Search Results (1,064)

This paper introduces a reduced Gaussian kernel filtered-x least mean square (RGKxLMS) algorithm for a nonlinear active noise control (NANC) system. This algorithm addresses the computational and storage challenges posed by the traditional kernel (i.e., KFxLMS) algorithm. Then, we analyze the mean weight behavior and computational complexity of the RGKxLMS, demonstrating its reduced complexity compared to existing kernel filtering methods and its mean stable performance. To further enhance noise reduction, we also develop the historical error correction RGKxLMS (HECRGKxLMS) algorithm, incorporating historical error information. Finally, the effectiveness of the proposed algorithms is validated, using Lorenz chaotic noise, non-stationary noise environments, and factory noise. Full article

This paper presents a novel machine learning framework useful for optimizing energy consumption in households. Home appliances have a great potential to optimize electricity consumption by mitigating peaks in the grid load or peaks in renewable energy generation. However, such functionality of home appliances requires their users to change their behavior regarding energy consumption. One of the criteria that could encourage electricity users to change their behavior is the cost of energy. The introduction of dynamic energy prices can significantly increase energy costs for unsuspecting consumers. In order to be able to make the right decisions about the process of electricity use in households, an algorithm based on machine learning is proposed. The presented proposal for optimizing electricity consumption takes into account dynamic changes in energy prices, energy production from renewable energy sources, and home appliances that can participate in the energy optimization process. The proposed model uses data from smart meters and dynamic price information to generate personalized recommendations tailored to individual households. The algorithm, based on machine learning and historical household behavior data, calculates a metric to determine whether to send a notification (message) to the user. This notification may suggest increasing or decreasing energy consumption at a specific time, or may inform the user about potential cost fluctuations in the upcoming hours. This will allow energy users to use energy more consciously or to set priorities in home energy management systems (HEMS). This is a different approach than in previous publications, where the main goal of optimizing energy consumption was to optimize the operation of the power system while taking into account the profits of energy suppliers. The proposed algorithms can be implemented either in HEMS or smart energy meters. In this work, simulations of the application of machine learning with different characteristics were carried out in the MATLAB program. An analysis of machine learning algorithms for different input data and amounts of data and the characteristic features of models is presented. Full article

This article examines how media outlets create a “border spectacle” (De Genova 2013) in schools, which contributes to the criminalization and deportability of undocumented immigrant students. Using content analysis, we studied n = 30 news articles that covered an incident in 2017 where two undocumented young men were accused of sexual assault and rape of a young woman in the school they all attended. This paper builds on the “school-to-deportation pipeline” by suggesting that, in addition to the zero-tolerance behavioral policies established by schools and teacher’s racist behaviors, the media coverage of alleged criminal acts also play a role in the expulsion and criminalization of undocumented students. The analysis of the news articles highlights four types of media frames employed to criminalize the young men involved in the case prior to these allegations being addressed by a court of law: (1) immigrant youth as sexual predators; (2) immigration as a correlation to a criminal act; (3) parents as the real victims of the case; and (4) sexual assault victims as collateral damage. Each of these media frames are built on xenophobic tropes that have historically facilitated the marginalization of Black and Latinx people, but in this case, it specifically targets undocumented young men. Collectively, the four media frames exemplify how media create a “border spectacle” in schools, manufacturing a moral hysteria to further marginalize and criminalize undocumented youth. We argue that, as a result of schools becoming border spectacles, undocumented young people’s fear of feeling targeted based on their “illegality” is intensified, and their sense of inclusion is hindered in an often thought to be safe and inclusive space for undocumented young people. Full article

US life expectancy now lags significantly behind the majority of high-income countries, having grown more slowly since 1980 for reasons that are not evident and have been debated. An exploratory system dynamics model is presented that reproduces the full pattern of US life expectancy from 1960 to the present. Multiple socioeconomic and behavioral factors help to explain the historical pattern, two of them apparently most responsible for the stagnation since 1980: the growth of obesity and the leveling off of growth in social spending. Some of the factors in the model are traced back to earlier causes, and obesity’s growth in particular is traced back to excess growth in private health care spending and its adverse effect on workers’ wages. The model’s base run does a good job of reproducing a variety of historical time series data going back to the 1960s, and counterfactual tests produce plausible results and clarify the model’s main themes. The model may thus be considered a reasonable starting point for more conclusive future modeling of US life expectancy. Full article

The failure of overhead transmission lines in the United States can lead to significant economic losses and widespread blackouts, affecting the lives of millions. This study focuses on analyzing the failure of transmission lines, specifically considering the effects of wind, ambient temperature, and current demands, incorporating minimal and significant pre-existing damage. We propose a multiphysics framework to analyze the transmission line failures across sensitive and affected states of the United States, integrating historical data on wind and ambient temperature. By combining numerical simulation with historical data analysis, our research assesses the impact of varying environmental conditions on the reliability of transmission lines. Our methodology begins with a deterministic approach to model temperature and damage evolution, using phase-field modeling for fatigue and damage coupled with electrical and thermal models. Later, we adopt the probability collocation method to investigate the stochastic behavior of the system, enhancing our understanding of uncertainties in model parameters, conducting sensitivity analysis to identify the most significant model parameters, and estimating the probability of failures over time. This approach allows for a comprehensive analysis of factors affecting transmission line reliability, contributing valuable insights into improving power line’s resilience against environmental conditions. Full article

Historically, society has labeled social communication differences in autistic individuals as disordered by comparing them to the social communication behaviors of the predominant non-autistic population. This study explores how autistic young adults view their social communication experiences and how their differences impact them when navigating social situations in predominantly non-autistic environments. This qualitative study utilized purposive sampling to recruit 15 autistic adults aged 18–28 in the United States. All participants were conversation-level speaking communicators and high school graduates. Each participant engaged in an individual semi-structured, conversational interview with the first author via Zoom video conferences between November 2019 and June 2020. The data analysis identified inductive themes through interpretive phenomenological analysis. Five major themes emerged from the data that captured the challenges of autistic adults: (a) “Communication definitely is a struggle at times”, (b) “if it’s a very comfortable situation, then it’s fine”, (c) my communication style has “been very misunderstood”, (d) “I have to learn people”, and (e) “we’re all human. Autistic too, we’re still human”. The participants’ experiences suggest that differences in their communication style and social behavior resulted in overwhelming feelings of uncertainty and marginalization as they put great effort into engaging with non-autistic individuals. Full article

The Ecuadorian electricity sector, until April 2024, presented losses of 15.64% (6.6% technical and 9.04% non-technical), so it is important to detect the areas that potentially sub-register energy in order to reduce Non-Technical Losses (NTLs). The “Empresa Eléctrica de Ambato Sociedad Anónima” (EEASA), as a distribution company, has, to reduce NTLs, incorporated many smart meters in special clients, generating a large amount of data that are stored. This historical information is analyzed to detect anomalous consumption that is not easily recognized and is a significant part of the NTLs. The use of machine learning with appropriate clustering techniques and deep learning neural networks work together to detect abnormal curves that record lower readings than the real energy consumption. The developed methodology uses three k-means validation indices to classify daily energy curves based on the days of the week and holidays that present similar behaviors in terms of energy consumption. The developed algorithm groups similar consumption patterns as input data sets for learning, testing, and validating the densely connected classification neural network, allowing for the identification of daily curves described by customers. The results obtained from the system detected customers who sub-register energy. It is worth mentioning that this methodology is replicable for distribution companies that store historical consumption data with Advanced Measurement Infrastructure (AMI) systems. Full article

The seismic assessment of historical masonry bell towers is of significant interest, particularly in Italy, due to their widespread presence and inherent vulnerability given by their slenderness. According to technical codes and standard practice, the seismic evaluation of masonry bell towers can be conducted using a range of methodologies that vary in their level of detail. This paper presents a case study of a historical masonry bell tower located in the Caserta Province (Italy). Extensive investigative efforts were undertaken to determine the tower’s key geometric and structural characteristics, as well as to document ongoing damage phenomena. The dynamic behavior of the tower was assessed through ambient vibration testing, which enabled the identification of the principal modal shapes and corresponding frequencies, also highlighting peculiar dynamical characteristics caused by the damage conditions. Subsequently, the seismic assessment was carried out using both Level 1 (simplified mechanical) and Level 2 (kinematic limit analysis) methodologies. This assessment helped identify the most probable collapse mechanisms and laid the foundation for employing more advanced methodologies to design necessary retrofitting interventions. The study emphasizes the importance of Level 2 analyses for structures where out-of-plane failure mechanisms are likely due to pre-existing cracking. Both approaches provide less-than-unity acceleration factors, ranging from 0.45 for Level 1 (assuming non-ductile behavior) to 0.59 for Level 2, in this case specifically using the information available about existing cracking pattern. Full article

The accurate prediction of vehicle behavior is crucial for autonomous driving systems, impacting their safety and efficiency in complex urban environments. To address the challenge of multi-agent trajectory prediction, we propose a novel model integrating multiple input modalities, including historical trajectories, map data, vehicle features, and interaction information. Our approach employs a Conditional Variational Autoencoder (CVAE) framework with a decoder that predicts control actions using the Gaussian Mixture Model (GMM) and then converts these actions into dynamically feasible trajectories through a bicycle model. Evaluated on the nuScenes dataset, the model achieves great performance across key metrics, including minADE₅ of 1.26 and minFDE₅ of 2.85, demonstrating robust performance across various vehicle types and prediction horizons. These results indicate that integrating multiple data sources, physical models, and probabilistic methods significantly improves trajectory prediction accuracy and reliability for autonomous driving. Our approach generates diverse yet realistic predictions, capturing the multimodal nature of future outcomes while adhering to Physical Constraints and vehicle dynamics. Full article

Futures trading analysis plays a pivotal role in the development of macroeconomic policies and corporate strategy planning. High-frequency futures data, typically presented as time series, contain valuable historical patterns. To address challenges such as non-stationary in modeling futures prices, we propose a novel architecture called FuturesNet, which uses an InceptionTime module to capture the short-term fluctuations between ask and bid orders, as well as a long-short-term-memory (LSTM) module with skip connections to capture long-term temporal dependencies. We evaluated the performance of FuturesNet using datasets numbered 50, 300, and 500 from the domestic financial market. The comprehensive experimental results show that FuturesNet outperforms other competitive baselines in most settings. Additionally, we conducted ablation studies to interpret the behaviors of FuturesNet. Our code and collected futures datasets are released. Full article

Measurements taken on a historical dike in the Netherlands over one year showed that interaction with the atmosphere led to oscillation of the piezometric surface of about 0.7 m. The observation raised concerns about the long-term performance of similar dikes and promoted a deeper investigation of the response of the cover layer to increasing climatic stresses. An experimental and numerical study was undertaken, which included an investigation in the laboratory of the unsaturated behavior of a scaled replica of the field cover. A sample extracted from the top clayey layer in the dike was subjected to eight drying and wetting cycles in a HYPROP™ device. Data recorded during the test provide an indication of the delayed response with depth during evaporation and infiltration. The measurements taken during this continuous dynamic process were simulated by means of a finite element discretization of the time-dependent coupled thermohydraulic response. The results of the numerical simulations are affected by the way in which the environmental loads are translated into numerical boundary conditions. Here, it was chosen to model drying considering only the transport of water vapor after equilibrium with the room atmosphere, while water in the liquid phase was added upon wetting. The simulation was able to reproduce the water mass balance exchange observed during four complete drying–wetting cycles, although the simulated drying rate was faster than the observed one. The numerical curves describing suction, the amount of vapor and temperature are identical, confirming that vapor generation and its equilibrium is control the hydraulic response of the material. Vapor generation and diffusion depend on temperature; therefore, correct characterization of the thermal properties of the soil is of paramount importance when dealing with evaporation and related non-steady equilibrium states. Full article

Adolescence is a stage of significant intrapersonal and interpersonal changes, influenced by cultural and historical shifts. This study aims to analyze the changes in heterosexual behaviors among adolescents in Western countries over the past 50 years approximately. For this purpose, we conducted a systematic review following the PRISMA model, utilizing the online databases EBSCO, WoS, and Scopus, covering the period from 1980 to 2024, across ten European countries. The review focused on studies published in scientific journals with regional or national samples. In addition to the 30 selected studies, three more were included due to their relevance as cited in the selected articles. Despite the considerable methodological heterogeneity, the results showed a relative stabilization of the differences in sexual behaviors of boys and girls throughout time. Those differences were more evident in Southern European countries. Some changes were also noted, such as the delay in the initiation of the first sexual intercourse, a trend observed over the last decade. The findings are discussed in terms of the sexual script theory, highlighting the importance of these mental frameworks in the heteronormative sexual socialization of boys and girls. Future research should emphasize the diversity of heterosexual behaviors, their significance, and the emotional experiences that accompany them. Full article

This paper examines the influence of emotions on political polarization, looking at online propagation of conspiracy thinking by extreme right movements in Southern Europe. Integrating insights from psychology, political science, media studies, and system theory, we propose the ‘polarization loop’, a causal mechanism explaining the cyclical relationship between extreme messages, emotional engagement, media amplification, and societal polarization. We illustrate the utility of the polarization loop observing the use of the Great Replacement Theory by extreme right movements in Italy, Portugal, and Spain. We suggest possible options to mitigate the negative effects of online polarization in democracy, including public oversight of algorithmic decission-making, involving social science and humanities in algorithmic design, and strengthening resilience of citizenship to prevent emotional overflow. We encourage interdisciplinary research where historical analysis can guide computational methods such as Natural Language Processing (NLP), using Large Language Models fine-tunned consistently with political science research. Provided the intimate nature of emotions, the focus of connected research should remain on structural patterns rather than individual behavior, making it explicit that results derived from this research cannot be applied as the base for decisions, automated or not, that may affect individuals. Full article

Background: This scoping review examines the risk factors considered in assessing the dangerousness of individuals found Not Criminally Responsible on Account of Mental Disorder (NCRMD) in Canadian administrative courts. This review aims to identify the factors used by mental health review boards during annual case reviews to guide decisions on detention or release. Methods: Using a scoping review approach following PRISMA guidelines, this study analyzed research across multiple databases to identify relevant studies focusing on risk assessment for NCRMD cases. Results: The findings indicate that five primary categories of risk factors—historical, clinical, behavioral, legal, and miscellaneous—are influential in the decision-making process. Historical factors, such as past violence and early psychiatric contacts, are critical in predicting future risk. Clinical factors, including psychiatric diagnosis and treatment adherence, are key to evaluating current and potential future risks. This study reveals variability in the application of standardized risk assessment tools, highlighting a need for more consistent practices across Canadian jurisdictions. Conclusion: This review concludes that, while a multifaceted approach to risk assessment is essential for balancing public safety with individual rehabilitation, further research is needed to refine these processes and establish more uniform standards for managing NCRMD cases in forensic psychiatry. Full article

Northern Nigeria has had historically low antenatal care (ANC) utilization rates with poor health outcomes. Previous studies have shown that group antenatal care (gANC) improves ANC behavior and pregnancy outcomes. The gANC has been adopted in Kaduna and Kano States, Nigeria. This paper presents baseline findings from the implementation of the gANC program in Kaduna and Kano States, Nigeria, based on data collected from 1269 and 1200 pregnant women, respectively, from March to April 2024. Analyses of sociodemographic and pregnancy behavior data were performed. Participants were mostly between the age of 19 and 31 years, married or living with a partner, with over 50% having their own businesses. Over 62% and 34% had completed secondary- or higher-level education, with 60% and 80% living in urban areas in Kaduna and Kano States, respectively. In Kano State, >60% of the women had their last delivery at home, with 41.6% not assisted by a skilled birth attendant. In Kaduna, >63% delivered in the hospital and >50% had skilled attendance during labor. Almost half had not used contraceptives previously. This study has provided baseline evaluation data for the implementation of gANC in two states in Nigeria. Subsequent longitudinal data will examine the impact of gANC utilization on perinatal outcomes and contraceptive behavior to inform the scaling of the program in the country. Full article