Consistent Information Research Articles

Multi-Criteria Evaluation of the Effectiveness of Computer Crime Disclosure Under Ecuadorian Legal Regulations

Decision-making based on multiple criteria is common in various contexts, recognized for its high complexity in seeking viable solutions. Computer crimes encompass any act with criminal intent that seeks to cause harm or put at risk a legally protected interest using computer tools. This study aims to determine whether residents of Santo Domingo are aware of computer crimes established in Ecuadorian legislation, employing multicriteria evaluation techniques and the TODIM and PROMETHEE methods. These methodologies are complemented by neutrosophic single-value sets, based on neutrosophic logic, to effectively manage the indeterminate and inconsistent information typical in real-world scenarios. In this way, the utility of these techniques for addressing complex problems in daily life and in various social domains is demonstrated.

Delayed predictive inference integration with and revision by low-competitive inference alternatives in Chinese narrative text reading.

When readers encounter information conflicting with the predictive inferences made earlier, they may update the outdated ones with new ones, a process known as predictive inference revision. The current study examined the revision of disconfirmed predictive inferences by the primarily weakly activated, thus low-competitive inference alternatives during Chinese narrative text reading among Chinese native speakers. We conducted an event-related brain potential (ERP) experiment to study the predictive inference revision with increasingly supportive information for the low-competitive predictive inference alternatives. It serves as the very first attempts to study the predictive inference revision mechanisms by combining a larger range of ERP components, including frontal-Post-N400-Positivity (f-PNP) as an index of revision to examine the influences of the alternative inferences at later stages of reading comprehension. Our results showed that readers could detect inconsistent information (P300), disconfirm the incorrect predictive inferences before successfully integrating the low-competitive alternative predictive inferences with their current situation model (N400), engaging themselves in a second-pass reanalysis process incurring processing costs (P600), and revising the disconfirmed predictive inferences (f-PNP) at a later stage of reading comprehension. Results of this study are supportive of relevant theories in assuming that predictive inference revision does not happen immediately upon encountering conflicting information but happens slowly and incrementally. Our results also unfold the post-revision mechanisms by suggesting the remaining activation and lingering influences of the disconfirmed inferences in the forthcoming reading process.

Multi‐UAV Cooperative Search for Moving Targets With Impaired Communication Using Improved Gray Wolf Optimizer

The study investigates the problem of collaborative target search by multiple unmanned aerial vehicles (UAVs) under the condition of communication information corruption. The problem involves the influence of target movement and environmental obstacles on the update of the UAV’s local target probability map (TPM), as well as communication information corruption among UAVs due to packet loss and resulting local information inconsistency. The objective is to efficiently plan paths for the UAV swarm to discover more targets. To address the above issues, this paper proposes the following methods. Firstly, an information diffusion model is introduced to simulate the TPM changes caused by target movement and the discovery of obstacles based on a two‐dimensional Gaussian distribution, thereby correcting the real‐time target information held by UAVs. Secondly, a radial basis function neural network (RBFNN) is employed to recover the corrupted information, and a weighted averaging method is used to fuse the information from multiple UAVs, achieving consensus among the UAVs. Lastly, the original gray wolf optimizer (GWO) is enhanced by incorporating strategies such as Levy flight and the nonlinear convergence factor. Subsequently, the improved GWO is integrated with the model predictive control (MPC) for UAV trajectory planning, enabling a more efficient search for optimal paths. Experimental results demonstrate that the proposed information recovery method outperforms the compared methods by 24.7% in terms of performance. The proposed comprehensive search approach shows higher efficiency in searching for more targets within a specified time, and it also exhibits advantages in terms of area coverage and other aspects.

Examining the Care Transition Experiences of Home Care Clients Using Journey Mapping

As a priority area of the Canadian government, home care is heavily relied on to support an integrated, accessible, and sustainable health system. VHA Home Healthcare is a large home care agency in Ontario, Canada. Home care provides services that span personal support, nursing, physical and occupational therapy and more to assist people in remaining in their homes and community. Although home care is associated with delayed and reduced hospital use, some home care recipients will require hospital admission. Unplanned hospital admissions in Ontario account for CAD 1.2 billion in healthcare spending. However, to date, little attention has been paid to the unique needs of clients receiving home care and experiencing a transition to and from the hospital. The proposed study aimed to answer the following research questions:
 1)What is the experience of home care clients transitioning from home to hospital and back home and having their home care services resumed?
 2)What are opportunities and recommendations to better support home care clients transitioning to and from the hospital?
 Our study used a descriptive qualitative design by conducting individual interviews and applying a patient journey mapping approach. Patient journey mapping involved identifying the clients’ experience, their touch points across the system, pain points and facilitators. A total of 7 clients and caregivers participated in 12 interviews across two phases. We analyzed the data using qualitative content analysis, and aggregated findings visually depicted the journey on a map. Our results demonstrated that participants experienced hospital admissions as challenging, whether planned or unplanned. Pain points involved having an adverse outcome while in the hospital, including missed or delayed care, attempting to seek additional information to fill knowledge gaps, and sensing not being listened to. Once discharged, clients reported managing multiple service providers and agencies for exacerbated care needs and receiving inconsistent and limited information when in the community. Participants described the system and individual facilitators that mitigated the pain points. These involved having consistent and supportive coordinators and providers in the community before and after the discharge home, having a discharge plan, and accessing an interprofessional team when hospitalized. At a personal level, many participants relied on past professional and personal experience when advocating and navigating the health care system. Self-advocacy skills were essential to ensure that participants had a quality care transition. Our study has several important implications. Notably, patient journey mapping is an effective tool to diagram the home care client journey. Findings also indicate that clients and caregivers often mitigate poor outcomes using advocacy skills and personal and professional experiences. To this end, we can learn from client journeys and leverage clients’ lived experiences to design interventions targeting pain points during care transitions.

The Misinfodemic and Its Effects on COVID-19 Preventive Strategies in Socially Vulnerable Populations

Throughout the coronavirus disease-2019 (COVID-19) pandemic, there has been disparate misinformation on those already experiencing health and social inequities. Misinformation from online communities, friends/family interpretation bias, and inconsistent information from content experts exacerbate an already weakened trust in the medical system among socially vulnerable populations. When trust in scientific evidence is low, susceptibility to misinformation is high. Although trust is an essential factor in susceptibility to misinformation, it cannot be the only lens through which the impact of misinformation on socially vulnerable populations is examined. Patients need scientifically valid information in the face of a flood of misinformation and conspiracy theories. This can be provided through public health campaigns, community workers, and all healthcare providers. Vigilance needs to be maintained to dispute any unwarranted information to keep the population healthy

Hallucinations in ChatGPT: An Unreliable Tool for Learning

Recently, ChatGPT has been upgraded to its newer version for its unsubscribed users – ChatGPT 3.5. Though ChatGPT has become an astonishing phenomenon all over the world for creating realistic texts within seconds, it can disseminate wrong information and misconceptions. Technical experts have identified this problem as hallucination. This paper has examined ChatGPT’s ability to differentiate between correct and incorrect relations in the questions that are set to it. It has also explored the efficacy of ChatGPT in helping students acquire linguistic and literary proficiency. The study took the form of exploratory interpretive research. The participants of the research study were students studying English at the undergraduate level. Data was collected through semi-structured interviews, FGDs, and input provided to ChatGPT. All data were analyzed qualitatively. The findings of this research indicate that ChatGPT tends to provide inconsistent information when a series of contextual questions are asked. Because of this hallucination, ChatGPT becomes an unreliable source for language and literature learning.

Multiple attribute group decision making approach for selection of robot under induced bipolar neutrosophic aggregation operators

In current piece of writing, we bring in the new notion of induced bipolar neutrosophic (BN) AOs by utilizing Einstein operations as the foundation for aggregation operators (AOs), as well as to endow having a real-world problem-related application. The neutrosophic set can rapidly and more efficiently bring out the partial, inconsistent, and ambiguous information. The fundamental definitions and procedures linked to the basic bipolar neutrosophic (BN) set as well as the neutrosophic set (NS), are presented first. Our primary concern is the induced Einstein AOs, like, induced bipolar neutrosophic Einstein weighted average (I-BNEWA), induced bipolar neutrosophic Einstein weighted geometric (I-BNEWG), as well as their different types and required properties. The main advantage of employing the offered methods is that they give decision-makers a more thorough analysis of the problem. These strategies whenever compare to on hand methods, present complete, progressively precise, and accurate result. Finally, utilizing a numerical representation of an example for selection of robot, for a problem involving multi-criteria community decision making, we propose a novel solution. The suitability ratings are then ranked to select the most suitable robot. This demonstrates the practicality as well as usefulness of these novel approaches.

Модели и методы онтологического анализа данных в задаче структурного анализа и синтеза технических решений

The article explores the potential and demonstrates the effectiveness of using some models and methods of ontological data analysis in the problem of structural analysis and synthesis of technical decisions. Morphological analysis and synthesis of layout diagrams of artifacts are considered as a working methodology for structural design. A set-theoretic model of the morphological decision space is constructed for the classical version of the methodology. The model in-cludes the functional subsystems of the designed artifact, implementation options for these subsystems, and potential technical decisions themselves. Moreover, in contrast to existing versions of such formalization, the emphasis is on the description of binary relationships between the entities of the knowledge domain of morphological analysis. It is pro-posed to describe the incompatibility of implementation options for a single functional subsystem of a designed arti-fact and the restricted compatibility of implementation options for various functional subsystems within the frame-work of a single model, known in ontological engineering and, in particular in ontological data analysis as “existence constraints”. Analysis of one of the regularities inherent in this model made it possible to strictly substantiate the ap-proach common in structural design to describing the restricted compatibility of implementation options for various functional subsystems in the form of prohibitions. The advantages of using existence constraints to describe the mor-phological decision space are shown, and the frame of a formal ontology for the knowledge domain of morphological analysis is constructed. The structural synthesis of a technical decision is compared with the method of semantic identi-fication of an object, developed within the framework of ontological data analysis for processing incomplete and in-consistent empirical information about the results of measuring object properties. A conclusion is made about the suit-ability of the latter for solving the problem of structural synthesis when correcting the concept of a “normal” subset of properties and information about the costs and/or benefits of including each individual implementation option for the functional subsystems of the designed artifact.

Comprehensive application system of BIM technology in MULTIBAY project

With the rapid development of urbanization, the number of super high-rise buildings with increased building volume and scale is gradually increasing. At present, there are still problems such as inconsistent engineering information transmission and low efficiency of collaborative management among various participants in the construction of international super high-rise buildings. Because of this, based on engineering practice, using the advantages of BIM technology such as visualization, simulation, and collaboration, this article explores the application process of BIM technology in the design stage of super high-rise buildings, such as multi-disciplinary detailed design and drawing collision inspection, as well as in the construction stage, such as visual technical disclosure and scheme selection. It also constructs a BIM whole process management system, enabling BIM technology to better participate in the whole process of project construction, improving the level of project information technology, and providing engineering reference for the application of BIM technology in similar super high-rise buildings.

“I can't be dealing with this brain fog”: A workplace focus group study investigating factors underpinning the menopausal experience for NHS staff

ObjectivesMultiple studies highlight that individuals undergoing menopause are not receiving sufficient support at work. An improved menopausal experience in the workplace has been found to be associated with increased job satisfaction, increased economic participation and reduced absenteeism. This work was undertaken to explore the impact of menopause on the working lives of NHS staff working in Wales, with specific emphasis on their experience of menopausal symptoms and management strategies in the workplace. Study designThis was a qualitative study using semi-structured focus groups and thematic analysis. 14 women working in the NHS in Wales attended four focus groups, lasting up to 1.5 h. Stem questions focused on participants' positive and negative experiences in the workplace, and their receipt of support. Transcripts were analysed using the framework approach. ResultsThree major themes were identified: experiences of menopausal symptoms and symptom management, the impact of menopause on work and the impact of work on the menopause. Menopause symptom experience in the workplace was multifaceted and varied, depending on factors such as ongoing or past symptom experience, expectations, social support and effectiveness of management strategies. Inconsistent information was highlighted as a reason why some participants felt confused both about the symptoms that they could attribute to the menopause and the management strategies available to them. A variety of symptom management strategies had been used by participants, including hormone replacement therapy, flexible working hours, working from home, changes to uniform, peer support and lifestyle changes, with varying levels of success. Some women were reticent to ask for support at work even though they felt the workplace response was likely to be supportive. Almost all the women felt that they had to persuade their GP to prescribe HRT and felt that their doctors were too reticent in prescribing this treatment. ConclusionsEmployers have a key role in supporting their staff experiencing menopausal symptoms, and such support has the potential to reduce sickness absence and boost retention. Based on the findings we recommend creating an open culture to break down taboos; protected time for peer support around shared experiences and effective symptom management techniques; and maximising the impact of non-menopause-specific policies such as flexible working to help all staff manage fatigue and become more productive in their roles.

(Mis)Matching Metadata: Improving Accessibility in Digital Visual Archives through the EyCon Project

Discussing the current AHRC/LABEX-funded EyCon (Early Conflict Photography 1890–1918 and Visual AI) project, this article considers potentially problematic metadata and how it affects the accessibility of digital visual archives. The authors deliberate how metadata creation and enrichment could be improved through Artificial Intelligence (AI) tools and explore the practical applications of AI-reliant tools to analyze a large corpus of photographs and create or enrich metadata. The amount of visual data created by digitization efforts is not always followed by the creation of contextual metadata, which is a major problem for archival institutions and their users, as metadata directly affects the accessibility of digitized records. Moreover, the scale of digitization efforts means it is often beyond the scope of archivists and other record managers to individually assess problematic or sensitive images and their metadata. Additionally, existing metadata for photographic and visual records are presenting issues in terms of outdated descriptions or inconsistent contextual information. As more attention is given to the creation of accessible digital content within archival institutions, we argue that too little is being given to the enrichment of record data. In this article, the authors ask how new tools can address incomplete or inaccurate metadata and improve the transparency and accessibility of digital visual records.

Graph neural network-based similarity relationship construction model for geospatial services

ABSTRACT During the development of service-based software systems, Geospatial Service (GS) replacement is often performed, which requires the discovery of functionally similar services in service registries to replace failed services. Compared to real-time similarity computations, direct extraction of similar services from constructed similarity relationships can yield higher replacement efficiency. However, missing and inconsistent service-registry information impedes accurate similarity relationship construction. Here, we propose a Graph Neural Network (GNN)-based model for GS Similarity Relationship construction considering service descriptions and tags, which is named GSSR-GNN. As the sparsity of the service similarity relationship graph constructed based on labeled samples limits the information propagation ability, a graph augmentation method for similarity relationship construction among second-order neighbors is proposed. Considering the differences in the semantic-information feature distributions, such as the service descriptions and tags, a feed-forward neural network-based fusion method is designed to embed them into the same vector space. Pre-trained Bidirectional Encoder Representations from Transformers (BERT) and WordNet models are introduced to enhance the service-representation expressiveness. When an enhanced service representation is input to the GNN, the similarity is calculated and the service similarity relationship is obtained. Experimental results show that the proposed model constructs service similarity relationships with high precision, thus improving the service replacement efficiency and reducing the computational cost of service registry during service replacement.

An improved TODIM-TOPSIS method for quality evaluation of college students employment and entrepreneurship education with probabilistic hesitant fuzzy sets

In recent years, employers have continuously raised their requirements for college students, not only requiring a solid professional foundation, but also emphasizing personal professional literacy. As the first base for cultivating college students, major universities should not only guide them in their correct employment and entrepreneurship, but also help them find employment and entrepreneurship faster and better. However, in the context of the new era, universities still face some problems in the process of carrying out employment and entrepreneurship education, which hinder the progress of employment and entrepreneurship education. The probabilistic hesitant fuzzy sets (PHFSs), as an extension of hesitant fuzzy sets (HFSs), can more effectively and accurately describe uncertain or inconsistent information during the quality evaluation of college student employment and entrepreneurship education. TODIM and TOPSIS methods are two commonly used multi-attribute decision-making (MADM) methods, each of which has its advantages and disadvantages. The quality evaluation of college student employment and entrepreneurship education is regarded as the defined multiple attribute group decision making (MAGDM). This paper proposes a novel method based on TODIM and TOPSIS to cope with multi-attribute group decision making (MAGDM) problems under PHFSs environment. After introducing the related theory of PHFSs and the traditional TODIM and TOPSIS methods, the novel method based on a combination of TODIM and TOPSIS methods is designed. And then, an illustrative example for quality evaluation of college student employment and entrepreneurship education proved the feasibility and validity of the proposed method. Finally, the result has been compared with some existing methods under the same example and the proposed method’s superiority has been proved.

Meta-analysis of the hazards of microplastics in freshwaters using species sensitivity distributions

The environmental hazards of microplastics have raised concerns about their potential ecological risks. However, our understanding of the true risks may be limited because most laboratory studies used pristine microplastics. Here, we analyzed the available literature about ecotoxicological effects of microplastics, including weathered microplastics in particular, on freshwater biota and performed probabilistic species sensitivity distributions. The predicted no-effect concentrations for pristine microplastics were lower than those for weathered microplastics, both in mass concentration (6.1 and 4.8 × 102 μg/L) and number concentration (2.6 × 104 and 2.0 × 106 part/m3). In addition, the toxicological studies on microplastics contains often inconsistent and inconclusive information due to the complexity of the microplastics and the employed exposure conditions. The available data for Daphnia magna and Danio rerio was analyzed in detail to understand the effects of microplastic size, shape and polymer type on their ecotoxicity. Microplastic size was the biggest driving factor, followed by shape and polymer type. There was a tendency for increasing toxicity with smaller size, however, a high variability of effect data was observed for small microplastics. This study provided further insights into the effect thresholds for ecological risk assessment of microplastics and the effects of microplastic characteristics on toxicity.

Going home positive: a qualitative study of the experiences of care for patients with COVID-19 who are not hospitalized.

Most Canadians diagnosed with COVID-19 have had mild symptoms not requiring hospitalization. We sought to understand the patient experience of care while being isolated at home after testing positive for SARS-CoV-2 infection. We conducted a phenomenologically informed qualitative descriptive study using in-depth semistructured interviews to identify common themes of experience for patients sent home from hospital with a positive COVID-19 diagnosis. Between July and December 2020, we conducted interviews with patients who were followed by the North York General Hospital COVID Follow-Up Clinic. Patients with mild to moderate symptoms were interviewed 4 weeks after their COVID-19 diagnosis. We conducted the interviews and performed a thematic analysis of the data concurrently, in keeping with the iterative process of qualitative methodology. We conducted interviews with 26 patients. From our analysis, 3 themes were developed regarding participants' overall experience: lack of adequate communication, inconsistency of information from various sources, and the social implications of a COVID-19 diagnosis. The implications of a positive test for SARS-CoV-2 infection are substantial, even when symptoms are mild and patients self-isolate as recommended. Participants noted communication challenges and inconsistent information, leading to exacerbated stress. Participants shared their experiences of the stigma of testing positive and the frustration of poor communication structures and inconsistent information. Experiencing care during self-isolation at home is an area of increasing importance, and these findings can inform improved support, ensuring access to equitable and safe COVID-19 care for these patients.

High-compressed deepfake video detection with contrastive spatiotemporal distillation

Deepfake detection in high-resolution videos has made significant progress in recent years, but detecting high-compressed deepfake videos remains challenging due to the low quality of synthesized videos. Existing video-level approaches fail to fully exploit the spatiotemporal inconsistencies in low-quality high-compressed deepfake videos, leading to poor generalization and robustness. In this work, we propose a Contrastive Spatio-Temporal Distilling (CSTD) approach that leverages spatial-frequency cues and temporal-contrastive alignment to improve high-compressed deepfake video detection. Our approach employs a two-stage spatiotemporal video encoder to fully exploit spatiotemporal inconsistency information. A fine-grained spatial-frequency distillation module is used to retrieve invariant forgery cues in spatial and frequency domains in high-compressed deepfake videos. Additionally, a mutual-information temporal-contrastive distillation module is introduced to enhance the temporal correlated information and transfer the temporal structural knowledge from the teacher model to the student model. We demonstrate the effectiveness and robustness of our method on low-quality high-compressed deepfake videos on public benchmarks against state-of-the-art competitors using extensive experiments and visualizations.

Assessing the sustainable energy storage technologies using single-valued neutrosophic decision-making framework with divergence measure

Energy storage technologies (ESTs) facilitate to handle intermittency of energy resources by storage surplus energy to utilize when it is required. Due to influence of numerous quantitative and qualitative attributes, evaluation of ESTs can be treated as a critical and vague multi-attribute decision-making (MADM) problem. As a type of neutrosophic set, single-valued neutrosophic set (SVNS) has been proven as one of the valuable and flexible ways to handle the imprecise, indeterminate and inconsistent information arises in real applications. Considering the uncertainty in ESTs selection process, this paper aims to propose an integrated MADM methodology by combining the MEthod based on the Removal Effects of Criteria (MEREC) and Evaluation based on Distance from Average Solution (EDAS) techniques with SVNSs. In this methodology, the score function and divergence measure-based procedure is proposed to derive the decision experts’ weights. In this respect, an innovative single-valued neutrosophic (SVN)-divergence measure is introduced with its desirable characteristics. Furthermore, the MEREC technique is extended under SVNS context to determine objective weights of the attributes. Later, an extended EDAS technique is introduced with the combination of SVN-divergence measure and MEREC model to assess and prioritize the alternatives under SVNS environment. To confirm the capability of introduced MADM methodology, a case study of ESTs selection is presented within the context of SVNS. Moreover, the merits of the proposed framework in terms of flexibility and robustness are shown by comparative analysis. Finally, the sensitivity analysis proves the stability of our obtained results.

Individual attitudes toward coerced confessions change perception of confession evidence: why jurors may accept or reject poor-quality confessions

Wrongful conviction statistics indicate that jurors will accept confession evidence even when it was coerced or contains inconsistent information. While research has considered the role of both inconsistencies and coercion in juror decisions about retracted confessions, little attention has been given to whether juror attitudes toward coerced confessions contribute to perception of suspect guilt. Using an experimental design, we manipulated the presence of inconsistencies and coercion in a fictional confession transcript. When presented with a coerced confession, low support for coercive interrogation techniques predicted lower belief in the suspect’s guilt, unlike those with higher support for coercion who did not alter their verdicts. This indicates that the effect of coercion on perceived suspect guilt differs depending on the juror’s individual attitude towards coercion. Inconsistencies in the confession similarly influenced judgements of guilt dependent on whether the person believed that confessions could be coerced from an innocent person. Implications are discussed further.

Novel Concepts on Domination in Neutrosophic Incidence Graphs with Some Applications

In graph theory, the concept of domination is essential in a variety of domains. It has broad applications in diverse fields such as coding theory, computer network models, and school bus routing and facility location problems. If a fuzzy graph fails to obtain acceptable results, neutrosophic sets and neutrosophic graphs can be used to model uncertainty correlated with indeterminate and inconsistent information in arbitrary real-world scenario. In this study, we consider the concept of domination as it relates to single-valued neutrosophic incidence graphs (SVNIGs). Given the importance of domination and its utilization in numerous fields, we propose the application of dominating sets in SVNIG with valid edges. We present some relevant definitions such as those of valid edges, cardinality, and isolated vertices in SVNIG along with some examples. Furthermore, we also show a few significant sets connected to the dominating set in an SVNIG such as independent and irredundant sets. We also investigate a relationship between the concepts of dominating sets and domination numbers as well as irredundant and independence sets in SVNIGs. Finally, a real-life deployment of domination in SVNIGs is investigated in relation to COVID-19 vaccination locations as a practical application.

Neutrosophic Multi-Criteria Decision-Making Framework for Sustainable Evaluation of Power Production Systems in Renewable Energy Sources

To make the change to a cleaner, more sustainable energy future, it is essential that power production systems be evaluated sustainably. To estimate the overall sustainability performance of various power production systems, this evaluation takes into account a wide range of parameters. Impact on the environment, availability of renewable resources, resource efficiency, social and economic implications, economic feasibility, grid integration and dependability, technical maturity, and scalability are all taken into account. Stakeholders may make better judgments and give higher priority to power production systems that take into account environmental impacts, resource efficiency, social impacts, and economic viability by evaluating these factors. So, we used the multi-criteria decision-making (MCDM) approach VIKOR to assess the power production systems in the sustainability criteria. The VIKOR method is used to rank the several alternatives. We integrated the neutrosophic set with the VIKOR method to deal with inconsistent information. We used seven criteria and ten alternatives in this study. The development of a sustainable and resilient energy sector is aided by the sustainable evaluation of power production systems, which in turn helps to reduce the effects of climate change and safeguard the environment.