Retrieval Mechanism Research Articles

Different retrieval mechanisms of overgeneral autobiographical memory for positive and negative cues in remitted major depressive disorder

Background and objectivesOvergeneral autobiographical memory (OGM) has been shown to occur in depressed and previously depressed populations regardless of the emotional valence of cues. However, recent research has pointed out that the retrieval process underlying OGM, generative retrieval (i.e., retrieval requiring effort or additional information) or direct retrieval (i.e., memory that comes to consciousness without effort or additional information), may differ depending on the emotional valence of cues. We examined the hypotheses that a remitted MDD (major depressive disorder), group compared with a control group, would show (a) more direct retrieval of categoric memories for negative cues, (b) more generative retrieval of categoric memories for positive cues, and (c) less direct retrieval of specific memories for positive cues. MethodsA remitted clinical MDD group (n = 21) and control group (n = 21) completed the Autobiographical Memory Test with minimal instruction, and were required to subjectively judge generative retrieval and direct retrieval. ResultsAs expected, results showed that the remitted MDD group reported more frequent direct retrieval of categoric memory for negative cues and more generative retrieval of categoric memory for positive cues than the control group. LimitationsOur paradigm for distinguishing between generative and direct retrieval relied on subjective judgements. ConclusionsThis extends the findings from student sample in previous studies to a help-seeking population. Increased availability of negative categoric memories and the attenuation of positive specific recall represent vulnerabilities for MDD. We discuss how these findings provide further rationale for memory therapeutics for MDD and refinement of those techniques.

Does the construction retrieval account apply to cross-modal inhibition of return in semantic context?

The traditional attentional reorienting hypothesis is insufficient to explain spatial and nonspatial inhibition of return (IOR). Therefore, a construction retrieval account that includes the influence of top-down attentional sets has been proposed and can explain both spatial and nonspatial IOR. However, it remains unknown whether the construction retrieval account can be applied to non-surface features of stimuli, as well as whether its construction and retrieval mechanisms are supra-modal. The present study manipulated semantic feature congruency and spatial location congruency between the prime and the target in cross-modal audio-visual and visual-audio experimental conditions, respectively, by orthogonally combining spatial and nonspatial IOR paradigms. Our results showed that there was an interaction between semantic feature congruency and spatial location congruency controlled by the attentional sets, and that this interaction was consistent in cross-modal audio-visual and visual-audio conditions. These results suggest that the construction retrieval account can be applied to abstract semantic features and that its construction and retrieval mechanisms are supra-modal. The present study extends the application scope of the construction retrieval account and promotes the interpretation of IOR under a unified theoretical framework.

Influence of Team Information Sharing on Collaborative Value Within Ashoka Fellows' Organizations in Africa

Purpose: The study sought to determine the influence of team information sharing on collaborative value within Ashoka Fellows' Organizations in Africa
 Methodology: The study applied pragmatism philosophy to offer several ways to bridge dichotomies in mixed methods approaches to social science. Explanatory sequential mixed-method research design consisting of two distinct phases, namely quantitative and qualitative, was adopted. Both qualitative and quantitative study methods were adopted. In the quantitative study, the target population constituted all the 154 Ashoka Fellows' Organizations working in 19 countries in Africa. Data was collected using a structured questionnaire administered online to the founders (Ashoka Fellows) or the Ashoka Fellows' Organizations' CEOs. One hundred responded by filling out the questionnaire, which translated to a 64.9% response rate. Additionally, qualitative data applied purposive sampling and selected six Ashoka regional team leaders in Africa for in-depth interviews. They all were available for the interviews translating to a 100% response rate. Data analysis techniques combined descriptive and inferential statistics. Statistical Package for Social Sciences and SmartPLS 3 software were used to analyze the collected data.
 Findings: Results revealed that the results confirm that team information sharing significantly influences collaborative value within Ashoka Fellows’ Organizations in Africa. Team Information Sharing has a significant influence on collaborative value witn an R2 = 0.300, chi-square X2 (10, N=100) = 63.010, p<.05, SRMR=0.088, Rms-theta=0.224 and NFI=0.805. The null hypothesis was rejected.
 Unique Contribution to Theory, Practice and Policy: The study recommends that Team members should ensure the team's information exchange is of good quality as the quality of information sharing that develops collaboration must go beyond necessary factual information to a point where team members can transfer their unique understanding. Also, team members should be willing to share information and engage in sequences of interpretation sharing to build on each other's interpretations and collectively make sense. Teams should embrace timely information sharing, which is critical for collaborative value and the use of technology. To ensure quality and timeliness of information shared, teams should consider both investments in information systems as sharing appropriate information allows the linkage of internal and external knowledge sources by facilitating information flow and providing effective data retrieval mechanisms

Risk response for critical infrastructures with multiple interdependent risks: A scenario-based extended CBR approach

Critical infrastructures (CIs) are elementary utility systems that maintain the normal society functioning. However, various risks constantly emerge, resulting in widespread human and economic losses. Effective risk response for CIs is a challenging task because of extreme time sensitivity. Especially, multiple risks and interdependencies among them also increase the response difficulties. To promptly generate effective response strategies for CIs with multiple interdependent risks, a scenario-based extended case-based reasoning (CBR) approach by combining the risk network and ontological modeling is proposed in this study. First, a three-stage solution framework is constructed. Subsequently, the historical cases are represented based on a scenario analysis and ontology model. A new case retrieval mechanism including three similarity measures and corresponding algorithm is proposed to precisely retrieve the most similar historical case, and the retrieved response strategies are adapted according to the risk loss severity and risk interdependencies. Furthermore, a case study is conducted to verify the practicability and superiorities of the proposed approach, and the necessity of considering multiple interdependent risks in CI risk response. It is indicated that the study provides a more suitable tool for CI risk response in risk interdependent scenarios, which is beneficial to the effectiveness of risk response in reality.

Membrane retrieval after Immediately Releasable Pool (IRP) exocytosis is produced by dynamin-dependent and dynamin-independent/protein kinase C-dependent mechanisms.

The importance of the immediately releasable pool (IRP) of vesicles was proposed to reside in the maintenance of chromaffin cell secretion during the firing of action potentials at basal physiological frequencies. To accomplish this duty, IRP should be replenished as a function of time. We have previously reported that an action potential-like stimulus (APls) triggers the release of ~50% IRP, followed by a fast dynamin-dependent endocytosis and an associated rapid replenishment process. In this work, we investigated the endocytosis and IRP replenishment produced after the exocytosis of variable IRP fractions in mice primary chromaffin cell cultures. Exocytosis and endocytosis were estimated by membrane capacitance measurements obtained in patch-clamped cells. In addition to the dynamin-dependent fast endocytosis activated after the application of APls or 5ms squared depolarizations, we found that depolarizations lasting 25-50 ms, which release >80% of IRP, are related with a fast dynamin-independent, Ca2+ - and protein kinase C (PKC)-dependent endocytosis (time constant <1s). PKC inhibitors, such as staurosporine, bisindolylmaleimide XI, PKC 19-31 peptide, and prolonged treatments with high concentrations of phorbol esters, reduced and decelerated this endocytosis. Additionally, we found that the inhibition of PKC also abolished a slow component of replenishment (time constant ~8s) observed after total IRP exocytosis. Therefore, our results suggest that PKC contributes to the coordination of membrane retrieval and vesicle replenishment mechanisms that occur after the complete exocytosis of IRP.

Touching while listening: Does infants' haptic word processing speed predict vocabulary development?

The present study examined the links between haptic word processing speed, vocabulary, and inhibitory control among bilingual children. Three main hypotheses were tested: faster haptic processing speed, measured by the Computerized Comprehension Task at age 1;11, would be associated with larger concurrent vocabulary and greater longitudinal vocabulary growth. Second, early vocabulary size would be associated with greater vocabulary growth at 3;0 and 5;0. Finally, faster haptic processing speed would be associated with greater concurrent inhibitory control, as measured by the Shape Stroop Task. The results revealed that haptic processing speed was associated with concurrent vocabulary, but not predictive of later language skills. Also, early decontextualized vocabulary was predictive of vocabulary at 3;0. Finally, haptic processing speed measured in the non-dominant language was associated with inhibitory control. These results provide insight on the mechanisms of lexical retrieval in young bilinguals and expand previous research on haptic word processing and vocabulary development.

Construction of artificial intelligence-assisted English learning resource query system.

English has become an important tool for China's opening to the outside world and exchanges with other countries. More and more people have the motivation and requirements to learn English, but under the traditional English learning mode and traditional teaching mode, the cultivation of learners' autonomous learning habits is ignored. This article aims to study the construction of artificial intelligence-assisted English learning resource query system and establish the relevant feedback mechanism of retrieval. This article applies this mechanism to the retrieval of learning resources, so as to provide learners with the learning resources they really need and improve learners' learning efficiency. This article proposes to find the relevant knowledge points by extracting the knowledge points of the retrieval content. It realizes the query expansion based on knowledge and then realizes the expansion of retrieval results. It realizes the mapping of knowledge points on the retrieval content, the query and expansion of knowledge points, and the presentation of learning resources of the knowledge point index. It also uses the relevant feedback mechanism to adjust the retrieval results to meet the retrieval needs of learners. The experimental results show that the number of knowledge points can be increased to 2–4 times by query expansion based on English resources. Thus, the number of learning resources of search results can be increased to 3–10 times, the expansion of search results can be realized, and the overall recall will be greatly improved. In this article, the related methods of artificial intelligence are applied to the construction experiment of the English learning resource query system, which has a certain promotion effect on the construction of the system.

Effectiveness of Modular Learning Approach among Secondary School Students during Pandemic in Zambales, Philippines

The world experienced the COVID-19 Pandemic, which severely affected the quality of Education, especially in the Philippines. The option was to change the face-to-face approach to a modular approach. This study aimed to determine the effectiveness of the modular learning approach to students’ academic performance. The study was conducted in Sta. Cruz District in the province of Zambales, Philippines. A Descriptive Research Design was employed. A total of one hundred fifty-four (154) teacher-respondents were randomly selected. There was a significant difference in the perception towards dimensions on the level of effectiveness in the implementation of modular learning approach as to Technical Support, Subject Content, Construction, Delivery and Retrieval Mechanism, Assessment of Student Achievement, Adherence to IATF Health Observance Protocols, and School and Local Government Unit Support. This modality's modular learning approach has improved and increased students’ academic performance. The students were rated “Satisfactory” in their academic performance. On the other hand, there is a negligible relationship between academic performance and the level of effectiveness in implementing the modular learning approach. The researcher recommends revisiting the learning contents and assessment of student output and providing item analysis to determine the vague or gray areas in the self-learning module for improvement. The School may conduct a time management plan for production, delivery, and retrieval to assure the modules had been delivered on time to the students and considerable time was allotted for assessing and accomplishing student learning outputs.

Are involuntary autobiographical memory and déjà vu natural products of memory retrieval?

Involuntary autobiographical memories (IAMs) and déjà vu are phenomena that occur spontaneously in daily life. IAMs are recollections of the personal past, whereas déjà vu is defined as an experience in which the person feels familiarity at the same time as knowing that the familiarity is false. We present and discuss the idea that both IAMs and déjà vu can be explained as natural phenomena resulting from memory processing and, importantly, are both based on the same memory retrieval processes. Briefly, we hypothesise that both can be described as "involuntary" or spontaneous cognitions, where IAMs deliver content and déjà vu delivers only the feeling of retrieval. We map out the similarities and differences between the two, making a theoretical and neuroscientific account for their integration into models of memory retrieval and how the autobiographical memory literature can explain these quirks of daily life and unusual but meaningful phenomena. We explain the emergence of the déjà vu phenomenon by relating it to well-known mechanisms of autobiographical memory retrieval, concluding that IAMs and déjà vu lie on a continuum.

Soil Moisture Retrieval Using Sail Squirrel Search Optimization-based Deep Convolutional Neural Network with Sentinel-1 Images

Soil Moisture (SM) is an environmental descriptor, which acts as the affiliation between the atmosphere and the earth’s surface. Various SM retrieval methods are developed to abolish the influence of vegetation cover attenuation, surface roughness, and scattering to find an association among SM and backscatter coefficient. To understand the relationship between various vegetation parameters and backscatter coefficient poses a great challenge in SM retrieval. Hence, an efficacious SM retrieval method is afforded using the proposed Sail Squirrel Search Optimization-based Deep Convolutional Neural Network (SSSO-based Deep CNN). Here, the proposed SSSO is derived by concatenating the Sail Fish Optimization (SFO) with Squirrel Search Algorithm (SSA). The Deep CNN performs the process of SM retrieval using vegetation indices. The fitness measure of the proposed optimization enables to find the best solution to update the weights of the classifier for increasing the efficiency of the retrieval mechanism. By training Deep CNN with the proposed optimization, the soil moisture of an area is effectively retrieved. However, the proposed SSSO-based Deep CNN obtained minimal estimation error and minimal RMSE of 0.550 and 0.726 using sentinel-1 data, respectively.

Design and Development of Inspection Test Rig with Stamping

Stamping and inspection are tasks that have been done manually since the beginning of time. Doing these tasks manually is time consuming, there is a possibility of error in it and it gets hectic after a while so it cannot be performed continuously. Automation has been simplifying and reducing the possibility of error over the recent years. In this project we will be designing and developing a prototype for stamping and inspection of metal cans using pneumatics and programmable logic controller (PLC). The stamping is done using the mechanism of quick retrieval by pressurized air. We aim to inspect cans of different sizes and make the system compatible to a wider range of industries. Different sizes of cans will require different pressure while stamping which will be programmed and stamping will be done accordingly with the required pressure. We can further develop the system to automate the whole production including pouring of material in cans, lidding, packaging, stamping and sorting. This work will propose a 3D design on the electropneumatic system and all the components used will also be discussed. This article is unique due to the concept of using one conveyor belt for two different sizes of cans. With the help of this design we can design different systems which can be compatible for multiple products in different industries. This might turn out to be a great solution for small scale industries that want to automate their plants but have not been able to due to high initial cost.

Case representation and retrieval for complex product design based on case-based reasoning

Reusing design knowledge of products is a useful way to solve the efficiency issue of complex product design. The design knowledge is tacit, empirical, and unstructured and there exists insufficient case matching and inefficient design reuse in complex products design process. Aiming at these problems, this paper presents an improved case-based reasoning methodology combining ontology with two-stage retrieval. Firstly, a knowledge domain ontology model of complex product design is constructed, and the technology of ontology-based data access is introduced to automatically generate a case knowledge base with semantic information. Then, a new two-stage case retrieval method integrated semantic query with similarity calculation is proposed. The case subset is selected by query statements. It has the characteristic of isomorphism with design problem. The retrieval mechanism is applied to compress the traversal space, reduce the redundancy of semantic similarity calculation, improve the retrieval efficiency, and fulfill the target of case reuse. Finally, a variant design of the chiller unit as an example is executed to illustrate the use of the proposed method, and experiments are organized to evaluate its performance. The result shows that the proposed approach has an average precision of 92% and high stability, outperforming existing methods.

Processing bound-variable singular they

AbstractThe pronouns they/them/their are readily available with a singular interpretation as bound variables (Balhorn 2004, Bjorkman 2017). Referential interpretations are possible, but subject to pragmatic considerations and changes in progress (Bjorkman 2017, Conrod 2019, Konnelly and Cowper 2020). In a series of experiments, we tested differences between bound and referential singular they in acceptability and incremental processing, asking whether bound they is sensitive to the gender of its antecedent, as referential they is (Doherty and Conklin 2017, Ackerman 2018, Ackerman et al. 2018, Conrod 2019). We found that bound singular they has an advantage over referential singular they in acceptability, even when the antecedent is gendered. In processing, however, bound-variable singular they showed a reading time advantage over referential singular they only with gendered antecedents. We evaluate these results against existing formal linguistic theories of singular they implemented within psycholinguistic models of pronoun processing. We submit that none of the theories fully captures the range of evidence we uncover, in particular the interaction between gender and quantification. We suggest a formal account that does: we propose, using representations from Kratzer (2009) and Sudo (2012), that gender and number features are differentially represented in referential versus binding dependencies. We speculate how this representational difference relates to the processing mechanisms of antecedent retrieval and to the limited processing advantage for bound singular they that we found.

Gradual adaption with memory mechanism for image-based 3D model retrieval

With the development of 3D modeling technology and its wide application in different fields, the number of 3D models increases rapidly, making 3D model retrieval a hot topic in current research. Compared with other 3D model retrieval methods, 2D image-based unsupervised 3D model retrieval takes the 2D images which have rich labels and are easy to obtain as the queries, and also takes into account the difficulties of labeling 3D models. 2D image-based unsupervised 3D model retrieval is a retrieval task involving cross-domain adaptation problem, which main challenge is the excessive domain gap. In this paper, we propose a cross-domain 3D model retrieval method of memory mechanism based on disentangled feature learning. The disentangled feature learning enables to disentangle the twisted original features into the isolated domain-invariant features and domain-specific features, where the former is to be aligned to narrow the domain gap. On this basis, the memory mechanism selects feature vectors from class memory modules constructed by class representative features of the opposite domain for every sample, which are used to update the domain-invariant features with gradient weight. The memory mechanism can gradually improve the adaptability of the model to the very different two domains. Experiments are conducted on the public datasets MI3DOR and MI3DOR-2 to verify the feasibility and the superiority of the proposed method. Especially on MI3DOR-2 dataset, our method outperforms the current state-of-the-art methods with gains of 7.71% for the strictest retrieval metric NN. • An end-to-end unsupervised 2D image-based 3D model retrieval framework. • Transfering knowledge from labeled 2D images to unlabeled 3D models. • Domain-invariant features are disentangled from the original features. • Memory module enhances domain-invariant features by representative features. • Experiments on MI3DOR and MI3DOR-2 verified the superiority of the method.

Preliminary Application of Space-Based Remote Sensing and Geospatial Technology for Investigation on the Geo-Environmental Consequences of Cyclone Aila 2009 in the NW Bangladesh

Climate change phenomena have become a great concern in the context of global change and increased frequency and magnitude of natural disasters throughout the world in the recent time. Natural disasters like floods, cyclones, storm surges etc. often cause significant losses of life, large-scale economic and social impacts, and considerable environmental damages. This paper dealt with study on the overall methodological development to investigate the consequences of climate change extremities particularly, the after effects of cyclone using Remote Sensing (RS) and Geographic Information System (GIS) technology. In this connection, individual functional components have been investigated, tested and verified. Time series multi-sensor satellite data particularly of Landsat, World view (from Google origin) have been utilized to infer information on the consequences of Cyclone Aila 2009 hitting part of southwestern Bangladesh as a test case under the present study. Information retrieval mechanism utilized is based on specially designed methodological framework using satellite-based RS technology along with GIS. Developed technical approach consists of a three-fold components – (i) Establishment of input foundation layer with high spatial details to support characterization, recognition and identification of important surface features using high resolution satellite data; (ii) Effective operational procedure to process, analyze, interpret and finally to archive the retrieved information; (iii) Functional computations were made using spatial modeler language (SML) programming environment under ERDAS Imagine image processing software. Satellite image processing, analysis operation together with image-based spectral characterization of surface features under different stressing conditions etc. have been exercised to derive useful surface information. Specially designed geospatial database has been established in GIS using ArcInfo professional software. Varieties of geospatial data from diverse sources have been incorporated categorically as column-based attributes in GIS. Dual spatial data layers have been generated in GIS for two different dates representing the pre-cyclone and post-cyclone time sequences utilizing appropriate high spatial resolution satellite images. Finally, a geospatial image-based combinational technique has been utilized employing high temporal and moderate spatial resolution time series satellite data with low temporal and high spatial resolution satellite data. Such an operation results in an improved spatial and temporal resolution of output products enabling capture of dynamics of surface features in the spatiotemporal domain providing more precision and details in the output. This study has been supplemented with necessary Ground Position System (GPS)-based ground truthing, selected field data collection and field-based group discussions outcomes.

Apoplastic sugar may be lost from grape berries and retrieved in pedicels.

In ripening grape (Vitis sp.) berries, the combination of rapid sugar import, apoplastic phloem unloading, and water discharge via the xylem creates a potential risk for apoplastic sugar to be lost from the berries. We investigated the likelihood of such sugar loss and a possible sugar retrieval mechanism in the pedicels of different Vitis genotypes. Infusion of D-glucose-1-13C or L-glucose-1-13C to the stylar end of attached berries demonstrated that both sugars can be leached from the berries, but only the nontransport sugar L-glucose moved beyond the pedicels. No 13C enrichment was found in peduncles and leaves. Genes encoding 10 sugar transporters were expressed in the pedicels throughout grape ripening. Using an immunofluorescence technique, we localized the sucrose transporter SUC27 to pedicel xylem parenchyma cells. These results indicate that pedicels possess the molecular machinery for sugar retrieval from the apoplast. Plasmodesmata were observed between vascular parenchyma cells in pedicels, and movement of the symplastically mobile dye carboxyfluorescein demonstrated that the symplastic connection is physiologically functional. Taken together, the chemical, molecular, and anatomical evidence gathered here supports the idea that some apoplastic sugar can be leached from grape berries and is effectively retrieved in a two-step process in the pedicels. First, sugar transporters may actively retrieve leached sugar from the xylem. Second, retrieved sugar may move symplastically to the pedicel parenchyma for local use or storage, or to the phloem for recycling back to the berry.

Modeling the Remote Associates Test as Retrievals from Semantic Memory

The Remote Associates Test (RAT) is a word association retrieval task that consists of a series of problems, each with three seemingly unrelated prompt words. The subject is asked to produce a single word that is related to all three prompt words. In this paper, we provide support for a theory in which the RAT assesses a person's ability to retrieve relevant word associations from long‐term memory. We present a computational model of humans solving the RAT and investigate how prior knowledge and memory retrieval mechanisms influence the model's ability to match human behavior. We expand prior modeling attempts by investigating multiple large knowledge bases and by creating a cognitive process model that uses long‐term memory spreading activation retrieval processes inspired by ACT‐R and implemented in Soar. We evaluate multiple model variants for their ability to model human problem difficulty, including the incorporation of noise and base‐level activation into memory retrieval. We conclude that the main factors affecting human difficulty are the existence of associations between prompt words and solutions, the relative strengths and directions of those associations compared to associations to other words, and the ability to perform multiple retrievals.

Serial and strategic memory processes in goal-directed selective remembering

People often rely on habitual, serial processing when presented with to-be-learned information. We tested how strategic processing can override more bottom-up, serial processes when remembering information by having participants study a list of word triads (e.g., “dollar phone pizza”). Participants' goal was manipulated by maximizing either (i) their recall for each of the studied words or (ii) their total score associated with recalling certain words in each triad that were more valuable (worth more points) to engage either serial or strategic processing and retrieval mechanisms. Results revealed that when learners were told to maximize their total recall, they frequently engaged in serial remembering—remembering guided by an item's location within the study phase (i.e., words were retrieved according to a habitual reading bias). However, when words were paired with point values that counted towards participants' scores if recalled, participants were not only selective for high-value words but also attempted to overcome the tendency to engage in serial remembering; instead, they appeared to engage in strategic remembering whereby retrieval is guided by value. Thus, to maximize memory utility, it may be beneficial to override habitual processes and initiate retrieval with high-value words, and when making recall transitions, to recall high-value words together. Importantly, when certain to-be-remembered words were more valuable than their neighbors, participants still demonstrated some serial processing of the to-be-remembered words, indicating that even when engaging in strategic memory, some habitual processes can persist.

Blockchain-Enabled Smart Surveillance System with Artificial Intelligence

Through the use of blockchain technology, sensitive information may be securely communicated without the need to replicate it, which can assist in decreasing medical record mistakes and saving time by eliminating the need to duplicate information. Furthermore, the information is timestamped, which further enhances the security of the data even further. The deployment of blockchain technology in a range of healthcare situations may enhance the security and efficiency of payment transactions. In this way, only those who have been allowed access to patient medical information can see or modify such information. It is proposed in this study that blockchain technology be used to provide an accessible data storage and retrieval mechanism for patients and healthcare professionals in a healthcare system that is both safe and efficient. As of 1970, a variety of traditional knowledge-based approaches such as Personal Identification Recognition Number (PIRN), passwords, and other similar methods have been made available; however, many token-based approaches such as drivers’ licenses, passports, credit cards, bank accounts, ID cards, and keys have also been made available; however, they have all failed to establish a secure and reliable transaction channel. Because they are easily misplaced, stolen, or lost, they are usually unable to protect secrecy or authenticate the identity of a legitimate claimant. Aside from that, personally identifiable information such as passwords and PINs is very prone to fraud since they are easily forgotten or guessed by an imposter. Biometric identification and authentication (commonly known as biometrics) are attracting a great deal of attention these days, particularly in the realm of information security systems, due to its inherent potential and advantages over other conventional ways for identifying and authenticating. As a result of the device’s unique biological characteristics, which include features such as fingerprints, facepalms, hand geometry (including the iris), and the device’s iris, it can be used in a variety of contexts, such as consumer banking kiosks, airport security systems, international ports of entry, universities, office buildings, and forensics, to name a few. It is also used in several other contexts, including forensics and law enforcement. Consequently, every layer of the system—sensed data, computation, and processing of data, as well as the storage and administration of data—is susceptible to a broad variety of threats and weaknesses (cloud). There does not seem to be any suitable methods for dealing with the large volumes of data created by the fog computing architecture when normal data storage and security technologies are used. Because of this, the major objective of this research is to design security countermeasures against medical data mining vulnerabilities that originate from the sensing layer and data storage in the Internet of Things’ cloud database, both of which are discussed in more depth further down. A key allows for the creation of a distributed ledger database and provides an immutable security solution, transaction transparency, and the prohibition of tampering with patient information. This mechanism is particularly useful in healthcare settings, where patient information must be kept confidential. When used in a hospital environment, this method is extremely beneficial. As a result of incorporating blockchain technology into the fog paradigm, it is possible to alleviate some of the current concerns associated with latency, centralization, and scalability.

Evaluating the neural mechanisms of exposure and retrieval of hedonic and utilitarian banners: A fMRI study

Traditional psychological theories of message persuasion typically conclude that messages that are able to facilitate an optimal allocation of cognitive resources in the audience will increase memory encoding, will be better retrieved and recalled, and will likely be more persuasive. The growing competition in online advertising has led to a need to evaluate which type of banners are able to allocate cognitive resources more efficiently, as this has a positive impact on the ability to remember the banner and potentially increase the purchase frequency of the advertised product. By means of functional Magnetic Resonance Imaging (fMRI), this study provides the first evidence of neural differences during the exposure and reimagination of two widely used banner appeals; namely, hedonic (i.e., banners that vividly emphasize the social, personal, and experiential benefits of buying the product) and utilitarian (i.e., banners focused on informative, convenient, and functional arguments). Our findings reveal that, when compared to utilitarian banners, hedonic static advertisements engage stronger neurocognitive processes, which translate into higher brain activations related to memory encoding and retrieval, ultimately correlating to higher recall. These findings advise the design of static and hedonic banners to improve the ad recall.