Related Information Research Articles

Distributed Fixed-Time Optimal Flocking for Second-Order Multi-Agent Systems

In this paper, we investigate the problem of distributed optimization for second-order multi-agent systems with fixed-time flocking. The objective is to concurrently steer the agents towards a common velocity while optimizing the global objective function. To solve the problem, we first design a fixed-time estimator to predict the global gradient of the objective functions, then based on which, each agent is endowed with fixed-time tracking controller to track the optimal solution. Motivated by the fact that the relative velocity information is difficult to obtain accurately, the proposed algorithm is designed without using neighbors’ velocity. The upper bounds of the settling time are provided without relying on the initial states of the agents, only requiring the adjustment of parameters. The effectiveness of the proposed control protocol is also demonstrated through numerical simulations.

A modified model of PANoptosisto identify prognosis and immunotherapy response in bladder cancer

BackgroundCell death, including apoptosis and necrosis, collectively known as widespread apoptosis. The present study aims to investigate the mechanism of action in widespread apoptosis-related modification patterns in bladder cancer. MethodsUsing a clinical genomics database, we obtained transcriptomic data and related clinical information of bladder cancer patients. By employing the least absolute shrinkage analysis, we were able to construct a risk model and single-cell sequencing analysis of differential genes in bladder cancer. ResultsFive differentially expressed genes (TMPRSS4, TPST1, FOXD1, ELOVL4, EMP1) associated with widespread apoptosis were identified as features for predicting the prognosis of bladder cancer patients. Survival curve analysis revealed significant differences in prognosis (P<0.05). Additionally, multivariate Cox regression analysis determined the independent risk factor for bladder cancer prognosis as the risk score (P<0.001), with high confidence in the scoring model validated internally (P<0.001). Single-cell sequencing reveals high expression of CDKN2A, ERBB2, and TMPRSS4 in B cells, while HRAS is significantly expressed in fibroblasts. ConclusionPANscore, as a potential prognostic and immunotherapeutic biomarker, will provide more precise and rational basis for personalized treatment strategies

The impact of agile supply chain strategy on manufacturers’ ambidextrous innovation—the role of relational governance and information technology capability

The impact of agile supply chain strategy on manufacturers’ ambidextrous innovation—the role of relational governance and information technology capability

Knowledge enhanced multi-task learning for simultaneous optimization of human parsing and pose estimation

Human parsing and pose estimation are both closely related to the human body structure, and it is advantageous to deal with both together in order to assist each other’s learning. Nevertheless, the two tasks have their own unique characteristics, and it is therefore difficult to efficiently and simultaneously obtain a good performance for both tasks in one single multi-task learning (MTL) network. This paper proposes a new MTL network (named “KDNet”) to enhance the communication between human parsing and pose estimation through knowledge distillation so as to improve the simultaneous learning of both tasks. In the proposed KDNet, a consistent representation module is used to enhance the related information sharing between the tasks, and a single-task model-based loss weighting method is developed to balance the loss levels. The proposed KDNet has been verified across three benchmark datasets, and in comparison to other state-of-the-art methods it achieves substantial performance gains without increasing model parameters during inference. The codes are shared at https://github.com/Yhdian/KDNet.git.

Transcriptomic analyses in the gametophytes of the apomictic fern Dryopteris affinis

Main conclusionA novel genomic map of the apogamous gametophyte of the fern Dryopteris affinis unlocks oldest hindrance with this complex plant group, to gain insight into evo-devo approaches.The gametophyte of the fern Dryopteris affinis ssp. affinis represents a good model to explore the molecular basis of vegetative and reproductive development, as well as stress responses. Specifically, this fern reproduces asexually by apogamy, a peculiar case of apomixis whereby a sporophyte forms directly from a gametophytic cell without fertilization. Using RNA-sequencing approach, we have previously annotated more than 6000 transcripts. Here, we selected 100 of the inferred proteins homolog to those of Arabidopsis thaliana, which were particularly interesting for a detailed study of their potential functions, protein–protein interactions, and distance trees. As expected, a plethora of proteins associated with gametogenesis and embryogenesis in angiosperms, such as FERONIA (FER) and CHROMATING REMODELING 11 (CHR11) were identified, and more than a dozen candidates potentially involved in apomixis, such as ARGONAUTE family (AGO4, AGO9, and AGO 10), BABY BOOM (BBM), FASCIATED STEM4 (FAS4), FERTILIZATION-INDEPENDENT ENDOSPERM (FIE), and MATERNAL EFFECT EMBRYO ARREST29 (MEE29). In addition, proteins involved in the response to biotic and abiotic stresses were widely represented, as shown by the enrichment of heat-shock proteins. Using the String platform, the interactome revealed that most of the protein–protein interactions were predicted based on experimental, database, and text mining datasets, with MULTICOPY SUPPRESSOR OF IRA4 (MSI4) showing the highest number of interactions: 16. Lastly, some proteins were studied through distance trees by comparing alignments with respect to more distantly or closely related plant groups. This analysis identified DCL4 as the most distant protein to the predicted common ancestor. New genomic information in relation to gametophyte development, including apomictic reproduction, could expand our current vision of evo-devo approaches.

Barriers and Challenges Faced by Women Shop Stewards in Two Major South African Trade Unions

There are fewer women shop stewards than men shop stewards in South Africa, which can be attributed to existing barriers and challenges. A need arose to investigate these obstacles. The study was qualitative and exploratory. Two themes emerged from the interviews we conducted, each with various sub-themes. The findings revealed barriers such as existing relationships, lack of nomination and not being elected if nominated as well as challenges such as the negative perception of women’s emotionality and a desire to become mothers, a general lack of support and victimisation. These barriers and challenges make it difficult for women to become shop stewards and, if they do, to be effective. According to South African labour legislation, equality should be encouraged in all roles. Women should be motivated to become shop stewards and offered support, not only by the trade unions through policies such as reserved seats for women in union leadership, women-only conferences and workshops and the funding of women’s initiatives, but also by employers, fellow women members and society. This study provides valuable information for industrial relations in South Africa and worldwide.

Evaluating the Use of Breast Self-Examination (BSE) for Recognizing Breast Cancer Awareness Among Jordanian Students and Workers in Medical Fields.

Globally, breast cancer (BC) is the most commonly detected neoplasm in women. Breast self-examination (BSE) is an effective screening technique that enables women to learn about the composition of their breasts and assist in the early identification of any potential breast abnormalities. This study aimed to assess the degree of BSE knowledge and attention among Jordanian females who are students or professionals in medical disciplines. Participants' knowledge about BSE and related issues was assessed using a self-administered questionnaire. The study invites participation from all females aged 18 and above, through both an online and in-person survey. The study extended invitations to female university students in Jordan across academic levels I, II, III, IV, V, and VI. A scoring system was employed, and the statistical analyses were performed using IBM SPSS Statistics (Version 20.0). The study had 946 female participants, with 98.41% of them being single. Low BSE practice was reported among 90.49% of the participants (n = 856) and this demonstrated a weak understanding of BC disease, including its possible risks, methods of detection, diagnosis, treatment, signs and symptoms, as well as knowledge about mammography and other related information. Only 27.27% (n = 258) of participants practice BSE once a month and on a regular basis. BC is considered the most prevalent malignant condition and the second largest cause of cancer-related deaths for women in Jordan. Screening strategies are essential for promptly identifying breast cancer and reducing the associated illness and death rates. It is recommended that women commence performing BSE starting at the age of 18. Furthermore, it is essential to incorporate a learning outcome in the cancer chapters that are directly relevant to the subject of BC and emphasize the significance of BSE for students pursuing a career in the medical area.

S2MRI-ADNet: an interpretable deep learning framework integrating Euclidean-graph representations of Alzheimer's disease solely from structural MRI.

To establish a multi-dimensional representation solely on structural MRI (sMRI) for early diagnosis of AD. A total of 3377 participants' sMRI from four independent databases were retrospectively identified to construct an interpretable deep learning model that integrated multi-dimensional representations of AD solely on sMRI (called s2MRI-ADNet) by a dual-channel learning strategy of gray matter volume (GMV) from Euclidean space and the regional radiomics similarity network (R2SN) from graph space. Specifically, the GMV feature map learning channel (called GMV-Channel) was to take into consideration spatial information of both long-range spatial relations and detailed localization information, while the node feature and connectivity strength learning channel (called NFCS-Channel) was to characterize the graph-structured R2SN network by a separable learning strategy. The s2MRI-ADNet achieved a superior classification accuracy of 92.1% and 91.4% under intra-database and inter-database cross-validation. The GMV-Channel and NFCS-Channel captured complementary group-discriminative brain regions, revealing a complementary interpretation of the multi-dimensional representation of brain structure in Euclidean and graph spaces respectively. Besides, the generalizable and reproducible interpretation of the multi-dimensional representation in capturing complementary group-discriminative brain regions revealed a significant correlation between the four independent databases (p < 0.05). Significant associations (p < 0.05) between attention scores and brain abnormality, between classification scores and clinical measure of cognitive ability, CSF biomarker, metabolism, and genetic risk score also provided solid neurobiological interpretation. The s2MRI-ADNet solely on sMRI could leverage the complementary multi-dimensional representations of AD in Euclidean and graph spaces, and achieved superior performance in the early diagnosis of AD, facilitating its potential in both clinical translation and popularization.

IDSV-GCN: Integrating Dual Syntactic Views Graph Convolutional Network for aspect-based sentiment analysis

Aspect-based sentiment analysis (ABSA) aims to predict the sentiment polarity towards given aspects in a sentence. Recent research has highlighted that graph neural networks based on syntactic dependency trees effectively convey rich syntactic information and are suitable for ABSA tasks. Most existing work typically adopt either dependency type or minimum distance between nodes in the original syntactic tree to construct different views. However, existing work overlooks the high-dimensional local feature information in dependency relations, resulting in inadequate exploration of this view. Meanwhile, most work ignores that the minimum distance view and the dependency view can be complementary and integrated in syntactic analysis. Unfortunately, directly adding the two views introduces noise issues, leading to unsatisfactory results after their integration. To address these issues, this paper introduces an integrating dual syntactic views graph convolutional network (IDSV-GCN) model that seamlessly integrates dependency type view and minimum distance view, enabling comprehensive utilization of syntactic structure and information. To better address the computation of weights in the dependency type view, we innovatively propose a fusion attention mechanism. This approach effectively captures both global and local feature information. To more effectively integrate the two views and reduce the impact of noise, we employ a syntactic mask layer to filter out noise in the dependency type view and highlighting important dependency feature information. Then, we construct a minimum distance weight matrix based on the matrix obtained from the dependency type view. Finally, we merge the two matrices and feed them into the GCN to facilitate the model’s learning of syntactic information. Experimental results on three widely-used benchmark datasets demonstrate that our proposed IDSV-GCN model outperforms state-of-the-art methods.

Psychological impact of physical distancing due to covid 19 pandemic on school and higher education students

MoHFW India, WHO, NHS, and CDC suggested imposing lockdown and closing educational institutions with the spread of covid 19 pandemic to reduce the risk of transmitting the disease. Students faced negative mental health symptoms due to unexpected transition from physical to virtual learning, increased screen time, physical distancing, altered habits, rumors through news, print media, social media, and other means. This study focuses on the psychological impact of covid 19 on school and higher education students due to psychosocial factors like physical distancing. 2140 students, mixed gender, in age from 17 years to 25 years (M = 22.73, SD = 6.12) are selected as sample population for the study. Mixed-method approach is used for data collection that include face to face interactions in clinics, Health Questionnaires &amp; Self-Reports, online counseling to assess the stress &amp; depression, Generalized Anxiety Disorder-7, rating scales for Behavior analysis, Warwick-Edinburgh Mental Well-being Scale to assess perceived stress, depression, anxiety, and mental health &amp; well-being, respectively. Analysis of data shows that 42.7% of school and higher education students have either depression or anxiety. 71.7% of students reported poor health and well-being. 36.7% of students reported fear of getting infected by virus, many students were reported with cognitive dissonance in myths and truths about Covid-19. 37.9% of students spend their time on media &amp; social media, including watching TV or reading Covid-19 related news and information on various platforms. About 56.2% students reported with increased use of social media. There was a minor difference between depression and anxiety, as reported by females and male students, however expression of separation fear was slightly higher in male students than female students. Physical distancing and lack of social interaction caused stress, anxiety (GAD) and depression symptoms in students due to imbalances in hormones &amp; neurotransmitters.

Localization of partial electrical discharges using compressive spherical frequency-difference beamforming.

Accurate localization of partial electrical discharges is essential for the diagnosis of high-voltage systems. The current study achieves this by employing an acoustic sensor array and a beamforming approach. The occurrence of a partial discharge is accompanied by the emission of high-frequency sounds in the ultrasonic range, making localization a challenging task requiring many sensors to avoid spatial aliasing. Compressive frequency-difference beamforming, as previously proposed, can be effective in addressing this issue. We expand the method to include near-field localization by utilizing a spherical wave and propose a two-step normalization process. This eliminates the bias associated with nonplanar waves and standardizes the field variables, thereby preserving only the phase and relative amplitude information. A distributed algorithm based on the alternating direction multiplier method is used to solve the associated convex optimization problem. The proposed method is demonstrated using simulated and experimental data.

Resilient cooperative platoon control for leader-following uncertain vehicular systems subject to cyber-physical attacks

This paper develops a resilient cooperative platoon control protocol for vehicle following systems under unreliable switching communication networks with cyber composite attacks, which includes actuator and sensors attacks, as well as denial of service (DoS) attacks. In this framework, to achieve leader-following platoon task, a novel resilient distributed adaptive control algorithm is designed by exploiting relative neighborhood information. First, a switching resilient turning mechanism with controller parameter updating laws is introduced into platoon controller architecture, which can compensate sensor and actuator attacks. Second, a topology-dependent analysis shows that cooperative leader-following platoon objective is achieved under some sufficient conditions, then the robust stability of the closed-loop coordination tracking error system is built. Finally, the simulation studies on a platoon of autonomous vehicles are provided to testify the design.

Hyperparameter Optimization for Convolutional Neural Network-Based Sentiment Analysis

The rapid development of digital technology brings about the importance of product or service opinions as a data source in business intelligence. Sentiment analysis is a business intelligence tool that can help gauge market trends by analysing the available opinions. Various studies have been developed to solve sentiment analysis problems, from selecting feature engineering techniques to selecting sentiment classification models based on classical or deep learning methods. However, these studies still select hyperparameters on a trial basis, which poses problems in terms of time and not optimal performance. Therefore, this research proposes the implementation of Bayesian Optimization to automate the selection of hyperparameters in sentiment analysis based on a Convolutional Neural Network (CNN). The results show that implementing the Bayesian Optimization method has the lowest computation time compared to Random and Grid Searches, which is only 175.746765 seconds, in the fifth trial. In addition, it made a reasonably large cut in terms of times compared to manual trial-based hyperparameter tuning that needs 540 trials. In the model assessment process, implementing Bayesian Optimization gives the highest values on Precision and Specificity, which are 0.8168 and 0.7176, respectively. Thus, implementing Bayesian Optimization is the right choice for sentiment analysis tasks since the precision of predicting negative sentiment classes is vital in hospitality business intelligence, especially the use of related information for product improvement or hotel service improvement. The implementation of Bayesian Optimization not only applied as a reliable hyperparameter selection technique for classification tasks but also regression prediction tasks.

Perancangan Sistem Informasi Sekolah SD Negeri 4 Kerta Payangan Menggunakan Metode Design Thinkin

School information systems are one of the most important needs in educational institutions. Building a school information system is the right step to help provide more relevant information and also in processing data in schools into useful information. However, there are some schools that have not implemented it such as SD Negeri 4 Kerta, In providing conventional information that is done manually where all data or documents are stored in hardcopy form, as well as in processing data such as school profile information, academic and school finances. The purpose of this study is to determine the design of the school information system user interface to be a solution to these problems. This research uses the Design Thinking method implemented to design a website-based school information system for SD Negeri 4 Kerta. The user interface design of the school information system is built in the form of a prototype using the figma application. This study used interview methods and literature studies in collecting data and related information. And the result of designing the user interface of the SD Negeri 4 Kerta school information system is to design a website to provide school information and to process school data and process the financial section.

Contraceptive access and use among women with migratory experience living in high-income countries: a scoping review

BackgroundWomen who have migrated often encounter difficulties in accessing healthcare and experience inequitable sexual and reproductive health outcomes in destination countries. These health inequities include contraceptive access and use. To better understand what influences contraceptive access and use, this scoping review set out to synthesize the evidence on contraceptive access and use and on associated interventions among women with migratory experience in high-income countries (HICs) in Europe, North America and Australasia.MethodsThe scientific databases PubMed, Web of Science and CINAHL were searched for peer-reviewed quantitative, qualitative and mixed method articles published between January 2000 and June 2023. Articles were included if they reported on studies exploring contraceptive use to prevent pregnancies among women of reproductive age with migratory experience living in HICs. Two researchers independently screened and extracted data from the articles. Findings were categorized by patient and health system level factors according to Levesque et al.’s framework of access to health care.ResultsA total of 68 articles were included, about half (n = 32) from North America. The articles focused on the individual level rather than the health system level, including aspects such as women’s contraceptive knowledge, the influence of culture and religion on accessing and using contraception, partner involvement, and differing health insurance coverage. On the health system level, the articles highlighted lack of information on contraceptive services, cultural (in)adequacy of services and communication aspects, contraceptives’ side effects, as well as geographic availability and cost of services. The review further identified three articles reporting on interventions related to contraceptive counselling.ConclusionsThere is a lack of knowledge regarding how health systems impose obstacles to contraceptive services for women with migratory experience on an organizational level, as research has focused heavily on the individual level. This review’s findings may serve as a foundation for further research and advances in policy and practice, specifically recommending early provision of health system related information and contraceptive education, engagement of male partners in contraceptive discourses, cultural competency training for healthcare professionals, and strengthening of interpretation services for contraceptive counselling.

Effectiveness of Public Health Education on the Uptake of Iron-And Folic Acid Supplements Among Pregnant Women: Findings from a Stepped Wedge Cluster Randomized Trial

Objective: To determine the effectiveness of public health education on the uptake of iron and folic acid supplements (IFAS) among pregnant women. Design: A stepped wedge cluster randomized trial. Setting: 12 antenatal care clinics (ANC) in Embu County - Kenya. The clinics are outpatient departments in health facilities dedicated to providing care for pregnant women. Participants: Pregnant women receiving antenatal care services in the study sites. Intervention: After a baseline observational phase, a multifaceted intervention bundle was implemented. The intervention included a 60-minute IFAS information session for health workers at the start of intervention. Health workers then provided daily IFAS literacy sessions to all the pregnant women and issued them with IFAS related information materials (a pill reminder card, and a personalized calendar). The study team conducted biweekly facility audits for quality assurance. The total duration of the study was 8 months, starting June 2022. Primary outcome: The primary outcome was the proportion of pregnant women taking IFAS daily in the intervention phase compared with control phase. This was measured through IFAS residual pill count during every ANC visit. Results: A total of 4,749 ANC visits were monitored for 2,132 women receiving antenatal care in the 12 clinics. Participant characteristics were similar across control and intervention phases. The uptake of IFAS significantly improved from 44.8% (95% CI 34.7 to 55.0) in control phase to 83.3% (95% CI 75.9 to 90.9) during the intervention. This translates to a 38.5 (95% CI 26.0 to 51.1) percentage points improvement. The number needed to treat (NNT) for this intervention was 3 patients (95% CI 2 to 4). Conclusion: IFAS uptake significantly improved following the intervention. The findings support public health education as an effective strategy for improving the uptake of IFAS in ANC settings.

Seasonal snow–atmosphere modeling: let's do it

Abstract. Mountain snowpack forecasting relies on accurate mass and energy input information in relation to the snowpack. For this reason, coupled snow–atmosphere models, which downscale input fields to the snow model using atmospheric physics, have been developed. These coupled models are often limited in the spatial and temporal extents of their use by computational constraints. In addressing this challenge, we introduce HICARsnow, an intermediate-complexity coupled snow–atmosphere model. HICARsnow couples two physics-based models of intermediate complexity to enable basin-scale snow and atmospheric modeling at seasonal timescales. To showcase the efficacy and capability of HICARsnow, we present results from its application to a high-elevation basin in the Swiss Alps. The simulated snow depth is compared throughout the snow season to aerial lidar data. The model shows reasonable agreement with observations from peak accumulation through late-season melt-out, representing areas of high snow accumulation due to redistribution processes, as well as melt patterns caused by interactions between radiation and topography. HICARsnow is also found to resolve preferential deposition, with model outputs suggesting that parameterizations of the process using surface wind fields may only be inappropriate under certain atmospheric conditions. The two-way coupled model also improves surface air temperatures over late-season snow, demonstrating added value for the atmospheric model as well. Differences between observations and model outputs during the accumulation season indicate a poor representation of redistribution processes away from exposed ridges and steep terrain and a low bias in albedo at high elevations during the ablation season. Overall, HICARsnow shows great promise for applications in operational snow forecasting and in studying the representation of snow accumulation and ablation processes.

Information-theoretic quantities as effective descriptors of electrophilicity and nucleophilicity in density functional theory.

Electrophilicity and nucleophilicity are two vastly important chemical concepts gauging the capability of atoms in molecules to accept and donate the maximal number of electrons. In our earlier studies, we proposed to simultaneously quantify them using the Kullback-Leibler divergence from the information-theoretic approach in density functional theory. However, several issues with this scheme remain to be clarified such as its general validity, predictability, and relationship with other information-theoretic quantities. In this work, we revisit the matter with bigger datasets and deeper theoretical insights. Five information-theoretic quantities including Kullback-Leibler divergence, Hirshfeld charge, Ghost-Berkowitz-Parr entropy, and second and third orders of relative Onicescu information energy are found to be reliable and robust descriptors of electrophilicity and nucleophilicity propensities. Employing these five descriptors, we design a list of new compounds and predict their electrophilicity and nucleophilicity scales. This work should markedly improve our confidence and capability in applying information-theoretic quantities to evaluate electrophilicity and nucleophilicity propensities and henceforth pave the route for more applications of these quantities from information-theoretic approach in density functional theory in the future. All structures were fully optimized at the M06-2X/6-311 + G(d) level of DFT functional using the Gaussian 16 package (version C01) with integration grids and tight self-consistent-field convergence. The solvent effect was taken into account by using the implicit solvent model (CPCM) in the CH2Cl2 solvent, and all 3D contour surfaces of Fukui function, local temperature, and ITA (information-theoretic approach) quantities were generated by GaussView. The Multiwfn 3.8 program was used to calculate the ITA indexes and atomic charges.

ChatGPT Enhances Programming Skills of Computer Engineering Students

This research paper aims to outline the role of ChatGPT as one artificial intelligence technology that has progressed in depth and positive effect on the field of education and research with highly capable language developed by OpenAI. This technology presents an exciting opportunity for students and educators, including personalize feedback, increased ability accesses, lesson preparation, assessment and new way of teaching complexity such as code correction and completion, automatic syntax error fixing, code snippet suggestion, code refactoring suggestions and optimization, generate of missing code, document, text to code, and answering technical queries. The experimented was adopted with 51 students in final grade of Computer Engineering, Souphanouvong University and selected 30 students in interviews to collect data for analysis and evaluation. The results show that students with using ChatGPT to help in programming they can more understandable by able to explain, giving examples, concepts, technology of complex method, searching related information and technical solving problem, which can improve programming skills and careers in software development in the future. However, students are still concerned about the accuracy of information provided, loss personal interaction, data privacy issue and price.

Readers encode absolute letter positions

Reading research has long been concerned with the question of whether the reading brain accesses lexical representations via absolute or relative letter position information. In recent years, important results have been obtained with the flanker lexical decision task. Studies have shown faster decisions about target words (e.g., ‘rock’) when flanked by related letters (‘ro rock ck’) than unrelated letters (‘st rock ep')—and crucially, equal facilitation upon switching flanker positions (‘ck rock ro'), pointing to relative rather than absolute letter position coding. Yet, a later study employing longer targets and flankers yielded detrimental effects of switching flanker positions. In order to get a better grasp on the equivocal evidence thus far, here we carried out an extensive test of flanker relatedness and position effects, using various target and flanker lengths, all within a single experiment. We observed a clear reduction of flanker relatedness effects upon switching flanker positions, and this held true across target and flanker lengths. The present results unambiguously suggest that lexical access is driven by absolute letter position information, and furthermore, are accurately predicted by the recent PONG model (Snell, 2024b).