Abstract. This paper examines the development over historical time of the meaning and uses of the term resilience. The objective is to deepen our understanding of how the term came to be adopted in disaster risk reduction and resolve some of the conflicts and controversies that have arisen when it has been used. The paper traces the development of resilience through the sciences, humanities, and legal and political spheres. It considers how mechanics passed the word to ecology and psychology, and how from there it was adopted by social research and sustainability science. As other authors have noted, as a concept, resilience involves some potentially serious conflicts or contradictions, for example between stability and dynamism, or between dynamic equilibrium (homeostasis) and evolution. Moreover, although the resilience concept works quite well within the confines of general systems theory, in situations in which a systems formulation inhibits rather than fosters explanation, a different interpretation of the term is warranted. This may be the case for disaster risk reduction, which involves transformation rather than preservation of the "state of the system". The article concludes that the modern conception of resilience derives benefit from a rich history of meanings and applications, but that it is dangerous – or at least potentially disappointing – to read to much into the term as a model and a paradigm.
Practicing and studying automated experimentation may benefit from philosophical reflection on experimental science in general. This paper reviews the relevant literature and discusses central issues in the philosophy of scientific experimentation. The first two sections present brief accounts of the rise of experimental science and of its philosophical study. The next sections discuss three central issues of scientific experimentation: the scientific and philosophical significance of intervention and production, the relationship between experimental science and technology, and the interactions between experimental and theoretical work. The concluding section identifies three issues for further research: the role of computing and, more specifically, automating, in experimental research, the nature of experimentation in the social and human sciences, and the significance of normative, including ethical, problems in experimental science.
Abstract Game theory is one of the key paradigms behind many scientific disciplines from biology to behavioral sciences to economics. In its evolutionary form and especially when the interacting agents are linked in a specific social network the underlying solution concepts and methods are very similar to those applied in non-equilibrium statistical physics. This review gives a tutorial-type overview of the field for physicists. The first four sections introduce the necessary background in classical and evolutionary game theory from the basic definitions to the most important results. The fifth section surveys the topological complications implied by non-mean-field-type social network structures in general. The next three sections discuss in detail the dynamic behavior of three prominent classes of models: the Prisoner's Dilemma, the Rock–Scissors–Paper game, and Competing Associations. The major theme of the review is in what sense and how the graph structure of interactions can modify and enrich the picture of long term behavioral patterns emerging in evolutionary games.
Language teacher education programs can become more reflective, inclusive, collaborative, situated, and inquiry-based. One such professional approach to incorporate these characters can be through personalized language teacher education (PLTE). Due to the importance of using AI and professional learning communities (PLCs) for developing a personalized teacher education, this study explored how AI-enhanced PLCs could be leveraged to create a more responsive, inclusive, and personalized teacher education. Still, a significant gap exists in understanding how AI can be specially integrated into PLCs to create personalized pathways for ELT preservice teachers, mainly in under-resourced contexts. To conduct this exploratory case study, 8 Iranian English language teaching (ELT) pre-service teachers were purposively selected from a teacher education university. Data was collected from group discussion, artifacts, and interviews, and the result of the thematic analysis revealed that AI-enhanced PLCs fostered personalized, reflective, and collaborative development by addressing individual teaching needs and providing innovative strategies. By addressing individual teaching needs and providing innovative instructional strategies, AI facilitated a dynamic learning environment. However, effective integration required overcoming challenges like limited AI literacy and contextual mismatches, highlighting the potential for tailored, impactful education. This study can inform teacher educators, policymakers, administrators, and teachers to integrate AI into their PLCs to develop a PLTE.
Variability is the predisposition of the elements in systems to assume different values over time and space. In biology, the variability is basic to explain differences and development in organisms but in the fields of scientific and technological information, the effects of variability on evolutionary dynamics of disciplines and technologies are unknown. In a broad analogy with the principles of biology, the variability within research fields can be a central argument to explain trajectories in scientific development and technological evolution. The purpose of the present study is to see whether statistical evidence supports the general hypothesis that the rate of growth in technologies can be explained by the level of variability in scientific fields and with this principal goal to analyze the relation between scientific variability and rate of growth in technologies. Proposed hypothesis of scientific variability here endeavors to explain basic sources of scientific development and technological evolution to lay the foundations for a general theory. The test here is based on emerging research fields of quantum technologies: Quantum Imaging, Quantum Meteorology, Quantum Sensing and Quantum Optics. A preliminary statistical evidence seems in general to support the hypothesis stated that the rate of growth in technological fields can be explained by the level of scientific variability in research fields, measured with relative entropy index. Nonparametric correlation based on Spearman’s rho shows a positive coefficient of 0.80 of these variables; linear model of the rate of technological growth = f(scientific variability) reveals a coefficient of regression equal to 1.63 (R2=0.60). Findings here suggest a general law that scientific variability positively drives scientific development and technological evolution. In particular, a higher variability within research fields can support scientific development and a high rate of growth in technological evolution (measured with scientific and technological information). Proposed hypothesis of scientific variability is especially relevant in environments of rapid change to explain determinants and dynamics of technological change within a general theoretical framework that supports technological management and forecasting of promising innovations.
Tinkama miego trukmė ir kokybė yra būtini optimaliai psichinei ir fizinei sveikatai. Karinės profesijos susiduria su unikaliais iššūkiais, tokiais kaip 36 valandų darbo pamainos, fiziškai alinantis darbas ir situacijos, dėl kurių galima susižaloti arba žūti. Pervargę kariai kenčia nuo sumažėjusio budrumo, su sprendimais susijusio reakcijos laiko, trumpalaikės atminties, navigacijos įgūdžių ir, kai kuriais atvejais, taiklumo per treniruotes suprastėjimo. Didelio fizinio ir psichinio nuovargio derinys gali padidinti traumų riziką ir sumažinti gebėjimą priimti tinkamus sprendimus reikiamu laiku. Šio tyrimo tikslas – nustatyti karinių pratybų poveikį profesionalių karių miego kokybei, kadangi dažniausi miego nepakankamumo atvejai pasitaiko per karines pratybas. Į tyrimą buvo įtraukta 10 profesionalių Lietuvos kariuomenės žvalgų būrio karių, 32,8 ± 6,9 metų amžiaus, atitinkančių pačius aukščiausius karių fizinio parengimo testo reikalavimus (≥ 270 balų). Profesionalių karių miego kokybė buvo vertinama 7 paras prieš karines pratybas, 7 paras karinių pratybų metu ir 9 paras iš karto po karinių pratybų. Visi tiriamieji dėvėjo laikrodžius „Garmin Decent G1“, kuriais buvo fiksuojami miego parodymai. Buvo registruojama bendra miego trukmė, gilaus miego, lengvo miego, REM ir būdravimo fazių trukmė. Nustatyta, kad karinių pratybų metu, karių bendra miego trukmė buvo 5,3 ± 2,9 val. per dieną (toliau – val./d.), o prieš pratybas buvo 2,3 ± 1,1 val./d. ilgesnė (p < 0,05). Po karinių pratybų karių bendra miego trukmė buvo 3,0 ± 1,1 val./d. ilgesnė nei karinių pratybų metu (p < 0,05). Taip pat po karinių pratybų karių bendra miego trukmė buvo ilgesnė 0,7 ± 0,5 val./d. lyginat su miego trukme prieš pratybas (p < 0,05). Karinių pratybų metu taip pat sutrumpėjo gilaus miego fazė. Gilaus miego fazės trukmė pratybų metu sutrumpėjo net 58 proc. (p < 0,05) lyginant su prieš pratybas buvusia, tačiau po pratybų išliko 18 proc. trumpesnė nei prieš pratybas (p < 0,05). Lengvo miego fazės trukmė buvo 93,7 ± 46,6 min. per dieną (toliau – min./d.) trumpesnė nei prieš pratybas (p < 0,05), tačiau vertinant procentais lengvo miego fazė sudarė apie 60 proc. bendros miego trukmės visuose etapuose ir tik apie 2 proc. buvo trumpesnė po karinių pratybų (p > 0,05). Karinių pratybų metu REM ir būdravimo miego fazių trukmė reikšmingai nesiskyrė nuo prieš pratybas buvusių trukmių (p > 0,05).
Computer applications to medicine. Medical informatics, Social Sciences