Miracle-dz/UniFAR-FTUs huggingface dataset:
Feedback Control of the Bending Response of Ionic Polymer Actuators

Ionic polymers are a class of active material that exhibit large bending deflections under the application of an electric field. Previous research has demonstrated that the response of an actuator in air is characterized by a large initial deflection and a relaxation to a steady-state value. The time constant of the relaxation is of the order 5–20 s and the overshoot of the step response is typically greater than 100%. These aspects of the step response limit the actuation bandwidth of the material. In this work we explore the use of feedback control techniques to eliminate the large overshoot and reduce the settling time of cantilevered ionic polymer actuators. Control models are developed from measurements of the actuator response to a step change in the applied voltage. The models demonstrate that the dynamics relevant to the control problem can be separated into a low-frequency (< 5 Hz) range in which the response is characterized by a series of time constants, and a high-frequency range (> 10 Hz) characterized by the resonance of the actuator. For shorter polymers the resonance is sufficiently high that it can be ignored in the control design, but the resonant response becomes significant as the length of the polymer increases. Control simulations based on Linear Quadratic Regulator (LQR) theory demonstrate that feedback control eliminates the overshoot in the step response and decreases the settling time by a factor of 10. Control of the actuator oscillation is accomplished by using an LQR weighting matrix that includes the states associated with the actuator resonance, whereas a state weighting matrix that does not include these terms results in a compensator that includes a notch at the actuator resonance. Experimental results on the shorter polymer demonstrate that the overshoot in the step response is eliminated and the settling time is reduced from 12–16 s to 1–2 s, thus verifying the ability of feedback control to shape the step response of the actuator. Experimental results on the longer polymer demonstrate that incorporating the resonance terms in the LQR weighting matrix is the superior control design method. Designs that included a notch in the compensator were unstable for very low gains because of variations in the actuator resonance due to changes in surface hydration. Designs that included phase lead in the compensator produced superior closed-loop performance and resulted in reductions in overshoot and settling time of the order 6–10 s.
Trust in Neuroethics

Dubljevi c et al. (2022) argue that neuroethics has a socio-political role that can “(1) serve to clarify and resolve conflicts, (2) orient the public with regards to the moral status of neurotechnology, (3) reconcile the public with the reasonable neurotechnological changes (for the public benefit), and (4) probe the limits of practical social and neurotechnological possibilities” (11). The authors argue that “[n]euroethics should serve as a ‘reality check’ in public discussion and be able to provide criteria for policymakers and the public to discriminate between technologies that produce ethical challenges within research itself from those that might entail subsequent (mis)use in society at large” (17). Our discussion will focus on how the neuroethics community can work to contribute responsible ethical analyses for public discussion, while also gaining the public’s trust.
Building Decision Tree for Imbalanced Classification via Deep Reinforcement Learning

Data imbalance is prevalent in classiﬁcation problems and tends to bias the classiﬁer towards the majority of classes. This paper proposes a decision tree building method for imbalanced binary classiﬁcation via deep reinforcement learning. First, the decision tree building process is regarded as a multi-step game and modeled as a Markov decision process. Then, the tree-based convolution is applied to extract state vectors from the tree structure, and each node is abstracted into a parameterized action. Next, the reward function is designed based on a range of evaluation metrics of imbalanced classiﬁcation. Finally, a popular deep reinforcement learning algorithm called Multi-Pass DQN is employed to ﬁnd an optimal decision tree building policy. The experiments on more than 15 imbalanced data sets indicate that our method outperforms the state-of-the-art methods.
Interactions Among Hawaiian Hawksbills Suggest Prevalence of Social Behaviors in Marine Turtles

Abstract. Social behaviors represent a central tenet of ecology and evolutionary biology, but remain widely undocumented in reptiles. Although marine turtles have been studied for decades, the prevalence, importance, and potential role of social behaviors have been largely overlooked. Consequently, marine turtles have predominantly been characterized as nonsocial animals in the literature. Here we report on visual observations of hawksbill turtles (Eretmochelys imbricata) inhabiting a nearshore coral reef in Hawai‘i that reveal a complex array of social behaviors. Combined with recent evidence for social behaviors in other marine turtle species, our results confirm that traditional views of nonsocial life histories are incomplete and that social behaviors are likely prevalent in many marine turtle species. Our findings have important implications for marine turtle management and suggest increased research into social behaviors is warranted across the taxon.
The Breast Cancer Weight Loss trial (Alliance A011401): A description and evidence for the lifestyle intervention

The Breast Cancer Weight Loss (BWEL) trial is a randomized controlled trial designed to determine whether weight loss after a breast cancer diagnosis can reduce the risk of cancer recurrence in women with overweight or obesity. The BWEL trial will compare the efficacy of a telephone‐based weight‐loss intervention plus health education materials versus health education materials alone on invasive disease‐free survival in 3,181 women with stage II or III breast cancer and BMI > 27 kg/m2. This report provides a detailed description of the goals and methods of the lifestyle intervention and the evidence supporting the intervention used in the BWEL trial. The intervention’s primary goal for participants is to achieve and maintain a weight loss ≥ 10% of baseline weight through increased physical activity and caloric restriction. The evidence supporting the diet, physical activity, and behavioral components of this telephone‐based weight‐loss intervention, as well as strategies to promote participant engagement and retention, is described. The intervention is provided through 42 sessions delivered by trained health coaches over a 2‐year period. If the BWEL lifestyle intervention is successful in improving cancer outcomes, then weight loss will be incorporated into the care of thousands of breast cancer patients.
SGLT inhibitors on weight and body mass: A meta‐analysis of 116 randomized‐controlled trials

Multiple trials have demonstrated the metabolic effects of sodium/glucose cotransporter 2 (SGLT2) inhibitors in patients regardless of diabetes status, and recent trials have been conducted on the combined sodium/glucose cotransporter 1 and sodium/glucose cotransporter 2 (SGLT1/SGLT2) inhibitors. Therefore, a meta‐analysis was conducted to investigate the weight reduction effects and dose‐response relationship of SGLT inhibitors and to assess the relative efficacy of SGLT1/SGLT2 inhibitors.
Proteomics Investigations of Potential Protein Biomarkers in Sera of Rabbits Infected With Schistosoma japonicum

Schistosomiasis is a chronic parasitic disease that continues to be a pressing public health problem in many developing countries. The primary pathological damage from the disease is granuloma and fibrosis caused by egg aggregation, and early treatment can effectively prevent the occurrence of liver fibrosis. Therefore, it is very important to identify biomarkers that can be used for early diagnosis of Schistosoma japonicum infection. In this study, a label-free proteomics method was performed to observe the alteration of proteins before infection, 1 and 6 weeks after infection, and 5 and 7 weeks after treatment. A total of 10 proteins derived from S. japonicum and 242 host-derived proteins were identified and quantified as significantly changed. Temporal analysis was carried out to further analyze potential biomarkers with coherent changes during infection and treatment. The results revealed biological process changes in serum proteins compared to infection and treatment groups, which implicated receptor-mediated endocytosis, inflammatory response, and acute-phase response such as mannan-binding lectin serine peptidase 1, immunoglobulin, and collagen. These findings offer guidance for the in-depth analysis of potential biomarkers of schistosomiasis, host protein, and early diagnosis of S. japonicum and its pathogenesis. Data are available via ProteomeXchange with identifier PXD029635.
The Negative Effect of Job Insecurity in the Virtuous Cycle Between Trust in the Organization, Subjective Well-Being, and Task Performance in the Current Volatility, Uncertainty, Complexity, and Ambiguity Context

Over the past decade, job insecurity referring to the employees’ perceived threat to the continuity and stability of employment as it is currently experienced has become a hot topic. A general assumption, supported by the findings, is that job insecurity causes far-reaching negative consequences for the employee health and well-being, attitudes toward organization and the job, and behaviors at work. However, the focus on behavioral outcomes, especially on employee performance at work, is still scant. Moreover, the literature remains fragmented concerning the impact of job insecurity on employee trust in the organization and how the trust influences employee subjective well-being (SWB), which in turn affects employee performance. Consequently, the link between job insecurity and SWB needs more investigation. Trying to narrow the gap, the paper aims at revealing the linkage between job insecurity, trust in the organization, SWB, and task performance. Quantitative data were collected in Lithuania. As predicted, the results revealed that job insecurity had a negative impact on trust in the organization and employee SWB. In case of linkage between job insecurity and task performance, the hypothesis was rejected. In general, these findings affirmed that job insecurity was a hindrance stressor, which needed to be considered when managing human resources in the current volatility, uncertainty, complexity, and ambiguity context.
Biological Pathways Associated With the Development of Pulmonary Toxicities in Mesothelioma Patients Treated With Radical Hemithoracic Radiation Therapy: A Preliminary Study

Radical hemithoracic radiotherapy (RHR), after lung-sparing surgery, has recently become a concrete therapeutic option for malignant pleural mesothelioma (MPM), an asbestos-related, highly aggressive tumor with increasing incidence and poor prognosis. Although the toxicity associated to this treatment has been reduced, it is still not negligible and must be considered when treating patients. Genetic factors appear to play a role determining radiotherapy toxicity. The aim of this study is the identification of biological pathways, retrieved through whole exome sequencing (WES), possibly associated to the development of lung adverse effects in MPM patients treated with RHR. The study included individuals with MPM, treated with lung-sparing surgery and chemotherapy, followed by RHR with curative intent, and followed up prospectively for development of pulmonary toxicity. Due to the strong impact of grade 3 pulmonary toxicities on the quality of life, compared with less serious adverse events, for genetic analyses, patients were divided into a none or tolerable pulmonary toxicity (NoSTox) group (grade ≤2) and a severe pulmonary toxicity (STox) group (grade = 3). Variant enrichment analysis allowed us to identify different pathway signatures characterizing NoSTox and Stox patients, allowing to formulate hypotheses on the protection from side effects derived from radiotherapy as well as factors predisposing to a worst response to the treatment. Our findings, being aware of the small number of patients analyzed, could be considered a starting point for the definition of a panel of pathways, possibly helpful in the management of MPM patients.
Clinical Audit in Emergency Medicine

Clinical audit is the review of clinical performance against agreed standards, and the refining of clinical practice as a result – a cyclical process of quality improvement in clinical care. The different steps of the clinical audit cycle are discussed. Publications on clinical audit in connection with Emergency Medicine are scarce in the medical literature. Clinical audit should be made compulsory for all healthcare professionals providing clinical care, and emergency physicians are no exceptions.
Diversity and Introduction History of Glycaspis brimblecombei Reflects a History of Bridgeheads and Distinct Invasions

Glycaspis brimblecombei is an invasive insect pest of Eucalyptus that has spread rapidly around the world since its first report in California in 1998. The pest now occurs on at least four continents where Eucalyptus is grown as a non-native plantation species. To characterize global routes of invasion for this insect, we characterized the sequences of a portion of the Cytochrome Oxidase 1 (COI) gene from 105 individuals from the invasive and native range, including from Australia, Brazil, Chile, La Réunion, Mauritius, South Africa and the United States. In addition, we developed 13 polymorphic microsatellite markers, of which we used 11 to characterize the diversity in the same 105 specimens. Our results suggest that there have been two independent introduction events from Australia, which is assumed to be the origin, to distinct parts of the adventive range. The first introduction was into the United States, from where it appears to have spread to South America and eventually to South Africa. This finding highlights the threat of bridgehead populations to accelerate pest invasions in Eucalyptus, even if those populations are on widespread non-commercial populations of Eucalyptus (as in California). A second introduction appears to have occurred on the islands of Mauritius and La Réunion and provides another example of the establishment of independent lineages of invasive global insect pests. This complex invasion pattern mirrors that found in other Eucalyptus pests.
Post-Traumatic Osteoarthritis Assessment in Emerging and Advanced Pre-Clinical Meniscus Repair Strategies: A Review

Surgical repair of meniscus injury is intended to help alleviate pain, prevent further exacerbation of the injury, restore normal knee function, and inhibit the accelerated development of post-traumatic osteoarthritis (PTOA). Meniscus injuries that are treated poorly or left untreated are reported to significantly increase the risk of PTOA in patients. Current surgical approaches for the treatment of meniscus injuries do not eliminate the risk of accelerated PTOA development. Through recent efforts by scientists to develop innovative and more effective meniscus repair strategies, the use of biologics, allografts, and scaffolds have come into the forefront in pre-clinical investigations. However, gauging the extent to which these (and other) approaches inhibit the development of PTOA in the knee joint is often overlooked, yet an important consideration for determining the overall efficacy of potential treatments. In this review, we catalog recent advancements in pre-clinical therapies for meniscus injuries and discuss the assessment methodologies that are used for gauging the success of these treatments based on their effect on PTOA severity. Methodologies include histopathological evaluation of cartilage, radiographic evaluation of the knee, analysis of knee function, and quantification of OA predictive biomarkers. Lastly, we analyze the prevalence of these methodologies using a systemic PubMed® search for original scientific journal articles published in the last 3-years. We indexed 37 meniscus repair/replacement studies conducted in live animal models. Overall, our findings show that approximately 75% of these studies have performed at least one assessment for PTOA following meniscus injury repair. Out of this, 84% studies have reported an improvement in PTOA resulting from treatment.
An online semi-definite programming with a generalised log-determinant regularizer and its applications

We consider a variant of the online semi-definite programming problem (OSDP). Specifically, in our problem, the setting of the decision space is a set of positive semi-definite matrices constrained by two norms in parallel: the L∞ norm to the diagonal entries and the Γ-trace norm, which is a generalized trace norm with a positive definite matrix Γ. Our setting recovers the original one when Γ is an identity matrix. To solve this problem, we design a follow-the-regularized-leader algorithm with a Γ-dependent regularizer, which also generalizes the log-determinant function. Next, we focus on online binary matrix completion (OBMC) with side information and online similarity prediction with side information. By reducing to the OSDP framework and applying our proposed algorithm, we remove the logarithmic factors in the previous mistake bound of the above two problems. In particular, for OBMC, our bound is optimal. Furthermore, our result implies a better offline generalization bound for the algorithm, which is similar to those of SVMs with the best kernel, if the side information is involved in advance.
Short-term load forecasting of Australian National Electricity Market by an ensemble model of extreme learning machine

Artificial Neural Network (ANN) has been recognized as a powerful method for short-term load forecasting (STLF) of power systems. However, traditional ANNs are mostly trained by gradient-based learning algorithms which usually suffer from excessive training and tuning burden as well as unsatisfactory generalization performance. Based on the ensemble learning strategy, this paper develops an ensemble model of a promising novel learning technology called extreme learning machine (ELM) for high-quality STLF of Australian National Electricity Market (NEM). The model consists of a series of single ELMs. During the training, the ensemble model generalizes the randomness of single ELMs by selecting not only random input parameters but also random hidden nodes within a pre-defined range. The forecast result is taken as the median value the single ELM outputs. Owing to the very fast training/tuning speed of ELM, the model can be efficiently updated to on-line track the variation trend of the electricity load and maintain the accuracy. The developed model is tested with the NEM historical load data and its performance is compared with some state-of-the-art learning algorithms. The results show that the training efficiency and the forecasting accuracy of the developed model are superior over the competitive algorithms.
Three-Dimensional Culture Decreases the Angiogenic Ability of Mouse Macrophages

Macrophages play important roles in angiogenesis; however, previous studies on macrophage angiogenesis have focused on traditional 2D cultures. In this study, we established a 3D culture system for macrophages using collagen microcarriers and assessed the effect of 3D culture on their angiogenic capabilities. Macrophages grown in 3D culture displayed a significantly different morphology and arrangement under electron microscopy compared to those grown in 2D culture. Tube formation assays and chick embryo chorioallantoic membrane assays further revealed that 3D-cultured macrophages were less angiogenic than those in 2D culture. Whole-transcriptome sequencing showed that nearly 40% of genes were significantly differently expressed, including nine important angiogenic factors of which seven had been downregulated. In addition, the expression of almost all genes related to two important angiogenic pathways was decreased in 3D-cultured macrophages, including the two key angiogenic factors, VEGFA and ANG2. Together, the findings of our study improve our understanding of angiogenesis and 3D macrophage culture in tissues, and provide new avenues and methods for future research on macrophages.
Contributions of Hedgerows to People: A Global Meta-Analysis

Hedgerows are linear landscape features of woody vegetation usually located around agricultural fields. An increasing number of studies have addressed the effects of hedgerows on biodiversity and ecosystem services. This study is aimed to synthesize these effects and compare the levels of biodiversity and ecosystem services in farmland with hedgerows and (1) farmland without hedgerows and (2) nearby natural habitat at the global scale. We hypothesized that farmland with hedgerows (1) enhances biodiversity and ecosystem services as compared to farmland without hedgerows but (2) supports lower levels of biodiversity and ecosystem services than natural habitat. Our systematic literature review retained 835 observations from 170 primary studies, which were analyzed following the standard methodology in meta-analyses. Our results partially support both hypotheses. Farmland with hedgerows exhibited higher levels of biodiversity and provisioning services than farmland without hedgerows (H1). Farmland with hedgerows provided similar levels of biodiversity (edge effects) but lower levels of ecosystem services than natural habitat (H2). The effects of hedgerows on biodiversity and ecosystem services depended on control ecosystem type (grassland/meadow or forest/woodland) but were largely independent of climate type (temperate or tropical) and the focus of spatial scale (field or landscape). In conclusion, conservation and restoration of hedgerows contribute to people in several ways by enhancing biodiversity and multifunctionality in agricultural landscapes.
QActor: Active Learning on Noisy Labels

Noisy labeled data is more a norm than a rarity for self-generated content that is continuously published on the web and social media from non-experts. Active querying experts are conventionally adopted to provide labels for the informative samples which don’t have labels, instead of possibly incorrect labels. The new challenge that arises here is how to discern the informative and noisy labels which beneﬁt from expert cleaning. In this paper, we aim to leverage the stringent oracle budget to robustly maximize learning accuracy. We propose a noise-aware active learning framework, QActor, and a novel measure CENT , which considers both cross-entropy and entropy to select informative and noisy labels for an expert cleansing. QActor iteratively cleans samples via quality models and actively querying an expert on those noisy yet informative samples. To adapt to learning capacity per iteration, QActor dynamically adjusts the query limit according to the learning loss for each learning iteration. We extensively evaluate diﬀerent image datasets with noise label ratios ranging between 30% and 60%. Our results show that QActor can nearly match the optimal accuracy achieved using only clean data at the cost of only an additional 10% of ground truth data from the oracle.