Relevant Description Research Articles

Mediterranean islands in Ptolemy's Geography: Mapping seas through geometrization

The Geography of Claudius Ptolemy is the sole work of ancient geography that presents an easily recognizable and rather realistic depiction of the Mediterranean Sea. The article ultimately aims to explain how Ptolemy achieved such results, given the available sources and methods of his time. It explores how Ptolemy structured the space of the Mediterranean Sea, examining how he positioned the major islands (Sicily, Sardinia, Corsica, Crete, Euboea, Rhodes) and two peninsulas (the Southern Balkans and the Crimea). It is argued that Ptolemy's outlines of the Mediterranean Sea can be accounted for as a result of using three theoretical tools available in his time for map construction: the so-called klimata or reference parallels, the wind rose for determining directions, and the so-called ‘opposite places’ or coastal points presumably situated on the same meridians. The overall outlines of Ptolemy's Mediterranean, and the position of Sicily in particular, are shaped by several latitudes established by earlier geographers. Two regions of Ptolemy's map, in the western and eastern parts of the Mediterranean Sea, are clearly structured based on the 12-point Timosthenes' wind rose, centered at Ostia and Rhodes, respectively. The position of Crete is found to be linked to the African coast by means of the ‘opposite places’ concept. In several cases, the outlines of Ptolemy's map distinctly correspond and can even serve as illustrations to the relevant descriptions found in Strabo's Geography.

Sources of bias in studies reporting birth prevalence of congenital anomalies: a scoping review and reporting checklist

Sources of bias in studies reporting birth prevalence of congenital anomalies: a scoping review and reporting checklist

Data managers and clinical research coordinators: a tool to collect the hourly activity

Background and Aims: the main purpose of this paper is analyzing the hourly activities performed by Data Managers (DMs) and Clinical Research Coordinators (CRCs) in a Clinical Trial Center (CTC) of an Italian Public Hospital in order to define and monitor the relevant job descriptions of these professionals Materials and Methods: the CTC of the Research and Innovation Departments (DAIRI) has created a tool to collect the hourly activity of DMs and CRCs. In the period between January 1 and December 31, 2022; the activities were collected and grouped in 24 macro areas. Results: the main activities conducted by staff are found in the macro areas "Study Management" (15.78%); "Briefing/Planning" (12.86%) and “Data Management/Data Entry” (12.42%). A difference was found between the activities carried out by CRCs and those conducted by DMs. Conclusions: the results of this monitoring, compared with data obtained at the national level, confirm the heterogeneity of the tasks performed by these professional figures. These differences increasingly underscore the need to obtain recognition of the figure by the National Health System (NHS) with the definition of a precise “job description”.

The Effects of Short-Range Intermolecular Repulsive Forces on Hamaker Constant Estimation Using Atomic Force Microscopy.

The atomic force microscope (AFM), as it is able to characterize surface topography as well determine the AFM cantilever tip-surface force, proves effective at estimating the value of the Hamaker constant, A, for a given solid material. Two main AFM-based methods have been proposed for estimating values of A. In the approach-to-contact (AtC) method, Hamaker constants are inferred from the deflections at which the AFM tip first jumps into contact with the substrate. In the pull-off (PO) method, the deflections that arise when the AFM tip is finally pulled away from the substrate are used to estimate values of A. In prior applications of these two methods, the short-range intermolecular repulsive forces that are known to arise between the AFM tip and the substrate were, however, ignored. Upon invoking a physically relevant description of these short-range and steeply repulsive forces, we investigate the effects of repulsive interactions on the values of A estimated from the existing AtC and PO methods when applied to neutrally charged systems under low humidity or vacuum conditions. For experimentally relevant surfaces, we find that repulsive forces have a modest effect on the AtC method, although they still need to be accounted for in order to generate improved estimates of the Hamaker constant. On the other hand, repulsive forces have a significant effect on the Hamaker constants inferred from the PO method, and must be properly accounted for when using this approach. Our analysis also includes an explicit incorporation of surface roughness into the PO method, which is not typically done in most prior applications of the PO method.

Effect Of Consumer Education By Consumer Federation Of Kenya On Consumer Protection In Commercial Banks Of Kenya

Background: In many jurisdictions, consumer protection law has not been enacted despite the financial services industry's development. This means that in order to boost client confidence and promote the adoption of innovative products, financial services clients must have access to fundamental protections. The prevalence of uninformed customers who rely on financial institutions for their information has led to a rise in the misuse of consumer confidence. Abuse by banks and its marketers, such as imposing exorbitant charges on clients' accounts without providing formal advice, is becoming more widespread. The purpose of the study was to ascertain the measures taken by the Consumer Federation of Kenya to ensure adequate access to information by Kenyan commercial banks customers in an effort to ensure their protection. approach was taken to get in touch with the COFEK staff. Methodology: Questionnaires were utilized in the study to collect data. Both inferential and descriptive statistics were used to analyze the data. Version 22 of the Statistical Package for Social Sciences (SPSS) was employed. Frequency tables, percentages, and relevant statistical descriptions were used to display the data. Results: Most of the respondents were fairly aware of the consumer education activities carried out by the protection agency in commercial banks. Consumer education had a significant coefficient (p-value = 0.0001; β = 0.469) according to the data analysis. Consumer education is statistically significant in ensuring consumer protection. Conclusion: COFEK should enhance the sensitization programs put in place for clients with a view of making them more informed. Public drives to raise the awareness levels should be carried out.

VITR: Augmenting Vision Transformers with Relation-Focused Learning for Cross-modal Information Retrieval

The relations expressed in user queries are vital for cross-modal information retrieval. Relation-focused cross-modal retrieval aims to retrieve information that corresponds to these relations, enabling effective retrieval across different modalities. Pre-trained networks, such as Contrastive Language-Image Pre-training networks, have gained significant attention and acclaim for their exceptional performance in various cross-modal learning tasks. However, the Vision Transformer (ViT) used in these networks is limited in its ability to focus on image region relations. Specifically, ViT is trained to match images with relevant descriptions at the global level, without considering the alignment between image regions and descriptions. This article introduces VITR, a novel network that enhances ViT by extracting and reasoning about image region relations based on a local encoder. VITR is comprised of two key components. Firstly, it extends the capabilities of ViT-based cross-modal networks by enabling them to extract and reason with region relations present in images. Secondly, VITR incorporates a fusion module that combines the reasoned results with global knowledge to predict similarity scores between images and descriptions. The proposed VITR network was evaluated through experiments on the tasks of relation-focused cross-modal information retrieval. The results derived from the analysis of the Flickr30K, MS-COCO, RefCOCOg, and CLEVR datasets demonstrated that the proposed VITR network consistently outperforms state-of-the-art networks in image-to-text and text-to-image retrieval.

Contributions to Astragalus sect. Dissitiflori in the light of morphological and molecular evidence: a new species and a new resurrect

Astragalus sublunatus is described as a new species from semi desert area of Semnan and Tehran provinces. The diagnostic characters and relevant description of the new species are given. It is distinguished from its closely related species, A. sitiens and A. ahangarensis, in having plicate inward leaflets, long and linear-lanceolate terminal leaflet and white petals in living time. It is also distinct from these species by molecular data, and along with them as well as A. bifoliolatus and A. moussavii formed a well-supported lineage. The distribution map and an illustration of the new species are also provided. Meanwhile, three accessions of A. sitiens were phylogenetically distinct from the rest of its conspecific members. They are morphologically well fitted with the characteristics of A. angustatus and thus, it was resurrected as a distinct species.

Sentinel mechanism for visual semantic graph-based image captioning

Image captioning aims to generate a description of a given image. However, inherent representation differences between images and sentences make it difficult to align semantic meanings for captioning. Inspired by the human cognitive processes of understanding and describing images, a visual semantic sentinel mechanism based image captioning framework is proposed in this paper. Specifically, we introduce attribute nodes to enable a more comprehensive description of the objects and model the high-level relationships within a visual semantic graph. Then, the visual semantic sentinel mechanism is proposed to simulate the process of sentence generation. visual semantic graphs, visual features and previous language information in generated words are integrated with a semantic sentinel mechanism to align vision-language information and get contextually relevant descriptions of images. Comprehensive experiments on the challenging MS-COCO demonstrate our method outperforms the previous state-of-the-art methods. The code is publicly available at https://github.com/superatops/SSVSG.

Non-Markovian quantum mechanics on comb.

Quantum dynamics of a particle on a two-dimensional comb structure is considered. This dynamics of a Hamiltonian system with a topologically constrained geometry leads to the non-Markovian behavior. In the framework of a rigorous analytical consideration, it is shown how a fractional time derivative appears for the relevant description of this non-Markovian quantum mechanics in the framework of fractional time Schrödinger equations. Analytical solutions for the Green functions are obtained for both conservative and periodically driven in time Hamiltonian systems.

Joint Imbalance Adaptation for Radiology Report Generation.

Radiology report generation, translating radiological images into precise and clinically relevant description, may face the data imbalance challenge - medical tokens appear less frequently than regular tokens; and normal entries are significantly more than abnormal ones. However, very few studies consider the imbalance issues, not even with conjugate imbalance factors. In this study, we propose a Joint Imbalance Adaptation (JIMA) model to promote task robustness by leveraging token and label imbalance. JIMA predicts entity distributions from images and generates reports based on these distributions and image features. We employ a hard-to-easy learning strategy that mitigates overfitting to frequent labels and tokens, thereby encouraging the model to focus more on rare labels and clinical tokens. JIMA shows notable improvements (16.75% - 50.50% on average) across evaluation metrics on IU X-ray and MIMIC-CXR datasets. Our ablation analysis proves that JIMA's enhanced handling of infrequent tokens and abnormal labels counts the major contribution. Human evaluation and case study experiments further validate that JIMA can generate more clinically accurate reports. Data imbalance (e.g., infrequent tokens and abnormal labels) leads to the underperformance of radiology report generation. Our curriculum learning strategy successfully reduce data imbalance impacts by reducing overfitting on frequent patterns and underfitting on infrequent patterns. While data imbalance remains challenging, our approach opens new directions for the generation task.

Attribute-Driven Filtering: A new attributes predicting approach for fine-grained image captioning

Fine-grained image captioning with attribute information has garnered significant attention in the realms of computer vision and natural language processing, demanding precise and contextually relevant descriptions of visual content. While previous attribute-driven image captioning models have shown improvements, challenges remain, such as the independence of attribute predictors and caption generators and the semantic gap between images and attributes. Another common issue is the inclusion of all attributes at every time step, despite most attributes being irrelevant to the word currently being generated. This can divert the model’s attention toward erroneous semantic details, resulting in a performance decline. To address these issues, we propose a novel Attribute-Driven Filtering (ADF) captioning network designed to provide rich and nuanced descriptions. This model incorporates a unique Attribute Predictor Module (APM) that dynamically predicts the most pertinent attributes in accordance with the textual context, utilizing different attributes at various time steps. The novelty of this approach lies in recognizing that not all attributes hold equal relevance at each time step, and the APM filters out irrelevant attributes to generate precise and contextually relevant captions. Furthermore, this model features a fusion mechanism that integrates visual information from a conventional attention module with attribute information predicted by the APM, aiming to reduce the visual semantic gap between images and attributes. Extensive experimentation demonstrates that the ADF model outperforms advanced models, achieving impressive CIDEr-D scores of 72.0 (Flickr30K) and 123.3 (MS-COCO) through reinforcement learning optimization. It consistently surpasses baseline models across diverse evaluation metrics, highlighting its effectiveness and robustness.

Rediscovering Bruegel: Art and History in Michael Frayn’s Headlong

British playwright and novelist Michael Frayn’s 1999 novel Headlong centers on an art historian’s discovery of the lost sixth painting from the collection Months or Seasons that Flemish painter, Pieter Bruegel is believed to have painted in 1565. Throughout the novel, driven by a quest to authenticate and possess the painting, the protagonist Martin Clay is engrossed in a thoughtful and thorough examination of Bruegel’s art renowned for busy tableaus of peasants, harvesters, hunters engaged in daily and rural activities. From religious conflicts, biblical conventions to allegorical landscapes, labors of the months within the framework of changing seasons, Bruegel’s artistic milieu has been interpreted and elicited in diverse and opposite ways. For that reason, Clay’s speculations about the political, intellectual and symbolic history behind the painting are not only the substance of the book, but the lengthy and diligent research he takes on also provides historical backdrop of the Netherlands in early sixteenth century, a time of atrocity and oppression as a result of Spanish rule. In view of that, this study aims to examine and understand how Martin’s journey turns into a headlong venture carrying thematic elements found in Bruegel’s works delving into the complexities of human behavior. Frayn not only brings the missing masterpiece to life with relevant descriptions projecting Bruegel’s idiosyncratic style and legacy, he also explicates its iconography-iconology in a political, and art historical context showing affinity with the narrative and artistic traditions of Dutch genre paintings.

The Relationship between Faith and Knowledge in the Works of Mykhailo Maksymovych

The paper deals with images of faith and knowledge in the works of Mykhailo Maksymovych, a famous botanist, folklorist, and historian of the 19th century and the first rector of the University of Saint Volodimir in Kyiv. Mykhailo Maksymovych’s way of solving the problem of the relationship between religion and science is analysed in the general context of the intellectual processes in Eastern Europe of the 19th century. The study is based on Mykhailo Maksymovych’s published works, memoirs, letters, and unpublished texts, held in the Institution of Manuscript at the Vernadsky National Library of Ukraine (Kyiv). The methodological foundation is the approaches of the Cambridge School of Intellectual History, theorising on cultural memory and quantitative content analysis with MAXQDA-2022. The paper shows that Mykhailo Maksymovych’s attitude to the demarcation problem of knowledge and faith resulted from a combination of his personal religiosity and his fascination with the ideas of Friedrich Schelling. Mykhailo Maksymovych perceived the Bible as a relevant description of the “factual” dimension of human history. He represented the philosophy of the heart, widespread in Ukrainian intellectual life of that period. Maksymovych’s deep personal religiosity, combined with his theoretical ideas about the correlation between faith and knowledge, led him to the idea of Orthodox coherence between Russia and Ukraine. This was an actualisation of the early modern idea, elaborated in the Kyivan Synopsis of the late 17th century. Mykhailo Maksymovych actualised these ideas on the basis of Romanticism. Early modern ideas were close to Maksymovych’s consciousness because he was religious in the traditional Orthodox sense. Religious images of Ukraine in the works of Mykhailo Maksymovych were similar to the ideas of Konstantin Leontiev, a famous Russian conservative philosopher of the second half of the 19th century.

SNP genotyping Dutch heritage apple cultivars allows for germplasm characterization, curation, and pedigree reconstruction using genotypic data from multiple collection sites across the world

The curation and preservation of Dutch apple germplasm depends on reliable accession level information. However, many accessions of Dutch heirloom apple cultivars maintained publicly by the “Centre for Genetic Resources, the Netherlands” (CGN) and privately by Dutch pomological societies lack information regarding true-to-typeness and pedigree ancestry. The aim of this study was to address this knowledge gap by genotyping 652 apple accessions maintained in the CGN collection and Dutch private collections, compare their genotypic information to each other and to a large database of apple cultivars from around the world to identify genotypic duplicates and pedigree relationships for the Dutch apple cultivars. Towards this aim, accessions were genotyped on the 20 K Illumina Infinium(R) apple SNP array and with 15 SSR markers. Each accession was assigned to a genotypic profile code (MUNQ codes, as used in previous studies) facilitating communication regarding genotypic duplicates. There were 211 (51.1%) genotypic profiles in the Dutch germplasm which were not identified in other germplasm collections. Private collections maintained many of these unique accessions, including important pedigree ancestors. The study identified a number of common pedigree ancestors of Dutch cultivars, such as ‘Herfst Bloem Soete’, ‘Huismanszoet’ (2), and ‘Reinette Rouge Étoilée’. The duplicate and pedigree reconstruction results and relevant literature descriptions were used to pomologically verify the identity of relevant accessions. The results of this study resolved identity disputes, helped to decide which accessions should be retained or included in the CGN collection, and benefited ongoing pomological studies in ancestry and provenance of traditional Dutch cultivars.

SAILoR: Structure-Aware Inference of Logic Rules.

Boolean networks provide an effective mechanism for describing interactions and dynamics of gene regulatory networks (GRNs). Deriving accurate Boolean descriptions of GRNs is a challenging task. The number of experiments is usually much smaller than the number of genes. In addition, binarization leads to a loss of information and inconsistencies arise in binarized time-series data. The inference of Boolean networks from binarized time-series data alone often leads to complex and overfitted models. To obtain relevant Boolean models of gene regulatory networks, inference methods could incorporate data from multiple sources and prior knowledge in terms of general network structure and/or exact interactions. We propose the Boolean network inference method SAILoR (Structure-Aware Inference of Logic Rules). SAILoR incorporates time-series gene expression data in combination with provided reference networks to infer accurate Boolean models. SAILoR automatically extracts topological properties from reference networks. These can describe a more general structure of the GRN or can be more precise and describe specific interactions. SAILoR infers a Boolean network by learning from both continuous and binarized time-series data. It navigates between two main objectives, topological similarity to reference networks and correspondence with gene expression data. By incorporating the NSGA-II multi-objective genetic algorithm, SAILoR relies on the wisdom of crowds. Our results indicate that SAILoR can infer accurate and biologically relevant Boolean descriptions of GRNs from both a static and a dynamic perspective. We show that SAILoR improves the static accuracy of the inferred network compared to the network inference method dynGENIE3. Furthermore, we compared the performance of SAILoR with other Boolean network inference approaches including Best-Fit, REVEAL, MIBNI, GABNI, ATEN, and LogBTF. We have shown that by incorporating prior knowledge about the overall network structure, SAILoR can improve the structural correctness of the inferred Boolean networks while maintaining dynamic accuracy. To demonstrate the applicability of SAILoR, we inferred context-specific Boolean subnetworks of female Drosophila melanogaster before and after mating.

Animal Pose Estimation Based on Contrastive Learning with Dynamic Conditional Prompts.

Traditional animal pose estimation techniques based on images face significant hurdles, including scarce training data, costly data annotation, and challenges posed by non-rigid deformation. Addressing these issues, we proposed dynamic conditional prompts for the prior knowledge of animal poses in language modalities. Then, we utilized a multimodal (language-image) collaborative training and contrastive learning model to estimate animal poses. Our method leverages text prompt templates and image feature conditional tokens to construct dynamic conditional prompts that integrate rich linguistic prior knowledge in depth. The text prompts highlight key points and relevant descriptions of animal poses, enhancing their representation in the learning process. Meanwhile, transformed via a fully connected non-linear network, image feature conditional tokens efficiently embed the image features into these prompts. The resultant context vector, derived from the fusion of the text prompt template and the image feature conditional token, generates a dynamic conditional prompt for each input sample. By utilizing a contrastive language-image pre-training model, our approach effectively synchronizes and strengthens the training interactions between image and text features, resulting in an improvement to the precision of key-point localization and overall animal pose estimation accuracy. The experimental results show that language-image contrastive learning based on dynamic conditional prompts enhances the average accuracy of animal pose estimation on the AP-10K and Animal Pose datasets.

Histologic Examination of Mastectomy Scars during Breast Reconstruction: A Systematic Review.

Breast reconstruction is a standard procedure in postmastectomy plastic surgery. The necessity of routine histological examinations for mastectomy scars during delayed reconstruction remains a topic of debate. We evaluated the need for histological examination of scars during delayed breast reconstruction. We conducted a systematic review using PubMed, TDnet, and Cochrane Central in August 2023. Inclusion criteria involved delayed breast reconstruction with histological scar analysis and malignancy reporting. Exclusion criteria encompassed noncancerous breast diseases, prophylactic mastectomies, articles lacking relevant information, case reports, technique descriptions, and reviews. We independently assessed articles. Differences in recurrence rates were determined using a Z-test for proportions. A linear regression model explored the relationship between reconstruction timing and pathological results. The number needed to treat was calculated based on the literature. The Wilcoxon test was used to compare mean reconstruction times and postreconstruction follow-up between groups. Our analysis covered 11 retrospective observational studies published between 2003 and 2018, including 3754 mastectomy scars. The malignancy recurrence rate was 0.19%, consistent with previous reports, with a number needed to treat of 144.93-188.68 patients. The timing of breast reconstruction postmastectomy averaged 19.9 months, without statistically significant association between reconstruction timing and recurrence rates. Postreconstruction follow-up periods ranged from 60 to 87 months. The postreconstruction adverse outcomes ratio was 2.21%. Assessing the necessity of histological examination in breast reconstruction is complex. Based on the literature and this study, we do not recommend routine histological examination of mastectomy scars during delayed reconstruction. A selective approach based on risk factors may be beneficial, warranting further research.

Development of a trend forecasting model for environmental pollution monitoring

A complex model for forecasting time series of environmental pollution indicators is described, considering the aggregation of various forecasting models, which are formed based on predictive statistical analysis of pollution indicators and have an adaptive nature. The model differs from known models by providing the possibility of adapting the model parameters to changes in the state of the environment, which is especially important in the conditions of using such models in monitoring systems. Th e complex forecasting model includes higher-order exponential smoothing, Holt, Winters, moving average, weighted moving average, and autoregression models. All the parameters set in these models are related to the Hurst index, which is calculated based on predictive fractal statistical analysis of the time series. Relevant descriptions and justifications are given. Using such a model as part of the econometric system will help predict and respond more effectively to possible changes in the values of pollution parameters. In particular, the persistence of the time series of pollution parameters can mean a stable trend of increasing or decreasing pollution. Suppose the time series becomes close to random or ergodic. In that case, this may mean an emergency or additional erratic emissions in the region that must be monitored. The described model is a forecasting model that is part of the system for monitoring environmental pollution parameters. In the future, a model for forecasting the pollution level in different regions of the People's Republic of China will be implemented.

Math Word Problem Generation via Disentangled Memory Retrieval

The task of math word problem (MWP) generation, which generates an MWP given an equation and relevant topic words, has increasingly attracted researchers’ attention. In this work, we introduce a simple memory retrieval module to search related training MWPs, which are used to augment the generation. To retrieve more relevant training data, we also propose a disentangled memory retrieval module based on the simple memory retrieval module. To this end, we first disentangle the training MWPs into logical description and scenario description and then record them in respective memory modules. Later, we use the given equation and topic words as queries to retrieve relevant logical descriptions and scenario descriptions from the corresponding memory modules, respectively. The retrieved results are then used to complement the process of the MWP generation. Extensive experiments and ablation studies verify the superior performance of our method and the effectiveness of each proposed module. The code is available at https://github.com/mwp-g/MWPG-DMR .

Select and Augment: Enhanced Dense Retrieval Knowledge Graph Augmentation (Abstract Reprint)

Injecting textual information into knowledge graph (KG) entity representations has been a worthwhile expedition in terms of improving performance in KG oriented tasks within the NLP community. External knowledge often adopted to enhance KG embeddings ranges from semantically rich lexical dependency parsed features to a set of relevant key words to entire text descriptions supplied from an external corpus such as wikipedia and many more. Despite the gains this innovation (Text-enhanced KG embeddings) has made, the proposal in this work suggests that it can be improved even further. Instead of using a single text description (which would not sufficiently represent an entity because of the inherent lexical ambiguity of text), we propose a multi-task framework that jointly selects a set of text descriptions relevant to KG entities as well as align or augment KG embeddings with text descriptions. Different from prior work that plugs formal entity descriptions declared in knowledge bases, this framework leverages a retriever model to selectively identify richer or highly relevant text descriptions to use in augmenting entities. Furthermore, the framework treats the number of descriptions to use in augmentation process as a parameter, which allows the flexibility of enumerating across several numbers before identifying an appropriate number. Experiment results for Link Prediction demonstrate a 5.5% and 3.5% percentage increase in the Mean Reciprocal Rank (MRR) and Hits@10 scores respectively, in comparison to text-enhanced knowledge graph augmentation methods using traditional CNNs.