Abstract
Insect pests are the main cause of loss of productivity and quality in crops worldwide. Insect monitoring becomes necessary for the early detection of pests and thus avoiding the excessive use of pesticides. Automatic detection of insects attracted by traps is a form of monitoring. Modern data-driven methods present great results for object detection when representative datasets are available, but public datasets for insect detection are few and small. Pest24 public dataset is extensive, but noisy resulting in a poor detection rate. In this work, we aim to improve insect detection in the Pest24 dataset. We propose the creation of three sub-datasets selecting the highest represented classes, the highest colour discrepancy, and the one with the highest relative scale, respectively. Several Faster R-CNN and YOLOv5 architectures are explored, and the best results are achieved with the YOLOv5 with an mAP of 95.5%.

Abstract

Abstract
This article presents a distributed resilient demand response program integrated with electrical energy storage systems for residential consumers to maximize their comfort level. A dynamic real-time pricing method is proposed to determine the hourly electricity prices and schedule the electricity consumption of smart home appliances and energy storage systems commitment. The algorithm is employed in normal and emergency operating conditions, taking into account the comfort level of consumers. In emergency conditions, the power outage of consumers is modeled for different hours and outage patterns. To evaluate the applicability of the proposed model, real samples of Southern California households are considered to model the smart homes and their appliances. Further, a sensitivity analysis is performed to assess the impacts of the number of households and number of persons per household on the output results. The results showed that the proposed model reduced the costs of utility in normal and emergency conditions by about 33.77% and 30.92%, respectively. The values of total payments of consumers in normal and emergency conditions were decreased by about 34.26% and 31.31%, respectively. Further, the consumers comfort level for normal and emergency conditions increased by about 146.78% and 110.2%, respectively. Finally, the social welfare for normal and emergency conditions increased by about 46% and 49.06%, respectively.

Abstract
The George B. Moody PhysioNet Challenge 2022 explored the detection of abnormal heart function from phonocardiogram (PCG) recordings. Although ultrasound imaging is becoming more common for investigating heart defects, the PCG still has the potential to assist with rapid and low-cost screening, and the automated annotation of PCG recordings has the potential to further improve access. Therefore, for this Challenge, we asked participants to design working, open-source algorithms that use PCG recordings to identify heart murmurs and clinical outcomes. This Challenge makes several innovations. First, we sourced 5272 PCG recordings from 1568 patients in Brazil, providing high-quality data for an underrepresented population. Second, we required the Challenge teams to submit working code for training and running their models, improving the reproducibility and reusability of the algorithms. Third, we devised a cost-based evaluation metric that reflects the costs of screening, treatment, and diagnostic errors, facilitating the development of more clinically relevant algorithms. A total of 87 teams submitted 779 algorithms during the Challenge. These algorithms represent a diversity of approaches from both academia and industry for detecting abnormal cardiac function from PCG recordings.

Abstract
On rugged slopes overlooking the Douro River we find the Alto Douro Wine Region in Portugal, populated by plantations in schist lands of difficult access and mostly manual work. The combined features of this region are a source of motivation to explore remote sensing techniques associated with artificial intelligence. In this paper, a preliminary approach for terrace vineyards detection is presented. This is a key-enabling task towards the achievement of important goals such as multi-temporal crop evaluation and cultures characterization. The proposed methodology consists in the application of a deep learning model (U-net) to detect the terrace vineyards using satellite images dataset acquired with Google Earth Pro. The proposed methodology showed very promising detection capabilities.

Abstract
This paper aims at contributing to personalize anesthetic drug administration during surgery. This study devel-ops an online robust model to predict the maintenance dose of atracurium necessary for the resulting effect, i.e. neuromuscular blockade, to attain a target profile. The model is based on the patient's neuromuscular blockade (NMB) response to the initial bolus only, overcoming the need for information on the patient's weight, age, height and Lean Body Mass usually associated to pharmacokinetic and pharmacodynamic models. To achieve this, a statistical analysis of the response of the patient to the initial bolus is carried out and a set of variables is established as predictors of the maintenance dose. The prediction is accomplished using Classification and Regression Trees, CART, which is a supervised learning method. Simulated data from a stochastic model for the NMB induced by atracurium is used as training set. All the 5000 doses predicted by the model lead to NMB level between 5% and 10%, which supports the proposed predictive model since it is clinically required that the steady state NMB level lies between this two values. The methodology is applied both to simulated and to clinical data sets and is found appropriate for online dose prediction.