Partial View Research Articles

Experimenting with Failure

In his article ‘Mindless Modernism’, published in 2013, Joshua Gang expressed his scepticism about the established narrative of modernism’s so-called inward turn. In his view, the notion that modernism was concerned with exploring the complex intricacies of human consciousness offers at best a partial view of the influence of psychology on the period’s most formally experimental literature. ‘In treating the introspective mind as a self-evident truth of modernism’, Gang warned, ‘we preclude rather than encourage new approaches to the period, particularly if those approaches are invested in the history of science, analytic philosophy, or the empirical study of mind.’1 The article was a necessary reminder that ‘psychology, and modernism’s relationship to psychology, did not end with introspection’.2 In many ways, Joshua Powell’s Samuel Beckett and Experimental Psychology: Perception, Attention, Imagery is the kind of scholarly work that Gang called for. Published in Bloomsbury’s Historicizing Modernism series, the book focuses on Beckett’s theatre and prose works from the late 1950s to the late 1980s. Powell argues that Beckett’s works are not simply artistically radical but can also be understood as scientific experiments. The book’s aim is to outline ‘the extent to which the psychological experiment comes to inform Beckett’s own experimental methods, and ultimately what these methods can be seen to achieve’ (p. 9). There are six main chapters in the book, most of which focus on a specific work (Not I, That Time, Footfalls, and Nohow On get individual chapters), through the lens of psychological concepts such as inattention or face-reading. Powell’s approach is significant in two respects. First, he shifts the focus from Beckett’s canonical early novels towards his less famous later plays such as Not I or That Time. Second, he largely bypasses the conversation about Beckett’s relationship to psychoanalysis (which he correctly argues has long dominated critical discussions) in favour of less frequently explored commonalities between Beckett’s work and behaviourism and gestalt psychology.

Collision Risk Assessment and Operation Assistant Strategy for Teleoperation System

Teleoperation robots remain superior to fully automated robots in complicated and unstructured environments (e.g., in-orbit assembly). However, the collision risk is also greatly increased in these environments. Therefore, the teleoperation robot should possess the capability of collision risk perception and be configured with security assistance strategy to improve safety and efficiency. With this objective in mind, this paper proposes a collision risk assessment system based on fuzzy theory, which comprehensively considers the effects of shortest distance, operation speed, and delay time. The introduction of fuzzy theory makes the risk assessment results more accurate. Furthermore, this paper also proposes a creative discrete expandable bounding box method to make the calculation of the nearest distance between complicated obstacles and the robot more efficient and faster. Secondly, to improve the safety and efficiency of teleoperation, this paper proposes a set of teleoperation assistance strategies for teleoperation robots based on collision risk. The strategies include partial view highlighting, variation in the motion mapping ratio, and haptic risk warning, all of which are achieved in the graphical interactive interface. Finally, this paper verifies the proposed collision risk model and the operation assistant strategy through experiments. The results show that the proposed fuzzy collision risk model has a correct trend with risk factors, and the proposed operation assistance strategies can effectively reduce the robot collision risk and improve the safety of teleoperation robots. In conclusion, this research contributes to the collision risk assessment and obstacle avoidance assistance strategy of teleoperation robots.

What is known and what is unknown about food buying and consumption behavior during the COVID-19 pandemic? A systematic literature review

PurposeThis study aims to analyze the management and marketing literature on food buying and consumption behavior during the COVID-19 pandemic to shed light on how consumers reacted to this global crisis, to help interpret consumer reactions to possible future crises and to identify future research avenues.Design/methodology/approachThis study adopts a systematic literature review as research methodology.FindingsThis study's outcomes reveal that, during the pandemic, people have deeply changed their food buying and consumption habits. The analysis identifies four main themes: food purchasing channel choice and buying behavior; food choices and consumption habits; food and “food-related” waste; and country influence on food buying and consumption.Research limitations/implicationsThis review uses only one database of records (Scopus). The Boolean modifiers chosen for extracting the papers may have influenced the type and number of retrieved publications. The analysis was limited to articles published in peer-reviewed academic journals.Originality/valueWhile most examined investigations do not provide a comprehensive picture of consumers’ food-related responses to the pandemic and individually offer only a partial view focusing on one or just a few aspects of food consumer behavior, this study offers an overall description of consumers’ responses to the crisis and identifies important research issues to be addressed in the future.

THE STRANGER AMONG US IN COMICS FROM CUTEZĂTORII MAGAZINE. CONSTRUCTING CHILDREN’S POLITICAL IDENTITIES THROUGH INTERACTION WITH THE OTHER IN COMMUNIST ROMANIA

A product of popular culture, comics are in tune with the societal context that created them. This article aims to present the hegemonic narrative, reading and messages of comics for communist pioneers in Cutezătorii [The Daring Ones] Magazine. To do so, the research material considered is made up of two comics focused on Otherness and Romanians’ relationships with state institutions close to the political factor. Qualitative content analysis was performed on both text and images. Findings uncovered a partial view of communist Romania and of the workings of propaganda, because, beside their entertainment value, these comics can be read as political texts or statements. These comics show how the political elite perceived those who wanted to leave Romania (illegally) and those who, of foreign origin, visited this country. Both comics series glorify the collaboration with the Militia, the State Security, and their surveillance methods (through technology and responsible employees and citizens). Communist pioneers are plain clothes detectives who help these institutions to gather their information, catch criminals, and keep the society under control. Because cultural representations impacted the Romanian society in multiple ways during the totalitarian communist rule, a critical reconsidering of cultural products of the past is necessary in order to better understand the present.

O 25 de Abril na imprensa da República Federal da Alemanha

The revolution of 25 April aroused public interest in Portugal for the first time in West Germany. Compared to other countries, however, the interest was rather modest. None of the Federal Republic of Germany’s newspapers sent a correspondent to Lisbon to report on the day-to-day events of the revolution, as did some European newspapers and even Neues Deutschland, the official organ of the German Democratic Republic’s ruling party. However, through the newspapers, the citizens of West Germany received a partial, superficial and biased view of the events in revolutionary Portugal. The treatment of the Carnation Revolution in the West German press says almost as much about Portugal as it does about the peculiar political culture of the Bonn Republic, which was deeply influenced by the Cold War.

Partial vs full glottic view with CMACTM D blade intubation of airway with simulated cervical spine injury: a randomized controlled trial

ABSTRACT Purpose A previous study reported a shorter time to tracheal intubation by reducing percentage of glottic opening (POGO) view to <50% when intubating a normal adult airway using the GlidescopeTM blade. We evaluate the efficacy of reducing POGO to <50% when intubating patients with rigid cervical immobilization using CMACTM D blade. Methods One hundred and four adult patients were randomized to group POGO 100% or POGO <50% . Laryngoscopy was performed by advancing tip of the D blade at vallecula. POGO 100% was achieved by exerting upward force to displace epiglottis until glottic opening from the anterior commissure to inter arytenoid notch. POGO < 50% was acquired by withdrawing the D blade tip dorsally from vallecula. The primary outcome was time to intubation. Results The median time (IQR) to successful intubation was 29 (25–35) seconds for group POGO < 50% and 34 (28–40) seconds for group with POGO 100% (difference in medians, 5 seconds; 95% confidence interval, 2 to 8, p = 0.003). Complications were minor. Conclusion Using the CMACTM D blade with a reduced POGO in patients with cervical spine immobilization resulted in faster tracheal intubation. Trial registration The trial is registered at ClinicalTrial.gov (CT.gov identifier: NCT 04833166)

Tutorial: Modeling of the extraction and acceleration of negative ions from plasma sources using particle-based methods

In this Tutorial, we consider plasma sources with applications to fusion devices and high energy accelerators. These ion sources typically produce negative ions from hydrogen-isotope gases, which are extracted through one or multiple apertures and accelerated to high kinetic energies. Next, they are either double stripped of two electrons to form positive ions used as precursors in accelerator devices or neutralized to produce a neutral beam injected in tokamak reactors. Contrary to the working conditions of most ion sources where volume production prevails, the mechanism of negative ion production by dissociative electron attachment on vibrationally excited molecules inside the plasma volume of fusion-type hydrogen-fueled high power discharges is mostly balanced by their destruction by detachment before being extracted rendering this means of producing negative ions rather inefficient. Surface production through the transfer of electrons from low work function metallic materials to the impacting atoms is the alternative solution to fulfill the requirements for the applications concerned. Negative ions are produced close to the aperture from which they are extracted. As a result, the analysis and understanding of the extraction mechanisms through experimental diagnostics is rather difficult due to the lack of accessibility and can only give a partial view. In addition, most of the experimental work is focused on the validation of requirements for the applications and not to the investigation of the fundamental processes that take place inside these types of sources. This Tutorial is focused on the description and understanding of the physical mechanisms behind the extraction and acceleration of negative ions from hydrogen plasma sources through modeling methods. We describe the numerical techniques of particle-based methods with a specific emphasis on particle-in-cell Monte Carlo collision algorithms. An analysis of the physical processes involved in driving the negative ions from the plasma source, across the apertures and inside the accelerator as reported in the literature, is presented in detail. This Tutorial concludes with additional and future works to be addressed in the coming years.

Evaluating the scalability of reconfigurable manufacturing systems at the design phase

To face market volatility, reconfigurable manufacturing systems (RMS) aim to efficiently and cost-effectively react to changes. We focus on one characteristic of RMS: the scalability (ability to adapt the volume of throughput). In the literature, the only few indicators for scalability are not always formally defined and usually only consider a partial view of scalability. Moreover, most of them are actually more suited for the configuration planning rather than for the design. However, the design of the RMS has a high impact on its scalability. We propose the first combinatorial definition of this problem and a new measure to fully assess the scalability of a system at the design phase. This measure, based on a multi-objective approach, can assess the scalability of single-product manufacturing systems, analysing all configurations that it can implement. We present numerical experiments to compare this indicator with a state-of-the-art scalability indicator and with some classical production line design indicators, and we show that future research should focus on scalability as a specific criterion to optimise during the design of an RMS. In addition, the results obtained allow us to infer some managerial insights on the best levers to use when performing a reconfiguration for scalability purpose.

A Partial Order View of Message-Passing Communication Models

There is a wide variety of message-passing communication models, ranging from synchronous "rendez-vous" communications to fully asynchronous/out-of-order communications. For large-scale distributed systems, the communication model is determined by the transport layer of the network, and a few classes of orders of message delivery (FIFO, causally ordered) have been identified in the early days of distributed computing. For local-scale message-passing applications, e.g., running on a single machine, the communication model may be determined by the actual implementation of message buffers and by how FIFO queues are used. While large-scale communication models, such as causal ordering, are defined by logical axioms, local-scale models are often defined by an operational semantics. In this work, we connect these two approaches, and we present a unified hierarchy of communication models encompassing both large-scale and local-scale models, based on their concurrent behaviors. We also show that all the communication models we consider can be axiomatized in the monadic second order logic, and may therefore benefit from several bounded verification techniques based on bounded special treewidth.

Distributed Multi-View Sparse Vector Recovery

In this paper, we consider a multi-view compressed sensing problem, where each sensor can only obtain a partial view of the global sparse vector. Here the partial view means that some arbitrary and unknown indices of the global vector are unobservable to that sensor and do not contribute to the measurement outputs. The sensors aim to collaboratively recover the global state vector in a decentralized manner. We formulate this recovery problem as a bilinear optimization problem relying on a factored joint sparsity model (FJSM), in which the variables are factorized into a node-specific sparse local masking vector and the desired common sparse global vector. We first theoretically analyze the general conditions guaranteeing the global vector's successful recovery. Then we propose a novel in-network algorithm based on the powerful distributed alternating direction method of multipliers (ADMM), which can reconstruct the vectors and achieve consensus among nodes concerning the estimation of the global vector. Specifically, each node alternately updates the common global vector and its local masking vector, and then it transfers the estimated global vector to its neighboring nodes for further updates. To avoid potential divergence of the iterative algorithm, we propose an early stopping rule for the estimation of the local masking vectors and further conceive an estimation error-mitigation algorithm. The convergence of the proposed algorithms is theoretically proved. Finally, extensive simulations validate their excellent performance both in terms of the convergence and recovery accuracy.

AlignBodyNet: Deep Learning-Based Alignment of Non-Overlapping Partial Body Point Clouds From a Single Depth Camera

This article proposes a novel deep learning framework to generate omnidirectional 3-D point clouds of human bodies by registering the front- and back-facing partial scans captured by a single-depth camera. Our approach does not require calibration-assisting devices, canonical postures, nor does it make assumptions concerning an initial alignment or correspondences between the partial scans. This is achieved by factoring this challenging problem into: 1) building virtual correspondences for partial scans and 2) implicitly predicting the rigid transformation between the two partial scans via the predicted virtual correspondences. In this study, we regress the skinned multi-person linear model (SMPL) vertices from the two partial scans for building virtual correspondences. The main challenges are: 1) estimating the body shape and pose under clothing from single partially dressed body point clouds and 2) the predicted bodies from the front- and back-facing inputs required to be the same. We, thus, propose a novel deep neural network (DNN) dubbed AlignBodyNet that introduces shape-interrelated features and a shape-constraint loss for resolving this problem. We also provide a simple yet efficient method for generating real-world partial scans from complete models, which fills the gap in the lack of quantitative comparisons based on real-world data for various studies including partial registration, shape completion, and view synthesis. Experiments based on synthetic and real-world data show that our method achieves state-of-the-art performance in both objective and subjective terms.

Localized Sparse Incomplete Multi-View Clustering

Incomplete multi-view clustering, which aims to solve the clustering problem on the incomplete multi-view data with partial view missing, has received more and more attention in recent years. Although numerous methods have been developed, most of the methods either cannot flexibly handle the incomplete multi-view data with arbitrary missing views or do not consider the negative factor of information imbalance among views. Moreover, some methods do not fully explore the local structure of all incomplete views. To tackle these problems, this paper proposes a simple but effective method, named localized sparse incomplete multi-view clustering (LSIMVC). Different from the existing methods, LSIMVC intends to learn a sparse and structured consensus latent representation from the incomplete multi-view data by optimizing a sparse regularized and novel graph embedded multi-view matrix factorization model. Specifically, in such a novel model based on the matrix factorization, a l1 norm based sparse constraint is introduced to obtain the sparse low-dimensional individual representations and the sparse consensus representation. Moreover, a novel local graph embedding term is introduced to learn the structured consensus representation. Different from the existing works, our local graph embedding term aggregates the graph embedding task and consensus representation learning task into a concise term. Furthermore, to reduce the imbalance factor of incomplete multi-view learning, an adaptive weighted learning scheme is introduced to LSIMVC. Finally, an efficient optimization strategy is given to solve the optimization problem of our proposed model. Comprehensive experimental results performed on six incomplete multi-view databases verify that the performance of our LSIMVC is superior to the state-of-the-art IMC approaches. The code is available in https://github.com/justsmart/LSIMVC.

Guest Editorial: Ecological knowledge co‐production and the contested imaginaries of development in Southeast Asia

Guest Editorial: Ecological knowledge co‐production and the contested imaginaries of development in Southeast Asia

Multimodal Human Facial Emotion Recognition Using DenseNet-161 and Image Feature Stabilization Algorithm

Human Facial Emotion Recognition (FER) is the technology to predict listener’s emotion of static images and videos to uncover data on one's enthusiastic states like happy, sad, frustration, anxiety, surprise, hate and neutral states. It's a part of the affective computing technology, which may be a collaborative area of research on listener’s emotions. The ability of a computer to recognise and understand and frequently explain human emotions and emotional states which relies on computing (AI) technology. The most difficult part of the FER is to plan the various looks with the separate enthusiastic states. The standard steps of Facial Emotion Recognition are i) Face RoI identification ii) Feature Extraction and iii) Emotion Recognition. Convolutional Neural Network models are the most commonly utilised to detect listener emotions. In this paper, an Image Feature Stabilization Algorithm (IFSA) is proposed to improve the efficiency of facial emotion recognition by implementing Deep Convolutional Neural Network (DCNN) model using the Transfer Learning (TL) technique. The architecture entails employing a FER-compatible pre-trained Densenet-161 based DCNN model and then fine-tuning the model for face emotion data. Initially, the dense layer(s) is/are trained, followed by the fine-tuning of each of the pre-trained DCNN blocks, resulting in an improvement in FER accuracy, particularly for difficult front face views like partial view. Experiments when performed on the CK+ dataset by employing a 10-fold cross validation method using various pre-train models like VGG 16, VGG19, ResNet-18, 34, 50, 152, Inception-V3 and DenseNet-161 showed accuracy of 85.9%, 94.6%, 91%, 91%, 91.1%, 95.1%, 97.1% and 98.7% respectively. Thus, Facial Emotion Recognition performed using the finetuned DenseNet-161 architecture demonstrated exceptionally improved accuracy compared to other pretrained models, along with the proposed image feature stabilization algorithm. The proposed architecture using Densenet-161 showed improved accuracy of 98.78% and 97.52% in other challenging FER datasets like KDEF and JAFEE.

‘Digitalized’ Competences. A Crucial Challenge beyond Digital Competences

Today’s societies are experiencing a digitalization that is transforming organizations and jobs. Scholars, practitioners, and policy makers tend to focus on digital competences as a way to adapt the general population and workers to the technological revolution, enhance competitiveness and employability, and reduce social exclusion. It is important to develop “digital competences”, but we propose that this strategy only provides a partial view of the necessary capabilities. To achieve a more complete picture, “digitalized competences” should also be considered, that is, non-digital competences that are transformed through the intervention of digital technologies. Increasingly, jobs in which purely digital skills are not core elements change dramatically when they incorporate digital objects and processes. This change forces workers and professionals to perform traditional tasks in a very different way. It is likely that the shift from non-digital to digitalized competences involves a more pervasive transformation for organizations, jobs, and workers than pure digital competences. With this in mind, this article has two main objectives. First, we provide a definition of digitalized competences, linked to job transformation. Second, we use a typology of competences (professional, core cognitive, transversal, and leadership) and some cases to illustrate this change.

Didactical characteristics of Dutch websites about kidney transplantation targeted for kidney patients and living donors: An exploratory study.

The aim was to explore the origin, content topics, teaching modes (instruction, interaction, and assessment), and corresponding social-epistemological dimensions (choices in knowledge transfer vs. knowledge building, and individual vs. group learning) of web-based information on kidney transplantation targeted for patients and living donors. Dutch websites on kidney transplantation were retrieved using the search engine Google.nl. From 24 websites, 250 webpages were examined on origin, content topics, teaching modes, and corresponding social-epistemological dimensions. The majority of the websites had a professional organization as origin (20/24). The number and distribution of content topics varied among the websites. Of the 16 different teaching modes found, 11 were instructional, 4 were interactional, and 1 assessment mode was found. The websites offered almost exclusively teaching modes on individual and passive learning, whereas group learning and interactive knowledge building was hardly encountered. The diversity in teaching modes and social-epistemological dimensions of Dutch websites on kidney transplantation targeted for patients and living donors is limited. The websites only provided a partial view on kidney transplantation; information regarding contact with others, e.g. support, was limited. A more balanced availability of teaching approaches and content topics is desirable to fit with the didactical goals to make well-considered health decisions.

A quantitative analysis of research trends in flood hazard assessment

Studies focusing on flood hazard assessment (FHA) reviews are still rare and usually approach mapping, uncertainty, spatial scale, and economic loss. Nevertheless, most of these studies only provide a snapshot of the current development of FHA, showing a partial view, and focusing on a limited number of selected approaches and methods. This study aims to analyze the historical development and emerging fields of FHA-related research and applies a bibliometric analysis method based on the Bibliometrix tool and the Web of Science (WoS) database. We refined the 4135 articles retrieved, established a dataset containing 723 articles, and downloaded the information of all articles, including journal, author, country, keywords, and abstracts. The dataset is entered bibliometric analysis model to study the development of FHA-related research from 2000 to 2020 (data as of 15 February 2021). The results show that the number of articles on FHA-related research has continued to increase and developed rapidly. The top five countries in the number of studies are China, Italy, the United States, Spain, and the United Kingdom. Compared to developed countries, the FHA-related research in developing countries lags significantly behind although it is developing rapidly. Future research is likely to focus on global change, coastal floods, compound disaster, and psycho-social aspects of flooding. Moreover, research using remote sensing and machine learning is becoming increasingly sophisticated. The purpose of this research is to provide direction for future FHA-related research and provide reference and scientific guidance for FHA-related researchers.

Contrastive 3D Shape Completion and Reconstruction for Agricultural Robots Using RGB-D Frames

Monitoring plants and fruits is important in modern agriculture, with applications ranging from high-throughput phenotyping to autonomous harvesting. Obtaining highly accurate 3D measurements under real agricultural conditions is a challenging task. In this letter, we address the problem of estimating the 3D shape of fruits when only a partial view is available. We propose a pipeline that exploits high-resolution 3D data in the learning phase but only requires a single RGB-D frame to predict the 3D shape of a <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">complete</i> fruit during operation. To achieve this, we first learn a latent space of potential fruit appearances that we can decode into an SDF volume. With the pretrained, frozen decoder, we subsequently learn an encoder that can produce meaningful latent vectors from a single RGB-D frame. The experiments presented in this letter suggest that our approach can predict the 3D shape of whole fruits online, needing only 4 ms for inference. We evaluate our approach in controlled environments and illustrate its deployment in greenhouses without modifications.

End-to-End Learning to Grasp via Sampling From Object Point Clouds

The ability to grasp objects is an essential skill that enables many robotic manipulation tasks. Recent works have studied point cloud-based methods for object grasping by starting from simulated datasets and have shown promising performance in real-world scenarios. Nevertheless, many of them still rely on ad-hoc geometric heuristics to generate grasp candidates, which fail to generalize to objects with significantly different shapes with respect to those observed during training. Several approaches exploit complex multi-stage learning strategies and local neighborhood feature extraction while ignoring semantic global information. Furthermore, they are inefficient in terms of number of training samples and time required for inference. In this letter, we propose an end-to-end learning solution to generate 6-DOF parallel-jaw grasps starting from the 3D partial view of the object. Our Learning to Grasp (L2G) method gathers information from the input point cloud through a new procedure that combines a differentiable sampling strategy to identify the visible contact points, with a feature encoder that leverages local and global cues. Overall, L2G is guided by a multi-task objective that generates a diverse set of grasps by optimizing contact point sampling, grasp regression, and grasp classification. With a thorough experimental analysis, we show the effectiveness of L2G as well as its robustness and generalization abilities.

Public administration and political science: From monogamous marriage to professional partnership

Public administration and political science: From monogamous marriage to professional partnership