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Abstract The use of biomass as a renewable energy source for electricity generation has gained attention due to its sustainability and environmental benefits. However, the intermittent electricity demand poses challenges for optimizing electricity generation in thermal systems. Time series forecasting techniques are crucial in addressing these challenges by providing accurate predictions of biomass availability and electricity generation. Here, wavelet transform is applied for denoising, convolutional neural networks (CNN) are used to extract features of the time series, and long short‐term memory (LSTM) is applied to perform the predictions. The result of the mean absolute percentage error equal to 0.0148 shows that the wavelet CNN‐LSTM is a promising machine‐learning methodology for electricity generation forecasting. Additionally, this paper discusses the importance of model evaluation techniques and validation strategies to assess the performance of forecasting models in real‐world applications. The major contribution of this paper is related to improving forecasting using a hybrid method that outperforms other models based on deep learning. Finally, future research directions and potential advancements in time series forecasting for biomass thermal systems are outlined to foster continued innovation in sustainable energy generation.

Agriculture stands as one of the major economic pillars worldwide, with food production contributing significantly to income growth. However, agricultural activities also entail risks associated with uncontrollable factors along the supply chain. To address these challenges, mathematical models have been developed for forecasting crucial variables in managing agribusiness activities. In this context, this article employs a combination of systematic bibliometric analysis and the Latent Dirichlet Allocation (LDA) method, a semi-automated approach. The main objective of this study was to automate the identification of relevant topics and construct a bibliographic portfolio (BP) covering the period 2015–2022, focusing on methodologies used in articles and other bibliometric analyses. The 30 articles included in the BP address issues related to methodologies applied in the temporal analysis of agricultural commodities. These articles were categorized based on the nature of the prediction models used, classified as (i) machine learning (ML), (ii) machine learning and artificial neural networks (ML-NN), (iii) machine learning and ensemble (ML-Ensemble), (iv) machine learning and hybrid (ML-hybrid), and (v) statistical. Regarding the results, the topic that stood out the most was termed ''Forecasting Methods Applied to Agribusiness Time Series.'' The most utilized classes were ML-hybrid (41.95 %) and statistical (29.31 %), followed by ML-NN (14.94 %), ML (9.20 %), and ML-Ensemble (4.60 %) types. The theoretical contribution of this study lies in identifying literary gaps concerning forecasting methods applied to agribusiness, while its practical implication is to identify forecasting methodologies to support decision-making.

Abstract Enhancements in the structural and operational aspects of transportation are important for achieving high-quality mobility. Toll plazas are commonly known as a potential bottleneck stretch, as they tend to interfere with the normality of the flow due to the charging points. Focusing on the automation of toll plazas, this research presents the development of an axle counter to compose a free-flow toll collection system. The axle counter is responsible for the interpretation of images through algorithms based on computer vision to determine the number of axles of vehicles crossing in front of a camera. The You Only Look Once (YOLO) model was employed in the first step to identify vehicle wheels. Considering that several versions of this model are available, to select the best model, YOLOv5, YOLOv6, YOLOv7, and YOLOv8 were compared. The YOLOv5m achieved the best result with precision and recall of 99.40% and 98.20%, respectively. A passage manager was developed thereafter to verify when a vehicle passes in front of the camera and store the corresponding frames. These frames are then used by the image reconstruction module which creates an image of the complete vehicle containing all axles. From the sequence of frames, the proposed method is able to identify when a vehicle was passing through the scene, count the number of axles, and automatically generate the appropriate charge to be applied to the vehicle.

This study introduces a pioneering approach for treating balance disorders, integrating advanced computer vision and engaging gamification techniques within a multi-agent system framework. Balancing disorders significantly impair quality of life, manifesting through symptoms like dizziness and muscle weakness. Addressing this, we developed a platform that employs depth imaging and real-time pose estimation to accurately capture and analyze patient movements, enabling personalized and adaptive rehabilitation exercises. The multi-agent system architecture ensures seamless communication and coordination, facilitating the delivery of tailored exercises that cater to the evolving needs of patients, either in person or remotely. Key to our approach is the use of gamification, which transforms rehabilitation exercises into interactive virtual activities, significantly enhancing patient engagement and motivation. Preliminary results demonstrate the platform’s efficacy in improving balance, highlighting its potential as a versatile tool for balance disorder treatment. This research advances the technological frontier in balance disorder rehabilitation and offers a blueprint for future telehealth and patient-centric care innovations.

Electricity load forecasting is important to planning the decision-making regarding the use of energy resources, in which the power system must be operated to guarantee the supply of electricity in the future at the lowest possible price. With the rise of the ability of forecasting based on deep learning, these approaches are promising in this context. Considering the attention mechanism promising to capture long-range dependencies, it is highly recommended for sequential data processing, where time series-related tasks stand out. Considering a sequence-to-sequence (Seq2Seq) time series data of the electricity load in Brazil, this paper proposes the use of the long short-term memory (LSTM) with the attention mechanism to perform the time series forecasting. The proposed Seq2Seq-LSTM with attention mechanism outperforms other well-established models. Having a mean absolute error equal to 0.3027 the proposed method is shown to be promising for field applications. The proposed method contributes to time series forecasting by implementing an attention mechanism to Seq2Seq data, therefore, more than one correlated signal can be used to perform the prediction enhancing its capacity when more data is available.

This paper analyzes time series forecasting methods applied to thermal systems in Brazil, specifically focusing on diesel consumption as a key determinant. Recognizing the critical role of thermal systems in ensuring energy stability, especially during low rain seasons, this study employs bagged, boosted, and stacked ensemble learning methods for time series forecasting focusing on exploring consumption patterns and trends. By leveraging historical data, the research aims to predict future diesel consumption within Brazil’s thermal energy sector. Based on the bagged ensemble learning approach a mean absolute percentage error of 0.089% and a coefficient of determination of 0.9752 were achieved (average considering 50 experiments), showing it to be a promising model for the short-time forecasting of thermal dispatch for the electric power generation system. The bagged model results were better than for boosted and stacked ensemble learning methods, long short-term memory networks, and adaptive neuro-fuzzy inference systems. Since the thermal dispatch in Brazil is closely related to energy prices, the predictions presented here are an interesting way of planning and decision-making for energy power systems.

Introduction: The term syndrome has been used interchangeably with disease, if it corresponds to a single nosological entity, in diagnostic discussions, and in some texts, in bronchial conditions such as acute and chronic bronchitis, bronchiectasis and especially bronchial asthma. Objective: To clarify the meaning of bronchial syndrome and its variants according to the medical diagnosis. Method: Qualitative research by means of a review of Semiology books and other texts related to the bronchial syndrome and the main conditions in which it manifests itself. Results: There is terminological ambiguity regarding bronchial syndrome and its variants, and some books do not even include them among the respiratory syndromes. In some texts, bronchial disorders are defined as syndromes while in other texts they are defined as nosological entities: acute and chronic bronchitis, bronchial asthma, and bronchiectasis. The use of bronchial syndrome is proposed, which according to the prevailing etiopathogenesis, will exhibit greater diagnostic specificity in two major sub-syndromes: the bronchitic and the obstructive bronchial. Conclusions: Bronchial syndrome expresses an affection of the bronchial tree with two major sub-syndromes of greater diagnostic specificity: the bronchitic (acute or chronic) and the obstructive bronchial (diffuse - bronchospastic or bronchostenotic - or localized). Cough and expectoration predominate in the former and dyspnea in the latter, both with hoarse, wheezing and with subcrepitant rales. To consider bronchial asthma as a syndrome or disease is a matter of focus. Defining bronchiectasis as a syndrome requires the presence of the imaging sign: bronchial dilatation. The upper airway obstructive syndrome is a syndromic entity with its own characteristics.

BackgroundRecurrent genetic alterations contributing to leukemogenesis have been identified in pediatric B-cell Acute Lymphoblastic Leukemia (B-ALL), and some are useful for refining classification, prognosis, and treatment selection. IKZF1plus is a complex biomarker associated with a poor prognosis. It is characterized by IKZF1 deletion coexisting with PAX5, CDKN2A/2B, or PAR1 region deletions. The mutational spectrum and clinical impact of these alterations have scarcely been explored in Mexican pediatric patients with B-ALL. Here, we report the frequency of the IKZF1plus profile and the mutational spectrum of IKZF1, PAX5, CDKN2A/2B, and ERG genes and evaluate their impact on overall survival (OS) in a group of patients with B-ALL.MethodsA total of 206 pediatric patients with de novo B-ALL were included. DNA was obtained from bone marrow samples at diagnosis before treatment initiation. A custom-designed next-generation sequencing panel was used for mutational analysis. Kaplan-Meier analysis was used for OS estimation.ResultsWe identified the IKZF1plus profile in 21.8% of patients, which was higher than that previously reported in other studies. A significantly older age (p=0.04), a trend toward high-risk stratification (p=0.06), and a decrease in 5-year Overall Survival (OS) (p=0.009) were observed, although heterogeneous treatment protocols in our cohort would have impacted OS. A mutation frequency higher than that reported was found for IKZF1 (35.9%) and CDKN2A/2B (35.9%) but lower for PAX5 (26.6%). IKZF1MUT group was older at diagnosis (p=0.0002), and most of them were classified as high-risk (73.8%, p=0.02), while patients with CDKN2A/2BMUT had a higher leukocyte count (p=0.01) and a tendency toward a higher percentage of blasts (98.6%, >50% blasts, p=0.05) than the non-mutated patients. A decrease in OS was found in IKZF1MUT and CDKN2A/2BMUT patients, but the significance was lost after IKZF1plus was removed.DiscussionOur findings demonstrated that Mexican patients with B-ALL have a higher prevalence of genetic markers associated with poor outcomes. Incorporating genomic methodologies into the diagnostic process, a significant unmet need in low- and mid-income countries, will allow a comprehensive identification of relevant alterations, improving disease classification, treatment selection, and the general outcome.

This paper presents a novel automated road damage detection approach using Unmanned Aerial Vehicle (UAV) images and deep learning techniques. Maintaining road infrastructure is critical for ensuring a safe and sustainable transportation system. However, the manual collection of road damage data can be labor-intensive and unsafe for humans. Therefore, we propose using UAVs and Artificial Intelligence (AI) technologies to improve road damage detection’s efficiency and accuracy significantly. Our proposed approach utilizes three algorithms, YOLOv4, YOLOv5, and YOLOv7, for object detection and localization in UAV images. We trained and tested these algorithms using a combination of the RDD2022 dataset from China and a Spanish road dataset. The experimental results demonstrate that our approach is efficient and achieves 59.9% mean average precision mAP@.5 for the YOLOv5 version, 65.70% mAP@.5 for a YOLOv5 model with a Transformer Prediction Head, and 73.20% mAP@.5 for the YOLOv7 version. These results demonstrate the potential of using UAVs and deep learning for automated road damage detection and pave the way for future research in this field.

Introduction: Rosai-Dorfman Disease (RDD) is reactive histiocytic disorder, which rarely occurs in the breast. Typically, it presents in women during their 5th decade of life, for which it is often confused with malignancy. Very few reports have reported its occurrence in the younger population, particularly pediatric patients. Case presentation: A 13-year-old female presented with a painful 3-cm mass the upper quadrant of the left breast. Ultrasound showed a hypoechoic mass with irregular margins abutting the skin, reported as a BI-RADS 5 lesion. Excisional biopsy of the mass showed Rosai Dorfman disease of the breast with involved margins. Follow-up PET/CT did not show any residual FDG-avid regions in the breast. Conclusion: RDD should remain in the differential diagnosis for breast masses for teenage patients, as we report the youngest known case of RDD of the breast.

An improved understanding of the pathobiology of Alzheimer’s disease (AD) should lead ultimately to an earlier and more accurate diagnosis of AD, providing the opportunity to intervene earlier in the disease process and to improve outcomes. The known hallmarks of Alzheimer’s disease include amyloid-β plaques and neurofibrillary tau tangles. It is now clear that an imbalance between production and clearance of the amyloid beta protein and related Aβ peptides, especially Aβ42, is a very early, initiating factor in Alzheimer’s disease (AD) pathogenesis, leading to aggregates of hyperphosphorylation and misfolded tau protein, inflammation, and neurodegeneration. In this article, we review how the AD diagnostic process has been transformed in recent decades by our ability to measure these various elements of the pathological cascade through the use of imaging and fluid biomarkers. The more recently developed plasma biomarkers, especially phosphorylated-tau217 (p-tau217), have utility for screening and diagnosis of the earliest stages of AD. These biomarkers can also be used to measure target engagement by disease-modifying therapies and the response to treatment.

This review delves into the critical role of automation and sensor technologies in optimizing parameters for thermal treatments within electrical power generation. The demand for efficient and sustainable power generation has led to a significant reliance on thermal treatments in power plants. However, ensuring precise control over these treatments remains challenging, necessitating the integration of advanced automation and sensor systems. This paper evaluates the pivotal aspects of automation, emphasizing its capacity to streamline operations, enhance safety, and optimize energy efficiency in thermal treatment processes. Additionally, it highlights the indispensable role of sensors in monitoring and regulating crucial parameters, such as temperature, pressure, and flow rates. These sensors enable real-time data acquisition, facilitating immediate adjustments to maintain optimal operating conditions and prevent system failures. It explores the recent technological advancements, including machine learning algorithms and IoT integration, which have revolutionized automation and sensor capabilities in thermal treatment control. Incorporating these innovations has significantly improved the precision and adaptability of control systems, resulting in heightened performance and reduced environmental impact. This review underscores the imperative nature of automation and sensor technologies in thermal treatments for electrical power generation, emphasizing their pivotal role in enhancing operational efficiency, ensuring reliability, and advancing sustainability in power generation processes.

Summary: β-glucan is the major component of the extracellular matrix (ECM) of many fungi, including wood degrading fungi. Many of these species also secrete oxalate into the ECM. Our research demonstrates that β-glucan forms a novel, previously unreported, hydrogel at room temperature with oxalate. Oxalate was found to alter the rheometric properties of the β-glucan hydrogels, and modeling showed that β-glucan hydrogen bonds with oxalate in a non-covalent matrix. Change of oxalate concentration also impacted the diffusion of a high-molecular-weight protein through the gels. This finding has relevance to the diffusion of extracellular enzymes into substrates and helps to explain why some types of wood-decay fungi rely on non-enzymatic degradation schemes for carbon cycling. Further, this research has potential impact on the diffusion of metabolites in association with pathogenic/biomedical fungi.

Abstract Insulators of the electrical power grid are usually installed outdoors, so they suffer from environmental stresses, such as the presence of contamination. Contamination can increase surface conductivity, which can lead to system failures, reducing the reliability of the network. The identification of insulators that have their properties compromised is important so that there are no discharges through its insulating body. To perform the classification of contaminated insulators, this paper presents computer vision techniques for the extraction of contamination characteristics, and a neural network (NN) model for the classification of this condition. Specifically, the Sobel edge detector, Canny edge detection, binarization with threshold, adaptive binarization with threshold, threshold with Otsu and Riddler–Calvard techniques will be evaluated. The results show that it is possible to have an accuracy of up to 97.50% for the classification of contaminated insulators from the extraction of characteristics with computer vision using the NN for the classification. The proposed model is more accurate than well‐established models such as support‐vector machine (SVM), k‐nearest neighbor (k‐NN), and ensemble learning methods. This showed that optimizing the model's parameters can make it superior to solve the problem in question.

BackgroundThe distribution of RUNX1-RUNXT1, PML-RARA, CBFB-MYH11, BCR-ABL1p210, and KMT2A-MLLT3 in the pediatric population with acute myeloid leukemia (AML) in many countries of Latin America is largely unknown. Therefore, we aimed to investigate the frequency of these fusion genes in children with de novo AML from Mexico City, which has one of the highest incidence rates of acute leukemia in the world. Additionally, we explored their impact in mortality during the first year of treatment.MethodsWe retrospectively analyzed the presence of RUNX1-RUNXT1, PML-RARA, CBFB-MYH11, BCR-ABL1p210, and KMT2A-MLLT3 by RT-PCR among 77 patients (

BackgroundIn Mexico, the incidence of acute myeloid leukemia (AML) has increased in the last few years. Mortality is higher than in developed countries, even though the same chemotherapy protocols are used. CCAAT Enhancer Binding Protein Alpha (CEBPA) mutations are recurrent in AML, influence prognosis, and help to define treatment strategies. CEBPA mutational profiles and their clinical implications have not been evaluated in Mexican pediatric AML patients.Aim of the StudyTo identify the mutational landscape of the CEBPA gene in pediatric patients with de novo AML and assess its influence on clinical features and overall survival (OS).Materials and MethodsDNA was extracted from bone marrow aspirates at diagnosis. Targeted massive parallel sequencing of CEBPA was performed in 80 patients.ResultsCEBPA was mutated in 12.5% (10/80) of patients. Frameshifts at the N-terminal region were the most common mutations 57.14% (8/14). CEBPA biallelic (CEBPABI) mutations were identified in five patients. M2 subtype was the most common in CEBPA positive patients (CEBPAPOS) (p = 0.009); 50% of the CEBPAPOS patients had a WBC count > 100,000 at diagnosis (p = 0.004). OS > 1 year was significantly better in CEBPA negative (CEBPANEG) patients (p = 0.0001). CEBPAPOS patients (either bi- or monoallelic) had a significantly lower OS (p = 0.002). Concurrent mutations in FLT3, CSF3R, and WT1 genes were found in CEBPAPOS individuals. Their contribution to poor OS cannot be ruled out.ConclusionCEBPA mutational profiles in Mexican pediatric AML patients and their clinical implications were evaluated for the first time. The frequency of CEBPAPOS was in the range reported for pediatric AML (4.5–15%). CEBPA mutations showed a negative impact on OS as opposed to the results of other studies.

Eutypa dieback and Esca complex are fungal diseases of grape that cause large economic losses in vineyards. These diseases require, or are enhanced by, fungal consortia growth which leads to the deterioration of the wood tissue in the grapevine trunk; however, pathogenesis and the underlying mechanisms involved in the woody tissue degradation are not understood. We examined the role that the consortia fungal metabolome have in generating oxygen radicals that could potentially play a role in trunk decay and pathogenesis. Unique metabolites were isolated from the consortia fungi with some metabolites preferentially reducing iron whereas others were involved in redox cycling to generate hydrogen peroxide. Metabolite suites with different functions were produced when fungi were grown separately vs. when grown in consortia. Chelator-mediated Fenton (CMF) chemistry promoted by metabolites from these fungi allowed for the generation of highly reactive hydroxyl radicals. We hypothesize that this mechanism may be involved in pathogenicity in grapevine tissue as a causal mechanism associated with trunk wood deterioration/necrosis in these two diseases of grape.

The usage of drones is increasingly spreading into new fields of application, ranging from agriculture to security. One of these new applications is sound recording in areas of difficult access. The challenge that arises when using drones for this purpose is that the sound of the recorded sources must be separated from the noise produced by the drone. The intensity of the noise emitted by the drone depends on several factors such as engine power, propeller rotation speed, or propeller type. Noise reduction is thus one of the greatest challenges for the next generations of unmanned aerial vehicles (UAVs) and unmanned aerial systems (UAS). Even though some advances have been made on that matter, drones still produce a considerable noise. In this article, we approach the problem of removing drone noise from single-channel audio recordings using blind source separation (BSS) techniques, and in particular, the singular spectrum analysis algorithm (SSA). Furthermore, we propose an optimization of this algorithm with a spatial complexity of $\mathcal {O}(nt)$ , which is significantly lower than the naive implementation which has a spatial complexity of $O(tk^{2})$ (where $n$ is the number of sounds to be recovered, $t$ is the signal length and $k$ is the window size). The best value for each parameter (window length and number of components used to reconstruct the source) is selected by testing a wide range of values on different noise-sound ratios. Our system can greatly reduce the noise produced by the drone on said recordings. On average, after the recording has been processed by our method, the noise is reduced by 1.41 decibels.

Although the skin effect has been widely studied over the years, many of its topics remain unclear for most electrical engineers, including undergraduate students and fresh graduates. Yet, in the cases of application of power frequency current, the knowledge gap to be bridged is more significant. Thus, for contributing to the widening in the knowledge of this effect, this work presents an analysis of the behavior of the current density distribution over the cross-section of differently combined rectangular conductors subject to power frequency current. In this case, since an analytical approach is not feasible, the numerical method of matrix solution of integral equations was adopted due to its simplicity, fast convergence, and its application is presented through some providentially chosen theoretical examples before a practical case may be analyzed. As the main results, the influence of a correlated effect that is the proximity effect shows itself as being predominant and the method presents itself as being very convenient for studying the current density distribution in conductors with a cross-section other than the circular, which allow bridging the knowledge gap in this theme. Moreover, the comparison of theoretical cases quite significant. Moreover, the method is adequate to planar configurations, by typical configurations, as in the case of bus bars of electrical panels.

Fomitiporia mediterranea M. Fisch. (Fmed) is a basidiomycete first described in 2002, and was considered up to then as part of Fomitiporia punctata (P. Karst) Murrill. This fungus can degrade lignocellulosic biomass, causing white rot and leaving bleached fibrous host residues. In Europe Fmed is considered the main grapevine wood rot (Esca) agent within the Esca disease complex, which includes some of the most economically important Grapevine Trunk Diseases (GTDs). This review summarises and evaluates published research on Fmed, on white rot elimination by curettage or management by treatments with specific products applied to diseased grapevines, and on the relationship between wood symptoms and Grapevine Leaf Stripe Disease (GLSD) in the Esca disease complex. Information is also reviewed on the fungus biology, mechanisms of pathogenicity, and their possible relationships with external foliar symptoms of the Esca disease complex. Information on Fmed control strategies is also reviewed.

Intussusception is the most common cause of intestinal obstruction among children up to three years of age. Though usually idiopathic, it may seldom be caused by a pathologic lead point. We present the case of a one-month-old female patient who was diagnosed with intussusception secondary to an ileal adenomyoma. To our knowledge, this case represents the youngest patient to be described in literature with an intussusception caused by a hamartoma.

Big Data applications are present in many areas such as financial markets, search engines, stream services, health care, social networks, and so on. Data analysis provides value to information for organizations. Classical Cloud Computing represents a robust architecture to perform complex and large-scale computing for these areas. The main challenges are the user's unknowledge about Cloud infrastructure, the requirement needed for improving performance, and the resource management to maintain stable processing. In these difficulties, an inadequate solution can lead to users overestimate or underestimate the number of computational resources, which drives to the budget increases. One way to work around this problem is to make use of Volunteer Computing since it provides distributed computational resources at free monetary cost. However, a volatile machine behavior is a problem to address in Big Data data distributions. Thus, this work proposes a data distribution model composed of Cloud Computing and Volunteer Computing environments in a hybrid fashion for Big Data analytics. The contributions of this work are: i) the required evaluation to enable efficient deployment of Big Data in hybrid infrastructures; ii) the development of an HR_Alloc Algorithm for establishing the data placement to Big Data applications; iii) a model to resource allocation in hybrid infrastructures. The obtained results indicate the feasibility of using a hybrid infrastructure with up to 35% of unstable machines in the worst-case scenario, without losing performance and a monetary cost lower than 20% in comparison to Classical Cloud Computing. Also, communication costs decrease up to 57.14% in the best-case scenario due to load balancing.

Background: Acute myeloid leukemia (AML) is the second most frequent leukemia in childhood. The FLT3 gene participates in hematopoietic stem cell proliferation. FLT3 mutations are recurrent in AML and influence prognosis. In Mexican pediatric AML patients, FLT3 mutational profile, and their clinical impact have not been evaluated.Aim of the study: This study aimed to identify the profile of FLT3 mutations in pediatric patients with de novo AML and to assess their possible influence on overall survival (OS) and other clinical features.Methods: Massive parallel target sequencing of FLT3 was performed in 80 patients.Results:FLT3 mutations [internal tandem duplication (ITD) or tyrosine kinase domain (TKD)] were identified in 24% of them. OS was significantly lower in FLT3POS cases than in FLT3NEG (p = 0.03). The average OS for FLT3POS was 1.2 vs. 2.2 years in FLT3NEG. There were no significant differences in the children's sex, age, percentage of blasts in bone marrow aspirate, or white blood cell count in peripheral blood at diagnosis between both groups. No differences were identified stratifying by the mutational load (high > 0.4) or type of mutation. The negative effect of FLT3 mutations was also observed in patients with acute promyelocytic leukemia (APL).Conclusions:FLT3 mutational profile is described in Mexican pediatric AML patients for the first time. Mutated FLT3 negatively impacts the outcome of AML patients, even considering the APL group. The clinical benefit from treatment with tyrosine kinase inhibitors in the FLT3POS pediatric patients needs to be assessed in clinical trials. FLT3 testing may contribute to better risk stratification in our pediatric AML patients.

Back-of-the-envelope (BOTE) calculations require the use of estimations and approximations. They are used by expert physicists to simplify complex problems to obtain quick and approximate solutions. Though BOTE calculations are routine for experts in physics, they are not well defined in academic literature. In this study we interviewed 9 physicists working in academia and industry and characterized experts’ responses in terms of the decisions made during different stages of the solution process. These findings characterize the individual skills that students need to develop in order to solve BOTE problems. We find that BOTE calculations involve most of the same decisions as bigger research problems. Thus, BOTE problems represent authentic, real-world physics problems that present challenges undergraduate students are often not prepared to solve. This provides an interesting foundation for future work characterizing other factors that influence both students’ and experts’ decisions to perform BOTE calculations.

Purpose: To explore teachers’ opinions about the design and deployment of teaching materials in class, with specific focus on the implications for professional development of self-produced resources. Design/methodology/approach: A mixed methods research design involving the administration of a questionnaire to a sample of 864 teachers and the implementation of two case studies of nursery teachers using interviews, observations, journals and recordings. Findings: The results confirm that textbooks are widely used in Schools for Nursery & Primary Education throughout Andalusia, where they exert a significant influence on classroom practices, even where teachers seek to complement them with more innovative material. Nevertheless, teacher-made material is highly valued by practitioners not only for promoting meaningful learning but also for its role in enhancing professional development, notwithstanding the obstacles and difficulties it frequently implies. Practical implications: The results endorse the need to provide support for teaching staff committed to the developing theirr own materials. Social implications: The region-wide policy of free textbooks for all schools should be reviewed in the light of the findings of the research. Originality/value: The research highlights a paradox at the heart of our educational system regarding the use of instructional materials: while the textbook represents the material of choice for the majority of teachers, these same teachers do not consider it the most appropriate for driving improvement in the teaching/learning process.

Interruptions in the supply of electricity cause numerous losses to consumers, whether residential or industrial and may result in fines being imposed on the regulatory agency’s concessionaire. In Brazil, the electrical transmission and distribution systems cover a large territorial area, and because they are usually outdoors, they are exposed to environmental variations. In this context, periodic inspections are carried out on the electrical networks, and ultrasound equipment is widely used, due to non-destructive analysis characteristics. Ultrasonic inspection allows the identification of defective insulators based on the signal interpreted by an operator. This task fundamentally depends on the operator’s experience in this interpretation. In this way, it is intended to test machine learning applications to interpret ultrasound signals obtained from electrical grid insulators, distribution, class 25 kV. Currently, research in the area uses several models of artificial intelligence for various types of evaluation. This paper studies Multilayer Perceptron networks’ application to the classification of the different conditions of ceramic insulators based on a restricted database of ultrasonic signals recorded in the laboratory.

Previous work has looked at the relationship between high school preparation and student performance in calculus-based introductory mechanics (physics 1) courses. Here, we extend that work to look at performance in introductory calculus-based electricity and magnetism (physics 2), and we look at the significance of what college math courses have been completed in addition to high school preparation. Using multiple linear regression including these measures of prior preparation, we examine the correlation between taking various math courses in college and final exam scores in introductory physics courses at a highly selective west coast university. In physics 1, we find that prior college math coursework is not a predictor of physics 1 final exam score. In physics 2, we find that having taken a course in vector calculus is a strong predictor of physics 2 exam performance (effect size=0.58 standard deviations, p