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This paper analyzes formulaic language in conference abstracts in Applied Linguistics and tracks an application of the findings to a second language English academic writing context. Specifically, the authors identify and profile phrase-frames (a form of discontinuous formulaic sequence) in terms of their frequency, internal variation, predictability, structure, and rhetorical functions in a corpus of approximately 1,600 conference abstracts in Applied Linguistics. The analysis informed a corpus- and genre-based second language writing pedagogical intervention on formulaic language, and the paper also presents the experiences of learners through sample student writing, survey, and interview data. Overall, findings highlight the pedagogical potential for including frame-based formulaic language in second language writing pedagogy due to the prevalence and inherent productivity of such features, the clear role they play in signaling functional intentions in rhetoric, and their ability to stimulate reflection and discussion of formulaicity in writing more broadly. • Nearly two-fifths of phrase-frames had strong relationships with lead variants. • Phrase-Frames were notably productive, with about one variant per two occurrences. • Phrase-Frames served as strong cues of rhetorical moves. • Students reported and demonstrated confidence with and awareness of formulaicity. • Challenges of corpus-based teaching surfaced and limited changes to writing found. [ABSTRACT FROM AUTHOR]

Given the idea that financial institutions can have a significant impact on environmental performance, green credit, which essentially involves capital allocation under environmental awareness, is worth to be explored and implemented by banking sectors worldwide. This paper explores the impact of environmental awareness on firms' loan rates, with a focus on borrowers who have a record of environmental penalties. Using a comprehensive loan dataset from one of China's "Big Five" banks between 2010 and 2013, we employ the volatility of particulate matter 2.5 (PM 2.5) as a proxy to measure environmental awareness. Our findings suggest that the volatility of air pollution has a positive effect on the floating ratio of the loan interest rate for firms with a history of environmental penalties. Furthermore, we find that this mechanism is particularly pronounced for private-owned enterprises (POEs), new loan customers, firms with low crediting ratings, and small-sized enterprises. We also confirm the role of environmental awareness in firms' loan costs by demonstrating that the volatility of air pollution is sensitive to variations in insolation duration, environmental attention, and PM2.5 monitoring stations. We suggest that the volatility of air pollution is a more meaningful indicator than the degree of air pollution, and that people's voluntary environmental awareness can transfer the environmental risk of firms to their internal operating costs. The significance of this study lies in its potential to aid policymakers and investors in advancing sustainable finance practices, as well as stimulating individual engagement with environmental awareness and prompting financial institutions to give greater importance to these issues. • Environmental awareness increases non-compliant firms' loan costs. • Environmental awareness can transfer the environmental risk of firms to their operating costs. • The mechanism more strongly impacts POEs and small-size firms. • Sensitivity of environmental awareness to insolation, attention, and PM 2.5 monitoring. • The volatility of air pollution is a more meaningful predictor than the degree of air pollution. [ABSTRACT FROM AUTHOR]

Our previously reported survey of the Low Earth Orbit (LEO) environment using the Murchison Widefield Array (MWA) detected over 70 unique Resident Space Objects (RSOs) over multiple passes, from 20 h of observations in passive radar mode. In this paper, we extend this work by demonstrating two methods that improve the detection sensitivity of the system. The first method, called shift-stacking, increases the statistical significance of faint RSO signals through the spatially coherent integration of the reflected signal along the RSO's trajectory across the sky. This method was tested on the observations used during our previous blind survey, and we obtained a 75 % increase in the total number of detections. The second method re-focuses the MWA to the near-field RSO's position (post-observation), by applying a complex phase correction to each visibility to account for the curved wave-front. The method was tested successfully on an MWA extended array observation of an ISS pass. However, the method is currently limited by signal de-coherence on the long-baselines (due to the hardware constraints of the current correlator). We discuss the sensitivity improvement for RSO detections we expect from the MWA Phase 3 correlator upgrade. We conclude the paper by briefly commenting on future dedicated Space Domain Awareness (SDA) systems that will incorporate MWA technologies. [ABSTRACT FROM AUTHOR]

Graph neural network (GNN) is a powerful model for learning from graph data. However, existing GNNs may have limited expressive power, especially in terms of capturing adequate structural and positional information of input graphs. Structure properties and node position information are unique to graph-structured data, but few GNNs are capable of capturing them. This paper proposes Structure- and Position-aware Graph Neural Networks (SP-GNN) , a new class of GNNs offering generic and expressive power of graph data. SP-GNN enhances the expressive power of GNN architectures by incorporating a near-isometric proximity-aware position encoder and a scalable structure encoder. Further, given a GNN learning task, SP-GNN can be used to analyze positional and structural awareness of GNN tasks using the corresponding embeddings computed by the encoders. The awareness scores can guide fusion strategies of the extracted positional and structural information with raw features for better performance of GNNs on downstream tasks. We conduct extensive experiments using SP-GNN on various graph datasets and observe significant improvement in classification over existing GNN models. [ABSTRACT FROM AUTHOR]

This paper examines the rising rates of autism spectrum disorder (ASD) in children in the United States (1 in 59), and explores the role academic libraries can play in helping college students with ASD. A literature review of how different types of libraries (school, public, academic) support students with ASD indicates research in this area in general is lacking. Findings point to lack of adequate training and awareness for librarians and staff, resources, services and spaces are universal challenges. Several initiatives that academic libraries and librarians may consider in helping students with ASD are identified and described. [ABSTRACT FROM AUTHOR]

Would a properly designed educational programme offered to young women improve their awareness and fundamental knowledge of menstrual pain and endometriosis? A multinational cross-sectional study using a pen-and-paper questionnaire among women aged 19–24 years was conducted between 2017 and 2019 to assess fundamental knowledge of menstrual pain and endometriosis. Improvement in knowledge was also analysed using a separate questionnaire completed before, and 1–3 months after, a group discussion, lecture on menstrual pain and endometriosis, or both. Among three groups of students (college [ n = 271], medical [ n = 877] and nursing [ n = 763]), knowledge of menstrual pain and endometriosis was lowest among college students, modest among nursing students and fair among medical students (P < 0.001 for each). The experience of cyclical pain, even when painkillers were taken, was reported by 15.5%, 4.6% and 3.8% of students, respectively. Most students managed their cyclical pain by enduring it or by taking over-the-counter medication. An informative education programme with group discussions, lectures, or both, was successful in improving knowledge and consequences of menstrual pain and endometriosis. Proper education and dissemination of knowledge to college students failed to motivate them to visit gynaecologists; however, medical and nursing students became highly interested in visiting gynaecologists. An educational programme can improve awareness and knowledge of endometriosis and dysmenorrhoea among young women. The programme motivated nursing and medical students, but not college students, to seek medical attention for early detection and management of endometriosis. [ABSTRACT FROM AUTHOR]

In this paper we investigate an SIRS-UAU disease-awareness spreading model on multiplex networks that incorporates the influence of mass media. Through the microscopic Markov chain approach, we derive the evolution equations for the probability of an individual being in each possible state and obtain the epidemic threshold, which is shown to be a continuous phase transition point between the disease-free state and the endemic state in the phase diagram. Our results show that increasing the immunity wanning rate will enlarge the epidemic prevalence and reduce the fraction of recovered individuals; however, the temporal immunity has no impact on the epidemic threshold. Moreover, implementing mass media broadcast helps raise the awareness incidence and decrease the fraction of recovered individuals. In addition, for the special case when awareness confers complete self-protection against infection, the mass media broadcast significantly diminishes the epidemic prevalence and increases the epidemic threshold. Nevertheless, when awareness provides only partial self-protective effectiveness, the mass media broadcast plays a limited role in changing the epidemic prevalence and the epidemic threshold. Furthermore, in the special case where the mass media broadcast is not included, we discover the metacritical point, a point above which the epidemic threshold starts to grow nonlinearly with the awareness spreading rate and below which the epidemic threshold is independent of the awareness spreading rate. Conversely, the metacritical point disappears as long as the mass media broadcast is incorporated into the model. Our analytical results are confirmed by extensive Monte Carlo simulations. • Studied a coupled model of awareness and disease spread with temporal immunity and media. • Temporal immunity affects prevalence, not threshold; media impact depends on protection. • Found a metacritical point for threshold on awareness; media presence removes it. • Provided phase diagram for epidemic prevalence and awareness, supported by simulations. [ABSTRACT FROM AUTHOR]

Existing ship dynamic collision avoidance methods mostly rely on the instantaneous motion information of surrounding ships to make decisions. This makes it difficult to adapt to changes in the motion states of surrounding ships, which may lead to collisions between ships. To improve the safety of dynamic collision avoidance methods, this paper combines the multi-ship trajectory prediction model GL-STGCNN with model predictive control for ship dynamic collision avoidance tasks. Firstly, the interaction between ships is extracted through GL-STGCNN to predict the future trajectories of surrounding ships. Then, the objective function based on the artificial potential field method and the velocity obstacle method is optimized to control the ship to complete the dynamic collision avoidance task. The performance of the dynamic collision avoidance method is verified and analyzed in the ship navigation scenario simulated by AIS data. The experiments show that the new ship dynamic collision avoidance method not only complies with the COLREGs, but also can flexibly select the collision avoidance method according to different scenarios. In addition, the theoretical collision avoidance threshold distance based on the MPC objective function shows a high degree of fit with the actual collision avoidance trigger distance observed in the simulation verification. • The integration of the GL-STGCNN multi-ship trajectory prediction model with model predictive control enhances ship dynamic collision avoidance. • The GL-STGCNN model extracts ship interactions to forecast future trajectories of neighboring vessels. • The effectiveness of the dynamic collision avoidance technique is assessed within a simulated ship navigation scenario utilizing AIS data. • Theoretical collision avoidance thresholds from MPC closely match observed distances in simulation verification. [ABSTRACT FROM AUTHOR]

In the context of disease spreading, two factors merit particular attention. One factor involves individuals instinctively seeking advantages while avoiding the risk of infection. The second factor pertains to the influence of the environment in which individuals reside. This paper introduces an adaptive mechanism and the surroundings reinforcement in a two-layer network. Meanwhile, a novel model, named aUAU-sSIR, is proposed to explore the impact of the adaptive mechanism and the surroundings reinforcement on disease spreading in the two-layer network. Then, based on micro Markov chain approach (MMCA), we derive the corresponding MMCA equation for the newly proposed model and articulate the mathematical expression of disease transmission threshold. Finally, comprehensive simulations demonstrate the feasibility and effectiveness of the theoretical analysis, elucidating the impact of both the adaptive mechanism and the surroundings reinforcement. The simulations reveal that adaptive behavior within the awareness-layer impedes the awareness diffusion and promotes the disease spreading. Additionally, our findings indicate that increasing the surrounding reinforcement factor can effectively accelerate disease spread. [ABSTRACT FROM AUTHOR]

In this paper, we make the first research effort to address the RGB-Thermal (RGB-T) crowd counting problem with the decision-level late fusion manner. Being different from the existing pixel-level or feature-level fusion methods, our proposition chooses to fuse the density maps yielded by RGB and thermal counterparts via spatially adaptive weighting with RGB illumination-aware attention. Our key intuition to conduct RGB-T density map fusion lies in 2 main folders. First, compared with the raw RGB-T images or convolutional feature maps, RGB-T density maps contain stronger counting-wise semantic meanings. Secondly, they are also of high spatial resolution for revealing fine local details. To fuse them adaptively, a spatial weighting map for each modality, together with an illumination-related RGB weight is generated. In this way, the issues of RGB illumination awareness and local counting pattern characterization ability are concerned jointly. To the best of our knowledge, we are the first to leverage RGB-T crowd counting concerning these 2 issues in a unified way. Meanwhile, cross-modality feature interaction is conducted between RGB and thermal modalities to facilitate spatial weighting map generation. The experiments on 2 well-established RGB-T crowd counting datasets (i.e. , RGBT-CC and DroneRGBT) verify the superiority of our proposition. The source code and pretrained models will be released upon acceptance at https://github.com/hustaia/DLF-IA. • The first decision-level fusion method towards RGB-Thermal crowd counting. • Joint RGB illumination awareness and counting pattern characterization ability. • Cross-modality feature interaction to facilitate spatial weighting map generation. [ABSTRACT FROM AUTHOR]

In this paper, we consider an epidemic model in well-mixed multiplex networks with distributed time delay. Specifically, the model consists of two layers of well-mixed networks in the physical and virtual worlds, respectively, where two diffusive processes interact and influence each other within the same individual. We assume that there is a distributed time delay for an individual to become infected, but no delay for an individual to transition from unawareness to awareness. Our main results are as follows: Let R 0 P and R 0 V represent the basic reproduction numbers in the physical and virtual worlds, respectively. First, we demonstrate that the disease will die out for any delay time, provided that R 0 P ≤ 1 or 1 < R 0 P ≤ R 0 V. The latter condition emphasizes the significance of effective information spreading in eradicating the disease. Secondly, in the case of R 0 P > max ⁡ { 1 , R 0 V } , we establish that the model exhibits an endemic and information saturated equilibrium, denoted as E 3. Additionally, we show that the model is uniformly persistent, indicating the sustained outbreak of the disease. • We explore an epidemic model influenced by human awareness within the framework of a multiplex network. • The latent period of the infectious disease is concerned. • The necessary and sufficient conditions for the uniform persistence of disease are derived. • Our results highlight the crucial role of information spreading in disease eradication. [ABSTRACT FROM AUTHOR]

Most existing image inpainting methods have achieved remarkable progress in repairing small image defects. However, when the holes are large, their filling contents suffer from structural distortion and center blur due to the weak correlation between known and unknown pixels. In this paper, we propose a Progressive Feature Generation (PFG) network which is mask awareable during the process of filling irregular holes. Specifically, in order to strengthen the constraint for the hole center, we propose dynamic partial convolution which can adaptively adjust the inpainting proportion according to mask ratio in each recurrence. To synthesize high-quality features in the feature generation phase, two parallel encoders are designed which could effectively improve the capability of model to restore large holes. We argue that during feature merging, the signals generated earlier at the same location are more credible. To this end, we develop the sub-regional weighted merging(SWM) method for PFG-Net to accurately merge the feature maps generated during the whole reconstruction process. Experiments on CelebA, Places2, and Paris StreetView datasets show that our model has excellent performance especially for large holes. • Dynamic partial convolution is designed to strength the correlation between valid and invalid pixels. • We design two parallel encoders to provide semantic understanding and image structure recognition for model. • Sub-regional weighted merging method is introduced to better utilize the earlier generated signals. • Experimental results show that our PFG-Net has significantly boost the inpainting performance. [Display omitted] [ABSTRACT FROM AUTHOR]

Summary Chordomas are very rare bone malignant tumours that have had a shortage of effective treatments for a long time. New treatments are now available for both the local and the metastatic phase of the disease, but the degree of uncertainty in selecting the most appropriate treatment remains high and their adoption remains inconsistent across the world, resulting in suboptimum outcomes for many patients. In December, 2013, the European Society for Medical Oncology (ESMO) convened a consensus meeting to update its clinical practice guidelines on sarcomas. ESMO also hosted a parallel consensus meeting on chordoma that included more than 40 chordoma experts from several disciplines and from both sides of the Atlantic, with the contribution and sponsorship of the Chordoma Foundation, a global patient advocacy group. The consensus reached at that meeting is shown in this position paper. [ABSTRACT FROM AUTHOR]

Currently, the majority of RGB-Depth salient object detection (SOD) methods utilize the encoder–decoder architecture. However, they often fail to utilize the encoding and decoding features fully. This paper rethinks the differences and correlations between them and proposes the Cross-Modal Inverse Guidance Network (CMIGNet) for SOD. Specifically, a Multi-level Feature Guidance Enhancement (MFGE) module is integrated into every layer of the foundational network. It employs a high-level decoding feature to guide the low-level RGB and depth encoding features, facilitating the rapid identification of salient regions and noise removal. The dual-stream encoding features guided by the MFGE module are combined using the proposed Dual-Stream Interactive Fusion (DSIF) module. It could simultaneously reduce dependence on two modal features during the fusion process. Thus, the impact on the results can be reduced in complex scenes when one modality is absent or confusing. Finally, the edge information is supplemented using the proposed Edge Refinement Awareness (ERA) module to generate the final salient map. Comparisons on seven widely used and one latest challenging RGB-D datasets show that the performance of the proposed CMIGNet is highly competitive with the state-of-the-art RGB-Depth SOD models. Additionally, our model is lighter and faster. • CMIGNet addresses the issue of low-level coding features interfered with by noise. • High-level decoding features guides low-level encoding features for varied feature interaction levels. • The dual-stream structure can reduce the interference of noise during fusion. [ABSTRACT FROM AUTHOR]

• Introduces Anticipation Reliance as a breakthrough concept for anticipation in driving. • Integrated in a generic multi-scale framework linking driver models to human factors. • AR acts as a descriptive compensative mechanism against driver task oversaturation. • Bridges the gap between classic collision-free idealized models and HF models. • Gives novel insights into applying anticipation and awareness in traffic modelling. The inclusion of human factors (HF) in mathematical models is proving crucial to allow complex driving behaviour and interactions to be explicitly considered to capture driving phenomena. An important area where such integration is required is for the role of anticipation by drivers to compensate for critical traffic situations. In this paper, we introduce the concept of Anticipation Reliance (AR), which acts as a demand lowering compensative effect for the driving task by relying more on anticipation. We implement AR in a generic multi-scale microscopic traffic modelling and simulation framework to explore and explain the effects of HF on traffic operations and safety in critical traffic situations. This concept addresses a disparity in the description of driver workload in relation to the execution of driving tasks in regard to the confidence that drivers place on tasks that are sub-consciously catered for. The crossover from HF to a mathematical description of this role of AR introduces a ground-breaking concept that explains and models the mechanisms that allow drivers to compensate and avoid accidents in many circumstances, even when driving errors or sub-optimal driving performance occurs. By and large, the HF effects can be subdivided in effects on perception and anticipation; effects on sensitivity and response to stimuli; and effects on personal attributes and characteristics. A key aspect of the framework are two intertwined driver-specific mental state variables—total workload and awareness—that bridge between classic collision-free idealized models for lane changing and car following, and HF models that explain under which conditions the performance of drivers deteriorates in terms of reaction time, sensitivity to stimuli or other parameters. In this paper, we focus on the awareness construct, as described by AR, and explore the effects. We prove the effectiveness of the approach with a case example that demonstrates the ability of the model to dissect a complex traffic situation with both longitudinal and lateral driving tasks, while endogenously considering human factors and that produces accident avoidance and occurrence within the same order of magnitude compared to real traffic accident statistics. [ABSTRACT FROM AUTHOR]

Municipal Solid Waste Management (MSWM) has clear implications on livability of cities and public health. Unfortunately, MSWM is often considered the responsibility of the Government alone. On the other hand, there is near unanimity world over among all key stakeholders on the need for citizens to involve themselves well in this process since they are the primary generators of municipal solid waste. However, lack of awareness among citizens and communication between Government and citizens is a major impediment in realizing this. Therefore, there is a need for some policy initiatives that would bridge this communication gap. Accordingly, this paper develops a framework for formulating a Waste Management Communication Policy (WMCP) that identifies the key concerns of the citizens and the appropriate channels to create awareness among them. In this paper, econometrics based Multinomial Logistic Regression (MLR) models are developed to identify appropriate communication channels based on socio-economic categories of the citizens. The models and their analyses provide actionable insights for planning a targeted outreach campaign for MSWM awareness. [ABSTRACT FROM AUTHOR]

Pursuing personal goals above communally shared interests is incompatible with socially sanctioned norms yet represents the hallmark of high levels of dark traits (DT; narcissism, Machiavellianism, and psychopathy). One yet unaddressed question is how this conflict affects subjective authenticity, traditionally defined as a natural tendency to perceive and present oneself as genuine rather than contrived. In this theoretical paper, we propose a novel perspective on the nature of the true self, recasting Kernis and Goldman's process-oriented framework of authenticity (2006) to accommodate high DT individuals' particular authenticity paths. We review how self-concept perceptions may affect high DT individuals' interaction with their environment, leading to a more agentic or communal external orientation, affecting their behaviours and social relations. Acknowledging antisocial tendencies can aid in recognising situational factors that trigger the expression or the concealment of this "dark true self". Machiavellianism is related to internal self-orientation challenges (e.g. low awareness), whereas narcissism supports a more functional internal self-orientation. By bringing together previously disjointed lines of research on authenticity and self-presentation, along with situational contingencies, we aim to elucidate the cause of disfluencies in one's self-system and potentially initiate pioneering research in characterising the dark correlates of the true self. [ABSTRACT FROM AUTHOR]

The performance of the infrared search and track system depends on the robust of the infrared small target detection. In this paper, a novel small target detection with multi-feature fusion is proposed. At first, the salient proposal region is obtained with phase spectrum Fourier transform (PFT). Then, geometric moment is utilized to design an adaptive steerable edges awareness factor, which effectively eliminates the strong edge clutters. Afterwards, we adopt a three-layers local contrast structure and the difference-ration form is introduced to enhance the target and suppress the complex backgrounds. Finally, the adaptive threshold is applied to extract the target in the saliency map. Experiments are conducted on four real IR image sequences, which demonstrate that the proposed method outperforms the comparing methods with various complex backgrounds. [ABSTRACT FROM AUTHOR]

The Cislunar region is crucial for expanding human presence in space in the forthcoming decades. This paper presents a comprehensive review of recent and anticipated Earth–Moon missions, and ongoing space domain awareness initiatives. An introduction to the dynamics as well as periodic trajectories in the Cislunar realm is presented. Then, a review of modern Cislunar programs as well as smaller missions are compiled to provide insights into the key players pushing towards the Moon. Trends of Cislunar missions and practices are identified, including the identification of regions of interest, such as the South Pole and the Near-rectilinear halo orbit. Finally, a review of the current state and short-comings of space domain awareness (SDA) in the region is included, utilizing the regions of interest as focal points for required improvement. The SDA review is completed through the analysis of the Artemis 1 trajectory. [ABSTRACT FROM AUTHOR]

Entity alignment refers to associate entities in different knowledge graphs if they are semantically identical. Embedding-based entity alignment approaches encode entities in a continuous embedding space where entities are aligned based on the similarity of learned embeddings. However, there exists ambiguity and uncertainty in entity alignment caused by single alignment metric. In this paper, a fuzzy entity alignment method FuzzyEA is proposed to model the uncertainty in alignment process based on intuitionistic fuzzy set (IFS). Iterative TransE model is designed to learn relational structure of knowledge graphs, where mutual selection and error correction mechanism is proposed to enhance the effect of iteration. The alignment results obtained by name/description embedding and structure embedding are fused based on Dempster's combination rule. Experiments on three benchmark datasets demonstrate that the proposed FuzzyEA consistently outperforms other entity alignment methods and contributes to promising improvement in alignment accuracy and discrimination ability. [ABSTRACT FROM AUTHOR]

• A collective POI recommendation model is given for visiting initial and next POIs. • The initial POI recommendation enables a joint modeling of various factors. • The next POI recommendation applies transfer probability and context information. [Display omitted] With the popularity of mobile devices, location-based services, such as Foursquare and Facebook, have attracted increasing attention in recent years. Users share their interests anytime, anywhere with their friends on the social network. Point-of-Interest (POI) recommendation, as one of key services on Location-Based Social Networks (LBSNs), can effectively enhance users' experience especially when they travel in a new city. Previous studies have made great success on POI recommendation by employing geographical influence and user preference. However, it is believed that the human decision on where to visit is very complex and involves the comprehensive factors such as POI popularity, POI location, user trajectory, and time context. In this paper, we propose a collective POIs recommendation framework which leverages the individual latent preference and contextual information. Firstly, to recommend top-K initial POIs, a scoring prediction model is constructed, which considers the influence of similarity, popularity and location of POIs. Furthermore, a next POI recommendation model based on personalized transfer probability is proposed, and the initial POI recommendation is combined to calculate the user's score on the next POI. Extensive experiments based on real datasets collected from Foursquare demonstrate the proposed framework outperforms the state-of-art ones. [ABSTRACT FROM AUTHOR]

• Investigates factors influencing public awareness of autonomous vehicles (AVs). • Discloses awareness is correlated with age, income, disability, public transport, and safety. • Reveals awareness is negatively correlated with increase in the number of household vehicles. • Provides insights into public awareness towards the potential benefits of AVs. • Emphasises opportunities and challenges associated with the public's expectations from AVs. Growing concerns, regarding transport disadvantage and increased socioeconomic disparities, demand innovative ways to improve equity and make transportation systems smarter. One of the most anticipated developments within the field of smart mobility is driving automation systems. Autonomous vehicles (AVs) have the potential to improve accessibility, affordability, safety and efficiency of transport systems, but come with risks including the intensification of private vehicle travel. Understanding public awareness is an important step to determine whether motivation and intention to use AVs will impact on its potential. This paper aims to investigate factors influencing public awareness of AVs as a solution to transport disadvantage. The study applies an ordinal logistic regression analysis to analyse survey data collected from the residents of Brisbane (Australia). Our findings reveal that awareness is positively correlated within young and middle-aged adults, low-income residents, disability, public transport users, and those who feel unsafe on public transport, while an increase in the number of household vehicles is negatively correlated. The study provides insights into public awareness towards the potential benefits of AVs, and highlights potential opportunities and challenges associated with these expectations. [ABSTRACT FROM AUTHOR]

Existing methods based on convolutional neural networks (CNNs) have progressed significantly in the salient object detection task. However, in some cases, the accurate detection of intact objects and maintenance of their original detailed structures, such as boundaries, was not possible. In this paper, we propose an Enhancing Region and Boundary Awareness Network (ERBANet), equipped with attentional feature enhancement (AFE) modules to improve the detection performance. The AFE modules act on high-level and low-level features to generate corresponding attentional features. High-level attentional features are used to highlight entire salient objects, while low-level attentional features help retain their boundaries. The proposed ERBANet aims to effectively aggregate high-level and low-level attentional features, fully utilizing their respective advantages. Furthermore, we propose a novel boundary maintenance loss (BML) for learning to preserve the original boundaries of salient objects. Meanwhile, dice loss is combined for learning to enhance the integrity of salient regions. The experimental results on five benchmark datasets demonstrate that our proposed method outperforms recent state-of-the-art methods and achieves better performance. [ABSTRACT FROM AUTHOR]

Complex tasks such as treating patients with chronic conditions and developing software products are typically accomplished by teams that collaborate over an extended time duration. To remain coordinated, team members need to be aware of others' activities if those activities are likely to affect their own actions. However, identifying such interactions and sharing information appropriately is challenging, especially when the activities of team members are loosely-coupled. In practice, team members often either lack important information about others' activities, or are overwhelmed by the need to review too much information. This paper presents Personalized Change Awareness , a new approach for supporting team coordination which aims to automatically identify and share the subset of information about others' activities that is most relevant to each of the team members. The paper formally defines the computational problem of information sharing in loosely-coupled teamwork, which underlies the personalized change awareness approach. It defines a new representation, Mutual Influence Potential Networks (MIP-Nets) and an algorithm, MIP-DOI, that uses this representation to determine the information that is most relevant to each team member. In contrast to existing information sharing algorithms in multi-agent teams, MIP-DOI does not assume the availability of a priori knowledge of a team's possible plans, because human teams rarely explicitly define detailed long-term plans in advance. We demonstrate the ability of MIP-DOI to identify relevant information using simulations of collaborative activities. We further evaluated the contribution of personalized change awareness to team performance in a controlled user study. To this end, we developed a personalized change awareness mechanism for collaborative writing, which used MIP-DOI to determine which changes to share with each author. This evaluation demonstrates that the Personalized Change Awareness approach resulted in higher productivity and lower perceived workload without any change in final quality compared to the currently prevalent approach of sharing all change information. Our results also demonstrate that merely reducing the amount of information shared with co-authors is not enough: sharing a random subset of changes resulted in significantly lower quality of work than sharing a personalized subset of changes. [ABSTRACT FROM AUTHOR]

Serving as the information backbone of an increasingly digitalized world and energy-intensive, the data center industry has to seek low-carbon developing way to properly meet the rapidly rising service demand. This paper designs 45 kinds of configuration choices for each decision units by integrating three data centers, three cooling technologies, as well as five energy supply mixes. The model is formulated as a bi objective optimization problem for the decision maker to decide newly established data location and configuration with objective of reducing data center expansion costs and well as minimizing power utilization associated with carbon emissions from a country level. The pilot study in China under the scenarios of different carbon emissions limitation and propagation delay that China can reduce at least 25% of newly established data center related emissions from 2020 to 2023. It further shows that future expansion can be focus on Inner Mongolia and Ningxia for the north, Hubei for the middle, and Yunnan and Guangxi for the south. This paper provides a fresh perspective and makes a valuable attempt at the low-carbon development of data centers. • Carbon awareness based integrated optimization model for data center in China is proposed. • Layout, scale, cooling technology and power supply mix are simultaneously considered. • 25% of newly established data center electricity related emissions in China can be avoided. [ABSTRACT FROM AUTHOR]

Charles Darwin's On the Origin of Species has an unusual format. After presenting his theory of Natural Selection in its first four chapters, there follows a series of five chapters presenting a large number of problems and objections to the theory which, he admits, appear overwhelming. Not until chapter 10 does he begin to present what he takes to be the positive evidence for his theory. In this paper I trace the evolution of this structure from its first hints in his Species Notebooks , through the 1842 Sketch and 1844 Essay to the Origin , showing that it reflects a growing awareness on Darwin's part of what I call 'In Principle Impossible' arguments against his theory, and of a systematic strategy for disarming them. [ABSTRACT FROM AUTHOR]

The development of efficient exact and approximate algorithms for probabilistic inference is a long-standing goal of artificial intelligence research. Whereas substantial progress has been made in dealing with purely discrete or purely continuous domains, adapting the developed solutions to tackle hybrid domains, characterized by discrete and continuous variables and their relationships, is highly non-trivial. Weighted Model Integration (WMI) recently emerged as a unifying formalism for probabilistic inference in hybrid domains. Despite a considerable amount of recent work, allowing WMI algorithms to scale with the complexity of the hybrid problem is still a challenge. In this paper we highlight some substantial limitations of existing state-of-the-art solutions, and develop an algorithm that combines SMT-based enumeration, an efficient technique in formal verification, with an effective encoding of the problem structure. This allows our algorithm to avoid generating redundant models, resulting in drastic computational savings. Additionally, we show how SMT-based approaches can seamlessly deal with different integration techniques, both exact and approximate, significantly expanding the set of problems that can be tackled by WMI technology. An extensive experimental evaluation on both synthetic and real-world datasets confirms the substantial advantage of the proposed solution over existing alternatives. The application potential of this technology is further showcased on a prototypical task aimed at verifying the fairness of probabilistic programs. [ABSTRACT FROM AUTHOR]

Most empirical researches in behavioral finance have found that the air pollution can affect the stock market in the developed world, while a few works show some different results in China. This paper deeply discusses the reasons for the difference of air pollution effect, and attributes it to the intensity of public environmental awareness. This paper selects some important environmental events which can enhance the public environmental awareness as samples, to investigate the influence of public environmental awareness on the air pollution effect. The results firstly show that the air pollution effect in Chinese stock market is not significant during a long-term sample period. Secondly, the empirical findings support that these events have significant impacts on the air pollution effect in Chinese stock market. Finally, this paper provides direct evidence on that the air pollution effect has a significant negative change after these events, which is consistent with the expected impact of the air pollution effect's mechanism. This research extends the behavioral financial issue of air pollution effect and provides the empirical evidence for the public environmental awareness's obstacle to the air pollution effect. [ABSTRACT FROM AUTHOR]

• Clinical and cognitive insight are associated with different brain areas. • Clinical insight is associated with spatially diffuse abnormalities across the brain. • Clinical insight might rely on a broad range of (social/meta-) cognitive functions. • Cognitive insight is mainly associated with ventrolateral prefrontal cortex and hippocampal areas. • Cognitive insight appears to rely more on the retrieval and integration of self-related information. In the past years, ample interest in brain abnormalities related to clinical and cognitive insight in psychosis has contributed several neuroimaging studies to the literature. In the current study, published findings on the neural substrates of clinical and cognitive insight in psychosis are integrated by performing a systematic review and meta-analysis. Coordinate-based meta-analyses were performed with the parametric coordinate-based meta-analysis approach, non-coordinate based meta-analyses were conducted with the metafor package in R. Papers that could not be included in the meta-analyses were systematically reviewed. Thirty-seven studies were retrieved, of which 21 studies were included in meta-analyses. Poorer clinical insight was related to smaller whole brain gray and white matter volume and gray matter volume of the frontal gyri. Cognitive insight was predominantly positively associated with structure and function of the hippocampus and ventrolateral prefrontal cortex. Impaired clinical insight is not associated with abnormalities of isolated brain regions, but with spatially diffuse global and frontal abnormalities suggesting it might rely on a range of cognitive and self-evaluative processes. Cognitive insight is associated with specific areas and appears to rely more on retrieving and integrating self-related information. [ABSTRACT FROM AUTHOR]

Deep-learning based image quality assessment (IQA) algorithms usually use the transfer learning method that transfers a pre-trained network for classification task to handle IQA task. Although it can overcome the problem of having insufficient IQA databases to some extent, it cannot distinguish between the important and unimportant deep features for the IQA task, which potentially leads to inaccurate prediction performance. In this paper, we propose a no-reference IQA method based on modelling of deep feature importance. A SE-VGG network is developed by using adaptive transfer learning method. It can suppress the features of local parts of salient objects of images that are not important to the IQA task, and emphasize the features of image distortion and salient objects that are important to IQA task. Moreover, the structure of the SE-VGG is investigated to improve the accuracy of the image quality assessment on a small IQA database. Experiments are conducted to evaluate the performance of the proposed method on various databases, including the LIVE, TID2013, CSIQ, LIVE multiply distorted and LIVE challenge. The results show the proposed method significantly outperforms the state-of-the-art methods. In addition, our method demonstrates a strong generalization ability. [ABSTRACT FROM AUTHOR]

With the prosperity of the location-based social networks, next point-of-interest (POI) recommendation has become an increasingly significant requirement since it can benefit both users and business. Obtaining insight into user mobility for the next POI recommendations is a vital yet challenging task. Existing approaches to understanding user mobility mainly gloss over the check-in sequence, making it fail to explicitly capture the subtle POI–POI interactions across the entire user check-in history and distinguish relevant check-ins from the irrelevant. In this paper, we proposed a novel recommendation approach, namely geographical-temporal awareness hierarchical attention network (GT-HAN) to resolve those issues. We first establish a geographical-temporal attention network to simultaneously uncover the overall sequence dependence and the subtle POI–POI relationships. Then, a context-specific co-attention network was designed to learn to change user preferences by adaptively selecting relevant check-in activities from check-in histories, which enabled GT-HAN to distinguish degrees of user preference for different check-ins. Finally, we make a POI recommendation using a conditional probability distribution function. Experimental results on real world datasets (obtained from Foursquare and Gowalla) show that the GT-HAN model significantly outperforms current state-of-the-art approaches, and demonstrating the benefits produced by new technologies incorporated into GT-HAN. [ABSTRACT FROM AUTHOR]

• Many business drivers were unaware of with which ADAS their car is equipped. • Others stated owning specific ADAS in contradiction to their vehicle's specifications. • Self-reported ADAS ownership is in fact awareness of ADAS ownership. • Vehicle specifications are required to assess drivers' ADAS usage by questionnaire. Now Advanced Driver Assistant Systems (ADAS) have entered the consumer market policymakers need instruments to monitor not only the amount of ADAS-equipped cars but also whether car owners actually use these systems; a prerequisite for many ADAS to achieve road safety and/or CO 2 reduction benefits. Although questionnaires provide the most common method for large scale fleet assessment, current questionnaires fall short in their ability to assess ADAS usage. Their main pitfall is that ADAS ownership – necessary to compute the gap between ADAS ownership and usage – is based on self-reported rather than actual ADAS ownership. In other words, it is implicitly presumed that car owners are aware of the ADAS with which their car is, or is not, equipped. To our knowledge, the presumption of awareness of ADAS ownership has not been acknowledged as a limitation in current questionnaire-based studies on ADAS usage. Therefore, this paper presents a methodology to address the awareness gap by combining survey and vehicle registration analysis to assess car owners' usage of ADAS in fleets. In this method, ADAS awareness constitutes of the difference between ADAS presence based on self-report and ADAS presence based on the vehicle specifications of participants' own car (obtained through participants' licence plate registration number). To test both the assumption that an awareness gap exists for ADAS as well as the feasibility of the method itself, a survey was performed under 1355 Dutch business drivers. It involved ADAS such as Adaptive Cruise Control, Lane Departure Warning and Emergency Brake. The study showed that many business drivers were unaware of with which ADAS their car is equipped and as a result, they were unable to report they own the ADAS concerned. However, drivers aware of owning a specific system generally reported that they use it. Additionally, the study revealed another group of participants: those who stated owning a specific ADAS while their vehicle specifications contradict this. In conclusion, awareness of ADAS ownership constitutes an important link that is currently missing when concatenating market penetration studies and regular questionnaires on ADAS usage. The proposed method may overcome this shortcoming, increase the validity of surveys on ADAS usage and provide large scale insights in the use of ADAS throughout an entire fleet of vehicles. [ABSTRACT FROM AUTHOR]

Ordinary human experience is embedded in a web of causal relations that link the brain to the body and the wider environment. However, there might be conditions in which brain activity supports consciousness even when that activity is fully causally isolated from the body and its environment. Such cases would involve what we call islands of awareness: conscious states that are neither shaped by sensory input nor able to be expressed by motor output. This Opinion paper considers conditions in which such islands might occur, including ex cranio brains, hemispherotomy, and in cerebral organoids. We examine possible methods for detecting islands of awareness, and consider their implications for ethics and for the nature of consciousness. Awareness may persist in fully disconnected cortical islands. We identify both natural and artificial examples of potential islands of awareness. Detecting islands of awareness poses difficult but often addressable challenges. The possibility of islands of awareness raises important ethical and legal issues. The discovery of islands of awareness would have important implications for debates about the nature of consciousness. [ABSTRACT FROM AUTHOR]

• Low emotional awareness (EA) is associated with multiple clinical conditions. • The neurocomputational processes underlying EA are poorly understood. • We present a deep active inference model of EA that can simulate these processes. • This model illustrates 7 distinct mechanisms whereby aberrant processing produces low EA. • This may offer distinct targets that could inform individualized treatment selection. Emotional awareness (EA) is recognized as clinically relevant to the vulnerability to, and maintenance of, psychiatric disorders. However, the neurocomputational processes that underwrite individual variations remain unclear. In this paper, we describe a deep (active) inference model that reproduces the cognitive-emotional processes and self-report behaviors associated with EA. We then present simulations to illustrate (seven) distinct mechanisms that (either alone or in combination) can produce phenomena – such as somatic misattribution, coarse-grained emotion conceptualization, and constrained reflective capacity – characteristic of low EA. Our simulations suggest that the clinical phenotype of impoverished EA can be reproduced by dissociable computational processes. The possibility that different processes are at work in different individuals suggests that they may benefit from distinct clinical interventions. As active inference makes particular predictions about the underlying neurobiology of such aberrant inference, we also discuss how this type of modelling could be used to design neuroimaging tasks to test predictions and identify which processes operate in different individuals – and provide a principled basis for personalized precision medicine. [ABSTRACT FROM AUTHOR]

Wireless network-on-chip (WiNoC) is a new paradigm to mitigate the long-distance transmission latency for conventional wired network-on-chip. The wireless routers in WiNoC have to handle a large number of packets which could cause data congestion, thus reducing the network performance. In this paper, we propose a novel wireless routing algorithm, called CPCA, which exploits the cross path congestion information as hints to route the packets. Under CPCA, the whole network is partitioned into sub-networks. In each subnet, the congestion information of the wireless router is propagated along the cross path. As a result, the routers in the same dimension can get the congestion degree of wireless router within the subnet. Based on the congestion information, CPCA can compute the suitable path for packets routing, which can prominently avoid the congestion aggravation in the wireless router. Experimental results show that our proposed method can effectively improve performance in terms of packets transmission latency and network throughput. • We established an efficient wireless routing algorithm in WiNoC for cross path congestion awareness (CPCA). • In the proposed topology, the wired routers in the same dimension can get the congestion degree of wireless routers. • CPCA can compute the optimal path for packets routing, which can prominently avoid the congestion aggravation. [ABSTRACT FROM AUTHOR]

The stability of random Boolean networks (RBNs) has aroused continuous attention due to its profound applications in genetic regulatory. With extensive research on the joint influence of network topology and update rules, the interaction between RBN stability and other dynamic processes remains to be investigated. In this paper we propose an interactive multiplex model and study the dynamical interplay between RBN stability and awareness propagation. Theoretical analysis reveals that the stability of RBN is enhanced by awareness propagation. Specifically, its transition point between stable and unstable phases is determined by the main eigenvalue of H , which is closely related to the prevalence of awareness propagation and coupling strength between two layers. In addition, we illustrate the existence of a meta-critical point, from which awareness propagates and thus inhibits chaotic behaviors in RBN. Our work sheds light on the interaction between RBN stability and awareness propagation, and provides theoretical guidance to intervention of real Boolean systems. • The dynamical interplay between RBN stability with awareness propagation is investigated in a multiplex model. • The stability of RBN layer is affected by the prevalence of awareness propagation and coupling strength between two layers. • The transition point between stable and unstable phases is determined by matrix H. • Awareness propagation defines a meta-critical point from which RBN stability is enhanced. [ABSTRACT FROM AUTHOR]

Sustainable construction can be defined as efficient resource use with ecological design. Three pillars are required to achieve sustainable construction: environmental protection, social well-being and economic prosperity. An increase in sustainable construction awareness among industry stakeholders could help improve levels of implementation. This paper aims to assess levels of awareness among UK practitioners and identify the barriers that inhibit sustainable construction. A mixed-method approach with questionnaire surveys and interviews was adopted. Questionnaire were distributed using MS Forms through social media (e.g., LinkedIn, Microsoft Teams, WhatsApp) and the researcher's contacts. The survey concluded with 127 useable responses. Interviews were also conducted over MS Teams with eight professionals with different backgrounds. Respondents for the interview were conveniently gathered through snowball sampling. Data from the questionnaire survey were analysed using SPSS. Descriptive statistics, Cronbach's Alpha and RII were calculated for the level of awareness of sustainable construction practices and barriers to adoption of sustainable construction. Transcripts from the interview were thematically analysed using NVivo. The findings showed that the industry practitioners have above-average levels of awareness. Sustainable construction practices that respondents were most aware of was reduce, reuse and recycle. Respondents were not as aware of newer sustainable practices such as digital twin. Surprisingly, the most prevalent barrier was not cost. According to the respondents, the most prevalent barrier was a tendency to maintain current practices. Many real-life experiences are documented in this research. Insights into current practice in the industry and key barriers to adopting sustainable construction are identified. Addressing these barriers could improve the adoption of sustainable construction in the UK. • investigates up-to-date techniques for attaining sustainable construction in the UK. • assess the level of awareness among industry practitioners. • identifies the hurdles faced by the industry to attain sustainable construction. • explains the current practices of construction and areas for potential improvement. [ABSTRACT FROM AUTHOR]

In this paper, we propose an adaptive, fault-tolerant, and congestion-aware (AFTC) routing algorithm. It improves the overall network performance of three-dimensional Network-on-Chip (3D NoC) in terms of deadlock-free, adaptability, fault tolerance, and balanced traffic. Firstly, the algorithm proposed 12 turn models to avoid deadlock problems. Then, the adaptability of the algorithm is improved by using the three-dimensional diagonal region division method. At the same time, the AFTC can reduce the number of turn-around steps during fault tolerance. Finally, the AFTC can balance traffic by setting different congestion thresholds. Compared with Vertical-Mesh-Conscious-Dynamic (VMCD) algorithm in terms of average packet delay and throughput, the AFTC algorithm can improve the throughput by an average of 27.29 % and 18.62 % while reducing the average delay time in different fault rates under uniform traffic and hotspot traffic patterns, respectively. [ABSTRACT FROM AUTHOR]

In this paper we expand on a theoretical-methodological framework for micro sociolinguistic multimodal analysis of situated practices in English-Medium Instruction (EMI) research (Moncada-Comas, & Sabaté-Dalmau, in press). We examine an interaction of a lecturer and his 21 students in an engineering class at a Catalan university. Drawing from video/audio-recorded classroom interactions, interviews, logs and visual materials, we argue that the EMI lecturer's praxis hinges on the use of multimodal-competence repertoires (non-verbal cues involving materiality, kinesics and positionality) and of multiple technology-based learning channels (Internet sources), for particular pedagogical functions. We show that his multimodal and multichannel lecturing intersects with his plurilingual practices (e.g., his monitored local-language(s) use), which become key to help students with insufficient English-language competence to access disciplinary content. Overall, our case study reveals that the combination of semiotic/kinesic modes and multichannel strategies with meaningful use of shared local languages makes "doing education" successful, without jeopardising the attainment of the required content and English-language level. This contributes to the exploration of how multimodal, multichannel and plurilingual practices interplay in effective classroom interactions, and of the affordances of our framework for the design and applicability of EMI pedagogies that consider all language ecologies in non-English speaking universities. [ABSTRACT FROM AUTHOR]

This paper discusses the relevance of information overload for explaining environmental degradation, insofar it can reduce individuals' awareness of the unsustainable side-effects of their choices. This "myopia" is reinforced by the increased distance from nature in everyday life brought about by the abundance of exosomatic energy. The departure point of the paper is to show that two outstanding intellectuals, engaged in very different fields, have set forth very similar reflections on the effects of information overload, namely the film director Wim Wenders and the social scientist, really a polymath, Herbert Simon, whose relevance to ecological economics has been recognised. The presentation of their ideas is then complemented by a presentation of the state of the art on information overload, which allows moving to our core argument about environmental degradation. • Information overload (IO) is a key notion both in Simon's and Wenders' works • IO and distance from nature lower awareness of environmental side effects of choice • Low awareness helps explaining environmental degradation [ABSTRACT FROM AUTHOR]

Public environmental awareness (PEA) is critical to the implementation of environmental policies by governments and product strategies by companies, however, there is less literature discussing its drivers. Environmental information disclosure (EID), as a bottom-up environmental policy, promotes public environmental management. This paper takes the Pollution Information Transparency Index (PITI) as a proxy for the degree of EID and discusses its impact on PEA. Using the PITI of 113 cities and the Baidu index-based PEA for 30 provinces from 2010 to 2019, we find a significant increase in PEA with the widening of EID, and the impacts of EID get larger as the quantile increases. Moreover, the effects of EID on PEA are amplified in areas with heavy pollution, high levels of green innovation, and stringent environmental governance capacities, but are diminished in areas with rampant corruption. The findings of this paper provide recommendations for optimizing EID implementation. • With the expansion of EID, PEA has increased significantly. • The impacts of EID on PEA are amplified in areas with high pollution, green innovation, and strict governance capacities. • The impacts of EID on PEA are diminished in areas with rampant corruption. [ABSTRACT FROM AUTHOR]

Alignment refers to the largely automatic tendency of interlocutors to re-use each other's language patterns in the course of authentic interaction (Pickering & Garrod, 2004). However, little is known about alignment during L2 interaction, where recycling of a partner's language - in contrast to L1 conversations - might be a more conscious, strategic behaviour (Costa, Pickering & Sorace, 2008). In this paper we explore attention and awareness of L2 speakers to their partner's language use during digitally mediated communication (text chat) and see whether and how they re-use each other's lexical phrases. Twelve English-L1 learners of L2 German participated in a task-based text chat discussion. Participants communicated under two different conditions: with a native speaker tutor vs. with a language learning peer. Three data sources were scrutinised: (1) chatlog analyses looked for re-used lexical phrases; (2) eye-gaze fixations on re-used text served as measures of attention; (3) awareness of language re-use was evaluated by means of cued interviews. Results reveal both unaware and strategic alignment; more of it happening in the tutor chat; and more advanced students being less inclined to copy their partner. Findings will be discussed in light of Costa et al.'s (2008) framework on L2 alignment. [ABSTRACT FROM AUTHOR]

Educational properties have recorded an average of 4980 structure fires between 2011 and 2015 according to US National Fire Protection Association. It has been determined that four out of five fires in schools have occurred between 6 a.m. and 6 p.m., when pupils were around. Even though the percentage of structure fires in educational properties has fallen over the years, there is still place for improvement and knowing how to react in such situations can contribute to reducing pupils' injuries and also to their lifesaving. Thus, the present paper analysis the possibility of using agent-based simulations in NetLogo for a classroom with two exits in order to increase pupils' awareness on how to act during an evacuation process caused by a fire. Based on an empirical study, it has been observed that after presenting the NetLogo evacuation simulation, the pupils' awareness has increased with up to 22.99%, having a direct result on the overall evacuation time and their safety. • A two-phases pilot study is conducted on pupils classroom evacuation. • The factors impacting classroom evacuation time are determined and analyzed. • Three evacuation criteria are used in the agent-based model. • Knowledge gathered increases pupils' awareness, reducing the evacuation time by 22.99%. • The agent-based model can be easily adapted for analyzing various real-life evacuations. [ABSTRACT FROM AUTHOR]

Abstract The demands placed on Higher Education Institutions (HEIs) to deliver sustainability initiatives alongside their long-standing social responsibility commitments has been recognised in literature. However, how these interrelate in practice continues to be relatively unexplored. The extant literature suggests that the integration of the two connected agendas can be problematic due to a range of factors, including a general lack of awareness or even misconceptions of the respective agendas. This paper explores the attitudes and practices related to the integration of social responsibility and sustainability initiatives at HEIs. Theoretically, this study highlights the ongoing relative positioning and importance of economic factors – as it relates to differentiation rather than integration – over others such as social responsibility and sustainability. The main implication of this study is that provide useful insights into how HEIs can closer integrate two contemporary but potentially competing agendas. Graphical abstract Image 10884 [ABSTRACT FROM AUTHOR]

In this paper the awareness and attitudes towards traceability are examined in the food supply chain (FSC) within UK Small and Medium-sized Enterprises (SMEs). The objective of this paper is to test some propositions found in the literature, and understand whether or not SMEs in the UK are aware of the main objectives of traceability and what importance is given in term of relevance and intention to invest in their traceability systems. This study employed a survey strategy by means of a questionnaire that was sent to food and drink companies operating in the FSC. 146 SMEs answered the questionnaire. Answers were analysed by means of quantitative tools (i.e. contingency tables; Chi-squared test; and Spearman's rank correlation). The findings show that UK SMEs are aware of the main purposes of traceability systems such as recall cost reduction, rapid recalls, and the improvement of food safety and quality, while there is a, misguided, belief that traceability systems can reduce the probability of recalls. The attitude towards traceability is found to be positive in term of recognition of its importance but an unwillingness to invest in traceability systems' enhancement is also found. The findings pose a question mark as to whether or not companies see traceability systems as a strategic tool. This study fills the gap found in the literature where few recent academic papers focused attention on SMEs awareness and attitudes towards traceability in the FSC. [ABSTRACT FROM AUTHOR]

The concept of mobility biographies has recently gained considerable currency in social-scientific transport research, reflecting increasing awareness of the long-term development of mobility practices. To complement existing work on the effects of major life events on mobility practices, we introduce the novel concept of ‘mobility milestones’, that is, mobility-related events across the life course that show a very high dependence on (infra)structural factors. Overall, the paper explicitly recognises the centrality of interactions between structural factors and human agency, thereby moving beyond a sole focus on individual travel behaviour. Initially, this paper reviews social and interdisciplinary research on mobility biographies, especially studies that use retrospective survey methodologies. Building on this review, we present an innovative online survey tool that has been deployed on the island of Ireland (n = 324) to record changes in individuals' mobility biographies and their connections with both life events and mobility milestones. This is followed by a discussion of key findings regarding the impact of starting college, residential relocation and changing employment, as well as buying a car or bicycle or getting a driving licence on individuals' modal choice. [ABSTRACT FROM AUTHOR]

The literature (2012-4) describing experimental pig surgery was reviewed to estimate the extent to which neuromuscular block (NMB) is used, to examine methods for ensuring unconsciousness, and to identify the rationale for use of NMB and establish the anaesthetist's training. In the first stage of a two-stage review, NMB use was estimated using Web of Knowledge to identify articles describing NMB during pig surgeries. In the second stage, PubMed and Google Scholar were used to increase the number of articles for determining measures taken to prevent accidental awareness during general anaesthesia (AAGA). The corresponding authors of screened articles were emailed four times to establish the reason for using NMB and the anaesthetists' backgrounds (medical, veterinary, or technical). The first search revealed NMB use in 80 of 411 (20%) studies. Of the 153 articles analysed in the second stage, two described strategies to reduce AAGA. Some (6%) papers did not provide information on anaesthetic doses; citations supporting anaesthetic efficacy were found in only 13. Five of 69 papers using inhalation agents measured end-tidal anaesthetic concentrations based on human, not porcine, minimal alveolar concentrations. The methods in 13% of articles reporting anaesthetic depth assessment were incomplete or questionable, or both; four described using somatic motor reflexes. Corresponding authors of 121 articles reported that the principal reason for NMB was improved 'surgical visualization' (26%). Medical or veterinary anaesthetists supervised anaesthesia in 70% of studies; non-anaesthetists provided NMB, unsupervised, in 23. Nine respondents prioritized experimental expediency over pig welfare. In laboratory pig studies, AAGA may be prevalent; reported details of its attempted prevention are woefully inadequate. [ABSTRACT FROM AUTHOR]