Human and Small Animal Detection Using Multiple Millimeter-Wave Radars and Data Fusion: Enabling Safe Applications.

Millimeter-wave (mmWave) radars attain high resolution without compromising privacy while being unaffected by environmental factors such as rain, dust, and fog. This study explores the challenges of using mmWave radars for the simultaneous detection of people and small animals, a critical concern in applications like indoor wireless energy transfer systems. This work proposes innovative methodologies for enhancing detection accuracy and overcoming the inherent difficulties posed by differences in target size and volume. In particular, we explore two distinct positioning scenarios that involve up to four mmWave radars in an indoor environment to detect and track both humans and small animals. We compare the outcomes achieved through the implementation of three distinct data-fusion methods. It was shown that using a single radar without the application of a tracking algorithm resulted in a sensitivity of 46.1%. However, this sensitivity significantly increased to 97.10% upon utilizing four radars using with the optimal fusion method and tracking. This improvement highlights the effectiveness of employing multiple radars together with data fusion techniques, significantly enhancing sensitivity and reliability in target detection.

Feasibility trial of the dialectical behavior therapy skills training group as add-on treatment for adults with attention-deficit/hyperactivity disorder.

OBJECTIVE: Our aim was to explore the feasibility, and efficacy of a Dialectical Behavior Therapy Skill Training Group (DBT-ST) as an add-on treatment for adult attention-deficit/hyperactivity disorder (ADHD) in Latin America. METHOD: Adults with ADHD (n = 31) with stable medication treatment for ADHD and residual symptoms (ASRS > 20) were randomly assigned to DBT-ST (n = 16) or treatment as usual (TaU; n = 15) for 12 weeks. Feasibility was accessed by attendance and completion rates at 12 weeks. Efficacy outcomes were measured with the ASRS, and performed at 0, 6, 12, and 16 weeks. RESULTS: The DBT-ST protocol had 81.25% completion rate, with a mean attendance of 87.25% of the sessions. No significant interactions between group and time were detected for outcome measures. DISCUSSION: The DBT-ST was feasible as add-on treatment for adult patients with ADHD in Latin America. Replicating previous findings, DBT-ST has shown no significantly higher improvement in ADHD symptoms in comparison with TaU. Registered at the Clinical Trials database (NCT03326427).

Computerized Cognitive Training for ADHD as an Add-On Treatment to Stimulants: A Randomized Clinical Trial.

Objective: Computerized cognitive training (CCT) as add-on treatment to stimulants for ADHD core symptoms is scarcely investigated. The purpose of this study is to assess the effect of CCT in a randomized controlled clinical trial for ADHD in children and adolescents treated with stimulants. Method: Fifty-three participants aged 6 to 13 years receiving stimulant treatment and presenting ADHD residual symptoms were randomized either to a CCT (n = 29) or to a controlled nonactive condition (n = 24) for four sessions/week during 12 weeks. The main outcome measure was inattentive symptoms assessed using the Swanson, Nolan, and Pelham-IV (SNAP-IV) Scale. Secondary outcomes include, among others, hyperactive/impulsive symptoms and cognitive tests. Results: There were neither significant group differences on ADHD-inattentive symptoms after the intervention nor on both ADHD-hyperactivity/impulsivity symptoms and cognitive measures. Conclusion: Our study does not provide evidence for the benefits of cognitive training over nonactive training on core ADHD symptoms in medicated ADHD children and adolescents.

Towards Improving TSCH Energy Efficiency: An Analytical Approach to a Practical Implementation.

The IEEE 802.15.4-2015 standard defines a number of Medium Access Control (MAC) layer protocols for low power wireless communications, which are desirable for energy-constrained Internet of Things (IoT) devices. Originally defined in the IEEE 802.15.4e amendment, the Time Slotted Channel Hopping (TSCH) has recently been attracting attention from the research community due to its reduced contention (time scheduling) and robustness against fading (channel hopping). However, it requires a certain level of synchronization between the nodes, which can increase the energy consumption. In this work, we implement the Guard Beacon (GB) strategy, aiming at reducing the guard time usually implemented to compensate for imperfect synchronization. Moreover, besides presenting a realistic energy consumption model for a Contiki Operating System-based TSCH network, we show through analytical and practical results that, without the proposed scheme, the power consumption can be more than 13% higher.

A Monitoring System for Online Fault Detection and Classification in Photovoltaic Plants.

Photovoltaic (PV) energy use has been increasing recently, mainly due to new policies all over the world to reduce the application of fossil fuels. PV system efficiency is highly dependent on environmental variables, besides being affected by several kinds of faults, which can lead to a severe energy loss throughout the operation of the system. In this sense, we present a Monitoring System (MS) to measure the electrical and environmental variables to produce instantaneous and historical data, allowing to estimate parameters that ar related to the plant efficiency. Additionally, using the same MS, we propose a recursive linear model to detect faults in the system, while using irradiance and temperature on the PV panel as input signals and power as output. The accuracy of the fault detection for a 5 kW power plant used in the test is 93.09%, considering 16 days and around 143 hours of faults in different conditions. Once a fault is detected by this model, a machine-learning-based method classifies each fault in the following cases: short-circuit, open-circuit, partial shadowing, and degradation. Using the same days and faults applied in the detection module, the accuracy of the classification stage is 95.44% for an Artificial Neural Network (ANN) model. By combining detection and classification, the overall accuracy is 92.64%. Such a result represents an original contribution of this work, since other related works do not present the integration of a fault detection and classification approach with an embedded PV plant monitoring system, allowing for the online identification and classification of different PV faults, besides real-time and historical monitoring of electrical and environmental parameters of the plant.

Effects of computerized cognitive training as add-on treatment to stimulants in ADHD: a pilot fMRI study.

The neurofunctional effects of Cognitive training (CT) are poorly understood. Our main objective was to assess fMRI brain activation patterns in children with ADHD who received CT as an add-on treatment to stimulant medication. We included twenty children with ADHD from a clinical trial of stimulant medication and CT (10 in medication + CT and 10 in medication + non-active training). Between-group differences were assessed in performance and in brain activation during 3 fMRI paradigms of working memory (N-back: 0-back, 1-back, 2-back, 3-back), sustained attention (Sustained Attention Task - SAT: 2 s, 5 s and 8 s delays) and inhibitory control (Go/No-Go). We found significant group x time x condition interactions in working memory (WM) and sustained attention on brain activation. In N-back, decreases were observed in the BOLD signal change from baseline to endpoint with increasing WM load in the right insula, right putamen, left thalamus and left pallidum in the CT compared to the non-active group; in SAT - increases in the BOLD signal change from baseline to endpoint with increasing delays were observed in bilateral precuneus, right insula, bilateral associative visual cortex and angular gyrus, right middle temporal, precentral, postcentral, superior frontal and middle frontal gyri in the CT compared to the non-active group. CT in ADHD was associated with changes in activation in task-relevant parietal and striato-limbic regions of sustained attention and working memory. Changes in brain activity may precede behavioral performance modifications in working memory and sustained attention, but not in inhibitory control.

ICENET: An Information Centric Protocol for Big Data Wireless Sensor Networks.

Wireless Sensors Networks (WSNs) are an essential element of the Internet of Things (IoT), and are the main producers of big data. Collecting a huge amount of data produced by a resource-constrained network is a very difficult task, presenting several challenges. Big data gathering involves not only periodic data sensing, but also the forwarding of queries and commands to the network. Conventional network protocols present unfeasible strategies for large-scale networks and may not be directly applicable to IoT environments. Information-Centric Networking is a revolutionary paradigm that can overcome such big data gathering challenges. In this work, we propose a soft-state information-centric protocol, ICENET (Information Centric protocol for sEnsor NETworks), for big data gathering in large-scale WSNs. ICENET can efficiently propagate user queries in a wireless network by using a soft-state recovery mechanism for lossy links. The scalability of our solution is evaluated in different network scenarios. Results show that the proposed protocol presents approximately 84% less overhead and a higher data delivery rate than the CoAP (Constrained Application Protocol), which is a popular protocol for IoT environments.

Computerized cognitive training in children and adolescents with attention deficit/hyperactivity disorder as add-on treatment to stimulants: feasibility study and protocol description.

Background: Cognitive training has received increasing attention as a non-pharmacological approach for the treatment of attention deficit/hyperactivity disorder (ADHD) in children and adolescents. Few studies have assessed cognitive training as add-on treatment to medication in randomized placebo controlled trials. The purpose of this preliminary study was to explore the feasibility of implementing a computerized cognitive training program for ADHD in our environment, describe its main characteristics and potential efficacy in a small pilot study. Methods: Six ADHD patients aged 10-12-years old receiving stimulants and presenting residual symptoms were enrolled in a randomized clinical trial to either a standard cognitive training program or a controlled placebo condition for 12 weeks. The primary outcome was core ADHD symptoms measured using the Swanson, Nolan and Pelham Questionnaire (SNAP-IV scale). Results: We faced higher resistance than expected to patient enrollment due to logistic issues to attend face-to-face sessions in the hospital and to fill the requirement of medication status and absence of some comorbidities. Both groups showed decrease in parent reported ADHD symptoms without statistical difference between them. In addition, improvements on neuropsychological tests were observed in both groups - mainly on trained tasks. Conclusions: This protocol revealed the need for new strategies to better assess the effectiveness of cognitive training such as the need to implement the intervention in a school environment to have an assessment with more external validity. Given the small sample size of this pilot study, definitive conclusions on the effects of cognitive training as add-on treatment to stimulants would be premature.

Computerized cognitive training in children and adolescents with attention deficit/hyperactivity disorder as add-on treatment to stimulants: feasibility study and protocol description / Treinamento cognitivo para crianças e adolescentes com transtorno de déficit de atenção/hiperatividade como tratamento complementar aos psicoestimulantes: estudo de viabilidade e descrição de protocolo

Abstract Background Cognitive training has received increasing attention as a non-pharmacological approach for the treatment of attention deficit/hyperactivity disorder (ADHD) in children and adolescents. Few studies have assessed cognitive training as add-on treatment to medication in randomized placebo controlled trials. The purpose of this preliminary study was to explore the feasibility of implementing a computerized cognitive training program for ADHD in our environment, describe its main characteristics and potential efficacy in a small pilot study. Methods Six ADHD patients aged 10-12-years old receiving stimulants and presenting residual symptoms were enrolled in a randomized clinical trial to either a standard cognitive training program or a controlled placebo condition for 12 weeks. The primary outcome was core ADHD symptoms measured using the Swanson, Nolan and Pelham Questionnaire (SNAP-IV scale). Results We faced higher resistance than expected to patient enrollment due to logistic issues to attend face-to-face sessions in the hospital and to fill the requirement of medication status and absence of some comorbidities. Both groups showed decrease in parent reported ADHD symptoms without statistical difference between them. In addition, improvements on neuropsychological tests were observed in both groups - mainly on trained tasks. Conclusions This protocol revealed the need for new strategies to better assess the effectiveness of cognitive training such as the need to implement the intervention in a school environment to have an assessment with more external validity. Given the small sample size of this pilot study, definitive conclusions on the effects of cognitive training as add-on treatment to stimulants would be premature.

Resumo Introdução O treinamento cognitivo tem recebido atenção especial como abordagem não medicamentosa para o tratamento do transtorno de déficit de atenção/hiperatividade (TDAH) em crianças e adolescentes. Poucos estudos avaliaram o treinamento cognitivo como abordagem complementar à medicação em ensaios clínicos randomizados controlados por placebo. O objetivo deste estudo foi explorar a viabilidade para a implementação de um programa de treinamento cognitivo computadorizado, descrever suas características principais e potencial eficácia em um pequeno estudo piloto. Métodos Seis pacientes com TDAH entre 10-12 anos de idade, em uso de psicoestimulantes e apresentando sintomas residuais, foram recrutados e randomizados para um dos dois grupos (treinamento cognitivo ou placebo) por 12 semanas. O desfecho principal foram os sintomas nucleares do TDAH avaliados através do Questionário de Swanson, Nolan e Pelham (SNAP-IV). Resultados Encontramos maior resistência do que a esperada no recrutamento dos pacientes em função de problemas logísticos para atender às sessões presenciais no hospital assim como para preencherem os critérios de status medicamentoso e ausência de algumas comorbidades. Ambos os grupos apresentaram diminuição nos escores dos sintomas de TDAH reportados pelos pais, mas sem diferença estatística entre eles. Além disso, foi observada melhora nos testes neuropsicológicos em ambos os grupos - principalmente nas tarefas treinadas pelo programa. Conclusão Este protocolo revelou a necessidade de novas estratégias para melhor avaliar a eficácia do treinamento cognitivo tal como a necessidade de implementar a intervenção no ambiente escolar a fim de obter uma avaliação com maior validade externa. Devido ao pequeno tamanho amostral deste estudo, conclusões definitivas sobre os efeitos do treinamento cognitivo como abordagem complementar aos psicoestimulantes seriam prematuras.

A distributed approach for a multiple sequence alignment algorithm using a parallel virtual machine.

Multiple sequence alignment is a central topic of extensive research in computational biology. Basically, two or more protein sequences are compared so as to evaluate their similarity. This work reports a methodology for parallel processing of a multiple sequence alignment algorithm (ClustalW) in an environment of networked computers. A detailed description of the modules that compose the distributed system is provided, giving special attention to the way a dynamic programming algorithm can be executed in parallel. Extensive experiments were done to evaluate performance and scalability of the method. Results show that the proposed method is efficient and offers a real advantage for large-scale multiple protein sequence alignment.