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Understanding the traits that define a leader is a perennial quest. An ongoing debate surrounds the complexity required to unravel the leader trait paradigm. With the advancement of machine learning, scholars are now better equipped to model leadership as an outcome of complex patterns in traits. However, interpreting those models is often harder. In this paper, we guide researchers in the application of machine learning techniques to uncover complex relationships. Specifically, we demonstrate how applying machine learning can help to assess the complexity of a relationship and show techniques that help interpret the outcomes of “black box” machine learning algorithms. While demonstrating techniques to uncover complex relationships, we are using the Big Five Inventory and need for cognition to predict leadership role occupancy. Among our sample (n = 3385), we find that the leader trait paradigm can benefit from modeling complexity beyond linear effects and generate several interpretable results.

Researchers, regulators, and practitioners have expressed concerns that the increase in disclosures in annual reports of firms over the past decades has resulted in unintended consequences for financial statement users. We investigate whether increases in disclosure requirements are associated with increased difficulty to use financial statements. We create an index measuring firms' increasing exposure to regulation-induced disclosures in their 10-K filings and show that a higher index is on average associated with longer analyst delay, lower analyst accuracy, and higher dispersion. Consistent with information overload theory, we also find evidence consistent with an inverted-U curve: As regulation-induced disclosures increase, the decision quality of analysts initially increases. However, above a certain level of disclosures, increases in disclosures are associated with a decrease in the decision quality of analysts, as evidenced by an increase in analyst delay and dispersion and a decrease in accuracy. In addition, these adverse effects are more pronounced when analysts are less experienced, follow more firms, and have access to fewer resources. Overall, the findings suggest that increases in regulation-induced disclosures, above a certain level, are associated with information overload.

A frequent criticism of knowledge-based planning tools is the apparent mismatch between information frameworks used in their spatial models and the information needs of planning actors. Increasingly, these actors are contributing their context-specific knowledge during the development of such tools. Transferring this knowledge from actors to the model remains a challenge. This study establishes a set of design requirements for knowledge elicitation in small group settings and introduces game co-design as a method allowing planning actors and planning support experts to meet halfway between the technology and user domains in the so-called third space. We present an initial case where in three nominal group sessions, actors encountered and critiqued parameterized assumptions of their planning issues in a tangible game environment. Findings indicate that the method can elicit different types of knowledge (divergence) about a spatial system in operationalized terms (formalization). We discuss the potential of tangible game co-design as a modeling as learning exercise and its complementarity to dedicated digital technologies for more holistic planning support.

As mechanical simulations play an increasingly role in engineering projects, an appropriate integration of simulations into design-oriented product development processes is essential for efficient collaboration. To identify and overcome barriers between design and simulation departments, the BRIDGES approach was elaborated for barrier reduction in design engineering and simulation. This paper shows the industrial evaluation of the approach using a multi-method study of an online survey and focus group workshops. The experts' assessments were statistically analysed to build a connection matrix of barriers and recommendations. 59 participants from multiple industries with practical experience in the field contributed to the online survey, while 24 experts could be acquired for the focus group workshops. As a result of the workshops, both the data-based and the workshop-based part of the BRIDGES approach were assessed as beneficial to raise the efficiency of collaboration and practically applicable. This provides an empirically secured connection of barriers and suitable recommendations, allowing companies to identify and overcome collaboration barriers between design and simulation.

Purpose The sudden arrival of Covid-19 severely disrupted the supply chain of personal protective equipment (PPE) in Australia. This paper aims to examine the development of a geographical cluster, which, through the application of additive manufacturing (AM), responded to the PPE supply crisis. Design/methodology/approach This longitudinal case study focuses on an AM cluster, which was developed to supply PPE in a responsive and flexible manner from 2019/2020. The study gathered data over three stages of cluster evolution: pre, during and post-peak Covid-19. Findings The type and nature of exchanges between organizations involved in the cluster established important insights into success factors for cluster creation and development. Using an established complexity framework, this study identifies the characteristics of establishing a cluster. The importance of cluster alignment created initially by a common PPE supply goal led to an emerging commercial and relational imperative to address the longer-term configuration after the disruption. Practical implications Clusters can be a viable option for a technology-driven sector when there is a “buzz” that drives and rapidly diffuses knowledge to support cluster formation. This research identifies the structural, socio-political and emergent dimensions, which need to be considered by stakeholders when aiming at improving competitiveness using clusters. Originality/value Covid-19 has rapidly and unexpectedly disrupted the supply chain for many industries. Responding to challenges, businesses will investigate different pathways to improve the overall resilience including on-/near-shoring. The results provide insights into how clusters are formed, grow and develop and the differentiating factors that result in successful impacts of clusters on local economies.

The COVID-19 (SARS-CoV-2) pandemic is changing the world, the way we socialize, as well as politics and public transport logistics and Mexico is not the exception. Authorities are designing new policies for public transport. The first restrictions include the close of different metro stations to ensure the social distance, avoid excess of passengers and reduce the demand, which also changes the metro system and the way it operates. In this paper a model is presented, based on the analysis and comparison of the Mexico’s City Metro System as a total network as well as the network with stations that are closed due to COVID-19. Using complex networks will give to the population information about the connectivity, efficiency and robustness of the system, in order to be able to make improvements, have adequate planning, set up different policies to improve and meet the needs of the system after COVID-19.

Purpose The purpose of this study is to gauge how tourism and hospitality students discern sustainability in the geographical context of the small island state of North Cyprus using systems theory and thinking framework. Design/methodology/approach This study used a qualitative approach and draws on data from focus group interviews with final year students enrolled in a tourism and hospitality program. Findings This study reveals that students lack a holistic understanding of sustainability and given this finding, the theoretical and practical implications are discussed. Originality/value In spite of the close linkages between tourism and sustainability and the fact that the United Nations designated 2017 as the “International Year of Sustainable Tourism for Development,” education for sustainability in tourism has received relatively little attention. Furthermore, there is a gap in the literature in terms of what tourism students know about sustainability and sustainable development.

Classification tree is a widely used machine learning method. It has multiple implementations as R packages, rpart, ctree, evtree, tree and C5.0. The details of these implementations are not the same, and hence their performances differ from one application to another. We are interested in their performance in the classification of cells using the single-cell RNA-Sequencing data. In this paper, we conducted a benchmark study using 22 Single-Cell RNA-sequencing data sets. Using cross-validation, we compare packages’ prediction performances based on their Precision, Recall, F1-score, Area Under the Curve (AUC). We also compared the Complexity and Run-time of these R packages. Our study shows that rpart and evtree have the best Precision, evtree is the best in Recall, F1-score and AUC, C5.0 prefers more complex trees, tree is consistently much faster than others, although its complexity is often higher than others.

Providing aid in times of increasing humanitarian need, limited budgets, and mounting security risks is challenging. This paper explores in what organisational circumstances evaluators judge, positively and negatively, the performance of international non-governmental organisations (INGOs) in response to disasters triggered by natural hazards. It assesses whether and how, as perceived by expert evaluators, CARE and Oxfam successfully met multiple institutional requirements concerning beneficiary needs and organisational demands. It utilises the Competing Values Framework to analyse evaluator statements about project performance and organisational control and flexibility issues, using seven CARE and four Oxfam evaluation reports from 2005-11. The reports are compared using fuzzy-set Qualitative Comparative Analysis. The resulting configurations show that positive evaluations of an INGO's internal and external flexibility relate to satisfying beneficiary needs and organisational demands, whereas negative evaluations of external flexibility pertain to not meeting beneficiary needs and negative statements about internal control concerning not fulfilling organisational demands.

Background: Providing the right care for each individual patient is a key element of quality palliative care. Complexity is a relatively new concept, defined as the nature of patients’ situations and the extent of resulting needs. Classifying patients according to the complexity of their care needs can guide integration of services, anticipatory discussions, health service planning, resource management and determination of needs for specialist or general palliative care. However, there is no consistent approach to interpreting and classifying complexity of patient needs. Aim: The aim of this article is to identify and describe classification systems for complexity of patient care needs in palliative care. Design: Narrative systematic review (PROSPERO registration number CRD42020182102). Data sources: MEDLINE, Embase, CINAHL and PsychINFO databases were searched without time limitations. Articles were included that described classification systems for complexity of care requirements in populations with palliative care needs. Results: In total, 4301 records were screened, with nine articles identified reporting the use of patient classification systems in populations with palliative care needs. These articles included the use of six classification systems: HexCom, Perroca Scale, AN-SNAP, Hui Major Criteria, IDC-Pal and PALCOM. These systems were heterogenous in the manner they determined complexity of care needs. The HexCom and IDC-Pal systems contained items that covered all domains of complexity as described by Hodiamont; personal, social support, health care team and environment. Conclusion: Although six classification systems have been developed, they access differing aspects of care needs and their application has been limited. The HexCOM and IDC-Pal systems offer the broadest determinations of complexity from an individual perspective. Further research is needed to apply these systems to populations external to those in which they were developed, and to appreciate how they may integrate with, and impact, clinical care.

VUСA is a chaotic and rapidly changing business environment that, based on the variability, uncertainty, complexity and ambiguity of the modern world, transforms the approach of banks to the analysis of financial stability. The aim of the paper is to improve tools for monitoring the impact of VUCA-world conditions on the financial stability of banks, namely a model for studying and analyzing the impact of the modern business space “VUCA” on the financial stability of the country's banks. To test the model, the method of constructing regression equations in multifactor regression analysis is used. For this study, data from some Eastern European countries (Ukraine, Belarus, Latvia, Lithuania, Moldova) were used, and time series data were used for 10 years from 2010 to 2019.Having considered the definition of “VUCA-world conditions”, the model of modern business space “VUCA” was developed when analyzing the activity of banks in the studied countries. Drivers, consequences, requirements and macroeconomic indicators of the countries’ activities in the VUСA-world conditions are determined. The VUCA-world conditions also consider the study of key macroeconomic indicators that allow building long-term relationships throughout the value chain. The analysis of the studied Eastern European countries showed that with the increase of factors of GDP growth, GNI per capita growth, research and development costs, foreign direct investment, and net inflow of 1%, the effective ratio of bank capital and assets also increases. The assessment, in contrast to the existing ones, makes it possible to consider the impact of the macroeconomic environment of banks on their financial stability.

The detection of motion artifacts in electroencephalogram (EEG) analysis is a high priority, especially for wearable, portable, or wireless EEG monitoring systems. Recently, many scholars have proposed numerous promising techniques to address this issue, e.g., blind source separation (BSS) and independent component analysis (ICA). However, real-time detection with low processing time and friendly use for embedded systems in wearable devices still needs more investigation. Therefore, in this paper, we considered an alternative method for approaching the motion artifact detection problem in EEG signals and proposed a new method called multiscale modified-distribution entropy (M-mDistEn). An efficient coarse-grained procedure was added into the modified-distribution entropy (mDistEn) to consider the various scales (frequencies) of the signals. The results showed that this method is suitable for distinguishing between noisy and normal portions of the signal with a maximum accuracy of approximately 93%. In addition, the M-mDistEn method led to $p$ -values of less than 0.05, indicating statistically significant results of differentiating the motion artifact EEG signals from the clean EEG signals. By comparing the different multiscale entropies, our proposed multiscale entropy was more accurate and robust. Thus, the proposed M-mDistEn method is an effective and efficient entropy method for the analysis of EEG signals corrupted by motion artifacts.

Aim of the study To evaluate the agreement between planned and delivered doses and its potential correlation with the plans' complexity subjected to dosimetric verification. Material and methods Four isocentre volumetric modulated arc therapy for total marrow irradiation plans optimized simultaneously with (P1) and without (P2) MU reduction were evaluated dosimetrically by γ method performed in a global mode for 4 combinations of γ-index criteria (2%/2 mm, 2%/3 mm, 3%/2 mm, and 3%/3 mm). The evaluation was conducted for 4 regions (head and neck, chest, abdomen and upper pelvis, and lower pelvis and thighs) that were determined geometrically by the isocentres. The Wilcoxon test was used to detect significant differences between γ passing rate (GPR) analysis results for the P1 and P2 plans. The Pearson correlation was used to check the relationship between GPR and the plans' complexity. Results Except for the head and neck region, the P2 plans had better GPRs than the P1 plans. Only for hard combinations of γ-index criteria (i.e. 2%/3 mm, 2%/2 mm) were the GPRs differences between P1 and P2 clinically meaningful, and they were detected in the chest, abdomen and upper pelvis, and lower pelvis and thighs regions. The highest correlations between GPR and the indices describing the plans' complexity were found for the chest region. No correlation was found for the head and neck region. Conclusions The P2 plans showed better agreement between planned and delivered doses compared to the P1 plans. The GPR and the plans' complexity depend on the anatomy region and are most important for the chest region.

Generally, for healthy adults, the entropy of electroencephalogram (EEG) signals gradually decreases from wake to sleep stages N1, N2, to N3, and increases during REM. However, some researchers found that multiscale entropy curves of sleep and wakefulness intercept, a cross-over phenomenon whose origin remains unexplored. The objective of the present work is to trace the origin of the cross-over phenomenon and to propose a workaround strategy. We simulated EEG by generating 1/f broadband signal and chirp signals with continuously varying frequencies. We then retrieved the rhythmic component from simulated EEG and real-world EEG and conducted MSE analysis of the instantaneous frequency variation (IFV) of the rhythmic component. The simulation revealed that this interception was ubiquitous in the MSE analysis of simulated EEG with rhythmic components of different frequencies. The cross-over point moved toward larger scale factors with the increasing sampling rate. We found that the MSE curve of IFV from real-world EEG for the wakefulness group was higher than that for sleep, showing no interception. These results suggest that (1) for a rhythmic signal like EEG, MSE analysis of the raw signal is highly affected by the rhythmic component, presenting artificial cross-over curves in sleep EEG study, (2) frequency variation of rhythmic components are complex signal which differs between wakefulness and sleep, in accordance with the complexity loss theory.

The data-driven approach is popular to automate learning of fuzzy rules and tuning membership function parameters in fuzzy inference systems (FIS) development. However, researchers highlight different challenges and issues of this FIS development because of its complexity. This paper evaluates the current state of the art of FIS development complexity issues in Computer Science, Software Engineering and Information Systems, specifically: 1) What complexity issues exist in the context of developing FIS? 2) Is it possible to systematize existing solutions of identified complexity issues? We have conducted a hybrid systematic literature review combined with a systematic mapping study that includes keyword map to address these questions. This review has identified the main FIS development complexity issues that practitioners should consider when developing FIS. The paper also proposes a framework of complexity issues and their possible solutions in FIS development.

Over the last decade, the emergence of military design theorists, educators, and practitioners has coagulated into a community of practice internationally and across multiple armed forces. This new group of military academics and professionals demonstrates a keen interest in developing and expanding the notion of “designing for war.” For military design theory, there are unexpected design contributions from postmodern philosophy, non-Western concepts, and other highly subjective and alternative fields and practices that provide insight and novel thought for human opportunities within the dynamic and complex conflict contexts of the 21st century. Many of these novel constructs are not available in mainstream commercial design applications, methods, or theory—they were developed for military design contexts and are largely unknown outside of the community of practice for security contexts. Part of this lack of exposure is inevitable, given the classification requirements of many design efforts. This article presents one of the most popular military design techniques used by the Joint Special Operations University (JSOU), a military education platform in Tampa, Florida. JSOU is part of the US Special Operations Command (USSOCOM) and provides tailored education to Special Operations Forces (SOF) worldwide.

Highlights • Edge area metric correlated to dosimetric evaluations of control point beam openings. • Distribution of edge area metric complexity scores were dependent on treatment site. • No correlation for mean edge area metric and evaluations of full treatment plans., Background and purpose Aperture-based complexity metrics have been suggested as a method to score complexity of volumetric modulated arc therapy (VMAT) plans. The purpose of this study was to evaluate the edge area metric (EAM) for clinical VMAT plans on a control point and treatment plan level. Materials and methods EAM on a control point level was evaluated based on film measurements of 18 static beam openings originating from VMAT plans. EAM on a treatment plan level (arithmetic mean value of EAM scores for control points) was evaluated based on measurements with the Delta4® for 200 VMAT plans for four different treatment sites: pelvic, thorax, head and neck, and prostate. Measurements were compared to calculations and dose difference and gamma pass rates were evaluated. Results EAM scores on a control point level correlated with Pearson’s r-values of −0.96 and −0.77 to dose difference and gamma pass rates, respectively. The prostate plans had the highest average EAM score. A connection between smaller PTVs and higher EAM scores was found. No correlation between the evaluation result and EAM on a plan level was found. Conclusions EAM on a control point level was shown to correlate to the difference between measured and calculated 2D dose distributions of clinical VMAT beam openings. No correlation was found for EAM on a plan level for clinical treatment plans.

Resumen La temática central de este ensayo es la necesidad de desplegar en la educación superior la visión integradora. El punto de partida es la exigencia del desarrollo científico-tecnológico de la formación de profesionales con una visión que integre saberes, la cual entra en contradicción con la mirada que reduce y fracciona, aún dominante en la educación superior. La base conceptual es el pensamiento complejo y la categoría condición humana. Entre las ideas que se desarrollan están: el valor de la categoría condición humana para desplegar la visión integradora en la educación superior; la lucha contra el modo de concebir la educación superior como una empresa; la importancia de lo afectivo en la educación superior. El objetivo es argumentar algunas manifestaciones en la educación superior del pensamiento que fragmenta y reduce. Entre las conclusiones están: La visión integradora en la educación superior impide que se sobrevalore la formación profesional y es poderosa para rebasar el racionalismo extremo y abrirse a la afectividad. Abstract The central theme of this essay is the need to deploy an integrative vision in higher education. The starting point is the requirement of scientific-technological development that professionals be trained with a vision that integrates knowledge, which contradicts the view that reduces and divides and still prevails in higher education. The conceptual base is complex thinking and the human condition category. Among the ideas developed are the value of the human condition category to deploy the integrative vision in higher education, the fight against the way of conceiving higher education as a business, and the importance of the affective dimension in higher education. The objective is to argue some manifestations of fragmentary and reductionist thought in higher education. One of the conclusions is that the integrative vision in higher education prevents professional training from being overvalued and is powerful to overcome extreme rationalism and open up to affectivity. Among the conclusions is that higher education’s integrative vision prevents professional training from being overvalued and is powerful in overcoming extreme rationalism and opening up to affectivity. Resumo O tema central deste ensaio é a necessidade de implantar uma visão integradora no ensino superior. O ponto de partida é a exigência do desenvolvimento científico-tecnológico de que os profissionais sejam formados com uma visão integradora de saberes, o que está em contradição com a visão que reduz e divide que ainda domina o ensino superior. A base conceitual é o pensamento complexo e a categoria condição humana. Entre as ideias que se desenvolvem estão: o valor da categoria condição humana para implantar a visão integrativa no ensino superior; a luta contra a forma de conceber o ensino superior como negócio; a importância do afetivo no ensino superior. O objetivo é discutir algumas manifestações na educação superior de um pensamento que se fragmenta e se reduz. Entre as conclusões: A visão integradora no ensino superior evita que a formação profissional seja supervalorizada; é poderosa para superar o racionalismo e se abrir à afetividade.

Good sleep quality is essential to our life and work. The two major challenges in evaluating sleep quality are the scoring of sleep stages and sleep apnea detection. In addition to sleep apnea, hypopnea is also a respiratory event that occurs at an equal or even higher frequency compared to that of apnea. However, the two phenomena are not only different in terms of physical manifestation, there are also significant differences in the methods and means of clinical treatment. Therefore, in this study we propose an effective algorithm to distinguish between hypopnea and apnea events. We consider two aspects of feature extraction. On the one hand, from the original time-domain waveform of electrocardiogram signals, we extract the time difference sequence between the appearance of the R-R wave in two adjacent QRS waves (RR intervals) and explore the way they change when apnea and hypopnea event occur. On the other hand, considering that the degree of sequence disorder caused by hypopnea and apnea events is different, multi-scale entropy and complexity are chosen as the features that describe this change. The results obtained using the proposed algorithm show that the classification between hypopnea events and any type of apnea events all reach an accuracy rate greater than 90%. For obstructive apnea events, which occur most frequently among the three different types of apneas, the classification accuracy, sensitivity, specificity and F1-score were 91.78%, 92.21%, 91.30%, 92.20%, respectively. The classification results between central apnea events and hypopnea events reached an accuracy of 93.18%, sensitivity of 95.00%, specificity of 91.67% and F1-score of 92.68%. At the same time, the algorithm achieved an accuracy of 92.59%, sensitivity of 90.38%, specificity of 94.64% and F1-score of 92.16% when classifying mixed apnea and hypopnea events. In the classification of normal breathing, hypopnea events and apnea events, the total accuracy rate achieved was 91.92%, with that for normal breathing reaching 93.62%, for hypopnea events reaching 90.39%, and 91.91% for apnea events, with a Kappa coefficient of 0.99. The algorithm proposed in this study constitutes an efficient and convenient approach for apnea and hypopnea event detection and classification.

Product-centric techniques to analyze mobile applications leverage traditional source code analysis, size, market success, complexity, and others. Many of these techniques converge in the strategy of evaluating metrics taken from the source code that delivers the functionality of the software product. However, when following the Model-View-Controller (MVC) architecture, mobile applications are typically constructed by a compound of at least two programming languages, one to deliver the functionality and the other to describe the visual aspects. The latter is commonly left out from source code analysis, even though critical parts of the application are present in the graphic User Interface (UI). In this paper, we identify an opportunity to strengthen the product-centric mobile app analysis by incorporating UI metrics. This approach aims to enhance the expressiveness of source code metrics and deliver a more comprehensive analysis of the complexity, maintainability, and effort estimation of a mobile app. To introduce the concept, we present a case study realized using a block-based programming language to create mobile apps, in which we describe and calculate functional and UI metrics, discover commonalities and differences, discuss traits, and open tracks for further research.

Complexity has been argued to limit operational efficiency, hinder decision-making and induce disruption in supply chain networks. The main aim of this paper is to investigate the architectural trade-off between complexity and modularity in modular assembly supply chain networks. Towards this, an information-entropic complexity model is developed and applied to the domain of assembly supply chains and logistics. This approach characterises complexity as a combination of the intrinsic complexity of the system modules/interfaces and the influence of the topological composition of the network. The model is then used within an optimisation framework, where the optimal granularity level for assembly supply chain design solutions for a given assembly product can be automatically verified by considering the trade-off between complexity and network modularity. It is concluded that the proposed methodology could help to minimise the complexity of supply chain assembly configurations while maximising their modularity and thereby help to increase both the reliability and performance of supply chain networks.

Glaucoma is a leading cause of irreversible visual field loss. The early detection and diagnosis of the disease are therefore necessary to prevent blindness. Pupillary light responses are an interesting new technique for the detection of glaucoma. However, the analysis of pupillary signals has been associated with manual supervision or involved high computational costs. The present paper is to propose an analysis framework to automatically investigate changes in the complexity of pupillary signals under ambient light conditions for the screening of glaucoma. In this work, pupillary data of 13 glaucoma patients, 13 age-matched controls, and 11 young controls were recorded at the light intensity of 100 cd/m2 using a commercial eye tracker. The pupillary complexity of the participants was analysed using Higuchi’s fractal dimension, permutation entropy, and conditional entropy. We found that there was a statistically significant difference in the pupillary complexity between glaucoma patients and control groups (P < 0.0001). Specifically, the difference was more pronounced when using the fractal dimension measure. These results confirm the potential of using pupillary complexity for the screening of glaucoma using commercial devices.

This study examined the effectiveness of implicit and explicit learning methods on the acquisition and retention of the decision-making skill in low and high complexity situations. 60 novice students were divided into explicit, implicit, and control group. Experimental groups followed 12 training courses. A pre-test, a post-test, and a retention test were used to evaluate the effectiveness of the instructional methods. All participants were evaluated in decision-making speed and accuracy in laboratory tests under simulated conditions. A three-way factorial ANOVA was conducted (3 Group X 2 Complexity X 3 Measurement periods) with repeated measurements on the last factor for the accuracy and reaction time. The analysis showed a significant improvement in decision-making accuracy, in low complexity for both experimental groups. In high complexity situations, the explicit method improved over time and was better than the implicit for decision accuracy. No differences were found among groups or measurements for the decision speed in either low or high complexity situations. It seems that in complex sport conditions, the use of explicit learning helps novices to develop decision-making accuracy more than the implicit learning method, since guided discovery may guide the participants to process contextual information from the sports environment more effectively.

PURPOSE: Current research seeks to create an economic model that connects strategic management and network theory. However, most theoretical models do not provide empirical evidence of network relationships’ real structure and attributes. The purpose of the paper is to explore the relation between enterprise characteristics and the characteristics of buyer–supplier relationships in supply chain networks. We are specifically interested in business relationships in networks with respect to the various enterprises’ sizes and sectors of industry. The subject of our research was characteristics, such as network relationship complexity, continuity of relationships, and strategic management in networks. The paper summarizes the results of an empirical study on buyer-supplier networks and accentuates the importance of developing and fostering business collaboration for strategic management. METHODOLOGY: We conducted the questionnaire research in 2016–2019 on 360 enterprises from the Czech Republic. We selected the research sample based on the non-probability purposive sampling method. The members of the research team collected data from an online survey and personal visits to enterprises. The statistical analysis of hypotheses is based on the frequency of managers’ answers. To evaluate results, a two-proportion Z-Test is used for comparing different categories of enterprises according to their enterprise size or prevailing sector of the industry. FINDINGS: The main results show that the differences between enterprises involved in the buyer–supplier structures lie mainly in their size. The survey did not identify differences between industry sectors. The findings show that the complexity of networks in the Czech Republic is not high in terms of the number of suppliers or involvement in many supply networks. The continuity of relationships with partners in buyer–supplier networks is relatively long-term oriented. Long-term partnerships reflect the higher quality of relationships and support future integration. However, large enterprises prefer to build contracts for shorter or longer periods. An overall decentralization strategy characterizes the strategic management of buyer–supplier networks. This finding means dividing competencies such as planning, managing, sourcing, decision-making, transporting (delivering) among more enterprises. IMPLICATIONS FOR THEORY AND PRACTICE: The paper provides an insight into understanding how the buyer–supplier network functions. The theory’s implication builds on the connection of supply chain management and strategic management from the network perspective. Supply chain management is viewed as a part of strategic management, and the synthesis of both research areas opens an innovative view to business theory. ORIGINALITY AND VALUE: The paper’s principal value is the connection between contemporary ideas of strategic management and supply chain management. The synthesis of supply chain management and network approach enhances strategic management theory.

Product variety is a growing trend of offering highly configurable products, but increases complexity costs throughout the entire product lifecycle. Modularization makes managing variety-induced complexity and ensuring profitable production possible. Unfortunately, the interfaces between modules represent joining domains and lack modularization and commonalization solutions. Meanwhile, joining element design finds increasingly more support from automation resulting in optimized solutions for individual product variants. Besides, continuous product development implies sequentially designed product variants that cause ambiguous and unnecessary design iterations for joining element design. This paper presents a five-step methodology to modularize joining elements in early product development, while balancing variety-induced complexity and production costs. It reduces complexity by commonalizing joining elements over product variants. The methodology unifies joining technologies, clusters joining locations and proposes an approach to increase modularity by balanced addition of joining elements.

Background To support a move towards a coordinated non-communicable disease approach in public health policy, it is important to conceptualise changes to policy on tobacco and alcohol as affecting a single interlinked system. For health economic models to effectively inform policy, the first step in their development should be to develop a conceptual understanding of the system complexity that is likely to affect the outcomes of policy change. Our aim in this study was to support the development and interpretation of health economic models of the effects of changes to tobacco and alcohol policies by developing a conceptual understanding of the main components and mechanisms in the system that links policy change to outcomes. Methods Our study was based on a workshop from which we captured data on participant discussions on the joint tobacco–alcohol policy system. To inform these discussions, we prepared with a literature review and a survey of participants. Participants were academics and policy professionals who work in the United Kingdom. Data were analysed thematically to produce a description of the main components and mechanisms within the system. Results Of the people invited, 24 completed the survey (18 academic, 6 policy); 21 attended the workshop (16 academic, 5 policy). Our analysis identified eleven mechanisms through which individuals might modify the effects of a policy change, which include mechanisms that might lead to linked effects of policy change on tobacco and alcohol consumption. We identified ten mechanisms by which the tobacco and alcohol industries might modify the effects of policy changes, grouped into two categories: Reducing policy effectiveness; Enacting counter-measures. Finally, we identified eighteen research questions that indicate potential avenues for further work to understand the potential outcomes of policy change. Conclusions Model development should carefully consider the ways in which individuals and the tobacco and alcohol industries might modify the effects of policy change, and the extent to which this results in an unequal societal distribution of outcomes. Modelled evidence should then be interpreted in the light of the conceptual understanding of the system that the modelling necessarily simplifies in order to predict the outcomes of policy change.

Objectives: The current management of chronic obstructive pulmonary disease (COPD) largely ignores its heterogeneous pulmonary and extrapulmonary manifestations in the individual patient. This study aimed to identify clusters of patients with COPD based on a thorough traits assessment.Design: An observational, prospective, single-center study.Setting and Participants: Patients with COPD referred by chest physicians for a comprehensive pulmonary rehabilitation program to CIRO (Horn, the Netherlands) were eligible to participate. CIRO is a specialized pulmonary rehabilitation center in the southern part of the Netherlands for patients with complex underlying respiratory diseases.Methods: Clinically stable patients with COPD underwent a comprehensive assessment, including pulmonary traits (airflow limitation, static hyperinflation, gas transfer, respiratory pressures, and arterial blood gases), extrapulmonary functional traits, and health status (quadriceps muscle strength, physical functioning, body composition, comorbidities, symptoms perception, and social and emotional functioning). Clusters were generated using the SOM-Ward Cluster algorithm, a hybrid algorithm that applies the classical hierarchical method of Ward on top of the self-organizing map topology.Results: Based on the abovementioned attributes of 518 patients with mild to very severe COPD (44% women, age 64.1 +/- 9.1 years, forced expiratory volume in the first second 48.6% +/- 20.0% of predicted), 7 clusters were identified. Clusters had unique patterns differing in demographics, pulmonary, extrapulmonary functional, and behavioral traits and/or health status.Conclusion and Implications: The tremendous heterogeneity in pulmonary, extrapulmonary functional and behavioral traits, and health status in patients with COPD supports the need for an individual comprehensive assessment and a goal-directed personalized management strategy. (C) 2020 AMDA - The Society for Post-Acute and Long-Term Care Medicine.

BACKGROUND: There are no current standardized and accepted methods to characterize the surgical complexity of a laparoscopic hysterectomy. This leads to challenges when trying to understand the relationship between the patient and the surgical features and outcomes. The development of core feature sets for laparoscopic hysterectomy studies would enable future trials to measure the similar meaningful variables that can contribute to surgical complexity and outcomes.OBJECTIVE: The purpose of this study was to develop a core feature set for the surgical complexity of a laparoscopic hysterectomy.STUDY DESIGN: This was an international Delphi consensus study. A comprehensive literature review was conducted to identify the features that were reported in studies on laparoscopic hysterectomy complexity. All the features were presented for evaluation and prioritization to key experts in 3 rounds of online surveys. A priori consensus criteria were used to reach agreement on the final outcomes for inclusion in the core feature set.RESULTS: Experts represented North America, South America, Europe, Africa, Asia, and Oceania. Most of them had fellowship training in minimally invasive gynecologic surgery. Sixty-four potential features were entered into round 1. Experts reached a consensus on 7 features to be included in the core feature set. These features were grouped under the following domains: 1) patient features, 2) uterine features, and 3) nonuterine pelvic features. The patient features include obesity and other nonobesity comorbidities that alter or limit the ability of a surgeon to perform the basic or routine steps in a laparoscopic hysterectomy. The uterine features include the size and presence of fibroids. The nonuterine pelvic features include endometriosis, ovarian cysts, and adhesions (bladder-to-uterus, rectouterine pouch, and other adhesions).CONCLUSION: Using robust consensus science methods, an international consortium of experts has developed a core feature set that should be assessed and reported in all future studies that aim to assess the relationship between the patient features and surgical outcomes of laparoscopic hysterectomy.

Automation is a collection of a whole set of theories and methods to make a system work automatically as intended, in our case the urban water supply system. A critical feature of automation is the feedback principle: a sensor is measuring a certain variable, e.g. a concentration; a computer tests that the measurement is valid; a computer algorithm calculates and decides what should be corrected; a pump or valve or some other device transforms the decision to action. All of this is untouched by human hand. The ‘intention’, or the goal, must be provided to the controller. The key component of automation is the system that can represent any component or process in the water supply system and even the complete system. Automation technology always must be combined with a true understanding of people at all levels. Otherwise, there is a high risk for misunderstandings and failures. Three categories of problems are highlighted, where automation can contribute: uncertainty, feedback, and complexity. A key challenge is the handling of disturbances. Integrated management of the whole urban water cycle will be required in future urban areas to acquire sustainable operations. Automation is a crucial condition to make integration possible in complex systems. HIGHLIGHTS Dealing with uncertainties by online monitoring.; Using feedback at all levels to correct deviations from ‘normal’ operation.; Handling disturbances, particularly in time scales too fast or too slow for human interaction.; Operating complex highly interactive systems as well as small-scale decentralized systems.; Operation of the integrated urban water supply system.

The past 25 years have seen many attempts to introduce defeasible-reasoning capabilities into a description logic setting. Many, if not most, of these attempts are based on preferential extensions of description logics, with a significant number of these, in turn, following the so-called KLM approach to defeasible reasoning initially advocated for propositional logic by Kraus, Lehmann, and Magidor. Each of these attempts has its own aim of investigating particular constructions and variants of the (KLM-style) preferential approach. Here our aim is to provide a comprehensive study of the formal foundations of preferential defeasible reasoning for description logics in the KLM tradition. We start by investigating a notion of defeasible subsumption in the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for both preferential and rational subsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level of entailment and present an algorithm for the computation of rational closure of a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DL ALC .

BACKGROUND At our academic tertiary care medical center, we have noted patients referred for endoscopic retrograde cholangiopancreatography (ERCP) who increasingly require advanced cannulation techniques. This trend is noted despite increased endoscopist experience and annual ERCP volume over the same period. AIM To evaluate this phenomenon of perceived escalation in complexity of cannulation at ERCP and assessed potential underlying factors. METHODS Demographic/clinical variables and records of ERCP patients at the beginning (2008), middle (2013) and end (2018) of the last decade were reviewed retrospectively. Cannulation approaches were classified as “standard” or “advanced” and duodenoscope position was labeled as “standard” (short position) or “non-standard” (e.g., long, semi-long). RESULTS Patients undergoing ERCP were older in 2018 compared to 2008 (69.7 ± 15.2 years vs 55.1 ± 14.7, P < 0.05). Increased ampullary distortion and peri-ampullary diverticula were noted in 2018 (P < 0.001). ERCPs were increasingly performed with a non-standard duodenoscope position, from 2.2% (2008) to 5.6% (2013) and 16.1% (2018) (P < 0.001). Utilization of more than one advanced cannulation technique for a given ERCP increased from 0.7% (2008) to 0.9% (2013) to 6.6% (2018) (P < 0.001). Primary mass size > 4 cm, pancreatic uncinate mass, and bilirubin > 10 mg/dL predicted use of advanced cannulation techniques (P < 0.03 for each). CONCLUSION Complexity of cannulation at ERCP has sharply increased over the past 5 years, with an increased proportion of elderly patients and those with malignancy requiring advanced cannulation approaches. These data suggest that complexity of cannulation at ERCP may be predicted based on patient/ampulla characteristics. This may inform selection of experienced, high-volume endoscopists to perform these complex procedures.

Background The need for better methods for evaluation in health research has been widely recognised. The ‘complexity turn’ has drawn attention to the limitations of relying on causal inference from randomised controlled trials alone for understanding whether, and under which conditions, interventions in complex systems improve health services or the public health, and what mechanisms might link interventions and outcomes. We argue that case study research—currently denigrated as poor evidence—is an under-utilised resource for not only providing evidence about context and transferability, but also for helping strengthen causal inferences when pathways between intervention and effects are likely to be non-linear. Main body Case study research, as an overall approach, is based on in-depth explorations of complex phenomena in their natural, or real-life, settings. Empirical case studies typically enable dynamic understanding of complex challenges and provide evidence about causal mechanisms and the necessary and sufficient conditions (contexts) for intervention implementation and effects. This is essential evidence not just for researchers concerned about internal and external validity, but also research users in policy and practice who need to know what the likely effects of complex programmes or interventions will be in their settings. The health sciences have much to learn from scholarship on case study methodology in the social sciences. However, there are multiple challenges in fully exploiting the potential learning from case study research. First are misconceptions that case study research can only provide exploratory or descriptive evidence. Second, there is little consensus about what a case study is, and considerable diversity in how empirical case studies are conducted and reported. Finally, as case study researchers typically (and appropriately) focus on thick description (that captures contextual detail), it can be challenging to identify the key messages related to intervention evaluation from case study reports. Conclusion Whilst the diversity of published case studies in health services and public health research is rich and productive, we recommend further clarity and specific methodological guidance for those reporting case study research for evaluation audiences.

Volumetric‐modulated arc therapy (VMAT) treatment plans that are highly modulated or complex may result in disagreements between the planned dose distribution and the measured dose distribution. This study investigated established VMAT complexity metrics as a means of predicting phantom‐based measurement results for 93 treatments delivered on a TrueBeam linac, and 91 treatments delivered on two TrueBeam STx linacs. Complexity metrics investigated showed weak correlations to gamma passing rate, with the exception of the Modulation Complexity Score for VMAT, yielding moderate correlations. The Spearman’s rho values for this metric were 0.502 (P

The depiction and description of body techniques of combat have always been a part of martial arts culture. For example, self-defence techniques of the Israeli system of Krav Maga have routinely been depicted and described in books. More recently, the dissemination of similar knowledge has been presented within emerging modern technologies such as online videos and blogs. We argue, however, that such approaches limit and may even harm the development of professional judgement and decision making of self-defence coaches. By focusing on (a) the distinction between declarative and procedural knowledge and (b) the complexity and non-linearity of self-defence situations, we point out the limitations of the depicted and described knowledge in these media (or channels) of communication. In essence, we argue that the focus on providing procedural knowledge to coaches promotes a view that self-defence skill development is linear. These foci ultimately will limit the adaptability of the coach in creating effective skill development programs for practitioners who need to be able to cope with complex dynamics of real world violence.

Complexity in MLC‐based radiosurgery treatment delivery can be characterized by the efficiency of monitor unit (MU) utilization and the average MLC leaf separation distance for a treatment plan. A reduction in plan complexity may be desirable if plan quality is not impacted. In this study, a number of strategies are explored to determine how plan quality is affected by efforts to reduce plan complexity. Ten radiosurgery cases of varying complexity are retrospectively planned using six optimization strategies: an unconstrained volumetric modulated arc therapy (VMAT) technique, a MU‐constrained VMAT technique, three techniques using various strengths of the aperture shape controller (ASC), and a hybrid technique consisting of a final‐stage VMAT optimization applied to a dynamic conformal arc leaf sequence (ODCA). The plans are compared in terms of MU efficiency, MLC leaf‐separation, conformity index (CI), gradient index (GI), and QA measurement results. The five VMAT techniques exhibited only minor differences in CI and GI values, though the ASC and MU‐constrained techniques did require 6–20% fewer MU and had mean field apertures 5–19% larger. On average, the ODCA technique had CI values 3.5% lower and GI values 1.0–2.5% higher than the VMAT techniques, but also had a mean field aperture 24–47% larger and required 16–32% fewer MU. The QA measurement results showed a 0.61% variation in mean per‐field 2%/1 mm gamma passing rates across all techniques (range 96.81%–97.42%), with no observed correlation between passing rate and technique. For simple targets, the ODCA technique achieved CI results that were equivalent to the unconstrained VMAT technique with an average 30% reduction in required MU, an average 50% increase in mean leaf separation distance, and brain V12Gy values within 0.38 cc of the VMAT technique for targets up to approximately 2 cm diameter. For MLC‐based single‐target radiosurgery, plan complexity can often be significantly reduced without an equivalent reduction in plan quality.

Heuristic algorithms play a significant role in synthesize and optimization of digital circuits based on reversible logic yet suffer with multiple disadvantages for multiqubit functions like scalability, run time and memory space. Synthesis of reversible logic circuit ends up with trade off between number of gates, quantum cost, ancillary inputs and garbage outputs. Research on optimization of quantum cost seems intractable. Therefore post synthesis optimization needs to be done for reduction of quantum cost. Many researchers have proposed exact synthesis approaches in reversible logic but focussed on reduction of number of gates yet quantum cost remains undefined. The main goal of this paper is to propose improved Ant Colony Optimization (ACO) algorithm for quantum cost reduction. The research efforts reported in this paper represent a significant contribution towards synthesis and optimization of high complexity reversible function via swarm intelligence based approach. The improved ACO algorithm provides low quantum cost based toffoli synthesis of reversible logic function without long computation overhead.

The method of scoring human resources capacity depending on the level of problem complexity and employment function and the level of responsibility has been presented. The complexity of professional activity is determined by the components: type of activity, variety of work, controllability of work performance, scale and complexity of management. The degree of responsibility is determined by the components: independence, initiative, commitment to the result, significance of the results of work. For each component, three characteristics have been determined, each of which is assigned a score: from 1 – minimum level, to 3 – a high level. The matrix model, formed on the basis of scoring data, with four groups of personnel has been proposed in the article: “Stars” (it is characterized by a high level of responsibility and high complexity of the labour functions perfomed), “Experts” (high complexity of work with a relatively low level of responsibility), «Soldiers» (high responsibility with a low level of complexity), «Cogs» (low level of responsibility and complexity of the work performed). Assessment of human resources capacity, determining the current state, is the basis for the development of management decisions and implementation of measures aimed at improving the efficiency of the organization through better use of human resources potential.

Brain complexity, which reflects the ability of the brain to adapt to a changing environment, has been found to be significantly changed with age. However, there is less evidence on the alterations of brain complexity in neurodegenerative disorders such as Alzheimer’s disease (AD). Here we investigated the altered complexity of resting-state blood oxygen level-dependent signals in AD-related neurodegeneration using multiscale entropy (MSE) analysis. All participants were recruited from the Alzheimer’s Disease Neuroimaging Initiative, including healthy controls (HC, n=62), amnestic mild cognitive impairment (aMCI, n =81) patients, and Alzheimer’s disease (AD, n=25) patients. Our results showed time scale-dependent MSE differences across the three groups. In scale=1, significantly changed MSE patterns (HC>aMCI>AD) were found in four brain regions, including the hippocampus, middle frontal gyrus, intraparietal lobe, and superior frontal gyrus. In scale=4, reversed MSE patterns (HC

As design continues to evolve so does our approach at ID, especially toward our curriculum and the competencies we build in our students. Major institutional changes in 2017–18 prompted an in-depth analysis of and update to our largest graduate program, the Master of Design (MDes). Dean Denis Weil and the ID faculty kept two things in mind as they upgraded the program content: the school’s pioneering 80-year history, and the position design holds in today’s tech-oriented world. Doing so revealed three new competencies to instill in our students and three important challenges we face in teaching those competencies, and today’s MDes course system structure reflects these considerations. After offering a brief background of ID and its roots, we will present our view of the role of design in today’s world. We will then outline the structure of our updated curriculum—which responds to our view of design’s contemporary role—and discuss some of the challenges we face as we build tomorrow’s most critical competencies in our students., Highlights • ID is building three unique competencies in its MDes students: embracing complexity, cultivating possibilities, and driving impactful change. • We face three important challenges in teaching these competencies: covering both breadth and depth, integrating application with theory, and managing diverse aptitudes and perspectives.

The impacts of the COVID-19 pandemic on food and nutrition insecurity are likely to be significant for Small Island Developing States due to their high dependence on foreign tourism, reliance on imported foods and underdeveloped local food production systems. SIDS are already experiencing high rates of nutrition-related death and disability, including double and triple burdens of malnutrition due to unhealthy diets. We consider the potential role for improved local food production to offset the severity of food system shocks in SIDS and identify the need for localized approaches to embrace systems thinking in order to facilitate communication, coordination and build resilience.

Product variety is a growing trend of offering highly configurable products at the cost of inducing complexity in manufacturing. Joining is a key manufacturing process and historically was a paper-based process with incomplete variety documentation. Nowadays, digital joining element design is a substitution of paper for 3D space. Nonetheless, it remains an ambiguous manual task with limited automation, resulting in time-consuming iterative error-prone development trajectories and costly reworks. This contribution addresses the state of the art in joining element design in both research and industry practice. It reviews product variety and its impact on joining processes. The paper identifies a need for integrating product variety into joining element design and it proposes a solution pathway using artificial intelligence methods.

Background: Health innovation has been dominant in the pharmaceutical, biomedical, and to some extent insurance institutions for quite some time. Now we are in an innovation age for healthcare delivery. Some note that the complexity of healthcare delivery may make innovation in this setting more difficult and may require more adaptive solutions. The aim of this study is to examine the relationship between departmental complexity and innovation, using a complex adaptive systems approach in a hospital setting. Methods: We conducted a quantitative study of 31 hospital units within one hospital and use complex adaptive systems (CAS) theory to examine how two CAS factors, autonomy and performance orientation, moderate the relationship between departmental complexity and innovation. Results: We find that departmental complexity is associated with higher innovation performance when autonomy is low rather than high. We also find that departmental complexity is associated with higher innovation performance when performance orientation is high rather than low. Our findings make three distinct contributions: we quantify the influence of complexity on innovation success in the health care sector, we examine the impact of autonomy on innovation in health care, and we are the first to examine performance orientation on innovation in health care. Conclusions: This study tackles the long debate about the influence of complexity on healthcare delivery, particularly innovation. Instead of being subject to the influence of complexity with no means of making progress or gaining control, hospitals looking to implement innovation programs should provide guidance to teams and departments regarding the type of innovation sought and provide support in terms of time and management commitment. Hospitals should also find ways to promote and make successful pilot implementations of such innovations visible in the organization. A close connection between the targeted innovation and the overall success and performance of the hospital unit is ideal.

BACKGROUND: Medication self-management is important for patients who are controlling diabetes. Achieving medication self-management goals, may depend on treatment complexity and patients' capacities such as health literacy, knowledge and attitude.OBJECTIVES: The aims of this study were to explore how patients with diabetes self-manage their medications, how patients seek support when experiencing problems and how primary healthcare providers identify patients' medication related problems and provide support.METHODS: Semi-structured interviews were conducted among patients with diabetes receiving primary care and with their primary healthcare providers - GPs, nurses, pharmacists and technicians - between January and June 2017. A purposive sampling strategy was used to identify and select participants. An interview guide based on the Cycle of Complexity model was developed. Interviews were audiotaped and transcribed verbatim. Transcripts were coded with a combination of deductive and inductive codes. A thematic analysis was performed to identify categories and themes in the data. Findings were compared with the Cycle of Complexity model.RESULTS: Twelve patients and 27 healthcare providers were included in the study. From the transcripts 95 codes, 6 categories and 2 major themes were extracted. Patients used practical solutions and gaining knowledge to manage their medication. Their problems were often related to stress and concerns about using medications. A trusted relationship with the healthcare provider was essential for patients to share problems and ask for support. Informal support was sought from family and peer-patients. Healthcare providers perceive problem identification as challenging. They relied on patients coming forward, computer notifications, clinical parameters and gut-feeling. Healthcare providers were able to offer appropriate support if a medication management problem was known.CONCLUSION: Patients are confident of finding their way to manage their medications. However, sharing problems with healthcare providers requires a trusted relationship. This is acknowledged by both patients and healthcare providers.

Background Healthcare represents a paradox. While change is everywhere, performance has flatlined: 60% of care on average is in line with evidence- or consensus-based guidelines, 30% is some form of waste or of low value, and 10% is harm. The 60-30-10 Challenge has persisted for three decades. Main body Current top-down or chain-logic strategies to address this problem, based essentially on linear models of change and relying on policies, hierarchies, and standardisation, have proven insufficient. Instead, we need to marry ideas drawn from complexity science and continuous improvement with proposals for creating a deep learning health system. This dynamic learning model has the potential to assemble relevant information including patients’ histories, and clinical, patient, laboratory, and cost data for improved decision-making in real time, or close to real time. If we get it right, the learning health system will contribute to care being more evidence-based and less wasteful and harmful. It will need a purpose-designed digital backbone and infrastructure, apply artificial intelligence to support diagnosis and treatment options, harness genomic and other new data types, and create informed discussions of options between patients, families, and clinicians. While there will be many variants of the model, learning health systems will need to spread, and be encouraged to do so, principally through diffusion of innovation models and local adaptations. Conclusion Deep learning systems can enable us to better exploit expanding health datasets including traditional and newer forms of big and smaller-scale data, e.g. genomics and cost information, and incorporate patient preferences into decision-making. As we envisage it, a deep learning system will support healthcare’s desire to continually improve, and make gains on the 60-30-10 dimensions. All modern health systems are awash with data, but it is only recently that we have been able to bring this together, operationalised, and turned into useful information by which to make more intelligent, timely decisions than in the past.

This paper responds to a growing literature arguing that change in higher education institutions might be better understood and managed if such institutions are understood as complex systems with emergent properties. Based on complexity theory, the paper articulates a set of characteristics that might be expected if institutions are in fact acting as complex systems. The utility of these characteristics for identifying complexity in the field is tested in the context of a large partnered research project involving the mathematics, science and education colleges from six Australian universities and their local K-12 schools. The study finds evidence of subsystem variations on the initial partnership priorities, including substantial boundary crossing, leading to emergence and co-evolution, indicating that a macroscopic view of emergent variation rather than ‘micromanaging’ may be an essential factor in scaling and sustaining collaborative partnerships. Refereed/Peer-reviewed

Computer programming skills have been growing as a professional competence also to unqualified end-users who need to develop software in their professional practice. Quality evaluation models of end-user-developed products are still scarce. In this paper, we propose a metric that leverages “When”, a condition typically found in block-based software development frameworks. We evaluated 80 Scratch projects collecting a metric related to the presence of the When condition and investigated common traits and differentiation with other metrics already proposed in the literature. We found that, in an evaluation with respect to the conditionals found in Scratch projects, When delivers a distinct and complementary approach to software complexity in products developed using block-oriented software development tools.

Nowadays, we are surrounded by devices collecting and transmitting private information. Currently, the two main mathematical problems that guarantee security on the Internet are the Integer Factorization Problem and the Discrete Logarithm Problem. However, Shor’s quantum algorithm can easily solve both problems. Therefore, research into cryptographic algorithms that run in classical computers and are resistant to quantum computers is extremely necessary. This area is known as post-quantum cryptography and usually studies asymmetric cryptography. By means of asymptotic analysis, the purpose of this paper is to provide an evaluation of security and its performance for the types of cryptographic systems considered safe against quantum attacks in the second-round NIST Post-Quantum Standardization Process, namely isogeny cryptosystems based on supersingular elliptic curves, error correction code-based encryption system, and lattice-based ring learning with errors. We performed a security comparison of Key Agreements protocols based on these three post-quantum cryptographic primitives and compared them with Discrete Logarithm Problem and Integer Factorization Problem. The comparison of security and its performance is presented by security level, the former by complexity analyses to achieve theoretical minimum key sizes, and the latter by simulation to assess a practical performance comparison. In the complexity analysis, as we increase the security level and then the size of the cryptographic keys increases, techniques based on isogeny outperform all other post-quantum algorithms in relation to key sizes at practical security level. In the performance comparison, the results show that the code-based protocol presents the best results among the others.

Heart rate variability (HRV) is a widely used measure for the variation of the heartbeat intervals controlled by the autonomic nervous system (ANS), which is primarily obtained by electrocardiogram (ECG) signals. In general, HRV of a healthy person exhibits long-range correlations with dynamic fluctuations, whether the complexity of HRV decreases with aging and incidence of disease. Recently, the complexity of differential inter-beat intervals, referred to differential R-R intervals, is known to be more effective than original R-R intervals to reflect HRV. The multiscale based entropy methods have been developed to quantify HRV using R-R intervals. In spite of their capability, it still remains unreliable quantification of HRV. Here, we propose a new multiscale complexity quantification measure with differential R-R intervals for HRV analysis. To verify the performance of the proposed method, we evaluate the complexity of differential HRV extracted from ECG signals of congestive heart failure (CHF) patients and healthy subjects. The results show that multiscale distribution entropy (MDE) of differential R-R interval has improved capability for quantifying the complexity of HRV regardless of the length of time series.

Termite mounds are fascinating because of their intriguing composition of nu- merous geometric shapes and materials. However, little is known about these structures, or of their functionalities. Most research has been on the basic com- position of mounds compared with surrounding soils. There has been some targeted research on the thermoregulation and ventilation of the mounds of a few species of fungi-growing termites, which has generated considerable inter- est from human architecture. Otherwise, research on termite mounds has been scattered, with little work on their explicit properties. This review is focused on how termites design and build functional structures as nest, nursery and food storage; for thermoregulation and climatisation; as defence, shelter and refuge; as a foraging tool or building material; and for colony communication, either as in indirect communication (stigmergy) or as an information channel essential for direct communication through vibrations (biotremology). Our analysis shows that systematic research is required to study the prop- erties of these structures such as porosity and material composition. High res- olution computer tomography in combination with nonlinear dynamics and methods from computational intelligence may provide breakthroughs in un- veiling the secrets of termite behaviour and their mounds. In particular, the ex- amination of dynamic and wave propagation properties of termite-built struc- tures in combination with a detailed signal analysis of termite activities is re- quired to better understand the interplay between termites and their nest as superorganism. How termite structures serve as defence in the form of disguis- ing acoustic and vibration signals from detection by predators, and what role local and global vibration synchronisation plays for building are open ques- tions that need to be addressed to provide insights into how termites utilise materials to thrive in a world of predators and competitors.