Information Content Research Articles

Auditing learner driver information about floodwaters: An environmental scan of government issued resources in Australia

Introduction: Vehicles driving, or being swept, into floodwaters is a leading cause of flood-related death. Establishing safe behaviors among learner drivers may reduce risk throughout their driving lifetime. Methods: An environmental scan of publicly available government issued learner and driver handbooks across the eight Australian jurisdictions was conducted to identify information provided regarding floodwaters. Search terms included ‘flood,’ ‘rain,’ ‘water,’ and ‘wet.’ A visual audit of flood-related signage was also conducted. Results: Twelve documents, across eight jurisdictions, were analyzed. Four jurisdictions’ documents provided no information on flooding. Of the four jurisdictions that provided information, content varied. This included highlighting risks and discouraging entering floodwaters in a vehicle, including penalties associated with travel on closed roads, to advising depth and current checks if crossing a flooded roadway, with recommendations based on vehicle size (preference given to bigger vehicles, i.e., 4wds). Information on flood-related signage was found in one jurisdiction. Discussion: Learner and driver handbooks represent a missed opportunity to provide flood safety information. Currently, information is not provided in all jurisdictions, despite flood-related vehicle drowning deaths of drivers and passengers being a national issue. Where information is presented, it is limited, often lacks practical guidance on how to assess water depth, current, and road base stability, and could better use evidence regarding the psychological factors underpinning, and behavioral prompts for performing, or avoiding, risky driving behavior during floods. Conclusions: The provision and content of information in learner driver and driver handbooks must be improved, particularly within the context of increasing flooding and extreme weather associated with the effects of climate change. Practical applications: We encourage all jurisdictions to provide practical information that draws on evidence-based risk factors and empirically established psychological factors for behavioral change to help establish safe driver behaviors around floods in the formative years of learning to drive.

Hybrid Block Chaotic Compressive Sensing and Chaotic Scrambling Algorithms for Color Image Encryption System

This paper suggests a new image encryption algorithm based on block compressive sensing (BCS) and chaotic scrambling techniques. With compressive sensing (CS), signals can be sampled much lower than the Nyquist-Shannon rate while preserving their information content. BCS can be used as a one-step process by using a 3D logistic chaotic map, the measurement matrix is used as a secret key for encryption. BCS algorithm used an individual image reconstruction algorithm that leverages l_1norm minimization to promote signal sparsity, and a smoothing operator to enhance image quality. An encrypted image is first decomposed into three sub-images based on the tricolor theory; then, a discrete wavelet transform is used to sparsely process the three decomposed images. A 2D Henon map is used to scramble the compressed image after compression. This process further enhances the security of the system by increasing the complexity of the encryption process. The results showed that the proposed system provides better security and has a lower computational complexity than other methods. Furthermore, the proposed system is resistant to known attacks such as brute force and statistical attacks.

Development of SSR markers related to agarwood production and genetic diversity of Aquilaria sinensis (Lour.) Spreng wild populations

Aquilaria sinensis (Lour.) Spreng is an economically important tree that produces agarwood when it is subjected to mechanical wounding. However, the information regarding its heredity is limited. The breeding of A. sinensis has been hindered due to ambiguity in the sources of germplasm, genetic background, and varietal information. Simple sequence repeat (SSR) markers are ideal for studying genetic diversity and germplasm identification in plants. They offer the advantages of high polymorphism, good reproducibility, and co–dominance. Here, we mined SSR markers within agarwood–inducing genes and used them to explore the genetic diversity among A. sinensis natural germplasm. A total of 407 SSR loci were identified within 92 genes. We designed 96 primer pairs, among which 16 showed polymorphism and were subsequently used to analyze the genetic diversity and population structure of 179A. sinensis individuals. A total of 107 alleles were identified, with an average of 7 alleles per locus, and their polymorphism information content ranged between 0.424 to 0.769, with an average of 0.569. The analysis of molecular variance revealed that genetic variation within the individuals accounted for 94% of the total variation, indicating that the variation of A. sinensis is derived from individual variation. Furthermore, the structure analysis indicated that the natural populations of A. sinensis could be theoretically divided into 16 subgroups. The 16 polymorphic SSRs had strong cross–species transferability and distinguished individuals of A. agallochum (Lour.) Roxb. ex Finl. and A. malaccensis Lam. Taken together, these findings provide a useful resource for germplasm identification and molecular marker–assisted breeding of A. sinensis.

SSR-BASED ASSESSMENT OF GENETIC DIVERSITY IN BARLEY COLLECTION FROM THE STATE COMMISSION FOR VARIETY TESTING OF AGRICULTURAL CROPS OF KAZAKHSTAN

Barley (Hordeum vulgare L.) is a globally significant cereal crop with a substantial production footprint. As a versatile commodity, it serves as a primary resource for animal feed, malting, and human consumption. Genetic diversity among 49 barley accessions procured from the State Commission for Variety Testing of Agricultural Crops in Kazakhstan was assessed using a set of 21 SSR markers associated with various agronomic traits. The collection exhibited moderate genetic diversity, as indicated by a mean polymorphic information content (PIC) of 0.548 ± 0.153 and a mean Shannon's information index (I) of 0.980 ± 0.317, aligning with previously reported estimates of barley genetic variation in the World. STRUCTURE analysis identified three distinct population clusters (K = 3) using Bayesian clustering and DeltaK, which was confirmed by PCoA results. However, neighbor-joining tree analysis revealed a more detailed population structure with six distinct clusters, indicating substantial genetic diversity within the barley collection. Distinct phenotypic variations were observed among identified genetic clusters, implying a strong association between genetic diversity and agronomic traits, including plant height, kernel number per spike, and grain yield. Seven barley accessions (GSI_B_60, GSI_B_82, GSI_B_216, GSI_B_85, GSI_B_96, GSI_B_141, and GSI_B_208) forming a cluster NJ_tree_2 demonstrated the highest mean grain yield of 50.99 ± 2.95 g/ m2. This specific cluster and its constituent accessions exhibit promising potential for development into high-yielding cultivars or as a valuable source of beneficial alleles for barley breeding initiatives.

Evaluation of Genetic Diversity in Sorghum Genotypes via Simple Sequence Repeat (SSR) Markers

This study aimed to evaluate the genetic diversity of 20 sorghum [Sorghum bicolor (L.) Moench] genotypes via simple sequence repeat (SSR) markers, with a focus on identifying drought-tolerant varieties. Leaf samples were collected from three-week-old seedlings, and genomic DNA was extracted via the CTAB method, a widely recognized protocol for obtaining high-quality DNA. The DNA samples were quantified to ensure consistent template concentrations for amplification via 25 SSR primers. PCR amplification was performed, followed by agarose gel electrophoresis to separate and visualize the SSR band patterns, which were then recorded in a binary matrix format on the basis of the presence or absence of bands. Among the SSR primers used, nine were polymorphic, generating 13 scorable markers, highlighting the genetic variability among the genotypes. The polymorphic information content (PIC) values ranged across the SSR loci, with the Xtxp145 locus exhibiting the highest PIC value of 0.998, indicating its high discriminatory power and informativeness in differentiating between genotypes. Genetic similarity indices were calculated via Jaccard's similarity coefficient [1], and the data were subjected to cluster analysis via the unweighted pair group method with arithmetic mean (UPGMA) method. The resulting dendrogram grouped the genotypes into seven main clusters at a 50% similarity threshold, underscoring the genetic diversity present within the sorghum genotypes studied. Cluster I contained a single genotype, SVD-1272R, whereas, Cluster II included seven genotypes with subcluster formation. Cluster III comprised one ungrouped genotype, SPV-486. Cluster IV included eight genotypes, whereas Clusters V, VI, and VII each contained a single ungrouped genotype. This dendrogram illustrates the genetic diversity and relationships among the sorghum genotypes on the basis of similarity indices. The findings of this study confirmed the efficacy of SSR markers in assessing genetic diversity and emphasized their potential utility in breeding programs aimed at improving drought tolerance.

What Drives Deep Reader Engagement with Socially-Generated Content? The Critical Role of Dual-Layer Information

Social media platforms have rapidly gained popularity due to features such as personalized recommendations and real-time push notifications, attracting a large number of creators to produce content. However, with the explosive growth of content and information overload, readers’ expectations for content and interaction quality have increased, making it increasingly difficult to engage them deeply. This poses management challenges to the platform’s ecosystem, such as high visitor volume but low retention rates. This study, leveraging the latest GPT technology, provides a more scientific approach to content metrics and a detailed exploration of interaction categories to examine the impact of information, authors, and interactions on reader engagement behavior. It particularly considers the effects of internal and external layers of social-generated content and different levels of reader engagement. Combining real community data, it is found that the impact of content information is the most crucial, with this impact being distributed across content and interaction aspects. It is suggested that social media platforms should strive to balance vivacity and informativeness when producing content. Additionally, the role of content, authors, and interactions varies across different product categories, so differentiated strategies should be employed accordingly.

Assessing the Information Quality, Accuracy and Content of YouTube Videos on the Endo-Perio Lesions: A Cross-Sectional Stud

Assessing the Information Quality, Accuracy and Content of YouTube Videos on the Endo-Perio Lesions: A Cross-Sectional Stud

Digital information repackaging: best practices from university library website managers

Purpose This paper aims to explore the digital information repackaging process from the perspective of library website operators. The digital info repackaging process consists of analysis, synthesis, editing and presenting. Design/methodology/approach Interviews with 17 library website operators regarding their experience in information repackaging. The library website observation also conducted to gather the data on information repackaging. Findings Librarians who manage library websites repackage digital information using a process of user analysis, content analysis, editing and transforming and translating. Librarians who are in charge of repackaging usually collect, check, group and present information based on the raw data and information. Librarians rarely did criticize or argue the information. They just deliver it in the pact and attractive format. In the future, librarians can add critical comments on their digital information content, such as give recommendation, provide point of view, add some suggestion or make comparison. Practical implications This paper can provide an overview of the process of repackaging digital information carried out by university libraries which can be provided by libraries via websites. Apart from that, it is hoped that the presence of this research can help librarians in carrying out the process of repackaging digital information. Originality/value This paper can add to the scientific development of information science and library studies related to the field of information repackaging, especially in the repackaging of digital information in creating website content to efficiently meet the information needs of university library users.

Design specifications and capabilities of a 3D simulation software for clinical education of physiotherapy students in the neurology department.

In this revolutionized era, thanks to cutting-edge technological breakthroughs like 3-dimensional (3D) computerized environments, physiotherapy trainers can improve their knowledge and confidence by using such training tools. Hence, there is room for developing these technologies for training medical students to expand their skills and expertise. This study aims to identify the design requirements and key functionalities of a 3D simulation software for the clinical education of physiotherapy students in neurology departments. First, by carefully reviewing neurological books, scientific articles, curriculum, and medical records, and consulting with experts, a scenario was compiled. In the next step, a researcher-developed questionnaire was designed. Then, experts' opinions were considered to confirm the validity and reliability of the questionnaire. The designed questionnaire was distributed among several neurological physiotherapists. Finally, the information elements, contents, and functional capabilities of the 3D software were determined by analyzing the data obtained from the questionnaire. The main components for the design of physiotherapy educational software were identified based on the findings of the literature review, curriculum analysis, and medical record review. A survey of physiotherapy professors was conducted using a questionnaire created by the researcher in order to enhance the capabilities of simulation software and ascertain its primary functions. Following an analysis of the data from the distributed questionnaire, 37 essential features and contents have been proven to be more crucial than the rest for the creation of 3D simulation software. As a result, the essential and fundamental needs for the patient's training in reading their medical records and performing muscle strength assessments were recognized and extracted. Based on these findings, a researcher-developed scenario for the various real cases was then established. In the patient's medical record reading scenario, the student is required to read the patient's record in text format. Similarly, in the section on cranial nerves, pictures are utilised to reinforce the student's assessment skills in addition to textual content. Together with the audio and pop-up texts, the simulated 3D environment also offers training for the assessment of muscle strength. As an educational tool, this software can enhance students' learning and assist in addressing the drawbacks of conventional teaching methods like lectures and hospital visits.

Identifying the physiological traits associated with DNA marker using genome wide association in wheat under heat stress.

Heat stress poses a significant environmental challenge that profoundly impacts wheat productivity. It disrupts vital physiological processes such as photosynthesis, by impeding the functionality of the photosynthetic apparatus and compromising plasma membrane stability, thereby detrimentally affecting grain development in wheat. The scarcity of identified marker trait associations pertinent to thermotolerance presents a formidable obstacle in the development of marker-assisted selection strategies against heat stress. To address this, wheat accessions were systematically exposed to both normal and heat stress conditions and phenotypic data were collected on physiological traits including proline content, canopy temperature depression, cell membrane injury, photosynthetic rate, transpiration rate (at vegetative and reproductive stage and 'stay-green'. Principal component analysis elucidated the most significant contributors being proline content, transpiration rate, and canopy temperature depression, which exhibited a synergistic relationship with grain yield. Remarkably, cluster analysis delineated the wheat accessions into four discrete groups based on physiological attributes. Moreover, to explore the relationship between physiological traits and DNA markers, 158 wheat accessions were genotyped with 186 SSRs. Allelic frequency and polymorphic information content value were found to be highest on genome A (4.94 and 0.688), chromosome 1A (5.00 and 0.712), and marker Xgwm44 (13.0 and 0.916). Population structure, principal coordinate analysis and cluster analysis also partitioned the wheat accessions into four subpopulations based on genotypic data, highlighting their genetic homogeneity. Population diversity and presence of linkage disequilibrium established the suitability of population for association mapping. Additionally, linkage disequilibrium decay was most pronounced within a 15-20cM region on chromosome 1A. Association mapping revealed highly significant marker trait associations at Bonferroni correction P < 0.00027. Markers Xwmc418 (located on chromosome 3D) and Xgwm233 (chromosome 7A) demonstrated associations with transpiration rate, while marker Xgwm494 (chromosome 3A) exhibited an association with photosynthetic rates at both vegetative and reproductive stages under heat stress conditions. Additionally, markers Xwmc201 (chromosome 6A) and Xcfa2129 (chromosome 1A) displayed robust associations with canopy temperature depression, while markers Xbarc163 (chromosome 4B) and Xbarc49 (chromosome 5A) were strongly associated with cell membrane injury at both stages. Notably, marker Xbarc49 (chromosome 5A) exhibited a significant association with the 'stay-green' trait under heat stress conditions. These results offers the potential utility in marker-assisted selection, gene pyramiding and genomic selection models to predict performance of wheat accession under heat stress conditions.

FRET calibration by symmetrization on the cell surface

Maximum position of Shannon-information of FRET frequency distribution represented as a function of the α (or G) calibrating factor as calculated in the framework of dual-laser flow cytometric FRET method – also called 3-cube FRET – gives a fairly good approximation of the real α, using only a single FRET sample. The estimation is accurate for completely symmetric FRET distributions like Gaussian and uniform. For non-symmetric distributions, the committed error has been found to depend on the direction and degree of deviation from the complete symmetry. The maximum information is characterized also by zero skewness, and minima in kurtosis and in the χ-value, defined as the “absolute deviation from the Gaussian”, as other α- functions characterizing shape. As to practice, information maximum of FRET distribution can be used to determine α, whenever symmetry can be assumed. In other cases, the goodness of estimation can be inferred from the information content and shape parameters (skewness, kurtosis and “χ-value”) of the primarily measured FRET parameter A′, based on their inverse correlations with their counterparts computed with the target FRET distribution. Above a threshold in FRET efficiency or its coefficient of variation, resonant peaks (“Ω-curves”) in FRET information appear on the α-scale, with their positions stabilized around α values with reduced fluctuations dictated by symmetry of the target FRET distribution. The phenomenon is explained by a FRET-induced broadening of the A′ distribution reaching the domain containing A′ = −α.

Convolutional neural network-based regression analysis to predict subnuclear chromatin organization from two-dimensional optical scattering signals.

Azimuth-resolved optical scattering signals obtained from cell nuclei are sensitive to changes in their internal refractive index profile. These two-dimensional signals can therefore offer significant insights into chromatin organization. We aim to determine whether two-dimensional scattering signals can be used in an inverse scheme to extract the spatial correlation length and extent of subnuclear refractive index fluctuations to provide quantitative information on chromatin distribution. Since an analytical formulation that links azimuth-resolved signals to and is not feasible, we set out to assess the potential of machine learning to predict these parameters via a data-driven approach. We carry out a convolutional neural network (CNN)-based regression analysis on 198 numerically computed signals for nuclear models constructed with varying in steps of between 0.4 and , and varying in steps of 0.005 between 0.005 and 0.035. We quantify the performance of our analysis using a five-fold cross-validation technique. The results show agreement between the true and predicted values for both and , with mean absolute percent errors of 8.5% and 13.5%, respectively. These errors are smaller than the minimum percent increment between successive values for respective parameters characterizing the constructed models and thus signify an extremely good prediction performance over the range of interest. Our results reveal that CNN-based regression can be a powerful approach for exploiting the information content of two-dimensional optical scattering signals and hence monitoring chromatin organization in a quantitative manner.

Effects of increasing spatial resolution on the spatial information content and accuracy of downward surface shortwave radiation

Downward Surface Shortwave Radiation (DSSR) plays a crucial role in ecological, hydrological, and solar photovoltaic studies. The estimation of DSSR at higher spatial resolutions is increasingly popular. However, the impact of increasing spatial resolution on spatial information content and accuracy of DSSR estimates is not well understood. Addressing this knowledge gap, an all-sky DSSR dataset at varying spatial resolutions (0.8° to 0.01°) in China was estimated using MODIS top-of-atmosphere reflectance data. Subsequently, the spatial heterogeneity and accuracy of the DSSR dataset as well as its driving factors were analyzed. The results indicate that the magnitude of the increase in spatial heterogeneity varies across different regions as the spatial resolution increases. On the edge of the Qinghai-Tibet Plateau, the spatial heterogeneity experiences the most significant increase. To quantify the factors driving the spatial heterogeneity of DSSR, the Correlation Coefficients (CC) were calculated between the Coefficient Of Variation (COV) of DSSR and the COVs of relevant factors. In cloudy-sky conditions, the CC between the COV of DSSR and cloud optical thickness ranged from 0.54 to 0.62, whereas the CC between DSSR COV and Column Water Vapor (CWV) COV was between 0.72 and 0.78. Under clear-sky conditions, the COV of CWV, aerosol optical depth, and PM2.5 showed more significant correlations with DSSR COV compared to altitude COV. Validation using ground-based measurements revealed that the accuracy of DSSR decreased at coarser spatial resolutions due to the presence of mixed pixel samples. Moreover, it was demonstrated that a spatial resolution of 0.05° offered the highest cost-effectiveness when estimating DSSR. These insights significantly advance our understanding of the role of spatial resolutions in DSSR estimation and are instrumental in guiding the selection of optimal resolutions for such studies.

Evaluation of YouTube as an Information Source for Use of Laser in Root Canal Treatment

Objective: At the present time patients in developed countries widely use online resources to access medical information. YouTube is a widely used website to get knowledge. In the past, medical and dental data were accessed only by doctor and dentist examinations. This research was aimed to assess the quality and quantity of the knowledge content in popular YouTube™ videos about laser root canal treatment (LRCT). Method: Google Trends showed that "laser root canal treatment" was the most searched keyword on the topic. Included videos were assessed as the video's name, universal resource locators, the number of video display, time of adding in website, total numbers of likes, dislikes and comments and total video time. Each video was classed into the 3 groups according to their information source as dentist, commercial, dental clinic. This video was classified according to the quality of information content as ‘good’, ‘poor’, or ‘bad’. In the evaluation of the data, the distribution of variables was examined with the Shapiro-Wilk normality test, the intergroup comparison of variables that did not show a normal distribution was examined with the Kruskal Wallis test. Comparison of subgroups was examined with Dunn's multiple comparison test and comparison of qualitative data was analyzed with Chi-square test. The results were evaluated at the significance level of p

Instagram as medium of information on feminism for Indonesia’s youth

Feminism has been an integral part of Indonesia’s history, yet it is commonly misunderstood as a western concept, causing fundamentalist religious groups and right-wing parties to strongly reject feminism. Therefore, social media that offer feminist literacy content is highly important, especially for Indonesia’s youth, as it can provide much-needed information that is often deemed taboo. The purpose of this research is to answer three research questions: (1) What is the use most sought by the followers of the @indonesiafeminis Instagram account? (2) What is the gratification most obtained by the followers of @indonesiafeminis Instagram account? And (3) Is the level of congruence between uses sought and gratifications obtained correlated with the intensity of consuming the @indonesiafeminis Instagram account? Using a quantitative survey method on 165 active followers of the @indonesiafemis Instagram account, results show that: (1) Cognitive needs are the use most sought by @indonesiafeminis followers; (2) Cognitive needs are also the gratification most obtained by the followers, and; (3) There is a positive but very weak correlation between the congruence of the uses sought and gratifications obtained and the intensity of consuming the Instagram account. Therefore, it is crucial that the administrators of @indonesiafeminis focus on providing informational content that satisfies the cognitive needs of the followers. However, despite the correlation, other factors beyond the congruence of the uses and gratifications possibly have an impact to the media consumption intensity.

JWST-TST DREAMS: Nonuniform Dayside Emission for WASP-17b from MIRI/LRS

We present the first spectroscopic characterization of the dayside atmosphere of WASP-17b in the mid-infrared using a single JWST MIRI/LRS eclipse observation. From forward-model fits to the 5–12 μm emission spectrum, we tightly constrain the heat redistribution factor of WASP-17b to be 0.92 ± 0.02 at the pressures probed by this data, indicative of inefficient global heat redistribution. We also marginally detect a supersolar abundance of water, consistent with previous findings for WASP-17b, but note our weak constraints on this parameter. These results reflect the thermodynamically rich but chemically poor information content of MIRI/LRS emission data for high-temperature hot Jupiters. Using the eclipse mapping method, which utilizes the signals that the spatial emission profile of an exoplanet imprints on the eclipse light curve during ingress/egress due to its partial occultation by the host star, we also construct the first eclipse map of WASP-17b, allowing us to diagnose its multidimensional atmospheric dynamics for the first time. We find a day–night temperature contrast of order 1000 K at the pressures probed by this data, consistent with our derived heat redistribution factor, along with an eastward longitudinal hotspot offset of 18.7−3.8+11.1◦ , indicative of the presence of an equatorial jet induced by day–night thermal forcing being the dominant redistributor of heat from the substellar point. These dynamics are consistent with general circulation model predictions for WASP-17b. This work is part of a series of studies by the JWST Telescope Scientist Team, in which we use Guaranteed Time Observations to perform Deep Reconnaissance of Exoplanet Atmospheres through Multi-instrument Spectroscopy.

Genetic characterization of white onion genotypes (Allium cepa L.) using ISSR markers

Understanding the genetic characteristics is crucial for preserving and developing Moroccan's white onion genotypes, with increased yield and quality as well as improved tolerance to biotic and abiotic stresses. In the present paper, inter simple sequence repeat (ISSR) markers were used to assess the genetic diversity and characteristics of white onion genotypes (Allium cepa L.) of Morocco. Based on the ISSR markers bands, 28 white onion genotypes were divided into four distinguished subgroups, with a genetic similarity value of 0.46 based on Jaccard index. Molecular markers analysis revealed that all ISSR markers were 100 % polymorphic with the white onions genotypes. Concerning markers performance, polymorphic information content (PIC) ranges from 0.051 to 0.39 with a mean of 0.301; however, the effective multiplex ratio (EMR) and marker index (MI) vary between 3–17 and 0.35–5.04, respectively. Based on resolving power (RP), it appears that the marker UBC825 has an efficient discriminatory power with an RP = 15.67. In contrast, the lowest RP value was detected with the marker UBC815 (RP = 1.58). Based on their high performance, markers UBC825, UBC835, UBC826, UBC840, and UBC811 ISSR seem to be specific and efficient for the identification of Moroccan white onion genotypes. Thus, given the genetic diversity shown by ISSR markers, the abundant resources of white onion genotypes in Morocco could be exploited in more valuable ways.

Multi-Energy Load Prediction Method for Integrated Energy System Based on Fennec Fox Optimization Algorithm and Hybrid Kernel Extreme Learning Machine.

To meet the challenges of energy sustainability, the integrated energy system (IES) has become a key component in promoting the development of innovative energy systems. Accurate and reliable multivariate load prediction is a prerequisite for IES optimal scheduling and steady running, but the uncertainty of load fluctuation and many influencing factors increase the difficulty of forecasting. Therefore, this article puts forward a multi-energy load prediction approach of the IES, which combines the fennec fox optimization algorithm (FFA) and hybrid kernel extreme learning machine. Firstly, the comprehensive weight method is used to combine the entropy weight method and Pearson correlation coefficient, fully considering the information content and correlation, selecting the key factors affecting the prediction, and ensuring that the input features can effectively modify the prediction results. Secondly, the coupling relationship between the multi-energy load is learned and predicted using the hybrid kernel extreme learning machine. At the same time, the FFA is used for parameter optimization, which reduces the randomness of parameter setting. Finally, the approach is utilized for the measured data at Arizona State University to verify its effectiveness in multi-energy load forecasting. The results indicate that the mean absolute error (MAE) of the proposed method is 0.0959, 0.3103 and 0.0443, respectively. The root mean square error (RMSE) is 0.1378, 0.3848 and 0.0578, respectively. The weighted mean absolute percentage error (WMAPE) is only 1.915%. Compared to other models, this model has a higher accuracy, with the maximum reductions on MAE, RMSE and WMAPE of 0.3833, 0.491 and 2.8138%, respectively.

A unified framework of semi-supervised community detection integrating network topology and node content

Detecting the community structure within networks is important because it aids in the analysis of complex networks. However, the existence of diverse complex structures in the network topology can limit the topological information’s ability to represent communities. For instance, the accuracy of many traditional methods based on topology alone decreases sharply when a link is missing or noisy. To solve the above-mentioned issues simultaneously, we propose a unified framework of semi-supervised community detection integrating content information. Three models are proposed, instead of a special model-handling problem. The framework used performs the same interpretation for a target problem. The inputs contain the adjacent and node content matrix. The proposed models describe the network topology using a non-negative matrix factorization (NMF) algorithm–based generative model or modularity maximization algorithm. Additionally, we use the equivalence theory of the pLSI, and NMF models the content information based on NMF. Community membership is also used as the relationship between communities and node contents. The models uniformly integrate content information and network topology. Further, a constraint matrix that depicts must-links is built by calculating the similarity of neighbors between any two nodes. Must-link pairs can represent the network topology’s prior information. The models’ community memberships are adjusted by using a strong constraint based on the connected component of this matrix. The models also introduce prior information into the topological and content information in the mentioned models. We compared the proposed models with the state-of-the-art community detection models on artificial networks and five real networks. The experimental results show that the proposed models outperformed the models combining topology and content and semi-supervised models. Fusing the topology with the content and the prior information in the models was also effective for community detection. Thus, the models under the framework are more advantageous than other semi-supervised models when incorporating the same amount of prior information into the models. The semi-supervised framework used also has a certain level of competitiveness in detecting communities.

Precise-color-mutation designing and programmable drop-on-demand printing for intelligent package of shrimp

Biosafe colorimetric labels that can accurately evaluate food freshness have been widely investigated in recent years. The colorimetric macro-array sensor, with the composite solution of curcumin, alizarin and acetic-acid as the indicator, was prepared by means of a self-developed 2D printing device to detect shrimp freshness. The results evidenced the sensor which had high stability, uniform color distribution and great sensitivity could be drop-on-demand printed in batches under the premise of micro-printing. When the TVB-N content of shrimp exceeded 20 mg/100g at all observed temperatures, the sensor abruptly turned from yellow to reddish-brown meanwhile the ΔE value raised sharply, indicating that there was a color mutation at the critical value of shrimp freshness. The macro-array sensor could be recognized by the smartphone to obtain the correlation between the color information (RGB) and TVB-N content, by which the shrimp freshness was determined. Furthermore, the correlation was verified through the WeChat mini-program. When the R value was less than 200 or the G value was below 140, the sensor was brown, suggesting the sample was inedible. Consequently, the system can be applied in food quality control throughout the supply chain.