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Cialdella, P., O. Chambon, J. P. Boissel, and E. Ravet-Cialdella. "La recherche d’une mesure unidimensionnelle de la dépression: à propos de l’échelle de dépression de Hamilton." Psychiatry and Psychobiology 4, no. 4 (1989): 203–10. http://dx.doi.org/10.1017/s0767399x00002765.
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RésuméL’utilisation du score global d’une échelle d’évaluation suppose l’unidimensionnalité de l’instrument, avantage censé permettre une plus grande puissance des tests statistiques. Classiquement, la vérification de cette propriété reposait sur l’isolement par l’analyse factorielle d’un facteur général, en rapport avec le pourcentage de variance extraite, mais aucun critère de choix du nombre de facteurs n’a été universellement accepté. De plus, les résultats d’une analyse donnée sont très dépendants des caractéristiques de l’échantillon, et non généralisables. Les récentes théories du trait latent, dont la plus connue est le modèle de Rasch (1960) preséntent l’avantage d’estimer la position des items sur une dimension (Fig. 1) indépendamment des caractéristiques (par exemple, diagnostiques) de l’échantillon et de procurer un test d’ajustement du modèle donnant un indice d’unidimensionnalité. La revue des analyses factorielles de l’échelle de dépression de Hamilton (HDS, 1960) montre qu’aucune étude n’a permis d’isoler un facteur général de dépression, qui autoriserait l’usage du score global de la HDS comme mesure de l’intensité de la dépression. Plus grave, les structures factorielles sont apparues comme généralement instables. En utilisant le modèle de Rasch, Bech a pu constater que 6 items extraits de la HDS remplissaient les critères d’unidimensionnalité, mais non l’échelle totale. Ces 6 items composent dorénavant une partie de la Bech-Rafaelsen Melancholia Scale (BRMES), dont l’unidimensionnalité a été récemment vérifiée par Maier & Philipp (1985), au sens du modèle de Rasch. L’analyse factorielle et le modèle de Rasch convergent donc vers la conclusion que l’utilisation du score global de la HDS ne se justifie plus, mais le modèle de Rasch semble appuyer la validite de la note globale de la BRMES comme mesure stable de la sévérité de la dépression.
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Korahiré, Joël Awouhidia. "Dynamiques socioéconomiques dans les villages riverains au ranch de gibier de Nazinga au Burkina Faso." Articles 40, no. 2 (March 24, 2011): 53–70. http://dx.doi.org/10.7202/1001388ar.
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Afin d’inciter les populations riveraines à la conservation des ressources fauniques, le Ranch de Gibier de Nazinga a adopté un modèle de gestion pour l’amélioration de leur condition de vie. Le but de cette recherche est d’étudier l’environnement socioéconomique dans lequel évoluent ces populations depuis la création dudit ranch. En utilisant principalement une démarche qualitative, la réalisation de cet objectif se fait en deux temps, soit primo en examinant les changements structurels socioéconomiques engendrés par le ranch dans la zone d’étude, puis secundo, en identifiant les impacts socioéconomiques du ranch et en explorant les perceptions des riverains sur ces impacts. Les résultats indiquent d’importantes améliorations sur les plans institutionnel et économique. Cependant, l’insuffisance d’infrastructures sociales de base et certains dysfonctionnements observés rendent les riverains quelque peu sceptiques et menacent la durabilité du modèle de gestion du ranch. En outre, il existe un conflit latent résultant d’une crise de confiance entre la direction du ranch et les populations riveraines.
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Descheneau-Guay, Amélie. "Les séries jeunesse et les stéréotypes sexuels : la récupération de l’idée d’émancipation et l’émergence d’une culture du consensus." Notes de recherche 19, no. 2 (March 21, 2007): 143–54. http://dx.doi.org/10.7202/014846ar.
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Cette note de recherche rapporte une exploration des stéréotypes sexuels féminins présents dans quatre séries télévisuelles que de jeunes québécois âgés de 7 à 12 ans ont dit préférer lors d’une enquête sur les systèmes représentationnels liés à la télévision. Dans une démarche mettant l’accent sur le contenu discursif latent, les séries sont décrites brièvement et les principaux thèmes sont relevés. Deux grands constats se dégagent du travail d’interprétation, soit l’émergence d’une culture du consensus dépolitisante et la récupération de l’idée féministed’émancipationcollective en l’idéologie du Girl Power. Ces deux tendances contribuent, chacune à leur façon, au renforcement d’un modèle féminin apolitique en adéquation avec l’ethos néolibéral contemporain, modèle suggéré comme celui de la femme libérée. L’analyse s’inscrit dans le large ensemble des travaux qui articulent les fonctionnements discursifs autour des conditions de production de connaissances, de visions spécifiques des genres et de positionnements idéologiques. Elle se situe dans le champ de l’analyse critique de discours dont l’objet consiste à explorer les formes de pouvoir qui s’établissent, s’affermissent et se reconstruisent, entre autres, à travers les contenus médiatiques.
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Childs, Ruth A., Orlena Broomes, and Monique B. Herbert. "Deciding Whether to Respond: A Latent Class Analysis of Nonresponse on Ontario’s Grade 9 Assessment of Mathematics." Alberta Journal of Educational Research 64, no. 1 (May 2, 2018): 70–87. http://dx.doi.org/10.55016/ojs/ajer.v64i1.56472.
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This study investigates nonresponse on Ontario’s Grade 9 Assessment of Mathematics – in particular, whether or not students responded to all multiple-choice or all open-response items in two test booklets. Whether students responded to all items of one type (multiple-choice or open-response) by booklet (for the first or second day of testing) was modeled, with and without proportion correct scores by item type as covariates, using latent class analysis. Both a 3-class model without the covariates and a 4-class model with the covariates but without direct effects distinguished among students who responded to all items, students who left both multiple-choice and open-response items blank, and students who left only open-response items blank. The results suggest that deciding to respond to all open-response items is distinct from deciding to respond to all multiple-choice items. Attitudes toward mathematics were also more related to the decision to respond to all open-response items than to the decision to respond to all multiple-choice items. Cette étude porte sur l’absence de réponse au test de mathématiques pour la 9e année en Ontario –nous cherchions notamment à savoir si les élèves avaient répondu à toutes les questions à choix multiples ou bien à toutes les questions ouvertes dans deux livrets d’examen. Une analyse de structure latente a permis la modélisation du comportement des élèves, à savoir s’ils avaient répondu à tous les items d’un type (questions à choix multiples ou questions ouvertes) dans un livret (lors du premier ou deuxième jour des tests) avec et sans des scores reflétant la proportion de bonnes réponses par type d’items comme covariables. Un modèle de classe 3 sans les covariables ainsi qu’un modèle de classe 4 avec les covariables mais sans effets directs ont tous les deux fait la distinction entre les élèves qui avaient répondu à tous le items, les élèves qui n’avaient ni répondu à certaines questions à choix multiples ni à certaines questions ouvertes et les élèves qui n’avaient pas répondu à certaines questions seulement dans le cas des questions ouvertes. Les attitudes face aux mathématiques ont également joué un plus grand rôle dans la décision de répondre à toutes les questions ouvertes que dans celle de répondre à toutes les questions à choix multiples.
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Burton, Réginald. "Influence des distributions du trait latent et de la difficulté des items sur les estimations du modèle de Birnbaum : une étude du type Monte-Carlo." Mesure et évaluation en éducation 27, no. 3 (2004): 41. http://dx.doi.org/10.7202/1087787ar.
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Nyambiya, T. E., T. Muromo, and K. Muchena. "‘My blood is now old and exhausted’: A Qualitative Study of Adults’ Behavioural Beliefs About Donating Blood in Harare, Zimbabwe." Africa Sanguine 22, no. 1 (May 25, 2020): 14–22. http://dx.doi.org/10.4314/asan.v22i1.3.
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Introduction: In Zimbabwe, though adults consume 80% of donated blood, their contribution to the national blood bank remains low.Considering that blood is an essential but scarce national resource it is important to gain insight into the beliefs that influence adults’ decisionsto donate blood or refrain from doing so.Aims and objectives: We sought to identify and describe the behavioural beliefs underlying adults’ blood donation intentions in Harare.Materials and methods: We used a qualitative study design based on the Integrated Behavioural Model. We interviewed 32 participants usinga semi-structured questionnaire. The responses were audio-recorded and transcribed verbatim. We analysed data thematically.Results: Concerning positive behavioural beliefs, our analysis yielded four positive latent themes: ‘donating saves lives’, ‘it is a social andreligious responsibility’, ‘enhances blood accessibility’ and ‘enhances the donor’s health’. Regarding negative behavioural beliefs, we alsofound four latent themes namely, ‘donated blood can negatively affect the donor’s health’, ‘donated blood can negatively affect the recipient’shealth’, ‘some cultural and religious beliefs discourage donating blood’ and ‘charging for donated blood is demotivating’.Discussion: Contemporary behavioural beliefs indicate that adults attach multiple and complex meanings to blood donation. The identifiedbeliefs can enable National Blood Service Zimbabwe to develop context-specific donor education and motivation strategies.Conclusion: Comprehensive knowledge about adults’ behavioural beliefs concerning blood donation is critical in shaping promotionalmessages that resonate with the target population.
French title: «Mon sang est maintenant vieux et épuisé»: Une étude Qualitative des Croyances Comportementales des Adultes Concernant le Don de Sang à Harare, Zimbabwe
Introduction: Au Zimbabwe, bien que les adultes consomment 80% du sang donné, leur contribution à la banque nationale de sang reste faible. Étant donné que le sang est une ressource nationale essentielle mais rare, il est important de mieux comprendre les croyances qui influencent les décisions des adultes de donner du sang ou de s’abstenir de le faire.Buts et objectifs: Nous avons cherché à identifier et à décrire les croyances comportementales qui sous-tendent les intentions de don de sang des adultes à Harare.Matériaux et méthodes: Nous avons conçu une étude qualitative basée sur le modèle comportemental intégré. Nous avons interviewé 32 participants à l'aide d'un questionnaire semi-structuré. Les réponses ont été enregistrées et transcrites textuellement. Nous avons analysé les données par thème.Résultats: Concernant les croyances comportementales positives, notre analyse a dégagé quatre thèmes latents positifs: «le don sauve des vies», «c'est une responsabilité sociale et religieuse», «améliore l'accessibilité au sang» et «améliore la santé du donneur». En ce qui concerne les croyances comportementales négatives, nous avons également trouvé quatre thèmes latents, à savoir: «le don de sang peut nuire à la santé du donneur», «le don de sang peut nuire à la santé du destinataire», «certaines croyances culturelles et religieuses découragent le don de sang» et «facturer le sang donné est démotivant ».Discussion: Les croyances comportementales contemporaines indiquent que les adultes attachent des significations multiples et complexes au don de sang. Les croyances identifiées peuvent permettre au National Blood Service Zimbabwe de développer des stratégies d'éducation et de motivation des donateurs spécifiques au contexte.Conclusion: Une connaissance approfondie des croyances comportementales des adultes concernant le don de sang est essentielle pour façonner des messages promotionnels qui résonnent avec la population cible.
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Fine, David. "Nursing’s latest module." Nursing Standard 6, no. 23 (February 26, 1992): 51. http://dx.doi.org/10.7748/ns.6.23.51.s53.
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Chaudhary, Neha, and Priti Dimri. "LATENT FINGERPRINT IMAGE ENHANCEMENT BASED ON OPTIMIZED BENT IDENTITY BASED CONVOLUTIONAL NEURAL NETWORK." Indian Journal of Computer Science and Engineering 12, no. 5 (October 20, 2021): 1477–93. http://dx.doi.org/10.21817/indjcse/2021/v12i5/211205124.
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Fingerprints are unique biometric systems (BSs) in which none of the human possesses similar fingerprint structures. It is one of the most significant biometric processes used in the identification of criminals. Latent fingerprints or latents are generated mainly by the finger sweat or oil deposits which is left by the suspects unintentionally. The impressions of latents are blurred or smudgy in nature and not viewed by naked eye. These fingerprints are of low quality, corrupted by noise, degraded by technological factors and exhibit minor details. Latents display consistent structural info when observed as an image. Image Enhancement is necessary in latents, to transform the latent (noisy) image into fine-quality (enhanced) image. In this work, a new image enhancement approach named BI-CNN (Bent Identity-Convolution Neural Network) with Spatial Pyramid Max Pooling (SPMP) model optimized using TSOA (Tunicate Swarm Optimization Algorithm) is presented to produce an enhanced latent at the output. This procedure involves the integration of ROI (Region Of Interest) Estimation, Anisotropic Gaussian Filter (AGF) based Pre-filtering, Fingerprint alignment using Sobel Filter, Intrinsic Feature patch extraction using Optimized BI-CNN, GAT (Graph Attention) network based Similarity Estimation followed by image reconstruction and feedback module. The implementation tool used in this work is PYTHON platform. The proposed optimized BI-CNN framework tested on dual public datasets namely IIITD-latent finger print and IIITD-MOLF have shown enhanced outcomes. Thus, the IIITD -latent fingerprint database obtained 83.33% on Rank-10 accuracy and 39.33% on Rank-25 accuracy.
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Chen, Fangdong, Yumeng Xu, and Li Wang. "Two-Stage Octave Residual Network for End-to-End Image Compression." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 4 (June 28, 2022): 3922–29. http://dx.doi.org/10.1609/aaai.v36i4.20308.
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Octave Convolution (OctConv) is a generic convolutional unit that has already achieved good performances in many computer vision tasks. Recent studies also have shown the potential of applying the OctConv in end-to-end image compression. However, considering the characteristic of image compression task, current works of OctConv may limit the performance of the image compression network due to the loss of spatial information caused by the sampling operations of inter-frequency communication. Besides, the correlation between multi-frequency latents produced by OctConv is not utilized in current architectures. In this paper, to address these problems, we propose a novel Two-stage Octave Residual (ToRes) block which strips the sampling operation from OctConv to strengthen the capability of preserving useful information. Moreover, to capture the redundancy between the multi-frequency latents, a context transfer module is designed. The results show that both ToRes block and the incorporation of context transfer module help to improve the Rate-Distortion performance, and the combination of these two strategies makes our model achieve the state-of-the-art performance and outperform the latest compression standard Versatile Video Coding (VVC) in terms of both PSNR and MS-SSIM.
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Jin Dai, Jin Dai, and Zhifang Zheng Jin Dai. "Disentangling Representation of Variational Autoencoders Based on Cloud Models." 電腦學刊 34, no. 6 (December 2023): 001–14. http://dx.doi.org/10.53106/199115992023123406001.
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<p>Variational autoencoder (VAE) has the problem of uninterpretable data generation process, because the features contained in the VAE latent space are coupled with each other and no mapping from the latent space to the semantic space is established. However, most existing algorithms cannot understand the data distribution features in the latent space semantically. In this paper, we propose a cloud model-based method for disentangling semantic features in VAE latent space by adding support vector machines (SVM) to feature transformations of latent variables, and we propose to use the cloud model to measure the degree of disentangling of semantic features in the latent space. The experimental results on the CelebA dataset show that the method obtains a good disentangling effect of semantic features in the latent space, which proves the effectiveness of the method from both qualitative and quantitative aspects.</p> <p> </p>
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Seol, Hyun-Soo. "Development and Application of Latent Transition Analysis Module in jamovi." Korean Human Resource Development Strategy Institute 19, no. 4 (December 31, 2024): 27–42. https://doi.org/10.21329/khrd.2024.19.4.27.
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This study presents the development and implementation of a novel statistical module for latent transition analysis within the jamovi software environment. jamovi is an open-source platform designed to make statistical analysis accessible to users with varying levels of expertise. This module integrates latent transition analysis, a complex method used for examining changes in latent variables over time. This integration significantly enhances the software's analytical capabilities. The primary objective of this development is to democratize access to advanced statistical methods, thereby facilitating their use in educational settings and diverse research fields such as psychology, sociology, and education. This paper discusses the technical aspects of the module's integration, evaluates its usability through user feedback, and highlights its potential impact on both teaching and research. The findings suggest that the module not only simplifies the execution of sophisticated analyses but also encourages broader adoption of latent transition analysis by reducing technical barriers. Consequently, this work contributes to the ongoing efforts to make advanced statistical tools more accessible and user-friendly, promoting their integration into everyday research practices.
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Bellecci, C., and M. Conti. "Transient behaviour analysis of a latent heat thermal storage module." International Journal of Heat and Mass Transfer 36, no. 15 (October 1993): 3851–57. http://dx.doi.org/10.1016/0017-9310(93)90065-e.
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de Jong, John H. A. L. "Le Modele De Rasch." Taaltoetsen 31 (January 1, 1988): 57–70. http://dx.doi.org/10.1075/ttwia.31.07jon.
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This paper provides an elementary introduction to the one parameter psychometric model known as the Rasch model. It explains the basic principles underlying the model and the concepts of unidimensionality, local stochastic independence, and additivity in non-mathematical terms. The requirements of measurement procedures, the measurement of latent traits, the control on model fit, and the definition of a trait are discussed. It is argued that the Rasch model is particularly appropriate to understand the mutual dependence of test reliability and validity. Examples from foreign language listening comprehension tests are used to illustrate the application of the model to a test validation procedure.
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Kapłon, Robert. "Modele analizy czynnikowej z dwoma zmiennymi ukrytymi." Przegląd Statystyczny. Statistical Review 2011, no. 3-4 (December 31, 2011): 242–55. http://dx.doi.org/10.59139/ps.2011.03-04.6.
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The goal of factor analysis is to reduce the redundancy among variables by using smaller number of factors that are treated as constructs or latent variables. Unfortunately, if we face with data heterogeneity, the estimates of a single set of means, factor loadings and specific variances may be misleading.One way of accounting for unobserved heterogeneity is to include another latent variable in a factor analysis model. As a consequence, the observations in a samples are assumed to arise from two or more subpopulations that are mixed in unknown proportions. Since putting some restrictions on parameters such as factor loadings and specific variancesone can get more parsimonious models. Therefore, the purpose of this paper is to present the eight factor analysis models. Methods of optimization to derive the maximum likelihood estimates based on EM algorithm as well as model selection procedure are considered. Proposed approach is illustrated by using a set of data referring to preferences.
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Erek, Aytunc, and Ibrahim Dincer. "An approach to entropy analysis of a latent heat storage module." International Journal of Thermal Sciences 47, no. 8 (August 2008): 1077–85. http://dx.doi.org/10.1016/j.ijthermalsci.2007.08.002.
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Al-Saadi, Saleh Nasser, and Zhiqiang (John) Zhai. "A new validated TRNSYS module for simulating latent heat storage walls." Energy and Buildings 109 (December 2015): 274–90. http://dx.doi.org/10.1016/j.enbuild.2015.10.013.
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Xiang, Zhengtao, Xing Wan, Libo Xu, Xin Yu, and Yuhan Mao. "A Training-Free Latent Diffusion Style Transfer Method." Information 15, no. 10 (September 26, 2024): 588. http://dx.doi.org/10.3390/info15100588.
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Diffusion models have attracted considerable scholarly interest for their outstanding performance in generative tasks. However, current style transfer techniques based on diffusion models still rely on fine-tuning during the inference phase to optimize the generated results. This approach is not merely laborious and resource-demanding but also fails to fully harness the creative potential of expansive diffusion models. To overcome this limitation, this paper introduces an innovative solution that utilizes a pretrained diffusion model, thereby obviating the necessity for additional training steps. The scheme proposes a Feature Normalization Mapping Module with Cross-Attention Mechanism (INN-FMM) based on the dual-path diffusion model. This module employs soft attention to extract style features and integrate them with content features. Additionally, a parameter-free Similarity Attention Mechanism (SimAM) is employed within the image feature space to facilitate the transfer of style image textures and colors, while simultaneously minimizing the loss of structural content information. The fusion of these dual attention mechanisms enables us to achieve style transfer in texture and color without sacrificing content integrity. The experimental results indicate that our approach exceeds existing methods in several evaluation metrics.
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Nam, Woojun, Jinho Choi, Gyugwang Kim, Jinhee Hyun, Hyungkeun Ahn, and Neungsoo Park. "Predicting Photovoltaic Module Lifespan Based on Combined Stress Tests and Latent Heat Analysis." Energies 18, no. 2 (January 11, 2025): 304. https://doi.org/10.3390/en18020304.
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In this study, long-term reliability tests for high-power-density photovoltaic (PV) modules were introduced and analyzed in accordance with IEC 61215 and light-combined damp heat cycles, such as DIN 75220. The results indicated that post light soaking procedure, light-combined damp heat cycles caused a 3.51% power drop, while IEC standard tests (DH1000 and TC200) caused only 0.87% and 1.32% power drops, respectively. IEC 61215 failed to assess the long-term reliability of the high-power-density PV module, such as the passivated emitter rear cell. Additionally, based on the combined test, the latent heat (Qmod) of the module was introduced to predict its degradation rate and to fit the prediction curve of the product guaranteed by the PV module manufacturers. Qmod facilitates in predicting a PV module’s lifespan according to the environmental factors of the actual installation area. The Qmod values of the PV stations in water environments, such as floating and/or marine PVs, indicated that they would last 7.2 years more than those on a rooftop, assuming that latent heat is the only cause of deterioration. Therefore, extending module life and improving power generation efficiency by determining installation sites to minimize latent heat would be advantageous.
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Chen, Yujun, Xin Tan, Zhizhong Zhang, Yanyun Qu, and Yuan Xie. "Beyond the Label Itself: Latent Labels Enhance Semi-supervised Point Cloud Panoptic Segmentation." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 2 (March 24, 2024): 1245–53. http://dx.doi.org/10.1609/aaai.v38i2.27887.
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As the exorbitant expense of labeling autopilot datasets and the growing trend of utilizing unlabeled data, semi-supervised segmentation on point clouds becomes increasingly imperative. Intuitively, finding out more ``unspoken words'' (i.e., latent instance information) beyond the label itself should be helpful to improve performance. In this paper, we discover two types of latent labels behind the displayed label embedded in LiDAR and image data. First, in the LiDAR Branch, we propose a novel augmentation, Cylinder-Mix, which is able to augment more yet reliable samples for training. Second, in the Image Branch, we propose the Instance Position-scale Learning (IPSL) Module to learn and fuse the information of instance position and scale, which is from a 2D pre-trained detector and a type of latent label obtained from 3D to 2D projection. Finally, the two latent labels are embedded into the multi-modal panoptic segmentation network. The ablation of the IPSL module demonstrates its robust adaptability, and the experiments evaluated on SemanticKITTI and nuScenes demonstrate that our model outperforms the state-of-the-art method, LaserMix.
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Yarmchuk, E. J., C. W. Cline, and D. C. Bruen. "Latent defect screening for high-reliability glass-ceramic multichip module copper interconnects." IBM Journal of Research and Development 49, no. 4.5 (July 2005): 677–85. http://dx.doi.org/10.1147/rd.494.0677.
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Zhang, Rongchao, Yiwei Lou, Dexuan Xu, Yongzhi Cao, Hanpin Wang, and Yu Huang. "A Learnable Discrete-Prior Fusion Autoencoder with Contrastive Learning for Tabular Data Synthesis." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 15 (March 24, 2024): 16803–11. http://dx.doi.org/10.1609/aaai.v38i15.29621.
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The actual collection of tabular data for sharing involves confidentiality and privacy constraints, leaving the potential risks of machine learning for interventional data analysis unsafely averted. Synthetic data has emerged recently as a privacy-protecting solution to address this challenge. However, existing approaches regard discrete and continuous modal features as separate entities, thus falling short in properly capturing their inherent correlations. In this paper, we propose a novel contrastive learning guided Gaussian Transformer autoencoder, termed GTCoder, to synthesize photo-realistic multimodal tabular data for scientific research. Our approach introduces a transformer-based fusion module that seamlessly integrates multimodal features, permitting for mining more informative latent representations. The attention within the fusion module directs the integrated output features to focus on critical components that facilitate the task of generating latent embeddings. Moreover, we formulate a contrastive learning strategy to implicitly constrain the embeddings from discrete features in the latent feature space by encouraging the similar discrete feature distributions closer while pushing the dissimilar further away, in order to better enhance the representation of the latent embedding. Experimental results indicate that GTCoder is effective to generate photo-realistic synthetic data, with interactive interpretation of latent embedding, and performs favorably against some baselines on most real-world and simulated datasets.
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GARNER, JAMES, and SCOTT CROSSLEY. "A Latent Curve Model Approach To Studying L2 N-Gram Development." Modern Language Journal 102, no. 3 (July 12, 2018): 494–511. http://dx.doi.org/10.1111/modl.12494.
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SARITA KUMARI and SOMNATH BAIDYA ROY. "Impact of dynamic vegetation on near-surface meteorology using a newly developed WRF_NOAHMP_SUCROS coupled model." Journal of Agrometeorology 24, no. 4 (December 2, 2022): 335–47. http://dx.doi.org/10.54386/jam.v24i4.1892.
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The study attempts to quantitatively understand the impact of dynamic vegetation on land-surface atmosphere interactions over spring wheat croplands in India. A new modeling tool capable of simulating these interactions was developed by incorporating the crop growth module of the Simple and Universal Crop growth Simulator (SUCROS) crop model into the Weather Research and Forecasting (WRF) mesoscale model. An earlier study had calibrated and evaluated the stand-alone SUCROS crop model with observed data for spring wheat collected from an experimental site in northwestern India. The crop growth module of the calibrated SUCROS model was implemented in the Noah-MP land module of WRF to build the coupled WRF_NOAHMP_SUCROS model. Numerical experiments were conducted with WRF_SUCROS that simulates the simultaneous evolution of meteorological drivers and crop Leaf Area Index (LAI) and the two-way interactions between these processes. These experiments were compared with WRF simulations driven by observed climatological mean LAI. These experiments only simulate the effects of changes in LAI on meteorology but not the other round. Results show that the coupled WRF_NOAHMP_SUCROS model is able to simulate the LAI better than the default dynamic vegetation module in WRF. It also produces realistic simulations of the near-surface meteorological parameters. The latent heat flux (LHF) varies directly with LAI, and sensible heat flux (SHF) varies inversely with LAI. As the crop grows, the energy transfer occurs more in latent heat flux than sensible heat flux due to increased evapotranspiration. Hence the growing crops result in near-surface cooling due to decreased Bowed Ratio. The mixing ratio is also increased due to increased latent heat flux. The uncoupled WRF model also shows similar patterns except in the juvenile crop stage where it overestimates the sensible heating and temperature but underestimates latent heat fluxes and mixing ratio.
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Bao, Yuting, Yuwen Tao, and Pengjiang Qian. "Image Compression Based on Hybrid Domain Attention and Postprocessing Enhancement." Computational Intelligence and Neuroscience 2022 (March 17, 2022): 1–12. http://dx.doi.org/10.1155/2022/4926124.
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Deep learning-based image compression methods have made significant achievements recently, of which the two key components are the entropy model for latent representations and the encoder-decoder network. Both the inaccurate estimation of the entropy estimation model and the existence of information redundancy in latent representations lead to a reduction in the compression efficiency. To address these issues, the study suggests an image compression method based on a hybrid domain attention mechanism and postprocessing improvement. This study embeds hybrid domain attention modules as nonlinear transformers in both the main encoder-decoder network and the hyperprior network, aiming at constructing more compact latent features and hyperpriors and then model the latent features as parametric Gaussian-scale mixture models to obtain more precise entropy estimation. In addition, we propose a solution to the errors introduced by quantization in image compression by adding an inverse quantization module. On the decoding side, we also provide a postprocessing enhancement module to further increase image compression performance. The experimental results show that the peak signal-to-noise rate (PSNR) and multiscale structural similarity (MS-SSIM) of the proposed method are higher than those of traditional compression methods and advanced neural network-based methods.
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Zhang, Jian, Jin Yuan, Chuanzhen Li, and Bin Li. "An Inverse Design Framework for Isotropic Metasurfaces Based on Representation Learning." Electronics 11, no. 12 (June 10, 2022): 1844. http://dx.doi.org/10.3390/electronics11121844.
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A hybrid framework for solving the non-uniqueness problem in the inverse design of isomorphic metasurfaces is proposed. The framework consists of a representation learning (RL) module and a variational autoencoder-particle swarm optimization (VAE-PSO) algorithm module. The RL module is used to reduce the complex high-dimensional space into a low-dimensional space with obvious features, with the purpose of eliminating the many-to-one relationship between the original design space and response space. The VAE-PSO algorithm first encodes all meta-atoms into a continuous latent space through VAE and then applies PSO to search for an optimized latent vector whose corresponding metasurface fulfills the target response. This framework gives the solution paradigm of the ideal non-uniqueness situation, simplifies the complexity of the network, improves the running speed of the PSO algorithm, and obtains the global optimal solution with 94% accuracy on the test set.
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Chen, Yueqiao, Wenxia Yang, Xi Fang, and Huan Han. "EC-GAN: Emotion-Controllable GAN for Face Image Completion." Applied Sciences 13, no. 13 (June 28, 2023): 7638. http://dx.doi.org/10.3390/app13137638.
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Image completion methods based on deep learning, such as Variational Autoencoders (VAEs) and Generative Adversarial Networks (GANs), have succeeded in producing semantically plausible results. However, existing facial image completion methods can either produce only one result or, although they can provide multiple results, cannot attribute particular emotions to the results. We propose EC-GAN, a novel facial Emotion-Controllable GAN-based image completion model that can infer and customize generative facial emotions. We propose an emotion inference module that infers the emotions of faces based on the unmasked regions of the faces. The emotion inference module is trained in a supervised manner and enforces the encoder to disentangle the emotion semantics from the native latent space. We also developed an emotion control module to modify the latent codes of emotions, moving the latent codes of the initial emotion toward the desired one while maintaining the remaining facial features. Extensive experiments were conducted on two facial datasets, CelebA-HQ and CFEED. Quantitative and qualitative results indicate that EC-GAN produces images with diverse desired expressions even when the main features of the faces are masked. On the other hand, EC-GAN promotes semantic inference capability with irregularly masked holes, resulting in more natural facial expressions.
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Goldsworth, J. "United Kingdom: Pensions: latest trustee toolkit module tackles fund management." Trusts & Trustees 13, no. 4 (April 9, 2007): 110. http://dx.doi.org/10.1093/tandt/ttm009.
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Fang, Zhiyu, Xiaobin Zhu, Chun Yang, Zheng Han, Jingyan Qin, and Xu-Cheng Yin. "Learning Aligned Cross-Modal Representation for Generalized Zero-Shot Classification." Proceedings of the AAAI Conference on Artificial Intelligence 36, no. 6 (June 28, 2022): 6605–13. http://dx.doi.org/10.1609/aaai.v36i6.20614.
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Learning a common latent embedding by aligning the latent spaces of cross-modal autoencoders is an effective strategy for Generalized Zero-Shot Classification (GZSC). However, due to the lack of fine-grained instance-wise annotations, it still easily suffer from the domain shift problem for the discrepancy between the visual representation of diversified images and the semantic representation of fixed attributes. In this paper, we propose an innovative autoencoder network by learning Aligned Cross-Modal Representations (dubbed ACMR) for GZSC. Specifically, we propose a novel Vision-Semantic Alignment (VSA) method to strengthen the alignment of cross-modal latent features on the latent subspaces guided by a learned classifier. In addition, we propose a novel Information Enhancement Module (IEM) to reduce the possibility of latent variables collapse meanwhile encouraging the discriminative ability of latent variables. Extensive experiments on publicly available datasets demonstrate the state-of-the-art performance of our method.
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Zhang, Guoqing, Ruixin Zhou, Yuhui Zheng, and Baozhu Li. "Binary Noise Guidance Learning for Remote Sensing Image-to-Image Translation." Remote Sensing 16, no. 1 (December 23, 2023): 65. http://dx.doi.org/10.3390/rs16010065.
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Image-to-image translation (I2IT) is an important visual task that aims to learn a mapping of images from one domain to another while preserving the representation of the content. The phenomenon known as mode collapse makes this task challenging. Most existing methods usually learn the relationship between the data and latent distributions to train more robust latent models. However, these methods often ignore the structural information among latent variables, leading to patterns in the data being obscured during the process. In addition, the inflexibility of data modes caused by ignoring the latent mapping of two domains is also one of the factors affecting the performance of existing methods. To make the data schema stable, this paper develops a novel binary noise guidance learning (BnGLGAN) framework for image translation to solve these problems. Specifically, to eliminate uncertainty of domain distribution, a noise prior inference learning (NPIL) module is designed to infer an estimated distribution from a certain domain. In addition, to improve the authenticity of reconstructed images, a distribution-guided noise reconstruction learning (DgNRL) module is introduced to reconstruct the noise from the source domain, which can provide source semantic information to guide the GAN’s generation. Extensive experiments fully prove the efficiency of our proposed framework and its advantages over comparable methods.
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Cho, Sun-Joo, Youngsuk Suh, and Woo-yeol Lee. "An NCME Instructional Module on Latent DIF Analysis Using Mixture Item Response Models." Educational Measurement: Issues and Practice 35, no. 1 (October 26, 2015): 48–61. http://dx.doi.org/10.1111/emip.12093.
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Nakhla, David, Hossam Sadek, and James S. Cotton. "Melting performance enhancement in latent heat storage module using solid extraction electrohydrodynamics (EHD)." International Journal of Heat and Mass Transfer 81 (February 2015): 695–704. http://dx.doi.org/10.1016/j.ijheatmasstransfer.2014.10.016.
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Selvakumar, R. Deepak, Jian Wu, and Ahmed K. Alkaabi. "Electrohydrodynamic acceleration of charging process in a latent heat thermal energy storage module." Applied Thermal Engineering 242 (April 2024): 122475. http://dx.doi.org/10.1016/j.applthermaleng.2024.122475.
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Chen, Hui-O., Yuan-Chi Cui, Peng-Chan Lin, and Jung-Hsien Chiang. "An Innovative Multi-Omics Model Integrating Latent Alignment and Attention Mechanism for Drug Response Prediction." Journal of Personalized Medicine 14, no. 7 (June 27, 2024): 694. http://dx.doi.org/10.3390/jpm14070694.
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By using omics, we can now examine all components of biological systems simultaneously. Deep learning-based drug prediction methods have shown promise by integrating cancer-related multi-omics data. However, the complex interaction between genes poses challenges in accurately projecting multi-omics data. In this research, we present a predictive model for drug response that incorporates diverse types of omics data, comprising genetic mutation, copy number variation, methylation, and gene expression data. This study proposes latent alignment for information mismatch in integration, which is achieved through an attention module capturing interactions among diverse types of omics data. The latent alignment and attention modules significantly improve predictions, outperforming the baseline model, with MSE = 1.1333, F1-score = 0.5342, and AUROC = 0.5776. High accuracy was achieved in predicting drug responses for piplartine and tenovin-6, while the accuracy was comparatively lower for mitomycin-C and obatoclax. The latent alignment module exclusively outperforms the baseline model, enhancing the MSE by 0.2375, the F1-score by 4.84%, and the AUROC by 6.1%. Similarly, the attention module only improves these metrics by 0.1899, 2.88%, and 2.84%, respectively. In the interpretability case study, panobinostat exhibited the most effective predicted response, with a value of −4.895. We provide reliable insights for drug selection in personalized medicine by identifying crucial genetic factors influencing drug response.
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Mohamed Abdul Cader, Akmal Jahan, Jasmine Banks, and Vinod Chandran. "Fingerprint Systems: Sensors, Image Acquisition, Interoperability and Challenges." Sensors 23, no. 14 (July 21, 2023): 6591. http://dx.doi.org/10.3390/s23146591.
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The fingerprint is a widely adopted biometric trait in forensic and civil applications. Fingerprint biometric systems have been investigated using contact prints and latent and contactless images which range from low to high resolution. While the imaging techniques are advancing with sensor variations, the input fingerprint images also vary. A general fingerprint recognition pipeline consists of a sensor module to acquire images, followed by feature representation, matching and decision modules. In the sensor module, the image quality of the biometric traits significantly affects the biometric system’s accuracy and performance. Imaging modality, such as contact and contactless, plays a key role in poor image quality, and therefore, paying attention to imaging modality is important to obtain better performance. Further, underlying physical principles and the working of the sensor can lead to their own forms of distortions during acquisition. There are certain challenges in each module of the fingerprint recognition pipeline, particularly sensors, image acquisition and feature representation. Present reviews in fingerprint systems only analyze the imaging techniques in fingerprint sensing that have existed for a decade. However, the latest emerging trends and recent advances in fingerprint sensing, image acquisition and their challenges have been left behind. Since the present reviews are either obsolete or restricted to a particular subset of the fingerprint systems, this work comprehensively analyzes the state of the art in the field of contact-based, contactless 2D and 3D fingerprint systems and their challenges in the aspects of sensors, image acquisition and interoperability. It outlines the open issues and challenges encountered in fingerprint systems, such as fingerprint performance, environmental factors, acceptability and interoperability, and alternate directions are proposed for a better fingerprint system.
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Namatēvs, Ivars, Artūrs Ņikuļins, Anda Slaidiņa, Laura Neimane, Oskars Radziņš, and Kaspars Sudars. "Towards Explainability of the Latent Space by Disentangled Representation Learning." Information Technology and Management Science 26 (November 30, 2023): 41–48. http://dx.doi.org/10.7250/itms-2023-0006.
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Deep neural networks are widely used in computer vision for image classification, segmentation and generation. They are also often criticised as “black boxes” because their decision-making process is often not interpretable by humans. However, learning explainable representations that explicitly disentangle the underlying mechanisms that structure observational data is still a challenge. To further explore the latent space and achieve generic processing, we propose a pipeline for discovering the explainable directions in the latent space of generative models. Since the latent space contains semantically meaningful directions and can be explained, we propose a pipeline to fully resolve the representation of the latent space. It consists of a Dirichlet encoder, conditional deterministic diffusion, a group-swap and a latent traversal module. We believe that this study provides an insight into the advancement of research explaining the disentanglement of neural networks in the community.
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Singh, Babita, and Waseem Ahmad. "Implementation of Latent Fingerprint Matching System." International Journal of Advance Research and Innovation 2, no. 2 (2014): 43–47. http://dx.doi.org/10.51976/ijari.221406.
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"Biometrics" means "life measurement" .The term is usually associated with the use of unique physiological characteristics to identify an individual. Biometrics is used in computer science as a means of identification and access control. It is also used to identify individuals in groups that are under surveillance. Latent finger prints are inadvertent impressions left by fingers on surfaces of objects. The main difficulties in latent fingerprint matching are unclear ridge structure, small finger area, and large non-linear distortion while rolled fingerprint are of larger size and contain more minutiae. Latent fingerprint identification is of critical importance to law enforcement agencies in identifying suspects. While tremendous progress has been made in plain and rolled fingerprint matching, latent fingerprint matching continues to be a difficult problem. The eventual goal of research is to propose a system for matching latent fingerprints to rolled fingerprints that is needed in forensics applications. The system will match latent fingerprints to rolled fingerprints that is needed in forensics applications. In this paper we will apply latent fingerprint algorithm to develop a minutiae-based fingerprint matcher that takes into account the specific characteristics of the latent matching problem.The experimental results indicate that singularity, ridge quality map, and ridge flow map are the most effective features in improving the matching accuracy. The matching module consists of minutiae matching, orientation field matching.
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Szaszák, Norbert, and Ákos Pozsa. "Experimental investigation of a high efficiency electric heater and dehumidifier prototype unit." Analecta Technica Szegedinensia 14, no. 2 (December 8, 2020): 43–49. http://dx.doi.org/10.14232/analecta.2020.2.43-49.
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In this paper the principle of operation and preliminary laboratory measurements of a prototype of a high-efficiency electrical air heater unit is presented. Unlike conventional heaters, which apply Joule-heat formed by electrical resistance, the developed device uses thermoelectric modules for heating ambient air. Just like in case of resistance heaters, most of the heat is produced as a result of the internal ohmic resistance of the thermoelectric module (resistance heating), however, in case of appropriate air conditions our device is capable of transforming the latent heat of the air moisture into heat energy. In case of condensation mode, some of the moisture condensates on the cold side of the module while its latent heat is transferred to the hot side of the module where it heats the dried air. In this mode, the heating efficiency of the device (e.g., the ratio of the heat added to air and the consumed electricity) is over unity. Following the idea and basic equations of the operation of this device, the results of the laboratory measurements in a climate test chamber is presented.
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Bian, Cheng, Xiaoyu Li, Qi Bi, Guangpu Zhu, Jiegeng Lyu, Weile Zhang, Yelei Li, and Zijing Zeng. "Constraint Latent Space Matters: An Anti-anomalous Waveform Transformation Solution from Photoplethysmography to Arterial Blood Pressure." Proceedings of the AAAI Conference on Artificial Intelligence 38, no. 10 (March 24, 2024): 11087–95. http://dx.doi.org/10.1609/aaai.v38i10.28985.
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Arterial blood pressure (ABP) holds substantial promise for proactive cardiovascular health management. Notwithstanding its potential, the invasive nature of ABP measurements confines their utility primarily to clinical environments, limiting their applicability for continuous monitoring beyond medical facilities. The conversion of photoplethysmography (PPG) signals into ABP equivalents has garnered significant attention due to its potential in revolutionizing cardiovascular disease management. Recent strides in PPG-to-ABP prediction encompass the integration of generative and discriminative models. Despite these advances, the efficacy of these models is curtailed by the latent space shift predicament, stemming from alterations in PPG data distribution across disparate hardware and individuals, potentially leading to distorted ABP waveforms. To tackle this problem, we present an innovative solution named the Latent Space Constraint Transformer (LSCT), leveraging a quantized codebook to yield robust latent spaces by employing multiple discretizing bases. To facilitate improved reconstruction, the Correlation-boosted Attention Module (CAM) is introduced to systematically query pertinent bases on a global scale. Furthermore, to enhance expressive capacity, we propose the Multi-Spectrum Enhancement Knowledge (MSEK), which fosters local information flow within the channels of latent code and provides additional embedding for reconstruction. Through comprehensive experimentation on both publicly available datasets and a private downstream task dataset, the proposed approach demonstrates noteworthy performance enhancements compared to existing methods. Extensive ablation studies further substantiate the effectiveness of each introduced module.
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Li, Hui, Tianpeng Pan, and Lili Zhang. "Enhanced Remote Sensing Image Compression Method Using Large Network with Sparse Extracting Strategy." Electronics 13, no. 13 (July 8, 2024): 2677. http://dx.doi.org/10.3390/electronics13132677.
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Deep neural networks based on hyper-encoders play a critical role in estimating prior distributions in remote sensing image compression issues. However, most of the existing encoding methods suffer from a problem on the hyper-encoding side, namely the mismatch of extraction ability with the encoder. This ability bias results in likelihood features that fail to extract sufficient information from latent representations. To solve this problem, the feature extraction capabilities of the hyper-encoder are enhanced to better estimate the Gaussian likelihood of the latent representation in end-to-end network optimization. Specifically, residual blocks and a parameter estimation module are incorporated to balance the performance of the encoder and the hyper-encoder. Furthermore, it is observed that the well-trained compression model tends to generate a fixed pattern of latent representations. Therefore, we incorporate a nonlocal cross-channel graph (NCG) on the backside of the encoder. Specifically, it aggregates features between similar latent representations in a graphical manner to further enhance the side information extraction capability of the hyper-encoder. Considering the computational cost, a sparse graph strategy is further developed to dynamically select the most relevant latent representations for aggregation operations, which greatly reduces the computational effort. The proposed algorithm is named nonlocal cross-channel efficient graph (NCEG). A long-dependent residual network is selected as the backbone, and a sparse attention module is inserted into the encoder/decoder side to enhance the perceptual field of the network. The experimental results on two evaluation datasets demonstrate that the proposed method achieves satisfactory results compared to other learning-based methods.
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Gong, Zhen-Xiang, and A. S. Mujumdar. "A New Solar Receiver Thermal Store for Space-Based Activities Using Multiple Composite Phase-Change Materials." Journal of Solar Energy Engineering 117, no. 3 (August 1, 1995): 215–20. http://dx.doi.org/10.1115/1.2847798.
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A new solar receiver latent thermal energy storage module for space-based activities is proposed. It is different from a conventional module (which uses a single phase-change material (PCM)); this module uses a composite configuration of multiple PCMs. A finite element model is presented which simulates the transient cyclic thermal process involved. Computations have been carried out to investigate the potential advantages of a composite multi-PCM store over a single PCM store. Numerical results indicate that variation of the fluid outlet temperature can be significantly reduced by appropriate selection of multiple composite PCMs.
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Grubor, Aleksandar, and Nikola Milićević. "The implementation of hierarchical latent models in the measurement of perceived service quality." Marketing 50, no. 3 (2019): 179–85. http://dx.doi.org/10.5937/markt1903179g.
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Wei, Tingting, Hongpeng Wu, Lei Dong, and Frank Tittel. "Acoustic Detection Module Design of a Quartz-Enhanced Photoacoustic Sensor." Sensors 19, no. 5 (March 4, 2019): 1093. http://dx.doi.org/10.3390/s19051093.
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This review aims to discuss the latest advancements of an acoustic detection module (ADM) based on quartz-enhanced photoacoustic spectroscopy (QEPAS). Starting from guidelines for the design of an ADM, the ADM design philosophy is described. This is followed by a review of the earliest standard quartz tuning fork (QTF)-based ADM for laboratory applications. Subsequently, the design of industrial fiber-coupled and free-space ADMs based on a standard QTF for near-infrared and mid-infrared laser sources respectively are described. Furthermore, an overview of the latest development of a QEPAS ADM employing a custom QTF is reported. Numerous application examples of four QEPAS ADMs are described in order to demonstrate their reliability and robustness.
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Aldawood, Faisal Khaled, Yamuna Munusamy, Mohamed Kchaou, and Mohammad Alquraish. "Effect of Elastomeric Coating on the Properties and Performance of Myristic Acid (MA) Phase Change Material (PCM) Used for Photovoltaic Cooling." Coatings 13, no. 9 (September 14, 2023): 1606. http://dx.doi.org/10.3390/coatings13091606.
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Nitrile butadiene rubber (NBR) latex exhibits excellent tensile properties, chemical resistance, and thermal stability in applications such as gloves and safety shoes due to vulcanization. In this research work, attempts have been made to manipulate the vulcanization to produce thin and compact elastomeric NBR coating on myristic acid (MA) phase change material (PCM) to produce shape-stabilized PCM. The proposal for the use of latex-based elastomeric coating for PCM has been rarely considered in the literature due to a lack of understanding of the crosslink of elastomers. Thus, in this research, the effects of sulfur formulation on the coating performance of NBR on the PCM in terms of latent heat and thermal stability were determined. Leakage analysis indicates that the MA pellet coated with 0.5 phr of sulfur-cured NBR layer (MA/NBR-0.5) successfully eliminates the leakage issue. A tensile analysis revealed that a durable PCM coating layer must possess a combination of the following criteria: high tensile strength, ductility, and flexibility. Fourier transform infrared analysis (FTIR) and electron microscopy images showed the formation of thin, compact, and continuous NBR coating when 0.5 phr of sulfur was used. The further increment of sulfur loading between 1.0 and 1.5 phr causes the formation of defects on the coating layers, while non-vulcanized NBR layers seem to be very weak to withstand the phase-change process. The recorded latent heat values of melting and freezing of MA/NBR-0.5 are 142.30 ± 1.38 and 139.47 ± 1.23 J/g, respectively. The latent heat of the shape-stabilized MA/NBR-0.5 PCM is reduced by 32.24% from the pure MA latent heat density. This reduction is significantly lower than the reported latent heat reduction in shape-stabilized PCMs in other works. The thermal cycle test highlights the durability of the coated PCMs by withstanding up to 1000 thermal cycles (2.7 years) with less than 2% changes in latent heat value. Cooling performance test on photovoltaic (PV) module shows that the fabricated shape-stabilized PCM could reduce the temperature of the PV module up to 17 °C and increase the voltage generation by 7.92%. Actual performance analysis of shape-stabilized PCMs on the cooling of the PV module has been rarely reported and could be considered a strength of this work.
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Chawla, Meenakshi, Tapas Mukherjee, Alvina Deka, Budhaditya Chatterjee, Uday Aditya Sarkar, Amit K. Singh, Saurabh Kedia, et al. "An epithelial Nfkb2 pathway exacerbates intestinal inflammation by supplementing latent RelA dimers to the canonical NF-κB module." Proceedings of the National Academy of Sciences 118, no. 25 (June 21, 2021): e2024828118. http://dx.doi.org/10.1073/pnas.2024828118.
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Aberrant inflammation, such as that associated with inflammatory bowel disease (IBD), is fueled by the inordinate activity of RelA/NF-κB factors. As such, the canonical NF-κB module mediates controlled nuclear activation of RelA dimers from the latent cytoplasmic complexes. What provokes pathological RelA activity in the colitogenic gut remains unclear. The noncanonical NF-κB pathway typically promotes immune organogenesis involving Nfkb2 gene products. Because NF-κB pathways are intertwined, we asked whether noncanonical signaling aggravated inflammatory RelA activity. Our investigation revealed frequent engagement of the noncanonical pathway in human IBD. In a mouse model of experimental colitis, we established that Nfkb2-mediated regulations escalated the RelA-driven proinflammatory gene response in intestinal epithelial cells, exacerbating the infiltration of inflammatory cells and colon pathologies. Our mechanistic studies clarified that cell-autonomous Nfkb2 signaling supplemented latent NF-κB dimers, leading to a hyperactive canonical RelA response in the inflamed colon. In sum, the regulation of latent NF-κB dimers appears to link noncanonical Nfkb2 signaling to RelA-driven inflammatory pathologies and may provide for therapeutic targets.
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Kim, Youn-Su, and Gwang-Sang Yoon. "The Efficiency and Repeatability of Multispectral Module for Close-up Photography to Latent Fingerprints." Korean Academy of Scientific Criminal Investigation 15, no. 3 (September 30, 2021): 197–204. http://dx.doi.org/10.20297/jsci.2021.15.3.197.
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Geng, Hongxin, Long Lin, Pingli Li, Chunyao Zhang, and Heying Chang. "Study on the heat and mass transfer in AGMD module with latent heat recovery." Desalination and Water Treatment 57, no. 33 (July 30, 2015): 15276–84. http://dx.doi.org/10.1080/19443994.2015.1074122.
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Engels, Niklas, Mark Merchant, Rajita Pappu, Andrew C. Chan, Richard Longnecker, and Jürgen Wienands. "Epstein-Barr Virus Latent Membrane Protein 2a (Lmp2a) Employs the Slp-65 Signaling Module." Journal of Experimental Medicine 194, no. 3 (July 30, 2001): 255–64. http://dx.doi.org/10.1084/jem.194.3.255.
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In latently infected B lymphocytes, the Epstein-Barr virus (EBV) suppresses signal transduction from the antigen receptor through expression of the integral latent membrane protein 2A (LMP2A). At the same time, LMP2A triggers B cell survival by a yet uncharacterized maintenance signal that is normally provided by the antigen receptor. The molecular mechanisms are unknown as LMP2A-regulated signaling cascades have not been described so far. Using a novel mouse model we have identified the intracellular adaptor protein Src homology 2 (SH2) domain–containing leukocyte protein (SLP)-65 as a critical downstream effector of LMP2A in vivo. Biochemical analysis of the underlying signaling pathways revealed that EBV infection causes constitutive tyrosine phosphorylation of one of the two SLP-65 isoforms and complex formation between SLP-65 and the protooncoprotein CrkL (CT10 regulator of kinase like). This leads to antigen receptor-independent phosphorylation of Cbl (Casitas B lineage lymphoma) and C3G. In contrast, phospholipase C-γ2 (PLC-γ2) activation is completely blocked. Our data show that in order to establish a latent EBV infection, LMP2A selectively activates or represses SLP-65–regulated signaling pathways.
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Budiman, Alexander C., Brian Azzopardi, Sudirja, Muhammad A. P. Perdana, Sunarto Kaleg, Febriani S. Hadiastuti, Bagus A. Hasyim, et al. "Phase Change Material Composite Battery Module for Thermal Protection of Electric Vehicles: An Experimental Observation." Energies 16, no. 9 (May 4, 2023): 3896. http://dx.doi.org/10.3390/en16093896.
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A composite container for an electric vehicle (EV) battery module filled with a phase-change material (PCM) was experimentally tested at various discharge rates. The average cell temperatures at 1 C, 2 C, and 4 C discharge rates, respectively, might reach 38 °C, 50 °C, and 70 °C in the absence of any heat-absorbing material. The temperature was noticeably lower with PCM present than with a conventional battery module. For instance, at 4 C discharge rates, none of the battery cells inside the PCM-filled module were able to reach 70 °C. Unfortunately, the PCM addition also degraded the composite’s tensile qualities. Further investigations used Paraffin-20 and Caprylone since PCMs provide a notably different thermal performance due to their distinctive latent heat profiles. It was observed that a high melting temperature of the paraffin mixture, despite its slightly lower latent heat capacity compared to Caprylone, could lead to a more uniform temperature. Overall, both PCMs can be used as passive protection against any potential thermal abuses in EV battery modules, while in terms of mechanical strength, the use of a composite reinforcement material is strongly encouraged.
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Sponagle, Benjamin, Dominic Groulx, and Mary Anne White. "Experimental Evaluation of a Latent Heat Storage Module with a Heat Spreader for Thermal Management of a Tablet Computer." Applied Sciences 11, no. 9 (April 28, 2021): 3983. http://dx.doi.org/10.3390/app11093983.
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The objective of this work was to experimentally determine the feasibility of using a phase change material (PCM)-based temperature control module, in conjunction with a heat spreader and thermal interface material, to improve the thermal management of a tablet computer. An experimental apparatus was designed to be representative of a tablet computer. This mock tablet was used to perform a series of transient heating and cooling experiments to compare the impact of the PCM module on the thermal response of the system. The PCM module consisted of n-eicosane encapsulated with heat-sealable laminated film forming a 2 mm thick sheet of encapsulated PCM. A full comparison, including the use of a heat spreader and a thermal interface material (TIM), was conducted at heat generation rates of 4.5 and 7 W. The temperature control module was able to reduce the mean and peak temperatures of the internal components and at a heat generation rate of 7 W it extended its operating time by 30% before it reached a critical threshold temperature.
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Braun, Michael, and Angelika Glöckner-Rist. "Perceived consequences of female labor-force participation: a multilevel latent-class analysis across 22 countries." OBETS. Revista de Ciencias Sociales 6, no. 2 (December 15, 2011): 163. http://dx.doi.org/10.14198/obets2011.6.2.01.
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This paper investigates whether there are different patterns of traditionality in different countries with regard to a perceived negative impact of labor-force participation of mothers on their children and family life. For this purpose, individual-level traditionality subgroups and segments of countries with different traditionality patterns of their nationals were identified simultaneously by means of multilevel latent-class (ML-LC) analysis of the answers to three items of the Changing Family and Gender Roles module of the International Social Survey Program (ISSP). This module was fielded in 22 countries in the years 1994 and 2002. Six individual-level subgroups and five country segments can be discerned. The structure of individual-level subgroups is almost identical in both years. Four individual-level subgroups differ only quantitatively in their level of traditionality. Two further subgroups are characterized by a unique tendency to defend working mothers against criticism. From 1994 to 2002 the sizes of traditional subgroups decrease, and there is also some change in the composition of country segments. This paper investigates whether there are different patterns of traditionality in different countries with regard to a perceived negative impact of labor-force participation of mothers on their children and family life. For this purpose, individual-level traditionality subgroups and segments of countries with different traditionality patterns of their nationals were identified simultaneously by means of multilevel latent-class (ML-LC) analysis of the answers to three items of the Changing Family and Gender Roles module of the International Social Survey Program (ISSP). This module was fielded in 22 countries in the years 1994 and 2002. Six individual-level subgroups and five country segments can be discerned. The structure of individual-level subgroups is almost identical in both years. Four individual-level subgroups differ only quantitatively in their level of traditionality. Two further subgroups are characterized by a unique tendency to defend working mothers against criticism. From 1994 to 2002 the sizes of traditional subgroups decrease, and there is also some change in the composition of country segments.