as depression. We also examined the relationship between changes in daily life and mental health. We found that answering affirmatively to “the family income has decreased,” “I’ve had family problems,” and “I cannot do housework” related to mental health disorders among the nurses. Consideration: Psychosocial factors that may have been exacerbated during the COVID-19 pandemic were found to affect nurses’ mental health. Thus, reducing these psychosocial factors and building a mental health support system for nurses is crucial.

ИЗМЕРИТЕЛЬНЫЙ ИСКУССТВЕННЫЙ ИНТЕЛЛЕКТ: МЕТОДОЛОГИЧЕСКИЕ ОСНОВЫ, ПРИНЦИПЫ СОЗДАНИЯ И ИНФОРМАЦИОННЫЕ ТЕХНОЛОГИИ НА ОСНОВЕ РЕГУЛЯРИЗИРУЮЩЕГО БАЙЕСОВСКОГО ПОДХОДА

The article is devoted to a new direction in the creation of information and measurement systems – measuring artificial intelligence. The paper gives the basic definitions and attributes of measuring artificial intelligence. The field of measurement tasks in which the application of methods and means of intellectualization of measurement processes is necessary is defined. The main types of intelligent measurements are given, their characteristics and specific properties are indicated. It is emphasized that measurement tasks in conditions of information uncertainty caused by inaccuracy, incompleteness, fuzziness of data, their small volumes and uniqueness, incompleteness of models of the measuring object and the environment of its functioning, should be solved on the basis of regularizing methods that ensure the stability of the solutions obtained, as well as with the involvement of knowledge about the object and the environment, which makes it possible to compensate for the lack of completeness data. Therefore, in the article, to solve measurement problems in these conditions, it is proposed to use a regularization approach (RBP) and technologies based on it: Bayesian intelligent measurements (BII) and Bayesian intelligent technologies (BIT).Methodological principles and basic analytical dependencies of BII and BIT are given. The article proposes a methodology for creating a system for evaluating the quality of artificial intelligence (AI) solutions, in particular, evaluating the reliability of solutions, which determines the degree of confidence in the solutions obtained. The methodology is based on scaling of measuring solutions and implemented as a system of metrological support of AI solutions. Within the framework of this methodology and technologies of metrological support, complexes of metrological characteristics are proposed that determine accuracy, reliability (level of errors of the 1-st and 2-nd kind), which ensures traceability and transparency at each stage of obtaining solutions. Practical examples of the implementation of RBP methods and technologies for solving applied problems and creating intelligent measuring systems are given.

On-Tube Attribute Visualization for Multivariate Trajectory Data.

Stylized tubes are an established visualization primitive for line data as encountered in many scientific fields, ranging from characteristic lines in flow fields, fiber tracks reconstructed from diffusion tensor imaging, to trajectories of moving objects as they arise from cyber-physical systems in many engineering disciplines. Typical challenges include large data set sizes demanding for efficient rendering techniques as well as a large number of attributes that cannot be mapped simultaneously to the basic visual attributes provided by a tube-based visualization. In this work, we tackle both challenges with a new on-tube visualization approach. We improve recent work on high-quality GPU ray casting of Hermite spline tubes supporting ambient occlusion and extend it by a new layered procedural texturing technique. In the proposed framework, a large number of data set attributes can be mapped simultaneously to a variety of glyphs and plots that are embedded in texture space and organized in layers. Efficient rendering with minimal data transfer is achieved by generating the glyphs procedurally and drawing them in a deferred shading pass. We integrated these techniques in a prototype visualization tool that facilitates flexible mapping of data set attributes to visual tube and glyph attributes. We studied our approach on a variety of example data from different fields and found it to provide a highly adaptable and extensible toolbox to quickly craft tailor-made tube-based trajectory visualizations.

Typological Analysis of Young People Health Behavior: on Specifics of the Four-Component Model Realization

The article deals with the methodical problems of the typological analysis of young people on health-saving behavior. Methodical solutions are proposed for the formation of a system of classification characteristics in the form of logical indices that allow for multidimensional classifications. The procedure for imputation of missing data that occurs when using the proposed indices for the purpose of young people typologization is described. The results of a comparative analysis of cluster profiles for three age groups among young people are presented. The conclusion is substantiated that the heuristic potential of indicators of health-saving behavior as basic type-forming attributes in these groups is different.

Evolution of genome size and GC content in the tribe Carduinae (Asteraceae)

Genome size and GC content are basic species-specific attributes often delimiting genera or higher taxa, which enable the identification of polyploidy, hybridization and other modes of genome or karyotype evolution. The evolution of these genomic traits can often occur as a result of the selective pressure of the environment. Here, we reconstruct the evolution of these genomic traits in subtribe Carduinae (Asteraceae) in the context of changes in chromosome numbers. Using flow cytometry, genome size and GC content were estimated for 119 taxa and mapped onto a phylogenetic tree constructed using sequences from seven genetic markers. In addition, the genomic data were compared with the length of stomatal guard cells and achene size (length, weight) to evaluate the extent to which genomic characters could evolve adaptively in this subtribe. We found strong phylogenetic signals for the analysed genomic and phenotypic traits, which delimited most Carduinae genera or clades in agreement with the reconstructed phylogeny. Monoploid genome size was positively correlated with genomic GC content and stomatal guard cell length. In Cirsium, whose species were the focus of the majority of the analyses, the large-genomed subgen. Lophiolepis had smaller guard cells, which might be related to it occurring in more xeric habitats compared to subgen. Cirsium. In contrast, the achenes of the large-genomed subgen. Lophiolepis were larger, possibly in response to the summer drought, whereas achene weight and length were independent of genome size across the subtribe. Thus, genome size in the subtribe Carduinae might evolve under weak environmental control, at least under that mediated by the size of guard cells or achenes. Achene size was related positively to GC content, which could have evolved adaptively in response to summer drought. In Carduus and the North American Cirsium taxa, there is an increase in average chromosome size with reduction in monoploid chromosome number, suggesting descending dysploidy associated with chromosomal fusion. Polyploidy is relatively rare in this subtribe and was confirmed only in five of the species studied, including Cirsium vulgare, an invasive species that likely originated via distant (intersubgeneric) hybridization, as suggested by its intermediate genomic and achene features combined with the conflict between its morphology and phylogenetic position. Phylogenetic reconstruction, differences in genomic parameters, as well as stomatal guard cell and achene sizes support the separation of a monophyletic Lophiolepis from the remainder of Cirsium. Genome and achene size results also indicate that the early diverging Cirsium italicum can be separated from the rest of the monophyletic Lophiolepis+Picnomon+Notobasis clade.

Scene-Text Oriented Referring Expression Comprehension

Referring expression comprehension (REC) aims to identify and locate a specific object in visual scenes referred to by a natural language expression. Existing studies of REC only focus on basic visual attributes and neglect scene text. Since scene text has the functions of object identification and disambiguation, it is naturally and frequently used to refer to objects. However, existing methods do not explicitly recognize text in images and fail to align scene text mentioned in expressions with the text shown in images, resulting in object localization errors. This study takes the first step toward addressing these limitations. First, we introduce a new task called scene-text oriented referring expression comprehension, which aims to align visual cues and textual semantics of scene text with referring expressions and visual contents. Second, we propose a scene text awareness network that can bridge the gap between texts from two modalities by grounding visual representations of expression-correlated scene texts. Specifically, we propose a correlated text extraction module to solve the problem of lacking semantic understanding, and a correlated region activation module to address the fixed alignment problem and absent alignment problem. These modules ensure that the proposed method focuses on local regions that are most relevant to scene text, thus mitigating the misalignment of scene text with irrelevant regions. Third, to conduct quantitative evaluations, we establish a new benchmark dataset called RefText. Experimental results demonstrate that the proposed method can effectively comprehend scene-text oriented referring expressions and achieves excellent performance.

Research on news coverage and image of homosexuals in Taiwan

Taiwan has made tremendous efforts on the road of equal rights for the same sex through mass media, and the affirmative action since1990s. Although the Taiwanese referendum on November 24th, 2018, failed to pass the proposal of same-sex marriage, Taiwan is expected to become the first Asian region to legalize same-sex marriage in 2019. This article conducted Framing theory and Discourse Historical Approach to research on how news coverage changed when framing homosexuality during year 2005 to 2018 on Taiwan China Times, specifically in diversity of topics, referring expression, basic personal image and role attributes. And found that media's description of homosexual groups has gradually changed from negative to positive, and the media has a great role in building the social image of homosexual groups.

Short‐term effects of the control of the invasive plant Asclepias syriaca: Secondary invasion of other neophytes instead of recovery of the native species

AbstractQuestionThis study aimed to assess the impact of the invasion of Asclepias syriaca, a perennial non‐native herbaceous species, on basic soil attributes and vegetation composition, and to study the effect of mechanical control, namely the cutting of Asclepias, on target and other plant species.LocationSandy region of Kiskunság National Park, Hungary, Central Europe.MethodsIn ten old‐fields, four 4 m × 4 m plots were established (n = 40), of which three were invaded by Asclepias, and one was uninvaded. The invaded plots were treated as: (1) all Asclepias shoots removed, (2) half of the Asclepias shoots removed, or (3) untreated. The treatment was conducted twice a year in summer during the period 2019–2021. Before the first treatment, soil and vegetation were sampled. Afterwards, vegetation monitoring was performed twice a year: the cover of each vascular plant species and the number of Asclepias shoots were recorded in each plot.ResultThere were no differences in the studied soil attributes between the uninvaded and invaded plots. However, there were differences in vegetation composition, namely, the cover of sand grassland specialists was higher in uninvaded plots. Short‐term cutting negatively affected Asclepias after two years. The cover of specialists did not change in response to treatments, but the cover of other neophyte plant species increased.ConclusionsBased on our results, the invasion of Asclepias changes the vegetation composition, but not the soil. Although short‐term mowing can reduce the cover of Asclepias, but the grassland specialist plants do not regenerate; instead, secondary invasion occurs. We conclude that more time or additional treatment is required for native plant recovery.

Relationship between the inserting sites of Fu's subcutaneous needling and traditional acupoints: revelation to acupuncture and moxibustion

Focusing on the phenomenon of "de-acupoints" of the needle insertion sites in Fu's subcutaneous needling (FSN), the authors allocated the evolution and characteristics of the needle insertion sites of FSN. From six aspects, named morphology and structure, location, nomenclature, numbers and meridian tropism, indications and acupuncture manipulations, the comparison was made between the insertion sites of FSN and traditional acupoints. It is believed: ①The needle insertion sites of FSN has the basic attributes of acupoint, which not only refers to the operation site, but also indicates the reaction of disease; moreover, it is the treatment site with significant therapeutic effect. ②The optimized sites of insertion in FSN should be named differently and their locations and numbers should be specified relatively. ③The insertion sites of FSN should be further intersected and integrated with traditional acupoints, and a part of traditional acupoints should become the insertion sites of FSN. ④Accepting and integrating the insertion sites of FSN, and expanding the scope of traditional acupoints may be the new project in the research of traditional acupoints.

Lineage Tracing of Spermatogonial Stem Cells Within the Male Germline.

Spermatogonial stem cells (SSCs) are the fundamental units from which continuous spermatogenesis arises. Although our knowledge regarding the basic properties of SSCs has grown, driven primarily through the advancement of techniques and technologies to study SSCs, the mechanisms controlling their fate remain largely unknown. Among the modern strategies to evaluate SSCs, lineage tracing is among the few established approaches that allow for functional assessment of stem cell capacity. As a result, lineage tracing continues to forge new discoveries underlying the basic attributes of SSCs as well as the molecular factors that govern SSC function. Traditional approaches to lineage tracing with dyes or radioactive labels suffer from progressive loss after successive cell divisions or unintentional label transfer to neighboring cells. To address these limitations, genetic approaches primarily leveraging transgenic technologies have prevailed as the preferred avenue for modern lineage tracing. This chapter will discuss current protocols for effective genetic lineage tracing and address applications of this technology, considerations when designing lineage tracing experiments, and the methods involved in utilizing lineage tracing to study SSCs and other cell populations.

RSNet: Relation Separation Network for Few-Shot Similar Class Recognition

Although deep learning methods have drastically improved the performance on visual recognition tasks in which large inter-class variances exist, similar-class recognition continues to pose significant challenges, mainly due to the close resemblance between similar classes. The challenge is further compounded in the case of few-shot learning because only a very small amount of training data is available; accordingly, a certain performance degradation has been observed when some few-shot methods are applied for classification tasks. To address the aforementioned issue, we propose a novel Relation Separation Network (RSNet) in this paper, aiming to boost few-shot learning by improving similar-class recognition performance. We assume that image features consist of common and private features, where the common features capture the basic attributes shared among similar classes and their private counterparts capture the unique attributes of each class. Our RSNet learns to decouple the common and private features of an image. As a result, the feature representation of an image is composed of two weakly associated but easily aligned components, and better classification performance is achieved by giving more attention to subtle features. Experimental results on the publicly available datasets miniImageNet, CUB, and CIFAR-FS show that the proposed model outperforms existing state-of-the-art methods. Specifically, compared to PT+MAP, RSNet improves the accuracy of classification on the CUB dataset by approximately 5% and that of similar-class classification by more than 10%.

Clothing Sales Forecast Considering Weather Information: An Empirical Study in Brick-and-Mortar Stores by Machine-Learning

Reliable sales forecasts are important to the garment industry. In recent years, the global climate is warming, the weather changes frequently, and clothing sales are affected by weather fluctuations. The purpose of this study is to investigate whether weather data can improve the accuracy of product sales and to establish a corresponding clothing sales forecasting model. This model uses the basic attributes of clothing product data, historical sales data, and weather data. It is based on a random forest, XGB, and GBDT adopting a stacking strategy. We found that weather information is not useful for basic clothing sales forecasts, but it did improve the accuracy of seasonal clothing sales forecasts. The MSE of the dresses, down jackets, and shirts are reduced by 86.03%, 80.14%, and 41.49% on average. In addition, we found that the stacking strategy model outperformed the voting strategy model, with an average MSE reduction of 49.28%. Clothing managers can use this model to forecast their sales when they make sales plans based on weather information.