Partial Knowledge Research Articles

Assessment of Knowledge, Attitude and Practices of Malaria among Mothers of patients between 5 and 15 Years of age

Background: Malaria continues to be a serious global public health and development issue. Plasmodium falciparum, the most lethal type of the malaria parasite, is responsible for the great majority of malaria-related death and morbidity in children. Aim: To ascertain malaria knowledge, attitudes, and practices among mothers of patients aged 5 to 15 in Bahawalgar District, Pakistan. Methods: The Cross sectional Descriptive study was done at District health quarter hospital Bahawalnagar. A questionnaire was used to collect the data from mothers of patients visiting medical OPD. Data was analyzed with SPSS version 25. Results: Findings of the study showed that a total of 241 malaria infected children were studied, to observe their plasmodium prevalence and their parental KAP of malaria. Most common age group of children was 13-15 years among 56.0%. Female’s children were commonest as 63.9%. Most of parents 46.1% were found with intermediate education. P-vivax was P-vivax was mostly seen among 66.4% children followed by p-falciparum 17.4%, p-malariae 3.3% and Plasmodium Vivax + falciparum 12.9%. The prevalence of plasmodiums was insignificantly related to demographic characteristics, with p-values that were relatively low. Conclusion: The study concluded that plasmodium vivax was the most prevalent malarial parasite. Parents had partial knowledge regarding malaria and its treatment. Parents had good attitude and agreed to participation in its prevention. Key words: Malaria, parents, knowledge, practice, children’s mothers

Power-Balancing Dual-Port Grid-Forming Power Converter Control for Renewable Integration and Hybrid AC/DC Power Systems

In this work, we investigate grid-forming (GFM) control for dc/ac power converters in emerging power systems that contain ac and dc networks, renewable generation, and conventional generation. We propose a change in control paradigm to a universal dual-port GFM control strategy that simultaneously forms the converter ac and dc voltage (i.e., dual-port GFM), unifies standard functions of grid-following (GFL) and GFM (e.g., primary frequency control, maximal power point (MPP) tracking) in a single universal controller, and is backward compatible with conventional machine-based generation. Notably, in contrast to state-of-the-art control architectures that use a mix of grid-forming and grid-following control, dual-port GFM control can be used independently of the converter power source or network configuration. Our main contributions are stability conditions that cover emerging hybrid ac/dc networks as well as machines and converters with and without controlled power source, which only require partial knowledge of the network topology. Finally, a detailed case study is used to illustrate and validate the results.

Prevalence and knowledge of cardiovascular disease risk factors among young adults in Saudi Arabia

Cardiovascular diseases (CVDs) are common disorders affecting a large portion of the population. The current study aims to assess the prevalence of CVDs risk factors among university students. Furthermore, to determine their knowledge level of CVD-related risk factors, such as lipid profile, blood sugar, and hypertension. In this cross-sectional study, 294 students were randomly recruited from a Saudi Arabian university. Blood glucose, cholesterol profile (total, HDL, and LDL cholesterol), and triglycerides were measured using standard protocols. Demographic characteristics and awareness about CVDs risk factors were obtained using a questionnaire. Data from 294 undergraduate students aged 18 and 22 were analyzed. The percentage of female participants was 35.4%. The prevalence of CVDs risk factors among the participants was as follows: there were 91 (31%) participants with elevated fasting blood sugar, 93 (31.6%) with high cholesterol levels, 92 (31.3%) with high triglyceride levels, 62 (21.4%) with high LDL cholesterol levels, 77 (26.2%) were smokers, and 105 (35.7%) were with elevated blood pressure. Chi-square analysis showed that smoking, Cholesterol among males, hip waist ratio, body mass index, and blood pressure among females were significantly associated with CVD events compared to other estimated risk factors. This study showed a high prevalence of CVD disease risk factors among Saudi University students. In addition, males who had high cholesterol levels were more likely to develop CVDs than females. • Saudi Arabian young adults are at a high risk of CVD and possess partial knowledge of risk factors. • The most common CVDs risk factor among young adults was excess weight. • Males with high cholesterol levels were more likely to develop CVDs than females. • Hip waist ratio, BMI, and BP risk factors among females were significantly associated with the development of CVD events.

DNB: A Joint Learning Framework for Deep Bayesian Nonparametric Clustering.

Clustering algorithms based on deep neural networks have been widely studied for image analysis. Most existing methods require partial knowledge of the true labels, namely, the number of clusters, which is usually not available in practice. In this article, we propose a Bayesian nonparametric framework, deep nonparametric Bayes (DNB), for jointly learning image clusters and deep representations in a doubly unsupervised manner. In doubly unsupervised learning, we are dealing with the problem of "unknown unknowns," where we estimate not only the unknown image labels but also the unknown number of labels as well. The proposed algorithm alternates between generating a potentially unbounded number of clusters in the forward pass and learning the deep networks in the backward pass. With the help of the Dirichlet process mixtures, the proposed method is able to partition the latent representations space without specifying the number of clusters a priori. An important feature of this work is that all the estimation is realized with an end-to-end solution, which is very different from the methods that rely on post hoc analysis to select the number of clusters. Another key idea in this article is to provide a principled solution to the problem of "trivial solution" for deep clustering, which has not been much studied in the current literature. With extensive experiments on benchmark datasets, we show that our doubly unsupervised method achieves good clustering performance and outperforms many other unsupervised image clustering methods.

Dynamic Caching for Files With Rapidly-Varying Features and Content

Proactive caching shows great potential to minimize peak download rates by caching popular data, in advance, at the edge. Fast-changing file features, such as fast-changing file popularities and fast-changing file contents (data freshness), represent a challenge for proactive caching if cache content update is much slower, which decreases the efficiency and usability of caching. We present a dynamic caching scheme that updates local user caches and optimizes the use of caching resources. The developed scheme index-code the updates with the delivery messages. The developed scheme is presented for a network with one cache-enabled server, that has a pool of files, communicating with <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$K$ </tex-math></inline-formula> cache-enabled receivers with requests limited to the server’s file pool. The developed scheme assumes partial knowledge of features variation. Asynchronous file delivery is assumed as a result of non-flexible receivers’ request timing. We show that the file delivery messages can be used to proactively and constantly update the receivers’ finite caches by index-coding the update messages with delivery messages at no additional rate-cost. We also show that this mechanism reduces the downloaded traffic and can be used to reduce other QoS metrics.

The Knowledge and Perception of Sustainability in Livestock Systems: Evidence from Future Professionals in Italy and Argentina

The preference of consumers for more sustainable consumption patterns has a great impact on what and how food is produced. Consumers’ interest in sustainable agricultural products can drive this type of production, and the primary sector should take advantage of this opportunity to propose new paradigms in the supply of livestock products. Although sustainable alternatives can be imposed through rules or regulations, it is important that market forces act through consumer choices so that the development of the sector may have long-lasting effects. Professionals involved in the primary sector and, in particular, in livestock production must be trained to face the new challenges of the sector and meet market demands. Improving production with a view to sustainability is one of the challenges that need to be addressed at a global level. The present study investigated the perception and knowledge of sustainability of some future professionals in the sector to understand whether the academic training delivered to them is suitable. The results show that respondents have partial knowledge of sustainability, especially of its environmental dimension. Their idea of sustainability is particularly linked to the extensification of production rather than sustainable intensification, and only marginally do they consider the social and economic dimensions of sustainability. Less knowledge of the social and economic components is particularly relevant if sustainability is pursued through an extensive approach that leads to an inevitable reduction in production. The academy must act to improve the knowledge of sustainability in its three main components in an essential balance.

Modelling temperature dynamics of the SAGD process in an oil reservoir by the discovery of parametric partial differential equations

AbstractMany chemical and industrial processes are complex, and the dynamics of such processes cannot be explained using a partial differential equation (PDE) or a system of PDEs with constant coefficients. Parametric PDEs, that is, PDEs with their coefficients varying across time or space, are utilized for this purpose. The non‐availability of data at all spatial locations and partially available process knowledge add to the complexity of modelling such processes. This paper proposes a framework to discover parametric PDEs using data‐driven and hybrid modelling approaches with the temperature dynamics of steam‐assisted gravity drainage (SAGD) process in an oil reservoir as the system under study. We utilize an ensemble of 200 realizations of the temperature dynamics generated using the variogram for the PDE discovery. Permeability, which is one of the oil reservoir's petrophysical properties, is used to develop the hybrid models. We infer that utilizing partial process knowledge aids in improving the model's accuracy.

A Multi-Attribute Decision-Making Procedure Based on Complex q-Rung Orthopair Fuzzy Weighted Fairly Aggregation Information

In this study, the authors explore the complex q-rung orthopair fuzzy sets, which are preferred to be enhanced of the complex intuitionistic fuzzy sets and the complex Pythagorean fuzzy sets, individually. The intention is regarding the buildup of certain innovative operational laws and their related weighted aggregation operators based on the complex q-rung orthopair fuzzy (CQROF) information. In this regard, they characterize certain original neutral or fair operational laws that involve the model of proportional distribution to accomplish a neutral or fair usage to the truth and falsity functions of CQROFSs. Consequently, with these operations, they acquire CQROF weighted fairly aggregation (CQROFWFA) and CQROF ordered weighted fairly aggregation (CQROFOWFA) operators which can neutrally or fairly provide the truth and falsity degrees. They implement an MADM (multi-attribute decision-making) methodology with multiple decision makers and partial weight knowledge in the structure of CQROFSs.

An Investigation into the Roles of Guessing and Partial Knowledge in the Vocabulary Size Test

Although an accurate measure of vocabulary size is integral to understanding the proficiency of language learners, the validity of multiple-choice (M/C) vocabulary tests to determine this has been questioned due to users guessing correct answers which inflates scores. In this paper the nature of guessing and partial knowledge used when taking the Vocabulary Size Test (VST) is examined. The analysis evaluates the thought processes of test takers through think-aloud protocols and self-reports. This provides a taxonomy of seven types of guesses used by learners while taking the VST and measures the frequency of their occurrence. Based on qualitative coding, guesses are investigated to determine if they exhibit partial word knowledge which is relevant to the test construct. The findings suggest that both guesses resulting from the use of partial knowledge and random guesses are included in estimates and this has a detrimental effect on test accuracy. The paper concludes that the VST does not provide an accurate measure of the vocabulary necessary for reading due to guessing and the meaning-recognition format. It also suggests that the role of partial knowledge should be considered when producing more sophisticated vocabulary tests in the future.

Robust Adaptive Learning-Based Path Tracking Control of Autonomous Vehicles Under Uncertain Driving Environments

This paper investigates the path tracking control problem of autonomous vehicles subject to modelling uncertainties and external disturbances. The problem is approached by employing a 2-degree of freedom vehicle model, which is reformulated into a newly defined parametric form with the system uncertainties being lumped into an unknown parametric vector. On top of the parametric system representation, a novel robust adaptive learning control (RALC) approach is then developed, which estimates the system uncertainties through iterative learning while treating the external disturbances by adopting a robust term. It is shown that the proposed approach is able to improve the lateral tracking performance gradually through learning from previous control experiences, despite only partial knowledge of the vehicle dynamics being available. It is noteworthy that a novel technique targeting at the non-square input distribution matrix is employed so as to deal with the under-actuation property of the vehicle dynamics, which extends the adaptive learning control theory from square systems to non-square systems. Moreover, the convergence properties of the RALC algorithm are analysed under the framework of Lyapunov-like theory by virtue of the composite energy function and the <inline-formula xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink"> <tex-math notation="LaTeX">$\lambda $ </tex-math></inline-formula> -norm. The effectiveness of the proposed control scheme is verified by representative simulation examples and comparisons with existing methods.

Partial Knowledge Transfer in Visual Recognition Systems via Joint Loss-Aware Consistency Learning

One of the key challenges for the implementation of visual recognition systems in the real world is to construct prediction models that can realize the knowledge transfer from the seen data to the unseen data. Specifically, partial knowledge transfer (PKT) aims to address a more common and realistic scenario in which we are accessible to a label-rich source domain while working on a relative label-scarce target domain. The essence of PKT is to explore the latent categories across different domains and simultaneously facilitate the positive transfer from these data. In this article, we propose a joint loss-aware consistency learning (JLACL) to effectively enhance the transferability of knowledge in visual recognition systems, which conducts an iterative optimization on three-level losses, including discrepancy loss, consensus loss, and cross-entropy loss. The discrepancy loss is designed to eliminate the class distribution bias by a similarity perception metric between the source and target domains. The consensus loss can assist to preserve domain-invariant and representative features for model learning by exploring correlation. Moreover, we also find that using the cross-entropy loss to determine the shared label space, which can help to alleviate the negative transfer by suppressing the features with nonshared labels. Finally, the PKT can be successfully achieved by joint optimization of total losses. Extensive experiments on several public and challenging datasets in visual recognition applications adequately demonstrate the superiority of our proposed JLACL over existing state-of-the-art PKT methods.

An Action Research Study on Teaching the Landform Concepts in a Fifth Grade Social Studies Course

This study aims to find out the effect of activities and practices on concept education to help carry out activities to teach these concepts more effectively and permanently, and determine student preconceptions about the concepts related to the landforms in the 5th-grade social studies curriculum in the learning subdomain called “People, Places and Environments.” It was conducted as an action research study, employing a qualitative research design. The participants of the study were 16 fifth graders who were studying at a state secondary school in Bekilli District of the province of Denizli, Turkey. Mind maps, worksheet activities, models and mock-up, focus group interviews and field notes were used as the data collection tools, concept development tools, and process support tools. Collected in four class hours, the data were subjected to statement-based content analysis, and then compared with the criteria developed for the related concept and converted into findings and conclusions. The findings indicate that the student group has a partial preliminary knowledge and perception of the distinctive landforms they see around them. However, the student group's preliminary knowledge and perception of the landforms they do not observe around them is insufficient. The results obtained at the end of the instructional intervention process indicate that the student group is confused about the definitions of the bay, gulf, cape, peninsula, island, strait, and ocean. With the activities implemented after the additional action plan, the student group made progress by effectively learning the basic and sub-dimensional landforms they were confused about. The student group found it useful to carry out mind maps and application activities and achieved effective learning with models and mock-ups. Based on research result, use of sightseeing –observation techniques can be recommended to social studies teachers.

A Physics-Guided Neural Operator Learning Approach to Model Biological Tissues From Digital Image Correlation Measurements.

We present a data-driven workflow to biological tissue modeling, which aims to predict the displacement field based on digital image correlation (DIC) measurements under unseen loading scenarios, without postulating a specific constitutive model form nor possessing knowledge of the material microstructure. To this end, a material database is constructed from the DIC displacement tracking measurements of multiple biaxial stretching protocols on a porcine tricuspid valve anterior leaflet, with which we build a neural operator learning model. The material response is modeled as a solution operator from the loading to the resultant displacement field, with the material microstructure properties learned implicitly from the data and naturally embedded in the network parameters. Using various combinations of loading protocols, we compare the predictivity of this framework with finite element analysis based on three conventional constitutive models. From in-distribution tests, the predictivity of our approach presents good generalizability to different loading conditions and outperforms the conventional constitutive modeling at approximately one order of magnitude. When tested on out-of-distribution loading ratios, the neural operator learning approach becomes less effective. To improve the generalizability of our framework, we propose a physics-guided neural operator learning model via imposing partial physics knowledge. This method is shown to improve the model's extrapolative performance in the small-deformation regime. Our results demonstrate that with sufficient data coverage and/or guidance from partial physics constraints, the data-driven approach can be a more effective method for modeling biological materials than the traditional constitutive modeling.

Special issue on PID control in the information age: Theoretical advances and applications

Special issue on PID control in the information age: Theoretical advances and applications

Stabilization of a Furuta’s Pendulum using Barrier Function-based Adaptive Lyapunov Re-design

One of the most challenging task in the control engineering is the stabilization of underactuated systems. This task can be even arduous if the system is affected by parametric uncertainties due to a partial knowledge of the parameters or external disturbances. These difficulties could lead to instability. The paper presents an adaptive approach to the Lyapunov redesign, stabilizing a Furuta’s pendulum, where the control signal follows the uncertainties with opposite sign. The controller is composed of a nominal term and a barrier function based adaptive sliding mode control. The gain guarantees the system trajectories converge to a vicinity of the surface in finite-time. Then, a barrier-function gain maintains the sliding surface inside a predefined vicinity. In this matter, the proposed controller guarantees the estimation of the uncertanties generating a continuous control signal from the semi-positive definite barrier function. Finally, some experiments were performed to prove the feasibility of the proposed controller.

Playing Russian Roulette with Every COVID-19 Injection: The Deadly Global Game

Are the promoters of the COVID-19 injections leading, pushing, and even forcing the people of the world to play a global game of Russian Roulette? The actuarial statistics and clinical facts are revealing harmful and deadly consequences on a global scale. For the sake of those still able to choose whether to spin the cylinder and pull the trigger one more time, I am conducting a guided tour. I will show why there are exponentially many more ways for the experimental injections to cause disease and death than health and well-being. Briefly put, valid transcription of mRNA from our own DNA leads to well-formed proteins that work as designed for cytoplasm, organelles, cells, tissues, and functional organ systems of the body. By contrast, the foreign (xeno) nucleic acid sequences, the mXNAs, in the “modified mRNA” in the COVID-19 injectables are more likely to harm the body’s systems than to benefit them. The relevant research shows why the mXNAs are incompetent to communicate effectively with the complex native context in vivo — the human biosignaling systems that are not mechanical but are articulated in multiple layers, interconnected, and for practical purposes, infinitely varied systems of information processing. Survival and longevity depend on valid communications among nuclear and mitochondrial DNA, their RNAs, and the protein language systems specified before a person’s birth. When these complex biosignaling systems fail, disorders and diseases follow. In catastrophic failures, death occurs. The story is not simple and the tour I am conducting is challenging with more than a few twists, turns, and digressions. However, I believe, persons experimented on — almost all of them unknowingly, some with partial knowledge who were unwilling recipients — account for more than half the world population. The stakes are high, even life or death, so I think many of the recipients of one or more injections will take the guided tour.

Few-Shot Continuous Authentication for Mobile-Based Biometrics

The rapid growth of smartphone financial services raises the need for secure mobile authentication. Continuous authentication is a user-friendly way to strengthen the security of smartphones by implicitly monitoring a user’s identity through sessions. Mobile continuous authentication can be viewed as an anomaly detection problem in which models discriminate between one genuine user and the rest of the impostors (anomalies). In practice, complete impostor profiles are hardly available due to the time and monetary cost, while leveraging genuine data alone yields poorly generalized models due to the lack of knowledge about impostors. To address this challenge, we recast continuous mobile authentication as a few-shot anomaly detection problem, aiming to enhance the inference robustness of unseen impostors by using partial knowledge of available impostor profiles. Specifically, we propose a novel deep learning-based model, namely a local feature pooling-based temporal convolution network (LFP-TCN), which directly models raw sequential mobile data, aggregating global and local feature information. In addition, we introduce a random pattern mixing augmentation to generate class-unconstrained impostor data for the training. The augmented pool enables characterizing various impostor patterns from limited impostor data. Finally, we demonstrate practical continuous authentication using score-level fusion, which prevents long-term dependency or increased model complexity due to extended re-authentication time. Experiments on two public benchmark datasets show the effectiveness of our method and its state-of-the-art performance.

ISegMSI: An Interactive Strategy to Improve Spatial Segmentation of Mass Spectrometry Imaging Data.

Spatial segmentation is a critical procedure in mass spectrometry imaging (MSI)-based biochemical analysis. However, the commonly used unsupervised MSI segmentation methods may lead to inappropriate segmentation results as the MSI data is characterized by high dimensionality and low signal-to-noise ratio. This process can be improved by the incorporation of precise prior knowledge, which is hard to obtain in most cases. In this study, we show that the incorporation of partial or coarse prior knowledge from different sources such as reference images or biological knowledge may also help to improve MSI segmentation results. Here, we propose a novel interactive segmentation strategy for MSI data called iSegMSI, which incorporates prior information in the form of scribble-regularization of the unsupervised model to fine-tune the segmentation results. By using two typical MSI data sets (including a whole-body mouse fetus and human thyroid cancer), the present results demonstrate the effectiveness of the iSegMSI strategy in improving the MSI segmentations. Specifically, the method can be used to subdivide a region into several subregions specified by the user-defined scribbles or to merge several subregions into a single region. Additionally, these fine-tuned results are highly tolerant to the imprecision of the scribbles. Our results suggest that the proposed iSegMSI method may be an effective preprocessing strategy to facilitate the analysis of MSI data.

A novel integration of MCDM methods and Bayesian networks: the case of incomplete expert knowledge

In this study, we propose an effective integration of multi criteria decision making methods and Bayesian networks (BN) that incorporates expert knowledge. The novelty of this approach is that it provides decision support in case the experts have partial knowledge. We use decision-making trial and evaluation laboratory (DEMATEL) to elicit the causal graph of the BN based on the causal knowledge of the experts. BN provides the evaluation of alternatives based on the decision criteria which make up the initial decision matrix of the technique for order of preference by similarity to the ideal solution (TOPSIS). We then parameterize BN using Ranked Nodes which allows the experts to submit their knowledge with linguistic expressions. We propose the analytical hierarchy process to determine the weights of the decision criteria and TOPSIS to rank the alternatives. A supplier selection case study is conducted to illustrate the effectiveness of the proposed approach. Two evaluation measures, namely, the number of mismatches and the distance due to the mismatch are developed to assess the performance of the proposed approach. A scenario analysis with 5% to 20% of missing values with an increment of 5% is conducted to demonstrate that our approach remains robust as the level of missing values increases.

Graph-Based Bayesian Optimization for Large-Scale Objective-Based Experimental Design

Design is an inseparable part of most scientific and engineering tasks, including real and simulation-based experimental design processes and parameter/hyperparameter tuning/optimization. Several model-based experimental design techniques have been developed for design in domains with partial available knowledge about the underlying process. This article focuses on a powerful class of model-based experimental design called the mean objective cost of uncertainty (MOCU). The MOCU-based techniques are objective-based, meaning that they take the main objective of the process into account during the experimental design process. However, the lack of scalability of MOCU-based techniques prevents their application to most practical problems, including large discrete or combinatorial spaces. To achieve a scalable objective-based experimental design, this article proposes a graph-based MOCU-based Bayesian optimization framework. The correlations among samples in the large design space are accounted for using a graph-based Gaussian process, and an efficient closed-form sequential selection is achieved through the well-known expected improvement policy. The proposed framework's performance is assessed through the structural intervention in gene regulatory networks, aiming to make the network away from the states associated with cancer.