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RESUMO A intensidade de pastejo é um dos principais fatores que moldam a estrutura da vegetação, influenciando a comunidade de aves campestres. Aves de grama alta, incluindo diversas espécies ameaçadas, dependem de baixas intensidades ou exclusão de pastejo, pouco praticadas no Pampa. Aqui, avaliamos a comunidade de aves em campos sob intensidade de pastejo moderada, baixa e sob exclusão de pastejo. Estabelecemos 3 transecções, uma em cada uma destas condições. As três transecções foram amostradas mensalmente, durante 13 meses, registrando as espécies e suas abundâncias e posteriormente relacionando-as com as condições da vegetação observadas. As três áreas apresentaram riqueza e diversidade (H’) de espécies muito semelhantes entre si, variando de 50 a 54 espécies, e de 2,79 a 2,95, respectivamente. A abundância de indivíduos, foi favorecida pela diminuição da intensidade e pela exclusão de pastejo. De modo geral, espécies de aves associadas à grama curta foram mais representativas na área sob pastejo moderado, enquanto as associadas à grama alta foram favorecidas pela baixa intensidade e exclusão de pastejo. A vegetação mais alta e densa, das áreas menos impactadas pelo gado, foi especialmente importante para as espécies ameaçadas de extinção, como Culicivora caudacuta Vieillot, 1818 e Sporophila cinnamomea Lafresnaye, 1839 fornecendo abrigo, poleiros e substrato para construção e fixação de ninhos. Corroboramos, dessa forma, a necessidade de áreas com distintas intensidades de pastejo, formando um mosaico com diferentes alturas de vegetação, mantendo a diversidade de aves campestres. São necessárias, porém, mais áreas com baixa intensidade ou exclusão de pastejo, das quais diversas espécies ameaçadas dependem. Contudo, estas áreas também devem ser manejadas, evitando o desenvolvimento de excessiva vegetação lenhosa, que pode levar à descaracterização da vegetação campestre, afetando a avifauna a ela associada.

Objective: A pathological tremor (PT) is an involuntary rhythmic movement of varying frequency and amplitude that affects voluntary motion, thus compromising individuals’ independence. A comprehensive model incorporating PT’s physiological and biomechanical aspects can enhance our understanding of the disorder and provide valuable insights for therapeutic approaches. This study aims to build a biomechanical model of pathological tremors using OpenSim’s realistic musculoskeletal representation of the human wrist with two degrees of freedom. Methods: We implemented a Matlab/OpenSim interface for a forward dynamics simulation, which allows for the modeling, simulation, and design of a physiological $\mathcal {H}_{\infty} $ closed-loop control. This system replicates pathological tremors similar to those observed in patients when their arm is extended forward, the wrist is pronated, and the hand is subject to gravity forces. The model was individually tuned to five subjects (four Parkinson’s disease patients and one diagnosed with essential tremor), each exhibiting distinct tremor characteristics measured by an inertial sensor and surface EMG electrodes. Simulation agreement with the experiments for EMGs, central frequency, joint angles, and angular velocities were evaluated by Jensen-Shannon divergence, histogram centroid error, and histogram intersection. Results: The model emulated individual tremor statistical characteristics, including muscle activations, frequency, variability, and wrist kinematics, with greater accuracy for the four Parkinson’s patients than the essential tremor. Conclusion: The proposed model replicated the main statistical features of subject-specific wrist tremor kinematics. Significance: Our methodology may facilitate the design of patient-specific rehabilitation devices for tremor suppression, such as neural prostheses and electromechanical orthoses.

Abstract Background Intimate partner violence (IPV) is a pervasive public health issue that affects millions of women worldwide. Women living below the poverty line experience higher rates of violence and fewer resources to escape or cope with the abuse, and the COVID-19 pandemic has significantly impacted women’s economic well-being worldwide. We conducted a cross-sectional study in Ceará, Brazil, on women in families with children living below the poverty line at the peak of the second wave of COVID to assess the prevalence of IPV and its association with common mental disorders(CMD). Methods The study population comprised families with children up to six years of age who participated in the cash transfer program “Mais Infância”. The families selected to participate in this program must meet a poverty criterion: families must live in rural areas, in addition to a monthly per capita income of less than US$16.50 per month. We applied specific instruments to evaluate IPV and CMD. To access IPV, we used the Partner Violence Screen (PVS). The Self-Reporting Questionnaire (SRQ-20) was used to assess CMD. To verify the association between IPV and the other evaluated factors with CMD, simple and hierarchical multiple logistic models were used. Results Of the 479 participant women, 22% were positively screened for IPV (95% CI 18.2–26.2). After multivariate adjustment, the chances of CMD are 2.32 higher in women exposed to IPV than in those not exposed to IPV ((95%CI 1.30–4.13), p value = 0.004). CMD was also associated with job loss during the COVID-19 pandemic (ORa 2.13 (95% CI 1.09–4.35), p-value 0.029). In addition to these, separate or single marital status, as well as non-presence of the father at home and food insecurity were associated with CMD. Conclusion We conclude that the prevalence of intimate partner violence in families with children up to six years of age living below the poverty line in Ceará is high and is associated with greater chances of common mental disorders in mothers. Also, job loss and reduced access to food caused by the Covid 19 pandemic exacerbated both phenomena, constituting a double burden generator factor on mothers.

Abstract The advent of the 5th generation of mobile networks brought a large number of new use case and applications to be supported by the physical layer (PHY), which must be more flexible than all previous radio access networks (RAN). The concept of the centralized RAN (C-RAN) allows all the baseband processing to be performed in the central office, simplifying the network deployment and also allowing the operators to dynamically control the PHY according with the applications requirements. The radio-frequency (RF) signal generated by the C-RAN can be transported to the remote radio unit (RRU) by using a radio over fiber (RoF) system. In this paper, we propose two RoF approaches for composing the transport and access networks of the next-generation systems. The first investigation relies on the implementation of a machine learning-based digital pre-distortion (DPD), designed for RoF systems. In the second approach, we implement an RoF system and characterize the optical and electrical power levels aiming to reduce the RoF non-linear distortions. The overall link performance is evaluated by measuring the error vector magnitude (EVMRMS) and 590 Mbit/s is achieved with EVMRMS as low as 4.4% in a 10 m reach cell.

Orthogonal Frequency Division Multiplexing (OFDM) is the modulation technology used in Fourth Generation (4G) and Fifth Generation (5G) wireless communication systems, and it will likely be essential to Sixth Generation (6G) wireless communication systems. However, OFDM introduces a high Peak to Average Power Ratio (PAPR) in the time domain due to constructive interference among multiple subcarriers, increasing the complexity and cost of the amplifiers and, consequently, the cost and complexity of 6G networks. Therefore, the development of new solutions to reduce the PAPR in OFDM systems is crucial to 6G networks. The application of Machine Learning (ML) has emerged as a promising avenue for tackling PAPR issues. Along this line, this paper presents a comprehensive review of PAPR optimization techniques with a focus on ML approaches. From this survey, it becomes clear that ML solutions offer customized optimization, effective search space navigation, and real-time adaptability. In light of the demands of evolving 6G networks, integration of ML is a necessity to propel advancements and meet increasing prerequisites. This integration not only presents possibilities for PAPR reduction but also calls for continued exploration to harness its potential and ensure efficient and reliable communication within 6G networks.

Abstract Multicarrier modulation allows for deploying wideband systems resilient to multipath fading channels, impulsive noise, and intersymbol interference compared to single-carrier systems. Despite this, multicarrier signals suffer from different types of distortion, including channel noise sources and long- and short-term fading. Consequently, the receiver must estimate the channel features and compensate it for data recovery based on channel estimation techniques, such as non-blind, blind, and semi-blind approaches. These techniques are model-based and designed with accurate mathematical channel models encompassing their features. Nevertheless, complex environments challenge accurate mathematical channel estimation modeling, which might neither be accurate nor correspond to reality. This impairment decreases the system performance due to the channel estimation accuracy loss. Fortunately, (AI) algorithms can learn the relationship among different system variables using a model-driven or model-free approach. Thereby, AI algorithms are used for channel estimation by exploiting its complexity without unrealistic assumptions, following a better performance than conventional techniques under the same channel. Hence, this paper comprehensively surveys AI-based channel estimation for multicarrier systems. First, we provide essential background on conventional channel estimation techniques in the context of multicarrier systems. Second, the AI-aided channel estimation strategies are investigated using the following approaches: classical learning, neural networks, and reinforcement learning. Lastly, we discuss current challenges and point out future research directions based on recent findings.

Terahertz (THz) band will play an important role in enabling sixth generation (6G) envisioned applications. Compared with lower frequency signals, THz waves are severely attenuated by the atmosphere temperature, pressure, and humidity. Thus, designing a THz communication system must take into account how to circumvent or diminish those issues to achieve a sufficient quality of service. Different solutions are being analyzed: intelligent communication environments, ubiquitous artificial intelligence, extensive network automation, and dynamic spectrum access, among others. This survey focuses on the benefits of integrating intelligent surfaces (ISs) and THz communication systems by providing an overview of IS in wireless communications with the scanning of the recent developments, a description of the architecture, and an explanation of the operation. The survey also covers THz channel models, differentiating them based on deterministic and statistical channel modeling. The IS-aided THz channels are elucidated at the end of the survey. Finally, discussions and research directions are given to help enrich the IS field of research and guide the reader through open issues.

Abstract The interest on applications where machine learning algorithms and communications are combined has been on a rising in recent years. Machine learning and neural networks are being advocated as a way of improving the performance of several functions across all layers of future communication systems. Furthermore, in applications where complexity reduction is essential for the system feasibility at the cost of an affordable performance loss, more efficient systems might be achieved with the aid of machine learning algorithms. Signal detection for multiple-input multiple-output (MIMO) systems has become a hot topic in recent years given its prominent role in fourth and fifth generations of mobile networks. However, the computational complexity in MIMO systems can become prohibitive when the number of antennas increases. Therefore, by leveraging neural networks architectures we propose a deep unfolded detector, whereby the algorithm of the probability data association (PDA) detector is adapted and enhanced by means of neural network learning capabilities. We unveil that the proposed detector is orders-of-magnitude less complex than the PDA detector, yet presenting no severe penalties in performance in terms of bit error rate (BER).

Objective: To explore the relationship of undernutrition and the short duration of breastfeeding with child development of children 0–66 months of age residing in Ceará, Brazil. Methods: The authors of the present study utilized population-based data from children enrolled in the Study on Maternal and Child Health in Ceará, Brazil (PESMIC). Children's development was assessed with the Ages and Stages Questionnaire third version, validated in Brazil. Undernutrition was accessed through anthropometric measures obtained by trained staff. Breastfeeding information was obtained through the mothers' report and confirmed in the child's governmental booklet. The authors used logistic regressions adjusted for sample clusters used in PESMIC design in a theoretical model for known determinants of child development following the World Health Organization nurturing framework. Results: A total of 3,566 children were enrolled in the sixth PESMIC study and had their development assessed. The authors found that 8.2%, 3.0%, 2.1%, and 3.6% of children were stunted, underweight, or wasted, at the time of the interview, respectively. All studied factors were associated with a higher prevalence of child development impairment in at least one of the assessed domains. Underweight was the factor with the strongest effect, with an adjusted odds ratio (AOR) of 4,14 (2,26–7,58), p

Objectives: To determine the inflammatory profile of CRSwNP in Brazil and characterize the subgroups of CRSwNP patients in this population through cluster analysis. Study Design: Multicenter cross‐sectional study involving 15 centers representing different regions of Brazil. Subjects and Methods: Clinical data of 166 patients and 80 controls, aged 18 to 70 years old, number of surgeries for CRS, history of asthma and aspirin sensitivity, and Lund‐Mackay scores on CT scans. During nasal endoscopy, we obtained the Lund‐Kennedy scores and collected 2 samples of nasal polyps: one for eosinophil and neutrophil tissue counts and one to quantify different cytokines. Results: 79.6% of our patients had 10 or more eosinophils/HPF. CRSwNP groups exhibited significantly lower concentrations of TNF‐alpha and significantly higher concentrations of IFN‐gamma, CCL11/Eotaxin, CCL24/Eotaxin‐2/MPIF‐2, and CCL26/Eotaxin‐3 versus the control group (Kruskal‐Wallis test). Comparison between CRSwNP groups (≥10 vs <10 eosinophils/HPF) showed no difference in cytokine concentration (Mann‐Whitney test). Hierarchical clustering and PCA according to cytokine concentrations revealed 2 main Clusters, with a significantly higher concentration of all cytokines in Cluster 1 (n = 35) than in Cluster 2 (n = 121), except IL‐6 and IL‐33 (Mann‐Whitney test). According to ROC curve analysis the best cut‐off to differentiate the 2 clusters was 43 eosinophils/HPF. The group with ≥43 presented a higher prevalence of men and a higher Lund‐Mackay score (Mann‐Whitney test). Conclusions: CRSwNP patients in Brazil present mixed inflammation, with 2 distinct groups (high and low inflammatory pattern) that can be distinguished by tissue eosinophilia of ≥43 eosinophils/HPF cut‐off in nasal polyps. [ABSTRACT FROM AUTHOR]

This work proposes a novel machine learning (ML)-based linearization scheme for radio-over-fiber (RoF) systems with external modulation. The proposed approach has the advantage of not requiring new training campaigns in case the Mach-Zehnder modulator (MZM) parameters are changed over time. Our innovative digital pre-distortion (DPD) was designed to favor enhanced remote areas (eRAC) scenarios, in which the non-linearities introduced by the MZM become more severe. It employs a multi-layer perceptron (MLP) artificial neural network (ANN) to model the RoF system and estimate its post-inverse response, which is then applied to the DPD block. We investigate the ML-based DPD performance in terms of adjacent channel leakage ratio (ACLR), normalized mean square error (NMSE), resultant signal root mean square error vector magnitude error (EVM$_\mathrm{RMS}$), and complexity. Numerical results demonstrate that the intended DPD method is less complex and outperforms the orthogonal scalar feedback linearization (OSFL) scheme, which has been considered a state-of-the-art DPD technique. The proposal has the potential to effectively and efficiently compensate for the RoF nonlinear distortions, especially in a time-variant system, without needing new training campaigns.

New applications for mobile networks are driving up data rates, requiring new solutions to meet the demand for throughput. In urban and densely populated areas, massive multiple-input multiple-output, high bands in millimeter waves, and ultra-dense networks based on small cells are being used to achieve high data rates. However, these solutions do not apply to the enhanced remote areas communications scenario, where large cells must be deployed using vacant VHF or UHF bands with an opportunistic spectrum allocation approach. In this case, a reasonable solution to increase the overall system data rate is to enhance the waveform spectral efficiency. This paper proposes a method to improve the spectral efficiency of the generalized frequency division multiplexing scheme by exploiting the faster-than-Nyquist principle in the time and frequency domains without introducing any penalties in terms of bit error rate. This achievement is obtained by under-sampling the time-frequency grid, which results in more data symbols being transmitted per waveform sample while introducing a controlled amount of interference into the transmitted waveform. On the receiver side, low complexity detectors, such as sphere decoder, successive symbol-by-symbol sequence estimation, successive symbol-by-symbol with go-back K sequence estimator, and frequency-domain equalization, can be used to reconstruct the transmitted data. This paper presents the details of adapting the mentioned detectors to the proposed waveform and analyzes their performance in terms of bit error rate under different channel models and implementation complexity. Among all the detectors considered in this paper, the sphere decoder stands out as it presents an interesting compromise between complexity and bit error rate, even in scenarios with high interference. Other detection techniques, originally proposed for multiple-input multiple-output systems, can be adapted to handle the interference introduced by the faster-than-Nyquist generalized frequency division multiplexing with reasonable complexity and acceptable bit error rate performance. Hence, one may conclude that the faster-than-Nyquist generalized frequency division multiplexing is a suitable candidate waveform for the enhanced remote areas communications scenario when high spectrum efficiency is required.

The advent of the fifth generation (5G) of mobile networks has introduced several new use cases that are pushing mobile networks in environments that are typically supported by wired technologies. The initial discussions around the sixth generation (6G) of mobile networks signalizes that different approaches are needed to address all contrasting requirements, where multiple-input multiple-output (MIMO) technique stands as a key technology for most future wireless systems. In this review, we present an introduction on classical linear estimators and coherent detectors along with an innovative and accurate complexity formulation within a common framework, allowing a fair comparison and providing an initial guideline for researchers that are looking for a general view of the main techniques available for spatial multiplexing (SM)-MIMO detection and estimation.

Abstract Background The first 1000 days of life are a critical period when the foundations of child development and growth are established. Few studies in Latin America have examined the relationship of birth outcomes and neonatal care factors with development outcomes in young children. We aimed to assess the association between pregnancy and neonatal factors with children’s developmental scores in a cross-sectional, population-based study of children in Ceará, Brazil. Methods Population-based, cross-sectional study of children aged 0–66 months (0–5.5 years) living in Ceará, Brazil. We examined the relationship of pregnancy (iron and folic acid supplementation, smoking and alcohol consumption) and neonatal (low birth weight (LBW) gestational age, neonatal care interventions, and breastfeeding in the first hour) factors with child development. Children’s development was assessed with the Ages and Stages Questionnaire (ASQ-BR). We used multivariate generalized linear models that accounted for clustering sampling to evaluate the relationship of pregnancy and neonatal factors with development domain scores. Findings A total of 3566 children were enrolled. Among pregnancy factors, children whose mothers did not receive folic acid supplementation during pregnancy had lower fine motor and problem-solving scores (p-values

Maternal educational attainment has been identified as relevant to several child health and development outcomes. This study aimed to evaluate the association of sociodemographic and maternal education factors with child development in families living below the poverty line. A cross-sectional study was conducted through telephone contact from May to July 2021 in Ceará, a state in Northeastern Brazil. The study population comprised families with children up to six years of age participating in the cash transfer program “Mais infância”. The families selected to participate in this program must have a monthly per capita income of less than US$16.50. The Ages and Stages Questionnaire version 3 was applied to assess the children’s development status. The mothers reported maternal educational attainment as the highest grade and or degree obtained. The final weighted and adjusted model showed that maternal schooling was associated with the risk of delay in all domains except for the fine motor domain. The risk of delay in at least one domain was 2.5-fold higher in mothers with a lower level of schooling (95% CI: 1.6–3.9). The findings of this study suggest that mothers with higher educational attainment have children with better child development outcomes.

Dinomyidae is a South American group of caviomorph rodents with a single extant species (Dinomys branickii), but with a rich fossil record including medium-sized to giant species, which reached its peak diversity during the Late Miocene. The dinomyids from the Cerro Azul Formation (late Middle Miocene to Early Pliocene) have been studied in detail from localities in La Pampa Province, Argentina. In this contribution, dinomyids from Chasicoan Stage/Age (Late Miocene) levels of the Cerro Azul Formation at Arroyo Chasicó locality (Buenos Aires Province, Argentina) are revised, including previously published material and unpublished ones. We confirm the presence of Tetrastylus sp. Diaphoromys gamayensis, and define two new taxa, Bondesiomys chasiquensis gen. et sp. nov. and Gyriabrus sokka sp. nov. The diversity of dinomyids from the Cerro Azul Formation at Arroyo Chasicó is higher than other Chasicoan faunas at the same latitude, identified in localities of La Pampa Province, since in Cerro Patagua there are not Dinomyidae records and in Cerro la Bota Tetrastylus sp. and Dinomyidae indet. were the only recorded taxa. [ABSTRACT FROM AUTHOR]

The modularization and manufacture of many-cores system-on-chip that involve several vendors open up a vulnerability: the inclusion of Hardware Trojans (HT). In addition to that, the reduced feature size of transistors may accelerate aging effects, leading to faults. The literature presents techniques to tackle security and fault-tolerance, such as cryptography, authentication codes, error correction codes, creation at runtime of flow profiles to detect anomalous behavior. However, at the communication level (i.e., NoC), there is a gap in generic methods to detect attacks or faults. As detailed in the state-of-the-art session, approaches targeting the NoC protection against attacks add additional hardware in the NoC itself, which is prone to security attacks or faults. This work decouples the detection of attacks or faults by using data and control NoCs. The adoption of a control NoC enables the proposal of the Communication Session Protocol to monitor message exchange, detect abnormal behavior, and recover the communication from an eventual failure or attack. The execution time overhead varies according to the application communication model, from 3.5% to 33%. Such overhead is acceptable because once detected an abnormal communication behavior, the protocol changes the path between communicating task pairs and resumes the application execution.

Abstract Recent advances in the communication systems culminated in a new class of multiple access schemes, named non-orthogonal multiple access (NOMA), where the main goal is to increase the spectrum efficiency by overlapping data from different users in a single time-frequency resource used by the physical layer. NOMA receivers can resolve the interference among data symbols from different users, increasing the overall system spectrum efficiency without introducing symbol error rate (SER) performance loss, which makes this class of multiple access techniques interesting for future mobile communication systems. This paper analyzes one promising NOMA technique, called sparse code multiple access (SCMA), where C users can share U

Recent exploration of Software-Defined Networking (SDN) for Many-Core Systems-on-Chip (MCSoCs) showed higher management flexibility and reduced physical complexity compared to other runtime communication management. In SDN, there is a software SDN Controller (control layer) that configures generic routers (data layer). The adoption of SDN makes the path establishment programmable and straightforward, according to different network policies, such as low power, QoS, fault-tolerance. It is also possible to change the path establishment policies at runtime without the need to redesign the NoC. Current works focus on proposing SDN architectures, lacking a systemic framework that describes the steps required to implement SDN into a Many-core environment. Security is an observed gap in SDN designs. A malicious task could configure SDN routers and take control of the NoC. The contribution of this work is to present a systemic and secure SDN framework (SDN-SS), detailing the steps required to support SDN in MCSoCs. This work also describes the iteration between the hardware, operating system, and user's tasks. The SDN-SS manages a Multiple-Physical NoC, with one packet-switching subnet and a set of circuit-switching subnets. The originality of SDN-SS includes (i) a step-by-step framework description addressing the phases required to support a secure SDN management; (ii) a secure SDN router configuration protocol; (iii) a protocol to change the subnet at runtime. Experimental results show the framework's capability to avoid DoS and Spoofing attacks while presents a low SDN router configuration overhead, corresponding up to 2% of a related work delay and a small impact over the user's task communication.

Resumo: O objetivo deste artigo foi avaliar os fatores socioeconômicos, familiares e individuais associados ao desenvolvimento infantil no primeiro ano de vida, entre famílias em vulnerabilidade social. Trata-se de uma análise transversal, com dados da linha de base de um ensaio randomizado. O estudo incluiu 3.242 crianças < 12 meses de idade, residentes em 30 municípios de cinco regiões do Brasil. A escolha de estados e municípios foi intencional, tendo como base a implementação do Programa Criança Feliz. A amostra foi selecionada a partir de crianças elegíveis para o Programa Criança Feliz, cujo objetivo é promover a estimulação e o desenvolvimento infantil. O Ages and Stages Questionnaire (ASQ) foi utilizado para avaliação do desenvolvimento infantil. Um modelo de análise multinível em três níveis (estado, município e indivíduos), usando teste de Wald para heterogeneidade e tendência linear, estimou a média do ASQ-3 e intervalo de 95% de confiança (IC95%). Análises foram ajustadas para potenciais confundidores. Foram analisadas informações de 3.061 (94,4%) crianças com dados disponíveis para ASQ-3. Escores de desenvolvimento infantil (total e em todos os domínios) foram cerca de 12% menores em crianças nascidas pré-termo e com restrição do crescimento intrauterino (pequenas para idade gestacional). Observou-se menores escores em filhos de mães com baixa escolaridade, com sintomas de depressão, com duas ou mais crianças menores de sete anos residindo no domicílio e que não relataram autopercepção de apoio/ajuda durante a gestação. Conclui-se que características potencialmente modificáveis (escolaridade, depressão materna e prematuridade/restrição do crescimento intrauterino) apresentaram maior impacto na redução do escore de desenvolvimento em todos os domínios avaliados.