Algorithms, indispensable to understand Artificial Intelligence (AI), are omnipresent in social media but users’ understanding of these computational processes and the way they impact their consumption of information is often limited. There is a need for Media and Information Literacy (MIL) research investigating a) how MIL can support Algorithm Literacy (AL) as a subset of competences, and with what working definition, b) what competences users need in order to evaluate algorithms critically and interact with them effectively, and c) how to design learner-centred interventions that foster increased user understanding of algorithms and better response to disinformation spread by such processes. Based on Crossover project research, this paper looks at four scenarios of use by journalists, developers and MIL experts that mirror users’ daily interactions with social media. The results suggest several steps towards integrating AL within MIL goals, while providing a concrete definition of Algorithm-literacy that is experience-based. The competences and design considerations are organised in a conceptual framework thematically derived from the experimentation. This contribution can support AI developers and MIL educators in their co-design of algorithm-literacy interventions and guide future research on AL as part of a set of nested AI literacies within MIL.

The purpose of this study is to establish whether digital literacy and insurtech adoption influence insurance inclusion in Uganda. Principally, we sought to determine whether insurtech adoption mediates the nexus between digital literacy and insurance inclusion. This study adopted a cross-sectional and quantitative correlational approach. The study’s sample was 391 individuals who had used digital platforms such as mobile phones and computers to access insurance products and services in Uganda. Data were collected using structured survey questionnaires. Partial Least Squares Structural Equation Modelling (PLSEM) was employed to test the hypothesised relationships. The results demonstrate that both digital literacy and insurtech adoption significantly and positively influence insurance inclusion. We also found digital literacy to be a significant and positive determinant of insurtech adoption. Markedly, it was found that insurtech adoption mediates the association between digital literacy and insurance inclusion in Uganda. However, this study was conducted in a developing country with an underdeveloped insurance market and with low technological advancement. This may affect generalisation of the study findings. This study’s novelty lies in establishing how digital literacy and insurtech adoption interplay to influence insurance inclusion in Uganda. This is the first study to examine the effect of digital literacy and insurtech adoption on insurance inclusion.

As with many of the non-deterministic search algorithms, particularly those are analogous to complex biological systems, there are a number of inherent difficulties, and the Bees Algorithm (BA) is no exception. Basic versions and variations of the BA have their own drawbacks. Some of these drawbacks are a large number of parameters to be set, lack of methodology for parameter setting and computational complexity. This paper describes a Grouped version of the Bees Algorithm (GBA) addressing these issues. Unlike its conventional version, in this algorithm bees are grouped to search different sites with different neighbourhood sizes rather than just discovering two types of sites, namely elite and selected. Following a description of the GBA, the results gained for 12 benchmark functions are presented and compared with those of the basic BA, enhanced BA, standard BA and modified BA to demonstrate the efficacy of the proposed algorithm. Compared to the conventional implementations of the BA, the proposed version requires setting of fewer parameters, while producing the optimum solutions much faster.

The key links of face recognition are digital image preprocessing, facial feature extraction and pattern recognition, this article aimed at the current problem of slow speed and low recognition accuracy of face recognition , from the above three key links, on the basic of analyzing the therories of Fractional Differential Masks Operator (FDMO), Principal Component Analysis (PCA) and Support Vector Machine (SVM), design a kind of FDMO+PVA+SVM coupling algorithm that applies to face recognition to improve the speed and accuracy of it. To realize FDMO+PCA+SVM coupling algorithm, first, we should apply FDMO to face image processing binary marginalization, the purpose is getting face contour; Then, we apply PCA to get the feature of face image which is disposed by binary marainalization. At last, we can apply One-Against All of the SVM classifier and LibSVM software package to realize face recognition. Beside, the article with nine different coupling algorithm design four groups of experimental reaults on the ORL face database verified by comparative analysic FDMO+PCA+SVM coupling algorithm in the superiority of face recognition accuracy and speed.

With the increasing concern of environment, the energy-efficiency scheduling of manufacturing industry is becoming urgent and popular. In turning processes, both spindle speed and processing time can affect the final energy consumption and thus the spindle speed and scheduling scheme need to be optimized simultaneously. Since the turning workshop can be regarded as the flexible flow shop, this paper formulates a mixed integer linear programming model for energy-efficient scheduling of flexible flow shop. Accordingly, a new decoding method is developed by considering of the optimization of spindle speed and scheduling scheme simultaneously, and an estimation of distribution algorithm adopting the new decoding method is proposed to solve large-size problems. The parameters of this algorithm are determined by statistical technique with a simplified practical case. In order to validate the proposed method, a case from practical factories is studied, in which the makespan can be shortened by 25.22%, and the consumed energy can be saved by 5.48%. These results demonstrate the effectiveness of the proposed mathematical model and algorithm.

In order to overcome the limitation of the traditional adaptive Unscented Kalman Filtering (UKF) algorithm in noise covariance estimation for statement and measurement, we propose a hybrid adaptive UKF algorithm based on combining Maximum a posteriori (MAP) criterion and Maximum likelihood (ML) criterion, in this paper. First, to prevent the actual noise covariance deviating from the true value which can lead to the state estimation error and arouse the filtering divergence, a real-time covariance matrices estimation algorithm based on hybrid MAP and ML is proposed for obtaining the statement and measurement noises covariance, respectively; and then, a balance equation the two kinds of covariance matrix is structured in this proposed to minimize the statement estimation error. Compared with the UFK based MAP and based ML, the proposed algorithm provides better convergence and stability.

Health literacy describes skills and competencies that enable people to gain access to, understand and apply health information to positively influence their own health and the health of those in the social environment. In an increasingly media saturated and digitized world, these skill sets are necessary for accessing and navigating sources of health information and tools, such as television, the Internet, and mobile apps. The concepts of Media Health Literacy (MHL) and eHealth Literacy (eHL) describe the specific competencies such tasks require. This article introduces the two concepts, and then reviews findings on the associations of MHL and eHL with several contextual variables in the social environment such as socio-demographics, social support, and system complexity, as a structural variable. As eHL and MHL are crucial for empowering people to actively engage in their own health, a growing body of literature reports of the potential and the effectiveness of intervention initiatives to positively influence these competencies. From an ethical standpoint, equity is emphasized, stressing the importance of accessible media environments for all - including those at risk of exclusion from (digital) media sources. Alignment of micro and macro contextual spheres will ultimately facilitate both non-digital and digital media to effectively support and promote public health.

Earth observation satellites can capture images of specific areas in Earth's orbit based on user requirements. Despite the high demand for satellite imagery, satellites remain a scarce resource. When there are many observation tasks that are large and dense, they may interfere with each other due to satellite mobility. To address this issue, a multi-satellite scheduling algorithm based on a task merging mechanism has been proposed. This paper proposes a two-stage approach to the problem: task merging phase and merging task scheduling phase. The task merging phase introduces two methods based on Mean Shift and Complete Graph to generate the merging task set. In the merging task scheduling phase, a multi-satellite scheduling algorithm based on merging tasks is employed to solve the problem. Finally, certain measurement experiments suggest the effectiveness of the algorithm.

In wireless sensor networks (WSNs), each sensor node can estimate the global parameter from the local data in distributed manner. This paper proposed a robust diffusion estimation algorithm based on minimum error entropy criterion with self-adjusting step-size, which are referred to as diffusion MEE-SAS (DMEE-SAS) algorithm. The DMEE-SAS algorithm has fast speed of convergence and is robust against non-Gaussian noise in the measurements. The detailed performance analysis of the DMEE-SAS algorithm is performed. By combining the DMEE-SAS with diffusion minimum error entropy (DMEE) algorithms, an Improving DMEE-SAS algorithm is proposed, in non-stationary environment where tracking is very important. The Improving DMEE-SAS algorithm can avoid insensitivity of the DMEE-SAS algorithm due to the small effective step-size near the optimal estimator, and obtain a fast convergence speed. Numerical simulations are given to verify the effectiveness and advantages of these proposed algorithms.

A more sustainable society and economy also implies a more sustainable behavior in the consumption of financial products. A possible change in focus can come from the demand side, so that more sustainable consumption of financial products have to go hand to hand with financial literacy. However, financial literacy, potential favoring of this sustainable behavior, is far from reaching an international consensus about its definition, object and scope. Two objectives are analyzed; the different interpretations of financial literacy in the academic literature, as well as its evolution and how, in what context and with what other concepts the term is used in social networks. Scientometric techniques and content analysis have been used to carry out a systematic review of literature and also NLP to analyze the comments on Social Networks. Critical moments are identified in the definition of financial literacy. Also ten sentiments are analyzed in social networks in wich positivity, trust, and anticipation predominate. Greater attention to this issue is necessary both from the private initiative and from public policies, so that financial literacy is an effective tool for a more sustainable behavior by consumers. Finally, a new definition is proposed based on our findings.

Significant challenges exist globally regarding literacy teaching and learning, particularly in poor socio-economic settings in countries of the Global South. In this paper we argue that to address these challenges, major features of how the brain works that are currently ignored in the educational literature should be taken into account. First, perception is an active process based in detection of errors in hierarchical predictions of sensory data and action outcomes. Reading is a particular case. Second, emotions play a key role in underlying cognitive functioning. Innate affective systems underlie and shape all brain functioning, including oral and written forms of language and sign. Third, there is not the fundamental difference between listening/speaking and reading/writing often alleged on the basis of evolutionary arguments. Both are socio-cultural practices driven and learnt by the communication imperative of the social brain. Fourth, like listening, reading is not a linear, bottom-up process. Both are non-linear contextually shaped psycho-social processes of understanding, shaped by current knowledge and cultural contexts and practices. Reductionist neuroscience studies which focus on decontextualized parts of reading cannot access all the relevant processes. An integrated view of brain function reflecting this non-linear nature implies that an ongoing focus on personal meaning and understanding provides positive conditions for all aspects of literacy learning. Assessment of literacy teaching at all its stages should include indicators that take into account these foundational features relating reading and writing to neuroscience.

In this paper, we first discuss the research status and application progress of the finite element method and the smoothed particle method. By analyzing the advantages of the smoothed particle method and the finite element method, a new coupling algorithm, namely FEM-SPH algorithm, is proposed. By the method of comparison, it shows that finite element method and SPH method in the simulation of large deformation problems each have advantages and disadvantages, the finite element method smoothed particle coupling algorithm is effective to achieve the performance of high computational efficiency and can naturally simulate large deformation problems across. In the process of calculation, the large deformation unit can be freely into an algorithm to facilitate the calculation accuracy and efficiency of three methods of numerical simulation. Through the study found, FEM-SPH algorithm not only overcome the defect of smooth particle tensile instability, but also overcomes the problem of low efficiency of finite element computation. To further test the FEM-SPH algorithm has advantages in the practical engineering, we have carried out the actual test to the example of the super high speed collision, concluded that, since the target of most of the computational domain is always finite element, smoothed particle focused only in contact with the projectile and target of local area, particle number is not much, the whole calculation process just ten minutes, computational efficiency has been greatly improved, at the same time in the simulation of large deformation, the advantage is very obvious .This provides a criterion for the actual project, depending on the specific material deformation mode and choose a more appropriate conversion algorithm.

This paper proposes a new approach for multi-focus images fusion based on Region Mosaicing on Contrast Pyramids (REMCP). A density-based region growing method is developed to construct a focused region mask for multi-focus images. The segmented focused region mask is decomposed into a mask pyramid, which is then used for supervised region mosaicking on a contrast pyramid. In this way, the focus measurement and the continuity of focused regions are incorporated and the pixel level pyramid fusion is improved at the region level. Objective and subjective experiments show that the proposed REMCP is more robust to noise than compared algorithms and can fully preserves the focus information of the multi-focus images meanwhile reducing distortions of the fused images.

Pavement crack detection is one of the key links in highway pavement maintenance management. In the current road crack detection, the network model easily ignores the shallow geometric features of cracks and cannot extract the key feature information of cracks. Aiming at the above problems, an EC-YOLOX network model is proposed. The CFPN is constructed to cross-fuse different scale features to solve the feature scale invariance. In the strengthen feature extraction layer, ECANet is fused to enhance the ability to pay attention to key information. The WEIoU loss function is proposed, which assigns different penalty terms to the target box in the vertical and horizontal directions. In terms of data, geometric distortion constraint correction is performed on the single-point perspective pavement image. The experimental data show that the accuracy of EC-YOLOX on the self-built data set reaches 76.34% [email protected], which is 4.45% higher than that of YOLOX. The loss curve of EC-YOLOX is smoother than that of YOLOX, and the minimum training loss value is 2.136, which is 0.648 lower than the minimum loss value 2.784 of YOLOX. Many experiments have verified the effectiveness of EC-YOLOX in improving the detection effect in pavement crack detection.

Classifying network flow subsequences in an Industrial IoT gateway is an effective method for identifying Industrial IoT faults. This paper proposes a subsequence classification algorithm SSGBUL-IKNN based on unsupervised learning encoding to address the problem of low classification accuracy. Firstly, we built a network module based on unsupervised learning to encode the flow sequence of the Industrial IoT gateway. In the encoding module, a subsequence calibration algorithm controls the increasing error during the coding process. Then, the specific fault type is obtained through an improved KNN classification algorithm based on the indication distance source from the integrated coding sequences converted by multi-dimensional network flow sequences. The test result on three Industrial IoT datasets of a sewage treatment plant shows that the accuracy of SSGBUL-IKNN in this paper exceeds 92%, significantly higher than the algorithm DTW and TSF. It reaches the classification requirements of Industrial IoT fault diagnosis.

With the integrated development of the Internet, wireless sensor technology, cloud computing, and mobile Internet, there has been a lot of attention given to research about and applications of the Internet of Things. A Wireless Sensor Network (WSN) is one of the important information technologies in the Internet of Things; it integrates multi-technology to detect and gather information in a network environment by mutual cooperation, using a variety of methods to process and analyze data, implement awareness, and perform tests. This paper mainly researches the localization algorithm of sensor nodes in a wireless sensor network. Firstly, a multi-granularity region partition is proposed to divide the location region. In the range-based method, the RSSI (Received Signal Strength indicator, RSSI) is used to estimate distance. The optimal RSSI value is computed by the Gaussian fitting method. Furthermore, a Voronoi diagram is characterized by the use of dividing region. Rach anchor node is regarded as the center of each region; the whole position region is divided into several regions and the sub-region of neighboring nodes is combined into triangles while the unknown node is locked in the ultimate area. Secondly, the multi-granularity regional division and Lagrange multiplier method are used to calculate the final coordinates. Because nodes are influenced by many factors in the practical application, two kinds of positioning methods are designed: the unknown node is in the positioning unit or not. When the unknown node is on the side of the positioning unit, we use the method of vector similarity. Moreover, we use the centroid algorithm to calculate the ultimate coordinates of unknown node. When the unknown node is not on the side of the positioning unit, we establish a Lagrange equation containing the constraint condition to calculate the first coordinates. Furthermore, we use the Taylor expansion formula to correct the coordinates of the unknown node. In addition, this localization method has been validated by establishing the real environment.

The paper presents an error model of a measurement chain containing link performing discrete wavelet transform algorithm which is most often the last stage of measurement signal processing. The goal is to determine the uncertainty budget of the input quantities of the wavelet transform algorithm. The error model takes into account parts of analog, analog-to-digital and digital processing, describing the properties of subsequent fragments of the chain using their transmittance and processing functions. The proposed model enables the description of both deterministic and non-deterministic properties of signal errors. The proposed model was validated using an example measurement chain created for this purpose.

The conventional well placement was done manually by using a numerical reservoir simulator, and it required a lengthy trial-and-error process. It required great experience and expertise to manipulate the variables and uncertainties that lie on the reservoir to determine the best placement of the well. In addition, the traditional gradient-based methods such as Line-search and Trust-region were not viable in terms of maximum results obtained. Gradient-based methods were too dependent on the surface gradient of the solution and may only converge to local optima instead of global optima. Complex reservoirs have rough surfaces with high uncertainties, which hinders the traditional gradient-based method from converging to global optima. Thus, genetic algorithms were utilized to automate the manual trial-and-error process and to overcome the limitations of the traditional gradient-based method. The objectives of this study were to analyse the effect of different initial well placement distributions, the number of random solution sizes, and the crossover rate on cumulative oil production. A synthetic reservoir model built using CMG Builder was used as the testing platform for the optimization problems. Well-placement parameterization and optimization set-up were carried out using the CMG CMOST optimization tool. The integration of CMG IMEX and CMOST optimized cumulative oil production based on the objectives of the study. The results obtained showed that the higher number of random solutions used resulted in higher cumulative oil production, with more generations needed to reach the optimum solution. It can be concluded that the larger number of random solutions used increased the probability of reaching the optimum solution, but will take more generations.

With an ever increasing amount of research data available, it becomes constantly more important to possess data literacy skills to benefit from this valuable resource. An integrative course was developed to teach students the fundamentals of data literacy through an engaging genome sequencing project. Each cohort of students performed planning of the experiment, DNA extraction, nanopore sequencing, genome sequence assembly, prediction of genes in the assembled sequence, and assignment of functional annotation terms to predicted genes. Students learned how to communicate science through writing a protocol in the form of a scientific paper, providing comments during a peer-review process, and presenting their findings as part of an international symposium. Many students enjoyed the opportunity to own a project and to work towards a meaningful objective.

Depth-sensing technology has led to broad applications of inexpensive depth cameras that can capture human motion and scenes in 3D space. Background subtraction algorithms can be improved by fusing color and depth cues, thereby allowing many issues encountered in classical color segmentation to be solved. In this paper, we propose a new fusion method that combines depth and color information for foreground segmentation based on an advanced color-based algorithm. First, a background model and a depth model are developed. Then, based on these models, we propose a new updating strategy that can eliminate ghosting and black shadows almost completely. Extensive experiments have been performed to compare the proposed algorithm with other, conventional RGB-D algorithms. The experimental results suggest that our method extracts foregrounds with higher effectiveness and efficiency.

This study explores the crucial roles of energy literacy, transition, and justice in addressing the global climate crisis. Through a systematic bibliometric survey and thematic analysis of existing literature, we identify a scarcity in research (<0.1%, n = 17,773) concurrently integrating the three crucial terms: energy literacy; energy transition; and energy justice. This study emphasizes the importance of energy literacy in empowering societies to make informed decisions, which is essential for facilitating equitable energy transitions and achieving sustainability goals. We delve into the interconnections between energy literacy and energy justice, proposing the integration of these concepts into educational systems, including social media platforms to foster an understanding of energy systems, their environmental impacts, and socio-economic implications. Emphasizing the strategic use of social media, the study also introduces the ‘COMPEL Justice’ framework—Communities Prepared for Energy Literacy and Justice—which aims to enhance public engagement and participation in energy transition initiatives. By analyzing publications from various academic and governmental sources, we provide insights into the challenges and opportunities that lie in promoting energy literacy and justice, particularly in the context of global efforts to mitigate climate change and enhance energy equity. Finally, exemplary application and implications associated with the adoption of the proposed framework within the context of oil-producing regions (e.g., Louisiana in the United States) are presented.

Underwater Sensor Networks (UWSNs) can enable a broad range of applications such as resource monitoring, disaster prevention, and navigation-assisted. It is especially relevant for sensor nodes location in UWSNs. Global Positioning System (GPS) is not suitable for using in UWSNs because of the underwater propagation problems. Hence some localization algorithms based on the precise time synchronization between sensor nodes have been proposed which are not feasible for UWSNs. In this paper, we propose a localization algorithm called Two-Phase Time Synchronization-Free Localization Algorithm (TP-TSFLA). TP-TSFLA contains two phases, namely, range-based estimation phase and range-free evaluation phase. In the first phase, we address a time synchronization-free localization scheme base on the Particle Swarm Optimization (PSO) algorithm to decrease the localization error. In the second phase, we propose a Circle-based Range-Free Localization Algorithm (CRFLA) to locate the unlocalized sensor nodes which cannot obtain the location information through the first phase. In the second phase, sensor nodes which are localized in the first phase act as the new anchor nodes to help realize localization. Hence in this algorithm, we use a small number of mobile beacons to help achieve location without any other anchor nodes. Besides, to improve the precision of the range-free method, an extension of CRFLA by designing a coordinate adjustment scheme is updated. The simulation results show that TP-TSFLA can achieve a relative high localization ratio without time synchronization.

Insulated Gate Bipolar Transistor (IGBT) may convey high current and sustain high breakdown voltages; it may provide high power control. In a sense, how IGBT demonstrates the capability of power control was not specifically described, and the equivalent bipolar junction transistors (BJT) activated by an applied bias to the insulated gate is intriguing. As seen in Figure 1, IGBT shows an equivalent circuit structure containing two types of BJT, (NPN-type Bipolar transistor and PNP-type Bipolar transistor), which can be modeled as diode-associated transistor. Apparently, as a bias applies to the controlling gate, the channel beneath the gate oxide gets strongly inversed and turns out to be conductive just like what happens on metal-oxide-silicon field effect transistor (MOSFET), which provides necessary igniting currents to both BJTs and thus causes high current gain flowing through them-selves from the bottom. A proposed and renewed formula provides addressing the above scenario and successfully fitting the measured data that forms the characteristic curves. Some parameters in the proposed formula, which includes size and mobility associated constant, threshold voltage, and Early-Voltage-related lambda for MOSFET, explain and explore the underlying physics. IGBT thus enjoy high input impedance and high current gain due to MOSFET (Insulated Gate) and BJT, respectively. This formula combines both merits of MOSFET and BJT in order to fit the measured data. Indeed, the formula intimately fits IGBT electrical characteristic curves, and thus may be qualified and served as a model. The new model is then advisable for designing power integrated circuit to exactly achieve power saving, power control, and power efficiency.

Financial literacy is a path to sustainability and has an important role in ensuring the financial sustainability of individuals, families, enterprises and national economies. The level of these economic indicators such as debt, payment discipline, savings and financial management all translate into prosperity or insolvency and bankruptcy and result partially from financial literacy. The higher the level of financial literacy, especially of young people, the more favorable the level of economic indicators, which translates into the economy and sustainable development. However, despite many years of research, there is still a lack of a uniform and coherent definition of financial literacy, as well as methods for measuring it. This study offers a small step forward, presenting the authors’ own view of the concept of financial literacy, the conceptualizations of financial literacy and methods used for investigating. Moreover, they present the results of a survey conducted on the financial behaviour, financial attitudes, and financial knowledge of the Polish youth and compare this to a PISA study on 15-year-old students. Results demonstrate a good, and partially very good, level of financial literacy among young Poles, showing that they are rational in their financial decision making.

Deadlock is a real problem that can cause expensive, time-dependent hang-ups or failures in distributed computing systems. Some studies have been conducted with deadlock detection and resolution in generalized request computing models, most of these studies are based on the diffusing computation scheme, where forward and backward propagated probe and reply messages respectively are diffused between dependent processes. The replies carry the dependency information between processes for the initiator. Using this dependency information initiator can detect and resolve the deadlock cycle. It has been proven in the literature that a deadlock cycle length distribution is skewed with a large majority of cycles (90%) having length two. Based on this fact an improved algorithm is proposed for handling deadlock detection in distributed computing systems under the distributed request model where each process has several requests at a time. The improved algorithm depends mainly on message propagation and timeout policy. Probe and reply messages are propagated between the initiator and its successors only to detect deadlock cycles between them. Global timeout policy is used to detect potential deadlock cycles with large length distribution. The proposed algorithm can handle concurrent executions. Simulation experiments are performed to see the effectiveness of the improved algorithm. It is found that the improved algorithm compares favorably with other existing algorithms and it shows better results for several performance metrics especially in reducing deadlock latency and execution time.

Preventive Maintenance (PM) is a periodic maintenance strategy, which has great results for devices in extending their life, increasing productivity, and most importantly, help avoid unexpected breakdowns and their costly consequence. A preventive maintenance scheduling (PMS) is determining the time for carrying out PM, and it represents a sensitive issue in terms of impact on production if the time for the PM process is not optimally distributed. In this study, hybrid heuristic methods were used to solve the PMS problem, as genetic algorithm and Tabu research were adopted. The optimal solution was reached in a short time compared to previous studies in which optimal methods were used, integer programming and nonlinear integer programming. Also, sensitivity analysis was applied to measure the robustness and strength of the method used, where an optimal solution was obtained for all experiments and in record time. This method can be used for power plants in privet or public sectors, to generate an optimal PMS, to save money, and to avoid water or electricity cuts.

Detection of subsurface archaeological remains using a range of remote sensing methods, poses several challenges until today. Recent studies regarding the detection of archaeological proxies like those of cropmarks highlight the complexity of the phenomenon. In this work we present three different methods, and associated indices, for identifying stressed reflectance signatures indicating buried archaeological remains, based on a dataset of measured ground spectroradiometric reflectance. Several spectral profiles between the visible and near infrared part of the spectrum, were taken over a controlled environment in Cyprus during the 2011-2012 and are re-used in this study. The first two (spectral) methods are based on a suitable analysis of the spectral signatures in (1) the visible part of the spectrum, and in particular in the neighborhood of 570 nm, and (2) the red edge-near infrared part of the spectrum, in the neighborhood of 730 nm. Machine learning (decision trees) allows for the deduction of suitable wavelengths to focus on, in order to formulate the proposed indices and the associated classification criteria (decision boundaries) that can enhance the detection probability of stressed vegetation. Noise in the signal is taken into account by simulating reflectance signatures perturbed by white noise. Applying decision tree classification on the ensemble of simulations and basic statistical analysis we refine the formulation of the indices and criteria for the noisy signatures. The success rate of the proposed methods is over 90%. The third method rests on the estimation of vegetation/canopy reflectance parameters through inversion of the physical-based PROSAIL reflectance model and the associated classification through machine learning methods. The obtained results provide further insights into the formation of stress vegetation that occurred due to the presence of shallow buried archaeological remains, which are well aligned with physical-based models and existing empirical knowledge. To the best of the authors' knowledge, this is the first study demonstrating the usefulness of radiative transfer models such as PROSAIL for understanding the formation of cropmarks. Similar studies can support future research directions towards the development of regional remote sensing methods and algorithms if systematic observations are adequately dispersed in space and time.

Amid the swift progression of the global automobile industry, the aspiration for lightweight vehicle design has surfaced as a crucial approach to boost fuel efficiency and curtail environmental impact. The objective of this study revolves around achieving holistic optimization of vehicle structure via detailed scrutiny of vehicle process characteristics, amalgamated with a Back Propagation neural network (BPNN) optimized by a genetic algorithm (GA) and a topology optimization (TO) method.The process initiates with an examination of the vehicle's technical characteristics, succeeded by an optimization protocol that couples GA with BPNN. This exhaustive optimization procedure derives the best-fit values of design parameters and identifies the globally optimal lightweight design approach. Subsequently, a comprehensive structural optimization is actualized by modifying the topological distribution of the structure.A simulation experiment of the conceptual car body beam skeleton is conducted as a conclusive step, and the anti-collision capacity of the car body beam skeleton is assessed by measuring deformation intrusion. The findings reveal that in the vehicle topology optimization, a comparative analysis of pre and post-optimization results indicates an improved topological density across diverse sections. The topological density of the vehicle's frontal structure escalates from 0.75 to 0.8. Following optimization, the stiffness assessment records a marginal increase (within the range of 0.02–0.05), implying that the lightweight design does not compromise the general rigidity of the structure.The optimized body structure manifests superior crashworthiness in the crash test: the deformation intrusion diminishes by 27% in front-end collisions. In side impacts, the deformation and intrusion at critical points are reduced by 13.15% to 10% respectively. The comprehensive method proposed herein proves to be an efficient strategy to actualize the lightweight design of vehicles.

Gaze tracking of high-speed moving targets is a novel application mode for low Earth orbit microsatellites. In this mode, small satellites are equipped with high-resolution narrow-field-of-view video cameras for stable gaze-tracking imaging of high-speed moving targets. This paper proposes a high-precision attitude adaptive integral sliding mode control method with a feedforward compensation disturbance observer to enhance the capability of a microsatellite attitude control system for gaze tracking of high-speed moving targets. Specifically, first, we present the attitude control system model for microsatellites and the calculation method for the desired attitude of target tracking based on image feedback. Then, an adaptive integral sliding mode attitude control algorithm with a feedforward compensation disturbance observer, which meets the requirements of high-precision tracking control, is designed. The developed algorithm utilizes the disturbance observer to observe the friction torque of the flywheel and compensates for it through feedforward control. It also employs the adaptive integral sliding mode control algorithm to reduce the impact of uncertain disturbances, decrease the steady-state error of the system, and enhance the attitude control precision. Simulation experiments demonstrated that the designed disturbance observer can successfully observe the frictional disturbance torque of the flywheel. The attitude Euler angle control precision for high-speed moving target tracking reached 0.009°, and the angular velocity control precision reached 0.005°/s, validating the effectiveness of the proposed approach.

Although advertising literacy leads to critical thinking in the face of advertising, but so far, no action has been taken in Iran regarding a tool to measure this type of literacy. And after the investigations, it was determined that although much research has been done on advertising, but the lack of appropriate measurement tools to measure the level of advertising literacy is clearly evident. Therefore, this research provides a valid tool for measuring advertising literacy from students' perspective. In this study, referring to the dimensions of advertising literacy from the perspective of Malmelin (2010) and the views of related professors, a questionnaire was developed and to determine the validity of the structure, confirmatory factor analysis was used. In this study, the statistical population was high school students, considering the impact of advertising in this age range; finally, a tool with four dimensions of informational literacy, aesthetic literacy, rhetorical literacy and promotional literacy was obtained. According to the confirmation of this tool in the present study, it can be used to examine the status of items, their order and prioritization from the perspective of the mentioned population.

Count data analysis presents unique challenges due to its discrete nature, often exhibiting excessive zeros and overdispersion. To address these complexities, count models, such as Poisson regression and Negative Binomial regression, have been developed, enabling modeling and prediction of count-based phenomena. Additionally, it’s important to notice that zero inflation, a phenomenon commonly observed in count data, requires specialized techniques for robust analysis. This article provides an overview of count data and count models, explores zero inflation, introduces Likelihood Ratio Tests, and explains how the Vuong Test can be used as a model selection criteria. Furthermore, we created a Vuong Test implementation from scratch using the Python programming language. This implementation enhances the accessibility and applicability of the Vuong Test in real-world scenarios, providing a valuable contribution to the academic community, since Python didn’t have an implementation of this statistical test.

While the role of safe riding behavior as a safety contributor for cyclists has been increasingly studied in recent years, there have been few studies analysing cycling behavior in relation to crash-related outcomes in Baltic countries. Indeed, to the best of our knowledge, this is the first time this issue has been addressed in the case of Latvia. Aim: The objective of this study was to assess the relationships among self-repotted cyclists’ behavior, traffic safety literacy, and their cycling crash involvement rates. Method: A total of 299 cyclists aged M=32.8 from across Latvia (42% females) participated in an online survey, which comprised questions regarding respondents’ demographics, frequency of riding, and the number of crashes in the past five years. The Cycling Behavior Questionnaire (CBQ) and the Cyclist Risk Perception and Regulation Scale (RPRS) were applied to assess cyclists' behavior patterns and traffic safety literacy. Results: According to the findings, it can be inferred that cyclists frequently engage in riding errors and traffic violations while cycling. Those who exhibit more antisocial behavior patterns are also more likely to be involved in road crashes. Conversely, cyclists with greater positive behavior rates more often also tend to possess better knowledge of traffic rules and exhibit a heightened risk perception, indicating a greater awareness of road traffic safety. Conclusion: All in sum, this study underscores key age differences, with older individuals significantly less involved in riding crashes, exhibiting fewer driving errors and a higher level of risk perception, which serves as a crucial factor in road safety. At the practical level, these results stress the need to address both traffic safety literacy and protective cycling factors of cyclists, in a manner to improve overall road safety and promote active transport modes in Latvia.

Digital literacy is the ability to use digital technologies to find, evaluate, create, and communicate information. People with disabilities benefit from having these skills because they allow them to access various opportunities in the digital world. To ensure that they can fully participate in the digital realm, digital literacy efforts should prioritize inclusive design principles. People with disabilities often rely on assistive technologies such as screen readers, alternative input devices, voice recognition software, or specialized hardware to navigate digital platforms. Digital literacy programs should not only focus on consuming content but also on creating and sharing it through documents, multimedia, and online platforms. Peer support and collaboration are important for enhancing their learning experience. Additionally, digital literacy programs address cybersecurity or online safety considerations specific to such people and provide them training and support for using assistive technologies effectively while educating them about potential risks, privacy settings, and safe online practices. This narrative review highights the meaning and levels of digital literacy, digital divide, history, before expanding on the quantative metrics in the available research on the theme of people with disabilities. Retrieved data from freely accessible internet search engines revealed 196 publications, including books, chapters in books, original research articles, proceedings of seminars, and reviews, which were classified into harvest plots to be analyzed by their format, timelines, topics, or themes, respectively. While results indicate an upward curve in the quantity of publications, especially in the post-millennium era, the topics addressed are unevenly distributed, leaving scope for the future to focus empirical and evidence based research on themes related to measurement as well as training on digital literacies for both teachers and students with disabilities. By promoting digital literacy in people with disabilities, we can bridge the digital divide and empower them to participate fully in today's increasingly digital society.

The world is facing an energy crisis. Governments are seeking to provide universal energy access and guarantee energy security while trying to mitigate climate change. One possible solution is energy transitions towards low carbon energy systems. Among other things (physical infrastructure, public policy and regulatory enablers and knowledge and capacities) changes in the energy systems require a well informed and participative citizenship. Within this context the concept of energy literacy appears. Energy literacy is the understanding of how energy is generated, transported, stored, distributed and used, awareness about its environmental and social impacts and the knowledge to use it efficiently in the different sectors of the economy. This paper provides a systematic literature review in the Web of Science’s Core Collection. Most of the work done around energy literacy addresses its evaluation among different groups, particularly students at different levels, and the construction, application and evaluation of tools for improving energy literacy. Other frequently studied issues are the influence of energy literacy in decision making, its drivers and conceptual research about the topic. Energy enables citizens to effectively contribute to energy efficiency and sustainable development, nevertheless energy literacy is not strongly correlated to energy consumption habits.

This article mainly proposes the point of view that financial literacy curriculum aims to make kinds of people, to make people with financial creativity of resource integration thinking and continuous ‘to conscience’ of morality, which is cultivate a way of thinking that can maximize benefits through the use of resource integration and constantly ‘to conscience’ to achieve goals for participants in financial structures with balanced interests. In this sense, this article argues that financial literacy is to make kinds of people with a financial mindset and constantly strengthen ‘to conscience’ through uniting of knowledge and action to achieve human well-being and benign operation of society. In this sense, this article attempts to determine what is really taught in financial literacy education in terms of curriculum theory. That is to improve creativity by cultivating financial thinking with constant ‘to conscience’. The views of this paper provide a new way of thinking about financial literacy education assessment. This changes the evaluation criteria of financial literacy education from the result-oriented of wealth growth to the process-oriented of financial creativity growth, trying to move the criticism of financial literacy education curriculum and evaluation from controversy to unity.

This paper discusses the impact of orthographic manuals on the state of literacy, i.e. the relation of orthographic literacy and orthographic standardisation. The established hypothesis claims that frequent changes of orthographic rules during the pupils’ primary and secondary education do not have any considerable impact on their orthographic habits. In other words, the quantity of orthographic mistakes observed during a longer period of time and in conditions of changed orthographic rules would not show significant oscillations in their spelling. In order to confirm the hypothesis, a questionnaire was conducted encompassing 41 tests among 526 students of a technical study programme during four consecutive academic years, pursuant to whose results a writing uniformity index and a categorisation of orthographic controversy into six classes is established. The Croatian language has been selected for the observation due to multiple orthographic changes in the last 30 years in the three major orthographic points: writing of the covered r, writing of d and t in front of c and č in declination of words ending in -tak, -tac, -dak and -dac, and the issue of compound or separate spelling of the negation particle and the auxiliary biti (to be). Moreover, the paper methodologically and quantitatively establishes criteria according to which the second established hypothesis on evolutionary orthographic literacy can be confirmed. The conclusions are expected to be able to contribute to the better understanding of orthographic planning and application of orthographic norms in schools.

Accurate forecasting of icing thickness has a great significance for ensuring the security and stability of power grid. In order to improve the forecasting accuracy, this paper proposes an icing forecasting system based on fireworks algorithm and weighted least square support vector machine (W-LSSVM). The method of fireworks algorithm is employed to select the proper input features with the purpose of eliminating the redundant influence. In addition, the aim of W-LSSVM model is to train and test the historical data-set with the selected features. The capability of this proposed icing forecasting model and framework is tested through the simulation experiments using real-world icing data from monitoring center of key laboratory of anti-ice disaster, Hunan, South China. The results show that the proposed W-LSSVM-FA method has a higher prediction accuracy and it may be a promising alternative for icing thickness forecasting.

The weight coefficients of the diaphragm spring depend on experiences in the traditional optimization. However, this method not only cannot guarantee the optimal solution but it is also not universal. Therefore, a new optimization target function is proposed. The new function takes the minimum of average compress force changing of the spring and the minimum force of the separation as total objectives. Based on the optimization function, the result of the clutch diaphragm spring in a car is analyzed by the non-dominated sorting genetic algorithm (NSGA-II) and the solution set of Pareto is obtained. The results show that the pressing force of the diaphragm spring is improved by 4.09%by the new algorithmand the steering separation force is improved by 6.55%, which has better stability and steering portability. The problem of the weight coefficient in the traditional empirical design is solved. The pressing force of the optimized diaphragm spring varied slightly during the abrasion range of the friction film, and the manipulation became remarkably light.

High Education Institutions (HEI) play a key role in fostering sustainability since students will be future active society members. By providing them with the knowledge, skills, and mindset needed to address environmental challenges, they can contribute to increase sustainability in a long-term approach. With the effects of climate change strongly affecting water availability across planet, it is increasingly relevant to understand if higher education students are aware of this situation, to develop adequate measures to promote sustainable behaviors regarding water uses. This study aimed to assess students’ perceptions regarding water efficiency through a questionnaire submitted in 7 HEI in Portugal. Students recognized the importance of water with older students, women and science students demonstrating a greater awareness. Inspite of this 27% students do not know what the average water consumption in Portugal is and only 18% accurately reported the consumption range in Portugal. There is also a lack of knowledge (&gt;50% students) if common water efficiency measures are applied in their HEI. The main information sources identified by the students were “social communication” and “internet/social media”, revealing that water efficiency knowledge transfer in high education can be improved.

This case study explores the transformative effects of emergent digital technologies, particularly augmented reality (AR), on literacy engagement and reading for pleasure. The United Arab Emirates Ministry of Education implemented the UAE Storytime programme, utilising AR to create an interactive literacy experience for young learners. The programme incorporated bilingual episodes, featuring stories based on local and global contexts, with 3D objects and interactive games to enhance understanding. The results indicate a positive correlation between the use of AR and literacy engagement. A high proportion of learners scanned the QR codes to access the AR objects, which enhanced their engagement and understanding of the stories. The programme fostered a learner agency, engaged family members in the learning process, and established a sense of global community. The study recommends the integration of similar initiatives into the curriculum to promote collaboration, creativity, and holistic learning experiences.

Health literacy is an indicator of a society’s ability to make better health judgement for themselves and the people around them. This study investigates the prevalence of health literacy among Malaysian adults and provides an overall picture of the current health literacy state of the society. The study also highlights socio-demographics markers of communities with limited health literacy which may warrant future intervention. A population based self-administered survey using the Health Literacy Survey Malaysian Questionnaire18 (HLS-M-Q18) instrument was conducted as part of the National Health Morbidity Survey 2019 in Malaysia. The nationwide survey utilized two-staged stratified random sampling method. A sample of 9478 individuals aged 18 and above participated in the study. The health literacy score was divided into three levels; limited, sufficient and excellent. Findings showed that majority Malaysian population has sufficient health literacy level, albeit leaning towards the lower end of the category with an average score of 35.5. The limited health literacy groups are associated with respondents with older age, lower education level and lower household income. Overall health literacy state for Malaysia is categorized at a lower sufficient level. Health literacy improvements should focus on communities with limited health literacy level.

The Covid-19 infodemic can be countered by scientific evidences, clear and consistent communication and improved health literacy of both individuals in need of information and those providing it. A rapid online survey was carried out to evaluate vaccine literacy (VL) skills in the general population and perceptions about candidate Covid-19 vaccines, as well as behavior and beliefs about current vaccinations. Observed VL levels were sufficiently high and consistent with previous observations - where comparable self-reported tools were administered face-to-face and paper-and-pencil - the mean functional score being =2.92, while the interactive-critical one was =3.27, on a maximum of 4. Perceptions regarding future Covid-19 vaccines, along with beliefs about vaccination, were mostly positive and significantly associated with functional and interactive-critical VL scales. Despite obvious limitations, the study confirms that rapid surveys via web are a suitable method to evaluate and trail attitudes during infectious disease outbreaks, and to assess health literacy skills about vaccination, which can be useful to adapt medical communication strategies, for a better understanding of the value of immunization.

P-band radar remote sensing applied during the Airborne Microwave Observatory of Subcanopy and Subsurface (AirMOSS) mission has shown great potential for estimation of root zone soil moisture. When retrieving the soil moisture profile (SMP) from P-band radar, a mathematical function describing the vertical moisture distribution is required. Because only a limited number of observations are available, the number of free parameters of the mathematical model must not exceed the number of observed data. For example, a second order polynomial that contains 3 free parameters was presumed based on in-situ SMP data. The polynomial is currently parameterized based on 3 backscatter observations provided by AirMOSS (i.e. one frequency at three polarizations of HH, VV and HV). In this paper, a more realistic, physically-based SMP model containing 3 free parameters is derived based on a solution to Richards’ equation for unsaturated flow in soils. Evaluation of the new SMP model based on both numerical simulations and measured data revealed that it exhibits greater flexibility for fitting measured and simulated SMPs than the currently applied polynomial. It is also demonstrated that the new SMP model can be reduced to a second order polynomial at the expense of fitting accuracy.

The field of financial literacy is intricate and developing. This systematic review of the literature provides a thorough overview of financial literacy. It examines the sample of 3182 papers of the period 1990 to 2023 from different databases such as Scopus, Web of Science, IEEE, Science Direct, and JSTOR and identifies important publications, knowledge gaps, and rising themes. The four major topics that emerged are the degree of financial literacy among various groups of people, gender disparity in financial literacy, the influence of financial literacy on financial behavior and planning, and the impact of financial education on day-to-day decisions. Groups with lower levels of financial literacy include adults, women, people in the lower-income band, rural people, etc. Financial planning and financial behavior are positively associated with financial literacy. Financial literacy could be enhanced with financial education; however, output depends upon program design and experimental group. The review also discusses financial literacy in the context of gender disparity, including its definition, difficulties encountered, and possible solutions. This systematic study will help researchers, practitioners, and policymakers by providing insightful information on financial literacy and its ramifications.

This research follows the publication of recent scoping and systematic reviews of existing literature on vaccine literacy (VL), its relationship to vaccine hesitancy (VH), and associated variables, such as vaccination beliefs, attitudes, and behaviors towards immunization. In this review, 17 articles were selected, published between December 2022 and November 2023, in which VL tools were used, most of which support the notion of a negative association between VL and VH, although not confirmed by others. Moreover, the definitions of the variables under consideration were not homogeneous between studies, some aimed to evaluate the association of VL with vaccine confidence, while others were more focused on outcomes such as willingness to be vaccinated or vaccine uptake. The complexity of the factors underlying VL, and the heterogeneity of the methods applied in the studies, in particular the limitations of cross-sectional surveys, still make it difficult to have a comprehensive understanding of the role of VL. Therefore, it is important to develop future assessment tools that consider not just the measurement of psychological factors connected to the motivational aspect of VL, but also those related to knowledge and competencies. For this purpose, a theoretical framework is proposed, where the positioning of VL at the intersection between antecedents and beliefs/attitudes leading to behaviors explains why and how VL serves as a tool and a critical, direct, or mediating driver of vaccination choices, overcoming VH and increasing vaccination rates, operating at personal, as well as at organizational and community level.

Widespread patterns of poor dietary behavior are a key factor causing the increasing prevalence of chronic diseases around the world. Research provides initial insights into the potential of food literacy (FL) to empower individuals to improve their dietary behavior. Yet, studies on FL interventions in working adults are scarce. This study aimed to investigate the short- and long-term effects of a comprehensive 3-week workplace health promotion program (WHPP) on FL and dietary intake (DI) and to examine the association between FL and DI in a sample of 144 German office workers (30.0% female). Using two random intercept mixed linear regression models, we found significant strong improvements for both FL (β = 0.52, p &lt; .0001) and DI (β = 0.63, p &lt; .0001) after the WHPP when compared to baseline. Significant long-term improvements at 18 months where strong for FL (β = 0.55, p &lt; .0001) and small for DI (β = 0.10, p &lt; .0001). FL showed a significant moderate effect on DI across all measurement time points (β = 0.24, p &lt; .0001). Our study fills a gap of long-term findings in the literature on FL interventions, offers insights into underlying mechanisms, and provides recommendations for effective WHPPs.

This research article examines the state-wise literacy rates in India, with a focus on male and female literacy, as well as the disparities between them. The data was sourced from a survey conducted by the National Statistical Office (NSO), with a special emphasis on Union Territories and Northeastern states based on the 2011 Census. Kerala boasts the highest literacy rate at 96.2%, while Andhra Pradesh reports the lowest at 66.4%. Notably, the national male-female literacy gap stands at 12.9%. Kerala also demonstrates the smallest gender gap at 2.2%, while Rajasthan exhibits the highest at 23.3%. Through descriptive and explorative analysis, this article provides a comprehensive overview of literacy rates across various Indian states and Union Territories, highlighting the significant disparities that exist.

This article introduces a novel nature-inspired metaheuristic algorithm called Frilled Lizard Optimization (FLO), which emulates the hunting behavior of frilled lizards in their natural habitat. FLO draws in-spiration from the sit-and-wait strategy observed in frilled lizards during hunting. The underlying theory of FLO is presented and mathematically formulated in two phases: (i) an exploration phase, simulating the frilled lizard's attack towards prey, and (ii) an exploitation phase, simulating the lizard's retreat to the top of the tree after feeding. To assess FLO's efficacy in solving optimization problems, the algorithm's performance is evaluated across fifty-two standard benchmark functions, encompassing unimodal, high-dimensional multimodal, fixed-dimensional multimodal, and the CEC 2017 test suite. Comparative analyses with twelve existing metaheuristic algorithms are conducted. The simulation results reveal that FLO, distinguished by its adeptness in exploration, exploitation, and balancing them during search process, outperforms competing algorithms. Additionally, FLO is implemented on twenty-two constrained optimization problems from the CEC 2011 test suite and four engineering design problems, demonstrating its effectiveness in addressing real-world optimization applications.

Introduction: In recent decades, health literacy, in connection with a broad range of public 1 health terms, has become a burgeoning field. This study aims to explore trends and biases in this 2 area through a bibliometric analysis. Methods: A Random Forest Model was utilized to identify 3 keywords and other metadata that predict annual citations in the field. In order to supplement 4 this machine learning analysis, we have also implemented a bibliometric review of the corpus. 5 Results: Findings indicate a high positive coefficient for the keyword ’Covid-19’ and ’Male’, whereas a 6 negative coefficient was observed for ’Female’, suggesting potential biases. Evolving themes such as 7 Covid-19, Mental Health, and Social Media were discovered. A significant shift was noted in the main 8 publishing journals, while the major contributing authors remained the same. Discussion: The results 9 hint at the influence of the Covid-19 pandemic and potential gender biases on citation likelihood, as 10 well as changing publication strategies despite the fact that the main researchers remain as the ones 11 that have been studying health literacy since its creation.

Background: Poor menstrual health literacy impacts adolescents’ quality of life and health outcomes across the world. The aim of this systematic review was to identify concerns about menstrual health literacy in low/middle (LMIC) and high-income (HIC) countries. Methods: Relevant social science and medical databases were searched for peer-reviewed papers from January 2008 to January 2020 identifying 61 relevant studies. Results: A thematic analysis of the data revealed that LMICs report detrimental impacts on adolescents in relation to menstrual hygiene and cultural issues, while in HICs, issues related to pain management and long term health outcomes were reported more frequently. Conclusions: In order to improve overall menstrual health literacy in LMICs and HICs, appropriate policies need to be developed, drawing on input from multiple stakeholders to ensure evidence-based and cost-effective practical interventions.

People must have particular personality traits and social resources, also known as health literacy, in order to access, comprehend, and use information to make decisions about their health. Patients' ability to engage in complex disease management and self-care is strongly related to their level of health literacy. It can help us stay healthy by preventing illness and effectively managing existing illnesses. People with low health literacy (LHL) may find it difficult to manage their condition and prevent illness, which may lead to increased use of healthcare services. Furthermore, LHL is associated with increased hospitalizations, increased use of emergency care, decreased use of preventative services, and a worsened ability to understand labels and health messages, a worsened state of health, higher mortality, and more expensive medical care.

Environmental literate citizenship is a lifelong learning process that aims to develop citizens who have the knowledge, skills, and commitment to make responsible decisions that will affect the quality of the environment. The purpose of the present study was to evaluate the environmental literacy levels of nursing students in terms of sustainable environmental by considering the dimensions of environmental literacy. It was also aimed at understanding the correlation among dimensions of environmental literacy and revealing the factors affecting the dimensions of environmental literacy. This research was designed as a descriptive study in the survey model. A total of 278 nursing students participated in this research. It was revealed that students obtained a high score for use and concern from the dimensions of environmental literacy, their attitudes towards the environment were moderate; however, nursing students took the lowest points for the environmental knowledge component, which indicates that students need support in environmental knowledge. It was put forward that there were statistically significant differences based on gender and taking environmental related courses with dimensions of environmental literacy. All dimensions of environmental literacy were found to be positively correlated with each other, ranging from low to moderate relations.

In this paper, a distinction is first made between environmental, sustainable and green chemistry; the last two are then examined in relation to the more general problem of the environmental education. A brief historical digression on the STS (Science, Technology and Society) movement tries to dissect the reasons why chemistry is seen by the general public as a problem, not as a decisive resource for the realization of the ecological transition. Although sustainable and green chemistry can be decisive in overcoming the insularity of chemical disciplines in the high school, it fails to effectively embed itself in educational practices. Specific operational goals, supporting a real scientifical and technological literacy in sustainable and green chemistry, can help chemical educators. They are provided at the end of an examination of the founding axes of sustainable and green chemistry, according to the criteria of scientific and technological literacy.

The goal of the project was to gain an understanding of the secondary school youth experience with food literacy and school gardens on their mental well-being. Over the course of five months, sixteen youth participated in a photovoice research project in which they expressed their personal experiences about food and gardening through photography and writing. The aspects of secondary school youths’ life experiences affected by exposure to food literacy and school gardens and their impact upon their well-being were identified. These included emotions and feelings, having a safe place, nutrition and relaxation. The youth explicitly connected relaxation with the themes of love and connectedness, growing food, garden as a place, cooking, and food choices. This was linked to nature, beauty, environment and sustainability. Youth clubs or groups were also identified as a key enabler for connection. Youth shared their food literacy experiences, observing that their engagement improved some aspect of their mental well-being. They identified food literacy and gardens as being the most important to mental well-being including: connecting, personal health and personal growth. The youth recognized that connecting comes from having community, relationships and respect. Fostering opportunities for food literacy such as growing and preparing food contributes to resiliency.

Consumer protection in the financial market has several dimensions. From a formal point of view, consumer rights are guaranteed by law. Market asymmetries are eliminated through the obligation to provide consumers with complete and objective information about financial products and services, honest marketing communication and professional sales practices. Programs are implemented in schools and the media to promote knowledge and responsible use of financial products and services. Despite the efforts made, the number of incorrect and suboptimal financial decisions is so high that the risk of households falling into excessive debt remains significant. The limited effectiveness of the law led to the claim that only effective education can reduce the risk of suboptimal financial decisions. Unfortunately, the efforts made in this area are not fully satisfactory. The aim of the article is to review scientific research on the financial knowledge of consumers in Central Europe and compare it to the results of a study conducted in Poland in January 2024. The results of the analysis could indicate the direction of further research on the possibilities of eliminating suboptimal financial decisions of consumers without limiting - for their better protection - access to financial products.

This study measured secondary students’ digital literacy using a digital game-based assessment system that was designed and developed based on the Evidence-Centered Game Design (ECGD) approach. A total of 188 secondary students constituted the valid samples in this study. Fine-grained behavioral data generated from students’ gameplay processes were collected and recorded with the assessment system. The Delphi method was used to extract feature variables related to digital literacy from the process data, and the Analytic Hierarchy Process (AHP) method was used to construct the measurement model. The assessment results of the ECGD-based assessment had a high correlation with standardized test scores, which have been shown to be reliable and valid in prior large-scale assessment studies.

Parental refusal of childhood vaccines is a growing public health concern. Numerous reasons exist for this refusal, including religious, personal, and philosophical beliefs, and safety concerns. However, parental refusal of childhood vaccines is not simply an individualized problem for the family; this impacts herd immunity and affects the entire community. To improve vaccination rates among the pediatric population, understanding the thought process and decision-making behind parental opposition and refusal of vaccinations is essential. Using a survey developed to assess attitudes towards recommended childhood vaccines and the COVID-19 vaccine, this study examined the correlation between vaccine literacy and hesitancy among parents and legal guardians of elementary school-aged children in the Midwest. Responses were analyzed using Chi-squared tests on “R” software. Significant negative correlations were found between COVID-19 hesitancy and vaccine literacy, and resistance towards all vaccines and vaccine literacy. No significant negative correlations were found between hesitancy towards all vaccines and vaccine literacy, or hesitancy and literacy among different income and education brackets. Our results suggest that vaccine education may lessen vaccine hesitancy among parents and may be an essential factor in improving vaccination rates among the pediatric population.

The Interdisciplinary Method of Musical Literacy, Education and Artistic Awareness (MILMESA) is developed and applied essentially in higher education (artistic, cultural and teacher training courses), promoting effective endogenous (interarts) and exogenous interconnection (artistic and other non-artistic areas) of an inter and pluridisciplinary matrix, as well as assuming itself as a privileged vehicle of relationship and extension to the community in the frame of the making of artistic-cultural and pedagogical-didactic events in the European, Luso-Brazilian, Luso-African and Hispanic American territories. It was created, tested and evaluated in five consecutive quadrennia (2002–2023), being regularly adapted, adapted and added according to the observed realities, evolving into the deepening of new concepts and models of intervention in these domains (e.g., Expressive and Proximal Didactics). Using the Ethnographic Method and the Case Study, and having the source of data collection as Document Analysis of written and iconographic or visual sources (n = 1386) and Participant Observation, this study aimed to describe and analyse the application and evolution of MILMESA in terms of education and extension to the community. It was concluded that there was a need to promote, with greater regularity and scope, greater technological interaction via digital platforms and social networks, within the framework of the participation of external specialists (e-DEP) and the very experience of MILMESA’s experiences and initiatives, giving it a more meaningful and participatory range.

Objective: This study aimed to determine impact of eHealth literacy on health promotion activities and preventive health practice among adolescents. Methods: This cross-sectional study was conducted between March and May 2022 with 706 adolescents in Çorum, Turkey. A face to face questionnaire form including socio demographic characteristics, preventive health practices, eHealth Literacy scale and Adolescent Health Promotion Scale were used. The data of the research were analyzed with the SPSS 22.0 program. Percentage, mean, Pearson Correlation analysis and multiple regression analysis were used in the analyzes. The p<0.05 value was considered statistically significant in the evaluations. Results: In the study, 55.8% were female and the mean age of the whole group was 16.09±2.63 years. The mean score of eHealth Literacy scale was 29.40±6.29. The mean of the total scores obtained from the Adolescent Health Promotion Scale was 137.97±21.87. Among the adolescents 96.0% didn’t use alcohol and 81% didn’t smoke. The rate of annual weight measure was 68.8%. Measurements of annual blood pressure, annual blood iron, annual cholesterol, annual dental examination and regular exercise rate were below 50%. There was a significant positive correlation between eHealth literacy and the Adolescent Health Promotion Scale (p<0.001). Multiple linear regression analysis was performed to predict eHealth literacy using the variables of Adolescent Health Promotion and preventive health practices. eHealth literacy was positively and significantly predicts health promotion behaviors (β= 0.27, t (695)= 7.54, p<0.001). eHealth literacy increased annual weight measurement by more than 0.13 (β=0.13), the annual blood iron measurement by more than 0.16 (β=0.16), annual dental examination by more than 0.11 (β=0.11). Conclusions: Adolescents ehealth literacy was high and effected via health promotion activities and preventive health practices. Developing eHealth litracy interventions will be important for environments with a high concentration of adolescents (schools, courses). Also, primary health care services should be entegrated with school environment.

Amid to the COVID-19 pandemic, digital health literacy (DHL) has become a significant public health concern. This research aims to assess information seeking behavior, as well as the ability to find relevant information and deal with DHL among university students in Pakistan. An online-based cross-sectional survey, using a web-based interviewing technique, was conducted to collect data on DHL. Simple bivariate and multivariate linear regression was performed to assess the association of key characteristics with DHL. The results show a high DHL related to COVID-19 in 54.3% of students. Most of the Pakistani students demonstrated ~50% DHL in all dimensions, except of reliability. Multivariate findings showed that gender, sense of coherence and importance of information were found to be significantly associated with DHL. However, a negative association was observed with students’ satisfaction with information. This led to the conclusion that critical operational and navigations skills are essential to achieve COVID-19 DHL and cope with stress, particularly to promote both personal and community health. Focused interventions and strategies should be designed to enhance DHL amongst university students to combat the pandemic.

Water literacy is the ability to feel familiar with and get actively involved in water to face issues about it. This study aims to analyze the water literacy of students who come from areas that have indigenous knowledge related to water conservation and do not have indigenous knowledge but have come from the same province with a similar abundance of water. The samples of this study were 439 people consisting of 184 boys and 255 girls aged 11-18 years from junior and senior high school students in Palembang Municipality and three sub-districts in Muara Enim Regency, namely Semende Darat Laut, Semende Darat Tengah, and Darat Ulu and both are indonesian. There were 39 questions for measuring water literacy in the form of a Likert scale (1-5) to measure practical and living literacy with Cronbach alpha values of 0.692 and 0.773, respectively, and were analyzed with SPSS version 21 through the Mann-Whitney (u) and Cronbach alpha statistical tests. The total alpha was 0.838. Social literacy was measured in the open-ended question instrument and analyzed descriptively. The results showed that practical water literacy in Palembang was significantly different from Semende's but not with living water literacy. Students' social water literacy in the Semende had two different answer patterns, while, in Palembang, it was more diverse with six different response patterns. Meanwhile, when asked to describe a diagram of the flow of water in nature in Semende, no students think that it is the neglect of customs that results in reduced water in Semende as understood by some students in Palembang who are not involved with these customs.

Nowadays genetic algorithm (GA) is greatly used in engineering pedagogy as an adaptive technique to learn and solve complex problems and issues. It is a meta-heuristic approach that is used to solve hybrid computation challenges. GA utilizes selection, crossover, and mutation operators to effectively manage the searching system strategy. This algorithm is derived from natural selection and genetics concepts. GA is an intelligent use of random search supported with historical data to contribute the search in an area of the improved outcome within a coverage framework. Such algorithms are widely used for maintaining high-quality reactions to optimize issues and problems investigation. These techniques are recognized to be somewhat of a statistical investigation process to search for a suitable solution or prevent an accurate strategy for challenges in optimization or searches. These techniques have been produced from natural selection or genetics principles. For random testing, historical information is provided with intelligent enslavement to continue moving the search out from the area of improved features for processing of the outcomes. It is a category of heuristics of evolutionary history using behavioral science-influenced methods like an annuity, gene, preference, or combination (sometimes refers to as hybridization). This method seemed to be a valuable tool to find solutions for problems optimization. In this paper, the author has explored the GAs, its role in engineering pedagogies, and the emerging areas where it is using, and its implementation.

Background: Industry revolution 4.0 (IR.4.0) paradigm, nurses were required to have adequate competencies to carry out their professional duties. Digital information should be mastered by the nursing student. Purpose: This study aims to explain digital literacy competencies on nursing students in the era of IR 4.0 in the Indonesian context. Methode: A literature review was conducted in this study using Rayyan Systematic Reviews Software. Results: There was 14 literature analyzed describing Student’s Information literacy competency and Internet of Thing. Conclusion: Digital literacy for nursing students today is an inevitability, and nursing students who have digital literacy competencies have the potential to be able to increase nursing knowledge and skills as ontological competencies in their learning process.

Late January 2020, COVID-19 unexpectedly imposed world-wide limitations on daily life. Deemed a pandemic mid-March 2020, lockdowns were imposed for an indefinite period, including at academic institutions. Consequently, interest in digital literacy—an on-going and increasing concern of academic institutions in the 21^st century—exponentially heightened. Continuing meetings of academic groups now necessitated online communication. In the almost overnight closure of all non-essential services, academic units at one post-secondary institution expeditiously selected Zoom—a popular video conferencing application—as the preferred platform for meetings until social distancing was lifted. In contrast to this widely accepted use of Zoom for scheduled meetings, one unique health-related group at the institution, tailored to the 4Cs of 21^st century learning of critical thought, communication, cooperation and creativity, found social networking through a private Facebook group a more appropriate and satisfying group experience than likely possible with the Zoom app. Pros and cons of both online platforms are presented along with when each choice is warranted. In promoting digital literacy as the primary goal in online communication for academic meetings, private Facebook groups hold promise for collaborative online academic meetings with similar features to this health-related group.

Sustainability, sustainable development and education for sustainable development are increasingly central concepts, both in social practice and in the field of scientific knowledge. Sociology, and in particular Sociology of Education as a specialised Sociology, can provide relevant contributions in its promotion. This article aims to explore the importance of Sociology of Education in promoting sustainability literacy in higher education, using the Sustainable Development Goals and key competencies (United Nations and UNESCO) as the central reference in this field, and intends, thus, to become an added contribution for this discussion. The article seeks to demonstrate that the learning of sustainability literacy would benefit from the use of a sociological stance throughout this whole process that considers dimensions that are often not directly emphasised and articulated between each other, such as: interconnection of scale levels, sociological imagination, multi-paradigmatic nature, heuristic interdisciplinarity, reflexivity and use of Sociology for action.

Human activities continue to degrade oceanic, coastal and inland waters. The generational change in the role of society in actively looking after the health of water resources can be achieved through the expansion of ocean and water literacy in schools. The Network of European Blue Schools established under the EU4Ocean Coalition for Ocean Literacy has improved ocean and water literacy; however, this Network needs to grow and be supported. Here we present ProBleu; a recently funded EU project that will expand and support the Network, partly through the use of citizen science. The core of the proposed methodology is facilitating school activities related to ocean and water literacy through funding calls to sustain and enrich current school activities, and kick-start and support new activities. The outcomes of the project are anticipated to have wide and long-term impacts across society, oceanic, coastal and inland water environments.

The primary barrier to understanding visual and abstract information in STEM fields is representational competence the ability to generate, transform, analyze and explain representations. The relationship is known between the foundational visual literacy and the domain specific science literacy, however how science literacy is a function of science learning is still not well understood despite investigation across many fields. To support the improvement of students’ representational competence and promote learning in science, identification of visualization skills is necessary. This project details the development of an artificial neural network (ANN) capable of measuring and modeling visual science literacy (VSL) via neurological measurements using functional near infrared spectrometry (fNIRS). The developed model has the capacity to classify levels of scientific visual literacy allowing educators and curriculum designers the ability to create more targeted and immersive classroom resources such as virtual reality, to enhance the fundamental visual tools in science.

Environmental lead exposure is a population health concern in many low- and middle-income countries. Lead is found throughout Myanmar and prior to the 1940s, the country was the largest producer of lead worldwide. The aim of this study was to examine any potential association between lead mining and adult literacy rates at the level of the 330 townships in Myanmar. Townships were identified as lead or non-lead mining areas and 2015 census data were examined with association being identified using descriptive, analytical and spatial statistical methods. Overall, there does appear to be a significant relationship between lead mining activity and adult literacy levels (P<0.05) among townships with both low access [OR= 2.701 (1.136-6.421)] as well as townships with high access to safe sanitation [OR=18.40 (1.794-188.745)]. LISA cluster maps confirm these findings. This exploratory analysis is a first step in the examination of potential environmental lead exposure and its implications in Myanmar.

(1) Background Diabetic Retinopathy (DR) is the primary cause of poor vision in young adults. Screening for DR is important but many patients do not attend; (2) Methods: We validated an algorithm for predicting DR and its progression by conducting a retrospective study of 11 children in a population of 120,389 T2DM (3) Results: By applying the algorithm to the population showed an AUC of 0.93 (95% CI, 0.92–0.94) for any-DR and 0.90 (95% CI, 0.89–0.91) for referable-DR(4) Conclusions: The algorithm is useful for predicting patients who could develop referable forms of DR and also for any-DR. This would allow a personalized screening plan to be drawn up for each patient.

Introduction: Different factors such as rapid growth of population, urbanization and industrialization of communities have detrimental effects on the environment. In regard to the importance of the environment and its fundamental role in sustainable development along with the awareness and attitude of the people of Yazd which has not yet been assessed, this study aims to assess the level of awareness, attitude and environmental literacy of the people of Yazd in relation to environmental issues and challenges in the year 2017. Materials and methods: In this descriptive-analytical study, the sample size was obtained from 410 Yazd city residents through prior studies and the Cochran formula. The sample was then selected by the stratified random sampling method. The data selection tool was a questionnaire with questions on demographic characteristics, awareness level, attitude and practices in relation to menopause. The data was analyzed upon collection by the SPSS-20 software and Mann-Whitney, Kruskal-Wallis, and Chi-Square non-parametric tests along with the Spearman correlation test. In this study, the significance level was considered as 0.05. Results: In this study, a total of 404 questionnaires were completed and evaluated. Based on the obtained results, 224 individuals (55.4%) male, 53.3% married, 31.2% had at least a bachelor’s degree and the majority of individuals (40.1%) were in the 18-27 age group. In terms of area of residence, 64.1% reside in region 2. From all the participants, 51.3% of the participants believed that the main environmental issue in Yazd was water deficiency and pollution. The awareness score was statistically significant with the age, education level and employment status variables. Attitude only had significant relationship with the employment status variable. The relationship between the practices score was statistically significant with the age, marital status, and area of residence variables. There is a significant relation between age, income level and knowledge level but there is no significant relation with attitude levels. There is no significant relation between gender, education level, marital status, area of residence, awareness and attitude levels (p-value > 0.05). There is a positive correlation between awareness, attitude and practices scores. The results indicated that the attitude score of most participants (51.5%) was lower than the average score but the awareness and practices score of most participants was average. Conclusion: Based on the results of this study, although the awareness of Yazdi citizens on some environmental issues and institutions that are active in the environmental pollution control field in the country appears low, but in general, the awareness of the people of Yazd, specifically regarding health issues stemming from pollution was assessed as adequate. Considering the importance of environmental factors’ role in communities’ health and its continuous improvement, due to the expansion of automated life namely in large cities within the country, it is necessary to conduct various researches to identify and control these factors and to carry out interventional studies to determine suitable solutions before implementing them.

In reservoir operations rule curves, it is necessary to apply rule curves to guide long-term reservoir management. This paper proposes an approach to optimize the reservoir operation rule curves (RORC) using intelligent optimization techniques from the Firefly Algorithm (FA) and a unique combination method utilizing the Artificial Bees Colony and Cultural Algorithm (ABC-CA). The aim is to establish a connection with the simulation model to determine the optimal RORC for flood control. The proposed model was used to determine the Nam-Oon Reservoir (NOR) is optimal flood control RORC in Northeastern Thailand. The minimum frequency and a minimum average of ex-cess water were given as an objective function for assessing the efficiency of the search process. The evaluation of the effectiveness of flood control RORC involved expressing water scarcity and excess water situations in terms of frequency, magnitude, and duration using historical inflow data syn-thesized from 1000 events. The results demonstrate that when using the obtained RORC to simulate the NOR system for reducing flooding in long-term operations, situations of excess water were smaller than those using the current RORC. The results indicated that situations of excess water using the obtained RORC from the proposed model were smaller than those using the current RORC situations. In decreasing flood situations, the newly acquired RORC from the suggested FA and ABC-CA models performed better than the current RORC.

Globalization has had an impact on the education system in the UAE, where increased use of bilingual curriculum (Arabic-English) is held in high regard. Nevertheless, literacy among Emirati children and teenagers remains low. This study uses a 15-item, open-ended questionnaire completed by Emirati parents and an 8-item, open-ended questionnaire completed by Emirati primary school children from 8 to 11 years of age, and compares the translation of The Little prince into Modern Standard Arabic (MSA) and into the Emirati dialect. The results suggest that not only the Emirati dialect, but any Arabic dialect (in its respective Arab country) could be used in primary schools to motivate children to read in Arabic and bridge the gap between their spoken language (dialect) and formal written Arabic. Not only must an Arab child learn how to read, but also, they have to understand a very formal language system that they are not used to speaking at home, i.e., Modern Standard Arabic (MSA). Having books in their own dialects may make children more interested in reading, as they can understand them more easily.

Maximum likelihood estimation (MLE) is a popular method for parameter estimation in both applied probability and statistics but MLE cannot solve the problem of incomplete data or hidden data because it is impossible to maximize likelihood function from hidden data. Expectation maximum (EM) algorithm is a powerful mathematical tool for solving this problem if there is a relationship between hidden data and observed data. Such hinting relationship is specified by a mapping from hidden data to observed data or by a joint probability between hidden data and observed data. In other words, the relationship helps us know hidden data by surveying observed data. The essential ideology of EM is to maximize the expectation of likelihood function over observed data based on the hinting relationship instead of maximizing directly the likelihood function of hidden data. Pioneers in EM algorithm proved its convergence. As a result, EM algorithm produces parameter estimators as well as MLE does. This tutorial aims to provide explanations of EM algorithm in order to help researchers comprehend it. Moreover some improvements of EM algorithm are also proposed in the tutorial such as combination of EM and third-order convergence Newton-Raphson process, combination of EM and gradient descent method, and combination of EM and particle swarm optimization (PSO) algorithm.

MAACS is a software architecture which layers multiple standard cipher algorithms together to increase the overall strength of the encrypted data stream, without modifying the standard algorithms, which have been heavily vetted from a cryptanalysis perspective and are already widely deployed. This layering technique makes the combination of algorithms significantly more resistant to traditional cryptanalysis attacks, while also addressing increased concerns over future quantum computing attacks against these algorithms. For example, concerns over published attacks against 256-bit AES and potential future attacks emanating from Grover’s algorithm and other unknowns in the quantum computing era are explored. Several approaches for evolving AES are considered. MAACS uses a concept termed "aperiodic modulation", whereby of a set of cryptographic algorithms are modified by an- other, independent software algorithm in an unpredictable, pseudo-random manner. In the MAACS reference architecture, a 256-bit ChaCha20 cryptographically secure pseudorandom number generator (CSPRNG) drives an aperiodic modulator that orchestrates encryption/decryption processes multiplexed across eight parallel 128-bit AES instances, yielding a cryptosystem with a composite strength of 1,376 bits or more in larger configurations.

In an era characterized by technological disruption and unprecedented challenges posed by the COVID-19 pandemic, this scholarly investigation explores the role of Artificial Intelligence (AI) in shaping Leadership Dynamics in Higher Education Institutions (LDHE) within the specific socio-cultural and regulatory environment of China. Utilizing Structural Equation Modeling (SEM) as operationalized through the SMART_PLS software, this research harnessed data from a robust sample of 381 key stakeholders in China higher education. The study's empirical findings indicate a noteworthy impact of AI-informed decision-making processes on LDHE, which was further mediated by the variable of AI Literacy among leadership. Moreover, the consequences of the ongoing pandemic served as a moderating factor in this relationship. These nuanced outcomes not only contribute to the theoretical discourse surrounding educational leadership and technological integration but also provide actionable insights for policy formulation and institutional strategies in an increasingly complex educational landscape.

Indonesian crypto asset owners are expanding despite the government's ban. Crypto assets are too risky for Indonesian investors. This study uses the Theory of Planned Behavior and exogenous variables like financial literacy, trust, and government regulation to determine the causes of the intention to invest in crypto assets. Exogenous variables and behavioral intentions were moderated by gender. The study employs a quantitative approach, collecting data through a questionnaire survey of 149 adults over the age of 17 who have invested in non-crypto assets. SmartPLS was used to analyze research data. Financial literacy and trust, according to the study's findings, have an impact on the intention to invest in crypto assets. Gender can moderate the relationship between belief and behavioral intention. Women are more likely than men to invest in crypto assets due to the block chain system's security, transparency, and ease of use. This research is expected to help the government address the risks associated with investing in crypto assets by increasing transaction security with futures clearing and monitoring by using third parties as depositors. It also governs the laws governing crypto exchanges' liability to their investors.

Abstract: Nutritional interventions have beneficial effects on certain psychiatric disorder symptomatology and common physical health comorbidities. However, studies evaluating nutritional literacy in mental health professionals (MHP) are scarce. This study aimed to assess the degree of self-rated training and literacy relating to nutrition in MHPs. We conducted a cross-sectional survey across 52-countries. Surveys were distributed via colleagues and professional societies. Data were collected regarding self-reported general nutrition knowledge, nutrition education, learning opportunities, and the tendency to recommend food supplements or specific diets in clinical practice. In total, 1056 subjects participated in the study: 354 psychiatrists, 511 psychologists, 44 psychotherapists, and 147 MHPs in-training. All participants believed the diet quality of individuals with mental disorders was poorer compared to the general population (p<0.001). The majority of the psychiatrists (74.2%) and psychologists (66.3%) reported having no training in nutrition. Nevertheless, many of them used nutrition approaches, with 58.6% recommending supplements and 43.8% recommending specific diet strategies to their patients. Only 0.8% of participants rated their education regarding nutrition as ‘very good’. Almost all (92.9%) stated they would like to expand their knowledge regarding ‘Nutritional Psychiatry’. There is an urgent need to integrate nutrition education into MHP training, ideally in collaboration with nutrition experts to achieve best practice care.

The chicken poultry business is becoming a crucial sector for our nation's sustainable food supply. To increase the health of chickens in coops and maybe increase the output of chicken meat and eggs, several innovations and inventions have been developed. Therefore, the creation of an automated chicken feeding device might be highly beneficial for the expansion of the poultry sector. This equipment may take the role of a worker in the chicken feeding process, solving the industry's labor shortage and introducing a semi-automatic procedure. The main processor of the automated chicken feeding system is a Raspberry Pi 3 Model B+. The machine consists of five main components: a Raspberry Pi with an amplifier that makes noise to draw the attention of free-range chickens, a Raspberry Pi with a pi camera that counts and detects the chickens, a Raspberry Pi with a servomotor that moves the food from storage to a container, a Raspberry Pi with a load cell that controls the number of feeds to avoid waste on foods, and a Raspberry Pi with a temperature sensor that monitors the food's freshness. This enhances the coop's atmosphere while lowering labor costs, conserving food, and ensuring that the chickens are fed on time. It also regulates the temperature sensor to ensure that the chicken food is fresh and prevents food from becoming polluted by insects and chicken excrement.

We present a new method of estimating fitness of countries and complexity of products by exploiting a non-linear non-homogeneous map applied to the publicly available information on the goods exported by a country. The non homogeneous terms guarantee both convergence and stability. After a suitable rescaling of the relevant quantities, the non homogeneous terms are eventually set to zero so that this new method is parameter free. This new map reproduces the findings of the method proposed by Tacchella et al. [1], and allows for an approximate analytic solution in case of actual binarized matrices based on the Revealed Comparative Advantage (RCA) indicator. This solution is connected with a new quantity describing the neighborhood of nodes in bipartite graphs, representing in this work the relations between countries and exported products. Moreover, we define the new indicator of country net-efficiency quantifying how a country efficiently invests in capabilities able to generate innovative complex high quality products. Eventually, we demonstrate analytically the local convergence of the algorithm.

This research delves into librarians' perceptions of artificial intelligence (AI) support in learning experiences, lifelong learning, and digital literacy within the library context, undertaking a comparative analysis between Malaysia and Indonesia. Employing a survey methodology, a self-developed questionnaire was administered to 59 librarians in Malaysia and 85 in Indonesia. The questionnaire, utilizing a Likert scale, systematically explored subjective judgments on AI's impact across various dimensions. A noteworthy finding is the consistent and harmonized perception among librarians in both countries, underscoring a universal recognition of AI's perceived support for learning experiences, lifelong learning, and digital literacy. Of particular interest is the emphasis on sustainability in librarians' perspectives, as they recognize the enduring value of AI in fostering educational growth. Despite the limitations inherent in perceptual measures and a relatively modest sample size, the empirical and practical contributions of this study are significant. It offers a nuanced exploration of librarians' viewpoints, providing valuable insights for future research endeavors. Moreover, the study's findings have immediate practical implications for the integration of AI tools in library services, policy-making, and professional development, with a focus on ensuring sustainable implementation for long-term benefits.

The American Academy of Arts and Sciences suggested the definition of science literacy emphasize how crucial understanding the scientific process and the ability to evaluate conflicting scientific evidence is. The purpose of this article is to present an evidence-supported curriculum covering the fundamentals of logic, reasoning, and argumentation skills to address the emphasized basic knowledge, skills, and abilities required to be scientifically literate, which will prepare the public to understand and engage with science meaningfully. An analytic-synthetic approach toward understanding the notion of public is taken using a theoretical biomimetics framework that identifies naturally occurring objects or phenomena that descriptively captures the essence of a construct to facilitate creative problem-solving. In the present case, the problem being solved is how to reconcile what is meant by public, how it ought to be interpreted, the different levels of confidence in science that exist, and various understandings of science all with one another. The results demonstrate there is an inherent denotative-connotative inconsistency in the traditional notion of public that can be explicated through the concept of a fractal allowing for comprehension of the relationship between public confidence in, and understanding of, science.

Artificial insemination (AI) is considered as the most efficient feasible tool to improve the dairy cows’ profitability and productivity. In bovines, AI helps to promote genetic improvement and can positively impact the rural economy. The achievement of education in rural populations was one of the Millennium Development Goals (MDGs), made by the United Nations. During the last World Summit on Sustainable Development (WSSD), it was concluded that most of the world’s poor, illiterate and undernourished population lives in rural areas; therefore, access to education is considered one of the greatest challenges for governments in countries with developing economies. To determine the effects of a theoretical-practical training program related to rural management and leadership, AI, and bovine genetic improvement, on the perception and level of knowledge in a rural Colombian population that has been affected by the armed conflict. Phase 1. Included the practical and theoretical training of undergraduate animal sciences students in three dimensions rural management; leadership (D-ML), AI in bovines (D-AI), and genetic improvement (D-GI) after training students were evaluated and selected according to their skills, characteristics, solidarity skills, and their social sensitivity was identified. Phase 2. Included the socioeconomic characterization and training of rural residents by students and field experts in three dimensions D-ML, D-AI, and D-GI. After training we evaluated the perception and level of knowledge before and after training in 63 rural residents using an evaluative instrument. The data before and after (post-test) of the theoretical-practical training, were analyzed to obtain a statistical significance. The perception of knowledge in rural residents was low for general knowledge (2.48±0.76, p ˂ 0.05), D-ML (2.89±1.18, p ˂ 0.05) and D-AI and D-GI (2.17±0.83, p ˂ 0.05). On the other hand, before the theorical-practical training the level of general knowledge was 45.9%, however, after the training increased at 77.6% (p<0.01), while the level of knowledge for the D-AI was increased from 38.5 % to 80.6 % (p ˂ 0.05) after training. Regarding the level of knowledge for the D-GI pre-training was 50.8 and increased to 73.0% (p<0.01) then after. Finally, the level of knowledge for the D-ML was increased (p<0.01) from 54.8 before training to 75% after training. Altogether, the rural extension programs contribute to the closing of knowledge gaps in relation to the use of reproductive biotechnologies and bovine management in rural areas affected by armed conflict.

This study aims to determine the Implication of innovation, proactivity, risk-taking, artistic orientation, and financial literacy on creative economy businesses during the COVID-19 pandemic. This was conducted on 120 creative economy businessmen in Bandar Lampung City, which is a miniature of Indonesia with multiethnic cultures. The results showed innovation is not significant, but proactive attitude, artistic orientation, and financial literacy have a significant implication on the performance of creative economic businesses during the Covid-19 pandemic.

The present study explores the potential impact of peers' omnipresence at school on children's academic performance. We tested 99 fourth-graders either alone or with a classmate in a task involving both numeracy and literacy skills: numerosity comparison and phonological comparison. Ninety-seven college-aged young adults were also tested on the same task, either alone or with a familiar peer. Peer presence yielded a reaction time (RT) speedup in children, and this social facilitation was at least as important as that seen in adults. RT distribution analyses indicated that the presence of a familiar peer promotes the emergence of adult-like features in children. This included shorter and less variable reaction times (confirmed by an ex-Gaussian analysis), increased use of an optimal response strategy and, based on Ratcliff’s diffusion model, speeded up non decision (memory and/or motor) processes. Peer presence thus allowed children to, at least, narrow (for demanding phonological comparisons), and, at best, virtually fill in (for unchallenging numerosity comparisons) the developmental gap separating them from adult levels of performance. These findings confirm the influence of peer presence on skills relevant to education and lay the ground for exploring how the brain mechanisms mediating this fundamental social influence evolve during development.

Development of Halal Tourism in Indonesia is the focus of the Indonesian government and MSMEs have an important role in supporting the development of halal tourism in Indonesia. This study aims to examine the relationship between marketing communication and Islamic financial literacy on Islamic financial inclusion and MSMEs performance in the halal tourism sector. A covariance-based SEM technique utilizing LISREL software was used to analyze the data from this investigation. Non-probability sampling was employed to collect the data, and the sample consists of 152 halal tourism entrepreneurs. We find that positive and significant association between Islamic financial inclusion and business performance. We also find that there are a positive and significant association between Islamic Financial Literacy and Islamic Financial Inclusion. Marketing communication and Islamic financial inlcuon a positive relationship but insignificant. This studi implies that to establish a halal tourist ecosystem for long-term development in Indonesia, commercial actors must lend their full support. This study demonstrates that they can thrive when MSMEs in the halal tourist ecosystem are backed by Islamic banking and Islamic rural banks. As a result, a more accommodating approach from Islamic banking is required to provide access to halal finance for business actors in Indonesia’s halal tourism ecosystem.

Resumo: Este estudo teve como objetivo avaliar a Saúde Mental Positiva (SPM) e a sua relação com características sociodemográficas, literacia em saúde mental e escala de vulnerabilidade psicológica (PVS) em estudantes universitários portugueses com idades compreendidas entre os 17 e os 62 anos. Foi realizado um estudo descritivo-correlacional. Uma pesquisa online foi realizada para avaliar variáveis ​​demográficas, e vários questionários foram aplicados para avaliar saúde mental positiva, vulnerabilidade psicológica e alfabetização em saúde mental. Os dados foram coletados de 1º de novembro de 2019 a 1º de setembro de 2020. No geral, 3.405 alunos participaram do estudo. Os resultados mostram que 67,8% dos alunos revelaram um alto nível de PMH, 31,6% apresentaram um nível médio de PMH e 0,6% apresentaram um nível baixo de PMH. Os estudantes do sexo masculino relataram maior satisfação pessoal (t (3170) = -2,39, p=0,017) e autonomia (t (3170) = -3,33, p=0,001), no PMH em relação ao sexo feminino. Alunos sem bolsa pontuaram mais alto (t (3.127) = -2,04, p=0,42) no PMH do que alunos com bolsa. Estudantes que não foram deslocados de casa relataram maior (t (3170) = -1,99, p=0,047) Autocontrole em PMH do que aqueles deslocados de sua casa. Estudantes universitários com resultados de PMH mais altos têm resultados de PVS mais baixos e resultados de Alfabetização mais altos. Os achados deste estudo contribuirão para identificar as necessidades de intervenção dos alunos na PMH. Estudantes universitários com resultados de PMH mais altos têm resultados de PVS mais baixos e resultados de Alfabetização mais altos. Os achados deste estudo contribuirão para identificar as necessidades de intervenção dos alunos na PMH. Estudantes universitários com resultados de PMH mais altos têm resultados de PVS mais baixos e resultados de Alfabetização mais altos. Os achados deste estudo contribuirão para identificar as necessidades de intervenção dos alunos na PMH.

This paper describes an $\sim {\cal O}(n)$ pre-compute technique to speed up the Karatsuba algorithm for multiplying two numbers.

The algorithmic complexity vulnerability (ACV) that may lead to denial of service attacks greatly disrupts the security and availability of applications, and due to the widespread use of third-party libraries, its impact may be amplified through the software supply chain. The existing work in the field is dedicated to abstract loop or iterative patterns and fuzzing the entire application to discover algorithm complexity vulnerabilities, but they still face efficiency and effectiveness issues. Our research focuses on: (1) Proposing a representation and extraction method for code features related to algorithmic complexity vulnerabilities, helping analysts quickly understand program logic; (2) Providing a new ACV detecting model, focusing on the spatial complexity anomalies caused by deep recursion structures, and proposing a new filtering method; (3) Aiming at the difficulty of efficiently generating complex-data-type-related payloads using existing symbol execution techniques, a call-chain-guided payload construction method is proposed. We tested third-party components in the open-source Java Maven Repository, identified many unexposed vulnerabilities and 8 of them received CVE (Common Vulnerabilities &amp; Exposures) identifiers, and demonstrated that our method can discover more algorithmic complexity vulnerabilities compared to existing tools with better performance.

This paper describes a polynomial time algorithm for solving graph isomorphism and automorphism. We introduce a new tree data structure called Walk Length Tree. We show that such tree can be both constructed and compared with another in polynomial time. We prove that graph isomorphism and automorphism can be solved in polynomial time using Walk Length Trees.

This study is based on the BP neural network regression model optimised by genetic algorithm and dedicated to predicting the amount of car purchase, this study is important for all parties in the automotive industry. By plotting the violins of six indicators, including gender, age, annual salary, credit card debt, net worth, and car purchase amount, we found that, except for gender, the other data showed a trend of dense in the middle gradually decreasing to the upper and lower sides, with an overall normal distribution. The correlation analysis shows that the amount of car purchase has a high positive correlation with age (0.63), followed by annual income (0.62), the correlation between net worth and the amount of car purchase is 0.49, while the correlation between credit card debt and the amount of car purchase is low (0.03). In the experimental part, we analyse the predicted-actual values of the training and test sets in comparison and find that the model predicted values are very close to the actual values. The RMSE of the training set is 11.559 and the RMSE of the test set is 11.4835, which is not much different from each other, indicating that the model has good generalisation ability. In addition, the R2 of both the training and test sets is 1, indicating that the model fits well. The MAE of the training set is 8.2512 and the MAE of the test set is 9.578; the MBE of the training set is -4.3035 and the MBE of the test set is -2.4547.These metrics indicate that the model performs well in predicting the amount of car purchase. In conclusion, this study provides an effective and reliable method to predict the amount of car purchase, creating a useful bridge between car manufacturers, dealers and consumers.

Background: This study aimed to assess the dementia literacy (DL) level of community-dwelling adults in the four cities (Hong Kong, Guangzhou, Macau and Zhuhai) of the Greater Bay Area of China and to determine the preferred mass media for receiving dementia information. Methods: A multi-city cross-sectional study with 788 community-dwelling adults completed the survey. Dementia literacy was indirectly measured by two validated scales, 30-item Alzhiemer’s Disease Knowledge Scale (ADKS) and 20-item Dementia Attitudes Scale (DAS). When the ADKS total score was <15 and DAS total score was <70, it was considered as ‘inadequate dementia literacy’. Participants were also asked to indicate whether they would like to receive dementia information via digital media or traditional media. Chi-square tests and logistic regressions were undertaken. Results: About one-third of the participants had inadequate dementia literacy. Those with young age or secondary education preferred to get dementia information from social media. But people living in public housing would like to get information from government or hospital websites. Middle-aged participants inclined to learn dementia from television or radio. Conclusion: It is worthy to conduct territory-wide public education in dementia and develop strategies according to their preferences in the types of mass media.

This paper models the factors explaining households members economic well-being in Burkina Faso, with a focus on the relative influence of gender inequality in literacy status. It does so, using data from the 2014 survey on household living conditions and a semi-parametric bi-variate sample selection modeling approach. This approach compared to the classic Heckman two-step estimator is methodologically innovative because it deals simultaneously with non-random sample selection using conventional systems of two equations, non-linear covariates' effects using spline approach, and the non-normal bivariate distribution using copula functions. The graphical results from the Lorenz curves combined with the numerical Atkinson and Gini coefficients suggest that inequality in overall per-capita consumption spending among households headed by literates is higher than that of their illiterate counterparts in 2009 and 2014. However, independently of the head of household’s literacy status, the level of inequality in total economic well-being decreased from 2009 to 2014. Using the poverty indices of Watts, Sen, Foster ( alpha= 1)) we found that poverty among households headed by literates is lower than that of their illiterates counterparts for both years, although overall poverty decreased nationally between 2009 and 2014. The results also show that although the gender inequality in literacy status does not translate into inequalities in non-food wellness, it does however for food-wellness as female headed households have 38.9% less per-capita food consumption spending than their men counterparts. Combining both food and non-food consumption spending, total economic well-being also seems to exhibit gender inequality as female headed households now have relatively 26.7% less combined per-capita consumption spending.

Nowadays, the wasting of resources is one of the fundamental challenges of the industrial sector. The rectangle packing problem can be very effective in this context. Practical applications of this issue in the timing and designing of the industries and businesses are very remarkable. The purpose of this issue is to arrange a set of rectangles with specific dimensions in a rectangular page with a specific width and unlimited height without overlapping. The fundamental challenge in this issue is that this is an NP-complete issue. Therefore, it is difficult to achieve the best arrangement, which has the maximum rate of resource utilization and also has a linear running time. Many algorithms have been presented to estimate a practical solution for this issue. In the past decades, the best fit method has been one of the most useful methods for this purpose. This study presents a combinatorial algorithm based on two algorithms, including the lowest front-line strategy and the best-fit algorithm. The running results indicate that the suggested algorithm performs well, despite its simplicity. The time complexity of the suggested algorithm is O(nm), in which n is the number of input rectangles and m is the number of the created front lines.

In this study, Acısu Basin, which is upper side of one of the most important basins (Gediz) in Türkiye, was modelled using three types of hydrological models and three different calibration algorithms. Lumped (GR4J), semi-distributed (SWAT+), and fully distributed (mHM) hydrological models were built and integrated with Parameter Estimation Tool (PEST) which is a model independent calibration tool including various algorithms. Levenberg Marquardt (L-M), Shuffled Complex Evolution (SCE), and Covariance Matrix Adoption Evolution Strategy (CMA-ES) were selected as calibration algorithms. Calibration period was 1991-2000, and validation results were obtained for 2002-2005. The effect of model structure and calibration algorithm selection on discharge simulation was evaluated via comparison of 9 different model-algorithm combinations. Results have shown that mHM and CMA-ES combination showed the best discharge simulation performance according to NSE values (calibration: 0.67, validation: 0.60). Although statistically the model results were classified as acceptable, the pattern comparison of discharge value shows that all of the models miss the peak values in significant amounts. This problem may be related to the interventions made in 2000-2001 years interval and possible to overcome by changing the calibration and validation periods, increasing the number of iterations or using the naturalized gauge data.

Objective: We tested a model of individual health literacy information sharing with family members, personal preventive behaviours and family well-being during the Coronavirus Disease 2019 (COVID-19) pandemic in Hong Kong. Methods: We analysed data of 1501 randomly selected Chinese adults from a cross-sectional survey in Hong Kong from 9 to 23 April, 2020. Individual health literacy about COVID-19 with the items extracted from the questionnaire in World Health Organization Risk Communication and Community Engagement (RCCE) Action Plan Guidance for COVID-19 preparedness and response, COVID-19 information sharing with family members, preventive behaviours against COVID-19 and family well-being were measured. Structural equation modelling analysis tested the proposed model. Findings: COVID-19 information sharing with family members partially mediated the association between individual health literacy and personal preventive behaviours. The direct effect of 0.24 was shown, and the indirect effect through COVID-19 information sharing with family members was small at 0.03 (Z = 3.66, p < 0.001). Family well-being was associated with personal preventive behaviours against COVID-19. The model was adjusted for sex, age, and socioeconomic status factors and had good fit with RMSEA = 0.04, CFI = 0.98, TLI = 0.96, and SRMR = 0.02. Conclusion: COVID-19 information sharing with family members was a partial mediator between individual health literacy and personal preventive behaviours against COVID-19. Strategies for enhancing health literacy and preventive measures against COVID-19 are needed to promote family well-being in the pandemic.

The relationship between Climate Change and Water is an obvious and key issue within the Sustainable Development Goals. This study aims to investigate the social representation created around this relationship in three different territorial contexts in order to evaluate the influence of the territory on the perception of the risk of Climate Change and its relationship with water. By means of a questionnaire completed by 1709 university students, the climatic literacy of the individual was evaluated in order to relate it to other dimensions on the relationship between Climate Change and Water (information, training previous on climate change and pro-environmental attitudes) in their different dimensions in three different territorial contexts. The results show that the socio-cultural context influences the social representation of Climate Change, but not from the climatological condition, so that it is reasonable to think that the social representation of this relationship is favoured by a common culture around this relationship.

Graph visualization has evolved significantly over time, offering various methods to effectively represent complex data structures. This study introduces a novel force-directed algorithm for 3D data visualization, specifically tailored for commonly used datasets in linked data visualization. Leveraging the power of graph theory and methods, the algorithm aims to address the challenge of creating visually appealing and easily interpretable visualizations, which often necessitate advanced user interaction. By utilizing 3D space and the Unity Engine, the research successfully visualizes data and develops interactive visualizations, overcoming limitations of basic force-directed implementations. The main contribution of presented research is in the force-directed algorithm with springs and controlled placement as a visualization technique that combines the use of springs and attractive forces to stabilize a graph in a 3D environment. The combination of these two techniques also helps prevent the formation of a single large cluster within the graph, enhancing the overall visualization quality.

The widespread adoption of electromobility constitutes one of the measures designed to reduce air pollution caused by traditional fossil fuels. However, several factors are currently impending this process, ranging from insufficient charging infrastructure, battery capacity, long queueing and charging time, to psychological factors. On top of range anxiety, the frustration of the EV drivers is further fueled by the lack the uncertainty of finding an available charging point on their route. To address this issue, we propose a solution that comes to bypass the limitations of the Reserve now function of the OCPP standard, enabling drivers to make charging reservations for the upcoming days, especially when planning a longer trip. We created an algorithm that generates reservation intervals based on the charging station's reservation and transaction history. Subsequently, we ran a series of test cases that yielded promising results, with no overlapping reservations.

Hyper-spectral images contain a wide range of bands or wavelength due to which they are rich in information. These images are taken by specialized sensors and then investigated through various supervised or unsupervised learning algorithms. Data that is acquired by hyperspectral image contain plenty of information hence it can be used in applications where materials can be analyzed keenly, even the smallest difference can be detected on the basis of spectral signature i.e. remote sensing applications. In order to retrieve information about the concerned area, the image has to be grouped in different segments and can be analyzed conveniently. In this way, only concerned portions of the image can be studied that have relevant information and the rest that do not have any information can be discarded. Image segmentation can be done to assort all pixels in groups. Many methods can be used for this purpose but in this paper, we discussed k means clustering to assort data in AVIRIS cuprite, AVIRIS Muffet and Rosis Pavia in order to calculate the number of regions in each image and retrieved information of 1st, 10th and100th band. Clustering has been done easily and efficiently as k means algorithm is the easiest approach to retrieve information.

Information security plays a major role in the dynamics of today’s interconnected world. Despite the successful implementation and effectiveness of modern cryptographic techniques, their inherent limitations can be exploited by quantum computers. In this article we discuss Grover’s quantum searching algorithm and its impact on the security of modern symmetric ciphers. More specifically, we present its formal description and give an implementation of the algorithm using IBM’s Qiskit framework, which allows us to simulate and run the program on a real device. The developed program is modular and scalable, managing to properly run on any input state. We compare the performance of the algorithm running on the local simulator against its execution on a quantum processor from IBM’s lineup, noting the discrepancies between the results due to physical characteristics of the chip.