Low-Cost Non-Wearable Fall Detection System Implemented on a Single Board Computer for People in Need of Care.

This work aims at proposing an affordable, non-wearable system to detect falls of people in need of care. The proposal uses artificial vision based on deep learning techniques implemented on a Raspberry Pi4 4GB RAM with a High-Definition IR-CUT camera. The CNN architecture classifies detected people into five classes: fallen, crouching, sitting, standing, and lying down. When a fall is detected, the system sends an alert notification to mobile devices through the Telegram instant messaging platform. The system was evaluated considering real daily indoor activities under different conditions: outfit, lightning, and distance from camera. Results show a good trade-off between performance and cost of the system. Obtained performance metrics are: precision of 96.4%, specificity of 96.6%, accuracy of 94.8%, and sensitivity of 93.1%. Regarding privacy concerns, even though this system uses a camera, the video is not recorded or monitored by anyone, and pictures are only sent in case of fall detection. This work can contribute to reducing the fatal consequences of falls in people in need of care by providing them with prompt attention. Such a low-cost solution would be desirable, particularly in developing countries with limited or no medical alert systems and few resources.

Enhancing Monitoring Performance: A Microservices Approach to Monitoring with Spyware Techniques and Prediction Models.

In today's digital landscape, organizations face significant challenges, including sensitive data leaks and the proliferation of hate speech, both of which can lead to severe consequences such as financial losses, reputational damage, and psychological impacts on employees. This work considers a comprehensive solution using a microservices architecture to monitor computer usage within organizations effectively. The approach incorporates spyware techniques to capture data from employee computers and a web application for alert management. The system detects data leaks, suspicious behaviors, and hate speech through efficient data capture and predictive modeling. Therefore, this paper presents a comparative performance analysis between Spring Boot and Quarkus, focusing on objective metrics and quantitative statistics. By utilizing recognized tools and benchmarks in the computer science community, the study provides an in-depth understanding of the performance differences between these two platforms. The implementation of Quarkus over Spring Boot demonstrated substantial improvements: memory usage was reduced by up to 80% and CPU usage by 95%, and system uptime decreased by 119%. This solution offers a robust framework for enhancing organizational security and mitigating potential threats through proactive monitoring and predictive analysis while also guiding developers and software architects in making informed technological choices.

Proactive esophageal cooling during laser cardiac ablation: A computer modeling study.

BACKGROUND AND OBJECTIVES: Laser ablation is increasingly used to treat atrial fibrillation (AF). However, atrioesophageal injury remains a potentially serious complication. While proactive esophageal cooling (PEC) reduces esophageal injury during radiofrequency ablation, the effects of PEC during laser ablation have not previously been determined. We aimed to evaluate the protective effects of PEC during laser ablation of AF by means of a theoretical study based on computer modeling. METHODS: Three-dimensional mathematical models were built for 20 different cases including a fragment of atrial wall (myocardium), epicardial fat (adipose tissue), connective tissue, and esophageal wall. The esophagus was considered with and without PEC. Laser-tissue interaction was modeled using Beer-Lambert's law, Pennes' Bioheat equation was used to compute the resultant heating, and the Arrhenius equation was used to estimate the fraction of tissue damage (FOD), assuming a threshold of 63% to assess induced necrosis. We modeled laser irradiation power of 8.5 W over 20 s. Thermal simulations extended up to 250 s to account for thermal latency. RESULTS: PEC significantly altered the temperature distribution around the cooling device, resulting in lower temperatures (around 22°C less in the esophagus and 9°C in the atrial wall) compared to the case without PEC. This thermal reduction translated into the absence of transmural lesions in the esophagus. The esophagus was thermally damaged only in the cases without PEC and with a distance equal to or shorter than 3.5 mm between the esophagus and endocardium (inner boundary of the atrial wall). Furthermore, PEC demonstrated minimal impact on the lesion created across the atrial wall, either in terms of maximum temperature or FOD. CONCLUSIONS: PEC reduces the potential for esophageal injury without degrading the intended cardiac lesions for a variety of different tissue thicknesses. Thermal latency may influence lesion formation during laser ablation and may play a part in any collateral damage.

Prevalence of computer vision syndrome: A systematic review and meta-analysis.

PURPOSE: This review aimed to estimate the prevalence of computer vision syndrome (CVS) in the general population and subgroups. METHODS: A search was conducted in the following the databases: PubMed, SCOPUS, EMBASE, and Web of Science until February 13, 2023. We included studies that assessed the prevalence of CVS in any population. The Joanna Briggs Institute's critical appraisal tool was used to evaluate the methodological quality. A meta-analysis of the prevalence of CVS was done using a random-effects model, assessing the sources of heterogeneity using subgroup and meta-regression analyses. RESULTS: A total of 103 cross-sectional studies with 66 577 participants were included. The prevalence of CVS was 69.0% (95% CI: 62.3 to 75.3; I2: 99.7%), ranging from 12.1 to 97.3% across studies. Point prevalence was higher in women than in men (71.4 vs. 61.8%), university students (76.1%), Africa (71.2%), Asia (69.9%), contact lens wearers (73.1% vs. 63.8%) in studies conducted before the COVID-19 pandemic (72.8%), and in those that did not use the CVS-Q questionnaire (75.4%). In meta-regression, using the CVS-Q scale was associated with a lower prevalence of CVS. CONCLUSION: Seven out of ten people suffer from CVS. Preventive strategies and interventions are needed to decrease the prevalence of this condition which can affect productivity and quality of life. Future studies should standardize a definition of CVS.

Cross-sectional and prospective associations between screen time and childhood neurodevelopment in two Brazilian cohorts born 11 years apart.

BACKGROUND: The aim of this study was to investigate the associations between screen time from ages 2 to 4 years and child neurodevelopment at age 4. METHODS: The participants were from the 2004 (N = 3787) and 2015 (N = 3604) Pelotas (Brazil) birth cohort studies. Childhood neurodevelopment was assessed at age 4 using the Battelle Development Inventory. The time children spent on screen devices was reported by their guardians at ages 2 and 4 years. Linear regression models were used to investigate the association of: (i) time spent on television at ages 2 and 4 years; (ii) time spent on other screens at age 4; and (iii) total screen time at age 4 (television + other screens) with childhood neurodevelopment at age 4. RESULTS: Average daily screen time among children born in 2004 and those born in 2005 aged 4 years were 3.4 (SD: 2.4) and 4.4 h (SD: 2.9), respectively. Overall, few associations of very small magnitude between screen time and child neurodevelopment were observed. Television time at 2 years of age was statistically associated with lower neurodevelopment at 4 years of age in the 2015 cohort (ß = -0.30, 95%CI = -0.55; -0.05). Conversely, television time (ß = 0.17, 95%CI = 0.07, 0.26) and total screen time (ß = 0.22, 95%CI = 0.13, 0.31) at age 4 were associated with higher neurodevelopment at age 4 in the 2004 cohort. CONCLUSIONS: The findings of this study suggest that the amount of time spent on screen devices might not be associated with neurodevelopment of children under 5 years of age. The small magnitude and inconsistencies in the direction of associations did not find evidence to support the current guidelines for screen time at this age. Therefore, more studies, especially those with longitudinal data, are important to comprehend the true effect of screen time on neurodevelopment and other health outcomes.

Deciphering the structure and mechanism of action of computer-designed mastoparan peptides.

Mastoparans are cationic peptides with multifunctional pharmacological properties. Mastoparan-R1 and mastoparan-R4 were computationally designed based on native mastoparan-L from wasps and have improved therapeutic potential for the control of bacterial infections. Here, we evaluated whether these peptides maintain their activity against Escherichia coli strains under a range of salt concentrations. We found that mastoparan-R1 and mastoparan-R4 preserved their activity under the conditions tested, including having antibacterial activities at physiological salt concentrations. The overall structure of the peptides was investigated using circular dichroism spectroscopy in a range of solvents. No significant changes in secondary structure were observed (random coil in aqueous solutions and α-helix in hydrophobic and anionic environments). The three-dimensional structures of mastoparan-R1 and mastoparan-R4 were elucidated through nuclear magnetic resonance spectroscopy, revealing amphipathic α-helical segments for Leu3-Ile13 (mastoparan-R1) and Leu3-Ile14 (mastoparan-R4). Possible membrane-association mechanisms for mastoparan-R1 and mastoparan-R4 were investigated through surface plasmon resonance and leakage studies with synthetic POPC and POPC/POPG (4:1) lipid bilayers. Mastoparan-L had the highest affinity for both membrane systems, whereas the two analogs had weaker association, but improved selectivity for lysing anionic membranes. This finding was also supported by molecular dynamics simulations, in which mastoparan-R1 and mastoparan-R4 were found to have greater interactions with bacteria-like membranes compared with model mammalian membranes. Despite having a few differences in their functional and structural profiles, the mastoparan-R1 analog stood out with the highest activity, greater bacteriostatic potential, and selectivity for lysing anionic membranes. This study reinforces the potential of mastoparan-R1 as a drug candidate.

BUS-BRA: A breast ultrasound dataset for assessing computer-aided diagnosis systems.

PURPOSE: Computer-aided diagnosis (CAD) systems on breast ultrasound (BUS) aim to increase the efficiency and effectiveness of breast screening, helping specialists to detect and classify breast lesions. CAD system development requires a set of annotated images, including lesion segmentation, biopsy results to specify benign and malignant cases, and BI-RADS categories to indicate the likelihood of malignancy. Besides, standardized partitions of training, validation, and test sets promote reproducibility and fair comparisons between different approaches. Thus, we present a publicly available BUS dataset whose novelty is the substantial increment of cases with the above-mentioned annotations and the inclusion of standardized partitions to objectively assess and compare CAD systems. ACQUISITION AND VALIDATION METHODS: The BUS dataset comprises 1875 anonymized images from 1064 female patients acquired via four ultrasound scanners during systematic studies at the National Institute of Cancer (Rio de Janeiro, Brazil). The dataset includes biopsy-proven tumors divided into 722 benign and 342 malignant cases. Besides, a senior ultrasonographer performed a BI-RADS assessment in categories 2 to 5. Additionally, the ultrasonographer manually outlined the breast lesions to obtain ground truth segmentations. Furthermore, 5- and 10-fold cross-validation partitions are provided to standardize the training and test sets to evaluate and reproduce CAD systems. Finally, to validate the utility of the BUS dataset, an evaluation framework is implemented to assess the performance of deep neural networks for segmenting and classifying breast lesions. DATA FORMAT AND USAGE NOTES: The BUS dataset is publicly available for academic and research purposes through an open-access repository under the name BUS-BRA: A Breast Ultrasound Dataset for Assessing CAD Systems. BUS images and reference segmentations are saved in Portable Network Graphic (PNG) format files, and the dataset information is stored in separate Comma-Separated Value (CSV) files. POTENTIAL APPLICATIONS: The BUS-BRA dataset can be used to develop and assess artificial intelligence-based lesion detection and segmentation methods, and the classification of BUS images into pathological classes and BI-RADS categories. Other potential applications include developing image processing methods like despeckle filtering and contrast enhancement methods to improve image quality and feature engineering for image description.

Prevalence of internet addiction and psychological factors in adolescents during the covid-19 pandemic / Prevalência de dependência da internet e fatores psicológicos em adolescentes durante a pandemia de covid-19 / Prevalencia de la adicción a internet y factores psicológicos en adolescentes durante la pandemia de la covid-19

Addictive behaviors related to Internet are becoming more common and this tool has been essential once it enables home office, entertainment, homeschooling, and easy access to information. Despite the easiness brought by technology, the exaggerated use has affected users in different ways, including in the development of psychiatric disorders. This study aimed to assess internet addiction, depression, anxiety, attention-deficit/hyperactivity disorder (ADHD), attention, impulsivity, and stress in 48 adolescents (26 young women and 22 young men), aged from 15 to 18 years, with a mean age of 16.74 (0.61), mostly students of public schools, during COVID-19, to investigate correlations between these variables according to sex and sociodemographic aspects. To assess the factors, the Internet Addiction Test (IAT); the Swanson, Nolan, and Pelham Rating Scale (SNAP-IV) Questionnaire ; the Depression, Anxiety, and Stress scale for brazilian adolescents (EDAE-A); the Barratt Impulsiveness Scale (BIS-11); and a sociodemographic questionnaire were applied. The data collection was performed in schools located in southern Brazil. The results indicated that 12 out of 48 adolescents were considered addicted to the Internet. Moreover, Internet addiction was a predictor of depression in regression analysis (p<0.001). In addition, participants classified as more addicted to the Internet scored lower averages in general attention (p<0.035) and higher averages in behavioral symptoms of inattention and ADHD (p<0.050), stress (p<0.003), anxiety (p<0.016), and depression (p<0.015), with effect sizes ranging from moderate to high. Therefore, the intense internet use by adolescents might cause psychological consequences such as depression in adolescents. Family support and professional intervention might help in the reduction of symptoms and consequences of internet addiction as well as in its prevention.(AU)

A dependência de internet é cada vez mais comum, pois essa ferramenta tem se tornado imprescindível, uma vez que possibilita home office, entretenimento, educação domiciliar e fácil acesso às informações. No entanto, o uso exagerado da tecnologia afeta os usuários de diversas formas, inclusive no desenvolvimento de transtornos psiquiátricos. Este estudo visou avaliar a dependência de internet, depressão, ansiedade, hiperatividade, atenção, impulsividade e estresse em 48 adolescentes (26 meninas e 22 meninos) de 15 a 18 anos, com idade média de 16,74 (0,61), estudantes de escolas públicas do Sul do Brasil durante a covid-19, para investigar correlações entre as variáveis anteriores de acordo com gênero e aspectos sociodemográficos. Para avaliar, aplicou-se o Internet Addiction Test (IAT), um teste de atenção, escala SNAP IV, escala de depressão, ansiedade e estresse para adolescentes (EDAE-A), escala de impulsividade de Barratt e um questionário sociodemográfico. Os resultados indicaram que 12 adolescentes foram considerados viciados em internet, e que a dependência desta foi preditora da depressão na análise de regressão (p < 0,001). Ainda, os participantes classificados como adictos tiveram médias mais baixas em atenção geral (p < 0,035) e mais altas em sintomas comportamentais de desatenção e hiperatividade (p < 0,050), estresse (p < 0,003), ansiedade (p < 0,016) e depressão (p < 0,015), com efeitos que variaram de moderado a alto. Portanto, o uso intenso da internet por adolescentes pode ter consequências psicológicas, como a depressão. Bom apoio familiar e intervenção profissional podem ajudar na redução dos sintomas e consequências, bem como na prevenção da dependência.(AU)

La adicción a Internet es cada vez más habitual, puesto que esta herramienta es esencial para el trabajo remoto, el entretenimiento, la educación domiciliar y el fácil acceso a la información. Sin embargo, su uso exagerado afecta a la vida de las personas de diferentes maneras, incluso en el desarrollo de trastornos psiquiátricos. El objetivo de este estudio fue evaluar la adicción a Internet, depresión, ansiedad, hiperactividad, atención, impulsividad y estrés en 48 adolescentes (26 muchachas y 22 muchachos), de entre 15 y 18 años, con una edad promedio de 16,74 (0,61), en su mayoría estudiantes de escuelas públicas del Sur de Brasil, durante la pandemia de la COVID-19, para investigar las correlaciones entre las variables mencionadas según género y aspectos sociodemográficos. Para evaluar los factores, se aplicaron el Test de Adicción a Internet (TAI), un test de atención, la escala SNAP IV, la Escala de Depresión, Ansiedad y Estrés para adolescentes (EDAE-A), la escala de impulsividad de Barratt y un cuestionario sociodemográfico. Los resultados indicaron que 12 adolescentes fueron considerados adictos a Internet, además, la adicción a Internet fue un predictor de la depresión en el análisis de regresión (p<0,001). Igualmente, los participantes clasificados como más adictos a Internet tuvieron promedios más bajos en atención general (p<0,035), y más altos en síntomas conductuales de falta de atención e hiperactividad (p<0,050), estrés (p<0,003), ansiedad (p<0,016) y depresión (p<0,015), con efectos que varían de moderado a alto. Por lo tanto, el uso intenso podría producir consecuencias psicológicas como la depresión en los adolescentes. Tener un buen apoyo familiar e intervención profesional puede ayudar a reducir los síntomas y las consecuencias de la adicción a Internet, así como prevenirla.(AU)

Influence of notebook computers screens and undergraduate level of dental students in the radiographic detection of carious lesions

Aim: To evaluate the influence of notebook computers screens and undergraduate level of dental students in the radiographic detection of carious lesions. Methods: Bitewing digital radiographs were presented to 3rd and 5th year dental students in three different notebooks computers: Notebook 1 with anti-glare screen (1366×768 pixels), Notebook 2 without anti-glare screen (1366×768 pixels), and Notebook 3 with anti-glare screen (1920×1080 pixels). A reference standard based on a consensus analysis was set by three senior professors of Oral Radiology and Cariology. Sensitivity, specificity and accuracy values were measured and submitted to two-way ANOVA at a significance level of 5%. Results: Notebook 2 provided significantly lower sensitivity values (Mean 56.5% ± 2.94) than notebook 3 (71.1% ± 2.82) (p = 0.002). We found no statistically significant differences between the two undergraduate years (p > 0.05). Conclusion: The anti-glare screen of notebook computers screens can influence the radiographic detection of carious lesions, but the undergraduate level of dental students does not influence this diagnostic task

Computer-aided autism diagnosis using visual attention models and eye-tracking: replication and improvement proposal.

BACKGROUND: Autism Spectrum Disorder (ASD) diagnosis can be aided by approaches based on eye-tracking signals. Recently, the feasibility of building Visual Attention Models (VAMs) from features extracted from visual stimuli and their use for classifying cases and controls has been demonstrated using Neural Networks and Support Vector Machines. The present work has three aims: 1) to evaluate whether the trained classifier from the previous study was generalist enough to classify new samples with a new stimulus; 2) to replicate the previously approach to train a new classifier with a new dataset; 3) to evaluate the performance of classifiers obtained by a new classification algorithm (Random Forest) using the previous and the current datasets. METHODS: The previously approach was replicated with a new stimulus and new sample, 44 from the Typical Development group and 33 from the ASD group. After the replication, Random Forest classifier was tested to substitute Neural Networks algorithm. RESULTS: The test with the trained classifier reached an AUC of 0.56, suggesting that the trained classifier requires retraining of the VAMs when changing the stimulus. The replication results reached an AUC of 0.71, indicating the potential of generalization of the approach for aiding ASD diagnosis, as long as the stimulus is similar to the originally proposed. The results achieved with Random Forest were superior to those achieved with the original approach, with an average AUC of 0.95 for the previous dataset and 0.74 for the new dataset. CONCLUSION: In summary, the results of the replication experiment were satisfactory, which suggests the robustness of the approach and the VAM-based approaches feasibility to aid in ASD diagnosis. The proposed method change improved the classification performance. Some limitations are discussed and additional studies are encouraged to test other conditions and scenarios.

Mechanical Power of Ventilation: From Computer to Clinical Implications.

Mechanical ventilation is a lifesaving intervention that may also induce further lung injury by exerting excessive mechanical forces on susceptible lung tissue, a phenomenon termed ventilator-induced lung injury (VILI). The concept of mechanical power (MP) aims to unify in one single variable the contribution of the different ventilatory parameters that could induce VILI by measuring the energy transfer to the lung over time. Despite an increasing amount of evidence demonstrating that high MP values can be associated with VILI development in experimental studies, the evidence regarding the association of MP and clinical outcomes remains controversial. In the present review, we describe the different determinants of VILI, the concept and computation of MP, and discuss the experimental and clinical studies related to MP. Currently, due to different limitations, the clinical application of MP is debatable. Further clinical studies are required to enhance our understanding of the relationship between MP and the development of VILI, as well as its potential impact on clinical outcomes.

Visual or computer-based measurements: Which is important for the interpretation of an athlete's electrocardiography?

OBJECTIVE: Preparticipation screening of athletes by electrocardiography is the most crucial step in determining sudden cardiac death risk factors. Several electrocardiography interpretation software programs have been developed for physicians practicing in this field. Our study aimed to assess cardiopoint sudden death screening module by comparing its findings with two cardiologists using Seattle and International criteria. METHODS: A total of 303 licensed national athletes (37% females) were enrolled. electrocardiographies were examined by the cardiopoint sudden death screening module using Seattle criteria and cardiologists. The consistency between cardiologists and software was compared, and the confidence assessment of the module was tested. RESULTS: With regard to Seattle criteria, moderate consistency was found between the cardiopoint sudden death screening module and the 1st (κ=0.41) and 2nd cardiologist (κ=0.59). Consistency between two cardiologists was moderate (κ=0.55). When we applied International criteria, there was moderate consistency between the module and the 1st cardiologist (κ=0.42), and good consistency between the module and the 2nd cardiologist (κ=0.63). Consistency between the two cardiologists was good (κ=0.62). CONCLUSION: The cardiopoint sudden death screening module had similar agreement with cardiologists based on both criteria. However, the software needs to be updated according to International criteria. Using computer-based measurements for preparticipation screening will help to save time and provide standardization of electrocardiography interpretation.

Accuracy of computer-assisted dynamic navigation when performing coronectomy of the mandibular third molar: A pilot study.

OBJECTIVES: The study represents a preliminary evaluation of the accuracy of the dynamic navigation system (DNS) in coronectomy of the mandibular third molar (M3M). METHODS: The study included participants with an impacted M3M near the inferior alveolar canal. The coronectomy planes were designed before the surgery using cone-beam computed tomography (CBCT) imaging data and then loaded into the DNS program. Intraoperatively, the navigation system was used to guide the complete removal of the target crown. Postoperative CBCT imaging was used to assess any three-dimensional deviations of the actual postoperative from the planned preoperative section planes for each patient. RESULTS: A total of 12 patients (13 teeth) were included. The root mean square (RMS) deviation of the preoperatively designed plane from the actual postoperative surface was 0.69 ± 0.21 mm, with a maximum of 1.45 ± 0.83/-1.87 ± 0.63 mm deviation. The areas with distance deviations < 1 mm, 1-2 mm, and 2-3 mm were 71.97 ± 5.72 %, 22.96 ± 6.57 %, and 4.52 ± 2.28 %, respectively. Most patients showed extremely high convexity of the surface area located in the mesial region adjacent to the base of the extraction socket. There was no observable evidence of scratching of the buccolingual bone plate at the base of the extraction socket by the handpiece drill. CONCLUSIONS: These results provide preliminary support for the use of DNS-based techniques when extracting M3M using a buccal approach. This would improve the accuracy of coronectomy and reduce the potiential damage to the surrounding tissue. CLINICAL SIGNIFICANCE: DNS is effective for guiding coronectomy.

Extroversion-Related Differences in Gaze Behavior during a Computer Task for Assessing Speed-Accuracy Trade-Off: Implications for Sensor-Based Applications.

The principle of Fitts' law explains that the difficulty of movement increases when targets are farther away and narrower in width, particularly when touching two parallel targets as quickly as possible. Understanding the differences in motor and gaze behaviors between extroverts and introverts when performing tasks that require speed and accuracy is crucial for the development of sensor-based interfaces for games and rehabilitation. This study aimed to investigate such differences in a computer task that assesses the speed-accuracy trade-off (Fitts' task). Twenty introverts and seventeen extroverts wore an eye tracker and an accelerometer attached to their hand while performing 12 trials through six levels of difficulty presented on a computer screen. The results showed that introverts had longer visual fixations at the higher difficulty levels and reduced pupil diameter variability when difficulty was intermediate, suggesting that their gaze behavior may be different from that of extroverts. However, no significant differences were found in the speed and accuracy performance or kinematic variables between extroverts and introverts. These findings have important implications for the design of interventions that require both speed and accuracy in movement, such as in the development of virtual reality/games for rehabilitation purposes. It is important to consider individual differences in motor and gaze behaviors, particularly in those who may struggle with longer visual fixations, for the design of sensor-based applications and to promote successful interventions and recovery.

Formation of silver halos by Sphingomonas paucimobilis MX8 and its bioleaching of silver from computer keyboard printed circuit boards.

Silver (Ag) is currently obtained from primary and secondary sources through hydrometallurgical and pyrometallurgical processes. However, these processes consume high amounts of energy and are environmentally unfriendly. The search for bacteria tolerant with a high leaching capacity for to Ag is therefore a necessary requirement as part of the development of bioleaching technologies with reduced impact on the environment and lower energy expenditure. In this sense, the objective of this research was to evaluate the tolerance of Sphingomonas paucimobilis MX8 to Ag added to nutrient agar, and to determine whether this tolerance could favor the bioleaching of Ag present on the printed circuit boards (PCBs) of computer keyboards. The bacteria Sphingomonas paucimobilis MX8 was cultured in Petri dishes with nutrient agar and four different concentrations of AgNO3 (200, 400, 600, and 800 mg L-1) at 28 °C for 10 days. For the bioleaching experiment, the bacteria were grown in a mineral medium with computer keyboard PCBs for 30 days at room temperature (17 to 22 °C) and centrifugation at 150 rpm. The results indicate that Sphingomonas paucimobilis MX8 is tolerant to Ag and forms a silvery halo around its growth in the presence of this metal. Furthermore, Sphingomonas paucimobilis MX8 was able to bioleach 12% of the Ag found in computer keyboard PCBs. The results obtained could help generate more environmentally friendly silver bioleaching processes in which the silver bioleaching capacity of this bacterium is increased.

Experimental Evaluation of EMKEY: An Assistive Technology for People with Upper Limb Disabilities.

Assistive technology can help people with disabilities to use computers more effectively and can enable them to access the same information and resources as people without disabilities. To obtain more insight into the factors that can bring about the design of an Emulator of Mouse and Keyboard (EMKEY) to higher levels of user satisfaction, an experimental study was conducted in order to analyse its effectiveness and efficiency. The experimental study involved 27 participants (Mage = 20.81, SD = 1.14) who performed three experimental games under different conditions (using the mouse and using EMKEY with head movements and voice commands). According to the results, the use of EMKEY allowed for the successful performance of tasks such as matching stimuli (F(2,78) = 2.39, p = 0.10, η2 = 0.06). However, the execution times of a task were found to be higher when using the emulator to drag an object on the screen (t(52,1) = -18.45, p ≤ 0.001, d = 9.60). These results indicate the effectiveness of technological development for people with upper limb disabilities; however, there is room for improvement in terms of efficiency. The findings are discussed in relation to previous research and are based on future studies aimed at improving the operation of the EMKEY emulator.

Open Science Drone Toolkit: Open source hardware and software for aerial data capture.

Despite the increased access to scientific publications and data as a result of open science initiatives, access to scientific tools remains limited. Uncrewed aerial vehicles (UAVs, or drones) can be a powerful tool for research in disciplines such as agriculture and environmental sciences, but their use in research is currently dominated by proprietary, closed source tools. The objective of this work was to collect, curate, organize and test a set of open source tools for aerial data capture for research purposes. The Open Science Drone Toolkit was built through a collaborative and iterative process by more than 100 people in five countries, and comprises an open-hardware autonomous drone and off-the-shelf hardware, open-source software, and guides and protocols that enable the user to perform all the necessary tasks to obtain aerial data. Data obtained with this toolkit over a wheat field was compared to data from satellite imagery and a commercial hand-held sensor, finding a high correlation for both instruments. Our results demonstrate the possibility of capturing research-grade aerial data using affordable, accessible, and customizable open source software and hardware, and using open workflows.

Computer-Aided Ankle Ligament Injury Diagnosis from Magnetic Resonance Images Using Machine Learning Techniques.

Ankle injuries caused by the Anterior Talofibular Ligament (ATFL) are the most common type of injury. Thus, finding new ways to analyze these injuries through novel technologies is critical for assisting medical diagnosis and, as a result, reducing the subjectivity of this process. As a result, the purpose of this study is to compare the ability of specialists to diagnose lateral tibial tuberosity advancement (LTTA) injury using computer vision analysis on magnetic resonance imaging (MRI). The experiments were carried out on a database obtained from the Vue PACS-Carestream software, which contained 132 images of ATFL and normal (healthy) ankles. Because there were only a few images, image augmentation techniques was used to increase the number of images in the database. Following that, various feature extraction algorithms (GLCM, LBP, and HU invariant moments) and classifiers such as Multi-Layer Perceptron (MLP), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), and Random Forest (RF) were used. Based on the results from this analysis, for cases that lack clear morphologies, the method delivers a hit rate of 85.03% with an increase of 22% over the human expert-based analysis.

Vehicle and Driver Monitoring System Using On-Board and Remote Sensors.

This paper presents an integrated monitoring system for the driver and the vehicle in a single case of study easy to configure and replicate. On-board vehicle sensors and remote sensors are combined to model algorithms for estimating polluting emissions, fuel consumption, driving style and driver's health. The main contribution of this paper is the analysis of interactions among the above monitored features highlighting the influence of the driver in the vehicle performance and vice versa. This analysis was carried out experimentally using one vehicle with different drivers and routes and implemented on a mobile application. Compared to commercial driver and vehicle monitoring systems, this approach is not customized, uses classical sensor measurements, and is based on simple algorithms that have been already proven but not in an interactive environment with other algorithms. In the procedure design of this global vehicle and driver monitoring system, a principal component analysis was carried out to reduce the variables used in the training/testing algorithms with objective to decrease the transfer data via Bluetooth between the used devices: a biometric wristband, a smartphone and the vehicle's central computer. Experimental results show that the proposed vehicle and driver monitoring system predicts correctly the fuel consumption index in 84%, the polluting emissions 89%, and the driving style 89%. Indeed, interesting correlation results between the driver's heart condition and vehicular traffic have been found in this analysis.