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Introduction Pure Neural Leprosy (PNL) is a rare clinical form of leprosy in which patients do not present with the classical skin lesions but have a high burden of the disability associated with the disease. Clinical characteristics and follow up of patients in PNL are still poorly described in the literature. Objective This paper aims to describe the clinical, electrophysiological and histopathological characteristics of PNL patients, as well as their evolution after multidrug therapy (MDT). Methods Fifty-two PNL patients were selected. Clinical, nerve conduction studies (NCS), histopathological and anti-PGL-1serology were evaluated. Patients were also assessed monthly during the MDT. At the end of the MDT, all of the patients had a new neurological examination and 44 were submitted to another NCS. Results Paresthesia was the complaint most frequently reported by patients, and in the neurological examination the most common pattern observed was impairment in sensory and motor examination and a mononeuropathy multiplex. Painful nerve enlargement, a classical symptom of leprosy neuropathy, was observed in a minority of patients and in the motor NCS axonal injuries, alone or in combination with demyelinating features, were the most commonly observed. 88% of the patients did not present any leprosy reaction during MDT. There was no statistically significant difference between the neurological examinations, nor the NCS pattern, performed before and after the MDT. Discussion The classical hallmarks of leprosy neuropathy are not always present in PNL making the diagnosis even more challenging. Nerve biopsy is an important tool for PNL diagnosis as it may guide therapeutic decisions. This paper highlights unique characteristics of PNL in the spectrum of leprosy in an attempt to facilitate the diagnosis and management of these patients., Author summary Neuropathy is responsible for most of the disability associated with leprosy disease. The pure neural leprosy (PNL), in spite of being a rare clinical presentation of that, has some singular characteristics that are still poorly studied. In this study, we selected 52 patients at the diagnosis to describe their clinical, neurophysiological and histopathological presentation patterns as well as their evolution during the multidrug therapy (MDT). We confirmed that sensitive symptoms were the most commonly described by PNL patients but neural pain is rare. Also confirmed that mononeuropathy mutiplex was the most common pattern observed at neurological examination and at the nerve conduction studies at the moment of the diagnosis. However we showed that painful nerve enlargement other cardinal signs of leprosy, besides the skin lesions, is not common in this group of patients, There was no statistically significant difference between the neurological examination and NCS performed before and after the MDT.During the MDT88.4% of the patients did not have any leprosy reaction. This may suggest that that MDT was effective in stopping disease progression. Our results highlight the difficulties in the diagnosis of PNL and the progression following MDT in an attempt to prevent further disability in PNL patients.

Background As the world’s largest coal producer, China was accounted for about 46% of global coal production. Among present coal mining risks, methane gas (called gas in this paper) explosion or ignition in an underground mine remains ever-present. Although many techniques have been used, gas accidents associated with the complex elements of underground gassy mines need more robust monitoring or warning systems to identify risks. This paper aimed to determine which single method between the PCA and Entropy methods better establishes a responsive weighted indexing measurement to improve coal mining safety. Methods Qualitative and quantitative mixed research methodologies were adopted for this research, including analysis of two case studies, correlation analysis, and comparative analysis. The literature reviewed the most-used multi-criteria decision making (MCDM) methods, including subjective methods and objective methods. The advantages and disadvantages of each MCDM method were briefly discussed. One more round literature review was conducted to search publications between 2017 and 2019 in CNKI. Followed two case studies, correlation analysis and comparative analysis were then conducted. Research ethics was approved by the Shanxi Coking Coal Group Research Committee. Results The literature searched a total of 25,831publications and found that the PCA method was the predominant method adopted, and the Entropy method was the second most widely adopted method. Two weighting methods were compared using two case studies. For the comparative analysis of Case Study 1, the PCA method appeared to be more responsive than the Entropy. For Case Study 2, the Entropy method is more responsive than the PCA. As a result, both methods were adopted for different cases in the case study mine and finally deployed for user acceptance testing on 5 November 2020. Conclusions The findings and suggestions were provided as further scopes for further research. This research indicated that no single method could be adopted as the better option for establishing indexing measurement in all cases. The practical implication suggests that comparative analysis should always be conducted on each case and determine the appropriate weighting method to the relevant case. This research recommended that the PCA method was a dimension reduction technique that could be handy for identifying the critical variables or factors and effectively used in hazard, risk, and emergency assessment. The PCA method might also be well-applied for developing predicting and forecasting systems as it was sensitive to outliers. The Entropy method might be suitable for all the cases requiring the MCDM. There is also a need to conduct further research to probe the causal reasons why the PCA and Entropy methods were applied to each case and not the other way round. This research found that the Entropy method provides higher accuracy than the PCA method. This research also found that the Entropy method demonstrated to assess the weights of the higher dimension dataset was higher sensitivity than the lower dimensions. Finally, the comprehensive analysis indicates a need to explore a more responsive method for establishing a weighted indexing measurement for warning applications in hazard, risk, and emergency assessments.

Background Pragmatic challenges remain in the monitoring and return to play (RTP) decisions following suspected Sports Related Concussion (SRC). Reliance on traditional approaches (pen and paper) means players readiness for RTP is often based on self-reported symptom recognition as a marker for full physiological recovery. Non-digital approaches also limit opportunity for robust data analysis which may hinder understanding of the interconnected nature and relationships in deficit recovery. Digital approaches may provide more objectivity to measure and monitor impairments in SRC. Crucially, there is dearth of protocols for SRC assessment and digital devices have yet to be tested concurrently (multimodal) in SRC rugby union assessment. Here we propose a multimodal protocol for digital assessment in SRC, which could be used to enhance traditional sports concussion assessment approaches. Methods We aim to use a repeated measures observational study utilising a battery of multimodal assessment tools (symptom, cognitive, visual, motor). We aim to recruit 200 rugby players (male n≈100 and female n≈100) from University Rugby Union teams and local amateur rugby clubs in the North East of England. The multimodal battery assessment used in this study will compare metrics between digital methods and against traditional assessment. Conclusion This paper outlines a protocol for a multimodal approach for the use of digital technologies to augment traditional approaches to SRC, which may better inform RTP in rugby union. Findings may shed light on new ways of working with digital tools in SRC. Multimodal approaches may enhance understanding of the interconnected nature of impairments and provide insightful, more objective assessment and RTP in SRC. Clinical trial registration NCT04938570. https://clinicaltrials.gov/ct2/results?cond=NCT04938570&term=&cntry=&state=&city=&dist=

With the increase of patients with retinopathy, retinopathy recognition has become a research hotspot. In this article, we describe the etiology and symptoms of three kinds of retinal diseases, including drusen(DRUSEN), choroidal neovascularization(CNV) and diabetic macular edema(DME). In addition, we also propose a hybrid attention mechanism to classify and recognize different types of retinopathy images. In particular, the hybrid attention mechanism proposed in this paper includes parallel spatial attention mechanism and channel attention mechanism. It can extract the key features in the channel dimension and spatial dimension of retinopathy images, and reduce the negative impact of background information on classification results. The experimental results show that the hybrid attention mechanism proposed in this paper can better assist the network to focus on extracting thr fetures of the retinopathy area and enhance the adaptability to the differences of different data sets. Finally, the hybrid attention mechanism achieved 96.5% and 99.76% classification accuracy on two public OCT data sets of retinopathy, respectively.

Background Self-management is a promising approach to improve quality of life after stroke. However, evidence for the appropriateness and effectiveness of self-management for stroke survivors with aphasia is limited. This article reports on the process used to develop a supported self-management intervention for stroke survivors with aphasia (SSWA) using co-production and behaviour change theory. Preparatory research included systematic reviews, and qualitative interviews and focus groups with SSWA, family members and speech and language therapists (SLTs). Materials and methods We conducted six, 2 hour long intervention development workshops with key stakeholders. The workshops were informed by principles of co-production and the intervention development process outlined by the Behaviour Change Wheel (BCW). We also incorporated the findings of our preparatory research within workshops. Each workshop included an introduction, 1–2 co-production tasks and time for feedback at the end of the session. Data were analysed on an ongoing basis so that findings could be used to feed in to subsequent workshops and intervention development. Results Workshop participants (n = 12) included; SSWA (n = 5), family members (n = 3) and SLTs (n = 4). Together, participants engaged with accessible and participatory co-production tasks which aligned with the BCW framework. Participants engaged in discussion to define self-management in behavioural terms (behavioural diagnosis) and to identify what needed to change to support self-management. Participant’s co-produced solutions for supporting self-management and discussed options to implement these in practice. Prototype materials were generated by the research team and evaluated by participants. Intervention functions and behaviour change techniques (BCTs) were mapped to the solutions generated by participants by the research team, after the final workshop. A supported self-management intervention for SSWA was developed which will be delivered by SLTs through community stroke services. Conclusions This paper reports the process we used to integrate co-production work with behaviour change theory to develop a complex self-management intervention. This is of relevance for researchers looking to harness the strengths of co-production methods and theory in intervention design. Future research will feasibility test the supported self-management intervention developed. This paper provides transparency to our intervention development process which will help others to better interpret the findings of our feasibility work.

The objective of the paper is to diagnose organisational culture of selected universities and analyse its impact on the innovation processes within them. The subject matter of the study was organisational culture and innovation at universities. The subjects were four selected universities in Poland, Austria, Germany, and Ukraine. The paper provided a definition of organisational culture and its typology. It further discussed the organisational culture of universities and the relationships between organisational culture and innovativeness. The literature review provided foundations for building a model for the formation of a type of organisational culture at universities that is innovation-friendly, which is the added value of the paper. It offers actions worth taking to shape innovation-friendly culture at universities. It is particularly important during difficult time of changing labour market, when universities greatly impact the attitudes of young people. The knowledge of how to shape innovation-friendly organisational culture at universities is necessary for academia to profile future employees in times of continuous changes. To investigate the relationship between organisational culture and the innovativeness of universities, we designed an original survey questionnaire [S1 File]. Organisational culture was diagnosed with the Organizational Culture Assessment Instrument by K.S. Cameron and R.E. Quinn. The analyses were conducted in Dell Statistica v. 13.1 (StatSoft Polska). We normalised data from the Likert rating scale using Kaufman’s and Rousseeuw’s formula. We used Spearman’s correlation coefficient and Kendall’s W to calculate correlations. The research shows that the investigated Polish and Austrian universities are dominated by hierarchy and market cultures. On the other hand, the German and Ukrainian universities host all cultures, but clan and adhocracy dominate there. Moreover, the analyses demonstrated that although the adhocracy culture was the least visible in the investigated organisations, it contributes to university innovativeness the most. The conclusions were used to build a model for promoting innovation-friendly organisational culture at universities. The model contains answers to the research questions. In addition, it offers guidelines for shaping organisational culture to bolster innovation at universities. The research identified relationships between organisational culture and university innovativeness and components that create innovation opportunities at universities as its contribution to management theory. When applied in practice, the guidelines can help form the university’s organisational culture bottom-up.

Traffic congestion continues to be a persistent problem throughout the world. As vehicle-to-vehicle communication develops, there is an opportunity of using cooperation among close proximity vehicles to tackle the congestion problem. The intuition is that if vehicles could cooperate opportunistically when they come close enough to each other, they could, in effect, spread themselves out among alternative routes so that vehicles do not all jam up on the same roads. Our previous work proposed a decentralized multiagent based vehicular congestion management algorithm entitled Congestion Avoidance and Route Allocation using Virtual Agent Negotiation (CARAVAN), wherein the vehicles acting as intelligent agents perform cooperative route allocation using inter-vehicular communication. This paper focuses on evaluating the practical applicability of this approach by testing its robustness and performance (in terms of travel time reduction), across variations in: (a) environmental parameters such as road network topology and configuration; (b) algorithmic parameters such as vehicle agent preferences and route cost/preference multipliers; and (c) agent-related parameters such as equipped/non-equipped vehicles and compliant/non-compliant agents. Overall, the results demonstrate the adaptability and robustness of the decentralized cooperative vehicles approach to providing global travel time reduction using simple local coordination strategies.

Informal learning environments provide the opportunity to study guests’ experiences as they engage with exhibits specifically designed to invoke the emotional experience of awe. The current paper presents insight gained by using both traditional survey measures and innovative mobile eye-tracking technology to examine guests’ experiences of awe in a science museum. We present results for guests’ visual attention in two exhibit spaces, one chosen for its potential to evoke positive awe and one for negative awe, and examine associations between visual attention and survey responses with regard to different facets of awe. In this exploratory study, we find relationships between how guests attend to features within an exhibit space (e.g., signage) and their feelings of awe. We discuss implications of using both methods concurrently to shed new light on exhibit design, and more generally for working in transdisciplinary multimethod teams to move scientific knowledge and application forward.

The control system designing of Unmanned Wave Glider (UWG) is challenging since the control system is weak maneuvering, large time-lag and large disturbance, which is difficult to establish accurate mathematical model. Meanwhile, to complete marine environment monitoring in long time scale and large spatial scale autonomously, UWG asks high requirements of intelligence and reliability. This paper focuses on the “Ocean Rambler” UWG. First, the intelligent control system architecture is designed based on the cerebrum basic function combination zone theory and hierarchic control method. The hardware and software designing of the embedded motion control system are mainly discussed. A motion control system based on rational behavior model of four layers is proposed. Then, combining with the line-of sight method(LOS), a self-adapting PID guidance law is proposed to compensate the steady state error in path following of UWG caused by marine environment disturbance especially current. Based on S-surface control method, an improved S-surface heading controller is proposed to solve the heading control problem of the weak maneuvering carrier under large disturbance. Finally, the simulation experiments were carried out and the UWG completed autonomous path following and marine environment monitoring in sea trials. The simulation experiments and sea trial results prove that the proposed intelligent control system, guidance law, controller have favorable control performance, and the feasibility and reliability of the designed intelligent control system of UWG are verified.

In two experiments, we used the simple zero-sum game Rock, Paper and Scissors to study the common reinforcement-based rules of repeating choices after winning (win-stay) and shifting from previous choice options after losing (lose-shift). Participants played the game against both computer opponents who could not be exploited and computer opponents who could be exploited by making choices that would at times conflict with reinforcement. Against unexploitable opponents, participants achieved an approximation of random behavior, contrary to previous research commonly finding reinforcement biases. Against exploitable opponents, the participants learned to exploit the opponent regardless of whether optimal choices conflicted with reinforcement or not. The data suggest that learning a rule that allows one to exploit was largely determined by the outcome of the previous trial.

The attentional blink (AB) phenomenon reveals a bottleneck of human information processing: the second of two targets is often missed when they are presented in rapid succession among distractors. In our previous work, we showed that the size of the AB can be changed by applying transcranial direct current stimulation (tDCS) over the left dorsolateral prefrontal cortex (lDLPFC) (London & Slagter, Journal of Cognitive Neuroscience, 33, 756–68, 2021). Although AB size at the group level remained unchanged, the effects of anodal and cathodal tDCS were negatively correlated: if a given individual’s AB size decreased from baseline during anodal tDCS, their AB size would increase during cathodal tDCS, and vice versa. Here, we attempted to replicate this finding. We found no group effects of tDCS, as in the original study, but we no longer found a significant negative correlation. We present a series of statistical measures of replication success, all of which confirm that both studies are not in agreement. First, the correlation here is significantly smaller than a conservative estimate of the original correlation. Second, the difference between the correlations is greater than expected due to sampling error, and our data are more consistent with a zero-effect than with the original estimate. Finally, the overall effect when combining both studies is small and not significant. Our findings thus indicate that the effects of lDPLFC-tDCS on the AB are less substantial than observed in our initial study. Although this should be quite a common scenario, null findings can be difficult to interpret and are still under-represented in the brain stimulation and cognitive neuroscience literatures. An important auxiliary goal of this paper is therefore to provide a tutorial for other researchers, to maximize the evidential value from null findings.

Improving travel time prediction for public transit effectively enhances service reliability, optimizes travel structure, and alleviates traffic problems. Its greater time-variance and uncertainty make predictions for short travel times (≤35min) more subject to be influenced by random factors. It requires higher precision and is more complicated than long-term predictions. Effectively extracting and mining real-time, accurate, reliable, and low-cost multi-source data such as GPS, AFC, and IC can provide data support for travel time prediction. Kalman filter model has high accuracy in one-step prediction and can be used to calculate a large amount of data. This paper adopts the Kalman filter as a travel time prediction model for a single bus based on single-line detection: including the travel time prediction model of route (RTM) and the stop dwell time prediction model (DTM); the evaluation criteria and indexes of the models are given. The error analysis of the prediction results is carried out based on AVL data by case study. Results show that under the precondition of multi-source data, the public transportation prediction model can meet the accuracy requirement for travel time prediction and the prediction effect of the whole route is superior to that of the route segment between stops.

In order to establish natural social synchrony between two humans, two requirements need to be fulfilled. First, the coupling must be bi-directional. The two humans react to each other’s actions. Second, natural social bodily synchronization has to be intentional or unintentional. Assuming that these essential aspects of human-human interactions are present, the present paper investigates whether similar bodily synchrony emerges between an interacting human and an artificial agent such as a robot. More precisely, we investigate whether the same human unintentional rhythmic entrainment and synchronization is present in Human Robot Interaction (HRI). We also evaluate which model (e.g., an adaptive vs non adaptive robot) better reproduces such unintentional entrainment. And finally, we compare interagent coordination stability of the HRI under 1) unidirectional (robot with fixed frequency) versus bidirectional (robot with adaptive frequency) rhythmic entrainment and 2) human intentional versus unintentional coupling. Fifteen young adults made vertical arm movements in front of the NAO robot under five different conditions of intentional/unintentional and unidirectional/bidirectional interactions. Consistent with prior research investigating human-human interpersonal coordination, when humans interact with our robot, (i) unintentional entrainment was present, (ii) bi-directional coupling produced more stable in-phase un-intentional and intentional coordination, (iii) and intentional coordination was more stable than unintentional coordination. To conclude, this study provides a foundation for modeling future social robots involving unintentional and bidirectional synchronization—aspects which seem to enhance humans’ willingness to interact with robots.

Online virtual museum tours combine museum authority and an academic approach with the diversity and interactivity of online resources; such tours have become an essential resource for online scientific research and education. Many important museums around the world are developing this type of online service. Comprehensive evaluation of such tours is, however, urgently needed to ensure effectiveness. This paper establishes a heuristic evaluation scale based on the literature. Taking the online virtual tour of the Exhibition of Architecture of the Forbidden City as a case study, confirmatory factor analysis was then carried out to improve the scale. Interviews were conducted to discuss and analyze the research results. The developed evaluation scale has four dimensions: authenticity, interaction, navigation, and learning. The results from the case study showed, first, that the exhibition had visual authenticity, but the behavioral authenticity was insufficient; second, the exhibition was generally interactive, but this aspect could be improved by enriching the links; third, the lack of effective navigation design for the exhibit was the main factor affecting experience quality. Fourth, the exhibition was informative and supported learning, but needs further improvement to the quantity and quality of information provided. Finally, the interviews revealed that the online exhibition did not entirely support people of different ages and abilities, so it needs further improvement to be wholly inclusive.

Visual Question Answering (VQA) is a learning task that combines computer vision with natural language processing. In VQA, it is important to understand the alignment between visual concepts and linguistic semantics. In this paper, we proposed a Pre-training Model Based on Parallel Cross-Modality Fusion Layer (P-PCFL) to learn the fine-grained relationship between vision and language. The P-PCFL model is composed of three Encoders: Object Encoder, Language Encoder, and Parallel Cross-Modality Fusion Encoder, with Transformer as the core. We use four different Pre-training missions, namely, Cross-Modality Mask Language Modeling, Cross-Modality Mask Region Modeling, Image-Text Matching, and Image-Text Q&A, to pre-train the P-PCFL model and improve its reasoning and universality, which help to learn the relationship between Intra-modality and Inter-modality. Experimental results on the platform of Visual Question Answering dataset VQA v2.0 show that the Pre-trained P-PCFL model has a good effect after fine-tuning the parameters. In addition, we also conduct ablation experiments and provide some results of Attention visualization to verify the effectiveness of P-PCFL model.

Economic globalization has swept the whole world. To focus on their main business, enterprises that are referred to as original equipment manufacturers (OEMs) outsource non-core production activities to contract manufacturers (CMs). By constructing a two-level supply chain consisting of two competing OEMs and one upstream CM, the strategic interaction of the OEMs between outsourcing and purchasing is studied. Specifically, the CM can offer custom- and predefined modes of original equipment manufacturing (namely, CO mode and PO mode, respectively). The former mode enables OEMs to determine product quality, while the latter only allows them to purchase from several quality configurations. The results show that, first, since the CO mode allows the adopter to lead the product design, whether to choose this mode depends on the required R&D cost. Interestingly, however, a lower R&D cost does not necessarily result in the adoption of the CO mode if the product quality difference is small under the PO mode. Second, the optimal purchasing strategy of an OEM is indifferent to the outsourcing mode (CO and PO) of its rival but significantly affected by the quality cost. However, compared to the PO mode, choosing the CO mode would cause the competitor to suffer more profit losses. Third, differing from the prior literature, this paper finds that when the downstream OEM can make quality decisions, although this may lead to profit loss of the contract manufacturer in some channels, it could benefit the CM overall.

Instance segmentation is more challenging and difficult than object detection and semantic segmentation. It paves the way for the realization of a complete scene understanding, and has been widely used in robotics, automatic driving, medical care, and other aspects. However, there are some problems in instance segmentation methods, such as the low detection efficiency for low-resolution objects and the slow detection speed of images with complex backgrounds. To solve these problems, this paper proposes an instance segmentation method with multi-scale attention, which is called a Hybrid Kernel Mask R-CNN. Firstly, the hybrid convolution kernel is constructed by combining different kernels and groups, which can complement each other to extract rich information. Secondly, a multi-scale attention mechanism is designed by assign weights to different convolution kernels, which can retain more important information. After the introduction of our strategy, the network is more inclined to focus on the low-resolution objects in the image. The proposed method achieves the best accuracy over the anchor-based method. To verify the universality of the model, we test Hybrid Kernel Mask R-CNN on Balloon, xBD and COCO datasets. The test results exceed the state of art methods. And the visualization results show our method can extract low-resolution objects effectively.

China is still one of the countries dominated by thermal power generation. In order to generate more efficient, stable and clean power, it is necessary to evaluate thermal power generation units (TPGU). Firstly, a comprehensive evaluation index system for TPGU with 20 secondary indicators was established from four aspects: reliability indicators, economic indicators, technical supervision indicators, and major operating indicators. Secondly, the entropy weight method can be used to calculate the weight of each second-level index. Mahalanobis Distance improved Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) method is coupled with the Grey Relational Analysis (GRA), and the comprehensive evaluation values of 5 units (600MW) are respectively 0.4516, 0.5247, 0.3551, 0.5589 and 0.6168 from both vertical and horizontal dimensions. Finally, by comparing and analyzing this method with the above research methods, it is found that the results obtained by this method which re-establishes the coordinate system based on the data set are more accurate. In addition, this method can effectively evaluate the operation of TPGU, which is of great significance for cleaner production while generating electricity. In conclusion, some suggestions on clean production of TPGU are put forward, and the innovation points and limitations of this paper are pointed out.

Background Over the last decade, mobile health applications (mHealth App) have evolved exponentially to assess and support our health and well-being. Objective This paper presents an Artificial Intelligence (AI)-enabled mHealth app rating tool, called ACCU3RATE, which takes multidimensional measures such as user star rating, user review and features declared by the developer to generate the rating of an app. However, currently, there is very little conceptual understanding on how user reviews affect app rating from a multi-dimensional perspective. This study applies AI-based text mining technique to develop more comprehensive understanding of user feedback based on several important factors, determining the mHealth app ratings. Method Based on the literature, six variables were identified that influence the mHealth app rating scale. These factors are user star rating, user text review, user interface (UI) design, functionality, security and privacy, and clinical approval. Natural Language Toolkit package is used for interpreting text and to identify the App users’ sentiment. Additional considerations were accessibility, protection and privacy, UI design for people living with physical disability. Moreover, the details of clinical approval, if exists, were taken from the developer’s statement. Finally, we fused all the inputs using fuzzy logic to calculate the new app rating score. Results and conclusions ACCU3RATE concentrates on heart related Apps found in the play store and App gallery. The findings indicate the efficacy of the proposed method as opposed to the current device scale. This study has implications for both App developers and consumers who are using mHealth Apps to monitor and track their health. The performance evaluation shows that the proposed mHealth scale has shown excellent reliability as well as internal consistency of the scale, and high inter-rater reliability index. It has also been noticed that the fuzzy based rating scale, as in ACCU3RATE, matches more closely to the rating performed by experts.

Research on the relationship between psychopathy and leadership effectiveness has adopted very different perspectives on psychopathy. To advance this field of research, the current paper introduces an overarching framework of “successful psychopathy” ( Lilienfeld, Watts, & Smith, 2015 ) to the leadership domain, comprising three conceptual models (the differential-severity model, the moderated-expression model, and the differential-configuration model) and their “hybrid” forms, which are combinations of two or three models. We test the three alternative conceptual models and four hybrid models in two independent samples of leader-subordinate dyads (N1 = 178 and N2 = 668) whereby leaders’ self-reported psychopathy is related to a range of subordinate-rated effectiveness criteria, including three performance dimensions and charismatic leadership. A recurrent pattern of findings across both studies provides evidence for differential effects for the various psychopathy subdimensions, whereas little support was found for the models assuming curvilinear and/or moderated effects. Implications for research on leader psychopathy are discussed.

This paper proposes a multiple criteria group decision making with individual preferences (MCGDM-IP) to address the robot selection problem (RSP). Four objective criteria elicitation approaches, namely, Shannon entropy approach, CRITIC approach, distance-based approach, and ideal-point approach, are proposed to indicate individual decision makers. A preliminary group decision matrix is therefore formulated. Both preferential differences representing the preference degrees among different robots, and preferential priorities representing the favorite ranking of robots for each individual decision maker, are analyzed to propose a revised group decision matrix. A satisfaction index is developed to manifest the merits of the proposed MCGDM-IP. An illustrative example using the data drawn from previous literature is conducted to indicate the effectiveness and validity of MCGDM-IP. The results demonstrate that the MCGDM-IP could generate a more satisfactory scheme to evaluate and select industrial robots, with an improvement of group satisfactory level as 2.12%.

Motivated by the two-decade-long scientific debate over the existence of the ego-depletion effect, our paper contributes to exploring the scope conditions of ego-depletion theory. Specifically, in a randomized experiment, we depleted students’ self-control with a cognitively demanding task that required students’ effort. We measured the effect of depleted self-control on a subsequent task that required self-control to not engage in fraudulent cheating behavior—measured with an incentivized dice-roll task—and tested ego-depletion in a large-scale preregistered field experiment that was similar to real-life situations. We hypothesized that treated students would cheat more. The data confirms the hypothesis and provides causal evidence of the ego-depletion effect. Our results provide new insights into the scope conditions of ego-depletion theory, contribute methodological information for future research, and offer practical guidance for educational policy.

School closures due to the COVID-19 global pandemic are likely to have a range of negative consequences spanning the domains of child development, education and health, in addition to the widening of inequalities and inequities. Research is required to improve understanding of the impact of school closures on the education, health and wellbeing of pupils and school staff, the challenges posed during reopening and importantly to identify how countries can ensure a safe return to education and to inform policy. This qualitative study aimed to reflect on the experience of primary school staff (pupils aged 3-11) in Wales regarding school closures and the initial reopening of schools and to identify recommendations for the future. A total of 208 school staff completed a national online survey through the HAPPEN primary school network, consisting of questions about school closures, the phased reopening of schools and a return to full-time education. Thematic analysis of survey responses identified five recommendations; (i) prioritise the health and wellbeing of pupils and staff. This includes more focus on wellbeing activities and less focus on attainment/assessments and protecting staff breaks to promote workplace wellbeing; (ii) focus on enabling parental engagement and support. Introduce support sessions for parents and include regular check ins during periods of home learning; (iii) improve digital competence amongst pupils, teachers and parents. Ensure the provision of equipment and training in building digital skills. Offer a combination of paper-based and digital home learning activities; (iv) consider opportunities for smaller class sizes and additional staffing. Ensure support is directed to need whilst providing pastoral care; and (v) improve the mechanism of communication between schools and families, and between government and schools. Ensure that schools receive advance notice of local or national changes in guidance.

Every day, people make many food decisions without thinking, repeatedly falling for the unhealthy option instead of the healthy option. While making these mindless decisions, people often rely on heuristics. In this paper, we demonstrate that these heuristics can be exploited to nudge consumers towards healthy alternatives. Specifically, we explore how the attraction effect (i.e., adding a decoy to a choice set) can nudge people to choose a healthy snack. The results of our choice experiment indicate that adding a decoy (i.e., a less attractive food alternative) to a self-control situation (i.e., choosing between a healthy and an unhealthy food alternative) can help people maintain self-control and choose the healthy option. This mixed choice set thus nudges people towards the healthy option. Moreover, our results show differential effects of the attraction effect depending on the (un)healthiness of the products in the choice set. Specifically, the attraction effect is prominent when the choice set consists of unhealthy products only (i.e., the unhealthy choice set), but not in the choice set that consists of only healthy products (i.e., healthy choice set). Importantly, our results indicate when the attraction effect can exploit consumers’ heuristics to help them make better, healthier food choices.

The lack of standardized language assessment tools in Russian impedes clinical work, evidence-based practice, and research in Russian-speaking clinical populations. To address this gap in assessment of neurogenic language disorders, we developed and standardized a new comprehensive assessment instrument–the Russian Aphasia Test (RAT). The principal novelty of the RAT is that each subtest corresponds to a specific level of linguistic processing (phonological, lexical-semantic, syntactic, and discourse) in different domains: auditory comprehension, repetition, and oral production. In designing the test, we took into consideration various (psycho)linguistic factors known to influence language performance, as well as specific properties of Russian. The current paper describes the development of the RAT and reports its psychometric properties. A tablet-based version of the RAT was administered to 85 patients with different types and severity of aphasia and to 106 age-matched neurologically healthy controls. We established cutoff values for each subtest indicating deficit in a given task and cutoff values for aphasia based on the Receiver Operating Characteristic curve analysis of the composite score. The RAT showed very high sensitivity (> .93) and specificity (> .96), substantiating its validity for determining presence of aphasia. The test’s high construct validity was evidenced by strong correlations between subtests measuring similar linguistic processes. The concurrent validity of the test was also strong as demonstrated by a high correlation with an existing aphasia battery. Overall high internal, inter-rater, and test-retest reliability were obtained. The RAT is the first comprehensive aphasia language battery in Russian with properly established psychometric properties. It is sensitive to a wide range of language deficits in aphasia and can reliably characterize individual profiles of language impairments. Notably, the RAT is the first comprehensive aphasia test in any language to be fully automatized for administration on a tablet, maximizing further standardization of presentation and scoring procedures.

Some evidence suggests that people behave more cooperatively and generously when observed or in the presence of images of eyes (termed the ‘watching eyes’ effect). Eye images are thought to trigger feelings of observation, which in turn motivate people to behave more cooperatively to earn a good reputation. However, several recent studies have failed to find evidence of the eyes effect. One possibility is that inconsistent evidence in support of the eyes effect is a product of individual differences in sensitivity or susceptibility to the cue. In fact, some evidence suggests that people who are generally more prosocial are less susceptible to situation-specific reputation-based cues of observation. In this paper, we sought to (1) replicate the eyes effect, (2) replicate the past finding that people who are dispositionally less prosocial are more responsive to observation than people who are more dispositionally more prosocial, and (3) determine if this effect extends to the watching eyes effect. Results from a pre-registered study showed that people did not give more money in a dictator game when decisions were made public or in the presence of eye images, even though participants felt more observed when decisions were public. That is, we failed to replicate the eyes effect and observation effect. An initial, but underpowered, interaction model suggests that egoists give less than prosocials in private, but not public, conditions. This suggests a direction for future research investigating if and how individual differences in prosociality influence observation effects.

The spread of infectious diseases such as COVID-19 presents many challenges to healthcare systems and infrastructures across the world, exacerbating inequalities and leaving the world’s most vulnerable populations most affected. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to disease spread. In this paper we present an agent-based modeling approach to simulating the spread of disease in refugee and IDP settlements under various non-pharmaceutical intervention strategies. The model, based on the June open-source framework, is informed by data on geography, demographics, comorbidities, physical infrastructure and other parameters obtained from real-world observations and previous literature. The development and testing of this approach focuses on the Cox’s Bazar refugee settlement in Bangladesh, although our model is designed to be generalizable to other informal settings. Our findings suggest the encouraging self-isolation at home of mild to severe symptomatic patients, as opposed to the isolation of all positive cases in purpose-built isolation and treatment centers, does not increase the risk of secondary infection meaning the centers can be used to provide hospital support to the most intense cases of COVID-19. Secondly we find that mask wearing in all indoor communal areas can be effective at dampening viral spread, even with low mask efficacy and compliance rates. Finally, we model the effects of reopening learning centers in the settlement under various mitigation strategies. For example, a combination of mask wearing in the classroom, halving attendance regularity to enable physical distancing, and better ventilation can almost completely mitigate the increased risk of infection which keeping the learning centers open may cause. These modeling efforts are being incorporated into decision making processes to inform future planning, and further exercises should be carried out in similar geographies to help protect those most vulnerable., Author summary The spread of infectious diseases presents many challenges to healthcare systems and infrastructures across the world. Given their density and available infrastructure, refugee and internally displaced person (IDP) settlements can be particularly susceptible to the dangers of disease spread. This study seeks to understand how COVID-19 spreads in settlements, focusing on the Cox’s Bazar refugee settlement in Bangladesh. Our model simulates the movements and interactions of each individual in the settlement, incorporating information about family structures and demographic attributes, to understand how COVID-19 might spread under various intervention strategies. Our analysis suggests that mask wearing in indoor locations can have a significant effect on disease spread, even when wearing reusable cotton masks, which the people in the settlement can make themselves. We also look at different ways to treat individuals who only have milder symptoms and don’t yet require hospitalisation, as well as various scenarios which might allow for the safe reopening of schools in the settlement. With almost 80 million forcibly displaced people in the world, we hope that this work will inspire more modeling groups to focus on these vulnerable populations, which have been traditionally under-served by such efforts, to ensure no one is left behind.

System identification techniques -- projection pursuit regression models (PPRs) and convolutional neural networks (CNNs) -- provide state-of-the-art performance in predicting visual cortical neurons' responses to arbitrary input stimuli. However, the constituent kernels recovered by these methods are often noisy and lack coherent structure, making it difficult to understand the underlying component features of a neuron's receptive field. In this paper, we show that using a dictionary of diverse kernels with complex shapes learned from natural scenes based on efficient coding theory, as the front-end for PPRs and CNNs can improve their performance in neuronal response prediction as well as algorithmic data efficiency and convergence speed. Extensive experimental results also indicate that these sparse-code kernels provide important information on the component features of a neuron's receptive field. In addition, we find that models with the complex-shaped sparse code front-end are significantly better than models with a standard orientation-selective Gabor filter front-end for modeling V1 neurons that have been found to exhibit complex pattern selectivity. We show that the relative performance difference due to these two front-ends can be used to produce a sensitive metric for detecting complex selectivity in V1 neurons., 22 pages

In this paper, we presentautopsych, a novel online tool that allows school assessment experts, test developers, and researchers to perform routine psychometric analyses and equating of student test data and to examine the effect of student demographic and group conditions on student test performance. The app extends current open-source software by providing (1) extensive embedded result narration and summaries for written reports, (2) improved handling of partial credit data via customizable item-person Wright maps, (3) customizable item- and person-flagging systems, (4) item-response theory model constraints and controls, (5) many-facets Rasch analysis to examine item bias, (6) Rasch fixed item equating for mapping student ability across test forms, (7) tabbed spreadsheet outputs and immediate options for secondary data analysis, (8) customizable graphical color schemes, (9) extended ANOVA analysis for examining group differences, and (10) inter-rater reliability analyses for the verifying the consistency of rater scoring systems. We present the app’s architecture and functionalities and test its performance with simulated and real-world small-, medium-, and large-scale assessment data. Implications and planned future developments are also discussed.

The diversified management ability of the non-family members in the top management teams (TMTs) can significantly increase the research and development (R&D) investment of the family firms. However, existing studies focus on family characteristics. To bridge the gap, this study explored the R&D investment propensity for family firms from the perspective of non-family members’ participation in TMTs. Based on the upper echelons and the socioemotional wealth theory, this paper incorporated the non-economic goals that influence strategic decisions on family firms into the analytical framework. According to the questionnaire data of Chinese private enterprises, the Tobit regression model was used to analyze the influence of family members on R&D investment decisions under non-economic goal orientations. The results indicated that the preference for control and influence among family members weakens the positive effect of non-family managers on R&D investment, while the preferences for status perception and social responsibility strengthen the positive effect.

In this paper, we assume that the supply chain for new energy vehicles (NEVs) consists of a manufacturer and N parts suppliers, considering that the R&D investment of both manufacturer and suppliers will affect the market demand of NEVs and NEVs credit, we construct decentralized and centralized decision-making models under the dual-credit policy to study the R&D investment strategy of supply chain enterprises. Furthermore, considering that suppliers can form alliances, we establish bargaining game models under the conditions of the non-alliance and alliance of suppliers, and discuss the coordination strategy for the NEVs supply chain. It is found that, under the dual-credit policy, the higher the credit coefficient of technology improvement, the higher the transaction price of credits, and the higher the R&D investment of supply chain. Dual-credit policy can effectively encourage NEVs supply chain to increase R&D investment, improve NEV technology level, and improve the profit of supply chain. Under the dual-credit policy, the increment profit distribution strategy based on a bargaining game model can coordinate the NEVs supply chain. When suppliers separately negotiate with the manufacturer, bringing the negotiation sequence forward, the supplier can get more profits. However, as the manufacturer has the right to determine the negotiation sequence, the supplier can only get the profit of the last round of negotiation, and the manufacturer can get excess profit. Forming a suppliers alliance can solve this problem effectively, and increase the profit of all suppliers when the alliance`s negotiating power is improved to a certain threshold.

Complex IT outsourcing relationships aptitude several benefits such as increased cost likelihood and lowered costs, higher scalability and flexibility upon demand. However, by virtue of its complexity, the complex outsourcing typically necessitates the interactions among various stakeholders from diverse regions and cultures, making it significantly more challenging to manage than traditional outsourcing. Furthermore, when compared to other types of outsourcing, complex outsourcing is extremely difficult because it necessitates a variety of control and coordination mechanisms for project management, which proportionally increases the risk of project failure. In order to overcome the failure of projects in complex outsourcing relationships, there is a need of robust systematic research to identify the key challenges and practices in this area. Therefore, this research implements systematic literature review as a research method and works as a pioneer attempt to accomplish the aforementioned objectives. Upon furnishing the SLR results, the authors identified 11 major challenges with 67 practices in hand from a total of 85 papers. Based on these findings, the authors intend to construct a comprehensive framework in the future by incorporating robust methodologies such as AHP and fuzzy logic, among others. Qatar National Library and Qatar University - grant No. IRCC- 2021-010.

Men are underrepresented in family planning (FP) research, and despite the widespread promotion of FP through mass media, there is no systematic evaluation on how mass media exposure influences their FP knowledge, attitudes and behavior. Using Demographic and Health Survey (DHS) data from 31 countries in Sub-Saharan Africa (SSA), collected between 2010 and 2019, this paper examines the associations between three types of traditional mass media (radio, television and print) with FP knowledge, attitudes and method choices among reproductive age men in SSA, relative to other socio-cultural factors. Estimates to quantify the relative contribution of each type of mass media, relative to other evidence-based socio-cultural influences on FP outcomes, were derived using the Shorrocks-Shapley decomposition. Radio exposure had the largest impact on FP knowledge, attitudes and method choice, accounting for 26.1% of the variance in FP knowledge, followed by Television (21.4%) and education attainment (20.7%). Mass media exposure had relatively minimal impact on FP method choice, and between the three types of mass media, television (8%) had the largest influence on FP method choice. Print media had comparatively lesser impact on FP knowledge (8%), attitudes (6.2%) and method choice (3.2%). Findings suggest that mass media exposure has positive influences on FP knowledge, attitudes and method choice but its influence on FP knowledge, attitudes and method choice is smaller relative to other socio-cultural factors such as education, household wealth and marital status. As such, efforts to increase FP uptake in Sub-Saharan Africa should take into consideration the impact of these socio-cultural economic factors.

The combination of blockchain and the sports industry is bound to be a trend in terms of innovation and the development of sports industry in the near future. Therefore, after analyzing the problems existing in the current business model of the sports industry, this paper takes 200 innovation indicators of 50 listed companies in the sports industry, among which 15 use blockchain and 35 do not use, as research samples and uses a three-stage DEA model to calculate their innovation efficiency. The results show that sports companies that use blockchain have better performance in terms of innovation efficiency than do those that do not, which illustrates the necessity of blockchain-based business model innovation. Then, by focusing on the internal structure and the case of the "vSport blockchain", this work shows that this business model innovation is feasible. The article first proposes a blockchain-based sports industry business model and then uses a combination of empirical and case studies to prove the necessity and feasibility of sports industry business model innovation.

The accurate segmentation of retinal vessels images can not only be used to evaluate and monitor various ophthalmic diseases, but also timely reflect systemic diseases such as diabetes and blood diseases. Therefore, the study on segmentation of retinal vessels images is of great significance for the diagnosis of visually threatening diseases. In recent years, especially the convolutional neural networks (CNN) based on UNet and its variant have been widely used in various medical image tasks. However, although CNN has achieved excellent performance, it cannot learn global and long-distance semantic information interaction well due to the local computing characteristics of convolution operation, which limits the development of medical image segmentation tasks. Transformer, currently popular in computer vision, has global computing features, but due to the lack of low-level details, local feature information extraction is insufficient. In this paper, we propose Patches Convolution Attention based Transformer UNet (PCAT-UNet), which is a U-shaped network based on Transformer with a Convolution branch. We use skip connection to fuse the deep and shallow features of both sides. By taking advantage of the complementary advantages of both sides, we can effectively capture the global dependence relationship and the details of the underlying feature space, thus improving the current problems of insufficient extraction of retinal micro vessels feature information and low sensitivity caused by easily predicting of pixels as background. In addition, our method enables end-to-end training and rapid inference. Finally, three publicly available retinal vessels datasets (DRIVE, STARE and CHASE_DB1) were used to evaluate PCAT-UNet. The experimental results show that the proposed PCAT-UNET method achieves good retinal vessel segmentation performance on these three datasets, and is superior to other architectures in terms of AUC, Accuracy and Sensitivity performance indicators. AUC reached 0.9872, 0.9953 and 0.9925, Accuracy reached 0.9622, 0.9796 and 0.9812, Sensitivity reached 0.8576, 0.8703 and 0.8493, respectively. In addition, PCAT-UNET also achieved good results in two other F1-Score and Specificity indicators.

In this paper, a general class of estimators is proposed for estimating the finite population mean for sensitive variable, in the presence of measurement error and non-response in simple random sampling. Expressions for bias and mean square error up to first order of approximation, are derived. Impact of measurement errors is examined using real data sets, including the survey conducted at Quaid-i-Azam University, Islamabad. Simulated data sets are also used to observe the performance of the proposed estimators in comparison to some other estimators. We obtain the empirical bias and MSE values for the proposed and the competing estimators.

This paper sought to understand the extent to which, and how individuals use personal or collective language when asked to articulate sense of place from a collective perspective. Understanding a collective sense of place could illuminate place-based connections in natural resource industries, where it is as groups or as institutions that organizations interact with the environment rather than as individuals. While there are well known methods for collecting information about sense of place at the individual level, there is a gap in understanding the best method to collect information at a collective level. We examined the use of key-informant interviews as a method to understand collective sense of place. In Bocas del Toro, Panama, ecotourism and environmentally based organizations are becoming more prolific due to abundant natural resources, making it an interesting case study for understanding sense of place from an organizational perspective. The use of personal and collective language is examined though in-depth semi-structured interviews from 15 environmentally-oriented organizations with a total of 17 interviews. This study specifically examined whether and how key informants, when prompted to speak for their organization, spoke collectively, reflecting a collective perspective versus their own. Methods included both quantitative analysis of personal versus collective language use frequency, and qualitative examinations of how individuals used personal versus collective language. Our results indicated no difference in the frequency with which individuals use personal versus collective language. We found that how individuals situated their perspectives into an organization reflects a complex personal and collective point of view reflecting five themes of personal versus collective language use: 1) sole personal perspective, 2) sole collective perspective, 3) distinction between collective and personal perspective; 4) organization perspective with insertion of “I think”; and 5) personal and collective perspective about organization and greater community. Our research identifies a previously undiscussed potential bias of key informant interviews. These findings have implications for how researchers approach collecting information beyond the individual level.

Introduction Quality antenatal care is a window of opportunity for improving maternal and neonatal outcomes. Numerous studies have shown a positive effect of women empowerment on improved coverage of maternal and reproductive health services, including antenatal care (ANC). However, there is scarce evidence on the association between women’s empowerment and improved ANC services both in terms of coverage and quality. Addressing this gap, this paper examines the relationship between multi-dimensional measures of women empowerment on utilization of quality ANC (service coverage and consultation) in Pakistan. Methods We used Pakistan Demographic and Health Survey 2017–18 (PDHS) data which comprises of 6,602 currently married women aged between 15–49 years who had a live birth in the past five years preceding the survey. Our exposure variables were three-dimensional measures of women empowerment (social independence, decision making, and attitude towards domestic violence), and our outcome variables were quality of antenatal coverage [i.e. a composite binary measure based on skilled ANC (trained professional), timeliness (1st ANC visit during first trimester), sufficiency of ANC visits (4 or more)] and quality of ANC consultation (i.e. receiving at least 7 or more essential antenatal components out of 8). Data were analysed in Stata 16.0 software. Descriptive statistics were used to describe sample characteristics and binary logistic regression was employed to assess the association between empowerment and quality of antenatal care. Results We found that 41.4% of the women received quality ANC coverage and 30.6% received quality ANC consultations during pregnancy. After controlling for a number of socio-economic and demographic factors, all three measures of women’s empowerment independently showed a positive relationship with both outcomes. Women with high autonomy (i.e. strongly opposed the notion of violence) in the domain of attitude to violence are 1.66 (95% CI 1.30–2.10) and 1.45 (95% CI 1.19–1.75) and times more likely to receive antenatal coverage and quality ANC consultations respectively, compared with women who ranked low on attitude to violence. Women who enjoy high social independence had 1.87 (95% CI 1.44–2.43) and 2.78 (95% CI 2.04–3.79) higher odds of quality antenatal coverage and consultations respectively, as compared with their counterparts. Similarly, women who had high autonomy in household decision making 1.98 (95% CI 1.60–2.44) and 1.56 (95% CI 2.17–1.91) were more likely to receive quality antenatal coverage and consultation respectively, as compared to women who possess low autonomy in household decision making. Conclusion The quality of ANC coverage and consultation with service provider is considerably low in Pakistan. Women’s empowerment related to social independence, gendered beliefs about violence, and decision-making have an independent positive association with the utilisation of quality antenatal care. Thus, efforts directed towards empowering women could be an effective strategy to improve utilisation of quality antenatal care in Pakistan.

Critical thinking is a requisite skill for college success, employability, and conducive active civic participation. Empirical studies have noted to the low achievement of Arab students on critical thinking assessments. Insufficient endeavors have attempted to propose effective interventions enhancing critical thinking abilities among Arab students. The current analysis provides a preliminary overview of a special course designed to improve critical thinking skills among Arab college students. Results indicated a great improvement in all areas of critical thinking including explanation of information, identification of strategies, implementing solutions, and formulating logical inferences. Students’ scores on a critical thinking assessment increased from sufficient to good as a result of participating in the program. The gains are consistent after controlling for gender, major, class seniority, and nationality. Notwithstanding these promising results, this paper is limited in several respects including the choice of critical thinking assessments represented by two questions, the highly contextualized setting making it difficult to be replicated, and the convenient sampling strategy used to recruit participants. This set of limitations, however, does not discourage proactive attempts like designing special courses to enhance students’ critical thinking acquisition in the Middle East.

This paper presents validation of the VR Simulation Realism Scale on a Polish sample. The scale enables a self-report measurement of perceived realism of a virtual environment in four main aspects of such realism–scene realism, audience behavior realism, audience appearance realism and sound realism. However, since the development of the original scale, the VR technology significantly changed. We aimed to respond to that change and revalidate the original measure in the contemporary setting. For the purpose of scale validation, data was gathered from six studies with 720 participants in total. Five experiments and one online survey were conducted to examine psychometric properties of the scale in accordance with the Standards for Educational and Psychological Testing. Evidence based on internal structure, relations to other variables and test content was obtained. The factorial structure of the original scale was tested and confirmed. The connections between realism and immersion, presence, aesthetics were verified. A suppressed relationship between realism and positive affect was discovered. Moreover, it was confirmed that scale result is dependent on the quality of VR graphics. Results of the analyses provide the evidence that the VR Simulation Realism Scale is a well-established tool that might be used both in science and in VR development. However, further research needs to be done to increase external validity and predictive power of the scale.

Little is known in Aotearoa New Zealand about experiences of homelessness amongst Takatāpui/LGBTIQ+ identifying people, despite growing international literature regarding LGBTIQ+ homelessness. Using data from semi-structured interviews with eight people who identified as Takatāpui/LGBTIQ+ and had experienced homelessness, this paper explores their experiences prior to becoming homeless. These experiences are placed into the categories of: the pervasiveness of instability (especially in regards to family relationships, finances, and housing), having to grow up fast due to social and material conditions, experiences of looking for housing in stressed markets, and systems failures that resulted in a lack of autonomy. These results show that instability and systems failures are key contributors to Takatāpui/LGBTIQ+ people becoming homeless in Aotearoa New Zealand.

Earlier findings suggest that men with daughters make judgments and decisions somewhat in line with those made by women. In this paper, we attempt to extend those findings, by testing how gender and parenting daughters affect judgments of the appropriateness of investing in and working for morally controversial companies (“sin stocks”). To do so, in Study 1 (N = 634) we investigate whether women judge the prospect of investing in sin stocks more harshly than men do, and test the hypothesis that men with daughters judge such investments less favorably than other men. In Study 2 (N = 782), we investigate the willingness to work in morally controversial companies at a significant wage premium. Results show that—for men—parenting daughters yields harsher evaluations of sin stocks, but no evidence that it lowers the propensity to work in such companies. This contrasts to the effect of gender: women reliably judge both investment and employment in morally controversial companies more harshly than men do. We suggest that an aversion towards morally controversial companies might be a partial determinant of the gender gap in wages.

Pain and inflammation are unpleasant experiences that usually occur as a result of tissue damage. Despite the number of existing analgesic drugs, side effects limit their use, stimulating the search for new therapeutic agents. In this sense, five hydrazone derivatives (H1, H2, H3, H4, and H5), with general structure R1R2C = NNR3R4, were synthesized with molecular modification strategies. In this paper, we describe the ability of hydrazone derivatives to attenuate nociceptive behavior and the inflammatory response in mice. Antinociceptive activity was evaluated through acetic acid-induced writhing and formalin-induced nociception tests. In both experimental models, the hydrazone with the greatest potency (H5) significantly (p < 0.05) reduced nociceptive behavior. Additionally, methods of acute and chronic inflammation induced by different chemicals (carrageenan and histamine) were performed to evaluate the anti-inflammatory effect of H5. Moreover, molecular docking analysis revealed that H5 can block the COX-2 enzyme, reducing arachidonic acid metabolism and consequently decreasing the production of prostaglandins, which are important inflammatory mediators. H5 also changes locomotor activity. In summary, H5 exhibited relevant antinociceptive and anti-inflammatory potential and acted on several targets, making it a candidate for a new multi-target oral anti-inflammatory drug.

The spatial distribution of visual items allows us to infer the presence of latent causes in the world. For instance, a spatial cluster of ants allows us to infer the presence of a common food source. However, optimal inference requires the integration of a computationally intractable number of world states in real world situations. For example, optimal inference about whether a common cause exists based on N spatially distributed visual items requires marginalizing over both the location of the latent cause and 2N possible affiliation patterns (where each item may be affiliated or non-affiliated with the latent cause). How might the brain approximate this inference? We show that subject behaviour deviates qualitatively from Bayes-optimal, in particular showing an unexpected positive effect of N (the number of visual items) on the false-alarm rate. We propose several “point-estimating” observer models that fit subject behaviour better than the Bayesian model. They each avoid a costly computational marginalization over at least one of the variables of the generative model by “committing” to a point estimate of at least one of the two generative model variables. These findings suggest that the brain may implement partially committal variants of Bayesian models when detecting latent causes based on complex real world data., Author summary Perceptual systems are designed to make sense of fragmented sensory data by inferring common, latent causes. Seeing a cluster of insects might allow us to infer the presence of a common food source, whereas the same number of insects scattered over a larger area of land might not evoke the same suspicions. The ability to reliably make this inference based on statistical information about the environment is surprisingly non-trivial: making the best possible inference requires making full use of the probabilistic information provided by the sensory data, which would require considering a combinatorially explosive number of hypothetical world states. In this paper, we test human subjects on their ability to perform a causal detection task: subjects are asked to judge whether an underlying cause of clustering is present or absent, based on the spatial distribution of those items. We show that subjects do not reason optimally on this task, and that particular computational short cuts (“committing” to certain world states over others, rather than representing them all) might underlie perceptual decision-making in these causal detection schemes.

IntroductionViolence against medical staff has been prevalent in China over the past two decades. Although Chinese authorities have released many laws and regulations to protect medical staff from violence since 2011, the legal approach alone is unlikely to resolve this complex issue. In particular, several cases of violence against medical staff in China have caused great media sensation.MethodThis paper proposes an integrated model that combines the environmental stimuli theory, broken windows theory, and rational choice theory. It adopts the fuzzy set qualitative comparative analysis (fsQCA) to untangle the causal relationship between violence against medical staff, media sensation, and judicial judgment. We examined reports of medical violence on media and news websites from January 1, 2010, to January 31, 2020, and selected 50 cases with detailed information for this study.ResultsThe results show that each condition is not sufficient for the absence ofjudicial judgment, but when combined, they are conducive to the outcome. The conditions ofhospital level,medical cost, andmedia sensationplay important roles. The providers, patients, and environmental factors are indicators of inadequate or lack ofjudicial judgment, which corresponds to previous expectations.ConclusionsThe integrated model greatly enriches the extant theories and literature, and also yields implications for preventing violence against medical staff in China. We suggest that sustainable and innovative healthcare reform should be initiated. For example, public hospitals should remain the cornerstone of national public health security. Medical staff in public hospitals must be regarded as “civil servants”. Therefore, the current legal system should be improved. The media should objectively report events concerning medical staff and improve public healthcare knowledge.

Background Aboriginal language interpreters are under-utilised in healthcare in northern Australia. Self-discharge from hospital is an adverse outcome occurring at high rates among Aboriginal people, with poor communication thought to be a contributor. We previously reported increased Aboriginal interpreter uptake and decreased rates of self-discharge during implementation of a 12-month hospital-based intervention. Interrupted time-series analysis showed sudden increase and up-trending improvement in interpreter use, and a corresponding decrease in self-discharge rates, during a 12-month intervention period (April 2018—March 2019) compared with a 24-month baseline period (April 2016 –March 2018). This paper aims to investigate reasons for these outcomes and explore a potential causal association between study activities and outcomes. Methods The study was implemented at the tertiary referral hospital in northern Australia. We used the Template for Intervention Description and Replication (TIDieR) as a framework to describe intervention components according to what, how, where, when, how much, tailoring, modifications and reach. Components of the study intervention were: employment of an Aboriginal Interpreter Coordinator, ‘Working with Interpreters’ training for healthcare providers, and championing of interpreter use by doctors. We evaluated the relative importance of intervention components according to TIDieR descriptors in relation to outcomes. Activities independent of the study that may have affected study findings were reviewed. The relationship between proportion of hospital separations among Aboriginal people ending in self-discharge and numbers of Aboriginal interpreter bookings made during April 2016-March 2019 was tested using linear regression. ‘Working with Interpreters’ training sessions were undertaken at a regional hospital as well as the tertiary hospital. Training evaluation comprised an anonymous online survey before the training, immediately after and then at six to eight months. Survey data from the sites were pooled for analysis. Results Employment of the Aboriginal Interpreter Coordinator was deemed the most important component of the intervention, based on reach compared to the other components, and timing of the changes in outcomes in relation to the employment period of the coordinator. There was an inverse association between interpreter bookings and self-discharge rate among Aboriginal inpatients throughout the baseline and intervention period (p = 0.02). This association, the timing of changes and assessment of intercurrent activities at the hospital indicated that the study intervention was likely to be casually related to the measured outcomes. Conclusions Communication in healthcare can be improved through targeted strategies, with associated improvements in patient outcomes. Health services with high interpreter needs would benefit from employing an interpreter coordinator.