Spatially distributed computation in cortical circuits

The traditional view of neural computation in the cerebral cortex holds that sensory neurons are specialized, i.e., selective for certain dimensions of sensory stimuli. This view was challenged by evidence of contextual interactions between stimulus dimensions in which a neuron's response to one dimension strongly depends on other dimensions. Here we use methods of mathematical modeling, psychophysics, and electrophysiology to address shortcomings of the traditional view. Using a model of a generic cortical circuit, we begin with the simple demonstration that cortical responses are always distributed among neurons, forming characteristic waveforms, which we call neural waves. When stimulated by patterned stimuli, circuit responses arise by interference of neural waves. Resulting patterns of interference depend on interaction between stimulus dimensions. Comparison of these modeled responses with responses of biological vision makes it clear that the framework of neural wave interference provides a useful alternative to the standard concept of neural computation.

Learning intents behind interactions with high-order graph for session-based intelligent recommendation

Session-based recommendation is an overwhelming task owing to the inherent ambiguity in anonymous behaviors. Graph convolutional neural networks are receiving wide attention for session-based recommendation research for the sake of their ability to capture the complex transitions of interactions between sessions. Recent research on session-based recommendations mainly focuses on sequential patterns by utilizing graph neural networks. However, it is undeniable that proposed methods are still difficult to capture higher-order interactions between contextual interactions in the same session and has room for improvement. To solve it, we propose a new method based on graph attention mechanism and target oriented items to effectively propagate information, HOGAN for brevity. Higher-order graph attention networks are used to select the importance of different neighborhoods in the graph that consists of a sequence of user actions for recommendation applications. The complementarity between high-order networks is adopted to aggregate and propagate useful signals from the long distant neighbors to solve the long-range dependency capturing problem. Experimental results consistently display that HOGAN has a significantly improvement to 71.53% on precision for the Yoochoose1_64 dataset and enhances the property of the session-based recommendation task.

Analysis of Enablers to Implement Ergonomic Interventions in Indian Manufacturing Industry: A Multi-criteria Decision-making Approach

The implementation of ergonomics intervention practices is the need of time in a highly global competitive working environment for cutting down musculoskeletal health issues and improving the productivity of workers. Thus far, no research has developed practical methodological approaches and techniques to look into the dynamic nature of ergonomics intervention practices, specifically in the glass artware industry. Therefore, the objective of this article is to identify and prioritize the enablers to implement ergonomics intervention practices in Indian glass artware industries. Nineteen ergonomics intervention (EI) practices enablers were identified from the literature review and 16 were screened from identified enablers with the help of fuzzy Delphi method (FDM). The screened important enablers were then examined using a hybrid Interpretive Structural Modeling- Matrix Cross-Reference Multiplication Applied to a Classification-based framework. The interpretive structural modelling (ISM) methodology helps in evaluating contextual interactions among the enablers while classifying them based on their driving and dependence power. The results revealed that stringent legal requirements from the government, along with management support and commitment, are the main drivers and play a significant role over other enablers. Further, this study attempts to explore the critical enablers that are most prevalent for implementing EI practices. The results would be helpful for industries in implementing EI practices within their production systems by introducing and boosting the dominant enabler. This research offers key insight for practitioners, researchers and policy-makers by identifying interrelationships among the enablers.

Italian Community Psychology in the COVID-19 Pandemic: Shared Feelings and Thoughts in the Storytelling of University Students

This study investigated how young Italian people experienced the period of peak spread of COVID-19 in their country by probing their emotions, thoughts, events, and actions related to interpersonal and community bonds. This approach to the pandemic will highlight social dimensions that characterized contextual interactions from the specific perspective of Community Psychology. The aim was to investigate young people's experiences because they are the most fragile group due to their difficulty staying home and apart from their peers and because they are, at the same time, the most potentially dangerous people due to their urge to gather in groups. The research involved 568 university students, 475 females, and 93 males, with an average age of 21.82 years (SD = 4.836). The collected data were analyzed with the Grounded Theory Methodology, using the Atlas 8.0 software. From the textual data, representative codes were defined and grouped into 10 categories, which reflect the individuals' prosocial attitudes, behaviors, and values. These categories formed three macro-categories, called: “Collective Dimensions,” which includes Connectedness, Solidarity, Italian-ness, Social Problems, and Collective Mourning; “Prosocial Orientation,” which includes Trust and Hope; and “Collective Values,” which includes Values of Freedom, Respect of Social Rules, and Civic-Mindedness. All these macro-categories are indicative of the shared feelings experienced by Italians during the first time of the pandemic. Further practical implications of these results will be discussed, including a consideration of the risk of developing distress and improving well-being, as well as promoting preventive behaviors.

Effect of Thermal, Acoustic and Air Quality Perception Interactions on the Comfort and Satisfaction of People in Office Buildings

Current research on human comfort has identified a gap in the investigation of multi-domain perception interactions. There is a lack of understanding the interrelationships of different physio-socio-psychological factors and the manifestation of their contextual interactions into cross-modal comfort perception. In that direction, this study used data from a post occupancy evaluation survey (n = 26), two longitudinal comfort studies (n = 1079 and n = 52) and concurrent measurements of indoor environmental quality factors (one building) to assess the effect of thermal, acoustic and air quality perception interactions on comfort and satisfaction of occupants in three mixed-mode university office buildings. The study concluded that thermal sensation (TSV) is associated with both air quality (ASV) and noise perception (NSV). The crossed effect of the interaction of air quality and noise perception on thermal sensation was not evident. The key finding was the significant correlation of operative temperature (Top) with TSV as expected, but also with noise perception and overall acoustic comfort. Regarding the crossed main effects on thermal sensation, a significant effect was found for the interactions of (1) Top and (2) sound pressure levels (SPL30) with air quality perception respectively. Most importantly, this study has highlighted the importance of air quality perception in achieving occupants’ comfort and satisfaction with office space.

Effects of the Level of Interactivity of a Social Robot and the Response of the Augmented Reality Display in Contextual Interactions of People with Dementia

The well-being of people with dementia (PWD) living in long-term care facilities is hindered due to disengagement and social isolation. Animal-like social robots are increasingly used in dementia care as they can provide companionship and engage PWD in meaningful activities. While most previous human–robot interaction (HRI) research studied engagement independent from the context, recent findings indicate that the context of HRI sessions has an impact on user engagement. This study aims to explore the effects of contextual interactions between PWD and a social robot embedded in the augmented responsive environment. Three experimental conditions were compared: reactive context-enhanced robot interaction; dynamic context-enhanced interaction with a static robot; a control condition with only the dynamic context presented. Effectiveness evaluations were performed with 16 participants using four observational rating scales on observed engagement, affective states, and apathy related behaviors. Findings suggested that the higher level of interactivity of a social robot and the interactive contextualized feedback helped capture and maintain users’ attention during engagement; however, it did not significantly improve their positive affective states. Additionally, the presence of either a static or a proactive robot reduced apathy-related behaviors by facilitating purposeful activities, thus, motivating behavioral engagement.

Future directions for marine disease research

Marine Disease Ecology synthesizes current work on disease in marine systems. Although we have learned a great deal about marine diseases, basic surveillance is difficult, disease mechanisms are complex, and general ecological principles are illusive. Fortunately, technology is no longer what thwarts our capabilities to sample and analyze individual samples for potential parasites, pathogens, or opportunists. Increasingly sophisticated advancements in remote sensing, molecular biology, computer technology, and data analytics open new avenues to study marine diseases. With such tools in place, we now need better surveillance, a quantitatively savvy workforce, and new visions that will produce clarity in how to understand, prevent, or mitigate marine diseases. Although we are making great strides, more holistic approaches can increase our reach in disease monitoring and disentangle the contextual interactions among hosts, their disease-causing agents, and the environment. Such new approaches will also allow us to look into the past, rediscover what we missed, and use this information to predict, and ideally prevent, the next outbreak in the ocean.

Challenges for adopting and implementing IoT in smart cities

Purpose The wider use of Internet of Things (IoT) makes it possible to create smart cities. The purpose of this paper is to identify key IoT challenges and understand the relationship between these challenges to support the development of smart cities. Design/methodology/approach Challenges were identified using literature review, and prioritised and elaborated by experts. The contextual interactions between the identified challenges and their importance were determined using Interpretive Structural Modelling (ISM). To interrelate the identified challenges and promote IoT in the context of smart cities, the dynamics of interactions of these challenges were analysed using an integrated Matrice d’Impacts Croisés Multiplication Appliqués à un Classement (MICMAC)-ISM approach. MICMAC is a structured approach to categorise variables according to their driving power and dependence. Findings Security and privacy, business models, data quality, scalability, complexity and governance were found to have strong driving power and so are key challenges to be addressed in sustainable cities projects. The main driving challenges are complexity and lack of IoT governance. IoT adoption and implementation should therefore focus on breaking down complexity in manageable parts, supported by a governance structure. Practical implications This research can help smart city developers in addressing challenges in a phase-wise approach by first ensuring solid foundations and thereafter developing other aspects. Originality/value A contribution originates from the integrated MICMAC-ISM approach. ISM is a technique used to identify contextual relationships among definite elements, whereas MICMAC facilitates the classification of challenges based on their driving and dependence power. The other contribution originates from creating an overview of challenges and theorising the contextual relationships and dependencies among the challenges.