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Titel Rörelsekvalitet, ground reaction force och skottprecision hos friska handbollsspelare: En tvärsnittsstudie av skillnader mellan sedvanlig träning och med integrerad mental träning. Bakgrund Främre korsbandsskador är vanliga bland unga idrottare och innebär stora utmaningar både fysiskt och psykiskt. Psykologiska faktorer som rädsla för ny skada, nedsatt självförtroende och bristande motivati

Bakgrund: Stress är en fysiologisk reaktion skapad av kroppen som en försvarsmekanism för överlevnad och är inte skadlig i sig, men som utan återhämtning kan leda till skador på kroppen, såsom utveckling av hjärt-kärlsjukdomar. Forskning har visat att fysisk aktivitet kan minska stress och leda till ett ökat välbefinnande. Syfte: Syftet med studien var att undersöka om fysioterapeutstudenter använ

We study a network formation game where n players, identified with the nodes of a directed graph to be formed, choose where to wire their outgoing links in order to maximize their PageRank centrality. Specifically, the action of every player i consists in the wiring of a predetermined number di of directed out-links, and her utility is her own PageRank centrality in the network resulting from the

The impact of climate on the annual cycle and interannual variability of CO2 fluxes is assessed in European evergreen needleleaf forests (ENFs) and deciduous broadleaf forests (DBFs) using observations from 19 sites, alongside outputs from process-based and data-driven models. All models capture the temporal phasing of CO2 fluxes, including a shorter sequestration period in northern rather than so

The structural integration of energy storage could play a pivotal role in designing more efficient, lightweight systems such as electric transport, internet of things (IoT) devices and autonomous systems. The current work focuses on the fabrication and characterization of novel multifunctional composites for sodium-ion structural batteries. A PEO-PVP polymer matrix with added InAs nanowires and gl

Metal–organic frameworks such as ZIF-8, grown in situ on nanocellulose (NC), have gained significant attention in recent years due to the versatility of the processing route and multifaceted application in the field of environmental remediation and biomedical applications. However, insights into the interactions between NC and MOF precursors and MOF structure evolution during in situ synthesis are

Inhibition of adenosine 5'-monophosphate (AMP)-activated protein kinase (AMPK) is under increasing investigation for its therapeutic potential in many diseases. Existing AMPK inhibitors are however limited, with poor selectivity and substantial off-target effects. Here, we provide mechanistic insights and describe the cellular selectivity of the recently identified AMPK inhibitor BAY-3827. A 2.5-Å

Amplification of the MYCN proto-oncogene serves as a key marker of aggressive disease and poor treatment outcomes in certain pediatric tumors originating from the nervous system, including neuroblastoma and medulloblastoma. However, the complex nature of the challenging MYCN protein underscores the urgent need for additional targets and therapies to tackle neuroblastoma and medulloblastoma. In thi

This paper examines how gender inclusion in transboundary water governance can be transformed from symbolic to substantive participation in the Brahmaputra Basin. Using qualitative methods including literature review, stakeholder mapping, and 11 semi-structured interviews across India and Bangladesh, the study applies an integrated conceptual framework linking transboundary water governance, multi

ObjectivesDespite regularly being found in blood cultures, there are few studies of bloodstream infection (BSI) with Citrobacter. In this population-based study, the aim was to explore patient characteristics, outcome, and incidence in a publicly funded single payer setting.MethodsPatients with growth of Citrobacter in blood culture were identified through the clinical microbiology laboratory in L

Learning a locomotion controller for a musculoskeletal system is challenging due to over-actuation and high-dimensional action space. While many reinforcement learning methods attempt to address this issue, they often struggle to learn human-like gaits because of the complexity involved in engineering an effective reward function. In this paper, we demonstrate that adversarial imitation learning c

Motion planning is a mature area of research in robotics with many well-established methods based on optimization or sampling the state space, suitable for solving kinematic motion planning. However, when dynamic motions under constraints are needed and computation time is limited, fast kinodynamic planning on the constraint manifold is indispensable. In recent years, learning-based solutions have

Genomgång av samtliga de tillfällen då Carl von Linné omnämns i Carl Tersmedens memoarer inklusive kommentarer, bland annat rörande kronologiska diskrepanser.

Machine learning methods have a groundbreaking impact in many application domains, but their application on real robotic platforms is still limited. Despite the many challenges associated with combining machine learning technology with robotics, robot learning remains one of the most promising directions for enhancing the capabilities of robots. When deploying learning-based approaches on real rob

Recent methods for imitation learning directly learn a Q-function using an implicit reward formulation rather than an explicit reward function.However, these methods generally require implicit reward regularization to improve stability and often mistreat absorbing states.Previous works show that a squared norm regularization on the implicit reward function is effective, but do not provide a theore

Quadruped robots are currently a widespread platform for robotics research, thanks to powerful Reinforcement Learning controllers and the availability of cheap and robust commercial platforms. However, to broaden the adoption of the technology in the real world, we require robust navigation stacks relying only on low-cost sensors such as depth cameras. This paper presents a first step towards a ro

Black-box policy optimization is a class of reinforcement learning algorithms that explores and updates the policies at the parameter level. This class of algorithms is widely applied in robotics with movement primitives or non-differentiable policies. Furthermore, these approaches are particularly relevant where exploration at the action level could cause actuator damage or other safety issues. H