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The increasing challenges of agricultural processes and the growing demand for food globally are driving the industrial agriculture sector to adopt the concept of ‘smart farming’. Smart farming systems, with their real-time management and high level of automation, can greatly improve productivity, food safety, and efficiency in the agri-food supply chain. This paper presents a customized smart farming system that uses a low-cost, low-power, and wide-range wireless sensor network based on Internet of Things (IoT) and Long Range (LoRa) technologies. In this system, LoRa connectivity is integrated with existing Programmable Logic Controllers (PLCs), which are commonly used in industry and farming to control multiple processes, devices, and machinery through the Simatic IOT2040. The system also includes a newly developed web-based monitoring application hosted on a cloud server, which processes data collected from the farm environment and allows for remote visualization and control of all connected devices. A Telegram bot is included for automated communication with users through this mobile messaging app. The proposed network structure has been tested, and the path loss in the wireless LoRa is evaluated.

Acute kidney injury (AKI) is a major medical problem that is of particular concern after cardiac surgery. Perioperative AKI is independently associated with an increase in short-term morbidity, costs of treatment, and long-term mortality. In this review, we explore the definition of cardiac surgery-associated acute kidney injury (CSA-AKI) and identify diverse mechanisms and risk factors contributing to the renal insult. Current theories of the pathophysiology of CSA-AKI and description of its clinical course will be addressed in this review. Data on the most promising renal protective strategies in cardiac surgery, from well-designed studies, will be scrutinized. Furthermore, diagnostic tools such as novel biomarkers of AKI and their potential utility will be discussed.

Sensors provide data which need to be processed after acquisition to remove noise and extract relevant information. When the sensor is a network node and acquired data are to be transmitted to other nodes (e.g., through Ethernet), the amount of generated data from multiple nodes can overload the communication channel. The reduction of generated data implies the possibility of lower hardware requirements and less power consumption for the hardware devices. This work proposes a filtering algorithm (LDSI—Less Data Same Information) which reduces the generated data from event-based sensors without loss of relevant information. It is a bioinspired filter, i.e., event data are processed using a structure resembling biological neuronal information processing. The filter is fully configurable, from a “transparent mode” to a very restrictive mode. Based on an analysis of configuration parameters, three main configurations are given: weak, medium and restrictive. Using data from a DVS event camera, results for a similarity detection algorithm show that event data can be reduced up to 30% while maintaining the same similarity index when compared to unfiltered data. Data reduction can reach 85% with a penalty of 15% in similarity index compared to the original data. An object tracking algorithm was also used to compare results of the proposed filter with other existing filter. The LDSI filter provides less error (4.86 ± 1.87) when compared to the background activity filter (5.01 ± 1.93). The algorithm was tested under a PC using pre-recorded datasets, and its FPGA implementation was also carried out. A Xilinx Virtex6 FPGA received data from a 128 × 128 DVS camera, applied the LDSI algorithm, created a AER dataflow and sent the data to the PC for data analysis and visualization. The FPGA could run at 177 MHz clock speed with a low resource usage (671 LUT and 40 Block RAM for the whole system), showing real time operation capabilities and very low resource usage. The results show that, using an adequate filter parameter tuning, the relevant information from the scene is kept while fewer events are generated (i.e., fewer generated data).

Despite the high nutritional value of fish, it is often under-consumed due to its characteristic odor and laborious cleaning process. This sensory barrier significantly diminishes the appeal of fish, particularly in regions or cultures where individual exhibit heightened sensitivity to fish odor. Fish processing systems have been developed to facilitate cutting and cleaning steps in aquatic supply centers and factories. In this study, to upgrade a fish processing system to an intelligent machine, four high-consumption fish classes were classified using Artificial Intelligence (AI), and the corresponding cutting point determination algorithms were developed using a multipurpose backlighted pure blue background for each class. As the classification algorithms developed, the best results were selected based on the least total MSE value. The best ANN structure was determined as 6–23–4 with 99.62%, 96.72%, and 95.06% with corresponding MSE values of 9.51 × 10 –5 , 2.03 × 10 –2 , and 2.54 × 10 –2 in the train, validation, and test sets, respectively. This structure was recorded as the best one with the ‘Logsig’ function in both hidden and output layers with the LM learning algorithm. The total classification accuracy of the SVM classifier resulted in 99.69% and 98.75%, with the corresponding MSE values of 1.23 × 10 –2 and 1.25 × 10 –2 in train and test data sets, respectively. As soon as the fish were classified, their unique cutting point determination algorithms were applied for fish processing. Finally, the head and belly cutting points accuracy of Silver Carp, Carp, and Trout fish were resulted in 98.36% and 99.49%, 97.85% and 98.07%, and 96.61% and 97.90%, respectively.

To review the intraoperative and postoperative complications after intracorneal ring segment implantation and to report the explantation rate among the available scientific literature. Three different databases (PubMed, Web of Science, and Scopus) were assessed from January 1995 to June 2019. The keywords used were: ring, rings, ICRS (intracorneal ring segments), segment, segments or Intacs, complication, explantation, explanted, retired, and removal. The selection process of this systematic review study is described in a flow diagram. A total of 39 studies published between 1995 and 2019 were included. Sixteen studies were case reports, 21 were case series studies, and 2 were chart analysis works. This study enrolled 1,946 participants, and 2,590 eyes were included. The postoperative complications described in most studies included migration, ring extrusion, corneal thinning, corneal melting, and some type of infective keratitis. These complications together with glare, halos, fluctuating vision, neovascularization, foreign body sensation, or pain represented most of the causes. The percentage rate of explantation ranged from 0.5% up to 83.3%. If we analyze those articles with a high number of implantations (2,124 eyes), an explantation rate between 0% and 1.4% was obtained. The complication rate and explantation ratio in segments of the intracorneal ring segments analyzed in the available scientific literature are minimal. Therefore, patient selection, surgery planning, and postoperative follow-up are critical to the success of surgery. [J Refract Surg. 2019;35(11):740-747.].

Today, industrial automation is being applied in a wide range of fields. The initial modeling of robots and mechanical systems together with simulation results in optimal systems. In this study, the designed system is simulated to obtain the required velocities, accelerations and torques of the actuating arms in a vision-based automatic system. Due to the slippery skin of fish and the low friction coefficient, it is not easy to design an optimal tool to handle fish. Since the fish-processing operation is undertaken step by step and provides fish stability, it is essential that the gripper enables different processing operations along the system. The proposed system performs belly-cutting, beheading, gutting, and cleaning stages for different fish sizes, based on the extracted dimensions of the vision system. In the head-cutting section, the average speed of the actuator jack was considered as 500 mm s−1. Under these conditions, the maximum required force to provide this speed was 332.45 N. In the belly-cutting subsystem, the required torque for the stepper motor resulted in 1.79–2.15 N m. Finally, the maximum required torque for the gutting stepper motor was calculated as 0.69 N m in the tested processing capacities.

Spiking neural networks (SNN) have gained popularity in embedded applications such as robotics and computer vision. The main advantages of SNN are the temporal plasticity, ease of use in neural interface circuits and reduced computation complexity. SNN have been successfully used for image classification. They provide a model for the mammalian visual cortex, image segmentation and pattern recognition. Different spiking neuron mathematical models exist, but their computational complexity makes them ill-suited for hardware implementation. In this paper, a novel, simplified and computationally efficient model of spike response model (SRM) neuron with spike-time dependent plasticity (STDP) learning is presented. Frequency spike coding based on receptive fields is used for data representation; images are encoded by the network and processed in a similar manner as the primary layers in visual cortex. The network output can be used as a primary feature extractor for further refined recognition or as a simple object classifier. Results show that the model can successfully learn and classify black and white images with added noise or partially obscured samples with up to ×20 computing speed-up at an equivalent classification ratio when compared to classic SRM neuron membrane models. The proposed solution combines spike encoding, network topology, neuron membrane model and STDP learning.

IntroductionThe COVID-19 pandemic, driven by the SARS-CoV-2 virus, has had a significant global impact, with over 775 million cases reported and more than 7 million deaths as of July 2024. In Chile, approximately 5.4 million people have been infected, with a substantial proportion experiencing persistent symptoms known as post-COVID-19 syndrome. This study aims to estimate the prevalence of post-COVID-19 syndrome in Punta Arenas, Chile, and to explore the associated symptoms, mainly focusing on psychological, physical and molecular impacts on the affected population.Methods and analysisThis cross-sectional study will use stratified random sampling to select a representative sample of 282 adults from Punta Arenas. Participants eligible for the study are those who had tested positive for SARS-CoV-2 by reverse transcription-quantitative PCR between July 2022 and July 2023. Data collection will include comprehensive clinical assessments, psychological evaluations and laboratory analyses of inflammatory biomarkers. Standardised instruments will be used to ensure consistency and reliability in measuring persistent symptoms. Statistical analyses will include descriptive statistics, regression models and subgroup analyses to identify risk factors and the prevalence of post-COVID-19 syndrome.Ethics and disseminationThe Human Research Ethics Committee of the Clinical Hospital of the University of Chile approved the study protocol (Memorandum No 007/2023). We will present the results in peer-reviewed publications and national and international professional and academic meetings.Trial registration numberNCT05855382.

Automated quality control is critical in modern manufacturing, especially for metallic cast components, where fast and accurate surface defect detection is required. This study evaluates classical Machine Learning (ML) algorithms using extracted statistical parameters and deep learning (DL) architectures including ResNet50, Capsule Networks, and a 3D Convolutional Neural Network (CNN3D) using 3D image inputs. Using the Dataset Original, ML models with the selected parameters achieved high performance: RF reached 99.4 ± 0.2% precision and 99.4 ± 0.2% sensitivity, GB 96.0 ± 0.2% precision and 96.0 ± 0.2% sensitivity. ResNet50 trained with extracted parameters reached 98.0 ± 1.5% accuracy and 98.2 ± 1.7% F1-score. Capsule-based architectures achieved the best results, with ConvCapsuleLayer reaching 98.7 ± 0.2% accuracy and 100.0 ± 0.0% precision for the normal class, and 98.9 ± 0.2% F1-score for the affected class. CNN3D applied on 3D image inputs reached 88.61 ± 1.01% accuracy and 90.14 ± 0.95% F1-score. Using the Dataset Expanded with ML and PCA-selected features, Random Forest achieved 99.4 ± 0.2% precision and 99.4 ± 0.2% sensitivity, K-Nearest Neighbors 99.2 ± 0.0% precision and 99.2 ± 0.0% sensitivity, and SVM 99.2 ± 0.0% precision and 99.2 ± 0.0% sensitivity, demonstrating consistent high performance. All models were evaluated using repeated train-test splits to calculate averages of standard metrics (accuracy, precision, recall, F1-score), and processing times were measured, showing very low per-image execution times (as low as 3.69×10−4 s/image), supporting potential real-time industrial application. These results indicate that combining statistical descriptors with ML and DL architectures provides a robust and scalable solution for automated, non-destructive surface defect detection, with high accuracy and reliability across both the original and expanded datasets.