Relevant Description Research Articles

A comprehensive review of the video-to-text problem

Research in the Vision and Language area encompasses challenging topics that seek to connect visual and textual information. When the visual information is related to videos, this takes us into Video-Text Research, which includes several challenging tasks such as video question answering, video summarization with natural language, and video-to-text and text-to-video conversion. This paper reviews the video-to-text problem, in which the goal is to associate an input video with its textual description. This association can be mainly made by retrieving the most relevant descriptions from a corpus or generating a new one given a context video. These two ways represent essential tasks for Computer Vision and Natural Language Processing communities, called text retrieval from video task and video captioning/description task. These two tasks are substantially more complex than predicting or retrieving a single sentence from an image. The spatiotemporal information present in videos introduces diversity and complexity regarding the visual content and the structure of associated language descriptions. This review categorizes and describes the state-of-the-art techniques for the video-to-text problem. It covers the main video-to-text methods and the ways to evaluate their performance. We analyze twenty-six benchmark datasets, showing their drawbacks and strengths for the problem requirements. We also show the progress that researchers have made on each dataset, we cover the challenges in the field, and we discuss future research directions.

CHAKSINI (PERISTROPHE BICALYCULATA (RETZ) NEES.: A COMPREHENSIVE REVIEW ON A LESSER-KNOWN HERB OF UNANI MEDICINE

Peristrophe bicalyculata (Retz.) Nees. family Acanthaceae is a lesser-known drug of Unani Medicine but commonly used drug in Ayurvedic medicine. The plant is found throughout India. In Aligarh and around it is locally called as “Chaksini”. In Unani medicine it is mentioned by the name of “Massi” and “Kakajangha”. The most relevant description of the plant is mentioned as “Massi”. It is used for the treatment of different ailments e.g. fever, cold, cough, insomnia, headache, leucorrhea etc. and also for snake poison and sprain. Preliminary neuropharmacological screening e.g. anticonvulsant, narcotic, CNS depressant activities of the plant have been done. The plant has also evaluated for its antibacterial, antidiabetic and antilipidaemic activities. This review is prepared to explore the identifying characteristics, ethanomedicinal importance and therapeutic uses of lesser known herb of Unani Medicine.

Hierarchical nanostructured MnF2 fabricated using rapid microwave synthesis as abnormal high-capacity of anode materials for Li-ion batteries

Tailoring various architectures of electrode material has been deemed a feasible route for improving the surface reactivity, the contact area between electrode and electrolyte, and enabling shortened electronic pathways to realize higher performance lithium-ion batteries. Here, we rapidly synthesized the manganese fluoride (MnF2) with various morphologies by microwave irradiation using ionic liquid as the fluoride source, soft template, and microwave absorber solvent. The hierarchical nanostructured MnF2 were optimized by controlling the purity of ionic liquid, microwave heating rate, and precursor concentration. The dominant factors influencing the morphologies of MnF2 were systematically investigated and relevant descriptions for the formation mechanism were suggested. Also, we demonstrated the abnormal high-capacity lithium-ion battery anode using the hierarchical nanostructured MnF2 anode based on the conversion mechanism. The prepared MnF2 anode yielded a maximum reversible specific capacity of 986 mAhg−1 at 0.1C after 30 cycles. This study provides a comprehensive understanding of the morphological control of the hierarchical structured MnF2 using the microwave-ionic liquid combination method for high-capacity lithium-ion battery anode.

Applications and Effects of EdTech in Medical Education

Rapid developments in technology as part of the Fourth Industrial Revolution have created a demand for educational technology (EdTech) and a gradual transition from traditional teaching and learning to EdTech-assisted learning in medical education. EdTech is a portmanteau (blended word) combining the concepts of education and technology, and it refers to various attempts to solve education-related problems through information and communication technology. The aim of this study was to explore the use of key EdTech applications in medical education programs. A scoping review was conducted by searching three databases (PubMed, CINAHL, and Educational Sources) for articles published from 2000 to June 2021. Twenty-one studies were found that presented relevant descriptions of the effectiveness of EdTech in medical education programs. Studies on the application and effectiveness of EdTech were categorized as follows: (1) artificial intelligence with learner-adaptive evaluation and feedback, (2) augmented/virtual reality for improving learning participation and academic achievement through immersive learning, and (3) social media/social networking services with learner-directed knowledge generation, sharing, and dissemination in medical communities. Although this review reports the effectiveness of EdTech in various medical education programs, the number of studies and the validity of the identified research designs are insufficient to confirm the educational effects of EdTech. Future studies should utilize suitable research designs and examine the instructional objectives achievable by EdTech-based applications to strengthen the evidence base supporting the application of EdTech by medical educators and institutions.

Transformer-Based Patent Novelty Search by Training Claims to Their Own Description

In this paper we present a method to concatenate patent claims to their own description. By applying this method, bidirectional encoder representations from transformers (BERT) train suitable descriptions for claims. Such a trained BERT could be able to identify novelty relevant descriptions for patents. In addition, we introduce a new scoring scheme: relevance score or novelty score to interprete the output of BERT. We test the method on patent applications by training BERT on the first claims of patents and corresponding descriptions. The output is processed according to the relevance score and the results compared with the cited X documents in the search reports. The test shows that BERT score some of the cited X documents as highly relevant.

Nutritional status determines apparent assimilative capacity and functional response of marine predatory fish

Abstract Marine predatory fish face unpredictable prey environments, ranging from abundance to scarcity of food. Dimensioning their assimilative system to accommodate gorging and fasting is therefore a central life history choice. Assimilative capacity experiments typically operate with sustained feeding to satiation, and therefore ignore the fluctuations in natural feeding opportunities. A more relevant description of the adaptive response is the episodic capacity associated with binge feeding (hyperphagia). We develop the theoretical foundation to define episodic and sustained capacity and its allometry. Extensive empirical evidence on marine piscivorous fish at higher latitudes confirms that the episodic capacity scales almost linearly with predator body mass (exponent approximately 0.95), producing an increasing factorial hyperphagic scope (exponent approximately 0.20). Our synthesis overturns the reigning steady state perspective on assimilative capacity. The fish can utilize an episodic capacity, typically twice the size of the sustained capacity, resulting in local dynamics of functional responses with profound implications for scaling-up to ecosystem level.

Overview on Image Captioning Techniques

Image captioning is a process to assign a meaningful title for a given image with the help of Natural Language Processing (NLP) and Computer Vision techniques. Captioning of an image first need to identify object, attribute and relationship among these in image and second is to generate relevant description for the given image. So it require both NLP and Computer vision techniques to perform image captioning task. Due to complexity of finding relationship between the attribute of the object and its feature makes it a challenging task. Also for machine it is difficult to emulate human brain however researches have shown a prominent achievement in this field and made it easy to solve such problems. The foremost aim of this survey paper is to describe several methods to achieve the same, the core involvement of this paper is to categorise different existing approaches for image captioning, further discussed their subcategories of this method and classify them, also discussed some of their strength and limitations. This survey paper gives theoretical analysis of image captioning methods and defines some earlier and newly approach for image captioning. This survey paper is basically a source of information for researchers in order to get idea of different approaches that were developed so far in the field of image captioning. Key words : Computer Vision, Deep Learning, Neural Network, NLP, Image Captioning, Multimodal Learning.

Industrially relevant Radioactive Particle Tracking study on the motion of adsorbent granules suspended in a pilot-scale water–air three-phase fluidized bed

The information obtained by one-dimensional Radioactive Particle Tracking is used to make an industrially relevant description of the macroscopic mixing of a three-phase bubble column. The objective is to characterize and compare the solid motion of granular activated carbon (dp = 1 mm) and calcium alginate beads (dp = 5 mm), widely used as adsorbents for pollutants and as support for catalysts, enzymes, and living organisms. The particles are suspended in water with upflowing air into a 0.1 m diameter and a 1 m height bubble column. The liquid–solid suspensions are in batch, and the air superficial velocity ranges within 0.01–0.12 m/s. The solid axial hold-up distribution of the carbon–water–air system is biased towards the bottom of the column, while the alginate–water–air system is more even. The axial dispersion coefficients obtained in both systems have a positive linear behavior within the operating range, which is uncannily marked in the carbon-water-air system, and it is an order of magnitude less than the alginate–water–air system. The contrasts of the two systems are mostly explained as a function of the solid densities. Axial mixing time distribution shows a sharp minimum at the second quartile of the column. Flow regime transition is assessed using information theory.

On the Limitations of Visual-Semantic Embedding Networks for Image-to-Text Information Retrieval.

Visual-semantic embedding (VSE) networks create joint image–text representations to map images and texts in a shared embedding space to enable various information retrieval-related tasks, such as image–text retrieval, image captioning, and visual question answering. The most recent state-of-the-art VSE-based networks are: VSE++, SCAN, VSRN, and UNITER. This study evaluates the performance of those VSE networks for the task of image-to-text retrieval and identifies and analyses their strengths and limitations to guide future research on the topic. The experimental results on Flickr30K revealed that the pre-trained network, UNITER, achieved 61.5% on average Recall@5 for the task of retrieving all relevant descriptions. The traditional networks, VSRN, SCAN, and VSE++, achieved 50.3%, 47.1%, and 29.4% on average Recall@5, respectively, for the same task. An additional analysis was performed on image–text pairs from the top 25 worst-performing classes using a subset of the Flickr30K-based dataset to identify the limitations of the performance of the best-performing models, VSRN and UNITER. These limitations are discussed from the perspective of image scenes, image objects, image semantics, and basic functions of neural networks. This paper discusses the strengths and limitations of VSE networks to guide further research into the topic of using VSE networks for cross-modal information retrieval tasks.

Driving Oscillatory Dynamics: Neuromodulation for Recovery After Stroke.

Stroke is a leading cause of death and disability worldwide, with limited treatments being available. However, advances in optic methods in neuroscience are providing new insights into the damaged brain and potential avenues for recovery. Direct brain stimulation has revealed close associations between mental states and neuroprotective processes in health and disease, and activity-dependent calcium indicators are being used to decode brain dynamics to understand the mechanisms underlying these associations. Evoked neural oscillations have recently shown the ability to restore and maintain intrinsic homeostatic processes in the brain and could be rapidly deployed during emergency care or shortly after admission into the clinic, making them a promising, non-invasive therapeutic option. We present an overview of the most relevant descriptions of brain injury after stroke, with a focus on disruptions to neural oscillations. We discuss the optical technologies that are currently used and lay out a roadmap for future studies needed to inform the next generation of strategies to promote functional recovery after stroke.

ORGANIZATIONAL JUSTICE PERCEPTION AND JOB BURNOUT AMONG NURSES AT PORT SAID SELECTED HOSPITALS

Background: Organization justice alludes to the degree to which nurses perceive working environment strategy, cooperation, and results to be reasonable, a major result of low levels of justice in organizations is increasing levels of job burnout among nurses. Study aim: is to investigate organizational justice perception and their relation to job burnout among nurses at Port Saidselected Hospitals. Subjects and Method: Descriptive correlational examination configuration was led in all inpatient divisions at Port-Saidselected Hospitals on 206 nurses. Data collection tools incorporated Colquitt's Organizational Justice Scale and Maslach Burnout Inventory and content validated by a panel of specialists. Results: The study results revealed that around half of nurses had a moderate degree of overall organizational justice and more than one third of nurses had moderate levels of emotional exhaustion. Additionally, less than three quarters of nurses had low levels of depersonalization. Likewise, overall organizational justice and it's all dimensions has a statistical significant negative correlation with total job burnout. Conclusion and recommendations: Nurses' perceived moderate degree of organizational justice, interactional justice scored the highest percentage. Whereas, a moderate level of burnout. Negative significant correlation between organizational justice and job burnout. However, negative significant correlations between all organizational justice and its dimensions with emotional exhaustion, depersonalization and total job burnout. The study recommends that a performance appraisal system with relevant job description should be applied to distributive justice by nurse manager.

A multicentric study on stigma towards people with mental illness in health sciences students

BackgroundThere is evidence of negative attitudes among health professionals towards people with mental illness but there is also a knowledge gap on what training must be given to these health professionals during their education. The purpose of this study is to compare the attitudes of students of health sciences: nursing, medical, occupational therapy, and psychology.MethodsA comparative and cross-sectional study in which 927 final-year students from health sciences university programmes were evaluated using the Mental Illness: Clinicians’ Attitudes (both MICA-2 and MICA-4) scale. The sample was taken in six universities from Chile and Spain.ResultsWe found consistent results indicating that stigma varies across university programmes. Medical and nursing students showed more negative attitudes than psychology and occupational therapy students in several stigma-related themes: recovery, dangerousness, uncomfortability, disclosure, and discriminatory behaviour.ConclusionsOur study presents a relevant description of the attitudes of each university programme for education against stigma in the formative years. Results show that the biomedical understanding of mental disorders can have negative effects on attitudes, and that education based on the psychosocial model allows a more holistic view of the person over the diagnosis.

Reinstatement of the Extension Principle in Approaching Mathematical Programming with Fuzzy Numbers

Optimization problems in the fuzzy environment are widely studied in the literature. We restrict our attention to mathematical programming problems with coefficients and/or decision variables expressed by fuzzy numbers. Since the review of the recent literature on mathematical programming in the fuzzy environment shows that the extension principle is widely present through the fuzzy arithmetic but much less involved in the foundations of the solution concepts, we believe that efforts to rehabilitate the idea of following the extension principle when deriving relevant fuzzy descriptions to optimal solutions are highly needed. This paper identifies the current position and role of the extension principle in solving mathematical programming problems that involve fuzzy numbers in their models, highlighting the indispensability of the extension principle in approaching this class of problems. After presenting the basic ideas in fuzzy optimization, underlying the advantages and disadvantages of different solution approaches, we review the main methodologies yielding solutions that elude the extension principle, and then compare them to those that follow it. We also suggest research directions focusing on using the extension principle in all stages of the optimization process.

Toward an Electrical Analog of the Cardiovascular System in Hemorrhage.

Current quantitative descriptions of the cardiovascular system in hemorrhagic shock focus on pressure based metrics. This approach is often incomplete; overlooking the important role of tissue perfusion. Electrical analogs to the cardiovascular system may offer a more complete description of hemorrhage. Application of fundamental concepts in electrical circuit theory (i.e.; Kirchhoff's Voltage Law and Ohm's Law) to analogs of the cardiovascular system offers a more refined description of this complex process. This manuscript hopes to serve as a starting point for a more mathematically robust, and clinically relevant description of hemorrhagic shock.

Learning to Check Contract Inconsistencies

Contract consistency is important in ensuring the legal validity of the contract. In many scenarios, a contract is written by filling the blanks in a precompiled form. Due to carelessness, two blanks that should be filled with the same (or different) content may be incorrectly filled with different (or same) content. This will result in the issue of contract inconsistencies, which may severely impair the legal validity of the contract. Traditional methods to address this issue mainly rely on manual contract review, which is labor-intensive and costly. In this work, we formulate a novel Contract Inconsistency Checking (CIC) problem, and design an end-to-end framework, called Pair-wise Blank Resolution (PBR), to solve the CIC problem with high accuracy. Our PBR model contains a novel BlankCoder to address the challenge of modeling meaningless blanks. BlankCoder adopts a two-stage attention mechanism that adequately associates a meaningless blank with its relevant descriptions while avoiding the incorporation of irrelevant context words. Experiments conducted on real-world datasets show the promising performance of our method with a balanced accuracy of 94.05% and an F1 score of 90.90% in the CIC problem.

Flood4castRTF: A Real-Time Urban Flood Forecasting Model

Worldwide, climate change increases the frequency and intensity of heavy rainstorms. The increasing severity of consequent floods has major socio-economic impacts, especially in urban environments. Urban flood modelling supports the assessment of these impacts, both in current climate conditions and for forecasted climate change scenarios. Over the past decade, model frameworks that allow flood modelling in real-time have been gaining widespread popularity. Flood4castRTF is a novel urban flood model that applies a grid-based approach at a modelling scale coarser than most recent detailed physically based models. Automatic model set-up based on commonly available GIS data facilitates quick model building in contrast with detailed physically based models. The coarser grid scale applied in Flood4castRTF pursues a better agreement with the resolution of the forcing rainfall data and allows speeding up of the calculations. The modelling approach conceptualises cell-to-cell interactions while at the same time maintaining relevant and interpretable physical descriptions of flow drivers and resistances. A case study comparison of Flood4castRTF results with flood results from two detailed models shows that detailed models do not necessarily outperform the accuracy of Flood4castRTF with flooded areas in-between the two detailed models. A successful model application for a high climate change scenario is demonstrated. The reduced data need, consisting mainly of widely available data, makes the presented modelling approach applicable in data scarce regions with no terrain inventories. Moreover, the method is cost effective for applications which do not require detailed physically based modelling.

Anatomical Emphasis for the Latissimus Dorsi Myocutaneous Flap for Breast Reconstructive Surgery Patient Education

Introduction & Objective Reconstructive surgeries among breast cancer survivors continue to increase due to public recognition of the disease and ever-increasing diagnoses. Multiple reconstructive techniques are presented to patients, each with indications relating to disease severity and patient presentation. The latissimus dorsi myocutaneous (LD) flap is one of several viable options for breast reconstruction with minimal post-operative complications and provides a natural-looking breast and sensation recovery. However, patient education materials with detailed anatomical illustrations are lacking. The objective of this study is to provide original patient education materials with detailed anatomical illustrations for breast reconstructive surgery, specifically the latissimus dorsi myocutaneous flap. Materials and Methods An extensive review of literature was conducted using a critical interpretive synthesis to obtain a detailed anatomical description of the LD flap as well as its indications for patient populations and post-operative complications. Results Articles containing relevant surgical descriptions of the LD flap procedure and the appropriate anatomy were used for incorporation in this review. Qualitative data analysis of literature revealed indications and anatomical details for original creation of illustrations for patient education. The LD flap is a useful graft for women with low BMI who wish to have a scar easily covered by undergarments. The LD flap is a skin, muscle, and fat flap supplied by the thoracodorsal artery and the thoracodorsal nerve. Variability exists but does not pose an issue to relocation of the flap through the tunnel created subcutaneously. The skin island is reattached in the area of the defect and can be cutaneously reinnervated via neurorrhaphy between the prominent intercostal nerves of the breast region. Women can have satisfactory results of the LD flap reconstruction, immediately or delayed. Reconstruction with a LD flap is widely tolerated with significant satisfaction and minimal post-operative complications. The LD flap had decreased satisfaction in those who selected an immediate reconstruction, potentially due to the expectation for the breast to look identical to the original. The recovery of sensation varies, but may never return fully. Conclusion With increasing demands for surgeons to make a perfect breast, numerous techniques presented to the patient can be overwhelming. It is important to supply adequate information concerning the LD flap operation for the patient to be informed and to have realistic expectations of the reconstruction outcome. Education with images and patient appropriate descriptions may help patients to have a better understanding of the surgery they are undergoing and the results to be expected. Significance As breast cancer maintains its place as one of the most common cancers among women in the United States, second only to certain forms of skin cancer, focus on the quality of life of survivors potentially can benefit millions. Future work aims to measure the effectiveness of anatomical illustrations in patient education materials pre- and post-surgery.

The classifications of subaxial cervical spine traumatic injuries. Part 4. AOSpine Subaxial Classification System

Cervical spine injuries are a fairly common consequence of mechanical impact on the human body. The subaxial level of the cervical spine accounts for approximately half to 2/3 of these injuries. Despite the numerous classification systems that exist for describing these injuries, the recommendations for treatment strategy are very limited, and currently none of them is universal and generally accepted. Consequently, treatment decisions are based on the individual experience of the specialist, but not on evidence or algorithms. While the classification system based on the mechanism of trauma originally proposed by B.L. Allen et al. and subsequently modified by J.H. Harris Jr et al., was comprehensive, but lacked evidence, which to some extent limited its clinical applicability. Similarly, the Subaxial Injury Classification System proposed by the Spine Trauma Group, had no distinct and clinically significant patterns of morphological damage. This fact hindered the standardization and unification of tactical approaches. As an attempt to solve this problem, in 2016 Alexander Vaccaro, together with AO Spine, proposed the AO Spine subaxial cervical spine injury classification system, using the principle of already existing AOSpine classification of thoracolumbar injuries. The aim of the project was to develop an effective system that provides clear, clinically relevant morphological descriptions of trauma patterns, which should contribute to the determination of treatment strategy. The proposed classification of cervical spine injuries at the subaxial level follows the same hierarchical approach as previous AO classifications, namely, it characterizes injuries based on 4 parameters: (1) injury morphology, (2) facet damage, (3) neurological status, and (4) specific modifiers. The morphology of injuries is divided into 3 subgroups of injuries: A (compression), B (flexion-distraction), and C (dislocations and displacements). Damage types A and B are divided into 5 (A0-A4) and 3 (B1-B3) subtypes, respectively. When describing damage of the facet joints, 4 subtypes are distinguished: F1 (fracture without displacement), F2 (unstable fracture), F3 (floating lateral mass) and F4 (dislocation). The system also integrates the assessment of neurological status, which is divided into 6 subtype). In addition, the classification includes 4 specific modifiers designed to better detail a number of pathological conditions. The performance evaluation of AOSpine SCICS showed a moderate to significant range of consistency and reproducibility. Currently, a quantitative scale for assessing the severity of classification classes has been proposed, which also, to a certain extent, contributes to decision-making regarding treatment strategy.

Joining language with data and data with data

Joining language with data and data with data

Electron Dynamics in Molecular Elementary Processes and Chemical Reactions

Abstract This account places a particular emphasis on recent progress in the theory and its applications of nonadiabatic electron dynamics in chemical science. After a brief description of the fundamental relevance of the breakdown of the Born-Oppenheimer approximation, we show examples of our extensive and systematic application of electron dynamics to highlight the significance and necessity of beyond-Born-Oppenheimer chemistry. The chemical subjects presented herewith cover (1) characteristic phenomena arising from nonadiabatic dynamics, (2) flow of electrons during chemical reactions and ionization dynamics, (3) symmetry breaking and its possible control in chemical reactions emerging from multi-dimensional nonadiabatic interactions, a special example which can cause possible breakdown of molecular mirror symmetry, (4) physical mechanism of charge separation in organic compounds and biomolecules, (5) essential roles of charge separation and elementary chemical reaction mechanisms in catalytic cycles of Mn oxo complexes up to Mn4CaO5 in water splitting dynamics (2H2O → 4H+ + 4e− + O2), (6) chemical bonds and huge electronic state fluctuation in densely quasi-degenerate electronic manifolds, which make chemistry without the notion of potential energy surfaces, and so on. All these materials and issues have been chosen because they are not directly resolved by the method of energetics based on time-independent quantum chemistry. We thus have been exploring, developing, and cultivating a new chemical realm beyond the Born-Oppenheimer paradigm. This account is closed with a scope about the theory of simultaneous electronic and nuclear quantum wavepacket dynamics.