Pieces Of Information Research Articles

Three-Dimensional Single Random Phase Encryption.

In this paper, we propose a new optical encryption technique that uses the single random phase mask. In conventional optical encryptions such as double random phase encryption (DRPE), two different random phase masks are required to encrypt the primary data. For decryption, DRPE requires taking the absolute value of the decrypted data because it is complex-valued. In addition, when key information is revealed, the primary data may be reconstructed by attackers. To reduce the number of random phase masks and enhance the security level, in this paper, we propose single random phase encryption (SRPE) with additive white Gaussian noise (AWGN) and volumetric computational reconstruction (VCR) of integral imaging. In our method, even if key information is known, the primary data may not be reconstructed. To enhance the visual quality of the decrypted data by SRPE, multiple observation is utilized. To reconstruct the primary data, we use VCR of integral imaging because it can remove AWGN by average effect. Thus, since the reconstruction depth can be another key piece of information of SRPE, the security level can be enhanced. In addition, it does not require taking the absolute value of the decrypted data for decryption. To verify the validity of our method, we implement the simulation and calculate performance metrics such as peak sidelobe ratio (PSR) and structural similarity (SSIM). In increasing the number of observations, SSIM for the decrypted data can be improved dramatically. Moreover, even if the number of observations is not enough, three-dimensional (3D) data can be decrypted by SRPE at the correct reconstruction depth.

Assessing the impact of marine renewable energy in Portugal: an analysis based on ACO-TCN-attention

As the global demand for renewable energy continues to increase, marine renewable energy has attracted much attention as a potential source of clean energy. As a country with rich marine resources, Portugal’s marine environment is of great significance to the development of marine energy. However, the current impact assessment of marine renewable energy projects has shortcomings such as incomplete understanding of ecosystems, incomplete consideration of fishery resources and socioeconomic impacts, lack of accuracy, and failure to consider geographical differences, thus lacking comprehensiveness and accuracy. To this end, we propose the ACO-TCN-Attention model to address these shortcomings in current impact assessments of marine renewable energy projects. The goal of this model is to provide a more comprehensive, precise and nuanced analysis to better understand the impacts of these projects on ecosystems, socio-economics and local communities. “ACO-TCN-Attention” is a model architecture that combines multiple machine learning and deep learning concepts. It includes three main parts: Ant Colony Optimization (ACO), Temporal Convolutional Network (TCN) and Attention mechanism. The ant colony optimization model simulates the behavior of ants and is used to optimize the operating strategies of marine renewable energy projects. Temporal Convolutional Network specializes in processing time series data and improves the prediction accuracy of the model. The attention mechanism allows the model to dynamically focus on the pieces of information that are most important for the current task. Extensive experimental evaluation shows that our method performs well on multiple datasets, significantly outperforming other models. This research is of great significance as it provides new methods and tools for improving the environmental impact assessment of marine renewable energy projects. By understanding the potential impacts of projects more accurately, we can better balance the relationship between the development of renewable energy and environmental protection, supporting the achievement of the Sustainable Development Goals. This research also provides useful guidance and reference for future research and practice in the field of marine energy.

Parameterized complexity of broadcasting in graphs

The task of the broadcast problem is, given a graph G and a source vertex s, to compute the minimum number of rounds required to disseminate a piece of information from s to all vertices in the graph. It is assumed that, at each round, an informed vertex can transmit the information to at most one of its neighbors. The broadcast problem is known to NP-hard. We show that the problem is FPT when parametrized by the size k of a feedback edge set, or by the size k of a vertex cover, or by k=n−t, where t is the input deadline for the broadcast protocol to complete.

Influence of Social Media on News Reportage and Authenticity of Covid-19 Stories

This work evaluates the influence of social media on news reportage and authenticity of covid-19 stories. In conducting this study, the researcher raised three research questions and three hypotheses. User and gratification theory was developed in 1974 by Katz, Blumler and Gurevitch and Diffusion of Innovations theory by Rogers were used to build theoretical support for the study. The survey research design was used to evaluate the opinions of 53 sampled respondents from Passion FM Uyo and NTA Uyo. After the analysis, findings revealed that, social media has an influence on news reporting in Nigeria and that it also has influence on the authenticity of news reported. Finally, it was found that social media had an influence on COVID-19 news reporting. Based on these findings, it was recommended that users of social media should be wary of some of the contents they consume as unscrupulous users have been found to display misleading information that could be harmful to the public. Again, members of the public are encouraged to desist from the various abuses of the use of social media for the interest of greater number of Nigerians found to be users of the sites in search of various relevant pieces of information.

An efficient breast cancer classification model using bilateral filtering and fuzzy convolutional neural network

BC (Breast cancer) is the second most common reason for women to die from cancer. Recent workintroduced a model for BC classifications where input breast images were pre-processed using median filters for reducing noises. Weighed KMC (K-Means clustering) is used to segment the ROI (Region of Interest) after the input image has been cleaned of noise. Block-based CDF (Centre Distance Function) and CDTM (Diagonal Texture Matrix)-based texture and shape descriptors are utilized for feature extraction. The collected features are reduced in counts using KPCA (Kernel Principal Component Analysis). The appropriate feature selection is computed using ICSO (Improved Cuckoo Search Optimization). The MRNN ((Modified Recurrent Neural Network)) values are then improved through optimization before being utilized to divide British Columbia into benign and malignant types. However, ICSO has many disadvantages, such as slow search speed and low convergence accuracy and training an MRNN is a completely tough task. To avoid those problems in this work preprocessing is done by bilateral filtering to remove the noise from the input image. Bilateral filter using linear Gaussian for smoothing. Contrast stretching is applied to improve the image quality. ROI segmentation is calculated based on MFCM (modified fuzzy C means) clustering. CDTM-based, CDF-based color histogram and shape description methods are applied for feature extraction. It summarizes two important pieces of information about an object such as the colors present in the image, and the relative proportion of each color in the given image. After the features are extracted, KPCA is used to reduce the size. Feature selection was performed using MCSO (Mutational Chicken Flock Optimization). Finally, BC detection and classification were performed using FCNN (Fuzzy Convolutional Neural Network) and its parameters were optimized using MCSO. The proposed model is evaluated for accuracy, recall, f-measure and accuracy. This work’s experimental results achieve high values of accuracy when compared to other existing models.

Research on the Effects of Doping

The background of this essay is that many athletes start to use illegal drugs to increase their physical ability. Many athletes tend to take a lot of drugs in order to enhance their physical performance, so the main topic that this essay wants to focus on is the pros and cons of using some drugs. Especially the positive or negative effects on athletes physical health. This research mainly includes several pieces of information from academic websites, books, and newspapers. The research data is collected from academic websites. The five important words of this essay are athletics, pros, cons, medicine or drug, effect of drug. The conclusion of this research is that medication can have irreversible effects on athletes, especially with huge pressure on the heart, and excessive medication can also mitigate the age of athletes, leading to early retirement. What athletes really need to do is improve their skills rather than using fraudulent means to improve their performance, which also leads to unfair competition for other athletes who have not taken medication.

Trends of Antibacterial Interactions in Multidrug – Resistant Isolates: Exploring Resistance Phenotypes in the Ndé Division, West-Cameroon

Background: Reliable pieces of information concerning bacterial-antibiotic interactions are key assets for therapeutic management of bacterial diseases.
 Objective: The present study aimed at detecting phenotypic characteristics of bacterial resistance in multidrug-resistant isolates recovered from clinical specimens at the “Université des Montagnes” Teaching Hospital.
 Methods: The total of 226 isolates (142 Gram-negative rods and 84 Gram-positive cocci) were subjected to phenotypic screening of resistance mechanisms. All procedural steps were conducted according to standard protocols on bacterial susceptibility to antibiotics.
 Results: Primary pieces of information revealed high rates of resistant isolates, especially with beta-lactams and Trimethoprim/Sulfamethoxazole while Nitrofurantoin and Imipenem were most effective. Amongst Gram-negative rods, 56% expressed one enzymatic resistance mechanism and 12% expressed two against beta-lactams. Also, with extended spectrum beta-lactamases, high level cephalosporinases and inducible cephalosporinases most commonly observed. About 62% and 14% of Gram-positive cocci expressed constitutive and Clindamycin-inducible resistance, respectively. Decreased susceptibility to Ceftriaxone and Penicillin G was also recorded in suspect mutant isolates selected by these antibiotics. Potential synergetic and other antagonistic interactions were evenly detected.
 Conclusion: Overall, the data could represent reliable clue for advocacy about personalized combination therapy, then capacity building for routine affordable susceptibility tests in caretaking.

KGSCS—a smart care system for elderly with geriatric chronic diseases: a knowledge graph approach

BackgroundThe increasing aging population has led to a shortage of geriatric chronic disease caregiver, resulting in inadequate care for elderly people. In this global context, many older people rely on nonprofessional family care. The credibility of existing health websites cannot meet the needs of care. Specialized health knowledge bases such as SNOMED—CT and UMLS are also difficult for nonprofessionals to use. Furthermore, professional caregiver in elderly care institutions also face difficulty caring for multiple elderly people at the same time and working handovers. As a solution, we propose a smart care system for the elderly based on a knowledge graph.MethodFirst, we worked with professional caregivers to design a structured questionnaire to collect more than 100 pieces of care-related information for the elderly. Then, in the proposed system, personal information, smart device data, medical knowledge, and nursing knowledge are collected and organized into a dynamic knowledge graph. The system offers report generation, question answering, risk identification and data updating services. To evaluate the effectiveness of the system, we use the expert evaluation method to score the user experience.ResultsThe results of the study showed that compared to existing tools (health websites, archives and expert team consultation), the system achieved a score of 8 or more for basic information, health support and Dietary information. Some secondary evaluation indicators reached 9 and 10 points. This finding suggested that the system is superior to existing tools. We also present a case study to help the reader understand the role of the system.ConclusionThe smart care system provide personalized care guidelines for nonprofessional caregivers. It also makes the job easier for institutional caregivers. In addition, the system provides great convenience for work handover.

The effects of mnemonic variability and spacing on memory over multiple timescales

The memory benefit that arises from distributing learning over time rather than in consecutive sessions is one of the most robust effects in cognitive psychology. While prior work has mainly focused on repeated exposures to the same information, in the real world, mnemonic content is dynamic, with some pieces of information staying stable while others vary. Thus, open questions remain about the efficacy of the spacing effect in the face of variability in the mnemonic content. Here, in two experiments, we investigated the contributions of mnemonic variability and the timescale of spacing intervals, ranging from seconds to days, to long-term memory. For item memory, both mnemonic variability and spacing intervals were beneficial for memory; however, mnemonic variability was greater at shorter spacing intervals. In contrast, for associative memory, repetition rather than mnemonic variability was beneficial for memory, and spacing benefits only emerged in the absence of mnemonic variability. These results highlight a critical role for mnemonic variability and the timescale of spacing intervals in the spacing effect, bringing this classic memory paradigm into more ecologically valid contexts.

The Role of Digital Tools in Enhancing the Interactivity of Teaching Cultural Industry Management

With the development of technology, digital tools have become an important part of education, this paper investigates the role of digital tools in enhancing the interactivity of cultural industry management teaching and discusses its impact on teaching effectiveness. 513 pieces of valid information were collected from a random sample of frontline teachers and students in a university in Jiangxi Province using questionnaires and interviews, the results of the study show that digital tools play an important role in enhancing the interactivity of teaching and learning. Specifically, teachers and students identified basic teaching skills, instructional design ability, collaborative teaching ability, data diagnosis ability, and instructional decision-making ability as the main skills required for the application of digital tools in teaching and learning, among which, instructional design ability performs the best in stimulating students’ motivation strategy, with a mean score of 3.938. In addition, multiple linear regression analysis revealed that digital tools have a significant impact on the development of cultural industry management and the development of cultural industry management. In addition, multiple linear regression analysis reveals that digital knowledge and digital skills have a significant positive effect on the interactivity of cultural industry management teaching, and the study concludes that the effective integration of digital tools can significantly improve the interactivity and effect of cultural industry management teaching and promote the innovation of teaching mode.

Multiple sources of unconscious-information processing affect a single response: independent unconscious priming effects

At present there is little knowledge on whether and how multiple pieces of unconscious information can simultaneously affect a single conscious response. In the present study, we manipulated the congruency relation between a masked prime arrow and the target arrow, as well as that between masked flankers and the target arrow. The results demonstrated that the masked prime and flankers produced independent unconscious priming effects on the response to the target. In the process of studying the above phenomenon, two secondary findings were made. First, although the prime congruency effect was obtained, the flanker congruency effect was smaller when the flankers were displayed simultaneously with the target than when they were displayed sequentially before the target. This suggested that priming stimulation required enough time to be processed to a sufficient extent to produce an unconscious priming effect. Second, when the prime stimulus was removed, leaving only the flankers, the flanker priming effect increased, suggesting that the attention attracted to the prime and its conscious mask could also reduce the flanker congruency effect. These results observed across several experiments were replicated in one within-subjects experiment. We proposed an “independent unconscious influence hypothesis” for the phenomenon. This hypothesis was further integrated into a more comprehensive unconscious information processing model. The possible causes of the observed phenomena were discussed.

Modeling students' preferences and knowledge for improving educational achievements

Student modeling is a fundamental aspect in customized learning environments. It enables unified representation of students' characteristics that supports creating personalized learning experiences. This paper aims to build an effective student model by combining learning preferences with skill levels. A student profile is formulated upon detecting the user's learning styles and learning preferences, as well as their knowledge level and misconceptions. The pieces of information are collected through an interactive online platform, by completing personal and knowledge assessment quizzes. Moreover, a learner can make his/her profile open for other learners as a starting point for supporting collaborative learning. The results showed an improvement of students' educational achievements who used the platform, and the satisfaction level reported by non-neutral users was averaged as a score of 90%. The evaluation of this platform showed promising results regarding its ability in describing students in a comprehensive manner.

Control Strategy for Building Air Conditioning Cluster Loads Participating in Demand Response Based on Cyber-Physical System

In recent years, with rising urbanization and ongoing adjustments in industrial structures, there has been a growing dependence on public buildings. The load of public buildings gradually becomes the main component of the peak load in summer, among which the load of air conditioning is particularly prominent. To clarify the key problems and solutions to these challenges, this study proposes a multi-objective optimization control strategy for building air conditioning cluster participation in demand response based on Cyber-Physical System (CPS) architecture. In a three-layer typical CPS architecture, the unit level of the CPS achieves dynamic information perception of air conditioning clusters through smart energy terminals. An air conditioning load model based on the multiple parameter types of air conditioning compressors is presented. Then, the system level of the CPS fuses multiple pieces of information through smart energy gateways, analyzing the potential for air conditioning clusters when they participate in demand response. The system of system level (SoS level) of the CPS deploys a multi-objective optimization control strategy which includes the uncertainty of the initial states of air conditioning clusters within the intelligent building energy management system. The optimal model takes into account the differences in the environmental conditions of each individual air conditioning unit within the cluster and sets different operating modes for each unit to achieve load reduction while maintaining temperatures within a comfortable range for the human body. The Multi-Objective Particle Swarm Optimization (MOPSO) algorithm based on Pareto frontiers is applied to solve this optimization control strategy and to optimize the operational parameters of the air conditioning clusters. A comparative analysis is conducted with single-objective optimization results obtained using the traditional Particle Swarm Optimization (PSO) algorithm. The case study results indicate that the proposed multi-objective optimization control strategy can effectively improve the thermal comfort of the human body towards the controlled temperatures of air conditioning clusters while meeting the accuracy of demand response. In the solution phase, the highest temperature within the air conditioning clusters is 24 °C and the lowest temperature is 23 °C. Adopting the proposed multi-objective optimization control strategy, the highest temperature is 26 °C and the lowest temperature is 23.5 °C within the clusters and the accuracy of demand response is up to 92%. Compared to the traditional PSO algorithm, the MOPSO algorithm has advantages in convergence and optimization precision for solving the proposed multi-objective optimization control strategy.

Visual features are processed before navigational affordances in the human brain

To navigate through their immediate environment humans process scene information rapidly. How does the cascade of neural processing elicited by scene viewing to facilitate navigational planning unfold over time? To investigate, we recorded human brain responses to visual scenes with electroencephalography and related those to computational models that operationalize three aspects of scene processing (2D, 3D, and semantic information), as well as to a behavioral model capturing navigational affordances. We found a temporal processing hierarchy: navigational affordance is processed later than the other scene features (2D, 3D, and semantic) investigated. This reveals the temporal order with which the human brain computes complex scene information and suggests that the brain leverages these pieces of information to plan navigation.

Numerical Covariance Evaluation for Linear Structures Subject to Non-Stationary Random Inputs

Random vibration analysis is a mathematical tool that offers great advantages in predicting the mechanical response of structural systems subjected to external dynamic loads whose nature is intrinsically stochastic, as in cases of sea waves, wind pressure, and vibrations due to road asperity. Using random vibration analysis is possible, when the input is properly modeled as a stochastic process, to derive pieces of information about the structural response with a high quality (if compared with other tools), especially in terms of reliability prevision. Moreover, the random vibration approach is quite complex in cases of non-linearity cases, as well as for non-stationary inputs, as in cases of seismic events. For non-stationary inputs, the assessment of second-order spectral moments requires resolving the Lyapunov matrix differential equation. In this research, a numerical procedure is proposed, providing an expression of response in the state-space that, to our best knowledge, has not yet been presented in the literature, by using a formal justification in accordance with earthquake input modeled as a modulated white noise with evolutive parameters. The computational efforts are reduced by considering the symmetry feature of the covariance matrix. The adopted approach is applied to analyze a multi-story building, aiming to determine the reliability related to the maximum inter-story displacement surpassing a specified acceptable threshold. The building is presumed to experience seismic input characterized by a non-stationary process in both amplitude and frequency, utilizing a general Kanai–Tajimi earthquake input stationary model. The adopted case study is modeled in the form of a multi-degree-of-freedom plane shear frame system.

LEGISLATIVE REGULATION OF BOTH CRIMES OF THREAT AND INFORMATIONAL BLACKMAIL- ANALYTICAL FUNDAMENTAL STUDY OF SAUDI INFORMATIONAL CRIMES LAW

Objective: This research discusses one of the most important issues resulting from the rapidly developed technological revolution, as coercion and electronic extortion have become one of the negative results of that revolution.
 
 Theoretical Framework: the coercion and electronic extortion is the most common negative results of that revolution, particularity that they do not require more tools or effort, as all the blackmailer needs is a picture, or an audio recording, or something that combines both of the picture and audio recording, or even a piece of information, on any social networking site, so that he, then, weaves the threads of his planned crime, behind which he intends to achieve specific goals, which are most cases achieving material or moral gain, engaging in a forbidden sexual relationship, or similar benefits. In addition, this crime is rapid spread due to its electronic nature. Accordingly, it is necessary to learn about these serious crimes.
 
 Methods: The researcher used the analytical fundamental approach to study the crime and its elements and analyze the legal provisions with the objective down to reach forms of the implementation of the crime and its elements and the penalty to be imposed on the criminal regarding this crime.
 
 Results and Discussion: This research work was divided into two sections. The first section discusses the definition of crime of coercion and its conditions, and the definition of crime of the electronic blackmail, and types and goals of the blackmail. As for the second section, it discusses elements of realization the crime and the punishment determined thereto by Law.
 
 Concluion: The crime of coercion and extortion goes through several stages, and the offender’s goal is to achieve his desires, whether material, moral, or sexual.

Comparing measured sound strength to theoretical sound strength for rooms of varying size

ISO Standard 23591 shows how the sound pressure level in a room can be estimated with the following two pieces of information: the sound power of sources in that room, and the sound strength G of that room as defined in ISO 3382-1. In an earlier study, the theory connecting room volume V, reverberation time T, and sound strength G was summarized, and a comparison of measured G to this theory was explored for a wide range of rooms. That test setup used Odeon software. In the present study, a different test setup is used implementing Dirac software to expand G testing to additional rooms of various sizes for evaluation under the same mathematical model. After a description of the equipment using Dirac software is discussed, a brief review of the additional rooms in which sound strength was measured is provided. Both measured and theoretical sound strength results are presented for these rooms. The difference between measured G and theoretical G using the mathematical model is analyzed statistically both for the Dirac test setup and compared to the results from the previous study.

Compatibility of Entomopathogenic Nematode (Steinernema sp.) and Coccinelid (Cheilomenes sexmaculata) with Insecticides registered against Fall Army Worm (Spodoptera frugiperda) in Corn

Cyantraniliprole, Chlorantraniliprole, Emamectin benzoate, Spinosad, Flubendiamide, Teflubenzuron, Novaluron+Indoxacarb, Broflanilide, Chlorafenapyr and Azadirachtin are some important registered insecticides against S. frugiperda in corn. Bioagents such as entomopathogenic nematodes (EPNs) and lady beetles have also role to reduce its population. Integration of bioagents with chemical insecticides is current need for sustaianable pest management. So, the present evaluation was conducted at laboratory condition to knowhow the fate of EPNs, Steinernema sp. and coccinelid (Cheilomenes sexmaculata) after their exposure with above mentioned insecticides used for S. frugiperda. Lab research was conducted during spring (2021) at BCKV, Burdwan campus (West Bengal). EPNs was multiplied on larva of Corcyra cephalonica. Cheilonemes sexmaculata (grubs and pupa) was collected from field. Ten novel insecticides recommended for S. frugiperda were evaluated against the mentioned bioagents. Corected mortality data of EPNs and coccinelid was analyzed statistically and accordingly insecticides were grouped into different toxicity class. Except Emamectin benzoate, all the chemicals were proved to be safe to Steinernema sp. E. benzoate caused 100 % reduction to insect infectivity by EPN, followed by Chlorafenapyr (80 %). Slightly harmful effect for grubs (C. sexmaculata) was noted in Cyantraniliprole and Emamectin benzoate and for pupa in emamectin benzoate and Novaluron + Indoxacarb. Otherwise all others were harmless both for grubs and pupal stage of C. sexmaculata. These pieces of information might be practical in decision making for IPM of S. frugiperda.

Implications of social network structures on socially influenced decision-making

Individuals often make decisions in a social environment where social influence can impact on people’s decision-making domains such as online purchasing, political voting and voluntary vaccine uptake. Social influence can be recognised as the intentional or unintentional change in an individual’s belief, perception, or behaviours caused by an information diffusion process embedded in a social network. However, there is limited research on how this diffusion process is shaped by the topology or structure of the social network. This work provides an exploratory and systematic analysis of how decision-making outcomes in a population can be affected by both the structure of the social network and the starting node of where new information starts to diffuse. Simulation results considering three common network structures highlight how social networks with clear community structures lead to a larger absolute impact on decision-making outcomes and networks where the social connections follow a preferential attachment rule show the largest relative impact than the others. The results also suggest scenarios in which introducing new pieces of information to the social network can facilitate the information diffusion process and produce a more significant impact in terms of the overall population decision-making process.

Traditional ethnobotanical knowledge and ethnomedicinal use of plants in the Tropical Rift Valley of Ethiopia

IntroductionA wide range of floral resources are used in Ethiopia for primary healthcare. Unfortunately, due to inadequate documentation and the fact that they were verbally passed down from one generation to the next, these indigenous medicinal practices are being forgotten. The study's goal was to identify and record indigenous peoples' traditional knowledge of plant usage in the Abala Abaya District of Wolaita, Ethiopia. MethodsThe collection of ethnobotanical data used a cross-sectional research design involving focus group discussions and semistructured questionnaires between January 2021 and April 2022. From each selected kebele (ward; smallest administrative unit), 50 informants (a total of 200) were selected as representative sample using a systematic random sampling technique. ResultsMore than 50 different human and animal illnesses have been documented to be treated by seventy-two species of plants grouped in thirty-two families. The most abundant families were Poaceae and Asteraceae (8 taxa in each). The two most often used plant parts were leaf (29%) and herbs (54%), respectively. Oral administration was the most typical mode of delivery (75%). For treating stomachache in the category of gastrointestinal illnesses, Hagenia abyssinica (Bruce) J.F.Gmel got the greatest fidelity level scores (FL = 100%). ConclusionsIn Abala Abaya District, there is broad access to traditional medicinal plants that can treat ailments in both humans and animals. This study, therefore, might be a baseline piece of information for further botanical related studies in the region. Plants with the highest FL values are highly recommended for novel drug discovery.