Low-cost Equipment Research Articles

A comprehensive study of machine learning-based methods to predict epileptic seizures

People with epilepsy have many difficulties as a result of this complicated brain condition, which is typified by frequent convulsions. Symptoms of these seizures include bizarre behaviors, odd sensations, and in extreme cases, loss of awareness. These seizures appear as episodes of aberrant electrical impulses in the central nervous system. Successful epilepsy management depends on early seizure detection and identification, which allows for appropriate intervention to minimize risks and improve patient outcomes. Two major reasons have contributed to the extraordinary advancements in the area of epilepsy investigations in the last few years: the explosive development of machine learning techniques and the decreasing cost of non-invasive electroencephalography (EEG) apparatus. The availability of low-cost EEG equipment has made it easier to gather information on brain activity, which has opened up new avenues for monitoring and analyzing episodes of epileptic seizures away from conventional medical settings. The abundance of data and the advancement of machine learning methods have created new opportunities for the early identification and forecasting of seizures. Machine learning algorithms can predict seizures based on EEG data, providing patients with epilepsy with more control and informed decision-making. This paper offers a current review of current methods for treating epileptic seizures. The feature extraction techniques and classification algorithms receive particular focus. The most popular EEG datasets and their accessibility are listed. The approaches that are examined range from those that use more established machine learning techniques, such as naive Bayes models, Support Vector Machines (SVM), and Linear Discriminant Analysis (LDA), to those that take advantage of more recent deep learning techniques, like (Long-Short Term Memory, or LSTM), and deep Convolutional-Neural-Networks (CNN).

Measurement of the damping coefficient of an elastic rubber band oscillator by a smartphone

The use of smartphones as laboratory tools for school physics experiments has recently received attention for the possibility of carrying out a wide variety of didactic experiments with low-cost equipments. This article presents a study on a damped oscillator consisting of an elastic rubber loop and a mass. The investigation of the oscillations was conducted by using a smartphone. The experimental data was interpreted by a simple model, obtaining information on the viscoelastic properties of the rubber material.

Hypersensitive detection of CYFRA21-1 by SERS dual antibody sandwich method

PurposeAs an important reference index for the early diagnosis of non-small cell lung cancer (NSCLC), CYFRA21-1 still lacks the detection of low equipment cost, wide linear range and high sensitivity. Surface-enhanced Raman scattering (SERS) is a vibration spectrum technology based on the surface plasma of precious metal nanoparticles, which has been effectively applied to the detection of tumor markers. The combination of SERS technology and sensors has great potential in the ultrasensitive detection of tumor markers. The purpose of this study was to develop a sensitive method using SERS to quantitatively detect CYFRA21-1 for early diagnosis of NSCLC. MethodsA double-antibody sandwich immunoassay based on SERS was designed and tested to implement the ultrasensitive detection of CYFRA21-1 in the serum of NSCLC patients. ResultsGold @Ag nanorods (Au @Ag NRs) with higher Raman signals were prepared and used as probes, while magnetic graphene oxide was used as a magnetic substrate. The immunized probe, immune substrate and CYFRA21-1 standard substance in the buffer system formed a double-antibody sandwich structure. The standard curve displayed a liner range from 1pg mL−1 to 10 ng mL−1, and the detection limit (LOD) is 0.8943pg mL−1. The Raman intensity exhibited a wide linear relationship with the logarithm of CYFRA21-1 concentration. ConclusionOur study successfully established a double-antibody sandwich immunoassay based on SERS. This method demonstrated high specificity and sensitivity for detecting CYFRA21-1 protein content in serum. It has the potential to be applied for early detection of lung cancer biomarkers.

A tightly coupled GNSS RTK/IMU integration with GA-BP neural network for challenging urban navigation

Abstract Intelligent transportation system is increasing the importance of real-time acquisition of positioning, navigation, and timing information from high-accuracy global navigation satellite systems (GNSS) based on carrier phase observations. The complexity of urban environments, however, means that GNSS signals are prone to reflection, diffraction and blockage by tall buildings, causing a degraded positioning accuracy. To address this issue, we have proposed a tightly coupled single-frequency multi-system single-epoch real-time kinematic (RTK) GNSS/inertial measurement unit (IMU) integration algorithm with the assistance of genetic algorithm back propagation based on low-cost IMU equipment for challenging urban navigation. Unlike the existing methods, which only use IMU corrections predicted by machine learning as a direct replacement of filtering corrections during GNSS outages, this algorithm introduces a more accurate and efficient IMU corrections prediction model, and it is underpinned by a dual-check GNSS assessment where the weights of GNSS measurements and neural network predictions are adaptively adjusted based on duration of the integrated system GNSS failure, assisting RTK/IMU integration in GNSS outages or malfunction conditions. Field tests demonstrate that the proposed prediction model results in a 68.69% and 69.03% improvement in the root mean square error in the 2D and 3D component when the training and testing data are collected under 150 s GNSS signal-blocked conditions. This corresponds to 52.43% and 51.27% for GNSS signals discontinuously blocked with 500 s.

Ternary nano-composite wireless ammonia sensor based on Polyaniline/CuTsPc/AgNPs for food intelligent packaging

The integration of gas sensing unit and low-cost wireless communication equipment enables real-time detection of various environmental gases, which is of great significance for food safety, medical diagnosis and environmental monitoring. In this study, copper phthalocyanine powder (CuTsPc) was prepared by high temperature solid phase melting technology. Ternary ammonia sensors with exceptional sensitivity to ammonia and conductivity were successfully fabricated by growing polyaniline/copper phthalocyanine/silver nanoparticles (PANI/CuTsPc/AgNPs) composites on polyethylene terephthalate films by in-situ polymerization. The morphology and composition of the films were characterized by FTIR, XRD and SEM, respectively. The ammonia sensing performance of the films at room temperature was systematically investigated. It is found that the flexible PANI/CuTsPc/AgNPs gas sensor exhibited rapid response time (61 s), fast recovery time (19 s), low theoretical detection limit (0.234 ppm), high response value (3.6 towards 500 ppm NH3), and excellent stability at room temperature. These remarkable response characteristics can be attributed to the synergistic effect between protonic acid-doped PANI, CuTsPc derivatives with an electron-withdrawing group, and AgNPs possessing both chemical sensitisation and electron sensitisation. Finally, the sensor material was transformed into a label antenna pattern as well as sensor transducer through series and parallel antenna loop access methods to create a wireless sensor system based on near-field communication (NFC). This system can offer real-time sensing and monitoring of changes in food packaging environment by non-contact recognition way, making it highly promising for future applications in intelligent packaging.

A low-cost vision system for online reciprocal collision avoidance with UAVs

In this article, we propose a reciprocal collision avoidance system for autonomous drones, based on computer vision and using relative positioning in an indoor environment. This dynamic environment represents a demanding challenge, but it is crucial for any future existence of multiple drones operating in urban areas. We use commercial AR Drone 2.0 robots, which represent that our proposal is suitable for low-cost equipment. In our case, we attempt to achieve the collision avoidance of two drones that fly one towards the other and react online autonomously to signals received by their computer vision systems with a decentralized control strategy. We test this in four different experiments with demanding conditions. For this purpose, we get the camera signal of the onboard drones and tune their behavior to react smoothly and precisely. We report encouraging positive results and provide the code we use in the experiments for replication.

An integrated low-cost automatic pipetting used in nucleic acid detection PCR instrument and clinical detection

Purpose This paper aims to introduce a custom-designed integrated nucleic acid detection polymerase chain reaction (PCR) instrument for clinical detection applications. Design/methodology/approach The PCR instrument can make rapid, sensitive, low-cost and quantitative molecular diagnosis compared with the current routine test flow from the pipette, series reagent to RT-PCR by manual manipulation. By integrating the multichannel automatic pipetting module, heat amplification module and real-time fluorescence detection module for the first time, the custom-designed integrated nucleic acid detection PCR instrument can achieve sample collection, subpackage, mixing, extracting, measuring and result presentation. Findings The multichannel automatic pipetting module was assembled with an accuracy of 0.4% (2 microliters) for accuracy measurement. Besides, the accuracy and sensitivity of nucleic acid using integrated low-cost nucleic acid detection PCR instruments were checked with COV-2019 virus (staining method) and African swine fever virus (probe method) under different concentrations. Practical implications Because of its high cost, complex system and bulky laboratory settings, including sample subpackage, mixing, extracting, measuring and finally result in presentation, the current nucleic acid detection system is not suitable for field operation and disease diagnosis in remote areas. The group independently designed and assembled an integrated low-cost multichannel nucleic acid detection PCR instrument, including a multichannel automatic pipetting module, a heat amplification module and a real-time fluorescence detection module. Originality/value The above equipment showed better reliability compared with commercial qPCR. These results can lay the foundation for functional, fast and low-cost PCR equipment for trace measurements.

Individualized Technique Feedback for Instant Technique Improvements and Knee Abduction Moment Reductions - A New Approach for 'Sidestepping' ACL Injuries?

Sidestep cutting technique is highly individual and has been shown to influence knee joint loading. However, studies assessing whether individualized technique feedback improves technique and ACL injury-relevant knee joint loads instantly in a sport-specific task are lacking. To determine the instant effects of individualized augmented technique feedback and instructions on technique and the peak external knee abduction moment (pKAM) in a handball-specific sidestep cut. Additionally, to determine the effects of technique modifications on the resultant ground reaction force and its frontal plane moment arm to the knee joint center. Controlled laboratory cohort study. Three-dimensional biomechanics of 48 adolescent female handball players were recorded during a handball-specific sidestep cut. Following baseline cuts to each side, leg-specific visual and verbal technique feedback on foot strike angle, knee valgus motion, or vertical impact velocity using a hierarchically organized structure accounting for the variables' association with performance was provided. Subsequently, sidestep cuts were performed again while verbal instructions were provided to guide technique modifications. Combined effects of feedback and instructions on technique and pKAM as well as on the resultant ground reaction force and its frontal plane moment arm to the knee joint center were assessed. On average, each targeted technique variable improved following feedback and instructions, leading to instant reductions in pKAM of 13.4% to 17.1%. High inter-individual differences in response to feedback-instruction combinations were observed. These differences were evident in both the adherence to instructions and the impact on pKAM and its components. Most players were able to instantly adapt their technique and decrease ACL injury-relevant knee joint loads through individualized augmented technique feedback, thereby potentially reducing the risk of injury. More research is needed to assess the retention of these adaptations and move towards on-field technique assessments using low-cost equipment. Level 3.

Utilization of cross‐correlation function for assessment of replication quality in ultrasonic embossing of microchannels on polymethyl methacrylate

AbstractUltrasonic embossing—a process where a horn vibrating at 20–30 kHz compresses on a thermoplastic substrate sitting on a mold to replicate the features on the mold to the substrate—is a promising method for fabricating microchannels on thermoplastics due to reduced cycle time and relatively low equipment cost. Replication quality in ultrasonic embossing has been investigated in the literature by primarily referring to the replication depth. However, this approach does not fully reflect the fidelity between the mold and the replicated feature, especially at the side walls, which are typically designed to be vertical in microchannels. We propose using cross‐correlation—a method that quantifies the similarity between two signals by measuring the overlap of their patterns—between the derivative of the mold profile and the embossed profile as a figure of merit that accurately reflects the fidelity of the side wall. For testing purposes, we embossed straight microchannels on polymethyl methacrylate (PMMA) using micron‐scaled protrusions on a brass mold fabricated by CNC milling. The mold comprised 100, 300, and 1000‐μm‐wide protrusions of same designed height of 300 μm, resulting in aspect ratios (AR) of 3, 1, and 1/3, respectively. Cross‐correlations between the derivatives of the profiles ranged between 0.50 and 1.00, while cross‐correlations between the profiles were greater than 0.97 in all cases, showing the sensitivity imposed by utilizing the derivatives. Setting average of all cross‐correlation values (0.8) as the quality threshold, we observed that high AR (3) features were replicated at low quality, while low AR (1/3) features were replicated at high quality, regardless of the process parameters. In replicating unit AR features, the process parameters were observed to affect the quality, where the combination of vibration time and pressure was the most significant compared to individual effects of the parameters.Highlights Parameters' effect on replication of varied aspect ratio features was assessed. Assessment was carried out by cross‐correlating mold and channel profiles. Cross‐correlating derivatives of profiles provide a more sensitive means. General rules for replication quality in ultrasonic embossing were driven.

A supplementary labor cost model for estimating the fixed capital cost of commercial-scale algae photobioreactors plants

Scaling up algae photobioreactors (PBR) for commercial production presents challenges in estimating fixed capital investment costs, particularly the labor costs associated with installing low-cost equipment. Using fractions of the equipment cost to estimate the installation labor cost does not necessarily provide a representative estimate for the labor cost as the labor required is no longer proportionate to the equipment cost. In this study, we developed a supplementary labor cost model to estimate equipment installation and overhead costs for scaling up algae PBRs using a manufacturing cost estimation approach. The model consists of four main steps: identifying and sequencing all processes, estimating labor time using the Methods-Time Measurement procedure, estimating process time, and calculating the overall labor cost. As a proof-of-concept, a pilot-sized PBR has been adapted from the literature to illustrate how to implement these four steps to estimate the labor cost for the commercial size. The model provides more accurate labor cost for fixed capital cost estimation to supplement existing fixed capital cost estimation methods. The model also provides insights into the main cost drivers in the equipment installation process that can be used for bioreactor redesign and further capital cost reduction.

Tensor recovery from binary measurements fused low-rankness and smoothness

Tensor compressed sensing (TCS) from binary measurements aims to recover a tensor with low-rankness from the binary quantization of its degraded linear measurements, which inherits the advantages of low equipment cost and high sampling efficiency of coarse quantization. Recently, numerous studies have demonstrated that incorporating the properties of low-rankness and local smoothness, which are commonly observed in practical tensor data, can significantly enhance conventional tensor recovery tasks. This motivates us to utilize the low-rank and smooth properties in solving TCS problems from binary measurements. Equipped with the recently proposed tensor correlated total variation (TCTV) that simultaneously regularizes low-rankness and smoothness, we present a regularized model for this problem in this paper. We rigorously prove the recovery guarantee for the proposed model and provide an explicit selection rule for the regularization parameter. Moreover, the theoretical results demonstrate the robustness of the proposed method against both bit flips and pre-quantization noises. An algorithm based on alternating direction multiplier method (ADMM) is presented for solving the proposed model, with its global convergence been proven. Finally, a series of experiments demonstrate the superior performance of our method compared to many other competing approaches.

Influence of Air Pollution and Climate on Daily Health of Gabonese Students in the Capital (Libreville): A Pilot Study

Air pollution is a global issue affecting billions of people, especially urban populations in low- and middle-income countries. Since air pollution is a health problem that also involves the climate, this study assessed the relationship between the individual health of 50 college students living in Libreville (Gabon) and air quality, temperature, and humidity using low-cost equipment. The study utilised 81 days of data from the air pollution monitoring network in Libreville. These data were paired with measurements of heart rate, breathing, and stress measured using 5 Garmin vívosmart® smartwatches. In addition to the environmental and health data, participants were also asked about their state of health and their lifestyle during the study. We found average concentrations of PM2.5 (25.25 µg/m3) and PM10 (29.50 µg/m3) that exceed the 24-h WHO air quality standards. The daily average of PM1 was around 17.84 µg/m3. Temperatures observed during the study period varied between 27 and 35 °C (mean = 30.15 °C), and humidity was around 51-77% (mean = 59.5%). Overall, the relationships between the environmental conditions and the health observations were negligible, with correlation coefficients R ≤ 0.36. Despite their weakness, coefficients between 0.31 and 0.36 showed that stress levels are associated with temperature, PM2.5 and PM10. The average stress level was associated with PM10 (R = 0.34) and PM2.5 (R = 0.36). Finally, the study reveals that smokers, mosquito repellent users, and fan users have reduced breathing capacities compared to non-smokers, non-users of mosquito repellent products, non-users of fans, users and non-users of air conditioning. The study recommends the use of portable sensors to measure individual exposure to environmental parameters for similar studies, which will resolve the problem of spatial representativeness highlighted in this study. It is also recommended that Gabon's health policies incorporate early warnings of forecast high temperatures or pollution peaks, in order to limit health risks

Incidence of Skin Melanoma in the Precarpathian Region. Overview of Trends of the Last Decade and Demonstration of Clinical Cases

Introduction. Melanoma is a malignant tumour that develops from melanocytes and in most cases affects the skin. Although melanomas are usually highly pigmented, they can also be amelanotic. In the structure of mortality from skin cancers, melanoma takes the leading place with a rate of 90%.
 Even small tumours can metastasise. Under such conditions, early diagnosis is of paramount importance, as its main task is to detect and eliminate the primary lesion before it becomes deeply invasive.
 However, naked eye examination is limited to a sensitivity of about 75%, and is more effective when the tumour size is more than 5 mm. Among the additional examination methods, dermoscopy has become widespread. The technique has been introduced into the world dermatological practice for a long time and is actively used in Ukraine, demonstrating high efficiency at a relatively low cost of equipment.
 Materials and methods. Statistical data on 901 cases of newly diagnosed skin melanoma in the Ivano-Frankivsk region were obtained from the bulletins of the National Cancer Registry of Ukraine for the period 2012-2022.
 Results and discussion. There has been a gradual decline in the number of undetermined melanoma cases according to the TNM classification. The indicator has changed by more than 50 percentage points, with the lowest values at the turn of 2017-2018, and a subsequent increase of 10 percentage points until 2022.
 The number of reported cases of skin melanoma where the primary tumour distribution corresponds to the TI-TII category has increased, with a peak value of 92.7% as of 2017. However, in the period 2019-2022, the trend changed dramatically. Since 2019, cases of skin melanoma diagnosed in the state of spread, which corresponds to category TIII, have prevailed – 51.1%, while cases of TI-TII accounted for 32.2%
 The global COVID pandemic that began in 2019 led to negative trends, contributed to the isolation of patients, and complicated their access to specialists. russia’s full-scale invasion of Ukraine in 2022 exacerbated this impact, which was reflected in the indicators of 2019-2022.
 The clinical cases from our practice in 2018 and 2021 were presented in the article.
 Conclusions. Melanoma is a malignant tumour with a high metastatic potential, with a better prognosis for recovery if diagnosed early. The increase in the number of registered cases of skin melanoma with primary tumour distribution in category TI-TII in 2013-2017 may indicate the effectiveness of measures aimed at promoting self-examination and timely treatment of patients for specialised care. The global COVID-19 pandemic and the full-scale russian invasion of Ukraine led to an increase in the proportion of melanomas diagnosed at the stage when the prevalence of the primary tumour corresponds to category TIII. It is necessary to resume measures that have had a positive impact on the health of residents of the Precarpathian region.

A teaching-learning sequence on colour in the context of a motivational stage for high school students

In this article we present a Teaching-Learning Sequence (TLS) on colour performed in the non-formal setting of a vocational stage with a sample of N=38 18-year-old high school students. Based both on previous educational research and the analysis of the characteristics of the Italian high school curriculum, the TLS adopts cooperative experimental investigation activities with low-cost equipment which help students distinguish different physical situations involving the combination of colours; furthermore, it focuses on developing a solid understanding of the connection between the physics and physiology of colour phenomena. The TLS was highly appreciated by students and, thanks in part to the inclusion of advanced topics such as photonic crystals and plasmons, it may have a significant motivational value.

3D Printed Devices for the Separation of Blood Plasma from Capillary Samples.

Sample preparation is a critical requirement for many clinical tests and diagnostic procedures, but it is difficult to perform on a lab-on-a-chip platform. The analytical side of microfluidic technologies has been gradually catching up with laboratory methods in terms of sensitivity, selectivity, and reliability. There is a growing need for the development of sample preparation modules that can either be connected or embedded into such devices and extract blood plasma in a fast, safe, and automated way. Achieving this functionality is an important step towards creating commercially viable products that can one day become part of everyday life. In this study, a range of simple, yet effective, 3D printed sample preparation devices was developed. The devices rely on snap-fit mechanisms and "resin-bonding" methods to fasten two layers and integrate a plasma separation membrane in between. The devices have excellent usability, with only one step required for their operation without any waiting time for the user, and could extract an average of 56.88% of the total available plasma from 50 μL capillary blood samples in 87 s without inducing any haemolysis. The manufacturing process is quick and straightforward, requiring only low-cost equipment and minimal training. The devices can either be used as a stand-alone device or integrated into an existing lab-on-a-chip system to provide blood filtration capabilities.

Economic impact of thermal energy storage on natural gas power with carbon capture in future electricity markets

As policies evolve to reflect climate change goals, the use of fossil fuel power plants is expected to change. Many fossil fuel power plants may opt to incorporate carbon capture and storage (CCS) technologies in response to evolving emissions policies. Additionally, fossil plants may need to be operated flexibly to accommodate the growing concentration of renewable energy systems. Unfortunately, most CCS technologies are very expensive, and they impose a parasitic load on the power plant, thereby decreasing net power output and the ability to operate flexibly. This investigation evaluated the economic potential of using hot and cold thermal energy storage (TES) to supplement the peak power output and flexibility of a natural gas combined cycle (NGCC) power plant with CCS capabilities. The two TES subsystems (hot and cold) were charged during periods of relatively low electricity prices and discharged during NGCC operation: resistively heated hot TES to offset the CCS parasitic heat load and vapor compression cooled cold TES to chill the inlet air to the power plant. Thermodynamic models were created for the base NGCC + CCS power plant, the hot TES equipment, and the cold TES equipment, to determine key performance and cost parameters such as net power output, fuel consumption, emissions captured, capital costs, and operational costs. These parameters were then used to simulate the operation of the power plant with and without the TES technologies in accordance with fourteen electricity pricing structures predicted for different future electricity market scenarios. The difference in net present value (NPV) between the base NGCC + CCS power plant and power plant with the TES technologies was used as the primary economic metric in this analysis. The NPV benefit from increased revenue due to TES utilization was found to outweigh the NPV penalty from the additional capital costs. This economic result was attributed to the low cost of the TES equipment and the ability to charge the storages using cheap electricity from high levels of renewable output. The results have shown that hot TES increased NPV in 12 of 14 market scenarios while the cold TES increased NPV in 14 of 14 market scenarios. A combination of both hot and cold TES yielded the largest increases in NPV.

EEG alpha and theta time-frequency structure during a written mathematical task.

Since the first electroencephalogram (EEG) was obtained, there have been many possibilities to use it as a tool to access brain cognitive dynamics. Mathematical (Math) problem solving is one of the most important cortical processes, but it is still far from being well understood. EEG is an inexpensive and simple indirect measure of brain operation, but only recently has low-cost equipment (mobile EEG) allowed sophisticated analyses in non-clinical settings. The main purpose of this work is to study EEG activation during a Math task in a realistic environment, using mobile EEG. A matching pursuit (MP)-based signal analysis technique was employed, since MP properties render it a priori suitable to study induced EEG activity over long time sequences, when it is not tightly locked to a given stimulus. The study sample comprised sixty healthy volunteers. Unlike the majority of previous studies, subjects were studied in a sitting position with their eyes open. They completed a written Math task outside the EEG lab, wearing a mobile EEG device (EPOC+). Theta [4 Hz-7.5 Hz], alpha (7.5 Hz-13 Hz] and 0.5 Hz micro-bands in the [0.5 Hz-20 Hz] range were studied with a low-density stochastic MP dictionary. Over 1-min windows, ongoing EEG alpha and theta activity was decomposed into numerous MP atoms with median duration around 3s, similar to the duration of induced, time-locked activity obtained with event-related (des)synchronization (ERS/ERD) studies. Relative to Rest, there was lower right-side and posterior MP alpha atom/min during Math, whereas MP theta atom/min was significantly higher on anteriorly located electrodes, especially on the left side. MP alpha findings were particularly significant on a narrow range around 10 Hz-10.5 Hz, consistent with FFT alpha peak findings from ERS/ERD studies. With a streamlined protocol, these results replicate previous findings of EEG alpha and theta activation obtained during Math tasks with different signal analysis techniques and in different time frames. The efficient application to real-world, noisy EEG data with a low-resolution stochastic MP dictionary shows that this technique is very encouraging. These results provide support for studies of mathematical cognition with mobile EEG and matching pursuit.

Ozone therapy as a method of prevention and treatment in surgery and oncology (a literature review)

The effectiveness of ozone therapy in the treatment of cancer pathology lies in the proven process of destruction of atypical cells due to the inhibition of vicious anaerobic metabolism. Ozone selectively causes the death of cancer cells, since healthy cells have their own antioxidant defense system, while atypical cells do not. Ozone therapy for cancer pathology is an auxiliary treatment method that involves the effect of the oxygen-ozone layer on cancerous cellular structures. Numerous clinical studies confirm that ozone therapy is well tolerated and is extremely rarely accompanied by side effects and does not cause negative reactions in the human body. The advantages of ozone therapy include: safety for human health, minimal side effects, proven effectiveness, low cost of equipment and the possibility of use as cancer prevention. In addition, this treatment method is indicated for people suffering from chronic diseases and disorders that increase the risk of cancer, such as atherosclerosis, hypertension, type 2 diabetes, obesity, immunosuppression, menstrual disorders, etc. The use of ozone in medicine is very promising due to its anti-inflammatory, immunomodulatory and antitumor effects, and its hepatoprotective effect makes it possible to reduce the patient’s time in the hospital due to the rapid restoration of liver function in the postoperative period.

Low-cost experiments for teaching eletrostatics in Brazilian high school

Many public schools in Brazil do not have the financial resources to obtain certain equipment for teaching physics. Considering that the Brazilian National Common Curricular Base, a normative document that defines the organic and progressive set of essential learning that all students must develop, determines that the curriculum for teaching electromagnetism for high school encompass electrostatics. It is very important that schools offer laboratory conditions. For this reason, it was proposed to design simple and low-cost construction equipment, so that teachers from the basic education network can build them for their schools, thus improving the quality of teaching for their students. This study aims to gather equipment and experiments, such as electroscope, electrophorus, Leyden jar and Van de Graaf generator, which can be used in the classroom for didactic purposes in physics teaching. The main focus of this work is the construction of equipment for the implementation of a laboratory for the study of electrostatics, which addresses physical concepts seen by high school students of the basic network in Brazil.

Mild Cognitive Impairment detection based on EEG and HRV data

Brain volume decrease is usually connected to neurodegeneration and aging. In this environment, an important percentage of elderly persons suffer from mild cognitive impairment (MCI), a kind of dementia that can lead to Alzheimer's disease (AD). Since the symptoms of cognitive impairment are scarcely discernible, developing a safe and effective method for early MCI detection has emerged as an important challenge. According to this regard, numerous cognitive training tests can be targeted to help aging people retain a good quality of life, especially in the case of fragility disorders. A Consortium to Establish a Registry for Alzheimer's Disease (CERAD) task was initially created to detect the early stages of AD. This task specifically targets various tests related to specific cognitive domains. However, it has since developed into a popular diagnostic tool for many kinds of dementia, such as MCI. Several low-cost equipment, such as electroencephalography (EEG) and heart rate variability (HRV), may be useful for predicting MCI. On the other side, various machine learning (ML) models can be employed to extract/analyze relevant features from biomedical and physiological signals, especially in the context of anomaly detection and classification. To this regard, we developed a new method based on ML models to categorize MCI and healthy control (HC) patients during the CERAD task using EEG and HRV multimodal data. Our dataset includes 15 subjects who were randomly assigned to training and testing groups of 7 HC and 8 MCI, respectively. Our raw EEG and HRV data are analyzed to extract time, frequency, and non-linear features. A scaling step is employed to reduce the significant disparity between features. For the classification task, five ML models are evaluated, including support vector machine (SVM), k-nearest neighbors (KNN), decision tree (DT), random forest (RF), and gradient boosting (GB). To enhance accuracy, a hybrid ML model with a voting system is developed, combining the top ML models with the highest accuracy rates. A comparison step is performed between the use of ML and hybrid ML models. The experimental findings demonstrated the efficacy of our proposed technique, which included a hybrid ML model. An average accuracy of 93.86%, a sensitivity of 93.87%, and a specificity of 93.53% are achieved. The obtained results allow one to conclude that the first CERAD test plays a prominent role as a novel biomarker with an ultra-short duration for early MCI identification through the combination of EEG and HRV signals.