Adaptive Knowledge Research Articles

Whole Body Vibration Improves Brain and Musculoskeletal Health by Modulating the Expression of Tissue-Specific Markers: FNDC5 as a Key Regulator of Vibration Adaptations.

Whole body vibration (WBV) is well known to exert beneficial effects on multiple tissues, improving synaptic transmission, muscle mass, bone quality, and reducing anxiety and depressive behavior. However, the underlying molecular mechanisms are not yet fully understood, and organs and tissues may respond differently to the vibratory stimulus depending on multiple factors. Therefore, we investigated the WBV effects on the brain and musculoskeletal tissue of 4-month-old young mice, evaluating synaptic plasticity by electrophysiological recordings and tissue organization by histology and histomorphometric analysis. Specifically, WBV protocols were characterized by the same vibration frequency (45 Hz), but different in vibration exposure time (five series of 3 min for the B protocol and three series of 2 min and 30 s for the C protocol) and recovery time between two vibration sessions (1 min for the B protocol and 2 min and 30 s for the C protocol). In addition, immunohistochemistry was conducted to evaluate the expression of fibronectin type III domain-containing protein 5 (FNDC5), as well as that of tissue-specific markers, such as brain-derived neurotrophic factor (BDNF) in brain, myostatin in muscle and collagen I (COL-1) in bone. Our results suggest that the WBV effects depend closely on the type of protocol used and support the hypothesis that different organs or tissues have different susceptibility to vibration. Further studies will be needed to deepen our knowledge of physiological adaptations to vibration and develop customized WBV protocols to improve and preserve cognitive and motor functions.

Novel rough set theory-based method for epistemic uncertainty modeling, analysis and applications

Considering the multi-source of uncertainty and the complex correlation of uncertain parameters in many engineering practices, the bounded set describing uncertainties is sometimes irregular and lacks a precise mathematical expression. To overcome the limitations of existing methods in handling this issue under incomplete information, this paper proposes a novel uncertainty modeling and analysis strategy based on the rough set theory. Firstly, in terms of limited experimental points, a data-driven partitioning method is introduced to establish an adaptive knowledge base. By means of the equivalence classes in knowledge base, a dual-approximate quantification model composed of upper and lower approximation sets is constructed to describe an arbitrary bounded-but-irregular uncertain set from both internal and external perspectives. In the subsequent uncertainty propagation analysis, a concept of rough approximate accuracy of response prediction is defined by four extreme values to quantitatively characterize the influence of model approximation on system response. Meanwhile, to improve the computational efficiency of extreme-value prediction in engineering application, an adaptive Kriging model combined with rough set theory is developed as the surrogate model of the original time-consuming simulations. Finally, two numerical examples are investigated to substantiate the effectiveness of the proposed method.

Maternal Left Ventricular Function in Uncomplicated Twin Pregnancies: A Speckle-Tracking Imaging Longitudinal Study.

Objective: The knowledge of maternal cardiovascular hemodynamic adaptation in twin pregnancies is incomplete. We aimed to longitudinally investigate maternal left ventricular (LV) function in uncomplicated twin pregnancies. Methods: 30 healthy and uncomplicated twin pregnant women and 30 controls with normal singleton pregnancies were prospectively enrolled to undergo transthoracic echocardiography at 10–15 week’s gestation (w) (T1), 19–26 w (T2) and 30–38 w (T3). LV dimensions and volumes, as well as LV ejection fraction (LVEF), mass (LVM) and diastolic parameters (at transmitral pulsed wave Doppler and mitral annular plane tissue Doppler), were calculated. Speckle-tracking imaging was also applied to evaluate LV global longitudinal (GLS), radial and circumferential 2D strains. Results: During twin pregnancy, maternal LV dimensions, volumes and LVM had an increasing trend from T1 to T3, similar to singletons, while LVEF remained stable. There was LV remodeling/hypertrophy in 50% of women at T2 and T3 in both groups. Diastolic function had a worsening trend from T1 to T3 with no differences between twins and singletons, except for higher LV filling pressure (i.e., E/E′) at T2 in twins. Two-dimensional strains did not vary during gestation in either group, except for a linear trend to increase (i.e., worsen) GLS in singletons. Radial and circumferential 2D strains were impaired in about half of the women at each trimester, while GLS was altered in one-fourth/one-third of them in both groups. Conclusion: Maternal LV geometry, dimensions and function are significantly impaired during twin pregnancies, in particular in the second half of gestation, with no significant differences compared to singletons.

Assessment of fetal circulation and mechanisms of heart adaptation during umbilical cord compression: An experimental model in fetal sheep

Perinatal asphyxia is a serious health issue with long-term neurologic morbidities. Cerebral lesions are induced by low cardiac output which occurs often during labour without reliable predictive factors. To assess hemodynamic changes during umbilical cord (UC) compression in fetal sheep. Experimental study on chronically instrumented sheep fetuses (surgery at 131 days of gestation, term = 141 days). Under general anesthesia, a hysterotomy is performed. An aortic catheter is inserted via the axillary artery for blood sampling, and a 6 Fr. Sheath is introduced to the left ventricle (LV) through the right carotid artery under pressures and echocardiographic control. Then a 5 Fr. Transonic PV loop catheter (EMKA Technologies, France) is inserted through the sheath into the LV, and the sheath is withdrawn towards the aorta to allow permanent measurement of the aortic pressure. A 16 mm inflatable occlusion device is placed around the UC (OC16, California, USA). Series of complete occlusion of the UC are done with progressive increasing duration to simulate labour. LV pressures and volumes are registered continuously. Pressure-volume loops and blood samples are analyzed before during and after the occlusions. During the first experimentations, short occlusions did not induce significant modifications. Longer occlusions induced progressive systolic and diastolic disorders which appeared before the metabolic acidosis ( Fig. 1 ). Knowledge of foetal heart adaptation could be interesting to prevent neurologic lesions. This hemodynamic model could also be applied in other situations such as congenital diaphragmatic hernia, pulmonary hypertension or aortic coartation.

Takagi–Sugeno fuzzy based power system fault section diagnosis models via genetic learning adaptive GSK algorithm

To effectively deal with the operating uncertainties of protective relays and circuit breakers existing in the power system faults, an improved fault section diagnosis (FSD) method is proposed by using Takagi–Sugeno fuzzy neural networks (T–S FNN). In this method an optimal T–S FNN-based diagnosis model is built with the idea of distributed parallel processing for each section instead of the whole power system. To obtain accurate T–S FNN-based diagnosis models, a genetic learning adaptive gaining-sharing knowledge-based algorithm (GLAGSK) is designed to optimize their structure parameters and consequent parameters. GLAGSK combines an adaptive knowledge ratio and a genetic learning strategy to balance population diversity and convergence speed to boost the optimization ability. After a fault occurs, selective optimal T–S FNN-based diagnosis models are triggered according to the alarm information. They work in parallel to improve the fault diagnosis efficiency. Simulation results of three test systems including an actual fault event show that, compared with other peer algorithms, GLAGSK can obtain more accurate T–S FNN-based diagnosis models with faster global convergence. Besides, compared with the BP and RBF neural networks and other FSD methods, the proposed FSD method based on optimal T–S FNN-based diagnosis models can diagnose different complex faults successfully with higher fault credibility.

Landscape genomics of the American lobster (Homarus americanus).

In marine species experiencing intense fishing pressures, knowledge of genetic structure and local adaptation represent a critical information to assist sustainable management. In this study, we performed a landscape genomics analysis in the American lobster to investigate the issues pertaining to the consequences of making use of putative adaptive loci to reliably infer population structure and thus more rigorously delineating biological management units in marine exploited species. Toward this end, we genotyped 14,893 single nucleotide polymorphism (SNPs) in 4190 lobsters sampled across 96 sampling sites distributed along 1000 km in the northwest Atlantic in both Canada and the USA. As typical for most marine species, we observed a weak, albeit highly significant genetic structure. We also found that adaptive genetic variation allows detecting fine-scale population structure not resolved by neutral genetic variation alone. Using the recent genome assembly of the American lobster, we were able to map and annotate several SNPs located in functional genes potentially implicated in adaptive processes such as thermal stress response, salinity tolerance and growth metabolism pathways. Taken together, our study indicates that weak population structure in high gene flow systems can be resolved at various spatial scales, and that putatively adaptive genetic variation can substantially enhance the delineation of biological management units of marine exploited species.

Cellular Bioenergetics: Experimental Evidence for Alcohol-induced Adaptations.

At-risk alcohol use is associated with multisystemic effects and end-organ injury, and significantly contributes to global health burden. Several alcohol-mediated mechanisms have been identified, with bioenergetic maladaptation gaining credence as an underlying pathophysiological mechanism contributing to cellular injury. This evidence-based review focuses on the current knowledge of alcohol-induced bioenergetic adaptations in metabolically active tissues: liver, cardiac and skeletal muscle, pancreas, and brain. Alcohol metabolism itself significantly interferes with bioenergetic pathways in tissues, particularly the liver. Alcohol decreases states of respiration in the electron transport chain, and activity and expression of respiratory complexes, with a net effect to decrease ATP content. In addition, alcohol dysregulates major metabolic pathways, including glycolysis, the tricarboxylic acid cycle, and fatty acid oxidation. These bioenergetic alterations are influenced by alcohol-mediated changes in mitochondrial morphology, biogenesis, and dynamics. The review highlights similarities and differences in bioenergetic adaptations according to tissue type, pattern of (acute vs. chronic) alcohol use, and energy substrate availability. The compromised bioenergetics synergizes with other critical pathophysiological mechanisms, including increased oxidative stress and accelerates cellular dysfunction, promoting senescence, programmed cell death, and end-organ injury.

Exploring Fine-Grained Cluster Structure Knowledge for Unsupervised Domain Adaptation

Unsupervised domain adaptation aims to leverage knowledge from a labeled source domain to learn an accurate model in an unlabeled target domain. However, many previous approaches propose to learn domain agnostic feature representations using a global distribution alignment objective, which does not consider the fine-grained cluster structures in the source and target domains. As such, the goal of this paper is to address two challenging problems: <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">1) how to thoroughly explore fine-grained cluster structure knowledge in the source and target domains, 2) how to effectively incorporate these structure knowledge for adaptation.</i> Regarding the first point, we are motivated by structural domain similarity assumption and propose structural representation learning, which is achieved by enforcing structural consistency between the source and target domains while retaining their individual discriminative properties. Regarding the second point, we firstly devise a novel structural centroid-based label prediction method, which explicitly models structural representations to form discriminative source and target cluster centroids, and estimates the label distribution of each target sample through the cosine similarity between its corresponding target cluster centroid and all the other source cluster centroids. Then, we adopt clustering learning to incorporate these discriminative structure knowledge for adaptation by minimizing the KL divergence between the predictive target label distribution and an introduced auxiliary one. Comprehensive experiments and analyses on four benchmark datasets demonstrate the superiority of the proposed discriminative clustering framework.

Evolutionary Foundations for Organizational Culture

Many research approaches exists in the literature to make sense of organizational culture. Some measure the content of culture quantitatively –for example, the degree of innovativeness (v/s tradition)– and seek to generate practitioner-relevant typologies; others focus on a single mechanism/aspect of culture, such as knowledge/communication, norms, shared beliefs, and frames/cognition in order to build a theory of culture; further, others move away from culture-as-a-constraint to propose culture-as-a-repository-of-tools that flexibly enables agents’ performance. The flipside of this burgeoning diversity, however, is a lack of consilience and common ground. In this paper we provide a formal model that may serve as an integrative framework for the field. The model is based on cultural evolution theory, whose wide scope is suited for integration efforts. The model consists of a changing environment, where agents acquire adaptive knowledge either via traditionalistic or innovative learning, agents may exert cooperative effort, and the organization’ leaders have two levers at its disposal: strength of prosocial norms and accuracy of the shared beliefs about the organization-environment interaction. The view that the model provides is that organizational culture evolves via a process of learning, that its function is adaptation to the environment, and that it does so in two fundamental ways, by external adaptation –enabled by shared beliefs and innovative learning behavior– and by internal integration –enabled by the strength of prosocial norms, cooperative effort, and traditionalistic learning behavior. The model yields several specific predictions which we map to previous empirical findings. The view of culture that emerges from our model is highly consistent with the (qualitative) canon established by Schein (2010, “Organizational culture and leadership”) and we discuss the model can provides a common theoretical foundation to the main cultural typologies of the literature.

Elevating Air Temperature May Enhance Future Epidemic Risk of the Plant Pathogen Phytophthora infestans.

Knowledge of pathogen adaptation to global warming is important for predicting future disease epidemics and food production in agricultural ecosystems; however, the patterns and mechanisms of such adaptation in many plant pathogens are poorly understood. Here, population genetics combined with physiological assays and common garden experiments were used to analyze the genetics, physiology, and thermal preference of pathogen aggressiveness in an evolutionary context using 140 Phytophthora infestans genotypes under five temperature regimes. Pathogens originating from warmer regions were more thermophilic and had a broader thermal niche than those from cooler regions. Phenotypic plasticity contributed ~10-fold more than heritability measured by genetic variance. Further, experimental temperatures altered the expression of genetic variation and the association of pathogen aggressiveness with the local temperature. Increasing experimental temperature enhanced the variation in aggressiveness. At low experimental temperatures, pathogens from warmer places produced less disease than those from cooler places; however, this pattern was reversed at higher experimental temperatures. These results suggest that geographic variation in the thermal preferences of pathogens should be included in modeling future disease epidemics in agricultural ecosystems in response to global warming, and greater attention should be paid to preventing the movement of pathogens from warmer to cooler places.

Acquired Body of Knowledge: a Core Valuation Influencing Factor in Inter-valuer Variance

Concerns over valuation accuracy and variance cannot be over-flogged, given the somewhat fluid nature of these concepts. It is however, more apt to dig into their fundamental causative factors. This paper realizes that specialist valuer or appraiser has a chain of sequential tasks anchored on his distinguishing competencies. At the heart of this is sufficient knowledge of the attributes of his subject of valuation. Twenty-two (22) Nigerian valuers based within Lagos Metropolis were made to carry out valuation assessment of selected landed and non-landed property assets and were also examined on their perception of the adequacy of their acquired Body of Knowledge (BOK) relevant to each asset category. The variation/dispersion in their valuations is revealed by the Standard Deviation of the distribution, for Landed Property, being 7.77 while that of Non-Landed Property is 32.24; By employing the 10% maximum variation rule of Glover (1985), 9% of the valuers fall outside the limit in respect of Landed Property whereas, the figure rose to 64% for Non-Landed Property assets. This is indicative of remarkably higher internal inconsistencies among respondent valuers on Non-Landed Property Assets. Multiple regression analysis of the results indicated that all the adaptive knowledge variables exert positive influence on valuer’s competence in valuation of both Landed Property and Non-Landed Property Assets. In view of these findings, there is urgent need to review and expand underlying curriculum for training prospective valuers towards aligning theory with practice and enhance their competence across property types.

Near-Infrared Spectroscopy Used to Assess Physiological Muscle Adaptations in Exercise Clinical Trials: A Systematic Review.

Simple SummaryIn recent years, physical exercise has been used as a therapeutic strategy in various clinical conditions, with pleiotropic benefits. Near-infrared spectroscopy (NIRS) has been positioned as a tool to analyze effects on muscle oxygenation, also allowing knowledge of adaptations on microvascular levels and muscle metabolism in subjects with central and peripheral vascular alterations, as well as cardiovascular, metabolic, and/or musculoskeletal diseases. This knowledge can help to guide therapeutic exercise specialists in decision making regarding the prescription and follow up of physical exercise as a therapeutic tool in the observation of acute or chronic adaptations to improve efficiency in the treatment and recovery of these patients. This review presents an overview of the effects of exercise clinical trials on muscle oxygenation in different pathologies and the technical characteristics related to the equipment used.Using muscle oxygenation to evaluate the therapeutic effects of physical exercise in pathologies through near-infrared spectroscopy (NIRS) is of great interest. The aim of this review was to highlight the use of muscle oxygenation in exercise interventions in clinical trials and to present the technological characteristics related to the equipment used in these studies. PubMed, WOS, and Scopus databases were reviewed up to December 2021. Scientific articles that evaluated muscle oxygenation after exercise interventions in the sick adult population were selected. The PEDro scale was used to analyze the risk of bias (internal validity). The results were presented grouped in tables considering the risk of bias scores, characteristics of the devices, and the effects of exercise on muscle oxygenation. All the stages were carried out using preferred reporting items for systematic reviews and meta-analyses (PRISMA). The search strategy yielded 820 clinical studies, of which 18 met the eligibility criteria. This review detailed the characteristics of 11 NIRS devices used in clinical trials that used physical exercise as an intervention. The use of this technology made it possible to observe changes in muscle oxygenation/deoxygenation parameters such as tissue saturation, oxyhemoglobin, total hemoglobin, and deoxyhemoglobin in clinical trials of patients with chronic disease. It was concluded that NIRS is a non-invasive method that can be used in clinical studies to detect the effects of physical exercise training on muscle oxygenation, hemodynamics, and metabolism. It will be necessary to unify criteria such as the measurement site, frequency, wavelength, and variables for analysis. This will make it possible to compare different models of exercise/training in terms of time, intensity, frequency, and type to obtain more precise conclusions about their benefits for patients.

Single Amino Acid Substitution the DNA Repairing Gene Radiation-Sensitive 4 Contributes to Ultraviolet Tolerance of a Plant Pathogen.

To successfully survive and reproduce, all species constantly modify the structure and expression of their genomes to cope with changing environmental conditions including ultraviolet (UV) radiation. Thus, knowledge of species adaptation to environmental changes is a central theme of evolutionary studies which could have important implication for disease management and social-ecological sustainability in the future but is generally insufficient. Here, we investigated the evolution of UV adaptation in organisms by population genetic analysis of sequence structure, physiochemistry, transcription, and fitness variation in the radiation-sensitive 4 (RAD4) gene of the Irish potato famine pathogen Phytophthora infestans sampled from various altitudes. We found that RAD4 is a key gene determining the resistance of the pathogen to UV stress as indicated by strong phenotype-genotype-geography associations and upregulated transcription after UV exposure. We also found conserved evolution in the RAD4 gene. Only five nucleotide haplotypes corresponding to three protein isoforms generated by point mutations were detected in the 140 sequences analyzed and the mutations were constrained to the N-terminal domain of the protein. Physiochemical changes associated with non-synonymous mutations generate severe fitness penalty to mutants, which are purged out by natural selection, leading to the conserved evolution observed in the gene.

Climate Change Adaptation and Mitigation Strategies in Madda Walabu District, Bale Zone, Southeast Ethiopia

Climate change is one of the most serious environmental challenges affecting people all over the world causing widespread agitation and having an impact on economic systems such as agricultural production. Local communities in Madda Walabu District of Bale Zone, Southeast Ethiopia, are heavily dependent on agriculture. In contrast, the agricultural activity of the local community was depressed by threats such as increased temperature, prolonged drought, and changes in rainfall distribution. To mitigate the adverse consequences of climate change, it is important to understand the local knowledge of adaptation and mitigation actions. This research was a look into the climate change adaptation and mitigation in Ethiopia’s Madda Walabu District households to survive. The objective of the study was to investigate climate change adaptation and mitigation strategies for the synergy of the communities in the Madda Walabu District. A multistage stratified random sampling procedure and three villages were randomly selected. A total of 150 sample households from the three villages were interviewed. Descriptive statistics were employed to analyse data, and the Statistical Package for Social Science (SPSS) was used for analysis. The results showed that most of the respondents have perceived an increase in temperature, fluctuation in the rainy season, and a decrease in the amount of rainfall. The respondents perceived that they are vulnerable to local climate variability. This study also revealed that 78% of the respondents followed different adaptation strategies to climate change, such as irrigation intensification, agroforestry, agronomic, and cultural practices. Adaptation and mitigation measures can be developed by enhancing the adaptive and mitigating capacity of forest-dependent communities. Therefore, awareness creation on climate change, variability, adaptation, and mitigation measures should be considered toward enhancing the adaptive capacity of the local communities in line with providing seasonal weather information. More research in the domain of climate change and mitigation techniques is needed as several statistical results were not as predicted, and the amount of adaptation and mitigation synergies was low. Furthermore, the study’s households were predominantly pastoralists, and their climate change adaptation and mitigation strategy for the livestock sector was an issue that needed to be addressed urgently.

Explainable deep learning powered building risk assessment model for proactive hurricane response.

Climate change and rapid urban development have intensified the impact of hurricanes, especially on the Southeastern Coasts of the United States. Localized and timely risk assessments can facilitate coastal communities' preparedness and response to imminent hurricanes. Existing assessment methods focused on hurricane risks at large spatial scales, which were not specific or could not provide actionable knowledge for residents or property owners. Fragility functions and other widely utilized assessment methods cannot model the complex relationships between building features and hurricane risk levels effectively. Therefore, we develop and test a building-level hurricane risk assessment with deep feedforward neural network (DFNN) models. The input features of DFNN models cover the meta building characteristics, fine-grained meteorological, and hydrological environmental parameters. The assessment outcomes, that is, risk levels, include the probability and intensity of building/property damages induced by wind and surge hazards. We interpret the DFNN models with local interpretable model-agnostic explanations (LIME). We apply the DFNN models to a case building in Cameron County, Louisiana in response to a hypothetical imminent hurricane to illustrate how the building's risk levels can be timely assessed with the updating weather forecast. This research shows the potential of deep-learning models in integrating multi-sourced features and accurately predicting buildings' risks of weather extremes for property owners and households. The AI-powered risk assessment model can help coastal populations form appropriate and updating perceptions of imminent hurricanes and inform actionable knowledge for proactive risk mitigation and long-term climate adaptation.

Qualitatively exploring the attributes of adaptability and resilience amongst recently graduated nurses

AimTo explore newly graduated nurse’s understandings and practices of adaptability and resilience in clinical environments. BackgroundThe everyday practice of nursing work involves managing emotional and practical everyday demands related to the role. Adaptability and resilience are two critical attributes that equip nurses for this by enabling them to manage challenges and be flexible with their practices and expectations in the face of rapidly changing and unpredictable circumstances. DesignInformed by the theoretical underpinnings of the Person-centred Practice Framework, semi-structured interviews using topic guides were conducted with nine newly graduated registered nurse participants recruited through purposive sampling. Interviews occurred between March-October 2020 with participants working across seven different healthcare settings in three Local Health Districts in NSW, Australia. ResultsAnalysis of the data generated the core themes of: 1) ‘Making sense’ explored how nurses defined resilience and adaptability; 2) ‘Surviving as the nurse’ focused on how nurses experienced adaptability and resilience as a newly qualified nurse; 3) ‘Trusting oneself’ reflected the interconnection of nurses’ developed adaptability and resilience to their clinical self-assurance; and 4) ‘Doing it again’ described how adaptability and resilience can be further supported by the university sector. Findings demonstrated that adaptability and resilience in combination are essential attributes and required for effective nursing practice post-graduation. However, both collegial and organizational support were found to be lacking in positively reinforcing these attributes in this study. ConclusionNewly graduated nurses can develop adaptability in clinical practice, so they are a more resilient future workforce. However, greater organizational leadership is required to model and strengthen these attributes for nurses. When perceptions, knowledge and experiences of adaptability and resilience are developed using person-centred approaches, they will be used in person-centred ways. Tweetable abstractNewly graduated nurses can develop adaptability in clinical practice, so they are a more resilient future workforce. However, greater organizational leadership is required to model and strengthen these attributes for nurses. When perceptions, knowledge and experiences of adaptability and resilience are developed using person-centred approaches, they will be used in person-centred ways.

Facilitating Knowledge Transfer in Project Management Environment

This paper presents an approach to adaptive knowledge management in the presence of management of projects for facilitation of knowledge transfer among project workers that promotes the effective completion of a project. The approach consists in coordinated dynamic management by the temporal sequence of project task groups and collaborative groups of project workers assigned for tasks performance. At that, tasks relevant knowledge, knowledge of the project workers, temporal and structural task parameters are jointly taken into account. The objective of the approach is to facilitate knowledge transfer between project workers for effective performance of the task work. The aim of the paper is to identify the key factors that influence the ability to transfer knowledge. The framework is represented by key factors characterizing significant influences on knowledge transfer. The results of the study indicate that the approach can be applied to project management.

Multiobjective Evolutionary Multitasking With Two-Stage Adaptive Knowledge Transfer Based on Population Distribution

Multitasking optimization can achieve better performance than traditional single-tasking optimization by leveraging knowledge transfer between tasks. However, the current multitasking optimization algorithms suffer from some deficiencies. Particularly, on high similar problems, the existing algorithms might fail to take full advantage of knowledge transfer to accelerate the convergence of the search, or easily get trapped in the local optima. Whereas, on low similar problems, they tend to suffer from negative transfer, resulting in performance degradation. To solve these issues, this article proposes an evolutionary multitasking optimization algorithm for multiobjective/many-objective optimization with two-stage adaptive knowledge transfer based on population distribution. The resultant algorithm named EMT-PD can improve the convergence performance of the target optimization tasks based on the knowledge extracted from the probability model that reflects the search trend of the whole population. At the first stage of knowledge transfer, an adaptive weight is used to adjust the search step size of each individual, which can reduce the impact of negative transfer. At the second stage of knowledge transfer, the search range of each individual is further adjusted dynamically, which can improve the population diversity and be beneficial for jumping out of the local optima. Experimental results on multitasking multiobjective optimization test suites show that EMT-PD is superior to other state-of-the-art evolutionary multitasking/single-tasking algorithms. To further investigate the effectiveness of EMT-PD on many-objective optimization problems, a multitasking many-objective optimization test suite is also designed in this article. The experimental results on the new test suite also demonstrate the competitiveness of EMT-PD.

The role of indigenous knowledge in climate change adaptation in Africa

Africa is particularly affected by climate change due to its exposure to climate hazards, high vulnerability, and low adaptive capacity. Yet, Africa is also a continent rich in Indigenous and Local Knowledge (ILK) that has a long history of informing responses to climatic variability and change. This paper explores the extent to which ILK has been used in climate change adaptation in Africa. It deploys a bibliometric analysis to describe the connections between ILK and climatic change adaptation in Africa, complemented by an analysis of ILK literature and case studies. We consider four key dimensions of ILK, 1) type, 2) contexts of application, 3) value for adaptation, and 4) outcomes and effects in responses to climate change in Africa. Examples drawn from 19 countries across Africa highlight ILK systems are closely connected with biocultural relationships associated with observed patterns of climate change and where adaptation can be more effective when informed by ILK. This body of knowledge is critical to the delivery of climate change adaptation in Africa. The paper suggests some measures through which ILK may be more widely leveraged, both for improved adaptation outcomes, as well as enhancing the biocultural heritage value of ILK systems across Africa. The study commends the remarkable value of ILK in Africa for climate change adaptation and its value for supplementing climate services, particularly in areas with limited access to modern climate and weather forecasts as well as the encouragement of Indigenous communities to develop senses of ownership and active contribution to the sustainability of the future interventions.

Developing tailored theoretically informed goal-setting interventions for rehabilitation services: a co-design approach

BackgroundSeveral active ingredients contribute to the purposes and mechanisms of goal-setting in rehabilitation. Active ingredients in the goal-setting process include, interdisciplinary teamworking, shared decision-making, having meaningful and specific goals, and including action planning, coping planning, feedback, and review. Clinicians have expressed barriers and enablers to implementing these active ingredients in rehabilitation teams. Interventions designed to improve goal-setting practices need to be tailored to address context specific barriers and enablers. Attempts to understand and enhance goal-setting practices in rehabilitation settings should be supported using theory, process models and determinant frameworks. Few studies have been undertaken to enhance goal-setting practices in varied case-mix rehabilitation settings.MethodsThis study is part of a larger program of research guided by the Knowledge to Action (KTA) framework. A multisite, participatory, codesign approach was used in five sites to address three stages of the KTA. (1) Focus groups were conducted to understand barriers and enablers to implementing goal-setting at each site. Following the focus groups three staff co-design workshops and one consumer workshop were run at each site to (2) adapt knowledge to local context, and to (3) select and tailor interventions to improve goal-setting practices. Focus groups were analysed using the Theoretical Domains Framework (TDF) and informed the selection of behaviour change techniques incorporated into the implementation plan.ResultsBarriers and enablers identified in this study were consistent with previous research. Clinicians lacked knowledge and understanding of the differences between a goal and an action plan often confusing both terms. Clinicians were unable to demonstrate an understanding of the importance of comprehensive action planning and review processes that extended beyond initial goal-setting. Interventions developed across the sites included staff training modules, a client held workbook, educational rehabilitation service flyers, interdisciplinary goal-based case conference templates, communication goal boards and a key worker model. Implementation plans were specifically established for each site.ConclusionsRehabilitation teams continue to struggle to incorporate a truly client-centred, interdisciplinary model of goal-setting in rehabilitation. Whilst clinicians continue to lack understanding of how they can use aspects of goal-setting to enhance client outcomes and autonomy in rehabilitation settings.