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OBJECTIVE: To evaluate the use of custom-made insoles adapted to flip-flops on pain intensity, foot function, and functional walking ability in individuals with persistent plantar heel pain in the short and medium term. DESIGN: Randomised controlled trial. SETTING: Flip-flop sandals in patients with persistent plantar heel pain. MAIN MEASURES: Participants (n = 80) were assessed at baseline, six and 12 weeks after the intervention, and 4 weeks post-intervention. RESULTS: For the primary outcomes, after 6 weeks of intervention, no between-group difference was observed in the intensity of morning pain or pain with walking, mean difference = -0.4 (95% confidence intervals = -1.5 to 0.8). Similarly, after 12 weeks of intervention, no between-group difference was observed in the intensity of morning pain or pain with walking, mean difference = -0.7 (95% confidence intervals = -1.9 to 0.6). Finally, at 4 weeks after the end of the intervention, there was no between-group difference in morning pain or pain on walking, mean difference = 0.01 (95% confidence intervals = -1.4 to 1.4). All differences and confidence intervals were smaller than the minimum clinically important difference for pain (2 points). There were no differences between the groups for the secondary outcomes. In addition, the mean differences were smaller than the minimum clinically important differences for pain intensity, foot function and functional walking ability. CONCLUSION: Custom-made insoles fitted to flip-flops did not differ from flip-flops with sham insoles in improving pain intensity, foot function and functional walking ability in people with persistent heel pain.Trial registration: ClinicalTrials.gov (Identifier: NCT04784598). Data of registration: 2023-01-20.

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Acute leukemias (ALs) are the most common cancers in pediatric population. There are two types of ALs: acute lymphoblastic leukemia (ALL) and acute myeloid leukemia (AML). Some studies suggest that the Renin Angiotensin System (RAS) has a role in ALs. RAS signaling modulates, directly and indirectly, cellular activity in different cancers, affecting tumor cells and angiogenesis. Our review aimed to summarize the role of RAS in ALs and to explore future perspectives for the treatment of these hematological malignancies by modulating RAS molecules. The database including Pubmed, Scopus, Cochrane Library, and Scielo were searched to find articles about RAS molecules in ALL and in pediatric patients. The search terms were "RAS", "Acute Leukemia", "ALL", "Angiotensin-(1-7)", "Pediatric", "Cancer", "Angiotensin II", "AML". In the bone marrow, RAS has been found to play a key role in blood cell formation, affecting several processes including apoptosis, cell proliferation, mobilization, intracellular signaling, angiogenesis, fibrosis, and inflammation. Local tissue RAS modulates tumor growth and metastasis through autocrine and paracrine actions. RAS mainly acts via two molecules, Angiotensin II (Ang II) and Angiotensin (1-7) [Ang-(1-7)]. While Ang II promotes tumor cell growth and stimulates angiogenesis, Ang-(1-7) inhibits the proliferation of neoplastic cells and the angiogenesis, suggesting a potential therapeutic role of this molecule in ALL. The interaction between ALs and RAS reveals a complex network of molecules that can affect the hematopoiesis and the development of hematological cancers. Understanding these interactions could pave the way for innovative therapeutic approaches targeting RAS components.

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This study presents performance and emissions data of an Otto cycle mono-cylinder combustion engine operating with two different compression rates and several mixtures of anhydrous ethanol fuel and water. The instrumented engine was mounted on a dynamometer with the ignition point and injection fuel advance calibrated to obtain the maximum torque and mixture in stoichiometric conditions. Characteristic engine performance parameters and emission fractions from its exhaust system were acquired from 2,000 rpm to 4,000 rpm with fuel mixtures of up to 50% water content. To our knowledge, data on this extreme operating condition are not available in the literature.

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Electroencephalography (EEG) is an exam widely adopted to monitor cerebral activities regarding external stimuli, and its signals compose a nonlinear dynamical system. There are many difficulties associated with EEG analysis. For example, noise can originate from different disorders, such as muscle or physiological activity. There are also artifacts that are related to undesirable signals during EEG recordings, and finally, nonlinearities can occur due to brain activity and its relationship with different brain regions. All these characteristics make data modeling a difficult task. Therefore, using a combined approach can be the best solution to obtain an efficient model for identifying neural data and developing reliable predictions. This paper proposes a new hybrid framework combining stacked generalization (STACK) ensemble learning and a differential-evolution-based algorithm called Adaptive Differential Evolution with an Optional External Archive (JADE) to perform nonlinear system identification. In the proposed framework, five base learners, namely, eXtreme Gradient Boosting, a Gaussian Process, Least Absolute Shrinkage and Selection Operator, a Multilayer Perceptron Neural Network, and Support Vector Regression with a radial basis function kernel, are trained. The predictions from all these base learners compose STACK's layer-0 and are adopted as inputs of the Cubist model, whose hyperparameters were obtained by JADE. The model was evaluated for decoding the electroencephalography signal response to wrist joint perturbations. The variance accounted for (VAF), root-mean-squared error (RMSE), and Friedman statistical test were used to validate the performance of the proposed model and compare its results with other methods in the literature, including the base learners. The JADE-STACK model outperforms the other models in terms of accuracy, being able to explain around, as an average of all participants, 94.50% and 67.50% (standard deviations of 1.53 and 7.44, respectively) of the data variability for one step ahead and three steps ahead, which makes it a suitable approach to dealing with nonlinear system identification. Also, the improvement over state-of-the-art methods ranges from 0.6% to 161% and 43.34% for one step ahead and three steps ahead, respectively. Therefore, the developed model can be viewed as an alternative and additional approach to well-established techniques for nonlinear system identification once it can achieve satisfactory results regarding the data variability explanation.

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INTRODUCTION: Hallux valgus (HV) is one of the most prevalent forefoot deformities, and its frequency increases with age, reaching nearly 23% in adulthood (females are usually more affected). Studies on customised insoles and orthoses for HV showed inconclusive results. There is no consensus in literature regarding the ideal insole or length of use for pain relief or functional improvement in individuals with HV. This study will assess the effects of a customised insole with retrocapital bar associated with an infracapital bar of the first metatarsal on pain and function of individuals with symptomatic HV. METHODS: This is the protocol for a blinded, sham-controlled randomised clinical trial. Eighty participants with symptomatic HV will be randomised into two groups (40 per group): customised insole or sham insole. Assessments will be performed at baseline (T0), six (T6) and 12 weeks (T12) of intervention. A follow-up will occur after 4 weeks of intervention (T16). The primary and secondary outcomes will be pain (Numerical Pain Scale) and function (Foot Function Index), respectively. STATISTICAL ANALYSIS: Analysis of variance with a mixed design or Friedman's test will be considered according to data distribution; post-hoc analyses will be performed using Bonferroni test. Time × group interaction and within-group and between-group differences will also be assessed. The intent-to-treat analysis will be used. A significance level of 5% and 95% s will be adopted for all statistical analyses. ETHICS AND DISSEMINATION: This protocol was approved by the research ethics committee of the Faculty of Health Sciences of Trairi/Federal University of Rio Grande do Norte (UFRN/FACISA; opinion number 5411306). The study results will be disseminated to participants, submitted to a peer-reviewed journal and presented in scientific meetings. TRIAL REGISTRATIONS NUMBER: NCT05408156.

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INTRODUCTION: Persistent heel pain is a prevalent complaint affecting up to 10% of the population. Insoles adapted in flip-flop sandals are an alternative treatment for pain and function of individuals with persistent heel pain, showing improvement within 12 weeks of treatment. Most studies considered foot posture and biomechanics to prescribe insoles for persistent heel pain, but few verified the effects of a 12-week treatment on pain catastrophising. This study will investigate the effects of insoles adapted in flip-flop sandals on pain intensity, function, functional walking capacity and pain catastrophising of individuals with persistent heel pain. METHODS AND ANALYSIS: This is a protocol for a sham-controlled randomised trial. Eighty individuals with persistent heel pain will be assessed and randomised into two intervention groups: insoles adapted in flip-flop sandals and flip-flop sandals with sham (ie, flat) insoles. Assessments will be conducted at baseline (T0), after 6 weeks (T6), 12 weeks postintervention (T12) and after a 4-week follow-up (T16). The primary outcome will be the pain intensity, and secondary outcomes will be foot function, functional walking capacity and pain catastrophising. Analysis of variance with mixed design (if normal distribution) or Friedman's test (if not normal distribution) will verify intergroup and intragroup differences. Bonferroni post hoc tests will be performed in case of significant group or time interaction. Intent-to-treat analysis will be used, and a significance level of 5% and 95% CIs will be considered. ETHICS AND DISSEMINATION: This study was approved by the research ethics committee of the Federal University of Rio Grande do Norte (registry no. 4,018,821). Results will be disseminated to individuals, submitted to a peer-reviewed journal and disclosed in scientific meetings. TRIAL REGISTRATION NUMBER: NCT04784598.

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Due to the increasing urban development, it has become important for municipalities to permanently understand land use and ecological processes, and make cities smart and sustainable by implementing technological tools for land monitoring. An important problem is the absence of technologies that certify the quality of information for the creation of strategies. In this context, expressive volumes of data are used, requiring great effort to understand their structures, and then access information with the desired quality. This study are designed to provide an initial response to the need for mapping zones in the city of Itajaí (SC), Brazil. The solution proposes to aid object recognition employing object-based classifiers OneR, NaiveBayes, J48, IBk, and Hoeffding Tree algorithms used together with GeoDMA, and a first approach in the use of Region-based Convolutional Neural Network (R-CNN) and the YOLO algorithm. All this is to characterize vegetation zones, exposed soil zones, asphalt, and buildings within an urban and rural area. Through the implemented model for active identification of geospatial objects with similarity levels, it was possible to apply the data crossover after detecting the best classifier with accuracy (85%) and the kappa agreement coefficient (76%). The case study presents the dynamics of urban and rural expansion, where expressive volumes of data are obtained and submitted to different methods of cataloging and preparation to subsidize rapid control actions. Finally, the research describes a practical and systematic approach, evaluating the extraction of information to the recommendation of knowledge with greater scientific relevance. Allowing the methods presented to apply the calibration of values for each object, to achieve results with greater accuracy, which is proposed to help improve conservation and management decisions related to the zones within the city, leaving as a legacy the construction of a minimum technological infrastructure to support the decision.

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The need to estimate the orientation between frames of reference is crucial in spacecraft navigation. Robust algorithms for this type of problem have been built by following algebraic approaches, but data-driven solutions are becoming more appealing due to their stochastic nature. Hence, an approach based on convolutional neural networks in order to deal with measurement uncertainty in static attitude determination problems is proposed in this paper. PointNet models were trained with different datasets containing different numbers of observation vectors that were used to build attitude profile matrices, which were the inputs of the system. The uncertainty of measurements in the test scenarios was taken into consideration when choosing the best model. The proposed model, which used convolutional neural networks, proved to be less sensitive to higher noise than traditional algorithms, such as singular value decomposition (SVD), the q-method, the quaternion estimator (QUEST), and the second estimator of the optimal quaternion (ESOQ2).

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The application of ubiquitous computing has increased in recent years, especially due to the development of technologies such as mobile computing, more accurate sensors, and specific protocols for the Internet of Things (IoT). One of the trends in this area of research is the use of context awareness. In agriculture, the context involves the environment, for example, the conditions found inside a greenhouse. Recently, a series of studies have proposed the use of sensors to monitor production and/or the use of cameras to obtain information about cultivation, providing data, reminders, and alerts to farmers. This article proposes a computational model for indoor agriculture called IndoorPlant. The model uses the analysis of context histories to provide intelligent generic services, such as predicting productivity, indicating problems that cultivation may suffer, and giving suggestions for improvements in greenhouse parameters. IndoorPlant was tested in three scenarios of the daily life of farmers with hydroponic production data that were obtained during seven months of cultivation of radicchio, lettuce, and arugula. Finally, the article presents the results obtained through intelligent services that use context histories. The scenarios used services to recommend improvements in cultivation, profiles and, finally, prediction of the cultivation time of radicchio, lettuce, and arugula using the partial least squares (PLS) regression technique. The prediction results were relevant since the following values were obtained: 0.96 (R2, coefficient of determination), 1.06 (RMSEC, square root of the mean square error of calibration), and 1.94 (RMSECV, square root of the mean square error of cross validation) for radicchio; 0.95 (R2), 1.37 (RMSEC), and 3.31 (RMSECV) for lettuce; 0.93 (R2), 1.10 (RMSEC), and 1.89 (RMSECV) for arugula. Eight farmers with different functions on the farm filled out a survey based on the technology acceptance model (TAM). The results showed 92% acceptance regarding utility and 98% acceptance for ease of use.